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Optimal Siting of Wind Farms in Wind Energy Dominated Power Systems

Author

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  • Raik Becker

    (Department of Bioenergy, Helmholtz Centre for Environmental Research GmbH-UFZ, Permoserstraße 15, 04318 Leipzig, Germany)

  • Daniela Thrän

    (Bioenergy Systems Department, DBFZ Deutsches Biomasseforschungszentrum gGmbH, Torgauer Straße 116, 04347 Leipzig, Germany)

Abstract

Electricity from renewable energy (RE) sources gained in significance due to green-friendly governmental initiatives in the form of either direct subsidizes, tax incentives or tradable certificates. Thereby, RE generators are incentivized to maximize energy feed-in or the remuneration from governmental subsidizes, meanwhile neglecting any market interaction. Consequently, wind farms are clustered in windy regions. Along with the governmentally initiated integration of RE generation into power markets, the siting of RE generators will change. In wind power dominated power systems that fully integrate RE generators into power markets, wind farms will compete against each other and try to maximize their market value. Hence, wind speed correlations with other wind farms will become increasingly important when choosing a site in a uniform or zonal pricing system. To quantify the impact of market integration on future wind farm siting, an approach is developed that takes into account the local wind potential of a certain site, wind speed correlations to other sites and their installed capacities. An optimization that minimizes the normalized sum of wind power correlations to all other sites and their respective normalized installed wind power capacity is performed. To achieve a predefined minimum energy output, the average wind yield is considered as an additional constraint. The outcome is an optimal wind farm site in a wind energy dominated system. Running this for a given wind power expansion scenario enables decision makers to foresee the spatial development of wind farm installations. To demonstrate the model’s applicability, a case study is performed for Germany. Thereby, wind speed data for four years from the European reanalysis model COSMO-REA6 is used. The results indicate that a full market integration of RE generators will space out more evenly new wind farms. Thereby, wind farms can economically benefit from the non-simultaneity of wind speed.

Suggested Citation

  • Raik Becker & Daniela Thrän, 2018. "Optimal Siting of Wind Farms in Wind Energy Dominated Power Systems," Energies, MDPI, vol. 11(4), pages 1-12, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:978-:d:141866
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    References listed on IDEAS

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    2. 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).
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    5. 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).
    6. Radu, David & Berger, Mathias & Dubois, Antoine & Fonteneau, Raphaël & Pandžić, Hrvoje & Dvorkin, Yury & Louveaux, Quentin & Ernst, Damien, 2022. "Assessing the impact of offshore wind siting strategies on the design of the European power system," Applied Energy, Elsevier, vol. 305(C).
    7. João Paulo N. Torres & Carlos A. F. Fernandes & João Gomes & Bonfiglio Luc & Giovinazzo Carine & Olle Olsson & P. J. Costa Branco, 2018. "Effect of Reflector Geometry in the Annual Received Radiation of Low Concentration Photovoltaic Systems," Energies, MDPI, vol. 11(7), pages 1-15, July.
    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. Drücke, Jaqueline & Borsche, Michael & James, Paul & Kaspar, Frank & Pfeifroth, Uwe & Ahrens, Bodo & Trentmann, Jörg, 2021. "Climatological analysis of solar and wind energy in Germany using the Grosswetterlagen classification," Renewable Energy, Elsevier, vol. 164(C), pages 1254-1266.
    10. Matthias Jordan & Volker Lenz & Markus Millinger & Katja Oehmichen & Daniela Thran, 2019. "Future competitive bioenergy technologies in the German heat sector: Findings from an economic optimization approach," Papers 1908.10065, arXiv.org, revised Aug 2019.
    11. Jianzhou Wang & Chunying Wu & Tong Niu, 2019. "A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network," Sustainability, MDPI, vol. 11(2), pages 1-34, January.
    12. 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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