A novel hybrid multi operator evolutionary algorithm for dynamic distributed generation optimization and optimal feeder reconfiguration.

This study addresses the integration of distributed generations (DG) and network reconfiguration in distribution networks, that has not been thoroughly investigated in prior research. The importance of technical objectives, such as power loss, voltage deviation, and voltage stability index, is emphasized in improving distribution network planning and operation. The study investigates the impact of changing sun irradiation and load demand on the IEEE 33 and 69-bus test systems. The issue at hand pertains to a mixed integer non-linear configuration, and four distinct research cases have been constructed in order to address and resolve it. Traditional evolutionary algorithms (EAs) are effective for such problems, but the study notes that using a single operator can limit performance. Hence, an innovative approach combines genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) to tackle multiperiod large-scale DG and network reconfiguration issues. Dealing with infeasible solutions during optimization poses a challenge, so penalty functions are often used in the literature. The penalty function can be limited by the selection of the penalty parameter, however; a large value of this parameter slows down the process, but a smaller value is stuck in infeasible space. Therefore, in the proposed hybrid method representative constraint handling techniques are incorporated to make a trade-off between exploration and exploitation. The simulation results illustrate the capability of the suggested strategy to converge towards the global optimal solution. Furthermore, taking into account the voltage stability index greatly improves the loading capacity as compared to the base situation. The hybrid multi-operator EA suggested in this study demonstrates a nearly global optimal solution for large-scale mixed integer non-linear problems, as evidenced by the comparison of simulation results with existing EAs. Moreover, the results demonstrate a substantial decrease in power loss by over 86%, a significant improvement in voltage deviation by more than 90%, and an increase in load capacity by over 700% through the effective integration of DGs with the voltage stability index as the objective function.