© Copyright 2013, First Solar, Inc.

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Isis-2: Energy Model Integrated with Business Systems

Parametric Generator

Cost Model

Isis Energy Model

Financial Model

Optimized Plant

Design & Layout

Sales Tool

Fleet Performance

Database

Expected Performance

Reporting

Energy Prediction Map

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Key Differentiating Features

Spectral Shift Native implementation – traditionally backed into with other gain/loss factors

Soiling Ramped model with rain-triggered and manually-triggered cleanings

Lifetime analysis DC-side degradation in voltage & current Multi-year performance estimates/performance analysis

Module temperature Transient model taking into account all heat fluxes

Inverter Redefined as state engine with zones User-selectable maximum power setpoint with temperature & elevation derate Efficiency curves at many voltages

Plant architecture Block-by-block breakdown with independent module characteristics, DC:AC loading factors, etc. with staggered installation & energization schedule for key financial analysis

Time Scale Sub-hourly modeling to better avoid modeling artifacts due to weather averaging (inverter clipping) Improved power plant analysis

Application Multi-user web application with shared components library Secure database of simulations results Integrated with other business systems

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Simulation

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Representation of a Power Plant

Preliminary?

As-Built?

Contractual?

Re-Usable Components

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Soiling Model with Ramp Rates & Cleaning Events

Direct Monitoring of Energy Lost due to Soiling on First Solar Modules in California, 38th PVCS

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CdTe Spectral Shift

Changes in Cadmium Telluride Photovoltaic System Performance due to Spectrum, 38th PVSC

H2O Absorption Bands

Monthly and hourly

calculation of the CdTe

spectral response using

precipitable water or

relative humidity

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Shading & Incidence Angle Modifier Losses

Increased Energy Production of First Solar Horizontal Single-Axis Tracking PV Systems without Backtracking, 39th PVSC

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AC

Pow

er

(kW

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Median System

System C

System D

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200

300

400

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AC

Pow

er

(kW

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Median System

System C

System D

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(kW

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Median System

System C

System D

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Dynamic Thermal Model

A Time Dependent Model for Utility Scale PV Module Temperature, 40th PVCS

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60

70M

odule

Tem

pera

ture

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els

ius

Hourly Avg. Module Cell Temperatures; Desert Southwest during the Summer

Measured

PVsyst Default Model

Updated PVsyst Model

FSLR Model

Reduces irradiance-weighted Tmod error at hot climate

sites from 3.2 °C (RMSE) to 1.5-2.0 °C, overall RMSE

reduction by 51%, MBE by 30%

Measured Default Static Model Updated Static Model Dynamic Model

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Non-Linear Temperature Coefficient

𝑖 = 𝐼ph − 𝐼0 𝑒𝑉d

𝑛cs ∙𝛾∙𝑉th − 1 −𝑉d

𝑅sh

− 𝐼ph ∙ 𝑏1

𝑛cs𝑉bi − 𝑉𝑑

𝑉d = 𝑣 + 𝑖 ∙ 𝑅s

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Array – Inverter Interaction

Once module temperature is computed, the

1-diode coefficients are temperature corrected, and the array MPP is solved

However, the inverter behavior is dynamic as well:

• Efficiency is a function of V & P

• Max capacity is a function of temperature and elevation

What inverter “zone” are we operating at?

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Parametric Analysis for Power Plant Design Optimization

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Benchmarking Goals

To quantify system-level prediction accuracy by using a well-

understood subset of extremely high-confidence data sets

• Deep look at performance to prediction

• Error analysis of energy prediction Reporting

• Feedback for energy model development by re-benchmarking

• Test-bed for advanced analytic methods

Continuous Improvement

• Demonstrate that Isis will hit the P50 for an ensemble of plant performance analyses in different climates & configurations

Acceptance

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Monitoring Points

Evaluating accuracy and error step-by-step through the model

with a final goal of understanding WHY prediction error occurs at

the energy meter

Energy Meter

AC Power

Inverter Efficiency

DC Power

DC Voltage

DC Current

Module Surface Temperature

Plane of Array Irradiance

Gigawattsbytes of powerdata reduced into

meaningful, easy-to- interpret results

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-5 -4 -3 -2 -1 0 1 2 3 4 50

0.5

1

1.5

2

2.5

3

3.5

4

4.5

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Model Error (%)

Num

ber

of

Syste

ms

Average Error = 0.43%

StDev Error = 2.53%

Span Error = 7.75%

Prediction Accuracy at the Energy Meter

Isis-2 overpredicted energy by 0.43% on average with a standard

deviation of 2.53%

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1

2

3

4

5Energy Meter

Model Error (%)

Num

ber

of

Syste

ms

Average Error = 0.06%

StDev Error = 1.96%

Span Error = 6.72%

Introduction of advanced lifetime model of voltage and current reduced

the error to 0.06%

10 sites | > 375 MW of PV modules | 15 system-years

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Look for First Solar at the 40th IEEE PVSC in Denver!

• “Evaluation of GHI to POA Models at Locations across the United States”

— Joint effort with SNL to quantify accuracy of irradiance decomposition/transposition models

• “A Time Dependent Model for Utility Scale PV Module Temperature”

— Updated module temperature module reduces RMSE by 51% and MBE by 30%

• “Measuring Soiling Losses at Utility-scale PV Power Plants”

— Continuing collaboration with Atonometrics to measure soiling losses

• “Performance Characterization of Cadmium Telluride Modules Validated by Utility Scale and Test Systems”

• “Self-Reported Field Efficiency of Utility-Scale Inverters”

• “Spectral Mismatch Considerations in Multi-irradiance Characterization of PV Modules”

• “Evaluation of a CdTe Spectrally Matched c-Si PV Reference Cell for Outdoor Applications”

• “Regional Atmosphere-Solar PV Interactions”