Dynamic reconfiguration strategy for islanded distribution networks based on improved gray wolf algorithm and considering network loss minimization

To address the problems of high network losses during the operation of islanded distribution networks with a high penetration of distributed power sources and the slow convergence and poor stability of existing algorithms used for network reconfiguration, a dynamic reconfiguration strategy for island distribution networks based on an improved grey wolf optimization (GWO) algorithm is proposed, with the primary objective of minimizing network losses.

A dynamic reconfiguration model for islanded distribution networks is established, with the minimization of active network losses and voltage deviation as the optimization objectives. To enhance the global search capability and convergence efficiency of the GWO algorithm, several strategies are introduced, including probabilistic perturbation, a dynamic tabu list, and adaptive parameter adjustment.

The results of the dynamic reconfiguration modelling and simulation of an islanded distribution network with a high penetration of distributed power sources show that the improved GWO algorithm achieves better stability, higher accuracy and greater computational efficiency compared to other algorithms, including the original GWO algorithm and the improved particle swarm optimization (PSO) algorithm. Under static reconfiguration conditions, the active power loss in the islanded distribution network is reduced by 21.8%, and the minimum bus voltage is increased by 2.03%. Under dynamic reconfiguration strategy, the active power loss is reduced by 27.98% over a 24-hour period. Furthermore, under extreme weather and line fault scenarios, the reconfiguration strategy continues to ensure stable network operation, with intra-day power losses reduced by 22.16% and 26.30%, respectively.

The results show that the improved GWO algorithm provides a novel theoretical framework and optimization approach for the dynamic reconfiguration of islanded distribution networks.