Bi-Level Game Strategy for Virtual Power Plants Based on an Improved Reinforcement Learning Algorithm

Zhu Liu, Guowei Guo, Dehuang Gong, Lingfeng Xuan, Feiwu He, Xinglin Wan and Dongguo Zhou ()
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Zhu Liu: China Southern Power Grid Research Technology Co., Ltd., Guangzhou 510663, China
Guowei Guo: Guangdong Electric Power Co., Ltd., Foshan Power Supply Bureau, Foshan 528061, China
Dehuang Gong: Guangdong Electric Power Co., Ltd., Qingyuan Yingde Power Supply Bureau, Yingde 513099, China
Lingfeng Xuan: Guangdong Electric Power Co., Ltd., Qingyuan Yingde Power Supply Bureau, Yingde 513099, China
Feiwu He: Guangdong Electric Power Co., Ltd., Qingyuan Yingde Power Supply Bureau, Yingde 513099, China
Xinglin Wan: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Dongguo Zhou: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Energies, 2025, vol. 18, issue 2, 1-16

Abstract: To address the issue of economic dispatch imbalance in virtual power plant (VPP) systems caused by the influence of operators and distribution networks, this study introduces an optimized economic dispatch method based on bi-level game theory. Firstly, a bi-level game model is formulated, which integrates the operational and environmental expenses of VPPs with the revenues of system operators. To avoid local optima during the search process, an enhanced reinforcement learning algorithm is developed to achieve rapid convergence and obtain the optimal solution. Finally, case analyses illustrate that the proposed method effectively accomplishes multi-objective optimization for various decision-making stakeholders, including VPP and system operators, while significantly reducing curtailment costs associated with the extensive integration of distributed renewable energy. Furthermore, the proposed algorithm achieves fast iteration and yields superior dispatch outcomes under the same modeling conditions.

Keywords: virtual power plant; bi-level game; reinforcement learning; power trading (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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