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

• Main Authors: Yang, Jing, Chen, Yen-Lin, Yee, Por Lip, Ku, Chin Soon, Babanezhad, Manoochehr
• Format: Article
• Published: MDPI 2023
• Subjects: Q Science (General); QA75 Electronic computers. Computer science; T Technology (General); TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.um.edu.my/38565/

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