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Towards robust renewable energy investment decisions at the territorial level

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  • Koponen, Kati
  • Le Net, Elisabeth

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

  • Koponen, Kati & Le Net, Elisabeth, 2021. "Towards robust renewable energy investment decisions at the territorial level," Applied Energy, Elsevier, vol. 287(C).
    • Handle: RePEc:eee:appene:v:287:y:2021:i:c:s0306261921001008
    DOI: 10.1016/j.apenergy.2021.116552
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    References listed on IDEAS

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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. 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.
    4. 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.
    5. 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.

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