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Potential utilization of battery energy storage systems (BESS) in the major European electricity markets

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  • Hu, Yu
  • Armada, Miguel
  • Jesús Sánchez, María

Abstract

Given the declining cost of battery technology in the last decade, nowadays the application of Battery Energy Storage Systems (BESS) becomes a more attractive solution in electrical power systems. The objective of this work is to analyze the potential utilization of BESS in the major European electricity markets. A general payoff model for BESS operation is proposed to correctly address the operational flexibility of battery systems. Utilization factors such as potentially profitable utilization time and rate are calculated for common applications including energy arbitrage and frequency support services using real market information. The result shows that under the current empirical estimation of the battery cost and lifetime, BESS is not feasible for energy arbitrage in most of the European electricity markets. However, BESS shows clearly and significantly higher potential in providing frequency support services. The result suggests that, when the frequency containment reserve is remunerable, BESS has already become potentially profitable in central European countries. For example in 2021, the potentially profitable utilization rate has reached almost 100% for the “Frequency Containment Reserve for Normal operation” (FCR-N) in the Danish market. Meanwhile, in the British Isles and some other islanded local markets, a remarkable level of scarcity of flexibility has been observed. As a result, the potential of BESS would also be encouraging in these markets with a declining battery cost in the future, if the market design would be improved to correctly address the flexibility provided by the battery systems.

Suggested Citation

  • Hu, Yu & Armada, Miguel & Jesús Sánchez, María, 2022. "Potential utilization of battery energy storage systems (BESS) in the major European electricity markets," Applied Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:appene:v:322:y:2022:i:c:s0306261922008340
    DOI: 10.1016/j.apenergy.2022.119512
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    Cited by:

    1. Pablo Carrasco Ortega & Pablo Durán Gómez & Julio César Mérida Sánchez & Fernando Echevarría Camarero & Ángel Á. Pardiñas, 2023. "Battery Energy Storage Systems for the New Electricity Market Landscape: Modeling, State Diagnostics, Management, and Viability—A Review," Energies, MDPI, vol. 16(17), pages 1-51, August.
    2. Md. Shafiul Alam & Tanzi Ahmed Chowdhury & Abhishak Dhar & Fahad Saleh Al-Ismail & M. S. H. Choudhury & Md Shafiullah & Md. Ismail Hossain & Md. Alamgir Hossain & Aasim Ullah & Syed Masiur Rahman, 2023. "Solar and Wind Energy Integrated System Frequency Control: A Critical Review on Recent Developments," Energies, MDPI, vol. 16(2), pages 1-31, January.
    3. Dirk Lauinger & Franc{c}ois Vuille & Daniel Kuhn, 2023. "Frequency Regulation with Storage: On Losses and Profits," Papers 2306.02987, arXiv.org, revised Mar 2024.
    4. Topalović, Zejneba & Haas, Reinhard & Sayer, Marlene, 2024. "Economic benefits of PHS and Li-ion storage. Study cases: Austria and Bosnia and Herzegovina," Applied Energy, Elsevier, vol. 362(C).
    5. Li, Junhui & Zhang, Jingxiang & Mu, Gang & Li, Cuiping & Yan, Gangui & Zhu, Xingxu & Jia, Chen, 2024. "Dynamic partitioning method for independent energy storage zones participating in peak modulation and frequency modulation under the auxiliary service market," Applied Energy, Elsevier, vol. 361(C).
    6. Samuel O. Ezennaya & Julia Kowal, 2024. "Optimizing Energy Arbitrage: Benchmark Models for LFP Battery Dynamic Activation Costs in Reactive Balancing Market," Sustainability, MDPI, vol. 16(9), pages 1-31, April.
    7. Ma, Qianli & Wei, Wei & Mei, Shengwei, 2024. "Health-aware coordinate long-term and short-term operation for BESS in energy and frequency regulation markets," Applied Energy, Elsevier, vol. 356(C).
    8. Sai, Wei & Pan, Zehua & Liu, Siyu & Jiao, Zhenjun & Zhong, Zheng & Miao, Bin & Chan, Siew Hwa, 2023. "Event-driven forecasting of wholesale electricity price and frequency regulation price using machine learning algorithms," Applied Energy, Elsevier, vol. 352(C).

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