Author, as appears in the article.: Elomari Y; Mateu C; Marín-Genescà M; Boer D
Department: Enginyeria Mecànica
URV's Author/s: Boer, Dieter-Thomas / Elomari, Youssef / Marín Genescà, Marc
Keywords: Optimization Multi-objective optimization Multi-criteria decision making Machine learning Life cycle cost Life cycle assessment Energy community
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.
Thematic Areas: Renewable energy, sustainability and the environment Química Nuclear energy and engineering Mechanical engineering Materiais Matemática / probabilidade e estatística Management, monitoring, policy and law Interdisciplinar Geociências General energy Fuel technology Farmacia Engineering, chemical Engenharias iv Engenharias iii Engenharias ii Engenharias i Energy engineering and power technology Energy (miscellaneous) Energy (all) Energy & fuels Economia Civil and structural engineering Ciências biológicas iii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Building and construction Biotecnología Biodiversidade Arquitetura, urbanismo e design
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: youssef.elomari@urv.cat marc.marin@urv.cat dieter.boer@urv.cat youssef.elomari@urv.cat
Author identifier: 0000-0002-7204-4526 0000-0002-5532-6409
Record's date: 2024-02-17
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.sciencedirect.com/science/article/pii/S0306261924000023
Papper original source: Applied Energy. 358
APA: Elomari Y; Mateu C; Marín-Genescà M; Boer D (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, 358(), -. DOI: 10.1016/j.apenergy.2024.122619
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Article's DOI: 10.1016/j.apenergy.2024.122619
Entity: Universitat Rovira i Virgili
Journal publication year: 2024
Publication Type: Journal Publications
Repositori URV