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A 33 yr climatology of extreme wind power generation events in Great Britain EMS Annual Meeting & ECAM (2013) Dirk Cannon a, David Brayshaw a, John Methven.

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Presentation on theme: "A 33 yr climatology of extreme wind power generation events in Great Britain EMS Annual Meeting & ECAM (2013) Dirk Cannon a, David Brayshaw a, John Methven."— Presentation transcript:

1 A 33 yr climatology of extreme wind power generation events in Great Britain EMS Annual Meeting & ECAM (2013) Dirk Cannon a, David Brayshaw a, John Methven a, Phil Coker b, David Lenaghan c, Andrew Richards c, David Mills c, David Bunney c d.j.cannon@reading.ac.uk a Department of Meteorology, University of Reading, UK b School of Construction Management and Engineering, University of Reading, UK c National Grid, Wokingham, Berkshire, RG41 5BN, UK

2 Motivation Needs of transmission system operators (TSOs) to understand the frequency and severity of extreme wind power generation events

3 Motivation Persistent wind power generation

4 Needs of transmission system operators (TSOs) to understand the frequency and severity of extreme wind power generation events Motivation Rapid changes in wind power generation

5 Wind speed record from reanalysis data (MERRA, Rienecker et. al., 2011. J. Clim. 24, 3624–3648 ) Long time series (1980–2012) Consistent assimilation of observations Gridded data (can be used with any wind farm distribution) Reproduces majority of observed variability in 10 m wind speed on – spatial scales > 200–300 km, – time scales > 3–6 hours Methodology Methodology | 33 yr climatology | Summary and future work

6 1. Wind farm distribution as of September, 2012; bi-linearly interpolated 2. Log-height extrapolation to turbine hub height 3. Transformation to Load Factor (LF) using idealised power curve Conversion to power Methodology | 33 yr climatology | Summary and future work

7 GB-aggregated over 215 wind farms: LF Note: MERRA-derived LF assumes constant wind farm distribution, whereas the real distribution constantly evolves Comparisons with NG data Methodology | 33 yr climatology | Summary and future work r = 0.96

8 Comparisons with NG data Methodology | 33 yr climatology | Summary and future work r = 0.73 r = 0.91

9 Persistent low wind How often do persistent low wind power generation events occur in an average year? Methodology | 33 yr climatology | Summary and future work

10 Persistent high wind How often do persistent high wind power generation events occur in an average year? Methodology | 33 yr climatology | Summary and future work

11 Rapid changes For how many hours in an average year is there a subsequent rapid change in wind power generation? Methodology | 33 yr climatology | Summary and future work

12 Inter-annual variability E.g., LF ≤ 6.3 % for persistence time ≥ 24 hr: Mean: 10 yr -1 Range: 2-18 yr -1 Methodology | 33 yr climatology | Summary and future work

13 Seasonal variability E.g., LF ≤ 2.2 % for persistence time ≥ 12 hr: Mean: 0.5 /season Range: 0.15 /winter 1.4 /summer Methodology | 33 yr climatology | Summary and future work

14 Summary Estimated the frequency and severity of extreme wind power generation events in Great Britain over the last 33 yr. Considered three extremes: 1)Persistent low wind power generation 2)Persistent high wind power generation 3)Rapid changes in wind power generation Return periods show large variations from year-to-year and season-to-season Quantitative results sensitive to power curve (not shown) Methodology | 33 yr climatology | Summary and future work

15 Future work Predictability of extreme wind power events GB-wide scales – On time scale of hours to days – Statistical and case study analysis Regional scales – Downscale to km-scale – Investigate extreme generation events & large forecast errors d.j.cannon@reading.ac.uk Methodology | 33 yr climatology | Summary and future work

16 Predictability of extreme wind power events GB-wide scales – On time scale of hours to days – Statistical and case study analysis Regional scales – Downscale to km-scale – Investigate extreme generation events & large forecast errors d.j.cannon@reading.ac.uk


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