Application of a MultiScheme Ensemble Prediction System for Wind Power Forecasting in Ireland

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Application of aMulti-SchemeEnsemble
Prediction Systemfor Wind Power Forecastingin
Ireland
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WEPROG ApS, DenmarkWeather and Wind Energy
PrognosisCorinna Möhrlen, com_at_weprog.comJess
Jørgensen, juj_at_weprog.comUniversity College
Cork, IrelandSustainable Energy Research
Group,Department of Civil and Environmental
EngineeringSteven Lang, s.lang_at_ucc.ie Brian
Ó Gallachóir, b.ogallachoir_at_ucc.ieE. McKeogh,
e.mckeogh_at_ucc.ie
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INTRODUCTION RATIONALEENSEMBLE PREDICTION
SYSTEMS (EPS)WIND POWER PREDICTION
UNCERTAINTYRESULTS VALIDATIONCONCLUSIONS
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INTRODUCTIONReliable wind power forecasting is
vitally important to Enable high wind
penetration Decrease costs of balancing
power Maximise CO2 benefit of wind
generation Ensure power system security and
stability, particularlyon weakly interconnected
grids
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IMPORTANCE OF FORECASTING ON IRISH GRID Total
installed generation on network is 6300MW
Maximum demand 4800MW minimum demand 2000MW
Installed wind generation was 500MW at end of
2005,and an additional 780MW with connection
agreements Further 2700MW applications to
connect to grid Weak interconnection of
Republic of Ireland grid with Northern Ireland
(NI) grid, and only weak interconnectionof NI
with Scotland and the rest of UK.
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TRADITIONAL WIND POWER FORECASTING
Persistence Physical models Statistical
models Hybrid models of the aboveMost rely
on input weather forecast datafrom national
meteorological servicesThese deterministic
forecasts of wind speed and direction are not
usually designed for wind power prediction, and
introduce the greatest errors to predicted wind
power
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ENSEMBLE PREDICTION SYSTEMS (EPS)A group, or
ensemble, of weather forecasts produced in
order to quantify the uncertainty of the
forecast.Different approaches Ensemble
Kalman Filter Singular vector Breeding
vector Multi-model EPS Multi-scheme EPS
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MULTI-SCHEMEENSEMBLE PREDICTION SYSTEM
(MS-EPS) 75-member, limited area EPS 75
different Numerical Weather Prediction
(NWP)model parameterisations, or schemes
Each members scheme differs in formulation of
fast meteorological processes Multi-scheme
method reduces ensemble bias and quantifies
forecast uncertainty
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BACKGROUND TO DEVELOPMENT OF MS-EPS Research
at UCC since 2000 Operational system launched
by WEPROG at Energinet.dk (then Eltra), 2003
Testing in research projects, e.g. Honeymoon,
2003-05 Currently forecasting 20GW wind
power Operating real-time, world-wide by
WEPROG Ongoing research and development by UCC
and WEPROG
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WEATHER PREDICTION WITH MS-EPS
12 hour Forecast 10m wind speed, UK and Ireland,
23/1/06
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WIND POWER PREDICTION MODULEConverts weather
forecast to wind power1 Calibration Step
Training of each ensemble member using
historicalpower production data Direction
dependent, time independent power curvesproduced
for each ensemble member 2 - Forecast Step
Predict power using directional power curves
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WIND POWER PREDICTIONEnerginet.dk - Operational
System since 2003
72 hour Wind Power Forecast for Eltra area,
Denmark, 12/1/06
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IRISH RESULTSValidation against data from
Golagh wind farm, Co. Donegal, northwest Ireland
(complex terrain, high load factor)
Photo courtesy B9 Energy
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VALIDATIONError Descriptors MAE mean
absolute error Bias Standard deviation and
RMSEAll normalised to the installed capacity
of the wind farm or the aggregate operational
area
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Golagh Wind Farm Verification 2/1/05 1/5/05
---- Observed power data with 1 hr smoothing
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IRISH RESULTSGolagh observed power data is
dominated by large fluctuations with amplitude
comparable to the EPS spread - similar effects
have been observed at Horns Rev
---- Observed power, raw 15 min data
Horns Rev output (from Eltra System Plan 2004)
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IRISH RESULTS Daily Forecasts for Golagh
Example 00UTC 48hr forecasts, 2/1/05 13/1/05
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MS-EPS IS ABLE TO QUANTIFY UNCERTAINTY
---- Observed power data with 1 hr smoothing
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QUANTIFICATION OF UNCERTAINTYIS AN IMPORTANT
FEATURE OF THE MS-EPS Physically realistic
uncertainty estimate Grid operators have
difficulty dealing with forecasting system which
uses single, deterministic weather forecasts from
national met services as input to forecasting
tool forecasts can be sometimes way out
Minimise balancing generation and associated
costs System security is enhanced with better
forecasts and information on uncertainty
assists in operating the system during atypical
weather events
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IRISH RESULTSVariation of forecast quality at
Golagh Wind Farm

• Error statistics generated from 24-48 hr
  forecasts
• 30m agl model wind speed
• Normalised to wind farm capacity of 15MW

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IRISH RESULTSVariation of forecast error with
forecast length - Golagh
Normalised mean absolute error out to 48 hour
horizon SOLID __ Statistical best guess Dashed
--- Mean Dotted Best member
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COMPARISON WITH DANISH GERMAN RESULTS To
study any differences between forecasting for
single sites and aggregate areas of wind power
production To investigate the effect of
geographical dispersion of turbines on
forecasting error
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RESULTS Germany / Denmark / Ireland
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DISTRIBUTION OF ERRORSFrequency distribution of
errors for single sitesand Danish and German
aggregate areas
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CONCLUSIONS Golagh and Horns Rev have
significant power output fluctuations and higher
forecast errorsthan aggregate wind power
production areas Forecast errors appear to
increase with increasing load factor, due to
increasingatypical weather events and the
greater number of hours at turbine cut-off
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CONCLUSIONS Study suggests the prediction
error in Ireland will be considerably lower with
geographical dispersion of wind farms
Forecasting for individual farms is more
difficult and less accurate than aggregatedwind
power forecasts
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CONCLUSIONS The Multi-Scheme Ensemble
Prediction System offers the possibility to
estimate the uncertainty of the forecasts This
provides operators more security when handling
wind power and hence enables higher wind
penetration
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ACKNOWLEDGEMENTSSustainable Energy
IrelandStudy funds under RE/W/03/006ESB
National GridData provision and support