Virtual Cluster Partitioning Method of Active Distribution Networks Using Quantum Particle Swarm Optimization and Sector Search

The presence of numerous distributed power sources in distribution grids leads to a diverse array of controlled object points and significant uncertainties, thereby posing a series of challenges to the control and operation of distribution grids. Hence, this study proposes a virtual cluster partitioning model for active distribution networks using a quantum particle swarm optimization (QPSO) algorithm and sector search, aiming to achieve autonomy within clusters and coordination between clusters. First, the article proposes a sector search model that transforms the topological connections of the distribution network into mathematical expressions. This model simplifies the search for node locations and improves the algorithm's convergence speed. Building upon the traditional particle swarm optimization (PSO) algorithm, this study introduces the wave function and Schrödinger equation to enhance algorithm performance. By treating the vectors obtained from sector searches as particles, the proposed QPSO algorithm significantly improves both the search efficiency and global convergence in solving the virtual cluster partitioning model. Finally, case studies conducted on the modified PG&E 69-node system demonstrated the proposed method's significant advantages. The method improved computational efficiency, with a cluster power supply rate over 0.6 and modularity above 0.7, ensuring balanced partitioning. The scalability and effectiveness of the proposed method were validated on an 85-node system, achieving balanced cluster partitioning with high operational efficiency and adaptability.