Optimal Clustering of Distributed Power Resources, Considering Communication Time Delay and Smart Grid Constraints

The implementation of clustering techniques in electric power networks yields improved reliability, self-healing capabilities, and controllability. This paper presents a clustering technique for a microgrid equipped with distributed power resources, aiming to minimize power imbalances and communication time delays within each cluster. An automatic clustering algorithm is employed to partition the microgrid into multiple clusters, and the Artificial Gorilla Troops Optimization (GTO) algorithm is utilized to determine the optimal number of clusters. The automatic clustering algorithm utilizes predefined objectives, namely power balance and communication time delays, to identify the most favorable clustering configuration for the microgrid. Furthermore, to achieve better convergence, the forward-backward power flow method is applied to assess power balance within each zone. Simulation results are presented both with and without the proposed algorithm, using the Bornholm microgrid in Denmark as a case study. The findings demonstrate a significant improvement in communication time delays when the proposed algorithm is employed. Additionally, clustering the microgrid yields several additional benefits, including enhanced reliability, self-healing capabilities, and fault clearance.