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

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  • 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
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    References listed on IDEAS

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    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.
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    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.

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