Research on Combination of Distributed Generation Placement and Dynamic Distribution Network Reconfiguration Based on MIBWOA

Abstract

This paper aims to address the combination of distributed generation placement and dynamic distribution network reconfiguration. Herein, a multi-strategy multi-objective improved black widow algorithm is proposed. A model is established, which considers the objectives of minimizing active power loss, voltage deviation, and carbon emission. The proposed algorithm significantly enhances the traversal capability and search speed by employing Cubic–Tent chaotic mapping, involving a novel formula with the fusion of optimal genes, and employing an adaptive mutation of Wald mutation and elite reverse learning mixing. The DeepSCN is employed to forecast the distributed generation (DG) output power and distribution network load. Through various test functions, the capability of the proposed algorithm is demonstrated. Whether single-objective or multi-objective, the algorithm has excellent performance. To showcase the practicality and effectiveness of the model and approach, a simulation experiment was performed on the IEEE-33 node configuration. The solution set provided by MIBWOA can reduce active network loss to improve operating efficiency, increase voltage offset to make operation more stable, and reduce carbon emissions to make operation more environmentally friendly. The proposed algorithm shows excellent performance in distributed generation placement and distribution network reconfiguration compared with the comparison algorithms. The results show that the solution proposed by MIBWOA can enhance the real-time operational parameters of the distribution network with considerable efficiency.