revisiting the model parameters of an existing system using the photovoltaic system analysis toolbox...

Revisiting the Model Parameters of an Existing System Using the Photovoltaic System Analysis Toolbox (PVSAT)

Kenneth J. Sauer, Ian C. Tse, and Ryan A. Desharnais

Originally Presented at the 42nd IEEE Photovoltaic Specialists ConferenceJune 17th, 2015

Original design plans / drawings

Manufacturer datasheets

3rd party test reports

Industry rules of thumb

Default parameters in software

Pro forma Energy Production Forecasts

2

Pro forma parameters come from:

How well do pro forma parameters represent a

PV system as built?

Pro forma Weather Data

Pro forma Model

Parameters

Energy Simulation Software (PVsyst,

PVWatts, SAM, etc.)

Pro forma Energy Production

Forecast

Procurement Phase

Presentation Outline

3

Photovoltaic System Analysis Toolbox (PVSAT)

Precision of PVSAT validated with PVsyst

Accuracy of PVSAT checked against test array data

Executing a modern performance guarantee

Three levels of model parameter true-up

Evaluating the impact of true-up on forecast accuracy

Photovoltaic System Analysis Toolbox (PVSAT)

4

Performance Metrics

Calculation

Energy Simulation

Model Validation

Data Import& Filtration

PVSAT

Sandia PV_LIB v1.2

Irradiance transposition

Heat transfer (U values)

Diode circuitry (.PAN files)

Power loss mechanisms (e.g., IAM)

Energy Sim submodels

• PVSAT able to take sub-hourly data

• Also can analyze periods > 1 year

• Capable of degradation rate modeling

Measurement Data

5

Roof-mounted

mc-Si

11 modules in series

1 inverter

PV system specification

1-minute data

September 2013 – April 2015

Global horizontal irradiance

Diffuse horizontal irradiance

Ambient temperature

Wind speed

DC current and voltage

Clear sky filter applied

Measurements collected on-site

Validation Test I: PVSAT vs. PVsyst

6

Measured weather + high-loss parameters

PVSAT PVsyst

Simulation results

Simulation results

Compare residuals

solar position algorithm error

PVSAT more precise and with less noise

Validation Test II: Model vs. Measurement

7

Measured weather + ext. as-built parameters

Simulation results

Simulation results

Compare residuals

Energy forecasts from PVSAT are as accurate as

those from PVSystMeasured energy

production

PVSAT PVsyst

PVSAT PVsyst

RMSD 1.92%

<1.94%

MBD -0.44% -0.47%

Energy Production

Deviation

-0.67% -0.72%

Power Capacity Test

8PPI > 100% = PASS

subset for regression

GpoaEff_RC

Month GpoaEff_RC Tcell_RC PmpDC_RC

Jan 519 26 1312

Feb 610 30 1518

Mar 762 37 1839

Apr 870 38 2084

May 895 38 2139

Jun 924 40 2186

Jul 918 39 2181

Aug 891 41 2105

Sept 860 40 2044

Oct 703 37 1700

Nov 534 31 1324

Dec 534 28 1342

Monthly Guarantee Table (Ext. As-Built)

Degradation Test

9

from the Guarantee Table execution

GpoaEff_RC Tcell_RC PmpDC_RC PmpDC’

Sept 860 40 2044 2040

Oct 703 37 1700 1686

Nov 534 31 1324 1340

Dec 534 28 1342 1321

Jan 519 26 1312 1305

Feb 610 30 1518 1523

Mar 762 37 1839 1828

Apr 870 38 2084 2072

May 895 38 2139 2127

Jun 924 40 2186 2170

Jul 918 39 2181 2166

Aug 891 41 2105 2084

Tim

e in

Op

erat

ion

Power Capacity Test (1st month)

PPI

time

RDEG < 0.7 %/yr : PASS

0.7% annual degradation rate per module manufacturer’s warranty

Energy Yield Test

10

EPI > 100% : PASS

Modeled RDEG = 0.7 %/yr

Typically run over one year period; here all 20 months is used

weather-adjusted

Three Models

11

Model Sources and Attributes

Original Pro Forma Original system design plans• Tilt: 12°• Azimuth: 190°

Datasheet• Power tolerances• γPmp

Default .PAN, IAM, and U values taken from PVsyst v6.38

Typical As-Built On-site survey• Tilt: 13.87°• Azimuth: 190.8°

Manufacturer flash test data• Power tolerances

Extended As-Built Custom .PAN, IAM, U values

Leve

l of

par

amet

er t

rue-

up

Model Parameter True-Up (1 of 2)

One-diode model optimization method from: Sauer et al., IEEE J. Photovoltaics, 2015.

12

Model Parameter True-Up (2 of 2)

Ex-post derivation of thermal parameters from: Faiman, Prog. Photovolt: Res. Appl., 2008.

BOM-specific analytical modeling of IAM: Fatehi & Sauer, Proc. 40th IEEE PVSC, 2014.

Orig. PF Ext. AB

UC [W/m2/°C] 20 19.9

UV [W/m2/°C/m/s] 0 2.413

Results (1 of 2)

Orig. PF Typ. AB Ext. AB

PPI [%] 107.50 105.82 99.81

Power Capacity Test for 1st month (Target = 100%):

Degradation Test over 20 months (Target = 0.7 %/year):


RDEG [%/year] 1.27 1.14 1.15

Energy Production Test over 20 months (Target = 100%):


EPI [%] 106.07 104.12 99.43

RMSD [% of nameplate] 4.53 3.57 1.10

MBD [% of nameplate] 4.15 2.87 -0.41

14

Results (2 of 2)

15

Presentation Summary

16

Fidelity of model parameters influences test results

Degradation test can detect long-term durability issues

Seasonal errors introduced or masked by inaccurate parameters

Possible to de-risk with efforts to true-up model parameters

Setting the right bar for performance is also useful for O&M

About Amplify Energy…

Our mission is to quantify and maximize the operating performance and value of installed photovoltaic (PV) systems

We provide services to owners, buyers, and developers of large commercial, industrial, and utility-scale systems

Customers choose us because our advanced diagnostics and experience enable us to efficiently see things others cannot

An engineering services company focused on the evaluation and improvement of PV systems

7/2/2015 18

Company at a Glance

Inspection & Testing- Field / On-Site Testing- In-House Laboratory

Performance Analysis- System Rating- Degradation Analysis- As-Built Parameters

Consulting Services- Forensic Engineering- Repair and Warranty Administration- Optimization and Repowering

Primary Services