Learning in Potential Games for Electric Power Grids: Models, Dynamics, and Outlook

Abstract

Noncooperative game-theoretic methods have been widely utilized in system-level engineering applications as they are capable of aggregating interests, information, and behaviors of many independent, self-interested entities with conflicting objectives via payoffs. Another major advantage is the existence of adaptive learning dynamics that involve realistic, constrained decision making, and converge to Nash equilibrium. If players are not fully rational (e.g., constrained or regulated), such convergence is generally not guaranteed except in some special games, such as potential games (PGs). Within each PG, there exists a potential function that connects the global objective with players’ payoffs and actions such as optimizers of the global objective align with Nash equilibrium. Therefore, PGs have been widely utilized in many engineering domains and recently to power grids. This article summarizes the state-of-the-art existing literature on applying various PG models in power grids and provides an overlook of potential models and algorithms that have not received sufficient attention but possess unique capabilities to solve existing challenges in power grids.