Belgian Wind Forecasting – Phase 1
Users’ Group 09/02/2012
Pieter-Jan Marsboom v12.02.09
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Overview 1. Context & Drivers 2. Forecast & Upscaling Model 3. Forecast Service 4. Wind Forecast Tool 5. Wind Forecast Quality 6. Challenges 7. Next Steps 8. Conclusion
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Context & Drivers
ELIA sells @ 1,1* P_Belpex_DAM ELIA buys @ 0,9*P_Belpex_DAM Imbalance tarrif
M W p -in s ta lle d c a p a c ity e v o lu tio n B e lg iu m 1800 1600 1400 1200 1000 800 600 400 200 0
SO LAR W IN D
2005
Ref : 30%-rule in KB Offshore 30/03/’09 13/02/2012
2006
2007
2008
2009
2010
2011
Ref : VREG,CWAPE,BRUGEL Belgian Wind Forecasting
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Context & Drivers
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Overview 1. Context & Drivers 2. Forecast & Upscaling Model • Forecast Model • Inventory • Upscaling Model 3. Forecast Service 4. Wind Forecast Tool 5. Wind Forecast Quality 6. Challenges 7. Next Steps 8. Conclusion
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Forecasting model (physical)
DSOLocation +
Wind [MW] + type
Connection Eliasubstation
PV [MWp] Cogen [MWe]
Meteo Forecast [D+1 – D+7]
4 km x 4 km
BLACKBOX Wind
Simulation
Powercurve i.f.o. turbine-type
Substation [D+1 – D+7] Wind
Temperature [°C] Windspeed [m/s] 10 m & 100 m
PV
PV
Powercurve & η of PV-cells
Wind direction [°] Irradiation [W/m²] 13/02/2012
Exhaustive inventory With – turbine-type & # – HUB height – coordinates Current content: Total = 930 MW Onshore = 735 MW Offshore = 195 MW
Cogen
Cogen Installation types industrial households Belgian Wind Forecasting
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Inventory of windfarms in Belgium
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Upscaling methodology
v
v v
Realtime :
15/07/11 - 08/09/11 Realtime + ex-post: 13/02/2012
v
= 70,5% => To compare with Germany : 23% = 78,1% Belgian Wind Forecasting
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Not static numbers Try to acquire more measurements 8
Overview 1. Context & Drivers 2. Forecast & Upscaling Model 3. Forecast Service 4. Wind Forecast Tool 5. Wind Forecast Quality 6. Challenges 7. Next Steps 8. Conclusion
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Forecast Service
Working in a probabilistic world
MW Probabilistic Forecast
Storm Indicator [0/1] 120 100
P90 [MW]
80
MW
Measured [MW] 60 40
Forecast [MW] 20
P10 [MW]
0 -20
Time (Hours)
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Overview 1. Context & Drivers 2. Forecast & Upscaling Model 3. Forecast Service 4. Wind Forecast Tool 5. Wind Forecast Quality 6. Challenges 7. Next Steps 8. Conclusion
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Wind Forecast Tool : 3 GUIâ&#x20AC;&#x2122;s GUI1: forecasts versus measurements For internal & external use (online publication, go-live expected 14th of february) Specifications : -Aggregated forecasts [D+1,D+7] in [MW] updated each day @ 11 A.M. -Upscaled measurements in [MW] updated each 15 min -Filtering possible : onshore <-> offshore, ELIA-connected <-> DSO-connected -Possibility to select period of interest (history) -Extracts in MS Excel possible
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Wind Forecast Tool : 3 GUIâ&#x20AC;&#x2122;s GUI2: realtime evolution of forecast error For internal use (national dispatching) Specifications : -Running average of forecast error [MW], updated each 2 min -Absolute forecast [MW] -Storm indicator [0/1] = which indicates a possible cut-off risk in the next 4 hours
GUI3 : detailed dashboard with wind farm resolution & quarter hour time scale For internal use (national dispatching) â&#x20AC;&#x201C; exports possible
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Overview 1. Context & Drivers 2. Forecast & Upscaling Model 3. Forecast Service 4. Wind Forecast Tool 5. Wind Forecast Quality 6. Challenges 7. Next Steps 8. Conclusion
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Forecast Quality – Day-ahead wind forecast-error over 1 month – RMSE% i.f.o. DA-prediction time – aggregate of [437MW] ~ Belgium [1000MW]
16 Offshore Wind BE
Onshore Wind BE 11 7 Aggregate Wind BE 5,5Aggregate Wind 50Hz 4,5 Aggregate Solar 50 Hz
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Predictability & Observability of Renewables : challenges for the TSO
Belgian Wind Forecasting
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Overview 1. Context & Drivers 2. Forecast & Upscaling Model 3. Forecast Service 4. Wind Forecast Tool 5. Wind Forecast Quality 6. Challenges 7. Next Steps 8. Conclusion
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Challenges â&#x20AC;&#x201C; wind power forecasting -
Gain experience : ex. correlations with other control zones BE <-> GE
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Challenges â&#x20AC;&#x201C; wind power forecasting Plausible correlation forecast error with system imbalance
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Challenges – wind power forecasting Storm management issues
EWP
V1 = cut-in windspeed V2 = Pmax windspeed V3 = cut-in after EWP(10’ avg) V4 = cut-off in EWP (10’ avg) EWP - Hysteresis [V4 V3] Typically [25 m/s 20 m/s] for one WT [22.5m/s 18m/s] for one WF RR = 4 à 5MW/min Ref: Twenties project “Assessment of storm forecast” Deliverable nº: 6.1
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Challenges solar power forecasting Variability wind > solar Forecast-error wind > solar Importance of aggregation effect Germany > Belgium
Ref: CORESO 13/02/2012
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Overview 1. Context & Drivers 2. Forecast & Upscaling Model 3. Forecast Service 4. Wind Forecast Tool 5. Wind Forecast Quality 6. Challenges 7. Next Steps 8. Conclusion
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Next Steps â&#x20AC;&#x201C; wind power forecasting
Red = high wind zone Blue = low wind zone
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Overview 1. Context & Drivers 2. Forecast & Upscaling Model 3. Forecast Service 4. Wind Forecast Tool 5. Wind Forecast Quality 6. Challenges 7. Next Steps 8. Conclusion
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Conclusion
Expected increase in installed renewables capacity (2015: wind > 2GW, solar > 2GWp)
Considering a minimum load of 6 GW during summer : at times >50% of load will be covered by wind & solar alone
Quid incompressibilities considering nuclear production & other not flexible units in base load
First steps have been made regarding predictability & observability of wind power • Go-live of external publication on ELIA website : expected 14th of February
Next steps will have to deal with: • an analogous project for solar power forecasting (considering the significant volume) • improved reserve dimensioning based on gained experience • further integration into decision support tools (congestion & balancing management) • renewables dispatching // traditional dispatching
MWp-installed capacity evolution Belgium
3000 2500 2000 Wind [MW]
1500
Solar [MW]
1000 500 0 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
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Belgian Wind Forecasting – Phase 1
Users’ Group 09/02/2012
Pieter-Jan Marsboom v12.02.09
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