Game theory and AI for strategic energy management

In an increasingly complex and interconnected energy system, decisions made by one party can significantly impact the outcomes for others. Game theory (GT) helps to model these interactions, enabling stakeholders to anticipate the actions of others, identify optimal strategies, and achieve desired outcomes. GT provides a mathematical framework for analyzing and optimizing the interactions between these stakeholders, such as energy producers, consumers, grid operators, and regulators. The integration of GT into energy networks has been driven by the advancement of smart grid technology, which includes a communication layer that facilitates the active management of grid assets and distributed energy resources (DERs) such as renewable energy sources (RESs), energy storage systems (ESSs), and electric vehicles (EVs). In this chapter, we present the basic principles of GT and investigate its applications to energy management.
This chapter examines the potential of integrating GT and AI in the context of energy management. The focus is on understanding how the strategic decision-making frameworks of GT, combined with the capabilities of AI, can enhance the efficiency and effectiveness of energy systems. An analysis of relevant applications from the literature is presented to provide insights into the current state of research and practice at this intersection.
Section 7.1 presents the principles of GT and explains the motivation behind its adoption for energy management. In Section 7.2, we analyze the areas of the smart grid where GT has been applied, introduce the concept of smart grid communities (SGCs), and investigate their characteristics. The game-theoretic approaches to energy management are investigated in Section 7.3. Section 7.4 explores the potential of combining AI and GT for energy management and presents a review of recent literature. Finally, conclusions and future research opportunities are discussed in Section 7.5.