Modified GWO algorithm-based optimal power flow analysis integrated with renewable energy sources and plug-in electric vehicles

Abstract

The increased integration of renewable energy sources (RESs) and plug-in electric vehicles (PEVs) into power systems brings new challenges in optimal power flow (OPF) management. These challenges arise primarily due to the intermittent nature of RESs and the unpredictable charging/discharging patterns of PEVs. As power systems transition towards low-carbon and decentralized power networks, achieving efficient and cost-effective OPF solutions has become a critical priority. Standard optimization algorithms like particle swarm optimization, grey wolf optimization suffer from premature convergence, imbalanced exploration-exploitation, and limited solution diversity, resulting in suboptimal OPF solutions. To address these limitations, this study proposed modified grey wolf optimization (MGWO), selected for its minimal control parameters, enhanced exploration-exploitation using greedy selection, and improved convergence through dynamic leader selection. Uncertainty in RESs and PEVs is modelled using Monte Carlo methods with probability density functions (Weibull, lognormal, and normal distributions), enabling realistic and robust OPF solutions. The proposed algorithm is validated on standard and modified IEEE 30 and 57-bus systems. Comparative and statistical analysis, reinforced by one-way ANOVA test, confirms that MGWO significantly reduces operating cost, emissions, and voltage deviation compared to standard grey wolf optimization, multi-verse optimizer, and arithmetic optimization algorithm. The results underscore MGWO’s potential for power system planning, grid stability, and economic dispatch under high-RESs and PEV penetration scenarios.