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The importance of forecasting regional wind power ramping: A case study for the UK

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  • Drew, Daniel R.
  • Cannon, Dirk J.
  • Barlow, Janet F.
  • Coker, Phil J.
  • Frame, Thomas H.A.

Abstract

In recent years there has been a significant change in the distribution of wind farms in Great Britain, with a trend towards very large offshore farms clustered together in zones. However, there are concerns these clusters could produce large ramping events on time scales of less than 6 h as local meteorological phenomena simultaneously impact the production of several farms. This paper presents generation data from the wind farms in the Thames Estuary (the largest cluster in the world) for 2014 and quantifies the high frequency power ramps. Based on a case study of a ramping event which occurred on 3rd November 2014, we show that due to the large capacity of the cluster, a localised ramp can have a significant impact on the cost of balancing the power system on a national level if it is not captured by the forecast of the system operator. The planned construction of larger offshore wind zones will exacerbate this problem. Consequently, there is a need for accurate regional wind power forecasts to minimise the costs of managing the system. This study shows that state-of-the-art high resolution forecast models have capacity to provide valuable information to mitigate this impact.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:114:y:2017:i:pb:p:1201-1208
    DOI: 10.1016/j.renene.2017.07.069
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    References listed on IDEAS

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    1. 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.
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    1. 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.
    2. Carlos Otero-Casal & Platon Patlakas & Miguel A. Prósper & George Galanis & Gonzalo Miguez-Macho, 2019. "Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter," Energies, MDPI, vol. 12(16), pages 1-19, August.
    3. Xiaosheng Peng & Kai Cheng & Jianxun Lang & Zuowei Zhang & Tao Cai & Shanxu Duan, 2021. "Short-Term Wind Power Prediction for Wind Farm Clusters Based on SFFS Feature Selection and BLSTM Deep Learning," Energies, MDPI, vol. 14(7), pages 1-18, March.
    4. Laura Cornejo-Bueno & Lucas Cuadra & Silvia Jiménez-Fernández & Javier Acevedo-Rodríguez & Luis Prieto & Sancho Salcedo-Sanz, 2017. "Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data," Energies, MDPI, vol. 10(11), pages 1-27, November.

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