pvgis

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 1

Integrating CM SAF data with PVGIS for Estimation of Solar Energy System

Performance

Thomas Huld, Richard Müller, Attilio Gambardella

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 2

Solar data needs for solar energy applicationsSolar radiation data are needed for solar energy system

planning, deployment and operation, but the type of data needed varies

• Spatial resolution: generally as high as possible

• Historical data needed for planning/deployment, recent data (or forecasts) needed for operation/monitoring

• Long-term averages are sufficient for some applications, while high time resolution is useful for others

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 3

Solar Energy Systems

Two main groups:• Thermal systems

– Domestic hot water and space heating– Concentrating solar thermal power plants

• Photovoltaic (PV) systems– Fixed mounted flat-plate systems, stand-alone– Fixed mounted flat-plate systems, grid-connected– Sun-tracking flat-plate systems– Sun-tracking concentrating systems

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 4

Data needs for solar energy estimation

• Flat-plate grid-connected PV: global and diffuse/beam long-term monthly averages. Tracking systems are more sensitive to the beam component

• Off-grid PV and solar hot water systems: long time series of hourly/daily global and diffuse/beam values

• Concentrating PV: direct normal irradiance (DNI), at least long-term monthly averages

• Solar thermal power plants: long time series of DNI

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 5

What is PVGIS?

• PVGIS is a database of solar radiation and temperature data combined with a web interface that lets users calculate the energy output of photovoltaic (PV) systems.

• PVGIS is also a scientific tool that allows us to do research on the performance of PV systems over large geographical areas and estimate the potential for solar energy deployment in Europe and Africa

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What is PVGIS? (2)

• The PVGIS solar radiation databases for Europe and Africa were obtained in the following way:– Europe: Long-term monthly averages global and diffuse irradiation

from ground stations are interpolated onto a spatial grid. Data collected by ESRA over the period 1981-1990

– Africa: Daily values of global irradiation sums from the HelioClim-1 database, estimated from MFG satellite data, with a spatial resolution of 15’. Time period 1985-2004

• The online calculation also includes the effects of shadows from nearby mountains. The shadow calculation is based on the SRTM-3 DEM with a resolution of 3 arc-seconds (~90m at the equator).

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 7

Web interface features

• Covers Europe and Africa• Various types of PV installations: fixed or tracking, models

for different PV technologies• High-resolution terrain data allows calculation of the effects

of shadowing• Google Maps interface with search and zoom facilities.

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Click here

Then click here

1000-2000 hitsper day

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Reasons for success

•Simple interface: 2 clicks and you have results•Easily understood results: energy production in kWh•Immediate access:

–Relevant information is not buried 8 menu levels deep–No registration needed–It’s free!

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 10

Research applications

• Mathematical models of PV performance, together with our colleagues of the ESTI laboratory

• Performance of PV technologies in different climates• Performance of different PV mounting strategies• Potential for PV in rural electrification in Africa

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 11

PVGIS: Known problems

• Europe: Heterogeneous spatial distribution of ground station data. Some ground station data not reliable. Data are old.

• Africa: Lack of diffuse component. Low primary spatial resolution, and lack of validation.

• General: beam irradiance is not good enough to calculate DNI with any confidence. Time resolution too low for some applications.

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 12

A wish list of Improvements

• Quality improvements in the solar radiation data• Higher temporal resolution, for research on intermittency and

for off-grid PV installations• Direct normal irradiance for concentrating PV applications• My pipe dream: PVGIS Asia

Collaboration with CM SAF via DWD on high-resolution satellite-based data. Incorporation into PVGIS in 2010.

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 13

Radiation data from CM SAF MSG:

• 4 years of hourly global and beam data, 06-2006 to 05-2010• Geographical coverage: Most of Europe and North Africa:

20N to 58N, 15W to 35E• Spatial resolution 1’30’’ (about 2.5km).

MFG:• 8 years of hourly global and beam data, 1998-2005• Covers most of Europe and North Africa: 0N to 58N, 15W to

35E• Spatial resolution 1’48’’

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 14

Construction of the new PVGIS dataset• Calculation of long-term monthly averages:

– Calculate multiyear average of each time slot for beam and global– Then sum all average time slots in each month– Overall average is arithmetic average of MFG and MSG data

• Calculate monthly beam and diffuse clear-sky indices as needed for the PVGIS algorithm

• Downscaling so far only includes terrain shadows in the online calculation

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 15

Comparison of PVGIS-3 with CM-SAF and other datasets

• Europe:– PVGIS vs. CM-SAF data from MFG and MSG– PVGIS vs. an average of four datasets:

Helioclim-3 (MSG-based)SatelLight (MFG-based)Meteonorm (mainly based on ground stations)NASA SSE (various sources)

• Africa:– PVGIS (HelioClim-1) vs. CM-SAF data from MFG

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 16

Differences from existing PVGIS

PVGIS-3 vs. CM-SAF PVGIS-3 vs. 4 datasets

Global horizontal, relative difference in annual average

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Global irradiation, comparison CM-SAF and PVGIS-3

Location MSG bias(%)

MFG bias (%)

CM-SAF –PVGIS (%)

Lindenberg (DE) -3.4 -3.0 +6.9

Cabauw (NL) +0.4 +1.5 +11.6

Carpentras (FR) +2.1 +5.1 +9.0

Payerne (CH) -3.0 +3.7 +13.2

Camborne (UK) - +6.2 +8.4

Ispra (IT) +8.0 - +15.0

Milano (IT) -0.5 - +13.0

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 18

Differences from PVGIS-3, Africa

PVGIS-3 CM-SAF MFG

Global horizontal, long-term annual average

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 19

Differences from PVGIS-3, Africa

Global horizontal, relative difference in annual average

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 20

First steps in validation of DNI

MSG MFGBSRN station Months MBE (%) Months MBE (%)

Sde Boqer (IL) 26 +5.5 21 +1.5

Cabauw (NL) 36 +4.8 11 +14.9

Carpentras (FR) 24 +2.5 22 +8.6

Payerne (CH) 22 -4.4 24 +9.6

Tamanrasset (DZ) 36 -9.1 24 -5.9

Camborne (UK) 23 +18.2

Relative MBE between CM SAF and BSRN stations

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 21

Future developments

• New version of PVGIS with choice of datasets (1-2 weeks)• Extension of spatial coverage of CM SAF data in PVGIS (if

Richard can fit it into his schedule)• Incorporation of high time-resolution data from CM SAF to

calculate off-grid systems in Europe (~end of year)• Making DNI available in the PVGIS web application (2011)• PVGIS Asia (2011?)

CM-SAF 3rd User Workshop, Rostock, 06-08/09/2010 22

Conclusions

• Successful collaboration between CM SAF and JRC• Clear improvements in some regions with high solar energy

penetration, such as Northern Italy• DNI data look very encouraging• Higher-resolution data allow us to do useful new stuff• PVGIS saved from slow decline into irrelevancy

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THANKS to:

The CM-SAF teams for producing a high-quality solar radiation data set, and making it available!

YOU, for your patience