A Validation of Multi-Period Dynamic Optimization Algorithm to Reconfigure Distributed Generations Along with Optimal Feeders Reconfiguration
Ghulam Abbas, Shu Zheng, Zhi Shen Wu · 2023
To enhance distribution network planning and operation, technical objectives including power loss, voltage deviation, and voltage stability index necessitates massive importance. The main goal of this research is to optimize the placement of dispatchable and non-dispatchable PV-type DGs along with network reconfiguration. Time-varying effects of solar irradiation and load demand on the IEEE 33-bus test system and six constrained mathematical benchmark functions are examined in the paper. The problem relates to a mixed integer non-linear configuration, and to solve it three distinct research cases are constructed. In order to solve such problems, the conventional evolutionary algorithms (EAs) are effective, but usage of a single operator limits the performance capability. Henceforth, genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) are combined via innovative approach to solve the problem of large-scale DG and network reconfiguration. To avoid getting algorithm trapped into an infeasible solution space and slower convergence, in this paper two representative parameter less constraint techniques are integrated with the proposed method to handle infeasible solutions and convergence. The simulation results validate that the proposed algorithm converges near the global optimal solution. Moreover, introduction of voltage stability index (VSI) significantly enhances the loading capacity of as compared to the base case. The proposed hybrid multi-operator EA remarkably reduces losses over 86% through optimal DG integration and network reconfiguration.