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Evaluating the prospect of utilizing excess energy and creating employments from a hybrid energy system meeting electricity and freshwater demands using multi-objective evolutionary algorithms

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  • Das, Pronob
  • Das, Barun K.
  • Rahman, Mushfiqur
  • Hassan, Rakibul

Abstract

Hybrid renewable energy systems have been accepted as one of the most convenient approaches towards the green energy future. As the renewable penetration propagates throughout the world, benefits of those systems other than providing clean energy have been brought to light. When hybridized, the stand-alone systems tend to generate significant energy excess to demand which needs to be disposed. These two aspects have been addressed in this study. Hybrid renewable energy systems containing solar photovoltaic, wind turbine, diesel generator and battery are optimized to supply reliable electricity and fresh water to a community of an Island not connected to the national grid in Bangladesh. The generated excess energy of the stand-alone hybrid system is utilized to run a reverse osmosis process for the desalination of sea water. Job creation, one of the prospective benefits of renewables, is maximized while minimizing cost and excess energy. The systems are optimized using genetic algorithm, particle swarm optimization and a hybrid of these two algorithms. The results reveal that, the hybrid algorithm exhibits better convergence and provides the optimum system with a cost of energy of 0.234 $/kWh, 1.64 jobs and 24,038 kWh/yr excess energy. By utilizing excess energy, a maximum of 11.8% and 9.3% reduction is achieved in overall system cost and electricity cost, respectively while job creation increases by 6%.

Suggested Citation

  • Das, Pronob & Das, Barun K. & Rahman, Mushfiqur & Hassan, Rakibul, 2022. "Evaluating the prospect of utilizing excess energy and creating employments from a hybrid energy system meeting electricity and freshwater demands using multi-objective evolutionary algorithms," Energy, Elsevier, vol. 238(PB).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pb:s0360544221021083
    DOI: 10.1016/j.energy.2021.121860
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    Cited by:

    1. Batista, Natasha E. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M. & Braga, Arthur P.S., 2023. "Optimizing methodologies of hybrid renewable energy systems powered reverse osmosis plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    2. Antonio Perrelli & Eduardo Sodré & Vinícius Silva & Alex Santos, 2023. "Maximizing Returns and Minimizing Risks in Hybrid Renewable Energy Systems: A Stochastic Discounted Cash Flow Analysis of Wind and Photovoltaic Systems in Brazil," Energies, MDPI, vol. 16(19), pages 1-19, September.
    3. Songkai Wang & Rong Jia & Xiaoyu Shi & Chang Luo & Yuan An & Qiang Huang & Pengcheng Guo & Xueyan Wang & Xuewen Lei, 2022. "Research on Capacity Allocation Optimization of Commercial Virtual Power Plant (CVPP)," Energies, MDPI, vol. 15(4), pages 1-18, February.
    4. Zhang, Weiping & Maleki, Akbar, 2022. "Modeling and optimization of a stand-alone desalination plant powered by solar/wind energies based on back-up systems using a hybrid algorithm," Energy, Elsevier, vol. 254(PC).
    5. Sun, Liangliang & Peng, Jiayu & Dinçer, Hasan & Yüksel, Serhat, 2022. "Coalition-oriented strategic selection of renewable energy system alternatives using q-ROF DEMATEL with golden cut," Energy, Elsevier, vol. 256(C).
    6. Vaziri Rad, Mohammad Amin & Kasaeian, Alibakhsh & Niu, Xiaofeng & Zhang, Kai & Mahian, Omid, 2023. "Excess electricity problem in off-grid hybrid renewable energy systems: A comprehensive review from challenges to prevalent solutions," Renewable Energy, Elsevier, vol. 212(C), pages 538-560.
    7. Zhang, Shuo & Yu, Yadong & Kharrazi, Ali & Ma, Tieju, 2023. "How would sustainable transformations in the electricity sector of megacities impact employment levels? A case study of Beijing," Energy, Elsevier, vol. 270(C).
    8. Nastro, Francesco & Sorrentino, Marco & Trifirò, Alena, 2022. "A machine learning approach based on neural networks for energy diagnosis of telecommunication sites," Energy, Elsevier, vol. 245(C).
    9. Zhao, Pan & Xu, Wenpan & Liu, Aijie & Wu, Wenze & Wang, Jiangfeng & Yan, Zhequan, 2022. "Performance evaluation of a renewable driven standalone combined power and water supply system with cascade electricity and heat storage," Renewable Energy, Elsevier, vol. 199(C), pages 1283-1299.

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