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Der Merit-Order-Effekt der erneuerbaren Energien - Analyse der kurzen und langen Frist

Author

Listed:
  • Fürsch, Michaela

    (Energiewirtschaftliches Institut an der Universitaet zu Koeln)

  • Malischek, Raimund

    (Energiewirtschaftliches Institut an der Universitaet zu Koeln)

  • Lindenberger, Dietmar

    (Energiewirtschaftliches Institut an der Universitaet zu Koeln)

Abstract

Unter dem Merit-Order-E ekt (M-O-E) der erneuerbaren Energien (EE) wird deren preissenkende Wirkung auf die Strompreise auf Großhandelsebene (Börsenpreise) verstanden. Sowohl die Höhe des Merit-Order-E ffekts in der kurzen und der langen Frist als auch dessen volkswirtschaftliche Bedeutung ist umstritten. In diesem Artikel analysieren wir zunächst, durch welche Faktoren Preise ffekte erneuerbarer Energien in der kurzen Frist bestimmt werden, unter welchen Bedingungen Preiseff ekte in einem theoretischen langfristigen Gleichgewicht möglich sind und welche Preise ffekte in der Realität durch Unsicherheiten und dynamische Anpassungsprozesse entstehen. In einem zweiten Teil berechnen wir mit einem Optimierungsmodell des europäischen Strommarkts den M-O-E für Deutschland bis 2030. Hierbei berücksichtigen wir im Unterschied zu bestehenden Berechnungen zwei wichtige Determinanten des M-O-E endogen: Anpassungsprozesse des Kraftwerksparks sowie Stromaustauschmöglichkeiten mit dem europäischen Ausland.

Suggested Citation

  • Fürsch, Michaela & Malischek, Raimund & Lindenberger, Dietmar, 2012. "Der Merit-Order-Effekt der erneuerbaren Energien - Analyse der kurzen und langen Frist," EWI Working Papers 2012-14, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    • Handle: RePEc:ris:ewikln:2012_014
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    References listed on IDEAS

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    Cited by:

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    2. Gawel, Erik & Korte, Klaas & Tews, Kerstin, 2015. "Energiewende im Wunderland: Mythen zur Sozialverträglichkeit der Förderung erneuerbarer Energien durch das EEG," UFZ Discussion Papers 2/2015, Helmholtz Centre for Environmental Research (UFZ), Division of Social Sciences (ÖKUS).
    3. Erik Gawel & Klaas Korte & Kerstin Tews, 2015. "Distributional Challenges of Sustainability Policies—The Case of the German Energy Transition," Sustainability, MDPI, vol. 7(12), pages 1-17, December.
    4. Moritz Nobis & Carlo Schmitt & Ralf Schemm & Armin Schnettler, 2020. "Pan-European CVaR-Constrained Stochastic Unit Commitment in Day-Ahead and Intraday Electricity Markets," Energies, MDPI, vol. 13(9), pages 1-35, May.
    5. Cludius, Johanna & Hermann, Hauke & Matthes, Felix Chr. & Graichen, Verena, 2014. "The merit order effect of wind and photovoltaic electricity generation in Germany 2008–2016: Estimation and distributional implications," Energy Economics, Elsevier, vol. 44(C), pages 302-313.
    6. Weigt, Hannes & Ellerman, Denny & Delarue, Erik, 2013. "CO2 abatement from renewables in the German electricity sector: Does a CO2 price help?," Energy Economics, Elsevier, vol. 40(S1), pages 149-158.
    7. Sergei Kulakov & Florian Ziel, 2019. "The Impact of Renewable Energy Forecasts on Intraday Electricity Prices," Papers 1903.09641, arXiv.org, revised Nov 2019.
    8. Strunz, Sebastian, 2014. "The German energy transition as a regime shift," Ecological Economics, Elsevier, vol. 100(C), pages 150-158.
    9. Dillig, Marius & Jung, Manuel & Karl, Jürgen, 2016. "The impact of renewables on electricity prices in Germany – An estimation based on historic spot prices in the years 2011–2013," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 7-15.
    10. Philip Beran & Christian Pape & Christoph Weber, 2018. "Modelling German electricity wholesale spot prices with a parsimonious fundamental model – Validation and application," EWL Working Papers 1801, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Mar 2018.

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    More about this item

    Keywords

    Merit-Order-E ekt; Strommarktoptimierung; Erneuerbare Energien;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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