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How to estimate wind-turbine infeed with incomplete stock data: A general framework with an application to turbine-specific market values in Germany

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  • Engelhorn, Thorsten
  • Müsgens, Felix

Abstract

This paper analyses market values of wind energy converters at the individual turbine level on a very large scale. Such an analysis is usually precluded by the lack of detailed public data on the stock of wind turbines. We therefore present a general method to estimate incomplete turbine stock data and generate hourly yields of individual turbines based on completed turbine stock data and highly disaggregated hourly wind speed data. On this basis, we calculate hourly infeed and annual market values of up to 25,700 wind turbines in Germany from 2005 to 2015. We show the spread in market values on turbine level, quantify regional differences and discuss the effect of turbine age on market values. We show that turbines in central Germany have, on average, lower market values than turbines in the north, south or far west of Germany. Furthermore, we show that modern turbines reach higher market values than older turbines. We also analyse the drivers of market values, differentiating between infeed-price correlation and standard deviation.

Suggested Citation

  • Engelhorn, Thorsten & Müsgens, Felix, 2018. "How to estimate wind-turbine infeed with incomplete stock data: A general framework with an application to turbine-specific market values in Germany," Energy Economics, Elsevier, vol. 72(C), pages 542-557.
    • Handle: RePEc:eee:eneeco:v:72:y:2018:i:c:p:542-557
    DOI: 10.1016/j.eneco.2018.04.022
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    Citations

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    Cited by:

    1. Eising, Manuel & Hobbie, Hannes & Möst, Dominik, 2020. "Future wind and solar power market values in Germany — Evidence of spatial and technological dependencies?," Energy Economics, Elsevier, vol. 86(C).
    2. Klie, Leo & Madlener, Reinhard, 2022. "Optimal configuration and diversification of wind turbines: A hybrid approach to improve the penetration of wind power," Energy Economics, Elsevier, vol. 105(C).
    3. Klie, Leo & Madlener, Reinhard, 2024. "Concentration versus diversification: A spatial deployment approach to improve the economics of wind power," Energy Policy, Elsevier, vol. 185(C).
    4. Dujardin, Jérôme & Schillinger, Moritz & Kahl, Annelen & Savelsberg, Jonas & Schlecht, Ingmar & Lordan-Perret, Rebecca, 2022. "Optimized market value of alpine solar photovoltaic installations," Renewable Energy, Elsevier, vol. 186(C), pages 878-888.
    5. Klie, Leo & Madlener, Reinhard, 2020. "Concentration Versus Diversification: A Spatial Deployment Approach to Improve the Economics of Wind Power," FCN Working Papers 2/2020, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised May 2021.
    6. Ulazia, Alain & Sáenz, Jon & Ibarra-Berastegi, Gabriel & González-Rojí, Santos J. & Carreno-Madinabeitia, Sheila, 2019. "Global estimations of wind energy potential considering seasonal air density changes," Energy, Elsevier, vol. 187(C).
    7. Höfer, Tim & Madlener, Reinhard, 2018. "Locational (In-)Efficiency of Renewable Power Generation Feeding in the Electricity Grid: A Spatial Regression Analysis," FCN Working Papers 13/2018, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised 01 Dec 2019.
    8. Bernath, Christiane & Deac, Gerda & Sensfuß, Frank, 2021. "Impact of sector coupling on the market value of renewable energies – A model-based scenario analysis," Applied Energy, Elsevier, vol. 281(C).
    9. Engelhorn, Thorsten & Müsgens, Felix, 2021. "Why is Germany’s energy transition so expensive? Quantifying the costs of wind-energy decentralisation," Resource and Energy Economics, Elsevier, vol. 65(C).
    10. Alain Ulazia & Ander Nafarrate & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia, 2019. "The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential," Energies, MDPI, vol. 12(13), pages 1-18, July.
    11. Reinhold Lehneis & Daniela Thrän, 2023. "Temporally and Spatially Resolved Simulation of the Wind Power Generation in Germany," Energies, MDPI, vol. 16(7), pages 1-16, April.

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    More about this item

    Keywords

    Renewable energy sources; Wind energy; Bottom-up modelling; Cost minimization; Market value; Power curves;
    All these keywords.

    JEL classification:

    • D49 - Microeconomics - - Market Structure, Pricing, and Design - - - Other
    • Q21 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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