To contribute to the realization of the goal of carbon peak and carbon neutrality, the non-polluting and sustainable nature of new energy sources such as wind, photovoltaic power, and energy storage has gained widespread attention, and new-energy distributed power generation technology is being applied on a large scale. Due to the high penetration, decentralization, and source–load uncertainty in new-energy distributed power generation, the traditional centralized regulation and control method struggles to meet the demand for scheduling flexibility in a distribution network. Hence, a cluster-optimization scheduling method for distribution networks considering source–load–storage synergy is proposed in this paper. Firstly, by using the comprehensive index of cluster-active power balance and electrical-distance modularity as the objective function, a simulated annealing algorithm is proposed to improve the genetic algorithm for solving a distribution network cluster division model. Then, based on the results of the distributed cluster segmentation, an optimal scheduling model is established, with the objective of minimizing the comprehensive operating costs by considering clusters as units. Inter-cluster power interactions are then used to reduce cluster operating costs and to meet intra-cluster power balance requirements by automatically setting time-sharing tariffs between the clusters. Finally, an IEEE33 node system is taken as an example for verification. The results show that the proposed distribution network cluster division method has better electrical coupling and active power balance and that the optimal scheduling method of clusters can effectively reduce the system operation costs. Hence, the method studied in this paper can increase the flexibility of regional distribution grid scheduling and the reliability of the power supply, reduce regional energy mobility to reduce energy consumption, improve the utilization efficiency of energy, and promote the sustainable development of new energy access to the distribution network.
