A Dual-Level Optimization Scheduling Method for Smart Distribution Networks with Multiple Flexible Resource Interactions Based on Multi-Strategy Enhanced Particle Swarm Optimization Algorithm

The rapid development of distributed renewable energy systems is swiftly transforming the structure of distribution networks. An essential issue in smart distribution networks is how to achieve interactive optimization among various entities with different forms. Against this backdrop, this paper investigates the regulation system and optimization scheduling algorithm for smart distribution networks with a high proportion of distributed generation. Firstly, it analyses the scheduling characteristics of various adjustable resources within smart distribution networks and examines the output characteristics of distributed energy sources, establishing an interactive model of “source-load-storage” that considers multiple adjustable resources. Secondly, it incorporates the topology of the distribution network into the day-ahead scheduling system, establishing a bi-level optimization scheduling model that considers dynamic network reconfiguration. The upper-level model is a dynamic reconfiguration model of the distribution network, aiming to maximize both the economic efficiency of scheduling and dynamic reconfiguration. The lower-level model is a day-ahead economic dispatch model for the distribution network, considering “source-load” coordination, with the goal of maximizing day-ahead scheduling economic efficiency. Subsequently, a multi-objective particle swarm optimization algorithm based on the three black holes capture strategy is used to solve this model. Finally, the IEEE 33-bus distribution system is used as a case study. The simulation results demonstrate that the bi-level optimization model can significantly enhance the economic efficiency and reliability of distribution network operations.