Maximization of Smoothing Effects for Photovoltaic Generation via Game Theoretic Learning Algorithm

Abstract

In this paper, we focus on smoothing effects for photovoltaic generation and investigate its maximization prob- lem based on game theory. We first confirm the smoothing effects through data analysis of global solar radiation profiles. We next formulate an optimal energy source selection problem and then reduce the problem to so-called resource allocation games. After pointing out that the game is reduced to so-called potential games, we present a real-time implementation method of a payoff-based learning algorithm leading players to the optimal action without prior knowledge on utility functions. Finally, we demonstrate its effectiveness through simulation.