IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v358y2024ics0306261924000023.html

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924000023
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.122619?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Orehounig, Kristina & Evins, Ralph & Dorer, Viktor, 2015. "Integration of decentralized energy systems in neighbourhoods using the energy hub approach," Applied Energy, Elsevier, vol. 154(C), pages 277-289.
    2. Parra, David & Norman, Stuart A. & Walker, Gavin S. & Gillott, Mark, 2017. "Optimum community energy storage for renewable energy and demand load management," Applied Energy, Elsevier, vol. 200(C), pages 358-369.
    3. Bahramara, S. & Moghaddam, M. Parsa & Haghifam, M.R., 2016. "Optimal planning of hybrid renewable energy systems using HOMER: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 609-620.
    4. Wagner, Markus & Day, Jareth & Neumann, Frank, 2013. "A fast and effective local search algorithm for optimizing the placement of wind turbines," Renewable Energy, Elsevier, vol. 51(C), pages 64-70.
    5. Tozzi, Peter & Jo, Jin Ho, 2017. "A comparative analysis of renewable energy simulation tools: Performance simulation model vs. system optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 390-398.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hassan Qudrat-Ullah, 2025. "A Thematic Review of AI and ML in Sustainable Energy Policies for Developing Nations," Energies, MDPI, vol. 18(9), pages 1-26, April.
    2. 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).
    3. 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).
    4. Lédée, François & Crawford, Curran & Evins, Ralph, 2025. "Improved surrogate modeling for multi-energy system design: Model architecture, sampling and scaling choices," Applied Energy, Elsevier, vol. 390(C).
    5. Jingyu Shi & Ran Xu & Dongfang Li & Tao Zhu & Nanyu Fan & Zhanghua Hong & Guohua Wang & Yong Han & Xing Zhu, 2025. "Multi-Criteria Optimization and Techno-Economic Assessment of a Wind–Solar–Hydrogen Hybrid System for a Plateau Tourist City Using HOMER and Shannon Entropy-EDAS Models," Energies, MDPI, vol. 18(15), pages 1-26, August.
    6. Tostado-Véliz, Marcos & Hasanien, Hany M. & Gómez-González, Manuel & Jurado, Francisco, 2025. "Robust rooftop photovoltaic planning in energy communities," Renewable Energy, Elsevier, vol. 243(C).
    7. Casella, Virginia & Ferro, Giulio & Parodi, Luca & Robba, Michela, 2025. "Maximizing shared benefits in renewable energy communities: A Bilevel optimization model," Applied Energy, Elsevier, vol. 386(C).
    8. El Kassar, Razan & Al Takash, Ahmad & El Rassy, Elissa & Hammoud, Mohammad & Py, Xavier, 2026. "Evaluating the sustainability of pv technologies and their cooling systems: a life cycle assessment review," Applied Energy, Elsevier, vol. 404(C).
    9. Shobo, Adedamola B. & Saez, Raul & Boer, Dieter & Vallès, Manel, 2026. "Unlocking the energy decarbonization potentials of rooftop photovoltaic-thermal collectors: A case study of Tarragona province's residential communities," Renewable Energy, Elsevier, vol. 256(PB).
    10. 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).
    11. Li, Yonggang & Su, Yaotong & Zhang, Yuanjin & Wu, Weinong & Xia, Lei, 2024. "Two-layered optimal scheduling under a semi-model architecture of hydro-wind-solar multi-energy systems with hydrogen storage," Energy, Elsevier, vol. 313(C).
    12. Sandra Corasaniti & Paolo Coppa & Dario Atzori & Ateeq Ur Rehman, 2025. "Renewable Energy Communities (RECs): European and Worldwide Distribution, Different Technologies, Management, and Modeling," Energies, MDPI, vol. 18(15), pages 1-60, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Scheller, Fabian & Bruckner, Thomas, 2019. "Energy system optimization at the municipal level: An analysis of modeling approaches and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 444-461.
    2. Azraff Bin Rozmi, Mohd Daniel & Thirunavukkarasu, Gokul Sidarth & Jamei, Elmira & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Stojcevski, Alex & Horan, Ben, 2019. "Role of immersive visualization tools in renewable energy system development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    3. Perera, A.T.D. & Nik, Vahid M. & Mauree, Dasaraden & Scartezzini, Jean-Louis, 2017. "Electrical hubs: An effective way to integrate non-dispatchable renewable energy sources with minimum impact to the grid," Applied Energy, Elsevier, vol. 190(C), pages 232-248.
    4. Husein, Munir & Chung, Il-Yop, 2018. "Optimal design and financial feasibility of a university campus microgrid considering renewable energy incentives," Applied Energy, Elsevier, vol. 225(C), pages 273-289.
    5. Binod Prasad Koirala & José Pablo Chaves Ávila & Tomás Gómez & Rudi A. Hakvoort & Paulien M. Herder, 2016. "Local Alternative for Energy Supply: Performance Assessment of Integrated Community Energy Systems," Energies, MDPI, vol. 9(12), pages 1-24, November.
    6. Jiaxin Lu & Weijun Wang & Yingchao Zhang & Song Cheng, 2017. "Multi-Objective Optimal Design of Stand-Alone Hybrid Energy System Using Entropy Weight Method Based on HOMER," Energies, MDPI, vol. 10(10), pages 1-17, October.
    7. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    8. Pueyo, Ana & Haas, Jannik, 2025. "Energy transitions in the Pacific, one island at a time: The case of Efate island, Vanuatu," Energy Policy, Elsevier, vol. 206(C).
    9. Eid, Cherrelle & Codani, Paul & Perez, Yannick & Reneses, Javier & Hakvoort, Rudi, 2016. "Managing electric flexibility from Distributed Energy Resources: A review of incentives for market design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 237-247.
    10. Pia Szichta & Ingela Tietze, 2020. "Sharing Economy in der Elektrizitätswirtschaft: Treiber und Hemmnisse [Title sharing economy in the electricity sector: drivers and barriers]," Sustainability Nexus Forum, Springer, vol. 28(3), pages 109-125, December.
    11. Ziad Ragab & Ehsan Pashajavid & Sumedha Rajakaruna, 2024. "Optimal Sizing and Economic Analysis of Community Battery Systems Considering Sensitivity and Uncertainty Factors," Energies, MDPI, vol. 17(18), pages 1-20, September.
    12. Rajbongshi, Rumi & Borgohain, Devashree & Mahapatra, Sadhan, 2017. "Optimization of PV-biomass-diesel and grid base hybrid energy systems for rural electrification by using HOMER," Energy, Elsevier, vol. 126(C), pages 461-474.
    13. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    14. Dimou, Andreas & Vakalis, Stergios, 2022. "Technoeconomic analysis of green energy transitions in isolated grids: The case of Ai Stratis – Green Island," Renewable Energy, Elsevier, vol. 195(C), pages 66-75.
    15. Terlouw, Tom & AlSkaif, Tarek & Bauer, Christian & van Sark, Wilfried, 2019. "Optimal energy management in all-electric residential energy systems with heat and electricity storage," Applied Energy, Elsevier, vol. 254(C).
    16. Cheng, Yaohua & Zhang, Ning & Kirschen, Daniel S. & Huang, Wujing & Kang, Chongqing, 2020. "Planning multiple energy systems for low-carbon districts with high penetration of renewable energy: An empirical study in China," Applied Energy, Elsevier, vol. 261(C).
    17. Abdel-Basset, Mohamed & Mohamed, Reda & Hezam, Ibrahim M. & Słowik, Adam, 2025. "Optimizing Wind farm layout using a one-by-one replacement mechanism-incorporated gradient-based optimizer," Energy, Elsevier, vol. 314(C).
    18. Tozzi, Peter & Jo, Jin Ho, 2017. "A comparative analysis of renewable energy simulation tools: Performance simulation model vs. system optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 390-398.
    19. Younessi, Hiva Seyed & Bahramara, Salah & Adabi, Farid & Golpîra, Hêmin, 2023. "Modeling the optimal sizing problem of the biogas-based electrical generator in a livestock farm considering a gas storage tank and the anaerobic digester process under the uncertainty of cow dung," Energy, Elsevier, vol. 270(C).
    20. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:358:y:2024:i:c:s0306261924000023. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.