A novel hybrid Salp Swarm Kepler optimization for optimal sizing and energy management of renewable microgrids with EV integration

The rapid proliferation of renewable energy sources (RES) and electric vehicles (EVs) has introduced significant challenges in the optimal design and energy management of modern microgrids. This study presents a novel hybrid metaheuristic, the Salp Swarm–Kepler Optimization Algorithm (SSAKOA), which synergistically combines the global exploration capability of the Salp Swarm Algorithm with the orbital-based exploitation efficiency of the Kepler Optimization Algorithm. The proposed algorithm is first rigorously evaluated on a suite of 23 standard benchmark functions to validate its convergence behavior, robustness, and solution quality relative to established metaheuristics. Subsequently, SSAKOA is applied to a grid-connected hybrid renewable energy system comprising photovoltaic panels, wind turbines, battery storage, hydrogen subsystems (electrolyzers, hydrogen tanks, and PEM fuel cells), and bidirectional EV charging capabilities. The microgrid model incorporates realistic meteorological, demand, and tariff data over an annual cycle to simulate real-world conditions. Comparative results against nine state-of-the-art optimization algorithms demonstrate SSAKOA's superiority in minimizing the levelized cost of energy (LCOE), enhancing the renewable energy fraction (REF), reducing grid dependency, and significantly lowering CO2 emissions. These findings substantiate SSAKOA as an effective and scalable optimization framework for integrated energy systems with high RES and EV penetration, offering tangible benefits in cost-efficiency, reliability, and environmental sustainability.