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Contract No. DE-AC36-08GO28308

Comparison of Photovoltaic 

Models in the System Advisor 

Model

Preprint

Nate J. Blair and Aron P. Dobos

National Renewable Energy Laboratory

Paul Gilman

Contractor

Presented at Solar 2013

Baltimore, Maryland

April 16-20, 2013

Conference Paper

NREL/CP-6A20-58057

August 2013
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Comparison of Photovoltaic Models in the System Advisor Model

Nate J. Blair

Strategic Energy Analysis Center

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

nate.blair@nrel.gov

Paul Gilman

3164 SE Salmon St

Portland, OR 97214

paulgilman@earthlink.net

Aron P. Dobos

Strategic Energy Analysis Center

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

aron.dobos@nrel.gov

ABSTRACT

The System Advisor Model (SAM) is free software

developed by the National Renewable Energy Laboratory

(NREL) for predicting the performance of renewable energy

systems and analyzing the financial feasibility of residential,

commercial, and utility-scale grid-connected projects. SAM

offers several options for predicting the performance of

photovoltaic systems. The model requires that the analyst 

choose from three photovoltaic system models, and 

depending on that choice, possibly choose from three 

module and two inverter component models. To obtain

meaningful results from SAM, the analyst must be aware of

the differences between the model options and their

applicability to different modeling scenarios. This paper

presents an overview the different photovoltaic model 

options, and presents a comparison of results for a 200 kW 

system using different model options

1. INTRODUCTION

The System Advisor Model (SAM) is software for

renewable energy project analysis that integrates a detailed

system performance model with a cost and financial model

for a range of applications. It is a package of performance 

and financial models that can represent different kinds of 

renewable energy systems for projects with different 

financial structures. As much as possible, SAM reports the

same metrics and uses consistent assumptions for the

different models to facilitate comparing technology and

financing options for project pre-feasibility studies or for 

academic research studies. SAM is developed by the

National Renewable Energy Laboratory in collaboration

with Sandia National Laboratories with funding from the

Department of Energy, and is available for download for 

free. [1]

SAM is among several photovoltaic system models, but is

unique in the way it integrates a photovoltaic performance 

model with a detailed financial model for a given project,

and in its ability to compare photovoltaic systems to other

renewable energy systems. It also offers additional analysis

tools for optimization, parametric, and statistical analysis.

Some other photovoltaic systems models are PVsyst, PV

Design Pro, PVSol, PVSim, PV F-Chart, and Polysun. [2]

Modeling a photovoltaic system in SAM involves choosing

whether to model the system using a model that represents 

the entire system with just a few inputs, or a more detailed 

model that uses separate component models for the module 

and inverter with additional inputs for the and array layout.

This paper describes each of the photovoltaic model options, 

and then compares results from the different models. 

2. PHOTOVOLTAIC MODEL OPTIONS

SAM offers three options for modeling a photovoltaic

system: The PVWatts System model is an implementation
2

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National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

of NREL's online photovoltaic calculator; the Flat Plate PV

model combines separate, user-selectable component

models for the module and inverter with a set of parameters

describing the array layout to represent the system; and the 

High-X Concentrating PV model is for concentrating PV 

(CPV) systems.

Fig. 1: Screenshot of the SAM user interface showing the

three photovoltaic model options.

All three photovoltaic models use an irradiance processor to 

determine the solar irradiance incident on the array (also 

called total plane-of-array irradiance) from hourly solar

radiation and meteorological data in a TMY2, TMY3, or

EPW file. [3,4,5] The solar resource data in the file may be 

measured, generated by a computer model, or a combination 

of the two. Depending on the photovoltaic model, the

irradiance processor may use one of three different

algorithms for calculating the incident irradiance for the 

array tracking type and location of the system. [6,7,8] The 

photovoltaic models use wind speed, and ambient

temperature data to model the effect of temperature on the 

array's performance.

The PVWatts System model uses a simple set of inputs that 

define the system's overall capacity and conversion 

efficiency, and relies on a fixed set of assumptions about the 

module and inverter performance characteristics.

The Flat Plate PV and High-X Concentrating PV models 

multi-step algorithm's use separate models for the module 

and inverter with a set of parameters describing the array to 

represent the system's performance. Each model first 

calculates the array's DC power output using the module 

model and parameters describing the array. Next it applies a 

user-specified DC derate factor to account for electrical 

losses from array tracking errors and module mismatch,

differences between nameplate rating and actual 

performance, and other factors not represented in the 

algorithm. The inverter model converts the derated DC

power value to the inverter's AC output power. SAM then 

applies an AC derate factor to account for losses on the AC 

side of the system, and sends the resulting AC power value 

to the financial model.

All three models use a set of user-specified shading factors 

that adjust the irradiance incident on the array to represent 

the effect of shading of the array by nearby objects. The Flat 

Plate PV and High-X Concentrating PV models also have

inputs for a set of monthly soiling factors that further reduce 

the incident irradiance. The soiling and shading factors are 

optional inputs, so each model can run with or without 

considering these effects.

SAM applies a set of performance adjustment factors to the 

system's AC output to determine the AC electricity 

delivered to the grid. These factors can be used to represent 

system availability, annual degradation in system output, 

curtailment, outages, or other operating losses or 

constraints. 

SAM is an hourly simulation model, so the calculations 

described above occur for each of the 8,760 hours in a 

typical year. Efforts are underway to develop subhourly 

versions of the model to allow for simulations based on 

solar resource data in smaller time steps.

TABLE 1: SUMMARY OF SAM'S PHOTOVOLTAIC 

MODELS

Flat Plate PVWatts High-X CPV

Array DC output • • •

Inverter AC output • • •

Temperature effects • • •

Array shading • • •

Tracking options • • 2-axis only

Row-to-row shading •

Backtracking •

Mounting options •

Multiple subarrays •

3. PVWATTS SYSTEM MODEL

PVWatts is a simple but widely used online photovoltaic 

calculator. [9,10] SAM includes an implementation of the 

model for analyses that do not require the component-level 

detail of the Flat Plate PV model, such as for preliminary 

output predictions and financial modeling before the details 

of a system's design are known, or for geospatial analysis 

involving systems in many locations where minimizing 

simulation is important. 

The PVWatts System Model is appropriate for modeling

rack-mounted systems with crystalline silicon modules. The

PVWatts model calculates cell temperature, array DC

output, and system AC output using a single derate factor

and internal assumptions about the inverter efficiency. 
3

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In PVWatts, the photovoltaic system is characterized by

four user-specified inputs: The array's DC power rating, a 

single DC-to-AC system derate factor, array tilt and azimuth

angles, and tracking type, which can be fixed for no 

tracking, one-axis tracking, or two-axis tracking. The derate 

factor can be chosen to represent shading, module 

mismatch, soiling of the array, and other effects not 

represented in the model calculations. A typical value for 

PVWatts derate factor is 0.77 [11,12]. The PVWatts model

is an adaptation of the Sandia PVFORM algorithms [13,14].

4. HIGH-X CONCENTRATING PV

The High-X Concentrating PV (HCPV) model represents a 

concentrating photovoltaic module using a table of userspecified cell efficiency values at different plane-of-array 

direct normal irradiance levels, and a set of factors to 

account for losses due to optical error, tracker misalignment, 

and wind flutter. The HCPV model also includes air mass 

modifier and temperature coefficients that can be adjusted to 

represent different modules. [15]

The HCPV module model is coupled with the Sandia 

inverter model (briefly described in Section 5.4) and array 

layout parameters to represent a complete system. Power for 

the tracking system is represented as a fraction of the array's 

DC nameplate capacity, and derate factors represent 

tracking error, module mismatch, and wiring and 

connections losses. User-specified limits on tracker rotation 

angles and a maximum wind speed that triggers tracker 

stowing further constrain the system's output.

The HCPV model calculates hourly cell efficiency and 

temperature, module efficiency and backplate temperature, 

array DC power output, and the inverter AC power output. 

It reports gross and net power output values to show the 

effect of the various derate factors. The model also 

calculates and reports hourly plane-of-array irradiance and 

air mass values.

NREL developed the HCPV model specifically for SAM to 

allow for some basic comparisons between HCPV and other 

renewable energy technologies. The model will be improved 

as results from ongoing research on characterizing CPV 

system performance becomes available.

5. FLAT PLATE PV MODEL

The Flat Plate PV model represents a photovoltaic system 

with separate performance models for the module and 

inverter, and a set of parameters describing the array layout. 

It can represent modules with crystalline or thin-film cells. 

The Flat Plate PV model offers three options for modeling a

photovoltaic module's performance, the Sandia, CEC, and or 

simple efficiency module model; and two options for 

modeling inverter, the Sandia or single-point efficiency 

model. Each option is described in more detail below.

The array layout parameters define the number of modules 

per string and number strings in parallel in the array, AC 

derate factors, and options for the sky diffuse irradiance 

model (Isotropic, HDKR, or Perez). [8]

The array can consist of up to four subarrays, where each 

subarray can have different orientation, tracking with 

optional backtracking for 1-axis tracking, shading and 

soiling factors, and DC derate factors. SAM assumes that 

each sub-array operates at its maximum power point. It also 

assumes that the modules in each subarray operate 

uniformly at the same cell temperature and maximum power 

point. Similarly, for systems with more than one inverter it 

assumes that all inverters have the same hourly conversion 

efficiency. 

Module mismatch losses in each subarray can be 

represented using a DC derate factor. For an array with 

more than one subarray used in combination with the CEC 

module model, SAM can calculate subarray mismatch 

losses caused by subarrays with different maximum power 

point voltages.

The Flat Plate PV model calculates the hourly cell 

temperature, DC output, and module conversion efficiency 

for each subarray, and the inverter DC input voltage and 

inverter AC output. The subarray mismatch option also 

calculates subarray DC voltages. Gross and net AC and DC 

power output values to show the effect of the AC and DC 

derate factors.

The array's hourly DC output depends on the results of the 

module performance model (described below), electrical 

configuration of modules and subarrays, and tracking type 

and soiling and pre-inverter derate factors for each subarray.

The array's DC output is the input to the inverter model, 

which determines the system's AC output.

5.1 Sandia Module Model

The Sandia PV Array Performance Model with Module

Database calculates hourly module efficiency values using a 

set of equations and coefficients developed from data

measured from modules (and, in some cases, arrays) in

realistic outdoor operating conditions. This empirical model 

requires 40 coefficients calculated from measurements taken 

over multiple days of module testing at a certified test

facility. [16]

SAM stores a database of module coefficients for 

commercially available modules in a library, which NREL 

updates as coefficients from new test results become

available from Sandia. The Sandia module library includes 
4

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National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

modules with thin-film cells, including amorphous silicon,

copper indium diselenide (CIS), cadmium telluride (CdTe),

and heterojunction with intrinsic thin layer (HIT). Because 

the Sandia model coefficients are based on measured data, 

the Sandia model may more accurately represent the 

performance of thin-film modules at low light levels than 

the CEC and simple efficiency models. This is an area that 

requires further study. [17]

5.2 CEC Module Model

The California Energy Commission (CEC) Performance

Model predicts module performance based on a database of

module characteristics for commercially-available modules 

determined from manufacturer's module ratings. Like the

Sandia model, the CEC model calculates hourly module 

efficiency values.

The CEC module model is an implementation of the fiveparameter model developed at the University of WisconsinMadison, with an additional temperature correction 

adjustment coefficient. It is a single diode model whose 

physical parameters can be calculated for a particular PV 

module from the specifications provided on a typical 

manufacturer datasheet. [9]

SAM offers two versions of the CEC module model, one 

that uses parameters stored in a library, and one that allows 

the user to specify parameters by hand from a 

manufacturer's datasheet. The library stores parameters from 

the database of modules maintained by the California 

Energy Commission, which regularly updates the database 

with data for modules currently available in the market. 

Over 10,000 modules are in the database as of this writing.

[18] For the user-specified parameter option, SAM uses an 

internal algorithm to calculate the model coefficients from 

the module parameters (Vmp, Imp, Voc, Isc, temperature 

coefficients, and number of cells in series). [19]

The CEC module model offers a choice of two temperature 

correction algorithms that calculate the cell temperature for 

different module mounting configurations, including on a 

rack that allows air to flow over the module back, flush with 

no airflow over the module back, or building-integrated. 

The NOCT cell temperature algorithm is from the original 

five-parameter model [9], and the Mounting-specific Cell 

Temperature Model is a first-principles heat transfer 

algorithm. [20,21] 

5.3 Simple Efficiency Model

The Simple Efficiency Module Model represents module 

performance using a set of parameters for the module area,

irradiance-to-DC-output conversion efficiency values for a 

range of irradiance levels. [22] It also uses a set of 

coefficients for the Sandia temperature correction algorithm

[16].

NREL developed the simple efficiency module specifically 

for SAM to allow for analyses that require specifying the 

module efficiency as an input, such as for parametric studies 

on module efficiency. The simple efficiency model is the

least accurate of the three models for predicting the

performance of specific modules.

We have excluded the simple efficiency model from this 

comparison because its results are strongly dependent on the 

user-specified module efficiency and temperature 

coefficients, which can be chosen to match the results of the 

other models. For the other photovoltaic module models, 

those parameters are determined by values stored in libraries 

that the analyst cannot easily change. 

5.4 Inverter Models

SAM's two inverter models use the DC output from one of 

the module models to calculate the system's AC output.

The Sandia Model for Grid-connected Inverters calculates 

the inverter's hourly efficiency using coefficients from a 

library of hundreds of inverters developed from 

manufacturer specifications and field test data. [23,24]

The Single Point Efficiency Model uses two user-specified 

inputs, the rated AC power in Watts, and rated DC-to-AC 

conversion efficiency. It assumes that the inverter's 

efficiency is constant from hour to hour. [25]

The PVWatts model represents the inverter using an internal 

algorithm without separate inputs for the module and 

inverter algorithms. [10]

This comparison uses the Sandia inverter model with 

coefficients for the SMA America 36 kW inverter from the 

default inputs for the Flat Plate PV model with commercial 

financing. Because we used the same inverter with both 

module models, the comparison does not provide insight 

into SAM's inverter models.

6. COMPARISON OF PV MODEL RESULTS

This section describes a comparison of SAM performance

model results for a 200 kW (rated DC kilowatts of array 

capacity) photovoltaic system with a fixed array (no 

tracking) using the Sandia module model, CEC module 

model, and PVWatts system model. The Sandia and CEC 

models use coefficients from their respective libraries for 

the SunPower SPR-210-WHT-U module.

The remaining model input values are the SAM default 

values for a commercial photovoltaic system. For each case 

in the comparison, only the module model was varied. For 

the PVWatts cases, a 200 kW system was modeled with two

DC-to-AC derate factors, 0.77 and 0.86.
5

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The graph of total monthly system AC output in Figure 2

shows that the simulation results for the Sandia and CEC

module models are in close agreement with monthly values 

within 2%. The graph also shows that the PVWatts derate 

factor of 0.86 more closely matches the other model results 

than the default value of 0.77. 

Fig. 1: Graph of system monthly output in kWh/month for 

the same 200 DC kW system modeled using the Sandia and 

CEC module models, and with the PVWatts model with two 

different DC-to-AC derate factors.

The monthly results also show that the difference between 

the CEC and Sandia module models is greater in the 

summer months than in the winter months. This difference 

is caused by the different temperature correction algorithms 

used by each model: The Sandia model predicts lower cell 

temperatures under the same ambient temperature and wind 

speed conditions than the CEC model with the NOCT cell 

temperature model.

Table 2 shows the system total annual AC output for each 

case along with the levelized cost of energy (LCOE) based 

on the SAM default cost and financing assumptions for a 

commercial photovoltaic project. The Sandia, CEC, and 

PVWatts with 0.86 derate factor models are in close 

agreement, while the PVWatts with 0.77 predicts a lower 

annual output and higher LCOE.

For the TMY2 weather data used for this comparison, the 

differences between photovoltaic model results is within the

uncertainty of the weather data. [26] The differences in 

LCOE are also within the uncertainty of the various cost and 

financial assumptions. This suggests that any of the three 

model options are suitable for estimates of a PV system's 

output or cost.

TABLE 2: ANNUAL OUTPUT AND LCOE FOR THE 

SIMULATIONS SHOWN IN FIGURE 1

Annual

Output

(kWh/yr)

LCOE

(¢/kWh)

Sandia Module 360,000 11.8

CEC Module 365,000 11.7

PVWatts (.86) 361,000 11.8

PVWatts (.77) 323,000 13.2

7. CONCLUSIONS

SAM's implements several different photovoltaic models, 

which each use different algorithms and databases to 

represent a photovoltaic system and predict its performance. 

For a system with crystalline silicon modules, the Flat Plate 

PV CEC and Sandia module models and the PVWatts 

System Model predict total monthly AC output values 

within 2.0% of each other, and for a financial analysis using 

SAM's commercial financing model with default values, an 

LCOE within 0.1 cent per kilowatt hour.

For the PVWatts model, the default derate factor of 0.86 

results in monthly AC output values much closer to the Flat 

Plate PV model than the default value of 0.77.

NREL has developed case studies comparing SAM results 

to data measured from installed systems [27] and is 

involved in ongoing efforts to validate model results against 

measured data. 

8. ACKNOWLEDGEMENTS

This work was supported by the U.S. Department of Energy

under Contract No. DE-AC36-08-GO28308 with the

National Renewable Energy Laboratory.

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at www.nrel.gov/publications.

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