IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i13p3944-d586741.html
   My bibliography  Save this article

Identification of Extreme Wind Events Using a Weather Type Classification

Author

Listed:
  • António Couto

    (LNEG—Laboratório Nacional de Energia e Geologia, 2610-999 Lisbon, Portugal)

  • Paula Costa

    (LNEG—Laboratório Nacional de Energia e Geologia, 2610-999 Lisbon, Portugal)

  • Teresa Simões

    (LNEG—Laboratório Nacional de Energia e Geologia, 2610-999 Lisbon, Portugal)

Abstract

The identification of extreme wind events and their driving forces are crucial to better integrating wind generation into the power system. Recent work related the occurrence of extreme wind events with some weather circulation patterns, enabling the identification of (i) wind power ramps and (ii) low-generation events as well as their intrinsic features, such as the intensity and time duration. Using Portugal as a case study, this work focuses on the application of a weather classification-type methodology to link the weather conditions with wind power generation, namely, the different types of extreme events. A long-term period is used to assess and characterize the changes in the occurrence of extreme weather events and corresponding intensity on wind power production. High variability is expected under cyclonic regimes, whereas low-generation events are most common in anticyclonic regimes. The results of the work provide significant insights regarding wind power production in Portugal, enabling an increase in its predictability.

Suggested Citation

  • António Couto & Paula Costa & Teresa Simões, 2021. "Identification of Extreme Wind Events Using a Weather Type Classification," Energies, MDPI, vol. 14(13), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3944-:d:586741
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/13/3944/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/13/3944/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Blanco, Herib & Faaij, André, 2018. "A review at the role of storage in energy systems with a focus on Power to Gas and long-term storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1049-1086.
    2. Correia, J.M. & Bastos, A. & Brito, M.C. & Trigo, R.M., 2017. "The influence of the main large-scale circulation patterns on wind power production in Portugal," Renewable Energy, Elsevier, vol. 102(PA), pages 214-223.
    3. Johann Baumgartner & Katharina Gruber & Sofia G. Simoes & Yves-Marie Saint-Drenan & Johannes Schmidt, 2020. "Less Information, Similar Performance: Comparing Machine Learning-Based Time Series of Wind Power Generation to Renewables.ninja," Energies, MDPI, vol. 13(9), pages 1-23, May.
    4. 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.
    5. Li Han & Yan Qiao & Mengjie Li & Liping Shi, 2020. "Wind Power Ramp Event Forecasting Based on Feature Extraction and Deep Learning," Energies, MDPI, vol. 13(23), pages 1-19, December.
    6. Madalena Lacerda & António Couto & Ana Estanqueiro, 2017. "Wind Power Ramps Driven by Windstorms and Cyclones," Energies, MDPI, vol. 10(10), pages 1-20, September.
    7. António Couto & Ana Estanqueiro, 2020. "Exploring Wind and Solar PV Generation Complementarity to Meet Electricity Demand," Energies, MDPI, vol. 13(16), pages 1-21, August.
    8. Hansen, Kenneth & Breyer, Christian & Lund, Henrik, 2019. "Status and perspectives on 100% renewable energy systems," Energy, Elsevier, vol. 175(C), pages 471-480.
    9. Ohlendorf, Nils & Schill, Wolf-Peter, 2020. "Frequency and duration of low-wind-power events in Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(8).
    10. 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.
    11. González-Aparicio, I. & Monforti, F. & Volker, P. & Zucker, A. & Careri, F. & Huld, T. & Badger, J., 2017. "Simulating European wind power generation applying statistical downscaling to reanalysis data," Applied Energy, Elsevier, vol. 199(C), pages 155-168.
    12. Zhang, Jie & Cui, Mingjian & Hodge, Bri-Mathias & Florita, Anthony & Freedman, Jeffrey, 2017. "Ramp forecasting performance from improved short-term wind power forecasting over multiple spatial and temporal scales," Energy, Elsevier, vol. 122(C), pages 528-541.
    13. Gallego-Castillo, Cristobal & Cuerva-Tejero, Alvaro & Lopez-Garcia, Oscar, 2015. "A review on the recent history of wind power ramp forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1148-1157.
    14. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
    15. Fang Liu & Ranran Li & Aliona Dreglea, 2019. "Wind Speed and Power Ultra Short-Term Robust Forecasting Based on Takagi–Sugeno Fuzzy Model," Energies, MDPI, vol. 12(18), pages 1-16, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Muyuan & Yao, Jinfeng & Shen, Yanbo & Yuan, Bin & Simmonds, Ian & Liu, Yunyun, 2023. "Impact of synoptic circulation patterns on renewable energy-related variables over China," Renewable Energy, Elsevier, vol. 215(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kies, Alexander & Schyska, Bruno U. & Bilousova, Mariia & El Sayed, Omar & Jurasz, Jakub & Stoecker, Horst, 2021. "Critical review of renewable generation datasets and their implications for European power system models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    2. Mayer, Martin János & Biró, Bence & Szücs, Botond & Aszódi, Attila, 2023. "Probabilistic modeling of future electricity systems with high renewable energy penetration using machine learning," Applied Energy, Elsevier, vol. 336(C).
    3. António Couto & Ana Estanqueiro, 2020. "Exploring Wind and Solar PV Generation Complementarity to Meet Electricity Demand," Energies, MDPI, vol. 13(16), pages 1-21, August.
    4. Alexis Tantet & Marc Stéfanon & Philippe Drobinski & Jordi Badosa & Silvia Concettini & Anna Cretì & Claudia D’Ambrosio & Dimitri Thomopulos & Peter Tankov, 2019. "e 4 clim 1.0: The Energy for a Climate Integrated Model: Description and Application to Italy," Energies, MDPI, vol. 12(22), pages 1-37, November.
    5. Garrido-Perez, Jose M. & Ordóñez, Carlos & Barriopedro, David & García-Herrera, Ricardo & Paredes, Daniel, 2020. "Impact of weather regimes on wind power variability in western Europe," Applied Energy, Elsevier, vol. 264(C).
    6. Shirizadeh, Behrang & Quirion, Philippe, 2022. "The importance of renewable gas in achieving carbon-neutrality: Insights from an energy system optimization model," Energy, Elsevier, vol. 255(C).
    7. Cosgrove, Paul & Roulstone, Tony & Zachary, Stan, 2023. "Intermittency and periodicity in net-zero renewable energy systems with storage," Renewable Energy, Elsevier, vol. 212(C), pages 299-307.
    8. Matti Koivisto & Kaushik Das & Feng Guo & Poul Sørensen & Edgar Nuño & Nicolaos Cutululis & Petr Maule, 2019. "Using time series simulation tools for assessing the effects of variable renewable energy generation on power and energy systems," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(3), May.
    9. Otero, Noelia & Martius, Olivia & Allen, Sam & Bloomfield, Hannah & Schaefli, Bettina, 2022. "A copula-based assessment of renewable energy droughts across Europe," Renewable Energy, Elsevier, vol. 201(P1), pages 667-677.
    10. Rabbani, R. & Zeeshan, M., 2020. "Exploring the suitability of MERRA-2 reanalysis data for wind energy estimation, analysis of wind characteristics and energy potential assessment for selected sites in Pakistan," Renewable Energy, Elsevier, vol. 154(C), pages 1240-1251.
    11. 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.
    12. Seljom, Pernille & Kvalbein, Lisa & Hellemo, Lars & Kaut, Michal & Ortiz, Miguel Muñoz, 2021. "Stochastic modelling of variable renewables in long-term energy models: Dataset, scenario generation & quality of results," Energy, Elsevier, vol. 236(C).
    13. Lee, Joseph C.Y. & Draxl, Caroline & Berg, Larry K., 2022. "Evaluating wind speed and power forecasts for wind energy applications using an open-source and systematic validation framework," Renewable Energy, Elsevier, vol. 200(C), pages 457-475.
    14. Moreno-Leiva, Simón & Haas, Jannik & Nowak, Wolfgang & Kracht, Willy & Eltrop, Ludger & Breyer, Christian, 2021. "Integration of seawater pumped storage and desalination in multi-energy systems planning: The case of copper as a key material for the energy transition," Applied Energy, Elsevier, vol. 299(C).
    15. Yilmaz, Hasan Ümitcan & Kimbrough, Steven O. & van Dinther, Clemens & Keles, Dogan, 2022. "Power-to-gas: Decarbonization of the European electricity system with synthetic methane," Applied Energy, Elsevier, vol. 323(C).
    16. Zerrahn, Alexander & Schill, Wolf-Peter & Kemfert, Claudia, 2018. "On the economics of electrical storage for variable renewable energy sources," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 108, pages 259-279.
    17. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren & Söder, Lennart, 2018. "Analysis of wind power intermittency based on historical wind power data," Energy, Elsevier, vol. 150(C), pages 482-492.
    18. Yun, Eunjeong & Hur, Jin, 2021. "Probabilistic estimation model of power curve to enhance power output forecasting of wind generating resources," Energy, Elsevier, vol. 223(C).
    19. Stetter, Chris & Wielert, Henrik & Breitner, Michael H., 2022. "Hidden repowering potential of non-repowerable onshore wind sites in Germany," Energy Policy, Elsevier, vol. 168(C).
    20. Yan, Jie & Möhrlen, Corinna & Göçmen, Tuhfe & Kelly, Mark & Wessel, Arne & Giebel, Gregor, 2022. "Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3944-:d:586741. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.