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

– 15 min time step until 12am CET

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German PV production 2016-08-07 (MW)

Actual production

PV capacity map

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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German PV production 2016-08-07 (MW)

Actual production

NWP

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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German PV production 2016-08-07 (MW)

Actual production

NWP

Satellite

Forecast delivery time

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

Satellite

Night Satellite

Forecast delivery time

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-learning combination

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)

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

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

2016-05-26 00:00 to 10:00 UTC

■ Approach of Hammer et al. (2015):

– Cloud maintain their distinctive features during few hours between night and day time

– Statistical relationships between HRV cloud index and brightness temperature related value are established

Cloud index 2016-05-26 00:00 to 10:00 UTC

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 Hammer et al. (2015)

Fog and stratus Very cold clouds

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 overestimate when the sun is low

■ An airmass correction is needed for pixel reflectance

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

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) based method

ᑭ’ = ln (1+ ᑭ/a)*a

a = 15/(|θ-75|+000.1 ) * 2

Empirically decreasing highest reflectance values

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)

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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Relative RMSE (%)

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Night

Day

Time horizon (h)

R el

at iv

e R

M SE

( %

)

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

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)

Training Country power

forecasts

NWP

(ECMWF, ICON, GFS)

Satellite

(Hourcast – Reuniwatt)

Night Satellite

(Hourcast – Reuniwatt)

PV power capacity

map

Seasonnality

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-learning combination

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