An Improved Artificial Ecosystem-Based Optimization Algorithm for Optimal Design of a Hybrid Photovoltaic/Fuel Cell Energy System to Supply A Residential Complex Demand: A Case Study for Kuala Lumpur

In this paper, the optimal design of a hybrid energy system (HES), consisting photovoltaic technology integrated with fuel cells (HPV/FC) and relying on hydrogen storage, is performed to meet annual demand residential complex find minimum total net present cost (TNPC), while observing reliability constraint as energy-not-supplied probability (ENSP) considering real meteorological data Kuala Lumpur city in Malaysia. The decision variables include size components, which are optimally determined by an improved artificial ecosystem-based optimization algorithm (IAEO). conventional AEO using dynamic lens-imaging learning strategy (DLILS) prevent premature convergence. results demonstrated that decrease (increase) leads increase (decrease) TNPC, well electricity (COE). For maximum 5%, show TNPC COE obtained USD 2.247 million 0.4046 million, respectively. superior performance IAEO has been confirmed AEO, particle swarm (PSO), manta ray foraging (MRFO), lowest higher reliability. addition, effectiveness tank efficiency load changes design.