Xiping Ma ¹ Yaxin Li ¹ Xiaoyang Dong ¹ Rui Xu ^1* Kai Wei ¹ Juanjuan Cai ² Juan Wei ²

• ¹ State Grid Gansu Electric Power Company, Lanzhou, China
• ² Lanzhou University of Technology, Lanzhou, Gansu Province, China

In response to the random characteristics of Distributed Generator (DG), its integration into the distribution network alters the topology structure and power flow distribution, subsequently causing changes in network loss. Moreover, existing distribution network optimization methods face issues such as high computational complexity, low efficiency and susceptibility to local optima. This paper proposes a scenario generation method using Generative Adversarial Network (GAN) to handle the uncertainty associated with DG and constructs a two-layer optimization model for the distribution network. The upper layer model determines the installation location and capacity of distributed power and energy storage systems with the lowest system economic cost. The lower layer model establishes an optimization model including wind, solar and storage with active power network loss and voltage deviation as objective functions. Both layers are solved using the Improved Whale Optimization Algorithm (IWOA). Then, the IEEE-33 node distribution system was taken as a simulation example to verify the effectiveness and superiority of the proposed model and algorithm.