An Evolutionary Game Theory-Based Optimal Scheduling Strategy for Multiagent Distribution Network Operation Considering Voltage Management

Distribution system operators (DSOs) have difficulty in scheduling distributed energy resources owing to the increasing power demand and penetration of renewable energy. The goal of this study is to determine the charging/discharging of PV energy-integrated energy storage system (PV-ESS), EV charging price, and demand response (DR) incentive values considering voltage management. To achieve the optimal energy operation for a distribution network, this study proposes an evolutionary game theory (EGT)-based new scheduling strategy, considering voltage management for a multi-agent system (MAS). The EGT, which is a decision-making strategy, is used by agents to cooperate and derive the best scheduling with their own behavior pattern functions to minimize the system operating cost. Photovoltaic-energy storage systems, electric vehicles charging power, and loads can perform charging/discharging scheduling, electric vehicle charging planning, and demand response participation, respectively. Under DSO supervision, a reward that stabilizes the voltage profile of the power distribution system is also implemented during the cooperation process. The proposed energy scheduling strategy combines an EGT-based decision-making with particle swarm optimization (PSO) to solve the optimization problem and determine the payoff function through self-evolutionary improvement. The effectiveness of the EGT-PSO has been analyzed for an IEEE 33-bus distribution system, and the results demonstrate that the proposed scheduling strategy not only achieves the most economical decision among agents but also manages the voltage profile.