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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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
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
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
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
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
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
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
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
0
1
2
3
4
5
6
7
8
0 5 10 15 20 25
rMAE / Pinst (%)
NWP
NWP + Day Sat
NWP + D&N Sat
Time horizon (h)
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
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