A multi-objective scheduling method for wind and solar energy storage and distribution network facing novel power system

With the proposal of dual carbon goals and novel power system, scheduling methods including distributed power generation and energy storage distribution network have become a research hotspot. The current multi-objective scheduling method for wind and solar energy storage and distribution network cannot balance economic, stability, and environmental friendliness, this paper establishes a multi-objective optimization scheduling model for peak shaving and valley filling in distribution network with wind and solar energy storage by minimizing the variance of peak shaving and valley filling, comprehensive economic costs, and pollution control costs within the region, the model is solved by improving the multi-objective quantum particle swarm optimization algorithm. The superiority of the proposed method is verified through case analysis. The results show that compared with conventional methods, the proposed method has better load peak shaving and valley filling variance, comprehensive cost, and pollution control cost, and higher computational efficiency, which can provide certain assistance for the construction of novel power system.