Performance evaluation and multi-objective optimization of hydrogen-based integrated energy systems driven by renewable energy sources

Fanhua Rong, Zeting Yu, Kaifan Zhang, Jingyi Sun and Daohan Wang

Energy, 2024, vol. 313, issue C

Abstract: This study proposes an integrated energy system using hydrogen storage to realize the efficient utilization of renewable energy sources and reduce the fluctuation when renewable energy is connected to grid. The system utilizes solar and wind energy to realize hydrogen production, desalination, and CCHP. First, the energy, exergy, and economic evaluations for the proposed system are carried out, and then an in-depth analysis of the key operating parameters is performed. The system achieves energy efficiency, exergy efficiency, and cost rate of 48.49 %, 19.98 %, and 7.969 $/h, respectively. And the exergy analysis shows that the main exergy destructions are caused by the parabolic trough solar collector and the transcritical CO2 power cycle. The parametric analysis demonstrates when solar radiation flux and wind speed increase, the exergy efficiency and hydrogen production are increased, but the cost rate is increased accordingly. Finally, two sets of multi-objective optimization schemes are performed combining the artificial neural network with the Non-dominant genetic algorithm-II. For the optimized fresh water output, cost rate, and exergy efficiency, it is achieving improvements of 51.73 %, 8.4 %, and 3.6 %, and for the optimized hydrogen production, cost rate, and exergy efficiency, it is increased by 12.53 %, 0.564 %, and 0.75 %, respectively.

Keywords: Integrated energy system; Renewable energy; Performance evaluation; Artificial neural network; Multi-objective optimization (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:313:y:2024:i:c:s0360544224034765

DOI: 10.1016/j.energy.2024.133698

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