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

Autor según el artículo: Elomari, Youssef; Mateu, Carles; Boer, Dieter

Departamento: Enginyeria Mecànica

Autor/es de la URV: Boer, Dieter-Thomas / Elomari, Youssef / Marín Genescà, Marc

Palabras clave: Systems; Performance; Optimization; Multi-objective optimization; Multi-criteria decision making; Multi -objective optimization; Multi -criteria decision making; Machine learning; Life cycle cost; Life cycle assessment; Generation; Energy community

Resumen: 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 , 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.

Áreas temáticas: 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

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

Direcció de correo del autor: marc.marin@urv.cat; youssef.elomari@urv.cat; dieter.boer@urv.cat

Fecha de alta del registro: 2025-02-24

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S0306261924000023

URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Applied Energy. 358 122619-

Referencia de l'ítem segons les normes APA: Elomari, Youssef; Mateu, Carles; 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, 358(), 122619-. DOI: 10.1016/j.apenergy.2024.122619

DOI del artículo: 10.1016/j.apenergy.2024.122619

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2024

Tipo de publicación: Journal Publications