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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

Author

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  • Jing Yang

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia)

  • Yen-Lin Chen

    (Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106344, Taiwan)

  • Por Lip Yee

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia)

  • Chin Soon Ku

    (Department of Computer Science, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia)

  • Manoochehr Babanezhad

    (Department of Statistics, Faculty of Sciences, Golestan University, Gorgan 49138-15759, Iran)

Abstract

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.

Suggested Citation

  • Jing Yang & Yen-Lin Chen & Por Lip Yee & Chin Soon Ku & Manoochehr Babanezhad, 2023. "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," Energies, MDPI, vol. 16(6), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2867-:d:1102187
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    References listed on IDEAS

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    1. Ramli, Makbul A.M. & Bouchekara, H.R.E.H. & Alghamdi, Abdulsalam S., 2018. "Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm," Renewable Energy, Elsevier, vol. 121(C), pages 400-411.
    2. Fodhil, F. & Hamidat, A. & Nadjemi, O., 2019. "Potential, optimization and sensitivity analysis of photovoltaic-diesel-battery hybrid energy system for rural electrification in Algeria," Energy, Elsevier, vol. 169(C), pages 613-624.
    3. Ghaffari, Abolfazl & Askarzadeh, Alireza, 2020. "Design optimization of a hybrid system subject to reliability level and renewable energy penetration," Energy, Elsevier, vol. 193(C).
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    1. Mustafa Kamal & Renzon Daniel Cosme Pecho & Hassan Falah Fakhruldeen & Hailer Sharif & Vedran Mrzljak & Saber Arabi Nowdeh & Igor Poljak, 2023. "Photovoltaic/Hydrokinetic/Hydrogen Energy System Sizing Considering Uncertainty: A Stochastic Approach Using Two-Point Estimate Method and Improved Gradient-Based Optimizer," Sustainability, MDPI, vol. 15(21), pages 1-30, November.

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