Bi-level four-stage optimization scheduling for Active Distribution Networks with Electric Vehicle integration using multi-mode dynamic pricing

This study presents a novel bi-level, four-stage optimization framework to address the operational challenges of renewable energy variability and uncoordinated Electric Vehicle (EV) charging in Active Distribution Networks (ADNs). By introducing a dynamic pricing mechanism, the model structures interactions between Active Distribution Network Operators (ADNOs) and Electric Vehicle Aggregators (EVAs) to foster efficient ADN management. This framework employs a multi-mode pricing strategy, categorizing EV charging into scheduled, real-time, and emergency modes to optimize network efficiency based on metrics like network losses, load distribution, and renewable energy integration. To accommodate renewable generation uncertainties, a max-max-min-min Stochastic Robust Optimization (SRO) model is embedded within the bi-level structure, enhancing both economic performance and robustness. The proposed method achieves significant improvements: reducing ADN load fluctuations by 18 %, renewable energy curtailment costs by 30.02 %, and network loss costs by 10.84 %. Solved with a bisection method and the Column-and-Constraint Generation-Alternating Iteration Strategy (C&CG-AIS), the model ensures computational efficiency, with a total cost of 3502.28 CNY and a 92.29 % reduction in iteration time. Validation on a modified IEEE 33-bus system confirms the proposed framework's effectiveness in improving network reliability and renewable resource utilization compared to conventional approaches.