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.

Contract No. DE-AC36-08GO28308

Acceptance Performance Test 

Guideline for Utility Scale 

Parabolic Trough and Other 

CSP Solar Thermal Systems

Preprint

Mark S. Mehos and Michael J. Wagner 

National Renewable Energy Laboratory

David W. Kearney 

Kearney & Associates

To be presented at SolarPACES 2011

Granada, Spain

September 20-23, 2011

Conference Paper

NREL/CP-5500-52467 

August 2011
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1 

ACCEPTANCE PERFORMANCE TEST GUIDELINE FOR UTILITY 

SCALE PARABOLIC TROUGH AND OTHER CSP SOLAR 

THERMAL SYSTEMS 

Mark S. Mehos1, Michael J. Wagner2, and David W. Kearney3

1

Program Manager, Concentrating Solar Power, National Renewable Energy Laboratory (NREL), 1617 Cole Blvd., Golden, 

CO 80401, USA (303) 384-7458, Mark.Mehos@nrel.gov 

2

Mechanical Engineer, National Renewable Energy Laboratory (NREL), 

1617 Cole Blvd., Golden, CO 80401 (303) 384-7430 

3

Principal, PhD, Kearney & Assoc., 12804 Ober Beach Rd, Vashon WA 98070, USA, (206) 910-3851 

Abstract 

Prior to commercial operation, large solar systems in utility-size power plants need to pass a performance 

acceptance test conducted by the engineering, procurement, and construction (EPC) contractor or owners. In 

lieu of the present absence of ASME or other international test codes developed for this purpose, the National 

Renewable Energy Laboratory has undertaken the development of interim guidelines to provide 

recommendations for test procedures that can yield results of a high level of accuracy consistent with good 

engineering knowledge and practice. Progress on interim guidelines was presented at SolarPACES 2010. 

Significant additions and modifications were made to the guidelines since that time, resulting in a final report 

published by NREL in April 2011. This paper summarizes those changes, which emphasize criteria for 

assuring thermal equilibrium and steady state conditions within the solar field. These criteria were derived 

using NREL’s Solar Advisor Model (SAM), which was modified to run at 5-second time steps to adequately 

capture the transient effects of changes in solar field inlet conditions. In addition to SAM, a model was 

developed that describes the time lag present between the observed delivered energy and instantaneous 

operating conditions. This time lag can result in a mismatch between the measured delivered energy and the 

delivered energy predicted by a solar field performance model. 

1. Introduction 

In May 2011 NREL published guidelines that provide recommendations for performance acceptance test 

procedures for utility-scale parabolic trough solar fields with an emphasis on thoroughly understanding the 

uncertainty associated with the test results [1]. The scope of the performance acceptance test guidelines is 

restricted to the solar field and heat transfer fluid (HTF) system as described in Fig. 1 below. 

Figure 1. Schematic of Solar System Boundary 
2 

While the interim guidelines released in 2010 described the methodology for determining the uncertainty of 

data collected from a short term performance test [2], subsequent internal NREL discussions and review by a 

community of stakeholders, including the SolarPACES Task I members, identified the need for more 

quantitative description of the impact of variations in test conditions on the uncertainty and thermal stability, 

including the solar field inlet temperature, incident solar radiation, and ambient weather conditions. 

2. Description of Short-Term Steady-State Tests 

Test Objectives: The objective of the short duration steady-state tests is to accurately measure both the solar 

field power (capacity) and efficiency based on a series of tests run under clear sky conditions. The thermal 

power delivered from the solar field is computed from: 

ܲ௠௘௔௦௨௥௘ௗ ൌ ݉ሶ C୮ ൫T୦୶,୧୬ െ T୦୶,୭୳୲൯ (1) 

where P୫ୣୟୱ୳୰ୣୢ is the calculated solar thermal power, mሶ is the HTF mass flow rate, C୮ is the temperature 

weighted specific heat of the heat transfer fluid (HTF), T୦୶,୧୬ is the HTF average bulk inlet temperature to 

solar heat exchanger train, and T୦୶,୭୳୲ is the HTF average bulk outlet temperature at the exit of the solar heat 

exchanger train (also at the inlet of the tough solar field). 

The solar thermal efficiency can be based on either DNI or aperture normal insolation (ANI) in the 

denominator, though the latter is preferred from a physical standpoint. The thermal efficiency based on ANI 

is calculated from 

ߟ௔௘௠௦௨௥௘ௗ ൌ ௉೘೐ೌೞೠೝ೐೏

஽ேூ·ୡ୭ୱ஘·஺ೌ೛೐ೝ೟ೠೝ೐

 (2) 

where િ܌܍ܚܝܛ܉܍ܕ is the thermal efficiency, ANI is the vector of direct normal insolation normal to the trough 

aperture, ܍ܚܝܜܚ܍ܘ܉ۯ is the solar field aperture area in tracking mode during the test period, and θ is the solar 

incidence angle associated with data collected during a test run. 

Test Uncertainty: Ascertaining the test uncertainty around the measured power and efficiency is an 

important element within any solar field acceptance test and has been described in detail in [1, 2]. The ASME 

in particular has placed critical importance on test uncertainty analyses of all measurements and calculations 

associated with performance test codes and the Guidelines have drawn heavily on the test uncertainty 

methodology required for all ASME performance test codes [3]. 

The expression for the standard measurement uncertainty of a calculated result, ࡾ࢛, based on multiple error 

sources can in many cases be calculated from the root-sum-square of the total uncertainty of the individual 

systematic and random uncertainties of a result ܀܊ and ܀܁ respectively. 

ݑோ ൌ ሾሺܾோሻଶ ൅ ሺܵோሻଶሿ

ଵ/ଶ (3)

where bR is the systematic standard uncertainty of a result and SR is the random standard uncertainty of a 

result as calculated by the following equations for bR and SR where the function R is defined as above for 

power or efficiency. 

ܾோ ൌ ൤∑ ቀ డோ

డ௑ത೔

ܾ௑ഢ തതതቁ

ଶ ூ

௜ୀଵ ൨

ଵ/ଶ

 and ܵோ ൌ ൤∑ ቀ డோ

డ௑ത೔

ݏ௑ഢ തതതቁ

ଶ ூ

௜ୀଵ ൨

ଵ/ଶ

 (4)

For the equations above, bXഥ is defined as the systematic standard uncertainty of a component and sXഥ is the 

random standard uncertainty of the mean of N measurements [4]. It is important to note that the “standard” 

uncertainties described above imply that the calculated result will capture the true result within a 68% 
3 

confidence level (one standard deviation). Typically a confidence level of 95% (two standard deviations) is 

desired by the performance test engineer. For this case, the expanded uncertainty in the result is given by 

ܷோ,ଽହ ൌ 2ݑோ (5)

Tables 1 and 2 below provide a summary of results derived a pretest uncertainty analysis for the power 

delivered from a “typical” parabolic trough solar field. Similar tables generated for solar field efficiency 

calculations are provided in the guidelines [1] where the methodology used for the pre-test analysis is 

described in detail. A similar pre-test analysis would be undertaken with specific instrumentation proposed or 

selected for the system under test. However, the systematic and random uncertainties of measurement 

parameters given below are representative of what may occur in a typical field installation. Note that the 

systematic uncertainty dominates the result, providing 99.6% of the total uncertainty. With respect to the 

individual contributors, all are of the same order with the HTF specific heat contributing approximately 34% 

of the total and each of the others about 22%. 

Table 1. Table of Data – Solar Field Power 

Independent Parameters 

Parameter Information 

(in Parameter Units) 

Uncertainty 

Contribution of 

Parameters to the 

Result 

(in Results Units 

Squared) 

Symbol Description Units 

Nominal 

Value 

Standard 

Deviation 

 

Ni

Absolute 

Systematic 

Standard 

Uncertainty 

ࢄ࢈ଙ തതത

Absolute 

Random 

Standard 

Uncertainty 

ࢄࡿଙ തതത

Absolute 

Sensitivity 

߲ܴ

߲ܺത௜

 Absolute 

Systematic 

Standard 

Uncertainty 

Contribution 

൤

߲ܴ

߲ܺത௜

ࢄ࢈ଙ തതത൨

૛

Absolute 

Random 

Standard 

Uncertainty 

Contribution 

൤

߲ܴ

߲ܺത௜

ࢄࡿଙ തതത൨

૛

 

m 

Mass flow 

rate kg/s 1200 3.5 180 12 0.3 255.4 9395941 4567 

Cp 

HTF 

specific 

heat 

kJ/kgK 2.48 .007 30 0.031 0.0013 1236000.0 14681159 24217 

Thxin 

Hot HTF 

temperature oC 393 1.2 180 1.0 0.09 2976.0 8856576 71065 

Thxin 

Cold HTF 

temperature oC 290 1.1 180 1.0 0.08 2976.0 8856576 59959 

 

Table 2. Summary of Data - Solar Field Power 

Symbol Description Units 

Calculated 

Value, 

R 

Absolute 

Systematic 

Standard 

Uncertainty, 

bR 

Absolute 

Random 

Standard 

Uncertainty, 

SR 

Combined 

Standard 

Uncertainty, 

uR 

Expanded 

Uncertainty 

of the 

Result, UR,95 

Expanded 

Uncertainty of 

the Result, 

UR,95 (%)

P Solar Field 

Power kJ/s 306528 5894 400 5908 11816 3.9% 

3. Stabilized Test Conditions and Thermal Equilibrium 

To achieve successful completion of a test run, the variations in the key test parameters should be low 

enough to contribute only in a minor way to the uncertainty band in the results. The solar system must be in a 

stable thermal condition and stable test condition prior to testing. This requires stable characteristics in the 

solar field inlet temperature and outlet temperature. Stability of the inlet and outlet solar field temperature 

implies that the HTF mass flow is adjusted adequately to accommodate changes in ANI over the course of 

the short duration test. Once thermal equilibrium and test condition stability have been reached, the criteria 

for valid test runs are primarily based on the level of uncertainty as described in Section 2 above. 
4 

Stability of Test Conditions: Table 3 shows an illustrative set of stabilization criteria for these conditions 

based on the influence of the variability of the test parameter on the total uncertainty of the test results. The 

variability described in Table 3 is defined as the standard deviation of the mean of N measurements of a 

parameter taken of a test run divided by the average value of the parameter over the test run period. Based on 

the examples provided by Tables 1 and 2, a combination of the allowable variations given in Table 3 will 

result in a negligible increase in the total uncertainty of the result. Final stabilization criteria for a specific 

project will be strongly influenced by the design of the solar system and associated instrumentation, and 

finally determined by the agreements between the testing parties. 

Table 3. Example Stabilization Criteria for Short-Duration Steady State Power Tests of a 

Utility-Scale Parabolic Trough Solar Field 

Parameter Allowable variability over test period 

ݏ௑ത⁄xത (%)

HTF volumetric flow rate, m3/s 0.5% 

ANI, W/m2 0.5% 

Solar field inlet temperature 0.2% 

Solar field outlet temperature 0.2% 

These criteria are to be applied to evaluate test conditions for stability. In general the potential test period for 

any given day will occur between 9 a.m. and 4 p.m. Observation of data collected from several operating 

plants indicates that the variability will be much smaller than the values described in Table 3. For example, 

5-second ANI data collected over a 15-minute period (180 data points) varied by approximately ± 2.5 W/m2

from an average value of 960 W/m2

. The related standard deviation of the data was 1.46 W/m2. For this 

instance, the variability as defined in Table 4-1 is calculated by dividing the standard deviation of the mean 

(1.46/√180) by the average ANI value of 960, resulting in a variability of 0.01%. 

Thermal Equilibrium: An estimate of the time necessary to establish thermal equilibrium within a typical 

solar field was derived using NREL’s Solar Advisor Model. The model was run using 5-second time steps to 

capture the impact of transient effects resulting from a sudden change in solar field inlet temperature. Figure 

2 describes the result of an analysis in which the error in a 15-minute average measurement of solar field 

power, caused by non-equilibrium conditions, is estimated based on varying step changes in inlet 

temperature. In the figure, Qsf,abs is the power (15-minute moving average) absorbed by the solar field less all 

thermal losses from the receivers, solar field, and header piping. Qsf,del is the average power delivered to the 

steam generator. If the solar field is not in equilibrium, e.g., the temperatures of the solar field HTF and the 

piping/insulation are still changing over time, the delivered power will be less than the absorbed power 

because some of the excess energy is used to heat the solar field HTF and piping. 
5 

Figure 2. Error Due to Non-equilibrium Resulting from Step Change in Solar Field Inlet Temperature 

Using Figure 2 as a guide, we can estimate the time required to minimize the error associated with nonequilibrium conditions to an acceptable level. For example, to limit the error to 0.1% for a 1oC step change 

in inlet temperature, approximately 45 minutes is required to establish adequate equilibrium beyond the 

initial upset, depending on pipe and insulation mass. 

4. Additional Uncertainty Considerations Resulting from Changing ANI 

Under conditions of a continuously changing ANI, which occurs even during a relatively short-term period, 

achieving true steady-state conditions is not possible and therefore the impact of varying ANI on an 

acceptance test must be well understood. 

As discussed previously, the solar field acceptance test seeks to compare the measured solar field thermal 

output to predicted output using a performance model. Consider this relationship as shown in Eqn. 6, where 

the measured energy on the left as described earlier in Eqn. 1 is compared to the model-projected energy on 

the right, based on the measured ANI and, in this case, thermal efficiency projected by the model. Any 

disparity between the left hand side (LHS) and the right had side (RHS) of the equation indicates 

disagreement between observed and modeled thermal power.

݉ሶ ܥҧ

௣ ൫ܶ௛௫,௜௡ െ ܶ௛௫,௢௨௧൯ ൌ ܣܰܫ · ܣ௥௘௣௔௧௨௥௘ · ߟ௧௛௘௥௠௔௟,௠௢ௗ௘௟ (6) 

The variables in this equation are identical in definition to those defined previously in equations 1 and 2. The 

percent disparity between instantaneous and observed energy can be expressed as (LHS/RHS – 1) x 100%, 

and results reported in the following discussion make use of this definition. 

Because parabolic trough systems require extensive piping, any energy absorbed within the HTF must often 

travel long distances before returning to the power cycle. Because changes in ANI levels will not be 

observed at the outlet of the solar field piping immediately, some time lag is always present between the 
observed delivered energy and the instantaneous operating conditions. To understand the impact of time lag 

on transient performance, NREL developed a model that considers the flow of HTF through a representative 

solar field collector piping system [5]. The NREL model tracks a large number of discrete “plugs” of HTF as 

they flow through the piping system at a constant velocity. Each plug of HTF interacts thermally with piping 

and insulation along the flow path, and incorporates a residence time at each calculation node to mimic the 

actual time delay observed in real piping systems. 

Simulation results: The model described above was used to analyze the impact when the ANI slowly 

increases or decreases over the course of the acceptance test, causing a continual mismatch between the 

observed delivered thermal power and the modeled incident power. This condition was simulated by 

applying a gradient to the ANI value once the modeled system reached initial steady-state conditions. To 

estimate gradients observed during different testing scenarios, we derived representative 30-minute ANI 

gradients for a summer, spring, and winter day using ANI data collected at NREL’s Solar Radiation Research 

Laboratory located in Golden, Colorado. Figures 3 and 4 show the results of varying the ANI rates for clear 

days in December and March. In these plots, ‘dt’ is the length of time variation since the ANI perturbation 

was applied, and the inset is an expanded view of the period of time after 50 minutes from the start of the 

simulation. 

Figure 3. Transient response of a steadily increasing ANI (typical December) 

in a system with ideal mass flow rate control. 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 20 40 60 80 100

Thermal Power (MW)

Time (min)

Whole Field: ANI of 850 + dt * 0.008 W/m2-s 

HTF Energy Balance

Incident Energy

2.40

2.45

2.50

2.55

2.60

50 60 70 80 90 100

Thermal Power (MW)

Time (min)

SS Disp. = -0.45%
 

Figure 4. Transient response of a steadily increasing ANI (typical March) 

in a system with ideal mass flow rate control. 

The resulting disparities for each case are presented in Table 4, assuming a system with ideal mass flow rate 

control to maintain a constant outlet temperature at the solar field exit. For comparison, the ANI gradient that 

corresponds to a steady-state disparity of 1% is also included. Note that these results are dependent on the 

base ANI level—850 W/m2

 in this case—and the geometry of the solar field. An increase in the incident 

thermal energy will cause a corresponding decrease in the steady-state disparity. 

Table 4. Summary of Results for Several Representative ANI Gradients 

Case Description 

Whole field Subfield 

Gradient

W/m2-s 

Disp.

% 

Gradient 

W/m2-s 

Disp. 

% 

March resource profile 0.00214 0.16 0.00214 0.12 

June resource profile 0.00743 0.40 0.00743 0.24 

December resource profile 0.00848 0.45 0.00848 0.27 

1% limiting case 0.02080 1.00 0.03900 1.00 

Note that all the errors from the equilibrium effects shown here fall below 0.5%. 

One potential pitfall in conducting acceptance testing during off-peak seasons is that the solar resource is 

typically significantly lower than during the summer months. While the DNI resource is still high in winter, 

the cosine (Θ) effect reduces the ANI resource considerably. During the January period, the ANI is 

approximately half of the 850 W/m2

 assumed throughout the analysis presented above. A reduced thermal 

resource results in a reduced field flow rate, and all transient effects are correspondingly drawn out. 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 20 40 60 80 100

Thermal Power (MW)

Time (min)

Whole Field: ANI of 850 + dt * 0.002 W/m2-s 

QSF [MW]

QIN [MW]

2.40

2.45

2.50

2.55

2.60

50 60 70 80 90 100

Thermal Power (MW)

Time (min)

SS Disp. = -0.16%
8 

5. Conclusion 

Criteria have been discussed and analyzed to assure thermal equilibrium and steady state conditions within 

the solar field during performance acceptance testing for stability of test conditions (specifically the HTF 

flow rate, the ANI solar resource, and solar field inlet and outlet temperatures), for sufficient pre-test time 

durations, and for changes in the solar resource during testing. In each condition, for a representative case 

example criteria are provided that are achievable in practice and should result in acceptably low uncertainty 

values or low test errors. For a given site or project, it is recommended that the testing parties examine these 

conditions through pre-test analyses using specific project parameters, and agree upon acceptable 

stabilization criteria in a detailed test plan developed prior to the testing period. 

Acknowledgements 

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

the National Renewable Energy Laboratory. 

References 

[1] D. Kearney, “Utility-Scale Parabolic Trough Solar Systems – Performance Acceptance Test Guidelines,” 

NREL Report No. SR-5500-48895, http://www.nrel.gov/docs/fy11osti/48895.pdf, May, 2011. 

[2] Kearney, D., Mehos, M., (2010), Development of Performance Acceptance Test Guidelines for Large 

Commercial Scale Parabolic Trough Solar Fields, SolarPACES 2010 Symposium, Sep. 21–24, 2010, 

Perpignan, France. 

[3] ASME PTC 19.1 - 2005 Test Uncertainty. 

[4] R.H. Dieck, “Measurements Uncertainty, Methods and Applications,” Fourth Edition, ISA, 2007. 

[5] Wagner, M., Mehos, M., Kearney, D., McMahan, D., “Modeling of a Parabolic Trough Solar Field for 

Acceptance Testing: A Case Study” , Proceedings of ASME 2011 5th International Conference on 

Energy Sustainability & 9th Fuel Cell Science, Engineering, and Technology Conference, Aug. 7-10, 

2011, Washington D.C.