Application of Multi-objective Differential Evolution Algorithm in Electric Vehicle Charging and Discharging Coordination Strategy
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Abstract
Traditional single-objective strategies for coordinating electric vehicle (EV) charging and discharging are often unable to balance grid stability, user costs, and battery degradation simultaneously, limiting their applicability in intelligent energy management systems associated with modern electromagnetic power infrastructures. This challenge is particularly significant for industrial microgrids, such as those serving manufacturing facilities, where EV fleet integration must preserve the reliable power quality required by sensitive electrical equipment and electromagnetic energy systems. To address these issues, this study proposes an improved Multi-Objective Differential Evolution (MODE) algorithm that explicitly optimizes multiple conflicting objectives in parallel. The conventional differential evolution framework is enhanced through non-dominated sorting and crowding distance mechanisms to improve solution diversity and convergence. A coordinated EV scheduling model incorporating four optimization objectives, including user satisfaction, together with constraints on bus voltage, charging/discharging power, feeder thermal limits, and battery state of charge, is established. A two-dimensional matrix encoding strategy and an external Pareto archive are adopted to enhance optimization stability. Experimental results demonstrate that the proposed MODE approach achieves a grid load standard deviation of 6.95 and a peak-to-valley ratio of 1.63 while maintaining an average battery depth of discharge of 15% and a user satisfaction level of 0.92 for commuting scenarios. These findings verify the effectiveness of MODE for multi-objective coordinated scheduling and provide a practical optimization framework for sustainable EV energy management and industrial microgrids requiring stable electromagnetic power delivery.
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