Optimization of Orderly-Charging Strategy of Multi-Zone Electric Vehicle Based on Reinforcement Learning

The disorderly charging of a large number of electric vehicles (EVs) intensifies the operational pressure on the distribution network and negatively impacts the users’ charging experience. This paper proposes an orderly-charging optimization strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm. First, a comprehensive EV charging behavior model is developed, incorporating regional functional characteristics, vehicle categories, and user behavioral diversity to more accurately reflect real-world charging patterns. Second, a closed-loop control architecture is designed, integrating charging load forecasting, dynamic energy storage regulation, and real-time power allocation. Finally, the DDPG algorithm is applied to enable intelligent dynamic power allocation, which effectively flattens peak–valley load disparities and minimizes user charging costs. The simulation results demonstrate that the proposed strategy significantly enhances distribution network performance and user satisfaction. Specifically, the strategy reduces peak load by 17.08% and achieves a total cost saving of USD 511.49 (17.08%). By considering real-world zones and diverse EV types, this strategy provides substantial engineering value for practical implementation in multi-zone charging systems.