Dirk Cannon a , David Brayshaw a , John Methven a , Phil Coker b ,

1. A 33 yr climatology of extreme wind power generation events in Great BritainEMS Annual Meeting & ECAM (2013) Dirk Cannona, David Brayshawa, John Methvena, Phil Cokerb, David Lenaghanc, Andrew Richardsc, David Millsc, David Bunneyc 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 frequencyand severityof extreme wind power generation events

3. Motivation • Needs of transmission system operators (TSOs) to understand the frequencyand severityof extreme wind power generation events Persistent wind power generation

4. Motivation • Needs of transmission system operators (TSOs) to understand the frequencyand severityof extreme wind power generation events Rapid changesinwind power generation

5. Methodology | 33 yr climatology | Summary and future work Methodology 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

6. Methodology | 33 yr climatology | Summary and future work Conversion to power 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

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

8. Methodology | 33 yr climatology | Summary and future work Comparisons with NG data GB-aggregated over 215 wind farms: r = 0.73 r = 0.91

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

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

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

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

13. Methodology| 33 yr climatology | Summary and future work Seasonal variability E.g., LF ≤ 2.2 % for persistence time ≥ 12 hr: Mean: 0.5 /seasonRange: 0.15 /winter1.4 /summer

14. Methodology| 33 yr climatology | Summary and future work Summary • Estimated the frequencyand severityof extreme wind power generation events in Great Britain over the last 33 yr. • Considered three extremes: • Persistent low wind power generation • Persistent high wind power generation • 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)

15. Methodology| 33 yr climatology | Summary and future work Future work Predictabilityof 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

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