Project code: RTI2018-093849-B-C33 (MCIU/AEI/FEDER, UE)

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

Record's date: 2023-07-31

Papper version: info:eu-repo/semantics/publishedVersion

Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/

Papper original source: Applied Energy. 267 (UNSP 114903):

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

Acronym: MATCE

First page: Article number 114903

Publication Type: Journal Publications

Author, as appears in the article.: Abokersh MH; Vallès M; Cabeza LF; Boer D

Department: Enginyeria Mecànica

URV's Author/s: Boer, Dieter-Thomas / Vallès Rasquera, Joan Manel

Abstract: © 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.

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/

ISSN: 03062619

Author's mail: manel.valles@urv.cat dieter.boer@urv.cat

Author identifier: 0000-0002-0748-1287 0000-0002-5532-6409

Journal volume: 267

Link to the original source: https://www.sciencedirect.com/science/article/pii/S0306261920304153

Funding program: Spanish Ministry of Economy and Competitiveness

Article's DOI: 10.1016/j.apenergy.2020.114903

Entity: Universitat Rovira i Virgili

Journal publication year: 2020

Funding program action: Retos Investigación