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A Stackelberg Game-based robust optimization for user-side energy storage configuration and power pricing

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  • Ding, Yixing
  • Xu, Qingshan
  • Hao, Lili
  • Xia, Yuanxing

Abstract

With the rapid development of demand-side management, battery energy storage is considered to be an important way to promote the flexibility of the user-side system. In this paper, a Stackelberg game (SG) based robust optimization for user-side energy storage configuration and basic electricity price decisions is proposed. Firstly, this paper put forward a two-stage energy management framework considering the interactive relationship between the supplier-side system and the user-side system. Secondly, based on the two-part electricity price mechanism, a bi-level optimal sizing of user-side energy storage is established in which robust dispatching is considered to deal with the uncertainty of renewable energy. Thus, a three-layer optimization model of “pricing on the power supply side–basic scenario configuration on the user side–worst-case scenario scheduling on the user side” is formulated. Through relaxing the state variables of energy storage in the configuration and scheduling models and combining Karush-Kuhn-Tucher conditions, the user-side model is transformed into a single-layer problem. A distributed algorithm based on the method of bisection is used to solve the two-stage SG problem. The simulation results demonstrate the basic electricity price and energy storage configuration suggestions and prove the superiority of the proposed method.

Suggested Citation

  • Ding, Yixing & Xu, Qingshan & Hao, Lili & Xia, Yuanxing, 2023. "A Stackelberg Game-based robust optimization for user-side energy storage configuration and power pricing," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223018236
    DOI: 10.1016/j.energy.2023.128429
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    2. Hanwen Wang & Xiang Li & Haojun Hu & Yizhou Zhou, 2024. "Distributed Dispatch and Profit Allocation for Parks Using Co-Operative Game Theory and the Generalized Nash Bargaining Approach," Energies, MDPI, vol. 17(23), pages 1-19, December.
    3. Zhichao Zhang & Xue Li & Yanling Zhao & Zhaogong Zhang & Bin Li, 2025. "Prediction Accuracy of Stackelberg Game Model of Electricity Price in Smart Grid Power Market Environment," Energies, MDPI, vol. 18(3), pages 1-16, January.
    4. Xu, Biao & Luan, Wenpeng & Yang, Jing & Zhao, Bochao & Long, Chao & Ai, Qian & Xiang, Jiani, 2024. "Integrated three-stage decentralized scheduling for virtual power plants: A model-assisted multi-agent reinforcement learning method," Applied Energy, Elsevier, vol. 376(PA).
    5. Wang, Yi & Jin, Zikang & Liang, Jing & Li, Zhongwen & Dinavahi, Venkata & Liang, Jun, 2024. "Low-carbon optimal scheduling of park-integrated energy system based on bidirectional Stackelberg-Nash game theory," Energy, Elsevier, vol. 305(C).
    6. Huang, Shanshan & Ye, Ze & Huang, Yunxiang, 2025. "Capacity tariff mechanism design for grid-side energy storage in China: A Stackelberg game approach," Utilities Policy, Elsevier, vol. 95(C).
    7. Li, Ningning & Gao, Yan, 2023. "Real-time pricing based on convex hull method for smart grid with multiple generating units," Energy, Elsevier, vol. 285(C).
    8. Zhang, Zhi & Chen, Yanbo & Ma, Tianyang & Tian, Haoxin & Liu, Jingyu & Zhou, Ming & Wang, Wei, 2025. "Multi-type energy storage expansion planning: A review for high-penetration renewable energy integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 219(C).
    9. Wang, Haibing & Wang, Fengxia & Han, Dong & Sun, Weiqing, 2024. "Multi-time scale optimal configuration of user-side energy storage considering demand perception," Renewable Energy, Elsevier, vol. 237(PA).

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