Optimizing Electricity Markets Through Game-Theoretical Methods: Strategic and Policy Implications for Power Purchasing and Generation Enterprises

This review proposes a novel integration of game-theoretical methods—specifically Evolutionary Game Theory (EGT), Stackelberg games, and Bayesian games—with deep reinforcement learning (DRL) to optimize electricity markets. Our approach uniquely addresses the dynamic interactions among power purchasing and generation enterprises, highlighting both theoretical underpinnings and practical applications. We demonstrate how this integrated framework enhances market resilience, informs evidence-based policy-making, and supports renewable energy expansion. By explicitly connecting our findings to regulatory strategies and real-world market scenarios, we underscore the political implications and applicability of our…

• SA
  Scientific and Technological Planning Project of Guangzhou City

  Awards: 2023A03J0124, 2023A04J1726
• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Award: 2022A1515010699