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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 a , Phil Coker b , David Lenaghan c , Andrew Richards c , David Mills c , David Bunney c d.j.cannon@reading.ac.uk

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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

motivation
Motivation
  • Needs of transmission system operators (TSOs) to understand the frequencyand severityof extreme wind power generation events
motivation1
Motivation
  • Needs of transmission system operators (TSOs) to understand the frequencyand severityof extreme wind power generation events

Persistent wind

power generation

motivation2
Motivation
  • Needs of transmission system operators (TSOs) to understand the frequencyand severityof extreme wind power generation events

Rapid changesinwind

power generation

methodology

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
conversion to power

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

comparisons with ng data

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

comparisons with ng data1

Methodology | 33 yr climatology | Summary and future work

Comparisons with NG data

GB-aggregated over 215 wind farms:

r = 0.73

r = 0.91

persistent low wind

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?

persistent high wind

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?

rapid changes

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?

inter annual variability

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

seasonal variability

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

summary

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)
future work

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

slide16

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