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Hierarchical game optimization of independent shared energy storage system considering multiple profits and penalty

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  • Wu, Nan
  • Wu, Qiong
  • Ren, Hongbo
  • Jin, Haojun
  • Li, Qifen

Abstract

With the rapid development of renewable energy, independent energy storage systems have garnered increasing attention. However, challenges such as limited revenue streams hinder their widespread adoption. In this study, a joint optimization scheme for multiple profit models of independent energy storage systems is proposed by introducing a storage configuration penalty mechanism for independent power producers with self-configured energy storage. A hierarchical optimization approach is employed, where the upper level optimizes the capacity allocation of independent energy storage systems to minimize construction costs, and the lower level utilizes a Stackelberg game model to maximize the benefits for both the independent shared energy storage operator and independent power producers through capacity leasing pricing. The numerical results demonstrate that the proposed penalty mechanism increases the independent shared energy storage operator's revenue by 35.6 %, while the revenue of independent power producers decreases by less than 4 %. Furthermore, the energy supply-demand balance is improved, significantly increasing the value of electricity sales and promoting the development of local spot markets. Additionally, the impacts of different energy storage technologies, regional factors, and market mechanisms are explored, and corresponding policies are proposed to support the deployment of independent energy storage systems.

Suggested Citation

  • Wu, Nan & Wu, Qiong & Ren, Hongbo & Jin, Haojun & Li, Qifen, 2025. "Hierarchical game optimization of independent shared energy storage system considering multiple profits and penalty," Energy, Elsevier, vol. 321(C).
    • Handle: RePEc:eee:energy:v:321:y:2025:i:c:s0360544225010904
    DOI: 10.1016/j.energy.2025.135448
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    References listed on IDEAS

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    1. Lombardi, P. & Schwabe, F., 2017. "Sharing economy as a new business model for energy storage systems," Applied Energy, Elsevier, vol. 188(C), pages 485-496.
    2. He, Ye & Wu, Hongbin & Wu, Andrew Y. & Li, Peng & Ding, Ming, 2024. "Optimized shared energy storage in a peer-to-peer energy trading market: Two-stage strategic model regards bargaining and evolutionary game theory," Renewable Energy, Elsevier, vol. 224(C).
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    Cited by:

    1. Liu, Jicheng & Song, Yanan & Xue, Xiaojun & Duan, Bingfan & Hadi, Dhaif Allah, 2025. "Value evaluation model study on shared energy storage adapted to the needs of new power system," Energy, Elsevier, vol. 330(C).
    2. Cao, Jinjia & Xu, Xu & Yao, Weitao & Xue, Fei & Long, Chao, 2025. "A distributed dynamic multi-agent reinforcement learning based cooperative game framework for multi-sectoral transaction in electricity markets considering dynamic revenue allocation optimisation and Renewable Energy Certificate trading," Energy, Elsevier, vol. 335(C).
    3. Zhang, Wei & Zhang, Maimai & Liu, Jiang, 2026. "Optimal multi-energy storage capacity allocation for electricity-heat-hydrogen-ammonia integrated energy systems with ammonia cycling," Renewable Energy, Elsevier, vol. 257(C).

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