Country scale solar irradiance forecasting for PV power...

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


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


Intraday trading

■ Solar energy is a variable source of electricity

■ Intraday power trading leads to fill the gaps at the best price


Unbalances adjustements

■ PV power forecast helps to :

• Optimize trading prices

• Avoid costly adjustment


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

0

5000

10000

15000

20000

25000

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)

Actualproduction

PV capacity map


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


Actual production

NWP




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


Actual production

NWP

Satellite

Forecast delivery time




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


Actual production

NWP

Satellite

Night Satellite

Forecast delivery time




■ Objective: quantifiying the benefit of night satellite-based forecast for

trading applications

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


Actual production

NWP

Satellite

Night Satellite

Machine-learningcombination


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)


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


■ 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


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


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



Airmass correction from Hammer et al., (2015)

Cloud index 2016-07-16 4:15 UTC Cloud index 2016-07-16 4:15 UTC



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


Irradiance night satellite forecast

assessment■ Full year 2016 over 23 DWD stations (hourly irradiation, free access)


Satellite-based forecast assessment

■ Night: θ > 89.8° ; Day θ < 89.8°

0

10

20

30

40

50

60

70

1 2 3 4 5 6

Relative RMSE (%)

All

Night

Day

Time horizon (h)

Rel

ativ

e R

MSE

(%

)


Satellite-based forecast assessment

■ Night: θ > 89.8° ; Day: θ < 89.8°

Night

All

Day

Time horizon: 3h


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


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

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


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


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)


■ 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


■ 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

Thank you!

Find more information on our website

www.reuniwatt.com

[email protected]

http://www.reuniwatt.com/

Country scale solar irradiance forecasting for PV power...

Documents

Transcript of Country scale solar irradiance forecasting for PV power...