Advancing sustainable electricity markets: evolutionary game theory as a framework for complex systems optimization and adaptive policy design
Lefeng Cheng, Runbao Sun, Kun Wang, Feng Yu, Pengrong Huang, Mengya Zhang
Abstract
Evolutionary Game Theory (EGT) provides a robust mathematical framework for addressing the complex, dynamic interactions in modern electricity markets, where evolving regulations and the integration of renewable energy sources present new challenges. This review synthesizes the application of EGT on both the supply and demand sides, presenting a comprehensive analysis of how EGT can support strategic optimization and enhance demand–supply equilibrium. On the supply side, EGT offers insights into the competitive strategies of power generation companies, helping them navigate price fluctuations, regulatory shifts, and environmental policies. By modeling interactions across various power generation technologies, EGT enables companies to optimize strategies that not only maximize individual profitability but also contribute to overall market stability. For demand-side applications, EGT analyzes consumer behavior in response to demand response (DR) programs, price signals, and incentive structures, with advanced tools such as smart meters and energy management systems enabling dynamic adjustments in consumption patterns. Through predictive modeling, EGT identifies stable consumption strategies that can enhance grid reliability and efficiency over time. This paper highlights EGT’s potential to serve as a foundational framework for policymakers and power companies, facilitating the design of market policies that promote resource allocation efficiency, carbon reduction, and competitive balance. By integrating EGT with practical market scenarios, this review underscores its value in advancing strategic planning and policy formulation for sustainable and resilient electricity markets.