Multi-strategy reference vector guided evolutionary algorithm and its application in multi-objective optimal scheduling of microgrid systems containing electric vehicles

Abstract

To solve the optimization scheduling problem of microgrids involving Electric Vehicles (EVs), in this paper, the integration of EVs into the microgrid is considered in the microgrid system composed of wind turbine, photovoltaic turbine, micro gas turbine, fuel cell, and storage battery, and the economic dispatch model of the microgrid system containing EVs is established, with the minimization of the operation cost and pollutant treatment cost as the optimization objective. Meanwhile, Chebyshev mapping is introduced into the reference vector guided evolutionary algorithm (RVEA) to initialize the distribution of the population, and then an improved angle-penalized distance (APD) method is used to balance the convergence and diversity of the algorithm. The improved RVEA algorithm is applied to the scheduling model proposed in this paper, Different operational scheduling scenarios are investigated, scheduling results with different optimization objectives are discussed. The results show that the multi-strategy RVEA algorithm proposed in this paper can improve the economical and environmentally friendly protection of the microgrid system, and promote the optimal operation of the microgrid.