Evolutionary Game-Based Battery Scheduling: A Comparative Study for Prosumers in Smart Grids

Integrating prosumers equipped with renewable energy resources (RES) and battery energy storage systems (BESS) in local energy markets (LEMs) offers economic benefits, in addition to significant environmental advantages. In this paper, a comprehensive energy management system (EMS) framework for battery scheduling is proposed to optimize the use of distributed energy resources (DERs) among prosumers in smart grid communities. A novel decentralized evolutionary game theory (EGT) algorithm is introduced to enhance energy management while preserving privacy and scalability. The primary objective is to develop an efficient, reliable, private, and scalable algorithm for battery scheduling that ensures economic efficiency and system stability, even under dynamic market conditions. The results show that, with different feed-in tariffs (FIT), the EGT algorithm reduces the standard deviation of hourly prices by 51% and 57% when having 20% and 80% FIT rates, respectively, in prosumer-based smart grid community (SGC), demonstrating its effectiveness in stabilizing market conditions. A comprehensive comparative analysis is conducted between the proposed EGT algorithm and established methods such as centralized optimization (CO), game theory (GT), and auction-based approaches. The comparison focuses on key performance metrics, including peak-to-average ratio (PAR), price volatility, economic efficiency, and computational performance. The results highlight the computational capabilities of the decentralized EGT algorithm in optimizing battery scheduling and effectively mitigating price fluctuations for SGCs with increasing prosumer participation.