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Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Conference Paper

NREL/CP-5500-72183

November 2018

CSP-Plant Modeling Guidelines and 

Compliance of the System Advisor 

Model (SAM) 

Preprint

Devon Kesseli1, Mike Wagner1, Rafael Guédez2

and Craig S. Turchi1

1 National Renewable Energy Laboratory

2 KTH Royal Institute of Technology

Presented at the SolarPACES Conference 2018

Casablanca, Morocco 

October 2-5, 2018
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

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

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303-275-3000 • www.nrel.gov

Conference Paper

NREL/CP-5500-72183

November 2018

CSP-Plant Modeling Guidelines and 

Compliance of the System Advisor 

Model (SAM) 

Preprint

Devon Kesseli1, Mike Wagner1, Rafael Guédez2

and Craig S. Turchi1

1 National Renewable Energy Laboratory

2 KTH Royal Institute of Technology

Suggested Citation 

Kesseli, Devon, Mike Wagner, Rafael Guédez and Craig S. Turchi. 2018. CSP-Plant 

Modeling Guidelines and Compliance of the System Advisor Model (SAM): Preprint. 

Golden, CO: National Renewable Energy Laboratory. NREL/CP-5500-72183.

https://www.nrel.gov/docs/fy19osti/72183.pdf.
NOTICE

This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable 

Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding 

provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy 

Technologies Office. The views expressed herein do not necessarily represent the views of the DOE or the U.S. 

Government. 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.

This report is available at no cost from the National Renewable 

Energy Laboratory (NREL) at www.nrel.gov/publications.

U.S. Department of Energy (DOE) reports produced after 1991 

and a growing number of pre-1991 documents are available 

free via www.OSTI.gov.

Cover Photos by Dennis Schroeder: (clockwise, left to right) NREL 51934, NREL 45897, NREL 42160, NREL 45891, NREL 48097, 

NREL 46526.

NREL prints on paper that contains recycled content.
1

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CSP-Plant Modeling Guidelines and Compliance of the 

System Advisor Model (SAM)

Devon Kesseli1, Mike Wagner1, Rafael Guédez2 and Craig S. Turchi1, a)

1 Thermal Sciences R&D Group, National Renewable Energy Laboratory, Golden, Colorado, USA 2 Department of Energy Technology, KTH Royal Institute of Technology, 10044, Stockholm, Sweden.

a) corresponding author: craig.turchi@nrel.gov

Abstract. Accurately modeling risks, costs, and electricity output is essential to the financing and advancement of 

concentrating solar power projects. To address this need, a group of CSP experts created a guideline document, titled 

SolarPACES Guideline for Bankable STE Yield Assessment [1]. To make this information more accessible and allow

stakeholders to test specific models against the recommendations, the guidelines have been condensed into a spreadsheetbased checklist. The checklist was applied to NREL’s System Advisor Model (SAM) software, providing useful feedback 

to both the checklist group and the SAM development team. This study showed strong agreement between SAM and the 

guidelines, demonstrated the use of the guidelines in model validation, and resulted in several recommended improvements

to SAM.

INTRODUCTION

In January 2017 SolarPACES published a guideline for bankable solar thermal electric (STE) yield assessment 

[1]. This report, led by Tobias Hirsch of the German Aerospace Center (DLR), documents appropriate methodology 

and best practices for modeling the performance of concentrating solar power (CSP) systems, and can be accessed on 

the SolarPACES website [2]. This work was motivated by the fact that there was no standard available to provide

guidance to persons involved in energy-generation predictions of STE systems. The lack of established guidelines 

creates uncertainty among stakeholders, including financial institutions, regarding the veracity of project performance 

estimates. This uncertainty translates into higher risk for developers and financiers, which drives up project costs. In 

addition to improvements in technical aspects of CSP plants, wider deployment of this technology requires that 

prediction of the electricity generation and thus the financial revenues are made with high precision and confidence.

Later in 2017, NREL staff met with researchers from DLR, KTH Stockholm (Sweden), Fraunhofer ISE (Germany), 

CENER (Spain), and SolarDynamics (USA) to review these guidelines and begin the development of an associated 

checklist. This checklist was designed to help CSP stakeholders compare their own models and modeling methods to

the recommendations outlined in the guidelines. This would allow stakeholders a higher degree of confidence in their 

CSP models and identify any potentially damaging model assumptions or omissions. 

One well-established CSP model is the System Advisor Model (SAM, https://sam.nrel.gov/); a performance and 

financial model for the renewable energy industry developed at NREL. SAM has been publicly available since 2007 

and has a wide array of tools for modeling a variety of renewable systems, ranging from solar to geothermal to wind. 

It has become an industry standard and has been downloaded by over 35,000 manufactures, project developers, 

academic researchers, and policy makers [2].

This paper documents the development of the checklist and showcases its use on SAM. Two frequently used SAM 

models were evaluated against the guidelines: the CSP parabolic trough (physical) model, and the CSP power tower 

molten salt model. This served to both refine the checklist and provide some feedback to the SAM development team 

regarding SAM’s compliance with the SolarPACES guidelines. 
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The checklist layout and methodology are described in the Approach section, followed by the results of the SAM 

model analyses. This work is intended to both be beneficial to SAM and encourage greater use of the guidelines by 

the CSP community. The completed checklist can be accessed on the SolarPACES website [2].

APPROACH

The initial checklist workshop, which met at DLR in Stuttgart, Germany reviewed the recently published 

guidelines and sought to develop a simplified method by which CSP stakeholders could test a specific modeling 

approach against the guideline recommendations. This workshop produced a draft checklist that asks a model 

developer a series of simple questions to compare the terminology and methods of their model(s) against the 

guidelines. In 2018, a joint effort was undertaken by NREL’s Thermal Sciences Research Group and KTH Stockholm 

to complete, clarify, and cross-reference the checklist back to the original guideline document. Consistent formatting 

was established across the checklist, descriptions and instructions were added, and the results-tabulation process was 

automated to streamline the user interface. Layout, rationale, and instructions for using the checklist are described 

below.

Checklist questions are given one of four classifications:

• Shall – These requirements are essential to achieve an accurate result.

• Should – These requirements will improve the quality of the model. Their significance is dependent on the 

system being modeled. These requirements also include “best practice” recommendations that do not affect 

model accuracy.

• Optional – These requirements are unlikely to have a major effect on the results but are worth considering 

and may be important in some situations.

• Info – These are suggestions for information to be reported by the model. This includes the “Reporting 

Variables” listed in the document. These values are updated on the sheet relating to each sub-system, and the 

results are summarized on the Reporting Variables sheet.

These classifications rank the importance of guideline requirements and affect how they are scored. Only “shall” and 

“should” requirements are counted in the results.

The spreadsheet-based checklist is divided into topical sections covering different aspects of the CSP plant. These 

sections align with the chapters of the guideline document. TABLE 1 lists each of the checklist sheets, followed by a 

short description and the relevant chapter in the guidelines. The checklist returns a score in the Overall Results section. 

This score shows the percentage of “shall” and “should” requirements met for each section, providing a quantitative 

measure of compliance with the guidelines.

A section of the SF (Point Focus) checklist sheet, with an expanded view of the first few lines, is shown in Fig. 1. 

The leftmost “Topic” column lists a question on each line. The second column, “Page”, lists the page number in the 

guidelines where this issue is raised. This ensures each checklist item is grounded in the guideline document and 

allows the user to seek further context and clarification directly in the guidelines. Each checklist question is then 

followed by two checkboxes. A checked box in the “yes” column signifies that the requirement is met. This should 

turn the cell from red to green and count this affirmative response on the Overall Results sheet. A checked box in the 

“na” (not applicable) column removes the requirement from consideration. This will turn the entire row gray, and the 

requirement will not be counted as either met or unmet on the Overall Results sheet. Checking the “na” box overrides 

the “yes” checkbox for that requirement. Finally, the “Category” column shows the classification of each question. 

Although it is not shown in the expanded portion of Fig. 1, to the right of the “Category” column is a space for user 

comments.

For simplicity, the two types of solar field (SF) were split into separate sheets, SF (Line Focus) and SF (Point 

Focus). This means that one of these sheets will not be applicable to the system being modeled. To deal with this, the 

topmost question in Fig. 1 was added to both solar field sheets. Checking the box in this row on whichever solar field 

type is not relevant to the model being examined will gray out the entire sheet and the appropriate line on the Overall 

Results page.
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TABLE 1. An overview of the individual sheets of the checklist spreadsheet.

Checklist Sheet Description Guideline 

Reference

Overall Results A summary of the compliance percentages of each section -

Reporting Variables A summary of all the “info” (reporting question) results Chapters 4-9

Financial Evaluation Financial modeling requirement checklist Chapter 2

General Modeling Checklist for general modeling practices Chapter 3

System Level System level modeling checklist Chapter 4

SF (Line Focus) Solar field checklist for line focus (trough, linear Fresnel) Chapter 5.2

SF (Point Focus) Solar field checklist for point focus (tower) systems Chapter 5.3

Power Block Power Block modeling checklist Chapter 6

Storage Thermal Energy Storage modeling checklist Chapter 7

AuxHeater Checklist for modeling auxiliary heating (fossil, heat trace) Chapter 8

Electrical Electrical subsystem modeling checklist Chapter 9

Op Strategy Operational Strategies modeling checklist Chapter 10

Annual Effects Checklist for modeling annual effects Chapter 11

Meteo Meteorological input modeling checklist Chapter 12

Uncertainties Uncertainty sources, measurements, and reporting checklist Chapter 13

FIGURE 1. A screenshot of the "SF (Point Focus)" sheet. The User Comments column (furthest to the right) is not expanded for 

clarity.

The completed checklist was then used to test NREL’s SAM models for molten-salt power tower and parabolic 

trough systems. Items and terminology found not to comply were discussed with NREL’s larger SAM team to 

determine if changes or clarifications should be made to SAM.

RESULTS – SAM TEST CASE

The SolarPACES checklist was applied to two different test cases within SAM; the CSP parabolic trough 

(physical) and the CSP power tower molten salt. The goal of this was to exercise the SolarPACES checklist and while 

also evaluating the compliance of SAM. The summary tables below show results taken directly from the Overall 

Results tab in the checklist. They show the percentage of mandatory “shall” and recommended “should” requirements 

met for each sub-system. All results presented here are for SAM release 2017.9.5. Specific discrepancies between 

SAM and the guidelines are described in detail in the discussion subsection. 
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CSP Parabolic Trough (Physical) Model

SAM’s CSP physical trough model is based on work by Wagner et al. and is presented in the “Technical Manual 

for the SAM Physical Trough Model” [2], available on the SAM website. 

TABLE 2. Overall results of the checklist applied to the CSP parabolic trough (physical) model in NREL’s SAM modeling 

system.

Category Percentage met Guideline 

Shall Should Reference

Financial Evaluation 100% 94.0% Chapter 2

General Modeling 100% 77.8% Chapter 3

System Level 100% 100% Chapter 4

SF (Line Focus) 95.2% 81.3% Chapter 5.2

Power Block 100% 66.7% Chapter 6

Storage 81.8% 71.4% Chapter 7

AuxHeater 66.7% 33.3% Chapter 8

Electrical 100% 100% Chapter 9

Op Strategy 100% 100% Chapter 10

Annual Effects 100% 100% Chapter 11

Meteo1 - - Chapter 12

Uncertainties 94.1% 71.4% Chapter 13

CSP Power Tower Molten Salt Model

SAM’s CSP Power Tower Molten Salt model is also based on work by Wagner [3]. Simulation techniques are 

described in documentation available on the SAM website.

TABLE 3. Overall results of the checklist applied to the CSP power tower molten salt in NREL’s SAM modeling system.

Category Percentage met Guideline 

Shall Should Reference

Financial Evaluation 100% 94.1% Chapter 2

General Modeling 100% 83.3% Chapter 3

System Level 100% 100% Chapter 4

SF (Point Focus) 94.2% 41.7% Chapter 5.2

Power Block 100% 66.7% Chapter 6

Storage 85.7% 100% Chapter 7

AuxHeater2 0.0% 0.0% Chapter 8

Electrical 100% 100% Chapter 9

Op Strategy 100% 100% Chapter 10

Annual Effects 100% 100% Chapter 11

Meteo1 - - Chapter 12

Uncertainties 94.1% 71.4% Chapter 13

These results show very good agreement in almost all subsystems. Often, discrepancies are the result of the breadth 

of the SAM tools. Instead of including inputs for every possible source of optical, parasitic, or thermal losses, many 

of which would not be applicable to a given CSP system, SAM passes this responsibility on to the user. An example 

of this is Operations and Maintenance (O&M) costs. SAM includes spaces for different types of O&M costs to be 

entered (fixed annual, variable by generation, etc.), but does not list every possible source of plant O&M cost. Careful 

consideration on the part of the user is essential to achieve an accurate cost model. For this reason, it would be more 

 1 While various meteorological data can be imported, the National Solar Radiation Data Base (NSRDB), which uses 30-min data, is built into 

SAM. 2 SAM release 2017.9.5 has a new power cycle and system dispatch model for CSP Power Tower Molten Salt that did not retain the fossil backup 

(auxiliary heater) option from prior versions. This feature will return in a future SAM release.
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telling to use the checklist on the SAM model for a specific CSP project, checking both SAM’s calculation, and that 

the user included all the appropriate sources of loss and inputs.

Alternatively, someone using SAM may want to keep the checklist on hand, to make sure each aggregate SAM 

input (such as “Heliostat image error”) includes all the relevant contributions (tracking error, mirror imperfections, 

motion due to wind, etc.). SAM’s own Help Menu often provides a similar reminder of the types of elements that 

should be accounted for in a single, general input variable.

Discussion

Differences between SAM and the guidelines are examined on a section by section basis below.

Financial Evaluation

SAM’s financial model is consistent across most SAM modeling systems. The user can add costs (fixed or variable) 

to account for all types of cost and benefit described in the checklist. However, it is often left to the user to account 

for various factors that compile into a single SAM input variable. For example, SAM lists EPC and Owner Costs as 

a single input and the user must account for the multiple cost elements that roll into this general category (see SAM 

Help Menu). An imperfect score in the “should” category is due to differences in a couple financial considerations

that are not mentioned in SAM but could be added by a user. Lastly, the guidelines recommend accounting for sitespecific costs that cannot be included in a generic SAM model, but could be added by the user.

General Modeling and System Level

SAM defaults to hourly simulation, so it may appear that the temporal resolution requirements were not met;

however, sub-hourly timesteps are acceptable within SAM as long as a full year is modeled. Although time steps are 

one hour by default, they can be set arbitrarily small by a user. SAM includes access to NREL’s National Solar 

Radiation Database (NSRDB) weather data, which has a resolution of 30 minutes (for the 1998-2014 update). While 

these data are greater than the 10-minute resolution recommended in the guidelines; SAM can accept shorter interval 

data supplied by the user. The guidelines also state that one-hour resolution is adequate for pre-feasibility studies. For 

projects closer to construction, a user should gather high-resolution data at the specific site, which can then be imported 

into SAM.

Other small discrepancies are the result of differences in sub-system structure between SAM and the guidelines. 

These are unlikely to affect model performance, and so are classified as “should” requirements. Using the checklist 

on SAM also resulted in clarifications and rewording on several System Level questions.

Solar Field and Power Block

Many Solar Field efficiency factors are aggregated into single input values by SAM. Thus, meeting the 

requirements of the guidelines requires the user to understand and account for the individual factors. For example, all

sources of optical losses must be incorporated into a single optical efficiency value. SAM specifies almost all sources 

of optical inefficiency that the guidelines say should be included in this value within the Help Documentation. A user 

can enter time-dependent losses to account for soiling and other forms of degradation as recommended by the 

guideline. Angle-dependent optical efficiencies and conductive thermal losses from the tower receiver are “should” 

requirements that are not included in SAM.

All requirements for the Power Block sub-system are met, excluding the recommendation that HTF properties be 

characterized by pressure. Instead, pumping power requirements for the HTF through the power block are 

characterized by a load-based coefficient that multiplies HTF mass flow rate. The user is free to modify this parameter 

as needed to match expected parasitic consumption, but the model does not predict parasitic power based on pressure 

considerations. This recommendation results in unchecked items across multiple sub-systems, including both Solar 

Field and Power Block.
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Thermal Energy Storage (TES)

The thermal energy storage section received the lowest percentages in both the “shall” and “should” categories.

This was due to SAM’s less-rigorous modeling of thermal losses from the salt storage tanks, and of the salt-HTF heat 

exchanger in the indirect storage system. For thermal losses to the storage tanks, the guidelines specify that the thermal 

loss through the foundation (including potential active cooling effect) needs to be considered. The guidelines also state 

that tank thermal losses should be a function of TES state of charge. SAM’s storage tank model does not meet either 

of these requirements. The rationale behind SAMs current model is that thermal losses through the ground are 

insignificant compared to losses to the atmosphere. If desired, a user would have to increase thermal losses through 

the walls to account for losses through the foundation. The current SAM TES model does not account for TES stateof-charge, but rather uses a single heat loss coefficient for all tank area. That is, SAM considers temperature along the 

inner tank surface to be constant, regardless of how full the tank is.

Lastly, in an indirect TES system, a heat exchanger is required to charge the salt in the storage tanks from the solar 

HTF. SAM does not consider heat losses (to ambient) or ramp-rate limits of this heat exchanger. Again, a user can 

account for these by increasing heat loss coefficients from other components and increasing system start-up times.

These discrepancies result in several unmet requirements. To resolve this, analysis will be conducted to look at the 

significance of these requirements. The SAM team will assess the relative magnitude of these terms and consider their 

addition in future versions of SAM.

Auxiliary Heater, Electrical, Operation Strategy, and Annual Effects

As mentioned above, the auxiliary heater model in the molten salt power tower is omitted from this version of 

SAM. The trough model achieves good results, the only shortcoming being that additional fuel and heating for startup 

is not accounted for. Once again, the user must make sure that the heater efficiency input is entered correctly, 

accounting for flue gas exhaust heat and heat lost to the surroundings. All requirements are met for the Electrical subsystem, as well as the Operational Strategy and Annual Effect checklists. 

Meteorological Modeling and Uncertainties

Both the Meteorological Modeling, and the Uncertainty requirements are partially outside the realm of what SAM 

is designed for, so were difficult to analyze. SAM accepts meteorological files as inputs, and these files are responsible 

for meeting the meteorological requirements. These requirements consider factors such as height at which data should 

be taken to how often the sampling instruments are cleaned and calibrated. SAM requires one year of data to run, but 

can accept any weather data, at any sampling rate. Simulations with less than one year of data can be made with 

“dummy” values for the remainder of the year, but annual outputs will not be relevant. SAM is installed along with 

weather files from the NSRDB, which is based on satellite-model values at 30-minutes intervals. SAM can simulate 

at the desired 10-minute interval of the guideline (or even shorter intervals), but this requires the user to provide the 

corresponding weather data. 

Similarly, measuring uncertainties is largely up to the user, but SAM provides several useful tools. The built-in

P50/P90 tool automatically characterizes inter-annual yield uncertainty when multiple years of weather data are 

available for a given site. The built-in stochastic simulation tools allow users to examine the effect of uncertainty of 

any (or multiple) of the input variables on an output metric. Between these two tools, most of the uncertainty 

requirements can be met. Other requirements are site-specific and must be examined by the user.

Checklist Difficulties and SAM Updates

A difficulty in applying the SolarPACES checklist to any model is that the relative importance or “weight” of 

different requirements is not assessed in the guidelines. This means that further work is required to quantify the 

significance of each requirement beyond “shall” or “should”. This is being undertaken for some of the discrepancies 

between SAM and the guidelines, to assess the importance of these potential additions before making changes to 

SAM. For some requirements, the effect on the overall results varies from case to case, adding another level of 

complexity. When the checklist is applied to a model, many missed requirements can be justified with good analysis 

from the model user. 
7

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CONCLUSIONS

Overall the SAM CSP models show good agreement with the SolarPACES guidelines. Where discrepancies appear 

to exist, the SAM tools are flexible enough that the additional losses, considerations, or requirements can be added by 

the user, following guidance in the SAM Help Menu. That is, many SAM inputs are aggregates with similar effects 

(e.g., efficiencies) and the user is instructed to account for the different factors in their input value. This flexibility

allows SAM to be applied to a wide variety of STE plant designs and locations while preventing an overly cumbersome

interface.

This test evaluated SAM’s default, i.e., example, cases. Applying the checklist to a complete SAM simulation of

a specific STE system would give a more accurate evaluation of the model’s capability for compliance. This would 

check both the equations and structure of SAM, as well as the inputs set by the user. In practice the challenge is to 

balance guiding the user to account for all practical factors while maintaining a clean and flexible user interface.

SAM’s compromise is to provide Help Menu documentation that lists factors that the user may need to consider.

Discrepancies were discussed with NREL’s SAM development team. These discussions resulted in several 

potential changes that would improve SAM’s alignment with the SolarPACES CSP modeling recommendations. 

Suggested additions to SAM are outlined below:

• Heat gain from pressure dissipation throughout the system, for both trough and tower systems.

• Pressure characterization throughout the system, for both troughs and towers. This will make fluid

pressure drops dependent on inlet pressure to each subsystem.

• Thermal losses through the TES foundation.

• More rigorous modeling of the salt-HTF heat exchanger in indirect systems (thermal losses, ramp rates, 

etc.).

• Operational limits for wind speed in trough systems.

• Start-up fuel consumption for auxiliary heating system.

• Difference in shading calculation for the outer rows in a parabolic trough system. (SAM treats all trough 

rows the same.), and 

• Re-inclusion of the auxiliary heater option in the Molten Salt Power Tower.

SAM is a freely available and flexible tool for CSP system modeling, with users in industry and academia from 

around the world. NREL remains dedicated to the continual improvement of SAM and will use the SolarPACES 

guidelines to assess and bolster SAM’s utility. The authors encourage other CSP stakeholders to test their own models 

and move the community toward more transparent, consistent, and accurate performance and cost modeling.

ACKNOWLEDGMENTS

This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for 

Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. 

Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy, Solar Energy 

Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. 

Government. 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.

We thank SolarPACES, for the opportunity to present this work, as well as all the contributors to the guidelines 

document, and to those at the DLR workshop in Stuttgart, Germany, who contributed to the original checklist draft.
8

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Schmidt, M. Seitz and M. Eck, "SolarPACES Guideline for Bankable STE Yield Assessment," IEA 

Technology Collaberation Programme SolarPACES, 2017. 

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National Renewable Energy Laboratory, System Advisor Model (SAM), Version 2017.9.5 ed., 

https://sam.nrel.gov/. 

M. J. Wagner and P. Gilman, "Technical Manual for the SAM Physical Trough Model," NREL Report 

No. TP-5500-51825, 2011.

M. J. Wagner, "Simulation and predictive performance modeling of utility-scale central receiver system 

power plants," University of Wisconsin--Madison, 2008.