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Critical weather situations for renewable energies – Part A: Cyclone detection for wind power

Author

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  • Steiner, Andrea
  • Köhler, Carmen
  • Metzinger, Isabel
  • Braun, Axel
  • Zirkelbach, Mathias
  • Ernst, Dominique
  • Tran, Peter
  • Ritter, Bodo

Abstract

A constantly increasing share of weather dependent renewable energies in Germany's power mix poses new challenges concerning grid management and security of energy supply. An evaluation of the three year period from 2012 to 2014 reveals, that 60% of days with largest errors in the day-ahead wind power forecasts for Germany are linked to cyclones and troughs traversing the North Sea, the Baltic Sea or Germany. A cyclone detection algorithm has been developed to automatically indicate these critical weather situations. The algorithm is based on Numerical Weather Prediction model forecasts of mean sea level pressure. The cyclone detection is used to design an automated weather information tool for end-users such as Transmission System Operators (TSOs). For 2014, it identified a critical weather development in 38% of all days. The root mean square error of day-ahead wind power forecasts increased by 1% of installed capacity during these periods. A real time application of the tool is being implemented in order to support a sustainable and save integration of the increasing wind power production. It will then be provided to, and will be tested by, three German TSOs with the purpose of an operative usage to guarantee the security of supply.

Suggested Citation

  • Steiner, Andrea & Köhler, Carmen & Metzinger, Isabel & Braun, Axel & Zirkelbach, Mathias & Ernst, Dominique & Tran, Peter & Ritter, Bodo, 2017. "Critical weather situations for renewable energies – Part A: Cyclone detection for wind power," Renewable Energy, Elsevier, vol. 101(C), pages 41-50.
    • Handle: RePEc:eee:renene:v:101:y:2017:i:c:p:41-50
    DOI: 10.1016/j.renene.2016.08.013
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    References listed on IDEAS

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    1. Gonçalves-Ageitos, María & Barrera-Escoda, Antoni & Baldasano, Jose M. & Cunillera, Jordi, 2015. "Modelling wind resources in climate change scenarios in complex terrains," Renewable Energy, Elsevier, vol. 76(C), pages 670-678.
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    Cited by:

    1. Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
    2. Renken, Volker & Sorg, Michael & Marschner, Volker & Gerdes, Lewin & Gerdes, Gerhard & Fischer, Andreas, 2018. "Geographical comparison between wind power, solar power and demand for the German regions and data filling concepts," Renewable Energy, Elsevier, vol. 126(C), pages 475-484.
    3. Köhler, Carmen & Steiner, Andrea & Saint-Drenan, Yves-Marie & Ernst, Dominique & Bergmann-Dick, Anja & Zirkelbach, Mathias & Ben Bouallègue, Zied & Metzinger, Isabel & Ritter, Bodo, 2017. "Critical weather situations for renewable energies – Part B: Low stratus risk for solar power," Renewable Energy, Elsevier, vol. 101(C), pages 794-803.
    4. Sharif, Arshian & Raza, Syed Ali & Ozturk, Ilhan & Afshan, Sahar, 2019. "The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: A global study with the application of heterogeneous panel estimations," Renewable Energy, Elsevier, vol. 133(C), pages 685-691.
    5. Tian Han & Ying Wang & Xiao Wang & Kang Chen & Huaiwu Peng & Zhenxin Gao & Lanxin Cui & Wentong Sun & Qinke Peng, 2023. "Mixed Multi-Pattern Regression for DNI Prediction in Arid Desert Areas," Sustainability, MDPI, vol. 15(17), pages 1-16, August.
    6. Raynaud, D. & Hingray, B. & François, B. & Creutin, J.D., 2018. "Energy droughts from variable renewable energy sources in European climates," Renewable Energy, Elsevier, vol. 125(C), pages 578-589.
    7. Berrueta, Alberto & Heck, Michael & Jantsch, Martin & Ursúa, Alfredo & Sanchis, Pablo, 2018. "Combined dynamic programming and region-elimination technique algorithm for optimal sizing and management of lithium-ion batteries for photovoltaic plants," Applied Energy, Elsevier, vol. 228(C), pages 1-11.

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