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The future spatial distribution of onshore wind energy capacity based on a probabilistic investment calculus

Author

Listed:
  • Yannik Pflugfelder
  • Christoph Weber

    (Chair for Management Sciences and Energy Economics, University of Duisburg-Essen)

Abstract

The spatial distribution of future renewable capacities is a key determinant for developing appropriate grid expansion plans. This is particularly relevant for onshore wind energy. Existing studiesmostly extrapolate future installations based on existing capacities and available sites. As wind farm projects are developed mainly by private investors, the economic rationale of investing at specific sites deserves more attention. Therefore, the present contribution develops a model of economic choice for wind investments based on site-specific computations of the achievable net present value, taking into consideration the land availability at the regional level. Therefore, sitespecific investment decisions are modeled as (partly aggregated) discrete choices. The net present value is computed from investment costs and expected yields, which can be estimated based on wind speed time series and power curves. Available land can be identified by excluding settlement, infrastructure, and nature conservation areas with appropriate buffers, as well as sites with topographically unsuitable profiles. The model is formulated as a nested logit model that captures the interdependencies between choices on two levels: the probability of investment in a particular region on the first level and the probability of installing a specific turbine type on the second level. In an application for Germany with the target capacities of the German Renewable Energy Act, the model delivers a spatial distribution of the capacities at the NUTS 3 level. The model also enables the derivation of the necessary compensation level and the most frequently installed turbine types.

Suggested Citation

  • Yannik Pflugfelder & Christoph Weber, 2025. "The future spatial distribution of onshore wind energy capacity based on a probabilistic investment calculus," EWL Working Papers 2501, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Jan 2025.
  • Handle: RePEc:dui:wpaper:2501
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    References listed on IDEAS

    as
    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, July.
    2. Michelsen, Carl Christian & Madlener, Reinhard, 2012. "Homeowners' preferences for adopting innovative residential heating systems: A discrete choice analysis for Germany," Energy Economics, Elsevier, vol. 34(5), pages 1271-1283.
    3. Conroy, Niamh & Deane, J.P. & Ó Gallachóir, Brian P., 2011. "Wind turbine availability: Should it be time or energy based? – A case study in Ireland," Renewable Energy, Elsevier, vol. 36(11), pages 2967-2971.
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    5. Pöstges, Arne & Weber, Christoph, 2023. "Identifying key elements for adequate simplifications of investment choices – The case of wind energy expansion," Energy Economics, Elsevier, vol. 120(C).
    6. de Vries, Bert J.M. & van Vuuren, Detlef P. & Hoogwijk, Monique M., 2007. "Renewable energy sources: Their global potential for the first-half of the 21st century at a global level: An integrated approach," Energy Policy, Elsevier, vol. 35(4), pages 2590-2610, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    wind energy; regionalization models; renewable energy sources; nested logit model;
    All these keywords.

    JEL classification:

    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy

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