Simultaneous allocation of renewable energy sources and custom power quality devices in electrical distribution networks using artificial rabbits optimization

Abstract

Abstract This study suggests an optimal renewable energy source (RES) allocation and distribution-static synchronous compensator (D-STATCOM) and passive power filters (PPFs) for an electrical distribution network (EDN) to improve its performance and power quality (PQ). First, the latest metaheuristic artificial rabbits optimization (ARO) is used to locate and size solar photovoltaic (PV), wind turbine (WT) and D-STATCOM units. In the second stage, ratings of single-tuned PPFs and D-STATCOMs at the RESs are determined, considering non-linear loads in the network. The multi-objective function reduces power loss, improves the voltage stability index (VSI) and limits total harmonic distortion. Simulations using the IEEE 33-bus EDN compared the ARO results with those of previous studies. In the first scenario, ideally integrated D-STATCOMs, PVs and WTs reduced losses by 34.79%, 64.74% and 94.15%, respectively. VSI increases from 0.6965 to 0.7749, 0.8804 and 0.967. The optimal WT integration of the first scenario outperformed the PVs and D-STATCOMs. The second step optimizes the WTs and PQ devices for non-linear loads. WTs and D-STATCOMs reduce the maximum total harmonic distortion of the voltage waveform by 5.21% with non-linear loads to 3.23%, while WTs and PPFs reduce it to 4.39%. These scenarios demonstrate how WTs and D-STATCOMs can improve network performance and PQ. The computational efficiency of ARO is compared to that of the pathfinder algorithm, future search algorithm, butterfly optimization algorithm and coyote optimization algorithm. ARO speeds up convergence and improves solution quality and comprehension.