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A data-driven framework for designing a renewable energy community based on the integration of machine learning model with life cycle assessment and life cycle cost parameters

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  • Elomari, Youssef
  • Mateu, Carles
  • Marín-Genescà, M.
  • Boer, Dieter

Abstract

This research paper presents a data-driven framework for design optimization of renewable energy communities (RECs) in the residential sector, considering both techno-economic challenges and environmental impact. The study's focus is to determine suitable sizes for photovoltaic systems, wind turbines, and battery electrical energy systems by evaluating energy, economic, and environmental criteria. To achieve this, we develop a data-driven model that incorporates Homer Pro and an in-house tool developed in Python programming language that integrates a machine learning algorithm, life cycle cost (LCC), life cycle assessment (LCA) calculations of the REC model. Furthermore, a multi-objective optimization model is established to minimize the LCC and LCA parameters while maximizing green energy use. Moreover, a multi-criteria decision-making approach based on Weighted Sum Model (WSM) is proposed to help the stakeholders to see beyond the selection criteria based on LCC and LCA to choose the most appropriate scenario optimal solution for the desired energy community and interpret the effect of various economic parameters on the sustainable performance of REC. The framework application is illustrated through a case study for the optimal design of REC for a residential community in Tarragona, Spain, consisting of 100 buildings. The results revealed a substantial improvement in economic and environmental benefits for designing REC, the optimal minimum cost solution with a levelized cost of energy (LCOE = 0.044 $/kWh) and a payback period of 7.1 years with an LCOE reduction of 85.04% compared to the base case. The minimum impact with an LCOE = 0.220 $/kWh and a payback period of 12.5 years with a reduction in environmental impact of 54.59% compared to the base case. Overall, the developed data-driven provides policy decision-making with an evaluation of REC in the residential sector.

Suggested Citation

  • Elomari, Youssef & Mateu, Carles & Marín-Genescà, M. & Boer, Dieter, 2024. "A data-driven framework for designing a renewable energy community based on the integration of machine learning model with life cycle assessment and life cycle cost parameters," Applied Energy, Elsevier, vol. 358(C).
    • Handle: RePEc:eee:appene:v:358:y:2024:i:c:s0306261924000023
    DOI: 10.1016/j.apenergy.2024.122619
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    References listed on IDEAS

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    Cited by:

    1. Andolfi, Laura & Lima Baima, Renan & Burcheri, Lorenzo Matthias & Pavić, Ivan & Fridgen, Gilbert, 2025. "Sociotechnical design of building energy management systems in the public sector: Five design principles," Applied Energy, Elsevier, vol. 377(PD).
    2. Pu, Yuchen & Li, Qi & Huo, Shasha & Breaz, Elena & Chen, Weirong & Gao, Fei, 2024. "Optimal configuration for shared electric-hydrogen energy storage for multiple integrated energy systems with mobile hydrogen transportation," Renewable Energy, Elsevier, vol. 237(PC).
    3. Pagnini, Luisa & Bracco, Stefano & Delfino, Federico & de-Simón-Martín, Miguel, 2024. "Levelized cost of electricity in renewable energy communities: Uncertainty propagation analysis," Applied Energy, Elsevier, vol. 366(C).

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