Multi-Microgrid Optimization With Electric Vehicle Mobile Energy Storage Considering Travel Characteristics

Abstract

To address the economic challenges posed by the integration of a large number of electric vehicles (EVs) into microgrids, while leveraging their mobile energy storage (MES) capabilities and accounting for the impact of EV users’ travel patterns on charging and discharging behaviors, a microgrid scheduling model is proposed that incorporates the MES characteristics of EVs under user travel habits. Firstly, based on the spatial and temporal characteristics of the EV travel chain, the upper and lower bounds of the state of charge (SOC) that EVs must maintain at specific moments during their driving process are determined. Secondly, a mathematical model of a microgrid operation incorporating EV mobile storage batteries, wind power, photovoltaic systems, stationary batteries, and micro-gas turbines is developed. This model considers the costs of electricity purchase and sale, wind and solar curtailment, and natural gas consumption, with the objective of minimizing the total operating cost. To validate the effectiveness of the proposed approach, the optimal scheduling model is implemented and solved using YALMIP and GUROBI. Simulation results demonstrate that the proposed model significantly reduces the total operating cost of the microgrid compared to traditional methods. It also improves the profitability of EV users to a certain extent, promoting new energy consumption when new energy resources are abundant.