A Novel Integration Approach for Photovoltaic/Wind/Fuel Cell-Based Hybrid Renewable Energy Systems With Reliability Indices for Sustainable Electric Vehicle Charging

Abstract

Hybrid energy systems that integrate renewable energy sources are driving the green energy revolution and playing an increasingly vital role in supporting sustainable transportation through electric vehicle charging infrastructure. This study involves the meticulous design of a reliable standalone multi-vector hybrid energy configuration comprising photovoltaic panels, wind turbines, and fuel cells (PV/WT/FC) for stochastic electric vehicle (EV) load. Significantly, the research presents a pioneering methodology that incorporates chaotic particle swarm optimization aligned with the Andean Condor algorithm (CPSO-ACA), providing a sophisticated optimization approach. The evaluation process is based on key measures like net present cost (NPC), levelized cost of energy (LCOE), and reliability indicators such as loss of load probability (LOLP), loss of load expectation (LOLE), and loss of energy expected (LOEE). With the proposed hybrid approach, a reliable hybrid energy system with the lowest renewable energy components and promising reliability (LOLP = 0.064) has been reported. From a financial perspective, the values of NPC, LCOE, and LOE ($4.06 M, $0.0636/kWh, and $0.7083 M) enable the hybrid system to be economically sound. Furthermore, the energy-oriented reliability indices, LOEE and LOLE, have significantly reduced to 5920 kWh and 564.144 h, respectively. The effectiveness of the proposed algorithm is compared with GA, GWO, MOPSO, and CPSO algorithms and is indicative of the strength achieved through proposed optimization in the evolving landscape of green energy technology.