Profiles and Projects: Graduate Yearbook 2009 Department of Mechanical Engineering Monash UniversityOPTIMUM ENERGY STORAGE FOR PORTLAND WIND FARMS WITH A FOCUS ON ECONOMIC FEASIBILITY DAVID BOWLY SUPERVISED BY: DR. M. A. HESSAMI ABSTRACT This report presents a study into the economic advantages of using large scale energy storage to complement a wind farm in a large, base load dominated electricity grid. A computer model is developed which simulates the operation of each energy storage system. A variety of operation strategies are compared with the results of a dynamic programming model which finds the maximum possible revenue a given system can generate. Three energy storage systems are modelled and costed: Pumped Seawater Hydro, Compressed Air Energy Storage (CAES), and Thermal Energy Storage. It is found that CAES is the most profitable storage medium, requiring a capital expenditure of $140M and generating a rate of return of 15.4%. PHS generates a ROR of 9.6%, and TES 8.0%. A significant investment opportunity exists; it is highly recommended that CAES is investigated in more depth with the aim of introducing large scale energy storage to Portland Wind Farms. INTRODUCTION Like all renewable energy sources, wind power has strengths and weaknesses. One of its biggest drawbacks is that wind is inherently variable, being classified as “non dispatchable” by electricity regulators. Energy storage ameliorates wind farms’ inherent variability, providing the ability to store power generated by wind turbines at times of low demand, and release that energy at times of high demand. Wind power operators receive higher revenues by providing power to the grid when the electricity spot price is high. This study investigates the economic viability of using an energy storage system with Portland Wind Farms (PWF) in Victoria, Australia. This will be Victoria’s largest wind farm by nameplate capacity (194.4MW) when completed in 2010 (Pacific Hydro, 2006). Three methods of energy storage will be investigated and compared – Pumped Seawater Hydro (PHS), Compressed Air Energy Storage (CAES), and Sensible Thermal Energy Storage (TES). All are commercially proven, although not all have previously been used on this scale. A large body of research into the use of energy storage to complement wind power exists. Many studies investigate technical and power control aspects, and a variety of papers present cases for using energy storage to complement wind power in island locations with isolated grids. Of the large-grid studies, Swider (2007) concludes that the ‘CAES can be economic in the case of large-scale wind power deployment’ in the German electricity grid. Ummels et al. (2008) concludes that ‘cost savings from energy storage increase with the amount of wind power installed’ in the Dutch grid. However, Lund et al. (2009) find that CAES is not economic for the grid of Denmark. The findings throughout literature, in conclusion, are that economic feasibility of energy storage is highly grid-dependent. In contrast to the above, this paper investigates the economics of various energy storage systems to enhance wind power in a low penetration, base load dominated, large capacity electricity grid. This project is an extension of the work by Hessami and Yeoh (2008) and Hessami and Rocha (2008), incorporating more sophisticated optimisation, further costing work, an investigation of TES, and a method which allows for reliable comparison between the different technologies. This research incorporates technical feasibility, operation optimisation, costing, and financial analysis elements, as shown in Figure 1 on the following page. Hydrogen fuel cells were considered. such as superconducting magnetic energy storage. This technology is already in operation in Okinawa. CAES is a commercially proven technology: a 290MW plant was commissioned in Huntorf. An ideal topography exists for the placement of a storage pond directly adjacent to the Cape Bridgewater wind farm. and a 110 MW plant in Alabama. US. Electric resistance is used to heat carbon blocks. in 1991. however none were suitable for this application. . 2008). a working fluid extracts the energy as steam and feeds a steam turbine. The system designed stores heat in the blocks at 750⁰C. have too short a service life to be practical for this application. Other storage devices considered A wide variety of other energy storage systems are also available. as the technology is very simple. Air is compressed and stored underground to charge the system. Compressed Air Energy Storage A CAES system separates the two components of a normal gas turbine. Germany. and to discharge. however capital costs are currently prohibitively expensive (Ibrahim et al. 2008). and fed directly into the combustion chamber to discharge. Other technologies. Japan. Thermal Energy Storage This research will use sensible heat storage with turbine. flywheels and super capacitors. with a 30MW high-head storage dam. and significant technical challenges exist in upscaling the technology. Most current battery technologies suffer from deep-cycle problems. Underground caverns are the cheapest solution for storing significant quantities of air at high pressures. and thus reliable (Ibrahim et al. are not capable of energy storage on the scale required.Profiles and Projects: Graduate Yearbook 2009 Department of Mechanical Engineering Monash University Figure 1 Key information collected and work completed over the course of this research to model the system and thus evaluate the economics of large scale energy storage DETAILS OF ENERGY STORAGE DEVICES Pumped Seawater Hydro Storage In the context of the current drought in Australia. The CAES turbine thus produces around three times as much specific power as a standard turbine when operating. It is an economical and practical alternative to the more common solution mining method. a pumped seawater hydro plant was chosen. in 1978. heat is extracted by a steam turbine operating at inlet conditions of 140 bar gauge and 700⁰C. providing an elevation of 135m within 250m of the shoreline... Hessami and Rocha (2008) identified a depleted gas field in the Waare formation of the Otway basin for this system. 500. Storage is “bypassed”. PHS n × 31. selling power from storage and wind farm to the grid 3.009.0% Turbine 12. it will be assumed that the plant operates at the same conditions for the whole of each half-hourly period.Profiles and Projects: Graduate Yearbook 2009 Department of Mechanical Engineering Monash University Table 1 Energy storage system parameters and capital costs.150.4MW pump/turbines Seawater at elevation Pump 2. each must be operated to obtain maximum revenue. The computer model is programmed in Matlab. but does not do well during periods of high or low spot price. There are many strategies available to operate the device to generate extra revenue. C and D were determined by exhaustive search.000 $17. Costings and plant specifications obtained from confidential industry quotations.970 $750.300 $973. The optimum modification function (1-S)15.7% Line loss 1. Cm and Dm.9% Transmission 0.000 pa CAES 4 × 20-80MW gas turbines Compressed air in cavern Compressor Diabatic expansion Transmission (64.000 pa $3. were obtained. Storage is “charged” using power from PWF.000 pa 1% 71% 0. W. Storage is “discharged”.2% of CapEx $400.000 $4. This strategy can be improved by modifying the C and D values. and discharge when above a “discharge” (D) price. The model calculates the return on investment for any system configuration. By choosing a range of possible values for storage and plant size (exhaustive search).500. .400 0. An optimum C and D can be found by exhaustive search.000 $5. OPERATING STRATEGY To compare the different energy storage systems. Three years of five minute instantaneous power readings from Yambuk wind farm (one of the four wind farms marking up PWF) were converted to half-hourly averages and scaled up to the output of PWF. selling no power (or reduced power) to the grid 2. Leakage losses are negligible for CAES and TES over the timescales of interest.000 $2/kW-yr $400. for example: Dm = D × 1 + W × (1 − S ) [ 15 ] (1) W is a weighting factor which determines how much of an effect the storage level is allowed to have on the operation. and are explained briefly below.0%) $15.2% of CapEx $400. The revenues generated are compared in Figure 2.9% 20% 1. Some of these were tested using the model. in half hourly intervals.3% $45. selling only power from the wind farm to the grid. A simple technique is to charge the system whenever the spot price is below a “charge” (C) price.900 $1. the parameters can be found which maximise the rate of return on capital investment in a storage system. Three years worth of Victorian electricity spot prices.3% 84.75/GJ 30. This technique is effective during normal operation. Both the data and the scaling process were shown to be reliable by independent verification with wind speed data from Port Fairy and Portland from the Bureau of Meteorology over the same period. to encourage the system to charge and discourage it from discharging when the storage level is low. If the storage is not close to being full or empty.7% $15.3% Plant Storage media Key losses Round trip η Fixed Cost Cost/MWh Cost/MW Maintenance cost Running costs Fuel cost THE COMPUTER SIMULATION The operation of the energy storage system is modelled using historical operational data to determine the extra revenue generated using storage.000 0. the following operations are possible: 1. For simplicity.0% TES 150-300MW steam turbines Heat in carbon blocks Heating Discharging Transmission 28. However. compared with the revenue generated by the wind farm without storage. using a method of dynamic programming adapted from Lund et al. By discretising the number of possible storage levels. the best possible path could be selected. (2009). on the basis that wind farm outputs and spot prices can be reliably forecast under most conditions several days in advance. and C on a bottom percentile. The extra revenue gained by the ability to store more of the wind farm’s output. The CAES system appears to be by far the most profitable energy storage device for PWF. A subroutine was written to predict the rounding error introduced by a given choice of discretisation in order to choose a number of discretisations to minimise the error introduced. Consider the “path” created by a given combination of charge and discharge cycles. shown in Figure 3. If two paths ending at a given time have the same storage level. buy buy Account for storage level Predictive c/d price Improved predictive Combination Optimum solution Revenue ($M over 3 years) Optimisation method Figure 2 The revenue generated using each operation strategy. This allows the system to supply power continuously during long high-demand periods. which quickly becomes too large to handle. With increasing sophistication. which is relatively cheap to store in large quantities. With three possible actions at each time step n. only the path corresponding to the highest storage level needs to be recorded – the other path is sub-optimal. Dynamic programming Figure 2 shows that increasingly sophisticated techniques can indeed generate extra revenue. and the storage level corresponding to this. This subroutine was found to predict the error with a high degree of accuracy. This method clearly introduces some rounding error. to reliably compare between different storage systems. The only items of interest at the end of each time period are the revenue generated up to that point. If the revenue from the paths could be compared. an optimum C and D are determined using future prices for a given period. must be balanced against the increased capital cost. RESULTS The model was run to determine the revenue which could be generated for a range of storage sizes and output ratings for each storage device. To take this into account. it is possible to reduce the number of outcomes. The profitability of the CAES system can be attributed to its storage medium. However. it is possible to make the storage levels overlap. the CAES system provides extra power to the grid on top of the output of PWF. A summary of the results for each storage device is shown in Table 2. the number of possible paths is 3n. Furthermore. fixed Vary sell. the best possible revenue should be found.Profiles and Projects: Graduate Yearbook 2009 Department of Mechanical Engineering Monash University Optimisation can be further improved based on the fact that the distribution of the spot price varies throughout the year. 140 130 120 110 100 90 80 No storage Vary sell. D is based on a top percentile of prices. the techniques approach the optimum solution. . or to provide more power to the grid during high demand periods. Each energy storage device has a set of characteristics resulting in the best rate of return. 4 6 x 31.00 $0. providing power when demand is high. x 10 1 50 4 0.20 $0.40 $19.5MW compression) $136 $23.622 1000 2000 3000 4000 5000 6000 Storage size (MWh) Figure 3 Optimisation run for the PHS system.8MW pumping) $260 $20. with the change in level over a day a function of the wind farm output and the spot price.4 Z: 9. The system is able to operate as a peaking plant for long periods.62% CAES 1887 7 3. In anticipation of a high spot price on the right.06 $0. the system charges and discharges on a daily basis.5MW turbines (16.40 $15. All costs are quoted in 2007 Australian dollars.5 x 10 kg @ 19. and a large proportion of the system is discharged over the following 12 hour period.22 $0. The point indicated has the highest rate of return. the system is fully charged. the capital expenditure required.03% Analysis of operation The following two figures show the operation of the CAES system under different conditions.43% TES 5500 18330 tonnes @ 750⁰C 240 1 steam turbine $229 $16.53 15. and the revenues generated by each. $M.14 9. Storage size (MWh) Storage size (physical) Output rating (MW) Output plant Capital Expenditure Extra revenue (2007) Fuel cost (2007) Maint cost (2007) Running cost (2007) Profit (2007) ROR on CapEx PHS 1800 7 3 4.00 $0.40 $17.33 8.Profiles and Projects: Graduate Yearbook 2009 Department of Mechanical Engineering Monash University ROR for these plant sizings 10 8 6 4 2 0 800 600 400 200 Output rating (MW) 0 0 X: 1800 Y: 188. Table 2 Summary of optimum specifications for each plant.4MW turbines (31.5 Storage level (MWh) Storage level (MWh) Spot price ($) 2000 0 2000 0 1000 0 23/03/2006 25/03/2006 27/03/2006 29/03/2006 Date (ticks at midnight) 31/03/2006 0 15/01/2007 16/01/2007 16/01/2007 17/01/2007 Date (ticks at midnight and midday) Figure 4 the operation of the CAES system under periods of normal spot prices (left) and very high spot prices.6MPa 110 4 x 27. Spot price ($) .35 $0.50 $5.89 x 10 m @ 135m 188.52 $0. During periods of normal spot prices.47 $0. REFERENCES Denholm. creating peaking plants in place of current non-dispatchable power sources. IEEE Transaction on Energy Conversion. Compressed Air Energy Storage appears to be the most profitable storage media for this wind farm and grid. M. Optimal operation strategies of compressed air energy storage on electricity spot markets. 2(1):34–46. A feasibility study of the use of seawater pumped hydro storage on wind farms in portland. Of the parameters on which the model depends. 5-7 November. 29(5-6):799–806. 12(5):1221–1250. http://www. Integration of large-scale wind power and use of energy storage in the netherlands’ electricity supply.. K. Cost estimation is a highly specialised business. fuel cost. L. This technology has the ability to greatly improve the usefulness of wind farms. It is highly recommended that these technologies are investigated in more depth with the aim of introducing energy storage to Portland Wind Farms. Furthermore.pacifichydro. IET Renewable Power Generation. and the annual increase in electricity price. Variations in maintenance and running costs.4%. E. W. H. Salgi. CONCLUSIONS AND RECOMMENDATIONS This study can conclude that large scale energy storage would almost certainly be a useful and profitable addition to Portland Wind Farms. 2008. Ummels.. Energy storage systems–characteristics and comparisons. had little effect on the profitability of the system. A. Thermal Energy Storage generates a rate of return of only 8. Swider. (2004). and Perron. Mexico. it is the capital cost which is the least certain. and charge and discharge efficiencies. Lund. To be completely confident would require a basic engineering package.. Wind Expo LAWEA 2008. some technical details could not be fully investigated. (2008). 45(13-14):2153–2172. the capital investment required being very much higher than CAES at $260M. C..6%.. Renewable and Sustainable Energy Reviews. australia. victoria. for his enthusiasm. (2007).Profiles and Projects: Graduate Yearbook 2009 Department of Mechanical Engineering Monash University Sensitivity analysis A sensitivity analysis was performed to quantify the effect of varying certain parameters on the calculated Rates of Return of each system. (2008). Ibrahim. B. and for his insights and advice on technical issues and writing. Recommendations The rates of return calculated. Pumped Seawater Hydro is predicted to generate a rate of return of 9. far more resources and expertise would be required to determine a very accurate capital cost. G.com.. ACKNOWLEDGEMENTS I would like to thank my supervisor Dr Mir-Akbar Hessami for introducing me to this very exciting topic. (2008). D. Mexico. Wind Expo LAWEA 2008. D.au/Default. Hessami. Hessami. (2009).-A. . and Kling. requiring a capital expenditure of $136M and generating a rate of return of 15. A. and Rocha. Compressed air energy storage in an electricity system with significant wind power generation.aspx?tabid=134. (2008). indicating that it is unlikely to be commercially feasible. A study of an integrated compressed air energy storage and large scale wind farm in australia. Life cycle energy requirements and greenhouse gas emissions from large scale energy storage systems. P. especially for the CAES system. and Kulcinski. H. Elmegaard. making it a feasible option in good economic times. M. and Yeoh. G. Pacific Hydro (2006). 2008. there is some contention in the academic world as to whether a CAES storage cavern at the depth of the Waare formation is feasible. N. Portland wind project. indicate a significant opportunity for investment. For example. the emissions from the power sources the energy storage system replace must be carefully quantified.-A. Applied Thermal Engineering. Pelgrum. Ilinca. and Andersen. J. Denholm and Kulcinski (2004) presents a study of electricity storage life cycle CO2 emissions which could be adapted to this study.0% on its capital cost. Future work To determine the change in CO2 emissions from the use of these energy storage systems. 22(1):95–102. The CAES system’s rate of return was found to be particularly sensitive to capital cost. B.. 5-7 November. Energy Conversion and Management.