A Generic CSP Performance 

Model for NREL’s System 

Advisor Model 

Preprint 

Michael J. Wagner and Guangdong Zhu 

To be presented at SolarPACES 2011 

Granada, Spain 

September 20-23, 2011 

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC. 

Conference Paper 

NREL/CP-5500-52473 

August 2011 

Contract No. DE-AC36-08GO28308 
 

 

 

 

 

 

 

 

 

 

 

 

 

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SolarPACES 2011
 

A GENERIC CSP PERFORMANCE MODEL FOR NREL’S SYSTEM
 

ADVISOR MODEL

Michael J. Wagner1, Guangdong Zhu2

1Mechanical Engineer, National Renewable Energy Laboratory, Golden, Colorado (USA)

Email: michael.wagner@nrel.gov, Phone: 1.303.384.7430

2Ph.D., Mechanical Engineer, National Renewable Energy Laboratory

Abstract

This paper presents a new CSP performance model for the SAM tool developed by NREL and Sandia

National Lab. The Generic Solar System (GSS) provides a general and flexible tool for predicting

the annual performance and financial viability of concentrating solar power (CSP) systems. With the

GSS, the modeler can specify the solar field optical and thermal performance using a lookup table and

polynomial correlations. Performance of the power conversion system is likewise specified in terms of

ambient conditions, incident solar radiation, and total load. This paper discusses the motivation and

formulation for the GSS and provides model verification by comparing predicted annual results from

a more detailed parabolic trough annual performance model with an equivalent implementation in the

GSS.

Abbreviations

CSP Concentrating Solar Power

GSS Generic Solar System

DNI Direct Normal Irradiation

DOE Department of Energy (US)

HTF Heat Transfer Fluid

NREL National Renewable Energy Laboratory

PhT Physical Trough

SAM System Advisor Model

TES Thermal Energy Storage

1 Introduction and Motivation

The suite of concentrating solar power (CSP) modeling tools in NREL’s System Advisor Model (SAM) [1]

includes technology performance models for parabolic troughs, power towers, and dish-Stirling systems.1

Each model provides the user with unique capabilities that are catered to typical design considerations

seen in each technology. Since the scope of the various models is generally limited to common plant configurations, new CSP technologies, component geometries, and subsystem combinations can be difficult

to model directly in the existing SAM technology models. To overcome the limitations imposed by representative CSP technology models, NREL has developed a “Generic Solar System” (GSS) performance

model for use in SAM. This paper discusses the formulation and performance considerations included in

this model and verifies the model by comparing its results with more detailed models.

1The Alliance for Sustainable Energy, LLC (Alliance), is the manager and operator of the National Renewable Energy

Laboratory (NREL). Employees of the Alliance, under Contract No. DE-AC36-08GO28308 with the U.S. Dept. of Energy,

have authored this work. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the

published form of this work, or allow others to do so, for U.S. Government purposes.
The value of a generic CSP model in SAM is severalfold. First, it allows modelers to tie together

the performance of various plant subsystems in a single annual-hourly model. One possible use of

the GSS model is to integrate off-design performance curves that are developed using more detailed

analysis tools outside of SAM. For example, a modeler may use a ray-tracing tool to characterize the

optical performance of their system for a variety of sun positions and import the performance table

into the generic model along with estimates for receiver thermal performance, power cycle performance,

etc. Second, SAM includes detailed financial models for several common markets. The modeler may

wish to take advantage of SAM’s capabilities in this area without restricting their performance model

to an existing technology in SAM. Finally, SAM’s advanced parametric, statistical, sensitivity, and

optimization tools can add value to a detailed model that would otherwise be developed outside of SAM.

2 Generic Solar System Model Description

The GSS model consists of several integrated subsystems; these include the solar field, thermal storage

system, power cycle, auxiliary fossil heater, and corresponding cost models. The performance equations

for each subsystem have generalized formulations that do not require specific component geometries or

plant layout. For example, the user can characterize the performance of the solar field by providing

optical derate factors, thermal losses as a function of solar irradiation, ambient temperature, and wind

speed, and an optical efficiency table as a function of solar position.

Like the solar field, the other subsystems are characterized using general performance equations. The

power generation cycle uses a part-load efficiency adjustment as well as an ambient temperature adjustment (with either wet-bulb or dry-bulb temperature as the basis) to quantify power cycle performance.

Thermal storage is modeled using a simple energy balance approach that includes charging and discharging energy derates, thermal losses, and general sizing inputs. The following discussion provides details

on the equations and relationships used to describe subsystem performance.

2.1 Solar Field

The solar field calculations predict the total thermal energy output by multiplying the total incident

thermal energy by optical and thermal efficiency reduction terms. The total incident thermal energy is

calculated by multiplying the Direct Normal Irradiation (DNI) by the total solar field aperture area, as

shown in Eq.[2.1].

Q ̇ inc = Ibn Aap,tot (2.1)

The total aperture area is held constant throughout the simulation, and is determined using the designpoint power cycle rating, field efficiency terms, and desired solar multiple2. The aperture area is calculated as shown in Eq.[2.2].

Q ̇ inc,des Aap,tot = (2.2) Ibn,des

Where:

Q ̇ sf,des Q ̇ inc,des = (2.3) ηopt,peak ηclean ηgen ηtherm,des

where the peak optical efficiency ηopt,peak is the maximum efficiency value provided by the user in the

efficiency table, mirror cleanliness is ηclean, general optical error not captured in the efficiency table is

ηgen, and thermal efficiency at design is given by ηtherm,des. The solar field thermal output at design

2The “solar multiple” indicates the ratio of the thermal output from the solar field at the design-point DNI value to the

design-point thermal input to the power block
(Q ̇ sf,des) is calculated with reference to the desired power cycle output and the solar multiple MS.

W ̇ pb,des · MS Q ̇ sf,des = (2.4) ηcycle,des

During the hourly simulation runs, the solar field thermal output is calculated by adjusting the designpoint thermal output value according to the actual incident solar radiation, the optical efficiency, and

the thermal efficiency.

 

 ̇  ̇ Ibn ηopt ηtherm Qsf = Qsf,des (2.5) Ibn,des ηopt,des ηtherm,des

The optical efficiency is determined by interpolating the optical efficiency table specified by the user on

the Solar Field page according to the solar position3. Solar position calculations are handled according

to equations developed in Duffie and Beckman [2] and Stine [3], and the reader is referred to these

publications and documentation for other SAM models [4] for more information. Thermal efficiency is

calculated using the product of three sensitivities; namely, sensitivity to (1) the irradiation value, (2)

ambient dry-bulb temperature, and (3) ambient wind speed. The polynomial form for each sensitivity

is given in Eq.’s[2.6-2.8].

 2 3 Ibn Ibn Ibn fhl,Ibn = C0 + C1 + C2 + C3 (2.6) Ibn,des Ibn,des Ibn,des

fhl,Tdb = C0 + C1 (Tdb − Tdb,des) + C2 (Tdb − Tdb,des)

2 + C3 (Tdb − Tdb,des)3 (2.7)

fhl,Vwind = C0 + C1 Vwind + C2 V 2

wind + C3 V (2.8) 3

wind

The product of these three heat loss factors is multiplied by the reference field thermal output and the

ratio of current irradiance to design-point irradiance to determine the total solar field heat loss. The

additional multiplication of the total thermal loss fraction by the irradiance ratio forces the heat loss to

scale with incident irradiation and remain a fraction of the total solar field output, rather than a fraction

of the design-point field output.

Ibn Q ̇  ̇ hl = Qhl,ref fhl,Ibn fhl,Tdb fhl,Vwind (2.9) Ibn,des

In addition to specifying thermal and optical performance, the user can limit solar field operation to

a range of solar elevation angles using the “Stow angle” and “Deploy angle” inputs on the Solar Field

page. During time steps when the solar elevation angle crosses the stow or deploy limit partway through

the hour, the performance of that time step is derated to account for partial operation.

2.2 Power Cycle

The power cycle model provides a simple mechanism for modeling the conversion from thermal energy

output from the solar field, thermal storage, and auxiliary backup into electrical energy. The model is not

directly tied to typical power cycle processes like turbine/compressor efficiency, heat rejection pressures,

etc., so the tool is not constrained to traditional electricity generation technologies. The model uses

the design-point power output (gross) and reference conversion efficiency to calculate the total thermal

power input requirement during each time step of the performance run, then adjusts the realized gross

power production according to ambient temperature and load sensitivities.

3The user may alternatively elect to not interpolate the values in the table and use the nearest position instead.
The load-based efficiency adjustment factor takes the same form as the polynomial relationship presented

in Eq.[2.6] above, but adds a quartic term. Efficiency is also scaled according to ambient temperature

deviation (either wet-bulb or dry-bulb, selected by the user) from the design point. The temperaturebased adjustment factor takes the form shown in Eq.[2.10], where the polynomial extends to the quartic

term.

2 fpb,T = C0 + C1 (Tamb − Tamb,des) + C2 (Tamb − Tamb,des) ... (2.10)

The final power output calculation is calculated as shown in Eq.[2.11].

W ̇ cycle = ηcycle,ref fpb,T fpb,load Q ̇ pb (2.11)

Parasitic consumption is characterized using two categories. The first applies a fixed parasitic loss

as a fraction of the total plant gross electric power output rating, and the second applies a variable

parasitic loss that can vary with actual electric power production and/or ambient temperature. Two cubic

polynomial equations describe how the variable parasitic responds to power production and deviation

in ambient temperature from the design point. The final net power output is equal to the gross power

production minus the two parasitic power losses.

 

W ̇ cycle W ̇  ̇  ̇  ̇ net = Wcycle − Wpar,f ixed − Wpar,var Tamb,  ̇ (2.12) Wcycle,des

2.3 Thermal storage and Balance of Plant

Thermal energy storage (TES) and auxiliary fossil backup systems are also included as options in the

GSS model. Like the solar field, these subsystem models use a simple energy-balance approach. The state

of the TES system is tracked and adjusted from time step to time step, and thermal losses can be applied

as a function of charge level and ambient temperature. Aside from these TES thermal loss sensitivities,

the TES, auxiliary system, and control strategy models are adopted from the SAM Empirical Trough

model, and the reader is referred to the SAM documentation [1] for more information.

3 Model validation

A major goal of developing the GSS model is to facilitate hourly/annual simulation of CSP systems

using a flexible set of inputs. The model is formulated on the thesis that the significant performance

effects of any complex CSP system can be adequately captured in a reduced-order model. If this is true,

then a modeler can use data generated by a detailed model as input into the reduced-order GSS model,

and the total predicted performance results will agree (reasonably) between both models. To test this

claim, we ran the SAM Physical Trough model [4] to generate performance data for a parabolic trough

system, and used the data to generate input for the GSS model. One strencth of the GSS is that it is not

limited to parabolic troughs or any particular CSP technology, so a successful comparison between the

Physical Trough model should show equal success with any other CSP technology. This section presents

the results of this validation exercise.

3.1 The SAM Physical Trough model and output data

The SAM Physical Trough tool approaches system performance modeling by using system geometry, optical properties, weather data, and thermodynamic fluid properties to calculate component performance.

The model formulations generally use first-principle or semi-empirical approaches and consequently ac­
count for a wide range of possible performance effects. For example, the model calculates receiver thermal

performance using absorber tube and glass envelope geometry, gas concentrations within the evacuated

annulus, surface emittance properties, ambient temperature, wind speed, and other properties. In this

way, the Physical Trough model accounts for primary and highly significant effects as well as secondary

or even negligible effects.

This model was selected for comparison with the GSS for several reasons, namely:

•	 The Physical Trough model accounts for a wide range of performance effects, and it considers many

effects that are not explicitly accounted for in the GSS.

•	 The formulation basis for the Physical Trough model differs from the GSS. For example, the

Physical Trough electrical parasitic model uses piping geometry to calculate expected pressure

loss and pumping power requirement, whereas the GSS expresses variable auxiliary consumption

as a function of ambient temperature and power cycle load. The ability to reproduce specific

performance results using a generalized formulation indicates the versatility of the generic model.

•	 Both models can run at hourly time steps and are easily compared within the SAM framework.

The comparison exercise makes use of the default inputs for the Physical Trough model. Table 1 highlights

the major system features.

Table 1: Major system features for the parabolic trough system comparison

Description Value Units

Gross power output

Net power output

Power cycle gross efficiency

Solar field aperture area

Thermal storage capacity

Plant location

Heat rejection type

Fossil backup

111

100

37.74

865,352

6

Daggett, CA

Wet cooled

None

MWe

MWe

%

m2

hours

-

-

-

GSS model design-point inputs were selected to match the Physical Trough model as closely as possible.

The GSS uses a linearly interpolated lookup table to determine solar field optical efficiency, where the

solar azimuth and zenith angles are the two independent variables. The optical efficiency values were

calculated for each solar position of interest using the optical code from the Physical Trough model.

Inputs for off-design response of the power cycle, solar field thermal losses, and parasitic loads were

created using one of two methodologies. The first and most direct method was to directly convert a lookup

table programmed in the Physical Trough model into a polynomial regression fit. This method applied

for the power cycle part-load and temperature efficiency adjustments. Table 2 shows the polynomial

coefficients derived directly from the Physical Trough model.

Table 2: Off-design performance coefficients for the GSS model

Effect / Order C0 C1 C2 C3 C4

Power cycle part load adjustment

Power cycle ambient temperature adjustment

Parasitics part load adjustment

Parasitics temperature adjustment

Irradiation thermal loss adjustment

Ambient temp. thermal loss adjustment

Wind speed thermal loss adjustment

0.5628

1.0

0.0636

1

4.75

1

1

0.8685

-0.002

0.803

0.0025

-8.0

0

0

-0.5164

0

-1.58

0

4.5

0

0

0.0844

0

1.7134

0

-0.25

0

0

0

0

-

-

-

-

-
The second method for obtaining GSS input values was to run the Physical Trough model annual

hourly simulation, plot the effect of interest as a function of the off-design basis variable, and generate

a polynomial fit to match the data. Figure 1 shows the results of this method for the parasitic electrical

consumption part-load adjustment and the solar field thermal loss irradiation adjustment. Both ambient

Figure 1: Parasitic electrical consumption (LEFT) and solar field heat loss (RIGHT) multiplying factors as functions

of normalized solar field load and solar irradiance, respectively. Cubic polynomial fits are applied to simulated output

data from the SAM Physical Trough model.

temperature and wind speed demonstrated negligible effects on solar field thermal efficiency, and they

were not included in the simulation. Note also that the GSS only considers the primary performance

effects, and possible interactions between effects are not modeled. All of the input coefficients are

presented in Table 2 above.

3.2 Model Comparison Results

Annual simulations were performed for both the GSS and Physical Trough model with one hour time

steps according to the input information specified above. The results show excellent agreement between

the two models in total annual predicted electricity production. When comparing model output on

shorter intervals, agreement was less precise. However, this finding is expected when considering the

nature of the curve fits and generic modeling approach; the inputs to the GSS represent the best match

for compiled annual data, so it stands to reason that the best match in performance prediction should

be on an annual basis. Where the detailed Physical Trough model is likely more accurate over shorter

intervals - when examining startup behavior of the solar field, for example - the large scale effects are

captured equally well with the detailed and generic models.

Figure 2 shows a comparison between the monthly net electricity production predicted by the GSS and

Physical Trough models. A comparison of monthly and annual energy flows are tabulated in Table 3.

4 Conclusions

This paper demonstrates the application of a generic model for CSP systems by comparing predicted

results with a more detailed parabolic trough model. The results of the comparative exercise show good

agreement between both models at several important stages in the thermal energy flow. The results

also show agreement on both a monthly and annual basis, with decreasing correspondence as the length

of the compared period decreases. This finding is consistent with the methodology used to formulate

the model input parameters, where we compiled annual data and defined regression curves according to
Figure 2: Monthly comparison of the net electric output of the SAM Physical Trough and GSS models.
 

Table 3: Comparison of results from modeling runs of the SAM Physical Trough and GSS.
 

Incident power Power from field Power to cycle Gross electricity Net electricity

GWht % GWht % GWht % GWhe % GWhe %

PhT GSS δ PhT GSS δ PhT GSS δ PhT GSS δ PhT GSS δ

Jan 206.1 206.1 0.0 57.2 55.5 -2.9 56.7 62.8 9.7 20.0 23.9 16.3 18.4 21.4 14.3

Feb 218.6 218.6 0.0 79.4 77.2 -2.9 78.3 81.4 3.8 27.9 31.2 10.5 26.1 27.7 6.0

Mar 265.9 265.8 0.0 125.5 122.0 -2.9 124.5 125.7 1.0 45.5 48.2 5.7 42.8 43.1 0.7

Apr 327.7 327.7 0.0 172.4 177.5 2.9 170.8 174.4 2.1 63.0 66.9 5.9 59.2 59.8 1.0

May 324.0 323.9 0.0 174.6 183.5 4.8 173.3 179.2 3.3 64.4 68.3 5.7 60.3 61.0 1.1

Jun 353.8 353.8 0.0 196.3 205.9 4.7 195.0 196.8 0.9 72.8 74.7 2.6 68.1 66.5 -2.4

Jul 331.2 331.1 0.0 182.1 189.9 4.1 180.8 184.7 2.1 66.7 69.8 4.3 62.3 62.0 -0.5

Aug 309.4 309.4 0.0 166.3 168.7 1.4 164.9 164.9 0.0 60.6 62.4 2.9 56.5 55.4 -2.0

Sep 298.6 298.5 0.0 151.9 145.9 -4.1 150.6 147.0 -2.4 55.5 55.7 0.4 51.9 49.5 -4.8

Oct 257.2 257.1 0.0 103.8 98.2 -5.7 102.8 105.5 2.6 37.1 40.1 7.6 34.9 35.8 2.6

Nov 220.5 220.5 0.0 66.9 61.1 -9.6 66.6 67.2 1.0 23.4 25.5 8.0 22.0 22.8 3.3

Dec 193.8 193.8 0.0 46.5 44.3 -5.0 46.4 52.0 10.8 16.0 19.5 17.6 14.8 17.5 15.5

Tot 3307 3306 0.0 1523 1530 -0.4 1511 1542 -2.0 553 586 -6.0 517 523 -1.0

trends in the Physical Trough annual simulation. Consequently, the GSS model tends to predict system

performance during any given time step that is closer to the annual average value than the Physical

Trough model predicts.

Based on this observation, the most appropriate use of the GSS model is for simulations that are consistent with the dataset from which the input is derived. For example, input values derived from an

annual dataset should not generally be used to examine predicted behavior on an hourly basis since

errors observed at high temporal resolution may cancel out over a longer simulation.

It is also important to note that the comparison between the Physical Trough and GSS models was an

example of a possible application, and the GSS is not limited to any particular CSP technology, parabolic

troughs included. This framework provides advanced modelers with the option to incorporate their

own external performance models into the SAM framework for sensitivity analysis, annual performance

predictions, or financial modeling.

Acknowledgments

This work was supported by the U.S. Department of Energy under Contract No. DE-AC36-08-GO28308

with the National Renewable Energy Laboratory.
References

[1] P. Gilman. Solar Advisor Model User Manual. Technical report, National Renewable Energy Laboratory, Golden, Colorado (USA), April 2010.

[2] J. Duffie and W. Beckman. Solar Engineering of Thermal Processes. John Wiley and Sons, Hoboken,

New Jersey, USA, 3rd edition, 2006.

[3] W. Stine and M. Geyer. Power from the Sun. Published online at www.powerfromthesun.net, 2001.

[4] M.J. Wagner and P.	 Gilman. Technical manual for the SAM physical trough model. Technical

Report NREL/TP-5500-51825, National Renewable Energy Laboratory, 1617 Cole Blvd., Golden,

CO (USA), June 2011.