Dynamic Network Partitioning for a Large-Scale Microgrid Cluster

A large-scale microgrid cluster (MGC) using centralized energy management faces computational and data processing burdens, making it rather hard to obtain real-time optimal energy dispatch strategies. Network partitioning method can reduce such complexity by dividing the MGC into self-adequate partitions. However, existing dynamic network partitioning methods normally update the network partitioning results at fixed time intervals, which limits the ability to adapt to spatiotemporal variations in output of renewable energy sources (RESs), load demand, and grid topology. To address this, a dynamic network partitioning method based on an improved genetic algorithm (IGA) combined with depth-first search (DFS) is proposed. IGA is employed to optimize the partitioning index, and DFS is subsequently applied to obtain the network partitioning result. Furthermore, a dynamic update mechanism is designed to adaptively update the partitioning results according to spatiotemporal variations in system conditions. Simulation results on the modified IEEE 123-bus system show that the proposed method achieves better partitioning quality than the genetic algorithm (GA), spectral clustering, and Louvain algorithms while maintaining computational efficiency.