Enhanced wombat optimization algorithm for multi-objective optimal power flow in renewable energy and electric vehicle integrated systems

In this study, the authors propose the Enhanced Wombat Optimization Algorithm (EWOA) as a solution for the optimal power flow (OPF) issue that occurs in transmission networks. With the incorporation of different types of uncertainties like wind energy, solar photovoltaic (PV) systems, and plug-in electric vehicles (PEVs), the conventional OPF was made to undergo transformation as a stochastic OPF. In order to enhance the method's diversity, a Levy flight mechanism was integrated into the algorithm. For this study, the OPF problem was developed as a Multi-Objective Optimization (MOO) problem with the following objectives such as active power loss, emissions and generation cost. Then, the authors deployed the Monte Carlo simulations to determine the generation costs incurred upon wind energy, solar PV, and PEV sources. This was done so to reduce the overall costs and also overcome the system issues like feasibility and affordability. Further, the authors also used Weibull, lognormal and normal probability distribution functions (PDFs) for characterizing the uncertainties faced in solar PV, wind energy and PEV sources. In various scenarios, the proposed method was validated for its efficacy on IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems. This was done so to demonstrate its capability and address the complexities involved in OPF problem under different conditions. The key advancement of the proposed EWOA is that it integrates the Levy flight mechanism and chaotic sine map, which in turn dramatically boost its optimization capabilities. These mechanisms further contribute to optimal outcomes in terms of less active power loss and low operation costs and emissions. To be specific, the proposed EWOA attained the finest outcomes in terms of generation cost ($731.41/h) and 0.1989 ton/h for emissions in the altered IEEE 30-bus system, $35,642.53/h for cost and 0.8683 ton/h for emissions in the altered IEEE 57-bus system, and $127,753.82/h for cost and 33.2763 MW for real power loss in the altered IEEE 118-bus system. In line with the outcomes, the EWOA presented in this study exhibits strong convergence characteristics and effectively explores the Pareto front. In summary, the EWOA method surpasses the standard WOA outcomes by providing superior exploration capabilities, rapid convergence, robust constraint management, and low sensitivity to variations in the parameters. These advantages make EWOA an effective solution for tackling optimal power flow and other such complex multi-objective optimization challenges.