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

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