Optimizing Virtual Power Plants Cooperation via Evolutionary Game Theory: The Role of Reward–Punishment Mechanisms

Lefeng Cheng, Pengrong Huang, Mengya Zhang, Kun Wang (), Kuozhen Zhang, Tao Zou and Wentian Lu ()
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Lefeng Cheng: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Pengrong Huang: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Mengya Zhang: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Kun Wang: Institute for Human Rights, Guangzhou University, Guangzhou 510006, China
Kuozhen Zhang: Law School, Shantou University, Shantou 515063, China
Tao Zou: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Wentian Lu: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China

Mathematics, 2025, vol. 13, issue 15, 1-86

Abstract: This paper addresses the challenge of fostering cooperation among virtual power plant (VPP) operators in competitive electricity markets, focusing on the application of evolutionary game theory (EGT) and static reward–punishment mechanisms. This investigation resolves four critical questions: the minimum reward–punishment thresholds triggering stable cooperation, the influence of initial market composition on equilibrium selection, the sufficiency of static versus dynamic mechanisms, and the quantitative mapping between regulatory parameters and market outcomes. The study establishes the mathematical conditions under which static reward–punishment mechanisms transform competitive VPP markets into stable cooperative systems, quantifying efficiency improvements of 15–23% and renewable integration gains of 18–31%. Through rigorous evolutionary game-theoretic analysis, we identify critical parameter thresholds that guarantee cooperation emergence, resolving longstanding market coordination failures documented across multiple jurisdictions. Numerical simulations and sensitivity analysis demonstrate that static reward–punishment systems enhance cooperation, optimize resources, and increase renewable energy utilization. Key findings include: (1) Reward–punishment mechanisms effectively promote cooperation and system performance; (2) A critical region exists where cooperation dominates, enhancing market outcomes; and (3) Parameter adjustments significantly impact VPP performance and market behavior. The theoretical contributions of this research address documented market failures observed across operational VPP implementations. Our findings provide quantitative foundations for regulatory frameworks currently under development in seven national energy markets, including the European Union’s proposed Digital Single Market for Energy and Japan’s emerging VPP aggregation standards. The model’s predictions align with successful cooperation rates achieved by established VPP operators, suggesting practical applicability for scaled implementations. Overall, through evolutionary game-theoretic analysis of 156 VPP implementations, we establish precise conditions under which static mechanisms achieve 85%+ cooperation rates. Based on this, future work could explore dynamic adjustments, uncertainty modeling, and technologies like blockchain to further improve VPP resilience.

Keywords: virtual power plants (VPPs); evolutionary game theory (EGT); reward–punishment mechanisms; cooperation optimization; market efficiency; renewable energy integration (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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