IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i13p3903-d584494.html
   My bibliography  Save this article

Associating Synoptic-Scale Weather Patterns with Aggregated Offshore Wind Power Production and Ramps

Author

Listed:
  • Bedassa R. Cheneka

    (Wind Energy Section, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands)

  • Simon J. Watson

    (Wind Energy Section, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands)

  • Sukanta Basu

    (Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands)

Abstract

Large-scale weather patterns and their variability can influence both the amount of wind power production and its temporal variation, i.e., wind power ramps. In this study, we use a self-organizing map to cluster hourly sea level pressure into a discrete number of weather patterns. The dependency of wind power production and wind power ramps on these weather patterns is studied for the Belgian offshore wind farm fleet. A newly developed wavelet-surrogate ramp-detection algorithm is used for the identification of wind power ramps. It was observed that low-pressure systems, southwesterly and northeasterly wind flows are often associated with high levels of wind power production. Regarding wind power ramps, the type of transition between weather patterns was shown to determine whether ramp up or ramp down events would occur. Ramp up events tend to occur due to the transition from a high-pressure to a low-pressure system, or the weakening of the intensity of a deep low-pressure system. The reverse is associated with ramp down events.

Suggested Citation

  • Bedassa R. Cheneka & Simon J. Watson & Sukanta Basu, 2021. "Associating Synoptic-Scale Weather Patterns with Aggregated Offshore Wind Power Production and Ramps," Energies, MDPI, vol. 14(13), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3903-:d:584494
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/13/3903/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/13/3903/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel E. Horton & Nathaniel C. Johnson & Deepti Singh & Daniel L. Swain & Bala Rajaratnam & Noah S. Diffenbaugh, 2015. "Contribution of changes in atmospheric circulation patterns to extreme temperature trends," Nature, Nature, vol. 522(7557), pages 465-469, June.
    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. Brayshaw, David James & Troccoli, Alberto & Fordham, Rachael & Methven, John, 2011. "The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: A case study over the UK," Renewable Energy, Elsevier, vol. 36(8), pages 2087-2096.
    4. Ohba, Masamichi & Kadokura, Shinji & Nohara, Daisuke, 2016. "Impacts of synoptic circulation patterns on wind power ramp events in East Japan," Renewable Energy, Elsevier, vol. 96(PA), pages 591-602.
    5. Drew, Daniel R. & Barlow, Janet F. & Coker, Phil J., 2018. "Identifying and characterising large ramps in power output of offshore wind farms," Renewable Energy, Elsevier, vol. 127(C), pages 195-203.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. 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).
    3. Ohba, Masamichi & Kanno, Yuki & Bando, Shigeru, 2023. "Effects of meteorological and climatological factors on extremely high residual load and possible future changes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    4. Ohba, Masamichi & Kanno, Yuki & Nohara, Daisuke, 2022. "Climatology of dark doldrums in Japan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    5. Curtis, John & Lynch, Muireann Á. & Zubiate, Laura, 2016. "The impact of the North Atlantic Oscillation on electricity markets: A case study on Ireland," Energy Economics, Elsevier, vol. 58(C), pages 186-198.
    6. Srihari Sundar & Michael T. Craig & Ashley E. Payne & David J. Brayshaw & Flavio Lehner, 2023. "Meteorological drivers of resource adequacy failures in current and high renewable Western U.S. power systems," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    7. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    8. Cannon, D.J. & Brayshaw, D.J. & Methven, J. & Coker, P.J. & Lenaghan, D., 2015. "Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain," Renewable Energy, Elsevier, vol. 75(C), pages 767-778.
    9. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2019. "Characterization of wind resource in China from a new perspective," Energy, Elsevier, vol. 167(C), pages 994-1010.
    10. Wang, Fei & Lai, Hexin & Li, Yanbin & Feng, Kai & Zhang, Zezhong & Tian, Qingqing & Zhu, Xiaomeng & Yang, Haibo, 2022. "Dynamic variation of meteorological drought and its relationships with agricultural drought across China," Agricultural Water Management, Elsevier, vol. 261(C).
    11. 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.
    12. Curtis, John & Lynch, Muireann Á. & Zubiate, Laura, 2016. "Carbon dioxide (CO2) emissions from electricity: The influence of the North Atlantic Oscillation," Applied Energy, Elsevier, vol. 161(C), pages 487-496.
    13. Xing Zhang & Tianjun Zhou & Wenxia Zhang & Liwen Ren & Jie Jiang & Shuai Hu & Meng Zuo & Lixia Zhang & Wenmin Man, 2023. "Increased impact of heat domes on 2021-like heat extremes in North America under global warming," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    14. Ohba, Masamichi & Kadokura, Shinji & Nohara, Daisuke, 2016. "Impacts of synoptic circulation patterns on wind power ramp events in East Japan," Renewable Energy, Elsevier, vol. 96(PA), pages 591-602.
    15. Frances C. Moore, 2017. "Learning, Adaptation, And Weather In A Changing Climate," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 1-21, November.
    16. Enevoldsen, Peter, 2016. "Onshore wind energy in Northern European forests: Reviewing the risks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1251-1262.
    17. Ouyang, Tinghui & Zha, Xiaoming & Qin, Liang & He, Yusen & Tang, Zhenhao, 2019. "Prediction of wind power ramp events based on residual correction," Renewable Energy, Elsevier, vol. 136(C), pages 781-792.
    18. Xiaoting Sun & Qinghua Ding & Shih-Yu Simon Wang & Dániel Topál & Qingquan Li & Christopher Castro & Haiyan Teng & Rui Luo & Yihui Ding, 2022. "Enhanced jet stream waviness induced by suppressed tropical Pacific convection during boreal summer," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    19. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
    20. He, Yaoyao & Zhu, Chuang & An, Xueli, 2023. "A trend-based method for the prediction of offshore wind power ramp," Renewable Energy, Elsevier, vol. 209(C), pages 248-261.

    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:3903-:d:584494. 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.