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

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

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

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

Contract No. DE-AC36-08GO28308

Evaluation Framework and 

Analyses for Thermal Energy 

Storage Integrated with 

Packaged Air Conditioning

Feitau Kung, Michael Deru, and Eric Bonnema

National Renewable Energy Laboratory

Technical Report

NREL/TP-5500-60415

October 2013
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

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

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

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Evaluation Framework and 

Analyses for Thermal Energy 

Storage Integrated with 

Packaged Air Conditioning 

Feitau Kung, Michael Deru, and Eric Bonnema

National Renewable Energy Laboratory

Prepared under Task No. BEC8.1122

Technical Report

NREL/TP-5500-60415

October 2013
NOTICE

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

owned rights. Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

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

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

Available electronically at http://www.osti.gov/bridge

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/help/ordermethods.aspx

Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

 Printed on paper containing at least 50% wastepaper, including 10% post consumer waste.
i 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Acknowledgments 

The authors would like to thank the U.S. Department of Energy (DOE) Building Technologies 

Office for its support of the project. This report was prepared by the National Renewable Energy 

Laboratory (NREL) under Task Number BEC8.1122. 

The authors greatly appreciated the valuable feedback about prior experiences, current industry 

needs, model approaches, and other grid considerations received from multiple individuals, 

including: Dennis Lilley, David Rubalcava, and Thomas Whitaker of Austin Energy; Eva 

Gardow of FirstEnergy; John Nix of Florida Power and Light; Bryan Cope of the Southern 

California Public Power Authority (SCPPA); Justin Hill of Southern Company; Ram 

Narayanamurthy of the Electric Power Research Institute (EPRI); Brian Parsonnet, Greg Tropsa, 

and Robert Willis of Ice Energy Technologies; and Stephen Frank, Edwin Lee, Ian Doebber, 

Paul Denholm, and Debra Lew of NREL. 

The authors would like to thank Brent Griffith of NREL for his work in developing new thermal 

energy storage (TES) modeling capabilities in EnergyPlus that were critical for this project. The 

authors also thank EPRI, Ice Energy Technologies, and their respective commercial building and 

utility collaborators for providing performance data from sites with TES systems. The authors 

also would like to thank the following individuals for reviewing drafts of various sections of this 

document: Mark MacCracken of Calmac; Ram Narayanamurthy of EPRI; John Nix of Florida 

Power and Light; Brian Parsonnet and Robert Willis of Ice Energy Technologies; Bryan Cope of 

SCPPA; Justin Hill of Southern Company; and Stephen Frank, Brent Griffith, Bob Hendron, and 

Daniel Studer of NREL. 
ii

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Executive Summary

Packaged air-conditioning (AC) systems are found in many commercial buildings. The Energy 

Information Administration estimated that in 2003, 1.6 million commercial, non-mall buildings 

in the United States were cooled with packaged AC units, and these buildings collectively 

represent about 30 billion ft2 of floor space (EIA 2006). This suggests that if thermal energy 

storage (TES) can be cost effectively integrated with packaged AC, the technology’s deployment 

potential could be high. 

Few third-party guidance documents or tools are available for evaluating TES integrated with 

packaged AC, as this technology category is relatively new compared to TES integrated with 

chillers or hot water systems. To address this gap, a project was conducted to improve the ability 

of potential technology adopters to evaluate TES technologies. The target audience for this work 

includes engineers and analysts who evaluate commercial building technologies for building 

owners and utilities. Major project outcomes included: 

1. An evaluation framework was produced to describe key metrics, methodologies, and 

issues to consider when assessing the performance of TES systems integrated with 

packaged AC. 

2. The project applied multiple concepts from the evaluation framework to analyze 

performance data from four demonstration sites.

3. A new simulation capability was produced to enable modeling of TES integrated with 

packaged AC in EnergyPlus, a whole building energy simulation application that is 

available for free to the public. The new simulation capability will be included in 

EnergyPlus, version 8.1, in the fall of 2013. An early version of this feature was used to 

model one combination of a TES technology and control strategy. The performance of 

this application was simulated across multiple U.S. climate zones and three building 

types: office, stand-alone retail, and strip mall.

The analyses show that TES integrated with packaged AC can successfully shift electric demand 

and energy to off-peak hours in a variety of circumstances. Preliminary analyses show that such 

strategies can increase or decrease site energy consumption, depending on site-specific 

conditions. In either case, for a comprehensive assessment of TES impacts, site metrics must 

then be converted to source metrics; for such calculations, this report identifies methods, 

example sources of data, and needs for future improvements to public data that can be used to 

evaluate source metrics. 

The analyses also indicate that emissions impacts are sensitive to hourly variations in load 

profiles. The results suggest that emissions and other source metrics may be sensitive to 

alternative control strategies that could be tested in the future to respond to real-time pricing 

signals, generate additional utility benefits, or improve renewable energy integration.
iii

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Nomenclature

AC air-conditioning 

AMPD Air Markets Program Data

ASHRAE American Society of Heating, Refrigerating and Air-Conditioning Engineers 

BPA Bonneville Power Administration

DOE U.S. Department of Energy 

DX direct expansion

EIA Energy Information Administration

EPA U.S. Environmental Protection Agency 

EPRI Electric Power Research Institute

ERCOT Electric Reliability Council of Texas

HVAC heating, ventilation, and air-conditioning 

NREL National Renewable Energy Laboratory

PCM phase change material

RTU rooftop unit 

TDSP Transmission/Distribution Service Provider

TES thermal energy storage
iv

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Contents

Acknowledgments ....................................................................................................................................... i

Executive Summary.................................................................................................................................... ii

Nomenclature ............................................................................................................................................. iii

Contents......................................................................................................................................................iv

Figures and Tables ....................................................................................................................................vi

Figures ................................................................................................................................................... vi

Tables ................................................................................................................................................... vii

1.0 Introduction........................................................................................................................................... 1

1.1 Target Audience ............................................................................................................................... 1

1.2 Technology Basics ........................................................................................................................... 2

1.3 Potential Impacts .............................................................................................................................. 3

1.4 Evaluating Performance at Different Levels of Detail ..................................................................... 5

1.4.1 General Performance Characteristics of Energy Storage Technologies ............................... 5

1.4.2 Metrics for Standardized Product Testing and Performance Rating Specific to a 

Technology Category ................................................................................................................ 6

1.5 Timescales for Collecting Data or Reporting Metrics ..................................................................... 6

2.0 Evaluation Framework: Site Power, Site Energy, and Associated Costs...................................... 8

2.1 Rate Structure Variations ................................................................................................................. 8

2.2 TES Impacts on Site Demand and Energy ....................................................................................... 9

2.3 Basic Considerations for Reviewing Data from Prior Demonstrations ......................................... 10

2.4 Combining Laboratory and Field Data with Simulations .............................................................. 11

2.5 Coordination with Other Facility Needs ........................................................................................ 13

3.0 Evaluation Framework: Translating Site Impacts into Source Impacts.......................................15

3.1 Source Energy Calculations for Building Owners’ Engineers ....................................................... 16

3.2 Source Emissions Calculations for Building Owners’ Engineers .................................................. 17

4.0 Evaluation Framework: Utility Impacts and Collaboration with Building Owners .....................19

4.1 Potential Utility Benefits................................................................................................................ 19

4.2 Load Leveling and Enhanced Renewable Energy Integration ....................................................... 21

4.3 Alternative Models for Building Owner Participation ................................................................... 25

5.0 Analysis of Performance Data from Demonstration Sites .............................................................26

5.1 Site Performance Data from Project Partner 1 ............................................................................... 26

5.1.1 Site 1 ................................................................................................................................... 27

5.1.2 Site 2 ................................................................................................................................... 33

5.2 Site Performance Data from Project Partner 2 ............................................................................... 36

5.2.1 Site 3 ................................................................................................................................... 36

5.2.2 Site 4 ................................................................................................................................... 40

5.3 Comparison of Source Metrics ...................................................................................................... 44

6.0 Simulation-Based Analysis of a TES System ..................................................................................47

6.1 EnergyPlus Feature Addition ......................................................................................................... 47

6.2 High-Level Check of Model Behavior........................................................................................... 47

6.2.1 Whole-Building Energy Model Development Process ....................................................... 48

6.2.2 TES Model Simulation Checks ........................................................................................... 49

6.3 Extension to Nationwide Climate Zones and Additional Example Building Types ...................... 51

6.3.1 Whole-Building Energy Model Development Process ....................................................... 52

6.3.2 Example Simulation Results for Site Metrics ..................................................................... 53

6.3.3 Future Data Required for Evaluating Source Energy and Emissions ................................. 55

7.0 Conclusions ........................................................................................................................................57

8.0 References ..........................................................................................................................................59
v 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Appendix A Additional Considerations for Source Energy Calculations ....................................62

A.1 Source Energy Factors ................................................................................................................... 62

A.2 Distribution Losses ........................................................................................................................ 64

A.3 Transmission Losses ...................................................................................................................... 69

A.4 Generation Plant Efficiency ........................................................................................................... 70

A.5 Precombustion Contributions to Source Energy ............................................................................ 72

A.6 Derivation of Average and Marginal Emission Factors ................................................................. 72

Appendix B Additional Background for Demonstration Data Analysis .......................................74

Appendix C Additional Simulation Results .....................................................................................77
vi 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figures and Tables 

Figures

Figure 5-1 Example 24-hour period of performance data from Site 1 .............................................. 28

Figure 5-2 Cooling electricity demand by tariff period for Site 1...................................................... 30

Figure 5-3 Summer cooling electricity demand for Site 1 ................................................................. 30

Figure 5-4 Cooling energy by tariff period for Site 1.......................................................................... 31

Figure 5-5 Summer cooling energy for Site 1 ..................................................................................... 31

Figure 5-6 Example 24-hour period of performance data from Site 2 .............................................. 33

Figure 5-7 Cooling electricity demand by schedule for Site 2 .......................................................... 34

Figure 5-8 Cooling energy by schedule for Site 2 .............................................................................. 35

Figure 5-9 Example 24-hour period of performance data from Site 3 .............................................. 37

Figure 5-10 Cooling electricity demand by schedule for Site 3 ......................................................... 38

Figure 5-11 Cooling energy by schedule for Site 3 ............................................................................. 38

Figure 5-12 Site 3 cooling electricity demand by hour for an example month................................. 40

Figure 5-13 Example 24-hour period of performance data from Site 4 ............................................. 41

Figure 5-14 Cooling electricity demand by schedule for Site 4 ......................................................... 42

Figure 5-15 Cooling energy by schedule for Site 4 ............................................................................. 43

Figure 5-16 Estimated emissions comparison for Site 1 .................................................................... 45

Figure 5-17 Estimated emissions comparison for Site 2 .................................................................... 45

Figure 6-1 June simulation results for the modified reference building model .............................. 50

Figure 6-2 June Site 1 performance scaled to the building’s total cooling capacity...................... 51

Figure 6-3 DOE climate zones (Credit: DOE 2004) ............................................................................ 52

Figure 6-4 Small office model peak demand differences for an example month............................ 54

Figure 6-5 Stand-alone retail model peak demand differences for an example month.................. 54

Figure 6-6 Strip mall model peak demand differences for an example month................................ 55

Figure A-1 Annual average distribution loss factors by time of day for ERCOT TDSPs................ 66

Figure A-2 Seasonal average distribution loss factors by time of day for Nueces Electric 

Cooperative with Loss Code A.......................................................................................................... 68

Figure A-3 Seasonal average ERCOT transmission load factors by time of day............................ 69

Figure A-4 Aggregated hourly electricity generation efficiency in August 2011, from Texas 

plants reporting emissions to the EPA Clean Air Markets Program............................................. 71

Figure C-5 Small office model: May facility electricity peak demand.............................................. 78

Figure C-6 Small office model: June facility electricity peak demand ............................................ 78

Figure C-7 Small office model: July facility electricity peak demand ............................................. 79

Figure C-8 Small office model: August facility electricity peak demand ........................................ 79

Figure C-9 Small office model: September facility electricity peak demand.................................. 80

Figure C-10 Small office model: October facility electricity peak demand....................................... 80

Figure C-11 Small office model: cooling electricity energy consumption........................................ 81

Figure C-12 Small office model: facility electricity energy consumption ......................................... 81

Figure C-13 Small office model: comparison of monthly load factors.............................................. 82

Figure C-14 Stand-alone retail model: May facility electricity peak demand.................................... 83

Figure C-15 Stand-alone retail model: June facility electricity peak demand .................................. 83

Figure C-16 Stand-alone retail model: July facility electricity peak demand ................................... 84

Figure C-17 Stand-alone retail model: August facility electricity peak demand .............................. 84

Figure C-18 Stand-alone retail model: September facility electricity peak demand........................ 85

Figure C-19 Stand-alone retail model: October facility electricity peak demand............................. 85

Figure C-20 Stand-alone retail model: cooling electricity energy consumption.............................. 86

Figure C-21 Stand-alone retail model: facility electricity energy consumption ............................... 86

Figure C-22 Stand-alone office model: comparison of monthly load factors................................... 87

Figure C-23 Strip mall model: May facility electricity peak demand ................................................. 88

Figure C-24 Strip mall model: June facility electricity peak demand ................................................ 88

Figure C-25 Strip mall model: July facility electricity peak demand ................................................. 89

Figure C-26 Strip mall model: August facility electricity peak demand ............................................ 89

Figure C-27 Strip mall model: September facility electricity peak demand...................................... 90
vii 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-28 Strip mall model: October facility electricity peak demand........................................... 90

Figure C-29 Strip mall model: cooling electricity energy consumption............................................ 91

Figure C-30 Strip mall model: facility electricity energy consumption ............................................. 91

Figure C-31 Strip mall model: comparison of monthly load factors ................................................. 92

Tables

Table 1-1 Summary of Potential TES Impacts..................................................................................... 4

Table 4-1 Potential Utility Functionalities and Current State of Interviewee Application Using 

TES Integrated with Packaged AC.................................................................................................... 19

Table 4-2 Potential Impacts of On-Site Storage on the Design and Operation of Facilities and 

Campuses with On-Site Generation ................................................................................................. 23

Table 5-1 Demonstration Sites 1 and 2............................................................................................. 27

Table 5-2 Tariff Periods at Site 1 ....................................................................................................... 29

Table 5-3 Cooling Electricity Demand at Site 1................................................................................ 32

Table 5-4 Cooling Electricity Average Energy Consumption at Site 1 .......................................... 32

Table 5-5 Cooling Electricity Annual Energy Consumption at Site 1 ............................................ 32

Table 5-6 Cooling Electricity Demand at Site 2................................................................................ 35

Table 5-7 Cooling Electricity Average Energy Consumption at Site 2 .......................................... 35

Table 5-8 Cooling Electricity Annual Energy Consumption at Site 2 ............................................ 36

Table 5-9 Demonstration Sites 3 and 4............................................................................................. 36

Table 5-10 Cooling Electricity Demand at Site 3................................................................................ 39

Table 5-11 Cooling Electricity Average Energy Consumption at Site 3 .......................................... 39

Table 5-12 Cooling Electricity Energy Consumption at Site 3 During Demonstration Period...... 39

Table 5-13 Cooling Electricity Demand at Site 4................................................................................ 43

Table 5-14 Cooling Electricity Average Energy Consumption at Site 4 .......................................... 43

Table 5-15 Cooling Electricity Energy Consumption at Site 4 During Demonstration Period...... 44

Table 5-16 Estimated Difference in Site Energy and Source Emissions for Cases with TES 

Relative to the Baseline Case............................................................................................................ 46

Table A-1 Variability in Annual Average Distribution Loss Factors for Two Cases ..................... 67

Table A-2 Variability in Seasonal Average Distribution Loss Factors for Nueces Electric 

Cooperative with Loss Code A.......................................................................................................... 68

Table A-3 Variability in Seasonal Average ERCOT Transmission Loss Factors........................... 69

Table A-4 Variability in Hourly Electricity Generation Efficiency in August 2011, From Texas 

Plants Reporting Emissions to the EPA Clean Air Markets Program........................................... 72

Table B-5 Linear Regression Models Used for Baselines in Section 5.0 ....................................... 74

Table B-6 Average DX Power (kW) Used for Baselines in Section 5.0 ........................................... 75
1 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

1 Introduction

Packaged air-conditioning (AC) systems are found in many commercial buildings. The Energy 

Information Administration estimated that in 2003, 1.6 million commercial, non-mall buildings 

in the United States were cooled with packaged AC units, and these buildings collectively 

represent about 30 billion ft2 of floor space (EIA 2006). This suggests that if thermal energy 

storage (TES) can be cost effectively integrated with packaged AC, the technology’s deployment 

potential could be high. 

Few third-party guidance documents or tools are available for evaluating TES integrated with 

packaged AC, as this technology category is relatively new compared to TES integrated with 

chillers or hot water systems. To address this gap, a project was conducted to improve the ability 

of potential technology adopters to evaluate TES technologies. Major project outcomes 

included: 

1. An evaluation framework was produced to describe key metrics, methodologies, and 

issues to consider when assessing the performance of TES systems integrated with 

packaged AC. 

2. The project applied multiple concepts from the evaluation framework to analyze 

performance data from four demonstration sites.

3. A new simulation capability was produced to enable modeling of TES integrated with 

packaged AC in EnergyPlus, a whole building energy simulation application that is 

available for free to the public. The new simulation capability will be included in the 

next release of EnergyPlus, version 8.1, in the fall of 2013. An early version of this 

feature was used to model one combination of a TES technology and control strategy. 

The performance of this application was simulated across multiple U.S. climate zones 

and three building types: office, stand-alone retail, and strip mall. 

In this report, Sections 2.0 through 4.0 present the evaluation framework, which is intended to 

help prospective technology adopters assess potential implementations of such TES systems. 

Sections 5.0 and 6.0 present the results of example analyses of system performance for an icebased TES technology. Additional methodology details are provided in the appendices. 

Though the evaluation guidance and analysis results in this document focus on TES integrated 

with packaged AC units, some of the concepts are applicable to other energy storage 

technologies as well.

1.1 Target Audience

This document is written for two main audiences. First, the report provides methodologies for 

engineers who evaluate building technologies for commercial building owners. The framework 

will help this audience to evaluate site-level performance of TES systems and source impacts 

attributed to building-sited TES operation. The field data analysis and simulation analysis results 

illustrate how concepts and methodologies from the framework can be applied. Building owner 

engineers can apply methodologies in this report to evaluate the performance of other TES 

applications, and the results of such evaluations can inform assessments of the business case for 

potential investments in TES.

Second, the report provides introductory evaluation guidance for technical experts who assist

utilities with technology evaluations and are considering distributed TES projects in 
2 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

collaboration with commercial building owners. The report explains analyses that can help to 

address operational and cost concerns of both utilities and building owners, and it suggests areas 

where future improvements to data would provide a more complete understanding of the impacts 

of TES.

1.2 Technology Basics

ASHRAE describes TES systems as those that “remove heat from or add heat to a storage 

medium for use at another time” (ASHRAE 2012, p. 51.1). A TES system can be charged by 

changing the temperature or phase of a substance in the storage section of the system. The 

system can be discharged by transferring heat between the storage section and a building load. 

TES systems can serve either cooling or heating needs, including space conditioning and process 

loads, though most use of TES today is for cooling applications (ASHRAE 2012). 

One TES application is the use of ice storage for peak load shifting, which changes the timing of 

energy consumption for space cooling. The objective in this case is for a site to shift space 

cooling energy consumption from on-peak hours, when electricity demand from most utility 

customers is high, to off-peak hours, when electricity demand from most utility customers is low, 

such as during the night or early morning. During off-peak hours, when space cooling loads are 

also typically low, the TES system charges: electricity is consumed to drive a refrigeration cycle 

and freeze liquid water into ice. When space cooling is needed, the TES system discharges: the 

ice melts as heat from supply air is delivered to the ice. In many TES systems, including unitary 

TES systems integrated with packaged AC, charging and discharging typically involve 

circulating a heat transfer fluid between the TES system’s refrigeration cycle equipment and its 

storage section (ASHRAE 2012). The energy required to pump and circulate heat transfer fluid 

during the discharging period is far lower than the energy required by the compressor for the 

refrigeration cycle during the charging period, so the bulk of the space cooling energy is 

consumed during off-peak hours. 

In an ice TES system, water is also a phase change material (PCM) because it changes phase 

within the designed operating conditions of the system. During charging, liquid water undergoes 

sensible heat transfer until it reaches its freezing point, and then it undergoes latent heat transfer, 

releasing its heat of fusion at a constant temperature as it solidifies. In other TES applications, 

water may be designed to remain in its liquid state for sensible heat storage only, as in 

conventional tank water heaters, solar hot water storage, or chilled water storage for space 

cooling applications. 

In the above case, water is the storage medium, the substance that maintains potential for later, 

controlled heat transfer when the substance is thermally discharged. Other storage media may 

also be used for TES. Selection of a storage medium will vary by application, but common 

considerations include availability, affordability, safety, stability, and favorable thermodynamic 

properties. Aside from water, thermal storage media can include other fluids (aqueous or 

nonaqueous); solids such as soil, rock, brick, ceramics, concrete, and components of a building 

structure; or other PCMs such as hydrated salts and polymers (ASHRAE 2012). 

The analyses in this report examine an ice TES system, but the evaluation framework concepts 

and EnergyPlus simulation capabilities can also be applied to TES technologies that use liquid 

water or other storage media.
3 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

1.3 Potential Impacts

Potential TES impacts are generally connected with one or more of the following high-level 

considerations: 

• Capacity deferral or reduction. Reduced peak power can decrease the cost of building 

or utility infrastructure. 

• Shifting the timing of loads. The schedule and variability of loads affect utility 

decisions about which power generation systems to operate at different times. Load 

leveling can help utilities to operate generators more effectively and reduce congestion in 

distribution systems. Charging storage during off-peak demand periods and displacing 

loads during on-peak demand periods is an example of a load leveling application 

(Sioshansi et al. 2012). 

• Balancing and reliability. For utilities, ancillary services, such as regulation and 

contingency reserves, “help grid operators maintain balance [between loads and generator 

output] on electric power systems” (NREL 2012). Utility-controlled, building-hosted 

TES systems could potentially help utilities to adjust the timing of electricity consumed 

by building systems that serve thermal loads, such as space cooling loads. For building 

owners, reliability needs may include on-site backup capabilities for critical thermal 

loads, such as cooling for data centers. 

• Source energy and emissions impacts. Utility interests include optimizing the 

generation, transmission, and distribution of energy to reduce costs. Increasing efficiency 

will reduce fuel costs, but other cost-driven operational decisions can have different 

effects on system efficiency and source energy. Generation plant efficiencies and 

transmission and distribution losses vary with times of day and seasons of the year, and 

since TES measures change the timing of electricity consumption, TES evaluators may 

want to know whether particular applications have a positive, negative, or negligible 

impact on the site-to-source efficiency associated with consuming electricity to serve 

building thermal loads. Similarly, emissions associated with power use depend on when 

and where power is used. In addition to generation mix differences between utilities, 

each utility operates a different combination of its generation systems at different times. 

These high-level considerations can be broken down into more specific potential technical and 

financial impacts listed in Table 1-1. Each impact conveys a different perspective or focus, 

though some necessarily involve overlapping concepts. The authors selected a subset of these 

impacts to target in this document based on: (1) the anticipated scope required to enable 

evaluation of the impact; and (2) the availability of relevant data from existing demonstration 

sites. Table 1-1 indicates which impacts are discussed in this document from either the building 

owner or the utility perspective.
4 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Table 1-1 Summary of Potential TES Impacts

Integration of TES with packaged AC 

could impact the following:

Discussion included from 

the building owner

perspective?

Discussion included from 

the utility perspective?

Peak power or electrical demand at the 

facility meter Yes Yes

Energy consumption at the facility 

meter Yes Yes

Demand and energy costs Yes

Partly. This document 

describes the role of TES in 

utility arbitrage.

Source energy attributed to facility

operations Yes Yes

Efficiency of utility power generation Yes, in the context of site-tosource energy calculations. Yes

Transmission and distribution losses Yes, in the context of site-tosource energy calculations. Yes

Emissions attributed to a facility’s 

energy consumption Yes Yes

Effectiveness of renewable energy 

integration with loads and the electric 

grid

Yes Yes

Sizing and installation of utility power 

generation, transmission, and 

distribution systems

N/A

Partly. This document 

explains an example case 

demonstrating an avoided 

cost.

Ancillary services, including regulation 

and contingency reserves N/A

Partly. Interviewed utility 

representatives have not 

documented specific cases 

where this capability was 

enabled by TES integrated 

with packaged AC, but 

stakeholders are interested 

in testing this with future 

projects.

In general, energy storage systems can be marketed toward building owners, utilities, or both 

audiences, depending on the values that they offer. The following sections of this document 

discuss a variety of potential impacts and how different audiences may assess them.
5 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

1.4 Evaluating Performance at Different Levels of Detail

System characteristics discussed in this document can also be estimated at varying levels of 

detail, depending upon the evaluation objective. 

1.4.1 General Performance Characteristics of Energy Storage Technologies

High-level performance results, such as demand and energy savings and associated cost savings, 

depend on a combination of site-specific conditions and technology-specific performance 

characteristics. Some performance characteristics are broadly applicable to many types of 

energy storage. These characteristics include: 

• Storage capacity. This is generally expressed in terms of energy. 

• Charging and discharging rates. These are generally expressed in terms of power. 

• Ramp rate. This is the change in power charged or discharged over time to meet 

variations in power requirements (DOE 2010). 

• Duration of time required for complete charging and discharging. 

• Electric power input in different operational modes (e.g., charging only, discharging only, 

charging and discharging, idle) and different operating conditions (e.g., entering air 

temperatures and flow rates).

• Storage loss rate. This refers to energy losses from the storage section when it is partially 

or fully charged. The relationship between loss rates and environmental conditions will 

vary depending on the type of energy storage (thermal, electrochemical, etc.). 

• Round trip efficiency. This is the useful energy discharged from storage divided by the 

energy input into storage during charging (DOE 2010). 

• Capacity degradation. This refers to changes in capacity over multiple charge/discharge 

cycles. Some forms of energy storage, such as batteries, lose capacity over the life of the 

system, while other forms of energy storage can be charged and discharged without a 

capacity penalty.

• Cycle limitations on system lifespan. Whether or not there is a strong connection 

between an energy storage system’s lifespan and charge/discharge cycles will depend on 

the type of energy storage. For example, the lifespan of an electrochemical battery 

system will likely be limited in terms of charge/discharge cycles, whereas the lifespan of 

a TES system could be limited by mechanical failure of a variety of components. 

• Dispatchability. In the case of building-hosted systems, dispatchability can be 

characterized in terms of (1) energy storage product properties (e.g., capacity, maximum 

charging and discharging rates) that are largely independent of other building systems; 

and (2) schedule availability or flexibility constraints. The latter can depend on the 

timing of facility needs and interactions with other building systems. 

• Downtime metrics. These may include: scheduled maintenance downtime; downtime 

associated with state of charge; unscheduled downtime; plant availability (reported at the 

end of operations); and the number and duration of failure incidents (DOE 2010). 
6 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

• Ability to follow a control signal, such as an Automatic Generation Control (AGC) signal 

or an Area Control Error (ACE) signal. An example metric that quantifies this ability is 

the ratio of energy provided by the storage system divided by the energy required by the 

AGC or ACE signal at each 4-second interval; this was used in a program administered 

by the DOE Office of Electricity Delivery and Energy Reliability (DOE 2010). 

Developers of specific programs that involve energy storage may include more or fewer metrics 

depending on the needs of the program. Some of these metrics also appear in technologyspecific testing standards, which may specify testing conditions under which these metrics 

should be measured and reported. 

1.4.2 Metrics for Standardized Product Testing and Performance Rating Specific 

to a Technology Category

Detailed data are necessary to describe the performance of specific technologies and products 

under standardized testing conditions. TES integrated with packaged AC is an example of a 

unitary system covered by American National Standard Institute (ANSI)/Air-Conditioning, 

Heating, and Refrigeration Institute (AHRI) Standard 900, which is titled the 2010 Standard for 

Performance Rating of Thermal Storage Equipment Used for Cooling (ANSI 2011). This 

standard specifies multiple values that manufacturers must provide to accompany published 

performance ratings. This detailed class of metrics is important for ensuring that manufacturers 

and third-party testing organizations can collect product-specific performance data under 

controlled, standardized, laboratory conditions. The laboratory data can be used to inform the 

design of evaluation tools, which could range from spreadsheet calculators to more robust 

simulation programs, depending on the accuracy desired and the complexity of the systems under 

consideration. For example, the inputs used for simulations in this project were based in part on 

data collected under standardized testing conditions. 

1.5 Timescales for Collecting Data or Reporting Metrics

For system performance data that can be viewed at different timescales, the decision to collect or 

report data at a particular timescale should take into account: (1) the audience for the data; (2) 

the accuracy or uncertainty of the values; and (3) the required effort or cost of data collection. 

Within a building owner’s organization, executive levels of management will likely be interested 

in annual values, such as total energy costs or savings, when making operational or procurement 

decisions. For facility managers, engineers, or energy consultants to conduct more detailed 

tasks, such as troubleshooting systems, optimizing controls, or estimating the performance of 

potential system purchases, more granular values are needed at hourly and subhourly timescales. 

These values may include time-series data—such as hourly or subhourly electricity demand—

and values used to define system characteristics—such as the time required for an energy storage 

system to fully charge or discharge. 

Though high-level reporting will often require summary values at annual or monthly timescales, 

the accuracy of such summary values will depend on underlying assumptions. For example, 

hourly data can enable estimation of energy cost savings for buildings with time-of-use tariffs. 

Similarly, hourly or subhourly data are important for estimation of demand savings, because 

monthly peak demand charges are often based on data collected at 15-minute intervals. More 

broadly, the decision to purchase a TES system integrated with AC may depend on an executivelevel review of annual cash flows or life cycle costs, but the savings estimates will be based on 
7 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

energy performance calculations, which are more accurate when they account for hourly or 

subhourly building and system data. 

On the other hand, the benefits of greater accuracy must be balanced with the required effort or 

cost of acquiring more granular data. For example, a comparison of annual source energy and

emissions for two alternative systems may be important for some decision makers. Engineers or 

analysts who prepare such estimates may want to know whether annual average loss factors, 

efficiencies, or emission factors are adequate for such calculations, or whether seasonal average, 

hourly average, or hourly marginal factors are necessary to arrive at an accurate conclusion. 

Such considerations are discussed further in Section 3.0. 
8 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

2 Evaluation Framework: Site Power, Site Energy, 

and Associated Costs 

Site metrics:

• Site power, including peak demand and the demand profile over time 

• Site energy consumption 

• Demand and energy costs to the building owner. 

The evaluation framework sections of this report describe key metrics, methodologies, and issues 

to consider when assessing the performance of TES systems integrated with packaged AC. 

When quantifying the impacts of building-sited energy storage on the electric grid, source 

metrics are more meaningful indicators of system performance than site metrics. Site metrics 

neglect impacts associated with off-site electricity generation and delivery, which vary with 

time; these off-site impacts are important for evaluations of energy storage, because energy 

storage technologies shift the timing of energy consumption. Nonetheless, some analysis of site 

metrics is also necessary because: (1) site metrics are connected to building owner’s costs; and 

(2) measurements at the system or building level are needed to derive source metrics that can be 

attributed to specific building owners. This section (2.0) discusses site metrics in greater detail, 

and Section 3.0 describes conversions of site metrics into source metrics. 

2.1 Rate Structure Variations

The cost effectiveness of energy storage depends in part upon utility rate structures. Some 

commercial building owners—typically those with smaller facilities—only pay energy charges 

for their electricity; other commercial building owners pay both demand and energy charges. In 

simpler rate structures, electricity charges may be fixed costs per unit of demand or energy. 

Some rate structures may also include a “ratchet” clause, which means that the minimum 

demand charge during any given billing period (e.g., for a particular month) is a certain 

percentage of the peak demand recorded during a longer period (e.g., the preceding 11 months). 

In cases where rate structures vary demand or energy charges with time-of-use, the rate 

structures are simplified reflections of how utility operating costs vary between different times of 

day and different seasons of the year. “Time-of-use” rate structures may divide a day into 

different periods with different electricity charges, such as “off-peak,” “partial-peak,” and “onpeak” hours; they may also divide the year into different seasons with different electricity 

charges. These rate structures are one mechanism by which utilities can translate utility 

operational cost savings into building owner cost savings. The translation is not perfect, though, 

as time-of-use rate structures are only approximations of time-dependent variations in the cost of 

generating and delivering electricity. 

A more sophisticated rate approach is “real-time pricing,” which involves updating rates 

frequently, generally on an hourly basis (DOE 2013). For example, Southern California 

Edison’s Real-Time Pricing RTP-2 rate structure option bills customers with “hourly electricity 

prices that vary based on time of day, season, and temperature,” where prices during a particular 

day depend on temperatures recorded in downtown Los Angeles the previous day (Southern 

California Edison 2010). Residential real-time pricing structures in Illinois are even more 
9 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

closely matched to utility operational costs, “charging customers for the electricity they consume 

each hour based on the corresponding wholesale hourly market price of electricity” (Illinois 

Commerce Commission 2013). For commercial customers that maintain a 30-minute demand of 

250 kW or more each month, Georgia Power offers day-ahead real-time pricing; for larger 

customers that maintain a 30-minute demand of 5 MW or more, it offers hour-ahead real-time 

pricing (Georgia Power 2013). 

As time-based pricing options become more prevalent, time-dependent impacts of energy storage 

will likely increase the financial motivation for building owners to consider energy storage 

options. At present, though, not all building owners have access to time-based rate structures. 

More information about rate structures available through various utilities is available through the 

OpenEI Utility Rate Database at http://en.openei.org/wiki/Gateway:Utilities (Open Energy 

Information 2013). 

2.2 TES Impacts on Site Demand and Energy

The ability of a particular TES technology to reduce electricity demand, energy, and associated 

costs varies with system design, environmental conditions, and local market factors. In the case

of chiller-integrated TES systems, manufacturers have been able to build business cases with

building owners who have access to time-of-use rates from their utilities. For example, the 

University of Central Florida estimated that its thermal energy storage facility, including a three 

million gallon tank that cost $3 million, will save close to $700,000 annually by shifting cooling 

loads to lower cost, off-peak periods (University of Central Florida 2013). 

On the other hand, current rate structures for small commercial buildings may not yield enough 

savings to lead to wide-scale building owner investment in present-day TES systems integrated 

with packaged AC. An alternative approach is for building owners to partner with utilities that 

have other mechanisms for realizing savings from TES systems, as discussed in Section 4.0. 

Regardless of which party purchases the TES system, the economic benefit to the building 

owner—which will be the primary motivation for a building owner to participate—will depend 

on capital costs and whether energy and demand charges increase, decrease, or stay the same 

when TES is added. Compared to a baseline system without TES, an alternative system with 

TES may consume more or less site energy, and several factors will contribute to the net result, 

including (but not limited to):

• The alternative system with TES may consume less energy than the baseline system if the 

alternative system operates its refrigeration cycle during periods when outdoor air 

temperatures are cooler. Faramazi et al. (2004) examined factors that affect the 

performance curves of packaged AC performance at high ambient temperatures and 

found that degradation rates vary with system design. 

• The alternative system with TES may consume less energy than the baseline system if the 

TES system’s compressor can be controlled to run constantly at or near its optimal 

efficiency point, whereas a constant-speed rooftop packaged AC unit may have to cycle 

to maintain space conditions and avoid overcooling. 

• The alternative system with TES may consume more energy than the baseline system 

because the temperature difference between ambient air and the cold side of the 

alternative system is greater than the temperature difference between ambient air and the 

cold side of the baseline system.
10 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

• The alternative system with TES may consume more energy than the baseline system 

because the alternative system must compensate for heat transfer losses from the storage 

compartment, as well as limitations in the effectiveness of any additional heat 

exchangers. 

• The alternative system with TES may consume more energy than the baseline system 

because additional circulation of heat transfer fluid requires additional pumping energy. 

Reliable estimation of site power and site energy impacts from TES depends on access to reliable 

data for baseline and alternative system performance.

2.3 Basic Considerations for Reviewing Data from Prior 

Demonstrations

A detailed discussion of field monitoring is outside the scope of this document, but technology 

evaluators should be aware of the following basic considerations when reviewing the results of 

prior field demonstrations of TES integrated with packaged AC. 

At a minimum, high priority system performance data to collect in field demonstrations include:

• Power and energy into the TES system

• Power and energy into the direct expansion (DX) packaged AC system (if separable from 

the TES system)

• Operational state of the TES system (charging, discharging, simultaneous charging and 

discharging, etc.)

• Operational state of the DX packaged AC system (cooling, idle, etc.). 

Monitoring the above values at smaller time intervals will improve estimates of various TES 

impacts. When selecting a time interval (hourly, 15-minute, etc.) for estimating such values, 

considerations include: 

• The energy data averaging interval used by the local utility to calculate peak demand

• The length of the TES system charge/discharge cycle

• The on/off cycling frequency of the packaged AC system under typical conditions 

• How rapidly energy storage must be dispatched to provide functionality sought by the 

technology evaluator.

Additional site data may help reveal environmental or operational factors that can contribute to 

differences between baseline and alternative performance. A building owner’s engineer may be 

able to use the additional site data to normalize performance values to account for differences in 

weather, facility schedules, or other variables. Such additional site data may include, but are not 

limited to: 

• Indoor air conditions, such as dry bulb temperature and relative humidity, in the space 

served by the TES system

• On-site measurements of outdoor conditions, such as dry bulb temperature, relative 

humidity, and insolation. Outside air temperature can differ significantly between 
11 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

rooftops and ground level, so temperature should be measured at the level where TES 

systems are mounted.

• Whole-building or whole-facility power consumption 

• Heating, ventilation, and air-conditioning (HVAC) schedules, including setpoints and 

outdoor air fraction 

• Operational schedules for other major building systems and occupancy schedules for the 

building 

• Utility rate structures, which can influence how building engineers schedule and control 

systems

• Energy consumption of any major submetered end uses (if available). For example, 

changes to lighting and other internal loads can affect cooling loads. 

The level of monitoring will depend on the objectives and scope of the demonstration; some 

projects may require more detailed data than those listed above, whereas other projects may have 

access to less data. 

If a savings claim is made as part of a report from a field demonstration, it is important to 

understand how the baseline was defined as a part of that claim. There are several ways to 

collect field data that can be used with other information or modeling techniques to establish 

baseline system performance:

• If the HVAC system has been retrofitted to integrate TES without upgrading the 

efficiency of the DX packaged AC unit, then pre-retrofit data can be combined with other 

data (as described in Section 2.4) to estimate baseline performance. 

• If the HVAC system is new, the monitoring period could be divided into two sets of days 

or weeks. One set would be the baseline set, during which the TES system is disabled, so 

that the baseline HVAC system provides all of the cooling. The other set would be the 

alternative set, during which the TES system operates as designed. The monitoring 

period should be subdivided in a manner that allows both sets to capture some similar 

weather and operating conditions. 

In either case, when collecting field demonstration data, it is inherently difficult to control 

environmental and operational variations between baseline and alternative performance periods. 

Prospective adopters of a technology will therefore benefit from combining field data with 

laboratory data collected under more controlled test conditions. Preferably, the laboratory tests 

would be administered by a qualified, accredited third party in accordance with industry 

standards; in particular, TES integrated with packaged AC is an example of a unitary system 

covered by ANSI/AHRI Standard 900 (ANSI 2011). One effective method for analyzing a 

combination of laboratory and field data is to use them in coordination with whole-building 

energy simulation. 

2.4 Combining Laboratory and Field Data with Simulations

Whole-building energy simulation programs can be used to assess how TES and HVAC systems 

perform under varying conditions. The TES and HVAC models in the energy simulation 

programs must have valid performance parameters and curves for the technologies being 

evaluated. Data collected under a range of controlled, laboratory test conditions should be used 
12 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

to generate individual performance parameters and curves that represent a physical system 

modeled by a simulation program. 

Due to variability in environmental and operational conditions in the field, it is not the intent of 

such simulation to replicate field-measured performance precisely. Rather, such simulation aims 

to capture performance trends realistically enough to enable evaluation of relative performance 

of alternative systems. Field data can be used as a high-level check to see if a model is realistic 

given site-specific inputs that describe the facility, weather conditions, and key specifications of 

the evaluated technology, such as storage capacity, cooling capacity during discharge, electrical 

power input in different modes of operation, and charge and discharge times. 

If the high-level check indicates that a model is realistic, a building owner’s engineer may create 

variations on the original model to simulate performance for additional cases. Example 

applications include: 

• If field data were collected for performance with TES, but not for a baseline case without 

TES, simulation can be used to estimate baseline system performance. This may be 

necessary if the HVAC system was either new or upgraded when the TES system was 

installed. Starting with a validated model of performance with TES, the modeler could 

remove the TES system and simulate performance with a DX-only cooling system. 

o If the TES system installation was paired with a replacement or upgrade for an old 

DX system that would have been replaced or upgraded anyway, then the old DX 

system should not be the basis of the baseline case. For a fair comparison, the DX 

units in the baseline case should have the same efficiency as the DX units in the 

alternative case.

o If the TES system installation was paired with an early DX unit replacement or 

upgrade that would not have otherwise occurred, then the modeler has more 

choices. The modeler could consider the TES system and new DX unit to be two 

parts of a single energy measure package. From this perspective, the baseline 

case would reflect the efficiency of the old DX unit. The alternative case would 

reflect the combined costs and savings of the TES system and new DX unit. 

(Optionally, the modeler could create an additional alternative case for a 

comparison representing use of the new DX unit without TES.) 

• If a monitoring period was divided into a baseline and an alternative set of days or weeks, 

then there will be inherent differences between the environmental and operational 

conditions of the baseline and alternative cases. Simulation can be used to produce two 

complete cases that span the same time period and are subject to the same environmental 

and operational conditions, making them easier to compare. 

• Simulation can be used to predict how a system will perform in conditions that were not 

captured by a monitoring period. For example, if field data were collected for a year, 

simulations could be used to infer performance during hypothetical years with milder 

weather or more extreme events. These simulated years offer a more complete picture of 

future performance.

The appropriate type of weather assumptions to select depends on the objectives of the 

building owner or utility that is considering TES. Varying weather assumptions can help 

a building owner or utility to construct a conservative savings estimate for a feasibility 
13 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

assessment or to compare the effectiveness of different options for critical load 

management under severe conditions. Typical meteorological year data, such as TMY2 

and TMY3 data sets, are useful for simulating system performance during periods with 

common diurnal and seasonal variations, but these data sets do not capture 

meteorological extremes (Wilcox and Marion 2008); if a building owner or utility is 

considering TES as a resource for reducing cooling demand, it would also be useful to 

simulate system performance using weather data inputs that include some days that are 

hotter than those found in typical years. 

• Simulation can also be used to estimate how performance would vary across different 

climates and applications. 

• Simulation can also be used to estimate performance of alternative systems for a new 

construction project. 

In combining laboratory and field data with simulations to compare baseline and alternative 

cases, an evaluator of a particular technology should also consider how the simulation platform 

represents the baseline system and how its approximations might affect the comparison of 

predicted results. Some limitations may be consequences of the simulation platform, while other 

limitations may result from a lack of adequate test methods for capturing and quantifying certain 

effects. One consideration is whether the evaluation approach can capture the difference in 

compressor cycling between baseline and alternative cases. Another consideration is whether the 

evaluation approach captures differences in dehumidification between the baseline and 

alternative cases, which would impact comfort. An awareness of these limitations will help a 

technology evaluator to determine whether an estimate of savings from a prospective TES 

technology is conservative or aggressive. 

2.5 Coordination with Other Facility Needs

In some TES applications, the packaged AC system is sized such that it can handle the cooling 

load if the TES system is offline. Whether the DX equipment or associated electrical system 

infrastructure can be downsized relative to a baseline design depends on the specifications of the 

TES system and the specifications of how the TES will be committed to providing utility 

functionality. Similarly, if a building owner’s engineer is considering integrating a TES system 

with backup cooling systems for critical loads, the design of any backup functionality will need 

to be coordinated with commitments to utility functionality. In evaluating whether separate 

functionalities can be coordinated, the technology evaluator will need to consider a variety of 

factors, including: the frequency, duration, and magnitude of utility-controlled discharge events; 

the capacity and charging and discharging times of the TES system; and any changes in electric 

demand from DX equipment in response to changes in TES system operation. Some constraints 

will be driven by physical conditions, while others may be set through contractual agreements. 

In the case of roof-mounted TES systems, a building owner’s engineer will also need to 

determine whether the building can handle the additional structural load from a potential TES 

installation.

Compared to demand response programs, one advantage of integrating TES with cooling loads is 

that TES systems can be designed to have no impact on the thermal comfort of building 

occupants. Such arrangements change the timing of cooling energy consumption without 

changing when or how much cooling is delivered to conditioned spaces. A TES-based approach 

contrasts with traditional demand response strategies, which typically curtail the operation of one 
14 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

or more building services—such as cooling or lighting—during periods of particularly high 

electricity demand. Traditional demand response strategies can sometimes have a negative

impact on occupant comfort and productivity, depending on the type, duration, and magnitude of 

the curtailment. A TES-based approach can provide utilities with demand reductions without 

disrupting other services to building occupants. 
15 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

3 Evaluation Framework: Translating Site Impacts 

into Source Impacts

Source metrics:

• Source energy (total or by fuel type)

• Source emissions 

Source energy and source emissions can be of interest to multiple audiences, each of which may 

have different assumptions about which factors in a site-to-source calculation can be simplified

or should be scrutinized. 

When evaluating a potential TES measure for a single, small commercial facility, the building 

owner’s engineer can usually assume that installing TES at a single facility would not 

appreciably change a utility’s current transmission and distribution loss factors, generation plant 

efficiency values, or source emission factors for different times of day. With this assumption, a 

building owner’s engineer would apply loss, efficiency, and emission factors that reflect the 

current state of the grid when estimating source impacts. Researchers can help by producing or 

identifying simple tables of factors that can be used by building owners’ engineers, because such 

engineers are unlikely to have access to or interest in using advanced grid analysis software to 

determine these factors. 

For large commercial and industrial facilities, campuses, and residential communities, 

introducing large quantities of TES can begin to impact distribution loss factors. Additionally, a 

utility that is evaluating the aggregate impact of a large collection of sites with TES may want to 

use grid analysis software to assess whether high penetrations of TES systems will change 

transmission and distributions loss factors, generation plant efficiency values, source emission 

factors, or changes in generator unit commitment and dispatch.

There are many different methods of estimating source energy and emission factors for an 

electric power system, and the optimal approach will depend on how the factors will be applied. 

Adjustments can be made for transmission and distribution losses, as well as imports and exports 

to and from geographic regions of interest. Factors generated by these various methods 

generally fall into two categories:

• Average source energy and emission factors are based on average source energy or 

emissions per unit of electricity generation in the system over a stated period (sum of 

source energy or emissions divided by sum of generation). Average factors reflect a 

snapshot of the electric power system, so they are suitable for developing emissions 

inventories or estimating emission footprints associated with building owner operations. 

• Marginal source energy and emission factors are based on the source energy 

consumption and emission rates of the generator(s) that are operated to provide more or 

less electricity in response to changing levels of demand. Marginal factors can be used to 

estimate the source energy and emission impacts of a project or policy that changes the 

system load (or generation) relative to a reference case, on a scale small enough not to 

change the mix of generators.
16 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Users of these factors should be aware that it is not possible to measure the actual source energy 

consumption and emissions attributable to site energy use at any given time on the grid, so any 

source energy and emission factors are estimates of the real behavior. Load, generation, 

transmission, and distribution systems are very dynamic, and their interactions can only be 

approximated. Estimating marginal factors is more difficult than estimating average values as it 

is particularly difficult to predict the highly dynamic behavior of the grid in response to 

incremental changes in load. Therefore, it is recommended that marginal factors be used to 

approximate trends in behavior rather than absolute impacts. 

3.1 Source Energy Calculations for Building Owners’ Engineers

Building owners’ purchasing decisions are primarily motivated by cost, but some building 

owners may also engage in initiatives to reduce the emissions impacts of their operations. Such 

building owner organizations may include private sector businesses that voluntarily pursue 

emissions reductions for social or marketing reasons. They may also include federal or other 

government agencies that are required to reduce emissions by law. 

To calculate source energy, the building owner’s engineer should start with hourly estimates of 

site energy consumption in case any time-of-use effects prove to be significant. If it turns out 

that site-to-source efficiency or loss factors vary significantly with time (e.g., varying with the 

hour of the day), the following calculation should be performed separately for each applicable 

time interval: 

Source Energy = Site Energy × SEF (3-1) 

Where:

SEF = source energy factor

The authors recommend defining the source energy factor in a manner that accounts for losses 

that occur during four major stages of energy harvesting and delivery: distribution, transmission, 

generation (energy conversion at power plants), and precombustion (including extraction, 

processing, and transportation of fuels used in power plants). 

To complete the site-to-source energy calculation, a building owner’s engineer must find or 

derive appropriate source energy factors. These values can vary with regional generation mixes, 

local transmission and distribution system properties, loads, and environmental conditions, such 

as temperature. Unfortunately, hourly data or separate on-peak and off-peak values for 

efficiency and loss factors are often difficult to find. Before investing additional time in seeking 

new data, an engineer may want to know which of these efficiency and loss factors can be 

assumed to be reasonably constant and which factors can vary significantly by season or time of 

day. An exploration of example data that can influence such decisions is provided in Appendix 

A.

Average and marginal factors can also be used together to estimate the percent change in source 

energy for a particular project or policy. For example, given a baseline case and an alternative 

case, the following equations can be applied: 

(Source Energy)Baseline = (Site Energy)Baseline × SEFA (3-2) 

∆Source= ∆Site × SEFM (3-3) 

PSource = 

∆Source

(Source Energy)Baseline

(3-4) 
17 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Where:

∆Site = alternative site energy minus baseline site energy

∆Source= alternative source energy minus baseline source energy

PSource= percent change in source energy relative to baseline source energy

SEFA = average source energy factor

SEFM = marginal source energy factor

3.2 Source Emissions Calculations for Building Owners’ Engineers

Hourly estimates of site energy consumption can be multiplied by published hourly source 

emission factors to estimate source emissions associated with a baseline and alternative case. 

Emission factors are often published with units of emitted mass divided by energy, but the 

energy unit may be site or source energy, depending on the publication. Before using an 

emission factor, it is important to note which energy format is used. This document references 

data sources that present emission factors in the following form: 

 Source Emissions = Site Energy × EF (3-5) 

Where:

EF = emission factor (in units of emitted mass divided by site energy)

As with source energy factors, emission factors can be produced in two forms: average and 

marginal. These can be used together to estimate the percent change in emissions for a particular 

project or policy. For example, given a baseline case and an alternative case, the following

equations can be applied: 

(Source Emissions)Baseline = (Site Energy)Baseline × EFA (3-6) 

∆Emissions= ∆Site × EFM (3-7) 

PEmissions = 

∆Emissions

(Source Emissions)Baseline

(3-8) 

Where:

∆Site = alternative site energy minus baseline site energy

∆Emissions = alternative emissions minus baseline emissions

PEmissions = percent change in emissions relative to baseline emissions

EFA = average emission factor

EFM = marginal emission factor

One resource for finding hourly source emission factors is the OpenEI website, which includes a 

set of simplified lookup tables at http://en.openei.org/datasets/node/488 developed through a 

2011 NREL project (Open Energy Information 2012). Resulting emission factors are provided 

in the form of pounds of power plant emissions per megawatt-hour of electricity consumed at a 

utility customer’s site. 
18 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

The developers of these tables combined demand profile data with simulation using an electric 

grid dispatch software package to estimate three types of emissions: carbon dioxide (CO2), 

sulfur dioxide (SO2), and nitrogen oxides (NOx). The emission factors for sites in the U.S. 

eastern interconnection are based on 2005 demand profiles; emission factors for sites covered by 

the Electricity Reliability Council of Texas (ERCOT) and the Western Electricity Coordinating 

Council (WECC) are based on 2008 demand profiles. Multiple sets of emission factor values are 

provided to account for the following types of variations: 

• Location: Emission factors are provided for 22 eGRID subregions spanning the 48 

contiguous states of the United States (EPA 2012a). 

• Time of use: For each location and each type of emissions, 12 month-specific sets of 24 

hour-specific average emission factors are provided to capture variations across months 

of the year and times of day.

The OpenEI website provides access to average emission factors. Additional results from the 

2011 project were leveraged to produce marginal emission factors used in Section 5.3. More 

details about the development of these factors are described in Appendix A.6. 

Another source of average and marginal emission factors is the Greenhouse Gas Tool for 

Buildings in California, developed by E3 (2010). This spreadsheet tool includes hourly emission 

factors for the state of California based on 2008 data, and these factors were adapted for use in 

Section 5.3, as well. The spreadsheet tool and documentation of E3’s methodology for 

estimating emission factors are available at http://www.ethree.com/public_projects/ghg.php. 
19 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

4 Evaluation Framework: Utility Impacts and 

Collaboration with Building Owners

Key metrics:

• Peak demand (monthly and average daily) 

• Load factor 

• Cost normalized by utility benefits (e.g., installed cost $ per unit peak demand reduction) 

compared to that of alternative utility investments

• Operational cost of providing a specific utility functionality (e.g., ancillary services) with 

or without energy storage 

• Load match, grid interaction, and other metrics for sites with on-site renewable energy 

systems

Understanding the potential for a technology to reduce source energy consumption and emissions 

is important to policymakers and regulators, but such metrics are only a part of a broader set of 

interests held by power system stakeholders. Other metrics are needed to characterize the 

potential for building-sited TES systems to reduce utility costs, including investments in 

infrastructure upgrades, purchases of electricity market products from electricity generators, and 

added costs from dispatching inefficient generators. 

4.1 Potential Utility Benefits

A utility may evaluate an investment in a collection of distributed, building-hosted TES systems 

in comparison to other utility-scale investments, such as: (1) new generation, transmission, or 

distribution systems; (2) alternative demand response programs; or (3) other energy storage 

projects. Table 4-1 lists several potential utility benefits that have been suggested by prior 

publications related to energy storage, and it notes which functionalities have been demonstrated 

specifically using TES integrated with packaged AC, according to interviews conducted during 

this project with representatives of six utility organizations. 

Table 4-1 Potential Utility Functionalities and Current State of Interviewee Application Using 

TES Integrated with Packaged AC

Functionality Description

State of Interviewee 

Application

Avoided cost of 

new generation, 

transmission, or 

distribution 

systems

Investment in energy storage can be 

considered as an alternative to investment 

in new or larger generation, transmission, 

and distribution systems that would be 

implemented in response to rising demand 

requirements. From this perspective, 

alternative investments may be compared 

by metrics such as installed cost per unit of 

peak demand reduction.

One SCPPA interviewee 

reported a case of a utility with 

an overloaded feeder that 

wants to defer an upgrade of a 

substation (Cope 2013). The 

peak load shift enabled by 

installing TES integrated with 

packaged AC will provide the 

same benefit at a lower cost 

than the substation upgrade 

option.
20 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Functionality Description

State of Interviewee 

Application

Improved load 

factor

The load factor during a particular period of 

time is the average electrical demand 

divided by peak electrical demand; load 

factors can be calculated at various levels, 

such as at the building, facility, or utility 

scale. Buildings often have load factors 

around 0.5 (Barley et al. 2005).

One interviewee said that he 

sees upcoming installations of 

TES integrated with packaged 

AC as a part of the solution for 

improving a currently 

undesirable (low) load factor

(Cope 2013).

Arbitrage

Energy arbitrage is the purchase of low-cost 

off-peak energy that can be resold during 

on-peak periods, and past research has 

examined the ability of electrical energy 

storage to facilitate arbitrage (Denholm et 

al. 2010; Sioshansi et al. 2009). In the case 

of TES, thermal energy is not typically sold, 

but arbitrage can be facilitated if TES is 

charged when electricity is less expensive 

and discharged when electricity is more 

expensive. 

Sioshansi et al. (2009) analyzed example

cases in the territory of PJM, a regional 

transmission organization, to quantify how 

arbitrage values vary with application and 

time (by time of day and from year to year); 

they reported that the drivers of the 

arbitrage value of energy storage include 

location, fuel price, fuel mix, and the 

efficiency and size of the storage system. 

They also discussed a method for 

forecasting arbitrage value and demonstrate 

that a simplified backcasting approach was 

able to capture about 85% or more of the 

potential arbitrage value. They suggest that 

predictive accuracy could be improved 

through use of near-term weather forecasts 

and refined dispatch rules. 

Though the PJM analysis focused on 

electrical storage, the arbitrage value of 

TES technologies that shift the timing of 

electricity demand can be assessed in a 

similar manner.

Utility-scale arbitrage has been 

demonstrated using water 

heaters (Narayanamurthy 

2013), but arbitrage benefits 

using TES integrated with 

packaged AC have not yet 

been documented

quantitatively by interviewed 

utility representatives.
21 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Functionality Description

State of Interviewee 

Application

Ancillary services

Ancillary services “help grid operators 

maintain balance on electric power 

systems,” and these services include 

regulation and contingency reserves (NREL 

2012). Energy storage can facilitate 

regulation if the net output of a storage 

system can be adjusted “to ensure real-time 

balance between system energy supply and 

demand,” which requires “a response time 

of seconds to minutes” (Sioshansi et al. 

2012, p. 48). Energy storage can assist 

with contingency reserves if the net output 

of a storage system can be increased to 

respond to a contingency event, such as a 

transmission outage, with “a response time 

of minutes” (Sioshansi et al. 2012, p. 48).

One interviewee indicated that 

SCPPA member utilities are 

considering the use of ice TES 

to provide ancillary services 

(Cope 2013). 

In addition to potential benefits that can be managed at the utility level, the benefits discussed in 

Section 4.2 are of interest to both building owners and utilities and represent opportunities for 

collaboration. 

4.2 Load Leveling and Enhanced Renewable Energy Integration

TES systems offer the potential to change the profile of building energy loads by shifting the 

timing of certain loads. This potential can be described in terms of two applications: (1) load 

leveling; and (2) enhancing renewable energy integration.

Load leveling flattens load profiles and includes strategies that “[dampen] cyclical daily load 

flows” (EIA 2013). This can help utilities to operate generators more effectively and reduce 

congestion in distribution systems. TES systems integrated with packaged AC can facilitate load 

leveling by shifting the timing of cooling energy consumption. Building owners could 

theoretically take more actions to support load leveling, such as by implementing more energy 

storage or other demand response technologies, but they are not strongly incentivized to do so 

with current rate structures. Alternatively, if utilities control TES systems integrated with 

packaged AC, the economic benefit will vary depending on such factors as how quickly TES 

cooling potential can be dispatched, the magnitude of the change in cooling demand, and the 

relative contribution of cooling loads to overall loads for specific utilities. 

Beyond traditional load leveling, TES integrated with packaged AC can potentially enhance 

renewable energy integration at multiple system boundary scales (e.g., utility, campus, building) 

and timescales (e.g., daily, hourly, subhourly). 

• At the utility power scale, an aggregation of utility-controlled, building-hosted TES 

systems could allow utilities to compensate for variable renewable energy generation by 

adjusting the timing of electricity consumption from building AC systems. For instance, 

a utility with a surplus of off-peak electricity generation from wind power systems could 

charge ice or chilled water storage in building-hosted systems during off-peak hours, 

reducing the need to curtail off-peak wind power generation. This particular approach

could be used with buildings that have cooling loads throughout the year. More 
22 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

generally, an effective solution will require alignment between the performance 

characteristics, controllability, and availability profiles of candidate storage, load, and 

generation systems. 

• At the individual building scale, energy storage technologies like TES integrated with 

packaged AC could help smooth fluctuations in grid electricity demand from sites with 

on-site renewable power systems. Currently, owners of buildings and campuses may 

install on-site renewable power systems for environmental or life-cycle reasons, but they 

are not necessarily incentivized to integrate renewable energy in a manner that reflects an 

understanding of grid impacts. For example, design teams have been able to produce net 

zero energy buildings, but the buildings’ imports and exports of energy are balanced on 

an annual basis, which is much longer than the balancing timeframes of interest to 

utilities. Owner-directed load-leveling practices could be encouraged by future rate 

structure revisions or incentive programs; or, utilities could apply the model of utilityowned, building-hosted equipment to larger systems that integrate loads, storage, and 

generation at building sites.

Prior research sponsored by the International Energy Agency (Voss et al. 2010; Salom et al. 

2011) can serve as a starting point for analyzing potential metrics that could capture impacts of 

storage on renewable energy integration. For example, Voss et al. define a “load match index” 

that can be evaluated at different time intervals (e.g., monthly). Voss et al. offer a few variations 

on the load match index, depending on whether net metering is allowed and whether battery 

storage is present. If the “battery balance” term in one of their equations is replaced with the 

more general term, “energy storage discharge,” a useful form of the load match index for 

evaluating TES would be: 

fload,i = min [1, onsite generation+energy storage discharge

facility load ], (4-9) 

where “i" denotes the time interval (e.g., hourly, daily, monthly). 

Voss et al. also defined a “grid interaction index,” fgrid, which reflects the stress that a building 

places on the electric grid if its import or export patterns fluctuate rapidly. In a review of 

multiple grid interaction indicators, Salom et al. (2011) include this version of the grid 

interaction index: 

fgrid,year = standard deviation[ Ei

max(|Ei|)

], (4-10) 

where Ei is the net import of grid electricity over each interval i, and where max(|Ei|) is the 

maximum value of Ei for the year. A negative value of Ei indicates a net export of electricity. A 

building with a relatively constant import or export pattern will have a low value for fgrid,year. 

Selecting a smaller timescale (e.g., one minute) for the interval i may be necessary if a site has 

large, rapid fluctuations in loads or on-site generation. An evaluator would then be able to assess 

whether fluctuations are severe enough to need mitigation and whether on-site storage, 

aggregation of distributed systems, or other load management strategies would be more effective.

Additional metrics may be necessary to adequately compare alternative designs. Such metrics

could include the maximum rate of change in electricity imported to and exported from a site. 

These rates of change could be calculated as step changes between power values measured at 

time intervals relevant to utility operations and billing. For example, the step changes could be 

calculated at a 15-minute interval if that timescale is used by a local utility for calculating 
23 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

demand charges. Building industry representatives could work with utility representatives to 

determine the most relevant timescales and associated step change thresholds that would be 

beneficial not to exceed. These thresholds could be informed by ramp rates and costs of utility 

resources (e.g., regulation services) that would otherwise be needed to respond to net load 

fluctuations of a particular scale. Alternative building and system designs could then be 

evaluated in part on their ability to avoid exceeding key thresholds. 

Table 4-2 lists potential impacts that could be evaluated in future research. Demonstration 

projects to date have not attempted to integrate control approaches for TES, packaged AC, and 

renewable generation systems with the intent of improving grid impacts of buildings that export 

renewable power. Future demonstration projects could be designed to quantify how the 

coordinated control of on-site generation, loads, and storage systems can optimize facility load 

profiles from a utility operations perspective. 

Table 4-2 Potential Impacts of On-Site Storage on the Design and Operation of Facilities and 

Campuses with On-Site Generation

Possible Objectives

for Integrating 

Storage with 

Distributed 

Generation Utility-Level Questions Facility-Level Questions

Possible Metrics and 

Scenarios to Compare 

Maximize on-site 

generation while 

avoiding exports 

to the grid

When I allow a customer 

to export electricity, how 

do the benefits relate to 

the cost of required 

infrastructure investments, 

such as enabling 

bidirectional flow in

applicable portions of the 

distribution system? How 

will these costs affect the 

price of energy exported 

from a customer’s site? 

Once I have decided to 

allow a customer to export 

electricity, are there other 

export thresholds that 

would be undesirable to 

exceed (e.g., to maintain 

voltage requirements or

comply with other 

regulations)? How might I 

incentivize building 

owners to avoid 

exceeding these export 

thresholds?

What is my on-site 

generation target?

Which systems are critical 

to remain operational 

during a fault event on my 

utility’s power system? For 

services that are noncritical but still highly 

beneficial (e.g., to maintain 

productivity), what is the 

value of ensuring that 

related systems remain 

operational?

Will installing a different

amount of on-site 

generation affect which rate 

structure applies to my 

facility?

Can storage provide more 

consistent demand 

reductions by mitigating 

times when gross on-site 

generation is mismatched 

with load?

Facility energy metrics and 

on-site generation totals 

with and without storage.

Incremental cost of storage

versus incremental cost to 

the utility for regulation 

resources or curtailment. 

Cost to building owner of 

building system design 

options sized with and 

without storage.

Peak electricity demand 

with and without storage.

Peak electricity export with 

and without storage.
24 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Possible Objectives

for Integrating 

Storage with 

Distributed 

Generation Utility-Level Questions Facility-Level Questions

Possible Metrics and 

Scenarios to Compare

Minimize 

unexpected 

variability in net 

load profiles

What are the costs of 

alternative strategies (e.g., 

regulation resources) for 

mitigating variability in net 

load profiles? 

Will storage help 

customers to participate 

more effectively in load 

management programs?

Will a particular storage 

technology pay back with 

current rate structures and 

incentives, or do I need to 

partner with a utility that 

can own and control the 

storage asset? 

Incremental cost of storage 

versus incremental cost to 

the utility for regulation 

resources, curtailment, or 

generation assets. 

Maximum step changes in 

electricity imports to and 

exports from a site with and 

without storage.

Load match metrics with 

and without storage. 

Grid interaction metrics

with and without storage.

Minimize 

investment in 

distribution 

infrastructure

What are the costs of 

alternative strategies (e.g., 

infrastructure investments) 

for relieving a system 

constraint that is limiting 

deployment of new loads 

or distributed generation?

Will design alternatives 

affect how much the utility 

will charge me to connect 

new loads or on-site 

generation with the grid?

Peak electricity demand 

with and without storage. 

Peak electricity export with 

and without storage. 

Optimize real-time 

pricing to 

maximize returns

How well does the 

customer’s electricity tariff 

reflect utility-level market 

prices for power 

generation and 

distribution?

Would alternative pricing 

structures provide greater 

opportunity for arbitrage?

Are any infrastructure 

upgrades (e.g., 

communications, 

metering) required to 

implement real-time 

pricing?

Which loads in my facility 

can be scheduled in a 

flexible manner? 

Will varying the operation of 

certain building systems 

affect the performance of 

other systems through 

interactive effects?

Will participation reduce my 

electricity costs?

Savings potential from 

participating in real-time 

pricing structures with and 

without storage.

Availability and output of 

storage capacity by hour 

under different control 

schemes.
25 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Possible Objectives

for Integrating 

Storage with 

Distributed 

Generation Utility-Level Questions Facility-Level Questions

Possible Metrics and 

Scenarios to Compare

Respond to utility 

control signals to 

provide ancillary 

services

What are the constraints 

on the availability of this 

resource for responding to 

utility control signals?

If there is a market for 

ancillary services in my 

region, what types of

resources are eligible for 

participation?

Which loads in my facility 

can be scheduled in a 

flexible manner? 

Which systems in my 

facility could respond to 

utility control signals 

without affecting services to 

occupants?

Will participation reduce my 

electricity costs?

Load match metrics with 

and without storage. 

Grid interaction metrics

with and without storage.

Availability of storage 

capacity by hour under 

different control schemes. 

Maximum charge and 

discharge rate in response 

to utility control signals.

4.3 Alternative Models for Building Owner Participation

Broad deployment of strategies that integrate on-site storage with generation will depend on the 

availability of adequate rate structures, incentive programs, or partnerships that benefit both 

utilities and building owners. 

As discussed in Section 2.0, facility-level savings under current rate structures may not be 

sufficient to lead to wide-scale building owner investment in presently available TES systems 

integrated with packaged AC. As time-based pricing options become more prevalent, however, 

more building owners are likely to consider technology options that can support load 

management strategies. 

In the near term, other methods for utilities to engage building owners include developing 

incentive programs or forming direct partnerships with building owners who are willing to share 

responsibilities for TES systems with utilities. For instance, one TES manufacturer has 

developed a marketing approach directed toward utilities (Ice Energy 2012) in which building 

owners host TES systems that are owned and maintained by the utility at little or no cost to the 

building owners. Both the building owners and the utility would be interested to know whether 

the system results in energy and demand savings, but the utility would be the primary party 

concerned with recovering the system installation cost. The utility could recover the TES 

installation cost through several means, such as energy arbitrage or avoided capital investments 

enabled by reducing peak demand. Feasibility for a specific utility would need to be analyzed on 

a case-by-case basis. 

In some of these partnerships, the utility controls the distributed TES systems and could 

potentially realize additional benefits described in Section 4.1, such as ancillary services. 

Additionally, the use of utility-controlled distributed TES systems is not limited to projects that 

integrate TES with packaged AC. For instance, Bonneville Power Administration has explored 

the feasibility of integrating utility control with other end uses, such as hot water heaters, that 

consume electricity and have thermal storage capacity (Ecofys 2012). Other utility stakeholders

have also implemented demonstrations of distributed hot water TES. 
26 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

5 Analysis of Performance Data from Demonstration 

Sites 

This section presents the results of applying multiple concepts from Sections 2.0 through 4.0 to 

evaluate the performance of TES systems from example demonstration sites. At the selected 

sites, TES systems and monitoring equipment had been installed as part of activities separate 

from the DOE/NREL project. NREL worked with industry partners to identify four sites where

stakeholders were willing to share anonymous performance data with NREL for an analysis that 

could be shared with the public. Cost impacts are not included here due to data limitations, but 

demand and energy impacts are presented for all four sites, and emissions impacts are presented 

for sites with adequate data.

At each of the four sites, baseline data were not collected by the technology developer or the 

building owner, so the authors have separately estimated baseline performance. As discussed in 

Section 2.3, there are several methods for establishing a baseline, such as by (1) combining preretrofit data with simulation, or by (2) dividing a monitoring period into baseline and alternative 

periods of days or weeks. In the case of the demonstration sites in this project, however, preretrofit data were not available, and the monitoring period did not include any baseline days. 

Instead, the authors employed a third approach to estimate DX power and run times in different 

modes of DX operation for the baseline case. One-minute measurements of DX power were 

binned into three groups based on which mode of operation the DX system was in when each 

measurement was taken. The three modes were “cooling,” “fan,” and “idle.” The group of 

minutes in DX cooling mode was further subdivided into two groups, depending on whether or 

not the DX system was transitioning between modes. 

DX power values during non-transitional cooling minutes were then correlated with ambient air 

temperature measurements to produce a simplified linear regression model of baseline 

performance for that mode of DX operation. For other modes of DX operation, average power 

values were estimated.

Fractional run times were estimated for each mode of DX operation, using separate assumptions 

to define representative periods for estimating maximum and average DX power. The baseline

power values for different modes were combined with the estimated fractional run times to 

estimate overall power for 15-minute periods used in peak demand calculations and one-hour 

periods used in average energy calculations. A detailed discussion of the baseline assumptions 

and development method is provided in Appendix B. 

5.1 Site Performance Data from Project Partner 1

A TES technology developer, Ice Energy Technologies, provided a year of TES and DX system 

performance data that it had collected in 2012 at two buildings in California. The buildings were 

in different cities, but both are located within Climate Zone 3B as defined by DOE and 

ASHRAE. (Climate zones are described in more detail in Section 6.2.1.) 

At both sites, each TES unit has a cooling capacity of five tons and a storage capacity of 30 tonhours, and each TES unit is integrated with a DX unit that also has a cooling capacity of five 

tons. Major components contained in each TES unit include: an ice storage section with a heat 

exchanger; a condensing unit with a heat exchanger; an R-410A refrigerant compressor; and a 

controller. 
27 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Each site has multiple TES units in operation, and Ice Energy Technologies provided 

performance data for one representative TES unit and DX unit per site. Ice Energy Technologies 

also provided on-site measurements of outside air temperature taken near the TES and DX units; 

this was helpful for reducing analysis error, because temperatures in rooftop microclimates can 

be several degrees higher than temperatures at ground level.

Basic characteristics for each site are summarized in Table 5-1. 

Table 5-1 Demonstration Sites 1 and 2

Case Site 1 Site 2 

State California California

Climate Zone 3B 3B

Facility Type Commercial office Commercial office

Facility Size One story; 3,300 ft2 One story, 21,000 ft2

Facility Vintage 2004 2010

HVAC System 

Vintage 2004 2010

Total Installed 

HVAC Cooling 

Capacity

10 tons 55 tons

Capacity of Each 

DX Unit 5 tons 5 tons

Number and 

Location of DX 

Units

2 units; ground-mounted 11 units; rooftop-mounted

Number and 

Location of TES 

Units

2 units (one TES unit integrated 

with each DX unit); groundmounted

11 units (one TES unit integrated with 

each DX unit); rooftop-mounted

Utility Pacific Gas & Electric California municipal utility

Demand Charge 

Rate Structure

A-10 TOU Medium General 

Service

Municipal Electric Utility Large 

Commercial Tariff

Monitoring 

Period for This 

Study

January 1, 2012 to December 31, 

2012

January 1, 2012 to December 31, 

2012

5.1.1 Site 1

An example day of performance data for one TES unit paired with one DX unit at Site 1 is 

shown in Figure 5-1. The 24-hour period starts at 12:00 p.m. (noon) to coincide with the 

scheduled start time for TES discharge, allowing the figure to depict a complete discharging and 

charging cycle. To mirror typical utility approaches for calculating electricity demand, the 

maximum power value for each hour was estimated by averaging power measurements over 15-

minute intervals and selecting the greatest 15-minute average recorded in that hour. 
28 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 5-1 Example 24-hour period of performance data from Site 1

At Site 1, most instances of TES cooling occurred between 12:00 p.m. and 9:00 p.m., reducing 

the average electrical energy consumption and maximum electrical demand during that time. 

The first six hours of this window (12:00 p.m. to 6:00 p.m.) are the times of day designated by 

the building’s utility tariff to be in the “Summer Peak Period,” when energy rates are highest. 

Utility tariff periods are summarized in Table 5-2. 

Between 9:00 p.m. and 12:00 p.m., when energy rates are lower, electricity demand and energy 

consumption values at Site 1 are higher, reflecting times when the DX system provides cooling 

and times when the TES system charges.
29 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Table 5-2 Tariff Periods at Site 1

Summer OffPeak Period

Summer 

Partial-Peak 

Period

Summer Peak 

Period

Winter OffPeak Period

Winter 

Partial-Peak 

Period

Days of the 

Year May 1 through October 31 November 1 through April 30

Days of the 

Week

Saturday, 

Sunday, and 

holidays

Monday through Friday 

(except holidays)

Saturday, 

Sunday, and 

holidays

Monday 

through Friday 

(except 

holidays)

Times of Day1 9:30 p.m. to 

8:30 a.m.

8:30 a.m. to 

12:00 p.m., 

and 6:00 p.m. 

to 9:30 p.m.

12:00 p.m. to 

6:00 p.m.

9:30 p.m. to 

8:30 a.m.

8:30 a.m. to 

9:30 p.m.

Site 1 system performance during the various tariff periods is depicted in Figure 5-2 through 

Figure 5-5. Key values are summarized in Table 5-3 through Table 5-5. TES successfully 

shifted electricity demand and energy consumption from summer peak tariff hours to summer 

partial-peak and off-peak tariff hours. Winter electricity demand for the case with TES is greater 

than baseline winter electricity demand due to infrequent use of TES, but winter energy use for 

both cases is approximately equivalent. Total annual cooling electricity consumption was 

estimated to be about 2% greater than baseline electricity consumption, but the difference is 

small relative to the uncertainty associated with the estimates of baseline performance described 

in Appendix B. 

 

1 The tariff also specifies that these start and stop times all are shifted one hour later during two periods of the year: 

(1) between the second Sunday in March and the first Sunday in April; and (2) between the last Sunday in October 

and the first Sunday in November.
30 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 5-2 Cooling electricity demand by tariff period for Site 1

Figure 5-3 Summer cooling electricity demand for Site 1
31 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 5-4 Cooling energy by tariff period for Site 1

Figure 5-5 Summer cooling energy for Site 1
32 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Table 5-3 Cooling Electricity Demand at Site 1

Summer OffPeak Period

Summer 

Partial-Peak

Period

Summer Peak 

Period

Winter OffPeak Period

Winter 

Partial-Peak 

Period

Observed Case: 

Max. TES 

Power, kW

4.04 4.00 0.26 2.79 2.77

Observed Case: 

Max. DX Power, 

kW

5.63 6.01 1.23 1.27 1.26

Observed Case: 

Max. Cooling 

Power, kW

9.34 9.16 1.45 3.91 3.98

Estimated 

Baseline Case: 

Max. Cooling 

Power, kW

6.26 6.15 6.29 1.27 3.09

Table 5-4 Cooling Electricity Average Energy Consumption at Site 1

Summer OffPeak Period

Summer 

Partial-Peak 

Period

Summer Peak 

Period

Winter OffPeak Period

Winter 

Partial-Peak 

Period

Observed Case: 

Hourly Average 

TES Energy, 

kWh

1.13 0.05 0.09 0.03 0.01

Observed Case: 

Hourly Average

DX Energy, kWh

1.76 1.98 1.18 0.71 0.94

Observed Case: 

Hourly Average 

Cooling Energy, 

kWh

2.90 2.03 1.27 0.74 0.95

Estimated 

Baseline Case: 

Hourly Average

Cooling Energy, 

kWh

2.08 2.55 2.95 0.71 0.94

Table 5-5 Cooling Electricity Annual Energy Consumption at Site 1

Summer OffPeak Period

Summer 

Partial-Peak 

Period

Summer Peak 

Period

Winter OffPeak Period

Winter 

Partial-Peak 

Period All Periods

Observed Case: 

Hourly Average 

Cooling Energy, 

kWh

6,701 1,887 893 1,719 1,450 12,651

Estimated 

Baseline Case: 

Hourly Average

Cooling Energy, 

kWh

4,815 2,372 2,079 1,651 1,445 12,362
33 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

5.1.2 Site 2

A similar control strategy was employed at Site 2, though the TES discharge window was 

shorter; an example day of performance data for one TES unit paired with one DX unit at this 

site is shown in Figure 5-6. The figure starts at 12:00 p.m. (noon), which is the start of the 

discharge window, so that it can depict a full discharging and charging cycle. 

Figure 5-6 Example 24-hour period of performance data from Site 2

Unlike Site 1, at Site 2, the electricity tariff did not include time-of-use charges. The TES 

technology developer also reported that this particular TES application was focused on reducing 

demand during summer months. To maximize demand savings, the TES systems were 

intentionally paired with high efficiency rooftop units (RTUs). The majority (97%) of the 

instances of TES discharge occurred in the afternoon between 12:00 p.m. and 5:59 p.m., so 

Figure 5-7 and Figure 5-8 summarize cooling system demand and energy consumption based on 

whether the values fell inside or outside this period of the day. Key values are summarized in

Table 5-6 through Table 5-8. 
34 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Cooling demand at this site was successfully shifted out of afternoon hours, which is when most 

office buildings in this location would be expected to have high electric loads. Total annual 

cooling energy consumption with TES was estimated to be about 11% greater than baseline 

energy consumption. An increase in site energy consumption is more likely if a case with TES is 

compared to a baseline with a high-efficiency RTU, as was the case for Site 2. The technology 

developer reports that other TES projects have typically been energy neutral or better in cases 

where the baseline used standard efficiency RTUs. 

Figure 5-7 Cooling electricity demand by schedule for Site 2
35 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 5-8 Cooling energy by schedule for Site 2

Table 5-6 Cooling Electricity Demand at Site 2

6:00 p.m.-11:59 a.m. 12:00 p.m.-5:59 p.m.

Observed Case: Max. TES 

Power, kW 3.70 0.25

Observed Case: Max. DX 

Power, kW 5.24 4.58

Observed Case: Max. 

Cooling Power, kW 7.79 4.59

Estimated Baseline Case: 

Max. Cooling Power, kW 5.24 5.29

Table 5-7 Cooling Electricity Average Energy Consumption at Site 2

6:00 p.m.-11:59 a.m. 12:00 p.m.-5:59 p.m.

Observed Case: Hourly Average 

TES Energy, kWh 0.50 0.07

Observed Case: Hourly Average

DX Energy, kWh 0.42 0.68

Observed Case: Hourly Average 

Cooling Energy, kWh 0.92 0.75

Estimated Baseline Case: Hourly 

Average Cooling Energy, kWh 0.42 1.86
36 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Table 5-8 Cooling Electricity Annual Energy Consumption at Site 2

6:00 p.m.-11:59 a.m. 12:00 p.m.-5:59 p.m. All Periods

Observed Case: Annual Cooling 

Energy, kWh 6,001 1,636 7,636

Estimated Baseline Case: Annual 

Cooling Energy, kWh 2,748 4,063 6,811

5.2 Site Performance Data from Project Partner 2

Electric Power Research Institute (EPRI) provided several months of data for TES systems that 

were tested at two Tennessee locations during EPRI projects in 2009 and 2010. The buildings 

were in different cities, but both are located within Climate Zone 4A as defined by DOE and 

ASHRAE. (Climate zones are described in more detail in Section 6.2.1.) 

Table 5-9 Demonstration Sites 3 and 4

Case Site 3 Site 4

State Tennessee Tennessee

Climate Zone 4A 4A

Facility Size One story; 23,000 ft2 One story; 14,700 ft2

Facility Vintage 2009 1995

HVAC System 

Vintage 2009 2008

Total Installed HVAC 

Cooling Capacity 25 tons 20 tons

Capacity of Each DX 

Unit 5 tons 5 tons

Number and 

Location of DX Units

5 split systems; rooftop- and groundmounted5 packaged system units; groundmounted

Space Type Served 

by TES Units Commercial office addition Laboratory and warehouse space

Number and 

Location of TES 

Units

One TES unit integrated with one DX 

split system

One TES unit integrated with one DX 

packaged unit

Monitoring Period for 

This Study June 23, 2010 to September 27, 2010 July 20, 2009 to October 12, 2009 

5.2.1 Site 3

At Site 3, the TES unit was scheduled to discharge and provide cooling between 10:00 a.m. and 

9:00 p.m. An example day of performance data is shown in Figure 5-9. The figure starts at 

10:00 a.m. in order to depict one complete discharging and charging cycle. 
37 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 5-9 Example 24-hour period of performance data from Site 3 

This site is part of a utility and does not have a time-of-use rate structure, so Figure 5-10 and

Figure 5-11 summarize cooling electricity demand and energy consumption based on whether 

electricity was used within the TES discharge window (10:00 a.m. to 8:59 p.m.) or during other 

hours (9:00 p.m. to 9:59 a.m.). Key values are summarized in Table 5-10 through Table 5-12. 

For each month in the monitoring period, demand and energy were successfully shifted from the 

TES discharge window to other hours. The total cooling energy consumption, though, was 

estimated to be 13% higher for the case with TES than for the baseline case.
38 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 5-10 Cooling electricity demand by schedule for Site 3

Figure 5-11 Cooling energy by schedule for Site 3
39 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Table 5-10 Cooling Electricity Demand at Site 3

9:00 p.m.-9:59 a.m. 10:00 a.m.-8:59 p.m.

Observed Case: Max. TES 

Power, kW 3.94 0.39

Observed Case: Max. DX 

Power, kW 4.84 3.83

Observed Case: Max. 

Cooling Power, kW 8.56 3.93

Estimated Baseline Case: 

Max. Cooling Power, kW 4.84 5.74

Table 5-11 Cooling Electricity Average Energy Consumption at Site 3

9:00 p.m.-9:59 a.m. 10:00 a.m.-8:59 p.m.

Observed Case: Hourly Average 

TES Energy, kWh 1.90 0.18

Observed Case: Hourly Average

DX Energy, kWh 1.67 0.70

Observed Case: Hourly Average 

Cooling Energy, kWh 3.56 0.88

Estimated Baseline Case: Hourly 

Average Cooling Energy, kWh 1.67 2.56

Table 5-12 Cooling Electricity Energy Consumption at Site 3 During Demonstration Period

9:00 p.m.-9:59 a.m. 10:00 a.m.-8:59 p.m. All Periods

Observed Case: Total Cooling 

Energy, kWh 4,384 957 5,340

Estimated Baseline Case: Total

Cooling Energy, kWh 2,069 2,577 4,646

It was observed that during the final hour of the discharge window at Site 3 (8:00 p.m. to 8:59 

p.m.), DX cooling was needed during about 6% of observed minutes. This is related to the fact 

that the discharge window at this site was longer than a typical application; ordinarily, the TES 

system used at this site is designed to provide 30 ton-hours of cooling for six hours if paired with 

a 5-ton DX system. As illustrated in Figure 5-12, the window of consistent peak demand 

reduction for this particular site and system design was 10:00 a.m. to 7:59 p.m. during the 

example month of July. 
40 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 5-12 Site 3 cooling electricity demand by hour for an example month

Since Site 3 is part of a utility and does not pay conventional utility bills, the instances of DX 

cooling in the final hour of the discharge window are not problematic from a billing standpoint. 

At customer sites with time-of-use rate structures, however, TES scheduling can be optimized to 

ensure that DX cooling does not start within a peak period. 

5.2.2 Site 4

At Site 4, the TES unit was scheduled to discharge and provide cooling between 12:00 p.m. and 

8:00 p.m. An example day of performance data is shown in Figure 5-13. The figure starts at 

12:00 p.m. (noon) so that it can depict a full discharging and charging cycle. 
41 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 5-13 Example 24-hour period of performance data from Site 4

For the purposes of analyzing overall energy consumption and potential to reduce peak demand 

in a building setting, a subset of the monitoring period was selected during which cycling 

patterns of the TES and DX systems were relatively typical. This shorter period was selected to 

mitigate two issues. First, compared to the three other sites, Site 4 had a higher incidence of 

missing and erroneous data points that had to be removed (e.g., negative meter values); in Figure 

5-13, for example, the hours of 12:00 p.m., 4:00 p.m., 11:00 a.m. are blank due to gaps in valid 

measurements. Second, for much of the remainder of the monitoring period, the TES system at 

Site 4 operated without cycling, which is different from how systems would be operated in a 

typical building setting.

The resulting subset was limited to September 18-28, which was the longest consecutive string 

of days with typical cycling patterns. Performance during this period is depicted in Figure 5-14

and Figure 5-15, with key values summarized in Table 5-13 through Table 5-15. During this 

period, demand is successfully shifted from the TES discharge window (12:00 p.m. to 8:00 p.m.) 

to other hours. This site does not have a time-of-use rate structure, so the figures summarize 

cooling electricity demand and energy consumption based on whether electricity was used within 
42 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

the TES discharge window. Additionally, total cooling energy during this period is about 6% 

lower than the estimated baseline cooling energy.

Figure 5-14 Cooling electricity demand by schedule for Site 4
43 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 5-15 Cooling energy by schedule for Site 4

Table 5-13 Cooling Electricity Demand at Site 4

8:00 p.m.-11:59 a.m. 12:00 p.m.-7:59 p.m.

Observed Case: Max. TES 

Power, kW 3.52 0.30

Observed Case: Max. DX 

Power, kW 7.09 1.31

Observed Case: Max. 

Cooling Power, kW 9.51 1.61

Estimated Baseline Case: 

Max. Cooling Power, kW 7.09 7.45

Table 5-14 Cooling Electricity Average Energy Consumption at Site 4

8:00 p.m.-11:59 a.m. 12:00 p.m.-7:59 p.m.

Observed Case: Hourly Average 

TES Energy, kWh 1.03 0.14

Observed Case: Hourly Average

DX Energy, kWh 0.89 0.47

Observed Case: Hourly Average 

Cooling Energy, kWh 1.91 0.60

Estimated Baseline Case: Hourly 

Average Cooling Energy, kWh 0.89 3.40
44 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Table 5-15 Cooling Electricity Energy Consumption at Site 4 During Demonstration Period

8:00 p.m.-11:59 a.m. 12:00 p.m.-7:59 p.m. All Periods

Observed Case: Total Cooling 

Energy, kWh 266 36 301

Estimated Baseline Case: Total

Cooling Energy, kWh 121 199 320

5.3 Comparison of Source Metrics

Published hourly location-specific conversion factors are available for calculating source 

emissions for the California demonstration sites, though insufficient public data are available for 

comparable source energy calculations at this time. In the case of source energy, some older loss 

rates distinguishing between peak, shoulder, and off-peak periods have been published (Wong 

2011), but the scope is limited, and the vintage of the data (1990s in one case) suggests that the 

loss estimates may not be applicable to this analysis. Completion of high quality source energy

calculations that account for differences in time of day will require future improvements in 

publically available data.

In the case of source emissions, however, data from prior research efforts were adequate to 

complete calculations. Using the OpenEI resource discussed in Section 3.2, hourly source 

emission factors for the CAMX eGRID subregion were selected. Hourly variations in emission 

factors result from differences in the mix of generator types operating in the CAMX eGRID 

subregion at different times of day and different times of the year.

To produce a second set of emissions estimates for comparison, the E3 tool described in Section

3.2 was also used. Hourly emission factors were averaged by hour of day and month; this step 

was taken to smooth out potentially non-replicable day-to-day variations in the original 8,760-

hour modeled dataset, while preserving diurnal and seasonal patterns that would be expected to 

recur in future time periods.

For each site, baseline emissions were estimated by multiplying hourly energy consumption by 

hourly average emission factors for the applicable month and time of day. The emissions 

difference between the case with TES and the baseline case was then estimated by multiplying 

hourly energy consumption differences by hourly marginal emission factors. The resulting 

emissions differences and the percent change in emissions relative to the baseline are 

summarized in Figure 5-16, Figure 5-17, and Table 5-16. 
45 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 5-16 Estimated emissions comparison for Site 1

Figure 5-17 Estimated emissions comparison for Site 2
46 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Table 5-16 Estimated Difference in Site Energy and Source Emissions for Cases with TES 

Relative to the Baseline Case

Site 1 Site 2

Annual Site Energy Difference +2.3% +10.8%

Annual CO2 Emissions Difference Using 

NREL Estimated CAMX Factors -2.3% +6.8%

Annual CO2 Emissions Difference Using 

Adapted E3 CA Factors +0.3% +12.2%

The results reveal sensitivity of annual emissions to variations in the times of day when 

electricity is used. In particular, source metric trends can differ from site metric trends. The Site 

1 calculation demonstrates that an increase in site energy can be accompanied by a decrease in 

source emissions of CO2. 

Additionally, even though the Site 1 and Site 2 calculations used the same hourly emission 

factors, the hourly differences in cooling load profiles between Sites 1 and 2 were sufficient to 

yield different emissions results. At Site 2, the increase in emissions is likely driven by an 

increase in site energy relative to the estimated baseline, which was large enough to overcome 

the region-specific emissions benefit of shifting load to off-peak hours. 

These emissions estimates should be viewed as preliminary, rather than conclusive, because the 

emissions changes are small relative to the uncertainty in estimating baseline energy 

consumption and hourly emission factors. For example, if baseline energy consumption is 

underestimated, then the emissions difference for the case with TES will appear worse than it 

actually is; alternatively, if baseline energy consumption is overestimated, then the emissions 

difference for the case with TES will appear better than it actually is. The results demonstrate 

the importance of developing a strong understanding of case-specific load assumptions and 

cooling system characteristics when evaluating prospective adoption of TES systems. 

An additional consideration is that both the NREL-estimated CAMX emission factors and the 

E3-estimated California emission factors reflect modeled snapshots of a particular year of grid 

operation. The basic methods illustrated here could be extended as a part of broader analyses 

that consider potential changes to the power system. 

The results also suggest that emissions may be sensitive to alternative control strategies that 

could be tested in the future to respond to real-time pricing signals, generate additional utility 

benefits, or improve renewable energy integration. 
47 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

6 Simulation-Based Analysis of a TES System 

6.1 EnergyPlus Feature Addition

As part of this project, NREL produced an enhancement to the simulation platform EnergyPlus 

that enables users to model the performance of TES integrated with packaged AC units. 

EnergyPlus is a DOE-sponsored software application that is free to public users; it previously 

included two methods for modeling ice storage integrated with central plant chillers, but prior to 

this project, it did not include a method for modeling TES integrated with packaged AC 

(EnergyPlus 2012). The enhancement to accommodate additional TES systems will be available 

to the public with the release of EnergyPlus version 8.1 in the fall of 2013. 

By adding the ability to model TES integrated with packaged AC to EnergyPlus, this project will 

potentially benefit the following types of technology evaluators in the commercial buildings 

community: 

• Building design teams that use whole-building simulation will be able to better assess 

TES integrated with packaged AC as an option for comparison with other design 

alternatives.

• Engineers and analysts will be better able to evaluate and optimize solutions for 

integrating energy efficiency, energy storage, and renewable energy technologies.

• Guidance documents or simplified evaluation tools could be developed in the future for 

public use by combining the new simulation capability with methodologies in this report. 

To represent a specific product, a user enters product-specific values as inputs to the model. 

These inputs include a wide range of performance characteristics, such as cooling capacities and 

coefficients of performance. The TES and DX units can be scheduled to operate in different 

modes of operation, including: DX cooling only; TES charging only; TES discharging only; 

simultaneous DX cooling and TES charging; simultaneous DX cooling and TES discharging; 

and off. The inputs also include performance curves that capture how power requirements vary 

with operating conditions. The ability to evaluate specific technologies depends on the 

availability of detailed system performance data that can be translated into performance curves 

or lookup tables used in an EnergyPlus model of the system. The new modeling capability is 

generic enough to represent a variety of technologies, including products available today and 

technologies that could be developed in the future to use different types of storage media or have 

specific performance characteristics. 

6.2 High-Level Check of Model Behavior

As discussed in Section 2.4, field data can be used as a high-level check to see if a model is 

realistic given site-specific inputs that describe the facility, weather conditions, and key 

specifications of the evaluated technology, such as storage capacity, cooling capacity during 

discharge, electrical power input in different modes of operation, and charge and discharge 

times. As part of the testing of the new EnergyPlus feature, field data from the Site 1

demonstration location were combined with a DOE Reference Building Model to perform a 

high-level check for realistic outputs. 
48 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

6.2.1 Whole-Building Energy Model Development Process

The starting point for the analysis was the DOE Small Office Commercial Reference Building 

Model for New Construction (Deru et al. 2011). The model for DOE Climate Zone 3B was 

selected because it includes Site 1. This model was adapted to incorporate known high-level 

characteristics of the Site 1 demonstration location, such as the building size and local weather 

data. Other details about system schedules, internal loads, building geometry, and building 

construction were not available for the demonstration site, so for these model elements, most 

attributes of the Commercial Reference Building Model were left unchanged.

Two models were produced for the purposes of the high-level check. The first model was a 

“baseline model” without TES. The second model was a model with TES. 

The baseline model was produced by making the following modifications to the Commercial 

Reference Building Model:

• Location-specific values (latitude, longitude, time zone, elevation, design days, ground 

temperatures, and water mains temperatures) in the Commercial Reference Building 

Model were changed to values for a nearby city with published data on environmental 

conditions. 

• The model was scaled down from 5,500 ft2 to 3,300 ft2 to match the size of the building 

from the Site 1 demonstration. The exterior façade lighting was manually scaled by the 

same ratio. A variety of other inputs such as interior lighting power and plug loads were

defined in the Commercial Reference Building Model on a per area basis, so they 

automatically scale when a user changes the building size. The HVAC systems and

service water heating (SWH) systems are “autosized” by EnergyPlus, so they also scale 

automatically with building size. The Commercial Building Reference Model is a fivezone model, so each zone was modeled with its own HVAC equipment autosized by 

EnergyPlus to meet the cooling capacity requirements of the zone. After reviewing initial 

results, a few internal loads, setpoints, and schedules were adjusted to reduce differences 

between modeled load profiles and high-level load profile patterns observed in data from 

Site 1.

• The HVAC control algorithm was modified to better reflect the algorithms of a typical 

rooftop unit based on work from a prior NREL project (Studer et al. 2012). This required 

changing the simulation time step in the model to one minute, and it involved the use of 

the optional Energy Management System (EMS) functionality in EnergyPlus to code and 

execute the more realistic RTU control algorithms. The HVAC fans were also modified 

to run continuously based on observations of the HVAC system at the Site 1

demonstration location. 

• Measured ambient air temperature data from the Site 1 demonstration location were used 

as outdoor air temperature inputs for the modeled HVAC system. For other weather data 

inputs, measured 2012 weather data from a nearby airport weather station were procured 

and used in simulation runs. 

• The model was modified to include HVAC systems capable of switching between DX 

units and TES units for cooling. For the baseline case, the TES system was turned off, 

and cooling loads were met with the DX units. 
49 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

The model with TES was produced by making the following additional modifications: 

• To represent the Ice Bear 30 product deployed at Site 1, NREL worked with technology 

developer Ice Energy Technologies to define appropriate performance curves and other 

product-specific inputs for use with the generic TES model object. 

• The resulting cooling system with integrated DX and TES components was controlled as 

follows for the months of May through October: 

o Between 9:00 p.m. and 8:00 a.m., charge the TES storage tank (make ice) if it is 

not fully charged,2 and use the DX system to meet any cooling loads.

o Meet cooling loads between 8:00 a.m. and 12:00 p.m. with DX cooling only. 

o Meet cooling loads between 12:00 p.m. and 9:00 p.m. by discharging TES. (The 

system is sized to provide TES cooling until at least 6:00 p.m., but on some days, 

more TES cooling will be possible.) If TES charge is depleted before 9:00 p.m. 

and a cooling load remains, switch to DX cooling. 

• For the remainder of the year, TES was not used, and cooling loads were met with DX 

cooling only. 

6.2.2 TES Model Simulation Checks

Detailed data necessary for a calibrated model were not readily available for the demonstration 

projects in this report, and collection of such data was outside the scope of this project. 

Simulation feature testing is an iterative process, however, and future projects could be designed 

to further test the EnergyPlus feature using more detailed data and calibrated models. As an 

initial step, the analysis in this section shows that high-level trends in a modeled building with 

TES were comparable to that of an actual office building with the same technology, though 

differences in building details cause performance details to differ accordingly. 

Figure 6-1 summarizes one month of cooling electricity demand and energy patterns for the 

simulation with the modified reference building model, combining results from all zones in the 

building. For comparison, Figure 6-2 depicts analysis results for Site 1 for the same month. 

Since measured data were provided for only half the cooling equipment at Site 1, measured 

values from Site 1 are doubled in Figure 6-2 to represent cooling electricity for the whole 

building. The cooling electricity values in both figures include power input to the DX and TES 

systems, excluding fan energy. 

The two figures show that both buildings have similar diurnal cycles. Cooling electricity 

demand and energy consumption values are higher during off-peak hours, and TES discharge 

reduces demand between 12:00 p.m. and 9:00 p.m. 

The magnitudes of the demand and energy values differ in a few respects between the two 

figures. The DX demand values are lower for the modified reference building model because the 

 

2 This is an illustrative example that is simpler than the charging logic used at Site 1, which adjusted charging start 

times based on state of charge and a target time for reaching full charge. The simplified approach was used for this 

analysis based on the level of data provided to NREL for this project, but more sophisticated controls can be 

implemented using the EMS feature in EnergyPlus.
50 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

combined cooling capacity of its autosized DX systems (7 tons) is smaller than the combined 

cooling capacity of the DX systems from the Site 1 demonstration (10 tons). The sizing 

difference is likely due to two factors: 

• Differences in loads, construction, setpoints, and schedules between the two buildings 

contribute to differences in cooling loads. In the absence of detailed building data for 

calibration, system sizing differences are not unusual. 

• The HVAC system at Site 1 may be oversized. Setpoint conditions were met in the 

modified reference building model, so the modeled system is not believed to be 

undersized. The hypothesis of an oversized system at Site 1 is supported by the ratio of 

maximum DX power to average DX energy observed in Figure 6-2: a high ratio suggests 

that the system cycles frequently, and the ratio for the system in Figure 6-2 is greater than 

that in Figure 6-1. 

Another difference is that modeled cooling energy for the case with TES is greatest when 

charging starts (Hour 21) and then drops over the next few hours. One reason for this difference 

is that the modified reference building model uses a simple schedule control to start charging at 

9:00 p.m. each night, whereas the system at Site 1 adjusts charging start times each day with a 

more complex algorithm based on state of charge. 

Figure 6-1 June simulation results for the modified reference building model
51 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 6-2 June Site 1 performance scaled to the building’s total cooling capacity

These differences are reasonable given that the models used in this section are variations on the 

Commercial Reference Building Model, and they are not calibrated models of the actual building

at Site 1. The model with TES shares a few qualities with the Site 1 demonstration building 

where possible—namely location, building footprint, and primary building activity. Many other 

details differ, including building construction, internal loads, HVAC equipment sizes, and 

system schedules. Given these differences, the outputs of simulations using the model with TES 

are not expected to closely match the data collected from the Site 1 demonstration, but the two 

buildings do share high-level trends in system behavior. 

6.3 Extension to Nationwide Climate Zones and Additional Example

Building Types

The next analysis demonstrates the ability of the new EnergyPlus TES model to facilitate 

comparisons of TES performance potential across climate zones and building types. 

The inputs used in this simulation study were informed in part by performance characteristics of 

a particular TES product, but a significant number of additional modeling assumptions and 

simplifications were also necessary to address the following project limitations:

• Available field and laboratory testing data for the specified TES technology were limited 

compared to the data necessary for comprehensive model validation. The available data 

provided a beneficial starting point, and initial simulations conducted during this project 

were able to replicate high-level trends from demonstration data. Examples of future data 

that would be beneficial for further validation of the TES model include: greater detail 

about HVAC system controls and operation; improved baseline performance data; more 
52 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

information about building characteristics, loads, and whole-building performance; and 

TES performance data collected under a wider range of conditions and applications.

• Simplifications were necessary to rapidly generate a large number of models. The impact 

of these simplifications could be examined in future analyses and adjustments to inputs 

could be made accordingly. For example, autosizing capabilities in EnergyPlus were 

leveraged in this project, so future analyses could examine the sensitivity of modeled 

TES performance to system sizing differences.

The preliminary results of this simulation study illustrate how impacts can vary if a test 

combination of TES system characteristics and controls is replicated in different settings. Future 

analyses could incorporate additional data to refine model inputs and conduct further validation 

activities to more accurately reflect the potential impacts of a particular TES application. 

6.3.1 Whole-Building Energy Model Development Process

The starting points for this analysis were the DOE Small Office, Stand-Alone Retail, and Strip 

Mall Commercial Reference Building Models for New Construction (Deru et al. 2011). These 

models represent examples of building types that often have packaged AC systems. For each of 

these three building types, a Commercial Reference Building Model is available for each of 16 

U.S. climate subzones. Fifteen of these subzones were defined by dividing eight DOE climate 

zones into smaller regions based on whether they have “moist,” “dry,” or “marine” conditions; 

these 15 subzones are depicted in Figure 6-3 (DOE 2004). An additional subzone was defined 

for the coast of southern California, which is highly populated and differs from the rest of 

climate subzone 3B. 

Figure 6-3 DOE climate zones (Credit: DOE 2004)
53 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

For each combination of location and building type, an applicable Commercial Reference 

Building Model was adapted as needed to produce two models: a baseline model without TES 

and a model with TES.

The process for developing these models followed the same general approach used in Section 6.2

with a few exceptions, including the following major differences: 

• Weather data adjustments specific to the Site 1 demonstration were not used, because 

such adjustments are not applicable to this analysis. Instead, Typical Meteorological 

Year 2 (TMY2) weather data were used for each of 16 U.S. climate subzones to reflect 

the generalized nature of this analysis. The usage of TMY data here is for illustrative 

purposes only; alternative or additional weather data sets may be more appropriate for 

other studies, particularly those intended to capture the benefits of TES during extreme 

weather and load events. Considerations for selecting weather data sets are discussed in 

Section 2.4. 

• Internal loads and HVAC setpoints from the original Commercial Reference Building 

Models were preserved.

• The standard method for representing DX cycling in EnergyPlus 8.0 was used in place of 

the custom approach used in Section 6.2. 

• To produce baseline models for the stand-alone retail and strip mall building types, the 

unitary DX packaged systems from the corresponding Commercial Reference Building 

Models were adjusted to make them compatible with the new TES coil model. 

For the purposes of this analysis, the same TES control logic from Section 6.2 was used in all 

test cases for consistency. (This approach is suitable for a generalized demonstration, but in sitespecific simulation studies or analyses focusing on controls optimization, the control options 

tested should reflect the specific objectives of the study.) 

6.3.2 Example Simulation Results for Site Metrics

For each location and building type, the analysis results show that the model with TES uses more 

annual site energy than the baseline model, but the timing of the energy consumption is 

consistently shifted into off-peak hours. These trends only reflect the implementations included 

in the example analysis; trends for other implementations may differ, and results will depend on 

case-specific details, including site conditions, building characteristics, TES and DX system 

performance details, and control logic. 

Results for an example month are presented in Figure 6-4 through Figure 6-6, including all 

climate subzones used in the study. 
54 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 6-4 Small office model peak demand differences for an example month

Figure 6-5 Stand-alone retail model peak demand differences for an example month
55 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure 6-6 Strip mall model peak demand differences for an example month

A complete set of energy and monthly peak demand differences for each combination of building 

type and climate subzone is provided in Appendix C. Rate structures vary widely, and a tariff 

sensitivity analysis was outside the scope of this project, so cost impacts are not included in the 

appendix. A building owner’s engineer can estimate cost impacts for a particular application by 

combining demand and energy performance results with the utility tariff specific to that site. A 

tariff sensitivity analysis could also be incorporated into future studies. 

Across all climate subzones, the cases with TES successfully shift demand from on-peak hours 

to off-peak hours during summer months. The peak demand reduction tends to be smaller in 

cooler months, as would be expected for a TES technology integrated with a cooling system. 

The modeled cases with TES consume more overall energy than their baseline counterparts, but 

the cases with TES reduce energy consumption during peak hours as intended. Demonstration 

data also indicated that annual site energy increases are possible for some TES applications, and 

future analyses could examine contributing factors in more detail.

6.3.3 Future Data Required for Evaluating Source Energy and Emissions

The EnergyPlus enhancement has provided improved capabilities for evaluating site metrics, but 

site metrics alone are insufficient for evaluating the overall impacts of deploying building-sited 

TES. An important next step would be to translate site metrics into source metrics using source 

energy and emission factors that vary with time of day. If hourly factors are unavailable, an 

alternative would be to seek factors that at least vary between on-peak and off-peak hours. As 

noted in Section 5.3, adequate factors were unavailable for calculating source energy impacts for 

the demonstration sites, and this condition is common throughout the country. (One partial 

exception is discussed in Appendix A.) Similarly, marginal emission factors are not readily 
56 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

available at this time for most of the United States. Completion of quality site-to-source 

calculations that account for differences in time of day will require future improvements in data 

that are available to the public. Such efforts could be furthered through future collaborations 

with stakeholders involved in the generation, transmission, and distribution of power, as these 

parties may already be collecting some of the prerequisite data for other operational purposes. 
57 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

7 Conclusions

This report presents an evaluation framework for engineers and analysts who assess technologies 

for building owners and utilities and are considering adoption of TES systems integrated with 

packaged AC. It provides methodologies for evaluating site demand and energy performance 

metrics and translating these values into source energy and emissions impacts. The framework 

does not provide methodologies for evaluating installation and maintenance requirements or 

conducting grid simulations, which are outside the scope of this report. 

In addition to providing guiding concepts, the report applies multiple concepts from the 

evaluation framework to analyze performance data from example demonstration sites. These 

analyses show that TES systems can successfully shift electric demand and energy to off-peak 

hours in a variety of circumstances. Analyses of demonstration data also show that such 

strategies can come with site energy savings or increases, depending on site-specific conditions. 

As part of this project, NREL produced an enhancement to the simulation platform EnergyPlus 

that enables users to model the building-level performance of TES integrated with packaged AC 

units. Expected benefits of this work include: 

• Building design teams that use whole-building simulation will be able to better assess 

TES integrated with packaged AC as an option for comparison with other design 

alternatives, including other storage technologies. 

• Engineers and analysts will be better able to evaluate and optimize solutions for 

integrating energy efficiency, energy storage, and renewable energy technologies.

• Guidance documents or simplified evaluation tools could be developed in the future for 

public use by combining the new simulation capability with methodologies in this report. 

Site metrics alone are insufficient for evaluating the overall impacts of deploying building-sited 

TES, because such metrics do not capture dynamic interactions between building systems and 

the grid. An important next step is to translate site metrics into source metrics using source 

energy and emission factors that vary with time. To help with this step, this report includes 

guidance and considerations for evaluating source energy and emission factors at different 

timescales. Completion of quality site-to-source calculations with appropriate inputs at relevant 

timescales will require future improvements in data that are available to the public. 

The example analyses in this report also indicate that emissions impacts are sensitive to hourly 

variations in load profiles. The results suggest that emissions and other source metrics may be 

sensitive to alternative control strategies that could be tested in the future to respond to real-time 

pricing signals, generate additional utility benefits, or improve renewable energy integration.

Realizing a broader range of TES benefits to utilities may require a combination of one or more 

of the following: reduction in technology costs, advancements in the design of electricity market 

products that capture the impacts of on-site energy storage, and collaboration between utilities 

and building owners. In particular, TES presents an opportunity to improve the integration of 

renewable energy systems, and multiple utility stakeholders have expressed interest in future 

efforts to advance the industry’s understanding of how to realize this potential. Improved 

integration could be measured with a combination of metrics, including source energy 

reductions, fossil fuel reductions, emissions reductions, and cost-effectiveness. Demonstration 

projects to date have not attempted to integrate control approaches for TES, packaged AC, and 
58 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

renewable generation systems with the intent of improving grid impacts of highly efficient 

buildings that export renewable power. Future demonstration projects could be designed to 

quantify how the coordinated control of on-site generation, loads, and storage systems can 

optimize facility load profiles from a grid perspective.
59 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

8 References

ANSI. (2011). ANSI/AHRI Standard 900: 2010 Standard for Performance Rating of Thermal 

Storage Equipment Used for Cooling. Approved by ANSI on January 4, 2011. Accessed April 

8, 2013, from 

http://www.ahrinet.org/App_Content/ahri/files/standards%20pdfs/ANSI%20standards%20pdfs/

ANSI.AHRI%20Standard%20900%20%28I-P%29-2010.pdf. 

ASHRAE. (2012). ASHRAE Handbook: HVAC Systems and Equipment. Atlanta, GA: 

American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. 

Barley, D.; Deru, M.; Pless, S.; Torcellini, P. (2005). Procedure for Measuring and Reporting 

Commercial Building Performance. NREL/TP-550-38601. Accessed April 9, 2013, from 

http://www.nrel.gov/docs/fy06osti/38601.pdf. 

Cope, Bryan. (8 March 2013). Interview. Southern California Public Power Authority. 

Glendora, CA. 

Denholm, P.; Ela, E.; Kirby, B.; Milligan, M. (2010). The Role of Energy Storage with 

Renewable Electricity Generation. NREL/TP-6A2-47187. Accessed September 4, 2013, from 

http://www.nrel.gov/docs/fy10osti/47187.pdf. 

Deru, M.; Torcellini, P. (2007). Source Energy and Emission Factors for Energy Use in 

Buildings. NREL/TP-550-38617. Revised June 2007. Accessed June 24, 2013, from 

http://www.nrel.gov/docs/fy07osti/38617.pdf. 

Deru, M.; Field, K.; Studer, D.; Benne, K.; Griffith, B.; Torcellini, P.; Liu, B.; Halverson, M.; 

Winiarski, D.; Rosenberg, M.; Yazdanian, M.; Huang, J.; Crawley, D. (2011). U.S. Department 

of Energy Commercial Reference Building Models of the National Building Stock. 118 pp.; 

NREL Report No. TP-5500-46861. 

DOE. (2004). Adapted from a climate zone map developed for the U.S. DOE and first 

published in ASHRAE Standard 90.1-2004. Available at 

http://apps1.eere.energy.gov/buildings/publications/pdfs/building_america/ba_climateguide_7_1.

pdf. Last accessed June 29, 2013. 

DOE. (12 November 2010). “Discussion of Data for Smart Grid Metrics and Benefits: Storage

System Performance Supplement.” Presentation accessed April 18, 2013, from 

http://www.smartgrid.gov/sites/default/files/pdfs/energy_storage_system_performance_supplem

ent.pdf. 

DOE. (2013). “Time-Based Rate Programs.” Accessed April 17, 2013, from 

http://www.smartgrid.gov/recovery_act/deployment_status/time_based_rate_programs. 

E3. (2010 December). Greenhouse Gas Tool for Buildings in California. Version 3. 

Spreadsheet tool accessed August 26, 2013, from 

http://www.ethree.com/public_projects/ghg.php. 

Ecofys. (16 October 2012). Smart End-use Energy Storage and Integration of Renewable 

Energy: A Pilot Project Overview. Accessed February 20, 2013, from 

http://www.ecofys.com/files/files/ecofys_2012_smart-end-use-energystorage_bpa_project_overview.pdf. 
60 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

EnergyPlus. (2012). “EnergyPlus Engineering Reference.” Revision dated October 6, 2012. 

http://apps1.eere.energy.gov/buildings/energyplus/pdfs/engineeringreference.pdf. 

EIA. (June 2006). 2003 Commercial Buildings Energy Consumption Survey. Tables B40 and 

B41. Accessed March 18, 2013, from http://www.eia.gov/consumption/commercial/data/2003/. 

EIA. (2013). “Glossary.” Accessed August 5, 2013, from http://www.eia.gov/tools/glossary/. 

EnergyPlus. (2012). “EnergyPlus Engineering Reference.” Revision dated October 6, 2012. 

http://apps1.eere.energy.gov/buildings/energyplus/pdfs/engineeringreference.pdf. 

EPA. (2012a). “eGRID.” Page last updated on December 13, 2012. Accessed March 7, 2013, 

from http://www.epa.gov/cleanenergy/energy-resources/egrid/index.html. 

EPA. (2012b). “Rules, Policy, and Instructions.” Page last updated on February 7, 2012. 

Accessed March 26, 2013, from http://www.epa.gov/airmarket/emissions/rules.html. 

EPA. (2013). “Air Markets Program Data.” Online database tool accessed March 25-26, 2013 

from http://ampd.epa.gov/ampd/. 

ERCOT. (1 December 2010). ERCOT Nodal Protocols, Section 13: Transmission and 

Distribution Losses. Accessed March 8, 2013, from 

http://www.ercot.com/mktrules/nprotocols/current.html. 

ERCOT. (2013a). “Data Aggregation 2012 Archive.” Accessed March 8, 2013, from 

http://www.ercot.com/mktinfo/data_agg/2012/index. 

ERCOT. (2013b). “Glossary.” Accessed March 22, 2013, from 

http://www.ercot.com/glossary. 

Faramarzi, R.; Coburn, B.; Sarhadian, R.; Mitchell, S.; Pierce, R. (2004, August 22). 

Performance Evaluation of Rooftop Air Conditioning Units at High Ambient Temperatures. A 

Proceedings Paper published by the American Council for an Energy-Efficient Economy. 

Accessed March 20, 2013, from http://aceee.org/proceedings-paper/ss04/panel03/paper05. 

Georgia Power. (2013). “Marginally Priced.” Accessed August 27, 2013, from 

http://georgiapower.com/pricing/business/marginally-priced.cshtml. 

Ice Energy. (2012). “Introducing Project Cool Move.” Accessed November 7, 2012, from 

http://www.ice-energy.com/project-cool-move. 

Illinois Commerce Commission. (2013). “Residential Real Time Pricing Programs.” Accessed 

April 18, 2013, from http://pluginillinois.org/realtime.aspx. 

Lazar, J.; Baldwin, X. (2013). Valuing the Contribution of Energy Efficiency to Avoided 

Marginal Line Losses and Reserve Requirements. Accessed May 21, 2013, from 

http://www.raponline.org/document/download/id/4537. 

Narayanamurthy, Ram. (August 2013). Interviews and peer review feedback. Electric Power 

Research Institute. Palo Alto, CA. 

NREL. (2012). “Glossary of Transmission Grid Integration Terms.” Accessed November 12, 

2012, from http://www.nrel.gov/electricity/transmission/glossary.html. 
61 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Open Energy Information. (2011). “Hourly Energy Emission Factors for Electricity Generation 

in the United States.” Last updated April 11, 2011. Accessed December 18, 2012, from 

http://en.openei.org/datasets/node/488. 

Open Energy Information. (2013). “Utilities.” Accessed August 6, 2013, from 

http://en.openei.org/wiki/Gateway:Utilities. 

Salom, J.; Widén, J.; Candanedo, J.; Sartori, I.; Voss, K.; Marszal, A. (November 2011). 

Understanding Net Zero Energy Buildings: Evaluation of Load Matching and Grid Interaction 

Indicators. From Proceedings of Building Simulation 2011: 12th Conference of International 

Building Performance Simulation Association. November 14-16. Sydney, Australia. 

Sioshansi, R.; Denholm, P.; Jenkin, T.; Weiss, J. (2009). “Estimating the value of electricity 

storage in PJM: Arbitrage and some welfare effects.” Energy Economics. Vol. 31., pp. 269-

277. Accessed April 9, 2013, from http://dx.doi.org/10.1016/j.eneco.2008.10.005. 

Sioshansi, R.; Denholm, P.; Jenkin, T. (2012). “Market and Policy Barriers to Deployment of 

Energy Storage.” Economics of Energy and Environmental Policy. Vol. 1, No. 2, pp. 47–63. 

Studer, D.; Romero, R.; Herrmann, L.; Benne, K. (2012). Energy Implications of Retrofitting 

Retail Sector Rooftop Units with Stepped-Speed and Variable-Speed Functionality. 70 pp.; 

NREL Report No. TP-5500-51102. Accessed from 

http://www.nrel.gov/docs/fy12osti/51102.pdf. 

Southern California Edison. (2010). “Real-Time Pricing RTP-2 General Service.” Accessed 

April 17, 2013, from https://www.sce.com/wps/wcm/connect/04ae933b-cafa-4412-bbe4-

8a1ab443f81c/RTP2.pdf?MOD=AJPERES. 

University of Central Florida. (2013). “Thermal Energy Storage Facility.” Accessed January 

10, 2013, from http://www.sustainable.ucf.edu/?q=node/88. 

Voss, K.; Sartori, I.; Napolitano, A.; Geier, S.; Gonzalves, H.; Hall, M.; Heiselberg, P.; Widén, 

J.; Candanedo, J.; Musall, E.; Karlsson, B.; Torcellini, P. (2010, March). Load Matching and 

Grid Interaction of Net Zero Energy Buildings. 

Wilcox, S.; Marion, W. (2008). Users Manual for TMY3 Data Sets. NREL/TP-581-43156. 

Revised May 2008. Accessed April 19, 2013, from 

http://www.nrel.gov/docs/fy08osti/43156.pdf. 

Wong, L. (August 2011). A Review of Transmission Losses in Planning Studies. Accessed July 

10, 2013, from http://www.energy.ca.gov/2011publications/CEC-200-2011-009/CEC-200-2011-

009.pdf. 
62 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Appendix A Additional Considerations for Source 

Energy Calculations

This section provides an initial examination of example data that can help when assessing the 

significance or negligibility of regional and time-dependent variations in factors used in site-tosource energy calculations for energy storage measures. This section does not describe a 

detailed analysis of regional and time-dependent factors, which would be outside the scope of 

this project; rather, this section discusses examples to demonstrate an approach and identify 

considerations that can be applied to other case-specific data. 

The following sections demonstrate an approach for translating site energy into source energy 

that can be applied when loss factors in the electric power system are available at different 

timescales.

A.1 Source Energy Factors

The general relationship between site and source energy can be defined as:

Source Energy = Site Energy × SEF (A-11) 

Where:

SEF = source energy factor

The authors recommend defining source energy factor in a manner that accounts for losses 

during four stages of activity: distribution, transmission, generation, and precombustion. 

Distribution losses and transmission losses result from the delivery of electricity over wires and 

through equipment located between electricity generators and end users. Generation losses occur 

at power plants during the conversion of fuels into electricity. Precombustion losses occur

during the “extraction, processing, and transportation” of fuels that are used in power plants 

(Deru et al. 2007). 

Each of the four stages of activity can be characterized by its own loss factor, which contributes 

to the overall source energy factor: 

SEF = 1

(1−DLF)(1−TLF)(1−GLF)(1−PLF) (A-12) 

Where:

DLF = distribution loss factor 

TLF = transmission loss factor

GLF = generation loss factor (at power plants) 

PLF = precombustion loss factor 
63 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

In this definition, each loss factor, LF, is dimensionless and takes the following general form3: 

LF1,2 = Loss1,2

Input1

(A-13) 

Where: 

LF1,2 = loss factor from Point 1 to Point 2 

Loss1,2 = power loss (kW) from Point 1 to Point 2 

 Input1,2 = power input (kW) into Point 1 

For example:

DLF = Distribution System Losses

Power Entering the Distribution System (A-14) 

If loss-related values are easier to find in other formats, the source energy factor equation can be 

rearranged for convenience. For example, a report by Deru et al. (2007) references a generation 

efficiency assumption instead of a generation loss factor. It also quantifies “precombustion 

effects” for different regions in units of precombustion energy loss per unit of site energy. To 

accommodate these two data formats, the source energy factor definition can be rearranged as:

SEF = 1

(1−DLF)(1−TLF)ηG

+ P (A-15) 

Such that: 

Source Energy = Site Energy × � 1

(1−DLF)(1−TLF)ηG

+ P� (A-16) 

Where:

DLF = distribution loss factor 

TLF = transmission loss factor

ηG = generation efficiency at power plants

 P = precombustion energy loss per unit of site energy 

 

3 As a caution, the term “loss factor” is not standardized in the industry; some references may define loss factors to 

be ratios of different values, such as losses over usable output power. Before using loss factors from other 

references in the equations described here, check how each reference defines its loss factors, and adjust them as 

needed.
64 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Marginal loss factors reflect the change in losses resulting from incremental changes in loads, so 

they are preferred over average loss factors when estimating source energy impacts for load 

management strategies, including deployment of energy storage. On the other hand, average loss 

factors are more readily accessible to the general public and may be used by stakeholders who 

lack access to marginal loss factors. When using average loss factors, it is important to 

understand potential sources of error. 

Lazar and Baldwin (2011) suggest that when analysts account for line losses as part of evaluating 

energy efficiency measures, most analysts use average line loss factors because they are 

measured and published more often than marginal loss factors. The use of average line losses, 

however, underestimates the benefit of a load reduction measure. Marginal line loss factors are 

greater than average line loss factors, and this disparity is greater when loads are high (Lazar and 

Baldwin 2011). 

Comparisons of marginal generation loss or efficiency would also be more effective than 

comparisons of average generation loss or efficiency. When loads are at their highest, hourly 

marginal generation losses would be expected to be greater than average generation losses, 

because highly inefficient generators are usually dispatched last. At other times, the error 

introduced by using average loss factors or efficiency values is more difficult to generalize, 

given that generation involves the dispatch of power plants that can vary dramatically in terms of 

fuel type and part-load performance curves. 

It is recommended that utility engineers and analysts use marginal loss factors where possible, as

these parties may be able to access necessary data within their organizations. On the other hand, 

a building owner’s engineer may lack access to marginal loss factors and need to start with 

average loss factors. If initial work with average loss factors suggests that more detailed analysis 

is warranted at a particular timescale of interest, a building owner’s engineer may wish to work 

with a partner utility to examine marginal loss factors at that timescale as a next step.

Utility and building stakeholders would benefit from future work that assesses the sensitivity of 

site-to-source calculations to region, timescale, and the replacement of average loss factors with 

marginal loss factors. In the near-term, the methods in this report can serve as a framework for 

conducting initial calculations and identifying information gaps.

A.2 Distribution Losses

In many regions of the United States, hourly estimates of transmission and distribution losses are 

not easily accessible to building owners’ engineers. One regional exception is ERCOT, which 

publishes forecasted and “deemed actual” transmission and distribution loss factors on its 

website (ERCOT 2013a). The deemed actual loss factors are estimates calculated as a function 

of electric load in 15-minute intervals. ERCOT’s calculations include coefficients that are 

adjusted based on season (winter, spring, summer, or fall) and whether the time of day is an onpeak versus off-peak period. Readers interested in more detail can find ERCOT’s methodology 

described in the ERCOT Nodal Protocols (ERCOT 2010). 

In this section, example loss factors from public ERCOT data are examined to assess whether 

time-of-day variations appear to be significant—thus warranting further investigation—or 

whether time-of day variations appear to be negligible for this case. 

ERCOT provides distribution loss factors for multiple Transmission/Distribution Service 

Providers (TDSPs). Some TDSPs are responsible for multiple service delivery points that are 
65 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

assigned unique loss codes; for these TDSPs, each loss code can be associated with a different 

loss factor (though this is not always the case). Figure A-1 summarizes distribution loss factors 

for each TDSP and loss code, using deemed actual values from 2012 as a recent example year. 

In this figure, distribution loss factors have been averaged by time-of-day. Thus, the figure 

represents an “annual average” day divided into 15-minute intervals, and each 15-minute interval 

has a different annual average distribution loss factor.
66 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure A-1 Annual average distribution loss factors by time of day for ERCOT TDSPs
67 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

High-level observations about the data in Figure A-1 include: 

• Distribution loss factors vary significantly between TDSPs. The average value for 

Texas-New Mexico Power Co. with Loss Codes A, C, and E (11.1%) is over eight times 

the average value for Centerpoint Energy Houston Electric with Loss Code E (1.3%). 

• Distribution loss peaks do not necessarily align with periods of highest demand or highest 

transmission losses (see Section A.3). Most TDSPs in this example have average 

distribution loss factors that peak in early morning hours, though a few cases have peaks 

in the afternoon or early evening. If marginal loss factors were available and plotted 

instead of average loss factors, the marginal loss factor would be expected to more 

closely follow trends in load, with marginal losses peaking during peak load hours. 

• Time-of-day variations are small for some TDSPs and large for others. The case with the 

greatest time-of-day variation is Texas-New Mexico Power Company with Loss Codes 

A, C, and E. (These three loss codes were assigned the same loss factors.) The case with 

the least time-of-day variation is Oncor Electric Delivery Company with Loss Code A. 

Table A-1 summarizes the variation in these two examples.

Table A-1 Variability in Annual Average Distribution Loss Factors for Two Cases

TDSP and Loss Code Average DLF

Range Between 

Maximum and 

Minimum DLF Range/Average

Standard 

Deviation

Oncor Electric Delivery 

Company with Loss Code 

A 

3.9% 0.2% 4.5% 0.05%

Texas-New Mexico Power 

Company with Loss Codes 

A, C, and E

11.1% 4.6% 41.6% 1.57%

Further analysis by season reveals additional patterns that are not captured by annual averages. 

As an example, Figure A-2 summarizes season average4 distribution loss factors by time of day 

for one TDSP and loss code: Nueces Electric Cooperative with Loss Code A. This case had 

annual values towards the middle of the range displayed in Figure A-1, and it was also one of the 

cases for which distribution losses appeared to vary significantly with time of day. 

 

4 Figure A-2 uses the same season boundaries as those used in the ERCOT methodology for calculating loss factors, 

with December through February assigned to winter, March through May assigned to spring, June through August 

assigned to summer, and September through November assigned to fall.
68 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure A-2 Seasonal average distribution loss factors by time of day for Nueces Electric 

Cooperative with Loss Code A

Figure A-2 shows that in this particular case:

• Winter distribution loss factors are higher than summer distribution loss factors. 

• All seasons show a daily peak for distribution loss factor in early morning hours. 

• The winter profile also shows a smaller, secondary peak in the early afternoon. 

Seasonal average distribution loss factors for this case are summarized in Table A-2. 

Table A-2 Variability in Seasonal Average Distribution Loss Factors for Nueces Electric 

Cooperative with Loss Code A

Season Average DLF

Range Between 

Maximum and 

Minimum DLF Range/Average

Standard 

Deviation

Winter 4.8% 1.2% 25% 0.38%

Spring 4.6% 1.8% 40% 0.63%

Summer 3.7% 1.5% 42% 0.56%

Fall 4.6% 1.6% 36% 0.57%

Engineers assisting building owners within the ERCOT interconnection can use Figure A-1 to 

judge whether they want to neglect or further investigate time-of-day or seasonal variations in 

distribution losses. 

At a minimum, the ERCOT data suggest that there would be value in improving the 

understanding of local and regional variations in distribution losses and providing supporting 

data in a simplified form accessible to building owners’ engineers without sophisticated grid 

analysis software.

At this time, data such as those from ERCOT are not readily available to building owners’ 

engineers in most other parts of the United States. Building owners’ engineers without adequate 

data will have to either neglect time-of-day variations in distribution loss factors or request 
69 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

supporting data from partner utilities. Additionally, marginal distribution loss factors are 

difficult to find in all regions, and the replacement of average loss factors with marginal loss 

factors may require collaboration with utilities as well.

A.3 Transmission Losses

ERCOT also provides data on ERCOT-wide transmission losses. Figure A-3 summarizes 

seasonal average transmission loss factors by time of day. The averages are based on 2012 

deemed actual values from ERCOT’s website archive (ERCOT 2013a). Unlike its distribution 

loss factors, which peak in early morning hours for most TDSPs, ERCOT’s transmission loss 

factors peak in late afternoon or early evening hours during spring, summer, and fall. The winter 

transmission loss factors differ in that they peak once in the early morning and again in the early 

evening.

Figure A-3 Seasonal average ERCOT transmission load factors by time of day

Within any given season, the difference between the maximum and minimum transmission loss 

factor is significant relative to the average transmission loss factor—the range divided by the 

average is greater than 10% in each case. The absolute difference, however, is small relative to 

the overall site-to-source calculation. The low absolute variability for this loss factor is 

summarized in Table A-3, which shows that the range between the maximum and minimum 

value for each season is less than 0.5%. 

Table A-3 Variability in Seasonal Average ERCOT Transmission Loss Factors

Season Average TLF

Range Between 

Maximum and 

Minimum TLF Range/Average

Standard 

Deviation

Winter 1.7% 0.2% 13% 0.07%

Spring 1.6% 0.3% 20% 0.11%

Summer 1.6% 0.4% 23% 0.13%

Fall 1.6% 0.3% 20% 0.11%
70 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

In this example, a building owner’s engineer within ERCOT boundaries could assume a 

consistent transmission loss factor of about 2% throughout the day and throughout the year. 

Future analyses may be improved with better access to marginal transmission loss factors that 

could replace average transmission loss factors in such calculations.

A.4 Generation Plant Efficiency

As with transmission and distribution loss factors, generation plant efficiency can vary with time. 

An early question for a building owner’s engineer with limited access to utility data is whether 

generation plant efficiency variations are significant or negligible when evaluating energy 

storage. This section describes a simplified approach for estimating time-of-day variation in 

generation plant efficiency.

The U.S. Environmental Protection Agency (EPA) publishes emissions, load, and heat input data 

from plants that must report emissions data to its Clean Air Markets program. Monitoring and 

reporting requirements and calculation methodologies are available through the EPA Clean Air 

Markets website (EPA 2012b). Public users can search for and download reported data through 

the online Air Markets Program Data (AMPD) tool (EPA 2013). 

Example data were selected from the AMPD tool to produce an example calculation of hourly 

generation plant efficiencies aggregated at the state level. To identify a manageable sample of 

hourly data to download, a single month and state were selected for initial examination: 

• August 2011 was selected as an example of a month in the cooling season.5

• Texas was selected for this example since it was also used in Sections A.2 and A.3, but 

this process could be repeated for other locations. 

The downloaded data included hourly values for gross load, steam load, and heat input for Texas 

monitoring locations covered by EPA emissions reporting programs.6

 The collective efficiency 

of the electricity-generating plants was assumed to be the aggregated gross load divided by the 

aggregated heat input. Since the focus here is on impacts on the electricity grid, the calculation 

excluded steam-generating plants (about 1% of the downloaded data points). To reduce the risk 

of methodological inconsistencies between monitoring locations, the calculation also excluded 

monitoring locations that were not identified by AMPD as being assigned to a specific emissions 

reporting program (also about 1% of the downloaded data points). The gross load and heat input 

of the remaining locations were aggregated at the state level for each hour of the day. Hourly 

average efficiency values are summarized in Figure A-4. 

 

5 A note of caution: If the user selects a date that is too recent, the AMPD tool may warn the user that only 

preliminary data are available for that date. At the time of this study, for example, summer 2012 data were still in a 

preliminary state, so a range from summer 2011 was selected instead, which did not generate a preliminary data 

warning.

6 For this example, the “Query” option was selected in the AMPD tool, focusing on the “Emissions” data set, 

organized by “Monitoring Location.”
71 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure A-4 Aggregated hourly electricity generation efficiency in August 2011, from Texas 

plants reporting emissions to the EPA Clean Air Markets Program

In Figure A-4, “View A” shows that time-of-day variations in electricity are fairly small 

compared to the magnitude of the average generation efficiency; “View B” is a closer look at the 

range over which generation efficiency varies, which reveals small drops in efficiency during 

early morning and the afternoon. The average efficiency and low overall variability is 

summarized numerically in Table A-4. 
72 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Table A-4 Variability in Hourly Electricity Generation Efficiency in August 2011, From Texas 

Plants Reporting Emissions to the EPA Clean Air Markets Program

Timeframe

Average 

Efficiency

Range Between 

Maximum and 

Minimum 

Efficiency Range/Average

Standard 

Deviation

August 2011 37.0% 1.6% 4.4% 0.46%

The overall consistency of values in Figure A-4 and Table A-4 suggests that, for this specific 

case, time-of-day variations in generation efficiency could be neglected by a building owner’s 

engineer during a site-to-source energy calculation if the included plants dominate the generation 

plants. If that were the case, the building owner’s engineer could assume an average plant 

generation efficiency of 37% for August calculations. 

One major limitation of this example, however, is that the data set excludes plants that do not 

report emissions to the Clean Air Markets program. For example, the contribution of renewable 

power systems to the grid is not captured. So, although the above example demonstrates a 

general process for examining the impact of time-of-day variations in generation efficiency, the 

inputs require improvement. For a building owner’s engineer to effectively assess the 

significance of these variations without access to grid analysis software, data sets or summary 

tables will be needed that capture local generation mixes and hourly efficiencies more 

comprehensively. 

Another limitation of this simplified approach is that the inclusion of electricity generation in the 

state-level aggregation was based solely on the location of the power plant; this approach does 

not account for imports and exports of electricity across state lines. While this simplification 

may be acceptable to a building owner’s engineer considering energy storage for a single site, a 

utility that is considering a larger project may opt to leverage grid analysis software to account 

for energy flows more accurately.

A.5 Precombustion Contributions to Source Energy

Source energy can also be defined to account for “precombustion effects,” including “extraction, 

processing, and transportation” of fuels used in power plants (Deru et al. 2007). Estimates of 

precombustion contributions to source energy are not typically available at hourly timescales, but 

annual average estimates of precombustion contributions to source energy for electricity 

generation are available in the appendix of the report titled, Source Energy and Emission Factors 

for Use in Buildings (Deru et al. 2007). For example, in the ERCOT interconnection, Deru et al.

estimate that an average of 0.253 kWh of additional energy was lost during precombustion 

activities for every 1 kWh of electricity delivered to consumers in 2004. 

A.6 Derivation of Average and Marginal Emission Factors

The OpenEI website referenced in Section 3.2 provides hourly average emission factors 

developed through a 2011 NREL project (OpenEI 2012). The emission factors were determined 

with a commercial unit commitment and hourly economic dispatch model that simulates the 

financial operation of the electric power system with a constrained transmission grid based on a 

direct current power flow. The model commits and dispatches electric generating units in order 

to minimize the production cost of the system as a whole while meeting electricity demand and 
73 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

reliability reserve requirements. The model simulated the dispatch of electricity generating units 

within each of the three synchronous interconnections (the eastern interconnection, WECC, and 

ERCOT). Each interconnection was modeled separately, and hourly electricity transfers between 

eGRID subregions were tracked for use in the emission calculations. Utilities and load areas 

were sorted into applicable eGRID subregions. 

The 2011 NREL project also included a preliminary examination of marginal emission factors

beyond the scope of the datasets at the OpenEI website. Base case and load decrement cases

were simulated to estimate changes in emissions that result from changes in load. For each 

eGRID subregion, the load decrement case involved reducing loads in the subregion by 500 MW 

during every hour of the year. The size of the load decrement (500 MW) was chosen to be large 

enough to represent more than one generator on the margin, but small enough that it is expected 

to reflect marginal effects rather than major changes in generation. Alternative load decrement 

sizes were tested, and lower values (less than 200 MW) led to unstable results. 

Marginal emission factors were estimated by comparing each base case and load decrement case 

to calculate emission changes per unit of load change. This work was leveraged to produce the 

marginal emission factors for the calculations used in Section 5.3. 
74 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Appendix B Additional Background for Demonstration 

Data Analysis

As indicated in Section 5.0, pre-retrofit data were not available for the demonstration sites, and 

their monitoring periods did not include any baseline days, so a simplified mathematical model 

of baseline performance was derived using data from hours when the DX system was the 

primary source of cooling. 

The approach leveraged a combination of one-minute-interval measured data and hourly 

aggregation. Starting with subhourly measurements was important for capturing peak demand 

(kW) values associated with DX RTU performance because DX compressors can cycle between 

idle and cooling modes multiple times within an hour.

One-minute measurements of DX power were binned into three groups based on which mode of 

operation the DX system was in when each measurement was taken. The three modes were 

cooling, fan, and idle. 

The cooling group was further subdivided into two groups, depending on whether the DX system 

was assumed to be in transition between modes or relatively stable. The transition minutes were 

assumed to include: (1) the minute before a DX system switches from cooling to another mode; 

(2) the first two minutes after a DX system switches to cooling. This approach isolates minutes 

that may be split between two modes, as well as minutes when power input may fluctuate 

immediately after cooling is initiated.

DX power during the non-transitional cooling minutes was then correlated with ambient air 

temperature to produce a simplified linear regression model of baseline performance for that 

mode of DX operation. Each equation takes the general form: 

Y = β0 + Xβ1 (B-17) 

Here, Y is DX power in kW, and X is ambient air temperature in degrees Fahrenheit. Each 

coefficient βo (intercept) and β1 (slope) is summarized in Table B-5, along with the coefficient of 

determination, R2 (the square of the correlation coefficient). The resulting correlations were only 

moderate in some cases, so the baseline regression models are considered to be only high-level 

approximations. This type of uncertainty is typical of field demonstration data, as discussed in 

Section 2.3. 

Table B-5 Linear Regression Models Used for Baselines in Section 5.0

Site Slope Intercept R2

Site 1 0.02 kW/°F 3.87 kW 0.76 

Site 2 0.02 kW/°F 3.08 kW 0.89

Site 3 0.04 kW/°F 2.05 kW 0.85

Site 4 0.06 kW/°F 2.18 kW 0.67

The linear regression model can be used for minutes when the system is assumed to be cooling 

and not in transition. For other modes of DX operation, average power values were estimated, as 

summarized in Table B-6. 
75 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Table B-6 Average DX Power (kW) Used for Baselines in Section 5.0

Site Fan Cooling Transition

Site 1 1.18 4.28

Site 2 0.97 3.39

Site 3 1.07 4.56

Site 4 1.27 6.66

After determining these values, the following algorithm was used to determine a maximum DX 

power for each hour of the baseline case:

• If no TES discharge occurred during the hour, then the maximum DX power in the 

baseline case was assumed to equal the maximum DX power in the case with TES.

• Otherwise, a new value was calculated for the maximum DX power for the hour in the 

baseline case. Since demand is still assumed to be based upon 15-minute averages, a 

baseline 15-minute interval was constructed for each hour. Time spent in different modes 

of DX operation during the 15-minute interval was estimated as follows:

o The fraction of time in DX cooling mode was assumed to be the greater of the 

following two values: 

 The fraction of time spent in TES discharge mode for the same hour in the 

case with TES.

 The maximum fraction of time spent in DX cooling mode when 

comparing all 15-minute intervals that month in the case with TES.

o The fraction of DX cooling time spent in transition was assumed to be: 

 3 minutes if total cooling time is 12 minutes or less. This reflects the 

presence of a transition between modes at some point in the 15-minute 

interval.

 0 minutes if total DX cooling time is 13-15 minutes. 

o Any part of the 15-minute interval not spent in DX cooling mode is assumed to be 

spent in DX fan mode. 

o The appropriate linear regression model from Table B-5 is applied to the fraction 

of time when the DX system is in cooling but not in transition. The appropriate 

average power value from Table B-6 is applied to the fraction of time spent in DX 

cooling transitions or DX fan mode. 

A separate algorithm was used to determine average DX energy consumption for each hour of 

the baseline case:

• If no TES discharge occurred during the hour, then the average DX energy in the baseline 

case was assumed to equal the average DX energy in the case with TES.

• Otherwise, a new value was calculated. Time spent in different modes of DX operation 

was estimated as follows:
76 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

o The fraction of time in DX cooling mode was assumed to equal the fraction of 

time spent in TES discharge mode for the same hour in the case with TES. 

o The fraction of DX cooling time spent in transition was assumed to be the lesser 

of the following two values: 

 The average fraction of time spent in DX cooling mode when comparing 

all hours that month in the case with TES.

 The total time spent in DX cooling mode.

o Any part of the 15-minute interval not spent in DX cooling mode is assumed to be 

spent in DX fan mode. 

o The appropriate linear regression model from Table B-5 is applied to the fraction 

of time when the DX system is in cooling but not in transition. The appropriate 

average power value from Table B-6 is applied to the fraction of time spent in DX 

cooling transitions or DX fan mode. 

This simplified approach reflects the limited amount of building data available within the scope 

of this project, which leveraged data from previous demonstrations conducted by project 

partners; other projects with better access to baseline data can employ other analysis methods as 

discussed in Section 2.3. 
77 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Appendix C Additional Simulation Results

The figures in this section summarize results from the nationwide simulations detailed in Section 

6.3. Three building types were modeled, including a small office building, a standalone retail 

building, and a strip mall building. 

In cases with TES, the TES system was used in the months of May through October to provide 

cooling between 12:00 p.m. and 5:59 p.m. This timeframe was selected as an illustrative 

example of a peak period, and it was based on a tariff schedule from a demonstration site 

discussed in Section 5.0. During other hours, cooling loads are met by packaged DX units. In 

baseline cases, TES is not used. Climate definitions and other modeling assumptions are 

provided in Section 6.3. 

For each building type: 

• The first six figures show how monthly peak demand differs between cases with and 

without TES. Each power difference is defined to be the power value from the case with 

TES minus the power value from the corresponding baseline case. The horizontal axis 

identifies the climate subzone where each building model was located. Across all climate 

subzones, the cases with TES successfully shift demand from on-peak hours to off-peak 

hours during summer months. 

• The next two figures show how total energy consumption differs between cases with and 

without TES. The cooling electricity figure includes energy for DX and TES system 

components associated with space cooling, except for fans. (This differs from the 

cooling electricity figures for demonstration data in Section 5.0, which included fan 

energy.) Fan energy is tracked separately by the simulation program used for this study, 

and its values did not change between cases with and without TES. The facility 

electricity figure includes all electricity-consuming end uses in the building. The 

modeled cases with TES consumed more overall energy than their baseline counterparts, 

but the cases with TES reduce energy consumption during peak hours as intended. 

Demonstration data indicate that such energy results are possible for some TES 

applications. 

• The final figure shows how load factor differs between cases with and without TES. The 

case with TES can have a greater or lower load factor than the baseline case, and the 

difference varies between climate zones. This study applied only one control strategy 

and did not attempt to optimize load factors, so load factor results in this section reflect 

indirect effects from an application that focused primarily on peak demand reduction. 

Future studies could examine whether variations in TES control strategies can improve 

load factor or other metrics. 

The inputs used in this simulation study were informed in part by performance characteristics of 

a particular TES product, but a significant number of additional modeling assumptions and 

simplifications were also applied, and the validation activities completed during this project were 

considered preliminary steps. The preliminary results illustrate how impacts can vary if a test 

combination of TES system characteristics and controls is replicated in different settings. Future 

analyses could incorporate additional data to refine model inputs and conduct further validation 

activities to more accurately reflect the potential impacts of a particular TES application. 
78 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-5 Small office model: May facility electricity peak demand

Figure C-6 Small office model: June facility electricity peak demand
79 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-7 Small office model: July facility electricity peak demand

Figure C-8 Small office model: August facility electricity peak demand
80 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-9 Small office model: September facility electricity peak demand

Figure C-10 Small office model: October facility electricity peak demand
81 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-11 Small office model: cooling electricity energy consumption

Figure C-12 Small office model: facility electricity energy consumption
82 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-13 Small office model: comparison of monthly load factors
83 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-14 Stand-alone retail model: May facility electricity peak demand

Figure C-15 Stand-alone retail model: June facility electricity peak demand
84 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-16 Stand-alone retail model: July facility electricity peak demand

Figure C-17 Stand-alone retail model: August facility electricity peak demand
85 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-18 Stand-alone retail model: September facility electricity peak demand

Figure C-19 Stand-alone retail model: October facility electricity peak demand
86 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-20 Stand-alone retail model: cooling electricity energy consumption

Figure C-21 Stand-alone retail model: facility electricity energy consumption
87 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-22 Stand-alone office model: comparison of monthly load factors
88 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-23 Strip mall model: May facility electricity peak demand

Figure C-24 Strip mall model: June facility electricity peak demand
89 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-25 Strip mall model: July facility electricity peak demand

Figure C-26 Strip mall model: August facility electricity peak demand
90 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-27 Strip mall model: September facility electricity peak demand

Figure C-28 Strip mall model: October facility electricity peak demand
91 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-29 Strip mall model: cooling electricity energy consumption

Figure C-30 Strip mall model: facility electricity energy consumption
92 

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Figure C-31 Strip mall model: comparison of monthly load factors