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REMIND-D: A Hybrid Energy-Economy Model of Germany

Author

Listed:
  • Eva Schmid

    (Potsdam Institute for Climate Impact Research)

  • Brigitte Knopf

    (Potsdam Institute for Climate Impact Research)

  • Nico Bauer

    (Potsdam Institute for Climate Impact Research)

Abstract

This paper presents a detailed documentation of the hybrid energy-economy model REMIND-D. REMIND-D is a Ramsey-type growth model for Germany that integrates a detailed bottom-up energy system module, coupled by a hard link. The model provides a quantitative framework for analyzing long-term domestic CO2 emission reduction scenarios. Due to its hybrid nature, REMIND-D facilitates an integrated analysis of the interplay between technological mitigation options in the different sectors of the energy system as well as overall macroeconomic dynamics. REMIND-D is an intertemporal optimization model, featuring optimal annual mitigation effort and technology deployment as a model output. In order to provide transparency on model assumptions, this paper gives an overview of the model structure, the input data used to calibrate REMIND-D to the Federal Republic of Germany, as well as the techno-economic parameters of the technologies considered in the energy system module.

Suggested Citation

  • Eva Schmid & Brigitte Knopf & Nico Bauer, 2012. "REMIND-D: A Hybrid Energy-Economy Model of Germany," Working Papers 2012.09, Fondazione Eni Enrico Mattei.
    • Handle: RePEc:fem:femwpa:2012.09
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    Cited by:

    1. Koppelaar, Rembrandt H.E.M. & Keirstead, James & Shah, Nilay & Woods, Jeremy, 2016. "A review of policy analysis purpose and capabilities of electricity system models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1531-1544.
    2. Knopf, Brigitte & Nahmmacher, Paul & Schmid, Eva, 2015. "The European renewable energy target for 2030 – An impact assessment of the electricity sector," Energy Policy, Elsevier, vol. 85(C), pages 50-60.
    3. Shafiei, Ehsan & Davidsdottir, Brynhildur & Leaver, Jonathan & Stefansson, Hlynur & Asgeirsson, Eyjolfur Ingi, 2014. "Potential impact of transition to a low-carbon transport system in Iceland," Energy Policy, Elsevier, vol. 69(C), pages 127-142.
    4. Schmid, Eva & Knopf, Brigitte, 2012. "Ambitious mitigation scenarios for Germany: A participatory approach," Energy Policy, Elsevier, vol. 51(C), pages 662-672.
    5. Schmid, Eva & Pahle, Michael & Knopf, Brigitte, 2013. "Renewable electricity generation in Germany: A meta-analysis of mitigation scenarios," Energy Policy, Elsevier, vol. 61(C), pages 1151-1163.
    6. Zhang, Shuwei & Bauer, Nico & Yin, Guangzhi & Xie, Xi, 2020. "Technology learning and diffusion at the global and local scales: A modeling exercise in the REMIND model," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    7. Martínez-Gordón, R. & Morales-España, G. & Sijm, J. & Faaij, A.P.C., 2021. "A review of the role of spatial resolution in energy systems modelling: Lessons learned and applicability to the North Sea region," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    8. Hanna, Richard & Gross, Robert, 2021. "How do energy systems model and scenario studies explicitly represent socio-economic, political and technological disruption and discontinuity? Implications for policy and practitioners," Energy Policy, Elsevier, vol. 149(C).
    9. Ueckerdt, Falko & Brecha, Robert & Luderer, Gunnar & Sullivan, Patrick & Schmid, Eva & Bauer, Nico & Böttger, Diana & Pietzcker, Robert, 2015. "Representing power sector variability and the integration of variable renewables in long-term energy-economy models using residual load duration curves," Energy, Elsevier, vol. 90(P2), pages 1799-1814.
    10. 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.
    11. Ludig, Sylvie & Schmid, Eva & Haller, Markus & Bauer, Nico, 2015. "Assessment of transformation strategies for the German power sector under the uncertainty of demand development and technology availability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 143-156.
    12. Morales-España, Germán & Martínez-Gordón, Rafael & Sijm, Jos, 2022. "Classifying and modelling demand response in power systems," Energy, Elsevier, vol. 242(C).

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    More about this item

    Keywords

    Hybrid Model; Germany; Energy System; Domestic Mitigation;
    All these keywords.

    JEL classification:

    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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