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Compromises in energy policy--Using fuzzy optimization in an energy systems model

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  • Martinsen, Dag
  • Krey, Volker

Abstract

Over the last year in Germany a great many political discussions have centered around the future direction of energy and climate policy. Due to a number of events related to energy prices, security of supply and climate change, it has been necessary to develop cornerstones for a new integrated energy and climate policy. To supplement this decision process, model-based scenarios were used. In this paper we introduce fuzzy constraints to obtain a better representation of political decision processes, in particular, to find compromises between often contradictory targets (e.g. economic, environmentally friendly and secure energy supply). A number of policy aims derived from a review of the ongoing political discussions were formulated as fuzzy constraints to explicitly include trade-offs between various targets. The result is an overall satisfaction level of about 60% contingent upon the following restrictions: share of energy imports, share of biofuels, share of CHP electricity, CO2 reduction target and use of domestic hard coal. The restrictions for the share of renewable electricity, share of renewable heat, energy efficiency and postponement of nuclear phase out have higher membership function values, i.e. they are not binding and therefore get done on the side.

Suggested Citation

  • Martinsen, Dag & Krey, Volker, 2008. "Compromises in energy policy--Using fuzzy optimization in an energy systems model," Energy Policy, Elsevier, vol. 36(8), pages 2973-2984, August.
    • Handle: RePEc:eee:enepol:v:36:y:2008:i:8:p:2973-2984
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    Cited by:

    1. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    2. Anastasia Soukhov & Ahmed Foda & Moataz Mohamed, 2022. "Electric Mobility Emission Reduction Policies: A Multi-Objective Optimization Assessment Approach," Energies, MDPI, vol. 15(19), pages 1-21, September.
    3. Tapia, John Frederick D., 2021. "Optimal synthesis of multi-product energy systems under neutrosophic environment," Energy, Elsevier, vol. 229(C).
    4. Stojiljković, Mirko M., 2017. "Bi-level multi-objective fuzzy design optimization of energy supply systems aided by problem-specific heuristics," Energy, Elsevier, vol. 137(C), pages 1231-1251.
    5. Connolly, D. & Lund, H. & Mathiesen, B.V. & Leahy, M., 2010. "A review of computer tools for analysing the integration of renewable energy into various energy systems," Applied Energy, Elsevier, vol. 87(4), pages 1059-1082, April.
    6. Lopion, Peter & Markewitz, Peter & Robinius, Martin & Stolten, Detlef, 2018. "A review of current challenges and trends in energy systems modeling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 156-166.

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