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

Utility-Scale Parabolic Trough 

Solar Systems: Performance 

Acceptance Test Guidelines 

April 2009 — December 2010

David Kearney

Kearney & Associates 

Vashon, Washington

Subcontract Report

NREL/SR-5500-48895

May 2011 
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

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

National Renewable Energy Laboratory

1617 Cole Boulevard

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Utility-Scale Parabolic Trough 

Solar Systems: Performance 

Acceptance Test Guidelines 

April 2009 — December 2010

David Kearney

Kearney & Associates 

Vashon, Washington

NREL Technical Monitor: Mark Mehos

Prepared under Subcontract No. AXL-9-99218-01

Subcontract Report

NREL/SR-5500-48895

May 2011
NOTICE

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iii

Acknowledgements 

This report has benefited considerably during its development by careful and frequent scrutiny 

from an active and experienced Advisory Committee and from critique by many stakeholders in 

the final review cycles. Throughout this process, the guidance, contributions and co-authorship 

of NREL Concentrating Solar Power Program Manager Mark Mehos have strengthened and 

honed the end product. Through his additional responsibility as Operating Agent of the 

SolarPACES Task 1 Committee on System Analysis, which is also partially focused on 

acceptance testing, members of that task group also participated in the review cycle. We 

sincerely thank all of the above reviewers for their time and effort.

The Advisory Committee and several others deserve special mention for their many 

contributions during this project and travel to NREL for periodic meetings. They are Gary Aron 

and Ryan Bowers (Worley Parsons), Geoff Baxter (NextLight), Georg Brakmann and Mathias 

Wiemann (Fichtner Solar), Gilbert Cohen (Eliasol), Jim Doubek and Todd Eagleston (NV 

Energy), Ray Dracker and Jake McKee (Solar Millennium), Ronald Flood and Phil Smithers

(APS), Michael Henderson (R.W. Beck), Ulf Herrmann (Flagsol), Chris Jensen and Marcus 

Weber (Fluor Power), Chuck Kutscher (NREL), Hank Price and Bruce Kelly (Abengoa Solar), 

and Kurt Westermann (Black & Veatch). The work of Mike Wagner (NREL) on modeling of 

equilibrium effects in the solar system has been of particular value. Finally, the excellent support 

of Teri Spinuzzi of NREL is gratefully acknowledged. 

David Kearney

Kearney & Associates
iv

Preface 

The purpose of these Guidelines is to provide recommendations for acceptance test guidance for 

large trough solar systems that can yield results of a high level of accuracy consistent with good 

engineering knowledge and practice. The recommendations have been developed under a 

National Renewable Energy Laboratory (NREL) subcontract and reviewed by stakeholders 

representing concerned organizations and interests throughout the concentrating solar power 

(CSP) community. These Guidelines recommend certain methods, instrumentation, equipment 

operating requirements, and calculation methods. When tests are run in accordance with these 

Guidelines, we expect that the test results will yield a valid indication of the actual performance 

of the tested equipment. But these are only recommendations—to be carefully considered by the 

contractual parties involved in the acceptance tests—and we expect that modifications may be 

required to fit the particular characteristics of a specific project.

These Guidelines do not specify means to compare test results to contractual guarantees. 

Therefore, we expect that the parties to a commercial test will address that issue and agree before 

starting the test and signing the contract on the method to be used for evaluating the test results. 

The approach taken in these Guidelines is that the measured test results will be compared to 

projections from a performance model based on the measured meteorological conditions and 

agreed-upon solar system characteristics.

The scope of the solar system discussed in these Guidelines does not include a thermal energy 

storage (TES) system. But even if the scope did include a TES system, the methods of testing the 

solar field/heat transfer fluid system itself would be similar, if not identical, to the guidance 

given in this document. A separate set of tests and objectives would be required for TES system 

acceptance.

A recently formed American Society of Mechanical Engineers (ASME) committee is working on 

Performance Test Code1

 52 – Concentrating Solar Power Plants that should supplant this 

Guideline within several years. This work will be provided to that committee as a reference for 

its deliberations.

 

1 Often abbreviated as PTC.
v 

Definitions and Description of Terms

For further reference, ASME PTC 2 – Definitions and Values contains definitions of terms and 

values of physical constants and conversion factors common to equipment testing and analysis. 

Definitions

• acceptance test: The evaluating action(s) to determine if a new or modified piece of 

equipment satisfactorily meets its performance criteria, permitting the purchaser to "accept" 

it from the supplier.

• accuracy: The closeness of agreement between a measured value and the true value. 

• aperture area: The projection on the aperture plane of the active mirror collecting area of a 

parabolic trough, i.e., projection of the reflective surface less gaps between the mirror panels.

• aperture of parabolic trough: The width between the reflector edges on a plane across the 

aperture of a parabolic reflector (the aperture plane).

• base reference conditions: The values of all the external parameters, i.e., parameters outside 

the test boundary to which the test results are corrected. 

• bias error: See systematic error. 

• calibration: The process of comparing the response of an instrument to a standard instrument 

over some measurement range and adjusting the instrument to match the standard if 

appropriate. 

• incidence angle (θ): The angle between a direct ray from the sun and the aperture plane of 

the parabolic trough collector (defined to be 0 degrees when the rays are normal to the 

aperture plane). The effect of incidence angle on performance is contained in the incidence 

angle modifier, or IAM. 

• influence coefficient: See sensitivity: the ratio of the change in a result to a unit change in a 

parameter. 

• instrument: A tool or device used to measure physical dimensions of length, thickness, width, 

weight, or any other value of a variable. These variables can include: size, weight, pressure, 

temperature, fluid flow, voltage, electric current, density, viscosity, and power. Sensors are 

included that may not, by themselves, incorporate a display but transmit signals to remote 

computer-type devices for display, processing, or process control. Also included are items of 

ancillary equipment directly affecting the display of the primary instrument, e.g., an ammeter 

shunt. Also included are tools or fixtures used as the basis for determining part acceptability.

• measurement error (δ): The true, unknown difference between the measured value and the 

true value. 

• parties to a test: Those persons and companies contractually interested in the results. 

• precision error: See random error. 

• primary variables: Those variables used in calculations of test results. Further classified as:

 - Class 1: primary variables have a relative influence coefficient of 0.2 or greater
vi

 - Class 2: primary variables have a relative influence coefficient of less than 0.2 

 - Refer to PTC 19.1 - Test Uncertainty for the determination of relative sensitivity

coefficients.

• random error (ε): Sometimes called precision; the true random error, which characterizes a 

member of a set of measurements. ε varies in a random, Gaussian-normal manner from 

measurement to measurement.

• random uncertainty: Estimate of ± limits of random error within a defined level of 

confidence. Often given for 2-σ (2 standard deviations) confidence level of about 95%.

• rated solar thermal design power (or capacity): The level of solar thermal output power 

from the solar system that will drive the turbine cycle at design electrical load.

• secondary variables: Variables that are measured but do not enter into the calculation. 

• sensitivity: See influence coefficient; the ratio of the change in a result to a unit change in a 

parameter. 

• serialize: Means that an instrument has been assigned a unique number and that number has 

been permanently inscribed on or to the instrument so that it can be identified and tracked. 

• soiling factor: A factor that describes the dirtiness, or soiling, of the reflective surface (e.g., 

glass mirrors or silvered film) of the parabolic trough collectors; specifically, at any given 

time, the actual reflectivity normalized by the design or as-new reflectivity. 

• solar multiple: Equals [ (solar field aperture area) / (solar field aperture area sufficient to 

deliver the design solar thermal output power at stated conditions of DNI and incident angle, 

e.g., 950 W/m2 at 0 degrees incidence angle) ]. 

• solar system thermal efficiency: Ratio of the solar thermal power output of the solar system 

normalized by the product of the incident direct beam radiation and the total aperture area of 

the solar field. See Equation (Eqn.) 3-2. 

• systematic error (β): Sometimes called bias; the true systematic or fixed error, which 

characterizes every member of any set of measurements from the population. It is the 

constant component of the total measurement error (δ). 

• systematic uncertainty (B): An estimate of the ± limits of systematic error with a defined 

level of confidence (usually 95%). 

• test boundary: Identifies the energy streams required to calculate corrected results. 

• test reading: One recording of all required test instrumentation. 

• test run: A group of test readings that satisfy the stated criteria for test conditions, e.g., with 

regard to thermally stability. 

• traceable: Records are available demonstrating that the instrument can be traced through a 

series of calibrations to an appropriate ultimate reference such as National Institute for 

Standards and Technology (NIST).

• ullage: The unfilled space in a container of liquid.
vii

• uncertainty (U): ±U is the interval about the measurement or result that likely contains the 

true value for a certain level of confidence.

Abbreviations and Acronyms

ANI aperture normal insolation (DNI vector normal to aperture plane) 

COD commercial operation date

CSP concentrating solar power 

DSC digital scanning calorimetry

DHI diffuse horizontal irradiance

DNI direct normal insolation (direct [beam] solar radiation on plane normal to sun’s ray) 

EPC engineering, procurement, and construction contractor

Eqn. equation 

GHI global horizontal irradiance

HCE heat collection element or receiver tube or receiver

HTF heat transfer fluid

HX heat exchanger

IAM incidence angle modifier – optical characteristic affecting performance 

IPP independent power producer 

KJOC Kramer Junction Operating Company 

L/D length/diameter ratio

LOC local controller

MOV motor-operated valves 

mRad milliradian – a measure of optical error

NREL National Renewable Energy Laboratory 

O&M operations and maintenance 

OJT on-the-job training 
viii

PTC performance test code

RSR rotating shadowband radiometer 

RTD resistance temperature detectors

SAM System Advisor Model, produced by NREL, Sandia National Labs, and DOE 

SCA solar collector assembly

SEGS solar electricity generating systems

SF solar field

SFC solar field control system

SM solar multiple

SRC steam Rankine cycle

STG steam turbine generator set

TC thermocouple 

TES thermal energy storage 

TP technology provider
ix

Table of Contents 

Acknowledgements ...................................................................................................................................iii

Preface ........................................................................................................................................................iv

Definitions and Description of Terms....................................................................................................... v

Definitions..................................................................................................................................v

Abbreviations and Acronyms ................................................................................................. vii

List of Figures ............................................................................................................................................xi

List of Tables ..............................................................................................................................................xi

Executive Summary ..................................................................................................................................xii

1 Introduction ........................................................................................................................................... 1

1.1 Technology Description .......................................................................................................1

1.2 Need for Guidelines .............................................................................................................4

2 Objectives and Scope .......................................................................................................................... 5

2.1 Parties to the Acceptance Tests ............................................................................................5

2.2 Example Project Scenario ....................................................................................................5

2.3 Pass / Fail Criteria ................................................................................................................5

2.4 Readiness for Acceptance Tests...........................................................................................5

2.5 Unique Characteristics of Solar System Performance Tests and the Role of a 

Performance Model ..............................................................................................................6

2.6 Utility-Scale Solar System Performance Acceptance Tests ................................................7

2.7 Solar System Boundaries .....................................................................................................7

3 Test Definitions..................................................................................................................................... 9

3.1 Performance Acceptance Test Guidelines ...........................................................................9

3.2 Short-Duration Steady-State Thermal Power Test ..............................................................9

3.2.1 Introduction and Purpose .........................................................................................9

3.2.2 Solar Resource and Weather ....................................................................................9

3.2.3 Specific Test Objectives ........................................................................................10

3.2.4 Calculations............................................................................................................10

3.2.4.1 Delivered Power Calculation ........................................................................10

3.2.4.2 Efficiency Calculation ...................................................................................11

3.2.4.3 HTF Solar Field Inlet and Outlet Temperatures ............................................11

3.2.4.4 HTF Physical Properties ................................................................................11

3.3 Multi-Day Continuous Energy Test ...................................................................................12

3.4 Performance Model Projection and Comparison to Measured Results .............................12

3.5 HTF and Solar System Parasitic Power Consumption ......................................................13
x 

4 Test Methods.......................................................................................................................................14

4.1 Introduction ........................................................................................................................14

4.2 Solar Field Test Conditions ...............................................................................................15

4.2.1 Solar Field Area, Thermal Power Output, and Design Capacity ...........................15

4.2.2 Solar Field Reflectivity ..........................................................................................15

4.2.3 Seasonal Example of Hourly ANI Variations ........................................................17

4.3 Criteria for Adequate Test Durations and Conditions .......................................................19

4.4 Criteria for Valid Test Results ...........................................................................................21

4.4.1 Thermal Equilibrium and Stabilized Test Conditions ...........................................21

4.4.2 Repeatability ..........................................................................................................21

4.4.3 Impact of ANI Variation on Trough Transient Behavior ......................................22

5 Instrument Selection..........................................................................................................................27

5.1 Required Data ....................................................................................................................27

5.2 Reference Solar Field Configuration and Measurement Conditions .................................27

5.3 Comments on Instruments and Measurements ..................................................................29

5.4 Temperature Measurement ................................................................................................30

5.5 Flow Measurement .............................................................................................................32

5.5.1 Velocity Profile ......................................................................................................32

5.5.2 Upstream/Downstream Requirements for Length/Diameter Ratio ........................37

5.6 Direct Normal Insolation ...................................................................................................37

5.6.1 Components of Solar Radiation .............................................................................37

5.6.2 Measurement Options ............................................................................................38

5.7 HTF Physical Properties ....................................................................................................39

5.8 Mirror Reflectivity .............................................................................................................39

6 Evaluation of Measurement Uncertainty..........................................................................................41

6.1 Measurement Uncertainty ..................................................................................................41

7 Comments on Test Procedures and Plan ........................................................................................47

7.1 Test Plan.............................................................................................................................47

7.1.1 Example of Contract Agreements Prior to Formulation of a Test Plan .................48

7.1.2 Some Elements of a Test Plan ...............................................................................48

7.1.3 Sample Test Report Outline ...................................................................................49

7.2 Further Observations ..........................................................................................................50

7.2.1 General ...................................................................................................................50

7.2.2 Summary of Agreements To Be Made Between Test Parties ................................51
xi

List of Figures

Figure ES-1. Schematic of solar system boundary and performance model ................................ xii

Figure 1-1. SEGS IV, Kramer Junction, California. (Photo from Luz International Ltd., circa 

1990) ..........................................................................................................................................1

Figure 1-2. Trough collector schematic. (Illustration from Flagsol GmbH) ..................................2

Figure 1-3. Plant operation based on utility load profile showing how TES can be used to 

better match the CSP plant’s output to utility needs. (Graphs from Abengoa Solar) ................3

Figure 2-1. Trough energy input. .....................................................................................................7

Figure 2-2. Schematic of solar system boundary and performance model. .....................................8

Figure 4-1. Patterns from ANI analysis: (a) during June, March, and December for the entire 

day, (b) for the daytime period between 8 a.m. and 4 p.m., and (c) the percent changes in 

the ANI over the previous 30 minutes during the daytime period for these three months. 

Location: Golden, Colorado. (Courtesy of NREL) ..................................................................18

Figure 4-2. Schematic representation of HTF system elements. ...................................................20

Figure 4-3. Location of two possible test boundaries for the acceptance testing procedure. .......23

Figure 4-4. Transient response of a steadily increasing ANI (typical December) in a system 

with ideal mass flow rate control. ............................................................................................24

Figure 4-5. Transient response of a steadily increasing ANI (typical March) in a system with 

ideal mass flow rate control. ....................................................................................................25

Figure 5-1. Fully developed velocity profile in a pipe. (Courtesy of NREL) ...............................33

Figure 5-2. Components of solar radiation. (Courtesy of NREL) ...............................................37

List of Tables

Table 4-1. Example Stabilization Criteria for Short-Duration Steady State Power Tests of a 

Utility-Scale Parabolic Trough Solar Field ..............................................................................19

Table 4-2. Example Conditions for Testing Periods, Durations and Data Points for ShortDuration Steady State Power Tests ..........................................................................................21

Table 4-3. Summary of Results for Several Representative ANI Gradients .................................25

Table 5-1. Parameters Required for Determining Thermal Power Output and Efficiency 

of the Solar System ..................................................................................................................28

Table 5-2. Temperature Measurement Devices .............................................................................31

Table 5-3. HTF Flow Measurement Devices .................................................................................34

Table 5-4. Estimated Direct Normal Sub-Hourly Measurement Uncertainties (Percent) .............38

Table 6-1. Table of Data – Solar Field Power ...............................................................................45

Table 6-2. Summary of Data – Solar Field Power .........................................................................45

Table 6-3. Table of Data – Solar Field Efficiency .........................................................................46

Table 6-4. Summary of Data – Solar Field Efficiency ..................................................................46
xii 

Executive Summary 

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. The 

Guidelines contained here are specifically written for parabolic trough collector systems with a 

heat-transport system using a high-temperature synthetic oil, but the basic principles are relevant 

to other CSP systems. The fundamental differences between acceptance of a solar power plant 

and a conventional fossil-fired plant are the transient nature of the energy source and the 

necessity to use a performance projection model in the acceptance process. These factors bring 

into play the need to establish methods to measure steady-state performance, comparison to 

performance model results, and the reasons to test, and model, multi-day performance within the 

scope of the acceptance test procedure. The power block and balance-of-plant are not within the 

boundaries of this Guideline. The current Guideline is restricted to the solar thermal performance 

of parabolic trough systems, and has been critiqued by a broad range of stakeholders in 

concentrating solar power development and technology. 

The solar system boundary is defined in this case to be the shutoff valves in the heat transfer 

fluid (HTF) piping at the inlet and outlet of the solar heat exchangers(s), as shown in Figure ES1. The solar system comprises the solar field and HTF subsystems. The boundary between these 

subsystems is defined as the shutoff valves on the interconnection piping for each loop to the 

cold HTF header and hot HTF header, respectively, in each segment of the solar field. The scope 

of these Guidelines does not include a thermal storage system. 

Figure ES-1. Schematic of solar system boundary and performance model 
xiii 

Performance acceptance tests are to be conducted with acceptable accuracy according to clearly 

stated procedures agreed upon between the parties involved. These Guidelines deal with issues 

specific to utility-scale parabolic trough solar fields. However, applicable performance test codes 

(PTCs) developed by ASME for other types of energy systems have very useful information for 

developing a detailed test plan, and are appropriately cited in these Guidelines. For example, 

applicable PTCs provide a general framework and information about instrumentation, data 

acquisition, data reduction, testing procedures, uncertainty levels, and test reports. These 

Guidelines focus particularly on the issues unique to the parabolic system shown in Figure ES-1. 

The primary objectives of the tests are the following: 

a. Power Test—Measure solar thermal power output (including the design-point solar 

thermal power, or capacity, if contractually required). 

b. Efficiency Test—Measure solar thermal efficiency at the same test conditions. 

c. Compare (a) and (b) to performance model projections. 

d. Monitor bulk solar field inlet and outlet temperatures at heat-exchanger inlet(s) and 

outlet(s), and/or at subfield boundaries. 

e. Measure HTF system and solar system parasitic power consumption. 

Two primary types of test runs are to be conducted. The first⎯the Short-Duration Steady-State 

Thermal Power Test⎯measures the thermal power output and thermal efficiency of the solar 

system under clear-sky conditions over a short period during which thermal equilibrium and 

stable steady-state conditions exist, and compares the measured results to performance model 

projections for those parameters. Important issues related to both thermal equilibrium and 

stabilized test conditions are dealt with in considerable detail in the Guidelines. 

The second⎯the Multi-Day Continuous Energy Test⎯gathers continuous daily thermal energy 

output (integrated power output using Eqn. 3-1; efficiency from Eqn. 3-2) and compares the 

results to projections from a performance model. Both clear-sky and partly cloudy conditions 

are acceptable. Additionally, the functionality of the solar system should be observed with regard 

to such items as daily startup, normal operation, and shutdown. 

Test methods are provided for both the short-term thermal power test and multi-day continuous 

energy test. Of special importance are the criteria that must be satisfied by the short-term power 

test. The recorded data can be viewed as having two components⎯the actual test measurements 

and the uncertainty interval associated with those measurements and other test conditions. Both 

are closely examined within these Guidelines, especially the magnitude of uncertainty in the 

results, and recommendations made on acceptable limits. The uncertainties in the power and 

efficiency results are influenced in several ways by the constantly changing energy input to the 

solar field, captured here in the aperture normal insolation (ANI) term. 

The solar system must be in a stable thermal condition (thermal equilibrium) and stable test 

condition (power measurement) prior to testing. This requires stable characteristics in the: 

• solar field inlet temperature 

• solar field outlet temperature 
xiv 

• mass flow rate change stabilized with change in ANI. 

These three conditions are dictated by the following influences, respectively: 

• power block stability 

• solar field HTF flow control system 

• time duration and magnitude of the ANI gradient. 

Once thermal equilibrium and test condition stability have been reached, the criteria for valid 

power and efficiency measurements (i.e., valid test runs) are primarily based on the level of 

uncertainty in the test results calculated using standard practice. Systematic uncertainties are the 

dominant consideration. 

However, and very important, it must be recognized that the purpose of these Guidelines is to 

provide information so that the testing parties can settle on project-specific, agreed-upon criteria 

and other test issues important to the overall purpose of the tests. For any given project, the 

performance acceptance tests will be conducted in accordance with a Test Plan written by the 

testing parties. The intent of these Guidelines is to provide insights into the issues and test 

methods that are critical to formulating a valid Test Plan, and to lay the groundwork for accurate 

test results. 
1 

1 Introduction 

1.1 Technology Description 

Parabolic trough systems with a high-temperature heat transfer fluid (HTF) are currently the 

most-proven concentrating solar power (CSP) technology because of a long commercial 

operating history starting in 1984 with the Solar Electric Generating System (SEGS) plants in the 

Mojave Desert of California. Consisting of nine plants with capacities ranging from 15 to 80 

MWe and a cumulative total 354 MWe, the SEGS plants continue to operate and demonstrate 

consistent and reliable performance. In the last several years, as a direct result of new and 

effective policies, project development activity in commercial parabolic trough power plants has 

strongly accelerated, with new plants being built in Spain and the United States. Specifically, a 

1-MWe trough system was commissioned in Arizona in 2006, a 64-MWe Nevada solar plant 

began operating in 2007, and a number of 50-MWe facilities have begun operating in Spain. By 

late 2010, announced trough projects in Spain and those with signed power purchase agreements 

(PPAs) in the southwest United States total more than 9 GW. 

Parabolic trough power plants consist of large fields of mirrored parabolic trough collectors, an 

HTF/steam-generation heat exchanger (HX) system, a power system such as a Rankine steam 

turbine/generator, and optional thermal storage and/or fossil-fired backup systems. Trough solar 

fields can also be deployed with fossil-fueled power plants to augment the steam cycle, 

improving performance by lowering the heat rate of the plant and either increasing power output 

or displacing fossil fuel-derived electricity. See Figures 1-1 and 1-2. 

The solar field (SF) comprises large 

modular arrays of single-axis-tracking 

parabolic trough solar collectors that are 

arranged in parallel rows, usually aligned 

on a north-south horizontal axis. Each solar 

collector has a linear parabolic-shaped 

reflector that focuses the direct beam solar 

radiation on a linear receiver (absorber 

tube) located at the focal line of the 

parabola. For a typical north-south 

orientation of the collector axes, the 

collectors track the sun from east to west 

during the day, with the incident radiation 

continuously focused onto the linear 

receiver where a heat transfer fluid is 

heated to nearly 400oC (750oF). 

Figure 1-1. SEGS IV, Kramer Junction, California. 

(Photo from Luz International Ltd., circa 1990) 
2 

Figure 1-2. Trough collector schematic. (Illustration from Flagsol GmbH) 

Because a concentrating collector must focus the sun’s rays on the receiver, the solar resource 

relevant to performance is the beam radiation from the sun, measured as the direct normal 

insolation2 (DNI) on a plane perpendicular to the rays. The DNI on the Earth’s surface is reduced 

from the extraterrestrial level by scattering in the atmosphere caused by moisture and other 

particles. In a linear-axis tracking trough collector, the effective beam radiation is the component 

perpendicular to the plane of the aperture of the parabolic-shaped mirrors. Later in this report, we 

shall refer to that component as the aperture normal insolation, or ANI.3 For purposes of 

Acceptance Testing, it will be shown that the ANI is an important characteristic of the solar 

resource for the purposes of these Guidelines. 

After circulation through the receivers, the HTF flows through a heat exchanger to generate 

high-pressure superheated steam (typically 100 bar/371oC [1450 psia/700oF]), and/or to a thermal 

storage system, if present. In a Rankine-cycle plant, the superheated steam is fed to a 

conventional reheat steam turbine/generator to produce electricity. The spent steam from the 

turbine is condensed and returned to the heat exchangers via condensate and feed-water pumps to 

be transformed back into steam. Steam condensation occurs via a surface condenser combined 

with an evaporative cooling tower (wet cooling), or directly in an air-cooled condenser (ACC) 

(dry cooling). The selection of condensation method will influence water usage, cycle 

performance, and capital cost. Dry cooling significantly reduces water use, but it increases 

investment and levelized electricity costs.4 After passing through the HTF side of the solar heat 

 

2

 Radiation on the plane normal to the ray from the sun, where “insolation” is a term originating from “in(coming) 

sol(ar radi)ation.”

3

 Use of this term is credited to remarks by Patrick Griffin of Thermoflow, Inc., Sudbury, Massachusetts. 

4

 Due to both higher capital costs of the cooling system and degraded performance during hot ambient conditions. 

Further, electricity revenues are lowered because the output reduction typically occurs during high-value time-ofdelivery periods. 
3 

exchangers, the HTF is then reheated as it recirculates through the solar field. Current research 

and development (R&D) programs are exploring the use of molten nitrate salts or pressurized 

gases for the HTF, as well as direct steam generation in the solar field.

A unique and very important characteristic of trough and power tower CSP plants is their ability 

to dispatch power beyond the daytime sun hours by incorporating highly efficient thermal energy 

storage (TES). During summer months, for example, plants typically operate for up to 10 hours a 

day at full-rated electric output without thermal storage. However, as illustrated in Figure 1-3, 

significant additional full-load generation hours can be efficiently added or shifted if thermal 

storage is available, allowing a plant to meet the morning and evening winter peaks that many 

utilities in the southwest United States experience.

Another approach is to configure the systems as hybrid solar/fossil plants, that is, a secondary 

backup fossil-fired capability can be used to supplement the solar output during periods of low 

solar irradiance. This alternative fossil-based method to match the utility system load profile is 

possible because of the thermal-cycle power plants. 

Figure 1-3. Plant operation based on utility load profile showing how TES can be used to better 

match the CSP plant’s output to utility needs. (Graphs from Abengoa Solar)

Considerable R&D is under way and planned in all aspects of trough systems: collector 

component improvements, power plant integration and configuration, thermal energy storage 

systems, cost reduction measures, and performance improvements. Funded both by internal 
4 

company resources and by government programs, a number of innovative approaches to trough 

technology improvement are being developed in all these areas. 

In solar trough plants that are configured with thermal storage systems, the solar field is typically 

increased in size to raise the energy collected. For example, on a clear day, a large solar field 

could provide enough energy in the morning and afternoon hours to both drive the turbine at full 

load all day and also charge storage to full capacity. For a given plant and utility application, the 

size of the solar field can be optimized for the local solar resource, grid demand pattern, 

electricity revenues (e.g., if time-of-use revenue multipliers are applicable), and financial 

parameters. For this purpose, a reference solar field size is determined at a nominal hourly DNI 

and solar field design parameters at rated solar thermal power capacity, and this is labeled as 

solar multiple (SM) = 1. Larger solar fields are identified as SM >1. For plants with no thermal 

storage in a high DNI region, the optimum solar field size will typically fall in the SM = 1.4 to 

1.5 range. For plants with thermal storage, SM values of 2.0 and higher will typically be chosen. 

1.2 Need for Guidelines 

Solar thermal trough power plants are being proposed with turbine capacities as large as 280 

MWe gross and, if significant thermal storage is included in the system, can require solar fields 

with up to 2.6 million m2 of reflector aperture in an area of high solar resource, and even larger 

solar fields in areas of less solar resource. The land requirement for a large plant with thermal 

storage is about 7.5 acres (~3 hectares) per MWe net. Heretofore, developers, debt providers, 

owners, and engineering, procurement, and construction (EPC) contractors have had no 

standardized test guidance for reference or use that is specifically associated with the 

performance acceptance of these large and capital intensive solar fields. This need will intensify 

as deployment of parabolic trough systems increases in the United States and internationally. 

The fundamental differences between acceptance of a CSP solar system and a conventional 

fossil-fired system are the transient nature of the energy source, and the need to use a 

performance projection model5 in the acceptance process. These factors bring into play the 

impacts of transient processes, uncertainties introduced by a model, and the need to test, or 

model, seasonal or annual performance within the scope of the acceptance test procedure in order 

to capture significant impacts on performance. 

The Performance Test Guidelines presented in this report are intended to provide guidance for 

the planning, preparation, execution, and reporting of performance test results on large-scale 

(utility-scale) parabolic trough solar systems that are made up of the solar field and HTF 

subsystems. 

It is anticipated that an official Performance Test Code (PTC) developed by the American 

Society of Mechanical Engineers (ASME) will, at some future time, supersede the parabolic 

trough Guidelines reported here. That code—ASME PTC 52 - Concentrating Solar Power 

Plants—is being planned to address other CSP technologies in addition to parabolic troughs. 

Typically, it takes several years to complete the preparation and approval of a PTC. 

 

5

 Use of a performance model in place of correction curves is also a trend in the testing of fossil-fuel power systems. 
5 

2 Objectives and Scope 

The objective of this document is to provide performance acceptance Guidelines for the solar 

system of parabolic trough power plants. The solar system includes both the solar field and HTF 

system. The power block and balance-of-plant (BOP) are not within the boundaries of this 

protocol. ASME PTC 46 - Overall Plant Performance would be the appropriate code for 

performance and acceptance testing of the power island.

Specifics on the type, duration, and frequency of tests for a particular project should be 

separately defined in a test plan that is part of the contractual agreement between the party 

turning over the solar system and the party taking control of the solar system. The test plan is a 

commercial agreement that will dictate the required testing for the transaction. This Guideline 

has been prepared to recommend and provide guidance for several tests that can be used to meet 

the objectives of the contractual agreements. 

2.1 Parties to the Acceptance Tests 

There are a large number of commercial scenarios under which acceptance tests on the solar 

system would be conducted. One such possible scenario is given below for purposes of 

illustration and clarity. Although this example is intended to help place various steps or 

responsibilities in perspective, it does not imply that other, equally valid, representative scenarios 

do not exist. The most important point is that the objective of these Guidelines is to present 

viable test methods that are applicable to acceptance testing of large parabolic trough solar 

systems regardless of the relationships of the parties involved.

2.2 Example Project Scenario 

• Test requirements are agreed upon between the EPC contractor and the solar system 

Technology Provider (TP). The TP is selected by the EPC contractor, either 

independently or by agreement with the Developer/Owner. 

• The EPC contractor sequentially carries out the solar system commissioning, startup, and 

acceptance testing with the Owner’s Engineer observance. Performance Acceptance Tests 

are to be conducted after the solar system is fully commissioned and operational, and is 

ready for turnover from the TP to the EPC. 

• At successful completion, the TP then turns over the solar system to the EPC contractor. 

2.3 Pass / Fail Criteria 

To be clear, the criteria for passing or failing the performance acceptance tests recommended in 

this report are a contractual matter to be agreed upon by the parties involved. 

2.4 Readiness for Acceptance Tests 

While the conditions to start acceptance testing are subject to contract conditions, in a typical IPP 

project development the acceptance performance test of the solar system occurs after 

commissioning of the system by the EPC construction and startup teams. Commissioning 

involves mechanical, electrical, instrumentation, and functional checks and walk-throughs to 

bring the solar system to an operating condition suitable for commercial operation and 

acceptance testing. All critical punch-list items from the walk-throughs are complete at this time, 
6 

as well as most lower-tier punch-list items. When the system is fully operational and performs all 

required functions, it is turned over to the responsible party (e.g., the EPC startup team) for 

Acceptance Testing. At this point, the performance of the solar system has not yet been 

rigorously tested, nor the system accepted by the Owner. 

2.5 Unique Characteristics of Solar System Performance Tests and the Role of a 

Performance Model 

A performance test of fossil-fired equipment, such as a boiler or combined-cycle plant, is a 

steady-state test based on thermal equilibrium, characterized by a controlled, steady heat input 

and a measurable output. Although in practice it requires care to achieve and maintain steadystate conditions, it is greatly facilitated by the fact that the input energy is controllable. In simple 

terms, a steady-state test is passed if the measured capacity or efficiency of the system meets the 

specified value. An important characteristic of the fossil-fired power system is that it often 

operates at or near its nameplate capacity. 

A solar system, on the other hand, is characterized by the continuous movement of the sun and 

the changing heat input to the solar field. This complicates the achievement of steady operating 

conditions during the test period. Furthermore, there are many daylight hours during the year 

when the solar system is not operating at capacity due to the diurnal and seasonal nature of the 

solar resource. 

The predicted performance of the solar system depends on the exact location of the sun and the 

specified characteristics of the solar collectors and HTF system. Because of the variable nature 

of the solar resource, a performance model (computer code) is required to predict the 

performance for given input conditions. 

A parabolic trough on a north-south axis tracks the beam radiation from the sun from morning to 

evening from east to west. For many promising solar plant locations the sun is never directly 

overhead. Because of its seasonal and diurnal position in the sky, in the winter, for example, the 

sun is low in the southern sky. The DNI, or direct normal insolation, is the direct beam radiation 

measured normal to the rays from the sun, and is independent of the collector position. The ANI, 

on the other hand, is the beam radiation component normal to the aperture plane of the collector. 

It is the energy that is useable by the collectors in the solar field, and is computed from the DNI 

and incidence angle, shown in the schematic diagram below. If the incidence angle is zero, the 

direct radiation from the sun is normal to the collector, and the ANI = DNI. If Θ is not zero, 

ANI = DNI * cos(Θ) 

A factor in the performance model called the incident angle modifier, or IAM, accounts for 

changes in optical properties resulting from non-normal incidence on receivers and mirrors 

excluding the cos(Θ) effect above. A plot of a typical IAM curve is also shown in Figure 2-1. 
7 

Figure 2-1. Trough energy input. 

2.6 Utility-Scale Solar System Performance Acceptance Tests 

This Guideline recommends a set of tests for use in the acceptance of a utility-scale solar system 

using parabolic trough technology. All or selected tests from this group can be chosen by 

agreement between the test parties. The data collected for each test are identical in nature. The 

tests, described in detail in Section 4, are the following: 

1. Short-Duration Steady-State Thermal Power Test 

2. Multi-Day Continuous Energy Test 

2.7 Solar System Boundaries 

The current version of these Guidelines does not include a thermal storage system. The solar 

system boundary is defined in this case to be the shutoff valves in the HTF piping at the inlet and 

outlet of the solar heat exchangers(s), as shown in Figure 2-2. The solar system comprises the 

solar field and HTF subsystems. The boundary between these subsystems is defined as the 

shutoff valves on the interconnection piping for each loop to the cold HTF header and hot HTF 

header, respectively, in each segment of the solar field. 
8 

Figure 2-2. Schematic of solar system boundary and performance model. 
9 

3 Test Definitions 

3.1 Performance Acceptance Test Guidelines 

This section elaborates on the objectives and elements of the two types of acceptance tests for 

large parabolic trough solar systems identified in Section 2. Performance acceptance tests are to 

be conducted with acceptable accuracy according to clearly stated procedures agreed upon 

between the parties involved. These Guidelines deal with issues specific to utility-scale parabolic 

trough solar fields, for which no ASME performance test code yet exists. However, applicable 

PTCs developed by ASME for other types of energy systems have very useful information for 

developing a detailed test plan, and are appropriately cited in following sections. For example, 

PTCs provide a general framework and information about instrumentation, data acquisition, data 

reduction, testing procedures, uncertainty levels, and test reports. However, note that many 

details are specific to a project and need to be stipulated in the context of the overall test plan. 

3.2 Short-Duration Steady-State Thermal Power Test 

3.2.1 Introduction and Purpose 

The purpose of this test is to measure the thermal power output and thermal efficiency of the 

solar system under clear-sky conditions over a short period during which thermal steady-state 

conditions exist, and to compare the measured results to performance model projections for those 

parameters. These tests are defined to be the Power Test and the Efficiency Test. (See the 

subsequent section, Specific Test Objectives.) 

The thermal power output of the solar field during a short test period will vary primarily with the 

magnitude of the solar resource and the time of day and season. Secondary impacts on power 

output result from variations in wind speed and ambient dry-bulb temperature. It is shown later 

that thermal steady-state conditions can be expected at most times of the year for short test-run 

durations. 

The purpose of this Guideline is to define or recommend methods to carry out valid acceptance 

tests. Criteria to judge successful completion of the tests are left to commercial agreements 

between the test parties. 

Commercial agreements may require that, at some stage, the rated solar thermal design power (or 

capacity) be measured at appropriate solar resource conditions. If such conditions do not exist in 

the period at the end of commissioning and turnover of the solar system, then provisional 

acceptance tests can be carried out, with a final acceptance test to follow for rated solar thermal 

design power when solar conditions allow. 

3.2.2 Solar Resource and Weather 

The tests are to be run on clear days during any time of year. Even with a high DNI, which can 

be experienced on a clear winter day, the important solar resource term that dictates the thermal 

energy input into the solar field is the direct insolation vector normal to the aperture plane of the 

parabolic trough, earlier defined as the ANI. During fall through winter seasons, for example, the 

sun is lower in the southern sky much of the day, resulting in a low ANI even when the DNI is 

high. Good resource conditions suitable for rated design-point power tests will be those with an 

ANI near the design point. The analysis presented just below suggests that a relatively constant, 
10 

or slowly changing, ANI is experienced by a parabolic trough collector on clear days during midday hours at any time of year, albeit over a broad range of power levels. 

During the performance test period, the solar field and HTF systems should not be operated 

beyond of their specified or suggested operating limits for solar resource (in terms of ANI), 

ambient temperature, or wind speed as provided by the solar system supplier(s). 

3.2.3 Specific Test Objectives

The primary objectives of the tests are the following: 

a) Power Test—Measure solar thermal power output (including the design-point solar 

thermal power, or capacity, if contractually required). 

b) Efficiency Test—Measure solar thermal efficiency at the same test conditions. 

c) Compare (a) and (b) to performance model projections. 

d) Monitor bulk solar field inlet and outlet temperatures at heat-exchanger inlet(s) and 

outlet(s), and/or at subfield boundaries. 

e) Measure HTF system and solar system parasitic power consumption. 

3.2.4 Calculations 

3.2.4.1 Delivered Power Calculation 

The thermal power output of the solar system is to be calculated at the solar heat exchangers, 

which, for a large solar plant, will be configured in a series of parallel heat-exchanger trains. For 

each train, the delivered thermal power can be computed from: 

ܲ௠௘௔௦௨௥௘ௗ ൌ ݉ሶ ∆h୦୶

 ൌ ݉ሶ න C୮ · dT

T౞౮,౟౤

T౞౮,౥౫౪

 

 ൌ ݉ሶ C୮ ൫T୦୶,୧୬ െ T୦୶,୭୳୲൯ (Eqn. 3-1)

where 

P = Solar thermal power 

݉ሶ = HTF mass flow rate 

∆h୦୶ = Enthalpy difference between inlet and outlet of heat exchanger

C୮ = Integral average specific heat of HTF 

Thxin = HTF average bulk inlet temperature to solar heat-exchanger train

Thxout = HTF average bulk outlet temperature from solar heat-exchanger train 

For a large solar power plant, the solar field design will likely be configured in solar system 

subfields that, together, make up the full solar field. In some cases, the HTF from an individual 

set of subfields will be collected to go to an associated set of solar steam-generator trains. The 

delivered thermal power measured across a set of subfields and accounting for the appropriate

calculated inlet and outlet piping heat loss can be used as a check against the delivered thermal 
11 

power at the heat-exchanger train, but not as the primary measurement, unless agreed upon 

between testing parties. 

3.2.4.2 Efficiency Calculation 

The solar thermal efficiency can be based on either DNI or ANI in the denominator, as mutually 

agreed upon by the test parties. ANI is recommended because it is the true energy input to the 

field. 

The solar thermal efficiency based on ANI is computed from: 

ߟ௔௘௠௦௨௥௘ௗ ൌ 

௉೘೐ೌೞೠೝ೐೏

஺ேூ·஺ೌ೛೐ೝ೟ೠೝ೐

 (Eqn. 3-2) 

where 

η = thermal efficiency 

θ = average solar incidence angle during a test run 

cosθ · DNI = ܫܰܣ

ܣ௥௘௣௔௧௨௥௘ = solar field aperture area in tracking mode during the test 

The active tracking aperture area will be used for the Aaperture term. Note that if the solar field 

condition for the test includes stowed solar collector assemblies (SCAs) then the receiver thermal 

losses will be abnormally high (because they are associated with the total solar field aperture 

area) and the efficiency result will be too low. The performance model projection should 

account for this condition. 

3.2.4.3 HTF Solar Field Inlet and Outlet Temperatures 

For completeness, measurements should include the average bulk inlet and outlet HTF 

temperatures at one or both of the following: 

• Each heat-exchanger train (primary measurement) 

• Each solar field subfield (secondary measurement). 

The heat-exchanger test is noted as “primary” because it gives the solar field power at the point 

of delivery to the power block. Measurement at the subfield level (“secondary”) requires a 

correction for heat loss in the headers to arrive at an equivalent value. 

3.2.4.4 HTF Physical Properties 

Calculation of the mass flow typically requires measurement of the volumetric flow rate and 

knowledge of the HTF density as a function of temperature. Calculation of the thermal power 

also requires knowledge of the HTF specific heat. These fluid properties of the HTF, although 

available from the manufacturer for fluid that meets as-new specifications, should be measured 

prior to the test per agreements between the test parties. These measurements are even more 
12 

important for performance tests that are run on solar systems where possible HTF degradation 

may have occurred (e.g., from off-design operation of the ullage6

 system). 

3.3 Multi-Day Continuous Energy Test 

The objective of this test is to gather continuous daily thermal energy output (integrated power 

output using Eqn. 3-1; efficiency from Eqn. 3-2) and to compare the results to projections from a 

performance model. Both clear sky and partly cloudy conditions are acceptable. Conditions in 

which cloud cover unevenly affects portions of a large solar field will need to be treated on a 

case-by-case basis, and agreed upon by both parties. In the event of multi-day fully cloudy or 

rainy weather—and perhaps in the event of non-uniform shadowing, as mentioned above—the 

test should be terminated and then restarted when appropriate. Additionally, the functionality of 

the solar system should be observed with regard to such items as daily startup, normal operation, 

and shutdown. 

It is recommended, based on similar typical tests on power systems, that the test be a continuous 

10-day test with data acquisition on 10-second intervals (i.e., test readings) to accurately capture 

morning startup, evening shutdown, and weather events. However, there is no hard, strong 

justification for these time durations or intervals, and specifics on duration of the test, data 

acquisition requirements, and contingencies in case of operational problems are to be agreed 

upon between the testing parties. It is also recommended that a stop/start pattern be permitted if 

other circumstances not related to the solar system require that the test be temporarily suspended 

due to, for example, shutdown of the power block. 

The measured energy output results over time are to be compared to the same values as predicted 

by the performance model agreed upon by both parties, e.g., the model that was used for proforma calculations and delivered by the equipment supplier or EPC contractor at contract 

signing. The input values to the performance model are also to be agreed upon by both parties 

and are to include the local DNI data and other appropriate weather conditions, specified 

warranty characteristics of the solar field, and mutually acceptable adjustments for the operating 

condition of the solar field (e.g., number of collectors not available). Treatment of the 

reflectivity values of the reflectors is discussed in Section 4.2.2. 

Because of the use of a performance model, no test-correction curves are involved because the 

model contains internal algorithms that adjust for off-design conditions. The contract shall 

define any commercial tolerance (which may or may not be linked to test uncertainty) allowed 

for in the pass/fail criteria (to be specified in the Test Plan). 

3.4 Performance Model Projection and Comparison to Measured Results 

The performance model used for the acceptance test must be mutually acceptable to both test 

parties, and typically would be the identical model using the system characteristics also used for 

the project financial projections. The input parameters for the performance model should be 

 

6

 The HTF expansion vessel(s) have an ullage volume above the liquid HTF pressurized to 10 bar to prevent boiling 

at 393o

C. This volume contains primarily HTF vapor and nitrogen, along with limited degradation products due to a 

very slow thermal decomposition of the HTF, resulting primarily in free hydrogen and a few other light 

hydrocarbons. The ullage system is designed to maintain the ullage volume pressure in the expansion vessel(s) and 

process the degradation products. 
13 

identical to those used in the commercial projections for the power plant. The weather file—

DNI, wind speed, ambient dry-bulb temperature—should be the actual weather file with the time 

stamps perfectly synchronized with the measured test data. 

The possible exception to this rule is the soiling condition of the solar field reflectors. This issue 

is treated in Section 4.2.2. 

The comparison of the measured capacity or efficiency to the model-projected value must take 

into account the uncertainty intervals for each quantity.7 The measured quantity has an 

uncertainty interval associated with it, and the result is a band defined by the measurement plus 

or minus (±) its uncertainty interval. 

The same is true of the value projected by the performance model. Because the performance 

model is the same as that used in the commercial projections, it is taken to have no uncertainty 

except that associated with the weather input and average mirror reflectivity value. Validation of 

the model algorithms and accuracy is a matter between the testing parties and is outside the 

scope of this Guideline. Although ambient temperature and wind speed could be taken into 

account, it is the parameters in Eqn. 3-1 that dominate the uncertainty. 

The uncertainty band for the result is very important and must be given in the test results. 

Comparison of the measured value to warranted levels—and whether or not the test uncertainties 

are incorporated into this comparison—is a commercial issue to be agreed to by the parties and is 

beyond the scope of these Guidelines or ASME PTCs. 

3.5 HTF and Solar System Parasitic Power Consumption 

This measurement is for information, unless otherwise guaranteed, and should include the 

instantaneous total parasitic power requirement of the HTF system, as a function of flow rate, 

during each test period and test run (a subset of the test period). Similarly, the solar field 

parasitic for the drive system should be measured. 

 

7

 That is, the uncertainty bands need to overlap for a valid test run. 
14 

4 Test Methods 

4.1 Introduction 

Ideally, the performance tests should be in thermal equilibrium and at a steady state condition. 

Neither goal is completely possible due to the effects of a changing heat source and changing 

ambient conditions. Failure to achieve either goal contributes to uncertainty in the test results. 

ASME Performance Test Codes 4 and 46 assert that thermal steady-state is defined as an 

operating condition in which the system is at thermal equilibrium, and that the guiding criterion 

for steady-state test conditions is that the average of the data reflects equilibrium between energy 

input and energy output to thermal and/or electrical generation. The continuously varying energy 

input from the sun, combined with other solar field characteristics, make this a particularly 

challenging objective. 

The receivers and entire subfield react fairly quickly to a change in the solar field heat input, or 

ANI. The main issues affecting thermal equilibrium are the heat exchange between the HTF and 

the headers (both walls and insulation) in the long header runs from the subfields to the power 

block, and warming or cooling of the HTF itself. Controlling a constant HTF outlet temperature 

at the exit of the subfields will greatly facilitate achieving thermal equilibrium in the headers. 

Because of the near-incompressibility of the HTF, the mass flow rate changes very rapidly 

throughout the entire solar field when the pump speed is changed, facilitating the HTF subfield 

exit temperature control. The objective in this regard is that the measurement of the ANI power 

to the solar field corresponds with the measurement of power delivered to the heat exchangers at 

the same point in time. With a changing ANI, errors will be introduced that add to the 

uncertainty of the result, as discussed in Section 4.4.3. However, a stabilized (steady-state) 

condition for purposes of performance testing is somewhat more difficult to achieve. Several 

key factors contribute to this issue: 

• Variations in the ANI input to the solar field 

• Solar field inlet temperature variation 

• Solar field outlet temperature variation 

• Thermal inertia within the subfield itself, that is, the response time of the subfield outlet 

temperature to a change in ANI. 

A crucial premise of this Guideline is that during short test runs, the combined effects of small 

variations in these conditions, along with the HTF outlet temperature flow control, can satisfy the 

requirements needed to reach a steady-state condition adequate for testing. The following 

paragraphs treat these issues more specifically. 
15 

4.2 Solar Field Test Conditions 

4.2.1 Solar Field Area, Thermal Power Output, and Design Capacity

Typically, the solar field will be larger than required for delivery of rated design solar thermal 

power during late spring and summer.8 At the high ANI levels, the peak solar thermal power 

output of a solar system with a high solar multiple will often be at or above the design capacity 

for a properly performing solar field. In practice, however, the allowable thermal output is 

nominally constrained by the design thermal input of the power block and the associated turbinecycle heat rejection at its design conditions. To control this condition, the solar field thermal 

output can be reduced by defocusing9

 individual SCAs to limit the thermal output to that which 

the turbine can accommodate at full load. The test plan should specify the exact procedure to be 

used under such high insolation conditions. 

If the test parties agree that demonstration of the full design capacity of the solar system is a 

requirement, the Thermal Power Test should be run at the rated design solar thermal output 

specification if possible, with the measured results compared to the projection of the solar 

performance model using the tracking aperture area of the solar field. 

For lower-insolation periods during the year, the solar thermal output may be less than the rated 

design output, though useful for efficiency demonstration purposes. If required by the test 

parties, final acceptance of the solar system may require a test at a time of year when a higher 

ANI condition occurs and the performance model indicates that design solar thermal output can 

be achieved. 

4.2.2 Solar Field Reflectivity 

The solar thermal output of the solar field is directly proportional to reflector soiling, which is 

characterized in most solar performance models by a soiling factor.10 The projected performance 

of the solar field at the time of contract signing is typically based on a specified average

 

8

 In solar trough plants that are configured with thermal storage systems, the solar field is typically increased in size 

to raise the energy collected. For example, on a clear day, a large solar field can provide enough energy in the 

morning and afternoon hours to both drive the turbine at full load all day and also to charge storage to full capacity. 

For a given plant and utility application, the size of the solar field can be optimized for the local solar resource, grid 

demand pattern, electricity revenues (e.g., if time-of-use revenue multipliers are applicable), and financial 

parameters. For this purpose, a reference solar-field size is determined at a nominal hourly DNI level and at solarfield design parameters, and this is labeled as solar multiple (SM) = 1. Larger solar fields are identified as multiples 

of the SM. For plants with no thermal storage, the optimum solar-field size will typically fall in the SM = 1.4–1.5 

range. For plants with thermal storage, SM values of 2.0 and higher will typically be chosen. 

9

 That is, placing collectors out of focus on the sun in the stow position or in a “follow-mode,” in which the 

collectors continue to track but are completely out of focus at a few degrees behind the sun’s movement. Placing a 

collector in a “feathering” or partially off-focus position to control the outlet temperature is not a suitable strategy 

during an acceptance test, because this does not allow a quantitative count of the SCAs in and out of focus. 

Therefore, for acceptance test purposes, the solar field must have SCAs fully in or out of focus and the active solar 

field should be maintained constant during the test. 

10 The soiling factor is one of a number of factors in the optical efficiency term of the collectors. 
16 

reflectivity. The decision on the cleaning and reflectivity measuring issue is very important to 

both the performance of the solar field and the uncertainty in the results. 

In normal operation, the reflectors are cleaned according to methods and a wash frequency that 

are specific to the site location, operation and maintenance (O&M) organization, and 

optimization of water and labor costs versus performance gains. Mirror reflectivity is typically 

returned to a high level after cleaning. 

For acceptance testing, agreements need to be reached between the testing parties on (a) reflector 

washing during the test period and (b) the soiling factor used in the model for comparison to the 

measured power delivery. At the time of acceptance testing of the solar system, the power plant 

will be operational—otherwise, it would be not be possible to reject the collected energy. 

One approach could be that the solar field collectors could be washed on a schedule similar to 

that proposed for normal operation. That is, prior to and during the multi-day test period, 

operators would wash the solar field mirrors using the normal planned O&M schedule and 

procedure in the solar field O&M plan. For a large plant, it could take one or several weeks to 

complete the washing cycle. A second choice could be to carry out no reflector panel washing at 

all during the short-term power test or multi-day test periods. 

Regardless of the approach to mirror washing, sufficient reflectivity readings should be taken to 

characterize the average reflectivity of the solar field for use in the performance model, with an 

appropriate uncertainty interval applied to the average solar field value. The testing parties need 

to agree on methods to optimize the spatial sampling to obtain a valid statistical average. This 

will likely require an additional quantity of instruments beyond the normal number at the plant. 

It is not possible in this general Guideline to specify in advance the number of required readings 

because of the site-specific character of both the solar field configuration and the soiling 

mechanisms at the site. 

And, further complicating this issue, to our knowledge no authoritative work has been carried out 

on methods to determine statistically valid reflectivity averages in large trough solar fields using 

a reduced number of readings. In the near future, NREL expects to be working with Sandia 

support to develop useful approaches regarding this problem. To date, a paper touching on this 

subject was published in 2006 for a test project on a small power tower heliostat field at the 

Plataforma Solar de Almeria test facility in Spain.11 Also, experience was gained at the Kramer 

Junction SEGS plants in the 1990s, where specific SCAs to be measured were chosen at random 

based on a pattern regarding the area of the solar field from which they were selected. Four 

SCAs were chosen from the exterior zone near the edges of the field where degradation rates are 

higher, whereas eight SCAs were selected from the interior zone, and the final eight SCAs were 

selected from the intermediate zone characterized by medium degradation.12 

 

11 Fernandez-Reche, J., “Reflectance measurement in solar tower heliostats fields,” Solar Energy 80 (2006) 779–

786. 

12 Cohen, G; Kearney, D.; Kolb, G. “Final Report on the O&M Improvement Program for CSP Plants”, excerpt from 

Appendix E on Mirror Cleanliness, Sandia National Laboratory Albuquerque, SAND99-1290, June 1999. 
17 

4.2.3 Seasonal Example of Hourly ANI Variations 

The following analysis presents data on variations in ANI on a parabolic trough collector surface 

for clear days during several seasons of the year. The primary result is that the ANI variation 

appears to be within reasonable and acceptable bounds even in winter periods. In other words, a 

small variation in the ANI does not necessarily preclude attainment of adequate thermal steadystate conditions. However, it is clear that the design and operation of the parabolic trough solar 

field must include the ability to control the bulk HTF outlet temperature at a near-constant value 

to achieve that state. 

With clear skies, there will be no cloud transients, and with HTF flow control the solar field 

outlet temperature will be held within close bounds. The temperature will not fluctuate much 

because the controlled small changes in mass flow will be near-instantaneous throughout the 

HTF system because the synthetic oil is, for all practical purposes, incompressible. By these 

means, there will be negligible heat exchange between the header pipe walls and the HTF fluid, 

and therefore, no thermal capacitance effects in the headers. The change in HTF mass flow rate 

will follow the variation in the ANI―that is, as the ANI changes, the HTF mass flow rate will be 

automatically adjusted accordingly after a short delay. 

The change in ANI was analyzed to examine the stability of the heat input to a trough collector 

for short periods in different seasons. Analysts used 1-minute, accurately measured DNI data 

from the NREL Solar Radiation Research Laboratory in Golden, Colorado, to show example 

patterns in ANI for clear days during June, March, and December for the entire day (Figure 4-

1(a)) and for the daytime period between 8 a.m. and 4 p.m. (Figure 4-1(b)). Also shown in 

Figure 4-1(c) are the percent changes in the ANI over the previous 30 minutes during the 

daytime period for these three months. These results show that the variations in ANI over short 

time periods, e.g., 15 to 30 minutes, are relatively small during certain periods, often within a 

few percent. 

We believe these results support the basic premise that a slowly changing heat input (ANI) and 

no transient heat exchange in the headers between fluid and pipe will allow valid measurements 

of power and efficiency at any time of year, averaged over short test runs of about 30 minutes in 

duration. However, if a demonstration of design-solar-system thermal power capacity is 

required, tests for that condition will likely need to be run in higher ANI periods. Furthermore, 

with continuous data acquisition and reduction on small time steps (e.g., 1-minute), the 

performance of the solar system can be accurately tracked and observations made on the 

steadiness of the results. 
18 

 

 

Figure 4-1. Patterns from ANI analysis: (a) during June, March, and December for the entire day, 

(b) for the daytime period between 8 a.m. and 4 p.m., and (c) the percent changes in the ANI over 

the previous 30 minutes during the daytime period for these three months. 

Location: Golden, Colorado. (Courtesy of NREL) 

a. 

b. 

c. 
19 

4.3 Criteria for Adequate Test Durations and Conditions 

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 

(thermal equilibrium) and stable test condition (power measurement) prior to testing. This 

requires stable characteristics in the: 

• solar field inlet temperature 

• solar field outlet temperature 

• mass flow rate change stabilized with change in ANI. 

These three conditions are dictated by the following influences, respectively: 

• power block stability 

• solar field HTF flow control system 

• time duration and magnitude of the ANI gradient. 

Once thermal equilibrium and test condition stability have been reached, the criteria for valid 

power and efficiency measurements (i.e., valid test runs) are primarily based on the level of 

uncertainty in the test results calculated using standard practice. Systematic uncertainties are the 

dominant consideration. 

Table 4-1 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 (see 

Section 6, Evaluation of Measurement Uncertainty, for more details on uncertainty analysis). 

The variability defined in Table 4-1 is defined as the standard deviation of the mean ݏ௑ത as 

described by Eqn. 6-1 divided by the average value of the parameter over the test run period. 

Based on the examples provided by Tables 6-1 through 6-4, a combination of the allowable 

variations given in Table 4-1 will result in a negligible increase in the total uncertainty of the 

result. It cannot be overemphasized, however, that 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 4-1. Example Stabilization Criteria for Short-Duration Steady State Power Tests of a 

Utility-Scale Parabolic Trough Solar Field 

Parameter Allowable variability over test 

period 

 (%) തܠ⁄ഥࢄ࢙

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, referring to 

Figure 4-1, the potential test period for any given day will occur between 9 a.m. and 4 p.m. 
20 

Observation of data collected from several operating plants indicates that the variability will be 

much smaller than the values described in Table 4-1. 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%. 

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 4-2. 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 and includes the energy 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. 

Figure 4-2. Schematic representation of HTF system elements. 

Using Figure 4-2. as a guide, we can estimate the time required to minimize the error associated 

with non-equilibrium conditions to an acceptable level. For example, to limit the error to 0.1% 
21 

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. 

Table 4-2 gives our recommendations for a pre-test period to show stability, duration of test runs, 

data sampling rate, number of test points, and allowable ambient conditions within a test run 

period. 

Table 4-2. Example Conditions for Testing Periods, Durations and 

Data Points for Short-Duration Steady State Power Tests 

Daily Test Period 0900-1600 (9a.m.-4p.m.) 

Pre-Test Steady-State Run 

Period 

Approx. 30-45 minutes 

Test Run Duration Approx. 15-30 minutes 

Test Run Data Points 10-second averages 

# of Test Points in Test Run 90 - 180 

Maximum Wind Speed Approx. ≤ 13 m/s

Minimum ANI Approx. 500 W/m2

4.4 Criteria for Valid Test Results 

A valid test result must satisfy the requirements of thermal equilibrium, stabilized test 

conditions, suitably low uncertainty, and repeatability. Valid test run results, over approximately 

15 to 30-minute durations, obtained during a single stable test run period should be averaged. 

Invalid test runs are those for which the run uncertainty intervals are not within acceptable limits, 

or if the test run result falls outside the uncertainty intervals of the test runs being compared. A 

limit should be set by the testing parties on the number of outlier test runs permitted. 

4.4.1 Thermal Equilibrium and Stabilized Test Conditions 

The criteria applied to the pre-test run period, and illustrated in Table 4-1, must continue to be 

satisfied through the test run period. 

4.4.2 Repeatability 

Repeatability of multiple test run results lends confidence to the methods incorporated in the 

testing. Accounting for the requirements for equilibrium and steady-state conditions, numerous 

15- or 30-minute test run results can be taken within the daily 7-hour test window. 

Invalid test runs are those that individually have total uncertainty intervals outside acceptable 

limits, or for which the test results do not lie within the uncertainty intervals of each other. A 

limit should be set on the number of invalid test runs that are permissible during a test run 

period, e.g., 10%.

It is recommended that this pattern be conducted during the best daily time periods (function of 

season) and repeated on three separate days during a 10-day window. In practice, once the solar 

system test conditions are set up, the data acquisition system can be run continuously during the 

full test-run period on the selected days, and the data can then be examined for suitable periods 

that satisfy the test run conditions for stability. 

The results of each test run, or an average of test runs close together in time, should be compared 

to the performance model output(s). However, the uncertainty of the average calculation will be 

the same as the uncertainty of an individual result. 
22 

4.4.3 Impact of ANI Variation on Trough Transient Behavior 

Under conditions of a continuously changing ANI, achieving true steady-state conditions during 

a short-duration acceptance test is not possible and therefore the impact of varying ANI on an 

acceptance test must be understood. 

As discussed throughout this report, the solar field acceptance test seeks to compare the 

measured solar field thermal output to predicted output using a performance model or correction 

factor. The measured thermal output considers the state of the HTF at the inlet and outlet of the 

power cycle heat exchangers and uses a first-law energy balance to calculate the total thermal 

power supplied to the power cycle. This measured data is compared to the predicted solar field 

output, given a particular solar resource level, the solar position, and ambient conditions. 

This relationship is shown in Eqn. 4-1, where the measured energy on the left as described earlier 

in Eqn. 3-1 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௛௘௥௠௔௟ (Eqn. 4-1)

The variables in this equation are identical in definition to those defined previously in equations 

3-1 and 3-2. 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, the details of which will be published separately as an NREL technical report. 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. 

Acceptance testing procedures may allow data collection either at the inlet/outlet of the power 

cycle heat exchangers or at the inlet/outlet of a single subfield, as shown in Figure 4-3. . 

Consequently, transient behavior at both locations is of interest. The analysis presented here is 

carried out for both data collection locations. The subfield boundary is selected to exclude the 

long-section header piping running from the power-cycle heat exchangers to the subfield header. 

As expected, the transient response for the smaller subfield boundary test is truncated relative to 

the larger solar field boundary at the heat exchangers. 
23 

Figure 4-3. Location of two possible test boundaries for the acceptance testing procedure. 

Simulation results: A number of cases—not all shown here—were analyzed to test the validity 

of the model against a physical understanding of solar field operation and at limits where the 

results could be readily compared to reality. For example, a default case was run with no ANI 

perturbation applied. The behavior showed that after 60 minutes the measured solar field thermal 

power matched the predicted solar field thermal power to within about one tenth of a percent. 

This behavior represents typical solar field startup conditions and is consistent with results 

described previously in this report. The conditions at the heat exchangers were reasonably stable 

after approximately 40 minutes, with slightly less than 1% disparity, while the subfield 

conditions were stable after approximately 25 minutes. 

The results indicate, as expected, that the thermal inertia of the header piping and insulation can 

be a significant contributor to the amount of time that the system takes to stabilize. Next, a case 

was run for a system with no thermal inertia in the piping and insulation, that is, with an active 

mass flow control. This case is indicative of the response time for a system beginning at steady 

state, and maintaining a desired HTF outlet temperature throughout the simulation by varying the 

HTF mass flow rate as required. Under this condition, the temperature gradient throughout the 

loop remains constant even under varying ANI conditions; therefore, thermal exchange between 

the HTF and the surrounding piping is negligible. The calculated transit times compare well with 

the steady-state time of 8.3 minutes for the entire solar field and 5.2 minutes for the subfield. 

In reality, 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 is simulated by applying a gradient to the ANI value once the system 

reaches 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 included in Figure 4-1. Figures 4-4 and 4-5 show the results of varying the 

ANI rates for clear days in December and March. In these plots, ‘dt’ is the length of time 
24 

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 4-4. 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%
25 

 

Figure 4-5. 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 4-3, assuming a system with ideal 

mass flow rate control. 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 4-3. 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 

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%
26 

presented above. A reduced thermal resource results in a reduced field flow rate, and all transient 

effects are correspondingly drawn out. 
27 

5 Instrument Selection

5.1 Required Data 

The defining equations for the solar system acceptance tests are straightforward. The power 

output of the solar system is calculated from Eqn. 3-1 for both types of tests. Eqn. 3-2 adds the 

need for measurement of the DNI, and the model input data require measurement of the 

reflectivity of the mirrors. Together, the tests require the measurement of or accurate data on the 

following: 

• HTF volumetric flow rate 

• HTF physical properties of density and specific heat (as agreed upon by the test parties) 

• HTF temperature 

• Direct normal radiation (ANI is calculated from this value and cos(θ)) 

• Mirror reflectivity 

• Ambient wind velocity (and optionally direction), dry-bulb temperature, and relative 

humidity 

• Latitude and longitude of the DNI instrument 

• Solar system parasitic power (including both HTF system and solar field) 

• Year, day of year, time of day. 

Following ASME PTC guidelines, these values are defined as primary or secondary variables in 

Table 5-1, and the source of the data is indicated. Primary variables are those that are required to 

calculate the results of the test. Secondary variables are variables of interest but are not required 

to calculate results. 

5.2 Reference Solar Field Configuration and Measurement Conditions 

The piping sizes and temperature levels in a utility-scale solar system can limit the type of flowand temperature-measurement devices that are suitable for use in that environment. A large solar 

field is typically divided into subfields, or sections, to optimize the field layout and header 

design. Large pipes are required to carry the HTF to and from the subfields from the heatexchanger area. Operating temperatures are on the order of 300°C at the inlet to the solar field, 

and 400°C at the outlet from the solar field (570oF and 750oF, respectively). Header sizes could 

vary from 61 to 91cm (24 to 36 inches) depending on design. The size of the headers feeding the 

heat-exchanger trains will depend on the number of parallel trains. 

For purposes of this Guideline, the following typical conditions were used for sample 

calculations: 

1. A nominal size of 24-inch (61-cm) hot and cold headers assumed for a typical subfield. 

2. A nominal size of 24-inch (61-cm) hot and cold headers assumed for the inlets and 

outlets of heat-exchanger (HX) trains. 
28 

3. For purposes of examining the flow condition and velocity profile in the headers, a 

nominal design velocity of 3 m/s (9.8 ft/s) is assumed. 

4. The expected method for flow measurement is to measure the cold HTF side of the 

system at multiple locations to arrive at the total flow, either at the inlets of all the 

subfields, or at the outlet of the HX trains. 

Table 5-1. Parameters Required for Determining Thermal 

Power Output and Efficiency of the Solar System 

 C – calculated M – measured P – physical property 

Pri – primary variable Sec – secondary variable 

Primary Term Parameter Typical Influence Typical Source

Solar Field 

Power 

Energy out Pri C 

HTF mass flow Pri M 

 HTF density Pri P 

 HTF specific heat Pri P 

HTF temperature entering HX train Pri M 

HTF temperature leaving HX train Pri M 

Solar Input Energy in Pri C 

Solar resource: DNI Pri M 

Effective trough aperture area Pri P 

 Solar time Pri M 

 Latitude Sec P 

 Longitude Sec P 

Solar field inclination Sec P 

Other Dry-bulb temperature Sec M 

 Wet-bulb temperature Sec M 

 Relative humidity Sec M 

 Wind velocity Sec M 

 Wind direction Sec M 

 Mirror reflectivity Pri M 
29 

5.3 Comments on Instruments and Measurements 

It is not the purpose of this Guideline to provide a complete treatise on measurement methods, 

systems, or their accuracy. Rather, the user of this Guideline should review the general 

considerations on engineering measurements as applied to these acceptance tests based on a 

thorough study of ASME PTCs relevant to this application. However, specific information on 

specific types of instruments is provided below with regard to measuring devices required to 

measure the variables noted previously—particularly at the HTF flow conditions encountered in 

utility-scale trough systems and for solar-unique variables. 

An instrument is a device for determining the value or magnitude of a quantity or variable. The 

variables of interest are identified above. Measurements may be direct or indirect. For example, 

measuring the temperature of the HTF with a thermocouple or resistance temperature detector 

(RTD) is a direct measurement. Measuring the flow rate of the HTF by use of a pitot tube or 

pressure drop across an orifice is an indirect measurement. Because of physical limitations of the 

measuring device and the system under study, practical measurements always have some error. 

The accuracy of an instrument is the closeness with which its reading approaches the true value 

of the variable being measured. Random error refers to the reproducibility of the measurement, 

that is, with a fixed value of a variable, how much successive readings differ from one another. 

Sensitivity is the ratio of the output signal or response of the instrument to a change in input of 

the measured variable. Resolution relates to the smallest change in measured value to which the 

instrument will respond. 

Errors other than human error may be classified as systematic or random. Systematic errors are 

those due to assignable causes; these may be static or dynamic. Static errors are caused by 

limitations of the measuring device or the physical laws governing its behavior. Dynamic errors 

are caused by the instrument not responding fast enough to follow changes in the measured 

variable, e.g., lag in a temperature reading. Random errors are those due to causes that cannot be 

directly established because of random variations in the system. 

There are several essential parts to an instrument measurement system, namely: 

• Primary sensing element 

• Transmitting means or system 

• Output or indicating element 

• Data storage system. 

Considerable literature exists on the accuracy, installation, data acquisition, and data storage of 

measurement devices and systems, from both independent sources and from suppliers of the 

devices and data acquisition systems. The ASME has spent considerable effort to supply 

valuable recommendations on this subject (see appropriate PTCs, e.g., on flow and pressure 

measurement). The primary considerations are selection of the measurement device and system, 

calibration, and estimation of the systematic uncertainty. Random errors need to be estimated 

from repeated test runs or by analytical means. 
30 

5.4 Temperature Measurement 

Resistance temperature detectors (RTDs) or thermocouples (TCs) are judged to be the most 

appropriate sensors to measure fluid temperature in the HTF stream. Table 4-2 summarizes the 

important characteristics of each for this application. Both are suitable, but as noted in the text 

below, the higher accuracy of the RTD suggests it is better suited for acceptance testing. 

The RTD element can be made of different metals depending on the application, but platinum 

appears to be the most popular and is highly accurate. RTDs have a narrower operating range 

and a slower response time than thermocouples, but are potentially more accurate. For short-term 

performance testing purposes, accuracy is more important than durability (and ease of wire 

installation without affecting accuracy is desirable); these factors favor the choice of RTDs. 

Some RTDs can have significant drift during the break-in period, which must be considered. 

Their slower relative response than thermocouples is likely not an issue for performance testing 

but it needs to be considered if they are used in control loops. However, the lower relative 

durability of an RTD is a significant consideration if the device will be installed permanently. 

Also note that either sensor type can be integrated with the recommended flow instrument (e.g., 

Annubar or vortex meter). 

Data measurement devices must be allowed to reach thermal equilibrium in the environment 

where the measurements will be taken. Thermocouple lead wires shall be placed in a nonparallel 

position relative to electrical sources to avoid possible electrical interference. 

It is presumed, but not yet measured in practice, that the temperature is constant over the 

diameter of the header piping due to the fully mixed flow. At the end of each loop, the HTF is 

injected perpendicular to the header flow. In the long headers leading to the power-block area, 

there is a negligible or very low heat flux to ambient temperature through the pipe wall and 

insulation. The thermal boundary layer under this condition is very thin. 
31 

Table 5-2. Temperature Measurement Devices 

Sensor Type

Resistance Temperature 

Detector (RTD) 

Platinum 4-Wire 

Thermocouple 

Type K 

Accuracy for 

Temperature Range, 

± °F 

~1.5°F (0.8oC) 

(DIN/IEC 60751 Class A, 

higher accuracy obtainable; e.g. 

1/3 DIN or 1/10 DIN, if desired) 

[see Note 1 Refs] 

~2.5°F (1.4oC) 

Suitable for 750°F Yes Yes 

Insertion 

Requirement Thermowell Thermowell 

Response Time Decent (~30 s) 

Good (depends on thermowell and 

thermocouple geometry, but generally 

better than RTD) 

Stability High High 

Repeatability Very Good Good 

Drift 0.1°–0.3°F (0.06°–0.17°C) per month 

(can be higher in break-in period)1

0.15°–0.4°F (0.08°–0.22°C) per 

month.1 May increase with cycling. 

Linear Very Good Good 

Covered by PTC 

19.3 

 [see Note 2 Refs] 

Yes Yes 

Durability/Life 

Good (more reliable types can 

experience higher drift during first 

several months; then stable) 

Better 

Cost Medium (industrial) to High (precision) Low to Medium 

Suitable Yes Yes 

Table 5-2 Notes 

1) Primary element and the transmitter should be calibrated before and after the test in accordance with manufacture’s 

specifications to ensure that the expected accuracy is met. This exceeds the DIN/IEC 60751 requirement, and is intended to 

improve instrument accuracy. 

2) References: 

1. EPRI-TR-106453 “Temperature Sensor Evaluation” (6/1996) 

2. ASME PTC 19.3 - Temperature Measurement

3. ASME PTC 46 – Overall Plant Performance

4. http://www.omega.com/ 
32 

5.5 Flow Measurement 

Accurately measuring the volumetric flow rate of the HTF is a significant engineering challenge, 

particularly in the large pipes that characterize utility-scale parabolic trough projects. ASME

PTC 19.5 – Flow Measurement is the primary reference for flow measurements. ASME PTC 6 - 

Steam Turbines and ISO 516713 provide further information on flow measurement techniques. 

These sources include design, construction, location, and installation of flow meters, connecting 

piping, and computations of flow rates. For the conditions of a solar system performance 

acceptance test, a number of flow measurement devices are suitable and have been evaluated on 

the basis of the most important criteria for this measurement. The results are given in Table 5-3. 

Usage in several plants has tended towards ultrasonic and vortex flow meters, but the choice for 

a specific project is up to the test parties. For good results, the Reynolds number in the 

measuring length should usually be turbulent, e.g., above 2300. 

5.5.1 Velocity Profile14

If the flow measurement entails measuring velocity at discrete points across the pipe, the issue of 

the velocity profile is important. The following analysis provides insight on the velocity profile 

in a circular pipe with turbulent flow conditions for any fluid. At a peak velocity of 3 m/s in a 

24-inch (61-cm) Schedule 40 header for the HTF at operating temperature, the Reynolds number 

is about 6.5 106

, and it is fully established turbulent flow at that value. This fact sets the velocity 

profile based on fluid mechanics analysis and empirical data. The analysis starts with the 

knowledge that flow in a pipe is a bounded flow and is subject to no-slip boundary conditions on 

the walls and bounded by a limited region. But there is no established theory for pipe flow under 

a high Reynolds number. The only practical method to solve high-Reynolds pipe flow is using an 

empirical formulation derived from experimental data, which is called the log-overlap law. This 

formulation defines three layers for a pipe flow: an inner layer close to the wall, an outer layer 

close to the centerline, and an overlap layer between them. Laminar shear is dominant in the 

inner layer (<0.001% of radius) and turbulent shear dominates in the outer layer. The resulting 

flow profile for the example case stated above is shown in Figure 5-1. 

 

13 ISO 5167 (International Organization for Standardization) Measurement of fluid flow by means of pressure 

differential devices inserted in circular cross-section conduits running full. Part 1: General principles and 

requirements; Part 2: Orifice plates; Part 3: Nozzles and Venturi nozzles; Part 4: Venturi tubes 

14 Data provided by Dr. Guangdong Zhu of NREL. 
33 

Figure 5-1. Fully developed velocity profile in a pipe. (Courtesy of NREL)
34 

Table 5-3. HTF Flow Measurement Devices15

ID Instrument Type Units 

Differential Pressure Type 

Ultrasonic 

(Clamp-On) 

Ultrason

(Insert

Type) 

Orifice, Nozzle, & Venturi 

V-Cone (McCrometer) 

Averaging Port Pitot Tube 

Segmented Wedge 

01 Indicative Price Range $/D2 (D=inch) 46–220 (Venturi) 62–170 8–90 57–80 18–225 33–139 02 Volumetric Accuracy +/- % ≤ 1.0–1.5 ≤ 1.0 0.8–1.1 0.5 1.2 ≤ 1.0 03 Maximum Turndown Ratio --- 4:1 (and greater) 10:1 7.5:1–14:1 8:1 30:1 15:1–15004 Permanent Pressure Drop psi 0.5–1 (Venturi) 1–2 < 0.1 2–4 None None 05 “Vena-Contracta” Pressure Drop psi 4 (Venturi) Unknown 2–3 4–8 None None 06 Suitable for Operation at 600°F Yes/No Yes Yes Yes Yes Yes Yes 07 Suitable for Operation at 750°F Yes/No Yes Yes Yes ~Yes (735°F) Yes Yes 08 Calibration Requirements --- Moderate Moderate Significant Moderate Minimal Minima09 Repeatability % 0.1 0.1 0.1 0.2 0.15 0.1–0.3 10 Piping Requirements 

(Diameters Upstream, Downstream) 

Dia. 3–30U, 

3–10D 

0–3U, 

0–1D 

7–30U, 

3–30D 

5–6U, 

3–5D 

10–40U, 

5–10D 

10U, 

5D 

11 Pipe Size Limitations --- All Sizes All Sizes 

One mnfr 

limited to 

~24” 

All Sizes All Sizes All Size

12 ASME MFC Specification --- 3M --- 12M --- 5M 5M 13 Conclusion --- Suitable Suitable Suitable Suitable Suitable Suitabl

15 The HTF flow meters used for acceptance testing and normal operation at the Andasol plants in Spain are permanently installed clamp-on ultr

meters were calibrated with water. 
35 

Table 5-3 Notes General magnetic meters were not included because they will not work due to low HTF electrical conductivity. Coriolis meters were not includ

limitations. Insertion turbine meters are included, although accuracy is questionable. All meters are subject to severe inaccuracies if two-phase f

Units: Inches*2.54 = cm ; psi/14.5 = bar ; psi*6895 = Pa. 01 “D” is nominal pipe size in inches. In general, the lower end of the costs, in $/D2, are representative of 36” pipe; the higher costs are represe

portion of the insert-type ultrasonic, V-Cone®, segmented wedge, and Venturi meter costs include piping/components (i.e., pipe spool) that 

accounted for in the piping system costs where insertion or clamp-on devices are employed. 

02 Accuracy is over turndown range. Most listed accuracies are for calibrated meters. 03 The turndown ratios listed are for a maximum pipe velocity of 15 ft/s. 05 The “Vena-Contracta” pressure drop is the local pressure drop, a portion of which is recoverable. Users are cautioned that flashing or cavita

line pressure is present, which can void flow measurement. With proper system design and operation, an adequate pressure margin above Hobtainable. 

08 Minimal = Internal diagnostics only; Moderate = Calibration of secondary transmitter equipment; Significant = “Moderate” plus periodic in

components. See “Instrument Notes” below for details on calibration and inspection for each instrument type. 

10 Upstream and downstream straight length, expressed as pipe diameters, required for an accurate measurement. The upstream length requirem

flow-straightening vanes. 

13 It is not the intent to provide a recommended technology but rather to screen all flow measurement technologies and provide users with man

pros/cons of each. See “Instrument Notes” below on Insertion Turbine. The orifice plate/flow nozzle/Venturi differential pressure types gen

capabilities and have significant pressure drop (with the exception of Venturi, as shown above) and will likely not be acceptable for measur

high incident angles (i.e., for reliability testing). The low turndown capability will be an issue on the steam/feedwater side as well, where thtraditionally been used in performance testing. 

Instrument Notes Orifice/Nozzle/Venturi: Although these technologies are the most mature and are well accepted in ASME PTCs, turndown ratio and pressure d

their use, with the exception of Venturi type. Within the Venturi family of meter types, low pressure drop options are available. Although these have accurate turndown ratios of 3:1 to 4:1, there are claims by certain manufacturers of 10:1+ with certain Venturi applications. Further investig

Periodic calibration of the external, secondary instruments (e.g., temperature, static P, delta P) is required. V-Cone®: A V-Cone meter, manufactured by McCrometer, is a differential pressure device, measuring pressure before and after a conical insert

the pipe. One of its main advantages is its nonprohibitive upstream and downstream piping requirements, although its pressure drop must be takcalibration of the external, secondary instruments (e.g., temperature, static P, delta P) is required. 
36 

Averaging Port Pitot Tube: Kramer Junction Operating Company (KJOC) reported clogging and plating problems with this technology. After 

Rosemount (the manufacturer of the Annubar), it was determined that an older, diamond-shaped model was originally used at the SEGS plants, offering is a “T” shape. There are other manufacturers of averaging port pitot tube devices, one of which claims to have a flow profile that is inh

plugging. Similar to the orifice/nozzle/Venturi, annual calibration of the secondary, external transmitters can be done while online. The internal periodically for fouling, which can be performed online (if appropriate “hot-tap” accessories are installed), although no measurement will be tak

Segmented Wedge: The segmented wedge meter is a differential pressure device, somewhat similar to an eccentric Venturi meter, and has histo

type applications where plugging or erosion can occur. Information in the table is representative of a remote seal connection type. Periodic calibsecondary instruments (e.g., temperature, static P, delta P) is required. Ultrasonic: Many manufacturers are temperature limited, with only a few manufacturers capable of meeting the application’s temperature requi

offering a clamp-on style that meets the temperature requirements and has reasonable accuracy. Ultrasonic (UT) meters are the mainstay in natu

applications that have high accuracy, reliability, and turndown requirements. UT meters have demonstrated, in other applications, as low as 0.15

turndown ratio and much higher turndown ratios at reduced accuracy. Note that the superior maintainability of UT meters described in the KJO

to the use of clamp-on type transducers; high accuracy UT meters are usually the insert type (e.g., wetted-transducer). Internal self-diagnostics ctransmitter while remaining online. Access to wetted transducers can only be obtained if fluid is evacuated from the meter section, which should 

design (i.e., up/down stream isolation valves). The use of transducers (or more likely intervening materials due to high temperature) that are not theoretically erode (protruding type), or collect with sediment (cavity type), which can affect meter accuracy. Insertion Vortex: Most manufacturers are temperature limited and KJOC Appendix R did report turndown issues. The internal devices need to 

fouling, which can be performed online (if appropriate “hot-tap” accessories are installed), though no measurement will be taken during inspecti

Insertion Turbine: Although published product literature theoretically would lend one to include an insertion turbine meter as an option (i.e., m

and turndown requirements), the quote obtained for the specific application noted an accuracy range likely unacceptable for the testing purposes

maintenance issues with moving parts, although the turbine can be inspected and/or repaired online with proper installation of “hot-tap” accesso

16 Cohen, G.; Kearney, D.; Kolb, G. “Final Report on the O&M Improvement Program for CSP Plants,” Sandia National Lab Report SAND99-1
37 

5.5.2 Upstream/Downstream Requirements for Length/Diameter Ratio 

For the instruments identified in Table 5-3, a certain amount of straight pipe upstream and 

downstream of the instrument is recommended to ensure a fully developed flow to achieve the 

expected accuracy of the instrument. The amount of downstream straight length pipe 

length/diameter ratio (L/D) is 4:5 for all devices. The amount of upstream straight pipe depends 

on the instrument and the fittings and or valves upstream of the instrument, but is generally at 

least 10 L/D of straight piping. For example, for a 24-inch (61-cm) header, an L/D=10 is about 

20 ft (6 m) of straight pipe. 

Flow conditioning devices can be located upstream of the instrument to reduce the recommended 

amount of straight length, but such conditioning will also introduce additional pressure drop. 

5.6 Direct Normal Insolation 

5.6.1 Components of Solar Radiation 

Radiation can be transmitted, absorbed, or scattered by an intervening medium in varying 

amounts, depending on the wavelength. Complex interactions of the Earth’s atmosphere with 

solar radiation result in three fundamental broadband components of interest to CSP technologies 

(see Figure 5-2). These components are: 

• Direct normal insolation17 (DNI)—Solar radiation available from the solar disk 

• Diffuse horizontal irradiance (DHI)—Scattered solar radiation 

• Global horizontal irradiance (GHI)—Geometric sum of the component of DNI normal to 

a horizontal plane and DHI (total hemispheric irradiance). 

 

17 The terms “insolation” and “irradiance” are often used interchangeably. 

Figure 5-2. Components of solar radiation. 

(Courtesy of NREL)
38 

A concentrating solar collector uses direct (or beam) radiation to redirect specular rays to the 

receiver. 

5.6.2 Measurement Options 

High accuracy is required for keeping the uncertainty low in the Acceptance Testing procedure. 

DNI is a very important input to the solar system performance model, and the heat input to the 

solar field is directly proportional to the DNI and the cosine of the angle of the sun’s rays to the 

aperture of the collector. 

Two conventional devices—the rotating shadowband radiometer (RSR) and the 2-axis tracking 

pyrheliometer—are available to measure DNI, the first indirectly and the second directly. The 

pyrheliometer option is highly preferred for Acceptance Test purposes because of its accuracy, 

despite its cost. 

The data in Table 5-4 are from the report, T. Stoffel, et. al, CSP: Best Practices Handbook for the 

Collection and Use of Solar Resource Data, NREL/TP-550-47465, Sept 2010. 

Table 5-4. Estimated Direct Normal Sub-Hourly Measurement Uncertainties (Percent) 

Type A Error Source Ustd

TP# 

Ustd

Si^ 

Type B Error Source Ustd

TP# 

Ustd

Si^ 

“Fossilized” calibration 

error 

0.615 0.615 “Fossilized” calibration 

error 

0.665 0.665 

Data logger precision 

(± 50 μV/10mV)* 

0.5 0.5 Data logger precision 

(1.7 μV/10mV)* 

0.02 0.02 

Si detector cosine 

response 

0 0.5 Si detector cosine 

response 

0 1.5 

Pyrheliometer detector 

temperature response 

(D20°C) 

0.25 0.05 Detector temperature 

response 

0.25 0.05 

Pyrheliometer detector 

Linearity 

0.10 0.10 Day-to-day temperature 

bias (10oC) 

0.125 0.10 

Solar alignment 

variations 

(tracker or shade band) 

and pyranometer level 

for Si 

0.2 0.1 Solar alignment variations 

(tracker or shade band) 

and pyranometer level for 

Si 

0.125 0.10 

Pyrheliometer window 

spectral transmittance 

0.1 1.0 Pyrheliometer window 

spectral transmittance 

0.5 1.0 

Optical cleanliness 

(blockage) 

0.2 0.1 Optical cleanliness 

(blockage) 

0.25 0.1 

Electromagnetic 

interference and 

Electromagnetic field 

0.005 0.005 Electromagnetic 

interference and 

electromagnetic field 

0.005 0.005 

TOTAL Type A** 0.889 1.382 TOTAL Type B** 0.934 1.938 

# Thermopile detector used for a pyrheliometer. 

^ Silicon diode pyranometer detector used for an RSR. 

* Typical manufacturer specified accuracy: ± 0.05% of full-scale range (typically 50 mV) -25o to 50 oC; 

 assume 10 mV signal to ± 50 microvolts (μV)(0.5%) with 1.67 μV resolution (0.02%). 

** Summed under quadrature. 
39 

As noted in the NREL report, the combined uncertainty can be determined from the standard 

uncertainties in the table for Type A and Type B errors for each detector type by summing in 

quadrature. The resulting standard uncertainty is 1.3% for a pyrheliometer option and 2.4% for 

an RSR option. The expanded uncertainty, representing a 95% confidence interval as described 

in Section 6 of these Guidelines, is 2.6% and 4.8% for the pyrheliometer and RSR, respectively. 

Maintenance considerations—which are markedly simpler for the RSR device—figure into the 

choice of a unit for remote use, but are not important for the Acceptance Test application. Again, 

cost of the tracking pyrheliometer is high, but easily justified for this purpose. 

For large solar fields (e.g., a 250-MWe trough plant may have 1.0 to 2.5 million square meters of 

aperture area and occupy a land area about three times larger), multiple DNI instruments are 

advised, although this is primarily a matter of judgment and agreement between the test parties. 

Under clear skies, the DNI should be uniform over areas of this size. For mental calibration, be 

advised that a single satellite measurement cell covers an area of about 10 km x 10 km, or 100 

million square meters. Regarding solar field instrument placement, multiple instruments might 

be placed in discrete subsections of the solar field, which could translate to five or more 

instruments. 

5.7 HTF Physical Properties 

The properties of density and specific heat of the HTF are required to calculate the energy 

transferred out of the solar system into the heat-exchange train. The parties to the test must agree 

whether to accept the manufacturer’s table of properties for a newly purchased fluid, or to have 

random samples tested. Samples will typically be sent to authorized laboratories. Test costs are 

expected to be on the order of several hundred dollars per sample per property. Representative 

samples of the HTF in the system during the performance test should be obtained using the 

methods described in ASTM D 4057 or ASTM D 5287. HTF oil should have a relatively 

consistent composition, similar to fuel oil. If HTF properties could vary because of outside 

factors, such as a changing source of HTF, a more rigorous sampling program will be required to 

ensure representative samples. 

ASTM E-1269 is the standard typically used for specific heat measurements using digital 

scanning calorimetry (DSC). Multiple runs are required to reduce uncertainty in the 

measurements coming from a variety of contributors. It appears that the uncertainty in the 

specific-heat measurement can be on the order of ±1% to 3%, and it is not clear how much that 

property might vary over the large quantity of HTF in a solar field. It is also noted that HTF 

properties are typically measured at lower temperatures, and properties at the solar system 

operating temperature may differ somewhat at the higher temperatures. 

The measurement of fluid density is more accurate, and one supplier states that the measured 

values are accurate well under 1%. 

5.8 Mirror Reflectivity 

Mirror reflectivity is an important input value to the solar system performance model. The 

primary instrument used at NREL and at operating plants is the D&S Portable Specular 

Reflectometer Model 15R-USB,18 designed for field and laboratory measurements of flat or 

 

18 Information on this device is provided for information only and does not constitute a recommendation by NREL. 
40 

curved reflectors. According to the June 2009 NREL TroughNet site, the Model 15R has been 

extensively used at (all) the SEGS parabolic trough plants to measure mirror soiling for 

determining when mirror washing is required. 

According to the manufacturer and reports from solar-field use, a single operator can take 

reflectance readings at the rate on the order of 3 to 4 per minute and record the readings to data 

sets stored in the instrument. Current detailed data from the operating plants is lacking. A USB 

port is provided for maintaining and downloading data sets and upgrading the firmware. 

Uncertainty appears to be less than 1% with proper usage; human error in taking readings may be 

an important factor. A study performed by PSA (Spain) found the resulting uncertainty was less 

than 1% for a D&S 15R reflectometer, using 23 samples of back-silvered glass mirrors of 

different qualities and taking 228 measurements with different operators and ambient 

conditions.19 

 

 

19 Personal communication with Eduardo Zarza, CIEMAT, September 2010. 
41 

6 Evaluation of Measurement Uncertainty 

6.1 Measurement Uncertainty 

Due to various influences, any test result will have an associated uncertainty. The uncertainty 

interval around a measured result describes our lack of knowledge about the true value of a 

measured quantity. Uncertainty can be reduced by many repeated measurements and in some 

instances can be further reduced through the use of redundant instruments.20 The uncertainty of 

an interval about a measured value is usually expressed with a probability or level of confidence. 

Uncertainties arise from possibilities for error arising from or classified as: systematic errors, 

random errors, and human errors. It is very important in the measurements discussed here to 

quantify the uncertainty intervals to make judgments on the validity of the test results.21

Test uncertainty is an important element within any performance test code. ASME in particular 

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

associated with performance test codes. Therefore, significant attention has been paid to this 

aspect of the Guidelines. ASME PTC 19.1 - Test Uncertainty is devoted to this topic, and 

sections in some other codes address uncertainty analysis related to their respective topics. 

Because of the resource variability and imperfections in control systems, variation in all of the 

measured parameters is inevitable. The frequency and period of data collection directly impacts 

the test uncertainty, and it is highly recommended that a pre-test uncertainty analysis be carried 

out prior to selection and subsequent installation of any instrumentation. An acceptance test will 

typically consist of more than one test run (data collected during a period of time in which the 

measured parameters are relatively steady). Conducting more than one test in addition to a posttest uncertainty analysis is recommended to verify the repeatability of the test results and the 

validity of the pre-test uncertainty analysis. 

The systematic error associated with a measurement of a single parameter can come from many 

sources, including the calibration process, instrument systematic errors, transducer errors, and 

fixed errors of method. The test engineer should be diligent in identifying all of these sources of 

error, although it is often the case that one or several will dominate within a particular 

measurement parameter. 

Random errors can similarly be based on a manufacturer’s specification. However, the random 

uncertainty for a given measurement can be reduced based on repeated measurements over the 

interval in which the system is considered to be at steady state (defined by a minimal change in 

the ANI plus HTF flow control over the test period, such that the effects of thermal exchange 

between the HTF and solar field piping are negligible). For repeated measurements, the random 

standard uncertainty can be defined by 

ݏ௑ത ൌ ܵ௑⁄√ܰ (Eqn. 6-1)

 

20 Redundant instruments are most readily used to reduce random errors associated with a measurement. To reduce 

systematic errors, the test engineer must calibrate the redundant instruments at different calibration laboratories to 

ensure that errors between the instruments are independent. 

21In ASME PTCs, a great deal of attention is paid to uncertainty estimates. See, for example, PTC 19-1 - Test 

Uncertainty and PTC 4-2008 - Fired Steam Generators, Sec. 7. 
42 

where SX is standard deviation of a series of sampled data and N is the number of data points 

collected over the test interval (e.g., 180 data points for a 30-minute test with data collected at 

10-second intervals). 

An example calculation is described below using the PTC 19.1 principles and notation, and it is 

described in detail below.22 The purpose is to describe how the uncertainties in each of the 

measured variables X associated with an acceptance test propagate into the value of a calculated 

resulting quantity R. 

Calculated results, such as the delivered power (Eqn. 3-1) and the solar thermal efficiency (Eqn. 

3-2), are not typically measured directly, but rather, are based on parameters measured during the 

course of one or multiple acceptance tests. For this case, the result, R, is a function of individual 

or average values of these independent parameters as described by 

ܴ ൌ ݂ሺܺଵ തതത, ܺଶ തതത,...,ܺప ഥ ሻ (Eqn. 6-2)

where the subscript i describes the number of parameters used in the calculation of the result and 

ܺത is either the value of a single measurement of the parameter or the average value of the 

parameter based on a number of N repeated measurements. 

The expression for the combined standard measurement uncertainty of a calculated result 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 error sources23

ݑோ ൌ ሾሺܾோሻଶ ൅ ሺܵோሻଶሿ

ଵ/ଶ (Eqn. 6-3)

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

uncertainty of a result as calculated by 

ܾோ ൌ ൤∑ ቀ డோ

డ௑ത೔

ܾ௑ഢ തതതቁ

ଶ

ூ

௜ୀଵ ൨

ଵ/ଶ

 (Eqn. 6-4) 

ܵோ ൌ ൤∑ ቀ డோ

డ௑ത೔

ݏ௑ഢ തതതቁ

ଶ

ூ

௜ୀଵ ൨

ଵ/ଶ

 (Eqn. 6-5)

 

22 Although this section is consistent with the methodology and notation used in PTC 19.1, the reader can 

additionally refer to “Measurement Uncertainty: Methods and Applications” by Ronald H. Dieck, ISA, Fourth 

Edition. This book describes the ASME methodology (among others) and uses consistent notation to the information 

presented in this Guideline. 

23 See ASME PTC 19.1 or Dieck for exceptions to this case. 
43 

For the equations above, ܾ௑ത is defined as the systematic standard uncertainty24 of a component 

and ݏ௑ത is the random standard uncertainty of the mean of N measurements. Definitions for the 

standard systematic and random uncertainty, as well as the methodology for calculating these 

values, are described in detail in PTC 19.1 and Dieck. 

The total standard measured uncertainty of the calculated result given by Eqn. 6-2 can be 

calculated using the methodology described above. It is important to note that this “standard” 

uncertainty implies that the calculated result will capture the true result within a 68% confidence 

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

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

ܷோ,ଽହ ൌ 2ݑோ (Eqn. 6-6)

Example Calculation of Total Uncertainty for the Measurement of Solar Field Power 

Applying Eqn. 6-4 to Eqn. 3-1 for the calculated solar field power, we get the following 

equation for the standard systematic uncertainty of the result25: 

ܾோ

ଶ ൌ ൬

߲ܴ

߲݉ ܾ௠൰

ଶ

൅ ቆ ߲ܴ

௣ܥ߲

ܾ஼೛ቇ

ଶ

൅ ൬ ߲ܴ

߲ܶ௛௫௜௡

்ܾ೓ೣ೔೙൰

ଶ

൅ ൬ ߲ܴ

߲ܶ௛௫௢௨௧

்ܾ೓ೣ೚ೠ೟൰

ଶ

 ൌ ቀܥ௣ሺܶ௛௫௜௡ െ ܶ௛௫௢௨௧ሻቁ

ଶ

ܾ௠ଶ ൅ ൫݉ሺܶ௛௫௜௡ െ ܶ௛௫௢௨௧ሻ൯

ଶ

ܾ஼೛

ଶ ൅ ൫݉ܥ௣൯

ଶ

்ܾ೓ೣ೔೙

ଶ ൅

 ൫݉ܥ௣൯

ଶ

்ܾ೓ೣ೚ೠ೟

ଶ

 (Eqn. 6-7)

Similarly, Eqn. 6-5 can be used to derive the equation for the absolute standard random 

uncertainty of the result. 

ܵோ

ଶ ൌ ቀܥ௣ሺܶ௛௫௜௡ െ ܶ௛௫௢௨௧ሻቁ

ଶ

ܵ௠ଶ ൅ ൫݉ሺܶ௛௫௜௡ െ ܶ௛௫௢௨௧ሻ൯

ଶ

ܵ஼೛

ଶ ൅ ൫݉ܥ௣൯

ଶ

்ܵ೓ೣ೔೙

ଶ ൅

 ൫݉ܥ௣൯

ଶ

்ܵ೓ೣ೚ೠ೟

ଶ (Eqn. 6-8)

 

24 The test engineer should be careful in understanding what uncertainty may be represented by a manufacturer for a 

given measurement device. The standard uncertainty implies a 68% (one standard deviation) confidence level that 

the systematic error will fall within the uncertainty limits. The manufacturer may provide information that a 95% 

confidence (two standard deviations) level was chosen for the uncertainty. For this case, the test engineer should 

typically divide this value by 2 for use in this analysis. As a conservative estimate (for which the manufacturer does 

not state a confidence level), one should assume that the value provided is based on one standard deviation. 

25 In actual practice, volumetric flow would likely be measured in lieu of mass flow resulting in a revised equation 

for power, Pൌ ρVC୮ ൫T୦୶,୧୬ െ T୦୶,୭୳୲൯, where both ρ and C୮ are a function of the HTF temperature. A more 

detailed analysis of the uncertainties associated with the measurement of solar field power and efficiency would 

need to include these expanded terms. 
44 

Example Calculation of Total Uncertainty for the Measurement of Solar Field Efficiency 

The above methodology can be applied identically to the equations for solar field efficiency to 

arrive at estimated uncertainties. The resulting equation for the standard systematic uncertainty 

associated with the solar field efficiency based on ANI is 

ܾோ

ଶ ൌ ቆ ஼೛

஽ேூ·ୡ୭ୱ஘·஺ೌ೛೐ೝ೟ೠೝ೐

ሺܶ௛௫௜௡ െ ܶ௛௫௢௨௧ሻቇ

ଶ

ܾ௠ଶ ൅

ቆ ௠

஽ேூ·ୡ୭ୱ஘·஺ೌ೛೐ೝ೟ೠೝ೐

ሺܶ௛௫௜௡ െ ܶ௛௫௢௨௧ሻቇ

ଶ

ܾ஼೛

ଶ ൅ ൬ ௠஼೛

஽ேூ·ୡ୭ୱ஘·஺ೌ೛೐ೝ೟ೠೝ೐൰

ଶ

்ܾ೓ೣ೔೙

ଶ ൅

൬ ௠஼೛

஽ேூ·ୡ୭ୱ஘·஺ೌ೛೐ೝ೟ೠೝ೐൰

ଶ

்ܾ೓ೣ೚ೠ೟

ଶ ൅ ൬ ௠஼೛ሺ்೓ೣ೔೙ି ்೓ೣ೚ೠ೟ሻ

ሺ஽ேூ·ୡ୭ୱ஘ሻమ · ஺ೌ೛೐ೝ೟ೠೝ೐൰

ଶ

ܾ஽ேூ

ଶ ൅

൬ ௠஼೛ሺ்೓ೣ೔೙ି ்೓ೣ೚ೠ೟ሻ

஽ேூ·ୡ୭ୱ஘ · ஺ೌ೛೐ೝ೟ೠೝ೐మ൰

ଶ

ܾ஺ಲ೛೐ೝ೟ೠೝ೐

ଶ

 (Eqn. 6-9) 

and 

ܵோ

ଶ ൌ ቆ ஼೛

஽ேூ·ୡ୭ୱ஘·஺ೌ೛೐ೝ೟ೠೝ೐

ሺܶ௛௫௜௡ െ ܶ௛௫௢௨௧ሻቇ

ଶ

ܵ௠ଶ ൅

ቆ ௠

஽ேூ·ୡ୭ୱ஘·஺ೌ೛೐ೝ೟ೠೝ೐

ሺܶ௛௫௜௡ െ ܶ௛௫௢௨௧ሻቇ

ଶ

ܵ஼೛

ଶ ൅ ൬ ௠஼೛

஽ேூ·ୡ୭ୱ஘·஺ೌ೛೐ೝ೟ೠೝ೐൰

ଶ

்ܵ೓ೣ೔೙

ଶ ൅

൬ ௠஼೛

஽ேூ·ୡ୭ୱ஘·஺ೌ೛೐ೝ೟ೠೝ೐൰

ଶ

்ܵ೓ೣ೚ೠ೟

ଶ ൅ ൬ ௠஼೛ሺ்೓ೣ೔೙ି ்೓ೣ೚ೠ೟ሻ

ሺ஽ேூ·ୡ୭ୱ஘ሻమ · ஺ೌ೛೐ೝ೟ೠೝ೐൰

ଶ

ܵ஽ேூ

ଶ ൅

൬ ௠஼೛ሺ்೓ೣ೔೙ି ்೓ೣ೚ೠ೟ሻ

஽ேூ·ୡ୭ୱ஘ · ஺ೌ೛೐ೝ೟ೠೝ೐మ൰

ଶ

ܵ஺ಲ೛೐ೝ೟ೠೝ೐

ଶ (Eqn. 6-10) 

Tables 6-1 through 6-4 summarize uncertainty data and results derived from the methodology 

described above as applied to the solar field power and efficiency calculations, and they can be 

considered an example of a pre-test uncertainty analysis. Although any such analysis must be 

undertaken with the specific system and instrumentation in mind, the systematic and random 

uncertainties of measurement parameters given in the tables represent what may occur in a 

typical field installation.26

 

26 Uncertainties within this Guideline are estimates based on discussions with various experts. Actual uncertainties 

will depend on specific solar field instrumentation and calibration procedures associated with an acceptance test. 
45 

Table 6-1. 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 6-2. 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% 

 
46 

Table 6-3. Table of Data – Solar Field Efficiency 

 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 തതത

૛

Absolute 

Random 

Standard 

Uncertainty 

Contribution 

߲ܴ

߲ܺത௜

ࢄࡿଙ തതത

૛

 

m 

Mass flow 

rate kg/s 1200 3.5 180 12 0.3 0.0006 0.000057 0.0000000 

Cp 

HTF specific 

heat 

kJ/kgK 2.48 0.007 30 0.031 0.0013 0.3040 0.000089 0.0000001 

Thxin 

Hot HTF 

temperature oC 393 1.2 180 1.0 0.09 0.0073 0.000054 0.0000004 

Thxin 

Cold HTF 

temperature oC 290 1.1 180 1.0 0.08 0.0073 0.000054 0.0000004 

DNI 

Direct 

Normal 

Insolation 

J/sm2 950 11.2 180 12.5 0.8 0.8 0.000098 0.0000004 

AAperture

Collector 

Aperture 

SM=1 m2 425000 -- -- -- -- -- -- -- 

 

Table 6-4. Summary of Data – Solar Field Efficiency 

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 (%)

η

Solar Field 

Efficiency -- 0.754 0.018 0.001 0.018 0.035 4.7% 
47 

7 Comments on Test Procedures and Plan 

The test plan needs to be organized and developed according to the specific solar system design 

and to carefully consider both the previous discussion and ASME test code information to arrive 

at uncertainty results that are agreed upon by the test parties. For example, ASME PTC 4-2008 – 

Fired Steam Generator gives excellent guidance on preparations and guidance of performance 

testing. That code recommends that the preparation for testing include: 

• Indisputable records on the equipment, test procedure, instrumentation characteristics 

locations, and calibrations 

• Agreements on a wide number of issues that might later lead to misunderstandings 

• Preliminary test runs to check out all aspects of the performance acceptance test 

procedures and data reduction 

• Issues related to the conduct of the tests, e.g., preparation, starting and stopping, and 

readjustment of equipment or instruments during the test run periods 

• Conduct of test and data collection. 

Further recommendations cover instruments, operating conditions, and records. 

7.1 Test Plan 

The Test Plan in the example organization scenario of these Guidelines would be prepared by the 

EPC contractor, with comments by the technology provider and likely an independent 

engineer(s) representing the owner and debt providers. The plan translates guiding principles 

into a detailed program related to the specifics of the solar system being tested, based on the 

contractual agreements between the test parties. It is crucially important to cover all aspects of 

the testing and to document all agreements and methods. Instrumentation would typically be 

identified by instrument type and measurement, with location later verified visually in the field. 

More instrument details, such as serial numbers and calibration information, would be provided 

as they become available. Testing plans should be spelled out in detail as to purpose, duration, 

methods, data reduction, and pass/fail criteria. Test reports should be complete, with ample use 

of photographs and diagrams. For further information, see: PTC 4-2008 – Fired Steam 

Generator; PTC 46 – Overall Plant Performance; PTC 50 - Performance Test Code for Fuel 

Cell Power Systems Performance; and PTC 19.1 - Test Uncertainty. 

To be clear, an independent Test Plan document must be written, based on these Guidelines 

and/or applicable ASME test codes, defining details of the performance acceptance testing. That 

document will become part of the contractual agreement between the party turning over the solar 

system and the party taking control of the solar system. Said differently, the Test Plan is a 

commercial agreement that will dictate the required testing for the transaction. The following 

bullet points and example outline in Section 7.1.3 are provided to show some elements of the use 

of the Guidelines in both the contract agreements and the test plan itself. In a general sense, the 

Guidelines (and eventually, an ASME PTC) provide the technical basis to produce a Test Plan 

that is specific to the project configuration, selected instrumentation, and other elements 

particular to the solar system under test. 
48 

7.1.1 Example of Contract Agreements Prior to Formulation of a Test Plan27

• Total plant electrical performance guarantees are agreed to prior to signing of the EPC 

contract. Typically, only one or at most two system load points are guaranteed, usually at 

design capacity or near design capacity. Subsystem guarantees would typically be set at 

that time. Guarantees and requirements for the solar system may vary considerably 

between projects, EPC firms, and the solar system technology provider. 

• To provide a wrap-around warranty, the usual EPC practice is to get back-to-back 

performance guarantees from major equipment suppliers. It is common to strive to pass 

through major equipment supplier terms, conditions, and exceptions to the contract. 

• The performance test procedure is not developed prior to contract signing; however, the 

contract language typically includes "mutually agreed performance test procedure to be 

developed and approved by owner ~6 months prior to testing." The contract will also 

include major test-related principles so that no surprises occur when the test procedure is 

issued. Test procedures typically follow the appropriate ASME PTC, including sample 

calculations, correction curves (if any), and sometimes a calculation spreadsheet. 

• A contract typically requires demonstration of values less than the guarantees (perhaps by 

5%) prior to retirement of scheduled liquidated damages (substantial completion). This is 

known as "Minimum performance or minimum performance levels." Achievement of 

guarantees (or better) is to be performed prior to Final Completion. The duration typically 

allowed for Substantial Completion to Final Completion is about 6 months to 1 year. 

• Contracts typically allow payment of performance-liquidated damages in lieu of 

achieving guarantee values, provided "minimum performance" criteria are achieved. 

7.1.2 Some Elements of a Test Plan 

• The guarantee values typically are included in the prime contract. Test specifics are 

usually covered in an Exhibit to the prime contract. 

• The contract defines any commercial tolerance (which may or may not be linked to test 

uncertainty) allowed for past/fail criteria. 

• Contract-defined tests are to follow appropriate ASME PTCs as possible. Because the 

ASME PTC on CSP systems (PTC 52) is currently under development and appears to be 

several years away from the issue date of these Guidelines, we recommend that these 

Guidelines and ASME PTCs such as those on uncertainty and instrumentation be among 

the sources used to formulate the Test Plan. 

• Both the EPC contractor and Owner nominate a person authorized to approve variations 

in test procedures, equipment/valve line-up, etc. 

• The Owner typically has an Owners Engineer or third-party testing contractor acting on 

their behalf. 

 

27 Comments on these two pages extracted from personal communication with Marcus Weber, Fluor, December 

2009, and David Ugolini, Bechtel Power Corp., July 2010. 
49 

• In practice, it is recommended that over test periods of several hours, or even days, per 

agreement between parties, data be taken continuously via the data acquisition system 

and processed in a post-test period through data reduction software. These data will not 

only document the Energy Test results, but will also serve to determine whether valid test 

run periods of 15 to 30 minutes can be identified in the Power Test. If desired, the data 

could be examined in real time using the same techniques to monitor progress through the 

test period. 

• Preliminary test results/reports are typically required soon after test completion. The 

final test report is due later and includes laboratory analysis of fuel samples, test data, 

operator logs, and calculations. (A typical example list is in ASME PTC 46 – Overall 

Plant Performance.) 

7.1.3 Sample Test Report Outline28

1. Scope and Objective 

2. Definitions 

3. Testing Methodology 

 Measured Parameters 

 Modeled Parameters 

 System Operation 

 Equipment in Service 

 DCS and Manual Data Collection 

 Description of Performance Model 

 Data Processing and Model Run 

 Responsibilities of Parties 

4. Testing Procedure 

 Pre-test Plant Condition 

 Operation prior to Data Collection 

 Operation during Testing 

 Operation after Testing 

5. Data Collection and Analysis 

 Calculation Procedures 

 Correction Factors 

 Uncertainty 

 Data and Results Distribution 

 

28 Based on input from M. Weber, Fluor and M. Henderson, and R.W. Beck 
50 

 Acceptance Criteria 

6. Appendices 

 Valve Positions 

 Instrument Data Points 

 Test Results 

 Performance Analysis 

 Representative Model Input/Output 

 HTF Thermal Properties 

 Sample Calculation 

7.2 Further Observations 

7.2.1 General 

In principle, the fundamental measured parameters and guiding equations for this Acceptance 

Test are straightforward, as shown in Section 3. The data required and computational methods 

for determining the performance of utility-scale solar thermal systems have been discussed in 

some detail in the previous sections, including data acquisition principles, instruments, and 

methods of measurement. Nevertheless, it is important to emphasize that there are many 

important considerations that must be addressed to ensure high-quality results. For this 

Acceptance Test, we reiterate a few important factors: 

• Thermal equilibrium and stabilized conditions in the test runs for solar thermal power 

output and efficiency 

• Acceptable measurement techniques for temperature, volumetric flow rate, and DNI, with 

all calibrations current 

• Accurate HTF property values that are accepted by the principal parties involved in the 

testing 

• Suitable locations for test measurements to ensure accurate results for a large solar 

system 

• Acceptable uncertainty analyses throughout the test(s), with predetermination of 

systematic and random uncertainties 

• Complete test logs and records of test data and data reduction methods 

• Preliminary (practice) test runs to identify any problems or inconsistencies 

• Pre-agreed-upon methods to deal with test anomalies and inconsistencies during testing. 

Relevant ASME performance test codes contain excellent, more detailed information about best 

practices for conducting performance acceptance tests. For example, they provide good guidance 

on measurement data reduction, including calibration corrections, handling test-point outliers, 

methods of averaging data, if required, and computations of random and systematic uncertainty. 

PTC 46 – Overall Plant Performance and PTC 4-2008 – Fired Steam Generators offer good 
51 

examples. Complete records of test data and data reduction methods are critical. All instrument 

calibrations should be current. 

7.2.2 Summary of Agreements To Be Made Between Test Parties 

The following list summarizes the various topics noted in these Guidelines that must be agreed to 

by the Test Parties: 

• Complete procedures and Test Plan for performance acceptance tests 

o Structure of tests (see below) 

o Selection and placement of flow and temperature instrumentation at solar subfields 

and at HX trains 

o Pass/fail criteria for evaluation of test results 

o Schedule of mirror washing during tests, pattern of reflectivity measurements, and 

basis for soiling factor to be used in performance model 

o Test schedule(s) 

o Requirements for demonstration of rated solar thermal design power capacity and 

rated solar thermal design efficiency 

o Selection of ANI or DNI as basis for efficiency measurement 

o Method of comparison between measured performance and model projected 

performance, and method of incorporation, or not, of test uncertainties into this 

comparison 

o Decision on measurements. 

 HTF properties (density; specific heat) 

 Multiple DNI locations 

 Single sensor or array of sensors at pipe measurement locations for flow and 

temperature measurements. 

• Solar System performance model 

o Model selection (recommend model used for commercial pro forma performance 

projections) 

o Input data set to model (both fixed and per test values). 

• Structure of short duration steady-state Thermal Power Test 

o Steady-state test run duration 

o Repeated number of steady-state tests per day, number of test days, and duration of 

multi-day steady-state test period 

o Criteria that would cause a temporary interruption in the agreed-upon test period. 

• Structure of multi-day Continuous Energy Test 

o Duration of test period. 

• Uncertainty analysis 
52 

o Random and systematic uncertainties for each parameter 

o Exact equations and methods to be used 

o Pre-test uncertainty analysis 

o Data collection 

o Post-test analyses. 

• Test report 

o Complete details and table of contents of test report 

o Data reduction procedures and equations 

o Schedule for completion.