Coordinated optimal bidding strategies methods of aggregated microgrids: A game theory-based demand side management under an electricity market environment

Due to substantial demand of expansion and application of distributed generations and energy storage systems (ESSs), the operation of microgrids (MGs) has become more flexible but more complicated with the role of active network loads. To deploying these potentials of MGs, in this paper, a novel demand side management (DSM) method based on the game theory with an effective payoff function is developed in a scrutinized manner. In order to be more compatible with reality, the interactions between some MGs are optimized in an electricity market environment using an evolutionary method. To implement this model, in the first step, each microgrid operator (MGO) tries to maximize its benefit considering the payoff function in an iterative process deploying the proposed game theory method. In this step, a new strategy is reconciled to DSM and its payoff function to reach the Nash equilibrium, effectively. The aggregated load demand of MGs will be participated in market mechanism by MGO through a Gray Wolf Algorithm (GWA) to maximize the MGO's profit. The results confirm the compatibility between DSM and game theory to find the Nash equilibrium point. Furthermore, the price-maker strategy makes more profit compared to price-taker one form the MGO point of view. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper presents a novel demand side management method based on game theory and an effective payoff function for optimizing the operation of microgrids and maximizing the profit of microgrid operators. By optimizing the interactions between different microgrids and using the Gray Wolf Algorithm to participate in market mechanism, the Nash equilibrium is achieved and the profit for microgrid operators is increased.