Aquila Optimizer Based Optimal Allocation of Soft Open Points for Multi-Objective Operation in Electric Vehicles Integrated Active Distribution Networks

Abstract

The appropriate position and sizing of soft open points (SOPs) for reducing the detrimental impact of electric vehicle (EV) load penetration and renewable energy (RE) variation on active distribution networks (ADNs) are provided in this study. Soft open points (SOPs) have been used to create a multi-objective framework that considers loss minimization and voltage profile enhancement. The non-linear multi-variable complicated SOP allocation problem is solved for the first time using a modern meta-heuristic Aquila optimizer (AO). The modified IEEE 33-bus benchmark and IEEE 69-bus ADNs are used in the simulations. Before SOPs, the average real power loss in IEEE 33-bus AND was 370.329 kW, but after SOPs, it was reduced to 259.356 kW (i.e., 29.96 percent reduction). Similarly, effective SOPs integration in the IEEE 69-bus resulted in a loss reduction of 81.07 percent. AO's computational efficiency is also compared to that of multiobjective particle swarm optimization (MOPSO), particle swarm optimization (PSO), and cuckoo search algorithm (CSA). The AO has produced better results in terms of lower losses, improved voltage profile despite variations in EV load penetration, and RE and load volatility in ADNs, according to the results