Game-theoretic evolution in renewable energy systems: Advancing sustainable energy management and decision optimization in decentralized power markets

Abstract

As power systems become increasingly decentralized and integrate higher shares of renewable energy, the complexity and uncertainty in electricity markets grow exponentially. Addressing these challenges requires innovative tools to optimize decision-making and manage distributed energy resources effectively. This paper provides an in-depth analysis of the applications of game theory and evolutionary game theory in modern power systems and electricity markets. By exploring generation planning, bidding strategies, demand response, and energy management, the study highlights the broad applicability of game-theoretic models, including Stackelberg games and Bayesian models, in optimizing decision-making processes. The core contribution lies in demonstrating the unique advantages of evolutionary game theory, particularly evolutionarily stable strategy and replicator dynamics, for managing the complex dynamics and uncertainties in distributed energy management and microgrids. These models offer critical insights into strategy evolution in dynamic and decentralized energy environments, addressing the challenges posed by the increasing integration of renewable energy. The findings underscore the potential of game theory to revolutionize energy systems, with implications for future research in power system intelligence and dynamic decision-making. This work provides a valuable framework for advancing sustainable energy management and inspires new directions in tackling uncertainty and optimization in electricity markets.