2014 PV Performance Modeling Workshop: Isis Power Plant Energy Simulation Tool, Bodo Littmann, First Solar

1. © Copyright 2013, First Solar, Inc.

2. 2
©Copyright2013,FirstSolar,Inc.
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

3. 3
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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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ACPower(kW)
Median System
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System D
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ACPower(kW)
Median System
System C
System D
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300
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500
ACPower(kW)
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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ModuleTemperature,Celsius
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

10. 10
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Non-Linear Temperature Coefficient
𝑖 = 𝐼ph − 𝐼0 𝑒
𝑉d
𝑛cs ∙𝛾∙𝑉th − 1 −
𝑉d
𝑅sh
− 𝐼ph ∙ 𝑏
1
𝑛cs 𝑉bi − 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 predictionReporting
• 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

14. 14
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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 5
Model Error (%)
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%
-5 -4 -3 -2 -1 0 1 2 3 4 5
0
1
2
3
4
5
Energy Meter
Model Error (%)
NumberofSystems
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
3
2
1
No.Systems

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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”