A Novel Approach for Optimal and Reliable Planning Framework of Multi‐Vector Hybrid Renewable Energy Systems for Sustainable Electric Vehicle Charging

ABSTRACT

The green energy revolution is being propelled by hybrid energy systems that incorporate renewable energy sources. These systems are also becoming more and more important in promoting sustainable transportation by providing infrastructure for electric vehicle (EV) charging. For a stochastic EV load, this work entails the careful design of a reliable and optimized multi‐vector hybrid energy configuration consisting of solar panels, wind turbines, and fuel cells. Importantly, the study offers a novel approach that combines the Bat algorithm (BA) with the Tunicate Swarm Algorithm (TSA), offering an advanced tunicate swarm Bat algorithm (TSBA) optimization technique. Key metrics including net present cost (NPC), levelized cost of energy (LCOE), and reliability indicators like loss of load probability (LOLP), loss of load expectation (LOLE), and loss of energy expected (LOEE) serve as the foundation for the evaluation process. A reliable hybrid energy system with the fewest renewable energy components and promising reliability has been described using the suggested hybrid technique. Financially speaking, the hybrid system is made viable by the NPC, LCOE, and LOE values. Additionally, the energy oriented reliability indices, LOEE and LOLE, have drastically decreased. The developed system delivers an advanced optimization framework which operates reliably while providing sustainable and cost effective solutions for green energy technology.