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The Electricity Generation Landscape of Bioenergy in Germany

Author

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  • Reinhold Lehneis

    (Department of Microbial Biotechnology, Helmholtz Centre for Environmental Research GmbH—UFZ, Permoserstraße 15, 04318 Leipzig, Germany)

Abstract

Disaggregated data on electricity generation from bioenergy are very helpful for investigating the economic and technical effects of this form of renewable energy on the German power sector with a high temporal and spatial resolution. But the lack of high-resolution feed-in data for Germany makes it necessary to apply numerical simulations to determine the electricity generation from biomass power plants for a time period and geographic region of interest. This article presents how such a simulation model can be developed using public power plant data as well as open information from German TSOs as input data. The physical model is applied to an ensemble of 20,863 biomass power plants, most of which are in continuous operation, to simulate their electricity generation in Germany for the year 2020. For this period, the spatially aggregated simulation results correlate well with the official electricity feed-in from bioenergy. The disaggregated time series can be used to analyze the electricity generation at any spatial scale, as each power plant is simulated with its technical parameters and geographical location. Furthermore, this article introduces the electricity generation landscape of bioenergy as a high-resolution map and at the federal state level with meaningful energy figures, enabling comprehensive assessments of this form of renewable energy for different regions of Germany.

Suggested Citation

  • Reinhold Lehneis, 2025. "The Electricity Generation Landscape of Bioenergy in Germany," Energies, MDPI, vol. 18(6), pages 1-12, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1497-:d:1614646
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    References listed on IDEAS

    as
    1. Reinhold Lehneis & Daniela Thrän, 2024. "In 50 Shades of Orange: Germany’s Photovoltaic Power Generation Landscape," Energies, MDPI, vol. 17(16), pages 1-12, August.
    2. Liu, Wan-Yu & Lin, Chun-Cheng & Yeh, Tzu-Lei, 2017. "Supply chain optimization of forest biomass electricity and bioethanol coproduction," Energy, Elsevier, vol. 139(C), pages 630-645.
    3. Akhtari, Shaghaygh & Sowlati, Taraneh, 2020. "Hybrid optimization-simulation for integrated planning of bioenergy and biofuel supply chains," Applied Energy, Elsevier, vol. 259(C).
    4. Olauson, Jon & Bergkvist, Mikael, 2015. "Modelling the Swedish wind power production using MERRA reanalysis data," Renewable Energy, Elsevier, vol. 76(C), pages 717-725.
    5. Rauner, Sebastian & Eichhorn, Marcus & Thrän, Daniela, 2016. "The spatial dimension of the power system: Investigating hot spots of Smart Renewable Power Provision," Applied Energy, Elsevier, vol. 184(C), pages 1038-1050.
    6. Jussi Ekström & Matti Koivisto & Ilkka Mellin & Robert John Millar & Matti Lehtonen, 2018. "A Statistical Modeling Methodology for Long-Term Wind Generation and Power Ramp Simulations in New Generation Locations," Energies, MDPI, vol. 11(9), pages 1-18, September.
    7. Thaker, Jayesh & Höller, Robert, 2024. "Hybrid model for intra-day probabilistic PV power forecast," Renewable Energy, Elsevier, vol. 232(C).
    8. 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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    Cited by:

    1. Reinhold Lehneis, 2025. "Effects of Climate Change on Wind Power Generation: A Case Study for the German Bight," Energies, MDPI, vol. 18(13), pages 1-13, June.
    2. Manske, David & Lehneis, Reinhold & Jordan, Matthias & Thrän, Daniela, 2025. "Spatial dynamics of residential heat pump integration and its potential role in the energy transition of German municipalities," Energy, Elsevier, vol. 332(C).

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