country scale solar irradiance forecasting for pv power...
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Country scale solar irradiance forecasting for
PV power trading
The benefits of the nighttime satellite-based forecast
Sylvain Cros, Laurent Huet, Etienne Buessler, Mathieu Turpin
7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
European power exchange (EPEX)
■ Power organised SPOT market
– Day-Ahead auctions:
• Electricity traded for delivery the following day at 24-hour time step
• The daily auction takes place at 12:00 pm, 7 days a week
– Intra-day trading:
• Electricity traded for delivery on the same or the following day at 15 min
time step
• Trading is continuous 7 days a week and 24 hours a day (up to 30 min.
before physical delivery
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
Intraday trading
■ Solar energy is a variable source of electricity
■ Intraday power trading leads to fill the gaps at the best price
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
Unbalances adjustements
■ PV power forecast helps to :
• Optimize trading prices
• Avoid costly adjustment
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
Intraday forecast for EPEX market: a
German use-case■ Every morning at 6 am CET, the customer wants:
– Forecast of total Germany PV power production eligible to EPEX
– 15 min time step until 12am CET
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German PV production 2016-08-07 (MW)
Actualproduction
PV capacity map
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
How to get an intraday country
scale PV power forecast?
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02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
German PV production 2016-08-07 (MW)
Actual production
NWP
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
How to get an intraday country
scale PV power forecast?
0
5000
10000
15000
20000
25000
30000
02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
German PV production 2016-08-07 (MW)
Actual production
NWP
Satellite
Forecast delivery time
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
How to get an intraday country
scale PV power forecast?
0
5000
10000
15000
20000
25000
30000
02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
German PV production 2016-08-07 (MW)
Actual production
NWP
Satellite
Night Satellite
Forecast delivery time
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
How to get an intraday country
scale PV power forecast?
■ Objective: quantifiying the benefit of night satellite-based forecast for
trading applications
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German PV production 2016-08-07 (MW)
Actual production
NWP
Satellite
Night Satellite
Machine-learningcombination
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
Country forecast using satellite data
■ Extrapolation of cloud index pattern (Cros et al., 2014) from
Meteosat-10 satellite
Cloud motion vector field on T0 HRV map Forecasted Cloud index maps – Up to 6 hours (step 15 min)
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
Forecasting before the sunrise
■ If the sun is below the horizon :
No cloud data available with VIS channel: => forecast is not possible
■ Delivering forecast at 6:00 CET only possible few days in summer
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
■ Using Meteosat-10 IR channels (10.8 and 3.6 µm) to synthetize a
« Heliosat compatible cloud index » before the sunrise
A night cloud index
2016-05-2600:00 to 10:00 UTC
■ Approach of Hammer et al. (2015):
– Cloud maintain theirdistinctive features duringfew hours between night and day time
– Statistical relationshipsbetween HRV cloud index and brightnesstemperature related value are established
Cloud index2016-05-2600:00 to 10:00 UTC
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
A night cloud index
■ Statistical relationships between HRV channel cloud index and
brightness temperature related value are established
■ Obtaining cloud index - not only a cloud mask - without NWP
profiles
From Hammeret al. (2015)
Fog and stratus Very cold clouds
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
HRV reflectance correction
■ Our modifications for night-day transition
85° < SZA < 89.8°
X = 1/(cos θ + 0.025*exp(-11* cos θ))When 85 < θ < 89.8°Rozenberg (1966)
θ: solar zenithal angle
■ Cloud index is overestimatewhen the sun is low
■ An airmass correction is neededfor pixel reflectance
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
HRV reflectance correction
Airmass correction from Hammer et al., (2015)
Cloud index 2016-07-16 4:15 UTC Cloud index 2016-07-16 4:15 UTC
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
HRV reflectance correction
Cloud index 2016-07-16 4:15 UTC Cloud index 2016-07-16 4:15 UTC
New airmass correction more appropriate for reflectance computed from calibrated radiance on Heliosat-2 (Rigollier et al., 2004) basedmethod
ᑭ’ = ln (1+ ᑭ/a)*a
a = 15/(|θ-75|+000.1 ) * 2
Empirically decreasing highest reflectance values
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
Irradiance night satellite forecast
assessment■ Full year 2016 over 23 DWD stations (hourly irradiation, free access)
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
Satellite-based forecast assessment
■ Night: θ > 89.8° ; Day θ < 89.8°
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1 2 3 4 5 6
Relative RMSE (%)
All
Night
Day
Time horizon (h)
Rel
ativ
e R
MSE
(%
)
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
Satellite-based forecast assessment
■ Night: θ > 89.8° ; Day: θ < 89.8°
Night
All
Day
Time horizon: 3h
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
Country forecast – Statistical approach
Random forest
Algorithm
Historical PV production
Measurements (EPEX SPOT archives)
TrainingCountry power
forecasts
NWP
(ECMWF, ICON, GFS)
Satellite
(Hourcast – Reuniwatt)
Night Satellite
(Hourcast – Reuniwatt)
PV power capacity
map
Seasonnality
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
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NWP
Night Satellite
Machine-learningcombination
Country Forecasting –
Statistical Approach■ Typical successful case: night satellite forecast « warns » NWP that
Germany is more cloudy than expected this early morning
■ Random forest algorithm behaves according to its training and to
the symmetry of the daily production profile
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
Impact evaluation of satellite forecasts
■ Producing day and night satellite forecast for years 2015 and 2016
■ Training random forest algorithm over 2015 and testing over 2016
– without satellite data
– with daytime satellite data
– with day and night satellite data
■ Comparison between forecasts and PV power on a daily basis (including night value). Installed capacity: 39.5 GW
Tests RMSE (MW)
rRMSE/Pinst (%)
MAE(MW)
rMAEPinst (%)
No sat 1997 5.0 1975 3.5
Day sat 1540 3.9 1264 3.2
D&N Sat 1066 2,7 987 2.5
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
Impact evaluation of satellite forecasts
■ MAE in function of time horizon
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0 5 10 15 20 25
rMAE / Pinst (%)
NWP
NWP + Day Sat
NWP + D&N Sat
Time horizon (h)
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
■ A country PV power forecast method has been presented and
evaluated
■ A satellite-based algorithm delivering forecast before sunrise has
been presented and assessed
■ The case-study clearly showed the benefits of satellite imagery for
PV power spot market trading
■ Further progress can be undertaken:
– Improving the cloud index mapping during night/day transition
– Using surface temperature from NWP for a better detection of low cloud
– Progress margin exists in random forest algorithm setting
Conclusion
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7th Solar Integration Workshop on integration of Solar Power into Power Systems | 24-25 October, Berlin, Germany
■ Cros S., Sébastien N., Liandrat O., Schmutz N., Cloud pattern prediction from geostationary meteorological satellite images for solar energy forecasting, SPIE – Remote Sensing Conference, September 22-25 2014, Amsterdam, The Netherlands. Proc. SPIE 9242, Remote Sensing of Clouds and the Atmosphere XIX; and Optics in Atmospheric Propagation and Adaptive Systems XVII, 924202 (21 October 2014)
■ Hammer, A., Kühnert, J., Weinreich, K., & Lorenz, E. (2015). Short-term forecasting of surface solar irradiance based on Meteosat-SEVIRI data using a nighttime cloud index. Remote Sensing, 7(7), 9070-9090.
■ Rigollier, C., Lefèvre, M., & Wald, L. (2004). The method Heliosat-2 for deriving shortwave solar radiation from satellite images. Solar Energy, 77(2), 159-169.
■ Rozenberg, G.V. Twilight: A Study in Atmospheric Optics; Plenum Press: New York, NY, USA,1966
References
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