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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

Technical Report

NREL/TP-5500-72856

May 2019

CSP Systems Analysis - Final Project Report

Craig S. Turchi, Matthew Boyd, Devon Kesseli, Parthiv Kurup, 

Mark Mehos, Ty Neises, Prashant Sharan, Michael Wagner, 

and Timothy Wendelin

National Renewable Energy Laboratory
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

Golden, CO 80401

303-275-3000 • www.nrel.gov

Technical Report

NREL/TP-5500-72856

May 2019

CSP Systems Analysis - Final Project Report

Craig S. Turchi, Matthew Boyd, Devon Kesseli, Parthiv Kurup, 

Mark Mehos, Ty Neises, Prashant Sharan, Michael Wagner, 

and Timothy Wendelin

National Renewable Energy Laboratory

Suggested Citation

Turchi, Craig S., Matthew Boyd, Devon Kesseli, Parthiv Kurup, Mark Mehos, Ty Neises, Prashant 

Sharan, Michael Wagner, Timothy Wendelin. 2019. CSP Systems Analysis – Final Project Report. 

Golden, CO: National Renewable Energy Laboratory. NREL/TP-5500-72856.

https://www.nrel.gov/docs/fy19osti/72856.pdf.
NOTICE

This work was authored 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.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.

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.
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Final Technical Report (FTR)

Project Title: CSP Systems Analysis

Summary Results of Cost Analysis and Analysis-Tool 

Development

Project Period: 10/01/15 – 09/30/18

Submission Date: 11/15/2018

Recipient: National Renewable Energy Laboratory (NREL)

Address: 15013 Denver West Parkway

Golden, CO 80401

Website: www.nrel.gov

Award Number: NREL-1651

Project Team: NREL

Principal Investigator: Craig Turchi

Phone: 303-384-7565

Email: craig.turchi@nrel.gov

Business Contact: Mark Mehos

Phone: 303-384-7458

Email: mark.mehos@nrel.gov

Technology Manager: Mark Lausten

Project Officer: Christine Bing 
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Executive Summary

The CSP Systems Analysis project was a three-year effort supporting the Concentrating 

Solar Power (CSP) Subprogram within the Solar Energy Technologies Office of the U.S. 

Department of Energy (DOE). The goal of the CSP Systems Analysis project was to 

provide timely and accurate CSP cost data to the DOE and project performance and cost 

for emerging technologies to inform research directions and industry investment. The 

project consisted of six, mostly independent tasks. Major accomplishments over the 

three-year period of performance were as follows.

• The NREL team maintained current cost estimates for CSP technologies through annual 

updates to the cost input parameters within NREL’s System Advisor Model (SAM). SAM 

is a performance and cost simulation software that includes technology models for the 

primary CSP technologies. During the period from 2016 to 2018, the estimated levelized

cost of energy (LCOE) for SAM’s default CSP molten-salt power tower technology 

dropped from 13.0 to 11.1 ¢/kWh (real dollars without incentives). 

• Power cycle models for the supercritical carbon dioxide (sCO2) Brayton Cycle were 

developed and made an option within SAM’s molten-salt power tower model. These 

models were subsequently used to evaluate the cost reduction potential of alternative 

salts. The partial-cooling model was found to provide the greatest cost-reduction 

potential due to its combination of efficiency and temperature differential across the 

turbine and thermal energy storage (TES) system. These studies were documented in 

two journal articles.

• NREL’s optical modeling software SolTrace and SolarPILOT were made more versatile 

and released as open-source software to the CSP community. These tools help 

researchers and developers to model and optimize the optical performance of CSP 

collector systems. The new features were highlighted in a recorded webinar that can be 

accessed from the NREL SAM website (https://sam.nrel.gov). 

• New models for solar-thermal systems, that is, applications for solar industrial process 

heat, were developed, validated, and installed within SAM. The analysis includes a 2018 

review of linear collectors in the market that are tailored for solar process heat 

applications. In 2018 the NREL team also developed an annual simulation for a direct 

steam generation system integrated with a latent-heat thermal energy storage (TES) 

system. This analysis was submitted for journal publication. 

• The NREL team continued to support the exchange of information and development of 

CSP guidelines within the international CSP community of SolarPACES. Much of the 

work under this project was presented at SolarPACES, including the development of a 

modeling checklist to help CSP stakeholders standardize the models used for estimating 

CSP performance and cost.

The CSP Systems Analysis project also supported DOE requests for tracking and 

reporting programmatic targets for the CSP Subprogram. 
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Contents

Executive Summary ........................................................................................................ 2

Background ..................................................................................................................... 4

Project Objective ............................................................................................................. 5

Task 1 – CSP Cost Tracking ....................................................................................... 5

Task 2 – Supercritical CO2 (sCO2) Power Cycle Modeling .......................................... 5

Task 3 – Optical Modeling of Power Tower Systems................................................... 5

Task 4 – Thermal-Application Modeling Tools for SAM............................................... 6

Task 5 – IEA SolarPACES Task I Operation Agent..................................................... 6

Task 6 – Assessment of Advanced CSP systems in the 600°C to 650°C temp. range 6

Project Results and Discussion....................................................................................... 9

Task 1. CSP Cost Tracking and Analysis .................................................................... 9

SAM 2018 Updates for Power Towers and Parabolic Troughs .............................. 12

Solar Field Costs.................................................................................................... 13

Heliostat Solar Field Installed Costs....................................................................... 13

Parabolic Trough Solar Field Installed Costs ......................................................... 15

Power Block and Balance of Plant Costs ............................................................... 17

Thermal Energy Storage (TES) tanks and costs.................................................... 19

Updated CSP costs within SAM............................................................................. 20

Potential Trough TES Improvements ..................................................................... 20

Task 2. Supercritical CO2 (sCO2) Power Cycle Modeling .......................................... 21

Preprocessor boosts computation speed for sCO2 Power Cycle Modeling............ 21

The sCO2 recompression model user interface updated within SAM..................... 22

Development of the Partial-cooling sCO2 Cycle within SAM .................................. 25

Comparing Annual System Performance with SAM’s sCO2 Power Cycle Models . 30

Task 3. Optical Modeling of Power Tower Systems................................................... 31

Accuracy of Updated SolTrace Code..................................................................... 31

SolTrace Cross-Platform Agreement ..................................................................... 34

SolTrace View Factor Validation ............................................................................ 35

Scripting Tools in SolarPILOT................................................................................ 39

Open-Source Code Release of SolarPILOT and SolTrace .................................... 44

Open-source platform ............................................................................................ 45

Github implementation: Organization..................................................................... 45

Documentation and Wiki ........................................................................................ 46

Tracking Use and Contributions............................................................................. 46

Parallel development.............................................................................................. 46

Contributing............................................................................................................ 47

Debugs and Other Code Improvements................................................................. 48

Task 4. Thermal-Application Modeling Tools for SAM............................................... 49

Validation of New Solar Industrial Process Heat (IPH) Models in SAM – Trough 

Model ..................................................................................................................... 49
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Validation of New Solar IPH Models in SAM – Direct Steam Generation (DSG) Model

............................................................................................................................... 52

Process Heat Collectors Review............................................................................ 55

Brewery Integration Case Study............................................................................. 71

Modeling Solar Process Heat with PCM Storage................................................... 73

Task 5. SolarPACES Task I Status Updates ............................................................. 74

Task 6. Assessment of Advanced CSP Systems in the 600°C to 650°C Temperature 

Range ........................................................................................................................ 75

Inventions, Patents, Publications, and Other Results.................................................... 76

Path Forward................................................................................................................. 78

References.................................................................................................................... 79

Background

The goal of the CSP Systems Analysis project is to provide timely and accurate CSP cost

data to the DOE and project performance and cost for emerging technologies to inform

research directions and industry investment. Much of NREL’s activity in CSP Systems 

Analysis is directed toward expanding the capabilities of NREL’s System Advisor Model 

(SAM). SAM is a performance and financial model designed to facilitate decision making 

for people involved in the renewable energy industry and includes simulation models for 

photovoltaic (PV), wind, geothermal, CSP and other power systems. SAM makes 

performance estimates and levelized cost of energy (LCOE) estimates for grid-connected 

power projects based on installation and operating costs and system design parameters 

that the user specifies as inputs to the model. 

SAM includes CSP models for trough, linear Fresnel, dish/engine, and power tower 

technologies, as well as linear-collector (trough and Fresnel) process heat models. SAM 

automatically populates input variables with a set of default values for the type of project. 

It is the responsibility of the user to review and modify all of the input data as appropriate 

for each analysis. Information about a project's location, the type of equipment in the 

system, the cost of installing and operating the system, and financial and incentives 

assumptions can be selected. To describe the renewable energy resource and weather 

conditions at a project location, SAM requires a weather data file. Depending on the kind 

of system being modeling, one can either choose a weather data file from a list, download 

one from the Internet, or create the file using one’s own weather data.

SAM’s CSP models have performed well in comparisons with other CSP estimation 

software and modeling guidelines (Wagner et al. 2011; Kesseli et al. 2018), and SAM has 

become the preeminent simulation package for estimating the performance and cost of 

solar energy technologies for NREL and the DOE Solar Energy Technologies Office. For 

example, SAM was used by NREL to help DOE develop the SunShot targets that have 

become the basic metrics for DOE’s CSP technology development portfolio (Mehos et al. 

2016). SAM also serves CSP stakeholders in the research, industrial, and financial 

sectors by providing a sophisticated, objective, and freely available tool for assessing the 

performance and cost of CSP systems. 
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SAM includes several technology models for different CSP configurations and relies on 

NREL-developed algorithms for modeling collector optical and thermal performance, 

power-cycle efficiency and behavior, and system dispatch (i.e., when to discharge storage 

to maximize revenue and minimize system operating costs), among others. Much of the 

work under the Systems Analysis project is directed toward improving the accuracy and 

utility of these subroutines. 

SolTrace and SolarPILOT are two such codes that were originally developed by NREL as 

stand-alone software for optical modeling and were partially integrated into SAM as part 

of this project. These two, formerly NREL-internal codes, were reviewed, cleaned, and 

documented and then released as open-source software.

Project Objective

Objectives under the CSP Systems Analysis project include enhancing the accuracy, 

speed and utility of NREL’s CSP models, including those models within SAM; providing 

new functionality in SAM; and using SAM and other tools to model CSP systems for 

specific analyses. The project also responds to specific DOE requests for CSP cost 

analyses. Six tasks were defined throughout the three-year period of performance: 

Task 1 – CSP Cost Tracking

• Track and assess CSP component and system costs as reported by the industry

• Update SAM’s CSP cost values

Task 2 – Supercritical CO2 (sCO2) Power Cycle Modeling

• Increase simulation speed and accuracy to make sCO2 cycle model codes compatible 

with NREL’s SAM framework,

• Address off-design operation and turbomachinery scaling so that the sCO2 power cycle 

models are consistent with constraints for CSP subsystems and dispatch optimization

algorithms,

• Examine sCO2 cycle performance and associated system cost impact as a function of 

the temperature differential (ΔT) across a 2-tank thermal energy storage (TES) system,

• Compare annual performance of two common sCO2 cycle configurations—

recompression and partial-cooling—within SAM’s molten-salt power tower (MSPT) 

model. Advise industry related to the selection and operating mode of sCO2 power 

cycles for CSP applications.

Task 3 – Optical Modeling of Power Tower Systems

• Develop and implement SolTrace performance and interface enhancements, enabling 

new analysis capabilities, and transfer SolarPILOT and SolTrace model capabilities to 

industry and researchers via a SAM webinar.

• Implement a method within the SolTrace engine to calculate view factors for multiple 

surfaces within selected stages. Availability of these data will significantly improve the 

efficiency of post-SolTrace radiative exchange analysis using thermal analysis codes. 

• Develop and consolidate scripting functionality among SAM, SolTrace, and SolarPILOT 

under SAM’s LK language.

• Update and release SolarPILOT as open-source code consistent with the open-source 

version of SAM. This will enable users to create custom geometry configurations (the 
NREL-1651

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former milestone) as well as other user-specific functions within SolarPILOT. Host 

informational webinar for user community.

Task 4 – Thermal-Application Modeling Tools for SAM 

• Separate SAM’s linear collector performance models from constraints associated with 

power conversion systems,

• Simplify and clarify SAM’s user interface for simulations of thermal-only systems,

• Validate performance estimates from a SAM thermal-system model versus experimental 

data, 

• Perform a case study to highlight the potential of solar industrial process heat to promote 

early adopters, 

• Analyze DSG+TES system for industrial steam production, showcasing the collector, 

TES, and project costs that lead to heat supply at lower cost than natural gas. Emphasis 

placed on the design and operation of the DSG system and TES to reduce cost and 

provide reliable steam delivery. 

Task 5 – IEA SolarPACES Task I Operation Agent

• This task supports NREL’s Mark Mehos as the SolarPACES Task I Operating Agent. 

SolarPACES Task I addresses the design, testing, demonstration, evaluation, and 

application of CSP systems, and supports an international project database for 

commercial CSP systems as well as the development of acceptance test procedures 

and standards for CSP systems. The active international market provides valuable 

markets for U.S. CSP developers and knowledge that can be applied to systems in the 

United States.

Task 6 – Assessment of Advanced CSP systems in the 600°C to 650°C temp. range

• Evaluate potential pathways and cost advantages of increasing power tower operating 

temperatures to 600-650°C as a prelude to later evolution to ≥700°C conditions. The 

task leverages prior studies by NREL, Sandia, and others and assesses construction 

material and thermal energy storage (TES) options.
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The project milestones are outlined in Table 1. 

Table 1. Project evaluation criteria (PEC) for the three-year scope. Page numbers refer to the initial 

page where information can be found in this report.

Metric Definition Success Value

Measured 

Value

Assessment Tool

(Quality Assurance)

Goal

Met?

PEC 1.1

Update to CSP technology 

costs in SAM

Report and presentation 

to DOE documenting the 

updated costs for CSP 

subsystems and overall 

plant cost within SAM

n/a

Referenced data sources 

with at least three survey 

responses for each cost 

category

Yes,

but 

superseded by 

PEC 3.1

PEC 1.2

Increase computation speed 

of the recompression code 

while retaining accuracy

Convergence of hourly, 

annual simulations in less 

than 25% of current 

system (SAM 2015-06-30) 

runtime

Achieved in 

~1% of 

current 

runtime

At least five different use 

cases will be evaluated; for 

all five, the deviation in 

annual generation and 

hourly generation RMS 

error from non-interpolated 

results will be < 5% 

Yes,

pg. 21

PEC 1.3.1

SolTrace simulation speed 

improvement

ΔΔssssss = ttssssss

0

ttssssss Δsim ≥ 20 > 300

Among population of ≥5 

heliostat field test cases, 

improvement mean 

Δssssss ≥ 20 with 90% 

confidence

Yes,

pg. 31

PEC 1.3.2

Deviation between raytrace and analytical view 

factors

Δff = �

ffaaaa − ffrr

ffaaaa

�

Δff mm ≤ 0.02 < 0.012

Max. error from set of ≥ 7 

analytical view factor 

solutions in Nellis & Klein 

(2009) F3D_X library

Yes,

pg. 31

PEC 1.3.3

Cross-platform simulation 

agreement

eeee mm = �

mmpp − mmrrrrrr

mmrrrrrr �

mm = �

qq̇

pp pp

qq̇

aaaa

nnrr rr,

0.001 < 0.001

Among population of ≥5 

heliostat field test cases, 

max error less than success 

value

Yes,

pg. 34

PEC 1.4

Validated, publicly available 

code to simulate trough, 

DSG trough, and Fresnel 

system performance for 

thermal applications.

Comparison of each new 

model with experimental 

data validating model 

energy-generation 

estimate within 90% 

confidence interval for 

test period. 

Agreement 

within 2% 

(trough) 

and 

approx. 3% 

(DSG)

Computational validation Yes,

pg. 49
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Metric Definition Success Value

Measured 

Value

Assessment Tool

(Quality Assurance)

Goal

Met?

PEC 2.2

MSPT/sCO2 recompression 

cycle model functionality 

and agreement across the 

following ranges:

Capacity: 10–200 MW

Load: 40–105%

Turbine Inlet: 550–750oC

Ambient: Temp. -10–50oC

MSPT/sCO2 model 

converges, is 

reproducible, and avoids 

HTF mass flowrate and 

power generation 

excursions outside 

specified bounds. 

Dispatch optimization 

improves upon level 

dispatch.

TBD

Simulations show that hourly 

HTF mass flowrate and cycle 

power do not violate 

equipment or operational 

constraints for annual 

simulations of 10 different 

designs. 

Yes, 

pg. 22

PEC 2.3.1

Demonstration of custom 

geometry configurations for 

heliostat and receiver

2 cases each TBD

Cases and functionality 

demonstrated in a SAM 

webinar

No, 

replaced by 

PEC 3.3

PEC 2.3.2

Deviation between ray-trace 

and analytical view factors 

for multiple surfaces on 

stage-by-stage basis

ΔFF ,jj = �

FF ,jj,aaaa − FF ,jj,rr

FF ,jj,aaaa �

ΔΔFF ,jj, mm ≤ 0.02 < 0.02

Criteria satisfied for ≥ 2

different cavity receiver 

geometries consisting of 5 

different surfaces.

Yes,

pg. 35

PEC 2.3.3

Scripting functionality 

replicates fraction of 

interface actions

80% 95% SolarPILOT & SolTrace 

interfaces

Yes,

pg. 39

PEC 2.4

Case study report defining 

conditions where NPV>0

Define a region of positive 

net present value based 

on parametric assessment 

of key design and financial 

parameters. 

n/a

Partner letter supporting the 

analysis methodology and 

primary assumptions 

Yes,

pg. 71

PEC 2.5

SolarPACES Task I status 

updates

Keep DOE apprised of 

activities within the 

international CSP 

community. 

Quarterly 

activity 

report

Summary of Task publications 

with relevance and benefit to 

U.S. CSP interests 

Yes,

pg. 74

PEC 2.6

DOE milestone report 

documenting the estimated 

performance and cost of 

MSPT systems operating at 

600 to 650°C 

Estimated LCOE at least 

10% less than the baseline 

design operating at 565°C 

with solar salt 

10-12%

Standardized financial 

parameters and a 

conceptually similar design 

will facilitate an accurate 

relative-cost evaluation.

sCO2 performance estimates 

based on engineering 

correlations of system 

performance through the 

range of temperature

Yes,

pg. 75
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Metric Definition Success Value

Meas’d

Value

Assessment Tool

(Quality Assurance)

Goal

Met?

PEC 3.1

Update CSP technology 

costs in SAM

Presentation to DOE 

documenting the updated 

costs for CSP subsystems 

and overall plant cost 

within SAM

n/a

Referenced data sources with 

at least three survey 

responses for each cost 

category

Yes,

pg. 12

PEC 3.2.1

Partial-cooling s-CO2 option 

for SAM MSPT Model

Public release to SAM; 

comparison with 

recompression and simple 

s-CO2 cycles. 

n/a

NREL report comparing

performance metrics between 

partial-cooling and 

recompression model

Yes,

pg. 25

PEC 3.2.2

sCO2 Power Cycle 

Optimization for Sensible 

TES

Sensitivity analysis of sCO2

cycle performance and 

associated system cost 

impact as a function of ΔT 

across a 2-tank TES 

system

n/a Peer-reviewed publication Yes,

pg. 30

PEC 3.3

Open-source code release 

of SolarPILOT to promote 

greater customization of 

heliostat/receiver modeling

Update code and release 

as open-source n/a

Release of open-source 

SolarPILOT and host 

informational webinar

Yes,

pg. 44

PEC 3.4

Analysis and optimization of 

linear collector system 

+thermal storage for 

industrial steam production

One or more scenarios 

illustrating solar-thermal 

levelized cost of heat 

(LCOH) less than cost from 

an industry standard fossil 

alternative

n/a

Comparison to industry 

standard (fossil boiler cost). 

Define a market sector (based 

on steam conditions, system 

capacity and DNI) where solar 

LCOH is less than natural gas 

boiler 

Yes,

pg. 73

PEC 3.5

SolarPACES Task I status 

updates

Keep DOE appraised of 

activities within the 

international CSP 

community.

n/aSummary of Task publications with relevance and benefit to 

U.S. CSP interests 

Yes,

pg. 74

Project Results and Discussion

Task 1. CSP Cost Tracking and Analysis

This task feeds new cost information into the latest release of SAM. Work in year three

focused on developing a better understanding of the capital expenditure (CAPEX) and 

key cost drivers for global CSP power tower and parabolic trough projects. 2017 and 2018 

had reported bids for CSP projects significantly lower than in previous years (Shemer 

2018b; Lilliestam et al. 2017), and levelized costs of electricity (LCOE), as low as $50/MWh 

in Chile were reported (Shemer 2018a).

Cost and project information were gathered from: (i) public sources of information (e.g.,

news sites related to CSP such as New Energy Update, CSP Focus and CSP Plaza), (ii) 

conference and journal articles, (iii) financing documents, e.g., from the World Bank (WB) 

and the Asian Development Bank (ADB), (iv) subscription access to the CSP Today 

Global Tracker which tracks projects globally, and (v) discussions with developers such 

as SolarReserve, Solar Dynamics and SUPCON Solar. SUPCON Solar is an emerging 

Chinese CSP technology developer and engineering, procurement and construction

(EPC) services provider.
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Table 2 summarizes power tower and parabolic trough plants where overall CAPEX has 

been determined. CSP and PV hybrid plants such as Cerro Dominador (SolarPACES 2018)

have been excluded if costs are not distinguished by technology. Table 2 highlights 

eleven power tower and eight parabolic trough projects, which use a variety of 

technologies such as molten salt tower (MS Tower), oil-heat transfer fluid (HTF) or MS 

parabolic trough.

Table 2. Chinese and other CSP plant projects that are active as of Sept. 2018.

# Country Project 

Name

Capacity 

(MW)

Technology 

Configuration

Total 

Investment 

($M)

Total 

Investment 

per kW 

($/kW)

TES 

Capacity 

(h)

Source

Power Tower

1 China Qinghai 

Delingha 50 MS Tower $163 $3,260 6

(Shemer 2018b; 

Yang, Zhu, and 

Guo 2018)

2 China Shouhang 

Dunhuang 100 MS Tower $472 $4,720 11

3 China Qinghai 

Gonghe 50 MS Tower $190 $3,800 6

4 China CPECC Hami 50 MS Tower $245 $4,910 8

5 China Gansu Jinta 100 MS Tower $388 $3,880 8

6 China Yumen 

Xinneng 50 Beam down $278 $5,560 12

7 China Shangyi 50 MS Tower $245 $4,890 6

(CSP Focus 

2017c; Yang, Zhu, 

and Guo 2018)

8 Australia Aurora 150 MS Tower $650 $4,333 8 (Shemer 2018b; 

Lilliestam and 

Pitz-Paal 2018) 9

United Arab 

Emirates 

(UAE)

DEWA IV 

(tower 

portion)

100 MS Tower

$553 

(tower 

portion)

$5,500 

(for 700-

MW site)

15

10 Chile Tamarugal 450 MS Tower $2,700 $6,000 13 (Shemer 2018b) 11 Chile Likana 390 MS Tower $2,400 $6,154 13

Parabolic Trough

1 China Royal Tech 

Yumen 50 Oil-Trough $209 $4,180 9

(Shemer 2018b; 

Yang, Zhu, and 

Guo 2018; NREL 

and SolarPaces 

2016)

2 China Rayspower 

Yumen 50 Oil-Trough Undisclosed Unknown 9

3 China Urat 100 Oil-Trough $435 $4,350 10

4 China CGN 

Delingha 50 Oil-Trough $301 $6,020 9

5 China Gansu 

Gulang 100 Oil-Trough Undisclosed Unknown 7

6 China Gansu 

Akesai 50 MS Trough $309 $6,170 15

7 China Zhangjiakou 64 MS Trough $280 $4,370 16

8 UAE

DEWA IV 

(Parabolic 

trough 

portion)

600 Oil-Trough

$3,317 

(parabolic 

trough 

portion)

$5,500

(for 700-

MW site)

11

(Shemer 2018b; 

Lilliestam and 

Pitz-Paal 2018)
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Table 2 includes the seven power tower and seven parabolic trough Chinese plants that 

are currently expected to finish as part of the Chinese Phase 1 demonstration projects. 

As previously highlighted, at least four of the original 20 projects have been stopped 

(HelioCSP 2018; Wang et al. 2017). The calculated CAPEX (in $M) of the active Chinese 

projects has used an exchange rate of 6.4364 Chinese Yuan (RMB) to $1 USD as of June 

15, 2018 (XE.com 2018a).

As seen in Table 2, the power tower investment range is approximately $3,300/kW to

$6,200/kW, even including the first commercial beam-down project (Yumen Xinneng).

These projects include 6 to 15 hours of thermal storage, which will influence the cost per 

kW. It is reported that the Shangyi 50-MW power tower project has been switched from 

direct steam generation (DSG) tower to MS tower with 6 hours of storage (CSP Focus 

2017c). Most power towers in construction or progressed development are less than 

$5,500/kW. Contrast this with the U.S. Crescent Dunes MS power tower plant, which 

came online in 2015 at $8,960/kW (NREL 2017a). The Chinese CAPEX range is slightly 

lower than the UAE and Chilean sites, although that can be at least partially attributed to 

lesser storage hours at the Chinese plants.

The parabolic trough investment range highlighted in Table 2 is $4,200 to $6,200/kW, 

even including two ongoing MS parabolic trough plants. The overall CAPEX bids for the 

Rayspower Yumen (50 MW oil-trough), and the Gansu Gulang (100 MW oil-trough) are 

undisclosed, but with the go-ahead to finish construction, it can be expected the 

investment is of the same order. Table 2 illustrates technology diversity within the Chinese 

program, not only MS power towers and oil-HTF troughs, but also a beam-down tower, 

and two MS parabolic troughs in the first set of demonstration projects. Aalborg, a key 

supplier of CSP MS steam generators, has been awarded the contract to supply two trains

of MS steam generator for the Gansu Akesai 50-MW MS trough project in China (Aalborg 

CSP 2017).

This tracking work shows that overall CAPEX and power purchase agreements (PPAs)

for CSP plants outside the U.S. has sharply decreased. There are several factors for this 

including the strong development of new markets like China and UAE, and very large 

capacity plants being built. Important factors in the CAPEX and PPA reduction include:

• Long financing and PPA durations. The PPA at the DEWA 700 MW site extends to 35 

years (Government of Dubai 2018). Had the period been 20 years like in other plants, the 

PPA price could have been $0.106/kWh instead of $0.073/kWh i.e., more than a 2¢

difference (Lilliestam and Pitz-Paal 2018). Another related important factor at DEWA, is the 

large amounts of storage capacity at each of the four plants, that then allows for very 

high energy generation and capacity factors. For example each of the parabolic trough 

plants have 11 hours of storage and could have for over 5,000 full load hours per year 

(Pitz-Paal 2017). Other important factors at DEWA include very low financing costs (e.g.,

interest rate) and very good financing conditions. For example, when the overall CAPEX 

($3.9 billion), the 35-year PPA life, and the 80% debt financing are considered (PR 

Newswire 2018a), it can be estimated that the Weighted Average Cost of Capital (WACC) 

is potentially only 4.8% with an internal rate of return (IRR) of 2.8% (Pitz-Paal 2017). This 

level of beneficial financing conditions supports the low reported PPA price.

• Alternative revenue streams outside of the PPA, i.e., the plant can sell electricity at 

different times to supplement the PPA. For Aurora, as part of the business model, the 
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plant can sell electricity at market prices during the evening peak times when electricity 

prices are significantly higher (Lilliestam and Pitz-Paal 2018).

• Cost-effective local suppliers. Low CAPEX costs are possible, for example though 

local heliostat manufacturing, automation and sourcing, rather than exclusively using 

low-cost labor countries. In Australia, which has labor costs similar in magnitude to the 

United States, SolarReserve has signed an agreement for the manufacture of at least 

13,000 heliostats needed for the Aurora project (CSP Plaza 2018b). This manufacturing 

capacity may then also be leveraged for other plants.

SAM 2018 Updates for Power Towers and Parabolic Troughs

Table 3 and Table 4 show the SAM 2017 and the updated 2018 cost values. The key 

areas that are discussed further for each technology are: Solar fields (both heliostats and 

parabolic troughs); Power Cycle and Balance of Plant (BOP); and the Thermal Energy 

Storage (TES). O&M and other cost inputs are not updated in the SAM 2018 update.

Table 3. Update of SAM 2017 to 2018 values of key parameters for the Molten Salt Power Tower

(n/c = no change).

Power Tower 

(Molten Salt and DSG) units SAM2017

SAM

2018 Comments

Site Improvements $/m2 16 n/c

Heliostat Field $/m2 145 140

Few data on real plants. $/m2

estimate created for China and then 

labor shifted to the U.S. 

Tower cost formula n/c

Receiver cost formula n/c

Thermal Energy Storage (TES) $/kWh-t 24 22

Calculations based on recent 

developer tank-cost estimates and 

SQM salt quote

Power cycle $/kWe 1,100 1,040 Adjusted learning rate for overall CSP 

plants and CEPCI 

Balance of plant (BOP) $/kWe 340 290 Adjusted learning rate for overall CSP 

plants and CEPCI

Contingency % 7 n/c

EPC & Owners Cost % 13 n/c

Sales Tax % 5 n/c Applied to 80% of total direct costs

O&M Fixed cost by capacity (MS) $/kW-y 66 n/c

O&M Fixed cost by capacity (DSG) $/kW-y 55 n/c

Variable cost O&M (both) $/MWh 3.5 n/c

Property Tax % 0.0 n/c

Insurance % 0.5 n/c
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Table 4. Update of SAM 2017 to 2018 values of key parameters for the for the Physical and 

Empirical Parabolic Trough models (n/c = no change).

Trough 

(Physical and Empirical) Units SAM2017

SAM

2018 Comments

Site Improvements $/m2 25 n/c

Solar Field $/m2 150 n/c

HTF System $/m2 60 n/c

Thermal Energy Storage (TES) $/kWh-t 65 62

Calculations based on recent 

developer tank-cost estimates and 

SQM salt quote

Power Plant $/kWe 930 910 Adjusted learning rate for overall 

CSP plants and CEPCI 

Balance of Plant (BOP) $/kWe 120 90 Adjusted learning rate for overall 

CSP plants and CEPCI 

Contingency % 7 n/c

EPC & Owners Cost % 11 n/c

Sales Tax % 5 n/c Applied to 80% of total direct costs

O&M Fixed cost by capacity $/kW-y 66 n/c

O&M Variable cost by generation $/MWh 4 n/c

Property Tax % 0 n/c

Insurance % 0.5 n/c

Solar Field Costs

A review of relevant published papers, project bids and discussions on the solar field 

costs (e.g., $/m2) has been completed for available heliostats and the parabolic troughs.

Part of the challenge when dealing with reported system costs or bids is discerning details 

on subsystem costs, for example, the $/m2 for the solar field. Normally only a small 

amount of detail is available, e.g., an overall solar field bid, rather than a detailed breakdown. 

Heliostat Solar Field Installed Costs

Task 1 compiled estimated, bid, installed and aspirational heliostat costs. Figure 1 shows 

the spread of estimated solar field costs for available data. The variation results from a 

combination of different heliostat designs and estimating teams. The R&D based heliostat 

installed costs (shown as cross-hatched column) are mainly found in scientific journals 

(e.g., new low-cost heliostat designs) and have not been built or realized. As such, they 

are excluded from the SAM 2018 heliostat cost update. The SAM 2017 value for the 

installed cost of heliostats is $145/m2.
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Figure 1. Estimated heliostat installed solar field cost spread (in $/m2). Cross-hatched bar shows 

R&D-based values.

The recently published Australian Solar Thermal Research Initiative (ASTRI) 2016 report, 

highlighted that “The actual cost of heliostats is presently estimated to be in the range 

150-200 USD/m2” (Coventry et al. 2016). The evidence in Figure 1 also shows that most 

expected or installed heliostats are less than $200/m2. Our review has found anecdotal 

evidence that heliostat costs have decreased; however, until more data on constructed 

solar fields are found, the SAM estimates—which are conceptually based on current year 

bid prices—will remain conservative.

As seen in Figure 1, the current sample size (of estimated or reported) installed solar 

fields for the power tower is 20, of which 8 are considered R&D-based projections at $75-

100/m2. There is one further R&D data point ascertained with confidential discussions 

with a solar developer who expects to build a solar field using their new design at $125/m2

in the U.S.

The lowest reported solar field bid was from SUPCON Solar, where an installed heliostat 

field was bid at approximately $180 M or $105/m2 (HelioCSP 2017; CSP Focus 2017b). This 

was for the Luneng Haixi 50-MW molten salt power tower plant in China.

The Shouhang Dunhuang project in China (100-MW molten salt project) heliostat field is 

estimated at $224/m2, for a solar field of approximately 1,380,000 m2 (CSP Focus 2018a). It 

must be noted that the Shouhang Dunhuang plant includes the receiver as part of the 

solar field in its reported subsystem CAPEX (CSP Focus 2018a), though the estimated value 

of ($224/m2) has attempted to exclude the receiver from the $/m2 cost.

For this cost update, an important plant to highlight is the CPECC Hami 50-MW molten 

salt power tower which is expected to use the low-cost heliostat developed by the Stellio 

Consortium, known as the Stellio (HelioCSP 2017; CSP Focus 2017b; SBP 2016). The estimated 

bid cost of an installed Stellio heliostat field in China is $124/m2. The Stellio heliostat has 

been reported as the solar field for the CPECC Hami site and the Redstone 100-MW plant 

in South Africa (ACWA Power 2017; Shemer 2017). As such, Stellio represents a suitable 
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surrogate for a state-of-the-art heliostat design and the SAM update uses the Stellio as a 

reference for the cost estimate.

Using the detailed SAM molten salt power tower cost models (Turchi and Heath 2013), we

estimate the labor portion of the solar field at 11% of the total installed solar field cost. 

Thus, the CPECC Hami installed cost of $124/m2 can be broken into: ~$14/m2 for labor 

and ~$110/m2 for non-labor (i.e., materials). To shift the Chinese plant to the United 

States., one must account for the difference between U.S. and Chinese labor cost and 

productivity. It is assumed that the same amount of tools and construction equipment are

used.

In 2017 in China, construction workers earn approximately 18 Chinese Yuan (CNY) per 

hour, or approximately 38,400 CNY/year (Economic Research Institute 2018). In dollars that 

would be $2.70/h or $5,775/yr as of 29th September 2017 (X-Rates 2018a). As a 

comparison, the hourly and yearly average construction labor wage for ‘Electric Power 

Generation’ in the U.S. as of 2017 was $24.55/h and $51,060/yr (BLS 2017). This is 

approximately 9.1 times more in the U.S. than in China for construction labor. If cheaper 

U.S. construction labor is used, e.g. approximately $15/h (PayScale 2018), the hourly rate 

for U.S. construction labor is approximately 5.8 times more than Chinese construction 

labor.

China as of 2017, had an estimated labor productivity of $14/h (Statista 2018a). If this is 

used as comparable for the construction industry, a China-to-USA productivity measure 

can be derived. Effectively it can be expected that for every 1.75 hours worked in China, 

1 h can be attributed to the U.S. labor (i.e., $24.55 of value in USA ÷ $14 value in China

= 1.75). Taking the estimated Stellio labor requirement of $14/m2 in China, this can be 

shifted to the U.S. as ($14/m2)*(5.8 to 9.1)/1.75 = 46 to 73 $/m2. When the materials cost 

of $110/m2 is added, the estimated installed Stellio field cost is $156/m2 to $183/m2 for 

the USA. The non-labor portion of the Stellio heliostat cost estimate is left unchanged, 

assuming globally sourced components and no tariffs.

If a similar installed cost estimate is undertaken using the installed heliostat costs for the 

Luneng Haixi plant (i.e., $105/m2), a lower U.S. estimate can be determined. Through 

similar calculation, it can be determined that the U.S. equivalent to install the Luneng 

Haixi heliostat field could be ~$133/m2 (i.e., [$12/m2 * 5.8]/1.75 + $93/m2 for non-labor 

costs). As such there is some evidence to suggest a $140/m2 installed cost heliostat in 

the U.S. (assuming the lower US-labor rate of $15/h) can be used for SAM 2018. For the 

SAM 2018 update, a heliostat cost of $140/m2 is recommended based on reported 

Chinese-project costs and near-term R&D projections. 

Parabolic Trough Solar Field Installed Costs

The installed solar field costs for the parabolic trough, as of SAM 2017 was $150/m2. 

Figure 2 shows the spread of estimated parabolic trough installed costs to date, including 

the number of aspirational R&D-based values (cross-hatched column) indicating potential 

of installing the solar field at less than $100/m2.
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Figure 2. Estimated parabolic trough installed solar field cost spread (in $/m2). Values reported as 

R&D-based are shown crosshatched.

The CGN Delingha 50-MW, oil-trough plant, which was the first grid-connected CSP plant 

in China in June 2018 and is expected to be fully operational by the end of September 

2018 (CSP Focus 2018b). ADB proposed loan documents have allowed the solar field 

installed cost to be estimated at $231/m2 in $2018 (ADB 2013), as such it is classified as 

> $200/m2. Even though the CGN Delingha solar field has an estimated installed cost of 

$231/m2, which is higher than the current SAM 2017 value of $150/m2, the use of the

EuroTrough (the most deployed parabolic trough in the market) is a significant step 

forward for the Chinese CSP market. Future deployments of such an established trough 

technology may lead to reduced installed solar field costs. 

The most significant parabolic trough estimate is for the installed cost of the 600-MW 

DEWA solar fields, which is estimated to be $105/m2. As mentioned, NREL has estimated 

the installed cost of the DEWA solar fields based on available project information, SAM 

parabolic trough breakdowns, and discussions with trough developer Solar Dynamics

(who have experience with Abengoa collectors). The estimated installed cost was found 

to be $105/m2 (Pizá de Silva 2018; Abengoa S.A. 2018; The Corner 2018; Solar Dynamics 2018; 

Turchi 2010). The 600-MW is made up of three separate 200-MW solar fields, and have 

sufficient solar field to supply the energy needed to store 11h of thermal energy at each 

of the 200-MW sites (New Energy Update 2018). For the SAM 2018 parabolic trough solar 

field update, an NREL estimate is provided to effectively create an analogous parabolic 

trough site (e.g. 600-MW) in the U.S., by labor shifting the DEWA site from the UAE to 

the U.S. It is assumed that each of the three solar fields can all be considered as a single 

installed project.

Using the detailed SAM parabolic trough cost models (C. Turchi 2010), the labor portion of 

the solar field is estimated at 22% of the total installed solar field cost. Effectively the 

$105/m2 can be broken into: ~$23/m2 for labor and ~$82/m2 for non-labor (i.e., materials). 

To shift the UAE plant to the U.S., one must account for the difference between U.S. and 

UAE labor cost and productivity. It is assumed that the same level of tools and 

construction equipment are used.
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In Dubai, day laborers in construction can earn approximately 1,500 Emirati Dirhams 

(AED) per month (Dubai FAQs 2018; Guide 2 Dubai 2017), or approximately $408. 

Construction supervision is more like 20,600 AED/mo (Guide 2 Dubai 2017; XE.com 2018b). 

As a comparison, the construction labor wage for ‘Electric Power Generation’ in the U.S. 

as of 2017 was approximately 10.4 times more than in UAE for construction labor (BLS 

2017). If cheaper U.S. construction labor is used e.g. at approximately $15/h (PayScale 

2018), it can be estimated the monthly labor wage could be approximately $2,800/mo, or 

approximately 7 times more than UAE construction labor. 

India, Pakistan and other Asian countries tend to supply low-cost manual labor in the UAE 

(Guide 2 Dubai 2017). Pakistan as of 2017, had an estimated labor productivity of $9/h

(Statista 2018b). If this is used as a comparable for the construction industry, a UAE-toUSA productivity measure can be derived as $15/h(US)/$9/h(Pakistan) = 1.67. Taking 

the estimated labor requirement of $23/m2 in UAE, this can be shifted to the U.S. as 

($23/m2)*(7)/1.67 = $96/m2. When the materials cost of $82/m2 is added, the estimated 

installed trough field cost is $178/m2 for the USA. The non-labor portion of the cost 

estimate is left unchanged, assuming globally sourced components and no tariffs.

In contrast to the above, confidential discussions with another U.S. developer suggest 

their large aperture trough has an estimated installed solar cost of $115/m2 in the U.S., 

suggesting that continued cost reduction in trough technology. 

For the SAM 2018 update, it is recommended that the current value of $150/m2 remain

until further data and evidence is gathered e.g. realized projects in China and UAE, 

international parabolic trough reviews from organizations such as NREL and DLR are 

undertaken, or project bids with good data are investigated.

Power Block and Balance of Plant Costs 

As highlighted in prior quarterly reports, steam-turbine package bids for some of the 

Chinese plants are lower than the U.S. market. For example, out of the four main steam 

turbine bidders for the Supcon Delingha 50-MW MS power tower plant it was found that 

the winner, Hangzhou Turbine Co., won the steam turbine bid at an estimated $177/kWe 

(CSP Focus 2017a; CNSTE 2017). Assuming this represents the steam turbine/genset, we can 

estimate the cost for the full power block system using SAM’s cost model. The SAM power 

block assumes inclusion of 17 components, of which the turbine/genset represents the 

largest single cost item, but only about 20% of the overall power block cost. Hangzhou 

Turbine Co, will provide the steam turbine, generator, auxiliary engine, regenerative 

system and the installation (CSP Focus 2017a; CNSTE 2017). Assuming a similar ratio of 

component costs, the total power block may be on the order of $177/kW ÷ 0.20 = 

$885/kWe. This is approximately 11% less than the current U.S. estimate for the power 

tower power cycle cost.

Other institutions such as the European Solar Thermal Electricity Association (ESTELA)

and the German Aerospace Center (DLR) have released studies that highlight the power 

cycle and power block costs are potentially decreasing (Kistner 2016; ESTELA 2016; IRENA 

2016). For example in an ESTELA market update, the highlighted Power Block cost in 

2016 was 720 – 765 €/kWe (ESTELA 2016). This equates to approximately $809 -

$860/kWe in $2016 (X-Rates 2018b). Then using the CEPCI ‘Equipment Index’ from 2016 

to 2018 (i.e. an 11.5% increase), the power block could be approximately $956/kWe at 
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the upper end, again lower than the SAM 2018 estimates. It is not clear that the Power 

Block includes everything that is included in SAM. While innovation itself has not been 

significant for CSP turbines using steam, the increasing demand and need to increase 

production is likely to help decrease costs as the global installed capacity increases, and 

players such as the Chinese turbine manufacturers enter the market.

In 2012, when the last detailed CSP cost study was undertaken by NREL, SAM’s power 

block values were $1,200/kWe (dry-cooled tower) and $1,050/kWe (dry-cooled trough).

(Turchi 2010; Turchi & Heath 2013). The current SAM 2017 estimate for the Power Cycle is 

$1,100/kWe and $930/kWe for the power tower and the parabolic trough respectively

(both dry cooled). These values represent survey-based updates from the initial detailedcost studies undertaken in 2012 (tower) and 2010 (trough). For this update, NREL returns 

to the original 2012 values and applies CSP learning rates and process-industry cost 

indexes to update the values to 2017. The Chemical Engineering Plant Cost Index 

(CEPCI) ‘Equipment Index’ is used. The change in the CEPCI Equipment Index from 2012 

to 2017 was 684.4/712.5 = 0.961. That is, the index decreased during this period by 3.9%. 

In the last 4 to 5 years, there has been significant growth in the global installed CSP 

capacity. From 2012 to 2017, the estimated reduction in CSP installed cost due to learning 

was approximately 20%, albeit with large uncertainty (Lilliestam et al. 2017). Effectively, for 

the doubling of overall capacity during that period, there has been on average a 20% 

decrease in overall costs. The learning rate for CSP includes factors such as economies 

of scale, high cost pressure, industry development and beneficial policies (Lilliestam et al. 

2017). The power block for CSP has not undergone significant technical change during 

this period but can be assumed to have benefited from greater industry maturity and 

project efficiency. Instead of applying the 20% learning associated with overall CSP 

plants—much of which is likely related to the solar components—we apply a lesser 10%

learning rate to the power cycle. If the power tower and parabolic trough dry-cooled power 

cycle costs are decreased by 10% from their original values in 2012, the respective 2017 

costs are $1,080/kWe and $945/kWe respectively. Further applying the CEPCI index 

change of a 3.9% decrease, yields the new Power Cycle estimates for 2017 of 

$1,040/kWe and $910/kWe for the power tower and the parabolic trough respectively.

These values are consistent with those reported by ESTELA above.

The balance of plant (BOP) cost has also been updated from the 2017 value, using the 

10% learning rate decrease and CEPCI cost index. In SAM, the BOP is made up of the 

steam generation system, as such the ‘Heat Exchanger and Tanks’ index is used. This 

index also decreased from 2012 to 2017: 599.9/661.7 = 0.907, i.e., an approximate 10% 

reduction. The recommended SAM 2018 Power Block and BOP values are shown in 

Table 5.
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Table 5. Power Cycle and BOP costs for SAM 2018 (2017$). By SAM convention, BOP costs 

represent the steam generation system. The benchmark values are from NREL’s detailed cost 

study in 2012.

Technology

Power 

Cycle Cost 

in 2012 

($/kWe)

Power Cycle Cost 

with 10%

decrease due to 

learning ($/kWe)

Power Cycle 

Cost indexed 

to 2017

($/kWe)

BOP Cost 

in 2012 

($/kWe)

BOP Cost with 

10% decrease 

due to learning 

($/kWe)

BOP Cost 

indexed to 

2017 

($/kWe)

Power 

Tower $1,200 $1,080 $1,040 $355 $320 $290

Parabolic 

Trough $1050 $945 $910 $110 $100 $90

Thermal Energy Storage (TES) tanks and costs

A cost estimate has been received from SQM, one of the key suppliers of solar salt in the 

CSP industry. The estimated cost for solar salt shipped to the U.S. (San Diego, CA) is 

$700-800/MT. This is less than the current $1,000/MT assumed for solar salt. Abengoa 

suggests a salt melting cost of $50 per ton (Kelly 2010). As such, 750 (mid value) + 50 

(melting) = $800/MT is used for installed salt.

The TES tank cost update relies on confidential information from SolarReserve. The 

similarity of construction for the cold and hot parabolic trough TES tanks (290°C and 

390°C) and the cold tank of the power tower system (290°C), allow a similar cost to be 

used for these three carbon-steel tanks. The hot tank for the power tower (565°C) requires 

stainless-steel construction with correspondingly higher cost. Information shared by 

SolarReserve provides new insight into the estimated costs for these tanks. Additional 

costs for controls, pumps, insulation and piping and heat exchangers (troughs only) are 

taken from the prior cost models (NREL 2017b).

The current SAM 2017 cost for the indirect, 2-tank parabolic trough TES is $65/kWhth, 

while the value for direct, 2-tank system in power towers is $24/kWhth. As seen in Table 

6, the estimated cost for the TES is calculated at $62/kWhth for troughs (~4% drop relative 

to SAM 2017) and $22/kWhth for power towers (10% drop). 

Table 6. Molten Salt Power Tower and Parabolic Trough 2018 SAM TES cost update

Power Tower Plant Value Unit

Hot Tank - Stainless Steel 11,900* m3

Cold Tank - Carbon Steel 10,800* m3

Salt inventory (includes heel) 27,400 MT

Piping, Insulation, Valves, & Fittings -

Foundations & Support Structures -

Instrumentation & Controls -

Thermal energy for system 2,350 MWhth

Cost subtotal

TES cost per unit thermal energy 22 $/kWhth

Parabolic Trough Plant Value Unit

Hot Tank - Carbon steel 11,900* m3
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Cold Tank - Carbon Steel 10,800* m3

Salt inventory (includes heel) 27,400 MT

Pumps & Heat Exchangers -

Piping, Insulation, Valves, & Fittings -

Foundations & Support Structures -

Instrumentation & Controls -

Thermal energy for system 943 MWhth

Cost subtotal

TES cost per unit thermal energy 62 $/kWhth

* Active volume (a 14% heel exists in each tank)

Compared to other reports, these TES costs are relatively conservative. For example,

ESTELA estimates the parabolic trough TES cost to be 26-30 €/kWhth (ESTELA 2016) or 

about 29-34 $/kWhth. The NREL TES cost estimate is about 80% higher than the ESTELA 

estimate. DLR estimates the 2-tank indirect TES for parabolic trough using the synthetic 

oil-HTF as 40 €/kWhth (Pitz-Paal 2017) in September 2017. This converts to about 

$50/kWhth; the NREL estimate is about 25% higher.

In a 2016 review of CSP technology, ESTELA estimated the cost for TES in molten salt 

power tower systems at 13-15 €/kWhth (ESTELA 2016). When this range is converted to 

2018$, the range is estimated at 17-19 $/kWhth. The NREL value of $22/kWhth is about 

20% higher than the ESTELA range.

Updated CSP Costs within SAM

The values outlined above were included in the release of SAM 2018 on November 11, 

2018. For comparison, the default cases for the MSPT and Physical Trough models are 

shown for SAM 2017 and SAM 2018 in Table 7. The only changes made in the default 

cases was setting the federal investment tax credit to zero and the selecting a uniform 

time of delivery schedule, i.e., each kWh of generation is valued equally. It should be 

noted that the default federal tax rate dropped from 35% to 21% and SAM’s assumed 

discount rate increased from 5.5% to 6.4% between 2017 and 2018, which also affect 

LCOE. These values are set by NREL’s SAM financial team and not the CSP group.

Table 7. Updated performance and cost estimates for two CSP models within SAM 2018.11.11.

Model and Parameter SAM 2017.09.05 SAM 2018.11.11

MSPT Model (115 MW gross, 10 hours TES, Solar multiple = 2.4)

 Annual generation (MWh) 569,103 577,916

 Total installed cost ($/kW)* 6,804 6,507

 LCOE, real$, no incentives (¢/kWh) 11.2 11.1

Physical Trough Model (111 MW gross, 6 hours TES, Solar multiple = 2.0)

 Annual generation (MWh) 354,221 362,298

 Total installed cost ($/kW)* 6,066 5,628

 LCOE, real$, no incentives (¢/kWh) 15.6 14.9

* overnight costs, i.e., does not include construction financing

Potential Trough TES Improvements

German company Wacker and DLR are working towards the use of synthetic silicone oilHTF for parabolic trough solar fields. Silicon oil increases the solar field outlet temperature 
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from 393°C (i.e. using today’s most common synthetic oil-HTF), to 425°C, decreases the 

hydrogen accumulation in the HTF, and has been tested at DLR since 2016 (Pitz-Paal 

2017). Analysis from DLR has found that the use of silicone oil instead of today’s most 

used synthetic oil-HTF (Therminol VP-1 or Dowtherm A), for the same sized plant, could 

reduce the LCOE by 5% (Jung et al. 2015).

The use of silicone oil-HTF and the increased solar field temperature allows for increased 

power block efficiency and decreased size and cost for the indirect 2-tank TES system. 

For example with the increased solar field temperature, the gross power block efficiency 

using air-cooled condensers (ACCs), could increase from 37.7% to 39.2% (Pitz-Paal 2017). 

Similarly due to the increased ΔT between the hot and cold tanks for the TES, DLR have 

highlighted that the TES cost in 2017 could be 33 €/ kWhth or $41/ kWhth in $2018 

(ChemEng Online 2018).

A partnership between Wacker and the Chinese Royal Sun Technology is expected to 

develop a first commercial CSP demonstration using silicone oil in China. Silicone oil has 

been tested in a demonstration loop operating at 425°C since 2016 (Dou, Stary, and 

Schaffer 2017). Wacker have developed a certified and tested silicone oil called Heliosol 

5A, which will be used at the 50-MW Royal Sun Technology parabolic trough plant 

(Schaffer 2016; Dou, Stary, and Schaffer 2017), which has a reported overall CAPEX of 

$4,180/kWe (Shemer 2018b). Currently there is no expectation for silicone oil-HTF 

parabolic trough plants in the U.S.

Task 2. Supercritical CO2 (sCO2) Power Cycle Modeling

Preprocessor boosts computation speed for sCO2 Power Cycle Modeling

In project year one, the primary objective for Task 2 was to develop a methodology to 

allow the recompression sCO2 power cycle to run within SAM with a simulation speed 

compatible with other SAM models, e.g., within a few minutes. The approach developed 

a “preprocessor” to develop a performance map for off-design situations that could be 

called instead of the slower first principles model during annual simulation runs. This 

preprocessor was developed to work with SAM’s existing user-defined power cycle 

(UDPC) and later moved to a separate sCO2 Power Cycle option on SAM’s Power Cycle 

page, thereby allowing detailed sCO2 cycle models to be integrated efficiently into an 

annual SAM simulation with SAM’s molten-salt power tower. 

In year one NREL reported success with the preprocessor concept, but implementation 

of the sCO2 recompression model led to poor integration between the power cycle and 

the other CSP components. That is, simulation speed improvements were accomplished, 

but the underlying sCO2 cycle behavior was found to challenge SAM’s existing CSP solver 

routine in ways that did not occur with the steam-Rankine model. This led to a 

restructuring of the year two objectives to resolve the issue. Although the sCO2 model 

struggled to accurately represent a sCO2/CSP plant, the speed advantage of the 

preprocessor methodology was successfully demonstrated in year one as summarized in 

Table 8. 

Compressor inlet pressure is a control variable that we can adjust (via CO2 inventory 

control) to optimize cycle performance. The off-design control algorithm first tries to 

achieve the design-point power output scaled by the normalized off-design HTF mass 
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flow rate. If there are multiple points that achieve this power output target, then the 

algorithm chooses the point with the best thermal efficiency. The model discards solutions 

where the selected compressor inlet pressure results in a (i) main compressor outlet 

pressure greater than the maximum in any compressor, (ii) surge in any compressor, or 

a (iii) tip speed greater than the speed of sound through the CO2 in the turbomachinery.

Table 8 shows a comparison of the 1st Principles and preprocessed-UDPC models. The 

sCO2 recompression model implemented in the UDPC option is roughly two orders of 

magnitude faster than the first principles model while achieving a root mean square 

(RMS) difference in the hourly data less than the 5%-error target and a very small (0.26%) 

cumulative energy difference for the annual data. These results show that the new 

framework is capable of quickly and accurately characterizing complex, power cycle 

models for an annual simulation.

Table 8: Comparison between the 1st principles and preprocessed UDPC models for an annual 

simulation with the sCO2 recompression model.

Parameter Result Target

1st principles model Approx. 3 hours -

User Defined Power Cycle (UDPC) 1.7 min order of minutes

UDPC vs. 1st Principles runtime < 1% < 25%

Hourly results RMS difference 3.46% < 5%

Annual Total Energy difference -0.26% < 5%

The sCO2 recompression model user interface updated within SAM

During year two NREL completed the SAM user interface section for this sCO2 cycle, and 

the sCO2 option is available on the Power Cycle page of the MSPT model. The first time 

a user selects this option in a case, a notice window appears that describes important 

differences between the sCO2-Brayton cycle and the default steam- Rankine cycle. The 

notice informs the reader that the cold HTF temperature is not an independent variable in 

the sCO2 model, and that the simulation will use the calculated value for this parameter. 

The notice box also instructs the user to review the number of panels in their receiver 

design, as the default value of 20 can lead to excessive pressure drops due to the smaller 

temperature difference across the receiver and corresponding larger HTF flowrates. 

Finally, the notice describes that the model will enforce partload constraints on mass flow. 

The sCO2 model is part of the open source SAM code that is available for download. 

Figure 3 shows an example of the sCO2 user interface. When the user first selects this 

option, there is no saved cycle design or off-design data, and the middle and right columns 

will be empty. If the user runs the SAM simulation without any changes, the annual 

simulation will begin by designing the cycle and preprocessing the off-design performance 

before beginning the time-series simulation. These steps can take a few minutes, so 

buttons in the user interface allow one to calculate both the design and off-design 

performance or just calculate the design point. If the user selects the off-design option, 

then the user will have the option to run the annual simulation with the calculated offdesign performance. This gives the user the opportunity to review the design point before 

simulating the case, as well as the option to run parametrics on system values that do not 

affect the cycle design. If the user changes an input value that affects the cycle design, 
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the stored calculations are invalid, and the option to run the simulation with the calculated 

performance disappears. In this scenario, the user must either change the input back to 

the value used for the cycle calculations or rerun the macro to develop new off-design 

performance parameters.

Figure 3: An example of the sCO2 user interface. Here, the top macro has already been run at a 

cycle design ambient temperature of 35°C. The screen shows what happens when the Ambient 

temperature at design is changed to 32°C. The grey-out cells indicate the user must either change 

the ambient temperature back to 35°C or rerun the macro.

Figure 4 and Figure 5 below show a snapshot of the system performance for a cold and 

hot day, respectively. The top plot in each snapshot shows the temperature and HTF 

mass flowrates for each component, while the bottom plot shows power cycle output and 

efficiency. Note that on the cold day, the sCO2 cycle can always operate at the critical 

point, so the cycle power output and efficiency are constant until the HTF mass flow 

reaches part-load. The top plot shows that mass is always conserved:

Rec._mass_flowrate = PC_HTF_mass_flowrate + TES_charge_mass_flowrate – TES_discharge_mass_flowrate

and that the HTF mass flowrate to the cycle stays within design constraints. On the hot 

day (Figure 5) the cycle becomes capacity limited, and the results show both power output 

and efficiency decreasing as the temperature increases. Note that the HTF mass flowrate 

to the power cycle is always decreasing as temperature increases. Mass is conserved 

and the HTF mass flowrate stays within design constraints on the hot day.
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Figure 4: MSPT-sCO2 cold day behavior

Figure 5: MSPT-sCO2 hot day behavior

Table 9 shows the results of these test cases. For each case, we analyzed the hourly 

model outputs and compared them to the design point values. As the three columns on 

the right show, the maximum normalized HTF mass flowrate (max m_dot) and maximum 
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normalized cycle power output (max W_dot) are very close to their design points. The 

normalized cycle efficiency (max efficiency) can be slightly greater than design as the 

system experiences ambient temperature colder than the design point throughout the 

year. The calculated capacity factors and PPA prices appear realistic of expectations. 

Dispatch optimization shows a small improvement in PPA price, although a reduction in 

capacity factor. This interesting result can be improved by modifying the dispatch model 

to better predict the sCO2 part load response, and we are currently working on this.

Table 9: SAM MSPT/sCO2 model test cases. Maximum (“max”) values are shown normalized to 

their design point values

TIT 

(°C)

Capacity

(MWe)

Efficiency 

at 35C (%)

Dispatch 

opt?

Capacity

Factor (%)

PPA

¢/kWh

max 

m_dot

max 

W_dot

max 

efficiency

554 200 42 no 58.4 10.29 0.995 0.99 1.05

554 200 42 yes 57.8 10.25 0.996 0.99 1.04

600 200 46 no 57 9.94 0.996 0.993 1.03

650 200 48.25 no 56.6 9.72 0.996 0.993 1.03

700 200 50.5 no 56.1 9.54 0.996 0.993 1.02

750 200 51.5 no 55.5 9.51 0.996 0.993 1.02

554 115 42 no 59 10.26 0.995 0.99 1.05

650 115 48.25 no 57.3 9.66 0.996 0.993 1.03

750 115 51.5 no 55.6 9.54 0.996 0.993 1.02

554 10 42 no 55.7 13.09 0.996 0.99 1.04

750 10 51.5 no 47.1 13.53 0.996 0.993 1.02

750 10 48.8* No 47.8 13.78 0.996 0.993 1.02

∗ This case was generated with the same total recuperator conductance as the 51.5% case the row 

above it, but the isentropic efficiencies of the turbomachinery were adjusted to more capacityrealistic values of 80% for the compressors and 88.5% for the turbine [Turchi 2014].

Development of the Partial-cooling sCO2 Cycle within SAM

In year three NREL developed the design and off-design performance models for the 

partial-cooling sCO2 cycle (Figure 6). Initial assumptions are made about how the cycle 

is configured and operated. The model assumes that the inventory of CO2 in the system 

is regulated such that the low pressure (i.e., the pre-compressor (inlet) in the system is 

an independent off-design control variable. “Inventory control” has been suggested for 

cycle control by multiple developers and researcher teams. The model also assumes that 

the main compressor (C) and pre-compressor (P-C) coolers adjust their fan speeds to

reach the main and pre-compressor inlet temperatures, respectively. Finally, the model 

assumes that the shaft speed of each turbomachinery component is fixed at its design 

speed (i.e. an “integrally geared” configuration). These assumptions mirror those in the 

recompression off-design model. 
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Figure 6. Equipment schematic of the partial-cooling cycle.

Figure 7: Logic diagram of the off-design partial-cooling cycle model. Numbers in parentheses 

refer to Figure 6.

Figure 7 shows the logic-flow diagram for the off-design model, which assumes that the 

pre-compressor inlet pressure and temperature and main compressor inlet temperature 
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are known. The model first guesses the turbine inlet temperature. Then it guesses the 

recompression fraction and turbine mass flow rate. With this information the model 

calculates the pre-compressor, main compressor, and turbine performance. If, given 

pressures provided by the compressor, the turbine calculates a different turbine mass 

flow rate than the guess, the model continues to iterate. After the turbine mass flow rate 

converges, the model solves for the re-compressor shaft speed required to achieve the 

mass flow rate and pressures that are constrained by the other turbomachinery 

components. If that shaft speed is not equal to the design shaft speed, then the model 

repeats the recompression fraction iteration (and the nested turbine mass flow rate 

iteration). Interestingly, although the partial-cooling cycle adds a compressor relative to 

the recompression cycle, its iteration is less complex because it can converge the 

turbomachinery performance without solving recuperator performance.

Next, the off-design model guesses the high-temperature recuperator (HTR) lowpressure outlet temperature. Then, the model can solve the low-temperature recuperator 

(LTR) performance that achieves its off-design conductance. With the LTR outputs, the 

model can solve the HTR performance that achieves its off-design conductance. If the 

calculated HTR low-pressure outlet temperature is not equal to the guess value, the 

recuperator iteration repeats. Finally, after the recuperator performance is solved, the 

model solves the primary heat exchanger performance that achieves its off-design 

conductance and calculates the turbine inlet temperature. If the calculated turbine inlet 

temperature is not equal to the value guessed at the beginning of the entire off-design 

iteration, then the entire iteration sequence repeats. The off-design model uses inventory 

control—the same algorithm as the recompression cycle—to find the pre-compressor inlet 

pressure that achieves the target power output.

Figure 8 illustrates the behavior of system parameters as a function of pre-compressor 

inlet pressure. Areas shaded red denote regions the control algorithm flags as infeasible. 

For example, when pre-compressor inlet pressure exceeds its design value, the cycle 

power is greater than design and the main compressor outlet pressure is greater than 

design. As the pre-compressor inlet pressure decreases, the power output and efficiency 

decrease until both the main and pre-compressors approach surge. Similarly, low precompressor inlet pressures are shaded red when surge occurs.
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Figure 8. Pre-compressor inlet pressure sweep at the design ambient temperature (35°C). Dots represent the design-point condition;

dotted lines show constraints; shaded zones are prohibited by the simulation model. 
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Figure 9 shows the optimized off-design performance of the partial-cooling cycle for 

varying off-design ambient temperatures. At the design ambient temperature (Tamb = 

35°C) the off-design model achieves the design point efficiency and power—as required. 

At warmer ambient temperatures, the power output decreases because the fluid is less 

dense at the design pressure and the compressors cannot move enough mass flow rate 

without exceeding cycle maximum pressure limits. At temperatures between 35°C 

(design) and 30°C, the cycle maintains the target power output and efficiency increases. 

However, at temperatures colder than 30°C, the recompressor is limited by surge 

constraints at the pre-compressor inlet pressures that would achieve the cycle target 

power output. To allow feasible recompressor behavior, the cycle power output decreases 

(In actual operation, one would reduce cooling by idling fans to maintain the target 

performance).

Figure 9: Optimized off-design performance of the partial-cooling cycle for varying off-design 

ambient temperatures.
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The trends in these results match those witnessed with the recompression off-design 

model. At ambient temperatures colder than design, where the power output declines, 

one simple solution is to turn down or shut off cooling fans to increase the difference 

between the ambient temperature and compressor inlet temperature so that the cycle can 

achieve its target power. The diminished cycle output at hotter temperatures is a more 

challenging problem, and possible solutions require more detailed cost-performance 

analysis.

Comparing Annual System Performance with SAM’s sCO2 Power Cycle Models

In project year three, NREL developed an optimized design and off-design partial-cooling 

cycle model that captured the attributes of the partial-cooling cycle on the solar field and 

thermal storage design and performance. A system model that addresses the interplay 

between the solar components and the power cycle will allow for informed analysis of the 

benefits and tradeoffs between the simple sCO2 cycle, recompression cycle, partialcooling cycle, and typical steam Rankine cycle to determine the potential of these cycles 

to achieve the SunShot performance targets.

The process of designing a sCO2 cycle for MSPT systems requires detailed 

understanding of how the cycle efficiency, temperature range for heat input, and cost 

affect the MSPT design, cost, and performance. MSPT electricity generation is 

proportional to cycle efficiency, but cycle designs with higher efficiencies are often more 

expensive and have a smaller temperature range for heat input. This smaller temperature 

range results in higher HTF mass flow rates through the tower and receiver as well as a 

fixed storage volume providing less storage capacity. It also causes the receiver to 

operate at a hotter average temperature, which increases thermal losses to the 

environment. In project year three NREL calculated efficiency, cost, and heat input 

temperature range as a function of heat exchanger conductance for three different sCO2

cycle configurations (simple, recompression, partial cooling), and then used these 

relationships to design the MSPT, simulate MSPT performance, and finally optimize each 

cycle configuration to minimize the MSPT levelized cost of energy (LCOE).

The results indicated the partial-cooling cycle minimum LCOE is about 6% less than either 

the simple or recompression cycle minimum LCOEs, and the partial-cooling cycle 

advantage is driven by two main factors. One factor is that the cycle has a larger PHX 

temperature difference. This results in less expensive thermal energy storage, which 

leads to lower total installed cost for the MSPT system. The second factor is that the 

larger temperature difference leads to a smaller HTF mass flow rate, which results in less 

HTF pump electricity consumption. The magnitude 

of this difference causes the partial-cooling MSPT 

system to generate more net electricity than the 

recompression cycle although it has a lower cycle 

efficiency. Finally, the simple cycle temperature 

difference is only slightly smaller than the partialcooling cycle temperature difference, but the simple 

cycle efficiency is lower. Consequently, the simple 

cycle results in a less expensive MSPT system that generates less net electricity than the 

recompression cycle, and the simple and recompression cycles have similar minimum 

LCOEs (Neises and Turchi 2018).

The analysis and results of the 

sCO2 power cycle comparison 

with SAM’s power tower system 

model are documented in a 

manuscript submitted to Solar 

Energy in November 2018. 
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Task 3. Optical Modeling of Power Tower Systems

During project year 1, NREL implemented improvements to NREL’s publicly available 

optical modeling tools to improve performance for power tower technologies. Binary-tree 

mapping methods were applied to NREL’s SolTrace ray-tracing code to greatly reduce 

computational expense for power tower systems. Geometry and statistical components 

were improved to expand the type and behavior set of systems that can be modeled, and 

finally, improvements to the SolTrace modeling interface and memory management 

protocols were provided for compatibility with SAM and SolarPILOT and for better 

utilization of previously simulated data.

The SolTrace speed improvements averaged greater than 1000x in eight example cases. 

This exceeded the metric target of 20x speed enhancement as seen in Table 10. 

Table 10. Summary of test cases for SolTrace speed improvement. The “Base” gives the 

simulation time in original form, while “New Time” gives the new simulation time. All speed 

enhancements exceed the target metric of 20x.

Case # Elements Description Base (s)

New

Time (s)

Improve. 

Factor

1 100,512 Default SolarPILOT case, 500 MWt 11,672 6.7 1742x

2 100,512 Same as Case 1, afternoon sun 

position, θ = 23°, α=253° 8,325 6.4 1300x

3 6,282 Default SolarPILOT case with singlefacet heliostats 927 2.5 370x

4 95,174 Ivanpah-like solar field 6,531 9.6 680x

5 34,188 eSolar-like hexagonal field 5,447 6.1 893x

6 79,812 North field, mixed heliostats (small 

near tower, large outer ring) 6,683 6.2 1078x

7 60,127 Ivanpah Unit 2 solar field with random 

vertical heliostat displacement 4,042 3.7 1092x

8 60,127 Ivanpah Unit 2 solar field with high 

optical slope error (~10 mrads) 3,830 3.7 1035x

Accuracy of Updated SolTrace Code

To demonstrate the accuracy of the revised code, SolTrace was used to indirectly 

calculate view factors for eight different surface geometries whose analytical and 

numerical solutions are provided in the Engineering Equation Solver (EES) library [EES, 

v.10]. The target accuracy was + 2%. An additional goal was to show that view factors 

could be calculated numerically using SolTrace. 

SolTrace is often used to predict flux on receiver surfaces. These data are then used in 

post-run analysis to calculate surface temperatures and thermal losses, including 

radiative losses. For purposes of efficient radiative transfer analysis, it would be extremely 

helpful if SolTrace could calculate and report surface-to-surface view factors in addition 

to surface flux for all surfaces. Theoretically SolTrace can be used used to calculate view 

factors numerically by taking advantage of the SolTrace Monte Carlo ray tracing engine. 
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SolTrace does not currently model radiating surfaces (only reflecting surfaces) and 

consequently does not directly calculate and report view factors. In order to use SolTrace 

to do these calculations it was necessary to mimic a radiative surface using reflection. 

SolTrace was temporarily modified to model a diffuse reflecting surface characterized by 

the diffuse gray radiative distribution [Siegel & Howell, 1992]. This distribution function 

includes θ, the angle between the outgoing ray and the reflecting surface normal vector, 

and φφ, the azimuthal angle of the outgoing ray about the surface normal vector axis: 

θ = sin−1 �� rr rrrr nnnn nn {0. .1}�, φφ = 2ππ( rr rrrr nnnn nn {0. .1}

As shown in Figure 10, a diffuse surface A1 reflects energy, part of which is intercepted 

by surface A2. The ratio of reflected energy intercepted by A2 to the energy incident on A1

is effectively the view factor. Reflecting incident rays normal to surface A1 using the above 

reflection distribution effectively mimics a gray diffuse radiating surface. The outoing 

reflected ray vector can be calculated from these two angular parameters.

Figure 10. Simulation of a radiating surface A1 for view-factor analysis in SolTrace.

SolTrace currently offers two options for both surface normal vector distributions (used in 

calculating the reflected-ray specular direction vector) and specular reflection 

distributions (further perturbation of the reflected-ray direction vector about the specular 

reflected direction). These are 1-D gaussian and 1-D uniform (pillbox) distributions neither 

of which can be used to adequately approximate the above diffuse distribution. In addition 

to reflection distributions, SolTrace also allows users to define a sunshape distribution 

which is applied in the same way as the specular reflection distribution. In SolTrace, when 

a ray is traced from the sun to the first stage of a SolTrace geometry, its initial vector 

direction is depicted as if the sun were an infinite distance away (i.e. parallel wavefront). 

Once the ray intersection on a surface normal to the sun direction is found then the 

sunshape distribution is applied and the ray direction is perturbed according to the applied 

distribution. As noted above, to mimic a diffuse radiative surface, SolTrace was 

temporarily modified such that the sunshape distribution was hardcoded to that of the 

diffuse equation above. Because of code structure, it was more expedient to modify the 

sunshape rather than the optical error distribution for purposes of this analysis. In FY17, 

SolTrace could include an option to calculate view factors between any set of surfaces 

φφ

θθ

Incident Energy Bundle

Surface Normal 

A

1

A

2

Reflected Energy Bundle
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and output these view factors in the form of a table. This table will be useful in postSolTrace thermal radiation exchange analysis. Optical error distributions, both surface 

normal and specular, were subsquently turned off in the analysis. A bundle of rays, (N = 

100 million) was then traced from the sun to the primary (or radiative) surface and 

reflected from this surface without losses according to the diffuse distribution. Some of 

these reflected rays intercept the secondary (receiving) surface. For a sufficiently large 

number of rays, the ratio of the number of rays intercepted by the secondary receiving 

surface to the number of rays incident upon the primary radiative surface should 

numerically approach the analytical view factor solution between these surfaces 

assuming diffuse gray characteristics.

The resulting calculations for eight different geometries were compared to analytical 

solutions for view factors available within EES. The geometries are shown in Figure 11, 

and the analysis was performed using the default parameters specified within EES.

Figure 11. Eight cases for view-factor analysis taken from EES.
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The SolTrace results are shown in Table 11 compared to the EES analytical solutions. In 

all cases except one (rectangle to circular disk in a parallel plane), the SolTrace result 

agreed exactly with the EES solution. Upon further inspection, the EES solution for this 

case was not analytical but also a Monte Carlo solution; therefore, one would expect 

some variation between this and the SolTrace Monte Carlo solution. This variation was 

on the order of 1% which satisfies the metric goal of less than 2%.

Table 11. Comparison of view factors calculated by SolTrace versus analytical solutions in EES.

Geometry

EES View 

Factor

SolTrace View 

Factor Difference

Finite parallel plates 0.4756 0.4756 0.00%

Finite perpendicular plates 0.1493 0.1493 0.00%

Disk to disk (same center) 0.1339 0.1339 0.00%

Rectangle to circular disk in a parallel plane 0.174 0.176 1.14%

Perpendicular offset rectangles 0.07235 0.07235 0.00%

Perpendicular rectangles with a common point 0.0605 0.0605 0.00%

Perpendicular rectangles with no common points 0.01626 0. 01626 0.00%

Finite adjacent plates 0.4833 0.4833 0.00%

SolTrace Cross-Platform Agreement

SolTrace results were checked for consistency when run on Linux, Windows, and iOS

operating systems. A set of eight test cases (Table 10) was run on each operating system. 

MS Windows is the reference operating system, and Mac OSX and Linux results were 

compared with the Windows results. The SolTrace input parameters used for this 

comparison included tracing one million rays, using two processor cores, and employing 

the same random number generator seed of 123. While the metric definition technically 

compares differences in peak flux, average flux and number of rays intercepted, one can 

start by comparing the traced ray information in the same way that Task 1.3.2 used these 

data. That is, identical ray-trace results will lead to identical flux and ray-intercept results. 

SolTrace outputs intersection and direction-cosine data for each ray. If this table of data 

is identical down to the last ray traced for each case, then one can be assured that the 

flux parameters are identical as well. The metric value would be exactly zero (0). 

This, in fact, was accomplished for five of the eight cases run comparing Mac OSX with 

MS Windows and five of the eight cases run comparing Linux with MS Windows. While 

one may expect exact agreement across platforms, it is often the case that variable 

initialization and memory management differences can lead to discrepancies, and these 

issues can be very difficult to diagnose because they arise from differences within the 

system architecture. Still, from a practical standpoint, the information generated by the 

same software running on different platforms should be equivalent in the context of the 

engineering requirements under consideration, e.g., within 0.1%. 

Table 12 summarizes the case-by-case differences between simulations executed on the 

three different operating systems. Flux intensity is characterized over a 30 × 30 grid on 

the receiver surface. The results show exact agreement for five of eight cases, with small 

discrepancies in cases 5, 6, and 8. 
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For the cases that were not identical, NREL examined the peak flux difference for Linuxto-Windows case 6 as an example. To determine whether this is due to a systematic error, 

Case 6 was re-evaluated with double the number (i.e., 2 million) rays. This result is also 

shown in Table 12. The values reported are substantially lower than the threshold 

requirement, and this implies that the peak flux error was not due to an underlying 

platform-based inconsistency. The original Case 6 error is caused by the relatively small 

number of rays run over a relatively large number of flux bins such that the peak flux 

uncertainty is 3.3%, which is greater than the observed error. Given this, NREL believes

that the Linux-Windows platform comparison shows excellent agreement and can be 

considered equivalent. Similar results are seen for the OSX comparison, although cases 

5 and 6 were not rerun in those systems. 

Table 12: Comparison of cross-platform error between Linux (Slackware 64 bit) and Windows 

(Windows 7 x64) and OSX and Windows. Values shown indicate relative metric error between 

corresponding cases on each platform. 

Linux to Windows OSX to Windows

Peak Flux

Average 

Flux

Number of 

ray hits Peak Flux

Average 

Flux

Number of 

ray hits

Case 1 0 0 0 0 0.000002 0.000001

Case 2 0 0 0 0 0 0

Case 3 0 0 0 0 0 0

Case 4 0 0 0 0 0 0

Case 5 0.000797 0.000013 0.000017 0.00159 -0.000011 -0.000018

Case 6 0.001098 0.000009 0.000015 -0.00111 -0.000028 -0.000022

 w/ 2e6 rays 0.000009 0.000004 0.000011

Case 7 0 0 0 0 0 0

Case 8 0.000033 0.000034 0.000005 0.000025 0.000037 0.000011

SolTrace View Factor Validation

Receivers used in CSP applications are often comprised of a number of surfaces in either 

an external or cavity configuration. NREL’s SolTrace software is routinely used to model 

incident flux on these surfaces from heliostats in a power tower field. These receiver 

surface flux maps are used in subsequent thermal analysis to calculate surface 

temperatures that account for radiative energy exchange between all surfaces. A 

necessary input to such thermal analyses is the view factor for each surface relative to all 

other surfaces in the receiver. View factor calculations can be extremely complex for 

multi-surface geometries, such as may be necessary for SunShot receiver designs. 

Often the solutions for these view factors end up as infinite series expansions and

approximations are made by limiting number of expansion terms to a practical value. 

Many such analytical approximations can be found in the open literature for a variety of 

simple surface configurations, but these published solutions often differ in accuracy and 

so can provide slightly different results. A numerical approach can also be used to 

calculate view factors. In Subtask 1.3.2, it was demonstrated that SolTrace can be used 

to accurately calculate view factors for a single pair of surfaces using its Monte Carlo 

numerical engine. This was done indirectly using SolTrace in a way in which it was not 

originally designed. Given this success, the goal of Subtask 2.3.2 was to develop the 
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capability within SolTrace to calculate not only receiver surface flux maps for which it was 

originally designed, but also calculate and output view factors for those same surfaces 

using the same SolTrace geometry input file. This capability will provide highly accurate 

numerical solutions and eliminate the need to calculate these view factors via 

approximations external to SolTrace. 

In project year two, SolTrace was first modified by adding a new “diffuse” optical error 

distribution option to the optical properties table. During year one this optical error 

distribution was temporarily hardwired into the code; now this capability is a userselectable option. A SolTrace script was then written which operates on existing SolTrace 

project files containing receiver geometries that are to be analyzed. The script requires 

that the receiver geometry to be analyzed for view factor data be defined inside one stage 

(which is almost always the case). The script prompts the user for the SolTrace project 

file, the stage number containing the receiver geometry and the number of rays to be 

traced. It then copies the geometry data for all receiver surfaces into memory and creates 

a temporary project in memory with two stages: a “radiative” first stage and a “target” 

second stage. It should be noted that in SolTrace, the first stage is where rays are 

generated from the source (e.g., sun) for subsequent tracing through the 1st stage to the 

remaining stages. The process then proceeds as follows:

One of the receiver surfaces is copied into the 1st (radiative) stage while the rest are 

copied to the 2nd (target) stage. Relative position and orientation are preserved between 

all surfaces. A SolTrace-defined sun position is then calculated for the temporary project 

such that the sun vector is normal to the single surface in the 1st (radiative) stage. Rays 

are traced from the sun and reflected off the surface in the radiative stage using the diffuse 

distribution option and a surface hemispherical reflectivity = 1.0 (no absorption). These 

reflected rays then continue to the 2nd (target) stage. The surfaces in the target stage are 

defined with a reflectivity = 0.0 (perfectly absorbing). Whatever rays from the radiative 

surface in the 1st stage that the target stage “sees”, i.e., intersects, are absorbed by that 

surface and tallied so that they cannot reflect to other surfaces. The ratio of the number 

of rays absorbed by any one surface to the number of rays reflected by the single surface 

in the 1st (radiative) stage is equivalent to the view factor between the that radiative 

surface and that particular absorbing surface. This value is calculated for each of the 

surfaces in the 2nd (target) stage and stored in memory. The project is cleared, and the 

process then repeats for each of the remaining surfaces in the receiver geometry until all 

view factors have been calculated. The script reports these values to the user in a file.

The PEC 2.3.2 evaluation criterion requires that surface view factors be calculated for two 

different cavity geometries each composed of at least five surfaces. Using the new 

version of SolTrace, view factors between any two surfaces were required to be within 

2% of analytic view factor solutions reported in the literature for those same surface 

geometries. As stated earlier, it should be noted here that many closed-form solutions in 

the literature are approximations to infinite series solutions and some discrepancy (on the 

order of 2-3%) has been found between solutions from different published sources. To 

provide a comparison with another numerical approach, Fluent, a computational fluid 

dynamics package based on a finite element numerical method, was used to calculate 

numbers for additional comparison. In some cases, published analytic solutions were not 

found for certain geometries and the comparison was limited to that between the two 

numerical approaches, SolTrace and Fluent.
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The two receiver geometries analyzed are shown in Figure 12. Figure 12 (left) is a 

rectangular box cavity comprised of five planar surfaces. The surface numbers are 

indicated in red. Similarly, a hexagonal box cavity is illustrated in Figure 12 (right). This 

cavity consists of seven planar surfaces. In both cases, the cavity aperture is shown as 

the relative sun direction.

Figure 12. Rectangular box cavity (left) and hexagonal box cavity (right)

The view factor Fi,j between any two surfaces is defined as the ratio of radiation absorbed 

by surface j (column index) to the diffuse radiation reflected from surface i (row index). 

View factors calculated using SolTrace, EES analytical solutions and the Fluent CFD 

code are presented together with relative error between SolTrace and each of the other 

solutions, EES and Fluent.

Table 13 shows results for the rectangular box cavity geometry shown in Figure 12. For 

the rectangular box geometry, the difference in results for all cases is insignificant. All 

agree to less than +1% (achieving the target of < 2% relative error) with most surface 

view factors agreeing to within +0.5%. Rectangular surfaces in parallel and orthogonal 

orientations to one another have simple analytical solutions and so one would expect that 

the analytical solution to compare well with the numerical solutions provided by both 

SolTrace and Fluent.
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Table 13. Surface view factor comparison for rectangular box receiver (Figure 12, left)

In summary, SolTrace was modified and a script was created to seamlessly calculate 

surface-to-surface view factors using existing SolTrace project files with single-stagedefined receiver geometry. Two cavity geometries were analyzed, view factors calculated 

and compared with both published analytical solutions and with the Fluent CFD code. 

Agreement was found to be excellent. This new SolTrace capability will allow more 

efficient thermal analysis using the expanded SolTrace output.
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Table 14. Surface view factor comparison for hexagonal box cavity (Figure 12, right).

Scripting Tools in SolarPILOT

NREL has developed a scripting language called “LK” that allows users to write and 

execute programs that directly interact with CSP-relevant modeling tools such as SAM, 

SDK, and SolTrace. Using a relatively small set of scripting functions, a user can perform 

a wide variety of tasks and interact more directly with the modeling tool. Using LK, a user 

might write a script that reads data from a file, performs trigonometric manipulations, 

executes a SAM or SolTrace simulation, generates plots and charts, and saves output 

data, for example. 

In previous work, NREL has supported access to SolarPILOT functionality by two 

separate means: a stand-alone graphical user interface (GUI) and integration of portions 

of the model into SAM via an application programming interface (API) that is not exposed 

to the user. Each option offers advantages, with the SAM implementation providing a 

simplified means of interaction while supporting SAM scripting, and the GUI providing a 

fuller set of options but without any scripting support. In this task, NREL augmented the 

standalone SolarPILOT GUI with an implementation of the LK scripting language that 

includes functions customized for this application. The primary goal of this work was to 

allow scripting to provide at least 80% of the actions that could be undertaken by a user 

within the GUI. To determine whether the scripting activity meets the 80% task milestone, 

NREL identified 21 functions that are provided by the SolarPILOT user interface. LK 

scripting was developed for 20 of these functions (95%) plus an additional five functions 

not available via the GUI, as summarized in Table 15.
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Table 15: Comparison of interface and scripting functionality

Feature GUI Scripting

1 Set numeric variable values X X

2 Set fixed-choice variable values X X

3 Specify location and weather file X X

4 Specify market payment allocation factor table X X

5 Display/retrieve calculated interface values X X

6 Specify land boundaries X X

7 Open case X X

8 Save case X X

9 New/reset case X X

10 Add and delete heliostat templates X X

11 Add and delete receiver templates X X

12 Run performance simulation using analytical engine X X

13 Run performance simulation using ray trace engine X X

14 Run parametric simulation X X

15 Generate layout X X

16 Specify optimization parameters X X

17 Run optimization X X

18 Modify individual heliostat positions X

19 Modify individual heliostat performance characteristics X

20 Select heliostats by geometrical region X

21 Select heliostats by scalable vector graphics region X

22 Defocus heliostats X

23 Create field performance plot X

24 Create receiver flux plot X X

25 Retrieve individual heliostat performance characteristics X X

26 Retrieve summary performance statistics X X

The ability to programmatically save heliostat field performance plots was not provided 

via a script. However, it is possible to execute a script and subsequently view the resulting 

field performance in the GUI, which is updated after completing the simulation. The 

scripting environment provides significant functionality beyond what is available via the 

GUI. Most notably, scripting allows users to programmatically modify heliostat locations, 

performance, and operational status, and it provides tools for selecting groups of 

heliostats. Additional description of these capabilities is provided below.

Setting and retrieving input values [Features 1-6, Table 15]

SolarPILOT includes approximately 375 settings, input variables, and parameters that 

can be manipulated by the user. These items are assigned one of several available data 

types, such as string, double, and integer. Several more complex data structures are also 

utilized, including point vector (a list of point structures with positional attributes), matrix

(a multidimensional list of data), and weather data (a structure that defines time-series 

weather information). A primary goal of the scripting interface is to provide a mechanism 

for assignment and retrieval of input and output parameters, and this is implemented via 

the sp_var(<name>, <value>) command, which takes as arguments the variable name 

and, optionally, the assignment value. If no value is provided, the function returns the 
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current value for the variable. The data type passed to this argument is flexible and 

corresponds to the data type of the variable to be assigned. 

Case management [7-9]

Scripting provides functionality to create, open, and save cases in accordance with the 

GUI. These actions are useful when operating a script that may pull information from 

various case files.

Template management [10-11]

SolarPILOT employs “templates” for managing heliostat and receiver geometries so that 

multiple configurations can be used in the same plant. For example, a heliostat field may 

consist of a finite set of heliostat 

focal lengths such that the system 

should be modeled using several 

heliostats with different focal length 

properties. In the GUI, this is 

accomplished by manually creating 

the necessary number of templates 

and adjusting the properties of 

each individually. The placement of 

templates within the field can be 

assigned in one of several ways, 

but the procedures are relatively 

restrictive or require multiple steps 

to achieve the desired outcome. 

We have added scripting functions 

to add and drop receiver and 

heliostat templates. Once added, 

the template is assigned a unique 

identifier that allows the user to 

manipulate the template 

characteristics. Individual heliostats may be assigned to one of the available templates 

upon layout of the field. Figure 13 illustrates the result of a random layout using two 

templates – one large and one small. Scripting provides the ability to construct any layout 

of interest with any number of associated heliostat geometries. Likewise, multiple 

receivers may also be programmatically constructed and configured.

Executing tasks [12-17]

The core functionality of SolarPILOT includes the generation of heliostat field layouts, 

characterization of their optical performance, optimization of design variables, and 

analysis of performance and design over a range of parameter values. It is therefore 

important to provide the following scripting functions that achieve these outcomes:

run_layout( ) Generate a heliostat field layout. Optionally, the user may provide heliostat 

positions to use in the layout.

run_performance( ) Calculate the optical performance of the solar field. This algorithm updates 

the overall performance metrics information and information specific to 

each heliostat.

Figure 13: Demonstration of script-based assignment of 

heliostat templates for random heliostat positions.
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run_optimization(T) Optimize the variables contained in the table ‘T’. The format allows the user 

to specify the variable name, optional upper and lower bounds, optional 

initial step size, and an optional initial guess value for each variable. 

The combination of these functions in a scripting environment allows a wide range of 

actions to be taken. In combination with the variable setting and retrieval functions 

described above, the script user may execute any parametric analysis or calculation 

procedure that can be programmed. 

Manipulating heliostat characteristics [18-22]

The SolarPILOT GUI lacks the tools to allow users to select and modify individual 

heliostats based on their location in the field and developing a case in which heliostat 

performance differs across the field is somewhat intractable. Scripting provides an 

opportunity for this capability, both in programmatically modifying heliostat performance, 

but also in providing methods for selecting groups of heliostats. The following options are 

now available to select heliostats by region within the solar field using the get_heliostats()

method:

all Returns a list of all heliostats in the field

cylindrical User provides radial and azimuthal angular bounds, and the function returns a 

list of heliostats within the region

cartesian User provides minimum and maximum Cartesian coordinates, and the function 

returns a list of heliostats within the region

polygon User provides a set of points defining a polygon...

SVG User provides geometry in scalable vector format...

SVG file User provides the file name of a drawing in scalable vector format...

The options “polygon,” “SVG,” and “SVG file” provide sophisticated means of selecting 

precise regions of heliostats. A user may adopt external software to generate polygons 

or vector graphics, and then impose those paths on the heliostat field to select heliostats. 

These options may be valuable for users seeking to model specific plants with geological 

features, fields with spatially varying soiling patterns, or other scenarios. A case study 

below illustrates the flexibility of the heliostat selection methods.

Generating data [23-26]

The scripting tool provides several functions that access and export data. These include 

plotting for receiver flux, export functions for variable definitions and simulation or layout 

results, and a function that provides summary information after a simulation.

Case study: Heliostat field soiling due to road proximity

We demonstrate the scripting functionality using two case studies. In the first, consider a 

surround heliostat field with an access road that crosses the field towards the southwest. 

The road is assumed to cause local soiling due to dust from vehicle traffic, and the soiling 

pattern follows the wind path, which blows predominantly from the west. Soiling is nonuniform, and decays quickly as the distance between a given heliostat and the road 

increases. Figure 14 shows the LK script used to implement this scenario: (i) a function 

“soil” is defined to calculate the position-dependent soiling, (ii) the access road is defined 

as an exclusion region with a long, narrow rectangle, (iii) the layout is generated, (iv) the 
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heliostats in the layout are retrieved and the reflectivity of each is modified depending on 

its position in the field, (v) a performance simulation is executed, and (vi) the results are 

updated in the SolarPILOT GUI. Figure 15 shows the layout and results of the 

performance simulation with a plot of heliostat reflectivity. 

Figure 14: Sample LK script used to calculate and assign soiling near an access road.

Figure 15: Reflectivity plot for heliostats with soiling applied by LK script.

/* 

Script to test modifying heliostat soiling

*/

//create an arbitrary function to affect soiling in an area near a road

function soil( loc )

{

x = loc[0];

y = loc[1];

if( y > 0 || x < y)

return 1.;

return 1. - 0.5*rand()*exp(-abs(x-y)/500);

} //.. end of function

//add the access road and assign variables

radmax = 2000; rw=40;

add_land_area([[0,0],[-radmax,-radmax],[-radmax,-radmax-rw],[0,-rw]], 'exclusion', false);

run_layout(); //Generate a new heliostat field layout

hels = get_layout(); //get information about all heliostats in the layout

//modify the soiling factor based on location

refs = []; hids = [];

for(i=0; i<#hels; i++)

{

refl = 0.95 * soil( [hels[i][1],hels[i][2]] ); //call the soiling function

hids[i] = hels[i][0];

refs[i] = refl;

}

modify_heliostats(hids, {'soiling'=refs}); //update heliostat soiling

run_performance(); //run a performance simulation

update_interface(); //update the solarpilot GUI with the results
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Case study: selecting and modifying heliostats using scalable graphics

Scalable vector graphics (SVG) files provide a systematic means of defining paths using 

lines, arcs, 2- and 3-point Bezier curves, and other path structures. Various external 

software packages are capable of generating SVG files, most prominently Adobe 

Photoshop® and InkScape. In this example, consider a heliostat field subject to passing 

clouds. The outline of the cloud can be measured and represented as a path in SVG 

format, and the cloud’s shadow has an opacity of 80%. We seek to determine the 

performance impact of the passing cloud on the solar field using LK scripting. Figure 16

and Figure 17 illustrates the implementation of this exercise.

Figure 16: LK script implementing the SVG cloud case study.

Open-Source Code Release of SolarPILOT and SolTrace

In project year three, NREL transitioned SolarPILOTTM and SolTrace optical modeling 

tools from their current package-release format into open source software (OSS). This 

approach offers several benefits, namely, OSS (i) increases interest by engaging users 

in the creation and improvement in the project, (ii) provides substantial transparency by 

exposing underlying algorithms and capabilities, (iii) relieves the software maintenance 

burden placed on NREL and DOE, (iv) takes advantage of CSP community knowledge in 

constructing new features or fixing bugs, (v) ensures that the software can live on beyond 

the loss of key staff or through reprioritization of funding, and (vi) preserves a detailed, 

/* 

Script to implement an SVG cloud (as soiling)

*/

run_layout();

hels = get_heliostats('svgfile', ‘cloud.svg', {'offset'=[-1500,1500], 'scale'=[20,25]});

refl = [];

for(i=0; i<#hels; i++)

refl[i] = 0.2;

modify_heliostats(hels, {'soiling'=refl});

run_performance();

update_interface();

Figure 17: SVG path drawn in InkScape and imposed as a cloud in SolarPILOT.
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public record of the software development process. One aspiration of OSS is to provide 

a hub for development of CSP optical models, whereas prior model development has 

been scattered and duplicative. 

Open-source platform

The selection of OSS platform is an important choice that will dictate much about the 

workflow and community involvement for the project. One key aspect of OSS is revision 

control – which defines a formal system that records and annotates the state of the code 

and documentation over time. Revision control systems can operate in different ways, but 

the most prevalent ones provide common functionality such as management of a central 

“master” repository, procedures for modifying the code, tools for conflict resolution should 

users introduce incompatible code, logs of changes made over time, and the ability to 

“checkout” historical versions of the code. The two most common version control systems 

are ApacheTM Subversion® and Git, with Git-based platforms in use by nearly 9 in 10 

developers. Git was selected as the deployment version control system. 

Git provides a system for developing code, but it does not serve as a platform (i.e., a web 

service). Several software development platforms are available, including Github.com, 

Bitbucket.org, Gitlab.com, or enterprise-hosted services. In addition to hosting the code 

and Git software, these sites provide tools for team communication, identifying and 

describing issues or sub-projects, downloading code, creating project wikis, etc. NREL 

maintains a repository library with a paid subscription at github.com/NREL, which 

provides various projects with the ability to develop code in public or private mode, control 

membership groups, track usage and contribution statistics, and take advantage of other 

features. Hence, the natural choice for this project is to also host the code on Github 

alongside other related projects, including SAM, SSC, WEX, and LK. 

The SolTrace and SolarPILOT codes were originally developed using Subversion 

repositories (repos) hosted on an NREL server. In year three, the code for both projects 

was transitioned to Github at url’s https://github.com/NREL/SolTrace and 

https://github.com/NREL/SolarPILOT, respectively. These were created as private repos 

with access only granted internally to NREL developers. The goal of the work was to fully 

transition the code to OSS, provide detailed instructions on building for different operating 

systems, document the project structure, and clean up vestigial code and extraneous 

comments. 

Github implementation: Organization

Both SolTrace and SolarPILOT are dependent on other projects, which means that users 

must first download and compile those dependencies. These are summarized in Table 

16. SolarPILOT is organized such that the code for the user interface, scripting, and help 

documentation is contained within the main SolarPILOT repository, while the underlying 

computation and layout algorithms are contained within the SAM simulation core (SSC). 

This convention is most convenient for use of the layout algorithms within SAM and 

prevents duplication of the code bases for the two projects. Other SSC functionality is 

required for SolarPILOT (e.g., weather file processing). 
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Table 16: Project dependencies for SolTrace and SolarPILOT. Checkmarks indicate dependence 

on the project. Note that SolarPILOT is also dependent on SolTrace.

Library Description Subdependencies SolTrace SolarPILOT

WXWIDGETS Graphical interface development 

toolkit

 

LK Scripting library wxWidgets  

WEX Extensions to wxWidgets wxWidgets, LK  

GOOGLE 

TEST Automated code testing 

SSC SAM simulation core wxWidgets, WEX, 

LK, Google Test



SOLTRACE Raytrace simulation engine (noted) 

Documentation and Wiki

While the organization of the project is intended to be intuitive, some documentation is 

required to instruct users on how to quickly compile the code, prepare dependencies, and 

contribute their material. Each project contains a “readme.md” (‘md’ for markdown) file 

that serves as a starting point for new users or a quick reference for those already familiar 

with the project. The readme provides a quick overview of build steps and dependencies, 

provides links to more detailed documentation, and gives a high-level description of the 

project. 

Other files include the “contributing.md” and “license.md.” The former states the 

procedure and requirements for contributing new code or fixing bugs, and the latter 

describes conditions of use for the code. Both topics are discussed in more detail below. 

Github also provides a wiki service for each project that can be used to provide more 

extensive documentation. Both SolTrace and SolarPILOT have wikis that contain detailed 

build instructions for all platforms, additional information on contributing, and usage of 

related projects. The wiki’s can be maintained or expanded by users in the future in 

accordance with changes to the code. 

Tracking Use and Contributions

Github provides several handy tools for tracking the projects. Code downloads, “clones” 

(downloading the repository via the Git client), “forks” (copies made with the intention of 

spawning a new derivative project), number of contributors, contribution statistics (lines 

of code created/deleted, frequency and timing of commits), and other interesting 

information is readily available through the Github project website. These data give insight 

into who is using the code and which aspects of it are particularly productive or ripe for 

future research. 

Parallel development

Collaborative code development allows contributors to work separately and merge 

changes or additions. The general process entails (i) cloning the code from the server 

onto the user’s local machine, where a full copy of the code exists independently of the 

server, (ii) making changes to the local code over some period of time, (iii) committing the 

code, in which changes are logged by Git on the local machine, and (iv) pushing the 

commits back to the server, effectively publishing them such that others can access them. 

Once commits have been pushed and are available, other users can pull from the server 

and incorporate those changes into their local copies of the code. 
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This process works well for continuous development, but commits are intentionally 

incremental, providing incomplete functionality or inadvertently introducing bugs into the 

software. To handle human error and the piece-wise development process, we adopt a 

common procedure that maintains a stable code version, an in-flux but functional 

development version, historical version placemarks, and in-progress feature development 

versions. Git enables these permutations without duplicating the code base with 

branching, which takes its name from the analogy of tree branches. A contributor creates 

a branch when developing new code or undertaking bug fixes. As the development 

progresses, changes are committed to the branch. Other branches are not affected by 

the commits. Once the new feature or bug fix has been fully developed and tested, the 

branch is merged back into the main code and the changes are available to all users who 

wish to begin their own branches. 

Two special branches are maintained, namely the “master” and “develop” branches. The 

“master” branch represents the stable version of the software and is only updated to 

include new commits at the time of a release. The “develop” branch is the main area for 

new development; users wishing to 

add features create a branch that is 

an offshoot of “develop,” and merge 

completed code back into it. Figure 18

shows an example of this scheme 

with two active feature branches. 

Furthermore, major versions are 

tagged, allowing any user to go back 

and access the code as it existed in 

the past. This is particularly useful for 

providing patches to existing versions 

or investigating changes in software 

output between versions.

Contributing

Contributors to the code follow a process that is designed to facilitate high-quality 

software and rigorous tracking. Contributors are also required to agree to NREL’s terms, 

relinquishing certain intellectual property (IP) rights and submitting to the licensing 

agreement. The agreement states:

I agree to contribute to SolarPILOT. I agree to the following terms and conditions 

for my contributions: First, I agree that I am licensing my contributions under the 

terms of the current SolarPILOT license. Second, I agree that, in order to conform 

to any future open source software license(s) under which SolarPILOT may be 

provided, the terms of my license may be modified without any notice to me and 

without my consent. Third, I represent and warrant that I am authorized to make 

the contributions and grant the license. If my employer has rights to IP that includes 

my contributions, I represent and warrant that I have received permission to make 

contributions and grant the required license on behalf of my employer.

Contributors need to prepare their machine to use Git and obtain the code as follows:

• Install the Git client on the local machine

Figure 18: Example of the branching scheme. Each 

dot represents a commit.
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• Clone SolarPILOT or SolTrace and all dependencies to create local copies of the code 

on the machine

• Build each project. This requires a C++ compiler like GCC or Visual Studio.

The process for contributing is as follows:

• Use the Git client to move to the branch or revision that is the starting point for the 

contribution

• Create a new branch

• Create a new issue on the Github site, describing the feature or bug fix to be added, the 

timeline for the changes, and any other notes that might help others assist on the 

project. The notes serve as documentation.

• Make changes and commit them

• Once changes are tested, create a pull request on the Github webpage. This notifies the 

NREL reviewers that code is available to merge back into the project. NREL will 

undertake a review to determine whether the contribution is suitable and meets the 

software standards. If so, the change is merged. Otherwise, the pull request is closed, or 

revisions are suggested. 

• A successful merged pull request also closes the associated issue that was created on 

Github. The branch that was created is deleted. 

This process ensures multi-party review and allows NREL to maintain control over the 

software, even with contributions coming from various external sources. 

Debugs and Other Code Improvements

In addition to the OSS transition, several noteworthy improvements were made to 

SolarPILOT during year three. The first improvement resolves a longstanding issue 

present on Linux-based systems that caused the interface to occasionally freeze. The 

issue was pervasive enough to prevent Linux users from adopting the software. In part 

due to the OSS transition, we were able to identify the problem and resolve it, and Linux 

systems now have a comparable tool to Windows users. 

The second improvement is 

the development of individual 

heliostat information reporting 

dialog on the heliostat field 

plot. Previously, users who 

sought information on 

individual heliostats were 

required to export data to a 

text file and search to find the 

heliostat of interest. In the 

latest version, we have added 

the ability to right-click any 

number of heliostats to display 

statistics that may be useful to 

designers, as shown in Figure 

19. Selected heliostats can 

furthermore be removed from 

the layout using controls on 

Figure 19: Demonstration of heliostat selection functionality in 

SolarPILOT.
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the plot page. If desired, removed heliostats can later be restored. This provides improved 

control and important information using mouse and keyboard functionality.

Lastly, an information webinar was given on July 18, 2018 to document the use and utility 

of the OSS versions of SolTrace and SolarPILOT. That video can be accessed at the 

NREL’s SAM website at https://sam.nrel.gov/webinars.

Task 4. Thermal-Application Modeling Tools for SAM

Validation of New Solar Industrial Process Heat (IPH) Models in SAM – Trough Model

The new thermal-application (i.e., solar IPH) models developed for SAM were validated 

against existing models and field data during project years one and two. The trough model 

was first checked by comparing the old C++ code with the new thermal-application code. 

The new thermal-application code was successfully debugged and the simulation results 

in the new code agreed with those in the old code within 0.001%, indicating that the only 

difference between models is due to numerical precision. 

Confirmation that the new thermal-application code agrees with the former code is 

significant because the former code has been validated against data from FLABEG’s 

Ultimate Trough loop at SEGS VIII (Zhu and Neises 2015). Zhu & Neises compared 

NREL’s SAM model to FLABEG’s theoretical model and found the maximum difference 

between the NREL model and the FLABEG model to be less than 1.2%. The thermal 

efficiency predicted by FLABEG’s theoretical model agreed with the measured thermal 

efficiency within the 95% confidence level. Thus, it may be inferred that SAM’s trough 

model matched the experimental data with less than 10% error as required for PEC 1.4. 

This analysis demonstrated that the new thermal-application trough code matched the 

previously validated output from the solar field predictions made in Zhu & Neises 2015. A 

comparison of the new and previous model results is shown in Figure 20, which indicates 

excellent agreement between the two codes. Occasionally, subtle differences in a 

decision variable can fall on a different side of a controller decision, which can result in 

the model solving for a different plant operating mode (e.g., shut down rather than operate 

in the next hour). This is the cause of the few larger deviations in the temporal data seen 

in Figure 20. These variations balance over the annual simulation; the difference in the 

annual cumulative data was 0.04%.
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Figure 20. Hourly thermal-energy generation comparison of existing SAM trough model versus 

new thermal-application model code for an annual simulation. Perfect agreement would plot a 45°

line.

Field data for model validation was obtained for Enel Green Power’s Stillwater, Nevada 

Solar/Geothermal hybrid plant. This plant has 24,780 m2 of SkyFuel SkyTroughs used to 

preheat geothermal brine. Data from the Stillwater solar field allows one to examine the 

model’s match to daily, transient energy outputs. NREL generated a SAM case file to 

match the physical conditions (trough, receiver and loop dimensions, HTF properties, 

design operating temperatures, etc.) of the Stillwater solar field. The thermal-energy 

output from this model was compared to data from the operating plant using local DNI 

values and operating conditions from the plant (mass flows, temperatures, and pressures) 

on 30-minute intervals for 58 consecutive days in 2015. The analysis used a satellite 

weather file for Stillwater, but substituted ground-measured DNI, temperature and wind 

speed data for the days in question. Comparison of model to data was then made for the 

58-day period. The summary results are shown in Table 17, which indicates good 

agreement with the data and achievement of the PEC 1.4 metric. The amount and quality 

of data for this site allowed the team to avoid resorting to stochastic estimates for system 

parameters. 

Table 17. Validation comparison of HTF-Trough model and Stillwater data based on 58 days of 

data.

Stillwater thermal energy 

output (MWht)

SAM thermal energy 

output (MWht) Difference Comments

4,832 4,919 +1.8% Meets PEC 1.4 target of 

<10% deviation

While the cumulative data for the 58-day period showed good agreement, there were 

discrepancies in the 30-minute temporal data. Figure 21 depicts the simulation results for 

a week in late March. SAM generally follows the trends well, but one can note periods 

where SAM’s prediction deviates from plant operations. Plant startup/shutdown behavior 

on March 30 and 31 appear to be due to operating decisions outside of weather

considerations. 
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Figure 21. Comparison of Stillwater plant data and SAM simulation for one week in late March.

Of greater interest is SAM’s ability to track startup and shutdown behavior based on 

energy considerations alone. Figure 22 highlights two cloudy days in March. SAM follows 

the overall trends well, but the match for individual time steps is not always accurate. This 

manifests itself in a large error when one examines the RMS error for the temporal data. 

The RMS value for the 58-day period was 2.2 MWt, which represents 14% of the design 

point power rating while the cumulative error was less than 2%, as noted in Table 17. 

Figure 22. Comparison of Stillwater plant data and SAM simulation on two partly cloudy days.
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Validation of New Solar IPH Models in SAM – Direct Steam Generation (DSG) Model

Working with Tubosol, who are the plant operators of the Puerto Errado 2 (PE-2) plant in 

Calasparra, Spain, NREL received and processed operational data of the entire 150 MWt

solar field (Novatec Solar 2011), for the month of June 2015. The data for the solar field 

(made up of 28 rows in total), is at 1-second intervals, and is split into two equal segments 

(PE-2.1 and PE-2.2), each segment being approximately 75 MWt. This resolution of data 

allowed for in-depth observation of the behavior of the solar field, both in normal and 

transient operation. The parallel solar field segments allow PE-2 to divert steam from both 

solar fields to one 15 MWe turbine in winter, rather than generating electricity from both 

15 MWe turbines at part load due to a lack of solar radiation. This leads to greater overall 

efficiency. 

Each independent solar field segment has the specifications shown in Table 18 (Novatec 

Solar 2011), which have been used for the SAM DSG model validation. It was decided 

because each segment can be operated independently that the validation would be done 

only on one solar field segment. A weather file which uses data from the site for DNI and 

ambient temperature was created for the validation.

Table 18. Data used for the SAM DSG validation of the PE-2 solar field segment

Parameter Value Unit

Product name Nova-1 NA

Number of rows in solar field 14 NA

Solar field length 940 m

Aperture area 151,000 m2

Operating Temperature 270 °C

Operating Pressure 55 bar

Peak Thermal Output (MWt) 75 MWt

The validation of the DSG process heat model was done by comparing the predicted 

thermal energy from the SAM model with the plant data from the PE-2 solar field segment. 

PE-2 is an electric power plant, and the data did not provide a direct measurement of the 

solar-thermal power output from the solar field. Outlet temperature and mass flow rate 

are known, but steam quality cannot be easily measured, thus the exiting enthalpy is not 

directly measured. Steam quality is the ratio of steam mass to total mass in a two-phase, 

water/steam system. Unlike the validation of the SAM liquid-HTF trough model, where the 

solar field thermal output was directly measured and compared to the SAM solar field 

thermal output, this was not possible for the DSG model validation. While SAM can 

undertake sub-hourly simulations, an annual dataset is required. As such, 1-s data are 

computationally burdensome, and the PE-2 solar field data were averaged to 10-min 

intervals.

The solar field segment output data from PE-2 consists of pressure, temperature and 

mass flow rate of the combined water/steam fluid. As noted above, the steam quality and 

enthalpy are unknown at each timestep. To calculate the solar field thermal power output 

each 10-min timestep, the steam quality is estimated in two ways (identified as Case 1 

and Case 2).
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Case 1: A constant steam quality of 0.748 was determined from the design-point

conditions obtained from PE-2 (Novatec Biosol 2011) and used to calculate the 

enthalpy at each 10-min step.

Case 2: The varying steam quality is estimated from the steam drum level given in 

the operations data at every timestep. The steam drum level, as confirmed with 

Tubosol, is a measure of the steam and water mix in the steam drum, where the 

steam drum separates the remaining water in the fluid from the steam produced by 

the solar field. The calculated enthalpy is multiplied by the mass flowrate to obtain 

an approximate value of thermal power delivered by the solar field. This estimated 

thermal power is then compared with the one predicted by the SAM simulation. This 

is potentially a more accurate depiction of time-dependent steam quality. 

There are differences between the way the SAM DSG model predicts the solar field 

operation compared to the way the real PE-2 solar field operates. Therefore, in order to 

make a more realistic comparison, a couple of constraints are added for Case 2:

i. The solar field has hot fluid in the field during the night periods (as well as the day), and 

the thermal energy can theoretically be calculated during this time. However, as no 

thermal power from the field is delivered during the night to generate steam for the 

steam turbine, the solar field thermal power output from 9 pm to 6 am is ignored.

ii. Time periods when sunlight is available, but PE-2 is not running for other reasons, are 

excluded from the SAM comparison. Figure 23 shows a plot of such a situation, where 

the thermal power predicted by the SAM model is shown against the real estimated solar 

field thermal power output. It can be seen the estimated solar field thermal output was 

barely present during the day (e.g. the green line), even though there was sufficient DNI 

for SAM to predict some thermal power generation. The thin blue line represents the 

SAM simulated power. As can be seen in Figure 23, due to the DNI variations (e.g. 

perhaps due to several clouds that day), the model responded to the spikes in DNI with 

a considerable power production, whereas the estimated real output was nearly zero 

through the day. Hence, the simulated thermal power for days like these were ignored, 

as operational decisions made at site cannot be simulated or accounted for in the DSG 

model. The startup constraints used in the DSG model are being explored as a means to 

address some of this issue.
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Figure 23. Comparison of the SAM-estimated output and the PE-2 solar field output on a day with 

transients, and showing the need for a constraint to exclude the SAM output when the estimated 

real output was minimal

Table 19 shows the comparison between thermal energy generation predicted by the 

SAM DSG model and that estimated using real field data (for both the constant and 

variable steam quality cases). 

Table 19. Comparison of the thermal energy generated between the real PE-2 solar field section 

and the SAM-simulated solar field, for the cases of constant and varaible steam quality (Case 1 

and Case 2 respectively). Analysis covers the month of June 2015.

Case Condition

PE-2 estimated 

thermal energy of 

solar field (MWh)

SAM thermal 

energy of solar 

field (MWh)

Difference 

(MWh)

Diff.

(%)

1 Constant steam quality at 

design conditions 141,105 126,534 14,571 -10.3

2

2.1: Estimated variable steam 

quality 130,864 126,534 4,330 -3.3

2:2: Estimated variable steam 

quality with constraints 127,082 126,128 954 -0.8

As can be seen in Table 19, with constant steam quality (Case 1), the PE-2 estimated 

thermal energy generated for June 2015, was approximately 141,100 MWh, compared to 

approximately 126,500 MWh from the SAM simulation. In other words, the SAM thermal 

output was approximately 10.3% less than the real estimated value. Case 1 can be 

considered an oversimplification, as there will be a varying steam quality produced by the 

real PE-2 solar field segment. Discussions with Tubosol confirmed that the steam quality 

can vary from 70-80% dependent on the DNI conditions. In Case 2 from Table 19, when 

variable steam quality is calculated for the PE-2 solar field, the estimated solar thermal 

energy generated was 130,800 MWh compared to the simulated output of 126,500 MWh 
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(Case 2.1). The difference in the energy generated between the SAM-simulated solar 

field and PE-2 is approximately -3.3%. When the constraints are added, the difference 

between the PE-2 thermal energy and the SAM output drops to 0.8%. Both scenarios 

meet the success metric of being within 10% of real DSG solar thermal field output. 

Further work might explore the settings of SAM’s startup constraints to better match with 

the behavior seen at PE-2 rather than apply a constraint on SAM output purely to match 

PE-2 behavior.

Figure 24 shows the comparison between the SAM simulation (blue) and the estimated 

PE-2 thermal power output, on days that had significant DNI variation (left), and a single 

transient in an otherwise very sunny day (right). The validation of the DSG SAM model 

was also presented in a conference in 2017 (Kurup et al. 2018).

 

Figure 24. Comparison of SAM-simulated solar field power output and real estimated output on 

days with significant variation in DNI (e.g. a cloudy day, left image), and a clear sunny day with 

one fast transient (right image)

Process Heat Collectors Review

Direct use of solar thermal energy (e.g. through non-concentrating and concentrating 

technologies) for IPH is a simpler application than for electric power production (C. Turchi, 

Kurup, and Zhu 2016). The heat can be used with little conversion loss, avoiding the heatto-electricity losses associated with using CSP for electricity production. For industrial and 

commercial sites, renewable heat for IPH has been rare in the United States.

The obvious competition for solar IPH within the United States is natural gas, and prior 

analysis indicates that solar collector cost reduction is required to provide solar heat at a 

competitive price for industrial processes such as sterilization, material drying, or thermal 

desalination (Kurup and Turchi 2015a; C. Turchi, Kurup, and Zhu 2016). In most of these IPH 

applications heat delivery via saturated steam is desired. Steam generated from solar 

collectors can either be made in the solar field itself, i.e. direct steam generation (DSG), 

or the more traditional approach where an HTF from the solar field (e.g. synthetic oil or 

pressurized water) transfers heat to a boiler through a heat exchanger. For solar IPH, the 

delivery of steam through DSG decreases complexity to the end-user and removes 

potentially high-cost capital items such as an organic HTF and boiler. 

Solar IPH with the concentrating collectors designed for power generation applications 

can supply heat at temperatures exceeding 400°C, which is significantly in excess of 

typical process heat requirements (Kurup and Turchi 2015a). Large collectors such as the 

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SkyTrough, EuroTrough, and Ultimate Trough (Adrian Farr 2009; Kurup and Turchi 2015b), 

have been optimized for power production at 400°C and higher; however, deploying these

large-aperture designs for smaller scale IPH applications can be problematic.

Consequently, several companies have developed and are marketing small concentrating 

collectors designs for solar IPH applications.

Specific solar IPH collectors are reviewed that exhibit the ability to install on roofs or small 

ground areas near an end-user, instead of the larger scale power generation CSP 

collectors common with CSP electricity generation projects. Typically, the size of the solar 

field will be constrained for solar IPH applications, and it is unnecessary to deploy such 

large collectors for typical IPH steam conditions. This review of concentrating solar IPH 

collectors is not exhaustive but represents known and active solar technology developers. 

The first section will look at liquid-HTF troughs and linear-Fresnel, followed by DSG 

troughs and linear Fresnel. Table 20 shows a summary of the solar IPH collectors and 

companies highlighted in this review.

Table 20. Summary table of the solar IPH collectors and companies as of Jan. 2019.

Technology Name of 

Company

Founding 

Country of 

Company

Main markets 

technology is 

deployed or 

planned in

Applications/inte

grations for the 

technology

Number of installations or 

capacity installed if known

Liquid-HTF 

Trough

Rackam Canada Canada, Brazil, 

Spain, USA

Solar IPH e.g. for 

paper and pulp, 

sludge drying

Approximately 7 

installations in Canada, 

Brazil, Spain and USA (~6 

MWth in total)

Heliovis Austria Spain, MENA Solar IPH e.g. 

food processing

1 MWth installed in Spain, 

similar project planned in 

MENA

Sunvapor USA USA Solar IPH e.g. 

food processing

Planned 1-MWth for 

California food processor

NEP Solar Australia Switzerland, 

Australia

Solar IPH e.g. 

food processing

Approximately 3 sites in 

Switzerland

Lucida Turkey

Jordan as first 

for first 

commercial 

plant

Solar IPH

Only demos installed, first 

commercial project in 

Jordan of 990 kWth

Inventive 

Power Mexico Mexico

Solar IPH e.g. for 

food processing, 

hotel

Estimated 40 installations 

in Mexico

Absolicon Sweden

Sweden, 

Austria, China 

and Greece

Solar IPH Approximately 7 

installations

Artic Solar USA USA Solar IPH e.g. 

food processing Approximately 3 sites

Ultra Lite Solar Canada USA, Canada, 

MENA

Solar IPH e.g. 

desalination Only demos installed.

Liquid-HTF 

Linear 

Fresnel

Skyven

Technologies USA USA Solar IPH No installation yet

Hyperlight USA USA

Solar IPH and 

geothermal 

hybrids

0.5 acre demonstration

Heliac Denmark Denmark Solar IPH e.g. for 

district heating 2-MWth demonstration
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DSG Trough

GlassPoint USA Oman, Kuwait, 

California

Solar enhanced 

oil recovery (solar 

EOR)

1,021 MWth in construction 

in Oman,

850 MWth planned in 

California

Solarlite Germany

Thailand, 

Germany and 

India

Solar IPH, 

electricity, solar 

EOR

5 MWe (~20 MWth) for the 

biggest site in Thailand

DSG Linear 

Fresnel

Industrial Solar Germany Jordan

Solar IPH e.g. for 

pharmaceutical 

processing

222 kWth for site in Jordan

SunCNIM France France Electricity 9 MWe (~27 MWth) site in 

construction

Liquid-HTF Trough

Parabolic trough collectors typically use a liquid-HTF in the solar field and there are 

several companies that produce troughs for solar IPH application.

1. Rackam

Rackam is a Canadian solar IPH developer and manufacturer. The main innovations 

include the use of micro-sun aluminum sheets from Alanod Solar for each collector 

module (Alanod Solar 2014), and the wheel tracking mechanism. Currently there are two 

models Rackam delivers, the S20 (2.34-m aperture) for ground mount and the S10 (1.16-

m aperture) for roof-mount cases (Rackam 2016). Rackam has deployed their system in 

Brazil, Canada, Spain, and the U.S. (Rackam 2018; CSP Plaza 2018a), and as of 2017 

has installed six fields with a total collector area of approximately 2,092 m2 (CSP Plaza 

2018a). The newest site under construction in Brazil is a 5.16-MWth solar field, which will 

include nearly 12,000 m2 of installed collector area once completed (CSP Plaza 2018a; 

Rackam 2017b).

IPH project developer Heat2Hydro Inc. has partnered with Rackam to deploy the Rackam 

trough systems. In 2017 Heat2Hydro setup a 100-kWth test loop using S20 parabolic 

troughs (Figure 25). The operational test loop in Surprise, Arizona provides heat for 

sludge drying, a byproduct of the wastewater treatment (Rackam 2017a). Heat2Hydro 

estimates a 5-MWth plant could be built to treat all of the City of Surprise’s wastewater 

sludge (Rackam 2017a). The S20 trough from Rackam is currently being certified by the 

U.S. Solar Ratings and Certification Corporation (SRCC).

 

Figure 25. Rackam S20 100-kWth loop being tested at the Heat2Hydro test site in Surprise, Arizona

for sludge drying. Photos from Heat2Hydro, credit: Heat2Hydro.

2. Heliovis
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Heliovis, an Austrian company, has developed an innovative inflated tube structure for 

solar IPH. The 7.8-m aperture collector is made of three plastic films; the first a robust 

and durable transparent film, the second the mirror film, and the third is the surrounding 

base film (Kazkaz 2018; Heliovis 2017). The schematic of the HelioTube can be seen in 

Figure 26, where an air pressure difference in the tube allows for the mirror film to be 

curved as needed. The overall length for one module is 220 m (Kazkaz 2018).

Figure 26. HelioTube inflated tube schematic. (Markham 2017)

Small system size and low project-development budgets mean it is important for the U.S. 

solar IPH market to have easy-to-install collectors with short payback periods. 

Discussions with Heliovis indicate that their inflated tube collector, which uses roll-to-roll 

manufacturing, could have low solar field installed costs. The design leverages 

inexpensive, easily available architectural plastics and has significantly less steel than 

other collector structures. The potentially low installed solar field costs will need to be 

understood better.

The HelioTube benefits from ease of manufacture, speed of installation and cheaper 

materials than current metal and glass/mirror film troughs. The 1-MWth module at the 

Heliovis test site in Spain (Figure 27) is connected to a mushroom producer and was 

constructed in eight weeks (Kazkaz 2018). Currently the test site uses a liquid-HTF, though 

future HelioTube developments could employ DSG.

Figure 27. Heliovis inflated HelioTube demonstration in Spain for mushroom drying (Kazkaz 2018). 

Credit: Heliovis.
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3. Sunvapor

Sunvapor, a California-based solar IPH project and technology developer, has developed 

the Green Trough through a $1.7 million DOE-funded project (DOE 2016). Sunvapor is 

aiming to reduce the installed collector cost to less than $100/m2 and cut manufacturing 

and material costs by nearly 50%, through the use of wood for the collector structure (DOE 

EERE 2017). The Green Trough, and other low-cost collectors in this report, could be vital 

in making CSP for solar IPH competitive in California. The demonstration of the Green

Trough in California is shown in Figure 28.

Figure 28. Sunvapor Green Trough demonstration at a site in California. Photo provided by 

Sunvapor, credit: Sunvapor.

Sunvapor recently were awarded a $1.5 million DOE project that combined the Green 

Trough with a TES system based on phase change materials (PCMs) (DOE EERE 2018). 

The aim of the project is to provide steam at ~180°C with a target LCOH of less than 

1.5¢/kWhth (DOE EERE 2018; PR Newswire 2018b). The NREL PCM model described 

later is being leveraged for use with the Sunvapor award.

4. NEP Solar

NEP Solar, an Australian company, is offering the PolyTrough 1800 (NEP Solar 2013; 

Minder 2013) that uses either an oil or water-glycol HTF. The 1.85-m aperture trough uses 

weather-resistant aluminum reflectors, is designed to produce a maximum solar field 

outlet temperature of 250°C, and is both roof and ground mountable (NEP Solar 2013). 

A previous paper in conjunction with the Bavarian Center for Applied Science (ZAE 

Bayern) and the Swiss Institute for Solar Technology (SPF), looked at the validation of 

operational data from a NEP Solar PolyTrough 1800 site in Saignelégier, Northwest 

Switzerland (Kurup et al. 2018). The 627 m2 solar field uses a pressurized water-glycol mix 

as the HTF (Frank et al. 2014), with solar field exit temperatures between 110 and 130°C 

(Minder 2013). The solar IPH site has a 360-kWth peak power, and the heat from the solar 

field is utilized as part of the milk processing. SAM was used to model the Swiss site, and 

found that the annual predicted thermal yield compared to the experimentally measured 

thermal output of the solar field to be within 5% of each other (Kurup et al. 2018). An image 

of the roof-mounted solar field in snowy conditions, at the Saignelégier dairy processing 

plant is shown in Figure 29.
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Figure 29. PolyTrough 1800 roof mounted solar field at the Saignelégier dairy processing plant in 

Switzerland (Minder 2013)

5. Lucida Solar

Turkish company Lucida Solar has developed a dual-axis tracking, modularized system

(Figure 30). Each modular hexagonal section is made up of 30 parabolic trough units with 

an aperture area of 81.2 m2 (Valenzuela and López-Martín 2017). The main innovation is the 

dual-axis tracker which allows each trough string unit to track elevation while the unit 

rotates in the azimuth direction to increase the efficiency of the thermal capture 

(Valenzuela and López-Martín 2017). A non-evacuated receiver is fixed, i.e., it does not 

rotate with the collector, which simplifies HTF handling.

 

Figure 30. Lucida Solar dual-axis tracking installation in Izmir, Turkey. Photos provided by Lucida 

Solar, credit: Lucida Solar.

Tests and modeling undertaken by the Spanish organization Ciemat-Plataforma Solar de 

Almería (PSA), have indicated that compared to a NEP Solar PolyTrough 1800 solar field 

of approximately the same aperture area, the thermal energy delivered from the Lucida 

field could be approximately 30% greater, mainly due to the dual-axis tracking (Valenzuela 

and López-Martín 2017). Discussions with Lucida Solar highlight that their first commercial 

solar IPH plant is in Jordan, where expensive diesel oil is burned for heat. This first project 

is estimated at 990-kWth.

6. Inventive Power

Inventive Power, a Mexican trough developer, has deployed some 40 systems across 

Mexico as of September 2018 (Inventive Power 2018). Inventive Power has installed 

parabolic troughs which utilize water/glycol or oil-HTF, mainly for food processing plants 

(e.g. dairy and animal feed processing), hotels, and agricultural plants such as fruit drying 
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(AEE INTEC 2018; Muang 2018). Mexico has high onsite-fuel costs and excellent DNI, as 

such the solar IPH is very suitable for industrial sites.

Inventive Power’s main collectors are the Power Trough 110 and the Power Trough 250, 

with 1.2-m and 2.5-m aperture widths respectively (Inventive Power 2016, 2017c). Both 

collectors use anodized aluminum for the reflector, can be either roof or ground mounted, 

and are designed to have solar field output temperatures of 50 to 200°C for the Power 

Trough 110, and up to 260°C for the Power Trough 250 (Inventive Power 2016, 2017c). 

Figure 31 shows both the roof mounted (left) and ground mounted (right) Power Trough 

110 solar fields for two sites.

 

Figure 31. Roof-mounted Power Trough 110 solar field (left), and ground-mounted solar field

(right). Photos from Inventive Power, credit: Inventive Power

Figure 31 left, highlights a 32-collector solar field (approximately 99 m2 aperture area) for 

domestic water heating at the Howard Johnson hotel in Guadalajara, Mexico (Inventive 

Power 2017b). Figure 31 right shows 80 collectors (approximately 247 m2) used for boiler 

preheating for an animal food manufacturer in Morelia, Mexico (Inventive Power 2017a; AEE 

INTEC 2017).

7. Absolicon

Swedish trough developer and manufacturer Absolicon has created iterations of an 

enclosed trough for solar IPH, which can be ground or roof mounted. The main offering 

is the T160, which is designed to heat a synthetic oil to 160°C (Absolicon 2017) and has 

been tested by SPF with a reported thermal efficiency of 76.6% (Absolicon 2018c). 

Absolicon has a 200-m2 installation of 10 collectors at the Hemab Energy Park in Sweden 

(Absolicon 2011), and is branching out to Austria, China and Greece (Absolicon 2018b). For 

example, in Athens, Greece, Absolicon and Solar ++ AB, are installing a 100 m2 T160 

solar field to provide heat for a Colgate Palmolive production plant (Absolicon 2018b). 

Figure 32 shows the 200 m2 solar field for the Smedjebacken site in Sweden (Absolicon 

2012), where approximately 80-kWth peak of heat can be provided in summer, to decrease 

the oil and fuel usage in the industrial area.
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Figure 32. Absolicon T160 roof mounted solar field at the Smedjebacken Industrial park in Sweden 

(Absolicon 2012). Credit: Absolicon.

Absolicon seeks to increase its manufacturing capacity and scale of production via mass 

manufacturing, automation and increased control of the supply chain (e.g. for parts and 

materials) to gain cost reductions of the collector. Figure 33 shows the robotic inspection 

ongoing at the first manufacturing plant in Sweden. 

Figure 33. Robotic inspection of Absolicon T160 trough in Swedish manufacturing line (Absolicon 

2018a). Credit: Absolicon.

Figure 34 shows Absolicon’s reported effect of increased manufacturing volume for the 

rear bulkhead that holds the reflector plates in the trough. By increasing the 

manufacturing volume from 100 units to 100,000 units per year, where rolls of aluminum 

sheet metal are pressed and formed, the cost reduction potential for that component is 

on the order of 90% (Absolicon 2018c). The cost is shown in Swedish Krona (SEK), with 

an exchange rate of approximately 9 SEK to 1 USD. Other components have also been 

identified for cost reduction through increased manufacturing investment and volume 

production, for example the use of dies for the plastic components, which could reduce 

the cost to one sixth of small volume production (Absolicon 2018c). These claims have not 

been verified through NREL analysis, but the premise is sound and similar has been found 

in other NREL manufacturing analysis (Akar et al. 2018). Using easy-to-manufacture rolls 

of material, cost reduction of this order is possible as volume increases. 
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Figure 34. Cost reduction potential (given in SEK) for the Absolicon rear bulkhead through mass 

manufacturing investments (Absolicon 2018c).

Absolicon have partnered with Heli New Energy Technology to ship their first robotic 

manufacturing line to China, and have plans to sell fully automated manufacturing lines 

in other countries and license the T160 (Epp 2018). The line in China has a yearly 

manufacturing capacity of 100,000 m2 of the T160 and a sales contract of approximately 

€2.5 million or $2.9 million USD. With the sales revenue, upgrades to the existing 

manufacturing plant in Sweden could allow for one collector to be manufactured every 6 

minutes (Epp 2018; Absolicon 2018a). This is an important step, where the design and mass 

manufacture of the collector, could allow cost decreases.

8. Artic Solar

Artic Solar is a Florida-based, Veteran Owned solar thermal manufacturer, targeting the 

100 – 200°C range for a variety of industrial processes (Artic Solar 2019), as shown in 

Figure 35 left. Artic Solar are developing and deploying the patented External 

Concentration Parabolic Collector (XCPC), where 3 mirrored elements make up a XCPC 

unit. Figure 35 right shows two XCPC units coupled together.

 

Figure 35. Artic Solar target operating range for industrial applications (left), and two XCPC units 

coupled together (right). Credit: Artic Solar.

The XCPC is a patented (U.S. Patent #9,383,120 B1) technology (Winston 2016), where 

a non-tracking, non-imaging concentrating collector allows the system to produce heated 

fluids up to ~400°F/200°C (Artic Solar 2019). The XCPC is also certified with the SRCC

(SRCC 2018), and Artic Solar is the exclusive licensee of the XCPC in the U.S. In the 

majority of cases the technology uses a liquid-HTF like pressurized water, and depending 
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on the temperature required, the fluid can be changed to a higher temperature HTF. For 

industrial applications, Artic Solar decrease O&M by removing the need for tracking of the 

collector, and there is additional benefit that the collector can provide heated fluid in direct 

and indirect radiation conditions.

While the technology has been patented, there is significant development and research 

still possible and needed for the optimization and improvement of the technology. For 

example, for the use of the technology and integration into areas such as desalination 

systems.

The latest deployed system from Artic Solar in December 2018, is for integration into a 

distillery in Jacksonville, Florida. Here 12 XCPC units provide heat for the solar steam 

boiler at the Four Father’s Distillery (Digital Journal 2018), and are designed to decrease 

the natural gas use (Dixon 2019). The Four Father’s Distillery is currently the only U.S. 

distillery to use a solar steam boiler, and uses a water-glycol mix to allow the water to be 

heated to steam conditions (Dixon 2019). The solar boiler integration is expected to save 

thousands of dollars annually from the avoided costs of burning natural gas (Dixon 2019).

9. CSP Ultra Lite Solar

CSP Ultra Lite Solar (referred to as Ultra Lite), is a Canadian company which was 

originally developing high temperature technologies for hydrogen production and fueling 

vehicles. The company has re-engineered its technology from hydrogen production to 

process heating and CSP applications (Ultra Lite Solar 2018a). Ultra Lite are targeting the 

use of the their collector technology towards medium and high temperature industrial 

applications of solar fuels, solar EOR, desalination and district heating (Ultra Lite Solar 

2018a).

Ultra Lite have developed three variations of the Ultra Lite (UL) trough. The UL1 trough 

is a developing technology that uses pressurized air to hold the primary mirror film 

reflector in place (Ultra Lite Solar 2018b). The UL3 is a more conventional design, though 

production ready, where a film is bonded to an aluminum sheet, and the trough structure 

is held by a support structure (Ultra Lite Solar 2018b). A rendering of the UL3 suitable for 

IPH can be seen in Figure 36, and can reportedly reach 200°C (Ultra Lite Solar 2018b). In 

terms of IP, it has been found that the company has 13 patents filed or pending.

Figure 36. Ultra Lite UL3 trough rendering, designed for solar IPH applications. Photo from Ultra 

Lite, credit: Ultra Lite.
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Benefits of the Ultra Lite troughs could include: lower-cost manufacturing and assembly 

if less support structure is needed (due to the air pressure assisted structure), thin film 

materials and local content (Ultra Lite Solar 2018c). 

Liquid-HTF Linear Fresnel

1. Skyven Technologies 

Skyven Technologies (based in Texas) is a recent company entering the solar IPH space. 

Founded in 2013, with private backing and grants from the National Science Foundation 

(Pitchbook 2018; Skyven Technologies 2018b), Skyven Technologies is looking to heat liquids 

up to 250°C for process heating (Skyven Technologies 2018a). Figure 37 (left) shows the 

modular design, which contains rows of mirror facets controlled to track the sun, 

concentrating the light onto a flat receiver, similar to linear Fresnel receivers. Figure 37

left, shows modules of the collector being installed, and the right image shows a closeup 

of the mirror facets. 

 

Figure 37. Skyven Technologies modular panels being installed (left), and close up (right). Photos 

from Skyven Technologies, credit: Skyven Technologies.

Innovations of Skyven Technologies include a very modular design for the collector, ease 

of installation (as the structure and mounting is based of PV panels), stamped aluminum 

mirror facets and injection molded facet backing structures (Protolabs 2016). This collector

has potential to be low-cost (e.g. through the mass manufacturing modular approach of 

key components like the mirror facets). Performance data are yet to be published or

reviewed by NREL.

2. Hyperlight Energy

Hyperlight Energy is a California-based solar thermal technology developer and 

manufacturer, who through the DOE COLLECTS program, have continued development 

of an innovative linear Fresnel collector and received a SunShot award of $1.5 million 

(DOE EERE 2016). Hyperlight Energy recently completed a demonstration system in 

Brawley, CA at San Diego State University, which covers 0.5 acre (Mungas 2016).

Hyperlight Energy have also received $750k from the California Energy Commission 

(CEC) for collector development (Hyperlight Energy 2016). Hyperlight Energy is aiming to 

reduce the installed collector cost to less than $100/m2 through the use of low-cost plastic

extrusions and water replacing traditional CSP collector structural steel and concrete 
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(Mungas 2016). The demonstration site in Brawley, CA can be seen in Figure 38 (left), and 

the right image shows the water support and plastic pipes which hold the mirrors.

 

Figure 38. Hyperlight Energy 1⁄2 acre demonstration at the San Diego State University in Brawley, 

CA (left), and illustration of the mirrors floating on the water support (right). Credit: Hyperlight.

Hyperlight are working with NREL in the further improvement of the collector design and 

modeling of the system (Zhu et al. 2016). An adaptive optimization approach for the 

secondary receiver has been developed, to then improve the optical performance of the 

system. The modelled results indicate that with an optimum secondary-reflector profile, 

the optical efficiency of the Hyperlight collector can be 90%, for a wide range of 

transversal and longitudinal incidence angles (Zhu et al. 2016).

3. Heliac

Heliac is a Danish solar thermal developer, manufacturer and installer, and are targeting 

low-cost large-scale solar fields for heat generation, for example at industrial sites or for 

district heating. For cost effectiveness, solar fields are expected to be greater than 200-

kWth (Heliac 2018a). The heat generated can either be used with a heat exchanger to 

integrate into a site’s existing IPH needs or for electricity generation.

Rather than traditional linear Fresnel based on mirrors, their innovative system, uses a 

thin film which has a refractive microstructure to concentrate the light to heat an HTF 

dependent on the temperature needed. Water is the cheapest option for temperatures 

below 100°C, and traditional synthetic HTFs like Therminol VP-1 or Dowtherm A would 

be used to reach 400°C. As seen in Figure 39, the concentrated light heats up a relatively 

cold fluid, and a heated fluid exits each individual system. 
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Figure 39. Heliac system design to concentrate light with thin film refractive les and produce heated 

fluid. Image provided by Heliac, credit: Heliac. (Heliac 2018c)

A demonstration of the solar technology is currently under construction. Heliac have been 

contracted to manufacture and install for E.ON (a large European power utility) a 2-MWth

system. This is being integrated into the existing district heating network at Lendemarke, 

Denmark. The installation can be seen in Figure 40.

Figure 40. Heliac 2-MWth demonstration for a district heat facility in Denmark. Photo provided by 

Heliac, credit Heliac.

The system is expected to produce 2,000-MWh annually, and the plant is expected to 

meet Lendemarke’s heat demand during the summer months and 20% in the rest of the 

year (Heliac 2018b).

DSG collectors

Few existing solar IPH collector developers have designed for steam production in the 

solar field itself. One notable application is the use of the steam in solar EOR.

1. GlassPoint

GlassPoint, a California-based developer, has developed and deployed multiple 

generations of DSG trough technology, with focus on injecting solar steam into existing 

oil fields for solar EOR. The key GlassPoint innovation is to place the collector and 

receiver hardware inside a transparent glasshouse, enabling the use of lightweight 
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structures in a zero-wind environment, see Figure 41 (Bierman, Treynor, et al. 2014; 

Bierman, O’Donnell, et al. 2014). GlassPoint reports the solar steam can decrease the onsite 

use of gas for EOR by 50-80% (Glasspoint 2018a).

Figure 41. GlassPoint schematic for once-through steam generation for EOR and other industrial 

use.

The biggest and most progressed GlassPoint project is the 1,021 MWth solar field in Amal, 

Oman, spanning nearly 3 km2 (Glasspoint 2018b). This is in construction, and as of 

February 2018, four of 36 planned glasshouse enclosures were in operation (SolarPaces 

2018). Once the site is complete, a daily solar steam output of 6,000 tons/day could be 

reached, saving 5.6 trillion British Thermal Units (BTU) of gas per annum (Glasspoint 

2018b). Progress of the site can be seen in Figure 42.

 

Figure 42. GlassPoint site Miraah. Construction as of September 2017. Credit: GlassPoint.

In 2017, GlassPoint also announced an 850-MWth solar EOR site in Belridge, California. 

When completed, this would be the largest concentrating solar site in the U.S. of any type. 

The site is expected to incorporate 26.5-MWe of PV to generate electricity for site needs 

(23 ABC News 2017; Business Wire 2017). 

2. Solarlite

Solarlite is a German company offering DSG troughs. Solarlite’s composite-facet DSG 

trough offers an innovative approach to reduce collector cost and enhance deployment 

of small-capacity systems. Most notably, composite-facet troughs are lighter and more 

rigid than the current state-of-the-art and composite facets allow a thinner, higher-
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reflectivity mirror (0.9 mm vs. 4 mm glass). Solarlite has developed composite-trough 

systems since 2001 and has deployed the 5-MWe TSE-1 power plant in Thailand (Solarlite 

2011). The installation of the SL 4600 trough can be seen in Figure 43.

 

Figure 43. Solarlite DSG Trough (SL 4600) installed in Thailand. Photos from Solarlite, credit: 

Solarlite.

Solarlite DSG systems for solar IPH applications could be very suitable, though they have 

yet to deploy solar field sizes less than 2-MWth as such small projects may not be cost 

effective, at least in terms of the fast payback needed by industrial end-users.

3. Industrial Solar

Industrial Solar, a German company, has developed a modular DSG linear Fresnel 

collector for solar IPH. Industrial Solar’s LF-11 is designed for industrial application and

direct production of saturated or superheated steam, up to 400°C. Roof mounting is 

available. The LF-11 collector with a 7.5 m collector width can be seen in Figure 44.

Figure 44. Industrial Solar DSG LF collector roof mounted in South Africa, where the heat is 

utilized for solar cooling (Industrial Solar 2016)

Industrial Solar are heavily working with the German Aerospace Center (DLR) for the 

development and deployment of their DSG collector into markets in Jordan (Berger et al. 

2016). The solar field at the RAM Pharma pharmaceuticals processing plant in Jordan can 

be seen in Figure 45. The site uses fuel oil for steam generation, and the Industrial Solar 

integration feeds into the existing steam line, where the steam supply is controlled through 

a steam drum (Willwerth et al. 2017).
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Figure 45. Industrial Solar collector integration at Ram Pharma in Jordan

The 394 m2 solar field produces 166°C steam at 6 bar (Berger et al. 2016), with a steam 

drum used for the integration to the pharmaceutical plant. Like most current IPH systems, 

there is no storage. Collaboration with DLR has shown that stable steam supply and 

control is achieved, both in winter and summer. During the spring and summer months, 

the solar field can produce sufficient steam to meet the requirements of the site for many 

hours. For example, on the 24th of April 2016, the integrated solar field and control system 

supplied steam for 5.5 hours, allowing fuel use to be switched off and represented a 33% 

saving in fuel oil. On winter days, the steam supply can be delivered in intervals, where 

steam supply from the steam drum is alternated with steam supply from the fossil power 

to meet the site’s needs. Throughout the testing and since the DSG solar field integration, 

there have been no technical issues, which is often a concern for operations staff (Krüger 

et al. 2017).

4. SunCNIM

SunCNIM, a French company, has developed a DSG linear Fresnel technology. The DSG 

collector and installation is designed to have high recyclable content (SunCNIM 2017). This 

novel technology has applications for power generation (with thermal storage), solar 

EOR, hybridizing conventional fossil generation plants, and for solar IPH, where steam is 

generated for process application. SunCNIM has built and tested their 500-kWth DSG 

solar field at the La-Seyne-Sur-Mer site since 2011 (SunCNIM 2017). Part of the novel 

aspect of the whole SunCNIM solution is a robotic, automatic cleaning system that 

ensures regular cleaning of the mirrors. The collector module and the robotic cleaning 

system at the test site can be seen in Figure 46 left and right respectively.

 

Figure 46: SUNCNIM DSG linear Fresnel collector module (left) and Robotic Cleaning System 

(right). Photos from SunCNIM, credit SunCNIM.

SunCNIM are in the construction phase of a 9-MWe DSG linear Fresnel power plant with 

4 hours of storage, where €60 million has been raised in financing (CNIM 2016; SunCNIM 

2016). The aperture of the field is 150,000 m2; steam is produced at 285°C at 70 bar 

(SunCNIM 2016). For France at least, SunCNIM can offer a vertically integrated solution to 
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customers, which could help decrease EPC costs to allow small solar IPH projects to be 

more cost competitive. Figure 47 shows the on-site progress of the solar field for the 9-

MWe project.

 

Figure 47. Site pictures of the solar field for the Ello project. Photos from SunCNIM, credit SunCNIM.

Brewery Integration Case Study

A case study of a potential 1-MWth solar field integration into an existing brewery in 

California was completed in project year three. The project was being pursued by 

developer Heat2Hydro Inc. (New York, NY), who shared information with NREL. Project 

finance sensitivities to solar field cost, natural gas price, project life, federal corporate tax 

rate, debt interest rate, and project incentives were explored. The biggest influences on 

project viability (measured by NPV) were the federal tax rate, the project life, and the solar 

field installed cost. For example, changing the tax rate from 35% to 21%, flips the Base 

Case from NPV>0 (+$9k) to economically unattractive NPV = -$7k. The pronounced 

influence of the federal tax is due to the large upfront costs of solar IPH projects that lead 

to tax-deductible losses in the early years of the project period. A higher tax rate produces 

larger deductions. It is important to realize that the commercial application model (from 

SAM 2017.09.05) used in this study assumes that the project owner has sufficient income 

to allow for tax deductions. Developer Heat2Hydro acknowledged the importance of 

owner tax appetite for project economics. 

Longer project life is also an important factor in project economics. After payoff of the 

project loan, project net income can be substantial. The importance of project life points 

to the need for development and demonstration of durable technologies for these 

applications—activities that may benefit from DOE R&D. However, this contrasts with the 

next lesson. 

Figure 48 shows a contour plot of NPV when installed solar field cost and project life are 

varied. The orange band highlights conditions where NPV transitions to positive values; 

thereby defining the border of economic viability for the given assumptions. As can be 

seen, the increase in life of the project significantly increases project NPV. A project with 

a 10-year financial life requires a solar field cost of less than about $100/m2.
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Figure 48. Contour plot of NPV, varying the solar field costs and the project life, with 21% federal 

tax rate.

The influence of tax rate and project life touch on the metric that is often most important 

to industrial users, namely payback period. Industrial users may require relatively short 

payback periods (e.g., less than 3-5 years), regardless of overall project NPV due to 

uncertainties in future cost and market projections. Under this constraint, sacrificing 

durability for lower upfront cost, may be the better economic choice. For example, lowcost collectors that last only 10 years, rather than 15 to 20 years, may be a more viable 

pathway for solar IPH. 

Sensitivities to the loan interest rate, salvage value, and the use of 100% equity (i.e. no 

debt used for the project) had lesser effect on the NPV.

The federal investment tax credit (ITC) and California solar-thermal production-based 

incentive (PBI) were also investigated as part of the case study. Despite these incentives 

and, as illustrated by this study, the favorable economics they suggest, deployment of 

solar IPH in California has been weak, and time is running out on these incentives. If new 

projects are constructed by the end of 2019, the project could at least gain the 30% ITC, 

and if they are built and finished in 2018, the California PBI also would apply in 2019. 

The potential to receive the $800k California PBI credit for a solar IPH installation in two 

years, yielding a positive NPV even with relatively high solar field costs of $300/m2, seems 

to provide a strong incentive to end-users wanting to install solar IPH. It should be noted 

that the PBI requires at least one year of measurements to document natural gas 

reduction after the operation of the solar field.

Yet, currently no new solar IPH projects have been built in California, even with the very 

lucrative California incentive in place, and potentially the use of the incentive in 

conjunction with the 30% ITC. There are likely several reasons for this. Solar IPH projects 

present risk due to the relative newness of the technology and the technology providers. 

The incumbency of low-cost natural gas remains a hurdle. Research from the Climate 

$100/m2

$150/m2

$200/m2

$250/m2

$300/m2

10 15 20 25 30

Installed Solar Field Cost 

Project Duration

$100,000-$150,000

$50,000-$100,000

$0-$50,000

-$50,000-$0

-$100,000--$50,000

-$150,000--$100,000

-$200,000--$150,000

-$250,000--$200,000

-$300,000--$250,000

-$350,000--$300,000
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Policy Institute indicates that large heat users like cement manufacturers, require a 

payback period of less than about three years before they will invest in carbon abatement 

options, even if the project can be profitable in the longer term (Zuckerman et al. 2014). This 

confirms what solar IPH developers are highlighting about the demand for quick (< 3 year) 

payback periods. Discussions with solar IPH developers like Heat2Hydro and Sunvapor

(Livermore, CA), have found it is still very difficult to get projects closed and constructed, 

especially when gas prices the end users are paying can be as low as $3.3/MMBTU in 

some locations.

Part of this lack of new projects is also related to uncertainty associated with Bill AB-797, 

which extended the natural gas incentive for only two years, and was signed in late 2017 

(California Legislative Information 2017). Solar developers working in 2017 with end users 

would have faced a situation where the original incentive could have been withdrawn by 

the end of 2017, and as such would have dissuaded the end users from committing to go 

ahead with the project. The extension of Bill AB-797 will make 2018 and 2019 the last 

years where this specific California natural gas incentive is available. But for end users to 

fully benefit from the incentive the project must be built by the end of 2018, to have the 

requisite year of operations data to show metered decreases in natural gas consumption.

In discussions with Heat2Hydro, it is clear the EPC costs in California can be a significant 

issue that can derail a project. Heat2Hydro has highlighted that EPC contractors in 

California are significantly over estimating prospective projects for relatively small solar 

fields (e.g., 1800 m2 or about 1-MWth). This indicates that 1-MWth projects in California 

could be significantly more costly due to the EPC risk margin added to building these first 

projects. While the installed solar field cost assumed for the Base Case has been 

achieved for large power projects, smaller IPH projects do not benefit from those 

economies of scale, and newer technical and business models will be needed to allow 

solar IPH to proliferate. 

Another reason for the high installed cost estimated by the EPC contractors could be the 

lack of experience with small IPH projects. Both the EPC contractors and the end-users 

are unfamiliar with the needs of solar IPH sites, and end-users also perceive solar IPH 

integrations as potentially high risk. This perception will be difficult to shake until there are 

multiple operating solar IPH plants in the region.

As of July 2017, the current CO2 Cap-and-Trade scheme for California was extended from 

2020 to 2030, via Bill AB-398 (Nemec 2017a). In January 2018, the average price of carbon 

emissions per ton was approximately $15/ton (CPI 2018), but currently the ‘pain’ of an 

emissions penalty is still quite low for most industrial users of natural gas. There is 

potential that by 2030, the cost per ton of CO2 could reach $55/ton (Nemec 2017b), which 

could force large industrial end-users to consider alternatives like solar thermal. The 

impact would need to be understood for small industrial sites like food processors. It is 

likely that California will continue to penalize CO2 emissions, which will raise the cost of 

natural gas usage and promote conservation and alternative heat sources such as solar 

IPH. 

Modeling Solar Process Heat with PCM Storage

Use of a solar collector for the DSG can be an efficient way of using solar energy for 

industrial steam supply. However, the intermittency of solar energy limits the capacity 

factor for the system, and hence, increases the cost of the steam produced. In project 
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year three, NREL used SAM’s solar DSG process heat model with a PCM-based TES 

system to simulate a DSG + TES system. The model examined the influence of PCM 

thermal conductivity and other system properties on overall performance and cost. 

With enhancement in the PCM thermal conductivity, the design-point performance of the 

storage system increases considerably, but these enhancements are expensive. In 

addition, the performance of the storage depends on the heat supplied from the DSG 

collector, which varies considerably over a year compared to the design point. A detailed 

yearly simulation needs to be carried out to predict the optimal PCM thermal conductivity. 

A literature review showed limited information available for the PCM storage optimization 

based on annual simulation. The objective of the study was to minimize the LCOH for the 

steam generated from PCM integrated with the solar-thermal system for solar IPH. 

Different available PCM materials for process-heat applications are identified, and a 

suitable PCM, sodium formate (Tmp = 258 °C), is selected based on cost and thermal 

properties. The optimal values of different design parameters (PCM thermal conductivity, 

hours of storage, pitch of the tubes, pipe diameter, and length of pipe) are calculated 

based on detailed annual simulation. Surface-area extending fins are modeled to improve 

heat transfer within the system and an approximate method for calculating the fin mass 

required to enhance the effective thermal conductivity is derived. Sensitivity analysis 

based on the solar installation cost is also carried out, and breakeven price for the solar 

field is calculated so that the cost of steam is comparable with steam produced from 

natural gas.

For this case study, the annual capacity factor for steam generation without storage is 

30.9%. PCM storage depends on several operating parameters, and detailed parametric 

analysis is done to calculate the optimal design parameters based on annual simulation. 

The thickness of the PCM surrounding the tube is inversely proportional to the cost of the 

storage. But with an increase in the PCM thickness, the resistance offered by the PCM 

increases significantly. To improve the heat transfer, either fins or some thermalconductivity enhancement technique for PCM is needed. To improve the heat transfer 

through PCMs, the use of longitudinal fins is proposed. But fins can be expensive, so a 

detailed technoeconomic analysis is done to minimize the levelized cost of heat for steam 

generation. The minimum LCOH for steam generation is 4.2 cents/kWhth with four hours 

of storage, 38.5% capacity factor, and 1.5 W/mK thermal conductivity for the PCM.

In a cost comparison with natural-gas-fired boilers, the steam generated from a solar 

thermal system is slightly more expensive for California, USA, but could be competitive if 

solar installation costs reduce to 220 $/m2. To be competitive with other sunny regions of 

the United States, the solar installation cost needs to be less than about 100 $/m2. 

The complete study is being prepared for journal submission (Sharan, Kurup, and Turchi 

2018).

Task 5. SolarPACES Task I Status Updates 

Throughout the last two years of the three-year project, funding was provided to support 

NREL’s participation as the operating agent for SolarPACES Task I. SolarPACES is an 

international association representing the CSP research and industrial community. 

SolarPACES Task I covers the design, testing, demonstration, evaluation, and application 

of CSP systems. For example: (i) the development and population of an international 
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project database for commercial CSP plants and projects, (ii) the development of 

acceptance test procedures and standards for CSP systems, (iii) the development of best 

practice guidelines for modeling CSP systems, and (iv) the analysis of the value of CSP 

in electricity markets with variable energy sources. Substantial CSP research and 

deployment activity is occurring internationally and knowledge of these activities helps

DOE assess the value to the U.S. CSP industry and DOE program objectives. Lessons 

from the international community are leveraged to improve U.S. plant performance and 

augment U.S. research objectives. The project reported SolarPACES activities to DOE 

on a quarterly basis.

In project year three, NREL completed the development of a model checklist for the 

SolarPACES Guidelines for Bankable STE Yield Assessment, also known as the GuiSmo 

project. 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 (Hirsh et al. 2017). To make this information more accessible and 

allow stakeholders to test specific models against the recommendations, the guidelines 

have been condensed into a spreadsheet-based checklist. The checklist was applied to 

SAM, providing useful feedback to both the checklist group and the SAM development 

team. The 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 (Kesseli et al. 2018).

Task 6. Assessment of Advanced CSP Systems in the 600°C to 650°C Temperature Range

In project year two, NREL examined the potential benefit of adopting the sCO2 Brayton 

cycle at 600°C to 650°C compared to the current state-of-the-art power tower operating 

a steam-Rankine cycle with solar salt at approximately 574°C. The analysis compared a 

molten-salt power tower configuration using direct storage of solar salt (60:40 weight% 

sodium nitrate : potassium nitrate) or single-component nitrate salts at 600°C or 

alternative carbonate- or chloride-based salts at 650°C.

The increase in power cycle efficiency offered by the sCO2 Brayton cycle is expected to 

reduce the size and cost of the solar field required for a given thermal energy input. Power 

cycle capital cost is expected to decrease compared to the superheated steam-Rankine 

cycle, based on projections from sCO2 cycle developers. Maximizing the ∆T of the storage 

system is required for viable deployment of sensible-salt TES. In this regard, the partialcooling sCO2 cycle is noted as a better option than the recompression sCO2 cycle. In the 

analysis it is assumed that a ∆T = 180K can be achieved with the partial-cooling cycle. 

Even with ∆T = 180K, the potential benefits of the sCO2 Brayton cycle are partially or 

completely offset by increased thermal storage cost, albeit for reasons that differ for the 

different salts. An approximate 5% reduction in levelized cost of energy (LCOE) is 

achieved with either solar salt at 600°C or ternary chloride salt at 650°C. 

The potential of using pure sodium nitrate or potassium nitrate is considered because the 

cold tank temperature for the sCO2 power cycle is estimated at 420°C, which would allow 

use of a salt with a higher melting point than solar salt. Sodium nitrate is the most cost 

effective, resulting in an overall LCOE reduction of 8.5%; however, sodium nitrate is 

known to have lower thermal stability than potassium nitrate. 
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The strong influence of salt cost and hot-tank cost on overall economics led to the analysis 

of single-tank thermocline options. The thermocline design significantly reduces salt 

inventory (by 50% or more) and in many cases also reduces the tank size versus the hot 

salt tank of the 2-tank system. It is speculated that integration of encapsulated phasechange material (PCM) in the thermocline could further increase the thermal-storage 

energy density and reduce storage tank volume. The thermocline cases led to three 

scenarios with relative LCOE reductions of approximately 10%; however, this must be 

tempered by possible operational inefficiencies of the thermocline temperature profile.

The full results of this study are documented in a Solar Energy journal article published 

in 2018 (C. S. Turchi, Vidal, and Bauer 2018).

Inventions, Patents, Publications, and Other Results

Patents and Patent Applications

T. Neises, “Hybridized Low-Temperature and High-Temperature Power Cycle,” U.S. provisional 

patent application 62/361,134.

Journal Submittals

C.S. Turchi, J. Vidal, and M. Bauer, “Molten salt power towers operating at 600–650 °C: Salt 

selection and cost benefits,” Solar Energy, 164, 38-46 (2018).

G. Mohan, M. Venkataraman, J. Gomez-Vidal, J. Coventry. “Assessment of a novel ternary 

eutectic chloride salt for next generation high-temperature sensible heat storage,” Energy 

Conversion and Management, 167, 156-165 (2018).

G. Mohan, M. Venkataraman, J. Gomez-Vidal, J. Coventry. “Thermo-economic analysis of hightemperature sensible thermal storage with different ternary eutectic alkali and alkaline earth 

metal chlorides,” Solar Energy, 176, 350-357 (2018).

M.J. Wagner et al., “Optimizing Dispatch for a Concentrated Solar Power Tower,” Solar Energy, 

174, 1198-1211 (2018). (documents SolarPILOT upgrades)

J. Lilliestam, T. Barradi, N. Caldés, M. Gomez, S. Hanger ,J. Kern, N. Komendantova, M.

Mehos, W.M. Hong, Z. Wang, A. Patt, “Policies to keep and expand the option of concentrating 

solar power for dispatchable renewable electricity,” Energy Policy, 116, 193-197 (2018).

T. Neises and C. Turchi, “Supercritical Carbon Dioxide Power Cycle Design and Configuration 

Optimization to Minimize Levelized Cost of Energy of Molten Salt Power Towers Operating at 

650°C,” Solar Energy, submitted, November 2018.

P. Sharan, P. Kurup, and C. Turchi, “Optimal System Design for Steam Production from Solar 

Collector and Phase-Change Material Storage,” Solar Energy, in preparation.

Conference Publications and NREL Reports

Tse and T. Neises, “Analysis and Optimization for Off-design Performance of the 

Recompression sCO2 Cycles for High Temperature CSP Applications,” 5th International 

Symposium – Supercritical CO2 Power Cycles, San Antonio, TX, March 29-31, 2016.

C. Turchi, P. Kurup, and G. Zhu, "Revisiting Parabolic Trough Concentrators for Industrial 

Process Heat in the United States,” PowerEnergy2016-59621, ASME 2016 Power & Energy 

Conference, Charlotte, NC, June 26-30, 2016.
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C. Turchi, J. Vidal, M. Bauer, “Intermediate-Temperature Molten Salt as a Pathway to CSP 

Performance and Cost Goals under the U.S. SunShot Initiative,” SolarPACES, Santiago, Chile, 

September 25-29, 2017. 

P. Kurup, A. Parikh, J. Möllenkamp, T. Beikircher, A. Samoli, and C. Turchi, “SAM Process Heat 

Model Development and Validation: Liquid-HTF Trough and Direct Steam Generation Linear 

Focus System,” Solar World Congress, Abu Dhabi, United Arab Emirates,October 29 to 

November 2, 2017.

P. Sharan, T. Neises, and C. Turchi, “Thermal Desalination as Cooling for a Supercritical 

Carbon Dioxide Brayton Cycle,” Paper 187, Supercritical CO2 Power Cycles Symposium

Pittsburgh, PA, March 26-29, 2018.

C. Turchi, “sCO2 Power-Related Research at NREL,” National Laboratory Panel Presentation

Supercritical CO2 Power Cycles Symposium, Pittsburgh, PA, March 26-29, 2018.

T. Neises, “Supercritical Carbon Dioxide Recompression Cycle Integration with a Molten Salt 

Power Tower CSP Plant, ASME 2018 Energy Sustainability Conference, Lake Buena Vista, FL, 

June 25-28, 2018.

D. Kesseli, M. Wagner, R. Guédez, and C.S. Turchi, “CSP-Plant Modeling Guidelines and 

Compliance of the System Advisor Model (SAM), SolarPACES 2018, Casablanca, Morocco, 

October 2-5, 2018.
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Path Forward

NREL will continue to support the DOE program with development and improvement of 

analysis tools for estimating the performance and cost of CSP systems. Proposed work 

for future years includes:

• Bottoms up cost analysis for a next-generation parabolic trough collector and heliostat 

subsystems from a U.S.-based developer to be used for SAM solar field updates. 

• Development of a cavity-receiver option for SAM’s molten-salt power tower model and 

extension of the model library to include additional HTFs. Many designs for future hightemperature CSP systems rely on cavity-receiver designs to ensure good thermal 

efficiency. The proposed SAM model and underlying SolarPILOT code will provide the 

user a path to evaluate the efficiency of cavity designs.

• The dispatch optimization code in SAM’s molten-salt power tower model will be 

converted for use in the other models that currently include TES, that is, the Physical 

Trough and Trough Process Heat models. This will allow the benefits of dispatch 

optimization to be extended to the trough-system models.

• The primary metrics for TES performance, e.g., thermal capacity, temperature profile, 

thermal loss rate, etc., will be provided as user input functions or tables to allow 

simulation of performance of novel TES systems.

• Continued support for NREL’s role as the operating agent for SolarPACES Task I. The 

active international sector provides valuable markets for U.S. CSP developers and 

knowledge that can be applied to systems in the United States.

• Development of a general model of the latent-heat storage in SAM’s C++ code base. 

Latent-heat TES offers unique opportunities for efficiency when integrated with the sCO2

Brayton cycle. Develop a CSP system model of a sensible-heat HTF power tower, 

cascaded latent-heat storage, and the reheat recompression cycle.

• Quantify the potential benefits of small-scale (e.g., less than 50 MWe) power tower 

designs featuring factory-fabricated power block and tower designs to drive down the 

cost of next-generation CSP technology deployment.

This scope will be discussed and refined with the DOE CSP program and is subject to 

funding availability and decisions of that office.
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