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Optimal valuation of wind energy projects co-located with battery storage

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  • Loukatou, Angeliki
  • Johnson, Paul
  • Howell, Sydney
  • Duck, Peter

Abstract

The electricity market in the UK has been reformed over recent years resulting in the introduction of ‘Contracts for Difference’ as an option for wind producers. They can use these contracts only if at the same time they enter into other market contracts, i.e., Power Purchase Agreements or participation in the wholesale market. Most wind producers prefer the option of holding a long-term Power Purchase Agreement with another party. However, these agreements do not always offer the best terms for a wind producer due to them selling energy at lower electricity prices than those of the wholesale market spot prices. In addition, direct participation in the wholesale market can lead to high imbalance costs for the wind producers. In this paper, we propose an optimal dispatch model solved via dynamic programming optimisation to examine these two different business cases for wind operators alongside the existence (or not) of subsidised Contracts for Difference. A case study with an onshore UK wind farm and a lithium-based battery is provided. The proposed methodology was designed flexibly, so that it can be applied to different wind farm and battery configurations, as well as energy market structures. It is found that it is not always more profitable for the wind operator to hold a Power Purchase Agreement with another party than owning a battery storage unit and trading wind energy directly into the wholesale market. The discount rate of the Power Purchase Agreements, the capital cost of the wind farm and the strike price of the Contracts for Difference are critical factors affecting the profitability of both business cases. In addition, if subsidies are totally removed from both of them, then these are no longer economically justifiable, unless significant reductions are achieved in the capital costs of wind farms and battery storage units. Lastly, we show that considering only the calendar life of the battery (and not the cycle life) leads to significant underestimation of the investment costs.

Suggested Citation

  • Loukatou, Angeliki & Johnson, Paul & Howell, Sydney & Duck, Peter, 2021. "Optimal valuation of wind energy projects co-located with battery storage," Applied Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920316391
    DOI: 10.1016/j.apenergy.2020.116247
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    as
    1. Xydas, Erotokritos & Qadrdan, Meysam & Marmaras, Charalampos & Cipcigan, Liana & Jenkins, Nick & Ameli, Hossein, 2017. "Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators," Applied Energy, Elsevier, vol. 192(C), pages 382-394.
    2. Johnston, Lewis & Díaz-González, Francisco & Gomis-Bellmunt, Oriol & Corchero-García, Cristina & Cruz-Zambrano, Miguel, 2015. "Methodology for the economic optimisation of energy storage systems for frequency support in wind power plants," Applied Energy, Elsevier, vol. 137(C), pages 660-669.
    3. Khalid, Muhammad & Aguilera, Ricardo P. & Savkin, Andrey V. & Agelidis, Vassilios G., 2018. "On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting," Applied Energy, Elsevier, vol. 211(C), pages 764-773.
    4. Jan Kloppenborg Møller & Marco Zugno & Henrik Madsen, 2016. "Probabilistic Forecasts of Wind Power Generation by Stochastic Differential Equation Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(3), pages 189-205, April.
    5. Verdejo, Humberto & Awerkin, Almendra & Saavedra, Eugenio & Kliemann, Wolfgang & Vargas, Luis, 2016. "Stochastic modeling to represent wind power generation and demand in electric power system based on real data," Applied Energy, Elsevier, vol. 173(C), pages 283-295.
    6. Zárate-Miñano, Rafael & Anghel, Marian & Milano, Federico, 2013. "Continuous wind speed models based on stochastic differential equations," Applied Energy, Elsevier, vol. 104(C), pages 42-49.
    7. Avinash K. Dixit & Robert S. Pindyck, 1994. "Investment under Uncertainty," Economics Books, Princeton University Press, edition 1, number 5474.
    8. Sinden, Graham, 2007. "Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demand," Energy Policy, Elsevier, vol. 35(1), pages 112-127, January.
    9. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    10. Li, Yang & Vilathgamuwa, Mahinda & Choi, San Shing & Xiong, Binyu & Tang, Jinrui & Su, Yixin & Wang, Yu, 2020. "Design of minimum cost degradation-conscious lithium-ion battery energy storage system to achieve renewable power dispatchability," Applied Energy, Elsevier, vol. 260(C).
    11. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
    12. Song, Ziyou & Feng, Shuo & Zhang, Lei & Hu, Zunyan & Hu, Xiaosong & Yao, Rui, 2019. "Economy analysis of second-life battery in wind power systems considering battery degradation in dynamic processes: Real case scenarios," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    13. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
    14. Díaz-González, Francisco & Sumper, Andreas & Gomis-Bellmunt, Oriol & Villafáfila-Robles, Roberto, 2012. "A review of energy storage technologies for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2154-2171.
    15. Daniel R. Jiang & Warren B. Powell, 2015. "Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage Using Approximate Dynamic Programming," INFORMS Journal on Computing, INFORMS, vol. 27(3), pages 525-543, August.
    16. Roy, Anindita & Kedare, Shireesh B. & Bandyopadhyay, Santanu, 2009. "Application of design space methodology for optimum sizing of wind-battery systems," Applied Energy, Elsevier, vol. 86(12), pages 2690-2703, December.
    17. Moreno, Rodrigo & Moreira, Roberto & Strbac, Goran, 2015. "A MILP model for optimising multi-service portfolios of distributed energy storage," Applied Energy, Elsevier, vol. 137(C), pages 554-566.
    18. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Optimal operation value of combined wind power and energy storage in multi-stage electricity markets," Applied Energy, Elsevier, vol. 235(C), pages 1153-1168.
    19. Malte Jansen & Iain Staffell & Lena Kitzing & Sylvain Quoilin & Edwin Wiggelinkhuizen & Bernard Bulder & Iegor Riepin & Felix Müsgens, 2020. "Offshore wind competitiveness in mature markets without subsidy," Nature Energy, Nature, vol. 5(8), pages 614-622, August.
    20. Früh, Wolf-Gerrit, 2013. "Long-term wind resource and uncertainty estimation using wind records from Scotland as example," Renewable Energy, Elsevier, vol. 50(C), pages 1014-1026.
    21. Guo, Li & Hou, Ruosong & Liu, Yixin & Wang, Chengshan & Lu, Hai, 2020. "A novel typical day selection method for the robust planning of stand-alone wind-photovoltaic-diesel-battery microgrid," Applied Energy, Elsevier, vol. 263(C).
    22. Kitapbayev, Yerkin & Moriarty, John & Mancarella, Pierluigi, 2015. "Stochastic control and real options valuation of thermal storage-enabled demand response from flexible district energy systems," Applied Energy, Elsevier, vol. 137(C), pages 823-831.
    23. Loukatou, Angeliki & Howell, Sydney & Johnson, Paul & Duck, Peter, 2018. "Stochastic wind speed modelling for estimation of expected wind power output," Applied Energy, Elsevier, vol. 228(C), pages 1328-1340.
    24. Engels, Jonas & Claessens, Bert & Deconinck, Geert, 2019. "Techno-economic analysis and optimal control of battery storage for frequency control services, applied to the German market," Applied Energy, Elsevier, vol. 242(C), pages 1036-1049.
    25. Khaloie, Hooman & Abdollahi, Amir & Shafie-khah, Miadreza & Anvari-Moghaddam, Amjad & Nojavan, Sayyad & Siano, Pierluigi & Catalão, João P.S., 2020. "Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model," Applied Energy, Elsevier, vol. 259(C).
    26. Matt Thompson & Matt Davison & Henning Rasmussen, 2004. "Valuation and Optimal Operation of Electric Power Plants in Competitive Markets," Operations Research, INFORMS, vol. 52(4), pages 546-562, August.
    27. Crespo-Vazquez, Jose L. & Carrillo, C. & Diaz-Dorado, E. & Martinez-Lorenzo, Jose A. & Noor-E-Alam, Md, 2018. "Evaluation of a data driven stochastic approach to optimize the participation of a wind and storage power plant in day-ahead and reserve markets," Energy, Elsevier, vol. 156(C), pages 278-291.
    28. Minh Y Nguyen & Dinh Hung Nguyen & Yong Tae Yoon, 2012. "A New Battery Energy Storage Charging/Discharging Scheme for Wind Power Producers in Real-Time Markets," Energies, MDPI, vol. 5(12), pages 1-14, December.
    29. Krieger, Elena M. & Cannarella, John & Arnold, Craig B., 2013. "A comparison of lead-acid and lithium-based battery behavior and capacity fade in off-grid renewable charging applications," Energy, Elsevier, vol. 60(C), pages 492-500.
    30. Iversen, Emil B. & Morales, Juan M. & Møller, Jan K. & Madsen, Henrik, 2016. "Short-term probabilistic forecasting of wind speed using stochastic differential equations," International Journal of Forecasting, Elsevier, vol. 32(3), pages 981-990.
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