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A combined modeling approach for wind power feed-in and electricity spot prices

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  • Keles, Dogan
  • Genoese, Massimo
  • Möst, Dominik
  • Ortlieb, Sebastian
  • Fichtner, Wolf

Abstract

Wind power generation and its impacts on electricity prices has strongly increased in the EU. Therefore, appropriate mark-to-market evaluation of new investments in wind power and energy storage plants should consider the fluctuant generation of wind power and uncertain electricity prices, which are affected by wind power feed-in (WPF). To gain the input data for WPF and electricity prices, simulation models, such as econometric models, can serve as a data basis.

Suggested Citation

  • Keles, Dogan & Genoese, Massimo & Möst, Dominik & Ortlieb, Sebastian & Fichtner, Wolf, 2013. "A combined modeling approach for wind power feed-in and electricity spot prices," Energy Policy, Elsevier, vol. 59(C), pages 213-225.
    • Handle: RePEc:eee:enepol:v:59:y:2013:i:c:p:213-225
    DOI: 10.1016/j.enpol.2013.03.028
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    7. Ciara O'Dwyer & L. (Lisa B.) Ryan & Damian Flynn, 2017. "Efficient large-scale energy storage dispatch: challenges in future high renewables systems," Open Access publications 10197/9103, School of Economics, University College Dublin.
    8. Florian Ziel & Rick Steinert & Sven Husmann, 2014. "Efficient Modeling and Forecasting of the Electricity Spot Price," Papers 1402.7027, arXiv.org, revised Oct 2014.
    9. Russo, Marianna & Bertsch, Valentin, 2020. "A looming revolution: Implications of self-generation for the risk exposure of retailers," Energy Economics, Elsevier, vol. 92(C).
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    15. Luňáčková, Petra & Průša, Jan & Janda, Karel, 2017. "The merit order effect of Czech photovoltaic plants," Energy Policy, Elsevier, vol. 106(C), pages 138-147.
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    17. Hain, Martin & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2017. "An Electricity Price Modeling Framework for Renewable-Dominant Markets," Working Paper Series in Production and Energy 23, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
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    19. Ziel, Florian & Steinert, Rick & Husmann, Sven, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Energy Economics, Elsevier, vol. 51(C), pages 430-444.
    20. Mira Watermeyer & Thomas Mobius & Oliver Grothe & Felix Musgens, 2023. "A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling," Papers 2304.09336, arXiv.org.
    21. 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.
    22. Hain, Martin & Kargus, Tobias & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2022. "An electricity price modeling framework for renewable-dominant markets," Working Paper Series in Production and Energy 66, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    23. Gao, Cuixia & Sun, Mei & Geng, Yong & Wu, Rui & Chen, Wei, 2016. "A bibliometric analysis based review on wind power price," Applied Energy, Elsevier, vol. 182(C), pages 602-612.
    24. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).

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