Temporally and Spatially Resolved Simulation of the Wind Power Generation in Germany

Author:

Lehneis Reinhold1ORCID,Thrän Daniela12ORCID

Affiliation:

1. Department of Bioenergy, Helmholtz Centre for Environmental Research GmbH—UFZ, Permoserstraße 15, 04318 Leipzig, Germany

2. Bioenergy Systems Department, DBFZ Deutsches Biomasseforschungszentrum gGmbH, Torgauer Str. 116, 04347 Leipzig, Germany

Abstract

Temporally and spatially resolved data on wind power generation are very useful for studying the technical and economic aspects of this variable renewable energy at local and regional levels. Due to the lack of disaggregated electricity data from onshore and offshore turbines in Germany, it is necessary to use numerical simulations to calculate the power generation for a given geographic area and time period. This study shows how such a simulation model, which uses freely available plant and weather data as input variables, can be developed with the help of basic atmospheric laws and specific power curves of wind turbines. The wind power model is then applied to ensembles of nearly 28,000 onshore and 1500 offshore turbines to simulate the wind power generation in Germany for the years 2019 and 2020. For both periods, the obtained and spatially aggregated time series are in good agreement with the measured feed-in patterns for the whole of Germany. Such disaggregated simulation results can be used to analyze the power generation at any spatial scale, as each turbine is simulated separately with its location and technical parameters. This paper also presents the daily resolved wind power generation and associated indicators at the federal state level.

Funder

Helmholtz Association of German Research Centres

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

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5. How to estimate wind-turbine infeed with incomplete stock data: A general framework with an application to turbine-specific market values in Germany;Engelhorn;Energy Econ.,2018

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