ISO-9060 Standard & Pyranometer Measurement Accuracy · research grade measurement applications •...

ISO-9060 Standard & Pyranometer Measurement Accuracy

Solar Radiation Spectrum & Pyranometer Spectral Response Function

Atmospheric solar radiation spectrum extends from 285 – 4000 nm UV-A / B Spectrum: 280 – 400 nm Visible Spectrum: 400 – 700 nm Infrared Spectrum: 700 – 4000 nm

Pyranometer Types

Thermopile Pyranometer

• ISO-9060 / WMO compliant

• standardized calibration method

• true solar response function

• WRR (World Radiometric Reference) traceable calibration

Si-Pyranometer (photodiode)

• non ISO-9060 / WMO compliant

• non standardized calibration method

• Spectrally limited & selective response function

Thermopile Pyranometer Construction

Basic Design Scheme Thermopile Element

outer dome

inner dome

thermopile detector

bubble level

leveling foot housing / heat sink

Fig. 2

Multi-junction radial thermopile element (64-series connected thermocouple junctions)

Fig. 1

• Domes serve as throughput filter: 0.3 – 3 µm • Spectrally black thermopile acts as blackbody • Bubble level ensures detector is planner • Sensor housing serves as detector heat-sink

Pyranometer Physics 101

Pyranometer GHI Thermal Dynamics

A thermopile is an electronic device which converts thermal heat energy to a voltage potential. Thermopiles generate an output voltage linearly proportional to the temperature differential (i.e. ΔT) between the thermopile hot junction receiver surface and cold junction pyranometer housing / heat-sink reference temperature. Symmetrically opposing convection currents form between the inner dome and detector surface when exposed to solar radiation in the level horizontal plane.

Hot Junction

Cold Junction

Tdome_1

T dome_2

T case

T hot junct

Note: When T dome_1 = T case, pyranometer thermal offset-A bias effect is 0 W/m².

T sky

T ambient

radiative IR exchange effect Fig. 3

Pyranometer Physics 101 Continued

Pyranometer POA Thermal Dynamics Pyranometer tilt response as referenced in the ISO-9060 Standard (i.e. Ref. 3-F), refers to the change in pyranometer sensitivity as a function of tilt angle relative to the horizontal plane. Convective sweeping pattern grows increasing asymmetrical as tile angle is increased.

Neither the ISO or WMO standards organizations have defined a formalized test procedure for validating pyranometer tilt response. Manufacture stated specifications are therefore subjective estimates and not necessarily representative of real-world tilt response performance.

Note: Inner dome convection pattern and detector convective sweeping effect changes with pyranometer tilt angle.

Fig. 4 radiative IR exchange effect

Asymmetrically opposing convection currents

Pyranometer Directional Response

Cosine Law Good vs. Poor Pyranometer Directional Response

1000 W/m²

Cos (45) * 1000 = 707.1 W/m²

With reference to the direct beam irradiance component, any deviation from theoretical ideal in pyranometer output signal vs. solar zenith angle is referred to direction error, or cosine error.

The blue plot above depicts a pyranometer with good directional response.

The red plot above depicts a pyranometer with poor directional response.

Pyranometer Performance Classification

ISO-9060 Pyranometer Classification

• Secondary Standard Most stringent performance compliance,

manufacturer directional response and temperature response validation required, research grade measurement applications

• First Class For research grade and routine

measurement applications.

Exceptions: for solar energy test applications, manufacture must validate directional response: ISO-9060, pg. 4 , 4.3.2

• Second Class For routine purpose measurement

applications, performance validation not required

• High Quality Most stringent performance compliance,

manufacturer directional response and temperature response validation required, research grade measurement applications

• Good Quality

For research grade and routine measurement applications.

• Mode For routine purpose measurement

applications, performance validation not required

WMO Pyranometer Classification

ISO-9060 Standard / Solar Energy Testing

Note: ISO-9060 section 4.3.2 states for purposes of solar energy test applications, a pyranometer meeting First Class criteria is recommend, so long as the directional response of the pyranometer has been validated and documented by the manufacturer.

ISO-9060 Pyranometer Specifications Ref No.

ISO-9060 Pyranometer Specifications Secondary Standard

First Class

Second Class

1 Response time: time to reach 95% response < 15 sec. < 30 sec. < 60 sec.

2 Zero-offset: Offset-A: response to 200 W/m² net thermal radiation, ventilated Offset-B: response to 5 K/h change in ambient temperature

+ 7 W/m²

± 2 W/m²

+ 7 W/m²

± 2 W/m²

+ 7 W/m² ± 2 W/m²

3a Non-stability: % change in responsivity per year ± 0.8% ± 1.5% ± 3%

3b Non-Linearity: % deviation from responsivity at 500 W/m² due to change in irradiance from 100 – 1000 W/m²

± 0.5% ± 1% ± 3%

3c Directional response (for beam irradiance): the range of errors caused by assuming that the normal incidence responsivity is valid for all directions when measuring from any direction, a beam radiation whose normal incidence irradiance is 1000 W/m²

± 10 W/m² ± 20 W/m² ± 20 W/m²

3d Spectral Selectivity: % deviation of the product of spectral absorbance and transmittance from the corresponding mean, from 0.35 – 1.5 µm

± 3% ± 5% ± 10%

3e Temperature response: % deviation due to change in ambient within an interval of 50K, (e.g. -10 to +40º C typical)

2% 4% 8%

3f Tilt response: % deviation in responsivity relative to 0º tilt, due to change in tilt from 0º to 90º tilt at 1000 W/m² beam irradiance

± 0.5% ± 2% ± 5%

Pyranometer Measurement Accuracy

At current there is NO officially recognized standard that addresses the issue of how to:

a. determine net accuracy of a pyranometer measurement in the field for any given measurement condition worldwide

b. transfer manufacture laboratory characterized performance results from the lab to real-world field conditions

Note: Published manufacturer accuracy estimates are subjective as they do not conform to any standardized test method for determining pyranometer measurement accuracy in the field. Some manufacturer characterize certain instrument offset characteristics in the lab, however it is not always possible to transfer laboratory test data from lab to real-world measurement conditions.

Critical Performance Criteria

So what are the critical performance criteria for achieving an accurate GHI / POA irradiance measurement?

• Calibration accuracy Best achievable calibration uncertainty is ± 1.5%, ± 0.1%

• Directional response Supplied standard with Secondary Standard and select First Class models

• Temperature response Limited to < 1% if equipped with internal compensation circuit, or when implementing temp

correction algorithm at logger/SCADA (e.g. linear, or second degree polynomial correction algorithm)

• Non-stability Non-stability, or change in instrument sensitivity, is typically the same for an given manufacturer’s

thermopile pyranometer line (i.e. not dependent on pyranometer Class)

Practical Methods for Estimating Pyranometer Measurement Accuracy

• Calculate the root sum square of the critical performance criteria for the pyranometer

Example: Assuming a pyranometer with a calibration uncertainty of ± 1.5% with a worst case directional response error of ± 2% and a temperature response of 1%, coupled with an annual non-stability spec of < 1%,

√

1.5² + 2² + 1² + 1² = 2.87% uncertainty

Practical Methods for Estimating Pyranometer Accuracy Continued

In the absence of a clearly defined standard for estimating pyranometer measurement accuracy, one practical method for assessing pyranometer real-world measurement accuracy is to ratio one average pyranometer data midwinter against collocated component sum derived GHI one minute average data, calculated from a well characterized WRR traceable 2-axos tracker mounted pyrheliometer and a shaded (i.e. diffuse) pyranometer, from sunrise to sunset.

Diffuse Pyranometer

First Class Pyrheliometer

Ratio of Midwinter Pyranometer GHI vs. Component Sum Derived GHI

0.80

0.85

0.90

0.95

1.00

1.05

1.10

1.15

1.20

1.25

1.30

7:08 8:08 9:08 10:08 11:08 12:08 13:08 14:08 15:08 16:08 17:08

February 5, 2012 (MST)

Second Class Pyran (TempCorrected)

Secondary Standard Pyran(Ventilated)

Second Class Pyran (w/oTemp Correction)

First Class Pyran(Ventilated)

Maximum Solar Elevation: 33.97º

Mean Daily Temp: 1.9º C

Pre-Construction PV Resource Assessment

Qualitative Analysis: Reference Cell vs. Pyranometer

World Renewable Energy 2012 Forum

By: Justin Robinson

Program Manager, DAS

ASES RAD Chair

Why Ground Based Resource Assessment

Risk Mitigation

• Low uncertainty (2-3%)

site specific irradiance

data

• Correlate GB data set to

long term historical data

(NSRDB) GH orientation

Thermopile Pyranometer

Thermopile detector:

• Chain of thermocouples in

series (Seebeck-Effect)

Designed for outdoor long term

applications

~Lambertian surface

Spectrally non-selective short

wave radiation measurement

Reference Cell

Encapsulated PV cell calibrated

at STC

Designed for solar simulator

intensity calibration at STC

Spectrally selective

measurement of short wave

radiation

Standard Test Conditions (STC)

STC Kyocera KD235 W Module

1000 W/m^2

Cell Temp 25o C

AM 1.5 Spectrum

AM1.5G Spectrum

(IEC 60904-3 & ASTM G173-03)

Tilt of incidence plane

Ground Reflectance *

Atmospheric Water Concentration

Atmospheric Ozone Concentration

Turbidity

Directional Error

Reference Cell Pyranometer

Hukseflux Thermal Sensors B.V., 2011

Directional Error


Directional Error vs. Angle of Incidence


Relative Spectral Response

Zehner et al., University of Applied Sciences Munich, 2009

Typical Spectral Response of Various Cell Types

Fraunhofer ISE CalLab

DHI Spectral Shift

Blackburn et al., University of Oregon, 2012

Solar Cell Efficiencies

NREL

Specifications

Pyranometer Reference Cell

Cell Type

Operating Temperature*

Typical Error

(Vertical beam)*

Non-Linearity *

ISO 9060 Classification

Response Time

Zero Offsets

Non-stability

Directional Error (<80 Deg)

Temperature Dependence of

Sensitivity

Tilt Error

Sensitivity

Spectral Range

Expected Daily Uncertainty

(Real World)

Outdoor PV Monitoring Standards

IEC 61724

ASTM E2848C

CAISO PIRP/ERIP Telemetry Standards

Additional Considerations

PV cells exhibit non-linear output across typical

operating temperatures, error rarely reported

Flat panel reference cell more prone to optical fouling

especially models with recessed detectors which

facilitate pooling of water over detector

GH orientation exasperates the directional response

issues associated with reference cells

The thermal dynamic properties of a reference cell do

not match those of an actual module which leads to

temperature de-rating bias

Over 50 years outdoor pyranometer data exists,

proven reliable method of irradiance measurement

Conclusions

Reference cells typically under report irradiance when

compared to pyranometers which can exceed 5%

• Hukseflux Thermal Sensors B.V., 2011

• Haeberlin et al., 13th EU PV Conference, 1995

Spectral mismatch between various cell types can lead

to a10% error band

• Zehner et al., University of Applied Sciences Munich, 2009

Thermopile pyranometers are the best option for Flat

Panel PV Resource Assessment when bankable data is

required

Draker Labs PV Prospector

Questions

Contact

Justin Robinson

Program Manager, DAS

ASES RAD Chair

[email protected]

Albany, New York | Barcelona, Spain | Bangalore, India | awstruepower.com | +1 877-899-3463

©2012 AWS Truepower, LLC

Marie Schnitzer Senior Director of Investor and Solar Services

May 2012

From Minimizing Risk to Maximizing Performance: Flat Panel PV Resource Assessment Best Practices (Forum)



1. The Importance of Solar Resource Assessment

2. Best Practices for On-Site Monitoring 3. Bankable Analysis 4. Key Messages 5. Questions



Importance of Solar Resource Information

Project Lifecycle Considerations:

• Early Development Phase – Prospecting and Site Screening

– Site Comparison and Selection

• Pre-Construction and Financial Readiness Phase – Long-Term Energy Assessment

– Economic Viability

• Operational Phase – Performance Verification

– Utility Forecasting

Characterize the Spatial and Temporal Variability of System Output



The Path to a Bankable Analysis

Conduct an On-Site Measurement Program

Procure High-Quality Reference Data

Analyze Data Sets and Predict Long-Term Resource

Quantify Data Uncertainties

Conduct Energy Production Analysis







Attributes of On-Site Monitoring

On‐site monitoring provides significant value to assessing a project’s potential, translating to higher confidence in energy estimates.

• Accurate Representation of the Project Site

• Customizable for Various Technologies (e.g., PV or CSP) and Various Users

• Flexible Equipment Options and Costs

• Small Environmental Footprint

• Straight-Forward Installation & Operation

• Self-Contained Communications and Power Supply



On-Site Monitoring Best Practices

Measurement Plan

• Solar instrumentation

• Meteorological: temperature, wind speed, precipitation

• Sampling/recording rate

• Measurement period

Installation and Commissioning

• Site selection

• Sensor verification

• Communications and data QA

• Documentation

Site Maintenance

• Regular schedule

• Clean, level instrumentation

• Site security

Data Validation and Quality Control

• Regular system monitoring

• Comparison with reference data and concurrent satellite data

• Visual data screening

• Clear sky / extreme values




2. Best Practices for On-Site Monitoring

3. Bankable Analysis 4. Key Messages 5. Questions



Developing a Long-Term Projection

Long-Term Meteorological Characteristics

Objective Review of Resource and Energy Potential

Observed Reference

Data

Modeled Data

On-Site Data



Other Sources for Solar Resource Data

Modeled Data • National Solar Radiation Database

(NSRDB)

• International Databases

• Solar Maps

Observed Reference Data • National Networks

• Regional and State Networks

• International Sources



Modeled Data Caution

• Understand Variation and Uncertainty

Schnitzer, et al; WREF 2012 – The Impact of Solar Uncertainty on Project Financeability

• Example: 14% difference in a 60km radius around Dallas, TX

1450

1500

1550

1600

1650

1700

1750

1800

1850

1 2 3 4 5 6 7

GH

I (

kWh

/m2

/ye

ar)

Long Term GHI from TMY3s near Dallas, TX



Data Source Advantages and Limitations

Data Source Advantages Limitations and Risks Intended Use

Modeled • Grid-cell specific

• Publicly available

• High data recovery

• Grid resolution

• Regional biases

• Greater uncertainty

• Initial prospecting

• Smaller projects

• Correlation with on-site data

Observed

Reference

Station

• Ground measurements

• Period of record may be

longer


• Scarcity of sites

• Location compared to

project site

• Uncertainty: quality of O&M,

instrumentation,

inconsistencies in data

• Confirm trends

• Identify regional biases


On-Site

Measurements

• Site-specific data

• Customized for project

needs

• Station details well-known

• Reduced uncertainty

• Shorter period of record

(correlate with long-term

data)

• High-confidence resource and

energy estimates

• Bankable reports

• In-depth characterization of

seasonal and diurnal climate



Considerations for Regionally Observed Data Sets

• Site Location and Exposure

• Proximity to Project Site

• Period of Record

• Data Trends

• Data Recovery Rate

• Site Maintenance

• Instrument Calibration

• Correlation Between Sites

Reference Station

Site



Adjusting to the Long-Term

Short Period of On-site Data

Long Period of Reference Data

Long-term Resource Estimation at the Project Site

Measure - Correlate - Predict

0

50

100

150

200

250

300

Jan

-02

Jul-

02

Jan

-03

Jul-

03

Jan

-04

Jul-

04

Jan

-05

Jul-

05

Jan

-06

Jul-

06

Jan

-07

Jul-

07

Jan

-08

Jul-

08

Jan

-09

Jul-

09

Mo

nth

ly I

rrad

iati

on

(kW

h/m

2)

Long-Term Reference Data On-Site Measured

0

200

400

600

800

1000

1200

0 200 400 600 800 1000 1200

Targ

et

Site

GH

I (W

/m2)

Reference Site GHI (W/m2)



Energy Production Analysis

Soiling, Shading, Incident Angle,

Mismatch, Wiring, Availability, etc

Component Selection,

Orientation, Tracking, Row Spacing, etc

Global/Diffuse Horizontal,

Temperature, Wind Speed

Sun Position, Surroundings, Horizon, etc

Energy Analysis

Project Location

Plant Design

System Losses

Resource



Uncertainty in the Long-Term Projections

Uncertainty Considerations • Measurement

• Inter-Annual Variability

• Representativeness of Monitoring Period

• Spatial Variability

• Transposition to Plane of Array

• Simulation and Plant Losses

Confidence in Energy Estimates • Probability Analysis

• P50, P75, P90, P95, P99

The uncertainty of data used in an assessment needs to be characterized and applied to the long-term projections




2. Best Practices for On-Site Monitoring 3. Bankable Analysis

4. Key Messages 5. Questions



Key Messages

• Solar resource assessment is a sound investment

• Utilize all available data sets in an resource analysis

• Consider the factors that impact the quality of the data sets

• Thorough resource assessments lead to more accurate energy estimates

• Detailed analysis of the resource and better characterization of the project site leads to a lower risk project



Effect of Uncertainty

On-Site Solar Data

Reduced Energy Uncertainty

Higher Confidence in Project Return

Reduced Project Risk



Marie Schnitzer Senior Director of Investor and Solar Services

Questions

+1 877-899-3463

[email protected]

awstruepower.com



Christopher Thuman and Marie Schnitzer

May 2012

From Minimizing Risk to Maximizing Performance: Flat Panel PV Resource Assessment Best Practices (Forum)







Importance of Solar Resource Information

Project Lifecycle Considerations:

• Early Development Phase – Prospecting and Site Screening

– Site Comparison and Selection

• Pre-Construction and Financial Readiness Phase – Long-Term Energy Assessment

– Economic Viability

• Operational Phase – Performance Verification

– Utility Forecasting

Characterize the Spatial and Temporal Variability of System Output



The Path to a Bankable Analysis

Conduct an On-Site Measurement Program

Procure High-Quality Reference Data

Analyze Data Sets and Predict Long-Term Resource

Quantify Data Uncertainties

Conduct Energy Production Analysis







Attributes of On-Site Monitoring

On‐site monitoring provides significant value to assessing a project’s potential, translating to higher confidence in energy estimates.

• Accurate Representation of the Project Site

• Customizable for Various Technologies (e.g., PV or CSP) and Various Users

• Flexible Equipment Options and Costs

• Small Environmental Footprint

• Straight-Forward Installation & Operation

• Self-Contained Communications and Power Supply



On-Site Monitoring Best Practices

Measurement Plan

• Solar instrumentation

• Meteorological: temperature, wind speed, precipitation

• Sampling/recording rate

• Measurement period

Installation and Commissioning

• Site selection

• Sensor verification

• Communications and data QA

• Documentation

Site Maintenance

• Regular schedule

• Clean, level instrumentation

• Site security

Data Validation and Quality Control

• Regular system monitoring

• Comparison with reference data and concurrent satellite data

• Visual data screening

• Clear sky / extreme values



Challenges to On-Site Monitoring

Adverse Weather

Solar Panel/Battery

Communications

Theft

Maintenance Technicians

Wiring

Rodents/Birds/Bees

Equipment and Failure




2. Best Practices for On-Site Monitoring

3. Bankable Analysis 4. Key Messages 5. Questions



Developing a Long-Term Projection

Long-Term Meteorological Characteristics

Objective Review of Resource and Energy Potential

Observed Reference

Data

Modeled Data

On-Site Data



Other Sources for Solar Resource Data

Modeled Data • National Solar Radiation Database

(NSRDB)

• International Databases

• Solar Maps

Observed Reference Data • National Networks

• Regional and State Networks

• International Sources



Modeled Data Caution

• Understand Variation and Uncertainty

Schnitzer, et al; WREF 2012 – The Impact of Solar Uncertainty on Project Financeability

• Example: 14% difference in a 60km radius around Dallas, TX

1450

1500

1550

1600

1650

1700

1750

1800

1850

1 2 3 4 5 6 7

GH

I (

kWh

/m2

/ye

ar)

Long Term GHI from TMY3s near Dallas, TX



Data Source Advantages and Limitations

Data Source Advantages Limitations and Risks Intended Use

Modeled • Grid-cell specific


• High data recovery

• Grid resolution

• Regional biases

• Greater uncertainty

• Initial prospecting

• Smaller projects


Observed

Reference

Station

• Ground measurements

• Period of record may be

longer


• Scarcity of sites

• Location compared to

project site

• Uncertainty: quality of O&M,

instrumentation,

inconsistencies in data

• Confirm trends

• Identify regional biases


On-Site

Measurements

• Site-specific data

• Customized for project

needs

• Station details well-known

• Reduced uncertainty

• Shorter period of record

(correlate with long-term

data)

• High-confidence resource and

energy estimates

• Bankable reports

• In-depth characterization of

seasonal and diurnal climate



Considerations for Regionally Observed Data Sets

• Site Location and Exposure

• Proximity to Project Site

• Period of Record

• Data Trends

• Data Recovery Rate

• Site Maintenance

• Instrument Calibration

• Correlation Between Sites

Reference Station

Site



Adjusting to the Long-Term

Short Period of On-site Data

Long Period of Reference Data

Long-term Resource Estimation at the Project Site

Measure - Correlate - Predict

0

50

100

150

200

250

300

Jan

-02

Jul-

02

Jan

-03

Jul-

03

Jan

-04

Jul-

04

Jan

-05

Jul-

05

Jan

-06

Jul-

06

Jan

-07

Jul-

07

Jan

-08

Jul-

08

Jan

-09

Jul-

09

Mo

nth

ly I

rrad

iati

on

(kW

h/m

2)

Long-Term Reference Data On-Site Measured

0

200

400

600

800

1000

1200

0 200 400 600 800 1000 1200

Targ

et

Site

GH

I (W

/m2)

Reference Site GHI (W/m2)



Energy Production Analysis

Soiling, Shading, Incident Angle,

Mismatch, Wiring, Availability, etc

Component Selection,

Orientation, Tracking, Row Spacing, etc

Global/Diffuse Horizontal,

Temperature, Wind Speed

Sun Position, Surroundings, Horizon, etc

Energy Analysis

Project Location

Plant Design

System Losses

Resource



Uncertainty in the Long-Term Projections

Uncertainty Considerations • Measurement • Inter-Annual Variability • Representativeness of Monitoring

Period • Spatial Variability • Transposition to Plane of Array • Simulation and Plant Losses • Degradation

Confidence in Energy Estimates • Probability Analysis • P50, P75, P90, P95, P99

The uncertainty of data used in an assessment needs to be characterized and applied to the long-term projections



On-Site Data Contribution to Project Uncertainty

P50

P75

P90P95

P99

70%

80%

90%

100%

50 60 70 80 90 100

Perc

ent

of

P5

0

Percentile (P-Value)

P-Values as Percent of P50

Modeled DataOn-Site Data




2. Best Practices for On-Site Monitoring 3. Bankable Analysis

4. Key Messages 5. Questions



Key Messages

• Solar resource assessment is a sound investment

• Utilize all available data sets in an resource analysis

• Consider the factors that impact the quality of the data sets

• Thorough resource assessments lead to more accurate energy estimates – on site data!

• Detailed analysis of the resource and better characterization of the project site leads to a lower risk project



Effect of Uncertainty

On-Site Solar Data

Reduced Energy Uncertainty

Higher Confidence in Project Return

Reduced Project Risk



Marie Schnitzer Senior Director of Investor and Solar Services Christopher Thuman Senior Meteorologist

Questions

+1 877-899-3463

[email protected]

[email protected]

awstruepower.com

Date post:	12-May-2018
Category:	Documents
Upload:	phamdien
View:	263 times
Download:	13 times

ISO-9060 Standard & Pyranometer Measurement Accuracy · research grade measurement applications •...

Documents