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Towards robust renewable energy investment decisions at the territorial level

Author

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  • Kati Koponen

    (VTT - VTT Technical Research Centre of Finland, TECH ECO (ex-ITESE) - Institut Technico-Economie - CEA-DES (ex-DEN) - CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) - CEA - Commissariat à l'énergie atomique et aux énergies alternatives - Université Paris-Saclay)

  • Elisabeth Le Net

    (TECH ECO (ex-ITESE) - Institut Technico-Economie - CEA-DES (ex-DEN) - CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) - CEA - Commissariat à l'énergie atomique et aux énergies alternatives - Université Paris-Saclay)

Abstract

Considerable and fast investments in renewable energy technologies are needed in order to reduce greenhouse gas emissions to achieve the Paris Agreement climate change mitigation targets. Many of these investment decisions are made at the territorial level, especially those related to the heating sector. When choosing the most suitable energy investments, decision makers need to consider several performance indicators—economic, social and environmental—simultaneously. In addition, decision makers face profound uncertainty concerning the future, as decisions on energy systems are always long-term investments. We aim to provide territorial decision makers with a simple decision-making framework that combines a robust decision-making method with multi-criteria analysis and allows the inclusion of territorial features. The main aim is to develope a simple tool that provides data to seek robust solutions which will perform well over a wide range of plausible futures. The method proposed is illustrated with a case study on renewable heating solutions based in France. Heat pumps or central biomass plants are robust in various future conditions, while current domestic natural gas based heating performs badly compared to the renewable technologies

Suggested Citation

  • Kati Koponen & Elisabeth Le Net, 2021. "Towards robust renewable energy investment decisions at the territorial level," Post-Print cea-03216464, HAL.
    • Handle: RePEc:hal:journl:cea-03216464
    DOI: 10.1016/j.apenergy.2021.116552
    Note: View the original document on HAL open archive server: https://cea.hal.science/cea-03216464
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    References listed on IDEAS

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    1. Theodor J. Stewart & Ian Durbach, 2016. "Dealing with Uncertainties in MCDA," International Series in Operations Research & Management Science, in: Salvatore Greco & Matthias Ehrgott & José Rui Figueira (ed.), Multiple Criteria Decision Analysis, edition 2, chapter 0, pages 467-496, Springer.
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    Cited by:

    1. Tianran Ding & Bernhard Steubing & Wouter Achten, 2022. "Coupling optimization with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/359529, ULB -- Universite Libre de Bruxelles.
    2. Telikani, Akbar & Rossi, Mosé & Khajehali, Naghmeh & Renzi, Massimiliano, 2023. "Pumps-as-Turbines’ (PaTs) performance prediction improvement using evolutionary artificial neural networks," Applied Energy, Elsevier, vol. 330(PA).
    3. Tianran Ding & Bernhard Steubing & Wouter Achten, 2022. "Coupling optimization with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/352783, ULB -- Universite Libre de Bruxelles.
    4. Karaaslan, Abdulkerim & Gezen, Mesliha, 2022. "The evaluation of renewable energy resources in Turkey by integer multi-objective selection problem with interval coefficient," Renewable Energy, Elsevier, vol. 182(C), pages 842-854.
    5. Netzah Calamaro & Avihai Ofir & Doron Shmilovitz, 2021. "Application of Enhanced CPC for Load Identification, Preventive Maintenance and Grid Interpretation," Energies, MDPI, vol. 14(11), pages 1-41, June.

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