Optimal microgrid configuration accounting for electric vehicle demand response: an improved snow goose optimization algorithm

A min-max-min two-stage robust optimization model is established to minimize the average daily total cost and configure the integrated photovoltaic (PV) storage and charging system for microgrid and the source and the load. Many studies are neglecting the potential of electric vehicles (EVs) to enhance the stability of microgrid systems by serving as dispatchable reserve power sources. The proposed model realizes multi-scenario application such as smoothing the fluctuation of PV output and participating in net load peak shaving. A hybrid supercapacitor-lithium-ion battery storage system is on the power side. This study considers the demand response mechanism guided by time-of-day tariffs to promote the balancing of the EVs’ supply and demand. The improved snow goose optimization algorithm is used to the proposed model characteristics. The result shows that reasonably optimize the configuration of microgrid optical storage and charging capacity to guide the construction of microgrid green energy system.