IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v127y2018icp195-203.html
   My bibliography  Save this article

Identifying and characterising large ramps in power output of offshore wind farms

Author

Listed:
  • Drew, Daniel R.
  • Barlow, Janet F.
  • Coker, Phil J.

Abstract

Recently there has been a significant change in the distribution of wind farms in Great Britain with the construction of clusters of large offshore wind farms. These clusters can produce large ramping events (i.e. changes in power output) on temporal scales which are critical for managing the power system (30 min, 60 min and 4 h). This study analyses generation data from the Thames Estuary cluster in conjunction with meteorological observations to determine the magnitude and frequency of ramping events and the meteorological mechanism.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:127:y:2018:i:c:p:195-203
    DOI: 10.1016/j.renene.2018.04.064
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148118304725
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2018.04.064?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kubik, M.L. & Brayshaw, D.J. & Coker, P.J. & Barlow, J.F., 2013. "Exploring the role of reanalysis data in simulating regional wind generation variability over Northern Ireland," Renewable Energy, Elsevier, vol. 57(C), pages 558-561.
    2. Huber, Matthias & Dimkova, Desislava & Hamacher, Thomas, 2014. "Integration of wind and solar power in Europe: Assessment of flexibility requirements," Energy, Elsevier, vol. 69(C), pages 236-246.
    3. Sinden, Graham, 2007. "Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demand," Energy Policy, Elsevier, vol. 35(1), pages 112-127, January.
    4. 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.
    5. Buttler, Alexander & Dinkel, Felix & Franz, Simon & Spliethoff, Hartmut, 2016. "Variability of wind and solar power – An assessment of the current situation in the European Union based on the year 2014," Energy, Elsevier, vol. 106(C), pages 147-161.
    6. Drew, Daniel R. & Cannon, Dirk J. & Barlow, Janet F. & Coker, Phil J. & Frame, Thomas H.A., 2017. "The importance of forecasting regional wind power ramping: A case study for the UK," Renewable Energy, Elsevier, vol. 114(PB), pages 1201-1208.
    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. Naemi, Mostafa & Brear, Michael J., 2020. "A hierarchical, physical and data-driven approach to wind farm modelling," Renewable Energy, Elsevier, vol. 162(C), pages 1195-1207.
    2. 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.
    3. 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.

    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. 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.
    2. Hdidouan, Daniel & Staffell, Iain, 2017. "The impact of climate change on the levelised cost of wind energy," Renewable Energy, Elsevier, vol. 101(C), pages 575-592.
    3. Hayes, Liam & Stocks, Matthew & Blakers, Andrew, 2021. "Accurate long-term power generation model for offshore wind farms in Europe using ERA5 reanalysis," Energy, Elsevier, vol. 229(C).
    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. Gruber, Katharina & Regner, Peter & Wehrle, Sebastian & Zeyringer, Marianne & Schmidt, Johannes, 2022. "Towards global validation of wind power simulations: A multi-country assessment of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the global wind atlas," Energy, Elsevier, vol. 238(PA).
    6. Olauson, Jon, 2018. "ERA5: The new champion of wind power modelling?," Renewable Energy, Elsevier, vol. 126(C), pages 322-331.
    7. Cradden, Lucy C. & McDermott, Frank & Zubiate, Laura & Sweeney, Conor & O'Malley, Mark, 2017. "A 34-year simulation of wind generation potential for Ireland and the impact of large-scale atmospheric pressure patterns," Renewable Energy, Elsevier, vol. 106(C), pages 165-176.
    8. Staffell, Iain & Pfenninger, Stefan, 2018. "The increasing impact of weather on electricity supply and demand," Energy, Elsevier, vol. 145(C), pages 65-78.
    9. Bowen Li & Sukanta Basu & Simon J. Watson & Herman W. J. Russchenberg, 2021. "A Brief Climatology of Dunkelflaute Events over and Surrounding the North and Baltic Sea Areas," Energies, MDPI, vol. 14(20), pages 1-14, October.
    10. Madeleine McPherson & Theofilos Sotiropoulos-Michalakakos & LD Danny Harvey & Bryan Karney, 2017. "An Open-Access Web-Based Tool to Access Global, Hourly Wind and Solar PV Generation Time-Series Derived from the MERRA Reanalysis Dataset," Energies, MDPI, vol. 10(7), pages 1-14, July.
    11. Ingeborg Graabak & Magnus Korpås, 2016. "Variability Characteristics of European Wind and Solar Power Resources—A Review," Energies, MDPI, vol. 9(6), pages 1-31, June.
    12. 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.
    13. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2015. "Designing an index for assessing wind energy potential," Renewable Energy, Elsevier, vol. 83(C), pages 416-424.
    14. 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.
    15. Mararakanye, Ndamulelo & Bekker, Bernard, 2019. "Renewable energy integration impacts within the context of generator type, penetration level and grid characteristics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 441-451.
    16. Coker, Phil J. & Bloomfield, Hannah C. & Drew, Daniel R. & Brayshaw, David J., 2020. "Interannual weather variability and the challenges for Great Britain’s electricity market design," Renewable Energy, Elsevier, vol. 150(C), pages 509-522.
    17. Ritter, Matthias & Deckert, Lars, 2015. "Site assessment, turbine selection, and local feed-in tariffs through the wind energy index," SFB 649 Discussion Papers 2015-046, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    18. Lucy C. Cradden & Francesco Restuccia & Samuel L. Hawkins & Gareth P. Harrison, 2014. "Consideration of Wind Speed Variability in Creating a Regional Aggregate Wind Power Time Series," Resources, MDPI, vol. 3(1), pages 1-20, February.
    19. Pennock, Shona & Coles, Daniel & Angeloudis, Athanasios & Bhattacharya, Saptarshi & Jeffrey, Henry, 2022. "Temporal complementarity of marine renewables with wind and solar generation: Implications for GB system benefits," Applied Energy, Elsevier, vol. 319(C).
    20. Strübing, Dietmar & Moeller, Andreas B. & Mößnang, Bettina & Lebuhn, Michael & Drewes, Jörg E. & Koch, Konrad, 2018. "Anaerobic thermophilic trickle bed reactor as a promising technology for flexible and demand-oriented H2/CO2 biomethanation," Applied Energy, Elsevier, vol. 232(C), pages 543-554.

    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:eee:renene:v:127:y:2018:i:c:p:195-203. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.