Modelling load

566Modelling and Aggregation of Loads in Flexible Power Networks Working Group C4.605 February 2014 MODELLING AND AGGREGATION OF LOADS IN FLEXIBLE POWER NETWORKS WG C4.605 Members Jovica V. Milanović, Convenor (GB), Julija Matevosiyan, Secretary (US), Anish Gaikwad, Web Officer (US); Members: Alberto Borghetti (IT), Saša Ž. Djokić (GB), Zhao Yang Dong (AU), Andrew Halley (AU), Lidija M. Korunović (RS), Sergio Martinez Villanueva (ES), Jin Ma (CN), Pouyan Pourbeik (US), Fernanda Resende (PT), Stefan Sterpu (FR), Fortunato Villella (BE), Koji Yamashita (JP); Corresponding Members: Odin Auer (AR), Karim Karoui (BE), Dimitry Kosterev (US), Shu Kwan Leung (AU), Dumisani Mtolo (ZA), Samila Mat Zali (MY); Contributors: Adam Collin (GB), Yizheng Xu (GB); Reviewers: Hans Abildgaard (DK), Jose Conto (US), Marian Piekutowski (AU), Walter Sattinger (CH), Toshio Inoue (JP), William Hung (GB) Copyright © 2013 “Ownership of a CIGRE publication, whether in paper form or on electronic support only infers right of use for personal purposes. Total or partial reproduction of the publication for use other than personal and transfer to a third party are prohibited, except if explicitly agreed by CIGRE; hence circulation on any intranet or other company network is forbidden”. Disclaimer notice “CIGRE gives no warranty or assurance about the contents of this publication, nor does it accept any responsibility, as to the accuracy or exhaustiveness of the information. All implied warranties and conditions are excluded to the maximum extent permitted by law”. ISBN : 978-2-85873-261-6 Modelling and Aggregation of Loads in Flexible Power Networks Modelling and Aggregation of Loads in Flexible Power Networks Table of Contents EXECUTIVE SUMMARY .........................................................................................................3 Index of Terms and Definitions .............................................................................................................. 6 Chapter 1 Introduction ..........................................................................................................................11 Chapter 2 Existing Methodologies for Load Model Development...............................................15 Chapter 3 Overview of the Existing Load Models ..........................................................................25 Chapter 4 Recommended Methodologies for Load Model Development ..................................42 Chapter 5 Load Models for Typical Classes of Customers (Load Sectors) .................................62 Chapter 6 Modelling of Active Distribution Network Cells............................................................73 Chapter 7 Conclusions and Future Work...........................................................................................86 References ...............................................................................................................................................88 Appendix 2-A Examples of the Effect of Load Modeling on Power System Dynamics ...........98 Appendix 2-B Overview of two exemplary structures used in the measurement based approach .............................................................................................................................................. 101 Appendix 2-C Combined Measurement and Component based Load Modelling Approach ................................................................................................................................................................ 106 Appendix 2-D Trend in Change in Dynamic Voltage Behaviour with Change in Induction Motor Ratio or Fault Duration [14] .................................................................................................. 107 Appendix 2-E Additional Procedure of Extracting the Measurements for Deriving More Reliable Load Model Parameters [30] ........................................................................................... 110 Appendix 2-F Detailed Results of the International Survey on Load Modelling .................... 112 Appendix 3-A Reported Parameters of Existing Load Models ................................................. 123 Appendix 3-B Models of Power Electronics Interfaced Loads ................................................... 132 Appendix 3-C Model of Directly Connected Single-phase Induction Motors ......................... 134 Appendix 3-D WECC Residential Air-Conditioning Stalling Motor Model ............................. 136 Appendix 3-E Model of Distributed Energy Storage Systems [63] .......................................... 137 Appendix 4-A Practical example of measurement based load modelling............................. 139 Appendix 4-B Load models with load self-disconnection ........................................................... 143 Appendix 5-A Example of Load Aggregation Methodology.................................................... 145 Appendix 5-B Generic UK LV Network Configurations and Component Values ................... 153 Appendix 5-C Additional residential load curves and models ................................................. 155 Appendix 5-D Commercial load curves ......................................................................................... 157 Appendix 5-E Reported load curves for different load classes ................................................ 158 Appendix 6-A Aggregated Models of Distributed Generation and Active Distribution Network Cells for Power System Studies – Literature Overview .............................................. 162 Appendix 6-B Black and Grey-Box Based Dynamic Equivalent Models ................................. 169 Appendix 6-C Dynamic Equivalents for Micro Grids................................................................... 175 Appendix 7 Bibliography on Load Modelling .............................................................................. 179 Appendix 8 List of publications based on the work presented in the WG report ................ 190 Page 2 Modelling and Aggregation of Loads in Flexible Power Networks EXECUTIVE SUMMARY The power system research community and industry acknowledge the importance of accurate load modelling for power system studies, however, many still use typical representation of static loads by the constant impedance/current/power load models, while dynamic loads, if represented, are usually modelled with an induction motor (IM) model. The last systematic update of load models was performed in the mid 1990s, since when significant changes have occurred in the structure, type and composition of loads at all network buses. General inadequacy of currently used load models was highlighted in several unsuccessful attempts to reproduce the behaviour observed in recent blackouts during the corresponding “post-mortem” simulations and analysis. Over the last several years, there has been a renewed interest in both industry and academia for load modelling due to appearance of new types of loads, offering increased efficiency and controllability. Different types of modern non-linear power electronic loads are now responsible for a significant part of the total demand in almost all load sectors. Furthermore, there are currently no appropriate load models available for the correct representation of various directly connected and inverter-interfaced micro and small-scale distributed generation technologies, which, in some of the future network scenarios, may strongly impact real and reactive power demands and behaviour in future network scenarios, as they would be installed in large numbers. In a response to this renewed interest in load modeling, CIGRÉ Study Committee C4 established, in late 2009, the Working Group (WG) C4.605: “Modelling and Aggregation of Loads in Flexible Power Networks”. The WG started work in February 2010 with the aim to: i) provide a critical and updated overview of existing load models and their parameters for power system studies at all voltage levels, and identify types of loads and load classes for which adequate load models are presently missing; ii) provide a comprehensive overview of existing methodologies for load modeling, with a critical overview of component based and measurement based approaches, clearly identifying their advantages and disadvantages; iii) develop a set of recommendations and step-by-step procedures for load model development and validation, using either component based or measurement based approaches, or their combination; iv) develop load models for all typical devices and classes of customers for which there are no existing models and recommend their typical parameter values and ranges; v) provide recommendations on developing equivalent static and dynamic models of networks with significant amount of distributed generation, including equivalent models of micro-grids and active distribution network cells. This report summarises major results of the WG achieved between February 2010 and February 2013. It starts with a critical overview of two existing most widely used methodologies for load modelling, the measurement based and the component based approaches. They are described in detail and their major advantages and disadvantages discussed. Following this, the report summarizes existing static and dynamic load models, the load classes these load models are valid for and load parameters that can be found in literature. The most frequently used static load models, the exponential, second order polynomial and linear load model, are discussed in detail. A comprehensive static load model that can be used for large voltage variations as well as a static load model of induction motors typically used to represent load consisting predominantly of induction motors are also discussed. Dynamic load models, including exponential dynamic load model, dynamic model of induction motor and different variants of composite dynamic load model, are discussed as well. A comprehensive questionnaire on load modeling practices was developed by the WG and distributed during the summer/autumn of 2010 to more than 160 utilities and system operators from over 50 countries on all continents. The report summarizes some of the key findings of that questionnaire, based on 97 responses (60.6% response rate) received by September 2011, and provides the full report on survey in the appendix. The survey revealed that the constant real and reactive power load model (constant P and Q) is the most Page 3 Modelling and Aggregation of Loads in Flexible Power Networks widely used load model for steady state power system studies. It also showed that the static load models are most commonly used even for dynamic system studies and that only about 30% of utilities and transmission system operators represent dynamic load by some form of induction motor model. The analysis of existing load models presented in the introductory part of the report is supported by findings of this survey. The report further develops a set of recommendations for load model development, for both measurement based and component based approaches. For the measurement based approach, it provides: i) types of signals to be measured (instantaneous or RMS values of voltages, currents or powers, frequency, etc.) or data to be collected, ii) data collection procedure, measurement requirements in terms of sampling rate, duration and location of monitoring equipment, iii) methodology of handling measured signals and conversion to the required values (e.g., formulae for conversion of measured voltage and current responses into power responses), iv) data filtering and processing, including required accuracy of filtering procedures, v) handling data where measured signals are clearly distorted due to presence of higher harmonics or noise, vi) type of field tests needed, vii) typical load model structures to be used and suitable parameter fitting procedures, and for the first time, viii) handling the issues resulting from load self-disconnection following system disturbances. For the component based approach, the chapter provides: i) types of individual loads that should be considered for data collection, ii) type of data to be collected, iii) mathematical models of individual devices/loads that should be considered, iv) methods for aggregating different devices, v) dealing with missing data and vi) representation of the distribution and subtransmission network. Due to their number, volume and complexity, system loads connected at medium voltage (MV) and low voltage (LV) distribution networks, i.e., at bulk load supply points (BLSPs), are generally represented as aggregate load models in power system analysis. These aggregate load models will include all network components (overhead lines, cables, transformers, etc.), the actual connected load and, possibly, some micro, small and medium-scale distributed generation systems, e.g. “micro-generation” connected at LV. The aggregate load model is, therefore, incorporating a significant “non-load” portion of the network, which must be correctly represented during the aggregation. One special category of aggregate load models are models of different Load Sectors, also known as Classes of Customers, or Load Classes, which are generally defined as an aggregation or collection of different types of loads, or load categories, representing the typical structure and composition of electrical devices and equipment found in a specific end-use application, where similar activities and tasks are performed, e.g. in residential or commercial load sector applications. This similarity in performed tasks and activities usually results in inherent similarities in characteristics and patterns of active and reactive power demands of end-users within the same load sector, which in turn allows use of similar aggregate load models for the representation of their aggregate demands. Aggregate load connected at network BLSPs will typically consist of several load sectors and possibly further sub-sectors, which must be identified during the aggregation. System loads can be generally grouped into the three main load sectors (classes of customers): residential, commercial and industrial, and a variety of other, typically smaller load sectors/classes: public, agricultural, service industry, etc. Variations in type, location and size of the network and electrical installations where residential, commercial and industrial loads are present, as well as in the ways these loads are used in the buildings containing them, will introduce further load sub- sectors, e.g. commercial load sector can be subdivided into office, retail, education and other sub-sectors. Due to the importance of these load classes for the operation of future power networks, where greater emphasis will be placed on active participation of small(er) customers, the report describes and discusses in detail aggregate models for residential and commercial load sectors and corresponding sub-sectors. Large scale integration of inverter interfaced small generation units with power ratings less than a few tens of kilowatts in LV networks and Distributed Generation (DG) in MV networks calls for the development of equivalent mathematical models of Active Distribution Network Cells (ADNC) and Micro Grids (MG) suitable to represent ADNC and MG in steady state and dynamic studies of large power networks. Since ADNC and MG properties cannot be adequately modelled using conventional dynamic aggregation methods, the report Page 4 Modelling and Aggregation of Loads in Flexible Power Networks suggests exploiting systems identification techniques for this purpose. Considering that there is no current industrial practice on development or use of this type of aggregate models, the recommendations made in this report are based on relatively limited academic research experience, presenting a pioneering work in this field. Two approaches are recommended to derive dynamic equivalents for ADNC and MG. The first one is a “black-box” modelling approach, based on Artificial Neural Networks (ANN) that tries to exploit the full response of the ADNC or MG after a disturbance. The second one is a “grey-box” modelling approach, exploiting the physical behaviour of the different components of the ADNC or MG. Recommended equivalent models could reduce the complexity of the ADNC and MG models and make them computationally feasible for application in large power system steady state and dynamic studies. Finally, the report identifies potential areas for further research and development in the field of power system load modelling. These include i) real-time load model identification, ii) introduction of new load classes to reflect significant participation of relatively new load devices, such as electronic-based load, electric vehicles, storage devices, etc., iii) extension of current ADNC dynamic equivalents to include additional models of DG units and characterized by increasing flexibility to allow mimicking the interaction with the automation systems of future smart distribution networks, iv) development of aggregated models for ADNC and MG operated as integrated energy systems, when a number of energy carriers such as gas, heat, electricity and potentially hydrogen will be optimally generated and dispatched to supply manifold of responsive and controllable loads, such as reversible heat pumps, electric vehicles, etc. v) exploiting computer intelligence methods for efficient power system load model development while taking advantage of the smart metering infrastructure that will supply large amount of data, vi) setting up pilot installations and experimental facilities for the purpose of load model validation. All specific tasks identified in the Terms of Reference of the WG have been completed. Additionally, an international survey on industry practice on load modelling, involving 97 utilities from all five continents, has been carried out and its findings summarized in the report. The survey’s results not only complement the work of the WG and serve a consolidated summary of the present modeling practices in the industry. Additionally, during the course of the work the members of the WG have widely disseminated the results of the work and published 1 international journal paper (IEEE Transactions on Power Systems) and 12 international conference papers. Finally, it should be noted that many regions continue to improve their load modeling practices, most notably is the Western Electricity Coordinating Council in the USA. Page 5 Modelling and Aggregation of Loads in Flexible Power Networks Index of Terms and Definitions All terms and definitions provided in this section are for the purpose of this CIGRE WG Report. Active Distribution Network Cell (ADNC): A network bus, or part of the network consisting of several buses, where a significant amount of distributed generation is connected and which over specific periods of time can be either a net exporter or net importer of active power either due to variations in load demand, distributed generation output or both. Combined (Hybrid) Component Based and Measurement Based Load Modelling Component based and measurement based load modelling are combined when: a) Measurements are performed at the locations where load categories/components can be or are identified (i.e. at network substations supplying specific residential or commercial load classes/sectors) which are then used to determine percentage contributions of known load components to the total aggregate demand; or b) When “load signatures”, “demand pattern identification” and other similar techniques are used to determine the actual structure and composition of the aggregate load from the measured aggregate demand data (i.e. to identify individual load categories/components and their contributions to the total demand), without previous knowledge of the modelled load. Component Based Load Modelling The methodology for developing a load model for a specific, and therefore known, individual load, group of loads, or an aggregate load based on the actual physical/electrical characteristics of the individual load type/category/component. Composite Load Model: A model representing a group of loads, or an aggregate load, in which both static and dynamic load components are present and clearly distinguished. Demand Factor: The ratio of the maximum demand of a group of loads during a specified time period to the corresponding total installed power of those loads. (At the transmission level: The ratio between system peak load and sum of substation peak load.) Demand Side Management: A set of measures, actions and interventions, initiated deliberately and with specific purpose by end-users and/or network operators, or a third party (e.g. energy suppliers/utilities), aimed at changing, re-structuring and/or re-scheduling power demands of a group of loads, load sector(s), part of a system, or a whole system, in order to produce desired changes in the actual amounts and time-patterns of power demands supplied at the dedicated point(s) of delivery for end-use consumption of electricity. Distributed Generation (DG): An individual source of electrical active power, or a group of such sources, which are connected to the distribution network, including on the customer side of tariff meters. Note: The definition of the distributed network is typically specified by the local/regional/national authority. Dynamic Equivalent of Active Distribution Network Cell An aggregated or reduced-order model of a network bus, or portion of the network, where significant amounts of distributed generation is connected and which is typically not of primary interest for the performed analysis, but which is capable of preserving all important features and characteristics of the represented network , while simultaneously reducing complexity and computational burden for dynamic analysis of the complete system. Page 6 Modelling and Aggregation of Loads in Flexible Power Networks Dynamic Load Model: A time-dependent load model that provides information on relevant load characteristics as a function of known or specified system parameters and time. End-use Load Type: A group of individual loads with the same specific purpose, which may have the same or different electrical characteristics. Note: E.g. incandescent lamps, compact fluorescent lamps, high-intensity discharge lamps and LED light sources all belong to the same “lighting load” type although they have different electrical characteristics and should be represented by different load model categories. Exponential Recovery Load: Represents the loads whose active and/or reactive power response after a step change in voltage can be modelled by an exponential dynamic load model (e.g., induction motor, heating load and tap-changing transformers). Generation: The total active and reactive power output of a generator, or power plant. Generic Load Model: A general formulation of a load model, usually expressed in a normalised analytical form, or assembled from the normalised circuit components, capable of qualitatively representing behaviour of one specific load model category. Note: Strictly speaking, the generic load model of one load category or sub-category should be able to correctly represent any individual electrical device from this load category or any combination of electrical devices from the same load category. A distinction should be made between the “formulation of a load model”, e.g. standard exponential or polynomial form, which can be used to represent different types/categories/components of load and a “generic load model” which typically represent one distinctive type/category/component of load, e.g. general/generic model of an induction motor or item of power electronic equipment. Inverter-based Load: Inverter based load is the load which uses inverters for the connection or interfacing to the main supply system such as single-phase and three-phase adjustable speed drives. Load: An electrical device or item of equipment, or any combination and number of these, connected in parallel to a power supply system and specifically designed to consume active power supplied at a dedicated point of delivery for end-use consumption of electricity. Also denotes actual active and reactive powers consumed by a load (i.e. electricity demand by a device). In that context, “load” and “demand” are interchangeable terms in this report. Load demand at a system bus is the sum of the actual demands of loads connected at the bus, as well as all loads and losses in the network downstream of that connection point. Note: As defined above, the, term “load” represents the power system component which during normal operation, is not generating active power, nor is participating in the transmission or distribution of power. Depending on the point of interest and type of power system study for which an appropriate load model is required (e.g. analysis of transmission system performance), the aggregate representation of the load may include not only the connected active power-consuming devices, but also other distribution and transmission network components present in the aggregated part of the system (i.e. downstream of the point of aggregation). This is particularly true in the context of “system load”, when the corresponding aggregate load model, may include transmission/distribution transformers and lines, capacitor banks, various control and voltage regulation equipment, as well as distributed generation. Page 7 Modelling and Aggregation of Loads in Flexible Power Networks Load Bus: A bus in the modelled system/network, which has a load connected to it and is a net consumer/importer of active power, and where total active and reactive power demands of the connected load are either known or predetermined. Note: When some generation is connected at the load bus (e.g. distributed generation), the local active power demand may be lower than the local active power generation, e.g. during the minimum loading conditions. In that case, or during this time, the bus cannot be treated as a load bus, as it is a net exporter of active power (see definition of Active Distribution Network Cell). Load Characteristics: A set of parameters and/or functional relationships describing and characterising the electrical behaviour and response of the load to changes in system operating and loading conditions, including following small and large disturbances. Note: The two most important load characteristics for the purpose of this report are variations of active and reactive power demands over the time and as a function of system voltage and system frequency. However, load models may also include other characteristics of interest (e.g. fundamental or harmonic currents, total harmonic distortion, efficiency, etc.). Load Class, or Load Sector, or Class of Customer: An aggregation or collection of loads from different load categories representing typical structures of electrical devices found in a specific end-use application, where similar activities and tasks are performed. The terms “load sector”, “load class” and “class of customer” are interchangeable in this report. Load Component: An individual electrical device or item of equipment, or a group of such items, typically used in the same end- use applications, which consume active and reactive power and respond to variations in voltage and frequency in a similar way. Load Composition or Load Mix: The fractional, per-unit or percentage contribution of different load model categories/components or types of loads within a modelled group of loads connected at one point of delivery for end-use consumption of electricity (e.g. at one system bus), to the actual total or aggregate demand of that group of loads. Note: Load structure specifies what individual load categories, components or types are connected at a given system bus (e.g. single-phase and three-phase induction motors, power electronic loads and resistive loads), while load composition specifies what are their individual contributions to the total demand (e.g. single-phase induction motors – 20%, three-phase induction motors – 30%, power electronic loads – 10%, and resistive loads 40%). Load Curve or Load Profile: A graphical depiction or analytical representation of the observed (i.e. measured), or estimated (i.e. forecasted) variations in active and/or reactive power demand of a load during a specified time period, where variations in demand are correlated with the actual time at which they occur. Load Density: The ratio of the power demand of a load to the geographic/network area in which that load is located. Load Duration Curve: A curve showing the duration for which active and/or reactive power demand of the load during a specified period of time is equal to, or smaller than a given value. Load Factor: The ratio of the actual consumption of a load within a specified time period (e.g. in kWh) to the consumption that would result from the continuous maximum demand of that load in the same period. Also denotes the ratio Page 8 Modelling and Aggregation of Loads in Flexible Power Networks of the average load (e.g. in kW) supplied during a specified time period to the peak load occurring in that period. (Load factor is also sometimes referred to as Equivalent full-load hours) Note: This term should only be used when the actual/average/maximum demands and period of time to which they relate are specified. Load Model: An analytical, mathematical, equivalent-circuit based, physical-component based, or otherwise established or formulated representation of a load, which can be used for the analysis, prediction or estimation of relevant load characteristics in power system studies and subsequent analysis of system-load interactions. Load Model Category: A group of different electrical devices used in various end-use applications, which, for the purpose of load modelling, have the same or similar characteristics with respect to their active and reactive power demands and also demonstrate similar responses to variations in voltage and frequency, therefore allowing them to be represented by the same load model. Note: One load model category may be further divided into several sub-categories (e.g. category of motor loads may be divided into single-phase and three-phase motor sub-categories, while the category of non-linear power electronic loads may be divided in three general sub-categories: without power factor correction circuit (PFC), with passive PFC, and with active PFC). Load Structure: The specification of main, or all important, load model categories, or load components, or end-use types of loads, which are present in the modelled group of loads (e.g. in an aggregate load, or in a model of a specific load class/sector) connected at one point of delivery for end-use consumption of electricity. Measurement Based Load Modelling The methodology for developing a load model, in which recording and measurements of suitable, normally occurring, or intentionally applied disturbances and events, are used to derive relevant characteristics of the modelled load and/or for matching an assumed or postulated load model to the measured data. Micro-generation: A small distributed generating system, representing one or several generating units, with a total rated power output of typically up to 50kW-100kW, connected to a low voltage distribution system through a power electronic-based or other control interface, and typically utilising some of the low-carbon technology (e.g. renewable energy sources or high efficiency fossil fuel-based combined heat and power applications). Micro Grid (MG): A type of a low voltage Active Distribution Network Cell (ADNC) being comprised of an aggregation of loads and micro-generation systems (including local storage devices), typically operated in a two-level hierarchical management and control scheme supported by communication infrastructure assuring its operation as a controlled entity (aggregated load or generator) either connected to the main distribution network, or autonomously when isolated from it. Model of Aggregate Load: A load model representing a group of loads from the same or different load categories (or load classes/sectors) connected to a single bus in the analysed system/network. Note: A distinction should be made between a “model of aggregate load” (as defined above) and “aggregate load model”, the latter being a load model formulated as an aggregation of separate/different load models (see Composite Load Model). Page 9 Modelling and Aggregation of Loads in Flexible Power Networks Peak or Maximum Load/Demand: The maximum value of a load, or a group of loads, expressed as active and/or reactive power demand during a specified period of time (e.g. “summer/winter peak load” is the greatest load on a power supply system during the summer/winter season). Note: Minimum load/demand is defined in a similar way, while average load/demand may simply represent typical or otherwise defined average system operating and loading conditions. Polynomial ZIP Load Model: One of the most common load model formulations, in which static load is represented by a second order polynomial relationship, specifying changes in load active and reactive power demands with the changes in supply voltage and/or frequency. Note: The ZIP load model consists of a constant impedance term (Z), constant current term (I) and constant power term (P), representing corresponding load components. In a “constrained ZIP model”, participations/contributions of all three load components (Z, I, P) in the total demand should be in the range from 0 to 1 p.u. and their sum should be equal to 1 p.u. In the unconstrained variant, termed as “accurate ZIP model”, the coefficients representing participations of three load components can be greater than 1 p.u. and/or lower than 0, but their sum should be again equal to 1 p.u. Although the latter model can be more accurate, its parameters may not have physical meaning. Static Load Model: A time-independent load model that provides information on relevant load characteristics as a function of known or specified system parameters. System Load: The total active and reactive power demanded from a power system, or a particular part of the system, including corresponding losses due to the transmission and distribution of electricity. Page 10 Modelling and Aggregation of Loads in Flexible Power Networks Chapter 1 Introduction Load modeling is one of the most important aspects of power system modeling. Most of the load models used today were developed several decades ago, and have not been adequately updated after the subsequent changes in load structure and load characteristics. The last systematic update of load models, on an industry- wide level, was performed in the mid 1990s. Additionally, load models and their parameters currently used by utilities and system operators for power system analysis are generally not in public domain, and there is a level of uncertainty regarding industry acceptance of research efforts in this area. There have also been some recent, and concerted efforts in the development of load models in some regions (e.g. Western Electricity Coordinating Council [1]), however, these efforts have focused primarily on the specific region in which they have been developed. Although the majority of current power system research and industry acknowledges the importance of accurate load modelling for power system studies, they still use typical static load models (constant impedance/current/power) while dynamic load models, if used, are usually in the form of standard induction motor (IM) models. Load characteristics have significant influence on both steady state and dynamic performance of power systems [2–4]. Accordingly, correct analysis of power systems require accurate load models, together with appropriate representations of generation, transmission and distribution parts of the system. Load modelling, however, is not a simple task, as there is a significant number of factors that should be taken into account such as the diversity in types and characteristics of the loads, the lack of information on the load structure and the difficulties during the assessment and validation of the load model. Furthermore, spatial and temporal load variability needs to also be taken into consideration to assess system behaviour at different times of the year and different times of the day, as well as in different demographic and geographic regions. Moreover, aggregate load models at MV and HV bulk supply buses adopted for power system studies include implicitly the distribution transformers, shunt compensation and the distribution network feeders, often without accounting appropriately for dynamics of operation of tap changing transformers and other voltage regulators that may be deployed at lower voltage levels. General inadequacy of currently used load models was highlighted in several unsuccessful attempts to reconstruct recent blackouts in the corresponding “post-mortem” simulations and analysis. During the power system stability analysis, the emphasis is mainly placed on modelling power generating units, while load models are regarded as of secondary importance, although the influence of load representation on the stability was recognized a long time ago. Power system engineers began to pay more attention to the load modelling since the Swedish blackout of 1983, as inappropriate representation of system loads has usually led to the discrepancies between the recorded and simulated system responses. The computer simulation of the power system using appropriate models of its components is one of the most important tools to understand the system dynamic behaviour and guide its operation and planning. Load models play a vital role in these simulations. Different load models may lead to different simulation results. Too conservative load model assumptions may lead to over-expenditure or situations that require exorbitant levels of investment to solve a problem that may be highly unlikely, whereas too simple and optimistic a load model may result in serious problems not being identified and thus leaving significant vulnerabilities in the system. These effects of the load model on the power system dynamics have been well documented all over the world and raised serious concerns related to the load modelling work. Some of the examples are discussed below. On August, 10th, 1996, a serious power failure occurred in the Western Systems Coordinating Council (WSCC) system resulting in its break-up into four islands, with loss of 30,390MW affecting 7.49 million customers in Western North America. The system lost its stability with increased oscillations. The post-fault simulation based analysis of the event carried out by Bonneville Power Administration (BPA) and using the WSCC database showed a very stable system. To match the simulation results with the measurements, the experts from BPA Page 11 Modelling and Aggregation of Loads in Flexible Power Networks modified the Pacific HVDC Intertie model, modelled Automatic generation Control (AGC), blocked certain turbine-governor models and also made changes to the voltage controls on the Lower Columbia generators. After all the above changes had been made, however, the simulation results still showed more damping in the system than the actual situation. In these simulations, the loads in Northwest and Canada were initially represented primarily by a constant current real power and constant impedance reactive power load model. When the load models were changed to a combination of the induction motor models and various static loads, the simulated and measured responses showed very good agreement [5]. Similar analysis carried out by Powertech Labs Inc. concluded “Our analysis has shown that by far the two most critical modelling elements in reproducing this oscillatory disturbance are load characteristics and generator excitation controls. Both are relatively uncertain (particularly load) and changes to either can profoundly change the system response”. More information about this outage is given in Appendix 2-A. As mentioned above, the inappropriate load model may cause quantitative analysis errors, even if it does not give a completely wrong judgment on the system stability. In the study of dynamic stability, in the Taiwan Power System, the effects of load models on the critical modes during an unstable low frequency oscillation were investigated. When the complete dynamic load model was used in the simulation, the results were much worse than the field measurements. When the composite load models were used, the results were in close agreement with the field-measurements. When the exponential load model was used, the results could not predict at all the undamped oscillations in the system [6]. In the study of transient stability of the power system in the Northeast of China following a three-phase short circuit test, three different load models were applied and different simulation results were observed compared to measurements [7]. Further details are provided in Appendix 2-A. Finally, in the study of voltage stability in the Argentinean power transmission system, the static load model could not predict the voltage collapse and gave a more optimistic analysis result, while the composite load models with the induction motor models included captured very well the observed voltage dynamics. In summary, different load models can make quite a difference to simulation results. Since the digital simulation is the fundamental tool of power system analysis and control, developing the appropriate load models is essential for ensuring secure and economic operation of power system. Examples of load modelling inadequacies on power system analysis are given in Appendix 2-A. In addition to the real life examples discussed above, the recent renewed interest in load modelling is also fuelled by the appearance of new types of loads, offering increased efficiency and controllability. Different types of modern non-linear power electronic loads are now responsible for a significant part of the total demand of the residential load sector in many power systems. Similarly, there are currently no appropriate load models available for the correct representation of various directly connected and inverter-interfaced micro and small-scale distributed generation technologies, which, in some of the future network scenarios, will strongly impact real and reactive power demands, as they will be installed in large numbers. In a response to this renewed interest in load modeling, CIGRÉ Study Committee C4 established in late 2009 the Working Group (WG) C4.605 on Modelling and Aggregation of Loads in Flexible Power Networks. The group started its work in February 2010, with an objective to: i) provide a critical and updated overview of existing load models and their parameters for power system studies at all voltage levels, and identify the types of loads and load classes for which adequate load models are presently missing; ii) provide a comprehensive overview of existing methodologies for load modeling, with a critical overview of component based and measurement based approaches, clearly identifying their advantages and disadvantages; iii) develop a set of recommendations and step-by-step procedures for load model development and validation using either component based or measurement based approaches, or their combination; iv) develop load models for all typical devices and classes of customers for which there are no existing models and recommend their typical parameter values and ranges; v) provide recommendations on developing equivalent Page 12 Modelling and Aggregation of Loads in Flexible Power Networks static and dynamic models of networks with a significant amount of distributed generation, including equivalent models of micro grids and active distribution network cells. The main objective of WG C4.605 is therefore to identify existing load models and to provide guidelines for development of new and more realistic generic static and dynamic models of power system loads for the analysis of transmission and distribution networks (i.e. networks with voltage levels ranging from 6kV, or 11kV, to 400kV, or above). Generally, the network buses where considered loads are connected are assumed to be predominantly passive (i.e. without significant generation connected to them). A part of the work, however, is dedicated to the representation of “load” models at network buses which during a specified period may contain a high active component, i.e., act as net exporters of active power to the network (due to, e.g., a high penetration of distributed generation). The intended applications of the load models presented in this report include both, steady state analysis and small/large disturbance studies of power networks. The emphasis is on the aggregate load models for representing power system loads at the bulk supply points. Therefore, single phase loads and load imbalance are not considered. Only the fundamental frequency component is taken into account in load model derivation. In order to inform the WG activities and to establish a critical overview of the current approaches to load modelling by the international industry, a comprehensive questionnaire on load modeling practices was developed by the WG and distributed during the summer/autumn of 2010 to more than 160 utilities and system operators from over 50 countries on all continents. The results of the survey, based on 97 responses (60.6% response rate) received by September 2011 are summarized in this report. The survey revealed that the constant real and reactive power load model (constant P and Q) is the most widely used load model for steady state power system studies. It also showed that the static load models are most commonly used even for dynamic system studies and that only about 30% of utilities and transmission system operators represent dynamic load by some form of induction motor model. The full analysis of the results of the survey is given in Appendix 2-F. In addition to this introductory chapter, the report contains further seven chapters and a number of appendices, where in-depth analysis, case studies and a range of tables with different load model parameters are given. Chapter 2 summarizes and discusses the existing methodologies for load modelling, with a detailed discussion of the component based and measurement based approaches. Chapter 2 also describes briefly the possibility to combine the measurement based and component based load modelling approaches in order to obtain more accurate load models. Chapter 3 provides an overview of existing load models divided into two groups - static and dynamic load models. The chapter also lists the most frequently used load models and load classes for which these models are valid. The areas of application of load models and identified load model parameters from the existing open source literature are also specified. At the end of the chapter, some of the results of the survey on load modelling practices are presented and discussed. Chapter 4 develops a set of recommendations for load model development, for both measurement based and component based approaches. Chapter 5 provides models of different Load Sectors, also known as Classes of Customers, or Load Classes, which are generally defined as an aggregation or collection of different types of loads, or load categories, representing the typical structure and composition of electrical devices and equipment found in a specific end- use application, where similar activities and tasks are performed. Chapter 6 presents equivalent mathematical models of Active Distribution Network Cells (ADNC) and Micro Grids (MG), suitable for their representation in steady state and dynamic studies of large power networks. Page 13 Modelling and Aggregation of Loads in Flexible Power Networks Chapter 7 brings together major conclusions of the report and provides suggestions for further research and development in the area of power system load modelling. Finally, the Appendices to this report provide supporting information and various examples and details of the material discussed in the main body of the report. Page 14 Modelling and Aggregation of Loads in Flexible Power Networks Chapter 2 Existing Methodologies for Load Model Development 2.1 Introduction In order to identify the current industry practices for development of power system load models, and to support findings coming from a literature review, a questionnaire was developed and distributed to more than 160 utilities and system operators from over 50 countries on all continents. One of the questions contained in the survey was intended to clarify the adequacy of presently available load models. The responses indicated that most engineers are satisfied with the load models available in existing software packages (85% on average) and have not developed their own models for system studies (78% on average). The survey also revealed that utilities and system operators updated load model parameters used in simulations relatively frequently. In 41% of cases, the load model parameters were updated within the last five years. Taking into account these findings, it can be concluded that many utilities and system operators haven’t used load models different from those available in commercial software, but have changed parameters applied to those models in order to improve the accuracy of the simulation results. During the past few decades, data capture and storage capabilities of various measurement devices have significantly improved and the number of measurement devices deployed in power systems has substantially increased. This has unlocked greater possibilities for the measurement and capture of data suitable for load model development and in particular, an opportunity to improve the quality of model parameters applied. The responses to the questionnaire revealed that the power industry is taking full advantage of available monitoring systems for such purposes. It has been reported that in over 50% of cases load model parameters are being identified based on field measurements. This chapter provides a critical overview of existing methodologies for load model development, with a detailed discussion on the component based and measurement based approaches, clearly identifying their advantages and disadvantages. Furthermore, considering the findings of the survey, the chapter discusses existing approaches to the load modelling that are based on the use of measurement devices, and the methods used for identifying load model parameters from field data. Note: The responses to questions Q4, Q5, Q7 and Q8 of the questionnaire completed by utilities and system operators are summarised above. Chapter 3 and Chapter 6 will provide further information on responses to questions Q1, Q2, Q3 and Q9, respectively. 2.2 Overview of Existing Methodologies for Load Model Development Load modelling has often been reported as one of the main sources of simulation inaccuracy within system planning and operational studies [3]. As previously mentioned, inadequate load models may lead to inaccurate and inconsistent results giving rise to reduced confidence in system simulations and resulting analysis outcomes. However load modelling is not a trivial task. The main factors which introduce complexity include:  Spatial and temporal diversity in the types of loads connected to the power system. Load variations are stochastic [7] and significantly different measured data are typically captured for the same load at different times, including time of day, day of week and across seasons.  Highly non-linear and discontinuous behaviour of some loads. Some examples are the stalling of residential air conditioners, high sensitivity to dynamic stability limits causing tripping of motor loads, as well as self- disconnection of power electronic loads above or below certain voltage thresholds. Page 15 Modelling and Aggregation of Loads in Flexible Power Networks  Lack of accurate information about the structure and composition of aggregate loads on a continuous basis.  Difficulties associated with the validation of load models. High voltage (HV) networks, starting from several tens of kV, are usually highly meshed and the load connected at these HV buses typically consist of a mix of different load classes. Therefore, only simple load models (such as the exponential load model) have been used to represent such buses in system studies. On the other hand, at the feeder level the loads response to disturbances contains more information about the load composition. Therefore, more complex or more detailed load models are appropriate for lower voltage network studies. In order to overcome these difficulties, different load modelling approaches have been developed. The necessary knowledge for the load model development process consists of information on load composition for different load types, obtained for different times of the day, days of the week and months/seasons of the year, either from end-use surveys or from measurements (including metered demand data at the load bus [14]). After that, a suitable load model is selected. This model might include a model of the part of the medium voltage and/or low voltage network (e.g. supply feeders and step-down transformers). Parameters for the selected load model can be derived from end-use survey results [8], from existing literature, or from measurements (such as time series data captured by various types of disturbance recorders). Figure 2-1 provides a general overview of the process of load model development. It consists of two steps: 1) Selection of a load model (structure); 2) Derivation of the load model parameters. Selection of a Load Model (with Load Model Structure) Percentage of each load components should be determined if combination of static and dynamic load model is applied. E.g. ZIP + Induction Motor (IM) model: 80% ZIP + 20% IM where, ZIP model: P = Pn[p1U2+p2U+p3] Derivation of the Load Model Parameters E.g. ZIP model: p1, p2, p3 ZIP + Induction Motor (IM) model: p1, p2, p3, inertia, slip, etc Figure 2-1: General Load Modelling Procedure. As previously mentioned, measured data can be used for identification of the fraction/percentage of each load component in the load mix, as well as for derivation of load model parameters. Measurements required for the former are mainly intermittently metered data (e.g. captured every 5 minutes [14]), while those required for derivation of load model parameters are usually continuously measured data (or data captured at a high sampling rate [14]). Considering that end-use survey and literature can also be taken advantage of identifying load components and their composition, as well as to derive load model parameters, general load modelling procedures are classified into six different approaches as shown in Figure 2-2. All possible approaches that can be derived from Figure 2-2, taking into account the different paths that can be taken from the top to the bottom of the diagram, are briefly described below: C  E: This approach is applied to a load model which consists of multiple (more than two) components, e.g. the ZIP load model and the composite load model, or a combination of an induction motor model and the ZIP load model. After the load model structure and composition are estimated using the intermittently metered data, the load model parameters are derived from the continuously measured data. Page 16 Modelling and Aggregation of Loads in Flexible Power Networks C  D: This approach also applies to a load model which consists of multiple components. After the load model structure and composition are estimated using the intermittently metered data, the load model parameters are derived from the information/knowledge available in literature, or from results coming from end-use surveys. A B C Assumption of the multiple Assumption of single load Assumption of the multiple load model structure using model structure load model structure using end use survey or literature E.g. Exponential static model measurements D E Derivation of the load model Derivation of the load model parameters using survey or literature parameters using measurements Figure 2-2: Classification of Load Modelling Approach. A  E: As for the two preceding examples, this approach is applied to a load model which consists of multiple components. After the load model structure and composition are estimated from the result coming from end-use surveys or literature, the load model parameters are derived from the continuously measured data. A  D: This is another approach which applies to a load model consisting of multiple components. After the load model structure and composition are determined based on the result of end-use surveys or literature, the load model parameters are then also derived from the same information and knowledge base. B  E: This approach is applied to a load model that ultimately consists of only a single model component, e.g. exponential load model, or constant power/current/impedance load model. The load model structure/composition is not required (it is assumed), and the load model parameters are then derived from (i.e. matched with) continuously measured data. B  D: This alternative approach applies to a load model having a single model component. Again, the load model structure/composition is known or assumed, but the load model parameters are derived from the result of end use surveys or literature. The results of the international survey conducted by the WG [8] relating to applied methodologies for load model developments, reveal some interesting information about the current practices of utilities and system operators. Approximately 50% use measurement data, while about 40% of them use end-use surveys or literature (see Figure 2-F-4). In addition, more than half use load models which consists of only single model component. Therefore, B  E and B  D appear to be the two most popular (most widely used) load modelling approaches presently being applied. Collecting adequate information and data from measurements alone is difficult. This is because in large interconnected bulk power systems, even the loss of a major power plant or circuit may lead to only slight changes in voltage and frequency. Moreover, such large disturbances do not occur frequently. Intentionally applied disturbances, where this is even possible, generally cannot be large or repeated on regular basis. Page 17 Modelling and Aggregation of Loads in Flexible Power Networks Given these difficulties, many utilities have used simplistic load models comprising voltage-dependent static polynomial or exponential load representations. The former is essentially a combination of constant impedance (Z), constant current (I) and constant power (P) loads (commonly referred to a ZIP load model), while the latter expresses power dependence with voltage as an exponential function with corresponding exponent. Such models can be used for the analysis of system steady state performance, but they are certainly not an appropriate for correct load representation for dynamic studies, particularly if there is a high penetration of induction motor loads (e.g. residential air conditioners). More recently, some utilities (mainly in the USA) have used more detailed load models that capture both the static and dynamic responses. Capturing the dynamic response of loads is critical for stability analysis, especially for simulating slow voltage recovery phenomenon due to the stalling of residential air conditioners (single-phase IM loads) following a system disturbance [9], [10]. The process of identifying a correct bulk system load model (i.e. a correct aggregate load model) can be broken down into the following problems:  Identifying a load model that can represent the structure of the given load as a combination of static and dynamic load components.  Determining the percentages of static and dynamic load components and their parameters. It should be pointed out that the above two challenges are related to not only the combination of static and dynamic load models, but also to any other load model which consists of multiple load components, such as the ZIP load model. To overcome the above two issues and to obtain data on load characteristics, two approaches have been predominantly used by the industry in the past [11]:  Component based approach (bottom-up or knowledge-based approach)  Measurement based approach (top-down or behaviour-based approach) In general, the component based approach is applied to a load model which consists of multiple model components, e.g. the ZIP load model and the composite load model in parallel. The measurement based approach can be applied to load models consisting of single and multiple components 2.3 Component Based Approach for Load Model Development 2.3.1 OVERVIEW OF COMPONENT BASED APPROACH The component approach (also known as knowledge-based approach) is a bottom-up modelling methodology, in which an aggregated load model is derived from: 1) Knowledge of load classes connected at a substation, 2) The structure and composition of load categories and/or components in each load class, and 3) Typical characteristics of each load category and/or component. This aggregate model incorporates main individual load components and is usually expressed as a second order polynomial model combined with an induction motor model or as a composite load model. The component based load modelling approach provides for a common load model structure (See Fig. 2.3) and an associated set of parameter values that are used throughout the system model. Referring to Figure 2-3, the aggregate load supplied at a bus can be categorised into load classes based on load consumptions which typically, at the highest level, are residential, commercial and industrial. On the other hand, it is recommended that large industrial loads should be modelled in as much detail as possible, using data specific to the plants in question [13]. Load representation for the typical load classes is far more challenging, due to the distributed nature of the load and potentially onerous data requirements. Page 18 Modelling and Aggregation of Loads in Flexible Power Networks Metered demand at the load bus, which is typically available at least every hour (in some cases more often), can be used to determine load class split. This can be combined with laboratory test results for individual loads using AI (Artificial Intelligence) techniques such as fuzzy regression method [14] or similar. Further discussion and more details of this are provided in Chapter 4. Transmission bus Distribution Feeder and OLTC impedance P +jQ Distribution bus Load classes Residential Commercial Industrial Agricultural Load components Cooling/heatpump Refrigeration Electronics Resistive lightning 95% 5% 0% 0% 0% 0% 0% 0% 100% 0% LM SM Z P I LM SM Z P I Load characteristics LM:Large Motors 5% 95% 0% 0% 0% 0% 0% 100% 0% 0% SM:Small Motors LM SM Z P I LM SM Z P I Figure 2-3: An Example of a Component Based Load Modelling Approach (adopted from [12]). After identifying load classes, the next step is to derive load components and their percentage contribution within each load class. Each load class contains typical load components that account for the majority of the power consumed by end users. For example, lighting, air conditioning, space heating, water heating and refrigeration. The challenge is to identify percentage contributions of each load component within the considered load class. Obtaining this information, which is different for each network and geographic location, and which also changes with time, is typically a time-consuming and complex process, and essentially relies on customer surveys. To overcome this issues, a simpler approach was devised in [15]. It requires the following information: i) Typical load composition of each load class (fractions of load consumed by each load component); ii) Combination of load classes at each bus. Much of the data requirements were determined and documented in [14]. As one of the latest data sets, the load component/category percentages for different load classes for various geographic regions within the Western Electricity Coordinating Council (WECC) in the United States are currently provided by the WECC's Load Modeling Task Force (LMTF) [15-16]. The data was compiled for both summer and winter seasons. Though approximate, reference [14] is the most complete source of information available for the utilities in WECC and it is also publicly available. An Asian country also provides a fraction of typical load components for residential commercial and industrial areas based on end use surveys as shown in Figure 2-4 [17]. Examples of composition for different load classes are given in the Appendix 2-B. Although the above approach might be applied to any region, typical compositions can differ. According to the most recent worldwide survey [8], there seems to be no discrimination in modelling different load classes, except in US and Oceania power systems. In the case of Oceania, industrial load is simply distinguished from other loads by inclusion of induction motors. A promising approach is the use of models representing actual electrical circuits of all load components, which are divided into only five general load categories (see Figure 2 3) based on their electrical characteristics (and not on their end-use). As circuit-based models allow for more accurate representation of load current/power responses to voltage variations, this approach provides a more detailed aggregate load model [18], [19]. For the approach presented in [12], each load component has its specific static and dynamic characteristics. Load characteristics emulate electrical behaviour of the load component relative to changes in voltage and frequency. Load characteristics comprise static (constant impedance, constant current and constant power) and dynamic (one or more types of induction motor) mathematical models. For example, resistive load components, Page 19 Modelling and Aggregation of Loads in Flexible Power Networks such as electrical heaters and incandescent lamps, are modelled as constant impedance loads, while inverter- based load components are modelled as constant power loads unless the voltage drop at the load bus is significant. Industries Motors , 69% 8% 11% 12% 443TWh/year Residential 273TWh/year 37% 21% Heater, 25% 18% Commercial 194TWh/year 41% Lamps, 34% Others , 25% Total 910TWh/year 53% 17% 13% 17% 0% 20% 40% 60% 80% 100% Fraction of typical load component in Japan Figure 2-4: Typical Load Components for Typical Load Classes, (adopted from [17]). In order to correctly aggregate load in medium-voltage networks, feeders transformers and reactive power compensation equipment should be included into the aggregate load model. Therefore, as shown in Figure 2-3, the composite load model not only consists of the equivalent load components, but also incorporates the substation, on-load tap changing transformer (OLTC) and other downstream equipment. It follows that the changes in both load composition and network configuration will cause changes in the characteristics of an aggregate load fed from a bulk supply point. 2.3.2 ADVANTAGES AND DISADVANTAGES OF THE COMPONENT BASED APPROACH The advantages of the component based load modelling approach can be summarised as follows:  It promotes correlation of developed/used load models with the physical characteristics of devices.  It utilises load class data from individual substations, which is generally available.  It can be easily applied to composite load models.  It does not require field measurements, providing that the structure and composition of load components (and their characteristics) in the aggregated load are known or can be obtained from surveys.  It is applicable to different systems and conditions.  It provides flexibility to demand control i.e. identification of demand-manageable components of the load.  It facilitates examination of system performance sensitivities to changes in load composition via simulation. Conversely, the disadvantages are as follows:  The resulting load models typically assume that the characteristics, structure and composition of the modelled load do not change over time, due to daily, seasonal, weather-related, or behavioural end- use variations. If time variations are considered, it is typically assumed that these changes can be described with a limited set of alternate models, e.g. winter/summer system loading conditions. It should be noted that this is also the case with the measurement based modelling approach, i.e. one set of parameters, or one model, is usually only valid for a particular time and location when and where the measurements were performed. Page 20 Modelling and Aggregation of Loads in Flexible Power Networks  If the information on load model structure is obtained for one substation, the same load model structure and composition cannot be directly applied to another substation.  Combinations of load classes and load composition data are not normally used by, nor available to power system operators and planners. Large-scale load surveys are usually required to gather data of sufficient quantity and quality.  If a new or undefined load type, which does not belong to any previously defined load component, is connected to the system, this will result in an error when identification of load model parameters is performed.  Even if the fraction of each load component is the same, their parameters can be different and vary widely depending on such variables as age, the manufacturer, end-use application etc. An example is the inertia constant of small induction motors.  In the case of transmission system operators, whose assets are typically associated with voltages at 110 kV or higher, ownership and location of load devices in customer facilities may not be directly accessible. This can make it difficult for transmission system operators to apply the component based load modelling approach. 2.4 Measurement Based Approach for Load Model Development 2.4.1 OVERVIEW OF MEASUREMENT BASED APPROACH Early digital disturbance recorders were developed in the 1980s and ever since there has been ongoing and continuous improvement in the technology covering both hardware components and supporting software platforms. Over the past decades, measurement devices have been widely installed throughout many power systems and the quality of measurements and the data capture has significantly improved. The measurement based approach is referred to as “top-down” methodology in which system events and disturbances recorded at representative substations and feeders, are used to derive the characteristics of the connected load. This approach is also sometimes referred to as a “behaviour-based” approach, as static and dynamic responses of the loads are recorded and subsequently used to derive the required load model. In order to develop load models and estimate credible parameters, it is necessary to postulate the initial load model structure in advance . Conceptually, the measurement based approach is illustrated in Figure 2-5, where circles represent current transformers monitored at the feeder head or on the low voltage side of step-down transformers, while square symbol represents a voltage transformer measurement. The associated recording equipment will capture the response of load to all events occurring upstream, at the higher voltage level. Whenever the system is disturbed, the load response is measured by the recorders [20], [21], but the load can also be monitored and recorded during normal operation [22], [23]. Different commercially available data acquisition devices, such as power quality meters and digital fault recorders can be used later as part of a measurement based load modelling effort [24], [25]. High Voltage Level Bus Voltage × Medium Voltage Level Feeders Disturbance Current Recorder Figure 2-5: Outline of an Example Digital Disturbance Recorder (adopted from [26]). Page 21 Modelling and Aggregation of Loads in Flexible Power Networks The parameters of the load model are estimated by fitting measured data to the assumed model structure using parameter identification and curve fitting techniques. Complex estimation techniques may be needed to obtain parameters for more complicated models. This may also be true depending on the types and number of disturbances in recording data sets. The process of identification involves identifying a suitable mathematical model, i.e. a performance (objective) function, and appropriate load model parameters, so that it can replicate the dynamic response of the loads during and following a disturbance. This is done by observing the relationship between the change in voltage and/or frequency with the corresponding changes in load active and reactive power demand. Figure 2-6 shows a representative flowchart of the measurement based load modelling approach. Note that Figure 2-6 is an example (generic) flowchart, and that various derivative flowcharts have also been used for the measurement based approach (See Chapter 4). Step 1 Data Collection Step 2 Data Processing Step 3 Selection of Load Model Structure Step 4 Parameter Derivation for the Load Model Step 5 Model Validation including Event Screening No Are parameters adequate? Yes Step 6 Selection of derived Load Model Parameters Figure 2-6: Measurement Based Approach. Step 1 Collect time stamped system disturbance data (time-domain voltages and currents for each phase and, if possible, frequency, active and reactive power). The necessary characteristics of an event that make it suitable for load modelling are described in Appendix 2-B. A typical location for data collection is the secondary side of a medium-voltage transformer (Figure 2-5) supplying radially connected loads. Step 2 Signal processing techniques, such as a Discrete Fourier Transform (DFT)-based signal processing algorithm [25], are applied in order to calculate fundamental components of voltage, current, active power and reactive power. Typical sampling rates for different devices are provided in Table 4-3 in Chapter 4. It should be noted though that these algorithms inherently result in some filtering of the input data. Step 3 Select a suitable load model and the corresponding load model structure. The initially adopted load model structure may be changed in Step (5) if appropriate load model parameters cannot be obtained. Step 4 Run an optimisation routine. Nonlinear optimisation techniques that minimise/maximise the performance (objective) function are normally used to determine the parameters of the load model. Given the performance (objective) function including model parameters to be estimated, the values of the parameters are selected through an optimisation process such that the error between the measured response and model response is minimised. Different optimisation techniques have been used in the past to estimate the parameters, e.g., a genetic algorithm [27], support vector theories [28], simulated annealing [29], etc. Page 22 Modelling and Aggregation of Loads in Flexible Power Networks As with any optimisation process, the user has to specify initial values of the parameters that need to be estimated. It is very important, therefore, to select reasonable initial values and their bounds, as this may affect convergence and accuracy of the optimisation routine and the final values of the estimated parameters. Step 5 Validate the derived load model using commercially available or otherwise developed time-domain simulation tools. The simulated power responses coming from the proposed model are compared with the actual recorded active and reactive power responses. If the responses do not satisfy pre-defined accuracy criteria, the procedure should be repeated by selecting different initial estimates, including changing the assumed load model if deemed appropriate. Step 6 If appropriate model parameters are obtained, they are ultimately accepted as the final load model parameters for implementation in system studies. If not, another load model structure is selected and corresponding performance function derived and the procedure is repeated from Step 3. If however, an appropriate load model cannot be found, it may be that the measured (or conditioned/filtered) data are not suitable for use in the measurement based approach. Alternate data should be obtained and the procedure reapplied on the new data set. The generic flow chart describing the measurement based approach and shown in Figure 2-6 can be generally applied to derive any load model. This approach has been implemented for fitting parameters to an exponential load model using different tools developed by different companies, e.g. EPRI [30], CRIEPI [31] and TRACTEBEL Engineering [32]. The same approach has been recently used to fit parameters to a composite load model using the Load Modelling Parameter Derivation (LMPD) routine developed by EPRI. The measurement based approach discussed above may be modified depending on the load models and the specifications of available measurement data. Two examples of slightly modified approaches are given below: The performance function can be modified to include a large number of events/sets of data [33]. The method divides available data into two groups: 1) Data for deriving load model parameters, 2) Data for validating load model parameters. In this case, the flow chart in Figure 2-6 would change, as there would be no need to repeat the estimation process in Steps 4 and 5. If the measurement recording time is too long for the type of the load model that needs to be developed (e.g. a load model particularly intended for voltage stability studies or for angular stability studies) and excess data exists within the data set, another activity can be included between Step 3 and Step 4 of Figure 2 6 [34] (See Appendix 2-E) The additional step is to extract/shorten suitable measurement data from the overall data set in a way that makes it suitable for deriving load model parameters for the intended model. 2.4.2 ADVANTAGES AND DISADVANTAGES OF THE MEASUREMENT BASED APPROACH The main advantages of the measurement based approach are as follows:  It is simpler than the component based approach because it uses directly recorded dynamic response of the load from the actual system.  It can capture temporal changes in connected load if sufficient measurements are recorded over a long enough time period.  It can be applied to any load. Page 23 Modelling and Aggregation of Loads in Flexible Power Networks The main disadvantages of this approach are:  It will not result in quality load models if appropriate (large) disturbance measurement data are not available (e.g. initial drop in voltage at the target bus exceeding 20% or 30%). Generally, large disturbances in the system cannot be forced, so continuous or at least long term measurement programs are required until a sufficiently data set is recorded. Intentionally applied disturbances cannot typically be large (limited to such things as transformer tap changes and capacitor/reactor switching) and may not provide sufficient information to fully define the load behaviour.  It does not facilitate straightforward load model identification when discontinuities in load response are observed during severe voltage sags or swells, e.g. delayed voltage recovery due to stalling of motor load, or load self-disconnection.  In the case of a composite load model or otherwise complex load model structure, multiple solutions to the optimisation problem yield many sets of parameter values, or result in no identification of optimal parameters at all. Divergence of the performance (objective) function is a risk.  It is required that natural load variations that are not related to voltage and/or frequency changes be detected in measured load responses and excluded from the parameter identified process. If not done, the accuracy of the derived load model parameters may be compromised. Filtering is recommended to include only data points from the time of the disturbance to a moment immediately before the load change.  Cannot capture changes in the load mix over time (unless continuously measured). 2.5 Summary This chapter provided a critical overview and clear identification of advantages and disadvantages of the two most widely used methodologies for load modelling, being the component based and measurement based approaches. The main advantage of the component based approach is that the load model can be developed from customer survey data on load composition (or even based on the data form literature) at each load bus and known characteristics of participating load components. The main disadvantage is the difficulty of establishing accurate load composition and its variation with time at any given bus. The main advantage of the measurement based approach is the use of recorded data from the actual system without the need to know actual load composition. The main disadvantage is usually the lack of appropriate (large and sufficiently time varied) disturbance measurement data required for use in load model validation and parameter estimation processes. Page 24 Modelling and Aggregation of Loads in Flexible Power Networks Chapter 3 Overview of the Existing Load Models 3.1 Introduction As outlined in Chapter 2, there are two main approaches for the development of load models being the component based and measurement based approaches. The first one implies a knowledge of the relevant modelling parameters applicable to a large number of individual load components, as well as the relative participation of those components in the total load at a given bus. The measurement based approach, on the other hand, does not require any knowledge about load composition and instead, relies on field measurements to develop load model(s) to represent aggregate loads at power system buses. For either approach, it is essential that the end result is a load model that accurately represents the actual load behaviour at the system bus. This chapter presents the most frequently used load models, irrespective of the approach used for their development. Existing load models can be conveniently divided into two basic groups - static and dynamic. Static load models include exponential, polynomial, linear, comprehensive, static induction motor and power electronic-interfaced models. Dynamic load models include exponential dynamic load model, dynamic induction motor (IM) models, transfer function IM model, composite, distribution, bulk power bus load and distributed energy storage system (DESS) models. Static load models describe the relationship between active and reactive power drawn by given load as an algeblic function of voltage and frequency. To model the dynamic properties of the load its dynamic model can be formulated independently from its static load model, or, if the dynamic load model is known, the static load model can be easily derived from the available dynamic load model. However, the opposite is not true. The dynamic load model cannot usually be formulated from the known static load model. Therefore, in case of static load, “model of the static load” is the same as the “static load model”, while both “static load model” and “dynamic load model” can be formulated in case of the “dynamic load”. The adjectives “static” and “dynamic” refer both to type of studies and to the types of load models used in those studies. Static studies, such as power flow, (or load flow) refer to steady state analysis. Dynamic studies refer to the full range of stability analyses [11] involving the transient responses of the system including the connected load, typically following disturbances of various types. Static loads are assumed to respond instantaneously to a change in supply voltage and/or frequency. Dynamic loads exhibit time-dependent responses, which are determined by the previous states/conditions of both the system and the load itself. The dynamic response is also influenced by the interactions and exchange of energy between the system and the load during and after the transition from the previous state/condition to the next. Dynamic load models are usually described in differential equation form relating real and reactive power with voltage and frequency. It should be pointed out, though, that both static and dynamic load models can be used in dynamic studies. In reality, there are no “static loads”, as all loads will respond to a large disturbance or change in supply voltage/frequency in a finite way, taking some time for the transition from pre-disturbance to post-disturbance states. However, “dynamic loads” may be expressed in the form of a “static load model” for: i) steady state power system analysis, when the changes of system conditions having an influence on the load characteristics are either very slow or small (can be assumed as constant in the considered time interval); ii) When a response of the modelled load to a voltage and/or frequency changes is very fast (e.g. cannot be captured by the measuring equipment); iii) If interest is in the load responses after the initial transient. An example of this duality is induction motor load, for which both static and dynamic load models can be formulated and used in the corresponding power system studies. The chapter also lists most frequently used load models and load classes for which these models are valid. The areas of application of load models and typical load model parameters identified from the existing open Page 25 Modelling and Aggregation of Loads in Flexible Power Networks source literature are also presented. At the end of the chapter, some of the results of the survey on load modelling practices are presented and discussed. 3.2 Static Load Models As described by Figure 3 1, load models can be broadly divided into two basic groups, being static and dynamic. Static load models are adequate for representing loads which exhibit simple, near instantaneous, time invariant changes in power demand following a deviation in either supply voltage and/or frequency at the connecting bus. Static load models can also be used to model the load whose response to voltage change is so fast that the dynamics of the process cannot be captured by the measuring equipment, or if the interest is focused on the load responses without the initial transients (e.g., for longer term voltage stability studies, initial load response to voltage change lasting a fraction of a second may not be of interest; the interest would be rather on new steady state value of the load following the initial fast recovery) . In the latter case, it can be said that the new steady-state is achieved “instantaneously”, i.e. after a very short period of time. The general form of a static load model, consisting of real and reactive power dependences on voltage (U) and frequency (f), is as follows: P  f P (U , f ) (3.1) Q  fQ (U , f ) (3.2) Static load models are mostly used for representing resistive load devices, lighting, general residential load and other similar aggregate loads that lack the participation of large induction motors and electrical drives in the overall load mix. They are most often implemented in power flow calculations and voltage stability studies. Existing static load models can be divided into groups as shown in Figure 3-1, being exponential, polynomial, linear, comprehensive, static induction motor and power electronic-interfaced load models. The generic mathematical formulations of a dynamic load model are the same as (3.1) and (3.2) with the exception of the inclusion of time dependence in the right hand side of the equality sign in (3.1) and (3.2). Dynamic load models are typically used for representing the loads having a significant participation of induction motors and electrical drives. Dynamic load models can be classified into the following groups: exponential, induction motor (IM), transfer function based IM model, composite, distribution, bulk power bus load and distributed energy storage systems (DESS) models. Figure 3-1: Load Model Classification. 3.2.1 EXPONENTIAL LOAD MODEL One of the most frequently used static load model is the exponential load model, given by (3.3) and (3.4): k pu k pf U   f  P  Pn     (3.3)  Un   fn  Page 26 Modelling and Aggregation of Loads in Flexible Power Networks kqu kqf U   f  Q  Qn     (3.4)  Un   fn  where P and Q are the real and reactive power drawn by the load at voltage U and frequency f, Pn and Qn are the real and reactive power drawn under rated voltage (Un) and frequency conditions (fn ), and the exponents kpu, kqu, kpf and kqf describe the change in load demand in response to variations in supply voltage and frequency away from nominal, respectively. Parameters of the exponential load model (and many of the other models described in this chapter), as reported in available open access literature, are summarised in Appendix 3-A. A load dependence on frequency is, however, often neglected since voltage changes are much more frequent and more pronounced than the changes in system frequency. With this simplification, (3.3) and (3.4) become: k pu U  P  Pn   (3.5)  Un  kqu U  Q  Qn   (3.6)  Un  Care should be taken with this simplification in the following cases:  Simulation of non-credible contingency events where the typical assumption of a stiff or stable network frequency may no longer be applicable due to the size of the disturbance being considered.  Simulation of small power systems, with a small equivalent inertia, or power systems dominated by slow acting governor controls (typical of hydro generators relative to thermal plant). In both cases, system frequency may vary by several or more percentage points following credible contingency events. Parameters kpu and kqu represent the partial derivatives of real and reactive power with respect to voltage in the vicinity of rated voltage, respectively [35], or real and reactive power sensitivities to voltage [36]. These parameters indicate the magnitude of real and reactive power changes in percent for a one percent in voltage in the vicinity of rated voltage [37]. If the voltage exponents in (3.5) and (3.6) are set to 0, 1 or 2, the load exhibits constant power, constant current or constant impedance characteristics, respectively. Although these load types predominantly have theoretical significance, they are and have been the most widely used load models in power system studies. This has been confirmed through the findings of the international survey carried out by this WG. Some resistive load devices, such as hot plates, heaters etc. are typically modelled as constant impedance load [11], [38], [39]. Frequency dependency terms in (3.3) and (3.4) can be modified at constant voltage Un by Taylor series expansion since the frequency change is much smaller than voltage. The alternate form of the model is obtained [35]. k pu U  P  Pn   1  k pf f  (3.7)  Un  kqu U  Q  Qn   1  k qf f  (3.8)  Un  where f represents the relative frequency change in frequency (f- fn) / fn. Page 27 Modelling and Aggregation of Loads in Flexible Power Networks In the case of reactive power compensation [40], expression (3.8) becomes: 2 k qu dQ  U   f  dQ  U   Q   Qn  Pn   1    Pn   1  k f  (3.9) dP  U n  qf   fn  dP  U n  The first term in equation (3.9) denotes the reactive power compensator, while the second term denotes the uncompensated reactive power load or inductive load. The capacity of reactive power shunt compensators can be estimated using a P-Q distribution data captured every hour of the year by metered demand at the load bus of interest. Once the slope dQ dP is derived from the P-Q distribution, the capacity of reactive power compensators can be calculated through dQ Qc  Pn  Qn (3.10) dP Note that positive Qc in this equation denotes capacitive load, while positive Q denotes inductive load. The method requires that the compensation can be assumed to be constant. 3.2.2 POLYNOMIAL LOAD MODEL Another static load model frequently used is the second order polynomial model of which there are several variants. The variant with frequency dependence neglected is as follows:   U 2 U   P  Pn  p1    p2    p3  (3.11)   U n   Un     U 2 U   Q  Qn  q1    q2    q3  (3.12)   U n   Un   This model is also called the “ZIP model”, since it consists of constant impedance (Z), constant current (I) and constant power (P) load components. Parameters p1 and q1 represent the relative participation of constant impedance load, p2 and q2 the relative participation of constant current load, and p3 and q3 relative participation of constant power load. Load participation of every load component (Z, I, P) in the total load is in the range from 0 and 1 p.u. such that their overall sum is 1 p.u. This variant is called the “constrained ZIP model” [38]. In another variant, the individual pi and qi parameters can be larger than 1 p.u. and/or less than 0, but their sum must still equal 1 p.u. Although the parameters of this variant may not look intuitive, the model may be more accurate than the constrained ZIP model. As such this variation is often referred to as the “accurate ZIP model” [37]. One frequently used form of the polynomial load model with frequency dependence taken into account [11] is:   U 2 U   P  Pn  p1    p2    p3  1  k pf f  , 3   U n   p i 1 (3.13)  Un  i 1   U 2 U   (3.14) Q  Qn  q1    q2    q3  1  kqf f  , 3   U n   q i 1  Un  i 1 Page 28 Modelling and Aggregation of Loads in Flexible Power Networks The variant is analogous to that shown in equations (3.7) and (3.8), but with additional terms for voltage dependence allowing participation of more than just one response characteristic. 3.2.3 LINEAR LOAD MODEL A linear load model can be used in studies where the voltage varies in a narrow range around the rated value such as in small-disturbance stability analysis. It is not recommended for larger voltage variations since it may introduce calculations inaccuracies. The linear load model shown in (3.15) and (3.16) has two parameters (a0 and a1) for real and two parameters (b0 and b1) for reactive power [41].  U  3 P  Pn  a0  a1 , p i 1 (3.15)  Un  i 1  U  3 Q  Qn  b0  b1 , q i 1 (3.16)  Un  i 1 The research presented in [41] showed however that reactive power typically varies according to the ZIP model. The same finding was confirmed by [23], where it was recommended to use the linear model for real power but a polynomial model for reactive power. It should be noted that in some studies [24], [41], [42], real (P0) and reactive (Q0) power at the initial (pre- disturbance) voltage (U0) are used, instead of real (Pn) and reactive power (Qn) at rated voltage, in formulae for exponential, polynomial and linear load models. 3.2.4 COMPREHENSIVE STATIC LOAD MODEL The comprehensive static load model is proposed for modelling load at extremely low voltages in [24]. The term “comprehensive” is intended to describe a load model that can capture static as well as dynamic characteristics of loads, including stalling phenomenon of residential air conditioners. Extremely low voltages cause static loads (particularly power electronics loads) to "drop-off", residential air conditioners to stall (or their magnetic contactors begin to open). Various devices have different threshold levels below which they will not operate and therefore cease to consume power. Some examples are:  High Definition TVs drop off between 48% and 65% of the nominal voltage  Compact fluorescent lamps extinguish between 17% - 35% of the nominal voltage  Power electronic devices may cut off when voltage are below 85 – 80% of nominal  Magnetic contactors of residential air conditioners may open between 40% and 52% of the nominal voltage and these same air conditioners may stall when voltage drops to values between 50%-73% of the nominal depending on the outdoor temperature. Further discussion on load self-disconnection is provided in Chapter 4. (Note: Some consumer electronic devices are designed to operate both at 120 and 230 V as well as in 50 and 60 Hz systems. Therefore, care should be taken when the above results are applied to other power systems.) The comprehensive static load model consists of one polynomial and two exponential models in tandem (each having different parameters): P  Pn  PZIP  PEX1  PEX 2  (3.17) Page 29 Modelling and Aggregation of Loads in Flexible Power Networks Where: 2 U  U  PZIP  p1    p2    p3 (3.18)  Un   Un  a1 U  PEX1  p4   1  k pf1 f  (3.19)  Un  a2 U  PEX 2  p5   1  k pf 2 f  (3.20)  Un  Reactive power is modelled using similar expressions. To capture dynamic behaviour of loads, the exponents a1 and a2 become voltage dependent below a set threshold value of bus voltage. This is mainly intended to capture the dynamic behaviour of the load. Typically, in most commercial software tools, the constant power and constant current load components in PZIP are switched to an elliptical current-voltage characteristic or constant impedance representation when the voltage drops below a certain voltage threshold (typically 0.7 p.u. or lower), that can be selected manually by the software tool user, to ensure numerical stability of the network solution in the simulation. If the load bus voltage is very low, as in the case of a fault, the power consumed by the load cannot remain constant, as current will dramatically increase and it will cause a failure of calculation. Figure 3-2 illustrates how constant power load characteristic (both for active and reactive power) is automatically switched to an elliptical characteristic when the load bus voltage goes below 0.7 p.u. after a voltage dip is provoked by a fault. 2.0 P [MW] 1.5 1.0 0.5 0.0 Q [Mvar] 0.6 0.4 0.2 0.0 0.7 p.u. Voltage Threshold 1.6 V [p.u.] 1.2 0.8 0.4 0.0 0 2 4 6 8 10 Time [s] Figure 3-2: Example of automatic change of load characteristic in the case of a significance voltage drop Load response to frequency deviation is included in (3.19) and (3.20). 3.2.5 STATIC MODEL OF INDUCTION MOTOR (IM) In some countries, especially those that are economically well developed, participation of induction motors in the total load demand is very significant (60-70%) [43]. The static IM model is often used to represent such load. It is derived from the IM equivalent circuit as shown in Figure Figure 3-3 [44]. Page 30 Modelling and Aggregation of Loads in Flexible Power Networks Figure 3-3: Equivalent Circuit of an Induction Motor. where in Figure 3-3: Rs – Stator resistance Rr - Rotor resistance X  s – Stator leakage reactance X  r – Rotor leakage reactance X s  X m  X  s - Shunt reactance X m – Magnetizing reactance s  s    / s – Operating slip s – Synchronous angular speed  - Rotor angular speed Equations for the real and reactive power that an induction motor consumes can be used to form a corresponding static load model:  R  U2 P   Rs  r   2  s   Rr  (3.21)    X s  X r  2  s R   s  U2 U2 Q   X s  X s   2   Rr  Xs (3.22)  Rs     X  s  X  r  2  s  3.2.6 POWER ELECTRONIC-INTERFACED LOAD MODEL The participation of various non-linear power electronic devices in the total load demand has increased steadily over the last few decades, and is expected to grow even further into the future. In this report, these loads are termed “power electronic-interfaced loads”, and their models can be divided into four general load categories: dc power supply (or switch-mode power supply, SMPS) loads, energy efficient light sources (compact fluorescent lamps, CFL and light-emitting diode (LED)), and drive-controlled motor loads (single-phase and three-phase motor with adjustable speed drive, ASD). All four categories of power electronic-interfaced loads require different load models and more detailed analysis (for their identification) compared to resistive and directly-connected motor load categories (see also Chapter 5 and Appendix 3-B). The latter are commonly available from existing literature and are simple to implement as they draw continuous sinusoidal currents. Work presented in [20], [45–48] used the component based load modelling approach to develop two general forms of models for power electronic-interfaced loads: which are termed “full-circuit” and “equivalent-circuit”. Full-circuit models are based on the actual electrical/electronic component circuits implemented in different Page 31 Modelling and Aggregation of Loads in Flexible Power Networks power electronic devices (some examples are presented in Appendix 3-B) and are therefore complex, need long simulation times and require specialised simulation software (e.g. PSPice, EMTDC). In comparison, the equivalent circuit models, on the other hand, are much simpler as they generally consists of an uncontrolled front-end diode bridge rectifier (“power electronic interface”), input impedance (R and L), dc link capacitor (Cdc) and an equivalent resistance (req), The basic topology is shown in Figure 3-4. In the corresponding equivalent circuit model, the input impedance and value of Cdc are based on typical (or estimated) component values present in the circuit. The equivalent resistance is given by an analytical relationship which describes the behaviour of all components beyond the Cdc, and is usually determined by investigating the current, voltage or power characteristics of the modelled power electronic device at the dc link. Further details about modelling of such loads are provided in Appendix 3-B. D1 D3 Rsys Lsys R L iin System Impedance Cdc req AC Power Supply System D4 D2 Figure 3-4: Equivalent-circuit Model of Power Electronic-interfaced Load. Power electronic-interfaced loads can be generally represented with exponential and polynomial models as detailed in Appendix 3-A. Besides the aforementioned power electronic load categories, there are also plug-in chargers for electric vehicles that will need to be considered going forward. These represent an important power electronic load category, which is expected to significantly increase in the (near) future, particularly in the residential load sector. 3.3 Dynamic Load Models By their very nature, there are many and varying dynamic load models. Only the most frequently used are presented in this chapter for reasons on practicality. 3.3.1 EXPONENTIAL DYNAMIC LOAD MODEL The exponential dynamic load model is based on generic forms representing the responses observed by induction motors, heating loads and tap-changers after a voltage change [49]. If an exponential recovery of power after a step change in voltage is assumed [50], and the functions Ps (U ) and Pt (U ) are defined using exponential functions, the model becomes [51]: s t dP U  U  Tp r  Pr  Ps (U )  Pt (U )  P0    P0   (3.23) dt  U0   U0  t U  Pl  Pr  P0   (3.24)  U0  In the exponential dynamic model: Pr - Real power recovery P0 - Initial value of real power before the voltage change Page 32 Modelling and Aggregation of Loads in Flexible Power Networks U 0 - Initial voltage value Tp - Real power recovery time constant  s - Steady-state real power voltage exponent  t - Transient real power voltage exponent Pl - Real power consumption of load Qr - Reactive power recovery Q0 - Initial value of reactive power before the voltage change Tq - Reactive power recovery time constant βs - Steady state reactive power voltage exponent βt - Transient reactive power voltage exponent Ql - Reactive power consumption of load The response of reactive power (Q) can be represented using the same form as equations (3.23) and (3.24) with corresponding change in parameters. The exponential dynamic load model can give inadequate results when used to reproduce the short-term response of loads having a high percentage of IMs because it does not take into account the inherent coupling between real and reactive power absorbed by IMs. The model however does give adequate results for long term voltage stability studies and for situations where there are limited voltage variations at the load buses (as typically experienced in distribution networks supplied through on load tap changing transformers [52]). In studies where only small disturbances are considered [50], the linearised form of the exponential dynamic load model can be used (only the real power model is shown below):  t   Tp s  1   (3.25) Pl  0  s  s  U P U0 Tp s  1 3.3.2 DYNAMIC MODEL OF INDUCTION MOTOR The exponential dynamic load model is typically used when the participation of residential load (having a limited amount of rotating load, e.g. AC/heat pumps) in the total load demand is significant [51], [53], i.e. the participation of IMs in the load mix is small. Otherwise, when the predominant components of the load are IMs, a specific induction motor model should be used. A fifth-order, three-phase induction motor model is given below [22]: d ds uds  Rs ids   s qs (3.26) d d qs uqs  Rs iqs   s ds (3.27) d d dr udr  Rr idr   s    qr (3.28) d d qr uqr  Rr iqr   s    dr (3.29) d Page 33 Modelling and Aggregation of Loads in Flexible Power Networks d  M e  M   (3.30) d bTm  ds  X s ids  X midr (3.31)  qs  X s iqs  X miqr (3.32)  dr  X mids  X r idr (3.33)  qr  X miqs  X r iqr (3.34) Where, in addition to the parameters used in Figure 3-3: uds , uqs - Stator voltage components udr , uqr - Rotor voltage components ids , iqs - Stator current components idr , iqr - Rotor current components  ds ,  qs - Stator flux linkages  dr ,  qr - Rotor flux linkages Rs – Stator resistance Rr - Rotor resistance X s  X m  X  s - Shunt reactance X r  X m  X  r Rotor reactance X  s – Stator leakage reactance X  r – Rotor leakage reactance X m – Magnetizing reactance s – Synchronous angular speed  - Rotor angular speed b - Base angular frequency M - Mechanical load torque   bt - Normalized time Tm - Mechanical time constant of the motor M e  X m  iqs idr  ids iqr  – Electromagnetic torque. In practical applications, the stator transients are neglected in the above model and the model reduces to a third order with (3.26) and (3.27) effectively converting to algebraic equations. Fifth order IM models should only be used for larger induction motors [54], or when the influence of IMs is very important with respect to the other parts of the load,. Otherwise a third order dynamic model is usually sufficient. Directly connected single-phase induction motors are widely used in low-voltage applications, in devices such as refrigerators, freezers, fans, pumps, dishwashers and washing machines. They are often referred to as asymmetrical two-phase induction motors, as an auxiliary winding is included and used during start-up. They can be represented using the d-q model given in [55]. An application model for this type of IM load is presented in Appendix 3-C. Page 34 Modelling and Aggregation of Loads in Flexible Power Networks 3.3.3 TRANSFER FUNCTION MODEL OF INDUCTION MOTOR Another form of frequently used load model to incorporate significant portions of IMs [56], [57] consists of static load and IMs combined and modelled using first, second or third order transfer functions [56]: First order transfer functions: k pf  Tpf s k pu  Tpu s P  s   f  s   U  s  (3.35) 1  T1s 1  T1s kqf  Tqf s kqu  Tqu s Q  s   f  s   U  s  (3.36) 1  T1s 1  T1s Second order transfer functions: P  s  K pu 1  T3 p s   (3.37) U  s  1  T s 1  T s  1p 2p Q  s  K qu 1  T3q s   (3.38) U  s  1  T s 1  T s  1q 2q Third order transfer functions: P  s  K 1  T4 p s 1  T5 p s   (3.39) U  s  1  T s 1  T s 1  T s  1p 2p 3p Q  s  K 1  T4 q s 1  T5q s   (3.40) U  s  1  T s 1  T s 1  T s  1q 2q 3q Through comparisons of responses against field measurements coming from network buses, it was found that the second-order and third-order load models are better at capturing load behaviour during transients, compared to the first-order load model. The above “combined IM - static load” model is a transition from relatively simple “single facet” dynamic load models to something more complex. The "composite load model" is probably the most widely used and most versatile dynamic load model available to practitioners in recent years. 3.3.4 COMPOSITE LOAD MODEL Since the load at most bulk supply buses is a combination of different static and dynamic devices, especially at buses that supply significant industrial load, a model should incorporate both static and IM (being the dominant dynamic device) components. The equivalent circuit of a composite load model consisting of an equivalent induction motor in parallel with total static load [58] is shown in Figure 3-5. In this model, a third order IM model is used, while d and q current components of resistive and capacitive load are represented as: idr  uds / R , iqr  uqs / R (3.41) idc  uqs / X c , iqc  uqs / X c (3.42) A similar composite model is used in [54], with static load represented with conductance (GSL) and susceptance (BSL) in parallel. Sometimes, the composite load model may incorporate equations for static load, equations of Page 35 Modelling and Aggregation of Loads in Flexible Power Networks an equivalent IM model (typically third order model) and equations for an equivalent synchronous motor [11]. The required complexity of the model depends on load composition, type of phenomenon which should be analysed, required accuracy of results, etc. Figure 3-5: Equivalent Circuit of Composite Load Model. There are several variants of composite load model. In [59], and [60] the static load component is represented by a ZIP model. This model is depicted in Figure 3-6 with separated constant power, constant current and constant impedance load components. The static part of Figure 3-6 can be described as [60]. 2 U  * U  P P  s *   PI  Z *   PP * (3.43)  0 U  0 U 2 U  * U  Q Q  * s   QI  * Z   QQ * (3.44)  U0   U0  with PZ*  PI*  PP*  1  K pm (3.45) Qmotor QZ*  QI*  QP*  1  (3.46) Q0 where Kpm is the ratio of initial motor load and initial real load of the bus, Qmot is the initial reactive power consumed by the motor and Q0 is the initial reactive load of the bus. Figure 3-6: ZIP - Motor Model. The parameter M lf that reflects the effect of the dynamic components on the total load can also be defined as Pmotor U0 M lf  , where S motorBase is the equivalent motor nominal apparent power, U 0 and U Base are S motorBase U Base initial bus voltage and base voltage, respectively. Thus, the dynamic part of the load is described as: Page 36 Modelling and Aggregation of Loads in Flexible Power Networks dEd' 1   '  Ed'  ( X  X ' ) I q   (  1) Eq' (3.47) dt T dEq' 1   Eq'  ( X  X ' ) I d   (  1) Ed' (3.48) dt T'  d 1  ( A 2  B  C )T0  ( Ed' I d  Eq' I q )  (3.49 dt 2H  1 Id   Rs (U d  Ed' )  X ' (U q  Eq' )  (3.50) R  X '2  2 s 1 Iq   Rs (U q  Eq' )  X ' (U d  Ed' )  (3.51) R  X '2  2 s where: E d' , E q' D-axis and q-axes transient EMF Ud , Uq D-axis and q-axis bus voltage H Rotor inertia constant A Torque coefficient in proportion to square of speed B Torque coefficient in proportion to speed C Constant torque coefficient T0 Load torque at rated frequency And: T '  ( X r  X m ) Rr Xr = Xm + Xr X  Xs  Xm X '  X s  X m X r ( X m  X r ) A B  C 1 Additionally, composite load models can include saturation characteristics of transformers and motors [61] to account for reactive power losses due to losses in the iron core of transformers and motors. 3.3.5 DISTRIBUTION LOAD MODEL A model of a distribution load that can be characterized by a significant proportion of induction motors connected downstream of a step-down transformer and distribution feeder, is available in the standard model library [32]. The model includes a step down transformer equipped with a continuously regulating on-load tap changer, shunt compensation connected to the secondary side of the transformer, a distribution feeder modelled by a lumped impedance, a resistive load connected at a point downstream of the distribution bus and one or more generic induction motor models connected to the same point. (It should be noted that different combinations of distribution load model can be assigned to the same load bus.) One variant of this model with a single induction load is presented in Figure 3-7. It includes a step-down transformer, whose turn ratio, resistance and reactance are , Rtf0 and Xtf0, respectively, a shunt compensation Page 37 Modelling and Aggregation of Loads in Flexible Power Networks device connected to transformer secondary with susceptance BC, feeder with resistance Rfed and reactance Xfed, conductance GL, corresponding to the resistive part of the load, and induction motor whose stator resistance and reactance are R1 and X1, magnetizing reactance is Xm, first rotor winding resistance and reactance are R2 and X2, and assuming a double cage rotor, second rotor winding resistance and reactance are R3 and X3. The tap changer is modelled as a controller that adapts the transformer ratio (modelled as a continuous variable) between user-defined limits to control the low voltage level to a reference value specified as a parameter. The controller acts following a time constant that can be specified by the user. Figure 3-7: Equivalent Model of Distribution Load. It should be noted that there are variants in modelling of equivalent distribution loads. One of these variants is the WECC composite load model described in [1] and [10]. It has four motors (A, B, C and D), static and electronic load, and shunt capacitance at the secondary of the transformer, as shown in Figure 3-8. Motor A represents three-phase motors driving constant torque loads, such as commercial and industrial air-conditioning and refrigeration compressors. Motor B represents so-called “speed squared” motors with large inertia such as motors driving fans. Motor C represents so-called “speed squared” motors with low inertia such as water pumps. Motor D represents single-phase motors driving constant torque loads such as residential air- conditioners, refrigerators and heat pump compressors. Appendix 3-D provides details on modelling of such small motors, which are prone to stall. Variable frequency drive controlled motors are classified as electronic loads. Static and electronic loads are represented by polynomial models, but some three-phase electronic loads may self-disconnect (turn-off) during voltage sags depending on the design of the inverter circuit (see Chapter 4 for more details). Figure 3-8: WECC Composite Load Model (adopted from [10]). The use of Variable Speed Drives (VSD) to connect large synchronous and asynchronous motors (in the range of hundreds of kW to tens of MW) in the oil and gas industry has considerably grown in the last few years. In parallel, an increasing number of smaller motors (e.g. refrigerators and air conditioners) that utilise inverter technologies are being installed in the networks. The correct representation of such industrial/residential loads is of fundamental importance when studying the effect of network disturbances on the dynamic behaviour of not only the loads themselves, but also the Page 38 Modelling and Aggregation of Loads in Flexible Power Networks connecting network. This is particularly true for isolated industrial systems or in regions of the network where large industrial plants are installed. The typical application of such models would be for studies related to industrial plant performance during system disturbances (from the connection point looking inward), and studies which aim to investigate the impact of such devices/plant on overall system stability and performance. 3.3.6 BULK POWER BUS LOAD MODEL Load modelling at bulk power buses depends both on the characteristics of the corresponding load components, as well as the physical interaction of the electrical power network that exist between elements and the loads. Therefore, when the parameters of the load are identified on the basis of measurements, it should be recognized that the parameters will inherently include three key network effects:  The effect of series reactance from the bulk power bus to the bus or buses at which the power in eventually consumed.  The net effect of saturation in distribution transformers  The effect of shunt capacitors and line charging characteristics of overhead lines and cables. These influences are shown by an equivalent circuit [39] as shown in Figure 3-9, representing a load model structure with a bulk power bus node. The model includes the parameters XT (effective series reactance), QS (saturation shunt reactance) and QC (effective shunt capacitance). The load model, excluding estimated capacitive compensation is: P(t )  1  k p U (t )  1  1  Pdrop   Pdyn  G(t )  1  U 2 (t ) (3.52) Q(t )  1  kq U (t )  1  1  Qdrop   Qdyn  G(t )  1  U 2 (t ) (3.53) where kp and kq are characteristic constants, Pdrop and Qdrop are the amounts of load drop related to minimum bus voltage, and Pdyn and Qdyn are the magnitudes of the dynamic load components. The conductance of the motor, G(t), follows the equation with the initial condition G(t=0)=1. dG / dt   1/ T   GV 2  1 (3.54) In this equation, T is the time constant of the dynamic load component. It is assumed that susceptance (B) of the motor also satisfies equation (3.54). In [62] one variant of the model (3.52)–(3.54) is presented. It includes one more equation for the susceptance B dynamics, and two damping time constants (Tp and Tq) for motor conductance and susceptance following a fault. Figure 3-9: Load Model Structure of Bulk Power Bus (adopted from [39]). Page 39 Modelling and Aggregation of Loads in Flexible Power Networks 3.3.7 GENERIC MODEL OF DISTRIBUTED ELECTRIC STORAGE SYSTEM A generic model presented in [63] is designed to represent any type of DC source from the grid connection perspective for transient analysis/simulation of power systems. A typical example of such devices is the distributed energy storage systems (DESS). The structure of the modelled DESS is given in Appendix 3-E. The model is organized as a library of parameterized block diagrams. This structure is flexible and allows modelling of various storage technologies and control algorithms. The programmable supervision part offers the possibility to set the system, to allow the study of different services such as frequency control. Three versions of the DESS model structure are described in [63]: i) An instantaneous/complete model for fast transient and harmonic analysis; ii) An average model that considers only fundamental electrical variables which significantly decreasing the computation time and facilitating the design of any supporting control schemes; iii) A model for small signal and transient stability analysis that is based on a power balance approach. These models were developed for power system analysis software for studying the related phenomena experienced in large power systems. 3.4 Parameters of Reported Load Models Appendix 3-A summarises model parameters for the majority of load models described in this Chapter. The parameters are identified for different load classes at MV and HV level and for LV devices. Even for the same load class and same season, model parameters differ in value. This is because the load composition is strongly influenced by many factors such are economic, social, climate etc. Also, the parameters identified for the same type of load devices can be significantly different because they depend on materials used in production, accessories, manufacturer etc. The tables in Appendix 3-A show that different load models are used for the same load class in existing literature. It means that the selection of load model depend both on load composition and on the scope and type of the power system study. 3.5 Load Models in International Industry Practice As mentioned in Chapter 2, a comprehensive questionnaire on load modelling practices was developed and distributed to more than 160 utilities and system operators from which a 60.6% response rate was achieved. A summary of the results coming from the questionnaire regarding the present real world application of load models provided below. Question one (Q1) of the survey identified that constant power (PQ) load model is by far the most dominant type of load model used in steady state power system studies (84% of all responses). This answer was expected, since the widely accepted practice for power flow analysis is to assume that distribution system tap changing transformers and voltage regulators return bus voltages close to nominal values (i.e. close to 1 p.u.) following any initial perturbation. In such a case, loads may be treated as constant real and reactive power demands, and a constant PQ load model can be legitimately applied. Other responses to (Q1) are listed in Table 3-1. On the other hand, the answers to question two (Q2) revealed that load models used in dynamic power system studies (for, e.g., transient stability and frequency stability, or short-term voltage stability analysis) by different network operators and utilities are very different. Seven types of different load models are used (see Table 3- 1) namely, constant power, constant current, constant impedance, ZIP model, exponential model (with parameters not equal to 0, 1 or 2) and two variants of composite load model - ZIP model with IM and detailed composite load model. Additionally, different load models are used for representing real and reactive power demands. Table 3-1 provides further details of the survey results coming from Q2. Page 40 Modelling and Aggregation of Loads in Flexible Power Networks There is a relatively even distribution of different load models used in dynamic system studies for modelling real power demand, but static load models are again dominant. Constant power and constant current load models account for about 42% of all used models. A similar dominance of static load models is observed in the modelling of reactive power demand. In this case, constant power and constant impedance load models account for 45% of all used load models used. For modelling both real and reactive power demand, about 30% of the reported models represent dynamic load by some form of induction motor model. Appendix 2-F presents the types of load models used in static and dynamic studies as reported across in different continents. Table 3-1: Types of load models used in static and dynamic power system studies Constant Constant Constant ZIP Exponential ZIP model with Exponential model with Detailed composite Load model P(Q) I Z model model IM IM model Static studies 84% 3% 3% 8% 2% - - - Real power 23% 19% 4% 19% 7% 16% 0% 10% Dynamic studies Reactive 23% 0% 22% 19% 9% 17% 0% 10% power Although the same type of load model can be used for representing loads at different buses, its parameters could vary depending on the modelled load class (e.g. residential, industrial, commercial, etc.). However, the current practice in 75% of utilities and system operators is that they do not discriminate between different load classes. This could be explained by the difficulties that transmission system operators (in particular) encounter in obtaining accurate information on load classes at different network buses. A possible explanation of this could be because the data are typically owned by distribution system companies. Another interesting observation from the survey is how frequently load model parameters are updated. According to the responses to question three (Q3), utilities and system operators are very well aware of the significance of load modelling issue as they update load model parameters relatively frequently. In 41% of cases, load model parameters were updated within the last five years. Further information from the questionnaire regarding the use of different load models for different load classes, and the updating of load model parameters, is given in Appendix 2-F. 3.6 Summary The chapter presented the typical existing load models that describe static or dynamic behaviour of different load devices and load classes. Provided as appendix materials are the parameters of the different load models found in publicly available literature. Based on the completed review of existing load models and the results of the international survey, it was found that the most frequently used static load models are exponential, second order polynomial, and linear model with frequency dependence neglected. The constant PQ load model is the most widely used for steady state analysis of power system. A comprehensive static load model is typically used for large voltage variations, while a static load model of induction motors (and loads dominated by induction motors) is valid depending on the type of analysis being undertaken. One of the most frequently used dynamic load models is exponential dynamic load model. It is predominantly used to model residential load. Induction motor dynamic load model is used to model load having a large participation of induction motors. The composite load model is adequate for representing loads consisting of both static load components as well as induction motors. There are different variants of composite dynamic models but ZIP- induction motor model is the most commonly used. Parameters of selected load model can be different even for the same load class and the same season, since load structure depends on many factors such as economic, social, climate etc. It is worth noting that the international survey on industry practices revealed that static load models (typically of constant PQ or ZIP) are the most commonly used for dynamic studies with only about 30% of utilities and transmission system operators using some form of IM model to represent dynamic loads. Page 41 Modelling and Aggregation of Loads in Flexible Power Networks Chapter 4 Recommended Methodologies for Load Model Development 4.1 Introduction Following the review of existing methodologies for load model development provided in Chapter 2, and the overview of load models used by industry as given in Chapter 3, this chapter provides recommendations for the development of aggregate load models at bulk power supply buses for power system steady state and dynamic studies. The first part of the chapter focuses on methodologies for applying the measurement based load modelling approach, while the second half discusses methodologies for component based load modelling. 4.2 Methodologies for Measurement Based Load Modelling As discussed in Chapter 2, measurement based load modelling provides an opportunity to examine real-time power system loads and their actual static and dynamic characteristics. Electrical power system steady state and dynamic measurements are currently available from a number of data acquisition devices, including Power Quality Meters (PQM), Digital Fault Recorders (DFR), Phasor Measurement Units (PMU), etc. Many of these devices are already widely deployed in numerous utilities around the world. With existing trends towards better observability of power networks, as an essential step towards future “Smart(er) Grids” development, the number of monitoring devices deployed in power systems around the world will continue to increase. In some cases however, the monitoring device may not be installed at the bus for which the load model is needed and an alternative approach must be used. In general, there are two main approaches that can be used for acquiring data for the purpose of load modelling:  A passive, top-down approach based on continuous monitoring at power system buses. This approach requires the installation of measurement equipment at appropriate locations, the determination of trigger criteria, and recording of appropriate signals at a resolution and with sufficient accuracy so as to be valid for load model development purposes. It is particularly suitable for gathering information on the load behaviour following large disturbances. The optimal location for undertaking measurements is the interface between the meshed and the radially connected grid, typical of bulk load supply points on the secondary side of step-down transformers.  An active, bottom-up approach that includes: i) Intrusive field tests, often limited to changing of on-load transformer tap positions, the switching of local shunt connected capacitor/reactor compensation equipment, and possibly the temporary disconnection of one transformer in a parallel pair (or more). Limiting the impact on the downstream distribution system is of primary concern. ii) Laboratory tests with standardized protocols to gather the behaviour of representative appliances. The field tests however are limited to recording only small disturbances in the network in order to limit the impact on distribution system. 4.2.1 CONTINUOUS FIELD MEASUREMENTS The main advantage of utilising recorded system responses obtained through the strategic deployment of measurement devices is that real-time load responses can be captured following both small and large system disturbances. This should ultimately result in the development of more accurate load models given that dedicated field tests are typically not able to produce large changes in voltage and/or frequency. It should be noted that there is a portion of the load behaviour that remains hidden until subjected to a sufficiently large perturbation. As one example, this section discusses the Electric Power Research Institute (EPRI) collaborative load modelling research program, which started in 2004. The program was specifically aimed at developing a systematic methodology and more accurate tools for the development and validation of load models using measured naturally occurring system disturbance data [64], [65]. Page 42 Modelling and Aggregation of Loads in Flexible Power Networks Figure 4-1 and Figure 4-2 summarise the general approach to measurement based load modelling developed in [12], [15], [16]. 1. Determine the load model structure 2. Estimate Load Parameters - Apply Parameter Estimation Techniques and Criteria - Estimate the parameters for the proposed model 3. Validate Load Model and Parameters - Compare the predicted output values (P, Q) with measurement - Estimate the errors in the estimated parameters and model 4. If unsatisfactory, try another model structure and repeat the Measured Input process until “satisfactory” load model is obtained Parameters (V & I) Extracted Parameters from measurement (P & Q) Continuous Acquisition of Disturbance Data (Voltage, Current, Real and Reactive Power) Detection of System Voltage Variations Identification Develop Custom Code for Data Extraction and Processing Accepted Load Model Figure 4-1: Simplified Schematic for Developing Load Models Based on Identification Aggregation (adopted from [64], [65]). Data Collection Event Selection Data Processing Load Model Parameter Derivation for Structure the Load Model Structure Model Validation Figure 4-2: Measurement Based Load Modelling Approach, (adopted from [12]). The methodology illustrated in Figure 4-2 involves the following steps: 1. Data Collection Ideally, time stamped, time domain voltages and currents for each phase following system disturbance should be recorded. Different commercially available data acquisition devices, such as Power-Quality (PQ) monitors, digital fault recorders (DFR) and digital relays, can be used for collecting system disturbance data. The ideal location for collecting data is at the LV side of distribution substation transformers. Either a single feeder or multiple feeders can be monitored depending on the load model development process being considered. Page 43 Modelling and Aggregation of Loads in Flexible Power Networks 2. Screening of Recorded Data (Event Selection) Recorded disturbance data are screened to identify events suitable for load model development. The necessary characteristics of the recorded event/disturbance that will likely make it suitable for modelling purposes are:  It should ideally be a three-phase disturbance event. This significantly restricts the number of directly usable events. An unbalance of up to 10% in voltage/current among phases may be acceptable. For unbalanced faults (which are the vast majority of faults that will be recorded) more detailed knowledge of the load attempting to be identified would be required to assess the applicability of the available data, i.e., prevalence of single phase versus three phase load devices likely to be connected downstream of the measurement point etc.  The event location should be either upstream on the transmission system, or on an adjacent feeder. Downstream events on monitored feeders cannot be used as they will likely result in the disconnection of faulted feeder sections (and associated loads) making response fitting almost impossible – such discontinuities cannot be emulated in the optimization process.  The event should not be an interruption (i.e. the voltage should not drop to zero) nor should there be discontinuities in the recorded real and reactive power responses. In general, optimization processes do not handle discontinuous functions very well.  The event should have sufficient drop in voltage (10% or more) so that sufficient dynamic response data can be invoked and captured. The post-event voltage should ideally come back to the pre-event value after the event is removed i.e. the optimisation is not able to fit stalling characteristics of motors etc.  The event should last at least four cycles so that sufficient dynamic response data can be captured.  The event should have as a minimum, several cycles of pre-disturbance data captured to initialise the state variables in the model. Several seconds of pre-disturbances data is ideal if this feature is included within the capabilities of the measurement equipment being utilised. 3. Data Processing A Discrete Fourier Transform (DFT) based sliding window algorithm is used to convert three-phase voltages and currents into positive sequence, per unit voltage, current, real power and reactive power. Sampling rate of 1 kHz, i.e., every 1ms, or 2 kHz, i.e., every 0.5ms, are typically sufficient for the optimization algorithm. Note that the sliding window algorithm inherently results in filtering of the input data. 4. Selection of Load Model Structure and Parameter Estimation To enable the integration of the developed physical load models into system planning and analysis tools such as PSS/E and GELF, the following load model structures will incorporate both static and dynamic characteristics of the load:  Polynomial static, ZIP, model augmented with 3rd order (differential equation based) IM model  Exponential static model augmented with Difference Equation (representing input-output 2nd order model) Once the load model topology is determined, the model parameters can be estimated by running an optimization routine. The recorded event data and selected load model structure are the inputs to the parameter identification procedure. For each load model, the optimisation estimates the composition percentages of static and dynamic loads, the coefficients of the static models, and the individual parameters of the dynamic model Page 44 Modelling and Aggregation of Loads in Flexible Power Networks 5. Model Validation Once a reasonable set of model parameters are obtained, they can be benchmarked using commercial power system analysis software. A validation process should be implemented to assess the level of agreement between simulated and measured load responses, ideally using data that has been guaranteed from the load model development process. If the performance of the model is determined to be adequate, it can be accepted for further general use in system studies. If not, further fine tuning of the model should be attempted via Step 4 of the process, whereby alternate model parameters are estimated, or if needed, a different model topology is selected. An example of a top-down approach to measurement based load modelling is provided in Appendix 4-A. It should be noted that these identification methodologies are common for various types of stability studies: transient stability, small disturbance stability and frequency stability, if a suitable load model is selected. 4.2.2 STAGED FIELD OR LABORATORY TESTS For staged field tests, the step change in voltage could be produced either by switching off one transformer out of a two transformers in parallel arrangement [66], [67], or by changing the taps of a single transformer [66], [68]. The former has the advantage of being able to induce a larger step change in voltage at the secondary bus. The latter approach can be used for creating voltage ramps, with a gradient dependant on the tap changer cycle time. In the case where parallel transformers are operated at different tap positions, care must be taken to avoid high circulating currents between them. The transformers should be operated at most with 2-3 taps difference, i.e., one operating at tap + 1 or +2 and the other at tap -1 or -2. The permissible voltage ariation is typically limited to about  7.5% of the rated voltage (and often to much less than that, i.e., one or two taps would only result in net change in voltage of up to about 3%) to keep the voltage within the statutory requirements. Consideration must also be given to the allowable voltage change that end users are subjected to noting that downstream regulating equipment (and controls) may be configured for a specific steady state voltage range at the bulk power delivery node. In the case of [69] for example, manual tap changing was performed causing step change in voltage from 3 to 10% while still maintaining the voltage magnitude between 0.95 and 1.1 p.u. The practical voltage change that can be induced by either of the above methods is likely to be site specific to a certain degree. Step change in voltage can be also produced by capacitor switching [41]. The deviation in voltage caused by capacitor switching is typically too small to induce sufficient change in real power and additional filtering may be required due to oscillatory transients. A Simplified diagram of typical equipment connections for staged field tests is shown in Figure 4-3 [69]. A digital data acquisition device is connected to existing current transformers (CT) and voltage transformers (VT). The effective (RMS) voltage and current values can be sampled at different rate if desired. In the case of [69], they were recorded every second (1 Hz sampling rate) as tap changers were manually adjusted. The field tests should ideally be carried out at different times of the day, on different days of the week and across different seasons to capture relevant changes in the load mix. Step changes in voltage should be applied when naturally occurring load variations are at their minimum. It is suggested to avoid periods of the day where load is either rapidly increasing (toward morning peak for example) or decreasing (on the midnight side of the evening peak as this may pollute gathered data with load change events that are not directly related to the imposed voltage change. Daily loading diagrams should be used to identify such operating periods. This is particularly important if load models are to be developed for longer term dynamic studies (e.g., voltage stability). In this case, longer continuous monitoring periods are required (several minutes or more) to capture Page 45 Modelling and Aggregation of Loads in Flexible Power Networks the response of the aggregate load, including variations coming from the operation of downstream tap changers and voltage regulation devices (which may or may not be directly controllable during the staged filed tests.) To mitigate such issues, it is recommended that several consecutive step change in voltage be carried out during each testing period in order to obtain a reliable data set. T 2´ 1000/5/5/5A F1 110kV F2 network CT F12 110 10´ 1.5%/10.5kV 31.5 MVA, Ynd5 VT 10 0.1 0.1 uk=14.29% / / kV 3 3 3 Recording equipment Figure 4-3: Simplified Diagram of Equipment Connections for Field Tests (adopted from [69]). For staged tests performed in a laboratory environment, the required step change in voltage can be produced in several different ways, including the switching of parallel transformers as already described above. In a laboratory, it may also be possible to directly after the turns ratio of the supply transformer. An advantage of this method is that a step change in voltage in the order of 30% can be produced. Other methods include inserting resistors between the source and the supply transformer, and connecting resistor in parallel at the secondary side of transformer. The conservative aspect of laboratory testing is that there is no impact on real time network operators or end-use customers which need to be managed. Examples of measurement based load modelling are given in Appendix 4-A and Appendix 4-B. 4.2.3 TYPES OF SIGNALS TO BE RECORDED, SAMPLING RATE, DURATION AND LOCATION OF MEASUREMENT Measurement based load modelling relies almost entirely on the availability of credible measurement data coming from the field, either during staged tests, or as part of ongoin monitoring program. The RMS and phase angle values of the three-phase voltages and currents, together with system frequency, are normally available from field measurement devices. Further analysis is needed to calculate the values of real and reactive powers in order to perform load model parameter identification. For example, given a two channel measurement (a common voltage with two sets of current signals), the real and reactive power can be calculated by the following equations [11]: Pa  U a  I a 1  cos(Va   Ia1 )  U a  I a 2  cos(Va   Ia 2 ) Pb  U b  I b 1  cos(Vb   Ib1 )  U b  I b 2  cos(Vb   Ib 2 ) (4.1) Pc  U c  I c 1  cos(Vc   Ic1 )  U c  I c 2  cos(Vc   Ic 2 ) Qa  U a  I a 1  sin(Va   Ia1 )  U a  I a 2  sin(Va   Ia 2 ) Qb  U b  I b 1  sin(Vb   Ib1 )  U b  I b 2  sin(Vb   Ib 2 ) (4.2) Qc  U c  I c 1  sin(Vc   Ic1 )  U c  I c 2  sin(Vc   Ic 2 ) In most cases, the average of the three phase voltages can be used as the terminal voltage at the load for load model identification purposes (noting that some voltage imbalance may exist in practice). EPRI’s measurement based approach, discussed in the introductory part of this chapter, requires disturbance (event) data from data acquisition devices installed at a selection of representative distribution feeders or Page 46 Modelling and Aggregation of Loads in Flexible Power Networks substations. The events include the aggregate response of loads connected to a distribution feeder due to either an upstream transmission fault or a fault on an adjacent feeder. The ideal location for collecting events for the measurement based approach is at the LV side of a distribution substation transformer. For each feeder for which a load model is to be identified, the following quantities should ideally be measured:  Waveform (time domain) bus voltages for all the three phases  Waveform (time domain) current data for all the three phases Many of the commercially available monitoring devices can be used for data collection, in particular power quality monitors (PQMs) and digital fault recorders (DFRs). A minimum set of requirements that monitoring device used for data collection for the measurement based approach should fulfil is given in Table 4-1. A number of software tools can be used for load modelling purposes. Some of them are listed in Table 4-12, however, this is not an exhaustive list of software tools. Ideally, it would be good to collect many cycles or even a few seconds worth of post-fault data for the EPRI’s LMPD algorithm. PQMs typically do not store more than 16 cycles of post-fault data, which is a notable limitation. Digital fault recorders (DFR) can store seconds worth of post-fault data and are more suitable; however, they are also typically more expensive than PQMs and not as widely installed as PQMs on distribution feeders. The sampling rate required for load modelling depends on the actual load models to be developed. It could range from 1 ms (1 kHz) to 1 s (1 Hz). Depending on the load model, the dynamics to be being represented by the model, the availability of data, and the capability of the measurement equipment, the sampling rate can be determined accordingly. The accuracy of the load model developed ultimately depends on the sampling rate used during data capture. With lower sampling rates, such as 1 Hz, static load models or exponential (1st order) dynamic load models (typically for voltage stability studies) can be developed. The quality of dynamic load models (as may be used for transient stability studies) developed with this sampling rate may not be appropriate. Load models used for transient stability studies should be developed using data having a sample rate of between 50 Hz and 1kHz. When high sampling rates are to be used, care should be taken when considering the length of the recording window as data storage limitations may be an issue. A possible solution to this is to use monitors with variable sampling rates, where slower sampling is used to record events over longer time period, and faster sampling is applied for short time periods immediately after triggered events. Tables 4-3 and 4-4 summarise different measurement devices, their associated sampling rates, and their implications on load modelling. Recommendations are also given in view of different power system stability issues. 4.2.4 CONDITIONING/FILTERING OF RECORDED DATA There are a number of specific signal analysis techniques needed for load modelling analysis, [70]. The key techniques required include: i) Signal analysis; ii) Adaptive filtering; iii) Normal equations and iv) Harmonic decomposition. Appendix 4-A describes filtering techniques suitable for load modelling as well as examples of filtered voltage and power responses from recorded data. For modelling of industrial loads, measurements should be undertaken at critical nodes and load take-off points. As these loads are likely to be dynamic, larger disturbances and higher sampling rates are required. The disturbance data recorded should have sufficient variations in voltage and real and reactive power data series (10% to 20% at least) to facilitate the load model parameter identification process. Since it is not practical to apply specific system disturbances to obtain response data, historical data on system faults could Page 47 Modelling and Aggregation of Loads in Flexible Power Networks be used for these purposes. As discussed in the previous section, depending on the type of load models involved, the sampling frequency should be at least 50 Hz. Table 4-1: Minimum Requirement of Hardware Configuration for Data Recording Devices (naturally occurring large system disturbances) INPUT SPECIFICATIONS Number of analog inputs 6/9/12/15 (channels) At least six inputs (three for phase voltages and three for phase currents to monitor one feeder) The voltage input would ideally be four-wire (neutral connection included). Number of digital channels Not necessary for load modelling however the position of certain circuit breakers can be useful additional information depending on substation topology and the types of field tests being undertaken (if any), e.g. transformer switching. Sampling rate At least 960 samples per second per channel (sampling rate of 1kHz or higher). Many specific data acquisition systems have adjustable sampling rates DATA SET SPECIFICATIONS Pre-fault Recording Time (cycles) At least 2 to 5 cycles of pre-fault data. Several seconds of data is preferred. Post-fault Recording time (cycles) Post-fault is a function of maximum storage capability and reset threshold. In the case of large disturbances (e.g., faults), several seconds of data may be sufficient for the development of dynamic models. Although , a several minutes data set is needed in the case where load models are to be used for long term voltage stability studies. . Trigger Condition Under/over-voltage; under/over-frequency should all be selectable as trigger conditions. Trigger Threshold The user should be able to set the voltage or frequency level that initiates an event record. Reset Threshold User defined (including specified recording duration) HARDWARE SPECIFICATIONS On-board RAM to store data. At least 512MB Hard drive storage This is largely dependent on whether the device can be downloaded remotely to recover stored data. Serial ports USB/RS232 Ethernet connection, network Chosen in order to facilitate the configuration updating of the unit and the download of data. protocols FILE FORMATS Desirable files formats are ASCII, binary and COMTRADE. The data recorded in system disturbances need to be processed and filtered before load model identification analysis is performed. For load model development based on fault data, the key steps involved include: i) Identification of data recorded at LV or HV side. ii) Data pre-processing to select data which contain sufficient voltage and power variations during recorded disturbances. iii) Calculation of real and reactive power values using recorded data (which may be three phase voltages and currents). iv) Load model identification based on the processed measurement data. Filtering may be needed if the original measurement data contain significant noise. However, filtering has to be carried out with care to ensure that the useful transients contained in the data series are adequately preserved. In most cases, the measurement noise is Gaussian noise, so simple averaging filters can be used to make the signals useful. For example, in a data series measured at 100 Hz, values of 5 to10 adjacent data points can be averaged. The average value will replace the original measurement value and used for load model identification purposes. An averaging filter is very effective even for higher sampling rates since the random Page 48 Modelling and Aggregation of Loads in Flexible Power Networks noise is the major target of filtering process. The application of Fast Fourier Transform (FFT) can then be used to extract the positive sequence component of recorded currents and voltages for further processing. Table 4-2: Example software tools that can be used for load modelling (non exhaustive list) SOFTWARE DESCRIPTION Siemens PTI PSS/E Transmission network modelling and analysis DiGSILENT PowerFactory Transmission, distribution modelling and analysis, optimisation toolkit can be used for identification of measurement based load modelling Eurostag Comprehensive power system analysis tool LMPD (EPRI tool) Measurement based load modelling GE PSLF Transmission network modelling and analysis PSS_SINCAL Mainly for distribution network analysis ASPEN Mainly for distribution network analysis DINIS Mainly for distribution network analysis EMTP Electromagnetic transients analysis, can be used for load models and power quality analysis GridLAB-D Open source distribution network simulation package CPAT (CRIEPI's Power Analysis Tools) Transmission network modelling and analysis Table 4-3: Sampling rate for various types of load model Sampling rate (up to) Load model can be obtained Confidence 1 ms Dynamic/ ZIP + Induction motor High Harmonic load model (lower harmonics only) Medium / Low 10 ms Dynamic /Induction motor Medium Static / ZIP High 100 ms Dynamic /Induction motor Low Static / ZIP for frequency and voltage stability. High Static / ZIP for transient stability Low 1 sec Static / ZIP for frequency stability Medium Static / ZIP for voltage stability Medium / High 2 sec Static / ZIP for frequency stability Low Static / ZIP for voltage stability Medium Phasor estimation is one of the most popular topics in the field of power system estimation. There are many algorithms that have been developed to estimate the frequency and fundamental phasors of voltage and current signals. These include level-crossing technique [75], Kalman filtering [76], adaptive notch filtering [77], Shank’s method [78], Short-Time Fourier Transform (STFT) based modified algorithms, etc. Among those techniques, level-crossing technique, infinite impulse response (IIR), short time Fourier transform, and Finite Impulse Response (FIR) based dynamic phasor estimator are commonly used. Detailed descriptions of these techniques are given in Appendix 4-A. For the measurement based load modelling approach, fundamental frequency (50Hz or 60 Hz) data are needed. The focus of load modelling described in this report is on positive sequence load models that can be used in performing steady state and stability studies using commercial power system analysis software (see Table 4-2). Therefore, the three phase voltage and current data recorded as field measurement need to be Page 49 Modelling and Aggregation of Loads in Flexible Power Networks processed to extract the fundamental frequency quantities. A Discrete Fourier Transform (DFT) based sliding window algorithm is a useful tool for such purposes. It should be noted, however that there are situations when it becomes necessary to consider harmonics and the modelling of non-linear load behaviours. These may include studies related to quantifying harmonic levels, capacitor placement investigations and filter design. Such studies are performed at both transmission and distribution levels and is important from a system compatibility perspective. These types of studies are outside of the scope of this report. 4.2.5 LOAD MODEL STRUCTURES AND PARAMETER FITTING PROCEDURES Typical load model structures include constant impedance, constant current, constant PQ, static polynomial based (ZIP), exponential, and dynamic models (induction motor models and differential equation models). EPRI has proposed a composite load model structure covering ZIP and induction motors suitable for use in PSS/E simulations. DiGSILENT Power Factory software uses a general model which supports ZIP and exponential static load model which can be further combined with IM dynamics. Additionally, differential equations can be used to represent load dynamics. For specific studies where power quality, especially harmonics, are needed such as modelling of a distribution feeder supplying a large industrial load, a harmonic load model structure may be justified. Details of various load model structures have been discussed in Chapter 3 of this report. 4.2.6 LOAD SELF-DISCONNECTION FOLLOWING SYSTEM DISTURBANCES Measurement based load modelling requires a dedicated post-processing algorithm to screen events suitable for load model parameter derivation. Preferably, the chosen event should not include power interruption periods, because some of the optimization algorithms cannot effectively handle discontinuity [12]. For the same reason, this is also true for discontinuities in real and reactive power responses. In addition to severe system faults, discontinuous behaviour (such as stalling of air-conditioner motors and self-disconnection of power electronic or inverter-based loads following voltage sag) lead to discontinuity in measurement data. Such issues should be addressed as part of measurement based load modelling practices since discontinuous load behaviour may have a significant effect on the overall stability of the system. Self-disconnection Characteristics of Representative Load Components According to a selection of laboratory test results in [79], some load devices start to disconnect from the power system when the voltage drops below 80% of rated. Virtually all loads that can physically disconnect will do so for voltages lower than 30% (See Figure 4-4). It should be noted that some load will stay connected for voltage drops even below 30%. The time before the low-capacity appliances can be restarted (following their disconnection) is between several seconds and several minutes (typically 3 min). Two examples are provided in Figure 4-5. The restarting sequence of large refrigerators and district cooling units is pre-programmed and will take several minutes (typically 3 ~ 5 min). This includes a number of logic checks related to the correct status of the cooling plant auxiliaries. In both example cases, for small and large load components, the restarting time is long enough that load restart has no effect (and should not be considered) in transient stability studies. However, the re- connection characteristics of loads should be considered for mid-term and long-term stability studies such as frequency stability and voltage stability studies. The Impact of Load Self-disconnection on System Response Large portions of small residential air-conditioners can have a significant impact on voltage recovery following network fault events. The risk of slow and delayed voltage recovery is well described in literature and has been observed in many power systems supplying a significant portion of air-conditioner load [80]. The risks introduced by the large scale motor stalling are high currents in the MV and HV networks, with the potential for undesired operation of Zone 3 distance relays and power plant over-current relays. This has the potential to lead to a partial or complete blackout of the power system. Possible countermeasure include the Page 50 Modelling and Aggregation of Loads in Flexible Power Networks implementation of fast capacitive MVAr reserves such as Static VAr Compensator (SVC) or special protection systems based on undervoltage activated load shedding. Such countermeasures may constitute a large capital investment. It should be noted that residential air-conditioner compressor motors tend to stall very rapidly (in a Table 4-4: Measurement Options and Their Implications on Load Modelling Digital Fault Recorders Protection relays with Power Quality Monitors or Digital recorder with Type EMS/SCADA (DFRs) / Phasor data logging capability Meters (PQMs) analogue interface units Measurement Units (PMUs) Permanent installation is Either permanent or Permanent installation is Either permanent or Either permanent or Installation typical although temporary installation are typical temporary temporary temporary is possible typical 50-60Hz or more are typical Typical sample Configurable up to kHz Configurable. Typically 1 Configurable up to few kHz 0.1 to 0.5 Hz rates depending on unit kHz (every ms) or lower depending on unit 5 - 6 Hz can be also set for longer data. Data can be stored on local Typically local storage Data can be stored on LSU Data can be stored on LSU Usually archived in a storage unit and/or unit (LSU) capacity is and/or transmitted to and/or transmitted to Data handling historical system for transmitted to central data limited unless relay is central data repository via central data repository via multiple years repository via communication networked (WAN, LAN) communication system communication system system OK Short term voltage Insufficient data resolution (Insufficient data resolution stability if sampling rate is 5-6Hz), Long term May be OK depending May be OK depending on voltage OK on record length record length stability (including pre-event buffer) and LSU Good for all types of load Good for all types of load May be OK Transient capacity Insufficient data resolution modelling activities, modelling activities stability (Insufficient data resolution if sampling rate is 5-6Hz), Recommended application Oscillatory May be OK for low OK stability frequency modes OK if record length Frequency Insufficient data resolution (including pre-event OK stability buffer is ≥ 10 sec) Normally 2 seconds, 10 Infinity for permanently seconds can be possible installed and limited for Data length Infinity Infinity 40-70 seconds are typical by adding optional those that store data on an features. LSU High sampling rate can Applicability for load Continuous measurement make it possible to Continuous measurement Possibility to analyse Advantages modelling for almost all data analyse harmonics of data harmonics of loads stability studies. loads. Limited data length if only Insufficient data length, LSU is available Only RMS values can be Relatively small sampling Relatively small data Insufficient resolution due stored. rate. (Non equvidistant length Disadvantages to CTs for measuring fault For modelling of specific measurements without Insufficient resolution due current load only Analysing harmonics of proper time stamping.) to CTs for measuring / Restricted locations of loads is impossible. fault current PMUs Page 51 Modelling and Aggregation of Loads in Flexible Power Networks 100 90 Non-inverter-based 80 high-pressure Air-conditioner Electric hot water discharge lamps for industrial use supply system 70 Control sequencer Inverter-based Voltage [%] for industrial use air-conditioner 60 Inverter-based Electric hot water fluorescent lamp Miniature relay Timer 50 supply system Digital Electromagnetic television 40 switch Induction DVD recorder heating cooker Control sequencer 30 Induction for industrial use heating cooker Personal Inverter-based 20 Computer high-pressure discharge lamps Inverter-based 10 fluorescent lamp Digital television 0 0.001 0.01 0.1 1 Voltage Sag Duration [s] Figure 4-4: Individual Load Self-disconnection Characteristics Following a Voltage Sag [79]. 200 VU-N[V] 100 0 -100 -200 20 10 I [A] 0 -10 -20 Restart 800 Compressor During Restart Stoppage P [W] 200 400 P [W] 100 0 0 -100 200 Q [var] 100 Preparation of Restart 100 Q [var] 50 0 0 -50 -100 0 5 10 15 20 25 -200 0 200 400 600 800 Time [s] Time [s] Figure 4-5: Example of Load Self-disconnection Characteristics Following a Voltage Sag [79] (Left: Digital Television Under 80% Voltage Sag with 500 ms duration, Right: Inverter-based Air-conditioner Under 40% Voltage Sag with 500 ms Duration). few cycles of disturbance initiation[9]) and thus the solution to the problem may be quite difficult. Where a large portion of the load are residential air-conditioners, the only way to prevent stalling is to have enough short-circuit capability at or near the load centre to maintain an adequate voltage profile during and following network events. The investigation of this problem is quite complex as some air-conditioner system may trip due to their own overcurrent protection, leading to a non-uniform response from load devices which are essentially equivalent until exposed to such extreme operating conditions. The advent of inverter-based appliances and their fast disconnection, or idling for several minutes, following system disturbances, might influence power system behaviour. For example, according to the test results shown in Figure 4-5, as the conventional induction motors are replaced with the inverter driven motors, many load types became more easily disconnected from the power system [79]. Their impact on the dynamic security assessment must be carefully considered by the grid operator. In low load density areas, or when load centres are remotely located from each other, the fault-induced voltage sags (dips) have a stronger influence around the location of the disturbance. Generally, load self- Page 52 Modelling and Aggregation of Loads in Flexible Power Networks disconnection facilitates the voltage recovery in the network and offers more flexibility to grid operators to secure the voltage stability of the transmission system. Net load self-disconnection amount: P+ P’ Active power load Fault occurrence Increase in active power load due to voltage rise:P’ Apparent load self-disconnection amount: P Fault clearing Post-fault voltage Fault occurrence Voltage rise Voltage Pre-fault voltage level Fault clearing Time Figure 4-6: Derivation of Load Self-disconnection Parameters. The effects of large portions of load being subject to disconnection need to be carefully considered. Two possible consequences are: (i) It may be beneficial in that the unintentional loss of load may partially relieve the system and arrest the voltage reduction, thus limiting the subsequent stalling of other motors, and (ii) The unintentional reduction in load may excessively relieve the system following fault clearance, resulting in surplus reactive power that would need to be absorbed by local dynamic reactive control devices (SVCs etc.). Depending on the amount and location of reserve capability, overvoltages may occur in parts of the system that have low short circuit levels or are electrically remote from reactive control devices. Activation of synchronous generator underexcitation limiters may also warrant consideration depending on local stability issues. While the determination of such load behaviours is not an easy task, it is considered important especially when large portion of load are at risk and/or the load is highly concentrated in a particular region of the power system. Identification of Self-disconnected Load from Measurement Data [81] Load self-disconnection leads to reduced load demand and may yield higher post-fault voltages. Therefore, the actual amount of disconnected load should not be derived from only the difference between pre-fault active/reactive load and that observed post-fault. This is because the change of the active and reactive load, P’ and Q’ is also influenced by the change in bus voltage (as well as frequency) depending on the system broader response to the fault). Separating the voltage and frequency induced response from the component of load that is physically disconnected, is an important part of the analysis process. 4.2.7 CONCLUDING REMARKS ON MEASUREMENT BASED LOAD MODELLING One of the difficulties associated with measurement based load modelling is the availability of suitable measurement data coming from the field. Utilities often resort to searching database of historical events in order to identify recorded disturbances that are suitable for the derivation of load models. It is quite common that data coming from events that happened several years ago have to be used for load model estimation. Continuous measurements are essential to ensure the availability of event data required to construct accurate load models that are representative of the current system. The first step in developing system-wide comprehensive load models is collection of suitable data from the field. The process includes gathering the following key pieces of information:  Data about load composition. This includes load component information coming from billing records and load surveys. Typically, the load class splits at each load bus are obtained for different times of the year (summer peak, winter peak, autumn, spring, etc.), so that a specific load model for the given season can be eventually determined.  System event data collected from DFRs or PMUs installed within the transmission system. The event recordings can be used for system-wide load model validation once they are suitably processed. Such Page 53 Modelling and Aggregation of Loads in Flexible Power Networks data is normally collected at a high (or extra high) voltage bus in the system. For developing load models for use in both voltage and angular stability studies, the recording devices should be set to collect data for tens of seconds, so that slow voltage recovery events can also be captured.  Events data coming from acquisition devices installed at a few representative distribution feeders or substations. These events include an aggregate response of loads connected to a distribution feeder due to either an upstream transmission fault or a fault on an adjacent feeder. This data can be collected using a PQM or a portable DFR. Load composition and load component information is essential for building a system-wide load model. System event data is not as critical, although it is eventually needed to test the performance of the load model being developed. Note that the collection of system events is a time-consuming process due to the random nature of network faults. Even if event recordings are available, they may not be suitable for model validation or deriving load model parameters. Therefore, a long term commitment is essential for the collection of system event data that are to be used for load modelling purposes. The aggregate response of loads connected to a distribution feeder that are induced due to either an upstream transmission fault or a fault on an adjacent feeder can be used for load modelling activities. Such data would include motor dynamics and the response of static loads and would allow the portion of each type to be estimated. It is important to distinguish between load model validation carried out at an individual feeder or a specific distribution voltage bus and validation that may be undertaken at a broader system level, i.e. at one or more Extra High Voltage (EHV) buses in the system. 4.3 Methodologies for Component Based Load Modelling When measurements are not available or a sufficient load model cannot be built using a combination of already validated models (that are representative of the load connected to a particular bus), the use of component based load modelling is an alternate approach. A description of this technique, that may be considered more traditional than the measurement based approach, has been presented for example in [82] and [83]. This section presents a description of the various steps that are required to develop load models using the component based approach. For completeness, and to provide some indication on how to deal with missing data, this section includes some information on models and data recommended for use when representing typical appliances. Moreover, this section also provides guidance on two specific issues: Combination of devices with analogous mathematical descriptions into a single model, and the representation of the distribution feeders and downstream voltage regulators. The overall component based load modelling approach is summarised in Figure 2-3 in Chapter 2. The figure groups the classification of the load at each substation into four classes, namely residential, commercial, industrial and general lighting. Each load class is characterised by specific load components. Each component is represented by a specific model and data set. As shown in the figure, the model of each component may be represented by a composition of basic load models, namely static such as constant impedance (Z), constant current (I), constant power (P), and dynamic ones such as small (SM) and large (LM) induction motors. The following procedure should be followed to construct a load model at a transmission network bus using the component based approach. Page 54 Modelling and Aggregation of Loads in Flexible Power Networks Step 1: Data Collection and Load Characterisation The load should be classified into broad load types extracted from billing data or load inventory surveys. It is typical to consider three classes of loads namely, residential, commercial and industrial. Other classes could be added, for example general lighting, which could be a significant load at night in metropolitan areas. Some utilities may have multiple sub-classes within each major class. For example, residential customers may be divided into customers with electric heating, customers with gas heating, customers with specific “time of use” tariff rates, etc. The availability of quality input information will certainly lead to a more accurate load model. The load supplied at each bulk power delivery point should be categorised into load classes in terms of real power consumed (MW) and not in terms of the number of customers. On the basis of the collected data, it is useful to define a set of typical loading conditions linked with the daily, weekly and seasonal variation of the load. These conditions are typically: light load conditions and heavy load conditions at summer time, in winter and in intermediate seasons (when neither space heating nor cooling is applied). This information is typically available with distribution companies and can be provided for an entire distribution substation or for individual feeders, in which case the data needs to be aggregated at the substation level corresponding to the bus represented in planning cases). Each load class has typical load components that account for the majority of the power consumed by end users within that load class. Like load classes, load component percentages also have temporal and geographic variations. The time required to collect this information, however, is often one of the biggest challenges in building component based load models. This information can be obtained by conducting customer surveys or monitoring power usage for a sample of customers. Some distribution companies have end-use load research programs which can provide useful information [10], [82], [84]. Each class of load is also subdivided into specific components. For example, the residential class may typically include the following load components: cooling/heating pumps, refrigeration, power electronics load, and lighting. There is a different load component mix (different percentage participation) within each load class. Different values are also expected for different loading conditions within the same class. These values are typically inferred by surveys and available data on the adoption of a specific appliance by end users. In [85], a process of synthesis based on the application of the Monte Carlo method is proposed, to define a residential load model. The method starts from knowledge of the most relevant socio-economic and demographic characteristics, Unitary Energy Consumption and the load profiles of individual household appliances. As an example, Table 4-5 presents the percentages adopted for the residential load class in the Western regions of the United States. Chapter 5 of this report presents a more detailed analysis on the load modelling for residential and commercial sectors by using further classification. Table 4-5: Example of load components percentages of the residential load class adopted by the U.S. WECC Load Modelling Task Force [12] Season Cooling/heat Ventilation Refrigeration Electronic Resistive Lighting Other pump and fans (cooking) Summer 34% 15% 10% 15% 16% 0% 10% Winter 10% 15% 15% 10% 25% 15% 10% Fall/Spring 15% 10% 15% 10% 25% 10% 15% Light Load 10% 15% 15% 10% 25% 15% 10% The load composition at the bulk supply bus reflects the percentage of different load types/categories participating in the overall demand at a given point in time. The participation of different customers in the overall demand at bulk supply buses varies with time and it depends on the type of the load class involved, devices constituting demand and the nature of processes in end user facilities. While global participation of different classes of demand can be established to a certain extent based on metering data, it is very difficult Page 55 Modelling and Aggregation of Loads in Flexible Power Networks to establish participation of different load types within each class at any given time. The essential requirement for developing appropriate load models at bulk supply points, and consequently the load response to system disturbances at given point in time (in addition to individual load component models) is information about demand composition at that time. Figure 4-7 shows a decomposed daily loading curve (DDLC) in terms of different load types for commercial load for a working day. It is derived from daily demand data for different types of loads [86]. The peak demand is normalized to 100 for ease of comparison. The key information needed for estimating load model at the bulk supply bus is the type and percentage of different load categories rather than appliance or end-users involved, i.e.: devices for which individual load models are available. Therefore, a daily load-by-category loading curve is needed instead of that shown in Figure 4-7. Considering demand decomposition shown in Figure 4-7, it is obvious that the same load category (e.g., IM) may be included in different load types as identified in the figure. The load types listed in Figure 4-7 therefore can therefore be categorized as follows:  Air conditioning load, motor load, process load, refrigerator load and ventilation load are grouped into the category of motor load. Figure 4-7: Decomposed Daily Loading Curve for Commercial Load Sector.  Office equipment is categorized as power electronics load (SMPS load).  Internal and external lighting is categorized as lighting load.  Cooking, water and space heating load can be categorized as resistive load. Due to different definitions of miscellaneous load, miscellaneous can be classified as a single category in its own right. Following this categorisation, new DDLC can be produced as shown in Figure 4-8. From this figure, it can be easily determined which load category is participating in the total demand at a given bus and what share of the total demand it constitutes. Since individual load models of each load category are known, and there are fewer of them, it is much easier to estimate aggregate load model at any chosen time by combining participating load categories after applying appropriate weighting factors (based on participation in total demand). A similar approach can be used to convert conventional (based on load classes) DDLC (as shown in Figure 4-9 a)) into DDLC based on different load categories for a general network bus (as shown in Figure 4-9 b)), or to develop DDLC for any season of the year. Further detail about decomposition of daily load curves and development of aggregate load models for different load classes is provided in Chapter 5 and 0. Page 56 Modelling and Aggregation of Loads in Flexible Power Networks Step 2: Definition of Models for the Various Classes of Loads Each load component is represented by a suitable model that, in general, is a combination of the basic load models already described in Chapter 3. These are large induction motors, small induction motors, constant impedance, constant power and constant current models or exponential models. The percentage contributions of individual loads and their parameters are defined based on generic data from literature and through laboratory tests, respectively. Figure 4-8: DDLC Based on Load Categories for Commercial Load Class. Figure 4-9: DDLC at Bulk Supply Bus Based on a) Load Class Mix, b) Load Category Mix. Table 4-6 presents the percentage of basic load models adopted in the Western regions of the United States to represent a residential load. Two specific issues that one should consider are  The aggregation of individual loads in a single model of corresponding type.  The inclusion into the aggregate model of other network components that are present in distribution and sub-transmission networks, e.g., feeders, transformers equipped with on load tap changers, capacitors, etc. Several methods to group the same type of load into a single aggregate model are presented in literature, in particular those related to aggregation of induction motors. The classical reference for aggregation of loads represented by exponential models is [87], while references [84], [88–92] deal with aggregation of induction motors. An important part of developing aggregate load models at bulk supply buses is the representation of distribution and subtransmission network elements. The main aspects that should be considered are:  The feeder representation to capture voltage drop and real and reactive power losses in primary distribution circuits, distribution transformers, and secondary distribution circuits;  The representation of shunt reactive compensation both at the substation and feeder ends with the relevant regulation controls (where these exist). Page 57 Modelling and Aggregation of Loads in Flexible Power Networks  The operation of the tap changers on regulating transformers. Network configurations with several tap changing transformers in series, e.g.: at different sub-transmission and distribution levels may be challenging. Approaches and solutions to this problem are provided in [93–95]. Table 4-6: Percentages of the basic load models adopted by the U.S. WECC Load Modelling Task Force to represent the residential load class [12] Cooling/heat Ventilation and Resistive Component Refrigeration Electronic Lighting Other pump fans (cooking) Large motor 5% 100% 5% 0 0 0 50% Small motor 95% 0 95% 0 0 0 0 100% Constant Z 0 0 0 0 100% 50% (incandescent) 100% Constant I 0 0 0 0 0 0 (fluorescent) Constant P 0 0 0 100% 0 0 0 Step 3: Selection of Parameters of Individual Load Models For each load component, the parameter values applied to the load models are evaluated on the basis of typical characteristics of the appliance as documented in literature or coming from laboratory measurements (where these are available). As discussed in the previous section, protection and controls may cause motors to drop off and reconnect following network disturbances. When developing models for longer term dynamic studies, it is important to model protection and control circuits that become relevant during such extreme operating conditions. Suggestions for modelling specific appliances are provided below:  Small induction motor model: the positive sequence 3-phase induction motor model is adequate for normal operation while a constant impedance model should be used to represent the stall condition.  Compact fluorescent lamps: as the coefficients of the exponential model vary much less than the coefficients of a polynomial model, the use of the exponential model is recommended. The average value of the real power voltage exponent used in the past is 1.06 (i.e. almost constant current model), whilst the average value of the reactive power voltage exponent is 0.65.  High definition TV sets: to reproduce reactive power variations, an exponential model is recommended. The average value of real power voltage exponent used in the past is -0.18 (i.e. almost constant power behaviour), while the average value of reactive power voltage exponent is 0.376. As shown by the tests described in [9], the representation of Residential Air Conditioner (RAC) has some peculiarities. Particularly in the case of delayed voltage recovery, attempts to simulate these events using conventional load models (i.e. polynomial and exponential form of static load models and motor models that do not adequately capture stalling behaviour) were not successful. The delayed voltage recovery was attributed to the stalling of RACs. It was evident that a suitable representation of single-phase RACs when they are present in high numbers is critical for successfully emulating the delayed voltage recovery phenomenon in dynamic simulations. The conclusions of the experimental activities undertaken to address the modelling deficiencies are summarized in the following dot points.  Under steady state operating conditions, 80-87% of real power is consumed by its compressor motor, 10-12% by its indoor fan, and 3-5% by its outdoor fan. Page 58 Modelling and Aggregation of Loads in Flexible Power Networks  RACs are in general equipped with magnetic contactors that trip the unit when the supply voltage drops below a certain threshold, typically between 40% and 52% of nominal voltage. This voltage is independent of outdoor temperature.  The stalling threshold voltage of RAC motors is between 50% and 73% of nominal voltage, depending on outdoor temperature.  RAC compressors can stall for voltage sag as short as 3 cycles if the voltage reduces to below the stalling threshold voltage for this duration. This means they will almost certainly stall for any transmission fault causing a large voltage dip in local supply voltage.  Thermal Overload Protection (TOL) disconnects the motor if it remains stalled for a considerable amount of time (seconds to tens of seconds). TOL operation time is inversely proportional to the applied voltage at the RAC terminals.  Once a unit stalls, TOL invariably operates for units with reciprocating compressors. In the case of scroll units, the unit may recover from the stalled mode without TOL operation if the voltage recovers quickly enough (roughly above 70%).  RAC motors have a very low moment of inertia, which makes them quite vulnerable to stalling.  Voltage sags may affect the efficiency of the RAC if it goes into “no load” mode. A model for Residential Air Conditioners that incorporates the above characteristic is given in [10]. When the model of the individual load components is chosen, a static model consistent with the developed dynamic model should be defined to calculate the initial steady state condition. This issue is particularly important for induction motor models. In [83], the following procedure is proposed. Time-domain simulations are carried out in which the motor models are subjected to step-wise voltage variations, making their terminal voltages vary in the range of 0.8 to 1.0 p.u., and the quasi steady-state responses are recorded. The obtained P-V and Q-V characteristics are then fitted with an exponential model of the following form:  P  Pn aU   bU   aU  0  bU 0  (4.3)  Q  rU0 Pn cU   dU   cU  0  dU 0  (4.4) Table 4-7 shows the results presented in [83] for the case of the data set proposed in [37] for small motor and large motor dynamic models. Table 4-7: Parameters of static models of induction motor models (adopted from [83]) a  b â c ã d ä r 0.6 Small motor 0.86 0 0.14 0.09 0.8 1.6 0.2 -3.3 0.627 U n Large motors 0.86 0 0.14 0.09 0.747 1.54 0.253 -3.07 0.519 U n0.35 Once the reactive power absorbed in steady state by all the load components at the bus of interest is determined, the reactive power output of fixed capacitors should be calculated such that the net reactive load at the bus matches the power flow data. This last step is typically done using commercially available power system analysis software. Page 59 Modelling and Aggregation of Loads in Flexible Power Networks 4.4 Model Validation and Sensitivity Analysis  Model validation at a distribution bus level At an individual feeder or bus level, it is essential to collect data about load class, load components and load characteristics to the extent possible. This data should be collected for different seasons of the year. In addition, monitors can be installed at a few representative distribution feeders (representing different mixes of load classes in the system) to collect disturbance data. The load class, load component and load characteristics data should be used to come up with the best estimate of load composition. If suitable disturbance data is available, these percentages can be compared with the percentages obtained from the measurement based approach. It is important to compare related data. For example, an event obtained on a hot summer day should not be used to compare load component percentages for a typical winter day. Also note that due to temporal variations in loads, and the fact that load class and load component data will have some uncertainty no matter how hard one tries to obtain the information, it is not possible to come up with the exact same answer with the two approaches. It is important, though, that the two approaches give results that are of the same order of magnitude have the same trend and are in agreement with the utility planner’s engineering judgment.  Model validation at the system level Validating the load model at the system level is the ultimate aim of developing a load model. The steps involved in the system-wide model validation process are as follows:  Collect system event data at one or more EHV buses in the system using either DFRs or PMUs. These devices should be set to collect continuous data for tens of seconds post-disturbance with adequate pre-disturbance record length to establish the initial steady state conditions.  If an event recording for a major fault is available, at one or more EHV buses in the system, then by comparing the actual recording to the simulated response one can obtain a better understanding of how good the load model is. The model can be refined if the measured and simulated responses don’t match with sufficient accuracy. This is an iterative process and engineering judgment needs to be carefully applied. This would require the additional activities of obtaining system information, such as status of generators and other equipment in the surrounding region at the time of the disturbance, to be able to faithfully simulate the event for comparison with the measured system response. It should be noted that in the real power system, there will always be variability (i.e. temporal and spatial variations in load due to climate and human behaviour) and uncertainty in the load model data. One can never have exact information about all the loads that are connected to the system at any given time. It is not possible to achieve an exact match between simulated and measured data at each load bus in the system and some discrepancy is to be expected. What is more important, however, is to compare the measured and simulated responses at a few EHV buses in the system where the measurements were taken. If there is a reasonable match between two sets of data/responses, then it indicates that the developed load models are appropriate for the system. If the match is not satisfactory, then the load model parameters (most likely percentages of static and dynamic load components) should be adjusted as necessary. During the model validation process, it is particularly important to assess sensitivity to variations in different parameters. Despite the best modelling efforts, the characteristics of actual loads make it difficult to remove all the uncertainties involved. To deal with these uncertainties, sensitivity analysis is always recommended to assess the effect of varying model parameters and the ability to reproduce actual measured responses. Page 60 Modelling and Aggregation of Loads in Flexible Power Networks The sensitivity analysis should identify the set of key parameters for the study of interest so that the load model tuning process can specifically focus on refining those parameters. In general, the sensitivity analysis should include an assessment of the effects of varying the amount of dynamic load component, induction motor load in particular, in the aggregate load model and the assessment of the effect of changes in motor parameters, e.g., stalling voltage as a function of temperature, or motor inertia. Additional analysis could include the sensitivity of the load model to self-disconnection and reconnection of part of the system load, as this could have significant effect on longer term dynamic studies, in particular, voltage and frequency stability studies. 4.5 Summary In this chapter, the measurement based and component based load modelling approaches have been presented. Recommendations are made with respect to all stages of the load model development process for both approaches. The issues of selection of monitoring location for both, continuous monitoring and staged field tests, selection and processing of monitoring data, sampling rates, signal filtering, selection of model structure and self-disconnection of part of the load are discussed in detail. The step-by-step recommendations for system load model development given in this chapter could be used by engineers in distribution and transmission companies for development of appropriate load models. Page 61 Modelling and Aggregation of Loads in Flexible Power Networks Chapter 5 Load Models for Typical Classes of Customers (Load Sectors) 5.1 Introduction A “Load Sector”, also known as a “Class of Customer” or a Load Class 1 , is generally defined as an aggregation or collection of different types of loads (or different load categories, see Chapter 3 and Appendix 5-A for a general overview), representing a typical structure and composition of electrical devices and equipment found in a specific end-use application, where similar activities and tasks are performed. This usually results in inherent similarities in characteristics and patterns of active and reactive power demands of end-users, allowing the use of the same or similar aggregate load models for the representation of their aggregate demands. In order to provide a more accurate load model, one load sector may be further divided into several sub-sectors. In power system studies, load models representing a group of loads from the same or different load categories/sectors connected to a single network bus (being the so called “bulk load supply point, BLSP) are usually termed as “Aggregate Load Models” or “Models of Aggregate Load”. They are often in a simplified form, e.g. use exponential or polynomial analytical expressions, in order to reduce the required computation times and overall complexity of the analysis of transmission and distribution networks. Accordingly, a model of a load sector is always an aggregate load model. This chapter mainly considers one special category, being load models of typical load sectors. Both in related publications (e.g. [3], [14], [30]), and in available statistical data (e.g. [96] and [97]), the corresponding representation of aggregate system loads are generally divided into three load sectors: residential (or domestic), commercial (or general service) and industrial. The residential load sector is generally defined as houses or buildings for dwellings, whose sole purpose is to provide residency to the occupants. The commercial load sector consists of public, private and voluntary establishments and businesses, which are generally aimed at providing a specific service to the public. The commercial load sector does not involve general product manufacturing and material processing activities, which are part of the industrial sector. Accordingly, industrial sector loads are defined as equipment and devices employed in any business that performs raw or any other level of material processing, fabrication or manufacturing, or similar activities. It is generally not possible to define general (i.e. generic) aggregate load models for the industrial sector, as the specific industrial processes and activities performed at different sites are usually not comparable across the whole industrial load sector. On the other hand, generic aggregate load models may be defined for residential and commercial load sectors, as there is less diversity within the loads and the general load structure is similar. However, this assumption is only applicable if load sub-sectors are introduced and defined, as the electrical characteristics of end-use equipment and their actual active and reactive power demands will vary between different users within the same load sector. Furthermore, the hourly and daily (24-hour load profile), weekly and monthly (difference between weekdays, weekends and holidays) and seasonal (summer-winter) variations will also differ within a given load sector. This suggests that it is often necessary to subdivide one specific load sector into corresponding “load sub-sectors” in order to correctly represent involved temporal, spatial and functional variations in their active and reactive power demands and other relevant characteristics. Variations in general electrical characteristics and active/reactive power demands within each load sector have been reported in a number of previous studies (e.g. [3], [98–104]). However, the production of detailed, time-varying load models for use in power system studies from this data is not readily available in existing literature. Therefore, 0 describes a general methodology for producing aggregate component based load models for different load sectors and sub-sectors, where load structure and composition are determined from available statistics and/or measured data (e.g. data collected by “smart meters”). Particular attention is given 1 In this report, terms “Load Sector”, “Class of Customer” and “Load Class” are interchangeable. Page 62 Modelling and Aggregation of Loads in Flexible Power Networks to the residential and commercial load sectors, as the industrial load sector will require specific load models which are generally determined by the actual manufacturing processes and involved end-use devices which form the loads. 5.2 Load Sectors (Classes) 5.2.1 RESIDENTIAL LOAD SECTOR (AND SUB-SECTORS) Although the purpose of every residential dwelling is identical and, generally, the individual loads used there will be similar, it is possible to divide the residential load sector into four sub-sectors based on the location, size and type of dwelling. The location and size will generally determine the network conditions, e.g. transformer rating, network configuration and strength, as well as type and length of cable or overhead line. The level of street/outdoor lighting, which is supplied by the same network, will also be influenced by the location. When aggregating to higher voltage levels, the influence of these differences will become more significant. Further details of the typical network components and parameter values for different residential load sub-sectors are given in Appendix 5-B, while Appendix 5-C gives additional residential load curves and models. Differences will also exist in terms of the size of renewable/distributed generation that is likely to be located in close proximity to the residential areas (see Chapter 6). Based on these general characteristics and parameters, the residential load sector is in this report is divided into highly-urban, urban, suburban and rural sub-sectors. Highly-urban Residential Sub-sector This sub-sector encompasses “metropolitan areas”, typically having high power density (both per m2 of the premises and km2 of the network) and consists of high-rise, multi-storey buildings with mostly flat-or unit-type dwellings. At these locations, the network is strong and meshed, while supply transformers have higher power ratings. The MV/LV feeders are usually cables (not overhead lines) and typical lengths are short (from few tens, up to few hundreds of meters), except in the case of high-rise buildings (e.g. “skyscrapers”), where the length of cables can be long. Furthermore, in this residential sub-sector, three-phase motors will be present to supply lifts, elevators, pumps and central air-conditioning systems, which are generally not found in other residential sub- sectors. In this residential sub-sector, only micro and small-scale generation technologies will be present due to space limitations (e.g. solar/PV, micro-CHP and some wind technologies). An exception again exists for high- rise buildings, where bigger electricity and/or heating (and cooling) generation systems are sometimes installed. Where district heating systems are present, bigger CHP systems may also be employed. It is also expected that the highly urban residential load sub-sector will have a higher penetration of modern power electronic loads, but this assumption is not yet validated. Urban Residential Sub-sector This sub-sector involves house-type dwellings, ranging from one to a few-storey tenement building, located outside the central areas of cities and large towns (e.g., terraced and semi-detached houses, or smaller blocks of flats or units). The supplying network is still strong, although somewhat reduced in comparison with the highly- urban residential load sub-sector. Meshed underground cable networks can be expected. It has a medium concentration/density of powers per m2/km2 and a relatively high penetration of modern power electronics loads (as discussed previously). Transformer ratings will be lower than in the highly-urban sub-sector, and the feeder lengths will also be longer, due to the lower load density. Again in this sub-sector, only micro and small- scale generation technologies will be present due to practical space limitations. Sub-urban Residential Sub-sector This sub-sector incorporates house-type dwellings ranging from one to a few-storey buildings located in suburban areas of cities and larger towns, or making up large parts of residential loads of smaller towns (typically semi-detached and detached houses). Due to the increased distance to the cities, the network is of Page 63 Modelling and Aggregation of Loads in Flexible Power Networks medium strength (although not significantly reduced compared to urban areas) and often radial in nature. It is further characterised by a medium concentration/density of powers per m2/km2 and a medium to high penetration of modern loads. The transformer ratings will be lower than in the urban sub-sector and the length of cable or overhead lines will be further increased due to the lower density of housing. Small/micro solar/PV, wind and CHP generation technologies can be expected to exist as space restrictions become less onerous. Rural Residential Sub-sector This sub-sector incorporates house-type dwellings ranging from one to a few storey buildings typically in remote locations and connected to weak radial networks. It is further assumed to have low concentration/density of powers per m2/km2, a smaller penetration of modern (power electronic) loads as an overall percentage of the load mix, and some three-phase motors for performing horticultural and/or agricultural activities 2 . Supply transformers will have lower power ratings and will be connected to MV networks by typically longer distance overhead lines. In this sub-sector, it is possible to have medium to large- scale distributed generation technologies, e.g. wind or PV systems, either located nearby, or implemented within a combined residential-agricultural object, such as a crop producing/processing farm. Load Structure and Composition As shown in Table 5-1, the overall load structure (expressed through the corresponding types of loads for all four residential sub-sectors) is expected to be similar, but the actual contributions of different load categories (i.e. load compositions) will be different. Additional variations in the characteristics of supplying LV and MV networks will influence the need for further differences in the corresponding aggregate load models. Table 5-1: Residential load composition Residential sector classification Load type Load category/sub-category Highly urban Urban Sub-urban Rural Cooking Resistive     Heating Resistive     Directly connected three-phase motors     HVAC Drive controlled three-phase motors     ICT equipment SMPS     GIL     Interior lighting Energy efficient lighting – CFL and LFL     Lifts/elevators Three phase motors     Power electronics SMPS     Refrigeration Single-phase motors     Abbreviations: HVAC – heating, ventilation and air-conditioning, ICT – information and communication technologies, GIL – general incandescent lamps, SMPS – switch-mode power supplies, CFL – compact fluorescent lamps, LFL – linear fluorescent lamps.  : included; : not included Load trends The residential load profile has two distinctive peaks – one in the morning, when the majority of the population are getting up and commencing their day, and one in the late afternoon and early evening hours, when dwellings are most heavily occupied. It is also possible for peaks to occur during the day, for example at lunch time or during the night-time, due to electric heating that is remotely/locally controlled by off-peak tariff meters. The morning and evening peaks are usually dominant as shown in Table 5-1. 2 It is often hard to make a clear distinction between rural residential and small agricultural load sectors. Page 64 Modelling and Aggregation of Loads in Flexible Power Networks Daily (i.e. weekday/weekend) and seasonal variations will exist within each sub-sector. However, as occupant’s habits, as well as the actual load devices present, are likely to be similar, these variations are expected to be consistent and similar between all sub-sectors. In the UK, seasonal variations are caused by longer times for use of heating, lighting and cooking equipment during the winter seasons and air-conditioning and refrigerator/cooling loads during summer. This is a direct result of the temperature variations and of the extent of available daylight. Generally, the seasonal variations will be significantly influenced by the climate zone in which modelled aggregate load is located. Variations between individual weekdays, although likely to happen (e.g. due to a public holiday, or some important social event influencing work-hours schedule) are in this report considered negligible. For residential load profiles, however, significant variations between the weekday and weekends can be expected. The main difference is that dwelling occupancy will increase during daytime hours at the weekend, ultimately resulting in increased demand. Demand will also be influenced by prevailing weather conditions and "cultural norms". For example in some countries, Sunday daytime load demand is expected to be higher than Saturday daytime load due to the “Sunday lunch effect”. Additionally, there is a less pronounced morning peak in weekend load profiles as occupants will become active at different times, resulting in a more gradual increase in the load demand. However, the evening peak for weekdays and weekends is expected to be similar. (Note: this behaviour may be different in different regions of the world) 5.2.2 COMMERCIAL LOAD SECTOR The commercial load sector contains greater variations than the residential load sector as the functions of the buildings and businesses contained within are much more diverse. Hence, there are potentially more sub-sectors for the representation of aggregate commercial loads, than in the case of the residential load sector. One of the possible break-down of commercial load sub-sectors is given in Figure 5-1. It is possible to further divide each sub-sector with respect to the building size (e.g. small, medium and large offices) and/or function (e.g. sale/wholesale, storing and distribution of durable or non-durable goods or products). Both factors will influence the actual structure and composition of the connected loads. Some examples of aggregate load curves for the commercial sector are given in Appendix 5-D. Hotel and catering Communication and transport 12.63% 5.63% Health 2.75% Education 5.77% Retail Commercial offices 36.03% 13.41% Other 5.08% Warehouses Sport and leisure 12.9% 5.8% Figure 5-1: Contribution of Sub-sectors to Commercial Load Sector Electricity Consumption (UK data, based on [105]). Commercial sub-sectors are not defined with respect to location as there is much greater variability in building location than in the residential sector. However, some sub-sectors are only expected to be found in certain locations, which are outlined where applicable in the discussion below. The following sections describe each of the commercial sub-sectors and their specific characteristics in terms of load composition and load trends over time. Page 65 Modelling and Aggregation of Loads in Flexible Power Networks Commercial Office Sub-sector – Load Composition There are three general types of commercial offices: public sector, private sector and voluntary/charities. The public sector is government-run and deals with the administration of state-regulated businesses, activities and projects. The private sector consists of privately owned businesses that generally trade for profit. The voluntary sector consists of non-profit organisations, e.g. charities. From a load modelling point of view, there is no strong distinction between the three. As mentioned previously, commercial offices can be further divided by building size. It is expected that ‘small’ commercial offices will have very limited air conditioning (i.e. certainly no three-phase motors), ‘medium’ commercial offices may have a mix of single-phase and small three-phase air conditioning units and ‘large’ (or “prestige/corporate”) commercial offices are likely to have large three-phase heating, ventilation and air conditioning (HVAC) units as well as commercial grade lifts. The load density is also expected to increase with sub-sector size, with ‘large’ offices exhibiting increased demand per m2/km2 in comparison to ‘small’ and “medium” offices. Furthermore, the contribution from power electronic loads is expected to increase with the sub-sector size, as the volume of the required information and communication technology (ICT) equipment and other supporting appliances will increase. It is also assumed that ‘large’ commercial offices will only be present in metropolitan areas, similar to the presence of the highly urban residential load sub-sector. Table 5-2: Loads present in commercial office load sub-sector Commercial office classification Load type Load category/sub-category Small Medium Large Cooking Resistive    Exterior lighting HID    Heating Resistive    Directly connected single-phase motors    HVAC Directly connected three-phase motors    Drive controlled three-phase motors    ICT equipment SMPS    Interior lighting Energy efficient lighting: CFL and LFL    Lifts/elevators Three-phase motors    Refrigeration Single-phase motors    Abbreviations: HID – high intensity discharge.  : included; : not included Commercial Office Sub-sector – Load Trends The opening hours of a given commercial premise will dictate its daily load profile (e.g. standard office hours in the UK, USA, Australia and many other countries are Monday to Friday, from 09:00 – 17:00). In the hours prior to opening, certain systems will automatically ‘switch-on’ (e.g. HVAC or space heating systems) causing a gradual increase in demand prior to the actual opening time. A ‘Switch-off’ effect is also present, but is generally less pronounced. During the night-time hours, a ‘base load’ continues to be present, e.g. ICT-support systems and night-lighting. This will exhibit small, if any, seasonal variations. Therefore, the total seasonal variations of the commercial office sub-sector are not expected to vary in wide ranges, as winter heating loads are replaced with cooling (air-conditioning) loads in summer. However, seasonal variations for ‘small’ commercial offices may be more pronounced, due to the lack of highly automated air-conditioning systems and the likelihood of such devices being manually switched based on personal comfort levels. Page 66 Modelling and Aggregation of Loads in Flexible Power Networks The weekend load profile is assumed to be constant and equal to the evening ‘base loading’ conditions. Communication and Transportation Sub-sector – Load Composition Although generally grouped together, it is possible to divide this commercial load sector into two distinctive sub-sectors. The communication sub-sector will include post-offices, various courier services, data centres and media. These are expected to be very similar to the commercial office sub-sectors and are not discussed in further detail. Transport services, e.g. railways, taxis, buses, air transport etc, will exhibit significantly different characteristics. The most notable difference is that this sub-sector will require more outdoor illumination than many other commercial sub-sectors. Size variations exist within the transport sub-sector, with ‘large’ facilities expected to utilise three-phase HVAC systems. (Near future) introduction of “fleet” electric vehicles will have a strong impact on load profiles, introducing night-time and day-time charging effects. It should be noted that power supply requirements for traction equipment is not included in this load sector and should be treated as an industrial load due to its specific characteristics and often significant power demands. Table 5-3: Loads present in communication and transport load sub-sector Transport classification Load type Load category/sub-category Small Large Cooking Resistive   Exterior lighting HID   Heating Resistive   Directly connected three-phase motors   HVAC Drive controlled three-phase motors   ICT equipment SMPS   Interior lighting Energy efficient lighting: CFL and LFL   Lifts/elevators Three-phase motors   Refrigeration Single-phase motors    : included; : not included Communication and Transportation Sub-sector – Load Trends The communication sub-sector is expected to have similar load trends to the commercial office sub-sector, with some notable additional considerations. ‘Large’ transport facilities, i.e. major airports and train/bus stations, will have extended opening hours (often 24 hours/7 days) and little variation between weekday and weekend consumption. Due to the large volume of lighting load, the seasonal variations are expected to be significant. Additionally, there may be a moderate to strong influence of holiday and vacation period on load profiles. Education Sub-sector – Load Composition The education sub-sector includes all types of educational facilities, e.g. primary schools, secondary schools and colleges/universities, and can be also divided with respect to typical building/campus size. Primary schools are classed as ‘small’ education facilities, secondary schools as ‘medium’ education facilities and colleges/universities as ‘large’ education facilities. Although the total power consumption will obviously increase as the size of facility grows (simply due to the increase in building/campus size), the loads present will also change in nature. For example, the number of computer/power electronics load devices can be expected to increase between a primary school and a major university campus.. Furthermore, universities may contain electrical installations with higher voltage levels and some three-phase equipment, e.g. for laboratories and performing specific research activities. It may also be cost effective for Page 67 Modelling and Aggregation of Loads in Flexible Power Networks some universities to have their own CHP schemes. Universities are usually expected to be present in cities and large towns, i.e. highly urban and urban areas, although some campuses may be located at the outskirts of large cities. Table 5-4: Loads present in education load sub-sector Education facility size Load type Load category/sub-category Small Medium Large Cooking Resistive    Exterior lighting HID    Heating Resistive    Directly connected three-phase motors    HVAC Drive controlled three-phase motors    ICT equipment SMPS    Interior lighting Energy efficient lighting    Lifts/elevators Three-phase motors    Refrigeration Single-phase motor    Three-phase lab equipment Three-phase motors     : included; : not included Education Sub-sector – Load Trends Official working hours are similar to the commercial office sub-sector, i.e. Monday – Friday (09:00 – 17:00). However, there are some noticeable differences between this sector and other commercial sectors. For all education sub-sectors, there are lengthy (ranging from one to several weeks) holiday and vacation periods, where demand is considerably reduced. The most notable is the summer period where such facilities may be closed for between six to eight weeks. Term breaks may last from a few to several weeks. Typical seasonal variations are also expected for this sub-sector, e.g., to use of lighting and heating loads. Health Sub-sector – Load Composition The health sub-sector includes any facility whose purpose is to provide healthcare and medical service, ranging from local general practitioners (GP) or dentist surgeries, to ambulances and large hospitals and polyclinics. Accordingly, it is possible to divide this sub-sector into 'small’ (e.g. doctor and dental surgeries) and ’large’ health facilities (e.g. hospitals and clinics), respectively. ‘Small’ health facilities will have load mix consisting of power electronics/SMPS load, motor load, LFL interior lighting load and some specialist equipment. ‘Large’ health facilities will require three-phase supply for high- power specialised medical and other equipment, and will require specific/dedicated load models for each individual site (similarly to industrial load sector). Further differences include the need for catering and HVAC units in ‘large’ facilities, which are not expected in ‘small’ health facilities. Some bigger on-site generation units, e.g., CHP or Combined cooling heating and power systems, or stand-by or emergency systems are expected to be found in large hospitals. ‘Large’ health facilities are expected to be located only in cities and large towns, i.e. highly urban and urban networks, but unlikely in down-town central locations. Accordingly, the appropriate network configuration should be determined on a case-by-case basis. Health Sub-sector – Load Trends ‘Small’ health facilities will operate around similar hours to commercial offices, i.e. Monday – Friday (09:00 – 17:00). Seasonal variations will depend on the heating and HVAC systems installed on the premises. Page 68 Modelling and Aggregation of Loads in Flexible Power Networks ‘Large’ health facilities will have a larger ‘base load’ because of the presence of various health-critical systems. This will contribute to a significant night-time load, which will also include the night-time staffing requirements of such facilities. Table 5-5: Loads present in health load sub-sector Health facility classification Load type Load category/sub-category Small Large Cooking Resistive   Exterior lighting HID   Heating Resistive   Directly connected three-phase motors   HVAC Drive controlled three-phase motors   ICT equipment SMPS   Interior lighting Energy efficient lighting – CFL and LFL   Lifts/elevators Three-phase motors   Refrigeration Single-phase motors   Three-phase equipment Three-phase motors    : included; : not included Table 5-6: Loads present in hotel and catering load sub-sector Hotel/catering classification Load type Load category/sub-category Small Large Cooking Resistive   Exterior lighting HID   Heating Resistive   Directly connected single-phase motors   HVAC Directly connected three-phase motors   Drive controlled three-phase motors   ICT equipment SMPS   Interior lighting Energy efficient lighting: CFL and LFL   Lifts/elevators Three phase motors   Power electronics SMPS   Refrigeration Single-phase motors    : included; : not included Hotel and Catering Sub-sector – Load Composition It is possible to further sub-divide the hotel and catering sub-sector into ‘small’ (local facilities) and ‘large’ (corporate facilities). ‘Small’ facilities are defined as one to a few storey buildings, while ‘large’ facilities are high-density, high-rise, multi-storey buildings. ‘Small’ hotel and catering facilities are not expected to include a significant number of (if any) three-phase devices, but ‘large’ facilities will require three-phase supplies to drive large air-conditioning systems as well as operate service lifts, public elevators and pumps. ‘Large’ hotels/catering are only expected to be present in cities and large towns, i.e. in highly-urban and urban networks, although they may be also present in certain destinations outside the urban areas (e.g. popular summer/winter tourist centres). Page 69 Modelling and Aggregation of Loads in Flexible Power Networks Hotel and Catering Sub-sector – Load Trends The hotel and catering facilities will exhibit large seasonal variations in demand, due to the nature of the business. However, the specific variations will be determined by the type of the activity/tourism and presence of local facilities, e.g. skiing, spa, beach, etc. Consumption is expected to increase during ‘peak season’ and, generally at weekends as the number of occupants will increase during such times. The type of heating and HVAC system will also influence the total seasonal variations. Retail Sub-sector – Load Composition The retail sector covers any business that is organised towards the sale of goods. This will cover a range of outlets; from small and local businesses (shops), up to large shopping centres (malls). Therefore, it is also necessary to divide this sub-sector into small, medium and large, again based on building size. Furthermore, food retail outlets must be defined separately due to the high concentration of lighting, cooling and cooking equipment required for their operation. Although the size of retail outlets is variable, it is expected that the total power consumption increase from small to large facilities is simply due to increased building size, with percentage contributions of different loads essentially not changed. However, the size of the facilities will determine the location. ‘Small’ local businesses are generally found in residential areas, but may be present in any network configuration. ‘Medium’ larger ‘chain’/corporate stores will be found in city streets and may comprise multi-level buildings. ‘Large’ shopping centres or supermarkets, consisting of many individual retail outlets, will only be present in highly-urban and urban sub-sectors. ‘Large’ retail outlets will also have outdoor illumination for dedicated (often extensive) public areas (e.g. car parks). Table 5-7: Loads present in retail load sub-sector Retail facility size Load type Load category/sub-category Small Medium Large Food Cooking Resistive     Exterior lighting HID     Heating Resistive     Directly connected single-phase motors     HVAC Directly connected three-phase motors     Drive controlled three-phase motors     ICT equipment SMPS     Interior lighting Energy efficient lighting: LFL and HID     Lifts/elevators Three phase motors     Single-phase motors     Refrigeration Three-phase motors      : included; : not included Retail Sub-sector – Load Trends The opening hours of the retail outlets will dictate the load trends, with customer volume unlikely to change the overall consumption. ‘Small’ retail outlets will have similar hours to commercial offices but will generally be open for a few hours before and after normal working hours. ‘Medium’ and ‘large’ retail outlets will have longer opening hours and will also be open at the weekends. The major variations in seasonal demand will be determined by the installed heating/HVAC systems. However, increased lighting demand will occur during winter months, while the outside temperature will influence the refrigeration motor loads (with increased consumption of this load category expected during the warmer summer months). Sport and Leisure Sub-sector – Load Composition Page 70 Modelling and Aggregation of Loads in Flexible Power Networks It is possible to divide this sub-sector into two main types being community/public recreation facilities, and sport grounds/stadiums. Community/public recreation facilities would typically include gyms and associated sports resources. Although the loads found in both types will be similar, the percentage contribution will be different. However for both applications, lighting is the dominant load. The contribution from HVAC systems will be determined by the size of the facility. HVAC is not expected to be present in high percentages in stadiums, but will be present in ‘large’ public recreation facilities, together with demand for hot water (showers and so forth). Sport and Leisure Sub-sector – Load Trends Community recreational facilities will operate during similar hours as retail outlets, but with extended evening opening hours (typically until late into the evening between 22:00 and 23:00 hours). Sport stadiums are essentially intermittent loads and will operate only during specific periods of the week/month/year. Table 5-8: Loads present in sport and leisure load sub-sector Sport and leisure facility classification Load type Load category/sub-category Small public Large public Stadium Cooking Resistive    Exterior lighting HID    Heating Resistive    Directly connected single-phase motors    HVAC Directly connected three-phase motors    Drive controlled three-phase motors    IT equipment SMPS    Interior lighting Energy efficient lighting – CFL and LFL    Refrigeration Single-phase motors     : included; : not included Warehouse Sub-sector – Load Composition It is necessary to divide this sub-sector into two main types being storage of non-durable goods that require a controlled environment (e.g. temperature, humidity, ventilation, etc.) and storage of durable goods that do not require a regulated environment. In this sub-sector, the main loads will be interior and exterior lighting, and the environment-regulating equipment, e.g. refrigeration for food storage. Some computer equipment is expected for stock monitoring purposes and on-site order processing although this is likely to be a small component of the overall load demand. Some warehouses may also have their own transportation facilities for distribution of goods. Night-time operation is likely, so exterior lighting (HID type) is expected in these sub-sectors. Warehouse Sub-sector – Load Trends The load profile will be determined by the hours of operation. However in controlled environments, the regulating equipment will be more or less constant. There may be seasonal variations in the regulating equipment consumption (as previously discussed for refrigeration equipment) but other seasonal variations are expected to be a result of increased or decreased demand for heating/HVAC and lighting demands. It is likely that warehouses will also be active during the weekend and night times, so daily variations are not expected to be as pronounced as for other commercial sub-sectors. Page 71 Modelling and Aggregation of Loads in Flexible Power Networks Table 5-9: Loads present in warehouse load sub-sector Warehouse classification Load type Load category/sub-category Controlled env. Uncontrolled env. Cooking   Exterior lighting HID   Heating Resistive   HVAC Directly connected single-phase motors   ICT equipment SMPS   Interior lighting Energy efficient lighting - CFL and LFL   Directly connected single-phase motors   Refrigeration Directly connected three-phase motors    : included; : not included 5.3 Summary This chapter has presented a detailed description of the two most diverse load sectors (classes of customers), commercial and residential, which are most likely to undergo the biggest changes in load types participating in the load class mix in the future. Commercial and residential load sectors (classes of loads) are described in detail, including all relevant sub-sectors. Additional material is provided in Appendix 5. : Page 72 Modelling and Aggregation of Loads in Flexible Power Networks Chapter 6 Modelling of Active Distribution Network Cells 6.1 Introduction Active Distributed Network Cells (ADNC) are distribution networks with a significant amount of distributed generation (DG) which at specific periods of time (e.g. at minimum loading conditions) is a net exporter of active power, but at other times (e.g. at maximum loading conditions) may be a net importer of active power. Micro Grid (MG) is a particular example of ADNC. The MG comprises a Low Voltage (LV) network with loads and several micro generation systems (usually with power electronic interfaces) connected to it, and a hierarchical control with management system comprising of two main control levels, being the local and central control mode, allowing the MG operation as a flexible active cell either when interconnected with the Medium Voltage (MV) distribution network or when isolated from it. Large-scale integration of various DG technologies into distribution networks leads to a gradual transition from passive to active distribution networks, with a large number of small to medium size generating units of various technologies spread over both MV and LV levels. Approximating the dynamics of these networks using passive lumped loads, as has been done previously, lacks the accuracy when simulating the dynamic behaviour of complex ADNC. Although most of the work done in this field has been focused on distribution networks connecting wind farms, aggregated models able to appropriately account for the ADNC dynamics (which may comprise a large number of active components with various technologies and sizes) have been developed. The aim has been to replace the whole detailed model (or a part of it)with a reduced order model exhibiting similar dynamic characteristics as the whole original ADNC. The ADNC should be represented through a combination of aggregated passive components and aggregated active components, so that power system operators can assess their impact, with a considerable computational time saving, but keeping the required accuracy regarding the phenomenon under study. Two methods to derive aggregated models of power systems (commonly known as dynamic equivalents) are system reduction and system identification techniques [106], [107]. Reduction techniques are based on aggregation and elimination of some components of the detailed model of the system. The two mostly found in literature are known as modal analysis [108], [109], and coherency based aggregation methods [110–112]. Modal analysis aims to reduce the system by aggregating similar modes and eliminating those modes that are not of interest. There are three main drawbacks: this approach is time consuming, the reduced order models do not have structural identity (they are purely mathematical in nature) and linear methods cannot properly capture complex power system dynamics following major disturbances. As an alternative to modal analysis based reduction techniques, coherency based aggregation became very popular by being able to identify coherent generators based on rotor angle swings, to aggregate those coherent generators into a single equivalent generator through the Zhukov’s method [113], [114] and to reduce the network by means of the Dimos method [114]. Several methods based on linear models have been used for coherency identification.[106], with techniques including inspection of time responses, pattern recognition, closest unstable equilibrium point, Lyapunov function, weakly coupled subsystems and modes of low frequency oscillations. Coherency identification without linearisation has also been attempted, as discussed in [115–119]. In system identification techniques, the dynamic equivalents are determined using a data set comprised of either by simulated data or real system measurements at a boundary bus between the study system and the system to be reduced. After a model structure is chosen, its parameters are adjusted so that the model response matches the measurements. Similar procedures have been adopted for aggregation of loads based on measurement based models (see Chapter 4). Among system identification techniques, Artificial Neural Networks (ANN) are the most preferred measurement based method due to its high inherent ability for modelling Page 73 Modelling and Aggregation of Loads in Flexible Power Networks nonlinear dynamic systems such as power system dynamic equivalents, with the system dynamic properties being obtained only from data [120–127]. On model performance, it is expected that the dynamic equivalent obtained from coherency based methods would be considerably more reliable and accurate than those set up by system identification method, because it is determined from a physical model rather than an approximation based on measurements. However, the reduction based equivalent method requires the physical knowledge about the network structure, its parameters, and the system operating status. In some cases, the available physical knowledge may not be sufficient to develop accurate and reliable dynamic equivalents [124–126], [128]. Moreover, the application of coherency based methods is restricted to power systems with a relatively small number of large synchronous generators concentrated in a few areas. In addition, when there is a need to update the dynamic equivalent, the tuning task may prove difficult since there is a little indication about which parameters should be adjusted [129], [130]. Also, dynamic equivalents derived from one class of event are typically not very successful for other classes [131], [132]. DG technologies based on induction machines or connected to a network through power electronic interfaces have quite different control systems which can be difficult to aggregate [133]. Some DG technologies do not have rotating parts (such as fuel cells and PV systems). Therefore, the term coherency widely adopted by classic reduction methods becomes less meaningful since induction generators do not have synchronizing torques and the power electronic based interfaces can almost completely separate the dynamic behaviour of the generator from the network. This results in different dynamic behaviour of DG strongly influenced by the controllers of their power electronic converters. Thus, system identification techniques have widen to derive aggregated models of complex power systems that include distribution networks integrating large amounts of DG that are not limited to synchronous generators.. It should be pointed out that they are currently no aggregated models of ADNCs and MGs that are in practical use by utilities. The aggregated modelling of ADNCs and MGs is in the earliest stages and recommendations for model development presented in this chapter are based on findings coming largely from academic research. This chapter presents several recommended modelling approaches in detail. A comprehensive literature review on the state of the art in aggregated DG modelling and ADNC equivalencing is presented in 0, along with conclusions and recommendations. (Note: Aggregation of different generation technologies should be done with care in case of scaling the response to other situations, e.g., wind and solar generation should be handled separately as their production pattern is determined by the meteorological conditions.) 6.2 Methodology to Develop Aggregate Models of ADNC and MG Using Nonlinear Techniques System identification based methods cover a number of issues ranging from data processing to validation of the measurement based model. With a given observed data set the main tasks of system identification are two- fold: i) Selecting the model structure; ii) Estimating the model parameters in a way that is consistent with the identification criterion. Another important step, however, is to evaluate the model performance regarding its intended purpose, i.e., model validation [134], [135]. Thus a general conceptual approach involves the main steps described in the sections that follow. 6.2.1 DESIGN OF THE IDENTIFICATION EXPERIMENT The data set representing the dynamic behaviour of the proposed model is the basis for successful system identification, because if the system behaviour is not represented within the data set, it cannot be represented by the model unless prior physical knowledge is explicitly incorporated. The design of appropriate excitation signals to create the set of data that describes how the system behaves over its entire operating range plays a key role in system identification methods. This task is even more decisive for more complex nonlinear models and the data set should contain considerably more information than for linear models. Page 74 Modelling and Aggregation of Loads in Flexible Power Networks Therefore, a suitable data acquisition process is required that deals with both input and output observations (either measured or simulated) of the system to be reduced. Once decided where and what to measure, the next steps comprise the sampling time definition, ( T ), and also the required number of measurements, ( N ). Thus, the data set of system inputs and outputs, u  kT  and y  kT  , respectively, is given by::  Z N  u 1 , y 1 u  2  , y  2   u  N  , y  N  (6.1) It should be assumed that there are always signals that cannot be controlled, such as measurement noise and possibly uncontrolled inputs, commonly denoted by v  kT  , that affect the system output. 6.2.2 SELECTION OF MODEL STRUCTURE The choice of an appropriate model structure is considered the most important and simultaneously the most difficult decision, that the user has to make, due to the lack of theoretical support. It is particularly important to select a model structure close to the intended purpose of the aggregated model, meaning that prior knowledge, engineering expertise and physical insights about the dynamics of the system to be reduced have to be combined with the formal properties of available models in order to select a suitable mathematical representation. In formal terms, after data set collection the user seeks a relationship between past observations ( u k 1 and y k 1 ) and future outputs, y  k  , as: y  k   g  u k 1 , y k 1   v  k  (6.2) It has been assumed that g   belongs to a family of functions that is parameterized in terms of a finite number of parameters commonly denoted by  , i.e. the model structure, M . Also, it should be useful to write g   as a concatenation of two mappings. One that takes the number of past observations and maps them into a vector   k ,  of fixed dimensions and another that takes this vector to the space of the model outputs, g  u k 1 , y k 1;   g   k ,  ;  . Thus, the model structure selection process should be decomposed into two partial problems [136]: i) How to choose the regression vector   k ,  from past inputs and outputs; ii) How to choose the nonlinear mapping g  ,  from the regressor space to the output space. Depending on the system identification based approach used, relying on “black-box” or “grey-box” methodologies, both partial problems should be considered or only the last one, respectively. 6.2.3 THE IDENTIFICATION METHOD The problem now is to decide how to use the information contained in the data set to find a proper vector of parameters  in order to obtain a model that provides predictions as close as possible to the outputs of the system to be reduced. The search for the optimal point in an n -dimensional parameter space is performed through a suitable parameter estimation technique. The basic search concept is illustrated in Figure 6-1. The system to be reduced and the parameterized aggregated model are fed with the same inputs and the corresponding outputs are compared yielding an error signal, which is computed for k  1, , N and used for adapting the vector of parameters, i.e.,  . The model is then augmented with a suitable identification criterion that measures how well the aggregated model fits the outputs of the system to be reduced. The optimisation algorithm will adapt the vector of parameters in order to achieve the minimum identification criterion. Page 75 Modelling and Aggregation of Loads in Flexible Power Networks vk  System to be + + y k  reduced  k  +  k  - Aggregated model Criterion  g  k ,  y k |   Figure 6-1: The Conceptual Self-adaptive Procedure of Parameters Estimation. 6.2.4 MODEL VALIDATION At this stage, the performance of the obtained aggregated model will be assessed in order to investigate whether or not it is valid for its intended purpose. Thus the aggregated model should be tested using as much information as it is practical, including measurements, physical prior knowledge and engineering experience in order to test whether the problem that motivated the modelling approach can be solved using the obtained aggregated model. Considering that in modelling ADNC and MG using the black or grey box approaches, estimated parameters may not always have physical meaning, the model validation should therefore rely more on comparison of dynamic responses of the model with measured responses coming from the field. Ultimately, the same model validation procedure can be followed as done for validation of aggregate load models at bulk supply point, i.e., by comparing simulated model response with responses obtained by monitoring at the point of connection of ADNC or MG to the distribution or transmission network. 6.3 Development of dynamic equivalent models of ADNC From the literature review on the dynamic equivalents for ADNC presented in 0, it is found that system identification techniques are the most practical to develop ADNC equivalent models. Obviously the component based (white-box) approach is difficult to adopt as the detailed parameters of ADNC components are needed beforehand. Also detailed modelling of ADNC(s) will significantly increase the size of the modelled system. Therefore, measurement based dynamic equivalencing techniques have been used to derive dynamic equivalent models of distribution networks with DG, representing the dynamic behaviour of these systems with respect to the upstream power system with acceptable accuracy. 6.3.1 BLACK-BOX MODELLING APPROACH: RECURRENT ANNS FOR DYNAMIC EQUIVALENTS A generic nonlinear dynamic equivalent model based on recurrent Artificial Neural Networks (ANN) was developed to replace a part of the distribution network, retaining the local DG dynamics impacting on the higher voltage level. The aggregated equivalent model is specified based on the model structure and parameters describing the ANN (activation functions, biases and weights) and only by measurements at boundary buses between reduced and retained subsystems, so that parameters, topology and complexity of the replaced subsystem will not affect the procedure of deriving the equivalent model. The use of complex mathematical analysis is thereby avoided. This represents a significant advantage especially when there is a limited understanding of the relations between system variables, as it can be observed from Figure 6-2. The recurrent structure of the ANN is required to capture the dynamic behaviour of the replaced network and to enable the online interaction with the retained network. In addition, loads (including induction motors) can be modelled as conventional aggregate loads represented at the boundary nodes with suitable voltage and frequency dependency using the general exponential relations. The separation between active and passive elements extends the validity of the equivalent model to simulate changes in the generating and load conditions inside the replaced system itself. However, if there is a difficulty in separating the passive loads separately, the entire distribution system can also be replaced by the recurrent ANN. Page 76 Modelling and Aggregation of Loads in Flexible Power Networks In addition to the ANN itself, the model requires two supplementary functions, being a mapping function to prepare the ANN inputs, and a de-mapping function to process the outputs from the ANN to calculate complex power. These two functions represent the interface between the ANN and the retained network. The equivalent model interacts with the retained system through the boundary buses. It is perturbed by the voltages at these buses and reacts by supplying the corresponding complex power at each time interval. The ANN itself acts as a Norton model, where the normalised deviations of voltage are used as the main inputs and the normalised deviations of currents represent the outputs. In addition to the input voltages, past values of currents and voltages are also introduced at the input layer to achieve the recurrent structure. Thus, the ANN is able to capture the dynamic nature of the original system and to maintain the continuous-time operation of the entire network. Figure 6-2: Basic Principles of the Recurrent ANN Based Dynamic Equivalent Approach. The current rather than power is used as output from the ANN, as it represents an independent variable, whereas power depends on voltage, which is an input to the ANN. Therefore, the use of current gives better convergence in the training process compared to complex power due to the complete decoupling between outputs and inputs of the ANN. The use of normalised deviations as inputs and outputs in the ANN allows the use of the equivalent model under new initial power-flow condition. The ANN in this case represents a normalised model scaled on initial conditions at the boundary buses. Augmenting this feature with the independent representation of active and passive elements results in a universal model, which is capable of simulating the original system under different operating conditions. Developing a recurrent ANN based dynamic equivalent model involves performing common system identification procedures, namely i) Data preparation; ii) Definition of the ANN structure; iii) Training procedure; iv) Model validation. Each of these tasks is briefly discussed below. Data Preparation Several three-phase short circuits were simulated at different locations in the retained network using the detailed model of the system implemented in the Power System Dynamics (PSD) simulation package [137],. The simulation of each fault is carried out for 10s, which is enough to restore the steady state conditions after the fault clearance. A 10ms integration time step is used in the simulation, which results in 1000 patterns with each fault. Complex voltages and injected currents at the boundary-buses are stored during the fault simulation and subsequently used to prepare suitable patterns for training the ANN. Definition of the ANN Structure Since the ANN is required to capture the dynamics of the replaced network, characterised by currents, recursive loops are used to feedback deviations of normalised current to the input layer with time delays. In Page 77 Modelling and Aggregation of Loads in Flexible Power Networks addition, the ANN interacts with the retained network and recognises its dynamic behaviour through the voltages. Therefore, past values of normalised voltage deviations are also used at the input layer. With two boundary buses, the ANN has 4 main inputs representing the real and imaginary components of normalised voltage-deviations. Furthermore, normalised deviations of these voltages at four previous time intervals are received at the input neurons. On the other hand, four outputs representing the real and imaginary components of normalised deviations of currents are obtained from the ANN. Four recursive loops with delay actions from each output are fed back to the input layer of the ANN. This results in a total number of 20 inputs in addition to 16 recurrent loops. The ANN contains two hidden layers with 10 and 5 neurons respectively. All hidden layers comprise neurons with nonlinear-sigmoid activation functions, while neurons in the output layer have linear functions. Training Procedure Patterns corresponding to eight different three-phase short circuits are used in the training process. Time- histories of voltage variables are involved in the input features to the network, while real recurrent loops from output current variables are considered. A special program is developed to train the ANN with actual recurrent loops. With 1000 pattern belonging to each waveform, 8000 patterns are used in the training process, which is accomplished offline without actual interaction with the retained network. However, the test and validation of the developed dynamic model are based on real-online interactions. The results from the training process (i.e. biases and weights) are saved to be used in the implementation procedures. Model Validation Since the equivalent model is intended for online applications, it has to be implemented in the PSD package as an unconventional power source [137] in such a way that it interacts with the retained subsystem at each time step in a similar way to that of the original network. Thus, the recurrent ANN based dynamic equivalent is simulated in a so-called regulator file and integrated into the network through one or more buses. The processing within the regulator files is accomplished using pre-constructed blocks to describe the dynamic behaviour of the element, as described in 0. After replacing the external system by the ANN-based dynamic model, the performance is investigated online and compared to that of the full system. For this purpose, disturbances not used in the training procedure should be used in order to evaluate the generalisation capability of the ANN. Detailed results can be found in [138–140]. 6.3.2 THE GREY-BOX APPROACH The development of equivalent ADNC model based on grey-box approach assumes a known structure of the cell (but not the exact composition of physical components) and estimates the parameters of the model from online or offline measurements. The adopted model structure represents the dominant behaviour of the ADNC system and leaves the mismatch part of the system to be approximated by an optimization method. The developed model can be used in large system stability studies to replace active distribution networks by equivalent models with suitable parameters and in that way improve the overall accuracy of system stability studies, while at the same time keeping the order of the simulated system model low. Generating the Data Set The rate of 10 samples per second was chosen to extract the data from the simulated responses in the DigSILENT PowerFactory software; which worked well during the parameter estimation process. A higher sampling rate can cause problems in the identification process, and a rate that is very fast relative to the system dynamics can also cause inaccuracies [134]. Model Structure Selection The dynamic equivalent model of DNC is composed of a converter-connected generator and a composite load model in parallel [141–143]. The equivalent model diagram is shown in Figure 6-3. The following assumptions are made in developing the equivalent model: i) The converter-connected generator model includes a third- order synchronous generator model and a full converter model; ii) The composite load model is represented by Page 78 Modelling and Aggregation of Loads in Flexible Power Networks constant impedance, constant power and a constant current load model (ZIP load model), accounting for the static load part, connected in parallel with an induction motor model accounting for the dynamic load part. The composite load model is referred to as the ZIP-IM load model [33], [59], [144], [145]; iii) The mechanical torques of both generator and induction motor are assumed to be constant. PL  jQ L IM ZIP P  jQ CONVERTER SG PG  jQ G Figure 6-3: The Equivalent Model Diagram. The proposed converter-connected generator model can be used to represent micro-turbines and wind turbines, especially the direct drive synchronous generator type [146–149]. The third-order of synchronous generator model was chosen as it is adequate to represent the dynamic behaviour of a synchronous generator [148– 150], while preserving the low order of the overall equivalent model, which was the main goal of development of equivalent model of ADNC. In addition, the full converter model used a simple model that is sufficient to represent the converter without neglecting its main principle, i.e. that the real power flowing through the converter is balanced. The chosen converter model preserves the dynamic characteristics represented by the DC-link equation (the capacitor linking between inverter and rectifier) [146], [147]. A more detailed description is presented in 0. Estimation of Parameters The parameter estimation procedure is shown in Figure 6-4 [141], [142]. Initially, the pre-processing procedure is performed on the input and output signals (voltage, v(t), frequency, f(t), real power P(t), and reactive power Q(t)). The pre-processing procedure, also known as data conditioning, filters out the noise contained in the signals. After the data conditioning process, the filtered input signals, voltage and frequency (V, f) are imported into the nonlinear grey-box model together with the initial values of model parameters to be estimated. The System Identification Toolbox in MATLAB is used to develop the grey-box model and to produce the output responses of real and reactive power ( Pˆ , Qˆ ). The simulated output responses ( Pˆ , Qˆ ) are obtained from the model using estimated model parameters based on nonlinear least square optimization, according to the specified identification criterion. The parameter estimation procedure is performed in MATLAB environment. Nonlinear least square is an optimization-based technique used to search for the best model parameters by fitting a curve through data. It fits data to any equation that defines Y as a function of X and one or more parameters. It finds the values of those parameters generating the curve that come closest to the data (minimizing the sum of the squares of the vertical distances between data points and curve) [151]. This technique requires a model of the analysed signal. For dynamic equivalent of ADNC, the signal model is defined by the grey-box model structure. Nonlinear least square optimization is generally used where the goal is to minimize the difference between the physical observation and the prediction from the mathematical model. More precisely, the goal is to determine the best values of the unknown nonlinear parameters of the model in order to minimize the squared errors between the measured values of the signal and the computed ones. The squared error functions for measured and simulated output are defined as follows: Page 79 Modelling and Aggregation of Loads in Flexible Power Networks 1 n 2 1 n min  P    min      Pkm    Pks   2  kP   min  n k 1 n k 1 n (6.3) 1 1 n min  Q    min   kQ     min  Qkm    Qks    2 2  n k 1 n k 1 where Pm and Qm are measured active and reactive power, Ps and Qs are simulated active and reactive power from the grey-box model. Input and output measured responses v(t),f (t) P(t),Q(t) P, Q Pre-processing for parameter estimation V, f + Initial parameters P̂, Q̂ Grey-box model _  Parameter updating Criterion Figure 6-4: The Parameter Estimation Procedure. The parameter estimation process is performed through iterations. In successive iterations, the sum of squared errors between the simulated and measured output signals is calculated. Based on this, the parameters are updated through the optimization algorithm. The iterative process and parameter tuning continues until the squared errors (ε) are within a predefined threshold. Three algorithms are available in MATLAB software for solving nonlinear least square problems: trust-region, Levenberg-Marquardt and Gauss-Newton [152]. The Levenberg-Marquardt algorithm was used for estimating grey-box model parameters in [141–143]. Model Validation Similar to the black box approach, the model validation can be performed using the comparison between the simulated output of the model corresponding to the original input with the original output of the system. The developed equivalent model has to be implemented in any simulation software for large system application under various dynamic system studies. The effectiveness and accuracy of the developed model can be confirmed by comparing the responses from original external system with responses from developed equivalent model. The results of model validation are given in 0 6.4 Development of dynamic equivalent models of MG Modelling nonlinear dynamic systems is considered a very complex task and general guidelines to derive a dynamic equivalent for MG requires making use of the available prior knowledge about the model and finding an acceptable trade-off between development effort and performance. Based on the available knowledge about physical structure of the MG used during the system identification procedure, two approaches relying either on black box modelling based on Artificial Neural Networks (ANN) or grey box modelling based on a physical model structure can be used. The first one tries to exploit the full response of the MG when excited after a disturbance, while the second one tries to understand the physical behaviour of the different components of the MG. These two approaches are discussed in more detail in the following sections. 6.4.1 THE MICRO GRID SYSTEM DEFINITION For the purpose of analysis and to get a better insight for reducing original into equivalent system, the detailed model of the whole modelled network is divided into two parts: The internal subsystem that has to be retained Page 80 Modelling and Aggregation of Loads in Flexible Power Networks for detailed analysis and the external subsystem that will be replaced by the equivalent model [153–155], as depicted in Figure 6-5. Boundary bus Internal subsystem – MV network External subsystem - MicroGrid LV MV IDE, IQE MG slow dynamics equivalent model I D, I Q IDS, IQS MC VD, VQ AC DC Storage device Figure 6-5: The Network Equivalent Model Including the MG Equivalent Aggregated Model. As it can be observed from Figure 6-5, the dynamic system to be identified consists of a set of differential and algebraic equations, corresponding to the dynamic models of the several micro generation systems describing the state evolution over time of the physical system – the Micro Grid [154] according to the MG concept presented in 0. Therefore, the dynamic equivalent to be developed will replace the MG detailed model, which is assumed to be the external subsystem, by a reduced order model according to the following guidelines: i) The MG dynamic equivalent must be an accurate representation of the detailed model concerning the transient analysis to be performed; ii) The cost of building the dynamic equivalent must be much smaller than the cost of performing the transient analysis using the MG detailed model; iii) The obtained dynamic equivalent has to be integrated into dynamic simulation tools. The MG detailed model is excited through generated disturbance scenarios into the internal subsystem and the corresponding MG dynamics are captured by means of the electrical variables measured at the system boundary bus. Thus, the MG dynamic equivalents will react to the boundary bus voltage and system frequency changes by varying the injected current into the retained subsystem. Based on the physical insights regarding the MG operation, two different time scales regarding the MG dynamic response can be distinguished [153–155]. The main storage device interfaced through a Voltage Source Inverter (VSI) has fast dynamic responses and the controllable micro-generation systems interfaced through PQ inverter controls have slow dynamic responses. Therefore, suitable dynamic equivalents for MG comprise the detailed model of the main storage device VSI control and the MG slow dynamics equivalent model corresponding to the aggregated model of the remaining MG components, as it can be observed from Figure 6-5. Then, the system identification process will also identify the equivalent model able to represent the MG slow dynamic behaviour. 6.4.2 TDNN BASED DYNAMIC EQUIVALENTS FOR MG The Time Delay Neural Network (TDNN) based on Multi Layer Perceptron (MLP) neural networks have high capability to deal with complicated nonlinear problems in a general framework, which allows their successful applications to new situations that were not used during a training phase [156]. Therefore, well trained TDNN can replace the MG slow dynamics and it is expected that it properly interacts with the retained network for a wide range of operating conditions. With the use of TDNN for dynamic modelling purposes, no information about the system structure is required and the use of complex mathematical analysis is avoided. This represents Page 81 Modelling and Aggregation of Loads in Flexible Power Networks a significant advantage, especially when there is a limited understanding of the relations between system variables. Data Processing As the data set is the basis of any successful identification procedure, a numerical experiment should be designed to produce a set of samples that describe how the system behaves over its entire range of operation. For this purpose the MMG detailed model is used taking into account the following issues: i) The design of input signals which lead to an informative data set; ii) The techniques for preparing data for neural network modelling. Based on the system definition presented before, as well as in the engineering expertise, adequate input signals have been designed. Thus, after MG islanding, several perturbations occurring in the MV network are simulated and both the input and output signals are measured according to a suitable sample time. As already mentioned previously, boundary bus voltages expressed in the synchronous reference frame, D  Q , and system angular frequency are considered as inputs, while the boundary bus injected current from the tie line, expressed in the D  Q reference frame, are considered as outputs. Thus, the TDNN is disturbed by both boundary bus voltage and network frequency variations. It reacts to these variations by varying the injected currents into the boundary bus, operating according to the principles of a Norton model [154]. Boundary bus voltages, system frequency and injected currents are stored in a database during the perturbation simulations in order to build suitable training patterns. Since the data set is almost the only source of information to build the TDNN based dynamic equivalent model, the number of samples should be large enough in order to form appropriate training and validation data sets. Since signals are likely to be measured in different physical units, it is recommended to remove the mean and scale all signals to the same variance to avoid the tendency that the signal of largest magnitude will be too dominating. Moreover, scaling makes the training algorithm numerically robust and leads to a faster convergence and tends to give better models [156]. In order to generate a more robust TDNN, which is able to simulate the MG dynamic behaviour under different operating conditions, normalized deviations of voltage, system frequency and currents from the corresponding steady state analysis could be used [121], [126], [138], [139], [156]. Model Structure Selection Selecting a TDNN model structure basically implies to select the structure of regressors and to specify how to combine them into a one-step-ahead prediction through the MLP neural network. However, a combinatorial explosion of possible solutions arises from this procedure and therefore it is impossible to investigate all configurations. In this sense, the working procedure is to separate the two components of the problem by first selecting a particular structure of the regression vector and subsequently to specify the number of hidden units in an attempt to determine good network architectures for this choice of regressors. The regressor vector is based on the Nonlinear Finite Impulse Response (NFIR) model structure. However, a wrong choice of the number of signals used as regressors will lead to poor results. Too small lag spaces obviously implies that essential dynamics will not be modelled, but too large ones will contain redundant information and increase significantly the input space dimensionality. Although it is desirable to decide the number of past inputs based on physical insight, when the knowledge about the system is limited the method based on the Lipschitz quotients can be considered, since it can often provide a reasonable estimate of the model order for deterministic systems. However, to compute all quotients, particularly if N is large, is a very time-consuming task [156]. Therefore, taking into account that MLP can cope well with redundant inputs by driving the corresponding hidden layer weights toward zero, the number of past inputs selection becomes less important and the main objective is to assure the representation of the MG relevant dynamics. Page 82 Modelling and Aggregation of Loads in Flexible Power Networks When an unlimited amount of training data is available, the MLP neural network architecture determination relies on fully connected neural networks. In this case, the architecture selection is reduced to a matter of choosing a number of hidden units and the activation functions types of an MLP neural network. A regularization technique is further applied in order to deal with the bias/variance dilemma. Estimation of the TDNN Adjustable Parameters In this stage the collected data set is used to pick the best model among the candidates contained in the specified MLP neural network architecture. The neural network is trained to provide the best possible one step ahead prediction in a mean square sense. The neural network toolbox of MATLAB is used for this purpose [157]. The back-propagation method with Levenberg-Marquardt algorithm is used during the learning procedure. Due to its simplicity, early stopping is also used in order to avoid over-fitting, realising a best bias/variance trade- off. Since it is always desirable that the trained neural network model is also validated on a validation data set not used to extract training patterns, the collected data set is then split between the training and test or validation data sets. Additional care is taken to guarantee similar properties between the two data sets regarding the representation of the system entire operating range.. As several MLP neural networks with randomly initialized parameters are trained, the validation error is also used as the first criterion to reject poor models. Model Validation After the training procedure, the performance of the TDNN based equivalent models with less generalization error is evaluated in the dynamic simulation platform. A MG slow dynamics equivalent model is then embedded in the validation module forming the MMG equivalent model. The model performance is evaluated by comparing its response following perturbations that occur in the retained subsystem not used during the training phase with the response obtained using the MG detailed model. Results of the MG equivalent model based on TDNN can be found in [154]. 6.4.3 DYNAMIC EQUIVALENTS FOR MG BASED ON PHYSICAL MODELLING (GREY BOX) In contrast with TDNN based MG dynamic equivalents, a second promising approach is based on the available physical knowledge about MG dynamics and composition. Therefore, the data set requirements are less demanding. Thus, the major issues are related to the model structure selection, the identification method and model validation, as described in the following subsections. Physically Parameterised Model Structure Selection When the available knowledge allows specifying a physical model structure, the mathematical representation of the MG slow dynamics reduced model is commonly done by a continuous state space model of a given order. In a study case, the physical laws that approximate the MG slow dynamics under the study were similar to those that govern the active power control in a diesel group [154], Figure 6-6 shows the block diagram representing the physically parameterized model structure. The model structure parameters whose values have to be estimated during the identification procedure are gathered into the parameter vector  , as:    R K1 K2 T2 TD  (6.4) Since the MG slow dynamics equivalent model should be a current source, the instantaneous power theory [158] was used in order to determine the network injected current as depicted in Figure 6-6. A more detailed description is presented in Appendix 6-C. Page 83 Modelling and Aggregation of Loads in Flexible Power Networks VD VQ I DR 1 Tm ,max Instantaneous  t power R theory  grid +  + m a K2 mb 1 Tm  Pm I QR - T2 s  1 TD s  1 + Qref K  1  ref  1 s Tm ,min Figure 6-6: Model Structure of the MG Slow Dynamics Equivalent Model. The Identification Method To estimate the parameters of the physically parameterized model structure, a suitable identification method is required. For this purpose the Evolutionary Particle Swarm Optimization (EPSO) tool was adopted as a global optimization tool together with the Sum Square Error (SSE) criterion [154], [155]. Under this context, the parameter vector  given by (6.4) provides particle phenotype descriptions corresponding to the particle positions into the parameter space. Since the identification method aims to find the values of parameters that minimize the SSE, this criterion plays the role of loss or cost function under system identification terminology as well as the particle fitness in an evolutionary sense. Thus, a suitable evaluation function was implemented for calculation of particles SSE. In contrast with TDNN, the determination of the parameter vector given by (6.4) in carried out on-line where the loss function of each particle is evaluated within the dynamic simulation platform. For this purpose the MG slow dynamics model structure is embedded in the validation package forming a potential MG equivalent model. Therefore, some interaction between the EPSO algorithm and the dynamic simulation platform, concerning the validation package, is required, as can be observed from the flow-chart depicted in Figure 6-7. After defining the search space through both the minimum and maximum values of each parameter into the parameter vector, it is expected that the EPSO algorithm will perform the search to the global optimum or, at least, to a good local optimum under the SSE sense. For this purpose, after mutation has been performed by the EPSO algorithm, the following sequence of steps has to be carried out for each particle in the swarm: i) The evaluation function sends the particle object parameters to the MG slow dynamics equivalent model; ii) A pre- specified set of disturbances occurring at defined time instants are simulated during a certain time period; iii) The MG equivalent model response is compared with the target response, which was generated from the MG detailed model, yielding an error sequence; iv) The SSE is then calculated and sent back to the evaluation function; v) Based on the error entropy information, EPSO algorithm performs selection in order to build the swarm corresponding to the next generation. The above procedure is repeated until the EPSO algorithm termination condition is verified. Model Validation Model validation is in some sense embedded into the parameter estimation stage, where models with poor performances have been eliminated by the selection operator during the parameter estimation procedure and the parameter vector is estimated on-line, on the environment in which it will be used,. The set of particle object parameters thus identified allows the best performance of the MG slow dynamics equivalent model evaluated on the validation software package. As a final validation test, the model performance is evaluated considering disturbances not used during parameter estimation. Results of the MMG equivalent model based on physical modelling approach can be found in [154]. Page 84 Modelling and Aggregation of Loads in Flexible Power Networks Search space definition EPSO algorithm Scenarios for the perturbation conditions  Replication  Mutation  Evaluation function Changing multimachine Power system  Selection power system matrices matrices in D-Q i Dynamic simulation of MMG equivalent model Hi Dynamic models Algebraic  Synchronous machines ED , EQ equations of MMG equivalent model SSE  Main storage device with VSI control calculation I DR , I QR  MG slow dynamics equivalent Simulink S-function coded in MatLab m-file model Data set Steady state network solution  t  Time domain solution of differential equations and controls - + Target  Figure 6-7: Flow-chart of Physical Parameters Estimation. 6.5 Summary This chapter presented two approaches to develop dynamic equivalents of ADNC and MG, namely black box (ANN based) and grey box (physical) approach. As a new area of power system modelling work, with very few reports and research papers available and limited practical use by utilities of ADNCs and MGs aggregated models, recommendations for development of aggregate models of ADNCs and MGs presented in this chapter are based on findings of academic research. The main conclusions of the chapter can be summarised as follows: i) Both black box (the recurrent ANN based) and grey box (based to a certain extent on physical understanding of the structure and composition of the network) methods can be used for development of aggregate dynamic models. ii) Both derived dynamic equivalent models are in a simple linear or nonlinear form e.g. state space form which is very flexible and compatible with various commercial simulation tools like PSS/E, DIgSILENT PowerFactory, MATLAB etc.; iii) The input data for both approaches can be either simulated or measured network responses at the boundary buses regardless of the size and complexity of the network. iv) The models facilitate significant simplification of dynamic analysis of large and complex power systems with interconnected ADNC containing DG of diverse technologies; v) Considerable computational effort and frequent user interaction is required to derive the TDNN based MG dynamic equivalent which is a very time consuming task, where the initial values of both inputs and outputs of TDNN, as well as their maximum deviations from initial values, have to be updated whenever the initial steady state conditions are changed and the model domain of validity is restricted to the test system used to generate the data set; vi) The grey box approach requires significantly less computational effort to develop the model and the models domain of validity is largely extended. Page 85 Modelling and Aggregation of Loads in Flexible Power Networks Chapter 7 Conclusions and Future Work 7.1 Conclusions This report presented the major results and conclusions of the group’s three year work. It presents a critical detailed overview of the two existing and most widely used methodologies for load modelling, the measurement based and component based approaches and identifies their major advantages and disadvantages. This overview clearly indicates the need for a hybrid approach in the future that will combine the individual strengths of the existing approaches, taking into account the data acquisition capabilities offered by modern measurement systems. The report also summarises the existing static and dynamic load models, the load classes that these load models are valid for, and the large amount of data for load model parameters that can be found in public literature. The analysis of existing load modelling methodologies and load models presented in Chapters 2 and 3, is supported by findings of an international survey on load modelling performed by the WG. A comprehensive questionnaire on load modelling practices was developed by the WG and distributed during the summer/autumn of 2010 to more than 160 utilities and system operators from over 50 countries on all continents. The report summarises some of the key findings of that questionnaire, based on 97 responses (60.6% response rate) received by September 2011. The survey revealed that the constant real and reactive power load model (constant P and Q) is the most widely used load model for steady state power system studies. It also showed that static load models are still the most commonly used even for dynamic system studies and that only about 30% of utilities and transmission system operators represent dynamic load by some form of induction motor model. Therefore, it can be considered that the number of models used, in actual studies, is limited although the variety of load models is wide. The report then presented, in Chapter 4, a set of recommendations for load model development, using both the measurement based and component based approaches. The recommendations were based on a combination of best practises coming from past analysis, and the practical experience of the WG members. The step-by-step recommendations for load model development given in Chapter 4 can be used by engineers in distribution and transmission companies for developing load models within their own systems. Accurate representation of aggregate loads for power system analysis requires detailed analysis of the loads connected downstream of the point of aggregation. Although system loads are typically classify in one of the three general load sectors (residential, commercial and industrial), this is typically not sufficient to accurately describe the diversity in load structure and load composition, as well as typical load profiles within each load sector. The general load sectors should be further divided into sub-sectors, for which similar generic aggregate load models can be developed. Large scale integration of inverter-interfaced small generation units, with power ratings less than a few tens of kilowatts in LV networks and Distributed Generation (DG) in MV networks, calls for the development of equivalent mathematical models of Active Distribution Network Cells (ADNC) and Micro Grids (MG). Such models should be suitable for representing ADNC and MG in steady state and dynamic studies of large power networks. As specific ADNC and MG properties cannot be adequately modelled using conventional dynamic equivalencing methods, the report suggests exploiting system identification techniques for this purpose. Considering that there is no current industrial practice on development or use of these types of aggregate models, the recommendations made in this report are based on relatively limited academic research experience. Two approaches are recommended to derive dynamic equivalents for DNC and MG. The first is a black-box modelling approach based on Artificial Neural Networks (ANN) that tries to exploit the full response of the MG when excited after a disturbance. The second is a grey box modelling approach exploiting the physical behaviour of the different components of the ADNC or MG. The chapter also provides conclusions related to modelling of ADNC and MG for large power system studies. Page 86 Modelling and Aggregation of Loads in Flexible Power Networks Finally, in spite of every effort taken to provide as comprehensive report on load modelling as possible some issues might have been missed. It is also important to re-emphasise that not all phenomena can be studied with one simple/single load model. The user should ultimately decide on the type of model to use based on the phenomena which is to be analysed, e.g., steady state, small disturbance or large disturbance power system analysis would require different types of load models to be used. 7.2 Future work Considering the allocated time of three years to complete the work specified in the WG terms of reference, some additional aspects of load modelling that were identified by the WG while preparing this report, could not be addressed. This section summaries some of the key areas that were identified as potential extensions of the work presented in this report. In order to regularly update time-varying load models and their parameters, which could be essential for "almost" real time control and operation of future power grids, online load modelling techniques could be a solution (continuous automatic update of load models). This work could include real-time identification of load model structure as well as real-time identification of load model parameters. For real-time load modelling to be possible, continuous measurement data must be available. The availability of appropriate data would need to be coupled with access to suitable software to automatically update load models and their parameters. This is an extremely challenging task, which might be facilitated by the increased deployment of sophisticated sensing and monitoring devices and advancements in data analysis and processing. Exploitation of data mining, classification and clustering techniques for the identification of load mix in different load classes and, more importantly, for the identification of the overall load mix (disaggregation of load) at bulk supply buses. The identification of load mix could be extended in order to identifying the controllable and non-controllable parts of the load at every bus in the network, which would significantly contribute to the efficient deployment of various demand side management schemes and functionalities Further work is also required in the area of detection of discontinuous load behaviour, such as load self- disconnection and improved modelling of load self-disconnection and incorporating these models into power system analysis packages. Work on development of load models for new load devices such as electronic-based or electronic-interfaced loads, electric vehicles, storage devices, etc. is of paramount importance, as participation of such new load devices in the load mix will continue to grow in the future. The model development of individual devices based on laboratory measurements and development of aggregate models comprising significant portion of power electronic based devices, should be carried out in parallel. The aggregate models should consider the possibility of load self-disconnection, as well as different restart times. Existing ADNC/MG dynamic equivalent models should be extended, in order to include additional models of DG units aggregated by technology and their ancillary services role and aggregation of controllable loads to be actively managed. The extended models should be characterised by increased flexibility to allow interaction with automation systems of future smart distribution networks. 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Haq, “A model for induction motor aggregation for power system studies,” Electric Power Systems Research, vol. 42, no. 3, pp. 225–228, Sep. 1997. Page 97 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 2-A Examples of the Effect of Load Modeling on Power System Dynamics 2-A.1 The August 10th, 1996 WSCC System Outage [5] At 15:42:37 on Aug. 10th, 1996, the Allston-Keeler 500kV line sagged close to a tree and flashed over. The line was tripped following unsuccessful single-pole enclosure. Due to the Keeler breaker configuration, the Keeler-Pearl 500kV line was also opened. A power shift from the Allston-Keeler line made the lower voltage lines load up to 115% of their thermal ratings. Consequently, the voltages in the lower Columbia area were depressed. About five minutes later, the Merwin-St.Johns 115kV line tripped due to a relay failure and the overloaded Ross-Lexington 230kV sagged into a tree. These lines are parallel to the Allston-Keeler 500kV line. About at the same time, at 15:47:37, sequential tripping of thirteen McNary units began due to exciter protection malfunctions at high field voltage. This started system power and voltage oscillations. Figure 2-A-1 shows recorded Malin 500kV bus voltage. Figure 2-A-2 shows the simulation voltage using the WSCC dynamic models and data. The figures show that the simulation results are too optimistic and cannot predict the system stability status. To match the simulated quantities to the recorded responses, modifications were made to various models. The load model plays an important role in reproducing the actual measurements. Meanwhile similar research found that the system damping was decreased when the percentage of constant MVA load was increased. Figure 2- A-3 shows the dramatic impact of the load model on the system simulation results. It shows how the system damping changes in response to modelling modifications. Figure 2-A-1: Recording of Actual Malin 500-kV voltage. Figure 2-A-2: Simulated Malin 500-kV voltage using WSCC data base. Figure 2-A-3: Impact of Load Modelling Page 98 Modelling and Aggregation of Loads in Flexible Power Networks 2-A.2 The Low Frequency Oscillation Happened in Taiwan China [6] Figure 2-A-4 is the single-line diagram of the Taiwan Power System. Various load models were applied to analyze the dynamics during the two major trunk lines outage condition. When the dynamic load model is used, the phase angles of the central and the southern generators differ by 180 degrees from the northern generators; when the composite load model is used, the phase angles of the central and the southern generators differ by 180 degrees from the northern generators except central generators 3010 and 3101; when using an exponential load model, the phase angles of the northern generators differ by 180°from central generator 3162. Table 2-A-1 shows the effects of different load models on the system dynamic stability respectively. It can be seen clearly that various load models have different effects on damping analysis results. Figure 2-A-4: A single-line diagram of the Taiwan Power System Table 2-A-1: The Effect of Load Models on the Critical Mode Eigenvalues During the Unstable Low Frequency Oscillation Event 2-A.3 The Test Performed in the NE Power Grid [7] Two three-phase short circuits in the 500kV transmission network are simulated to investigate the transient stability for one big power system in China. To observe the effects of load model on the system transients, the network topological structure, steady-state operation point, fault forms, all the models as well as their parameters except for the load are fixed. Figure 2-A-5 shows the simulation results from three different load models and the measurements at one 500kV bus. It can be seen clearly that the different load models lead to different simulation fidelities to the real dynamics. Page 99 Modelling and Aggregation of Loads in Flexible Power Networks Figure 2-A-5: Bus voltage angle and amplitude of the simulated results and the measured data at LY 500kV bus 2-A.4 Effects of Load Model on Voltage Stability Analysis [159] In certain locations of the Argentinean power transmission system there are 132 kV radial grids that are feeding supplying points which demand increases gradually, among other reasons, due to the amount of air conditioned (A/C) devices. Consequently, these grids are prone to suffer voltage collapse events, which have been observed during the high A/C utilization and without any previous contingency. Two load models are used to simulate the voltage dynamics. The first load model is a typical static load model, the simulation based on which is shown in Figure 2-A-6 as the solid line (“con modelo estático tradicional” means “with static traditional model”). The second load model includes 50% induction motors, the simulation based on which is shown in Figure 2-A-6 as the dotted line (“caso base con modelo CLODBL” means “base case with CLODBL model”). It can be clearly seen that the static load model cannot reproduce the voltage collapse dynamics. A further analysis using PV curves also indicates that the static load model gives an optimistic conclusion for voltage stability. Figure 2-A-6: Bus voltage dynamics Page 100 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 2-B Overview of two exemplary structures used in the measurement based approach 2-B.1 1-MACHINE STRUCTURE The first structure named the “1-machine” structure is shown in Figure 2-B-1. Load Bus T ransmission bus Rfeeder : Derived Xfeeder : Derived 230, 115 , 69- kV M Induction Motor Feeder Equivalent Rfdr , Xfdr Derived Param: Optimized Param : xls,xlr,rr,rs,xm,H Tm(=Te) LTC Derived Data Input Data Vm ag_load time stamp Pm _load Vmag Qm _load Pm Qm Vang ZIP Optimized Param : ap,bp,cp (Z,I,P coefficients of real B2 power) Load Capacitance aq,bq,cq (Z,I,P coefficients of reactive power) Derived Param: Xcapacitor Figure 2-B-1: 1 Machine structure The “Optimized” parameters in Figure 2-B-1 are those which are optimized during the non-linear optimization process. For these parameters, the user has to provide an initial estimate. The “Derived” parameters are those which are calculated either based on user input or during the optimization process, but are not optimized. “Input Data” is the voltage and current at the secondary of the substation transformer where monitors are located. The “Derived Data” node is the bus where the actual load response is modelled. The complete set of input and output parameters required for the single machine structure is shown in Figure 2-B-2. The salient features of this model are: INITIAL ESTIMATES AND BOUNDS The optimization process is sensitive to the initial estimates and the bounds on the parameters that are to be optimized. In any optimization process, multiple solutions exist and the answer depends on the initial estimate of the parameters. In the last phase of the project [16], a literature review was performed to come up with different sets of machine and static parameters that have been used in previous load modelling efforts. These parameters can be used as initial estimates. Different initial estimates available in Load Model Parameter Derivation (LMPD) for both the structures are given in [16]. In addition, the user can change lower and upper bounds on the parameters as well. LOAD BUS CAPACITOR The load bus capacitor is a derived parameter that is used to match the reactive power flow at the meter point where the measurements are taken. The capacitor reactive power is calculated as: Qcapacitor  Qmeas  Qmotor  Qstatic  Q feeder (2-B.1) The detailed equations for calculating Qmotor , Qstatic , and Q feeder are given in [16]. Sign of Qcapacitor follows load convention. That is, a. negative value indicates vars being supplied to the system and a positive value Page 101 Modelling and Aggregation of Loads in Flexible Power Networks indicates vars being absorbed (in essence an inductor). In practice, Qcapacitor should always come out to be negative. Optimized Parameters Induction Motor Static kp % of Dynamic Machine Real Power ap Z coefficient of Real Power Measured Data For each time step: xls Stator Reactance bp I coefficient of Real Power Time Voltage magnitude xlr Rotor Reactance cp P coefficient of Real Power Voltage angle Real power rs Stator Resistance aq Z coefficient of Reactive Power Reactive power rr Rotor Resistance bq I coefficient of Reactive Power xm Magnetizing Reactance cq P coefficient of Reactive Power LMPD Algorithm H Inertia Constant pf Machine Power Factor Calculated Parameters User Input Voltage drop Feeder X/R Rfeeder Distribution Feeder Resistance Machine power factor Static load power factor Xfeeder Distribution Feeder Reactance Initial guesses Bounds of parameters Kq % of Dynamic Machine Reactive Power Tm0 Steady-state Machine Torque Xcap Capacitance at the load Bus Figure 2-B-2: 1-Input and Output for the LMPD Algorithm – 1-Machine Structure Note that Qcapacitor includes the net effect of substation capacitor banks as well as capacitor banks along the feeder. Depending on the time of the day, capacitor banks would be in or out of service. In some cases, utility engineers might have accurate information of the status of the banks at the time of the event. In that case, Qcapacitor may be a constant value supplied by the user. If Qcapacitor is entered by the user, the match for reactive power will be sensitive to the accuracy of Qcapacitor . Therefore it is recommended that user should not try to force this value unless he or she is confident. The present version of the LMPD algorithm does not allow the user to enter a fixed value of Qcapacitor . INDUCTION MOTOR MODEL In the load model structures used, the dynamic characteristics of loads are represented by an “equivalent” induction machine. The level of detail in modelling the induction motor depends on the study. For load modelling purpose, a 3rd order model is adequate. This model considers rotor flux and speed dynamics. Specifically, the three states used to represent the machine are: rotor d-axis and q-axis fluxes and rotor speed. Stator dynamics are neglected in the model. Kp is an optimized parameter and represents percentage of dynamic (motor) real power. The default lower and upper bounds on Kp are 0.2 and 0.8 respectively. The allowable range of values for Kp takes into account a wide range in variation of motor load content depending on the day of the year and also time of the day. For example, for a summer peaking utility, Kp might be as high as 70% on a hot summer afternoon because of residential air conditioners. On the other hand, Kp might be 20% for a cool spring night where the majority of the motor load might be industrial motors. However, the 0.2 – 0.8 range is reasonably broad and should be narrowed down if better load information is available. Motor power factor is set to 0.87. Motor power factor is an optimized parameter; however it should be tightly bounded. In some cases using a lower value of motor power factor might give a better fit which can be explained by the fact that in our load structure we are using a single motor (or two motors) to represent the aggregated effect of a large number of motor, all of which are not running necessarily at rated load and power factor. Page 102 Modelling and Aggregation of Loads in Flexible Power Networks Percentage of dynamic reactive power is not an optimized parameter. It is calculated based on the real power consumed by the motor and static loads, and load power factors as shown in the following equations. Qmotor Kq  (2-B.2) Qstatic  Qmotor Where Qmotor and Qstatic are calculated as follows: Pmotor 2 (1  pf _ motor 2 ) Qmotor  (2-B.3) pf _ motor 2 Pstatic 2 (1  pf _ static 2 ) Qstatic  (2-B.4) pf _ static 2 Pmotor is the induction motor real power at the load bus while Pstatic is the real power consumed by static load. MECHANICAL TORQUE EQUATIONS There are two ways of representing mechanical torque characteristics for the motor load:  Constant torque  Polynomial torque In the constant torque implementation, mechanical load is not a function of motor speed. This is the most conservative representation for the load torque of an induction motor. The polynomial torque equation is represented as T  T0 ( A 2  B  C ) (2-B.5) Where, T0 is the steady state torque. From the results obtained so far, there is no advantage in using polynomial torque representation for the mechanical load. STATIC LOAD The static load equations are given by: P  P0 (a pV 2  b pV  c p ) (2-B.6) Q  Q0 (aqV 2  bqV  cq ) (2-B.7) Where ap(aq),bp(bq) and cp(cq) are constant impedance, constant current and constant power coefficients of real (reactive) power, respectively. The coefficients are not percentages of each static load type. They are algebraic coefficients and can take negative values as well. This is because in reality there are many loads (compact fluorescent light, discharge lighting, electronic loads etc.) that cannot necessarily be categorized as an ideal constant impedance or constant current or constant power load. Thus, representing loads as a combination of constant impedance (Z), constant current (I) and constant power (P) (ZIP model), loses some physical significance and the exponential model may be just as applicable in these cases. The default bounds on the static coefficients are 0 and 1. However, in some cases a lower bound of -1 on the static parameters can give a better fit. Some literature suggests using upper bounds greater than 1 to allow more flexibility in the static reactive model. Having a wide range of coefficients for the reactive model allows for non-linearity in the load response [160]. Bounds greater than 1 were not used for the test cases. The default value of static load power Page 103 Modelling and Aggregation of Loads in Flexible Power Networks factor is 0.9. For some static loads the power factor can be as high as or higher than 0.95. However, because we are trying to model the static load at the substation a value of 0.9 is more realistic to take into account reactive losses as we go down in voltage (i.e. transformation that is not explicitly modelled). 2-B.2 2-MACHINE STRUCTURE The other structure, named the “2-machine” structure is shown in Figure 2-B-3. This structure has two induction motors to represent motor dynamics. Load Bus Transmission bus Rfeeder : Derived 230, 115, 69- kV Xfeeder : Derived Large Induction M Feeder Equivalent Motor Rfdr, Xfdr Optimized Param: xls,xlr,rr,rs,xm,H LTC M Small Induction Derived Data Vmag_load Motor Input Data time stamp Pm_load Vmag Qm_load Pm Qm Vang ZIP Optimized Param: ap,bp,cp (Z,I,P coefficients of real B2 power) aq,bq,cq (Z,I,P coefficients of reactive Load Capacitance power) Derived Param: Xcapacitor Figure 2-B-3: 2-Machine Structure The complete set of input and output parameters required for the two machine structure is shown in Figure 2-B- 4. The purpose of adding the second machine is to be able to separate out typically large (and heavier) industrial motors loads from typically smaller (and much lighter inertia) residential motor loads such as residential air conditioners. This structure can be used for summer peak events to explicitly represent residential air conditioners. Most of the parameters are already described in the previous section. Some of the features unique to the 2- machine structure are described in this section. MACHINE INERTIA CONSTANTS (HL AND HS) The inertia constant of the two machines have different initial estimates and bounds. As the name suggests the small motor has a smaller inertia. The lower and upper bounds for the small motor are 0.03 and 0.3, respectively. This upper bound is smaller than the lower bound for the large motor (0.5). This ensures that the small motor H will always come out to be smaller as compared to the large motor. The lower bound of 0.03 for the small machine is based on the lab tests performed on rotors of residential air conditioners [15]. Percentage of Dynamic Real Powers (over total) – Kpl and Kps For the two machine structures there are two percentages of dynamic real power: Re al power consumption of the l arg e motor K pl  (2-B.8) Total real power measured at the load bus Page 104 Modelling and Aggregation of Loads in Flexible Power Networks Re al power consumption of the small motor K ps  (2-B.9) Total real power measured at the load bus Care should be taken in setting the bounds for Kp for the two machines. The default bounds on Kpl are set to 0.2 and 0.8 (same as the 1-machine structure). However, these bounds should be changed as needed. Optimized Parameters Small Motor kp % of Dynamic Machine Real Power xls Stator Reactance xlr Rotor Reactance rs Stator Resistance rr Rotor Resistance xm Magnetizing Reactance Measured Data H Inertia Constant For each time step : Time pf Machine Power Factor Voltage magnitude Voltage angle Real power Optimized Parameters Reactive power Large Motor Static kp % of Dynamic Machine Real Power ap Z coefficient of Real Power xls Stator Reactance bp I coefficient of Real Power LMPD Algorithm xlr Rotor Reactance cp P coefficient of Real Power rs Stator Resistance aq Z coefficient of Reactive Power User Input rr Rotor Resistance bq I coefficient of Reactive Power Voltage drop Feeder X/R xm Magnetizing Reactance cq P coefficient of Reactive Power Machine power factor Static load power factor H Inertia Constant Initial guesses Bounds of parameters pf Machine Power Factor Calculated Parameters Rfeeder Distribution Feeder Resistance Xfeeder Distribution Feeder Reactance Kq % of Dynamic Machine Reactive Power Tm0 Steady -state Machine Torque Xcap Capacitance at the load Bus Figure 2-B-4: Input and Output for LMPD Algorithm – 2-motor Structure Kps has bounds of 0.1 and 0.3. The upper bound of 0.3 is probably the upper limit of the percentage of residential air conditioners on a hot summer day. However, these bounds should be changed as needed based on the season and time of the day, and any survey data that is available to give a better understanding of the actual potential range of values for a given utility. Page 105 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 2-C Combined Measurement and Component based Load Modelling Approach One of challenges in the load modelling work lies in the wide spatial distribution of the load. Using one single load model is obviously inappropriate in large-scale power grid, since the load characteristics may be very different at various locations. It seems more reasonable to build its own load model at each load bus from measured data. But it is impractical for a large-scale power grid that comprises too many load buses. If the load characteristic recorder is installed at each load bus, it would require immense investments and cause difficulties in updating and maintaining the parameter database of the load model. A solution to this problem is to combine the measurement based load modelling method with the component based method. First, the component based load modelling method will be applied at the system level and the information will be taken from the system operators focusing on the load composition [3]]. In general, the load buses comprise five load classes (industrial, agricultural, commercial, residential and other loads). Some load classes can be subdivided if more detailed characteristics need to be taken account into, for example, industrial load class can be further divided into light industry, heavy industry etc. After taking into account the influence of seasonal changes on loads, ten clustering indices were adopted. Namely, maximum summer and maximum winter load proportion of the five load classes. Based on the load composition data, the load substations can be clustered. Various clustering algorithms are available. The fuzzy clustering analysis is one of them. Based on the load clustering results, the load bus close to the cluster centre in each cluster group should be chosen as the typical load bus. Then the load dynamic data monitors are installed at the selected representative load buses. The measurement based load modelling methodology will be applied to build the load model for each bus load, which takes the same procedure as stated in this chapter. The thus built load model will be applied to all the other load buses in the same cluster. The model validation work is necessary, which consists of two levels. At the local level, the measured load dynamics will be used to check whether the load model built on the measurements could describe the load dynamics; while on the system level, the simulations on the whole system including the load models on all the load buses will be made to check whether such load clustering is good to reflect the system dynamics. The load model validation work may be combined with the sensitivity analysis to adjust the load clustering if necessary. Page 106 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 2-D Trend in Change in Dynamic Voltage Behaviour with Change in Induction Motor Ratio or Fault Duration [14] Dynamic voltage behaviour in medium-voltage load buses with induction motors are introduced with relative to change in static load model parameters. 2-D.1 EVALUATION ITEMS OF DYNAMIC VOLTAGE BEHAVIOUR Because induction motors (IMs) can also affect the dynamic voltage behaviour following the voltage sags, the change in dynamic voltage behaviour resulting from the voltage sags with the IMs was examined through time domain simulations in terms of the five evaluation items shown in Figure 2-D-1. Note that load self-connection is ignored in this study. (a) Initial voltage during voltage sags (d) Maximum voltage after voltage sags (e) Steady state voltage V0 after voltage sags Voltage (c) Initial recovery voltage (b) Minimum voltage during voltage sags 0 0.05 5 Time [s] Figure 2-D-1: Evaluation items of dynamic voltage behaviour following voltage sags 2-D.2 DYNAMIC VOLTAGE BEHAVIOUR WITH CHANGE IN MAXIMUM SELF-DISCONNECTION AMOUNT OF LOADS Thirteen load bus voltages at each evaluation point (from A to J) for five IM ratios (0% to 40%, in increments of 10%) are shown separately in Figure 2-D-2. Figure 2-D-3 shows an example of the dynamic voltage response at point H following the voltage sags with IM. 1 A B 0.8 A B Voltage (a) (p.u.) Voltage (b) (p.u.) 0.8 C D1 C D1 0.6 0.6 D2 E D2 E 0.4 0.4 F G F G 0.2 0.2 H I1 H I1 0 I2 J 0 I2 J 0 10 20 30 40 0 10 20 30 40 Induction motor ratio (%) D3 Induction motor ratio (%) D3 (a) Initial voltage during voltage sags (b) Minimum voltage during voltage sags 1 1.1 A B 1.08 A B Voltage (d) (p.u.) Voltage (c) (p.u.) 0.9 C D1 1.06 C D1 0.8 1.04 0.7 D2 E 1.02 D2 E 0.6 F G 1 F G 0.98 0.5 H I1 0.96 H I1 0.4 0.94 I2 J I2 J 0 10 20 30 40 0 10 20 30 40 Induction motor ratio (%) D3 Induction motor ratio (%) D3 (c) Initial recovery voltage (d) Maximum voltage after voltage sags Page 107 Modelling and Aggregation of Loads in Flexible Power Networks 1.1 1.08 A B Voltage (e) (p.u.) 1.06 C D1 1.04 1.02 D2 E 1 F G 0.98 0.96 H I1 0.94 I2 J 0 10 20 30 40 Induction motor ratio (%) D3 (e) Steady-state voltage after voltage sags Figure 2-D-2: Evaluation items of dynamic voltage behaviour following voltage sags 1.0 Voltage (p.u.) 0.8 Induction motor ratio: 0% 0.6 Induction motor ratio: 10% 0.4 Induction motor ratio: 20% Induction motor ratio: 30% 0.2 Induction motor ratio: 40% 0.0 0 1 2 3 4 5 Time in second Figure 2-D-3: Voltage response after voltage sags at point H 2-D.3 DYNAMIC VOLTAGE BEHAVIOUR WITH CHANGE IN FAULT DURATION Thirteen load bus voltages at representative one point for three fault durations (50 ms, 100 ms, 150 ms) are shown in Figure 2-D-4. Figure 2-D-5 shows an example of the dynamic voltage response at point H following the voltage sags with IM. 1 A B Volatage (c) (p.u.) 0.9 0.8 C D1 0.7 D2 E 0.6 F G 0.5 H I1 0.4 I2 J 0.05 0.1 0.15 Fault duration (s) D3 Figure 2-D-4: Evaluation items of dynamic voltage behaviour following voltage sags Table 2-D-1 shows the mean values of the voltage change at all bus load voltages of points A to I for a 10% change in IM ratio (from 40% to 30%, from 30% to 20%, from 20% to 10%, and from 10% to 0%). The table also shows the mean values of the voltage change at all bus load voltages for a 50 ms change in fault duration (from 150 ms to 100 ms, from 100 ms to 50 ms). As shown in Table 2-D-1, the IM ratio does not affect b), d), and e), but does affect a) and c). The trend in the change in dynamic voltage behaviour resulting from the change in load self-disconnection amount and load voltage characteristics index is illustrated based on the sensitivity analysis, respectively in Figure 2-D-6. Page 108 Modelling and Aggregation of Loads in Flexible Power Networks 1.2 Voltage (p.u.) 0.8 Fault duration: 50ms 0.4 Fault duration: 100ms Fault duration: 150ms 0.0 0 1 2 3 4 5 Time in second Figure 2-D-5: Voltage response after voltage sags at point H Table 2-D-1: Dynamic voltage behaviour resulting from change in induction motor ratio and fault duration Voltage change when the IM ratio or Trend in change in voltage when IM Evaluation fault duration changes ratio decreases or fault duration is Item in Fig. 16 IM Ratio Fault Duration shortened (a) 0.050 (Large) 0.000 (---) Voltage drop (b) 0.011 (Small) 0.017 (Small) Voltage rise (c) 0.076 (Large) 0.031 (Medium) Voltage rise (d) 0.005 (Small) 0.020 (Medium) Voltage drop (e) 0.000 (---) 0.002 (---) co Change Note: Brackets ( ) denote the effect of voltage change when the IM ratio or the fault duration decreases. (a) Initial voltage during voltage sags (d) Maximum voltage after voltage sags (e) Steady state voltage V0 after voltage sags Voltage (c) Initial recovery voltage (b) Minimum voltage during voltage sags 0 5 Time [s] －：Trend of voltage when IM ratio decreases －：Trend of voltage when fault duration is shortened Figure 2-D-6: Trend in change in dynamic voltage behaviour with change in induction motor ratio or fault duration Page 109 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 2-E Additional Procedure of Extracting the Measurements for Deriving More Reliable Load Model Parameters [30] Inappropriate load models could cause discrepancies between the measured and simulated responses in both steady-state and transient state. Therefore, more accurate load models and their parameters need to be derived with the aid of measured data. Although more sophisticated measurement devices has been developed, the whole measured data such as 30 seconds data should not be used for deriving load model parameters, because the natural change in load structures regardless of voltage and frequency dependent load can deteriorate the accuracy in the derived voltage and frequency dependent load model parameters. In order to extract the measured data which do not include the natural change in load structure, an automatic extraction method suitable for load model parameter derivation using Fuzzy Inference System (FIS) is developed. The suitable data length can be specified using the correlation index between active power load and load bus voltage provided by the FIS. The measured data which are not used for learning algorithm are used to validate the performance of the developed method. In this research, the FIS technique is proposed for the ex-traction of the measured data which do not include the natural change in load structures. P(t)/P, V(t)/V, P(t)/P － V(t)/V are used to obtain the starting point of the occurrence of the natural change in load changes from active power load. V(t)/V , Q(t)/P. are used to obtain the same starting point from reactive power load. The training datasets are selected from the past measured data with the expert judgment. 1.01 1.00 Measured 0.99 Simulated P [pu] 0.98 0.97 0.96 0.95 -0.060 -0.070 Q [pu] -0.080 -0.090 -0.100 1.000 V [pu] 0.996 0.992 0.988 0.1 0 F [Hz] -0.1 -0.2 -0.3 -0.4 -0.5 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Time in second (a) without the extraction method (b) with the extraction method Figure 2-E-1: The example measured data and the model response Figure 2-E-1 shows the measured data and the response of active and reactive power load model without the developed extraction method for large frequency change. Figure 2-E-1(a) shows the measured data and the response of active and reactive power load model without the developed extraction method using FIS. At 11 seconds, a significant load drop occurs caused by probably a feeder trip. When the measured data to be identified the load model parameters includes this load change at 11 seconds, the mismatch between measured data (blue solid line) and the response of the load models (red solid line) becomes large as shown in Figure 2- E-1(a)). That results in the improper load model parameter derivation for this case and shows the necessity of removing the measured data which include load change caused by the change in load structures. Figure 2-E-1(b) shows the measured data and the response of active and reactive power load model with the developed extraction method using FIS. It is recognized that the model response after applying the extraction method coincide with the measurement data, while the model response without applying the method is not Page 110 Modelling and Aggregation of Loads in Flexible Power Networks identical with the measured data. Therefore, it can be concluded that the developed extraction method can extract the measured data which are suitable for load model parameter derivation. Figure 2-E-2 shows the measured data and the response of active and reactive power load model without the developed extraction method for small frequency change. It also reveals that the developed extraction method can extract those measured data which are suitable for load model parameter derivation. The developed extraction method using FIS is expected to derive more appropriate load model parameters more speedy. The development of the automated process enables to achieve unified and neutral judgment. 1.010 P [pu] 1.005 Measured Simulated 1.000 -0.256 Q [pu] -0.258 -0.260 -0.262 1.004 V [pu] 1.003 1.002 1.001 1.000 F [Hz] 0.04 0.02 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Time in second (a) without the extraction method (b) with the extraction method Figure 2-E-2: The example measured data and the model response with and without the developed extraction method for small frequency change Page 111 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 2-F Detailed Results of the International Survey on Load Modelling In order to survey and identify current industry practice for power system load modelling, a comprehensive questionnaire on load modelling practices was developed and distributed by the members of the WG C4.605 in to an important amount of utilities and system operators worldwide. 2-F.1 ORGANIZATION OF THE QUESTIONNAIRE A two-part questionnaire was developed for the purpose of conducting the survey on load modelling practices. The first part was shorter and contained only nine general questions, which were sent to all participants in order to facilitate high response rate and provide a basic, but comprehensive overview of the international load modelling approaches and practices. The second part was a longer questionnaire, which was sent only to those respondents who expressed willingness in completing it after returning the short questionnaire. This paper considers only responses to the questions from the short questionnaire (Table 2-F-1), which are classified into four categories: 1) types of load models for static and dynamic power system studies, 2) approaches for identification of load models and model parameters, 3) adequacy of load models and used load simulation tools, 4) approaches for including small distributed generation (DG) in load models. 2-F.2 SURVEY PARTICIPANTS Potential survey participants were identified by WG members and contacted by e-mail. The questionnaire was sent to 160 contacts around the world (different utilities and system operators) between June and December 2010. By September 2011, the responses to short survey were received from 97 contacts from different continents, resulting in the overall response rate of about 60.6% (see 2nd column, 3rd column and last row in Table 2-F-2). Per-continent overall response rate was higher than 50% for 4 out of 5 continents (see 4th column in Table 2-F-2). If the received responses are only considered, the relative per-continent response rates are shown in the last column in Table 2-F-2. In case of a detailed questionnaire, the response rates were much lower, as these responses were received from only 19 initial participants. Table 2-F-1: Survey questions and categories Question No Question Category Types of load models used in static power system studies (e.g. Q1 power flow analysis) Types of load models used in dynamic power system studies 1 Q2 (e.g. stability analysis) Q3 Load models for different load categories/classes Approaches for load model data collection and parameter Q4 identification 2 Q5 Most recent update of load model parameters Q6 Load simulation tools used for system studies Adequacy of available load models for system stability Q7 3 studies Q8 Extent of use of user-defined load models Accounting for or inclusion of small distributed generation in Q9 4 load models Page 112 Modelling and Aggregation of Loads in Flexible Power Networks 2-F.3 THE RESULTS OF THE SURVEY Q1: Load models used in steady state power system studies Several sample answers were offered to the respondents to this question: constant power load model (PQ); simple polynomial load model, i.e., combination of constant impedance, constant current and constant power (ZIP) load model; combination of ZIP and equivalent induction motor model; detailed composite load model; and exponential load model, [161]. Exponential and polynomial (ZIP) load models (only for real power demand) are given by (2-F.1) and (2-F.2): np V  P  P0    (2-F.1)  V0  0 1 2 V  V  V  P  P1    P2   V   P3   V   (2-F.2)  V0   0   0  where: np=0 → Constant P (Power); np=1 → Constant I (Current); np=2 → Constant Z (Impedance); P and P0: Actual and initial real power of modelled load; V and V0: Actual and initial load bus voltage magnitudes; Pi, i=1-3: corresponding shares of constant P, I or Z load, and P1 + P2 + P3 = P0. The widely accepted practice for power flow analysis of electric networks is to assume that the distribution system tap changing transformers and voltage regulators have brought bus voltages close to nominal values (i.e. close to 1 p.u. value). In this case, loads may be treated as a constant real and reactive power demands, and constant PQ load model can be used. Therefore, as expected, the responses to this question (Q1, Figure 2- F-1) identified constant power PQ load model as by far the most dominant type of load model used in steady state power system studies (84% of all responses). Table 2-F-2: The participants and response rates of the survey Response Response Rate Number of Number of relative to all 97 Continent Utilities/Operators Received Rate responses[%] Contacted Responses [%] Africa 21 7 33.3 7 Americas 34 17 50.0 18 Asia 35 24 68.6 25 Europe 62 41 66.1 42 Oceania 8 8 100.0 8 Total 160 97 60.6 Note: “Americas” denotes both North and South America. Page 113 Modelling and Aggregation of Loads in Flexible Power Networks Constant PQ Other Exponential Model ZIP Model Oceania 80% 20% Asia 88% 13% Americas 74% 5% 21% Europe 89% 11% Africa 78% 11% 11% Overall 84% 6% 8% 0% 20% 40% 60% 80% 100% Figure 2-F-1: Load models used for steady state power system studies Q2: Load models used in dynamic power system studies Load models used in dynamic power system studies (for, e.g., transient stability and frequency stability, or short-term voltage stability analysis) by different network operators and utilities are very different. In total, 8 different types of load models are used, featuring additional differences between load models used for representing real and reactive power demands. The responses to this question (Q2) suggest that there is no dominant dynamic load modelling practice, which is summarized in Figure 2-F-2 separately for real and reactive power load models. It can be seen from Figure 2-F-2 that although there is a relatively even spread of different load models used in dynamic system studies for modelling real power demand, static load models are again dominant. Constant power and constant current load models account for about 42% of all used models. Similar dominance of static load models is observed in case of modelling reactive power demand. In this case, however, constant power and constant impedance load models account for 45% of all used load models. For modelling both real and reactive power demand, about 30% of the used models represent dynamic load by some form of induction motor model (IM with ZIP or exponential, or composite load model). In case of load models for dynamic system studies, there was no uniformity between different utilities, countries and continents, as in case of load models for static power system studies. More than a half of utilities and system operators surveyed in Americas use load models which include different types of induction motor models, i.e. dynamic load models for dynamic system studies. In contrast, 100% of utilities in Africa use static load models for dynamic power system studies. In general, the constant power PQ load model is still the most widely used in a majority of power system stability studies (as it is the most conservative approach), together with the constant current for real power and constant impedance for reactive power model. Arguably, this might be a consequence of a recommendation given almost twenty years ago in [3], that in the absence of a detailed information on the load structure/composition, real power demand can be represented using constant current and reactive power demand using constant impedance load models. Q3: Load models for specific load classes Although the same type of the load model can be used for representing loads at different buses, its parameters could vary depending on the modelled load class (e.g. residential, industrial, commercial, etc.). However, and according to the responses to this question (Q3, Figure 2-F-3), the current practice mostly does not discriminate between different load classes. In large majority of the cases, the same load model and model parameters are used throughout the modelled system. This could be explained by the difficulties that transmission system operators encounter in obtaining accurate information on load classes at different network buses, because these data are typically owned by distribution system companies. Page 114 Modelling and Aggregation of Loads in Flexible Power Networks Constant P Constant I Constant P ZIP Model Constant Z ZIP Model Constant Z ZIP Model with IM Exponential Model ZIP Model with IM Exponential Model Detailed Composite Model Exponential Model with IM Detailed Composite Model Exponential Model with IM Oceania 14% 43% 14% 29% Oceania 15% 15% 38% 31% Asia 15% 32% 6% 21% 3% 18% 6% Asia 12% 41% 18% 6% 18% 6% Americas 13% 13% 26% 4% 30% 13% Americas 13% 13% 26% 4% 30% 13% Europe 29% 8% 6% 21% 10% 16% 8% Europe 31% 16% 21% 6% 16% 8% Africa 57% 29% 14% Africa 57% 29% 14% Overall 23% 19% 4% 19% 7% 16% 10% Overall 23% 22% 19% 9% 17% 10% 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% a) real power load model b) reactive power load model Figure 2-F-2: Load models used for dynamic power system studies Q4: Load model collection and parameter identification During the past few decades, capabilities of various measurement devices have been significantly improved and their numbers in power systems have substantially increased. This opened a possibility for the measurement and capture of data required for load modelling and, particularly, load model parameter identification and acquisition. According to received responses to this question (Q4, Figure 2-F-4), it is clear that power industry is taking advantage of available measuring and monitoring systems, as the current practice for load model parameter identification is based on the measurements in over 50% of the cases (on average), while in Americas and Asia this figure is 60% and 65%, respectively. Measurements Survey Yes No Literature Don't know Experience Have never been collected Other Oceania 63% 38% Oceania 43% 29% 21% 7% Asia 17% 83% Asia 65% 12% 8% 4% 12% Americas 35% 65% Americas 43% 29% 19% 10% Europe 22% 78% Europe 46% 17% 25% 5% Africa 100% Africa 60% 20% 10% 10% Overall 25% 75% Overall 50% 19% 19% 5% 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% Figure 2-F-3: Q3: Use of different load models for different Figure 2-F-4: Q4: Approaches for load model parameter load classes in dynamic power system studies identification It is interesting to note that in 19% of the cases (on average), the utilities rely solely on model parameters available in the literature. This highlights the importance of publishing the results of load model parameter identification studies based on measurements by utilities around the world. These results are likely to be used by other utilities, e.g. those that might not have resources to conduct their own load modelling studies. Page 115 Modelling and Aggregation of Loads in Flexible Power Networks Q5: Most recent update of load model parameters Another interesting observation from the survey is how frequently load model parameters are updated. This has been traditionally one of the major difficulties associated with load modelling, as the appearance of new types of loads/devices with different characteristics may invalidate load models and parameters established in the past. Regular update of load model parameters is, therefore, essential to ensure accurate simulation results. According to the responses to this question (Q5, Figure 2-F-5), the utilities and system operators are very well aware of this issue, and they relatively frequently update load model parameters used in simulations. In 41% of the cases, the load model parameters were updated within the last five years. Significance that utilities in general, and those in Europe, Africa and Americas in particular, are currently placing on load modelling is demonstrated through the fact that in over 22% of the cases load model parameters have been updated during the past year. Less than 1 year ago 2 5 years ago 6  10 years ago 11 20 years ago More than 20 years ago Don't know Oceania 30% 20% 30% 10% 10% Asia 13% 17% 4% 38% 29% Americas 22% 33% 17% 6% 11% 11% Europe 29% 12% 5% 15% 7% 32% Africa 14% 43% 14% 29% Overall 20% 21% 7% 12% 15% 25% 0% 20% 40% 60% 80% 100% Figure 2-F-5: Q5: Frequency of updating load model parameters Q6: Load simulation tools used for system studies Whatever mathematical model of power system load is developed or used, it has to be incorporated with the models of other system components into the overall model of power system, which is then applied in computer simulations by in-house developed or commercially available software. According to the responses to this question (Q6, Figure 2-F-6), currently the most widely used simulation software by industry for power system analysis are PSS/E (41% of users) and DIgSilent (13% of users), indicating that standard load models (i.e. “load model libraries”) from these software packages are also the most widely used. PSS/E [17] Digsilent [18] CPAT [19] PSLF [20] PSASP [21] Eurostag [22] PSCAD [23] Other Oceania 53% 20% 13% 13% Asia 33% 37% 15% 15% Americas 50% 15% 19% 15% Europe 35% 13% 4% 8% 6% 34% Africa 56% 33% 11% Overall 41% 13% 7% 5% 5% 23% 0% 20% 40% 60% 80% 100% Figure 2-F-6: Q6: Power system analysis and load simulation tools, [31], [32], [162–166] Page 116 Modelling and Aggregation of Loads in Flexible Power Networks Q7 & Q8: Adequacy of available load models According to the received responses, most engineers are satisfied with the load models available in existing software packages (85% on average, Q7 in Figure 2-F-7) and do not develop their own load models for power system studies (78% on average, Q8 in Figure 2-F-8). This places significant pressure on power system software developers to keep load models and model parameters regularly updated in their simulation tools. In spite of wide reliance on models available in commercial software packages, it should be noted that a relatively large portion of surveyed utilities and system operators in Europe and Americas (about 35%) are basically not satisfied with provided load models and they either modify them, or would like them to be modified. Yes No Don't know Yes No Oceania 100% Oceania 100% Asia 92% 8% Asia 4% 96% Americas 59% 35% 6% Americas 35% 65% Europe 88% 7% 5% Europe 34% 66% Africa 86% 14% Africa 100% Overall 85% 12% 3% Overall 22% 78% 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% Figure 2-F-7: Q7: Adequacy of available load models for Figure 2-F-8: Q8: Extent of use of user-defined load models system stability studies Q9: Modelling of small distributed generation Increasing penetration levels of both medium/large distributed generation (DG) systems in medium and high voltage networks, as well as highly dispersed micro/small DG in low voltage networks, require development of new load models for correct representation of aggregate system demands and power flows. According to responses to this question (Q9, Figure 2-F-9), however, 40% of the surveyed network operators and utilities do not currently consider at all these technologies when modelling demand at bulk load supply points (BLSP) in system studies. In further 28% of the cases, DG is simply modelled as a negative load. Some utilities and system operators, however, model DG as an independent component from the system load, especially when DG penetration in the distribution system is relatively high. In most of cases, current penetration levels of various DG technologies are reasonably low, which may justify neglecting their influence on bulk-supplied aggregate loads. Anticipated increase of various DG technologies (particularly those based on power electronic-interfaced intermittent/variable renewable energy resources), however, will effectively change the nature of BLSP responses to network disturbances. Generally, it is not feasible to model large number of small and highly dispersed individual DG units as individual generators due to significant increases in model complexity and computational time. Therefore, their representation will require some form of aggregation and equivalenting, either as an “aggregate generation model”, or with the DG correctly incorporated as a part of the overall aggregate (BLSP) load model. Page 117 Modelling and Aggregation of Loads in Flexible Power Networks Not Considered Negative Load Generation Equivalent Dynamic Model No Response Oceania 63% 38% Asia 76% 16% 8% Americas 33% 33% 6% 28% Europe 20% 30% 4% 37% 9% Africa 43% 29% 29% Overall 40% 28% 3% 23% 6% 0% 20% 40% 60% 80% 100% Figure 2-F-9: Q9: Inclusion of small DG in load models 2-F.4 PREVALENT LOAD MODELS Based on the results of the survey, some additional analysis is performed in this section, in order to identify prevalent (i.e. dominant or overall average) load models used by utilities and network operators around the World. It is difficult to specify a single prevalent or typical dynamic load model, as load models currently used in dynamic system studies vary in relatively wide ranges (Figure 2-F-2). Regardless of the type of power system study (static or dynamic), the most common types of static load models by far are individual constant power, constant current and constant impedance loads, or their combination expressed in a form of a ZIP load model (Figure 2-F-1 and Figure 2-F-2). Based on the individual constant power/ current/ impedance and ZIP load models reported in the survey, parameters of equivalent exponential load model, i.e. voltage exponents np and nq, are calculated next. The conversion from polynomial to exponential load model consists of two steps: 1) conversion of different polynomial load models into the exponential load model, and 2) calculation of equivalent values of exponential load model coefficients for real and reactive power, np and nq. The main point of this analysis is to uniformly convert all static load models used for power system studies into a single exponential load model. For example, Figure 2-F-2 shows that used static load models include “constant Z”, “constant I”, “constant P”, ZIP, and exponential load models. As shown in (2-F.1), in case of the constant Z, I, and P load models, the corresponding exponential load model coefficients are 2, 1, and 0, respectively. However, for the conversion of the ZIP model into the exponential model, a separate procedure is required. A simple conversion between ZIP load models reported in the survey (2-F.1) and equivalent exponential load model (2-F.1) can be done by (2-F.3) and (2-F.4):  V  0 V  1 V  2 V  np  P  P1    P2    P3    P0   V  V  V  V    0  0  0  0  0 1 2 nq (2-F.3)  V  V  V  V  Q  Q1    Q2    Q3    Q0     V0   V0   V0   V0  P1 ´ 0  P2 ´ 1  P3 ´ 2 Q ´ 0  Q2 ´ 1  Q3 ´ 2 np  , nq  1 (2-F.4) P1  P2  P3 Q1  Q2  Q3 where: np and nq are active and reactive power coefficients of the equivalent exponential load model, and all other quantities are defined in (2-F.1) and (2-F.2). Page 118 Modelling and Aggregation of Loads in Flexible Power Networks The conversion by (2-F.4) is based on Taylor expansion, which is more accurate if the voltage changes are smaller. Table 2-F-3 shows an example of the conversion and equivalent np and nq coefficients for five network operators and utilities from North and South America. (Note: Some respondents from Americas did not provide any specific information for the selected ZIP model as the answer to Q2.) On the other hand, Table 2-F-4 shows four examples of the coefficients of exponential load models used by one respondent from Europe. (Note: In this case, the conversion is not performed, as this respondent indicated four exponential load models with the corresponding coefficients as the actually used load models). Table 2-F-3: Parameters of zip model and equivalent exponential model: for all respondents from North and South America P1/Q1 P2/Q2 P3/Q3 Equivalent np Equivalent nq (Constant P) (Constant I) (Constant Z) 0%/0% 80%/50% 20%/50% 1.2 1.5 20%/20% 10%/10% 70%/70% 1.5 1.5 0%/0% 100%/0% 0%/100% 1.0* 2.0** 0%/0% 100%/0% 0%/100% 1.0* 2.0** 0%/0% 100%/0% 0%/100% 1.0* 2.0** * Presented as the constant current (I) load type in Fig. 2 ** Presented as the constant impedance (Z) load type in Fig. 2 Table 2-F-4: Examples of Exponential Load Model Parameters Provided by One Respondent from Europe Model 1 Model 2 Model 3 Model 4 np 1.4 0.65 1.3 1.5 nq 1.0 4.0 1.8 2.3 Table 2-F-5: Further results of the analysis of equivalent np coefficient Median, Mean, Standard Range, Continent Min Max deviation np np  np    np   Oceania 0.00 1.70 1.00 1.03 0.52 0.511.55 Asia 0.00 2.00 1.00 0.85 0.57 0.281.42 Americas 0.00 1.50 1.00 0.72 0.60 0.121.32 Europe 0.00 2.00 0.65 0.62 0.69 -0.071.31 Africa 0.00 1.50 0.00 0.50 0.64 -0.141.14 World 0.00 2.00 1.00 0.74 0.63 0.111.37 Following the above conversion procedure, currently used static load models for dynamic stability studies, shown in Figure 2-F-2, are converted into equivalent exponential load models shown in Figure 2-F-10. The conversion of static load models used by the survey respondents in dynamic system stability studies into equivalent exponential load models is further statistically analysed in Table 2-F-5 and Table 2-F-7, as well as Page 119 Modelling and Aggregation of Loads in Flexible Power Networks in Figure 2-F-11 and Figure 2-F-12. These results show minimum, maximum, median and mean values of coefficients np and nq, their standard deviations and ranges for different continents and for the World as a whole. Constant P Constant I Constant P Constant Z Constant Z ZIP Model ZIP Model Exponential Model Exponential Model ZIP Model with IM ZIP Model with IM Exponential Model with IM Exponential Model with IM Detailed Composite Model Detailed Composite Model Before Before 23% 19% 4% 19% 7% 16% 10% 23% 22% 19% 9% 17% 10% Conversion Conversion After After Static Load Model 72% 16% 10% Static Load Model 73% 17% 10% Conversion Conversion 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% (a) real power load model (b) reactive power load model Figure 2-F-10: Identified prevalent static load models used for dynamic power system studies, represented as equivalent exponential load model It can be seen that in case of Asia, Americas, Africa and Europe, np varies from 0 to 2, while in Oceania it ranges from 0 to 1.7. Mean values of np are less than 1 for all continents except for Oceania, where it is slightly greater than 1. Consequently, mean value of np for the World is approximately 0.7, while its median value is 1.0 (Figure 2-F-10(a)). Standard deviations are of the same order as the corresponding mean values, indicating a large dispersion of np coefficient for all continents and worldwide. Using these standard deviations and corresponding mean values, somewhat narrower ranges of np are obtained according to the formulae:  np  np   , i.e. np  np   ; np    (2-F.5) where: np is mean value and  is standard deviation. These ranges are shown in the last column in Table 2-F-5, and the same approach is repeated for calculated equivalent nq coefficient. Table 2-F-6: The analysis of values of NP coefficient without conversion Median, Mean, Standard Range, Continent Min Max np np deviation  np    np   Oceania 0.00 1.70 1.00 1.00 0.54 0.461.54 Asia 0.00 2.00 1.00 0.77 0.62 0.151.39 Americas 0.00 1.30 0.59 0.56 0.56 0.001.12 Europe 0.00 2.00 0.00 0.55 0.74 -0.191.29 Africa 0.00 1.00 0.00 0.33 0.52 -0.190.85 World 0.00 2.00 1.00 0.67 0.66 0.011.33 For comparison, Table 2-F-6 presents the results of statistical analysis of processing of np excluding those values that are obtained by conversion. Most of the differences between Table 2-F-5 and Table 2-F-6 are small, but generally greater standard deviations and therefore wider ranges are obtained in Table 2-F-6. Page 120 Modelling and Aggregation of Loads in Flexible Power Networks mean median 50 40 Frequency 30 20 10 0 -0.5 0 0.5 1 1.5 2 2.5 np Figure 2-F-11: Histogram of np coefficients of equivalent exponential models for static load models used worldwide in dynamic power system studies Table 2-F-7: Further results of the analysis of equivalent NQ coefficient Median, Mean, Standard Range, Continent Min Max deviation, nq nq nq    nq    Oceania 0.00 3.17 2.80 2.25 1.08 1.173.33 Asia 0.00 3.00 2.00 1.46 0.84 0.622.30 Americas 0.00 3.10 1.50 1.27 1.07 0.202.34 Europe 0.00 4.00 0.37 0.96 1.07 -0.112.03 Africa 0.00 2.00 0.00 0.79 0.99 -0.201.78 World 0.00 4.00 1.80 1.30 1.08 0.222.38 Table 2-F-8: The analysis of values of NQ coefficient without conversion Median, Mean, Standard Range, Continent Min Max deviation, nq nq nq    nq    Oceania 0.00 3.17 2.80 2.25 1.08 1.173.33 Asia 0.00 3.00 2.00 1.55 0.94 0.612.49 Americas 0.00 3.10 1.30 1.21 1.21 0.002.42 Europe 0.00 4.00 0.00 0.91 1.13 -0.222.04 Africa 0.00 2.00 0.00 0.67 1.03 -0.361.70 World 0.00 4.00 2.00 1.35 1.12 0.232.47 Table 2-F-7 shows that the values of nq are spread over wider range than the values of np. Mean values of nq vary from 0.79 for Africa, to 2.25 for Oceania; while the mean worldwide value of nq is 1.3 (worldwide median value is 1.8, Figure 2-F-12). Standard deviations of nq values are again of the same order as the corresponding mean values, confirming a rather widely spread of nq values. Table 2-F-8 presents the results of the analysis of nq values that are not obtained by conversion. As expected, the greater standard deviations and therefore wider ranges are obtained in Table 2-F-8 in comparison with corresponding standard deviations in Table 2-F-7. Exception is in the case of Oceania where none of nq values is obtained by conversion. Page 121 Modelling and Aggregation of Loads in Flexible Power Networks mean median 50 40 Frequency 30 20 10 0 -1 0 1 2 3 4 5 nq Figure 2-F-12: Histogram of nq coefficients of equivalent exponential models for static load models used worldwide in dynamic power system studies Since the dispersion of parameters np and nq used in dynamic studies is significant, both mean values and median values can be treated as prevalent, i.e. typical. For example, prevalent value of np for Europe is approximately 0.6, while for Americas and the World it is approximately 0.7/1.0 (Note: mean value/median value). Prevalent or typical value of nq is almost 1/0.4 for Europe, but it is approximately 2.3/2.8 for Oceania and 1.3/1.8 globally. 2-F.5 CONCLUSIONS The survey indicated that there are large differences between the load models used for static and dynamic power system studies by different system operators and utilities. Therefore, additional analysis of the received responses is performed, including the calculation of equivalent exponential load model, in order to identify prevalent (i.e. dominant or overall average) load models used by system operators and utilities in different continents and worldwide. The following main conclusions can be drawn from the results of the survey:  About 70% of utilities and system operators around the World use only static load model for power system stability studies.  About 30% of utilities and system operators use some form of induction motor model to represent dynamic loads in power system stability studies.  Dominant practice in the USA is to use a combination of static (typically ZIP) and dynamic load model (typically induction motor), while use of static load models is prevalent in the rest of the World.  In about 40% of the cases, currently used load model parameters have been updated within the last five years.  Typical static load model used in steady state studies is constant power (PQ) load for both real (active) and reactive power, i.e., np = nq=0.  Typical values of np and nq coefficients of the equivalent exponential static load models used in dynamic studies worldwide are 0.7 and 1.3, respectively.  Most of the utilities and system operators represent distributed generation as a negative load in system studies, without modelling them explicitly. Some of them, however, recognize the importance of appropriate DG models, particularly for future power system studies. Page 122 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 3-A Reported Parameters of Existing Load Models Table 3-A-1 to Table 3-A-5 summarise reported parameters for most of the load models described in Chapter 3. Table 3-A-1: Parameters of the model (3.3)–(3.4) for the load device/load class [11] (1994) Load device/load class kpu kqu kpf kqf 3-phase central 0.088 2.5 0.98 -1.3 Air conditioner 1-phase central 0.202 2.3 0.9 -2.7 window type 0.468 2.5 0.56 -2.8 Water heaters, oven, deep fryer 2 0 0 0 Dish washer 1.8 3.6 0 -1.4 Clothes washer 0.08 1.6 3.0 1.8 Clothes dryer 2.0 3.2 0 -2.5 Refrigerator 0.77 2.5 0.53 -1.5 Television 2 5.1 0 -4.5 Incandescent lights 1.55 0 0 0 Fluorescent lights 0.96 7.4 1 -2.8 Industrial motors 0.07 0.5 2.5 1.2 Fan motors 0.08 1.6 2.9 1.7 Agricultural pumps 1.4 1.4 5 4 Arc furnace 2.3 1.6 -1 -1 Transformer (unloaded) 3.4 11.5 0 -11.8 summer 1.2 2.9 0.8 -2.2 Residential load winter 1.5 3.2 1 -1.5 summer 0.99 3.5 1.2 -1.6 Commercial winter 1.3 3.1 1.5 -1.1 Industrial 0.18 6 2.6 1.6 Power plant auxiliaries 0.1 1.6 2.9 1.8 Table 3-A-2: Parameters of the model (3.5)–(3.6) for the load device/load class Reference Load device/load class kpu kqu dryer heater 1.95 0 dryer motor 0.77 2.13 advanced washing machine 0.34 1.51 advanced frequency drive 1 1.47 1.34 advanced frequency drive 2 2.12 1.98 heat pump 1 (split design) 0.34 4.12 heat pump 2 blower -3.34 0.86 heat pump 2 compressor 0.33 5.74 [38] (1996) refrigerator/freezer 2.11 1.89 battery charger 2.59 4.06 electronic compact fluorescent 1 0.95 0.31 electronic compact fluorescent 2 1.03 0.46 conventional magnetic compact fluorescent 1 2.07 3.21 electronically ballasted fluorescent 2 0.89 1.21 external fluorescent dimmer 1 5.84 high pressure sodium lamps 1.9 -4.25 Page 123 Modelling and Aggregation of Loads in Flexible Power Networks office equipment 1 0.24 0 office equipment 2 0.2 0 microwave oven 0.83 24.17 industrial heater/blower 1.98 1.42 baseboard heater 2 0 heater 1.93 0 hot plate 1.95 0 fluorescent lamp 1.69 4.67 mercury lamp 1 2.52 3.45 [167] (2002) mercury lamp 2 2.52 3.58 loaded, p=0.8p.u. 0.5 4.5 induction motor, for loaded, p=0.55p.u. 0.7 5.3 U=1p.u. loaded, p=0.3p.u. 0.9 5.7 unloaded 4.3 7.8 winter 1.761 3.656 [168] (2008) residential load summer 1.572 4.101 year 1.629 3.968 Table 3-A-3: Parameters of the model (3.7)–(3.8) for the load device/load class Reference Load device/load class kpu kqu kpf kqf incandescent light 1.55 0 0 0 fluorescent light 1.96 7.38 1 -26.6 air conditioner 0.2 2.3 0.9 -2.67 dryer 2.04 3.27 0 -2.63 refrigerator, freezer 0.77 2.5 0.53 0 [169] (1982) electrical range cooking 2 0 0 0 pump-fan-induction motors 0.08 1.6 2.9 1.8 heaters 2 0 0 0 TV, computer, etc. 2 5.2 0 -4.6 summer 0.78 3.29 0.69 -8.89 Mixed load winter 1.21 3.88 0.77 -10.85 [57] (2007) industrial load 0.772 4.522 0.331 6.479 Table 3-A-4: Parameters of the model (3.11)-(3.12) for the load device/load class Reference Load device/load class p1 p2 p3 q1 q2 q3 dryer heater 0.96 0.05 -0.01 0 0 0 dryer motor 1.96 -2.23 1.33 2.51 -2.34 0.83 advanced washing machine 0.05 0.31 0.63 -0.56 2.2 -0.65 advanced frequency drive 1 0.43 0.61 -0.05 -1.21 3.47 -1.26 advanced frequency drive 2 3.19 -3.84 1.65 1.09 -0.18 0.09 heat pump 1 0.72 -0.98 1.25 14.78 -23.72 9.93 [38] (1996) (split design) blower 5.46 -14.21 9.74 -14.85 31.59 -15.74 heat pump 2 compressor 0.85 -1.4 1.56 22.92 -40.39 18.47 refrigerator/freezer 1.19 -0.26 0.07 0.59 0.65 -0.24 battery charger 3.51 -3.94 1.43 5.8 -7.26 2.46 electronic compact fluorescent 1 0.14 0.77 0.09 -0.06 -0.34 -0.6 Page 124 Modelling and Aggregation of Loads in Flexible Power Networks electronic compact fluorescent 2 0.16 0.79 0.05 0.18 -0.83 -0.35 conventional magnetic compact 0.34 1.31 -0.65 3.03 -2.89 0.86 fluorescent 1 electronically ballasted fluorescent 1 -2.48 5.46 -1.97 0 0 0 electronically ballasted fluorescent 2 -1.6 3.58 -0.98 0.79 -0.16 0.36 electronic dimming ballast -0.16 1.77 -0.62 0 0 0 external fluorescent dimmer -0.48 1.89 -0.41 12.21 -18.38 7.16 high pressure sodium lamps 0.98 -0.03 0.06 29.84 -45.26 14.41 office equipment 1 0.34 -0.32 0.98 0 0 0 office equipment 2 0.08 0.07 0.85 0 0 0 microwave oven -2.78 6.06 -2.28 0 0 0 industrial heater/blower 0.98 0.02 0 0.69 0.25 0.06 baseboard heater 1 0 0 0 0 0 heater 0.771 0.389 -0.163 0 0 0 hot plate 1.293 -0.635 0.336 0 0 0 fluorescent lamp 0.584 0.528 -0.111 8.045 -11.419 4.376 mercury lamp 1 0.589 1.344 -0.935 3.534 -3.612 1.079 mercury lamp 2 -0.756 4.033 -2.277 1.388 -0.804 1.192 [167] loaded, 4.148 -7.793 4.646 16.836 -29.142 13.306 (2002) p=0.8p.u. loaded, 3.075 -5.465 3.391 13.678 -22.072 9.39 induction motor p=0.55p.u. loaded, 2.928 -4.901 2.973 13.909 -22.074 9.165 p=0.3 unloaded 13.099 -21.889 9.788 20.071 -32.356 13.285 winter 1.089 -0.45 0.361 11.095 -18.942 8.847 [168] residential load summer 0.877 -0.213 0.336 14.953 -26.351 12.396 (2008) year 0.827 -0.049 0.222 14.14 -24.838 11.696 Table 3-A-5: Load model and identified parameters for the load device/load class According to [57] (2007)  U  2 U   P  Pn  p1    p 2     p 3  1  k pf f  industrial load p1=0.189, p2=0.42, p3=0.391, kpf=0.3398, q1=2, q2=-1, q3=0, kqf=3.355,   U n  Un    U  2 U   Q  Qn q1    q 2     q 3  1  k qf f    U n  Un   Residential load [41] (2000)  U  P  Pn  a 0  a1  with capacitors OFF  U n  a0=0.55, a1=0.45, b0=9.2, b1=-20.4, b2=12.2 with capacitors ON   U   2 Q  Qn  b0  b1 U a0=0.51, a1=0.49, b0=9.5, b1=-21.4, b2=13.2  b2      Un  Un   P  Pn 1  p1U  Data for peak load [23] (1984)   Primarily residential load Q  0,5Pn 1  q1U  q2 U 2 summer: p1=1.1, q1=4.7, q2=23  0,5P 1  2U  U   winter: p1=1.5, q1=2.6, q2=4  Qn n 2 Primarily commercial load summer: p1=0.8, q1=3.9, q2=15 winter: p1=1.2, q1=3.3, q2=7 Page 125 Modelling and Aggregation of Loads in Flexible Power Networks dPr According to [51] (1994) Tp  Pr  Ps (U )  Pt (U ) mostly residential load dt s t winter s=0.05-0.54, t=1.85-2.46, Tp=127-159.6s, U  U  s=2.1-4.99, t=4.18-6.73, Tq=22.9-131.7s  P0    P0    U0   U0  summer s=0.17-1.39, t=1.5-1.83, Tp=83-363.8s, s=1.72-5.34, t=4.64-6.32, Tq=0.1-1024.8s t U  Pl  Pr  P0   According to [69] (2008) U0  residential load dQr winter s=1.19, t=1.63, Tp=142s, s=3.93,  t=4.15, Tq  Qr  Qs (U )  Qt (U ) dt Tq=127s s t summer s=1.35, t=1.76, Tp=169s,  s=3.43, t=3.71, U  U   Q0    Q0   Tq=138s  U0   U0  year s=1.24, t=1.67, Tp=150s,  s=3.74, t=3.98, t Tq=131s U  Ql  Qr  Q0   According to [170] (2009) U0  heater s=1.952, t=1.952, Tp0s, incandescent lamp s=1.483, t=1.483, Tp0s fluorescent lamps s=2.466, t=2.466, Tp0s, s=7.893, t=7.388, Tq=63.72s mercury lamp 1 s=2.389, t=2.389, Tp0s,  s=3.17, t=3.387, Tq=43.41s mercury lamp 2 s=2.497, t=2.497, Tp0s,  s=3.327, t=3.565, Tq=24.47s refrigerator 2 s=0.533, t=0.533, Tp0s,  s=2.506, t=2.506, Tq0s induction motor s=0.219, t=0.219, Tp0s, s=3.835, t=3.835, Tq0s Note: Sampling rate used was 1s that is not sufficient to identify short time constants accurately According to [171] (1982), M=K(1-s) Larger paper mill (base 115kV, 30MVA) R=7.76, XC=4.75, Rr=0.00178, Rs=0.000121, Xr=Xr+Xm=2.5515, Xs= Xs+Xm=2.5515, Xm =2.43, H=3.45, Composite load model =0.00017, K=0.8816 Smaller paper mill (base 115kV, 10MVA) R=9.267, XC=1.2567, Rr=0.00199, Rs=0.00043, Xr=Xr+Xm=0.97767, Xs=Xs+Xm=1.142, Xm =0.88, H=1.92, =0.001, K=0.772 According to [59] (1997) for mostly industrial load in p.u.: PP=0.199, QQ=-0.2121, Idl=0.086, Iql=0.129, RZ=24.14, ZIP - motor model LZ=171.31, Rs=0.043, Rr=0.054, Ls=Ls+Lm=0.462, Lm=3.058, Lr=Lr+Lm=0.462, H=0.7361, T0=0.656, r=360.781, =1.061 Data for system peak load [39] (1994) Load model of bulk power bus Qs=500GVAr, XT=21% on 24.8GW base, PL=24.8GW, QL=12.4GVAr Table 3-A-6: IEEE recommended typical induction motor data [161] (1995) Type Rs Xs Xm Rr Xr H A B Load factor 1 0.031 0.1 3.2 0.018 0.18 0.7 1 0 0.6 2 0.013 0.067 3.8 0.009 0.17 1.5 1 0 0.8 3 0.013 0.14 2.4 0.009 0.12 0.8 1 0 0.7 4 0.013 0.14 2.4 0.009 0.12 1.5 1 0 0.7 Page 126 Modelling and Aggregation of Loads in Flexible Power Networks 5 0.077 0.107 2.22 0.079 0.098 0.74 1 0 0.46 6 0.035 0.094 2.8 0.048 0.163 0.93 1 0 0.6 7 0.064 0.091 2.23 0.059 0.071 0.34 0.2 0 0.8 Type 1: Small industrial motor Type 2: Large industrial motor Type 3: Water pump Type 4: Power plant auxiliary Type 5: Weighted aggregate of residential motors Type 6: Weighted aggregate of residential and industrial motors Type 7: Weighted aggregate of motors dominated by air conditioning Table 3-A-7: Motor parameters obtained from the Southold substation of Long Island Lighting Company [84] (1987) Type Rs Xs Xm Rr Xr H A B Load factor REFR 0.056 0.087 2.4 0.053 0.082 0.280 - - 0.500 DWSH 0.110 0.140 2.8 0.110 0.056 0.280 - - 0.500 CWSH 0.110 0.120 2.0 0.110 0.130 1.500 - - 0.400 DRYR 0.120 0.150 1.9 0.130 0.140 1.300 - - 0.400 REFR: a refrigerator and freezer type motor DWSH: a dishwasher type motor CWSH: a clothes washer type motor DRYR: a dryer type motor Table 3-A-8: Parameters for models of many load components [37] (1994) index Rs Xs Xm Rr Xr A B H Load factor 1 0.033 0.067 2.4 0.048 0.062 0.2 0.0 0.28 0.6 2 0.033 0.067 2.4 0.048 0.062 0.2 0.0 0.28 0.6 3 0.033 0.067 2.4 0.048 0.062 0.2 0.0 0.28 0.6 4 0.10 0.10 1.8 0.09 0.06 0.2 0.0 0.28 0.6 5 0.056 0.087 2.4 0.053 0.082 0.2 0.0 0.28 0.5 6 0.11 0.14 2.8 0.11 0.056 1.0 0.0 0.28 0.5 7 0.11 0.12 2.0 0.11 0.13 1.0 0.0 1.5 0.4 8 0.12 0.15 1.9 0.13 0.14 1.0 0.0 1.3 0.4 9 0.53 0.83 1.9 0.036 0.68 0.2 0.0 0.28 0.6 10 0.53 0.83 1.9 0.036 0.68 0.2 0.0 0.28 0.6 11 0.53 0.83 1.9 0.036 0.68 0.2 0.0 0.28 0.6 12 0.10 0.10 1.8 0.09 0.06 0.2 0.0 0.28 0.6 13 0.079 0.12 3.2 0.052 0.12 1.0 0.0 0.7 0.7 14 0.031 0.1 3.2 0.018 0.18 1.0 0.0 0.7 0.6 15 0.013 0.067 3.8 0.009 0.17 1.0 0.0 1.5 0.8 16 0.025 0.088 3.2 0.016 0.17 1.0 0.0 0.4 0.7 17 0.013 0.14 2.4 0.009 0.12 1.0 0.0 1.5 0.7 Page 127 Modelling and Aggregation of Loads in Flexible Power Networks 1. Heat pump space heating 10. Heat pump commercial A/C 2. Heat pump central air conditioner 11. Commercial central A/C 3. Central air conditioner 12. Commercial room A/C 4. Room air conditioner 13. Pumps, fans, other motors 5. Refrigerator and freezer 14. Small industrial motors 6. Dish washer 15. large industrial motors 7. Clothes washer 16. Agricultural water pumps 8. Clothes dryer 17. Power plant auxiliaries 9. Commercial heat pump Table 3-A-9: The induction motor parameters adopted in China [172] (2008) Type Rs Xs Xm Rr Xr Tj á P S0 I 0 0.295 3.497 0.02 0.12 2.0 0.15 2.0 0.0116 II 0.013 0.11 3.0 0.012 0.12 2.0 0.15 2.0 0.011 T M =K L [α+(1-α)(1- S0)P], K L is a coefficient, α is the damp torque coefficient which is independent of the speed, P is the exponential coefficient which relates to the damp torque. Table 3-A-10: Induction motor parameters recommended by WSCC [172] (2008) Parameter Default Large industrial motor Small industrial motor Rs/p.u. 0.0068 0.0130 0.0310 L1/p.u. 0.1000 0.0670 0.1000 Lm/p.u. 3.4000 3.8000 3.2000 Ls/p.u. 3.5000 3.8700 3.3000 R2/p.u. 0.0180 0.0090 0.0180 L2/p.u. 0.0700 0.1700 0.1800 H(MW.s/MVA) 0.5000 1.5000 0.7000 T’ 0/s 0.5300 1.1700 0.5000 L’ /p.u. 0.1700 0.2300 0.2700 D/p.u. 2.0000 2.0000 2.0000 Table 3-A-11: Induction motor parameter [24] (2009) Rs/p.u. Xs0/p.u. Xm/p.u. Rr/p.u. Xr0/p.u. T j/s The motor of commercial load 0.001 0.23 3.0 0.02 0.23 1.326 Large industrial motor 0.007 0.0818 3.62 0.0062 0.0534 3.2 Small industrial motor 0.078 0.065 2.67 0.044 0.049 1.0 Table 3-A-12: Parameters of dynamic model of induction motor [173] (1995) Load class Rs Xs Xm Rr Xr Hm loading factor Mixed load 0.046 0.097 2.571 0.056 0.115 0.627 0.619 Page 128 Modelling and Aggregation of Loads in Flexible Power Networks Table 3-A-13: Exponential and polynomial model parameters for power electronic (SMPS) load category Exponential PF1 Polynomial model Load Type model (cap) kpu kqu p1 p2 p3 q1 q2 q3 SMPS with no PFC 0.994 0 2.36 0 0 1 -3.63 9.88 -7.25 SMPS with p-PFC 0.97 0 -0.5 0 0 1 0.45 -1.44 1.99 SMPS with a-PFC 1.00 0 N/A 0 0 1 0 0 0 Note: PFC, p-PFC and a-PFC are abbreviations for power factor correction, active power factor correction and passive power factor correction, respectively. PF1 is displacement/fundamental power factor (capacitive in case of SMPS load). Table 3-A-13 to Table 3-A-17 present the results of exponential (3.5)–(3.6) and polynomial (3.11)–(3.12) model interpretations of power electronics interfaced loads: SMPS load category, energy efficient light sources (CFL and LED) load category, single-phase drive-controlled motor load category and three-phase drive- controlled motor load category [20], [45–48] (2007-2009). The parameters of exponential and polynomial load model parameters are obtained on the basis of data analysis from steady-states for different values of supply voltage. Table 3-A-14: Exponential and polynomial model parameters for energy efficient lighting (CFL/LED) load category PF1 Exponential model Polynomial model Load (cap) kpu kqu p1 p2 p3 q1 q2 q3 CFL 0.91 0.94 0.52 0.01121 0.9176 0.07086 0.10 -0.73 -0.37 LED 0.137 1.32 2.06 0.3112 0.6865 0.0009538 -1.05 0.04 0.01 Note: Load models of high-intensity discharge (HID) light sources, which are often used in commercial load sector, are currently being developed. Table 3-A-15: Exponential and polynomial model parameters for single-phase drive-controlled motor (SASD) load category Exponential model Polynomial model Loading PF1 kpu kqu p1 p2 p3 q1 q2 q3 Higher power V/Hz open-loop SASDs CT 0.896 -0.10 -0.88 0.40 -0.89 1.49 1.54 -3.95 3.41 LT 0.896 0.08 -0.71 0.02 0.0 0.98 0.95 -2.60 2.66 QT 0.896 0.22 -0.57 -0.27 0.76 0.51 0.54 -1.65 2.11 CP 0.896 -0.19 -1.11 1.08 -2.5 2.42 2.47 -6.04 4.56 Higher power V/Hz closed-loop SASDs All 0.896 -0.19 -1.11 1.08 -2.5 2.42 2.47 -6.04 4.56 Higher power V/Hz advanced SASDs All 0.896 0 -0.73 0 0 1 1.45 -3.66 3.19 Lower power V/Hz open-loop SASDs CT 0.99 -0.10 -3.33 0.40 -0.89 1.49 -3.32 10.50 -8.18 LT 0.99 0.08 -2.72 0.02 0.0 0.98 -3.65 10.34 -7.69 QT 0.99 0.22 -2.63 -0.27 0.76 0.51 -3.67 10.31 -7.64 CP 0.99 -0.19 -3.91 1.08 -2.5 2.42 -3.02 10.62 -8.60 Lower power V/Hz closed-loop SASDs All 0.99 -0.19 -3.91 1.08 -2.5 2.42 -3.02 10.62 -8.60 Lower power V/Hz advanced SASDs All 0.99 0 -2.81 0 0 1 -3.61 10.39 -7.78 Note: Abbreviations CT, LT, QT and CP denote constant torque, linear torque, quadratic torque and constant power mechanical load, respectively. Page 129 Modelling and Aggregation of Loads in Flexible Power Networks Table 3-A-16: Exponential and polynomial model parameters for three-phase drive-controlled motor (ASD) operated in continuous mode Exponential model Polynomial model Loading PF1 kpu kqu p1 p2 p3 q1 q2 q3 Lower power V/Hz open-loop ASDs CT 0.99 -0.17 -0.08 0.45 -1.07 1.62 1.75 -3.55 2.8 LT 0.99 0.04 0.17 0.02 0 0.98 1.18 -2.18 2.0 QT 0.99 0.22 0.35 -0.27 0.76 0.51 0.66 -0.97 1.31 CP 0.99 -0.19 -0.28 1.08 -2.5 2.42 2.64 -5.5 3.9 Lower power V/Hz closed-loop ASDs All 0.99 -0.19 -0.28 1.08 -2.5 2.42 2.64 -5.5 3.9 Higher power V/Hz open-loop and closed-loop ASDs All 0.99 -0.07 0.20 0.13 -0.33 1.20 1.30 -2.38 2.08 Lower and higher power V/Hz advanced ASDs All 0.99 0 0 0 0 1 0.99 -1.96 1.97 Table 3-A-17: Exponential and polynomial model parameters for three-phase drive-controlled motor (ASD) operated in discontinuous mode Exponential model Polynomial model Loading PF1 kpu kqu p1 p2 p3 q1 q2 q3 Lower power V/Hz open-loop ASDs CT 0.984 -0.10 0.09 0.40 -0.89 1.49 1.60 -3.10 2.50 LT 0.984 0.08 0.28 0.02 0 0.98 1.10 -1.91 1.81 QT 0.984 0.22 0.24 -0.27 0.76 0.51 0.70 -0.98 1.28 CP 0.984 -0.19 -0.98 1.08 -2.5 2.42 2.67 -5.49 3.81 Lower power V/Hz closed-loop ASDs All 0.984 -0.19 -0.98 1.08 -2.5 2.42 2.67 -5.49 3.81 Higher power V/Hz open-loop and closed-loop ASDs All 0.984 -0.07 -0.3 0.13 -0.33 1.20 0.55 -1.7 2.15 Lower and higher power V/Hz advanced ASDs All 0.984 0 -0.5 0 0 1 1.22 0.45 -0.67 The results of processing parameters of exponential static load model (3.5)-(3.6) for different low voltage devices from existing literature showed that they attain normal probability distribution [240]. The centres of probability of kpu and kqu are 0.158 and 0.292, respectively. However, the parameters vary in very wide ranges and corresponding standard deviations are more than 2.5 times larger than the corresponding centers of probability. Much larger standard deviations are obtained for the parameters of polynomial/ZIP model (3.11)-(3.12). Furthermore, the analysis showed that the load devices of the same type can have quite different parameter values because the parameters can vary depending on the manufacturer, used electrical circuits, production process, auxiliary components, operating conditions, etc. Table 3-A-18 and Table 3-A-19 present load models and the parameters for residential and commercial load class, respectively, found in existing literature. Page 130 Modelling and Aggregation of Loads in Flexible Power Networks Table 3-A-18: Aggregate load models of residential load class Ref Load model Variant or season Parameter values PF = 0.85, ZP = 0.29, IP = 0.1, PP = 0.61, ZQ = 3.22, IQ = -4.53, PQ ZIP With motors included = 2.31 [14] With motor modelled PF = 0.88, ZP = 0.39, IP = 0.13, PP = 0.48, ZQ = 4.74, IQ = -6.19, ZIP + motor separately PQ = 2.45 Summer kpu=1.2, kqu=2.9, kpf=0.8, kqf=-2.2 [11] Exponential model Winter kpu=1.5, kqu=3.2, kpf=1, kqf=-1.5 Winter kpu=1.761, kqu=3.656 [168] Exponential model Summer kpu=1.572, kqu=4.101 Year kpu=1.629, kqu=3.968 Capacitors on a0=0.51, a1=0.49, b0=9.5, b1=-21.4, b2=13.2 [41] Linear load model Capacitors off a0=0.55, a1=0.45, b0=9.2, b1=-20.4, b2=12.2 Linear load model Summer p1=1.1, q1=4.7, q2=23 [23] + polynomial Winter p1=1.5, q1=2.6, q2=4 s=0.05-0.54, t=1.85-2.46, Tp=127-159.6s, s=2.1-4.99, Winter t=4.18-6.73, Tq=22.9-131.7s [51] Summer s=0.17-1.39, t=1.5-1.83, Tp=83-363.8s, s=1.72-5.34, t=4.64- Exponential dynamic 6.32, Tq=0.1-1024.8s load model Winter s=1.19, t=1.63, Tp=142s, s=3.93,  t=4.15, Tq=127s [69] Summer s=1.35, t=1.76, Tp=169s, s=3.43,  t=3.71, Tq=138s Year s=1.24, t=1.67, Tp=150s, s=3.74,  t=3.98, Tq=131s Table 3-A-19: Aggregate load models of commercial load class Variant or Ref Load model Values season With motors PF = 0.82, ZP = 0.21, IP = 0.15, PP = 0.64, ZQ = 4.74, ZIP included IQ = -6.19, PQ = 2.45 [14] With motor PF = 0.88, ZP = 0.29, IP = 0.15, PP = 0.56, ZQ = 4.5, ZIP + motor modelled IQ = -6.19, PQ = 2.45 separately Summer kpu=0.99, kqu=3.5, kpf=1.2, kqf=-1.6 [11] Exponential model Winter kpu=1.3, kqu=3.1, kpf=1.5, kqf=-1.1 Linear load model Summer p1=0.8, q1=3.9, q2=15 [23] + polynomial Winter p1=1.2, q1=3.3, q2=7 Page 131 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 3-B Models of Power Electronics Interfaced Loads This appendix firstly presents examples of full-circuit models of power electronics interfaced loads [20], [45– 48], and dg equivalent circuit for three-phase induction motor model [19]. Figure 3-B-1: Full-circuit SMPS model Bridge Inverter Lemi Toroid esr Lres sqrt((i_tube^2)/time)) i_tube i_tube^2 Rin N N IT 1 3 System Cdc N Cres Impedance 2 VT/IT v_tube R_tube Supply system Figure 3-B-2: Full-circuit CFL model (a) (b) Figure 3-B-3: DQ equivalent circuit for three-phase induction motor: a) d-axis b) q-axis Switch-mode power supply (SMPS) loads are modern power electronic devices (“consumer electronics”, PCs, TVs, CD/DVD players, etc.) that are sensitive to voltage variations and require a regulated dc voltage supply. The SMPS include a front-end rectifier, dc link and dc-dc converter operating with feedback control to provide regulated output, as well as other power electronic components which should be accurately represented in the full-circuit load model. For the SMPS load category, the equivalent-circuit model is introduced based on the principle that, for a steady-state dc load, this equipment operates as a constant power load, as the SMPS is able to regulate the dc load voltage over a range of supply system voltages. The implementation of the equivalent resistance for this load category is represented by the equation 2 vdc req  (3-B.1) Prated where: vdc is the instantaneous value of dc link voltage and Prated is the rated power of the supplied dc load. Energy efficient lighting (CFL/LED) load category is currently dominated by compact fluorescent lamps (CFLs), but it is expected that (organic) light-emitting diode, (O)LED, light sources will increase in popularity and in numbers Page 132 Modelling and Aggregation of Loads in Flexible Power Networks in the future. Currently implemented LED light source circuit topologies consist of a diode bridge rectifier directly supplying the LED chain (the current characteristics of which can be easily modelled using the diode equation), and is therefore no need to simplify the circuit, as it already has the form of the equivalent-circuit model. Standard circuit topology of modern CFLs consists of an electronic ballast circuit with self-oscillating inverter, used for controlling the voltage across the fluorescent tube as presented in the full circuit CFL model in Figure 3-B-1. It is possible to represent steady state behaviour of all components behind the Cdc with an equivalent resistance. The mathematical formulation of this resistance changes depending on instantaneous dc link voltage vdc, which is determined by the state of charge of the Cdc. One formulation of the resistance should be used during charging and the other during the discharging stage: req,ch arging  11vdc  0.021vdc 2  1300 (3-B.2) req,discharging  23.7vdc  274 (3-B.3) Single-phase and three-phase adjustable speed drive (ASD) controlled motors may also be represented by the equivalent circuit models. For these two load categories, the equivalent resistance is determined by the motor loading conditions, which can be represented with the four general types of mechanical loads: constant torque, linear torque, quadratic torque, and constant power. The final results are again obtained after investigating the relationship between the active power/current drawn at the dc link and the dc link voltage for considered mechanical loading conditions. These results are then converted to the equivalent resistance using the standard relationship (3-B.1) with Pdc (the active power drawn at the dc link) instead of Prated. The vast majority of single-phase ASDs consist of a single-phase front end rectifier and a three phase inverter controlling a small three-phase motor. Therefore, the full circuit model consists of a 4th order dq motor model connected to front-end rectifier/inverter. Three-phase ASDs can be divided into two general categories: those that operate in continuous mode and those that operate in discontinuous modes. In continuous current conduction mode, the dc link filter inductor current never falls to zero, while in discontinuous current conduction mode the dc link filter inductor current does fall to zero. Mode of operation is determined by the dc link filter components (Ldc and Cdc). Page 133 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 3-C Model of Directly Connected Single -phase Induction Motors The model of directly connected single-phase induction motors is     rs   X ss 0 b X ms 0  s   vqs b  i s   s     X mS   s  qs 0 rS  X SS 0 vds   b b  ids    's    (3-C.1) 1 r  ' 1 r '  i ' s  vqr   X ms  X ms rr'  X rr  X RR  qr  v ' s   b n b b n b   's   dr        idr   n r X ms X mS ' n r X rr rR'  ' X RR   b b b b  where: X ss  X ls  X ms , X SS  X lS  X mS , X rr'  X lr'  X ms , X RR '  X lR '  X mS , ωb is the base electrical angular NS frequency used to calculate the inductive reactances, ωr denotes the rotor reference frame, n  and Ns Lls , rs , rr' , L'lr are the main winding stator and rotor inductances and resistances, LlS , rS , rR' , L'lR are the auxiliary winding stator and rotor inductances and resistances, and Lms and LmS are the magnetising inductance of the main winding and auxiliary winding respectively. The equivalent circuit model of directly connected single-phase induction motors is shown in Figure 3-C-1. Figure 3-C-1: Stationary reference dq single-phase induction motor circuit representation The values used for typical single-phase induction motor (SPIM) parameters in Table 3-C-1. The parameters of exponential and ZIP model which are developed from this model are presented in Table 3-C-2. In this table abbreviations IR and CR denote motors without run capacitors (inductor run SPIM) and motors with them (capacitor run SPIM), respectively. The abbreviations CT, LT, QT and CP are the types of motor mechanical loads: constant torque, quadratic torque, linear torque and constant mechanical power, respectively. Table 3-C-1: Values of SPIM [55] Parameter (Ù) Main winding Magnetising Auxiliary winding X ls  2.79 X ms  66.8 X lS  3.22 rs  2.02 X mS  92.9 rS  7.14 rr'  4.12 rR'  5.74 X lr'  2.12 ' X lR  2.95 Page 134 Modelling and Aggregation of Loads in Flexible Power Networks Table 3-C-2: Exponential and Polynomial model coefficients of SPIMs Exp. Model Polynomial Model Loading PFnom np nq ZP IP PP ZQ IQ PQ IR CT 0.62 0.06 1.92 0.63 -1.20 1.57 1.4 -0.91 0.50 IR LT 0.62 0.19 1.92 0.31 -0.43 1.11 1.40 -0.91 0.50 IR QT 0.62 0.30 1.92 0.10 0.10 0.80 1.4 -0.91 0.50 IR CP 0.62 -0.12 1.92 1.16 -2.42 2.26 1.40 -0.91 0.50 CR CT 0.90 0.38 1.68 0.50 -0.62 1.11 1.54 -1.43 0.89 CR LT 0.90 0.46 1.68 0.34 -0.22 0.88 1.54 -1.43 0.89 CR QT 0.90 0.53 1.68 0.22 0.08 0.69 1.54 -1.43 0.89 CR CP 0.90 0.29 1.68 0.73 -1.16 1.43 1.54 -1.43 0.89 Page 135 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 3-D WECC Residential Air-Conditioning Stalling Motor Model A simple user written model was developed that controls the small motor model and a fictitious shunt in parallel with the motor. It is shown in Figure 3-D-1. The state transition diagram of this model is presented in Figure 3-D- 2. According to these figures, the user written stalling motor model may be explained as follows: 1. The model starts in steady state, in State 1 – the motor model is in service and the ‘fictitious shunt’ that emulates stalling is out of service. 2. Now if during the simulation the terminal voltage of motor falls below the magnetic contactor trip point, then the motor is also taken out of service to emulate magnetic contactor drop out (State 2). A delay of 10 ms is assumed between sensing this voltage and opening the magnetic contactor. The reclosing time of the magnetic contactor is simulated as 10 ms/V2. 3. Once the voltage recovers enough for the magnetic contactor to close, it is assumed that the unit will initially stall. Thus, the motor model is kept out-of-service and instead the ‘fictitious shunt’, which represents the constant impedance behaviour of the motor under stalled conditions, is switched into the circuit. The reason for this is that the magnetic contactor drop out point is lower than the stall voltage, thus the fact that initially the voltage had dropped below the magnetic contactor drop out point (and thus also the stall voltage) means that the unit will most likely stall. If subsequently the voltage rises above the point at which the motor can restart, then it will come out of the stall condition and the motor will be allowed to restart. It is assumed that once the unit stalls, it remains stalled until it either trips on thermal overload (TOL) or restarts if voltage recovers enough and the TOL does not operate within 10 seconds. 4. Now if the voltage remains low and the motor remains stalled, it will eventually trip if the total I2t is greater than a user setpoint (I2t represents the accumulated thermal energy over a period of time). To emulate the behaviour of scroll compressors, one may allow the compressor motor to re-start if it does not trip on TOL after the voltage recovers above a given threshold for more than a given time period (10 seconds). Figure 3-D-1: The stalling motor model used in EPRI’s composite load model [80] The functionality of motor D from WECC composite load model (that represents single-phase motors mainly residential air conditioners) is implemented using what is called a “performance model”. In this methodology the actual measured response of a single-phase a/c compressor motor to a gradual ramp down and up in voltage is taken and two pairs of polynomial equations are fitted to the response. One pair is fitted to the real power response and one pair to the reactive power response. The polynomial equations are quadratic equations. One represents the behaviour of the unit while running normally and the other when the unit has stalled (effectively a constant impedance curve). This can be seen in Figure 3-D-3, where we see the two regions of operation of the motor: normal and stalling. The transition from the normal running curve to the stalled curve is based on the stalling voltage, Vstall. That is, the voltage at which the motor stalls. So once again a state-transition algorithm Page 136 Modelling and Aggregation of Loads in Flexible Power Networks determines when the voltage has fallen below the stall voltage and thus switches the model response from one curve to the other. Detailed description of the final development of the recently released and approved WECC composite load model “cmpldw” can be found in [1]. Figure 3-D-2: State transition diagram for stalling motor model [80] Figure 3-D-3: Performance model approach (reproduced with permission from [174]). Appendix 3-E Model of Distributed Energy Storage Systems [63] The principle of the model of DESS is presented in this appendix. The storage unit is connected to the grid through a power electronic conversion system including a PWM boost chopper and a PWM voltage source inverter, modeled by the so-called “system scheme”. The DESS supervision selects the appropriate operating mode and computes the active/reactive references Pr_ref /Qr_ref that are tracked by the automated control routines. A phase locked-loop is used to synchronize the DESS to the grid. The control of the inverter is carried out in the rotating d-q frame. In order to track the active reference, the control routines alter the storage current isj through the chopper: the resulting power flow is followed by the inverter since its direct current Page 137 Modelling and Aggregation of Loads in Flexible Power Networks component is used to control the voltage of the DC-bus. The reactive power of the DESS is controlled through the quadratic current component irq of the inverter. The use of a controlled DC voltage and the general layout of the control scheme are chosen in a way to be able to facilitate any future upgrade of this DESS model to a multi-source power station. Figure 3-E-1: Structure of the modeled DESS Page 138 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 4-A Practical example of measurement based load modelling The DFR monitoring campaign lasted 6 months to gather summer peak and winter light load behaviours. The 3 phase voltages and currents at both incomer MV side of the HV/MV transformers and one dedicated MV feeder were monitored in each of the substations (see Figure 4-A-1). HV 3-phase V& I in HV Meshed MV X 3-phase V& I Radial for one feeder Figure 4-A-1: DFR location in substations An 8 kHz sampling frequency was adopted to derive continuously the 10 cycle RMS average values. Following each detected disturbance, the signals are recorded during 130 s with a 4kHz sampling frequency (see Figure 4-A- 2). trigger time t1 (10 sec.) t2 (120 sec.) Figure 4-A-2: DFR recording period following a disturbance In this particular case, a sampling rate used for collecting data was 16 samples/cycle/channel (1kHz). Almost all modern dedicated monitoring devices can sample at this, or much higher, rates. Load behaviour: the top-down approach Four main relevant categories of disturbances were identified during the monitoring campaign:  voltage dips (of small amplitude): active and reactive powers return to their initial values;  voltage dips provoking a bifurcation: active and reactive powers do not return to their initial level. It is observed that the positive sequence equivalent voltage dip can be limited in amplitude;  voltage steps induced by large motor start or tap change operation;  frequency transients corresponding to real systems unbalance induced by a system frequency deviation. They are seen by all the installed devices. Voltage dips larger than 30% of the nominal voltage induced a loss of load comprised between 8.5% for a dip of 13% and 50% for a dip of 35% of the equivalent positive voltage (see Figure 4-A-3). Analysis of the load disconnection phenomenon During the monitoring campaign of the hot summer peak load season, a large part of the load is of AC type. Its size and cooling technology vary strongly with the type of supplied customer ranging from the classic air window or inverter interfaced 1 kWe split AC appliance to the large district cooling plant based on 1-2 MW motors. Page 139 Modelling and Aggregation of Loads in Flexible Power Networks 10 8 s 9.50 9.75 10.00 10.25 10.50 10.75 11.00 11.25 11.50 11.75 12.00 [DFR_signal_20080913_093743_02 (imported)] Direct Voltage Amplitude(kV) 3 2 1 s 9.50 9.75 10.00 10.25 10.50 10.75 11.00 11.25 11.50 11.75 12.00 [DFR_signal_20080913_093743_02 (imported)] Direct Current Amplitude(kA) 51 50 s 9.50 9.75 10.00 10.25 10.50 10.75 11.00 11.25 11.50 11.75 12.00 [DFR_signal_20080913_093743_02 (imported)] Actual frequency (Hz) 30 20 s 9.50 9.75 10.00 10.25 10.50 10.75 11.00 11.25 11.50 11.75 12.00 [DFR_signal_20080913_093743_02 (imported)] Direct Active Power(MW) 10 -0 s 9.50 9.75 10.00 10.25 10.50 10.75 11.00 11.25 11.50 11.75 12.00 [DFR_signal_20080913_093743_02 (imported)] Direct Reactive Power (Mvar) Figure 4-A-3: Recorded loss of load following a voltage dip (a) Small size appliances A set of 4 single phase appliances has been tested in laboratory. One air window and three split inverter interfaced AC units have been exposed to the following voltage and frequency profiles:  steady state change of frequency covering the range 30 to 70 Hz;  steady state change of voltage covering the range 0% to 130% of the nominal voltage;  voltage sag and swell of various duration ( 3, 10 and 25 cycles) and amplitude (+10, +20, +30, -30, - 40, -50 and -80%);  frequency profile emulating an idealized primary frequency control response. Sensitivity to frequency changes: Main results indicated that during short periods representative of power system electromechanical transients, the rotating load displays a constant behaviour with respect to frequency when connected through an inverter and a constant torque when directly coupled to a compressor. Sensitivity to voltage sag: The obtained results are summarized in Table 4-A-1 (green: successful ride through, orange: trip or idling after the second sag, red: trip or idling after the first sag). It indicates that a variable proportion of the load can be lost following phase-neutral voltage sags lasting more than 3 cycles (60ms). The proportion increases with the sag duration and depth. (b) Large size appliances The behaviour of large district cooling, large industrial motors and large chillers during grid disturbances is governed by the large induction motor driving the compressor unit (approximately 80% of the total absorbed load) and the auxiliary pump driven motors (20% of the total absorbed load). Manufacturer tests have shown that voltage sags larger than 30% of the nominal voltage often identified as short-term power interruptions (STI) can damage the motor and compressor if the chiller is reconnected to the line while the motor and line phases do not match. STI of 2.5 cycles or longer are detected and cause the shut down of the unit (see Table 4- Page 140 Modelling and Aggregation of Loads in Flexible Power Networks A-1). The unit is disconnected within few cycles of STI detection. This activation time is below usual transmission systems fault clearing times. Table 4-A-1:Sensitivity to voltage sag amplitude and duration Appliance 1 Appliance 2 Duration -30% -40% -50% -80% Duration -30% -40% -50% -80% 1c-1c-1c OK OK OK OK 1c-1c-1c OK KO (1x) KO (3x) KO (1x) 1c-8c-1c OK KO(1x) 1c-8c-1c OK 1c-23c-1c KO (1x) 1c-23c-1c OK Appliance 3 Appliance 4 (without inverter) Duration -30% -40% -50% -80% Duration -30% -40% -50% -80% 1c-1c-1c OK OK OK OK 1c-1c-1c OK OK OK OK 1c-8c-1c OK OK KO (1x) 1c-8c-1c OK OK OK KO (3x) 1c-23c-1c OK OK 1c-23c-1c OK OK KO (3x) KO (1x) Sensitivity to voltage swell: Two of four appliances tripped for voltages above 125% of the nominal phase-neutral voltage. Voltage dip (%) 100% Might shut down! 30% Do not shut down 1.5 cycles Dip duration Figure 4-A-4: STI protection activation zone If the STI protection is not activated, the stalling of the large induction motor will activate the current overload protection (COP) that is set to trip after 1.5 s with a current higher than 140% of nominal one. Example of filtering of recorded load responses Figure 4-A-5 shows the obtained data responses for the average voltage and the real and reactive powers of transformer. The data were recorded for 90 min with 10Hz sampling rate. Instead of the full time range, a window of approximately 20mins is chosen in order to focus the efforts on the relevant dynamic time frame. This window in Figure 4-A-6 below is focused around the time when the voltage was tapped, i.e. 50s before the identified event and 1200s after the tapping of the transformer. For the data set below, this was identified to be approximately from 2758s to 4008s. Focusing on the window as stipulated above, different smoothing techniques were applied to the data in order to extract relevant dynamics. The different techniques are based on the Fast Fourier Transform (FFT) filter, the Adjacent Averaging (AA) technique and the Savitzky-Golay (SG) technique. Of which, the FFT filter is based on the fourier analysis of the data and the cutting off of unwanted frequency components from the data. The Savitzky-Golay method does a polynomial regression over a given window of data points in order to find the smoothed value for each data point. Adjacent averaging essentially does what its name implies and smoothen the data by finding the average of the value over a given window. With the FFT filter, the most stringent filtering is applied; this cuts off the most of the high frequency components and this is used as a benchmark for comparison with the other methodologies as it is one of the most widely recognized and theoretically grounded techniques. Page 141 Modelling and Aggregation of Loads in Flexible Power Networks Vav 34.4 34.3 VfitFFT Vav, KV 34.2 34.1 34.0 33.9 33.8 33.7 33.6 44 0 1000 2000 3000 4000 5000 6000 42 Power1 MW 40 P1 PfitFFT 38 0 1000 2000 3000 4000 5000 6000 22 20 18 Qpower1 MVAR 16 Q1 14 QfitFFT 12 0 1000 2000 3000 4000 5000 6000 Time, s Figure 4-A-5: System Time Responses to voltage step Power1 P1fitFFT 42.0 P1fitSG200 P1fitSG500 41.5 P1fitSG1000 P1fit200 P1fit500 41.0 P1fit1000 40.5 Power1 40.0 39.5 39.0 38.5 0 200 400 600 800 1000 1200 1400 Time Figure 4-A-6: Filtered Power Responses using different smoothing techniques All three methods were implemented; the SG and AA were implemented with different windows of 200s, 500s and 1000s, and the resulting curves for active power are shown in Figure 4-A-6. It is important to note that all the methods yielded curves with similar trends but with varying accuracies. This would indicate that the major trends have been captured by the filtering. For the AA method, the most obvious effect would be the averaging has reduced the peaks quite drastically. In addition, as the window for the averaging increases, the discrepancy between the FFT smoothed signal and that processed by AA increases as expected. The next step would be to decide between the filtering methods. FFT filtering is deemed not to be suitable due to the large errors that are introduced at the start and the end of the filtered data; typical of the FFT due to offsets. For the AA method, the most obvious effect is the averaging has reduced the peaks quite drastically. In addition, as the window for the averaging increases, the discrepancy between the FFT smoothed signal and that processed by AA increases as expected. Page 142 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 4-B Load models with load self -disconnection 4-B.1 Embedded Under Voltage Load Shedding (EUVLS) model [175] A dedicated EUVLS model is attached to the dynamical load model of EUROSTAG. As equivalent load models are connected to the MV level (e.g. 11, 33 kV) in the positive sequence, the self-disconnection of loads connected at lower voltage levels should be as well modeled. Doing this it must be taken into account that at LV (e.g. 6, 0.4 kV) a proportion of the load is connected between phase and neutral voltage. The proposed EUVLS model is based on the following assumptions (see Figure 4-B-1):  ⍺% of the total supplied load can be disconnected in case of large voltage sags;  % of the load subject to EUVLS connected between phase and neutral. Total load Total load affected by the EUVLS not affected by the α% EUVLS Load supplied in 1ph Load supplied (100-α)% in 3ph α(100-β)/3% (α.β)% for each phase 1ph 1ph 1ph 3ph A B C Figure 4-B-1: Load categorization for EUVLS Single, two and three phase faults of various depth can be simulated in the HV system to assess the voltage sag seen by in MV and LV taking into account the grounding and connection arrangements of HV/MV transformers and MV/LV transformers. The resulting voltage sags are compared to load sensitivity voltage ranges [Vtrip_min;Vtrip_max]. Above Vtrip_max, 100% of the load will remain connected, while below Vtrip_min, 100% of the load subjected to EUVLS will be disconnected. Vph 0.4 kV Vmes 11 kV 100.00 1 p.u. Load shed in % of the total load 80.00 Vtrip max 0.4 kV 60.00 Vtrip min 0.4 kV 40.00 Vtrip max 11 kV 20.00 Vtrip min 11 kV 0.00 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Positive voltage (p.u.) Phase 1 Phase 2 Three phase load Vpos 11kV Load shed envelope Recording campaign (11kV) Phase 3 disconnection window Single phase load ESG implemented load shed envelope disconnection windows Figure 4-B-2: EUVLS disconnection windows Figure 4-B-3: EUVLS envelope characteristic This model leads to voltage disconnection windows (see Figure 4-B-2). The amount of load disconnected is related to the MV positive magnitude of voltage sag seen from the MV side. This characteristic shows a piecewise linear function shape and depends on the type of electrical appliances supplied by the MV substation. Page 143 Modelling and Aggregation of Loads in Flexible Power Networks The availability of recordings allows identifying a EUVLS curve by tuning Vtrip_min and Vtrip_max levels in LV and MV and the proportions α and . The EUVLS envelope can be adjusted to cover various severity scenarios and discretized in few steps to simplify the parameters coding in the simulation tool (see Figure 4-B-3). This model justifies the EUVLS phenomenon observed for positive voltage falling just below 0.9 p.u.. 4-B.2 Load model with load self-disconnection characteristics [17] At the system perspective, the load self-disconnection characteristics are as shown in Figure 4-B-4. The load self-disconnection characteristics, which consist of the maximum load self-disconnection amount, the load self- disconnection starting voltage and the saturation voltage, can be considered for static and dynamic load models of CRIEPI's Power Analysis Tools (CPAT) [31]. Note that the initial active and reactive power loads of all load models of CPAT are varied based on the load self-disconnection characteristics shown in Figure 4-B-4. The detailed load self-disconnection characteristics shown in Figure 4-B-4 were often determined with reference to several hundred historical data on the amounts of voltage sags and load self-disconnections measured in Japan in 1987 (Table 4-B-1). The load models with the load self-disconnection characteristics are often utilized for frequency and voltage stability studies in Japan. 100 100% Residual Voltage [%] Load Self-disconnection Starting Voltage 80% (Residual) Voltage [%] 60% 50 Load Self-disconnection Saturation Voltage 40% 20% 0 50 100 [%] 0% Load Self-disconnection Ratio 0% 20% 40% 60% 80% 100% Maximum Load Self-disconnection Amount Load self-disconnection amount [%] Figure 4-B-4: An image of a piecewise linear approximation of self-disconnection characteristics and self-disconnection of system loads obtained from measurements on 29 August 2008 [17]. Table 4-B-1: Parameters of Load Self-disconnection Characteristics [17] Parameter Historical Values Values on 29-08-2008 Load self-disconnection starting voltage 78 [%] 80 [%] Load self-disconnection saturation voltage 45 [%] 36 [%] Maximum load self-disconnection amount 21 [%] 25 [%] Page 144 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 5-A Example of Load Aggregation Methodology This appendix describes a general methodology for producing aggregate component based load models for different load sectors and sub-sectors, where load structure and composition, i.e. load profiles, are determined from available statistics and/or measured data (e.g. data collected by “smart meters”). The main purpose is to further illustrate aggregation methodologies discussed in Chapter 4 and specify input data requirements for building accurate aggregate load models. It is important to regularly update existing load models, as new technologies emerge and then gain significant market share. One example is ongoing wholesale substitution of traditional general incandescent lamps (GILs) with compact fluorescent lamps (CFLs) and light-emitting diode (LED) light sources. Another example is electrification of road transportation sector, where battery chargers for electric vehicles will present a new type of the load, significant in numbers and installed powers. Similarly, it is important to re-evaluate how loads are represented in power system analysis as the operation of the electricity network changes, e.g. after a large-scale deployment of distributed generation, or a wide-scale implementation of demand-side management. The move from centralised to distributed generation requires to model both distribution networks and connected loads in greater detail, while increased and more advanced level of active network control (as in so-called “smart grids”) will also require further modelling efforts. Modern loads, particularly those based on power electronic converter/inverter technologies, often contain sophisticated control circuits and, therefore, impose additional requirements for load modelling. The load control circuits may have significant impact on the performance of both the local electricity network and power supply system as a whole in a range of different areas, e.g. power flow, voltage stability, harmonic propagation, voltage distortion, power quality, etc. Therefore, modern load models should be able to represent all relevant load characteristics, which may be required for different types of power system studies. The load aggregation methodology described in this section uses component-based approach that results in aggregate load models capable of reproducing input current waveforms for a given voltage supply conditions/waveforms, therefore retaining most of the important electrical characteristics. 5-A.1 General component-based load aggregation methodology The suggested component-based load aggregation methodology can be broken down into six main steps, which are summarised in the “flow chart” shown in Figure 5-A-1. Three sets of input data are required: a) measured or estimated load curves and statistical or otherwise obtained information on the load structure and load composition, b) accurate models of the main load categories (discussed in Chapter 3 and Section 5- A.3 of this appendix), and c) network configurations and parameters (i.e. models) of network components. As the first step, the corresponding load structure and load composition (i.e. load components and their contributions to the total demand) of the modelled load sector should be identified from either measured, or estimated load curves and demand profiles. This should include both short-term (i.e. sub-hourly up to hourly) and medium to long-term (weekly to monthly/seasonal) variations in active and reactive power demands of the considered aggregate load (e.g. in a particular load sector). Next, the load structure should be converted into the main load categories based on the general electrical characteristics of the modelled electrical equipment and devices. The identified load categories are then represented using the corresponding generic load models, which, based on their contributions to the total demand of a modelled load sector, are combined to create the corresponding low-voltage (LV) aggregate load model. The LV aggregate load model is then connected to typical LV/MV distribution network configurations, where the total demand of the aggregate LV load model is varied, in order to identify how active and reactive power demands change at the MV side of the supplying transformer. This information is used for the formulation of the aggregate MV load model, and process of aggregation may proceed further, to HV levels. Page 145 Modelling and Aggregation of Loads in Flexible Power Networks Input data Load structure Models of main and Network data load categories Load curves Load aggregation (Low-voltage) Network representation (with low-voltage aggregate load connected) Aggregate load model at medium/high-voltage Figure 5-A-1: The “flow chart” of the proposed load aggregation methodology. The type of the target power system analysis/study will determine the actual form of the aggregate MV load model. For steady-state power system analysis, for example, the MV load model will typically take exponential or polynomial/ZIP forms. However, by considering the distribution network impedance, it is possible to represent component-based models (e.g. motor models) at higher voltage-levels. 5-A.2 Data requirements for identifying load structure and load compositio n The development of accurate aggregate load models for every load sector and load sub-sector requires either extensive long-term and wide-scale measurement campaigns (as in various measurement-based modelling approaches), or access to the representative and detailed statistical data (as in various component-based approaches). The information that should be collected is usually related to active and reactive power demands of the aggregate load (and individual load components in the aggregate demands) with certain resolution – typically 30-min or 60-min intervals, but sometimes shorter periods may be required (e.g. 1-min intervals). If measurements are used, and if sufficient processing, storage and metering resources are available, instantaneous current and voltage waveforms may be used to accurately identify the actual load composition/mix, using various non-invasive approaches (e.g. “load signatures” and “demand pattern identification”). Both in component and measurement-based modelling approaches it is important to allow the inclusion of hourly, daily, weekly and seasonal variations in the final aggregate load model. 5-A.3 Main load modelling categories Published and other generally available electricity consumption statistics usually divide loads into groups (i.e. types), based on the specific end-use of electricity, or actual tasks and activities performed in a given load sector (e.g. lighting load, heating load, cooking load, etc.). From a load modelling point of view, this is generally not a suitable categorisation/classification of loads. For load modelling purposes, loads should be grouped into load modelling categories, based on the similarity of their electrical characteristics and/or circuit topologies, rather than on specific end-use applications. This approach allows to use same load model to represent different electrical equipment and devices the same load category. Practically all types of electrical equipment and devices that can be found in residential and commercial load sectors can be divided into the five following general load categories: Page 146 Modelling and Aggregation of Loads in Flexible Power Networks - resistive loads - single-phase and three-phase directly-connected motor loads - dc power supplies, or switch-mode power supply (SMPS) loads - energy efficient lighting, consisting of compact fluorescent lamps (CFL) loads and light-emitting diode light sources (LED) loads - single-phase and three-phase drive-controlled motors, or adjustable speed drive (ASD) loads In addition to the loads, and as a part of the aggregate LV network/load representation, some micro and small-scale distributed generation (DG), e.g. micro-PV, micro-Wind, micro-CHP etc., may be connected in parallel to the LV loads within the end-users’ premises, while medium and large-scale DG units may be connected at MV and higher voltage levels. This is illustrated in Figure 5-A-2. Load models of resistive and directly-connected motor load categories are commonly available in existing literature and simple to implement, as they draw continuous sinusoidal currents from the supply. Accordingly, they can be accurately represented using the standard steady state and dynamic load model formulations (e.g. exponential or polynomial/ZIP load models and associated power factors – very close to unity for resistive loads). The three other load categories (SMPS loads, CFL/LED loads and ASD loads), however, represent non- linear power electronic equipment, which require different load models and more detailed simulations for accurate representation. The loads from these categories have increased significantly in numbers in recent years, and their accurate (aggregate) models for the use in power system studies and analysis are missing in existing literature (see Chapter 3). Additionally, the influence of harmonic legislation, differences in technologies and circuit topology variations effectively introduced several sub-categories within each category of these loads. An overview of the load categories and sub-categories is given in Table 5-A-1 (see also Figure 5-A-2 and Chapter 3 for more detail), while the subsequent text provides further details. Bulk Supply System 11kV Busbar Transformer Medium/ Line/Feeder Impedance Large DG Low-voltage Busbar Power Energy Directly Drive Electronics/ Resistive Efficient Connected Controlled SMPS Lighting Motor Motor Micro/ Small DG Low- High- Single- Three- Single- Three- CFL LED power power phase phase phase phase Passive Active Low- High- Low- High- RSIR CSCR PFC PFC power power power power CT QT LT CP Figure 5-A-2: Representation and composition of aggregate load based on the five general load categories (DG included). Page 147 Modelling and Aggregation of Loads in Flexible Power Networks Table 5-A-1: Main load modelling categories and sub-categories Load Category Sub-categories Further information no-PFC Devices with rated power less than or equal to 75 W are not expected to have power factor correction (PFC) circuit. Power electronics – SMPS loads a-PFC Above 75 W, p-PFC is currently more common, but the contribution of p-PFC a-PFC is expected to increase in future CFL Low- and high-power variations possible. For CFLs, higher power LED devices are expected to contain vf-PFC. Energy efficient lighting – CFL/LED loads LFL The details of LED loads are not commonly established, due to the HID infancy of this technology Cooking Cooking is assumed to be ideally resistive Resistive loads GILs GILs exponential coefficient is 1.55 Single-phase/three-phase Low/high power Any combination of motor type and mechanical loading condition is Directly connected induction motor loads possible; however, specific motors are suited to specific applications RSIR/CSCR/CSIR (discussed in further text). CT/QT/LT/CP Single-phase/three-phase Low/high power Any combination of motors and loading conditions is possible; Drive-controlled induction motors – ASD loads however, specific motors are suited to specific applications (discussed Open/closed loop control in further text). Scalar/vector control where: PFC-power factor correction, a-PFC-active PFC, p-PFC-passive PFC, vf-PFC-valley-fill PFC, CFL-compact fluorescent lamp, LED-light-emitting diode light sources, LFL-linear fluorescent lamp, HID-high intensity discharge lamp, GIL-general incandescent lamp, RSIR-resistor start-inductor run induction motor, CSIR-capacitor start-inductor run induction motor, CSCR-capacitor start-capacitor run induction motor, CT/QT/LT/CP-constant/quadratic/linear torque and constant power mechanical loading. Step one – identification of load structure and load profile It is generally possible to obtain information on load structure and composition from the measured data. However, demands of LV customers are not currently widely measured or available, but this may change in the (near) future, when recordings and information collected from so called “smart-meters”, which are recently being deployed and implemented, or are planned to be deployed in high numbers, can be used for that purpose. Although this will allow to perform wide-scale measurements of demands of LV customers, and in this way make measured data available for improved load modelling, it is still not clear whether these measurements will include the actual recordings of instantaneous current/voltage waveforms, or allow to monitor operation of individual equipment, as this is required to accurately determine structure and composition of the modelled load. Information on aggregate demands and load profiles is, generally, easier to obtain, as power flows are widely recorded in power systems. Often, this data is only recorded at higher voltage levels, with the lowest wide-scale measurements typically recorded at MV ‘bulk load supply points’, BLSPs (at 11 kV, 33 kV or higher). Therefore, these measurements represent an aggregate mix of discussed load sectors and sub-sectors. Sources of load profile data include: government level reports [104], research organisations [176] or distribution companies [177]. The ideal data resolution is 30-min or better, in order to match generator scheduling/balancing conditions, but should be in no case lower than 1-hour. Detailed information on load composition may be found in government level reports, e.g. [100], [104], [178], or research papers, e.g. [102], [179]. If this information is not readily available, processing of habitual and equipment end-use statistics, e.g. [180], can be used as an alternative way for determining load composition. If obtained from statistics, load information is normally given per “end-use load types”, and must be further processed to build an aggregate load model (i.e. to decompose the aggregate load curve). Figure 5-A-3 shows a UK based example. Page 148 Modelling and Aggregation of Loads in Flexible Power Networks 100 Consumer 90 electronics / ICT Cooking 80 Wet Active power demand (p.u.) 70 Cold 60 Storage hot water 50 Direct hot water 40 Top up heating 30 Direct heating 20 Storage heating 10 Lighting 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (hr) Figure 5-A-3: Decomposition of the load curve into end-use load types for the aggregate UK residential load sector during average loading conditions, where 1 p.u. (100%) corresponds to the peak winter demand, [104]. Step two – conversion of “load type” structure into main “load modelling categories” The determined load structure must then be converted from “end-use load types” into the general or main “load modelling categories” (and sub-categories), discussed in Chapter 3 and Section 5-A.3. This again may be achieved without direct measurements, e.g. by using national statistics ([96], [181]) and associated legislation (e.g. [182]). “Wet load” Wet load consists of dishwashers, tumble-dryers, washer-dryers and washing machines. Due to the high running torque requirements, tumble-dryers, washer-dryers and washing machines will require run capacitors. The running torque of dishwashers does not require a run capacitor, so they will utilise inductor run SPIMs. All wet loads are assumed to have constant torque (CT) mechanical loading. “Cold load” The cold load type covers all variants of refrigerators and freezers. As the compressors used in such devices do not require high running torque, it is expected that 100% of this load uses inductor run SPIM. The motor mechanical loading is represented by quadratic torque conditions. Information and communication technologies (ICT) load This load category covers all types of home computers and related information/communication technology equipment (e.g. modems, printers, etc.). This load is dominated by desktop computers and monitors, all with relatively large rated powers. Based on the implemented power factor correction (PFC) circuit (active, passive or none), electrical equipment/load here can be divided into three categories: load with a-PFC, load with p- PFC and load with no-PFC. Many laptop chargers will not have to satisfy harmonic legislation, due to the low rated powers. Laptop chargers that do have to adhere to legislation will have a-PFC, as the size and weight of the p-PFC inductor is not suited to portable applications. As mentioned, desktop computers have larger rated powers and the vast majority will have to have PFC, as there is no benefit from using a-PFC. Monitors and printers of high enough rated power will incorporate p-PFC. All other loads in this load type will have low-rated powers (≤ 75 W) and are expected to have no-PFC. Consumer electronics This load category is dominated by televisions/TV sets, with the remaining load consisting of various devices with rated power below 75 W (e.g. CD/DVD players). As with the ICT load type, these loads also can be divided into SMPS loads with a-PFC, with p-PFC and with no-PFC. Page 149 Modelling and Aggregation of Loads in Flexible Power Networks Larger (primary) TV’s will incorporate p-PFC, with more recent technologies likely to have a-PFC. Smaller (secondary) TV’s below 75 W will have no-PFC. Cooking load Hobs and ovens are modelled as resistive load category, while microwaves are modelled as p-PFC SMPS. Lighting load Many lighting solutions exist, and national statistics must be used to determine the expected contribution for each location. In the residential sector, the majority of lighting is either from general incandescent lamps (GILs) or compact fluorescent lamps (CFLs) light sources. The contribution from CFLs is expected to increase in the near future, due to a recent ban and phase-out of GILs (EU [183], US [184]), with light-emitting diode (LED) light sources expected to increase in the future. Applying the above assumptions for the UK case, it is possible to convert the load type demand profile (Figure 5-A-3) into a load category demand profile. This is shown in Figure 5-A-4. 100 SMPS: 90 a_PFC p_PFC 80 no_PFC 70 Contribution to load (%) SPIM: 60 IR QT IR CT 50 CR CT 40 Resistive: 30 20 Lighting: 10 GIL CFL 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (hr) Figure 5-A-4: Decomposition of the aggregate UK residential load curve for average loading conditions into general load categories and sub-categories. Step three – low-voltage aggregate load model Once the load structure has been resolved into load categories, the generic load models (presented in Chapter 3) are connected together in the identified percentages for given load sector, to produce the LV aggregate load model for each 30-min (or 1-hour) period. An example of the LV aggregate exponential load model is shown in Figure 5-A-5, with the corresponding polynomial/ZIP model given in Figure 5-A-6. 1.8 np nq Exponential model coefficient value 1.6 1.4 1.2 1.0 0.8 0.6 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (hr) Figure 5-A-5: Low-voltage aggregate exponential load model. Page 150 Modelling and Aggregation of Loads in Flexible Power Networks 1.0 2.0 Constant impedance coefficient, Zq Constant impedance coefficient, ZP 0.9 Constant current coefficient, Iq Constant current coefficient, IP 0.8 Constant power coefficient, PP 1.5 Constant power coefficient, Pq Polynomial model coeffcient Polynomial model coeffcient 0.7 1.0 0.6 0.5 0.5 0.4 0.0 0.3 0.2 -0.5 0.1 -1.0 0.0 0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (hr) Time (hr) (a) active power coefficients (b) reactive power coefficients Figure 5-A-6: Low-voltage aggregate polynomial load model. Step four –network configuration Data on the network configuration and network component values for the modelled sector/sub-sector should be collected and used to build a network model in suitable power system simulation software. An example of the UK urban network configuration is shown in Figure 5-A-7, with more details on the network components given in Appendix 5-B. 1 2 30 Representative of Grid all 0.4 kV feeders Supply 3 Representative of all 11kV feeders 31 System 4 32 5 6 34 Zsys 35 36 37 38 39 7 33 8 9 10 11 12 13 14 15 33kV 11kV 0.4kV Figure 5-A-7: UK urban network configuration. Step five – connection of low-voltage aggregate model to network The low-voltage aggregate load model is then connected at the designated load points in the network model (Note: For modelling practical/realistic cases, this is likely to include a mix of sub-sectors). Step six – formulation of high-voltage aggregate load model The low-voltage (LV) aggregate load is then used to produce the medium-voltage (MV) aggregate load model. This can be achieved by increasing/decreasing the aggregate power demand (while keeping the load mix the same) to instigate changes in the supply voltage, or by changing the supply voltage using the supply transformer (e.g. OLTC transformer). The changes in active/reactive power demands at MV bus, expressed as a function of voltage, are then used to formulate the MV aggregate load model. A comparison of LV and MV exponential aggregate load models is shown in Figure 5-A-8, with the corresponding polynomial/ZIP model are shown in Figure 5-A-9. Page 151 Modelling and Aggregation of Loads in Flexible Power Networks 1.6 LV Aggregate Model (0.4kV) 1.8 MV Aggregate Model (11kV) Exponential model coefficient value 1.4 1.6 Exponential model coefficient value 1.4 1.2 1.2 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (hr) Time (hr) (a) active power coefficient, np (b) reactive power coefficient, nq Figure 5-A-8: Comparison between low-voltage and medium-voltage aggregate exponential load models 1.0 2.0 LV Aggregate Model (0.4kV): ZP IP PP LV Aggregate Model (0.4kV): ZQ IQ PQ 0.9 MV Aggregate Model (11kV): ZP IP PP MV Aggregate Model (11kV): ZQ IQ PQ 1.5 0.8 Polynomial model coefficient Polynomial model coefficient 0.7 1.0 0.6 0.5 0.5 0.4 0.0 0.3 -0.5 0.2 0.1 -1.0 0.0 0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (hr) Time (hr) (a) active power coefficients (b) reactive power coefficients Figure 5-A-9: Comparison between low-voltage and medium-voltage aggregate polynomial load models Page 152 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 5-B Generic UK LV Network Configurations and Component Values 380 customers 190 customers PMAX AV.= 2.27kW/customer Total Load MAX AV.= 862.6 kW (at 1p.u.) PMAX AV.= 2.27kW/customer Total Load MAX AV.= 431.3kW (at 1p.u.) L L HU18 (16 cust.) U18 (6 cust.) C 76m E 90m A A B L C D E L HU19 U19 20m 16m 43m 27m 26m 53m (4 cust.) (20 cust.) D 82m B 66m C C E E 30m 11m 40m 17m L L L L (15 cust.) (15 cust.) (33 cust.) (33 cust.) HU14 HU15 U14 U15 HU16 HU17 U16 U17 11kV 0.4kV (21 cust.) 11kV 0.4kV (8 cust.) (8 cust.) (21 cust.) L L L L A A B D D E 9m 64m 71m 15m 80m 85m L U9 L U10 HU9 HU10 (9 cust.) (16 cust.) (8 cust.) (6 cust.) D 22m E 32m L L E E C C 41m 6m 53m 12m 1000kVA D 26m 500kVA E 41m (20 cust.) (10 cust.) (12 cust.) (6 cust.) U11 Transformer HU11 Transformer U4 E 70m HU4 HU7 B 55m L (8 cust.) U7 L (6 cust.) ZT=1.1 + j4.62 L L ZT=2.04 + j9.28 L L A A A A A A A A B C D E (p.u. on 100MVA) (p.u. on 100MVA) 32m 90m 41m 35m 33m 17m 22m 72m 30m 25m 23m 10m B 50m B 55m B 30m E 73m E 67m E 93m E 68m E 40m B 60m B 75m C C E E L L L L 14m 40m L 24m 52m L L L HU8 L L U8 HU12 L (12 cust.) U12 U13 L (22 cust.) HU13 (12 cust.) HU5 HU6 (22 cust.) (14 cust.) U5 U6 (9 cust.) HU3 (24 cust.) (14 cust.) U3 (14 cust.) (9 cust.) L L (12 cust.) U1 (20 cust.) HU1 (31 cust.) E 36m (15 cust.) B 26m D A 63.6m 78.6m E 85m B 70m L L U2 HU2 * L type line length = 30m (12 cust.) * L type line length = 30m (27 cust.) (a) highly urban network (b) urban network 76 customers 19 customers PMAX AV.= 2.27kW/customer Total Load MAX AV.= 172.52kW (at 1p.u.) PMAX AV.= 2.27kW/customer Total Load MAX AV.= 43.13kW (at 1p.u.) R15 R16 R17 R18 R19 E SU7 L M M M M M 30m (11 customers) SU9 Overhead line L (4 customers) R14 main feeders D 60m K 30m K 30m E 30m H H H H H M K 30m K 30m K 30m H 30m 30m 30m 30m 30m 30m R8 R9 R10 R11 K J J J J J 30m 35m 35m 35m 35m 35m E 30m E 30m M M M M 11kV 0.4kV J 35m L M SU6 R12 K 30m K 30m (6 customers) (3 customers) L K 30m K 30m K 30m Underground SU4 11kV 0.4kV cable lateral J J J J J J K M R13 SU8 35m 35m 35m 35m 35m 35m 30m feeders L (13 customers) E L 200kVA R4 R6 30m SU2 M M Transformer (16 customers) pole-to-pole M R2 ZT=7.5 + j22.5 distance D 90m E 30m 50kVA pole-to-pole Overhead lines (p.u. on 100MVA) Transformer distance K 30m K 30m K 30m H H H H H H H H ZT=43.72 + j78.6 J J J J J J J 30m 30m 30m 30m 30m 30m 30m 30m (p.u. on 100MVA) 35m 35m 35m 35m 35m 35m 35m K 30m Single E 30m E 30m E 30m K 30m K 30m K 30m Customers M R7 L SU1 M M M L L (5 customers) R1 R3 R5 * M type line length = 30m SU3 SU5 (10 customers) (8 customers) * L type line length = 35m (c) suburban network (d) rural network Figure 5-B-1: Generic UK LV network configurations Page 153 Modelling and Aggregation of Loads in Flexible Power Networks Table 5-B-1: Typical configurations and parameters of LV lines in the UK Cross Maximum Positive sequence Neutral Zero-phase sequence LV Line type Sectional sustained Z Z Z Area current (CSA) Rph Xph Rneutral R0 X0 Izph Id. Configuration (mm2) (Ω/km) (Amps) A 300 0.1 0.073 0.168 0.593 0.042 465 B Underground Line (Cable) 185 0.164 0.074 0.168 0.656 0.05 355 C 120 0.253 0.071 0.253 1.012 0.046 280 D EPR or XLPE 95 0.320 0.075 0.320 1.280 0.051 245 E 0.6/1 kV 4x(CSA) Al / Cu (earth) CNE 70 0.443 0.076 0.443 1.772 0.052 205 F 35 0.87 0.085 0.87 3.481 0.058 156 G 120 0.284 0.083 - 1.136 0.417 261 Overhead Line H 95 0.32 0.085 - - - 228 I 70 0.497 0.086 0.63 2.387 0.447 195 Aerial Bundled Conductor (ABC) J XLPE 4x(CSA) Al 50 0.397 0.279 - - - 168 K 35 0.574 0.294 - - - 148 Service Connection L 35 0.851 0.041 0.9 3.404 0.03 120 PVC or XLPE M 0.6/1 kV 1x(CSA) Al / Cu (neutral / earth) CNE 25 1.191 0.043 1.26 4.766 0.03 100 Public Lighting N 25 1.18 0.043 0.9 4.72 0.03 100 PVC or XLPE 0.6/1 kV 1x(CSA) Cu / Cu (neutral / earth) CNE Table 5-B-2: Parameters of typical 11/0.4 kV secondary distribution transformers Basic Model Parameters Operating Transformer Load Losses No-Load Transformer Tapping Impulse Impedance (Impedance on 2ary side) Voltage Sub-sector Rating Connection at 75ᵒC Losses Type Range Level (%) RLV XLV (kV) (kVA) (W) (W) (kV) (p.u. on Transf. Rating) 1500 15810 1400 5 0.01054 0.048876 Highly Prefabricated 1000 11000 1350 4.75 0.011 0.0462 Urban substation 800 7410 1000 4.75 0.00926 0.04658 Urban 500 ± 5% in 5100 680 0.0102 0.0464 11 / 0.4 Ground / Dyn11 75 315 2.5% taps 3420 580 4.75 0.01085 0.04624 Sub-urban Pad mounted 200 2900 540 0.015 0.045 Pole 100 1750 320 0.0175 0.04145 Rural mounted 4.5 50 1100 190 0.02186 0.0393 Page 154 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 5-C Additional residential load curves and models 5-C.1 Additional residential load curves 100 100 Consumer SMPS: 90 electronics / ICT 90 a_PFC Cooking p_PFC 80 80 no_PFC Wet Active power demand (p.u.) 70 70 Contribution to load (%) Cold SPIM: 60 60 RSIR_QT Storage DHW RSIR_CT 50 50 RSCR_CT Direct DHW 40 40 Top up heating Resistive: 30 30 Direct heating 20 20 Lighting: Storage heating GIL 10 10 CFL 0 Lighting 0 2 4 6 8 10 12 14 16 18 20 22 24 2 4 6 8 10 12 14 16 18 20 22 24 Time (hr) Time (hr) (a) decomposition of minimum loading conditions into load (b) decomposition of minimum loading conditions into load types, [104]. categories and sub-categories 100 Consumer 100 90 electronics / ICT SMPS: Cooking 90 a_PFC 80 p_PFC Wet 80 no_PFC Active power demand (p.u.) 70 70 Contribution to load (%) Cold SPIM: 60 60 RSIR_QT Storage DHW RSIR_CT 50 50 RSCR_CT Direct DHW 40 40 Resistive: Top up heating 30 30 Direct heating 20 20 Lighting: Storage heating 10 GIL 10 CFL 0 Lighting 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (hr) Time (hr) (c) decomposition of maximum load conditions into load (d) decomposition of maximum load conditions into load types, [104]. categories and sub-categories Figure 5-C-1: Characteristic loading conditions for residential load sector 5-C.2 Additional residential load models 1.8 np 1.8 np nq nq Exponential model coefficient value Exponential model coefficient value 1.6 1.6 1.4 1.4 1.2 1.2 1.0 1.0 0.8 0.8 0.6 0.6 0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (hr) Time (hr) (a) minimum loading conditions (b) maximum loading conditions Figure 5-C-2: Low-voltage aggregate exponential load models for minimum and maximum residential loading conditions Page 155 Modelling and Aggregation of Loads in Flexible Power Networks 1.0 2.0 Constant impedance coefficient, Zq Constant impedance coefficient, ZP 0.9 Constant current coefficient, Iq Constant current coefficient, IP 0.8 Constant power coefficient, PP 1.5 Constant power coefficient, Pq Polynomial model coeffcient Polynomial model coeffcient 0.7 1.0 0.6 0.5 0.5 0.4 0.0 0.3 0.2 -0.5 0.1 -1.0 0.0 0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (hr) Time (hr) (a) minimum loading conditions active power coefficients (b) minimum loading conditions reactive power coefficients 1.0 Constant impedance coefficient, ZP Constant impedance coefficient, Zq 0.9 2.0 Constant current coefficient, Iq Constant current coefficient, IP 0.8 Constant power coefficient, PP Constant power coefficient, Pq 1.5 Polynomial model coeffcient Polynomial model coeffcient 0.7 1.0 0.6 0.5 0.5 0.4 0.0 0.3 0.2 -0.5 0.1 -1.0 0.0 0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (hr) Time (hr) (c) maximum loading conditions active power coefficients (d) maximum loading conditions reactive power coefficients Figure 5-C-3: Low-voltage aggregate polynomial load models for minimum and maximum residential loading conditions Page 156 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 5-D Commercial load curves 100 100 Lifts Lifts 90 90 Cooling Cooling Active power demand (% of peak load) Active power demand (% of peak load) ICT + CE 80 ICT + CE 80 Catering Catering 70 70 Cold Cold Direct WH Direct HW 60 60 Direct SH Direct SH Lighting 50 Lighting 50 40 40 30 30 20 20 10 10 0 0 0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (hr) Time (hr) (a) decomposition of minimum loading conditions into (b) decomposition of average loading conditions into load types, load types, [104]. [104]. 100 100 Lifts 3PIM Drive 90 ICT + CE SMPS: Active power demand (% of peak load) Catering npPFC Contribution to load (% of peak load) 80 80 Cold pPFC 70 Direct WH aPFC Direct SH 3PIM 60 60 Lighting SPIM 50 R LFL 40 40 HID CFL 30 GIL 20 20 10 0 0 0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (hr) Time (hr) (c) decomposition of maximum loading conditions into (d) decomposition of maximum loading conditions into load load types, [104]. categories and sub-categories Figure 5-D-1: Low-voltage aggregate polynomial load models for minimum and maximum residential loading conditions Page 157 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 5-E Reported load curves for different load classes Figure 5-E-1: Load class mix in US [185] Figure 5-E-2: Residential load composition data in US [185] Figure 5-E-3: Overall load composition data in California Figure 5-E-4: Residential load composition data in [179] California [179] Figure 5-E-5: Commercial load composition data Figure 5-E-6: Industrial load composition data (California) (California) Page 158 Modelling and Aggregation of Loads in Flexible Power Networks Figure 5-E-7: Decomposed load curves in Swedish house with direct electric heating (workday and holiday) [186] Figure 5-E-8: Decomposed load curves in Swedish house without direct electric heating (workday and holiday) [186] Figure 5-E-9: Decomposed load curves in Swedish Apartment (workday and holiday) [186] Figure 5-E-10: Decomposed daily loading curves (DDLC) in Figure 5-E-11: DDLC in a “typical ” house in Greece [187] “typical” house in Denmark [187] Page 159 Modelling and Aggregation of Loads in Flexible Power Networks Figure 5-E-12: DDLC in a typical house in Italy [187] Figure 5-E-13: DDLC in a typical house in Portugal [187] Figure 5-E-14: Load class mix under total load in winter Figure 5-E-15: Residential load composition data in winter Germany [188] Germany [188] Figure 5-E-16: Residential load composition in Italy (with Figure 5-E-17: Residential load composition in Spain Air conditioners) [189] (summer) Figure 5-E-18: Residential load composition in summer in Figure 5-E-19: Residential load composition in summer in Scandinavia New Member States Page 160 Modelling and Aggregation of Loads in Flexible Power Networks Figure 5-E-20: Residential load composition in Summer Figure 5-E-21: Residential load composition in Summer UK Germany/Austria Figure 5-E-22: Residential load decomposition data in Taiwan, China [190] (This curve is created in 1995) Page 161 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 6-A Aggregated Models of Distributed Generation and Active Distribution Network Cells for Power System Studies – Literature Overview This Appendix presents a critical overview of the existing literature on aggregated models of DG technologies, with a special focus on wind-based generation systems, and active distribution network cells, including Micro Grids, for purposes of dynamic power system studies. In addition, conclusions and recommendations are provided. 6-A.1 Aggregated Models of Wind-based Generation Wind-based generation is the prevailing technology in the distributed generation technology mix. It is currently present at all scales and levels of implementation, ranging from multi-megawatt wind turbine generator (WTG) units installed in large wind farms (WF), to sub-kilowatt units operating in individual installations. The dynamic response of wind-based generation can be represented with detailed models of all individual generators. However, with hundreds of WTGs connected in transmission and distribution networks, it is unreasonable to model each WTG in detail for distribution and transmission system studies. There is a need for equivalent WTG models that accurately represent aggregate WF behaviour at grid connection point without significantly increasing computational efforts. Aggregated Wind Farm Models Comprehensive literature overview on modelling of individual WTG and other DG technologies is given [191]. This section focuses on aggregated models of wind farms only. Depending on the nature of the studies and assumptions made different aggregation approaches can be used: 1. The ultimate simplification is the aggregation of the entire wind farm into an equivalent WTG, [192–195]. This approach assumes either the same wind incident on all WTGs or average wind incident on individual WTGs, which is applied on aggregated model of the WF. Generator rating is scaled and the rest of the individual WTG parameters remain unchanged in per unit (p.u.) on aggregated machine base3. 2. Another approach is aggregating the WTGs into groups. Wind turbines with similar input wind speeds can be grouped together and represented by an equivalent WTG, [192–200]. Grouping can also be applied if several WTG technologies are used within a particular wind farm, then each technology can be represented by an equivalent WTG. 3. In order to reflect different wind speed conditions within a wind farm, mechanical system, i.e. drive train, can be modelled separately for each WTG, whereas the generator and electrical control system, e.g. converter controls, can be aggregated into its equivalent representation, [192], [193], [201], [202]. Methods 2 and 3 have an advantage of simulating WTG speed fluctuations more accurately which may be important if over-speeding due to faults leads to individual WTG disconnection. For planning studies involving future WTGs, whose capabilities are still unknown or need to be defined, the most conservative assumption is a strict fulfilment of the grid code requirements. For this type of system studies it might be sufficient to simply emulate performance of a WF at the grid connection point, based on the different WTG technologies and their technical capabilities such as low voltage fault ride through, reactive current 3 It is assumed that individual WTG parameters are provided in per unit on individual machine base Page 162 Modelling and Aggregation of Loads in Flexible Power Networks supply, synthetic inertia4, etc. in accordance with grid code requirements [203], [204]. This type of models are not component based models but performance-based models. A further consideration in aggregated modelling is the representation of the WF cable network. In [193], [195], [201] and [202] an approximate method is presented using short circuit fault calculations for deriving the values of a series reactance that represents the cable network. This reactance is placed in series with the WF transformer. The size of this reactance is much smaller than that of the leakage reactance of the WF transformer, so it may also be neglected without a significant loss of accuracy. Conclusions and recommendations In aggregating wind turbine generators the final decision on the level of simplification depends on the type of the study and the level of accuracy required. Generally, for the studies related to localized issues (e.g. local voltage stability) and in the electrical vicinity of the WF it is desirable to represent the WF in more detail (e.g., WTGs inside the WF could be scattered over several kilometers and voltage can vary through the WF causing the number of machines being tripped on low voltage). On the other hand, WFs remote to the studied region may be modelled with aggregated models and still yield adequate study results with a fraction of computational effort. Further details and recommendations on wind farm modelling and aggregation can be found in [192]. 6-A.2 Aggregated Models of Micro and Small-scale Wind Generation Although micro and small-scale WTG units (with rated power < 50-100 kW) are highly dispersed and small in size, their total number can be high in certain parts of a distribution network (e.g. in a large urban area). A few thousands of micro-WTGs even with a very small individual rated powers could easily match a medium-size WF with the power in the region of few (tens of) MWs. In such cases, aggregate effects micro/small WTGs is, in essence, similar to those of medium to large-scale technologies. Nevertheless, aggregate modelling of micro and small-scale wind-based generation is a topic which is virtually non-existent in the available literature. A database with more than 150 micro/small WTGs from more than 60 different manufacturers is collected for the analysis presented in [205]. It is shown that characteristics (i.e. power curves) of majority of micro WTGs currently available in the market can be correctly represented using only four "generic wind turbines" and corresponding "generic power curves", normalised using WT swept areas, as illustrated in Figure 6-A-1. Expected annual energy outputs and cost-benefit analysis are obtained using steady state and dynamic models of three actual and four generic micro/small WTGs. Using generic models proposed in [205] aggregate representation of a number of micro/small WTG units in an urban area is presented in [207]. The impact of urban micro-wind generation on the steady state network performance (power flows and voltage profiles) is analysed taking into account the variability of wind energy inputs. Conclusions and recommendations As micro/small WTGs operate in parallel with loads connected within the end-users’ LV installations, their aggregation should be performed together with the aggregation of LV system loads. Correct assessment of the performance of aggregated WTGs, therefore, requires both spatial and temporal correlation of input wind energy resources and output powers with the corresponding variations in characteristics of modelled/aggregated system loads. The initial results of the work presented in [18] and [19] demonstrate that the aggregation of micro/small WTGs can be performed by using generic WT models from [205] and taking into account both variations between the different locations and long-term (seasonal), as well as medium to short-term (daily or hourly) variations in available wind energy resources. 4Synthetic inertia or simulated inertia - a facility provided to replicate the effect of inertia of a synchronous generating unit to a prescribed level of performance. Page 163 Modelling and Aggregation of Loads in Flexible Power Networks 400 400 Power Output (W/m ²) Generic 1 Power Output (W/m ²) 350 350 Generic 2 300 250 300 200 250 150 200 100 150 50 100 0 50 0 1 2 3 4 5 6 7 8 9 10 11 12 0 Wind Speed (m/s) 0 1 2 3 4 5 6 7 8 9 10 11 12 (a) Wind Speed (m/s) (b) 180 180 Power Output (W/m ²) 160 Power Output (W/m ²) Generic 3 160 Generic 4 140 140 120 120 100 100 80 80 60 60 40 40 20 20 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 0 1 2 3 4 5 6 7 8 9 10 11 12 Wind Speed (m/s) Wind Speed (m/s) (c) (d) Figure 6-A-1: Comparison of four generic and a number of actual wind turbine power curves, all normalised using the corresponding swept areas: a) to d) individual generic and the closest matches with actual wind turbines, adopted from [206]. 6-A.3 Aggregated Models of Other DG Reference [191] gives a literature overview of individual model for other DG including small synchronous generator models, generic model for inverter coupled devices, micro turbines and fuel cells. In [106] details on PV array modelling are provided. No literature has been found on aggregation of distributed generators other than wind turbine generators. 6-A.4 Equivalent Models of Active Distribution Network Cells Technical literature reports several papers dealing with the problem of the identification of aggregated models of distribution networks with a high degree of DG integration intended for both steady state and dynamic power system studies. Regarding static equivalent models, papers [208–212] suggest techniques based on network matrices for network reduction. However, this class of equivalent models suffer the important limitation that they cannot be easily adopted in networks with high degree of penetration of DG due to the computational problems in handling high matrix dimensions. Aiming to reduce the time consuming required by these procedures, techniques such as Dimo, Zhukov, Radial Equivalent Independent (REI) and Equivalent Distribution Network (EDN) have been proposed [114], [213], [214]. The Dimo and Zhukov methods [114] aggregate, respectively, through fictitious impedances and ideal transformers, nodes where the equivalent network is interfaced with the study system. The REI method [213] aggregates the injections of a group of buses into a single bus. The aggregated injection is distributed to these buses through radial network called the REI network. Equivalent network parameters derived by this method depend on the operating point, so that retained constant values imply significant analysis errors. This problem can be overcome by adopting the EDN method, which is based on the principle of the REI method with some modifications presented in [214]. Page 164 Modelling and Aggregation of Loads in Flexible Power Networks For dynamic equivalent models for ADNC, strictly mathematical techniques that originate from control systems theory have been proposed in [215], [216], where they perform quite well. Most of these model order reduction methods, also known as Singular Value Decomposition (SVD) based methods, are based on modal analysis to directly identify and preserve the modes of interest regarding the dynamic behaviour of distribution networks while the others are eliminated. Thus, a balanced realization method with truncation performed through singular perturbations theory was proposed in [215] and Hankel norm approximation, focusing on the observability and controllability properties of the system, was used in [216]. The method of Hankel norm approximation was considered the most efficient out of SVD based methods and, therefore, it was successfully applied for dynamic equivalency purposes of a medium size distribution network with DG of various types and size, including WTGs (squirrel cage induction generator and doubly-fed induction generator), split shaft microturbines, combine heat and power (CHP) plants equipped with microturbine units and a diesel generator. Another family of model reduction techniques is the Krylov methods, also referred as moment matching methods, which are based on the leading coefficients of a power series expansion of the reduced system transfer function around a user-defined point that have to match those of the original system transfer function [119], [217], [218]. These methods have certain advantages and disadvantages when compared with strictly mathematical techniques. Generally, SVD based methods hardly applicable to systems of very large dimensions (thousands of states) since singular values of the detailed model of the system have to be computed and such a computation might be cumbersome for large order systems. The Krylov methods are iterative in nature and can be used for systems with large number of states. However, these methods do not provide the bounds for the approximation error while SVD based methods do so. Therefore the selection of the reduction method has to be performed in accordance with both the desired goals and the system structure. Krylov methods are simple, they can be automated and they can provide a significant order reduction while retaining small error with respect to the original model. They are especially profitable when applied to large systems. Dynamic equivalents obtained by means of strictly mathematical techniques or Krylov methods lose direct physical interpretation. The system is represented in state space form with states being some linear combination of real physical variables. This raises the problem of how to integrate such equivalents into software for power system simulations. It can be done by the introduction of controlled current sources in the lines connecting the study network with the neighbouring external power system. The controllers of such current sources will represent dynamic relationship between the current and the voltage [215–217]. Although the procedure for obtaining them can be automated [216], [217], new reduced order models should be derived for different operating conditions. Since restrictions may arise when applying these methods to high dimensional systems [219] and linear models provide a limited accuracy to represent nonlinear ADNC with effective dynamic impact in the study system when major disturbances occur, system identification based methods have been proposed recently in the technical literature [138–143], [220–223]. These methods consist in defining suitable model structures based on the available prior knowledge and physical insights about the detailed system to be reduced, with the model parameters being estimated from simulated data by means of a self-adaptive learning procedure. For this purpose an identification criterion is defined, based on the error between output trajectories of the physical system detailed model and the corresponding aggregated model, in order to measure how well the model fits the system response. Then, with a proper optimization algorithm the model parameters are adjusted so that the identification criterion is minimized. The resulting aggregated models present behaviours close to those exhibited by physical systems, even if they do not have physical correspondence with them. Depending on the physical insights effectively used to define the model structure black box modelling and grey box modelling approaches have been reported on the literature. Black-box modelling approaches have been exploited in [138–140], [220–222], avoiding the need of detailed information about the network structure and parameters and also the need of complex mathematical analysis. These facts represent a significant advantage especially when there is a limited understanding of the relationships between system variables. Page 165 Modelling and Aggregation of Loads in Flexible Power Networks Thus, in [220], [221] the models are developed in the form of state space and auto-regressive model with exogenous input (ARX), with simplicity being their main advantage. The whole distribution network model containing steam, diesel and hydro generators was implemented in PSS/E dynamic tool to simulate the system behaviour following different types of disturbances taking place on the study system. Simulated data comprising time series voltage and frequency are used as inputs and time series of active and reactive power are used as outputs. These time series are imported into the MATLAB System Identification Toolbox in order to perform the estimation of the model parameters. These models were introduced later in PSS/E to replace the distribution network for validation purposes. The performance of the proposed model is highly dependent on the type and location of the disturbance. A generic nonlinear dynamic equivalent model based on recurrent Artificial Neural Networks (ANN) was proposed in [138], [139] and used to replace a MV distribution network containing several tens of DG units, such as fuel cells and micro turbines with several ratings, connected near the end user terminals at the LV levels. The model structure is defined through the recurrent ANN structure, which allows capturing the dynamic behaviour of the replaced ADNC and to enable the interaction with the study system through the boundary buses at a wide range of operating conditions, keeping the continuous time operation of the entire network. The ANN acts as a Norton model, where the normalized deviations of boundary bus voltages are used as the main inputs and the normalized deviations of currents represent the outputs. In addition to the input voltages, past values of both current and voltages deviations are introduced as the input layer to achieve the recurrent structure. Three-phase short-circuits are simulated in the study system using the system detailed model implemented in the PSD simulation package [137]. During the faults, both complex voltages and injected currents are measured at boundary buses and stored to be used subsequently to prepare suitable patterns for ANN training purposes. The training process was accomplished offline. Passive loads are represented as lumped equivalent elements connected at boundary nodes using constant impedance equivalent elements. Loads voltage and frequency dependence can also be modelled using the general exponential relationships. The separation between active and passive elements extends the validity of the equivalent model to simulate changes regarding the generation and load conditions inside the replaced system itself, giving more flexibility regarding power system analysis. However, the entire distribution system can also be replaced by the recurrent ANN if there is a difficulty in representing the passive loads separately. The use of normalized deviations as model inputs and outputs allows the equivalent model to be used under new initial power flow conditions and, therefore, the ANN based aggregated model represents a normalized model scaled on initial operating conditions at the boundary buses. Augmenting this feature with the independent representation of both active and passive elements, a general model is derived with capability for simulating the original system under different operating conditions. The ADNC aggregated model proposed in [141–143] comprises a composite load model also referred as ZIP- IM load model [60], connected in parallel with a converter interfaced generator. This model is intended to be connected to the ADNC point of connection and is represented in the form of a sixth-order nonlinear state space model resulting from the differential and algebraic equations that typically describe the dynamics of the model individual components. Voltage and frequency are defined as the model inputs whereas both active and reactive powers are used as the outputs. All the input and output signals are measured at the ADNC point of connection following various disturbances simulated using the system detailed model implemented in DIgSILENT PowerFactory software. These recorded signals are used for parameters estimation purposes through nonlinear least square optimization techniques using the MATLAB System Identification Toolbox. The performance of the ADNC aggregated model was evaluated by comparing its response with the one obtained from the system detailed model implemented in DIgSILENT PowerFactory simulation tool, demonstrating the model effectiveness. However, further developments are required in order to extend the dynamic equivalent considering the presence of other inverter interfaced DG technologies, such as fuel cells and PV systems. Page 166 Modelling and Aggregation of Loads in Flexible Power Networks 6-A.4.1 EQUIVALENT MODELS OF MICRO GRIDS Large deployment of active distribution network technologies will allow new system concepts to be implemented such as the SmartGrid and Micro Grid (MG) concepts [224]. Nowadays MG have become a popular concept under the framework of setting up smart power grids and used in many ways to describe the concepts of managing energy supply and demand at distribution level. This is a broad definition addressing also the advanced metering infrastructures, demand response, energy efficiency and the trend towards de- centralization of generation with the effective deployment of Distributed Generation (DG) technologies and storage devices. MG can be implemented by different owners to meet different objectives. Industrial and commercial users might implement a MG to provide cheap combined power and heat generation or for assuring them of a supply that meets their demanding specifications which cannot be realized by the utility grid. Government entities are especially interested in MG to provide resiliency and security of supply as essential services, exploiting the MG capability to island from the utility. Therefore, there is not a single MG definition. Some authors discuss MG in terms of residential sized system while others discuss them as community wide systems sharing, however, the following common features:  A MG is a localized group of electricity sources and loads operating interconnected to and synchronous with the traditional centralized utility grid but can disconnect and operate autonomously as physical islands in emergency conditions or when justified by market conditions, providing a way to improve the grid reliability and to reduce the dependency of long distance transmission networks. The IEEE P1547.4/D12 standard (IEEE Standard for Interconnecting Distributed Resources with Electrical Power Systems) provide alternative approaches and good practices for the design, operation and integration of DG islanded systems, including the ability to separate from and reconnect to the electric power system area while providing power to the islanded systems [225];  The capability to control the balance of local generation and demand in order to ensure a stable energy supply to the power consumers served by the MG according to their own requirements regardless of whether the MG is connected to the main utility system or operated autonomously. The MICRO GRIDS Project, Micro Grids: Large Scale Integration of MicroGeneration to Low Voltage Grids is one of them and was the first attempt at EU level to deal in-depth with the MG concept [226]. This concept was exploited further in the 6th FP under the framework of the MORE-MICRO GRIDS Project, More-Micro Grids: Advanced grid architectures for the integration of DER within local distribution networks including Micro Grids, aiming to increase the DG integration [227]. Below, a brief description of the MG management and control architecture is presented in the next section followed by the literature overview regarding dynamic modelling of MG systems. 6-A.4.2 THE MG MANAGEMENT AND CONTROL ARCHITECTURE Europe, North America and Japan have addressed the challenges being faced in the conventional electric power systems operating paradigm by actively promoting research, development, demonstration and deployment of MG [228–230]. In contrast with the conventional “fit and forget” policy to connect DG to distribution systems, the MG concept developed within the EU MICRO GRIDS project offer the possibility of fully profiting from active management strategies applied in Low Voltage (LV) systems with large integration of DG units. The MG is defined as a LV network comprising several feeders connected to the secondary winding of the distribution transformer, as it can be observed from Figure 6-A-2, with DG sources together with local storage devices and controllable loads and a hierarchical-type management and control scheme supported by a communication infrastructure allowing the MG operation mostly interconnected to the distribution network and in islanded mode in case of faults in the upstream network, riding automatically from grid connected to islanded operation and synchronizing after restoration of the upstream network voltage [231–233]. Page 167 Modelling and Aggregation of Loads in Flexible Power Networks DG systems include modular and small scale generation systems based on renewable energy sources such as Photovoltaics (PV) or micro wind generators and fuel-based generators in Combined Heat and Power (CHP) applications, such as microturbines and fuel cells. Due to the type of energy conversion system used, microsources are interfaced through power electronic converters to the LV network. Therefore, the inverter dominated MG requires that power balance during transients have to be provided by energy storage devices, either flywheels connected to the LV network through AC/DC/AC power electronic interfaces or batteries and supercapacitors connected to the dc-link of microgeneration systems [231]. PV Microturbine MC AC DC AC AC LC MC PV MC DC LV LC AC LC MV MC Wind Generator LC Fuel Cell MC AC DMS MGCC DC MC MC AC AC AC DC LC Storage device Microturbine Figure 6-A-2: The Micro Grid management and control architecture In order to achieve the desired flexibility, the MG is centrally controlled and managed by the Micro Grid Central Controller (MGCC) installed in the LV side of the MV/LV distribution transformer, which communicates with controllers located in a lower hierarchical level comprising local Microsource Controllers (MC) and Load Controllers (LC). The MC takes advantage of the MS power electronic interface and can be enhanced with various degrees of intelligence in order to control both voltage and frequency of the MG during transient conditions based on only local information. The MGCC functions can range from monitoring the active and reactive power of MS to assuming full responsibility of optimizing the MG operation by sending set points to the MC and LC in order to control microgenerators and controllable loads, respectively [231]. 6-A.4.3 MG DYNAMIC MODELLING Mathematical models suitable to represent the MG dynamic behaviour for purposes of dynamic and stability studies that have been reported on the available literature can be grouped in the following two main categories:  MG detailed models, comprising suitable models to simulate the dynamic behaviour the of individual DG units connected to the LV networks and, therefore, the whole MG dynamics impacting inside the MG and with respect to the utility system;  MG aggregated models, aiming to simulate the relevant dynamics of the MG with respect to the utility system. Detailed models of MG focus on microgeneration systems technologies, on their power electronic based interfaces and on the control strategies suitable to operate the inverter dominated MG in both interconnected and islanded modes of operation. Thus, a commonly adopted procedure is to model the power electronic converters through their control functions only, neglecting switching transients, harmonics and inverter losses, because fast transient phenomena is not a matter of concern for purposes of analysis of system dynamic behaviour [234–238]. Also three-phase models have been used for both DG units and power electronic based Page 168 Modelling and Aggregation of Loads in Flexible Power Networks interfaces, describing the MG dynamic behaviour only under balanced conditions. However, recently, models able to simulate the MG operation under unbalanced conditions have been reported in the literature [239]. Aggregated models for MG have been successfully developed under the framework of the MORE MICRO GRIDS Project, then focusing the MMG concept, exploiting system identification techniques [153–155]. Therefore, the system identification procedure consists on finding just another mathematical representation of the MG built upon the corresponding MG detailed model, being the MG operated under balanced conditions according to the SMO control approach, such that the MG is able to provide primary frequency control through the VSI interfacing the main storage device and secondary load frequency control by means of the controllable DG units. The MG dynamic equivalents are able to replace the MG in dynamic simulations following the occurrence of several disturbances at the MV level, like MMG islanding and load following in islanded mode. Short-circuits were not addressed since the contribution of power electronic based interfaces for short-circuit currents is limited by their low over current capacity when compared to conventional generators. The MG equivalent model is basically divided into two parts taking into account the dynamics going on the several active devices, i.e. fast dynamic response and slow dynamic response. The MG fast dynamic response is represented by the detailed model of the Voltage Source Inverter (VSI) interfacing a constant voltage source that represents the main storage device. On the other hand, the MG slow dynamic response is represented by reduced order models that can be derived from two different approaches: Black box and grey box modelling. A black box modelling approach based on ANN was proposed in [153]. The MG dynamic equivalent is based on a Time Delay Neural Network (TDNN) model structure combining a Nonlinear Finite Impulse Response (NFIR) input structure with a Multilayer Perceptron (MLP) neural network as the nonlinear mapping. The TDNN based model was trained through the Levenberg-Marquardt method and Mean Square Error (MSE) criterion using the MATLAB Neural Network Toolbox, with the MLP neural network structure being optimized using early stopping. The training process is performed offline using a data set based on time series obtained from time domain simulations using the MG detailed model implemented in Matlab®/Simulink® environment. The grey box modelling approach was exploited in [155] to derive a physically based MG dynamic equivalent. The selected model structure is similar to the active power control system found in diesel engine run power generators. The parameter identification task is performed using Evolutionary Particle Swarm Optimization (EPSO) as the global optimization tool and the Sum Square Error (SSE) as the identification criterion. The loss function of each particle is evaluated using time domain simulation. For this purpose the model structure was embedded in the dynamic simulation platform built in Matlab®/Simulink® environment, replacing the MG slow dynamics detailed model. The EPSO sends the parameter vector to the dynamic simulation platform, being its performance evaluated through the SSE calculated using the error of the time domain responses of both the MG equivalent model and the MG detailed model. The SSE is then sent to the EPSO algorithm in order to perform the search towards the optimum solution in the SSE sense. This modelling approach is presented in detail in this chapter. 6-A.5 Conclusions from ADNC equivalent modelling review Since ADNC is no longer passive network, the DGs have to be also considered in developing equivalent ADNC models. A reasonable balance between detail ADNC model and simplified model must be found to avoid computational constraints and to preserve required accuracy. Linearization and model reduction of ADNC are only appropriate under certain conditions [215], [216] as they generally limit the application of the model and render it unsuitable for large disturbance studies [215], [216], [223]. System identification techniques in combination with some general knowledge of network structure, if applied adequately, are suitable for ADNC model development as they rely on actual measured system responses and can be easily validated [141], [142], [155], [220–222]. Generally most of the work done in this area only considered the static loads as the load models in the dynamic equivalent model of ADNC [138], [139], [215–217]. Only a few papers take into account the dynamic load models [141–143], [223]. Page 169 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 6-B Black and Grey-Box Based Dynamic Equivalent Models 6-B.1 Recurrent ANN based dynamic equivalent models Since the ANN based dynamic equivalent model is intended to be integrated in dynamic simulation tools, it is implemented in such a way that it interacts with the retained subsystem at each time step to represent with the required accuracy the dynamic behaviour of the detailed model. For this purpose, the ANN equivalent model is implemented in the PSD simulation package as an unconventional power source to interact with the retained network in time domain simulations, as depicted in Figure 6-B-1. Figure 6-B-1: Implementation and interaction of the recurrent ANN based dynamic equivalent with the retained network As it can be observed from Figure 6-B-1, at each time step state variables and boundary bus voltages are captured and processed to get a complete input set to the ANN through the mapping function, f1 , as follows: U i  U i ,0 f1  U i  , i  1, 2, ..., j n (6-B.1) U i ,0 Where: U i is the normalized voltage deviation at boundary bus i ; n U i , U i is the voltage at boundary bus i and its initial value; 0 j is the number of boundary buses. A block simulating the behaviour of the ANN is used to process the inputs with the help of the information about the ANN structure, such as biases, weights and types of activation functions, to get the corresponding output currents. The information describing the ANN structure is saved in a supplementary file to be used through the simulation process. The outputs of the ANN are then used to define the corresponding normalized deviations of currents for the active components,  I na , computed through the demapping function f 2 , as: I a , i  I a ,i 0 f 2   I a ,i  , i  1, 2, ..., j n 0 (6-B.2) I a ,i Where:  I a ,i is the normalized current deviation of active components at boundary bus i ; n I a ,i , I a ,i is the current of active sources at boundary bus i and its initial value; 0 Page 170 Modelling and Aggregation of Loads in Flexible Power Networks Real and imaginary parts of U in are used as separate inputs to the ANN while real and imaginary components of  I na ,i are obtained separately at the output layer. The complex power is then calculated and supplied to the retained network. 6-B.2 State space model used in grey box based approach The nonlinear state space model can be summarized as follows: x  Ax  Bu  f ( x) (6-B.3) y  Cx  Du  f ( x) where A, B, C and D are the coefficient matrices, x is the state vector, u is the input vector, y is the output vector and f(x) is a function that represents the nonlinear parts of the model. The schematic of the composite equivalent circuit for the ZIP-IM load model is shown in Figure 6-B-2 [141–143]. The dynamic part of this model is represented by internal voltage relationships of the third-order induction motor [144]. Constant Constant Constant power current impedance loads loads loads V IM ZIP Figure 6-B-2: The equivalent circuit of the ZIP-IM load model The composite load model used in the DNC model is described by the following equations: dEm 1     Bm Em  CmV cos  m  (6-B.4) dt Tdm d m CV  r  s  m sin  m (6-B.5) dt  Em Tdm dm 1   Em V    sin  m  Tm  (6-B.6) dt H m  X m  Xm X  X m where Bm  , Cm  m , m is the subscript of motor, E' is voltage behind the transient reactance, T'd is X m X m d-axis time constant, V is the bus voltage, ωr is the angular velocity of rotor, ωs is the angular velocity of stator, ω is the angular frequency, δ is the angle between E' and V, H is the inertia, X is the reactance, X' is the transient reactance and Tm is the mechanical torque. The output equations of the composite load model are expressed in the following way: Page 171 Modelling and Aggregation of Loads in Flexible Power Networks   V 2 V  1  PL  PZIP 0  PZ    PI    PP    Bm Em sin  m    Vo   Vo   (6-B.7)   V 2 V  1   V2  QL  QZIP 0 QZ    QI    QQ     Bm Em cos  m    Vo   Vo    X m  where PL and QL are the real and reactive power of the ZIP-IM model, respectively; PZIP0 and QZIP0 are the real and reactive power of the static ZIP model at steady state,; PZ and QZ are the constant impedance part of the ZIP model; PI and QI are the constant current part; and PP and QQ are the constant power part. The converter-connected generator is composed of a second-order synchronous generator model and a back- to-back full converter model [146], [147], [150]. The synchronous generator interfaces with the grid via a back-to-back full converter as shown in Figure 6-B-3 [146], [147]. The real power flow through the converter is balanced via the DC-link (the capacitor linking inverter and rectifier). Generator Grid side Inverter Rectifier side IG Ig + + IDC + VG VDC Vg _ _ _ Figure 6-B-3: The back-to-back full converter model. Adopted from [146], [147] The dynamic parts of the converter-connected generator can be described by (6-B.8) to (6-B.10). dEg dt  1 Tdg EFD  Eg   X g  X g  I d  (6-B.8)  1 Tdg  EFD  Bg Eg  CgV cos  g  dg dt  1 Hg Tm  Te  Dg  (6-B.9)  1 Hg Tm  Bg Eg sin  g  Dg  dVDC dt  1 CVDC VD I D  VQ IQ  Vd I d  Vq I q  (6-B.10) Xg X  X g where Bg  , Cg  g , g is the subscript of generator, E' is voltage behind the transient reactance, T'd X g X g is d-axis time constant, V is the bus voltage, Vo is the nominal bus voltage, ωr is the angular velocity of rotor, ωs is the angular velocity of stator, ω is the angular frequency, δ is the angle between E' and V, H is the inertia, X is the reactance, X' is the transient reactance, Tm and Te is the mechanical and electrical torque respectively, EFD is the excitation voltage, D is the damping factor, C is the capacitance, VDC is the capacitor DC voltage, IDC is the capacitor DC current, VD and ID is the d-axis voltage and current of the grid side of converter respectively, VQ and IQ is the q-axis voltage and current of the grid side of converter respectively, Vd and Id is the d-axis Page 172 Modelling and Aggregation of Loads in Flexible Power Networks voltage and current of the generator side of converter respectively, Vq and Iq is the q-axis voltage and current of the generator side of converter respectively. The output real and reactive power of the converter-connected generator are computed as follows: PG  Bg Eg sin  g  VDC I DC  V2  (6-B.11) QG   Bg Eg cos  g    VDC I DC  X g   where PG and QG are the active and reactive power of the converter-connected generator part. Based on (6-B.4) to (6-B.11), the system states, inputs and outputs are defined as follows:  x1   Em  x      2  m  x    u   V  P x   3   m  , u   1   , y     x4   Eg  u2  s  Q   x5    g       x6  VDC  The output equations (P and Q) of the equivalent model can be obtained from the following equation: P  PG  PL (6-B.12) Q  QG  QL Therefore, the nonlinear state space model of DNC in its final form can be described as follows:   Bm   T 0 0 0 0 0   dm   Cm   0 0 1 0 0 0   T  cos  m 0   x1    dm   0    Am 0 0 0 0  0   x   m sin  m C  1  0   Hm   2  Tm E       x3    u    0  x Bg   0     1 0   x4    0 (6-B.13)  0 0 0 0    u2   EFD  Tdg   x   g cos  g  C  0  0    Ag D     Tdg 5    0 0 0 0   x6     0   Hg Hg   0 0      0 K1   0 0 0 0 0 0  CVDC  2 Page 173 Modelling and Aggregation of Loads in Flexible Power Networks  x     1     V P    V sin  V sin  g  I DC  x 2   P  P   I  0 0 0  u   X   x 0  X g    V V   P        m ZIP 0 Z P 2 y  m  3    o o 1 ZIP 0 P  (6-B.14)   V cos  V  x   Q  Q V  Q   V V  u   Q  Q    cos  g  I DC    0 ZIP 0 Q   X   x 4 I 2 0 0 0 m X g     V V  X ZIP 0 Z 2 X m       g 5 o o g   x 6    Xm X m  X m Xg X  X g V V where Bm  , Cm  , Bg  , Cg  g , Am  sin  m , Ag  sin  g and X m X m X g X g X m X g K1  VD I D  VQ IQ  Vd I d  Vq I q The unknown parameters in this equivalent model are represented as P1, P2, P3, ….., P20 for model simplicity purpose. Finally, the nonlinear state space model of DNC can be summarized by matrix equation (6-B.15).  P1 0 0 0 0 0   x1   P7 0   0  0 1 0 0 0     0   x2   P8 1   0      P2 0 0 0 0 0   x3   0 0   u1   0  x        0 0 P3 00 0   x4   P9 0  u2   P10  0 0 P 4 P5 0   x5   0 0  0  0          0 0 0 0 P6   x6   0 0  0  0  (6-B.15)  x1  x   2  P11 0 0 P12 0 P13  x3   P17 0   u1   P19  y           P14 0 0 P15 0 P16   x4   P18 0  u2   P 20   x5     x6  Obviously the dynamic equivalent model of DNC as shown in (6-B.15) is a nonlinear model and has twenty unknown parameters. These twenty parameters have to be identified using a suitable parameter estimation procedure, custom-made or commercially available. Parameters P10, P19 and P20 featuring in (6-B.15) are the nonlinear parts of the equivalent DNC model. Page 174 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 6-C Dynamic Equivalents for Micro Grids 6-C.1 TDNN based Micro Grid equivalent model The TDNN based Micro Grid equivalent model is represented in Figure 6-C-1. TDNN  v D f1 q-1 VD , VQ  q-1 I DR , I QR vQ MLP i D q-1 Boundary neural bus  network iQ q-1  q-1  q-1 f2 TDNN based MG slow dynamics equivalent model Figure 6-C-1: TDNN based MG slow dynamics equivalent model At each time step, the MG slow dynamics equivalent model recognizes the operating status of the retained network through the boundary bus voltage and system frequency. The normalized voltage and system frequency deviations, vD , vQ and  , respectively, are computed through the function f1 . The TDNN is then used to determine the corresponding normalized current deviations, iD and iQ . Therefore the current to be injected into the retained network is computed using the function f 2 . These normalized deviations are computed based on their initial steady state values as follows VD  VD 0 VQ  VQ 0     0 vD  ; vQ  ;   (6-C.1) VD ,max VQ ,max max  0   I DR  iD ´ I D,max  I DR ; IQR  iQ ´ IQ,max  IQR 0 (6-C.2) where vD , vQ , iD , iQ are the normalized deviations of both voltage and current D  Q components;  is the normalized deviation of frequency; VD ,max , VQ ,max , I DR ,max , I QR ,max are the maximum variations considered to normalize both voltage and current D  Q components; max is the maximum frequency deviation considered to normalize frequency; VD 0 , VQ 0 , I DR  0  0 , I QR are the initial steady state values of both voltage and current D  Q components;   0 is the nominal value of system frequency. The initial steady state values of boundary bus voltage and injected current of the MG slow dynamics equivalent model are determined through the initial load flow calculations. Their maximum deviations as well as Page 175 Modelling and Aggregation of Loads in Flexible Power Networks frequency maximum deviation are obtained from the dynamic simulation of the largest amount of load connection and disconnection upon MMG islanding. 6-C.2 Physical Micro Grid equivalent model: Power instantaneous theory VD VQ I DR Instantaneous t power theory Pm I QR Qref Figure 6-C-2: Interface between the MG slow dynamics equivalent model and LV network The current source assumes the role of the inverter, by determining the current from both the active power delivered by the MG slow dynamics equivalent model, Pm , and a given reactive power, Qref , which corresponds either to a pre-defined value linked to a given MS power factor or a reactive power set-point sent by the MGCC. The instantaneous power theory was proposed in [211] for control of active power filters and has been used to control the PWM-VSI (Pulse Width Modulation – Voltage Source Inverter) or PWM-CSI (Pulse Width Modulation – Current Source Inverter). Voltages or current reference signals employed to turn on and turn off the switches of the inverter can be obtained from this theory [240], [241]. In this case, the instantaneous voltages and currents in three-phase circuits are adequately expressed as instantaneous space vectors in abc coordinates as depicted in Figure 6-C-3. b  axis   axis vb , ib v , i 2 / 3 v a , ia v , i 2 / 3 a  axis   axis 2 / 3 v c , ic c  axis Figure 6-C-3: Coordinates transformation In a balanced three-phase system the abc space vectors are easily transformed into  and  coordinates through the Clark transformation as follows: va  t    v  t        C ´  vb  t   (6-C.3)     v t  vc  t   Page 176 Modelling and Aggregation of Loads in Flexible Power Networks ia  t   i  t        C ´ ib  t   (6-C.4) i  t    ic  t   where va  t  , vb  t  and vc  t  are the instantaneous voltages in abc coordinates, respectively; ia  t  , ib  t  and ic  t  are the instantaneous currents in abc coordinates, respectively; v  t  and v  t  are the instantaneous voltages in    coordinates, respectively; i  t  and i  t  are the instantaneous currents in    coordinates, respectively; C is the Clark transformation given by  1 1  1  2  2  2   C ´ (6-C.5) 3    3 3 0 2  2  So, as described in [241], the instantaneous active and reactive powers are defined as:  p  t    v  t  v  t   i  t     ´  (6-C.6)  q  t    v  t  v  t   i  t   where p  t  is the instantaneous active power in W ; q  t  is the instantaneous reactive power in VAr . In systems with sinusoidal balanced voltages and currents, the average value of q  t  is equal to the conventional reactive power and the instantaneous active power, p  t  , is always equal to the conventional active power [158]. Thus, from equation (6-C.6) it is possible to obtain the currents reference signals to control the PWM-CSI depicted in Figure 6-C-2 as follows ia*  t   *  i  t   ib  t    C ´ i  t   1 (6-C.7) ic*  t       Where 1 i  t    v  t  v  t    Pm     ´  (6-C.8) i  t    v  t  v  t   Qref  Page 177 Modelling and Aggregation of Loads in Flexible Power Networks And    1 0    3  1 3  C 1  ´   (6-C.9) 2  2 2     1 3   2  2  The references of currents, ia* , ib* and ic* are calculated instantaneously without any time delay by using the boundary bus instantaneous voltages and both active and reactive power values. The procedure described above was implemented in a Simulink S-function coded in MatLab m-file, following the scheme presented in Figure 6-C-4. va VD V  D Q v VD  p.u. abc 2 vb ´ Vbase v VQ  p.u. 3 vc VQ V  abc   t ia* Pm W  i   I D  A I DR  p.u. Pm  p.u. abc 1  v v  ib* 1 S base  v Qref  p.u.   v  ic* 2 ´ I base D  Q I  A I QR  p.u. Qref VAr  i abc Q t Figure 6-C-4: Schematic representation of instantaneous power theory implementation Page 178 Modelling and Aggregation of Loads in Flexible Power Networks Appendix 7 Bibliography on Load Modelling Load modelling is a mature topic, because it has been long recognized that the results of power system voltage and angular stability studies strongly depend on the selection of accurate load models and their parameters. The knowledge about load model parameters, capable of correctly describing load behaviour during and after electric power system disturbances, enables proper power system planning, reliable prediction of prospective operating scenarios and application of adequate control actions for preventing undesired system behaviour and, ultimately, system instability. The bibliography paper that includes large number of references dealing with load modelling is published in 1995 [1]. It lists static and dynamic load models, the references in which these models are used, load classes, parameter values and conditions for which the values are valid. Two more summary papers that represent the overview of load modelling studied by the early nineties, [2] and [3], deal with load models typically used for power flow and dynamic performance simulation and the way of the representation of the load for dynamic performance analysis, respectively. From that time much work has been done on load modelling all over the world due to appearance of new types of loads and modern nonlinear electrical and electronic equipment offering increased efficiency and controllability. The CIGRE working group (WG) C4.605: “Modelling and aggregation of loads in flexible power networks” was established in February 2010, in order to address above mentioned and other relevant issues related to the general area of load modelling. This bibliography overview is the result of CIGRE WG C4.605 and its aim is to help both engineers and academics working on load modelling. The appendix summarizes the references starting from 1993 that describe various aspects of load modelling. References are sorted according to the themes that they address in order to facilitate easier use. Two main approaches to load modelling, component-based and measurement-based approach are documented extensively in the past work. Component-based approach is described in [4–47]. Measurement-based approach to load modelling is given in [28–30], [37], [46–119]. The issues related to selection of load model are discussed in [10], [14], [16], [53], [55], [65], [82], [92], [107], [120–132]. Load model parameter estimation is an essential part of load modelling and different procedures for parameter estimation are presented in [13], [27], [29], [30], [38], [56], [57], [59], [60], [65], [72–74], [82], [83], [85], [86], [89–93], [95], [97–99], [102], [103], [105–108], [110], [115], [125], [133–148]. Adequate load modelling is crucial for different power system studies. Thus, numerous papers, reports and books deal with load modelling influence on load flow analysis [11], [15], [20], [32–35], [42], [44], [45], [94], [149–154], small disturbance and transient stability analysis [1], [14], [15], [31], [53], [70], [74], [81], [109], [121], [143], [153], [155–178], voltage stability studies [11], [18], [25], [56], [57], [60], [64], [67], [71], [72], [84], [87], [90], [95], [109], [121–123], [126], [130], [131], [139], [152], [155], [179–205], and frequency deviations analysis [12], [14], [55], [57], [72], [83], [185], [206–209]. Some of the papers also emphasize load modelling influence on voltage sags [43], [60], [127], [135], [148], [210], [211], relay settings [124], voltage recovery [101], [187], [212], load shedding studies [100], [101], [128], [205], [213] and planning studies [16]. However, proper load modelling is not possible without adequate equipment. Recording equipment for load modelling is described in [48], [75], [76], [97], [214], [215]. 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Milanovic, “Aggregated Models of Wind-based Generation and Active Distribution Network Cells for Power System Studies – Literature Overview”, in 2011 IEEE Trondheim PowerTech, Jun., 2011. [3] K. Yamashita, S. M. Villanueva, and J. Milanović, “Initial Results of International Survey on Industrial Practice on Power System Load Modelling Conducted by CIGRE WG C4.605,” in CIGRE symposium, Bologna, Italy, Sep., 2011. [4] K. Yamashita, S. Djokic, J. Matevosyan, F. O. Resende, L. M. Korunovic, Z. Y. Dong, and J. V. Milanovic, “Modelling and Aggregation of Loads in Flexible Power Networks – Scope and Status of the Work of CIGRE WG C4.605,” in IFAC Symposium on Power Plants and Power Systems Control, Sep., 2012. [5] S. Martínez Villanueva, K. Yamashita, L. M. Korunović, S. Z. Djokić, J. Matevosyan, A. Borghetti, Z.Y. Dong, J. V. Milanović, “Modelling and Aggregation of Loads in Flexible Power Networks– Scope and Status of the Work of CIGRE WG C4.605”, in Proc. CIGRE SC C4 Colloquium, Hakodate, Japan, Oct., 2012. [6] L.M. Korunović, K.Yamashita, S. Z. Djokić, F. Villella, J.V. Milanović, A. Gaikwad, S. M. Villanueva, “Overview of Existing Load Models and Their Applications”, in Proc. CIGRE SC C4 Colloquium, Hakodate, Japan, Oct., 2012. [7] K. Yamashita, S.M. Villanueva, S.Z. Djokić, J. Ma, A. Gaikwad, J.V. Milanović, “Overview of Existing Methodologies for Load Model Development”, in Proc. CIGRE SC C4 Colloquium, Hakodate, Japan, Oct., 2012. [8] Z.Y. Dong, Alberto Borghetti, K. Yamashita, J.V. Milanvoic, “CIGRE WG C4.065 Recommendations on Measurement Based and Component Based Load Modelling Practice”, in Proc. CIGRE SC C4 Colloquium, Hakodate, Japan, Oct., 2012. [9] L. M. Korunovic, S. Sterpu, S. Djokic, K. Yamashita, S. M. Villanueva, and J. V. Milanovic, “Processing of load parameters based on Existing Load Models,” in 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Berlin, Germany, 2012, pp. 1–6. [10] K. Yamashita, S. Z. Djokić, F. Villella, J.V. Milanović “Self-disconnection and Self-recovery of Loads Due to Voltage Sags and Short Interruptions”, in Proc. CIGRE Symposium, Lisbon, Portugal, Apr., 2013. [11] S. Martínez Villanueva, K. Yamashita, J.V. Milanović, “Estado del arte en el modelado de cargas en grandes sistemas eléctricos de potencia”, in Proc. CIGRE ERIAC, Foz de Iguazu, Brazil, May, 2013 [12] F. O. Resende, J. Matevosyan, J. V. Milanovic, “Application of Dynamic Equivalence Techniques to Derive Aggregated Models of Active Distribution Network Cells and MicroGrids, A Critical Overview”, in Proc. IEEE PowerTech, Grenoble, France, Jun., 2013 Page 190