Multi-Objective Optimization Scheduling of Active Distribution Network Considering Large-Scale Electric Vehicles Based on NSGAII-NDAX Algorithm

With the rapid growth of electric vehicle (EV) ownership, the problem of increased peak loads on distribution networks due to large-scale EV integration needs to be addressed. This study proposes an active distribution network multi-objective optimization scheduling method. It takes into account the charging demands of large-scale EVs and aims to minimize distribution network operating costs, reduce net load variance, and maximize the photovoltaic (PV) consumption rate. First, the Monte Carlo sampling method is used to analyze the charging load demands of a large number of EVs. Next, we construct a multi-objective optimization scheduling model for the active distribution network. This model integrates the charging demands of EVs with the operating constraints of the distribution network. To tackle the multi-objective optimization problem, we propose the NSGAII-NDAX algorithm. Additionally, we employ a fuzzy theory-based method to select the Pareto optimal solution set, addressing the challenge of decision-making complexity posed by the large size and information-rich content of the optimal solution set. Finally, the effectiveness of the proposed method in the comparative analysis of multiple scenarios is verified by an improved IEEE 33-node example. The experimental results show that the proposed model and method can effectively utilize EV charging load optimization to reduce the peak-to-valley difference in the system load while ensuring the system’s economic operation and the maximum PV consumption rate. Compared with the other algorithms, the NSGAII-NDAX algorithm has stronger optimization ability and can better handle multi-objective optimization problems.