A Multi-Objective Dung Beetle Optimization-Based Optimal Scheduling Strategy for Active Distribution Networks with Large-Scale Electric Vehicle Integration

The large-scale integration of electric vehicles (EVs) can increase load fluctuations, operating costs, and security risks in active distribution networks (ADNs). To address these challenges, this study proposes a multi-objective optimal scheduling strategy based on a Multi-Objective Dung Beetle Optimization (MODBO) algorithm. A Monte Carlo simulation is first used to model stochastic EV charging behavior, followed by representative scenario selection using a minimum-distance criterion. A multi-objective scheduling model is then established considering photovoltaic utilization, ADN operating cost, system net-load variance, and voltage deviation. Case studies on a modified IEEE 33-bus system with 200 EVs show that uncoordinated charging increases the maximum load from 5672 kW to 6321 kW and raises the net-load variance to 4.15. With coordinated scheduling, the proposed method reduces the maximum load to 5672 kW and the variance to 1.29, while achieving 96.69% photovoltaic utilization and an operating cost of CNY 17,573. The results demonstrate that the proposed strategy effectively coordinates EV charging with distributed energy resources, mitigates load fluctuations, and improves the operational performance of ADNs.