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Configuration Selection for Renewable Energy Community Using MCDM Methods

Author

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  • Hamza Gribiss

    (Laboratory of Computer Science and Digital Society (LIST3N), University of Technology of Troyes, 10000 Troyes, France)

  • Mohammad Mohsen Aghelinejad

    (Laboratory of Computer Science and Digital Society (LIST3N), University of Technology of Troyes, 10000 Troyes, France)

  • Farouk Yalaoui

    (Laboratory of Computer Science and Digital Society (LIST3N), University of Technology of Troyes, 10000 Troyes, France)

Abstract

By 2050, the European Union plans to make Europe the first carbon-neutral continent and a global leader in climate-green industries. Recently, many decisions have been taken in the world to ensure the energy transition from fossil fuel to renewable energy. The creation of renewable energy communities (REC) is among the solutions used to increase this transition. This study presents 16 different configurations for energy self-consumption in RECs containing different industrial factories. One mathematical model is proposed for each configuration, and they have been solved according to different criteria. The comparisons are made between these configurations according to economic, environmental, technical, and social criteria. Then, four multi-criteria decision-making (MCDM) methods are used to choose the best configurations considering all the criteria. For this purpose, the achieved results from the mathematical models are used as input for the MCDM methods. The findings demonstrate that the most effective configurations combine both individual and collective self-consumption. Furthermore, the inclusion of collective production results in multiple advantages, including a 64.71% rise in economic gains, a 26.95% decrease in CO 2 emissions, a 21.39% improvement in self-sufficiency, and a significant increase in job creation by 175.24%. In addition, incorporating storage enables a substantial rise in the degree of self-sufficiency, leading to reduced reliance on the power grid and consequent reduction in CO 2 emissions.

Suggested Citation

  • Hamza Gribiss & Mohammad Mohsen Aghelinejad & Farouk Yalaoui, 2023. "Configuration Selection for Renewable Energy Community Using MCDM Methods," Energies, MDPI, vol. 16(6), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2632-:d:1094014
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    References listed on IDEAS

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    Cited by:

    1. Yang Tang & Yifeng Liu & Weiqiang Huo & Meng Chen & Shilong Ye & Lei Cheng, 2023. "Optimal Allocation Scheme of Renewable Energy Consumption Responsibility Weight under Renewable Portfolio Standards: An Integrated Evolutionary Game and Stochastic Optimization Approach," Energies, MDPI, vol. 16(7), pages 1-22, March.
    2. Shoaib Ahmed & Amjad Ali & Alessandro Ciocia & Antonio D’Angola, 2024. "Technological Elements behind the Renewable Energy Community: Current Status, Existing Gap, Necessity, and Future Perspective—Overview," Energies, MDPI, vol. 17(13), pages 1-40, June.
    3. Shoaib Ahmed & Amjad Ali & Antonio D’Angola, 2024. "A Review of Renewable Energy Communities: Concepts, Scope, Progress, Challenges, and Recommendations," Sustainability, MDPI, vol. 16(5), pages 1-34, February.

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