SOLAR POWER FORECASTING BASED ON NUMERICAL

Detlev Heinemann, Elke LorenzEnergy Meteorology Group, Institute of Physics, Oldenburg University

SOLAR POWER FORECASTING BASED ONNUMERICAL WEATHER PREDICTION, SATELLITEDATA, AND POWER MEASUREMENTS

Workshop on Forecasting, Balancing and Scheduling of Renewable Energy Sources in IndiaShangri-la Hotel, New Delhi5 May 2014

SOLAR POWER FORECASTING

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(I) German power sector today(II) Components of solar power forecasting system(III) Irradiance forecasting based on satellite data(IV) Combination of numerical weather models and satellite data(V) PV power forecasts

(VI) Forecasting of Direct Normal Irradiance (DNI)(VII)Advanced techniques

CONTENT


Workshop on Forecasting, Balancing and Scheduling of Renewable Energy Sources in India – New Delhi – 5 May 2014

NET POWER PRODUCTION FROM WIND AND SOLAR, 2013GERMAN POWER SECTOR TODAY

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Data: Fraunhofer ISE, BSW, DEWI

Installed capacity, end of 2013: PV: 35,7 GW Wind: 34,7 GW (compare with average load: ~63 GW)

Power production 2013: PV: 29,7 TWh (5,3 % of net electricity consumption) Wind: 47,2 TWh (8,4 % of net electricity consumption)

Max. combined production from wind & solar (18.04.2013):

35,9 GW = 52 % of load (PV 19,2 GW, Wind 16,7 GW)

Partial supply of load from wind and solar up to 50% each

Problem: peak load

Excess production very likely to occur soon



NET POWER PRODUCTION FROM WIND AND SOLARGERMAN POWER SECTOR TODAY

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Data: EEX

Maximum of combined production from wind & solar

18 April 2013

35,9 GW



NET POWER PRODUCTION FROM WIND AND SOLARGERMAN POWER SECTOR TODAY

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Data: EEX

Maximum of combined production from wind & solar

14 April 2014

37,8 GW


MONTHLY PRODUCTION SOLAR, WIND2013

Workshop on Forecasting, Balancing and Scheduling of Renewable Energy Sources in India – New Delhi – 5 May 2014 6

Sources: B. Burger, Fraunhofer ISE, Leipziger Strombörse EEX

GERMAN POWER SECTOR TODAY

Min:1,7 TWh

Max: 8,2 TWh

WIND

Min:3,9 TWh

SOLAR

TWh

7,0

6,0

5,0

4,0

3,0

2,0

1,0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec




WIND

SOLAR

SOLAR & WIND

Max: 0,2 TWhMin: 0,002 TWh



DAILY PRODUCTION SOLAR, WIND2013



MAXIMUM* POWER SOLAR, WIND2013* 15-min averages

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WIND

SOLAR

26,3 GW

24,0 GW




BALANCING OF SOLAR POWERACCORDING TO RENEWABLE ENERGY SOURCES ACT

‣ Mandatory purchase of all available renewable electricity by grid operators

‣ Marketing and balancing of PV power by transmission system operators (TSOs) based on their respective share of the all German electricity supply (horizontal burden sharing)

‣ Additional option of direct marketing of PV power is getting more important

Need for regional forecastscontrol areas of German transmission system operators



BALANCING OF SOLAR POWER

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BY TRANSMISSION SYSTEM OPERATORS

hourly forecast for next day

PV power feed-in, Germany ‣ day-ahead:selling of PV power at the European Power Exchange (EPEX) - hourly contingents - 12:00 for the next day



15min resolution forecast for next hours


‣ intra-day:trading of electricity at the EPEX - hours or 15minute periods - until 45 minutes before deliveryforecast errors influence prices

BY TRANSMISSION SYSTEM OPERATORSBALANCING OF SOLAR POWER



online-estimation from measured values


‣ intra-day:trading of electricity at the EPEX - hours or 15minute periods - until 45 minutes before deliveryforecast errors influence prices

‣ remaining deviations are adjusted with balancing power

BY TRANSMISSION SYSTEM OPERATORSBALANCING OF SOLAR POWER


OVERVIEW OF SCHEME


REGIONAL PV POWER PREDICTION

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Regional PV power forecast

Simulation of PV power

Solar irradiance forecast

Numericalweather prediction

Cloud motionfrom satellite

PV power measurements



Meteosat Second Generation (HR VIS)‣ Cloud index from Meteosat images (Heliosat methode)

Resolution:

MSG (HRV): 1.2 km x 2.2 km

(Germany) 15 Minuten

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IRRADIANCE FORECAST USING SATELLITE DATACLOUD TRACKING/CLOUD MOTION VECTORS



‣ Cloud index from Meteosat images (Heliosat methode)

‣ ‘Cloud motion vectors‘ (CMV) from identification of pattern in consecutive images

‣ Extrapolation of cloud motion to forecast next cloud index image


Meteosat Second Generation (HR VIS)



Satellite-based solar irradiance map

200W/m2 900W/m2

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‣ Cloud index from Meteosat images (Heliosat methode)

‣ ‘Cloud motion vectors‘ (CMV) from identification of pattern in consecutive images

‣ Extrapolation of cloud motion to forecast next cloud index image

‣ Solar irradiance from forecasts of cloud index images using the Heliosat method



DATA BASIS


Period01.01.-30.09.2012

Measurementsirradiance measurementsof 270 stations* in Germany

Forecasts DWD ECMWF CMV Combination of forecasts

EVALUATION OF IRRADIANCE FORECASTS

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* operated by DWD and meteomedia GmbH


COMBINATION OF ECMWF, DWD & CMV* FORECASTS


date

I in

W/m

2

* CMV: Cloud Motion Vectors

EVALUATION

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date

I in

W/m

2

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COMBINATION OF ECMWF, DWD & CMV* FORECASTSEVALUATION

Combination of forecasts increases accuracy!

* CMV: Cloud Motion Vectors



3h ahead: Satellite-based forecasts show improved results than NWP-based forecasts(MVF == CMV)

mean Germany

rmseECMWF 41W/m2

rmseCMV 34W/m2

EVALUATION: 3-HOUR FORECASTS

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COMBINATION OF ECMWF, DWD & CMV FORECASTS

CMV-

CMV,



Combination shows significant improvement: ccerror(ECWMF,DWD)=0.51, ccerror(ECWMF,CMV)=0.29

mean Germany

rmseECMWF 41W/m2

rmsecmv 34W/m2

rmsecombi 25W/m2

improvement * vs ECMWF 39%

improvement * vs CMV 26%

*

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EVALUATION: 3-HOUR FORECASTSCOMBINATION OF ECMWF, DWD & CMV FORECASTS

CMV-

CMV,


RMSE AS FUNCTION OF FORECAST HORIZON


UNCERTAINTY OF COMBINED FORECASTS

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CMV forecasts superior to NWP based forecast up to 4 hours ahead

CMV forecast superior to persistence of cloud situation from 2 hours onwards

significant improvement with combined model

Calculation was done only for solar zenith angle < 80° and only for hours for which all models were available (depending on the forecast horizon)



RMSE AS FUNCTION OF FORECAST HORIZONUNCERTAINTY OF COMBINED FORECASTS

‣ significant improvement by combination of forecasts with different time horizons‣ more pronounced for regionally smoothed forecasts


OVERVIEW OF SCHEME



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OVERVIEW OF SCHEME



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DATA BASIS


PV POWER FORECAST

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Control area 50Hertz

December 2011 – November 2012

Hourly data

PV measurements:

Monitoring data meteocontrol: > 1000 PV systems,approx. 20% of installed power

Single site evaluation:80 selected systems

No measurement of overall PV power feed-in in control area!



ESTIMATION OF PV POWER FEED-ININ CONTROL AREA

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Upscaling using Meteocontrol monitoring data all systems in the control areaconsidering spatial distribution with 2-digits post code resolution*considering distribution of PV system size *overall installed power Pnom (given by 50Hertz)

Forecast data basis

06:00, 1-3 days11/11-05/12: ECWMF only06/12-11/12: ECMWF & DWD

11:00, ‘intra-day‘09/12-11/12: combination of NWP and CMV

* based on EEG data, 16.8.2012



daylight values only, normalization to installed power

control areacontrol area single sitessingle sites

bias rmse bias rmse

Intra-day 0.9% 4.9% 1.1% 12.0%

Day-ahead 0.7% 5.7% 1.2% 12.8%

3 days 0.5% 6.2% 0.9% 13.4%

EVALUATION

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ANNUAL COURSE OF FORECAST ERRORS


Largest rmse values:February – April 2012

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EVALUATION


ANNUAL COURSE OF FORECAST ERRORS


Largest rmse values:February 2012--> Snow

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EVALUATION



o NWP, 06:00x combi, 11:00

o NWP, 06:00x combi, 11:00

Large improvement by combination of models!

COMBINATION OF NWP AND SATELLITE-BASED CMV FORECASTS

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EVALUATION



THANK YOU FOR YOUR ATTENTION!



FORECASTING OF DNI‣ CSP needs a high DNI forecast accuracy especially in cloud-free cases

with high DNI (different from, e.g., PV)‣ Good forecasts in case of low DNI are required (-> good water cloud

mask forecast) as well as for medium DNI situations ( ->good forecast of cirrus cloud optical properties)

‣ CSP technologies generally operate only in areas with high DNI and low cloud cover‣ depending on the geographical region of interest and its vicinity to

global aerosol sources, the priority is set either on good aerosol or cirrus forecasts

‣ The MACC (Monitoring Atmospheric Composition and Climate) project of ECMWF in the scope of GMES provides analysis and forecast of aerosols integrated into the Integrated Forecast System (IFS)

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PRINCIPLE

ECWMFirradiance forecast

Hourlysite-specific GHI forecasts

Measurement data

Post-processing

Direct/diffusemodel

DNI

forecasts

Clear sky model

FORECASTING OF DNI

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Smoothed with weighting function

Parameterization for beam fraction b = Ibeam /G in dependence of k*

k*-range separated in three intervals:

MODEL FOR DIRECT IRRADIANCEFORECASTING OF DNI

cloudy situationclearskybroken cloud effect

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EVALUATION (2005 & 2007)

Stations in Southern or Central Spain, DNI: rel. rmse: 40% – 50%Stations at the Northern Spanish coast, DNI: rel rmse: >70 %

FORECASTING OF DNI

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SOLAR POWER FORECASTING BASED ON NUMERICAL