Código de proyecto: RTI2018-093849-B-C33 (MCIU/AEI/FEDER, UE)

Palabras clave: Solar assist district heating system Multi-objective optimization Life cycle assessment Global sensitivity analysis Bayesian optimization approach Artificial neural network

Fecha de alta del registro: 2023-07-31

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

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

Referencia al articulo segun fuente origial: Applied Energy. 267 (UNSP 114903):

Referencia de l'ítem segons les normes APA: Abokersh MH; Vallès M; Cabeza LF; Boer D (2020). A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis. Applied Energy, 267(UNSP 114903), -. DOI: 10.1016/j.apenergy.2020.114903

Acrónimo: MATCE

Página inicial: Article number 114903

Tipo de publicación: Journal Publications

Autor según el artículo: Abokersh MH; Vallès M; Cabeza LF; Boer D

Departamento: Enginyeria Mecànica

Autor/es de la URV: Boer, Dieter-Thomas / Vallès Rasquera, Joan Manel

Resumen: © 2020 The Authors A promising pathway towards sustainable transaction to clean energy production lies in the adoption of solar assisted district heating systems (SDHS). However, SDHS technical barriers during their design and operation phases, combined with their economic limitation, promote a high variation in quantifying SDHS benefits over their lifetime. This study proposes a complete multi-objective optimization framework using a robust machine learning approach to inherent sustainability principles in the design of SDHS. Moreover, the framework investigates the uncertainty in the context of SDHS design, in which the Global Sensitivity Analysis (GSA) is combined with the heuristics optimization approach. The framework application is illustrated through a case study for the optimal integration of SHDS at different urban community sizes (10, 25, 50, and 100 buildings) located in Madrid. The results reveal a substantial improvement in economic and environmental benefits for deploying SDHS, especially with including the seasonal storage tank (SST) construction properties in the optimization problem, and it can achieve a payback period up to 13.7 years. In addition, the solar fraction of the optimized SDHS never falls below 82.1% for the investigated community sizes with an efficiency above 69.5% for the SST. Finally, the GSA indicates the SST investment cost and its relevant construction materials, are primarily responsible for the variability in the optimal system feasibility. The proposed framework can provide a good starting point to solve the enormous computational expenses drawbacks associated with the heuristics optimization approach. Furthermore, it can function as a decision support tool to fulfill the European Union energy targets regarding clean energy production.

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/

ISSN: 03062619

Direcció de correo del autor: manel.valles@urv.cat dieter.boer@urv.cat

Identificador del autor: 0000-0002-0748-1287 0000-0002-5532-6409

Volumen de revista: 267

Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S0306261920304153

Programa de financiación: Spanish Ministry of Economy and Competitiveness

DOI del artículo: 10.1016/j.apenergy.2020.114903

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2020

Acción del progama de financiación: Retos Investigación