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

Yannik Pflugfelder () and Christoph Weber ()
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Christoph Weber: Chair for Management Sciences and Energy Economics, University of Duisburg-Essen

No 2501, EWL Working Papers from University of Duisburg-Essen, Chair for Management Science and Energy Economics

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.

Keywords: wind energy; regionalization models; renewable energy sources; nested logit model (search for similar items in EconPapers)
JEL-codes: C35 Q42 Q48 R58 (search for similar items in EconPapers)
Pages: 24
Date: 2025-01, Revised 2025-01
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