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Identification of typical district configurations: A two-step global sensitivity analysis framework

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  • Chuat, Arthur
  • Terrier, Cédric
  • Schnidrig, Jonas
  • Maréchal, François

Abstract

The recent geopolitical conflicts in Europe have underscored the vulnerability of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy systems ensuring sustainable energy supply in the future, the imperative of leveraging renewable indigenous energy sources becomes evident. However, as such technologies are integrated into the existing system, it is necessary to shift from the current centralized infrastructure to a decentralized production strategy. This paper presents a method to identify a panel of technological solutions at the district level, intended to reduce complexity for the integration of decentralized models into a national-scale model. The framework’s novelty lies in combining a global sensitivity analysis for solution generation with clustering to identify typical configurations. The global sensitivity analysis is performed on a mixed integer linear programming model, which optimally sizes and operates district energy systems. The sensitivity analysis determines the most influential parameters of the model using the Morris method and provides a representative sampling of the solution space by leveraging the Sobol sampling strategy. The latter is then clustered using a density-based algorithm to identify typical solutions. The framework is applied to a suburban and residential Swiss neighborhood. The first outcome of the research is the high sensitivity of the model to energy carrier prices. As a result, Sobol’s sampling space separates itself into two system types: those based on a natural gas boiler and those relying on a combination of electrical heaters and heat pumps. For both types, the electricity demand is either fulfilled by PV panels or electricity imports. The identified configurations showcase that the framework successfully generates a panel of solutions composed of various system configurations and operations being representative of the overall solution space.

Suggested Citation

  • Chuat, Arthur & Terrier, Cédric & Schnidrig, Jonas & Maréchal, François, 2024. "Identification of typical district configurations: A two-step global sensitivity analysis framework," Energy, Elsevier, vol. 296(C).
    • Handle: RePEc:eee:energy:v:296:y:2024:i:c:s0360544224008880
    DOI: 10.1016/j.energy.2024.131116
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    References listed on IDEAS

    as
    1. Limpens, Gauthier & Moret, Stefano & Jeanmart, Hervé & Maréchal, Francois, 2019. "EnergyScope TD: A novel open-source model for regional energy systems," Applied Energy, Elsevier, vol. 255(C).
    2. Gupta, Rahul & Sossan, Fabrizio & Paolone, Mario, 2021. "Countrywide PV hosting capacity and energy storage requirements for distribution networks: The case of Switzerland," Applied Energy, Elsevier, vol. 281(C).
    3. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Solbrekke, Ida Marie, 2018. "A review of modelling tools for energy and electricity systems with large shares of variable renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 440-459.
    4. Di Somma, M. & Yan, B. & Bianco, N. & Graditi, G. & Luh, P.B. & Mongibello, L. & Naso, V., 2017. "Multi-objective design optimization of distributed energy systems through cost and exergy assessments," Applied Energy, Elsevier, vol. 204(C), pages 1299-1316.
    5. Maroufmashat, Azadeh & Elkamel, Ali & Fowler, Michael & Sattari, Sourena & Roshandel, Ramin & Hajimiragha, Amir & Walker, Sean & Entchev, Evgueniy, 2015. "Modeling and optimization of a network of energy hubs to improve economic and emission considerations," Energy, Elsevier, vol. 93(P2), pages 2546-2558.
    6. Åberg, M. & Widén, J. & Henning, D., 2012. "Sensitivity of district heating system operation to heat demand reductions and electricity price variations: A Swedish example," Energy, Elsevier, vol. 41(1), pages 525-540.
    7. Prataviera, Enrico & Vivian, Jacopo & Lombardo, Giulia & Zarrella, Angelo, 2022. "Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis," Applied Energy, Elsevier, vol. 311(C).
    8. Allegrini, Jonas & Orehounig, Kristina & Mavromatidis, Georgios & Ruesch, Florian & Dorer, Viktor & Evins, Ralph, 2015. "A review of modelling approaches and tools for the simulation of district-scale energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1391-1404.
    9. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2016. "Optimising urban energy systems: Simultaneous system sizing, operation and district heating network layout," Energy, Elsevier, vol. 116(P1), pages 619-636.
    10. Alanne, Kari & Saari, Arto, 2006. "Distributed energy generation and sustainable development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(6), pages 539-558, December.
    11. Schütz, Thomas & Schraven, Markus Hans & Fuchs, Marcus & Remmen, Peter & Müller, Dirk, 2018. "Comparison of clustering algorithms for the selection of typical demand days for energy system synthesis," Renewable Energy, Elsevier, vol. 129(PA), pages 570-582.
    12. Tian, Wei, 2013. "A review of sensitivity analysis methods in building energy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 411-419.
    13. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Uncertainty and global sensitivity analysis for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 214(C), pages 219-238.
    14. Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 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, Elsevier, vol. 267(C).
    15. Pickering, B. & Choudhary, R., 2019. "District energy system optimisation under uncertain demand: Handling data-driven stochastic profiles," Applied Energy, Elsevier, vol. 236(C), pages 1138-1157.
    16. Cédric Terrier & Joseph René Hubert Loustau & Dorsan Lepour & François Maréchal, 2024. "From Local Energy Communities towards National Energy System: A Grid-Aware Techno-Economic Analysis," Energies, MDPI, vol. 17(4), pages 1-16, February.
    17. Trutnevyte, Evelina & McDowall, Will & Tomei, Julia & Keppo, Ilkka, 2016. "Energy scenario choices: Insights from a retrospective review of UK energy futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 326-337.
    18. Alhamwi, Alaa & Medjroubi, Wided & Vogt, Thomas & Agert, Carsten, 2018. "Modelling urban energy requirements using open source data and models," Applied Energy, Elsevier, vol. 231(C), pages 1100-1108.
    19. Ren, Hongbo & Zhou, Weisheng & Gao, Weijun, 2012. "Optimal option of distributed energy systems for building complexes in different climate zones in China," Applied Energy, Elsevier, vol. 91(1), pages 156-165.
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