Improved Skill Optimization Algorithm Based Optimal Power Flow Considering Open-Access Trading of Wind Farms and Electric Vehicle Fleets

The paradigm shift towards sustainable energy sources has propelled research into enhancing the efficiency and reliability of power systems. This study introduces an advanced methodology for optimal power flow (OPF) by integrating the skill optimization algorithm (SOA) with considerations for open-access trading of wind farms and the integration of electric vehicle (EV) fleets. In order to improve exploration features of SOA, opposition-based learning (OBL) is used for diversifying the population. The proposed ISOA-based OPF model addresses the optimization of power flow in a multi-objective framework, aiming to minimize generation cost and minimize transmission loss while accommodating the open access trading between wind farms and EV fleet demands. The incorporation of open-access trading enables the effective utilization of surplus wind energy among interconnected power systems, fostering grid resilience and sustainable benefits. Simulation results on standard IEEE 30-bus and 57-bus test systems validate the efficacy of the proposed approach, showcasing improved system performance, reduced cost and power losses, and enhanced utilization of renewable resources in modern power grids. In the IEEE 30-bus, fuel costs are 803. 13/hrstandard and 935. 2408/hr with extra trading. In the IEEE 57-bus, costs are 37589. 34/hrstandard and 37628. 8/hrwith additional trading. These results align with benchmarks, showcasing the method's efficacy in intricate problem-solving.