2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar

3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA [1]

SolarGIS

New generation solar resource

database and PV online assessment tools

geomodelsolar.eu

solargis.info

Artur Skoczek

Branislav Schnierer

GeoModel Solar, Slovakia

3rd PV Performance Modeling Workshop, May 5-7, 2014, Santa Clara, CA


About GeoModel Solar

Development and operation of SolarGIS online system

• Solar resource and meteo database

• PV simulation software

• Data services for solar energy and PV:

Consultancy and expert services

geomodelsolar.eu

solargis.info


PVGIS

Research and demonstration project

Promotion of PV in Europe

by European Commission,

Joint Research Centre

SolarGIS

Commercial database and software

Focus on industry needs

by GeoModel Solar

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

GeoModel Solar: history

2012 2014 2013


1. Solar and meteo database

2. Interactive services

• Solar prospection

• PV prefeasibility and planning

• PV performance monitoring

3. Computer-to-computer services

• Web services and regular data supply

• Solar and PV forecasting

http://solargis.info

SolarGIS platform


Topics

SolarGIS


2. PV simulation tools

3. Online applications

4. Summary


Photovoltaics (PV)

What is required?

Concentrated Solar Power (CSP)

Concentrated Photovoltaics (CPV)

GHI (Global Horizontal Irradiation) DNI (Direct Normal Irradiation)

Solar resource


What data sources are available for Japan

Free infor sources:

• NASA SSE

• NREL

• PVGIS

• ...

Commercial suppliers

Source: NASA/SWERA, Meteonorm , 3Tier, SolarGIS

SolarGIS database


Data issues:

• Limited accuracy

=> difference between data sets can be seen

• Some databases are static

What data source are available for Japan

Input data sources

Data resolution

Methods

Level of validation

SolarGIS database


Requirements for solar resource data

• Data to be available at any location (continuous coverage)

• Longer climate record

• High accuracy (validated)

• High level of detailed (temporal, spatial)

• Continuous:

• Historical data

• Data for monitoring, nowcasting

• Data for forecasting

This is available with satellite-based data,

supported by high-quality ground measurements

SolarGIS database


Solar radiation – sources of information

1. Ground sensors

• Pyranometers or photo cells

• Installed on the site

2. Satellite-based solar models

• Input: satellite & atmospheric data

• Data are available globally



1. Ground sensors







Option 1: Ground (on-site) measurements

ADVANTAGES LIMITATIONS

High frequency measurements (sec. to min.)

Higher accuracy, if properly managed and

controlled

Historical data

Meteo stations are irregularly distributed

Limited time availability

Sensor accuracy

Recent data

Costs for acquisition and operation

Regular maintenance and calibration

Data quality checking


Issues in ground measurements

Quality-control procedures

Missing data

Time shift

Unrealistic values

Shading

Misaligned and miscalibrated sensors



1. Ground sensors







ADVANTAGES CHALLENGES

Available everywhere

Spatial and temporal consistency

Calibration stability

High availability >99% (gaps are filled)

History of up to 20+ years

Lower instantaneous accuracy

(spatial resolution approx. 3.5 km)

Lower frequency of measurements

(15 and 30 minutes)

Source: EUMETSAT, ECMWF, NOAA, SRTM-3, SolarGIS

Option 2: Satellite data

SolarGIS database


Ground-measured vs. satellite-derived

Distance to the nearest meteo

stations – interpolation gives only

approximate estimate

Source: SolarGIS

Resolution of the input data used in

the SolarGIS model: AOD: Atmospheric Optical Depth

WV: Water Vapour

MFG/MSG: Meteosat First/Second Generation


SolarGIS: innovation in satellite-based solar modeling

Solar resource data

• Geography-adapted models

with numerous improvements

• New cloud model

• New-generation atmospheric data

• High level of detail

• Extensive validation

• Online global services, fast availability

• Customized services

SolarGIS database

Source: SolarGIS


Corrected geometric and radiometric

distortions

Multi-spectral analysis of satellite data

• 2 to 4 channels

Multi-temporal analysis

SolarGIS: improved use of satellite data

Source: MTSAT (JMA)

SolarGIS database


SolarGIS: improved use of satellite data

Source: NOAA

SolarGIS database


• Snow/ice/fog conditions

• Tropical clouds

• High mountains

• Deserts (reflecting surfaces,

high clouds, dust)

• Coastal zones

SolarGIS models: adapted to different geographies

Source: EUMETSAT

SolarGIS database


New

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Da

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: M

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SolarGIS: improved identification of aerosols

Source: ECMWF

SolarGIS database


Ilorin, Nigeria

Tamanrasset, Algeria

Riyadh, Saudi Arabia

DNI

SolarGIS: improved identification of aerosols

Atmospheric pollution changes rapidly

Source: AERONET, ECMWF

SolarGIS database


SolarGIS innovation: detailed terrain modelling

Source: SolarGIS (c) 2014 Google

Global coverage (iMaps): 250 metres

GHI in Central Japan

Regional maps: 90 metres

GHI in Kosrae, Micronesia

• Primary data (satellite): 3 to 5 km

• Terrain postprocessing: data available

at resolution up to 90 metres

SolarGIS database


GHI solar resource in the world context

Source: SolarGIS

SolarGIS database


Uncertainty of satellite-based solar resource: SolarGIS

Typical range of uncertainty

(annual values at 80% occurrence):

• Global Horizontal Irradiation (GHI): ±4%

• Direct Normal Irradiation (DNI): ±8%

180+ GHI & DNI measurements

230+ aerosol measurements (AERONET)

Theoretical uncertainty of the best ground sensors:

• ±2% for GHI

• ±1% for DNI

-> In real conditions difficult to achieve

Conference SolarPACES 2012, 13 September 2012, Marrakech, Morocco [18]

Use of AOD correction for improvement of SolarGIS database

- Regional adaptation of the AOD database used in SolarGIS model

- Based on the AERONET data and ground measurements

- Aim: identify and remove regional bias of the MACC AOD database, reduce DNI

uncertainty

SolarGIS database

Source: AERONET stations


Uncertainty of satellite-based solar resource: SolarGIS

SolarGIS database

SolarGIS GHI validation data

Typical range of uncertainty

(annual values at 80% occurrence):

• Global Horizontal Irradiation (GHI): ±4%

• Direct Normal Irradiation (DNI): ±8%

180+ GHI & DNI measurements

230+ aerosol measurements (AERONET)

Theoretical uncertainty of the best ground sensors:

• ±2% for GHI

• ±1% for DNI

-> In real conditions difficult

to achieve


Typical uncertainty of satellite-derived SolarGIS data

Global Horizontal Irradiation

Hourly: ±12% to 45% Daily: ±5% to 23% Monthly: ±4% to 14%

Annual: ±3% to 7%

80% probability of occurrence (example of Almeria, Spain)

Uncertainty of Direct Normal Irradiation is about 1.5 to 2x higher

SolarGIS database


Hourly values Daily Monthly Yearly

SolarGIS: Uncertainty of Global Horizontal Irradiance

The uncertainty for ground sensors considers that they are well maintained, calibrated and data are quality controlled

±4 to ±8%

SolarGIS high uncertainty

• high latitudes

• high mountains

• high and changing aerosols

• reflecting desert surfaces

• snow and ice

SolarGIS low uncertainty

• arid and semiarid regions

• low and medium aerosols

SolarGIS database


Hourly values Daily Monthly Yearly

±8 to 15%

SolarGIS high uncertainty

• high latitudes

• high mountains

• high and changing aerosols

• reflecting desert surfaces

• snow and ice

SolarGIS low uncertainty

• arid and semiarid regions

• low and medium aerosols

SolarGIS: Uncertainty of Direct Normal Irradiance

SolarGIS database


Summary: How SolarGIS compares to ground measurements

Limits

• Accuracy lower than best sensors

• Inherent discrepancy of high frequencey measurements (e.g. hourly)

Advantages

• Comparative accuracy with good quality sensors in many regions

• Better than low quality sensors

• Radiometric stability and continuity

• Easy calculation of solar radiation for any PV surface (fixed or suntracking)

• Historical data available (up to 20+ years)

• Can be correlated (site-adapted) by local measurements

SolarGIS database


• Derived from meteorological models

• Validated

• Air temperature

• Ancillary data: Wind speed, Relative humidity…

SolarGIS meteo parameters

Source: SolarGIS, Google, NOAA

Air temperature


Topics

SolarGIS




4. Summary


PV performance in Standard Test Conditions: 2308 kWh/kWp

PV annual output: 1782 kWh/kWp, losses 22.8% (PR=77.2%), uncertainty: 5.5%

PV simulation chain example Cairo (SolarGIS)

Global irradiation

(module surface)

Mismatch and cable losses

Inter-row shading losses

Angular reflectivity

Shading by terrain

Losses in the conversion of

irradiance into DC in modules

Transformers and AC losses

Technical availability

-1.2% ±0.7%

Dirt, dust and soiling

-2.0% ±0.8%

-2.5% ±0.6%

-1.0% ±0.7%

±4.5%

-2.6% ±0.5%

-2.5% ±2.0%

-11.7% ±2.0%

Losses in the inverters

-1.5% ±0.5%

LOSSES UNCERTAINTY

-0.0% ±0.0% Irradiation

received by

PV modules

DC power

in PV modules

Conversion

to AC, transformation

and feed to 22 kV

Air temperature

SolarGIS PV tools


Scatergram of SolarGIS satelite derived

Global Horizontal Irradiation vs. ground

measured data

Scatergram of postprocessed

SolarGIS air temperature vs. ground

measured data

Match between ground measured GHI and temperature

with SolarGIS values

In collaboration with SUPSI, Switzerland

SolarGIS PV tools


PV simulation algorithms (SolarGIS)

Simulation of energy yield and performance ratio of triple junction roof-integrated and

free-standing amorphous silicon modules mounted horizontally

Red: measured PV data

Black: simulated data

In collaboration with SUPSI Switzerland

SolarGIS PV tools


Topics

SolarGIS




4. Summary


Fast access to data

• Interactive access

• Web services, FTP

Coverage: world

Availability: within few hours for any location

• Historical: from 1994/1999/2006 up to yesterday

• Nowcast: daily update

• Forecast: up to 48 hours ahead

SolarGIS data services

SolarGIS database


Data products are tuned to save time and money

Prefeasibility and prospection

• Long-term monthly averages

iMaps

pvPlanner

Project development and operation

• Time series

• Typical Meteorological Year

• 15 and 30-minute, hourly monthly

climData

Automatic data services

SolarGIS data services

SolarGIS database

• Annual

subscription

• Aggregated data

Lower price

• Data to be

purchased per site

• High information

content

Higher price


Prospecting and site evaluation

iMaps: High-resolution satellite-based data and maps

• Online and fast access to long-term annual and monthly averages

• Detailed and accurate maps

Source: SolarGIS

SolarGIS online applications


Prospecting and site evaluation

pvPlanner: PV planning tool

• Easy search of site

• Accurate simulation

• High-resolution data

• Technology options

• Access to data (xls, csv and pdf)

Source: SolarGIS



High resolution data

climData: Purchase site-specific data:

• Time series

• Typical Meteorological Year (TMY)

Where to use

• Project development

• Site adaptation

• Performance assessment

of power plants

• Quality control of ground measurements



Regular monitoring

pvSpot: performance assessment

• Independent view on the performance of the system

• Daily update



15-minute profile of PV power generation

SolarGIS database


Topics

SolarGIS




4. Summary


Summary 1: SolarGIS database

Solar and meteo data

• Low uncertainty of raw SolarGIS data

• High detail

• History of satellite-based solar radiation

and meteo data 15+ years

• Near real-time data update

Source: SolarGIS


Summary 2: SolarGIS online tools

• Detailed data resolution

• Interactive maps

• Fast access (interactive and automated)

• Accurate PV simulation

• Scaled products and services

Source: SolarGIS


Thank you!

Artur Skoczek

Branislav Schnierer

GeoModel Solar, Slovakia

http://solargis.info

http://gemodelsolar.eu

2014 PV Performance Modeling Workshop: New Generation Solar Resource Database and PV Online Assessment Tools: Artur Skoczek, GeoModel Solar

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