Local and regional PV power forecasting based on PV … · 2020-03-06 · October 22nd th2015, 4 PV Modelling Workshop, Köln, Germany 11 Local and regional PV power forecasting based

October 22nd 2015, 4th PV Modelling Workshop, Köln, Germany 1 1

Local and regional PV power forecasting based on

PV measurements, satellite data and numerical weather predictions

Elke Lorenz, Jan Kühnert, Björn Wolff,

Annette Hammer, Detlev Heinemann

1)Energy Meteorology Group, Institute of Physics, University of Oldenburg

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October 22nd 2015, 4th PV Modelling Workshop, Köln, Germany 2

Outline

grid integration of PV power in Germany

overview on PV power prediction system

evaluation:

different data and models for different forecast horizons

combination of different models

summary and outlook

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October 22nd 2015, 4th PV Modelling Workshop, Köln, Germany

Grid integration of PV in Germany

installed Power: 38.5GWpeak (end of 2014)

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marketing of PV power at the European Energy Exchange

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control areas





by transmission system operators regional forecasts

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control areas






direct marketing local forecasts

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control areas







day ahead: for the next day

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control areas








intraday: until 45 minutes before delivery

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control areas








intraday: until 45 minutes before delivery

here: 15 min to 5 hours ahead

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control areas


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PV power measurement

hours forecast horizon

Overview of forecasting scheme

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satellite cloud motion forecast CMV

hours forecast horizon


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days hours

NWP: numerical weather prediction

forecast horizon


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PV power predictions



days hours

PV power forecasting: PV simulation and statistical models


forecast horizon


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Numerical weather predictions

global model forecast (IFS) of the European Centre for Medium-Range Weather Forecasts (ECWMF)

regional model forecasts (COSMO EU) of the German Meteorological service (DWD)

COSMO EU, dir. irradiance 2104-05-02, 12:00

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Numerical weather predictions

global model forecast (IFS) of the European Centre for Medium-Range Weather Forecasts (ECWMF)

regional model forecasts (COSMO EU) of the German Meteorological service (DWD)

Post processing:

bias correction and combination with linear regression

COSMO EU, dir. irradiance 2104-05-02, 12:00

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Meteosat Second Generation (high resolution visible range)

cloud index from Meteosat images with Heliosat method* resolution in Germany

1.2km x 2.2 km 15 minutes

Irradiance prediction based on satellite data

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*Hammer et al 2003


cloud index from Meteosat images with Heliosat method

cloud motion vectors by identification of matching cloud structures in consecutive images

extrapolation of cloud motion to predict future cloud index


Meteosat Second Generation (high resolution visible range)

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satellite derived irradiance maps

cloud index from Meteosat images with Heliosat method

cloud motion vectors by identification of matching cloud structures in consecutive images

extrapolation of cloud motion to predict future cloud index

irradiance from predicted cloud index images with Heliosat method


200W/m2 900W/m2

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Measurement data

March- November 2013

15 minute values

921 PV systems1) in Germany

information on PV system tilt and orientation

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1)Monitoring data base of Meteocontrol GmbH

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days hours

PV power forecasting: PV simulation and statistical models


forecast horizon


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days hours


forecast horizon


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Pmeas(t-Dt)

Pclear(t-Dt)

persistence: constant ratio of measured PV power Pmeas to clear sky PV power Pclear

Ppers (t)= Pclear(t)


Different input data and models

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days hours


forecast horizon

persistence

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PV simulation:

Diffuse fraction model: Skartveith et al, 1998

Tilt model: Klucher 1970

Parametric model for MPP efficiency: Beyer et al 2004

Linear regression





days hours

PV simulation


forecast horizon


PV simulation

persistence

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Regional forecasts: persistence, CMV and NWP based forecasts

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Rmse in dependence of forecast horizon

15 minute values

normalization to installed power Pinst only daylight values, calculation time of CMV: sunel > 10° only hours with all models available included in dependence of forecast horizon

N

iinst

pred

inst

meas

P

P

P

P

Nrmse

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2

1

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forecasts for German average

CMV forecasts better than NWP based forecast up to 4 hours ahead

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N

iinst

pred

inst

meas

P

P

P

P

Nrmse

1

2

1

15 minute values normalization to installed power Pinst only daylight values, calculation time of CMV: sunel > 10° only hours with all models available included in dependence of forecast horizon



forecasts for German average

CMV forecasts better than NWP based forecast up to 4 hours ahead

persistence better than CMV forecasts up to 1.5 hour ahead

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N

iinst

pred

inst

meas

P

P

P

P

Nrmse

1

2

1

15 minute values normalization to installed power Pinst only daylight values, calculation time of CMV: sunel > 10° only hours with all models available included in dependence of forecast horizon



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comparison of German average and single site forecasts:

rmse for German about 1/3 of single sites rmse for NWP forecasts

German average single sites



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comparison of German average and single site forecasts:

rmse for German about 1/3 of single sites rmse for NWP forecasts

improvements with persistence and CMV larger for regional forecasts



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days hours

PV simulation*


forecast horizon


PV simulation*

persistence

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*)PV simulation with bias correction


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days hours

PV simulation


forecast horizon

Combination of different models

PV simulation

persistence

combination

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Combination of forecasting methods

combination of forecast models with linear regression:

Pcombi=aNWPPNWP + aCMVPCMV + apersistPpersist + a0

coefficients aNWP, aCMV, apersist, a0 are fitted to measured data


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in dependence of

forecast horizon

hour of the day


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in dependence of

forecast horizon

hour of the day

training data:

for single site forecasts: each PV system separately


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in dependence of

forecast horizon

hour of the day

training data:

for single site forecasts: each PV system separately

for regional forecasts: average of sites


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How many days to train forecast combination?

Improvement score:

with respect to best single model

𝑟𝑚𝑠𝑒𝑟𝑒𝑓 − 𝑟𝑚𝑠𝑒𝑐𝑜𝑚𝑏𝑖

𝑟𝑚𝑠𝑒𝑟𝑒𝑓

all sites average, May to November, 2012


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Improvement score:




all sites average, May to November, 2012 independent test year for model configuration


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Improvement score:


last 30 days



all sites average, May to November, 2012


Regional forecasts

regression coefficients in dependence of forecast horizon

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bars:

standard deviation for all hours

regression coefficients (weights) reflect horizon dependent forecast performance of different models


Regional forecasts

Rmse in dependence of forecast horizons

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Considerable improvement with combined model over single model forecasts


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Single site forecasts

regression coefficients in dependence of forecast horizon

bars:

standard deviation

for all hours & sites

regression coefficients (weights) reflect horizon dependent forecast performance of different models for single sites


Regression coefficients

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bars:

standard deviation

for all hours & sites

horizon dependent regression coefficients different for regional and single site forecasts




forecast combination outperforms single model forecasts for all horizons

improvements with combination larger for regional forecasts

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Summary

PV power prediction contributes to successful grid integration of more than 38 GWpeak PV power in Germany

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Summary


PV power forecasts based on satellite data (CMV) significantly better than NWP based forecasts up to 4 hours ahead

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Summary


PV power forecasts based on satellite data (CMV) significantly better than NWP based forecasts up to 4 hours ahead

significant improvement by combining different forecast models with PV power measurements, in particular for regional forecasts

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Outlook

combination of machine learning with PV simulation for integration of additional data sources:

additional meteorological parameters

additional NWP systems

uncertainty information

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Thank you for your attention!

EU-FP7 grant agreement no: 308991

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Documents

Local and regional PV power forecasting based on PV … · 2020-03-06 · October 22nd th2015, 4 PV Modelling Workshop, Köln, Germany 11 Local and regional PV power forecasting based