Articles producció científica> Enginyeria Mecànica

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

  • Identification data

    Identifier: imarina:6238562
    Authors:
    Abokersh MHVallès MCabeza LFBoer D
    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 produc
  • Others:

    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
    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
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    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
  • Keywords:

    Building and Construction,Civil and Structural Engineering,Energy & Fuels,Energy (Miscellaneous),Energy Engineering and Power Technology,Engineering, Chemical,Fuel Technology,Management, Monitoring, Policy and Law,Mechanical Engineering,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment
    Solar assist district heating system
    Multi-objective optimization
    Life cycle assessment
    Global sensitivity analysis
    Bayesian optimization approach
    Artificial neural network
    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
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