A multi-timescale energy scheduling model for microgrid embedded with differentiated electric vehicle charging management strategies

Abstract

This paper presents a novelty energy management model embedded with differentiated charging management strategies for optimal scheduling of the microgrid (MG), including electric vehicles (EVs) and renewable energy sources (RES) in similar official buildings and industrial parks. The study firstly synthetically considers the objectives of multiple stakeholder entities in the MG and various uncertainties and employs a multi-timescale energy schedule model to satisfy each entity’s different objective and optimize the power procurement strategy. In the day-ahead stage, an enhanced variant multi-objective particle swarm optimization algorithm (EVMOPSO) is used to solve the multi-objective equilibrium optimization model, while in the real-time stage, a rolling correction model based on model predictive control (MPC) is introduced. Secondly, considering the significant diversity of driving behaviors of the EV users unique to the MG above, this study presents a set of differentiated charging management strategies for the first time in the multi-timescale energy management model. These strategies are based on credibility, which assigns different credibility levels to users by evaluating the degree of regularity of their driving behaviors to assign corresponding charging and price strategies. Simulation results verify the method’s practicality and validity, and the model’s robustness is also verified by sensitivity analysis.