IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i10p1475-d113000.html
   My bibliography  Save this article

Wind Power Ramps Driven by Windstorms and Cyclones

Author

Listed:
  • Madalena Lacerda

    (Unidade de Energias Renováveis e Integração de Sistemas de Energia, Laboratório Nacional de Energia e Geologia, I.P. (LNEG), 1649-038 Lisbon, Portugal)

  • António Couto

    (Unidade de Energias Renováveis e Integração de Sistemas de Energia, Laboratório Nacional de Energia e Geologia, I.P. (LNEG), 1649-038 Lisbon, Portugal)

  • Ana Estanqueiro

    (Unidade de Energias Renováveis e Integração de Sistemas de Energia, Laboratório Nacional de Energia e Geologia, I.P. (LNEG), 1649-038 Lisbon, Portugal)

Abstract

The increase in the wind power predictability assumes a very important role for secure power system operation at minimum costs, especially in situations with severe changes in wind power production. In order to improve the forecast of such events, also known as “wind power ramp events”, the underlying role of some severe meteorological phenomena in triggering wind power ramps must be clearly understood. In this paper, windstorm and cyclone detection algorithms are implemented using historical reanalysis data allowing the identification of key characteristics (e.g., location, intensity and trajectories) of the events with the highest impact on the wind power ramp events in Portugal. The results show a strong association between cyclones/windstorms and wind power ramp events. Moreover, the results highlight that it is possible to use some features of these meteorological phenomena to detect, in an early stage, severe wind power ramps thus creating the possibility to develop operational decision tools in order to support the security of power systems with high amounts of wind power generation.

Suggested Citation

  • Madalena Lacerda & António Couto & Ana Estanqueiro, 2017. "Wind Power Ramps Driven by Windstorms and Cyclones," Energies, MDPI, vol. 10(10), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1475-:d:113000
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/10/1475/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/10/1475/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ricardo Bessa & Carlos Moreira & Bernardo Silva & Manuel Matos, 2014. "Handling renewable energy variability and uncertainty in power systems operation," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 3(2), pages 156-178, March.
    2. 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.
    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. 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.
    2. 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.

    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. Gallego-Castillo, Cristobal & Bessa, Ricardo & Cavalcante, Laura & Lopez-Garcia, Oscar, 2016. "On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power," Energy, Elsevier, vol. 113(C), pages 355-365.
    2. Ricardo J. Bessa & Corinna Möhrlen & Vanessa Fundel & Malte Siefert & Jethro Browell & Sebastian Haglund El Gaidi & Bri-Mathias Hodge & Umit Cali & George Kariniotakis, 2017. "Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry," Energies, MDPI, vol. 10(9), pages 1-48, September.
    3. Talan, Amogh & Rao, Amar & Sharma, Gagan Deep & Apostu, Simona-Andreea & Abbas, Shujaat, 2023. "Transition towards clean energy consumption in G7: Can financial sector, ICT and democracy help?," Resources Policy, Elsevier, vol. 82(C).
    4. Taylor, James W., 2017. "Probabilistic forecasting of wind power ramp events using autoregressive logit models," European Journal of Operational Research, Elsevier, vol. 259(2), pages 703-712.
    5. EunJi Ahn & Jin Hur, 2022. "A Practical Metric to Evaluate the Ramp Events of Wind Generating Resources to Enhance the Security of Smart Energy Systems," Energies, MDPI, vol. 15(7), pages 1-16, April.
    6. Wang, Qin & Wu, Hongyu & Florita, Anthony R. & Brancucci Martinez-Anido, Carlo & Hodge, Bri-Mathias, 2016. "The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales," Applied Energy, Elsevier, vol. 184(C), pages 696-713.
    7. Ma, Yixiang & Yu, Lean & Zhang, Guoxing, 2022. "Short-term wind power forecasting with an intermittency-trait-driven methodology," Renewable Energy, Elsevier, vol. 198(C), pages 872-883.
    8. 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.
    9. Mathieu Pichault & Claire Vincent & Grant Skidmore & Jason Monty, 2021. "Short-Term Wind Power Forecasting at the Wind Farm Scale Using Long-Range Doppler LiDAR," Energies, MDPI, vol. 14(9), pages 1-21, May.
    10. Amirhossein Sajadi & Luka Strezoski & Vladimir Strezoski & Marija Prica & Kenneth A. Loparo, 2019. "Integration of renewable energy systems and challenges for dynamics, control, and automation of electrical power systems," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(1), January.
    11. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
    12. 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.
    13. Carlos Adrian Correa-Florez & Andrea Michiorri & Georges Kariniotakis, 2019. "Comparative Analysis of Adjustable Robust Optimization Alternatives for the Participation of Aggregated Residential Prosumers in Electricity Markets," Energies, MDPI, vol. 12(6), pages 1-27, March.
    14. 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).
    15. Song, Weiye & Yan, Jie & Han, Shuang & Liu, Shihua & Wang, Han & Dai, Qiangsheng & Huo, Xuesong & Liu, Yongqian, 2024. "A multi-task spatio-temporal fusion network for offshore wind power ramp events forecasting," Renewable Energy, Elsevier, vol. 237(PB).
    16. Gao, Qiang & Yuan, Rui & Ertugrul, Nesimi & Ding, Boyin & Hayward, Jennifer A. & Li, Ye, 2023. "Analysis of energy variability and costs for offshore wind and hybrid power unit with equivalent energy storage system," Applied Energy, Elsevier, vol. 342(C).
    17. Ines Würth & Laura Valldecabres & Elliot Simon & Corinna Möhrlen & Bahri Uzunoğlu & Ciaran Gilbert & Gregor Giebel & David Schlipf & Anton Kaifel, 2019. "Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36," Energies, MDPI, vol. 12(4), pages 1-30, February.
    18. Peng, Xinghao & Li, Yanting & Tsung, Fugee, 2024. "A graph attention network with spatio-temporal wind propagation graph for wind power ramp events prediction," Renewable Energy, Elsevier, vol. 236(C).
    19. Xu, Bin & Zhu, Feilin & Zhong, Ping-an & Chen, Juan & Liu, Weifeng & Ma, Yufei & Guo, Le & Deng, Xiaoliang, 2019. "Identifying long-term effects of using hydropower to complement wind power uncertainty through stochastic programming," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    20. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.

    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:10:y:2017:i:10:p:1475-:d:113000. 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.