A NATIONAL ASSESSMENT OF 

DEMAND RESPONSE POTENTIAL
 

Federal Energy Regulatory Commission 

STAFF REPORT 

Prepared by 

The Brattle Group 

Freeman, Sullivan & Co 

Global Energy Partners, LLC 

JUNE 2009 

The opinions and views expressed in this staff report do not necessarily represent those of the Federal 

Energy Regulatory Commission, its Chairman, or individual Commissioners, and are not binding on the 

Commission. 

ACKNOWLEDGEMENTS

The analysis presented in this report was produced by a team of consultants from The Brattle Group 

(TBG), Freeman, Sullivan & Co. (FSC) and Global Energy Partners (GEP). Each firm led different parts 

of the project, typically with significant input from the other firms. TBG managed the project and was the 

lead contractor to the Federal Energy Regulatory Commission (FERC). TBG also had the lead in 

producing this report. FSC was the lead contractor on model development, and also developed the state 

and customer-segment level load shapes that were used as starting points for developing demand response 

impacts. FSC and TBG worked together to develop price impacts that reflect the extensive research that 

has been done in this area. GEP had the lead on data development with input from both TBG and FSC. 

Gary Fauth, an independent consultant specializing in advanced metering business case analysis, had the 

lead role in producing the advanced metering deployment scenario that underlies one of the potential 

estimates. Senior staff from all three firms worked jointly to develop scenario definitions and to provide 

defensible input assumptions for key drivers of demand response potential. 

We are grateful to Dean Wight, the FERC project manager, and Ray Palmer, David Kathan, Jessica L. 

Cockrell, George Godding, Jignasa Gadani and Judy L. Lathrop at FERC for their guidance through all 

stages of this project and for providing comments on earlier drafts of this report. We are also grateful to 

these experts who provided guidance and review: Charles Goldman, Anne George, Mark Lauby, 

Lawrence Oliva, Andrew Ott, and Lisa Wood. 

The Brattle Group 

Ahmad Faruqui 

Ryan Hledik 

Freeman, Sullivan & Co. 

Stephen S. George 

Josh Bode 

Paul Mangasarian 

Global Energy Partners, LLC 

Ingrid Rohmund 

Greg Wikler 

Debyani Ghosh 

Sharon Yoshida 

Federal Energy Regulatory Commission i 
 

TABLEOFCONTENTS
 

Acknowledgements ........................................................................................................................................... i
 

Table of Contents .............................................................................................................................................. ii
 

Table of Figures................................................................................................................................................. iv
 

List of Tables....................................................................................................................................................... vi
 

Executive Summary.......................................................................................................................................... ix
 

Energy Independence and Security Act of 2007......................................................................... ix
 

Estimate of Demand Response Potential .................................................................................... ix
 

Other Results of the Assessment................................................................................................xii
 

Barriers to Demand Response Programs and Recommendations for Overcoming the 

Barriers...................................................................................................................................... xiv 

Chapter I. Purpose of the Report .............................................................................................................17
 

Introduction................................................................................................................................ 17
 

Structure of the Report............................................................................................................... 19
 

Chapter II. Key Assumptions ......................................................................................................................21
 

Customer Classes ....................................................................................................................... 21
 

Demand Response Program Types ............................................................................................ 21
 

Demand Response Scenarios ..................................................................................................... 23
 

Comparing the Key Scenario Assumptions ............................................................................... 24
 

Chapter III. Key Results.................................................................................................................................27
 

National Results ......................................................................................................................... 27
 

Regional Results ........................................................................................................................ 30
 

State-level Results...................................................................................................................... 31
 

State Case Studies ...................................................................................................................... 32
 

Summary of State Impacts ......................................................................................................... 41
 

Benchmarking the Estimate for the Business-as-Usual Scenario .............................................. 44
 

Chapter IV. Trends and Future Opportunities .....................................................................................47
 

Areas for Further Research ........................................................................................................ 48
 

Chapter V. Overview of Modeling and Data ........................................................................................51
 

Model Overview ........................................................................................................................ 51
 

Database Development .............................................................................................................. 56
 

Development of Load Shapes .................................................................................................... 57
 

AMI Deployment ....................................................................................................................... 58
 

ii A National Assessment of Demand Response Potential 
Estimating the Impact of Dynamic Prices.................................................................................. 59
 

Cost-Effectiveness Analysis ...................................................................................................... 63
 

Chapter VI. Barriers to Demand Response ...........................................................................................65
 

The Barriers to Demand Response............................................................................................. 65
 

Assessing the Barriers................................................................................................................ 66
 

Chapter VII. Policy Recommendations ...................................................................................................69
 

Statutory Requirement ............................................................................................................... 69
 

General Recommendations to Overcome Barriers to Achieving Demand Response 

Potential ..................................................................................................................................... 69 

Recommendations to Achieve Specific Demand Response Potential Scenarios....................... 71
 

National Action Plan on Demand Response .............................................................................. 73
 

References..........................................................................................................................................................75
 

Appendix A. State Profiles...........................................................................................................................79
 

Appendix B. Lessons Learned in Data Development.....................................................................185
 

Appendix C. Detail on Barriers Analysis ..............................................................................................189
 

Appendix D. Database Development Documentation..................................................................201
 

Appendix E. Uncertainty Analysis ..........................................................................................................243
 

Appendix F. Energy Independence and Security Act of 2007, Section 529...........................247
 

Appendix G. Glossary of Terms ..............................................................................................................249
 

Federal Energy Regulatory Commission iii 
TABLEOFFIGURES

Figure ES-1: U.S. Peak Demand Forecast by Scenario.............................................................................. x 
 

Figure ES-2: Census Regions............................................................................................................................. xii 

Figure ES-3: Demand Response Potential by Census Division (2019).............................................xiii
 

Figure 1: U.S. Summer Peak Demand Forecast by Scenario ................................................................27
 

Figure 2: U.S Demand Response Potential by Program Type (2019)................................................28
 

Figure 3: U.S. Demand Response Potential by Class (2019).................................................................29
 

Figure 4: The Nine Census Divisions .............................................................................................................30
 

Figure 5: Demand Response Potential by Census Division (2019) ....................................................30
 

Figure 6: Comparison of Demand Response Impact Distribution across States..........................32
 

Figure 7: Georgia BAU and EBAU Peak Demand Reduction in 2019................................................33
 

Figure 8: Georgia BAU, EBAU, and AP Peak Demand Reduction in 2019 .......................................34
 

Figure 9: Georgia Potential Peak Demand Reduction in All Scenarios, 2019................................35
 

Figure 10: Connecticut BAU and EBAU Peak Demand Reductions in 2019 ...................................36
 

Figure 11: Connecticut BAU, EBAU, and AP Peak Reductions in 2019.............................................37
 

Figure 12: Connecticut Potential Peak Demand Reduction in All Scenarios, 2019 .....................38
 

Figure 13: Washington BAU and EBAU Peak Demand Reduction in 2019.....................................39
 

Figure 14: Washington BAU, EBAU, and AP Peak Reduction in 2019...............................................40
 

Figure 15: Washington Potential Peak Demand Reduction in All Scenarios, 2019 .....................41
 

Figure 16: Top Ten States by Achievable Potential in 2019 (GW)......................................................42
 

Figure 17: Top Ten States by Achievable Potential in 2019 (% of Peak Demand).......................43
 

Figure 18: Bottom Ten States by Achievable Potential in 2019 (GW) ..............................................43
 

Figure 19: Bottom Ten States by Achievable Potential in 2019 (% of Peak Demand)................44
 

iv A National Assessment of Demand Response Potential 
Figure 20: Comparison of BAU Estimate to Other Data Sources (2008)..........................................45
 

Figure 21: Key Building Blocks and Inputs for Demand Response Potential Model ..................52
 

Figure 22: User Friendly Input Sheet from Demand Response Potential Model .........................55
 

Figure 23: User Friendly Input Sheet from Demand Response Potential Model (continued).56
 

Figure 24: Cumulative AMI Installations under Two Scenarios...........................................................59
 

Figure 25: Significance of Barriers to Demand Response in California as Identified by 

Stakeholders............................................................................................................................................................67
 

APPENDICES 

Figure C-1: Regulatory Mechanisms for Promoting DSM at Electric Utilities.............................194
 

Figure D-1: Data Development for Model Inputs: Relationships Between Data Elements and 

Key Information Source....................................................................................................................................202
 

Figure D-2: Predicted vs. Actual Results for Commercial and Industrial Classes ......................214
 

Figure D-3: Residential Actual vs. Predicted by Temperature..........................................................215
 

Figure D-4: Residential Actual vs. Predicted by Temperature; CAC Quadrant...........................215
 

Figure D-5: Percent of Meters in State That Are AMI Meters in 2019...........................................230
 

Figure D-6: Comparison of Impacts from Recent Pricing Pilots to Calibrated PRISM 

Simulations............................................................................................................................................................234 

Figure E-1: Results of Uncertainty Analysis for the Achievable Potential Scenario in 2019..244
 

Figure E-2: Share of Achievable Participation Potential for Each Demand Response Program 

Type, 2019 .............................................................................................................................................................245
 

Federal Energy Regulatory Commission v 
LISTOFTABLES

Table 1: Key Differences in Scenario Assumptions..................................................................................24
 

Table 2: Explanation of Difference between FERC Staff Report and BAU Estimate ....................46
 

Table 3: Summary of Key Data Elements and Sources...........................................................................57
 

Table 4: Percent Reduction in Peak Period Energy Use for Residential Customers in Selected 

States..........................................................................................................................................................................60
 

Table 5: Final Participation Rates for Non-Pricing Programs ..............................................................60
 

Table 6: Drivers of Final Participation Rates for Pricing Programs ....................................................61
 

Table 7: Per-Customer Impacts for Non-Pricing Programs..................................................................63
 

Table 8: Assumed Per-Customer Impacts from Pricing Programs.....................................................63
 

Table 9: The Barriers to Demand Response ...............................................................................................66
 

Appendices 

Table A-1: Summary of Key Data by State..................................................................................................80
 

Table A-2: Potential Peak Demand Reduction by State (2014)...........................................................81
 

Table A-3: Potential Peak Demand Reduction by State (2019)...........................................................82
 

Table D-1: Number of Accounts by Rate Class ......................................................................................205
 

Table D-2: Electricity Sales by Rate Class .................................................................................................206
 

Table D-3: Peak Demand Forecast by State: .........................................................................................208
 

Table D-4: Summary of Utility Data Used in Regression Analysis ..................................................209
 

Table D-5: Average Energy Use per Hour (2 - 6 pm) on Top 15 System Peak Days................211
 

Table D-6: Average Per-Customer Peak Load by Rate Class ............................................................216
 

Table D-7: Growth Rate in Population and Critical Peak Load by Rate Class.............................218
 

Table D-8: Residential CAC Saturation Values by State......................................................................220
 

vi A National Assessment of Demand Response Potential 
Table D-9: Classification of Utilities by AMI Status...............................................................................228
 

Table D-10: Assumed Probability and Schedule for Utilities Underlying Each AMI Deployment 

Scenario..................................................................................................................................................................229
 

Table D-11: Annual and Cumulative Deployment for Each Forecast Year under EBAU and 

AP/FP Scenarios ..................................................................................................................................................230
 

Table D-12: Pilot Impacts Excluded from Assessment........................................................................235
 

Table D-13: Assumed Elasticities by Customer Class...........................................................................236
 

Table D-14: Percent Reduction in Peak Period Energy Use for the Average Residential 

Customer ...............................................................................................................................................................238 

Table D-15: Enabling Technology Equipment Costs .........................................................................239
 

Table D-16: Economic Screen Results for Dynamic Pricing with Enabling Technology .........241
 

Table D-17: Economic Screen Results for Direct Load Control........................................................242
 

Federal Energy Regulatory Commission vii 

EXECUTIVESUMMARY

Energy Independence and Security Act of 2007 

Section 529 (a) of the Energy Independence and Security Act of 20071 (EISA 2007) requires the Federal 

Energy Regulatory Commission (Commission or FERC) to conduct a National Assessment of Demand 

Response Potential 2

 (Assessment) and report to Congress on the following: 

•	 Estimation of nationwide demand response potential in 5 and 10 year horizons on a State-by-State 

basis, including a methodology for updates on an annual basis; 

•	 Estimation of how much of the potential can be achieved within those time horizons, 

accompanied by specific policy recommendations, including options for funding and/or 

incentives for the development of demand response; 

•	 Identification of barriers to demand response programs offering flexible, non-discriminatory, and 

fairly compensatory terms for the services and benefits made available; and 

•	 Recommendations for overcoming any barriers. 

EISA 2007 also requires that the Commission take advantage of preexisting research and ongoing work 

and insure that there is no duplication of effort. The submission of this report fulfills the requirements of 

Section 529 (a) of EISA 2007. 

 

This Assessment marks the first nationwide study of demand response potential using a state-by-state 

approach. The effort to produce the Assessment is also unique in that the Commission is making 

available to the public the inputs, assumptions, calculations, and output in one transparent spreadsheet 

model so that states and others can update or modify the data and assumptions to estimate demand 

response potential based on their own policy priorities. This Assessment also takes advantage of 

preexisting research and ongoing work to insure that there is no duplication of effort. 

Estimate of Demand Response Potential 

In order to estimate the nationwide demand response potential in 5 and 10 year horizons, the Assessment 

develops four scenarios of such potential to reflect different levels of demand response programs. These 

scenarios are: Business-as-Usual, Expanded Business-as-Usual, Achievable Participation and Full 

Participation. The results under the four scenarios illustrate how the demand response potential varies 

according to certain variables, such as the number of customers participating in existing and future 

demand response programs, the availability of dynamic pricing3

 and advanced metering infrastructure 

 

1

 Energy Independence and Security Act of 2007, Pub. L. No. 110-140, § 529, 121 Stat. 1492, 1664 (2007) (to be codified at 

National Energy Conservation Policy Act § 571, 42 U.S.C. §§ 8241, 8279) (EISA 2007). The full text of section 529 is attached 

as Appendix F. 2

 In the Commission staff’s demand response reports, the Commission staff has consistently used the same definition of “demand 

response” as the U.S. Department of Energy (DOE) used in its February 2006 report to Congress: 

Changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the 

price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale 

market prices or when system reliability is jeopardized. 

U.S. Department of Energy, Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them: 

A Report to the United States Congress Pursuant to Section 1252 of the Energy Policy Act of 2005, February 2006 (February 

2006 DOE EPAct Report). 3

 In this Assessment, dynamic pricing refers to prices that are not known with certainty ahead of time. Examples are “real time 

pricing,” in which prices in effect in each hour are not known ahead of time, and “critical peak pricing” in which prices on certain 

days are known ahead of time, but the days on which those prices will occur are not known until the day before or day of 

consumption. Static time-varying prices, such as traditional time-of-use rates, in which prices vary by rate period, day of the 

week and season but are known with certainty, are not part of this analysis. 

Federal Energy Regulatory Commission ix 
(AMI) 4

, the use of enabling technologies, and varying responses of different customer classes. Figure 

ES-1 illustrates the differences in peak load starting with no demand response programs and then 

comparing the four scenarios. The peak demand without any demand response is estimated to grow at an 

annual average growth rate of 1.7 percent, reaching 810 gigawatts (GW) in 2009 and approximately 950 

GW by 2019. 5 

 

This peak demand can be reduced by varying levels of demand response under the four scenarios. Under 

the highest level of demand response, it is estimated that there would be a leveling of demand between 

2009 and 2019, the last year of the analysis horizon. Thus, the 2019 peak load could be reduced by as 

much as 150 GW, compared to the Business-as-Usual scenario. To provide some perspective, a typical 

peaking power plant is about 75 megawatts 6

, so this reduction would be equivalent to the output of about 

2,000 such power plants. 

1,000 

GW
 

950 BAU 

No DR (NERC) 

1.7% 

38 GW, 

4% 

82 GW, 

9% 

138 GW, 

14% 

20% 

Change in peak 

demand from 

No DR scenario 

BAU: Business-as-Usual 

DR: Demand Response 

900 

1.7% Average Annual 

Growth Rate 

(AAGR) 

850 

800 

750 

700 Expanded Achievable Full 

650 

BAU 

1.3% 

Participation 

0.0% 

Participation 

0.6% 

188 GW, 

2009 2011 2013 2015 2017 2019 

Figure ES-1: U.S. Peak Demand Forecast by Scenario 

Executive Summary 

The amount of demand response potential that can be achieved increases as one moves from the Businessas-Usual scenario to the Full Participation scenario. 

It is important to note that the results of the four scenarios are in fact estimates of potential, rather than 

projections of what is likely to occur. The numbers reported in this study should be interpreted as the 

amount of demand response that could potentially be achieved under a variety of assumptions about the 

types of programs pursued, market acceptance of the programs, and the overall cost-effectiveness of the 

 

4

 A system including measurement devices and a communication network, public and/or private, that records customer 

consumption, and possibly other parameters, hourly or more frequently and that provides for daily or more frequent transmittal 

of measurements to a central collection point. AMI has the capacity to provide price information to customers that allows them 

to respond to dynamic or changing prices. 5

 The “No DR (NERC)” baseline is derived from North American Electric Reliability Corporation data for total summer demand, 

which excludes the effects of demand response but includes the effects of energy efficiency. 2008 Long Term Reliability 

Assessment, p. 66 note 117; data at http://www.nerc.com/fileUploads/File/ESD/ds.xls 6

 Energy Information Administration, Existing Electric Generating Units in the United States, 2007, available at 

http://www.eia.doe.gov/cneaf/electricity/page/capacity/capacity.html 

x A National Assessment of Demand Response Potential 
 

Executive Summary 

programs. This report does not advocate what programs/measures should be adopted/implemented by 

regulators; it only sets forth estimates should certain things occur. 

As such, the estimates of potential in this report should not be interpreted as targets, goals, or 

requirements for individual states or utilities. However, by quantifying potential opportunities that exist 

in each state, these estimates can serve as a reference for understanding the various pathways for pursuing 

increased levels of demand response. 

As with any model-based analysis in economics, the estimates in this Assessment are subject to a number 

of uncertainties, most of them arising from limitations in the data that are used to estimate the model 

parameters. Demand response studies performed with accurate utility data have had error ranges of up to 

ten percent of the estimated response per participating customer. In this analysis, the use of largely 

publicly-available, secondary data sources makes it likely that the error range for any particular estimate 

in each of the scenarios studied is larger, perhaps as high as twenty percent.7 

Business-as-Usual Scenario 

The Business-as-Usual scenario, which we use as the base case, considers the amount of demand response 

that would take place if existing and currently planned demand response programs continued unchanged 

over the next ten years. Such programs include interruptible rates and curtailable loads for Medium and 

Large commercial and industrial customers, as well as direct load control of large electrical appliances 

and equipment, such as central air conditioning, of Residential and Small commercial and industrial 

consumers. 

The reduction in peak demand under this scenario is 38 GW by 2019, representing a four percent 

reduction in peak demand for 2019 compared to a scenario with no demand response programs. 

Expanded Business-as-Usual Scenario 

The Expanded Business-as-Usual scenario is the Business-as-Usual scenario with the following additions: 

1) the current mix of demand response programs is expanded to all states, with higher levels of 

participation (“best practices” participation levels); 8

 2) partial deployment of advanced metering 

infrastructure; and 3) the availability of dynamic pricing to customers, with a small number of customers 

(5 percent) choosing dynamic pricing. 

The reduction in peak demand under this scenario is 82 GW by 2019, representing a 9 percent reduction 

in peak demand for 2019 compared to a scenario with no demand response programs. 

Achievable Participation Scenario 

The Achievable Participation scenario is an estimate of how much demand response would take place if 

1) advanced metering infrastructure were universally deployed; 2) a dynamic pricing tariff were the 

default; and 3) other demand response programs, such as direct load control, were available to those who 

decide to opt out of dynamic pricing. This scenario assumes full-scale deployment of advanced metering 

7 For example, an estimated demand response potential of 19 percent could reflect actual demand response potential ranging from 

15 to 23 percent. See Chapter II for a description of one source of error resulting from data limitations, and Appendix E for an 

analysis of uncertainties arising from the study assumptions. 8 For purposes of this Assessment, “best practices” refers only to high rates of participation in demand response programs, not to a 

specific demand response goal nor the endorsement of a particular program design or implementation. The best practice 

participation rate is equal to the 75th percentile of ranked participation rates of existing programs of the same type and 

customer class. For example, the best practice participation rate for Large Commercial & Industrial customers on interruptible 

tariffs is 17% (as shown in Table 5). See Chapter V for a full description. 

Federal Energy Regulatory Commission xi 
 

 

Executive Summary 

infrastructure by 2019. It also assumes that 60 to 75 percent of customers stay on dynamic pricing rates, 

and that many of the remaining choose other demand response programs. In addition, it assumes that, in 

states where enabling technologies (such as programmable communicating thermostats) are cost-effective 

and offered to customers who are on dynamic pricing rates, 60 percent of the customers will use these 

technologies. 

The reduction in peak demand under this scenario is 138 GW by 2019, representing a 14 percent 

reduction in peak demand for 2019 compared to a scenario with no demand response programs. 

Full Participation Scenario 

The Full Participation scenario is an estimate of how much cost-effective demand response would take 

place if advanced metering infrastructure were universally deployed and if dynamic pricing were made 

the default tariff and offered with proven enabling technologies. It assumes that all customers remain on 

the dynamic pricing tariff and use enabling technology where it is cost-effective. 

The reduction in peak demand under this scenario is 188 GW by 2019, representing a 20 percent 

reduction in peak demand for 2019 compared to a scenario with no demand response programs. 

Other Results of the Assessment 

As shown in Figure ES-1, the size of the demand response potential increases from scenario to scenario, 

given the underlying assumptions. 9

 Comparing the relative impacts of the four scenarios on a national 

basis, moving from the Business-as-Usual scenario to the Expanded Business-as-Usual scenario, the peak 

demand reduction in 2019 is more than twice as large. This difference is attributable to the incremental 

potential for aggressively pursuing traditional programs in states that have little or no existing 

participation. However, more 

demand response can be achieved 

beyond these traditional programs. 

By also pursuing dynamic pricing the 

potential impact could further be 

increased by 54 percent, the 

difference between the Achievable 

Participation scenario and the 

Expanded Business-as-Usual 

scenario. Removing the assumed 

limitations on market acceptance of 

demand response programs and 

technologies would result in an 

additional 33 percent increase in 

demand response potential (the 

difference between the Achievable 

Potential and Full Potential 

scenarios). A conclusion of this 

Assessment is that at the national 

level the largest gains in demand 

response impacts can be made 

9

 There are other technologies that have the potential to reduce demand. These include emerging smart grid technologies, 

distributed energy resources, targeted energy efficiency programs, and technology-enabled demand response programs with 

the capability of providing ancillary services in wholesale markets (and increasing electric system flexibility to help 

accommodate variable resources such as wind generation.) However, these were not included in this Assessment because 

there is not yet sufficient experience with these resources to meaningfully estimate their potential. 

Figure ES- 2: Census Regions 

xii A National Assessment of Demand Response Potential 
Executive Summary 

through dynamic pricing programs when they are offered as the default tariff, particularly when they are 

offered with enabling technologies. 

A mapping of states divided into the nine Census Divisions is provided in Figure ES-2. Regional 

differences in the four demand response potentials are portrayed by Census Division in Figure ES-3. To 

adjust for the variation in size among the divisions, the impacts are shown as a percentage of each 

Division’s peak demand. 

0% 

5% 

10% 

15% 

20% 

25% 

30% 

% of Peak Demand 

12% 12% 12% 13% 10% 20% 17% 20% 19% 

"DR Gap" between BAU and Full Participation: 

Pacific East Middle West New West East Mountain South 

North Atlantic North England South South Atlantic 

Central Central Central Central 

Business-as-Usual Expanded BAU Achievable Participation Full Participation 

Figure ES-3: Demand Response Potential by Census Division (2019) 

Regional differences in the estimated potential by scenario can be explained by factors such as the 

prevalence of central air conditioning, the mix of customer type, the cost-effectiveness of enabling 

technologies, and whether regions have both Independent System Operator/Regional Transmission 

Organization (ISO/RTO) and utility/load serving entity programs. For example, in the Business-as-Usual 

scenario, the largest impacts originate in regions with ISO/RTO programs that co-exist with utility/load 

serving entity programs. New England and the Middle Atlantic have the highest estimates, with New 

England having the ability to reduce nearly 10 percent of peak demand. 

The prevalence of central air conditioning plays a key role in determining the magnitude of Achievable 

and Full Participation scenarios. Hotter regions with higher proportions of central air conditioning, such 

as the South Atlantic, Mountain, East South Central, and West South Central Divisions, could achieve 

greater demand response impacts per participating customer from direct load control and dynamic pricing 

programs. As a result, these regions tend to have larger overall potential under the Achievable and Full 

Participation scenarios, where dynamic pricing plays a more significant role, than in the Expanded 

Business-as-Usual scenario. 

Federal Energy Regulatory Commission xiii 
Executive Summary 

The cost-effectiveness of enabling technologies 10 also affects regional differences in demand response 

potential. Due to the low proportion of central air conditioning in the Pacific, New England, and Middle 

Atlantic Divisions, the benefits of the incremental peak reductions from enabling technologies, as 

determined in this study, do not outweigh the cost of the devices, so the effect of enabling technologies is 

excluded from the analysis. As a result, in some of these states and in some customer classes the demand 

reductions from dynamic pricing reflect only manual (rather than automated) customer response and so 

are lower than in states where customers would be equipped with enabling technologies. This also 

applies to the cost-effectiveness of direct load control programs. 

The difference between the Business-as-Usual and Full Participation scenarios represents the difference 

between what the region is achieving today and what it could achieve if all cost-effective demand 

response options were deployed. Regions with the highest potential under the Full Participation scenario 

do not necessarily have the largest difference between Business-As-Usual and Full Participation. 

Generally, regions in the western and northeastern U.S. tend to be the closest to achieving the full 

potential for demand response, with the Pacific, Middle Atlantic, and New England regions all having 

gaps of 12 percent or less. Other regions, particularly in the southeastern U.S., have differences of as 

much as 20 percent of peak demand. 

Comparing the results for these four scenarios provides a basis for policy recommendations. For 

example, the difference between the Business-As-Usual scenario and the Full Participation scenario 

reveals the “gap” between what is being achieved today through demand response and what could 

economically be realized in the future if appropriate polices were implemented. Similarly, the difference 

between the Expanded Business-as-Usual and the Achievable Participation scenarios reveals the 

additional amount of demand response that could be achieved with policies that rely on both dynamic 

pricing and other types of programs. The Assessment also provides valuable insight regarding regional 

and state differences in the potential for demand response reduction, allowing comparisons across the 

various program types – dynamic pricing with and without enabling technologies, direct load control, 

interruptible tariffs, and other types of demand response programs such as capacity bidding and demand 

bidding – to identify programs with the most participation today and those with the most room for 

growth. 

Complete results for each of the fifty states and the District of Columbia are shown in Appendix A. 

Barriers to Demand Response Programs and Recommendations for 

Overcoming the Barriers 

A number of barriers need to be overcome in order to achieve the estimated potential of demand response 

in the United States by 2019. While the Assessment lists 25 barriers to demand response, the most 

significant are summarized here. 

Regulatory Barriers. Some regulatory barriers stem from existing policies and practices that fail to 

facilitate the use of demand response as a resource. Regulatory barriers exist in both wholesale and retail 

markets. 

• Lack of a direct connection between wholesale and retail prices. 

• Measurement and verification challenges. 

• Lack of real time information sharing. 

• Ineffective demand response program design. 

 

10 The Assessment evaluates the cost-effectiveness of devices such as programmable communicating thermostats and excludes 

them where not cost-effective. See Chapter V for a complete description of the methodology. 

xiv A National Assessment of Demand Response Potential 
Executive Summary 

•	 Disagreement on cost-effectiveness analysis of demand response. 

Technological Barriers. 

•	 Lack of advanced metering infrastructure. 

•	 High cost of some enabling technologies. 

•	 Lack of interoperability and open standards. 

Other Barriers. 

•	 Lack of customer awareness and education. 

•	 Concern over environmental impacts. 

 

As discussed above, three scenarios estimating potential reductions from the Business-as-Usual scenario 

have been developed. These scenarios estimate at 5 and 10 year horizons how much potential can be 

achieved by assuming certain actions on the part of customers, utilities and regulators. Each utility, 

together with state policy makers, must decide whether and how best to move forward with adoption of 

demand response, given their particular resources and needs; however, steps can be taken to help inform 

individual utility decisions and state policies, as well as national decisions. 11 

The increase in demand response under the Expanded Business-as-Usual scenario rests on the assumption 

that current “best practice” 12 demand response programs, such as direct load control and interruptible 

tariff programs, are expanded to all states and that there is some participation in dynamic pricing at the 

retail level. To encourage this expansion to all states and some adoption of dynamic pricing, FERC staff 

recommends that: 

•	 Coordinated national and local education efforts should be undertaken to foster customer 

awareness and understanding of demand response, AMI and dynamic pricing. 

•	 Information on program design, implementation and evaluation of these “best practices” 

programs should be widely shared with other utilities and state and local regulators. 

•	 Demand response programs at the wholesale and retail level should be coordinated so that 

wholesale and retail market prices are consistent, possibly through the NARUC-FERC 

Collaborative Dialogue on Demand Response process. 

•	 Both energy efficiency and demand response principles should be included and coordinated in 

education programs and action plans, to broaden consumers’ and decision makers’ understanding, 

improve results and use program resources effectively. 

•	 Expanded demand response programs should be implemented nationwide, where cost-effective. 

•	 Technical business practice standards for evaluating, measuring and verifying energy savings and 

peak demand reduction in the wholesale and retail electric markets should be developed. 

 

11 On a separate track FERC issued the Wholesale Competition Final Rule, which recognized the importance of demand response 

in ensuring just and reasonable wholesale prices and reliable grid operations. As part of the Final Rule, FERC required all 

RTOs and ISOs to study whether further reforms were necessary to eliminate barriers to comparable treatment of demand 

response in organized markets, among other things. Most RTOs and ISOs submitted filings that identified the particular 

barriers and possible reforms for their specific markets. Wholesale Competition in Regions with Organized Electric Markets, 

Order No. 719, 73 Fed. Reg. 64, 100 (Oct. 28, 2008), FERC Stats. & Regs. ¶ 61,071 (2008). 12 See definition of “best practices” at note 7. 

Federal Energy Regulatory Commission xv 
Executive Summary 

•	 Open standards for communications and data exchange between meters, demand response 

technologies and appliances should be encouraged and supported, particularly the efforts of the 

National Institute of Standards and Technology to develop interoperability standards for smart 

grid devices and systems. 

•	 Cost-effectiveness tools should be developed or revised to account for many of the new 

environmental challenges facing states and the nation, and to reflect the existence of wholesale 

energy and capacity markets in many regions. 

•	 Regulators and legislators should clearly articulate the expected role of demand response to allow 

utilities and others to 1) plan for and include demand response in operational and long-term 

planning, and 2) recover associated costs. 

The Achievable Participation and Full Participation scenarios estimate that the largest demand response 

would take place if advanced metering infrastructure were universally deployed and consumers respond 

to dynamic pricing. The Achievable Participation scenario is realized if all customers have dynamic 

pricing tariffs as their default tariff and 60 to 75 percent of customers adopt this default tariff, while the 

Full Participation scenario is based on all consumers responding to dynamic prices. For this to occur, in 

addition to the recommendations above, 

•	 Dynamic pricing tariffs should be implemented nationwide. 

•	 Information on AMI technology and its costs and operational, market and consumer benefits 

should be widely shared with utilities and state and local regulators. 

•	 Grants, tax credits and other funding for research into the cost and interoperability issues 

surrounding advanced metering infrastructure and enabling technologies should be considered, as 

appropriate. 

•	 Expanded and comprehensive efforts to educate consumers about the advantages of AMI and 

dynamic pricing should be undertaken. 

The Full Participation scenario is dependent upon removal of limitations to market acceptance through 

implementation of these recommendations, and all customers must be able to respond under dynamic 

pricing. 

FERC is required by Section 529 of EISA 2007, within one year of completing this Assessment, to 

complete a National Action Plan on Demand Response. The Action Plan will be guided in part by the 

results of this Assessment. 

xvi A National Assessment of Demand Response Potential 
CHAPTERI.PURPOSEOFTHEREPORT
 

Introduction 

This report fulfills the requirements of the Energy Independence and Security Act of 2007 (EISA 2007) to 

conduct a national assessment of demand response (“the Assessment”) using a state-by-state approach. 

As required by the EISA 2007, the analysis examines the potential for demand response over a ten year 

forecast horizon, with 2010 being the first year of the forecast and 2019 being the final year. In addition, 

the report identifies the barriers to achieving demand response potential, as required in EISA 2007. The 

work has been informed by preexisting research on the topic. The analysis concludes with policy 

recommendations by Federal Energy Regulatory Commission (FERC) staff for ways to overcome the 

barriers to demand response. FERC has commissioned The Brattle Group, along with Freeman, Sullivan 

& Co. and Global Energy Partners LLC to conduct this analysis. 

As used in this report, the term demand response is defined as follows: 13 

Changes in electric usage by end-use customers from their normal consumption patterns in 

response to changes in the price of electricity over time, or to incentive payments designed to 

induce lower electricity use at times of high wholesale market prices or when system reliability is 

jeopardized. 

The Assessment quantifies demand response potential for four scenarios, each designed to answer a 

different question: 14 

•	 Business-as-Usual Scenario (“BAU”): What will demand response and peak demand be in five 

and ten years? 

•	 Expanded BAU Scenario (“EBAU”): What will demand response and peak demand be in five 

and ten years if the current mix of demand response programs is expanded to all states and 

achieves “best practices” levels of participation, and there are modest amounts of pricing 

programs and advanced metering infrastructure (AMI) 15 deployment? 

•	 Achievable Participation Scenario (“AP”): What is the potential for demand response and peak 

demand in five and ten years if AMI is universally deployed, dynamic pricing is the default tariff, 

and other programs are available to those who decide to opt out of dynamic pricing? 

•	 Full Participation Scenario (“FP”): What is the total potential amount of cost-effective demand 

response that could be achieved in five and ten years? 

Comparing and contrasting the results for these four scenarios can answer a number of important 

questions. For example, the difference between the BAU scenario and the FP scenario reveals the “gap” 

between what is being achieved today through demand response and what could economically be realized 

in the future if the barriers are removed. Similarly, the difference between the EBAU and AP scenarios 

reveals the additional amount of demand response that could be achieved if policies shifted to an 

approach that relies on both economic and reliability based programs. 

 

13 U.S. Department of Energy, Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them: A 

Report to the United States Congress Pursuant to Section 1252 of the Energy Policy Act of 2005, February, 2006. 14 For more detail on the assumptions behind these scenarios, see Chapter V. 15 A system including measurement devices and a communication network, public and/or private, that records customer 

consumption, and possibly other parameters, hourly or more frequently and that provides for daily or more frequent transmittal 

of measurements to a central collection point. AMI has the capability to provide customers with price information, allowing 

them to respond to dynamic or changing prices. 

Federal Energy Regulatory Commission 17 
 

Chapter I – Purpose of the Report 

The study also provides insight regarding regional differences in demand response potential. The statelevel nature of the analysis allows for comparisons across different regions of the U.S. to identify areas 

where there is opportunity for substantial growth and adoption of demand response. Comparisons can 

also be made across various program types - dynamic pricing with and without enabling technologies, 

direct load control, interruptible tariffs, and other types of demand response programs such as capacity 

bidding and demand bidding – to identify those programs with the most participation today and those 

with the most room for growth. 

It is important to note that the results of the four scenarios are in fact estimates of potential, rather than 

projections of what is likely to occur. The numbers reported in this study should be interpreted as the 

amount of demand response that could potentially be achieved under a variety of assumptions about the 

types of programs pursued, market acceptance of the programs, and the overall cost-effectiveness of the 

programs. This report does not advocate what programs/measures should be adopted/implemented by 

regulators; it only sets forth estimates should certain things occur. 

As such, the estimates of potential in this report should not be interpreted as targets, goals, or 

requirements for individual states or utilities. However, by quantifying potential opportunities that exist 

in each state, these estimates can serve as a reference for understanding the various pathways for pursuing 

increased levels of demand response. 

As with any model-based analysis in economics, the estimates in this Assessment are subject to a number 

of uncertainties, most of them arising from limitations in the data that are used to estimate the model 

parameters. Demand response studies performed with accurate utility data have had error ranges of up to 

ten percent of the estimated response per participating customer. In this analysis, the use of largely 

publicly-available, secondary data sources makes it likely that the error range for any particular estimate 

in each of the scenarios studied is larger, perhaps as high as twenty percent.16 

The bottom-up, state-specific nature of the Assessment has led to a number of key developments which 

will contribute to future research on the topic. Of primary importance is the development of a flexible, 

user-friendly model for assessing demand response potential. The model is an Excel spreadsheet tool that 

contains user friendly drop-down menus which allow users to easily change between demand response 

potential scenarios, import default data for each state, and change input values on either a temporary basis 

for use in “what if” exercises or on a permanent basis if better data are available. 

Highlights of additional unique contributions are as follows: 

•	 The Assessment is the first nationwide, bottom-up study of demand response potential using a 

state-by-state approach. Previous national studies have taken a top-down approach and as a result 

have not captured the varying regional effects of some of the key drivers of demand response 

potential, such as market penetration of central air conditioning. Other studies have utilized a 

bottom-up approach, but have been limited to specific geographical regions and do not allow for a 

consistent comparison across all parts of the U.S. 

•	 The Assessment led to the development of an internally consistent, state-by-state database 

containing all inputs needed to do a bottom-up estimate of demand response potential. 

•	 Normalized load shapes were developed for five sectors (Residential with central air 

conditioning, Residential without central air conditioning, Small commercial and industrial, 

Medium commercial and industrial, and Large commercial and industrial). Historical usage data 

from twenty-one states and a newly-developed load shape estimation model created load shapes 

for the other twenty-nine states. 

16 For example, an estimated demand response potential of 19 percent could reflect actual demand response potential ranging from 

15 to 23 percent. See Chapter II for a description of one source of error resulting from data limitations, and Appendix E for an 

analysis of uncertainties arising from the study assumptions. 

18 A National Assessment of Demand Response Potential 
 

Chapter I – Purpose of the Report 

•	 Price elasticities and impacts estimates from 15 dynamic pricing pilots were synthesized to 

produce impacts estimates for each state. The impacts take into account differences in central air 

conditioning (CAC) saturation for residential customers, climate, and the effect of enabling 

technology. 

•	 The Assessment led to the development of a comprehensive and thorough summary of barriers to 

the achievement of demand response at the retail and wholesale level. 

Structure of the Report 

Chapter II of the Assessment identifies the key assumptions for each of the four demand response 

scenarios, along with a brief justification for the definitions of the scenarios. 

Chapter III provides a summary of the results, identifying important trends and insights at the national, 

regional, and state levels. 

Chapter IV is a qualitative discussion of future trends and opportunities for reducing peak demand, 

particularly in light of recent developments in smart grid technology. Ideas for future research are also 

recommended. 

Chapter V provides more detail on how the results were developed. It includes a description of the 

modeling methodology as well as a summary of the data development process. More detailed backup is 

provided in Appendix D. 

Chapter VI identifies existing barriers to demand response. These are barriers that are currently 

contributing to the “gap” between the amount of demand response in place today and the potential 

estimates that are described in this report. 

Chapter VII concludes the report by presenting policy recommendations for addressing the demand 

response barriers and moving closer to achieving the identified potential. 

Contained in the appendices of this report are documents which support the findings and 

recommendations of this Assessment. 

Appendix A provides detailed information on the demand response potential projections for each 

state. 

Appendix B offers lessons learned in the development of the data used in this Assessment. 

Appendix C provides detail on the analysis of barriers to achieving demand response potential. 

Appendix D contains documentation of the database development process used to create the model 

inputs for the report. 

Appendix E is an uncertainty analysis, which represents the magnitude and impact of the uncertainty 

related to the results of this Assessment. 

Appendix F is the full text of the Energy Independence and Security Act of 2007, Section 529 which 

applies to this Assessment. 

Finally, Appendix G contains a glossary of terms. 

Federal Energy Regulatory Commission 19 

CHAPTERII.KEYASSUMPTIONS

This chapter identifies the key assumptions that are important for interpreting and understanding the 

results of the Assessment. This includes the type of demand response programs that were included in the 

Assessment, definition of the customer classes considered, and the key distinctions between the four 

demand response scenarios. The purpose of this chapter is to provide context for the discussion of the 

key results in Chapter III. For details on specific assumptions and their justification, as well as on 

modeling methodology and data development, see Chapter V. 

Customer Classes 

Retail customers are divided into four segments based on common metering and tariff thresholds. Much 

of the data used in this Assessment was segmented in this way. 

•	 Residential: includes all residential customers. 

•	 Small commercial and industrial: commercial and industrial customers with summer peak 

demand17 less than 20 kilowatts (kW). 

•	 Medium commercial and industrial: commercial and industrial customers with summer peak 

demand between 20 and 200 kW. 

•	 Large commercial and industrial: commercial and industrial customers with summer peak 

demand greater than 200 kW. 18 

Demand Response Program Types 

The analysis includes five types of demand response programs: dynamic pricing without enabling 

technology, dynamic pricing with enabling technology, direct load control, interruptible tariffs, and 

“other” demand response programs such as capacity/demand bidding and wholesale programs 

administered by Independent System Operators (ISOs) and Regional Transmission Operators (RTOs). 

These demand response program categories are defined below. 

Dynamic pricing without enabling technology: Dynamic pricing refers to the family of rates that offer 

customers time-varying electricity prices on a day-ahead or real-time basis. Prices are higher during peak 

periods to reflect the higher-than-average cost of providing electricity during those times, and lower 

during off peak periods, when it is cheaper to provide the electricity. The rates are dynamic in the sense 

that prices change in response to events such as high-priced hours, unexpectedly hot days, or reliability 

conditions. 19 Customers respond to the higher peak prices by manually curtailing various end-uses. For 

example, residential customers might turn up the set-point on their central air conditioner or reschedule 

their kitchen and laundry activities to avoid running their appliances during high priced hours. The higher 

 

17 Summer peak demand is the customer's highest instantaneous level of consumption during the summer season. 18 There is some justification for further dividing this class to separately analyze very large C&I customers (i.e. with peak demand 

greater than 1 MW), as these customers would behave differently and potentially be eligible for different demand response 

programs. However, this group of customers is heterogeneous in size, end-uses, and consumption patterns. To separately 

analyze them is very challenging from a data perspective and is an area where further research could lead to additional 

valuable insights. 19 This definition excludes time-of-use (TOU) rates. TOU rates, in which prices typically vary by rate period, day of week and 

season, have higher prices during all peak rate periods and lower prices during all off-peak rate periods. They have not been 

included in the portfolio of demand response options because they are static rates and do not provide a dynamic price signal to 

customers that can be used to respond to unexpectedly high-priced days or reliability events. Other forms of dynamic pricing 

include critical peak pricing, in which the prices on certain days are known ahead of time, but the days on which those prices 

occur are not known until the day before or day of, and real time pricing, in which prices in effect in each hour are not known 

ahead of time. 

Federal Energy Regulatory Commission 21 
Chapter II – Key Assumptions 

priced peak hours are accompanied by lower priced off-peak hours, providing customers with the 

opportunity to reduce their electricity bills through these actions. 

Examples of dynamic rates include critical peak pricing, peak time rebates, and real-time pricing. Peak 

time rebate is different than critical peak pricing and real-time pricing rates in that rather than charging a 

higher price during critical events, customers are provided a rebate for reductions in consumption. The 

analysis assumes that advanced metering infrastructure (AMI) must be in place to offer any of these rates. 

AMI includes “smart meters” that have the capability to measure customer usage over short intervals of 

time (often 15 minutes), as opposed to many conventional meters that are read manually on a monthly 

basis. 

Dynamic pricing with enabling technology: This program is similar to the previously described dynamic 

pricing program, but customers are also equipped with devices that automatically reduce consumption 

during high priced hours. For Residential and Small and Medium commercial and industrial customers, 

the automated technology (known as a programmable communicating thermostat) adjusts air conditioning 

energy use where such devices are determined to be cost-effective. Large commercial and industrial 

customers are assumed to be equipped with automated demand response 20 systems, which coordinate 

reductions at multiple end-uses within the facility. 

Direct load control (DLC): Customer end uses are directly controlled by the utility and are shut down or 

moved to a lower consumption level during events such as an operating reserve shortage. For residential 

customers, an air-conditioning DLC program is modeled. 21 

 Direct control of other residential end uses, 

such as water heating, was not included. 22 Non-residential DLC programs include air-conditional load 

control as well, but could also include other forms of DLC in some states, such as irrigation control. 

Interruptible tariffs: Customers agree to reduce consumption to a pre-specified level, or by a prespecified amount, during system reliability problems in return for an incentive payment of some form. 

The programs are generally only available for Medium and Large commercial and industrial customers. 

Other DR programs: The Other DR category includes programs primarily available to Medium and Large 

commercial and industrial customers such as capacity bidding, demand bidding, and other aggregator 

offerings, whether operated by an ISO, RTO, or a utility in an area without an ISO or RTO. This category 

also includes demand response being bid into capacity markets. Some of these programs are primarily 

price-triggered while others are triggered based on reliability conditions. 

We have excluded certain options from the scope of our study that are sometimes included in the 

definition of demand response. These include static time-of-use (TOU) rates, back up generation, 

permanent load shifting and plug-in hybrid vehicles (PHEVs). The reasons are briefly described below. 

Often, demand response studies will include the impacts of all rates that are “time varying.” Time 

varying rates typically are structured such that customers are offered higher prices during peak periods 

when demand for electricity is at its highest. This higher peak price is accompanied by a discounted, 

lower price during the remaining hours. By providing customers with rates that more accurately reflect 

the true cost of providing electricity over the course of the day, customers have an incentive to shift load 

from the peak period to the off-peak period, thus reducing the overall cost of providing electricity. 23 

 

Within the family of time-varying rates, there is a distinction between rates that are “static” and those that 

are “dynamic.” For dynamic rates, as described previously, the peak period price can be triggered to 

 

20 Automated demand response is a communications infrastructure that provides the owner of the system with electronic signals that 

communicate with the facility’s energy management control system to coordinate load reductions at multiple end-uses. 21 Such DLC programs could be based on a programmable communicating thermostat or a conventional “switch” that cycles the air 

conditioner. For the purposes of this analysis, a switch is the basis for the DLC program. 22 These other forms of DLC were excluded because they represent a fairly small share of aggregate DLC program impacts and the 

state-level appliance saturation data necessary to conduct such an analysis was not readily available. 23 Alternatively, a rebate could be offered for consumption curtailment during peak periods. 

22 A National Assessment of Demand Response Potential 
Chapter II – Key Assumptions 

target specific system events, such as high-priced hours, unexpectedly hot days, or reliability conditions. 

Customers are typically notified of the higher peak period price on a day-ahead or day-of basis. Static 

rates, on the other hand, do not have this feature and instead use fixed peak and off-peak prices that do not 

change regardless of system conditions. TOU rates fall under this category of static time-varying rates. 

While TOU rates provide incentive to permanently shift load from peak periods to off-peak periods, they 

do not have the flexibility to allow for an increase in response on short notice. 

In addition, in many parts of the country TOU rates have been in place for decades and as a result their 

impacts are already factored into the reference load forecast. Further, FERC’s Demand Response Survey 

database 24 impact estimates are not available for many TOU rates. It is for these reasons that TOU rates 

were excluded from the analysis. 

Programs that specifically target back-up generation were excluded as well. However, if back-up 

generation as a technology underlies demand response for a more general program, that program was 

included. Additionally, permanent load shifting was excluded because it cannot be dispatched 

dynamically to meet system requirements. It is analogous to energy efficiency, which is also excluded 

from the scope of this report. Finally, we have excluded PHEVs because there is insufficient data to 

analyze their impacts and because, given the current absence of significant market penetration of PHEVs, 

their impact over the 10 year analysis horizon will likely be small. 

Demand Response Scenarios 

Four scenarios have been considered in this analysis. The first, Business-as-Usual, is simply a measure of 

existing demand response resources and planned growth in these resources. The other three scenarios are 

measurements of demand response potential under varying assumptions. All three of the demand 

response potential scenarios are limited only to cost-effective demand response programs, meaning that 

the net present value of the benefits of a given program exceeds the costs. 25 

Business-as-Usual (BAU) is an estimate of demand response if current and planned demand response 

stays constant. This scenario is intended to reflect the continuation of current programs and tariffs. In 

most instances, growth in program impacts is not modeled, although where information is available that 

explicitly states likely growth projections, that information has been included. The value in this scenario 

is that it serves as the starting point against which to benchmark the three other demand response potential 

scenarios. 

Expanded BAU (EBAU) is an estimate of demand response if the current mix of demand response 

programs is expanded to all states and achieves “best practices” levels of participation, along with a 

modest amount of demand response from pricing programs and AMI deployment. 26 The key assumption 

driving participation in the non-pricing programs is that all programs achieve participation rates that are 

representative of “best practices.” This scenario provides insight regarding what could be achieved 

through more aggressive pursuit of programs that exist today. However, it does not account for those 

programs that are not heavily pursued today but have significant potential, such as residential dynamic 

pricing. 

Achievable Participation (AP) is an estimate of demand response if AMI is universally deployed, 

dynamic pricing is the default tariff, and other programs are available to those who decide not to enroll in 

 

24 Available at http://www.ferc.gov/industries/electric/indus-act/demand-response/2008/survey.asp 25 For the purposes of this Assessment, the Total Resource Cost (TRC) test is used. More information on the cost-effectiveness 

screening is provided in Chapter V. 26 For purposes of this Assessment, “best practices” refers only to high rates of participation in demand response programs, not to a 

specific demand response goal nor the endorsement of a particular program design or implementation. The best practice 

participation rate is equal to the 75th percentile of ranked participation rates of existing programs of the same type and 

customer class. For example, the best practice participation rate for Large Commercial & Industrial customers on interruptible 

tariffs is 17% (as shown in Table 5). See Chapter V for a full description. 

Federal Energy Regulatory Commission 23 
dynamic pricing. Customer participation rates were developed to reflect the reality that not all customers 

will participate in demand response programs. In this scenario, participation in dynamic pricing programs 

is not limited as it is in the EBAU scenario, and all demand response programs can be equally pursued. 

This scenario considers the potential inherent in all available demand response programs while restricting 

the total potential estimate to maximum participation levels that could likely be achieved in reality. 

Full Participation (FP) is an estimate of the total amount of cost-effective demand response. This 

scenario assumes that there are no regulatory or market barriers and that all customers will participate. 

The value of this scenario is that it quantifies the upper-bound on demand response under the assumptions 

and conditions modeled in this Assessment. 27 

Comparing the Key Scenario Assumptions 

The four scenarios are differentiated by a set of distinguishing assumptions. The differentiation is driven 

mostly by assumptions about pricing programs. Table 1 summarizes these key differences. 

Table 1: Key Differences in Scenario Assumptions 

Assumption Business-as-Usual Expanded 

BAU 

Achievable 

Participation 

Full 

Participation 

AMI deployment Partial Deployment Partial deployment Full deployment Full deployment 

Dynamic pricing participation (of eligible) Today's level Voluntary (opt-in); 

5% 

Default (opt-out); 

60% to 75% 

Universal 

(mandatory); 

100% 

Eligible customers offered enabling tech None None 95% 100% 

Eligible customers accepting enabling 

tech None None 60% 100% 

Basis for non-pricing participation rate Today's level "Best practices" 

estimate 

"Best practices" 

estimate 

"Best practices" 

estimate 

Chapter II – Key Assumptions 

In the Full Participation and Achievable Participation scenarios, AMI is assumed to reach 100 percent 

deployment in all states by 2019. In the EBAU scenario, only partial deployment of AMI is achieved, 

depending on the current status of utility deployment plans in each state. This is consistent with the 

definition of the EBAU scenario as focusing heavily on non-pricing demand response programs, which do 

not require AMI for operation. By 2019, in the EBAU scenario, AMI market penetration ranges from 20 

percent to 100 percent with a national average of about 40 percent. The BAU scenario assumes the 

existence only of those AMI systems that are in place today or for which plans for deployment have been 

announced. 

Dynamic pricing is assumed to be widely available in the AP and FP scenarios. In the FP scenario, it is 

the only rate that is offered to customers. In the AP scenario, dynamic pricing is offered on a default 

basis, meaning that all customers are enrolled in a dynamic rate but they can “opt out” to a different rate 

type. Forty percent of Medium and Large commercial and industrial customers are assumed to opt out of 

the dynamic rate, as are 25 percent of Residential and Small commercial and industrial customers. 28 The 

EBAU scenario assumes a minimal amount of participation in dynamic pricing, with the rate being 

 

27 Technologies not modeled in the Assessement also have the potential to reduce demand. These include emerging smart grid 

technologies, distributed energy resources, targeted energy efficiency programs, and technology-enabled demand response 

programs with the capability of providing ancillary services in wholesale markets (and increasing electric system flexibility to 

help accommodate variable resources such as wind generation.) However, these were not included in this Assessment 

because there is not yet sufficient experience with these resources to meaningfully estimate their potential. 28 For details on the basis for these assumptions, see Chapter V. 

24 A National Assessment of Demand Response Potential 
 

Chapter II – Key Assumptions 

offered on a voluntary (opt-in) basis and only five percent of the customers in each customer class 

choosing to enroll. 29 

Another significant driver of the difference between the three demand response potential scenarios is the 

share of customers equipped with enabling technologies. Customers with enabling technology are a 

subset of those enrolled in dynamic pricing. In addition to being enrolled in dynamic pricing, for a 

customer to be equipped with enabling technology in a given scenario it must meet three criteria. It must 

first have load that is suitable for the technology, 30 then it must be offered the technology, and finally it 

must accept the technology. 

In the FP scenario, all eligible customers with load suitable for the technology are assumed to be offered 

the technology where it is cost-effective. Further, all of the customers who are offered the technology are 

assumed to accept it. In the AP scenario, acceptance rates for both the utility and the customer reflect the 

reality that the equipment will not be utilized in all instances where it makes economic sense to do so. In 

this scenario, 95 percent of eligible customers are offered the technology and 60 percent of eligible 

customers who are offered the technology accept it. Enabling technologies are not part of the EBAU or 

BAU scenarios. These market acceptance rates are largely assumption-driven for the purposes of 

defining the scenarios. Given the illustrative nature of these assumptions, they are ideal candidates for an 

uncertainty analysis. 

Participation rates in the non-dynamic pricing programs (DLC, interruptible tariffs, and Other DR) are 

determined using estimates of “best practices” developed using survey data from FERC’s 2008 

Assessment of Demand Response and Smart Metering. These participation rates are held constant on a 

percentage basis across all three scenarios and are applied to the segment of the population that is not 

participating in dynamic pricing. Thus, the major difference between the scenarios is that the 

participation rates are applied to a different population of eligible customers. More details on the 

development of the final participation rates are provided in Chapter V. 

In most studies of demand response, data from multiple data sources must be brought together and 

reconciled to create a coherent and internally consistent picture. That is especially true of this study, 

where multiple scenarios of demand response potential have been created for the fifty states and the 

District of Columbia. In the construction of the BAU scenario, the Assessment has relied on a top-down 

approach that yields aggregate impacts of demand response potential. The main data source has been the 

FERC demand response survey. The construction of the other three scenarios has relied on a bottom-up 

approach that expresses demand response potential as the product of existing peak-demand, percent drop 

in load per participating customer and number of participating customers. In most cases, the assumptions 

underlying these other scenarios are consistent with the data underlying the BAU scenario. 

However, in a few cases where the BAU numbers are a high proportion of the peak demand forecast, 

intrinsic discrepancies between the bottom-up and top-down approaches have prevented a complete 

reconciliation of the data from different sources. Empirically, the effect of these discrepancies is likely to 

be very small in magnitude and confined to small states with large amounts of existing demand response. 

In these states, the demand response potential may be slightly overstated, by not more than a percentage 

point or so. For the majority of states in the Assessment, the impact would be negligible and is dwarfed 

by other uncertainties in factors such as the peak load forecast, the per-customer impact of specific 

demand response programs and projections of the number of participating customers. In the future, this 

discrepancy could be reduced with more-detailed survey data to support the BAU scenario. FERC staff is 

evaluating changes to its survey methodology with this objective in mind. Also, the North American 

Electric Reliability Corporation (NERC) has designed and is refining a systematic approach to collecting 

demand response data that will contribute to the accuracy and usefulness of future analyses.31 

29 For programs in states where enrollment is already greater than five percent, the existing participation rate overrides this value. 30 For example, for residential customers, only those with central air conditioning would be eligible for a programmable 

communicating thermostat since it specifically applies to air conditioning load. This assumption does not vary across scenarios 

but does vary across customer classes and states. 31 See NERC, Demand Response Data Availability System (DADS) Preliminary Report, Phase I&II, June 3, 2009. 

Federal Energy Regulatory Commission 25 

CHAPTERIII.KEYRESULTS

This chapter summarizes the key results of the Assessment, identifies important trends in the findings, 

and compares demand response potential across scenarios, classes, program types, and regions. These 

findings are summarized for the U.S. as a whole, at the Census Division level, and at the state level. 

National Results 

A comparison of the demand response estimates under the four scenarios illustrates the potential impact 

of demand response on peak demand over the analysis horizon. This is illustrated in Figure 1. For the 

purposes of this Assessment, 2009 is considered to be the base year, and 2010 through 2019 is considered 

to be the analysis horizon. 

Figure 1: U.S. Summer Peak Demand Forecast by Scenario 

GW
 

1,000 

950 

900 

850 

800 

750 

700 

650 

38 GW, 

4% 

82 GW, 

9% 

138 GW, 

14% 

188 GW, 

20% 

BAU 

1.7% 

Expanded 

BAU 

1.3% 

Full 

Participation 

0.0% 

Achievable 

Participation 

0.6% 

No DR (NERC) 

1.7% 

Change in peak 

demand from 

No DR scenario 

Average Annual 

Growth Rate 

(AAGR) 

BAU: Business-as-Usual 

DR: Demand Response 

2009 2011 2013 2015 2017 2019 

The black line represents a U.S. peak demand forecast that does not include any demand response, as 

provided by the North American Electric Reliability Corporation (NERC). 32 

 Peak demand begins at 

about 810 GW in 2009 and grows at an average annual growth rate (AAGR) of 1.7 percent, reaching 

slightly more than 950 GW by 2019. Peak demand in the BAU scenario grows at a very similar rate, but 

is lower overall. The reduction in peak demand under BAU, relative to the NERC forecast without 

demand response, is 37 GW in 2009 and 38 GW by 2019, representing a four percent reduction in peak 

demand. The EBAU demand response scenario produces a peak demand estimate that grows at an 

AAGR of 1.3 percent per year as a result of further reduction in peak demand of 82 GW, or nine percent, 

by 2019. The AP scenario produces even larger reductions in peak demand, reducing the AAGR to 0.6 

 

32 The “No DR (NERC)” baseline is derived from NERC data for total summer demand, which excludes the effects of demand 

response but includes the effects of energy efficiency. 2008 Long Term Reliability Assessment, p. 66 note 117; data at 

http://www.nerc.com/fileUploads/File/ESD/ds.xls. http://www.nerc.com/page.php?cid=4|38|41 

Federal Energy Regulatory Commission 27 
Figure 2: U.S Demand Response Potential by Program Type (2019) 

0 

50 

100 

150 

200 

Peak Reduction (GW) 

0% 

5% 

10% 

15% 

20% 

25% 

% of Peak Demand 

Other DR 

Interruptible Tariffs 

DLC 

Pricing w/o Tech 

Pricing w/Tech 

38 GW, 

4% of peak 

82 GW, 

9% of peak 

138 GW, 

14% of peak 

188 GW, 

20% of peak 

Business-as- Expanded Achievable Full 

Usual BAU Participation Participation 

Chapter III – Key Results 

percent by reducing the peak by 138 GW, or 14 percent, by 2019. The FP scenario produces the largest 

reductions. Under this scenario, peak demand growth is approximately zero, and by 2019 would be 188 

GW (20 percent) less than if there were no demand response programs in place. 33 

The peak demand reduction estimates under the three demand response potential scenarios show a dip 

between 2010 and 2013, after which the reductions increase at varying rates. This pattern is a result of 

the assumed market penetration schedule of new demand response programs. For the traditional 

programs (i.e. direct load control, interruptible and curtailable, and RTO-sponsored), states are assumed 

to ramp-up to final participation rates over the five year period between 2009 and 2014 in an “S-shaped 

curve.” In other words, between 2009 and 2010, these programs experience relatively little incremental 

growth and the growth in peak demand is greater than the growth in demand response reductions. Then, 

between 2010 and 2013, the incremental increase in demand response is much higher, resulting in 

negative peak load growth during those years. After that, the incremental increase is smaller and the new 

programs mature and reach full participation (as a percentage of total customers) by 2015. Further, the 

effect of dynamic pricing over time is dependent on AMI market penetration, which increases throughout 

the forecast horizon. The more aggressive AMI deployment assumption in the AP and FP scenarios 

explains why demand response increases more significantly in the later years of those scenarios. 

It is interesting to compare the relative impacts of the four scenarios. Moving from the BAU scenario to 

the EBAU scenario, the peak demand reduction in 2019 is more than twice as large. This difference is 

attributable to the incremental potential for aggressively pursuing non-pricing programs in states that have 

little or no existing participation. However, more demand response can be achieved beyond these nonpricing programs. By also pursuing dynamic pricing the potential impact could further be increased by 68 

percent, the difference between the AP scenario and the EBAU scenario. Removing the assumed 

limitations on market acceptance of demand response programs and technologies would result in an 

 

33 This study assumes demand response occurs for four hours a day during the 15 highest load days of the year. Thus it reduces 

peak demand, but not necessarily demand in other (non-peak) times, and it may not reduce overall load growth in proportion to 

the reduction in peak demand. 

28 A National Assessment of Demand Response Potential 
additional 36 percent increase in demand response potential (the difference between the AP and FP 

Scenarios). A conclusion of this Assessment is that at the national level, the largest gains in demand 

response impacts can be made through pricing programs, particularly when offered with enabling 

technologies. This is more pronounced in the FP scenario, where roughly 70 percent of the impacts come 

from pricing programs. These findings are presented in Figure 2. 

Just as demand response programs contribute to total demand response potential in varying degrees, so do 

the customer segments. Today, the majority of demand response comes from Large commercial and 

industrial customers, primarily through interruptible tariffs and capacity and demand bidding programs. 

However, it is the residential class that represents most untapped potential for demand response. As seen 

below, the impacts from this class drive the major differences in the demand response potential scenarios. 

Based on the assumptions underlying this study, residential customers provide the greatest per-customer 

impacts from pricing programs. While residential customers provide only roughly 17 percent of today’s 

demand response potential, in the AP scenario they provide over 45 percent of the potential impacts. This 

is illustrated in Figure 3. 

Figure 3: U.S. Demand Response Potential by Class (2019) 

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Business-as- Expanded Achievable Full 

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Chapter III – Key Results 

Federal Energy Regulatory Commission 29 
Regional Results 

To identify regional differences in 

demand response potential, the

results can be broken out at the 

level of the nine Census Divisions. 

A mapping of states to these

regions is provided in Figure 4. 

Regional differences in demand

response potential are driven by

many factors, including the

customer mix, the market

penetration of central air

conditioning equipment, costeffectiveness of new demand

response programs, per-customer

impacts from existing programs,

participation in existing programs, 

and AMI deployment plans. A

summary of the regional demand 

response potential estimates by

scenario is provided in Figure 5. 

Figure 4: The Nine Census Divisions 

Figure 5: Demand Response Potential by Census Division (2019) 

0% 

5% 

10% 

15% 

20% 

25% 

30% 

% of Peak Demand 

12% 12% 12% 13% 10% 20% 17% 20% 19% 

"DR Gap" between BAU and Full Participation: 

Pacific East Middle West New West East Mountain South 

North Atlantic North England South South Atlantic 

Central Central Central Central 

Business-as-Usual Expanded BAU Achievable Participation Full Participation 

Chapter III – Key Results 

30 A National Assessment of Demand Response Potential 
Chapter III – Key Results 

The largest existing (BAU) impacts are in regions with both wholesale demand response programs and 

utility/load serving entity programs. Thus, New England and the Middle Atlantic have the highest 

estimates for the BAU scenario, with New England reporting to have the ability to reduce nearly 10 

percent of peak demand through demand response programs. Regions without significant wholesale 

organized markets demand response activity and relatively small existing programs, such as the West 

South Central and Mountain Divisions, have lower BAU estimates. 

Central air conditioning saturation plays a key role in determining the magnitude of AP and FP demand 

response potential. Hotter regions with high central air conditioning saturations, such as the South 

Atlantic, Mountain, East South Central, and West South Central Divisions could achieve greater average 

per-customer impacts from DLC and dynamic pricing programs. As a result, these regions tend to have 

larger overall potential under the AP and FP scenarios where dynamic pricing plays a more significant 

role than in the EBAU scenario. 

Demand response potential in the EBAU scenario is driven partly by the customer mix in a given region. 

Specifically, regions with a higher share of load in the Large commercial and industrial sector will tend to 

have larger potential under this scenario. By definition, the EBAU scenario focuses on programs, such as 

interruptible tariffs and Other DR, that are geared toward these customers. Large commercial and 

industrial customers participating in these programs tend to produce large peak reductions, so regions 

with more load in the commercial and industrial class have higher potential. This potential will partly be 

determined by the average per-customer impacts that have been reported for these programs in each state. 

Those states reporting very high impacts will demonstrate the most potential. 

The cost-effectiveness of enabling technologies also plays a role in driving regional differences in 

demand response potential. Due to lower per-customer air conditioning loads in the Pacific, New 

England, and Middle Atlantic Divisions, the benefits of the incremental peak reductions from enabling 

technologies do not outweigh the cost of the devices, and several states in these regions do not pass the 

cost-effectiveness screen. 34 As a result, in these states the impacts from dynamic pricing are only a 

function of manual customer response and are lower than in states where customers would be equipped 

with the technologies. This also applies to the cost-effectiveness of DLC programs, although these 

programs are found to be cost-effective for customer classes in most states. 

It is interesting to quantify the “demand response gap” between the BAU scenario and the FP scenario. 

This gap represents the difference between what the region is achieving today and what it could achieve if 

all cost-effective demand response options were deployed. It is not necessarily the regions with the 

highest FP potential that have the largest demand response gap. Generally, regions in the western and 

northeastern U.S. tend to be the closest to achieving the full potential for demand response, with the 

Pacific, Middle Atlantic, and New England regions all having demand response gaps less than or equal to 

12 percent. Other regions are significantly farther from achieving the full potential for demand response, 

falling short of FP potential by as much as 20 percent of peak demand. 

State-level Results 

At the most granular level, demand response potential was estimated for each of the fifty states and the 

District of Columbia. Across the states, there is significant variation in both existing demand response 

impacts and in the potential for new demand response. This variation can be seen in a comparison of the 

distribution of impacts across the states for the four scenarios, as provided in Figure 6. 

 

34 For more information on the cost-effectiveness analysis, see Chapter V and Appendix D. 

Federal Energy Regulatory Commission 31 
 

Business-as-Usual 

Distribution of State-Level Peak Reductions (2019) 

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Frequency 

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10th percentile = 0% 

50th percentile = 3% 

90th percentile = 8% 

Peak Demand Reduction 

0%2%4%6%8%

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Expanded BAU 

Distribution of State-Level Peak Reductions (2019) 

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Summary of Impacts 

10th percentile = 5% 

50th percentile = 8% 

90th percentile = 13% 

Peak Demand Reduction 

Frequency 

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4%6%8%

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0%2%

32%34%36% 

Achievable Participation 

Distribution of State-Level Peak Reductions (2019) 

20 

18 Summary of Impacts 

10th percentile = 9% 

16 50th percentile = 13% 

14 90th percentile = 18% 

y nc12 ue

10 

eqFr

8 

6 

4 

2 

0 

0%2%4%6%8%

10%12%14%16%18%20%22%24%26%28%30%32%34%36% 

Peak Demand Reduction Peak Demand Reduction 

Full Participation 

Distribution of State-Level Peak Reductions (2019) 

20 

6 

4 

2 

0 

Frequency 

8 

10 

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4%6%8%

10%12%14%16%18%20%22%24%26%28%30%32%

0%2%

36% 

Summary of Impacts 

10th percentile = 13% 

50th percentile = 18% 

90th percentile = 25% 34%

Figure 6: Comparison of Demand Response Impact Distribution across States 

Chapter III – Key Results 

There is the least variation in impacts in the BAU scenario. In this scenario, demand response reductions 

are generally clustered between zero and five percent, with half of the state reductions being three percent 

or less. There are a few states that have reported the ability to achieve peak reductions greater than or 

equal to 10 percent today. These states are generally in the New England and Middle Atlantic regions 

and are reporting significant demand response enrollment by large commercial and industrial customers 

in wholesale demand response programs. The presence of strong wholesale programs plays a very 

significant role in the amount of existing demand response potential. 

State-level impacts in the EBAU demand response scenario increase significantly relative to the BAU 

scenario. In Figure 6, this is shown by the rightward shift of the green bars along the horizontal axis 

relative to the red bars. In this scenario, the median demand response reduction is nine percent, while the 

range of the potential impacts is between two and 18 percent. 

The AP impacts further shift to the right, with a median impact of 14 percent and a range of impacts from 

five percent up to 23 percent. The FP potential presents the widest distribution of potential impacts, 

ranging from seven percent to 31 percent and a median of 17 percent. This widening of the distribution 

across the scenarios is attributable to the increasingly important role of state-specific end-use 

characteristics such as central air conditioning saturation. To fully interpret the state-level impacts, it is 

necessary to consider some case studies in more detail. These are presented in the following section. 

State Case Studies 

To illustrate the details of the demand response potential estimations at the state level, it is helpful to walk 

through case studies of a few states that are distinctly different from each other yet generally 

representative of a larger group of states. Three such states have been selected: Georgia, Connecticut, 

and Washington. Georgia has existing demand response and some AMI in place and is not a member of 

an ISO/RTO while Connecticut has a significant amount of existing demand response, particularly in 

32 A National Assessment of Demand Response Potential 
ISO/RTO programs. Washington, on the other hand, has essentially no existing demand response. It is a 

region that historically has had a large amount of hydropower capacity and as a result has been energy 

constrained but not capacity constrained. 35 Washington also has low central air conditioning saturation, 

limiting the potential for future growth in demand response in this analysis. 

Case Study #1: Georgia 

Today, Georgia’s level of demand response is similar to the national average. The majority of peak 

impacts come from one of the nation’s largest real-time pricing programs for Large commercial and 

industrial customers, as well as an interruptible tariff. Some additional impacts come from Residential 

and Small commercial and industrial DLC programs. In total Georgia is achieving a peak demand 

reduction of roughly 1.2 GW, or about 3.4 percent of the projected 2019 peak demand for Georgia of 34.7 

GW. 

In the EBAU scenario (Figure 7), participation in existing programs increases and new, primarily nonpricing programs are added. Significant growth takes place in the residential DLC program due to 

Georgia’s high central air conditioning saturation rate of 82 percent. Medium and Large commercial and 

industrial customers are assumed to participate in a new capacity/demand bidding type of program (Other 

DR) 36 and a small amount of peak reduction could come from Small commercial and industrial DLC as 

well. Participation in these programs is assumed to achieve “best practices” levels that are the 75th 

percentile of participation rates in existing programs. 

Pricing impacts remain significant in the existing Large commercial and industrial program, but under the 

EBAU scenario assumptions of a mild, voluntary rate offering, they do not play a significant role for the 

other customer classes. Relative to the BAU scenario, total impacts grow from 1.2 GW to 4.2 GW, or 

from 3.4 percent of peak demand to 12 percent. 

Figure 7: Georgia BAU and EBAU Peak Demand Reduction in 2019 

0 

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2,000 

3,000 

4,000 

5,000 

6,000 

Residential

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Large

Residential

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Peak Reduction (MW) 

BAU Expanded BAU 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

 

Chapter III – Key Results 

35 In other words, hydropower resources can be ramped up to meet peak demands for a few hours but there are seasonal limits on 

energy production. 36 Outside of RTO markets, capacity payments could be set at avoided capacity cost levels or could be negotiated on a case-by 

case basis with demand response providers. 

Federal Energy Regulatory Commission 33 
 

Georgia’s high residential central air conditioning saturation means that average per-customer impacts 

from dynamic pricing will be significant. As a result, in the AP scenario (Figure 8) impacts for the 

residential class increase under the assumption that dynamic pricing is offered as the default (opt-out) rate 

for all customers and 75 percent of the customers remain on the rate. A fraction of these customers (60 

percent of those with central air conditioning) accept enabling technology – customers who, under the 

EBAU scenario and in the absence of the availability of enabling technology might have chosen to enroll 

in the DLC program. Additionally, of the customers who do not enroll in dynamic pricing, some are 

assumed to instead enroll in the DLC program. Based on a high-level assessment of the cost effectiveness 

of these programs, both were found to be economic for all customer classes in the state under the EBAU 

scenario. 37 

Interestingly, total impacts for the Large commercial and industrial class decrease in the AP scenario. 

The reason for this is that some customers who would have enrolled in Other DR programs under the 

EBAU scenario are instead assumed to have enrolled in dynamic pricing. The average per-customer peak 

reductions in Other DR programs (40 percent reduction) are higher than those of dynamic pricing (seven 

percent without enabling technology, 14 percent with enabling technology) and, as a result, the Large 

commercial and industrial potential drops in the AP scenario. 38 While this defining assumption of the AP 

scenario results in small impacts for the Large commercial and industrial class relative to the EBAU 

scenario, demand response potential for the entire state is higher. In total, the AP scenario potential 

system peak impacts increase to 6.4 GW, or 18 percent of peak demand. 

Figure 8: Georgia BAU, EBAU, and AP Peak Demand Reduction in 2019 

0 

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Residential

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Peak Reduction (MW) 

BAU Expanded BAU Achievable Participation 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

By definition, impacts are largest for the FP scenario (Figure 9). All customers are enrolled in dynamic 

pricing, with enabling technology being accepted by all customers. Customers currently enrolled in DLC 

are assumed to remain in that program. Total Large commercial and industrial impacts drop relative to 

 

37 Details on the cost effectiveness assessment are provided in Chapter V and Appendix D. 38 It should be noted that the per-customer impacts from Other DR programs are based on the average of reported per-customer 

impacts in the 2008 FERC Demand Response survey. It is possible that impacts of this magnitude would not be achieved on a 

regular basis in practice and this is a topic that should be examined further. 

Chapter III – Key Results 

34 A National Assessment of Demand Response Potential 
the AP scenario, as the remaining participants in the Other DR programs are assumed to participate in 

dynamic pricing with enabling technology. However, on a system basis the total impacts increase to 8.5 

GW, or 25 percent of peak demand in 2019. This is the total amount of cost-effective demand response 

potential in the state under the assumptions of this scenario. For more information on Georgia, see 

Appendix A. 

Figure 9: Georgia Potential Peak Demand Reduction in All Scenarios, 2019 

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Residential

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Peak Reduction (MW) 

BAU Expanded 

BAU 

Achievable 

Participation 

Full 

Participation 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

Chapter III – Key Results 

Case Study #2: Connecticut 

Relative to Georgia, Connecticut is currently achieving significantly greater peak reductions from demand 

response on a percentage basis. In fact, Connecticut has one of the largest BAU demand response 

estimates of this Assessment. Where Georgia was achieving a 3.4 percent reduction, Connecticut is 

anticipating nearly a 13 percent reduction by 2019 in the BAU case. Much of this is due to large impacts 

being reported through participation in the ISO New England Forward Capacity Market. For the purposes 

of this Assessment, those impacts have been reported in the Other DR program category for Large 

commercial and industrial customers. Utility demand bidding programs in Connecticut are included in 

this category as well. The Other DR category represents nearly the entirety of the BAU peak reduction 

potential of 1,369 MW, or 16 percent of peak demand. 

The EBAU scenario (Figure 10) assumes that programs will be put in place for other customer classes as 

well. DLC programs would increase demand response potential, although the low central air conditioning 

load in the residential class means that the impacts are not as significant as were seen in Georgia. Some 

additional Large commercial and industrial customers are assumed to participate in an interruptible tariff, 

but participation in Other DR does not increase as it is already beyond the 75th percentile of existing 

programs. (This study caps participation at the 75th percentile, unless participation in a program already 

exceeds that). Therefore, the total impact increases relative to the BAU scenario, but not to the degree 

that was seen in Georgia. Peak reduction potential increases from 1,369 MW to 1,798 MW or from 16 

percent of peak demand to 21 percent. 

Federal Energy Regulatory Commission 35 
Peak Reduction (MW) 

Figure 10: Connecticut BAU and EBAU Peak Demand Reductions in 2019 

1,400 

1,200 

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Residential

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Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

Chapter III – Key Results 

Inclusion of default dynamic pricing in the AP scenario (Figure 11) increases overall demand response 

potential, but the incremental increase again is significantly smaller compared to Georgia. In the 

residential sector, this is driven by the low central air conditioning saturation rate. For Large commercial 

and industrial customers, existing participation in Other DR programs persists in the AP scenario impacts. 

The customers currently enrolled in Other DR programs are assumed to remain on those programs rather 

than enrolling in dynamic pricing. As a result, impacts from dynamic pricing are small but total impacts 

for the class remain large. The small potential impacts from dynamic pricing are further amplified by the 

fact that enabling technologies were not found to be cost-effective for Small and Large commercial and 

industrial customers in Connecticut, and therefore were assumed not to be available to customers in these 

classes. The end result is an increase in total demand response potential to 2181 MW, or 26 percent of 

peak demand in 2019. 

36 A National Assessment of Demand Response Potential 
Peak Reduction (MW) 

Figure 11: Connecticut BAU, EBAU, and AP Peak Reductions in 2019 

1,800 

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BAU Expanded BAU Achievable Participation 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

Chapter III – Key Results 

Mandatory dynamic pricing further increases demand response potential in the FP scenario (Figure 12). 

This is coupled with a higher assumed acceptance rate for enabling technologies across the customer 

classes, and total demand response potential increases to 2,458 MW, or 29 percent of peak demand. The 

fairly small incremental increase relative to the AP scenario is partly attributable to enabling technologies 

not being cost effective for Small and Large commercial and industrial customers. 

Relative to Georgia, the total potential for demand response is higher in Connecticut across the scenarios. 

While most categories of demand response programs actually have a lower potential in Connecticut, the 

presence of an ISO program that is reporting very large impacts makes for a higher overall potential 

estimate. It is also interesting to note that the incremental increase in demand response potential relative 

to the BAU scenario is smaller in Connecticut due to the large amount of existing demand response in the 

state. One interpretation of this finding is that Connecticut is currently achieving more of its potential. In 

other words, the “gap” between today’s impacts and the total amount that could be achieved is smaller. A 

side-by-side comparison of all four scenarios is presented in Figure 12. 

Federal Energy Regulatory Commission 37 
Figure 12: Connecticut Potential Peak Demand Reduction in All Scenarios, 2019 

Peak Reduction (MW) 

1,800 

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BAU Expanded Achievable Full

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Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

Chapter III – Key Results 

Case Study #3: Washington 

In contrast to both Georgia and Connecticut, no impacts from existing demand response programs were 

identified in the 2008 FERC survey for the state of Washington. This is generally a reflection of the state 

of demand response in the Pacific Northwest. Historically, low energy prices and a surplus of hydro 

capacity have made demand response seemingly less attractive in this region. However, as peak demand 

continues to grow and constraints on the operation of hydro facilities become more restrictive 39, utilities 

in the region are beginning to take a more serious look at demand response as a resource option. 40 

For Washington, the EBAU scenario (Figure 13) represents the addition of an entirely new portfolio of 

non-pricing demand response programs which are assumed to reach “best practices” levels for the U.S. 

Dynamic pricing is included on a voluntary opt-in basis. Impacts are spread somewhat evenly across 

DLC and interruptible tariffs, with the largest impacts coming from Other DR programs. Total demand 

response potential for the scenario is 864 MW, or four percent of peak demand. 

 

39 Environmental constraints related to wildlife preservation have become more stringent. 40 For example, Bonneville Power Administration, the wholesale provider of electricity for the region, has recently begun to explore 

opportunities to partner with its retail electric utility customers to integrate demand response into its portfolio of resource 

options. Source: http://www.bpa.gov/Energy/N/utilities_Sharing_EE/Utility_Brown_Bag/pdf/120408DR_BrownBag.pdf 

38 A National Assessment of Demand Response Potential 
Peak Reduction (MW) 

Figure 13: Washington BAU and EBAU Peak Demand Reduction in 2019 

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Chapter III – Key Results 

In the AP scenario (Figure 14), the inclusion of default dynamic pricing results in significantly higher 

demand response potential, particularly in the residential class. Acceptance of enabling technology 

replaces some of the participation in DLC in the EBAU scenario. As in the Georgia analysis, the Large 

commercial and industrial impacts are lower in the AP scenario than in the EBAU scenario. The 

explanation is the same in that the per-customer impacts of the new Other DR programs are larger than 

those of the dynamic pricing programs, and the total class potential drops in the AP scenario as a result. 

The total system demand response potential, however, increases to 2 GW, or nine percent of peak 

demand. 

Federal Energy Regulatory Commission 39 
Figure 14: Washington BAU, EBAU, and AP Peak Reduction in 2019 

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Chapter III – Key Results 

Demand response potential under the FP scenario is dominated by dynamic pricing with enabling 

technology. Impacts from interruptible tariffs are still reported for some Medium and Large commercial 

and industrial customers, as customers simultaneously enrolled in these programs might be expected to 

provide larger reductions from the interruptible tariff. The FP potential for Washington is 2.8 GW, or 12 

percent of peak demand. This is lower than that of Georgia or Connecticut, due to the lack of existing 

demand response and the state’s low saturation of central air conditioning. Results are provided in Figure 

15. 

40 A National Assessment of Demand Response Potential 
Figure 15: Washington Potential Peak Demand Reduction in All Scenarios, 2019 

Peak Reduction (MW) 

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Chapter III – Key Results 

Summary of State Impacts 

The previous three case studies demonstrate that each state has unique characteristics that will make its 

demand response potential different from that of other states. A comparison across these case studies has 

identified some of the key drivers of demand response potential. This includes: 

•	 Central air conditioning saturation: High central air conditioning market penetration leads to 

larger demand response potential, because customers with central air conditioning are more 

responsive to dynamic pricing. Additionally, higher central air conditioning saturation means that 

a larger share of the population is eligible to participate in DLC programs. This is evident when 

contrasting residential demand response potential in Georgia and Connecticut. 

•	 Cost-effectiveness: If a program does not pass the economic screen for a given customer class, 

then it will not be offered to those customers and demand response potential will be lower as a 

result. This was illustrated in Connecticut, where enabling technologies were not cost effective 

for Large commercial and industrial customers, and their dynamic pricing potential was low as a 

result. 

•	 Customer mix: States with a higher concentration of load in the Residential and large 

commercial and industrial classes will often have higher demand response potential, as these 

classes tend to provide the largest per-customer peak reductions. A higher than average share of 

peak demand in these customer classes drives the relatively high demand response potential seen 

in Georgia. 

•	 Regional price elasticity: Customers in the western U.S. have been found to be more price 

responsive than customers east of the Rocky Mountains. This drives regional differences in 

Federal Energy Regulatory Commission 41 
dynamic pricing potential. In Washington, customers on dynamic pricing would potentially be 

more responsive to dynamic pricing (on a percentage basis) than customers in more humid states 

in the east due to the lower loss of comfort that they would experience when reducing air 

conditioning load on hot summer days. 41 

•	 Existing program impacts: States that are reporting above-average per-customer impacts from 

non-pricing programs will tend to have higher total demand response potential in those programs. 

In other words, it is assumed that as participation in the existing programs increases, customers 

will continue to provide large impacts. Further, a high participation rate in existing programs will 

contribute to higher overall demand response potential. In particular, the ability of demand 

response to participate in wholesale markets increases demand response potential, as seen in the 

Connecticut case study. 

•	 AMI deployment: To the extent that dynamic pricing contributes to demand response potential in 

the EBAU scenario, its impact is limited by the final market penetration rate of AMI under the 

partial deployment scenario. The rate at which AMI is deployed over time affects the amount of 

dynamic pricing under all scenarios. 

Demand response potentials were estimated for all 50 states and the District of Columbia. Figures 16 

through 19 illustrate the potential of the ten states with the highest potential in 2019 and the ten states 

with the lowest 2019 potential (based on the AP scenario). On a gigawatt basis, California, Florida and 

Texas predominate because they have the highest peak demands. Ranked by demand response potential 

as a fraction of peak demand, Connecticut, Maryland and Maine are highest; each has substantial amounts 

of existing demand response, Maine has an above-average share of peak demand in the Large commercial 

and industrial customer class, and Maryland has a relatively large amount of residential central air 

conditioning. 42 There is a significant amount of variation across the states, both in terms of demand 

response potential and the amount of demand response that exists today. Complete state results appear in 

Appendix A. Peak Demand Reduction Potential (GW) 

20 

18 

16 

14 

12 

10 

8 

6 

4 

2 

0 

Figure 16: Top Ten States by Achievable Potential in 2019 (GW) 

Business-as-Usual Expanded BAU Achievable Participation Full Participation 

IL TN NY OH PA NC GA CA FL TX 

 

41 This is based on a survey of recent dynamic pricing pilots. More detail is provided in Appendix D. 42 Maryland is also assigned a high price elasticity based on results of Baltimore Gas & Electric Company’s dynamic pricing pilot. 

More detail is provided in Appendix D. 

Chapter III – Key Results 

42 A National Assessment of Demand Response Potential 
Peak Demand Reduction Potential

(% of Peak Demand) 

35% 

30% 

25% 

20% 

15% 

10% 

5% 

0% 

Figure 17: Top Ten States by Achievable Potential in 2019 (% of Peak Demand) 

Business-as-Usual Expanded BAU Achievable Participation Full Participation 

TN AZ FL GA NV UT NE ME MD CT 

Peak Demand Reduction Potential

(GW) 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

Figure 18: Bottom Ten States by Achievable Potential in 2019 (GW) 

Business-as-Usual Expaned BAU Achievable Participation Full Potential 

AK VT HI MT ND NH RI SD DE WY 

Chapter III – Key Results 

Federal Energy Regulatory Commission 43 
0% 

2% 

4% 

6% 

8% 

10% 

12% 

14% 

16% 

18% 

Peak Demand Reduction Potential (% of Peak

Demand) 

Business-as-Usual Expanded BAU Achievable Participation Full Participation 

AK HI WI MT OR WA WY ND NH KY 

Figure 19: Bottom Ten States by Achievable Potential in 2019 (% of Peak Demand) 

Chapter III – Key Results 

Benchmarking the Estimate for the Business-as-Usual Scenario 

The estimate for the BAU scenario serves as the starting point for much of this analysis, so it must be 

carefully validated through comparisons to other available data sources. Specifically, the 2008 BAU 

estimate of 36.7 GW has been benchmarked against three recent estimates of existing demand response: 

• 2008 FERC Assessment of Smart Metering and Demand Response (“2008 FERC Staff Report”); 

• NERC 2008 Summer Reliability Assessment; and 

• Data from the Energy Information Administration’s (EIA’s) Form-861 database. 43 

Figure 20 shows a comparison of the load reduction potential estimation for the BAU scenario with data 

from the three other sources. 

 

43 Table 9.2 ‘Demand Side Management Program Annual Effects by Program Category, 1996 through 2007’, which reports a 

potential peak load reduction of 23.1 GW from load management programs offered by large utilities in 2007. This is based on 

the EIA Form-861 reporting by utilities. http://www.eia.doe.gov/cneaf/electricity/epa/epaxlfile9_3.pdf 

44 A National Assessment of Demand Response Potential 
Figure 20: Comparison of BAU Estimate to Other Data Sources (2008) 

37 

41 

29 

23 

0 

5 

10 

15 

20 

25 

30 

35 

40 

45 

Potential load reduction (GW) 

Business-As-Usual (BAU) FERC Staff Report 2008 NERC 2008 Summer EIA data 

estimate Reliability Assessment 

Sources 

Chapter III – Key Results 

The BAU scenario estimate in the present analysis is based on the 2008 FERC Demand Response Survey 

Database which supports the staff report. The BAU potential estimate is lower than the 41 GW of 

potential indicated in the Staff Report and excludes two categories of programs that were included in the 

FERC analysis: ‘Time-Of-Use (TOU)’ rates and ‘Back-Up Generation’; and also excludes additional 

state-specific adjustments (see Table 2). The reasons for excluding these three items from our BAU 

estimation are as follows: 

1.	 Time-Of-Use (TOU) rates: For the purposes of this analysis, it is recognized that TOU rates can lead 

to significant reductions in peak demand. However, this generally happens through permanent load 

shifting rather than through demand response with short response time. See the discussion in Chapter 

II for more details on this exclusion. 

2.	 Back-up generation: Programs that explicitly target back-up generation are not included in the BAU 

estimation, as back-up generation is not considered to be a demand response option by itself. But, 

back-up generation is included in cases where it is an underlying option in a general demand response 

program. 

3.	 State-specific adjustments: An additional adjustment was made for an outlier program that is likely 

to have dramatically overstated impacts. 44 

 

44 In the 2008 FERC survey database, a Minnesota utility, Great River Energy, reported a load reduction potential of around 50% of 

the total potential for the state. However, the EIA Form-861 database indicates that the summer peak load contribution from 

this utility was 13% of the total summer peak load for the state. We therefore adjusted the load reduction potential reported by 

this utility in the FERC survey database to represent approximately 13% of the total load reduction potential for the entire state. 

This led to a reduction of 1.9 GW of potential for the State of Minnesota (Item 3 in Table 2). This observation was confirmed 

through a review of Electric Power Research Institute, “Energy Efficiency Potential Assessment for Great River Energy.” EPRI 

Technical Report 100891, July 2003. 

Federal Energy Regulatory Commission 45 
Table 2: Explanation of Difference between FERC Staff Report and BAU Estimate 

Potential load reduction 

(GW) 

2008 FERC Staff Report 41 

1. TOU impacts - 1.7 

 2. Backup generation - 0.7 

 3. State-specific utility adjustment - 1.9 

BAU Estimate = 36.7 

Chapter III – Key Results 

The BAU scenario estimate is higher (by around 8 GW) than the amount of existing demand response 

provided in the 2008 NERC Summer Reliability Assessment report. 45 This discrepancy is most likely due 

to the fact that NERC’s assessment is primarily focused on ISO/RTO estimates for demand response 

resource participation, while the BAU estimation based on FERC survey data was developed through a 

bottom-up estimation approach through aggregated utility reporting on demand response programs. 

Lastly, the BAU scenario estimate is also substantially higher than the EIA estimate (by roughly 14 GW). 

This difference can be explained by the fact that the EIA estimate only includes data reported by large 

utilities, which leads to the estimation of a lower level of load reduction potential. 

 

45 In its subsequent 2009 Summer Reliability Assessment Report (May 2009) NERC reports the demand response potential for 

summer 2009 peak load reduction to be about 33 GW. This study estimates the BAU load reduction potential in 2009 to be 

36.8 GW, higher by almost 3.8 GW than NERC’s 2009 report. 

46 A National Assessment of Demand Response Potential 
CHAPTERIV.	 TRENDSANDFUTURE

OPPORTUNITIES

This report estimates the potential for demand response in the United States at the national, regional, and 

state levels using four different definitions of potential. The four concepts of potential have been 

estimated for five program types across four customer classes. It relies on readily available information 

and data. As such, ideas and concepts that could not be quantified were excluded. This chapter briefly 

addresses some of these non-quantifiable aspects of demand response and suggests the role they may play 

in the future. 

A wave of new technologies is emerging that falls under the broad rubric of the smart grid. At this point, 

these technologies are too new for their likely market penetration or impact per participating customer to 

be determined. These include advanced, grid-friendly appliances which communicate with each other 

and whose operation can be managed remotely or locally by households through a digital home energy 

management system. Early versions of these technologies have been shown to be very promising but also 

very expensive in the California statewide pricing pilot and the Olympic Peninsula pilot. It is important 

to keep an eye on the continued development, testing and consumer acceptance of these technologies. 

Increasingly sophisticated in-home displays are being introduced that have the potential to reduce overall 

energy consumption. Future versions will be able to estimate how much of the bill was spent on the 

major end-uses, giving customers essential information to prioritizing their energy use during expensive 

times. These devices have the potential for lowering customer peak demands, thereby contributing 

indirectly to demand response. Some of these devices can work with time-of-use rates and future variants 

will probably be able to work with dynamic pricing rates. 

In a similar vein, new pricing designs continue to be developed that can enhance the appeal of dynamic 

pricing to large numbers of customers by tailoring the risk-reward trade-off inherent in such rates to the 

preferences of individual customers. For example, various types of real-time pricing products are under 

consideration featuring either a two-part structure in which customer-specific baseline usage is priced at 

the existing rate and only usage that deviates from the baseline is priced through real-time rates. Other 

products are being introduced where customers buy a price-cap to insulate all their usage from excessive 

levels of price volatility. Other examples include variable peak pricing rates under which prices on 

critical days are not pre-specified but based on real-time costs in wholesale markets and dynamic pricing 

rates where, for a fee, customers can over-ride the price signal on certain days that are important to their 

business. 

Today, codes and standards instituted by federal and in many cases state agencies affect energy used by 

appliances and by buildings. They are not designed to affect peak demand. However, that could change 

if agencies began to set standards for demand response. For example, the California Energy Commission 

is considering “load management” standards that may require all new Residential and Small commercial 

and industrial buildings to come equipped with programmable communicating thermostats. 

Another trend that is beginning to be observed in states with large energy efficiency and demand response 

programs is the desire to integrate these two program offerings. The idea is that ultimately both involve 

the same customer and often the same end-uses. To promote faster adoption of both programs, the value 

proposition has to be conveyed clearly to customers and the actions required of them have to be 

streamlined. The combined effect of integrated programs on demand response could be significant. 

Future assessments should address this. 

Distributed energy resources, such as photovoltaic arrays mounted on roof tops, hold the potential for 

having a significant effect on peak demand. Currently, their high capital cost poses a barrier to rapid 

Federal Energy Regulatory Commission 47 
Chapter IV – Trends and Future Opportunities 

market penetration. However, federal and state policies are addressing the cost barrier. As economies of 

scale increase, the cost should go down. When combined with appropriate rate designs, such as time-ofuse rates, the impact of these dispersed resources on peak loads could be significant. Other examples 

include battery storage and thermal energy storage. Both items hold the potential to significantly reduce 

peak demand on a permanent basis by shifting it to off-peak periods. As in the case of photovoltaic 

arrays, cost is a significant barrier to their rapid market penetration today. Another example is behindthe-meter generation which includes a diverse set of technologies including small conventional generation 

units that are used as back-up generation during emergencies and cogeneration systems that combine heat 

and power, largely in industrial process applications. 

Finally, another development to watch is the introduction of plug-in hybrid vehicles (PHEVs). If PHEVs 

can be charged during off-peak hours, they can improve capacity utilization in the power system and 

lower costs for all customers. However, if they are charged during peak hours, the load factor will 

worsen. The penetration of PHEVs will depend on several unknowns, including the price of gasoline, the 

price of electricity, customer driving habits and the incremental cost of PHEVs over conventional 

gasoline-power vehicles based on the internal combustion engine. 

Time-of-use (TOU) rates are not considered a form of demand response in this report because they cannot 

be used to produce reductions in peak demand during critical periods. However, they do represent a way 

of reducing peak demand over the long-run and reducing the need for peaking generation units. While 

TOU rates have been in existance for a long time, their penetration of the market, especially for 

Residential and Small commercial and industrial customers, has been limited. There are two major 

limitations. The first one is that the peak period encompasses far too many hours to allow customers an 

opportunity to curtail usage during that period or to move it to off-peak periods. The second one is that 

the price differential between the peak and off-peak periods is not big enough to create significant savings 

opportunities. Both are being addressed in the TOU rate designs that are now being introduced by several 

utilities. Of particular interest is the idea of a super peak period which may be as narrow as three hours 

and which may be applied only during the two or three months of the summer where the system is likely 

to peak. 

Another set of influences that will shape the future of demand response are utility and ISO/RTO 

administered energy efficiency programs. Many of these programs target end-uses such as central air 

conditioning which are a major driver of system peaks. As these appliances become more efficient, peak 

loads may diminish, albeit not by the same percentage amount as overall energy consumption. Similar 

comments can be made about inclining block rates which charge higher rates for usage in the upper tiers. 

Since that usage is highly correlated with the operation of peak-inducing appliances, reductions in upper 

tier usage brought about by inclining block rates can also lower peak demands. 

Technology-enabled demand response programs can be activated on short notice and have the capability 

of providing ancillary services in restructured wholesale markets. There is insufficient evidence on 

whether demand response is being actively used in this fashion in ancillary service markets. Experience 

to date is largely limited to energy and capacity markets. However, this will change in the future as 

ancillary service markets are opened to demand resources. 

Areas for Further Research 

This study has relied upon the best available data to make projections of demand response potential. For 

example, several pilots with dynamic pricing rates have yielded results that have been used to inform the 

study’s assumptions about likely customer response to such pricing programs. This required making 

some assumptions about the impact of humidity on customer response, as predicted from PRISM46 that 

 

46 The California Statewide Pricing Pilot produced estimates of price elasticity for residential customers that captured variations in 

customer price responsiveness across four different climate zones in the state. These estimates were codified in the Pricing 

Impact Simulation Model (PRISM) which allows price elasticities to vary as a function of a zone’s saturation of central air 

48 A National Assessment of Demand Response Potential 
Chapter IV – Trends and Future Opportunities 

was estimated using data from California’s dynamic pricing pilot. It would be worthwhile to test the 

validity of this assumption by combining the data from the various pilots. 

There is a long history with utility direct load control programs for Residential and Small commercial and 

industrial customers and curtailable/interruptible tariffs for Large commercial and industrial customers. 

Results from these programs have been used to inform this study’s assumptions. However, in several 

areas, further research is warranted to improve the quality of the assumptions. Many of these deal with 

Large commercial and industrial customers. For example, there is need for much better information on 

the likely effect of automated demand response on peak loads and the response of these large customers 

to dynamic pricing rates. 

As noted earlier, not much is known about the impact of dual-purpose programs that combine energy 

efficiency with demand response. More research is needed on this topic. 

Most of these gaps in knowledge can be addressed by designing and implementing pilot programs. These 

pilots should focus on topics on which not much is known today and not repeat investigations that have 

already been carried out. It would be useful to conduct a pilot screening exercise to identify high priority 

areas. One approach to doing this is to focus future pilots on areas which simultaneously satisfy two 

criteria: (a) high potential savings and (b) high uncertainty (for example, where newer technologies are 

involved). Areas with low potential may not be worth piloting. Areas with high potential savings but low 

uncertainty do not require piloting and should instead be considered for full-scale implementation. The 

lowest priority should be given to areas with low potential savings and low uncertainty. 

Another area in which further research is needed involves the prediction of customer participation rates. 

For certain programs with long histories, such as direct load control and curtailable/interruptible rates, 

considerable information on customer participation rates is available. Information from the distribution of 

participation rates has been widely used in this study. In other areas, such as customer participation in 

dynamic pricing programs, relatively little is known. New work is needed in this area. The traditional 

type of impact evaluation pilots will not help address the issue. Market research involving customer 

surveys, conjoint analysis and discrete choice modeling can provide initial answers. But all of these rely 

on stated preferences rather than observed (or revealed) preferences. Other creative research 

methodologies will need to be developed that combine information on stated preferences with information 

on revealed preferences (where it is readily available or where it can be inferred by analogy). 

 conditioning equipment and weather conditions. For more information, see Charles River Associates, Impact Evaluation of the 

California Statewide Pricing Pilot, Final Report. March 16, 2005 

Federal Energy Regulatory Commission 49 

CHAPTERV.OVERVIEWOFMODELING
 

ANDDATA
 

This chapter provides an overview of the model and data that underlie the demand response potential 

estimates presented in prior sections and in the detailed, state-level summaries contained in Appendix A. 

The chapter is divided into the following subsections: 

•	 High-level summary of modeling methodology 

•	 High-level summary of data development 

•	 Cost-effectiveness methodology 

Model Overview 

Development of the demand response potential model and default data underlying the estimates presented 

in this report was guided by the following objectives: 

•	 Produce defensible estimates of demand response potential based on the definitions and 

assumptions underlying this analysis; 

•	 Develop internally consistent estimates of demand response potential at the state, regional and 

national level; 

•	 Provide defensible default data for all required model input variables at the state level, from 

publicly available sources; 

•	 Ensure that there is no double counting of demand response load impacts; 

•	 Provide a user friendly, extremely flexible model that can be used to update the estimates as 

better data become available, as policies change, and to aid in policy analysis and development. 

Demand response potential estimation is inherently a “bottom-up” process. Load impacts associated with 

demand response programs are fundamentally driven by changes in consumer behavior, and demand 

response potential and load impacts vary significantly across customer segments. For example, the 

extensive literature on electricity demand developed over the last 30 years, and more recent evidence 

from time-based pricing pilots, indicates that residential customers are more responsive to time-varying 

price signals than are commercial and industrial customers (e.g., residential customers have higher price 

elasticities than do non-residential customers). 47 On the other hand, the average commercial and industrial 

customer has larger loads than does the average residential customer, so smaller percentage impacts still 

often translate into larger absolute impacts on a per customer basis. Residential customer impacts vary 

significantly across customers with and without central air conditioning and, for customers with central 

air conditioning, potential impacts vary across climate regions. These are a few examples of why it is 

important that the development of demand response potential estimates start at the customer level and 

work up to the segment and region level of interest. 

There are three fundamental building blocks needed to estimate demand response potential: 

 

47 See, for example, Charles River Associates. Impact Evaluation of the California Statewide Pricing Pilot, Final Report, March 16, 

2005 and Stephen S. George, Ahmad Faruqui and John Winfield. California’s Statewide Pricing Pilot: Commercial & Industrial 

Analysis Update. Final Report, June 28, 2006. 

Federal Energy Regulatory Commission 51 
 

Chapter V – Overview of Modeling and Data 

•	 An estimate of average energy use during peak periods before demand response impacts take 

effect; 

•	 An estimate of the change in energy use during peak periods resulting from customer 

participation in demand response programs and response to demand response price signals or 

incentives; and 

•	 An estimate of the number of customers that participate in demand response programs. 

These three building blocks are displayed in the blue shaded boxes in Figure 21 which also illustrates 

some of the primary input values that are needed to predict demand response effects. 

Figure 21: Key Building Blocks and Inputs for Demand Response Potential Model 

A significant challenge in developing demand response potential estimates is the general lack of data on 

energy use during peak periods, when demand response is needed most and the benefits are greatest. 

Most utilities do not have hourly load data for a representative sample of customers and the lack of such 

information can be a stumbling block for developing demand response load impacts for utilities and 

states. Original work was done through this project to develop representative, hourly load data to use as 

input to the model for five customer segments: residential consumers with and without central air 

conditioning, small non-residential consumers (demands less than 20 kW), medium non-residential 

consumers (demands between 20 and 200 kW) and large non-residential consumers (peak demands 

exceeding 200 kW). These load estimates were developed using regression analysis based on hourly load 

data from utilities in 21 states, representing a broad cross section of customer segments and climate 

conditions. Normalized load shapes were developed using statistical analysis and combined with annual 

energy use, weather data and system load data (to identify top system load days) from each state to 

produce the starting values for energy use during peak periods depicted in the first blue box in Figure 21. 

These estimates are primarily used as input to load impact estimates for price-based demand response. 

This original work could be a valuable resource for states and utilities that want to refine the demand 

response potential estimates presented here or that might find hourly load data useful for demand 

response program planning or other purposes. 

52 A National Assessment of Demand Response Potential 
 

 

 

Chapter V – Overview of Modeling and Data 

The demand response potential model uses two different approaches for determining load impacts for 

various demand response options. Load impact estimates for non-price based demand response options, 

such as direct load control and interruptible rates, are based on average values determined through 

analysis of data from existing programs. Load impact estimates for price-based demand response are 

determined using the normalized load shapes summarized above and estimates of the percentage change 

in energy use during peak periods based on price elasticities and the assumed change in prices during 

peak periods for demand response tariffs relative to non-time varying rates. 

Price elasticities depict the percentage change in energy use given a percentage change in price. In recent 

years, there have been numerous studies done by utilities around the country that estimate price 

elasticities associated with time-based pricing. 48 Estimates from various studies were used here to 

determine price impacts that vary across states and customer segments based on key drivers of demand 

response, such as air conditioning saturation, climate and the presence or absence of enabling technology 

such as programmable communicating thermostats that can help to automate some forms of price 

response in regions where the technology is cost effective. 

The percent reductions for price based demand response options used in each scenario are based on an 

assumption that prices during the peak period on high demand days are eight times higher on a dynamic 

time-varying rate than they are based on the average price associated with the non-time varying, 

otherwise applicable tariff. 49 This price ratio is intended to depict the ratio between an average price and 

a dynamic price that incorporates a large portion of the avoided cost of capacity50 into the small number 

of hours in which peak-period dynamic price signals are in effect. In reality, price ratios could vary 

significantly across states if every state fully reflected the avoided cost of capacity in the dynamic rate, 

since the avoided capacity cost does not vary greatly across states but current average prices do. As such, 

the price ratio might be much higher in Idaho, for example, where current prices are relatively low, than it 

would be in California, where current prices are much higher. 51 However, tariff design is not just based 

on cost analysis—there is always a concern about extreme changes in prices whether or not they are costreflective. As such, using the same 8 to 1 price ratio across states may more accurately reflect how prices 

might evolve as they move closer to reflecting both avoided capacity costs and other requirements to be 

reflected in a rate design. 

The third key element of demand response potential estimation is the number of customers that participate 

in each demand response program. The number of participants is a function of the number of eligible 

customers and the assumed participation rate. The number of eligible customers is based on the number 

of customers by segment and, in some cases, to the number of customers with specific end use equipment, 

such as central air conditioning. 52 For residential customers, the breakdown between those with and 

without central air conditioning is determined from data on air conditioning saturation in each state. The 

eligible population for price based demand response options is also driven by the presence or absence of 

48 A useful summary of numerous pilots is contained in Ahmad Faruqui and Sanem Sergici, "Household response to dynamic 

pricing of electricity: A survey of the experimental evidence," January 10, 2009. 

http://www.hks.harvard.edu/hepg/Papers/2009/The%20Power%20of%20Experimentation%20_01-11-09_.pdf. Price elasticities 

determined from a large, multi-year experiment conducted in California formed the starting point for the values used in the 

demand response potential model. These values are documented in Stephen S. George and Ahmad Faruqui, Impact 

Evaluation of the California Statewide Pricing Pilot, Final Report. March 16, 2005 and in Stephen S. George, Ahmad Faruqui 

and John Winfield. California’s Statewide Pricing Pilot: Commercial & Industrial Analysis Update. Final Report, June 28, 

2006. These starting values were modified, as discussed in Appendix D, based on information from other pilots and variation 

in key drivers of demand response such as air conditioning saturation and climate. 49 The price ratio used for the large C&I customer segment is 5 to 1. This lower ratio is based on the fact that most large C&I 

customers are already on static time-of-use rates and, thus, have a higher peak-period price as part of their standard tariff than 

do other customers. As such, the ratio between the standard (TOU) peak price and a price that more fully reflects the avoided 

capacity cost is less for this customer group than it is for the other customer segments. 50 "Avoided cost of capacity" refers to the amount of investment in new power plants that could be avoided or deferred through a 

reduction in peak demand. 51 A recent rate filing by PG&E that reflects the full avoided cost of capacity in critical peak price hours has a peak period price of 

roughly $1.50/kWh for residential customers. This represents roughly a 10 to 1 price ratio compared to current average prices 

in CA but a state like Idaho, it would be closer to 20 to 1. 52 Central air conditioning is a necessary condition to participate in air conditioning load control programs and also for the 

technology-enabled price responsive demand response options. 

Federal Energy Regulatory Commission 53 
 

Chapter V – Overview of Modeling and Data 

advanced metering infrastructure (AMI), which varies across years and scenarios. The number of 

customers assumed to participate in a demand response program is based on the assumed pricing policy 

(e.g., whether dynamic pricing is mandatory, is based on default, opt-out enrollment policies, or is based 

on opt-in enrollment). Enrollment assumptions for other demand response programs are based in part on 

enrollment in “best practices” programs that currently exist. 

Much more detailed documentation of the model and input values is contained in Appendix E. The 

demand response potential model used to generate the estimates contained in this report is available from 

FERC. It was developed with the idea that state and utility policy makers may wish to use the model with 

different input data and assumptions to develop alternative, state-specific demand response potential 

estimates. 

The demand response potential model is an Excel spreadsheet tool that contains user friendly drop-down 

menus that allow users to easily change between demand response potential scenarios, import default data 

for each state, and change input values on either a temporary basis for use in “what if” exercises or on a 

permanent basis if better data are available. Figure 22 shows half of the front-end, user input page of the 

spread sheet where scenarios can be selected and input values changed. Figure 23 shows the second half 

of the same input sheet. These “screen shots” are examples for a specific state and are shown here simply 

to give the reader a quick perspective on how input values can be changed and new scenarios created. 

Detailed documentation of the model and all variable names and input values are contained in Appendices 

D and E. 

As seen in Figure 22, the first part of the input sheet contains pull-down menus that can be used to select 

the geographic region of interest (each of 50 states plus D.C., 9 census regions and the nation as a whole) 

and the demand response potential scenario (Business-as-Usual, Expanded BAU, Achievable 

Participation or Full Participation potential). The user can also select from among a wide range of price 

ratios (and differing ratios for each customer segment) that drive price-based demand response load 

impacts. Once these selections are made, the “Load Default Inputs” button is used to load the default data 

from the state-level database that pertains to the options selected. If the user changes input values in the 

other portions of the database through the input screen, the “Save As Default” button can be used to make 

those changes permanent. 

The lower portion of Figure 22 shows the input values used by demand response program type and 

customer segment. Many of these values are either loaded in from the default database or are user 

defined. The line labeled “Customers with load suitable for enabling technology” is tied to the saturation 

of central air conditioning in each state and customer segment, as this value determines the percent of 

total customers where programmable communicating thermostats or direct load control options apply. 

The next line, “Offered Technology” is a function of whether such technology is determined to be cost 

effective in that state. This is typically 100 percent or 0, the latter being used in states where the 

particular technology is not cost effective. As indicated previously, all of the variables shown in Figure 

22 are documented in the appendices. Figure 23 shows the remaining portion of the input sheet from the 

model. The top part contains input values for the number of customers by type and growth rates for the 

number of customers, peak demand and energy use. It also shows the AMI deployment schedule for each 

customer segment. The bottom part of this portion of the input sheet has values for the remaining key 

variables that drive load impacts. They include average use during the peak period by customer segment, 

and percent reductions in average use for customers who participate in various demand response options. 

The percent reductions for price-based demand response are based on the price elasticities underlying the 

default database and the assumed price ratios that drive each scenario. However, these values can be 

overridden by the user if, for example, there is more current or relevant data from a pricing pilot at a 

specific utility or state indicating that the estimated values based on the default price elasticities might be 

inappropriate for the scenario of interest to a specific user. 

The demand response potential model produces a wide variety of numeric and graphical output reports 

and files. The graphs and tables shown in each of the state reports contained in Appendix A are examples 

of a few of the model outputs. In general, tables and/or graphs are produced that show the breakdown of 

54 A National Assessment of Demand Response Potential 
demand response impacts (in both absolute and percentage terms) by program type, customer segment, 

and year under each of the four demand response potential scenarios. The model also creates a database 

containing all output values with built in pivot tables that can be used to easily manipulate the data and to 

produce customized output tables and figures. There are also output files and graphs that show the results 

for all four demand response potential scenarios in the same sheet. 

Figure 22: User Friendly Input Sheet from Demand Response Potential Model 

A B C D E F G H I 

1 FERC National DR Potential Assessment 2 SCENARIO INPUTS - GA Achievable 

3 

4 

5 State GA Load Instructions 

Default Inputs 6 Type of Potential Achievable

7 New Peak to Old Peak Price Ratio - Residential 8.00

Save As Default Update Results 8 New Peak to Old Peak Price Ratio - Small C&I 8.00

Inputs and Results Database (Cycles 

9 New Peak to Old Peak Price Ratio - Medium C&I 8.00 through 50 States) 

10 New Peak to Old Peak Price Ratio - Large C&I 5.00

11 Data from year 2008

12 

13 Commercial & Industrial 

14 DR TYPE SPECIFIC INPUTS Residential Small Medium Large 

15 Dynamic Price Induced DR 

16 Max Percent Enrolled or Notified 75.0% 75.0% 60.0% 60.0%

17 Rates become effective at (% AMI penetration) 0.0% 0.0% 0.0% 0.0%

18 Enabling technology 

19 Customers with load suitable for enabling technology (%) 82.2% 78.0% 85.0% 40.0%

20 Offered technology (% of eligible) 95.0% 95.0% 95.0% 95.0%

21 Accept technology (%) - used for achievable 60.0% 60.0% 60.0% 60.0%

22 Automated or Direct Control DR 

23 Current Market Penetration (% of eligible customers) 2.8% 0.3% 0.0% 0.0%

24 Max Market Penetration (% of eligible customers) 25.0% 1.2% 7.2% 0.0%

25 Years required to achieve max penetration 5.0 5.0 5.0 5.0

26 Interrutiple Tariffs 

27 Current Penetration (% of customers in segment) 0.0% 0.0% 0.0% 6.5%

28 Current Penetration (% of MW in segment) 0.0% 0.0% 0.0% 4.9%

29 Max Penetration (% of customers in segment) 0.0% 0.0% 0.3% 6.9%

30 Max Penetration (% of MW in segment) 0.0% 0.0% 1.7% 16.8%

31 Years required to achieve max penetration 5.0 5.0 5.0 5.0

32 Other DR Programs 

33 Current Penetration (% of customers in segment) 0.0% 0.0% 0.0% 0.2%

34 Current Penetration (% of MW in segment) 0.0% 0.0% 0.0% 1.1%

35 Max Penetration (% of customers in segment) 0.0% 0.0% 0.1% 18.9%

36 Max Penetration (% of MW in segment) 0.0% 0.0% 0.0% 23.4%

37 Years to achieve max penetration 5.0 5.0 5.0 5.0

Chapter V – Overview of Modeling and Data 

Federal Energy Regulatory Commission 55 
Figure 23: User Friendly Input Sheet from Demand Response Potential Model (continued) 

J K L M N O 

4 

5 Commercial & Industrial 

6 GENERAL INPUTS Residential Small Medium Large 

7 Population and Load Growth Factors 

8 Starting Customer Population 4,039,005 483,576 66,628 11,363 

9 Population Growth Rate (Annual) 1.29% 1.57% 1.57% 1.57%

10 Annual Consumption Growth (Annual) 1.24% 1.13% 1.13% 1.13%

11 Critical Peak Growth (Annual) 0.60% 0.33% 0.33% 0.33%

12 AMI Deployment 

13 2009 10.7% 10.7% 10.7% 10.7%

14 2010 21.5% 21.5% 21.5% 21.5%

15 2011 32.2% 32.2% 32.2% 32.2%

16 2012 43.0% 43.0% 43.0% 43.0%

17 2013 53.7% 53.7% 53.7% 53.7%

18 2014 57.5% 57.5% 57.5% 57.5%

19 2015 61.2% 61.2% 61.2% 61.2%

20 2016 70.0% 70.0% 70.0% 70.0%

21 2017 80.0% 80.0% 80.0% 80.0%

22 2018 90.0% 90.0% 90.0% 90.0%

23 2019 100.0% 100.0% 100.0% 100.0%

24 

25 

26 

27 Commercial & Industrial 

28 AVERAGE PARTICIPANT CRITICAL DAY LOAD AND LOAD RED Residential Small Medium Large 

29 Critical peak avg. hourly load (kW) 3.36 5.44 59.68 601.70 

30 Critical peak avg. hourly load - CAC owners (kW) 3.73 DNA DNA DNA 

31 Critical peak avg. hourly load - no CAC (kW) 1.63 DNA DNA DNA 

32 Pricing - customers without central a/c (% reduction) 8.5% 0.7% 8.7% 7.5% 

33 Pricing - customers with central a/c but no enabling tech (% redu 19.3% 0.7% 8.7% 7.5% 

34 Pricing - customers with central a/c and enabling tech (% reductio 33.8% 14.9% 13.9% 13.9% 

35 Automated or Direct Load Control DR (kW reduction per custom 1.24 2.48 7.44 37.18 

36 Interruptible Tariffs - (% reduction) 0.0% 0.0% 69.9% 94.5% 

37 Other DR - committed load reduction programs (% reduction) 0.0% 0.0% 39.4% 50.0% 

Chapter V – Overview of Modeling and Data 

Database Development 

Each of the data elements that contribute to the model inputs were developed through careful review of a 

number of publicly available data sources. Table 3 lists the data elements developed and the sources used 

for developing these elements. Appendix D to the report describes in detail how the different data 

elements were developed and the interrelationships between the different elements and the sources. 

Appendix B details the challenges of developing state level data for this Assessment. 

56 A National Assessment of Demand Response Potential 
Table 3: Summary of Key Data Elements and Sources 

Data Category Data Elements Data Sources 

Number of customer accounts by 

rate class 

•

•

EIA State-Level data 

FERC Form No. 1 database 

• EIA State-Level data 

Market 

Characteristics 

Data 

Electricity sales by rate class • FERC Form No. 1 database 

System peak load forecast by 

state 

•

•

2008 NERC Long Term Reliability 

Assessment report 

EIA State-Level data 

Average Peak Load per customer 

by rate class 

•

•

•

•

•

Utility/ISO system load data 

Hourly load shapes by state 

 CAC saturation 

Average energy use by customer 

segment 

State weather data 

Growth rate in per customer peak 

load 

•

•

 U.S. Census Bureau 

Supplemental Tables to Annual 

Energy Outlook 2008 

Central air conditioning market 

saturation data 

•

•

•

•

Utility and state level appliance 

saturation survey reports 

Direct utility contacts 

EIA data on Regional Energy 

Consumption Survey (RECS) 

American Housing Survey, U.S. 

Census Bureau 

• EIA data on Commercial Building 

Energy Survey (CBECS) 

AMI deployment schedule by 

state 

•

•

•

•

 KEMA report 

 FERC survey 

 Utilipoint 

 Enernex 

Demand 

Response 

Program 

Related Data 

Business-As-Usual demand 

response potential estimation 

• 2008 FERC demand response 

Survey data 

Current participation in demand 

response programs 

•

•

•

2008 FERC Demand Response 

Survey data 

Demand response program 

evaluation reports 

Direct contacts with utilities 

Demand response program 

impacts 

•

•

•

2008 FERC Demand Response 

Survey data 

Demand response program 

evaluation reports 

Direct contacts with utilities 

Chapter V – Overview of Modeling and Data 

Development of Load Shapes 

One of the key inputs to demand response potential estimation is average electricity use per customer per 

hour during time periods when demand response programs are likely to be used but before any demand 

response occurs. We refer to the time period representing when demand response has a high probability 

of being used as the “peak period” on a “typical event day” and represent that period by the hours 

Federal Energy Regulatory Commission 57 
Chapter V – Overview of Modeling and Data 

between 2 and 6 pm on the top 15 system load days in each state. 53 Note that average energy use across 

the top 15 system load days will produce demand response load impact estimates that are significantly 

lower than if they were based on the single hour of system peak or based on fewer than the top 15 system 

load days. Utility system load data were used to identify top system load days in each state. 

As previously discussed, hourly load data were not available for all utilities and states or for all customer 

segments within states. Indeed, no data at all were found that distinguished between residential customers 

with and without central air conditioning. Fortunately, hourly load data were available on a large enough 

cross section of utilities and states (21 states in total) that it was possible to use regression analysis to 

estimate normalized load shapes for each relevant customer segment and to use these models to develop 

load shapes for all other states and customer segments. Data from these utilities were used to estimate 

regression models that relate normalized hourly load to a variety of variables that influence load in each 

hour, including weather, central air conditioning saturation and seasonal, monthly, day-of-week and 

hourly usage patterns. This statistical analysis was used to separate weather sensitive and non-weather 

sensitive load for residential customers. The normalized load shapes were then combined with estimates 

of average annual energy use and central air conditioning saturation by customer segment for each state 

and state-specific weather data to produce hourly load estimates for each customer segment and state. 

The average, hourly energy use between 2 and 6 pm on the top 15 system load days was used as the basis 

for estimating load impacts for price-based demand response options for each customer segment. 

AMI Deployment 

Advanced metering is a necessary technology to support price-responsive demand response for massmarket customers. As such, estimates of the penetration of AMI must be developed for each demand 

response potential scenario. However, having advanced meters is a necessary but not sufficient condition 

to support price-responsive demand response—a utility also needs a meter data management system and 

billing system that will support price-responsive demand response options. Quite often, utilities install 

meters that qualify as advanced meters in that they gather hourly or sub-hourly data daily, but use them as 

an automated meter reading system to produce monthly meter reads—they do not install the meter data 

management system and billing systems needed to support wide scale price-responsive demand response. 

The AMI deployment scenarios described below recognize that more than just metering is needed to 

support price-responsive demand response. The deployment time lines for each scenario are based on the 

understanding that only systems that have MDMS and billing systems are considered AMI for purposes 

of supporting demand response potential. 

Two AMI deployment scenarios were developed for each state. 

•	 The “Full Deployment” scenario is used to support the Achievable Participation and Full 

Participation demand response scenarios and assumes that all utilities will have AMI meters in 

place for all customers, along with the MDMS and billing systems required to support pricebased demand response, by the end of the analysis horizon, 2019. Deployment timing is based on 

a set of assumptions described in Appendix D, and varies significantly across states based on 

current plans, the mix of utilities in each state, and other factors. 

•	 The “Partial Deployment” scenario is used to support the Expanded BAU potential scenario and 

includes AMI deployment plans for each state based largely on a continuation of current trends. 

It includes utilities that already have or are currently deploying AMI systems and other utilities 

that, based on a variety of data sources, have expressed interest in or are believed to have a higher 

probability of installing such systems over the next ten years. 

The following figure shows the cumulative number of AMI meters underlying the partial and full 

deployment scenarios. 

 

53 In recent AMI business cases and dynamic pricing pilots, the number of load days used varies roughly between the top 10 and top 

20 days. The top 15 days were used in this study as an approximate midpoint. 

58 A National Assessment of Demand Response Potential 
Chapter V – Overview of Modeling and Data 

Figure 24: Cumulative AMI Installations under Two Scenarios 

0 

20,000,000 

40,000,000 

60,000,000 

80,000,000 

100,000,000 

120,000,000 

140,000,000 

160,000,000 

2009 2011 2013 2015 2017 2019 

Cumulative Meters Installed 

Limited Deployment 

Full Deployment 

These two alternative

scenarios should not be

considered forecasts of

actual AMI meter and

system deployment. The

full deployment scenario is 

predicated on the

assumption that all

customers will have smart

meters by the end of the tenyear forecast horizon. This 

assumption is combined

with a variety of

information and

assumptions that drive the

likely sequence of

installations across utilities

in a state and across states 

that are described below.

The partial deployment scenario is probably closer to what might actually occur, but it is not a true 

forecast, since a true forecast would require conducting business cases on hundreds or perhaps thousands 

of utilities and an assessment of the likely barriers to deployment in each state. Such work was beyond 

the scope of this analysis. 

Estimating the Impact of Dynamic Prices 

The AP and FP potential estimates rely heavily on price-based demand response options, specifically on 

dynamic tariffs that deliver high price signals on relatively few high-demand days when demand response 

benefits are greatest. Estimates of the load impact associated with pricing options are based on variables 

known as price elasticities. Economists define the “own” price elasticity as the percentage change in the 

quantity purchased of a good or service divided by the percentage change in the price of that good or 

service. There is a similar concept, known as the elasticity of substitution, which summarizes the 

relationship of two goods or services that are substitutes for each other. The elasticity of substitution is 

equal to the percentage change in the ratio of the quantities purchased of two goods to the ratio of the 

prices of the two goods. Put another way, the elasticity of substitution summarizes the rate at which 

consumers substitute one good for another based on the relative prices of the two goods. 

In the case of electricity demand, if prices are higher at one time of day relative to another, consumers 

may be willing to shift their load from the high priced to the low priced period. An example would be a 

consumer shifting the timing of their laundry from the peak to the off peak period. Alternatively, or in 

addition, a consumer might just forgo some energy use during the high price period. An example would 

be switching off lights during high priced periods—consumers don’t use more lighting during low priced 

periods because they used less during high priced periods. 

One approach to estimating how electricity demand would change in response to time varying prices 

involves estimating a two-equation demand system, where one equation determines the rate at which 

consumers substitute off-peak energy use for peak-period energy use and the second equation estimates 

the overall demand for energy. In combination, the two equations can predict the change in energy use in 

each time period as consumers move from non-time varying to time-varying prices. This is the approach 

that underlies the estimates of time-based price response in the demand response potential model. 

A variety of pricing experiments and other studies have been conducted that allow for estimation of 

demand models and price elasticities such as those described above. These studies show that price 

Federal Energy Regulatory Commission 59 
 

 

 

 

 

Chapter V – Overview of Modeling and Data 

responsiveness for residential customers varies across regions based in part on differences in the use of air 

conditioning. Climate differences can also impact price responsiveness, as can the presence or absence of 

enabling technology such as programmable communicating thermostats and other load control devices. 

Price responsiveness also differs between residential and non-residential customers with residential 

customers generally being more price responsive than non-residential customers. These factors have been 

taken into account in developing estimates of price response that reflect variation in the characteristics of 

customers across states. More detail on these regional factors is provided in Appendix D. 

The price elasticities summarized above for residential customers produce quite different percent 

reductions across states as a function of the variation in climate and air conditioning saturations. There 

are also differences in the estimated percent reduction in peak period energy use based on differences in 

the assumed ratio of prices during the peak period. The following table shows the percent reduction in 

peak period energy use for residential customers for two price ratios for each of three states that vary with 

respect to central air conditioning saturation and climate. Note that the relationship between price and 

energy use is not linear. That is, while the price ratio doubles going from 4 to 1 to 8 to 1, the percent 

reduction in peak demand increases by less than 100 percent. For example, the doubling of the price ratio 

in Massachusetts leads to a 58 percent decrease in peak period energy use. 

Table 4: Percent Reduction in Peak Period Energy Use for Residential Customers in Selected States 

State CAC Saturation Percent Peak Period 

Reduction for 4 to 1 Price 

Ratio 

Percent Peak Period 

Reduction for 8 to 1 Price 

Ratio 

Massachusetts 12.70% 6.20% 9.83% 

Maryland 78.00% 12.56% 19.66% 

Arizona 86.80% 14.28% 22.33% 

Key Assumptions 

The products of the previously described data collection and modeling approach are participation rates 

and impacts by program type, class, and state. These form the basis for the demand response potential 

estimates. Summary values of participation rate assumptions for non-pricing programs in the three 

potential scenarios are provided in Table 5.

54 Note that program participation is expressed as a 

percentage of the eligible population, which changes by scenario as the role of pricing programs changes. 

Participation rates in Table 5 represent the 75th percentile of participation in existing programs at the 

state-level. 55 The 75th percentile was chosen as the “best practices” estimate because it represents the 

participation rate that a state would need to achieve to be a “top quartile performer” which is a metric 

commonly used to identify best practices in potential studies. States with participation rates higher than 

the 75th percentile are assumed to remain at existing levels, rather than derated to the 75th percentile. 

Table 5: Final Participation Rates for Non-Pricing Programs 

Residential Small C&I Medium C&I Large C&I 

Direct Load Control 25% 1% 7% N/A 

Interruptible Tariffs N/A N/A 2% 17% 

Other DR N/A N/A 0% 19% 

Assumptions driving the final participation rate in pricing programs for the three demand response 

potential scenarios are provided in Table 6. Ranges reflect differences across states. 

54 BAU participation rates span a broad range encompassing these best practices estimates and are provided in Appendix D. 55 The assumed participation rate for residential DLC is higher than the 75th percentile. This assumption is based on general 

industry experience with these programs and a proven history of utilities consistently being able to achieve participation rates 

of 25 percent of the eligible population. 

60 A National Assessment of Demand Response Potential 
 

 

 

 

 

 

Chapter V – Overview of Modeling and Data 

Table 6: Drivers of Final Participation Rates for Pricing Programs 

Expanded 

BAU 

Achievable 

Participation 

Full 

Participation 

Residential 

Final AMI Market Penetration 19% to 100% 100% 100% 

Peak Price Ratio (New Peak-to-Existing Peak) 8 8 8 

Final Enrollment in Dynamic Pricing 5% 75% 100% 

Percent of Customers Eligible for Enabling Tech 3% to 91% 3% to 91% 3% to 91% 

Percent of Eligible Customers Offered Enabling Tech 0% 95% 100% 

Percent of Eligible Customers Accepting Enabling Tech 0% 60% 100% 

Small C&I 

Final AMI Market Penetration 19% to 100% 100% 100% 

Peak Price Ratio (New Peak-to-Existing Peak) 8 8 8 

Final Enrollment in Dynamic Pricing 5% 75% 100% 

Percent of Customers Eligible for Enabling Tech 70% to 78% 70% to 78% 70% to 78% 

Percent of Eligible Customers Offered Enabling Tech 0% 95% 100% 

Percent of Eligible Customers Accepting Enabling Tech 0% 60% 100% 

Medium C&I 

Final AMI Market Penetration 19% to 100% 100% 100% 

Peak Price Ratio (New Peak-to-Existing Peak) 8 8 8 

Final Enrollment in Dynamic Pricing 5% 60% 100% 

Percent of Customers Eligible for Enabling Tech 79% 79% 79% 

Percent of Eligible Customers Offered Enabling Tech 0% 95% 100% 

Percent of Eligible Customers Accepting Enabling Tech 0% 60% 100% 

Large C&I 

Final AMI Market Penetration 19% to 100% 100% 100% 

Peak Price Ratio (New Peak-to-Existing Peak) 5 5 5 

Final Enrollment in Dynamic Pricing 5% 60% 100% 

Percent of Customers Eligible for Enabling Tech 40% 40% 40% 

Percent of Eligible Customers Offered Enabling Tech 0% 95% 100% 

Percent of Eligible Customers Accepting Enabling Tech 0% 60% 100% 

As stated in the definition of the potential scenarios, AMI market penetration is assumed to reach 100 

percent by 2019 in both the AP scenario and the FP scenario. Final AMI market penetration is lower in 

the EBAU scenario, in which only those utility AMI deployments that were deemed “likely” through a 

review of industry data are included. 

The assumed price ratio of 8-to-1 for Residential, Small commercial and industrial, and Medium 

commercial and industrial customers is driven by the range of rates tested in recent dynamic pricing 

pilots, some of which have been greater than 10-to-1. 56 For Large commercial and industrial, the price 

56 The PSE&G residential pilot program price ratio was 14-1o-1. BGE recently tested price ratios of roughly 9-to-1 and 12-to-1. 

Federal Energy Regulatory Commission 61 
 

 

 

Chapter V – Overview of Modeling and Data 

ratio is lower to account for the fact that many of these customers are already enrolled on TOU rates, the 

impacts of which would be reflected in the load forecast. 

The assumptions for participation in price-based demand response options are based on market research 

and the limited experience that has been gathered to date. For the EBAU scenario, a participation rate of 

five percent is used for all sectors. There is very little experience and research to date upon which to base 

these assumptions. The most recent experience for a dynamic rate for residential customers has to do 

with Pacific Gas & Electric Company's SmartRate tariff, which is a critical peak pricing tariff that was 

offered in 2008 to residential customers in the part of the PG&E service territory where AMI meters had 

been installed. 57 The program was offered through direct mail and roughly eight percent of customers 

enrolled after a single mailer. Thus, five percent could be quite conservative for a program that would be 

marketed over an extended period of time. Given the limited experience for other customer segments, 

this assumption was used for all customer segments for the EBAU scenario. 

The assumptions for the opt-out enrollment strategy underlying the AP scenario are based on market 

research and recent experience in California. In conjunction with California’s Statewide Pricing Pilot, 

research was conducted on the opt-out rates that might occur for residential customers that were defaulted 

onto a CPP rate. 58 The opt-out rates for customers depended on assumptions about the level of customer 

awareness of alternatives and ranged from a low of 10 percent at a low level of awareness to a high of 33 

percent based on complete customer awareness of all options. The opt-out rate of 25 percent assumed 

here (75 percent retention) is consistent with an awareness level of 70 percent from that study. This value 

is also reasonably close to what was actually observed following completion of California’s Southwest 

Power Pool, when some customers were allowed to stay on the rate after the end of the pricing pilot. 

Roughly 65 percent of participants remained on the critical peak pricing tariff one year after the end of the 

Southwest Power Pool even though the participation incentive provided as part of the experiment was 

discontinued and customers had to start paying a monthly meter charge of between $3 and $5 depending 

on the utility serving them. 59 

The retention/opt-out rate for small commercial and industrial customers was assumed to be the same as 

for residential customers. For medium and large commercial and industrial customers, a retention rate of 

60 percent was assumed. This assumption is based in part on recent analysis of the opt-out rate 

experienced by San Diego Gas & Electric Co., which placed all of its commercial and industrial 

customers that had interval meters on default CPP/TOU rates in 2008. 60 This study found that 75 percent 

of all customers placed on the rate stayed on the rate after the initial opt-out period had passed. However, 

this may not represent the long term retention rate since customers were offered first-year bill protection 

as part of the transition strategy for the rate. How many customers might leave at the end of that period, 

which occurs in late 2009, is currently unknown. Thus, a lower retention rate seemed prudent. Another 

relevant data point for this assumption is the experience of large commercial and industrial customers in 

New York who were placed on an RTP rate several years ago. Roughly 66 percent of customers stayed 

on this rate. Based on these two data points, an assumption of 60 percent retention seemed reasonable. 

Customers are assumed not to be offered enabling technologies in the EBAU scenario, as the focus of this 

scenario is on non-pricing demand response programs. In the AP scenario, 95 percent of all eligible 

customers are offered enabling technology (in states where it is cost-effective to do so), reflecting the 

assumption that some states or utilities would choose not to pursue enabling technology. Sixty percent of 

customers accept the technology in this scenario, which is another illustrative assumption designed for the 

purposes of defining the scenario and reflecting that only a subset of customers will make the decision to 

57 Stephen S. George and Josh Bode. (Freeman, Sullivan & Co.). 2008 Ex Post Load Impact Evaluation for Pacific Gas and 

Electric Company’s SmartRateTM Tariff. Prepared for Pacific Gas and Electric Co. December 31, 2008. 58 Momentum Market Intelligence. Customer Preferences Market Research: A Market Assessment of Time Differentiated Rates 

Among Residential Customers in California. December 2003. 59 Dean Schultz and David Lineweber, Real Mass Market Customers React to Real Time-Differentiated Rates: What Choices Do 

They Make and Why? 16th National Energy Services Conference. San Diego, CA. February 2006. 60 Steven D. Braithwait, Daniel G. Hansen, Jess Reaser and Michael P. Welsh (Christensen Associates Energy Consulting, LLC) 

and Stephen S. George and Josh Bode (Freeman, Sullivan & Co.) 2008 Load Impact Evaluation of California Statewide 

Critical Peak Pricing (CPP) for Non-Residential Customers Ex Post and Ex Ante Report (May 1, 2009) 

62 A National Assessment of Demand Response Potential 
 

 

 

 

Chapter V – Overview of Modeling and Data 

install enabling technologies even if they are cost-effective. In the FP scenario, all customers are offered 

enabling technology where it is cost effective and it is assumed that all of the customers accept the 

technology. Given the limited basis for setting participation rates, readers may wish to carry out some 

type of uncertainty analysis on these assumptions. 

Per-customer impacts from non-pricing programs are provided in Table 7. These specify the amount by 

which an average customer participating in a given demand response program would reduce its peak 

demand. In Table 7, the per-customer impact is represented as a percent of the average customer’s peak 

demand. 61 These values are based on the range of reported impacts from existing programs. For states 

without an existing interruptible tariff or Other DR program, or with lower-than average impacts in these 

programs, the average per-customer impact was used. For states without an existing interruptible tariff or 

Other DR program, the average per-customer impact was used. For states without existing DLC impacts 

a 50 percent air-conditioning cycling strategy was assumed. 

Table 7: Per-Customer Impacts for Non-Pricing Programs 

Residential Small C&I Medium C&I Large C&I 

Direct Load Control 19% to 52% 7% to 17% 2% to 5% N/A 

Interruptible Tariffs N/A N/A 27% to 100% 13% to 100% 

Other DR N/A N/A 39% to 100% 10% to 100% 

Per-customer impacts from pricing programs are presented in Table 8. The range of residential impacts is 

a function of both the central air conditioning saturation in the state as well as the regional price 

elasticity. 62 Pricing impacts were simulated using the results of recent dynamic pricing experiments and 

studies as described earlier in this chapter and in Appendix D. 

Table 8: Assumed Per-Customer Impacts from Pricing Programs 

Without 

Technology 

With 

Technology 

Residential 7% to 18% 21% to 34% 

Small C&I 1% 15% 

Medium C&I 9% 14% 

Large C&I 7% 14% 

Cost-Effectiveness Analysis 

For the purposes of economic screening, the five demand response programs being considered in the 

analysis can be divided into two broad categories – those that do not require an enabling technology for 

participation (e.g. dynamic pricing such as critical peak pricing, peak time rebate, real-time pricing) and 

those that do (e.g. dynamic pricing equipped with devices that automate or reduce consumption). The 

demand response options that do not require an enabling technology for participation were deemed to be 

cost-effective for all states. For the demand response options that do require an enabling technology for 

participation, an economic screen was conducted to assess their cost-effectiveness in each state. The two 

types of options for which an economic screen was conducted are: 1) Dynamic Pricing with Enabling 

Technology, and 2) Direct Load Control. 

The economic screen uses a simple version of the Total Resource Cost (TRC) Test that compares the 

lifetime benefits of the demand response option (i.e., avoided capacity costs) relative to the associated 

costs to enable each option (i.e., costs related to technology adoption, program implementation and 

61 However, in modeling the demand response potential, nominal impact values are used for DLC programs. 62 CAC saturation is the percent of customers with central air conditioning. 

Federal Energy Regulatory Commission 63 
Chapter V – Overview of Modeling and Data 

delivery, etc.). Inputs for the economic screen include impact estimates per participant by state, capacity 

costs, equipment costs per participant, implementation costs, and economic parameters such as discount 

and cost escalation rates. The benefits are obtained by multiplying the unit demand reduction for each 

technology by avoided capacity costs over the ten year time horizon and discounting the dollar savings to 

a present value equivalent basis. For this type of preliminary analysis, the effects of incentives and 

participation rates are ignored. If the benefit-cost ratio is greater or equal than 1.00, the demand response 

option is considered cost-effective and is included in the state’s full participation potential results. 

The economic screening results show that Dynamic Pricing with Enabling Technology is a cost-effective 

option for the majority of states. However, there are a number of states for which it fails the economic 

screen. The results vary by customer type. Dynamic Pricing with Enabling Technology for residential 

customers is cost-effective for 42 states (84% of states). The option for small C&I customers is costeffective for 40 states (80% of states) as well as for the District of Columbia. For the medium C&I 

customers, the option is cost-effective for 43 states (86% of states) and the District of Columbia, while for 

the large C&I category it is cost-effective for 45 states (90% of states) and the District of Columbia. The 

results indicate that Dynamic Pricing with Enabling Technology is cost-effective primarily for those 

states with high critical peak loads associated with large cooling or other end-use requirements. In 

particular, this option is highly cost-effective in Arizona and Nevada. 

A few observations are worth noting for the results of the Dynamic Pricing with Enabling Technology 

screen: 

•	 Because a state does not pass the cost-effectiveness screen, it does not suggest these programs 

should not be pursued in that state. The estimates are based on price response using class-average 

load shapes. Many of the states that did not pass in fact have varying weather characteristics that 

would lead to different impacts. Some regions might have higher impacts and thus these 

programs may indeed be cost-effective. 

•	 As the customer class size increases and approaches the large C&I class (starting with the small 

C&I), more states become cost-effective. 

•	 These trends suggest that as dynamic pricing tariffs are introduced across the country, utilities 

that are considering adopting one of their own might consider starting with the larger customer 

classes and gradually introduce the tariffs to the smaller classes once more information is 

available. 

•	 Careful attention should be given to the economic analysis for these types of programs, 

particularly when looking at the residential class, which in some regions of the country may not 

provide the needed level of savings to justify the cost of enablement technologies such as 

programmable communicating thermostats and automated demand response. 

Direct Load Control is a cost-effective demand response option for most states because of the higher per 

participant savings associated with this option. The analysis showed that Direct Load Control is costeffective for residential customers in 48 states (96%) and the District of Columbia. The only states for 

which it is not cost-effective for residential customers are Alaska and Hawaii. Among both small and 

medium C&I customers, Direct Load Control is cost-effective for all states and the District of Columbia. 

A few observations are worth noting for the results of the Direct Load Control with Enabling Technology 

screen: 

•	 Most states passed the economic screen. However, for those states that failed the screen, methods 

of direct load control other than air conditioning might be viable. 

•	 Methods to control water heating and pumping loads may be more viable in these regions. 

For more details on the cost effectiveness analysis as well as state-level benefit-cost ratios, see Appendix 

D. 

64 A National Assessment of Demand Response Potential 
CHAPTERVI.	 BARRIERSTODEMAND

RESPONSE

A number of barriers are preventing demand response from reaching the full potential identified through 

this study. Some of these barriers are regulatory in nature, stemming from existing policies and practices 

that are not designed to facilitate the use of demand response as a resource. These barriers exist in both 

wholesale and retail markets. Other barriers are economic in nature. Certain technological limitations are 

also standing in the way. In total, 24 unique barriers to demand response have been identified through 

this study. Consistent with the requirements of EISA 2007, this chapter briefly summarizes these barriers 

to demand response. Further detail on the barriers is provided in Appendix C. 

The Barriers to Demand Response 

The barriers to demand response fall into four major categories: regulatory barriers, economic barriers, 

technical barriers, and other barriers. 

•	 Regulatory: Regulatory barriers are caused by a particular regulatory regime, market design, 

market rule, or the demand response program itself. They can be divided into three subcategories: general, wholesale-level, and retail-level. 

•	 Economic: Economic barriers refer to situations where the financial incentive for utilities or 

aggregators to offer demand response programs, and for customers to pursue these programs, is 

limited. 

•	 Technological: Potential technological barriers to implementation of demand response include 

the need for new types of metering equipment, metering standards, or communications 

technology. 

•	 Other: Some additional barriers do not fall into the categories described above. These are 

generally related to customer perceptions of demand response programs and a willingness to 

enroll. 

An extensive survey of the existing literature led to the identification of the 24 barriers to demand 

response identified in Table 9. Detailed descriptions of the barriers are provided in Appendix C. 

Federal Energy Regulatory Commission 65 
Chapter VI – Barriers to Demand Response 

Table 9: The Barriers to Demand Response 

Type Barrier 

Regulatory (General) 

1. Retail-wholesale disconnect (lack of dynamic pricing) 

2. M&V challenges 

3. Shared State and Federal Jurisdiction 

4. Perception of gaming 

5. Lack of real-time info sharing (ISOs and utilities) 

6. Lack of reliability/predictability in demand response 

Regulatory (Retail) 

7. Policy restrictions on demand response 

8. Ineffective demand response program design 

9. Financial disincentives for utilities 

10. Disagreement on cost-effectiveness analysis 

11. Lack of retail competition 

Regulatory (Wholesale) 12. Market structures oriented toward accommodating supply side 

resources 

Economic 

13. Inaccurate price signals 

14. Lack of sufficient financial incentives to induce participation 

Technological 

15. Lack of AMI 

16. Lack of cost-effective enabling technologies 

17. Concerns about technological obsolescence and cost recovery 

18. Lack of interoperability and open standards 

Other 

19. Lack of customer awareness and education 

20. Risk aversion 

21. Fear of customer backlash 

22. Perceived lack of ability to respond 

23. Concern over environmental impacts 

24. Perceived temporary nature of demand response impacts 

Assessing the Barriers 

A review of the existing literature has identified a study in which many of the barriers to demand response 

were ranked in terms of their level of overall significance to impeding further market penetration of 

demand response programs. The study was conducted by The Brattle Group through a recent project with 

the California Energy Commission. 63 Stakeholders were interviewed, asked to identify barriers to 

demand response, and asked to rate the significance of the barriers on a scale from one to five, with one 

being “highly insignificant” and five being “highly significant.” The results of the respondents’ ratings 

are summarized in Figure 25 below. 

 

63 Ahmad Faruqui and Ryan Hledik, “The State of Demand Response in California,” prepared for the California Energy Commission, 

April 2007. 

66 A National Assessment of Demand Response Potential 
Figure 25: Significance of Barriers to Demand Response in California as Identified by Stakeholders 

0.0 

0.5 

1.0 

1.5 

2.0 

2.5 

3.0 

3.5 

4.0 

4.5 

5.0 

Average Response 

Average 

Standard Deviation 

Low AMI Ineffective Low AB 1X Lack of DR Confusion Fear of Fear of Environmental 

Penetration Program Consumer Restrictions Options for with EE Inability to Customer Concerns 

Design Interest Customers Programs Recover Backlash 

Costs 

Chapter VI – Barriers to Demand Response 

Low AMI penetration topped the Brattle list of today’s barriers to demand response. The second and 

third most significant barriers, both with average scores above 3.5, were ineffective program design and 

low consumer interest. It is interesting to note that these two barriers are probably highly correlated, as a 

more effective program design would be likely to encourage customer interest in demand response 

programs. Environmental concerns associated with demand response were deemed to be the least 

significant barrier. 

Federal Energy Regulatory Commission 67 

CHAPTERVII.	 POLICY

RECOMMENDATIONS

This chapter provides policy recommendations that, if implemented, could serve to remove the most 

significant barriers to achieving the demand response potential estimated in this report. At the outset, it is 

important to note that many of the opportunities to increase demand response potential lie at the retail 

level. The States and local governments will need to play a central role in promoting demand response 

programs needed to reach the full potential. The expansion of demand response programs will involve 

technologies that affect the electricity system across State and Federal jurisdictions. Some decisions may 

be made at the Federal level, while others will need to be made by State and local regulators or 

legislatures. 

Statutory Requirement 

EISA 2007 requires that, in addition to estimating nationwide demand response potential, FERC must 

include “specific policy recommendations that if implemented can achieve the estimated potential.” 64 

EISA 2007 states, “[s]uch recommendations shall include options for funding and/or incentives for the 

development of demand response resources.” 65 EISA 2007 also directs FERC to note any barriers to 

demand response programs offering flexible, non-discriminatory, and fairly compensatory terms for the 

services and benefits made available, and shall provide recommendations for overcoming these barriers. 66 

Through the recommendations provided below, this chapter is responsive to all three Congressional 

directives. 

The preceding chapters of this report analyze three scenarios under which demand response potential 

could increase beyond the base case level of currently planned growth in demand response programs 

reflected by the Business-as-Usual scenario. These are the Expanded Business-as-Usual scenario, the 

Achievable Participation scenario, and the Full Participation scenario. Nationally, each is estimated to 

produce a significant increase in demand response potential relative to the Business-as-Usual scenario. 

However, as detailed in Chapter III, the estimated effect of each scenario in any particular state differs 

depending on a range of factors in that state, such as the level of central air conditioning saturation, the 

price of electricity, the generation capacity level, and the existing level of demand response, including 

whether the state has access to spot electricity and capacity markets with demand response programs. 

Thus, how much and how best to increase demand response may differ from state to state. Further, given 

that each scenario is based on different approaches to increasing demand response, the critical barriers 

confronting each of these scenarios may differ. A more complete discussion of these barriers is found in 

Chapter VI and Appendix C. 

General Recommendations to Overcome Barriers to Achieving Demand 

Response Potential 

Several of the barriers identified in Chapter VI significantly impede the ability to implement the estimates 

of demand response potential identified in this report. These barriers are highlighted below, along with 

recommendations for overcoming those barriers. 

 

64 EISA 2007, sec. 529(a). 65 Ibid. 66 Ibid. 

Federal Energy Regulatory Commission 69 
Chapter VII – Policy Recommendations 

Sharing of Information on Effective Program Design 

As noted in Chapter VI, improved program design represents one of the most significant means for 

improving the market penetration of demand response programs. To ensure the maximum impact for 

demand response programs, regulatory authorities and industry stakeholders should have access to tools 

and information to assist them in establishing programs that respond to their particular situation. Such 

assistance could include, for example, case studies on regulatory provisions, model state laws and retail 

tariffs, conferences and regional workshops, and technical papers on program implementation. In 

particular, many large customers complain that the wide variation in demand response programs offered 

by RTOs and by individual utilities increases their costs to monitor demand response programs, reducing 

the incentive to participate. Sharing demand response program alternatives across states or regions would 

encourage participation by large multi-state customers. 

Increasing Customer Awareness of and Education on Demand Response 

Achieving higher participation in demand response programs would require greater efforts by 

governments (federal, state and local), electric utilities and demand response providers to educate 

customers about the benefits, availability and operation of programs. Many consumers are unfamiliar 

with the benefits of demand response and may be averse to the perceived burdens of participation or risks 

in demand response programs. 67 Research shows, however, that customers who experience time varying 

rates have high levels of satisfaction and that, when offered the option of staying on such rates, most will 

do so and even recommend such rate programs to their friends. 68 Therefore, any plan to expand and 

increase participation in any type of demand response program should be accompanied by a plan to 

promote customer awareness and conduct targeted consumer education. This plan would raise awareness 

of the concept of demand response and educate consumers about the benefits of demand response, 

including an increased ability to control consumption, lower electric bills and possible environmental 

improvements. Strategies to build consumer acceptance could include marketing campaigns, customer 

outreach, coordination with the Environmental Protection Agency’s Energy Star program on energy 

efficiency, development of cost effectiveness tools and implementation of a web-based clearinghouse of 

demand response information. Budgets for prudently deployed education and marketing efforts would 

need to be fully funded, and would likely be higher under the Achievable Participation and Full 

Participation scenarios given the estimated expansion of dynamic pricing to include most ratepayers in the 

former scenario and all ratepayers in the latter scenario. 

Coordination of Wholesale and Retail Demand Response Strategy 

In order for any demand response strategy to be effective, programs at the wholesale and retail level 

should be coordinated so that wholesale and retail market designs are complementary. For example, 

changes to RTO or ISO market rules could create opportunities for retail demand response. Industry and 

regulators should develop a comprehensive strategy for demand response that, mindful of respective 

jurisdictions, includes RTO market design changes necessary to accommodate retail demand response 

programs and retail tariff and pricing changes that are consistent with wholesale market designs. 

Interoperability and Open Standards 

Advanced metering infrastructure (AMI) will be encouraged by improving and expanding 

interoperability, open standards for communications protocols and meter data reporting standards. 

Development of these standards would allow the flow of information that is currently impeded by the 

existence of multiple, competing state and local requirements. Interoperability also would enable the 

development of new technologies, such as smart appliances, to support broader application of demand 

 

67 See discussion in Appendix C, p. 218. 68 Ibid, p. 219. 

70 A National Assessment of Demand Response Potential 
Chapter VII – Policy Recommendations 

response programs and dynamic pricing. Congress recognized the need for such standards in EISA 2007, 

granting the FERC authority to approve standards developed through the NIST consensus process. 

Regulators and industry participants should continue to support the development of adequate standards 

through the ongoing NIST process. 

Coordination of Demand Response and Energy Efficiency Policies 

Policies on demand response and energy efficiency should be coordinated, as appropriate. Demand 

response actions and energy efficiency investments are linked. Customer involvement in demand 

response activities typically leads to increased attention to electricity consumption and heightened interest 

in energy efficiency. In order to ensure that demand response and energy efficiency policies do not work 

at cross purposes, these policy initiatives should be coordinated. State energy master plans like those 

developed by Maryland, Michigan, and New Jersey represent a good example of explicit incorporation 

and linkage of demand response and energy efficiency goals and policies. Major initiatives such as the 

National Action Plan for Energy Efficiency and the National Action Plan on Demand Response should be 

closely coordinated. 

Role of Demand Response in Operational and Long-Term Planning, and Recovery of 

Associated Costs 

Demand response resources can play an important role in operational and long-term planning. 

Incorporating demand response resources into planning horizons and load forecasts allows transmission 

providers and load-serving entities to depict more accurately the energy needs of their areas, thereby 

potentially deferring or offsetting costly investments in new peaking generation and transmission. 

Demand response resources can also provide an important role in real-time operations, including 

providing emergency response and ancillary services. The Commission has recognized the value of 

demand response resources in long-term and operational planning in several key recent orders. Order No. 

890 required transmission providers to establish a coordinated, open planning process that allows for the 

incorporation of demand response resources in all phases of the planning process on a basis comparable to 

other resources. 69 Order Nos. 693 and 719 recognized the ability of demand response resources to 

provide certain ancillary services when technically feasible. 70 Further integration of demand response 

will depend on a clear articulation by regulators and legislators of the expected role of demand response 

into operational and long-term planning. Key issues that should be resolved include how to account for 

and plan for customer electricity consumption changes in response to dynamic pricing, the ability of 

demand response resources to provide sustainable, long-term resources consistent with reliability 

requirements, appropriate compensation of demand response resources, and proper treatement of costs 

related to incorporation of demand response resources. 

Recommendations to Achieve Specific Demand Response Potential 

Scenarios 

Below are recommendations tailored to each scenario, consistent with the requirement to estimate how 

much of the potential can be achieved within five and ten years, accompanied by specific policy 

recommendations to achieve the potential. 

Expanded Business-as-Usual Scenario 

 

69 Preventing Undue Discrimination and Preference in Transmission Service, Order No. 890, FERC Stats. & Regs. ¶ 31,241 at P 

479, order on reh’g, Order No. 890-A, FERC Stats. & Regs. ¶ 31,261 (2007), order on reh’g, Order No. 890-B, 123 FERC ¶ 

61,299 (2008), order on reh’g, Order No. 890-C, 126 FERC ¶ 61,228 (2009). 70 Mandatory Reliability Standards for the Bulk-Power System, Order No. 693, FERC Stats. & Regs. ¶ 31,242, order on reh’g, Order 

No. 693-A, 120 FERC ¶ 61,053 (2007) and Wholesale Competition in Regions with Organized Electric Markets, Order No. 719, 

73 Fed. Reg. 61,400 (Oct. 28, 2008), FERC Stats. & Regs. ¶ 31,281 (2008), reh’g pending. 

Federal Energy Regulatory Commission 71 
 

Chapter VII – Policy Recommendations 

The increase in demand response estimated under the Expanded Business-as-Usual scenario is based on 

an assumption that current best-practice demand response programs are expanded to all states. To 

implement such an expansion of demand response activities, it would be necessary to increase 

significantly the number and extent of direct load control programs and interruptible tariffs, particularly in 

regions and states that currently lack programs. A means of broadly sharing information on the 

development, implementation and evaluation of direct load control programs and interruptible tariffs 

would be helpful to states and localities considering similar programs. Development and updating of 

model cost-effectiveness tools, particularly to include environmental challenges facing the states and the 

nation, and to reflect the existence of spot wholesale and capacity markets in many regions, would also be 

useful. 

The Expanded Business-as-Usual scenario also assumes at least some amount of participation in dynamic 

pricing at the retail level. In particular, all currently planned and announced AMI deployments would 

need to be approved and installed to achieve the estimated demand response potential. This would 

require broad-based support from utilities, governors, legislatures and state and local regulators. In 

addition, funding issues would need to be addressed in order to consider the rate impact and benefits 

associated with AMI for all customers. 

There are two additional recommendations for actions that could significantly expand the demand 

response programs necessary to achieve the potential represented by the Extended Business-as-Usual 

scenario. First, in order to encourage more aggressive participation in expanded direct load control 

programs and use of interruptible tariffs, payments to demand response resources should be designed to 

compensate them for the value they provide. Some direct load control programs and interruptible tariffs 

may not provide a sufficient financial incentive to participate. From an operational planning perspective, 

reliable and cost-effective demand response is valuable whether it is used or not, because it serves as an 

available resource that can be called upon during low probability events, such as system emergencies. 

Regulators and industry should examine compensation methods to assure that demand response is 

appropriately compensated. 

Second, development of standardized practices for quantifying demand reductions would greatly improve 

the ability of system operators to rely on demand response programs of all kinds and would minimize 

gaming opportunities. For example, payments under direct load control programs and interruptible tariffs 

are dependent on estimates of demand reductions. The lack of standards for measuring and verifying 

reductions in demand has made it difficult to plan reliably for these resources, and has fueled concern 

about potential gaming by participants. Central to the issue of measurement is a determination of the 

customer baseline, or the estimate of what metered load would have been without the reduction in 

demand. The North American Energy Standards Board (NAESB) is in the process of developing 

business practice standards for measuring and verifying energy savings and peak demand reduction in the 

wholesale and retail electric markets. Upon completion, federal and state regulators should work with 

RTOs and utilities to incorporate these standards into their processes for settlement, operations and longterm planning. In addition, efforts by states such as California to develop protocols for estimating 

demand reduction should be encouraged and possibly adopted by other states. 

Achievable Participation and Full Participation Scenarios 

The increase in demand response participation estimated under the Achievable Participation and Full 

Participation scenarios is primarily driven by widespread implementation of dynamic pricing. Universal 

deployment of AMI is assumed in every state, along with implementation of cost-effective enabling 

technologies for those customers participating in a dynamic pricing program. Examples of enabling 

technologies include in-home displays, programmable communicating thermostats, or home area 

networks. In order to achieve the demand response potential estimated in these scenarios, it would be 

necessary for utilities to adopt and implement AMI. AMI has benefits to utilities beyond the facilitation 

of dynamic pricing; for example it can substantially reduce the cost to read meters. Moreover, all 

funding-related issues associated with AMI deployment would need to be addressed. In addition to 

72 A National Assessment of Demand Response Potential 
 

 

 

Chapter VII – Policy Recommendations 

directly funding AMI and implementing technologies, tax credits or accelerated depreciation could be 

offered for investments. 

The Achievable Participation scenario assumes universal adoption of default tariffs that impose dynamic 

pricing on customers unless they expressly choose not to participate in the program. The Full 

Participation scenario assumes mandatory participation in dynamic pricing programs by all customers. 

To achieve the estimated demand response potential under either scenario, it would be necessary for retail 

regulators to modify existing electric utility rates and rate structures to implement dynamic pricing on a 

default or mandatory basis. Such rates would need to be designed to ensure that dynamic prices provide 

for adequate recovery of investments, while also offering time-varying electricity prices to customers. 

Funding for, or incentives to participate in, default dynamic pricing programs could be addressed by 

national energy policy leaders, the electric industry, consumer organizations, governors, state legislatures, 

and local and retail regulators. This is especially important as all these entities consider demand response 

programs in the context of climate change and renewable portfolio requirements. 

A significant additional barrier exists for implementation of dynamic pricing under the Achievable 

Participation or Full Participation scenarios. This report notes that dynamic pricing with enabling 

technologies is not cost-effective for all customers in all states. For example, in cooler states without a 

large presence of central air conditioning, implementation of dynamic pricing with enabling technology 

for residential customers may not be cost-effective. While the cost of some technologies, such as 

programmable communicating thermostats, has declined (they are less than one-third of the price three 

years ago), government funding may be appropriate to expedite the development and deployment of other 

innovations, such as AMI and related technologies. 

To support these scenarios, the customer education and technical assistance recommended above should 

be expanded and enhanced. Many customers, particularly residential customers, will need extensive help 

understanding the advantages of AMI and dynamic pricing. Sufficient resources should be expended 

under both the Achievable and Full Participation Scenarios to ensure that customers are comfortable with 

the new technology and are capable of adjusting their usage patterns and investment decisions. Similarly, 

better and more current information on AMI technology, costs, operational, market, and consumer 

benefits should be shared with regulators to support their decision-making on the full deployment of AMI 

and dynamic pricing. 

National Action Plan on Demand Response 

In addition to developing a national estimate of demand response that is documented in this report, EISA 

2007 requires FERC to develop a National Action Plan within one year of submission of this report. 

EISA 2007 provides that the National Action Plan will, in the context of supporting demand response, 

develop: (1) a national, customer-based communications program; (2) a technical assistance strategy to 

states; and (3) a set of tools, information and support materials for use by stakeholders. The Action Plan 

will be guided in part by the results of this Assessment. 

Federal Energy Regulatory Commission 73 

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Company, D. 08-07-045, July 31, 2008.
 

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California Energy Commission, April 2007. 

Faruqui, Ahmad and Ryan Hledik. “California’s Next Generation of Load Management Standards,” 

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Journal, April 2009. 

Federal Energy Regulatory Commission 75 
 

 

 

 

 

 

References 

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the experimental evidence," January 10, 2009. 

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76 A National Assessment of Demand Response Potential 
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Federal Energy Regulatory Commission 77 

APPENDIXA.STATEPROFILES
 

The following state profiles provide detailed information on the demand response potential projections for 

each state in the Assessment. The case studies presented in Chapter V of this report should be used as a 

guide for interpreting the results. 

Some of the state profiles make reference to the "share of peak demand" that each sector contributes. 

This refers to the fraction of the entire state peak demand that is represented by that sector. In other 

words, if a state has peak demand of 10 GW and the residential class peak demand is 4 GW, the share of 

peak demand belonging to the residential class is 40 percent. 

To provide context for interpreting the results, Table A-1 provides basic descriptive statistics for each of 

the states and the District of Columbia. 

Also, in Table A-2 and Table A-3 are summaries of the potential peak reductions from demand response 

for 2014 (year five of the analysis horizon) and 2019 (year ten of the analysis horizon) for all states, as a 

fraction of the estimated summer peak demand without demand response. (In a few instances, estimated 

growth in peak demand between 2014 and 2019 exceeds estimated growth of demand response potential 

over the same period, causing the 2014 fraction to exceed the 2019 fraction) 

Federal Energy Regulatory Commission 79 
Table A-1: Summary of Key Data by State 

Number of accounts by rate class Average peak load per customer (kW) 

Annual average gr

rate in peak (%

State 

Total 

population Residential Small C&I Medium C&I Large C&I 

System Peak 

Demand (MW) Residential Small C&I Medium C&I Large C&I Res C&

Alabama 4,661,900 2,077,677 362,448 12,354 3,801 19,000 3.4 15.1 192 748 1.6 0.Alaska 686,293 266,671 45,183 3,270 62 1,417 0.9 4.5 80 1,029 0.4 0.Arizona 6,500,180 2,567,749 280,527 15,965 1,381 18,456 3.8 16.9 165 822 0.2 0.Arkansas 2,855,390 1,301,517 199,604 6,629 3,442 9,875 2.8 9.1 93 801 1.2 0.California 36,756,666 12,971,924 1,567,550 301,662 17,772 57,137 1.2 3.2 38 555 0.8 0.Colorado 4,939,456 2,068,055 282,139 88,021 1,531 10,837 1.5 1.9 40 901 0.9 0.Connecticut 3,501,252 1,449,983 141,998 11,261 8,044 7,524 1.6 3.9 63 206 0.9 0.Delaware 873,092 390,239 47,323 1,475 374 2,503 1.9 15.2 125 951 0.4 0.District of Columbia 591,833 206,047 24,506 1,842 1,229 2,403 1.6 9.5 158 745 1.5 0.Florida 18,328,340 8,615,249 921,368 224,874 9,195 49,453 3.1 2.9 40 696 0.2 0.Georgia 9,685,744 4,039,005 483,576 66,628 11,363 28,215 3.4 5.4 60 602 0.6 0.Hawaii 1,288,198 409,581 55,808 7,482 632 1,790 1.0 4.2 45 842 0.9 0.Idaho 1,523,816 647,581 65,923 55,692 928 4,962 2.9 3.9 31 636 0.4 0.Illinois 12,901,563 5,054,895 541,263 26,791 21,435 30,465 1.7 7.3 28 450 1.3 0.Indiana 6,376,792 2,734,788 286,888 65,468 8,038 22,890 2.4 6.3 52 798 1 0.Iowa 3,002,555 1,320,241 183,320 30,471 3,507 9,169 1.9 4.1 47 709 1.6 1.Kansas 2,802,134 1,213,189 221,809 10,962 7,594 8,630 2.8 6.4 44 318 1.3 0.Kentucky 4,269,245 1,918,247 272,458 27,771 3,050 18,889 3.0 10.5 176 959 1.3 0.Louisiana 4,410,796 1,870,160 196,805 89,052 3,192 16,332 3.5 14.6 39 771 1.6 0.Maine 1,316,456 693,400 75,666 13,927 1,065 2,812 0.8 2.0 30 571 0.7 0.Maryland 5,633,597 2,187,996 230,938 17,496 4,054 13,583 2.6 13.1 32 606 0.4 0.Massachusetts 6,497,967 2,631,568 367,459 22,605 4,510 12,695 1.0 6.0 24 642 0.8 0.Michigan 10,003,422 4,336,390 485,729 44,172 10,836 23,292 1.5 6.2 48 609 1.2 0.Minnesota 5,220,393 2,283,083 189,477 75,091 10,044 14,123 1.7 3.2 42 327 1.3 1.Mississippi 2,938,618 1,222,047 228,202 1,565 2,228 9,835 3.5 8.8 78 1,215 1.6 0.Missouri 5,911,605 2,670,172 347,394 25,739 4,651 17,362 3.1 5.0 110 748 1.3 0.Montana 967,440 456,112 103,892 890 238 2,991 1.6 12.3 157 1,101 1.3 0.Nebraska 1,783,432 787,312 178,123 10,854 2,889 5,771 2.6 4.5 128 291 1.6 1.Nevada 2,600,167 1,079,306 145,469 4,497 1,963 7,538 3.1 12.1 112 931 0.2 0.New Hampshire 1,315,809 600,399 102,868 831 1,875 2,539 1.1 4.7 32 306 0.2 0.New Jersey 8,682,661 3,414,289 461,304 10,998 10,375 17,273 2.2 7.1 77 395 0.8 0.New Mexico 1,984,356 829,100 122,560 16,755 1,296 4,671 1.3 4.8 61 707 1.2 0.New York 19,490,297 6,855,544 958,009 66,351 5,265 33,809 1.3 5.7 81 820 0.8 0.North Carolina 9,222,414 4,128,231 619,832 29,169 3,277 26,548 3.2 5.6 168 1,373 0.5 0.North Dakota 641,481 310,222 54,365 2,211 699 2,379 2.2 9.7 129 614 1.6 1.Ohio 11,485,910 4,908,791 569,999 59,607 13,010 33,238 2.0 8.5 65 604 1.3 0.Oklahoma 3,642,361 1,629,818 243,831 30,398 3,097 11,919 3.3 3.8 70 778 1.2 0.Oregon 3,790,060 1,610,829 220,262 36,132 1,521 10,476 1.9 4.5 75 680 0.7 0.Pennsylvania 12,448,279 5,217,010 618,439 75,656 10,577 31,488 1.7 8.2 43 644 1.2 0.Rhode Island 1,050,788 432,307 48,623 8,614 864 1,785 1.0 2.7 32 393 0.8 0.South Carolina 4,479,800 2,028,361 326,244 15,666 2,327 16,947 3.6 7.6 172 1,696 1 0.South Dakota 804,194 355,714 66,375 658 875 2,128 2.2 9.3 87 402 1.6 1.Tennessee 6,214,888 2,660,110 428,663 30,312 3,735 22,475 3.9 11.5 186 376 1 0.Texas 24,326,974 9,397,317 1,269,490 411,961 5,756 72,723 3.3 3.7 47 2,086 0.3 0.Utah 2,736,424 911,744 103,864 16,754 791 5,742 1.6 4.9 86 1,322 0.4 0.Vermont 621,270 310,842 46,230 3,075 313 1,085 0.9 2.2 49 773 0.6 0.Virginia 7,769,089 3,170,126 369,208 32,352 7,886 22,412 2.5 4.6 88 708 0.7 0.Washington 6,549,224 2,762,275 345,256 26,145 3,568 18,538 1.8 6.5 110 771 0.6 0.West Virginia 1,814,468 855,919 135,823 11,181 1,199 6,916 2.3 6.3 78 1,431 1.6 0.Wisconsin 5,627,967 2,581,840 290,192 44,419 4,518 14,845 1.4 4.1 61 782 1.5 0.Wyoming 532,668 245,648 61,758 3,587 585 3,236 1.7 14.9 66 1,551 1.6 0.Appendix A – State Profiles 80 A National Assessment 
 - 

 

 

 

 

Appendix A – State Profiles 

Table A-2: Potential Peak Demand Reduction by State (2014) 

Business-as 

Usual 

Expanded 

BAU 

Achievable 

Participation 

Full 

Participation 

Alabama 6% 10% 13% 17% 

Alaska 

Arizona 

0% 2% 2% 2% 

1% 5% 14% 22% 

Arkansas 

California 

3% 13% 13% 14% 

7% 7% 12% 16% 

Colorado 

Connecticut 

4% 5% 6% 7% 

17% 22% 23% 24% 

Delaware 

District of Columbia 

4% 7% 11% 15% 

8% 18% 18% 21% 

Florida 

Georgia 

5% 9% 13% 17% 

4% 12% 16% 19% 

Hawaii 

Idaho 

2% 5% 7% 9% 

1% 6% 11% 15% 

Illinois 

Indiana 

7% 9% 9% 9% 

5% 7% 8% 10% 

Iowa 

Kansas 

6% 9% 10% 12% 

3% 7% 8% 9% 

Kentucky 

Louisiana 

2% 5% 6% 7% 

0% 5% 6% 7% 

Maine 

Maryland 

17% 19% 20% 21% 

11% 14% 22% 28% 

Massachusetts 

Michigan 

7% 10% 11% 11% 

8% 13% 14% 15% 

Minnesota 

Mississippi 

12% 13% 15% 16% 

1% 7% 8% 9% 

Missouri 

Montana 

1% 9% 11% 13% 

0% 4% 4% 5% 

Nebraska 

Nevada 

10% 14% 14% 15% 

0% 9% 10% 12% 

New Hampshire 

New Jersey 

4% 8% 8% 8% 

4% 8% 9% 10% 

New Mexico 

New York 

1% 6% 6% 7% 

8% 9% 10% 11% 

North Carolina 

North Dakota 

5% 10% 10% 11% 

1% 5% 6% 6% 

Ohio 

Oklahoma 

1% 11% 12% 12% 

0% 9% 10% 10% 

Oregon 

Pennsylvania 

0% 3% 6% 9% 

7% 11% 14% 16% 

Rhode Island 

South Carolina 

7% 10% 11% 11% 

4% 9% 10% 11% 

South Dakota 

Tennessee 

1% 6% 6% 6% 

5% 8% 9% 9% 

Texas 

Utah 

1% 8% 12% 16% 

8% 12% 13% 14% 

Vermont 

Virginia 

8% 9% 10% 11% 

1% 6% 7% 8% 

Washington 

West Virginia 

0% 4% 5% 7% 

3% 10% 10% 11% 

Wisconsin 

Wyoming 

1% 5% 6% 7% 

0% 6% 7% 7% 

Federal Energy Regulatory Commission 81 
 - 

 

 

 

 

Appendix A – State Profiles 

Table A-3: Potential Peak Demand Reduction by State (2019) 

Business-as 

Usual 

Expanded 

BAU 

Achievable 

Participation 

Full 

Participation 

Alabama 5% 10% 15% 21% 

Alaska 

Arizona 

0% 2% 5% 7% 

1% 5% 18% 28% 

Arkansas 

California 

2% 13% 17% 21% 

6% 7% 13% 17% 

Colorado 

Connecticut 

3% 5% 12% 17% 

16% 21% 26% 29% 

Delaware 

District of Columbia 

4% 7% 13% 19% 

7% 18% 17% 20% 

Florida 

Georgia 

5% 9% 18% 25% 

3% 12% 18% 25% 

Hawaii 

Idaho 

2% 5% 8% 11% 

1% 6% 14% 21% 

Illinois 

Indiana 

6% 8% 12% 15% 

5% 7% 13% 18% 

Iowa 

Kansas 

5% 8% 13% 17% 

2% 7% 13% 17% 

Kentucky 

Louisiana 

1% 5% 11% 18% 

0% 5% 12% 18% 

Maine 

Maryland 

16% 19% 22% 24% 

11% 13% 24% 32% 

Massachusetts 

Michigan 

7% 10% 14% 17% 

8% 12% 14% 16% 

Minnesota 

Mississippi 

12% 13% 16% 19% 

1% 7% 13% 19% 

Missouri 

Montana 

1% 9% 14% 19% 

0% 4% 9% 14% 

Nebraska 

Nevada 

9% 13% 19% 24% 

0% 9% 18% 26% 

New Hampshire 

New Jersey 

3% 8% 10% 13% 

4% 8% 12% 18% 

New Mexico 

New York 

1% 6% 11% 15% 

7% 9% 13% 17% 

North Carolina 

North Dakota 

4% 10% 17% 25% 

1% 5% 10% 14% 

Ohio 

Oklahoma 

1% 11% 14% 17% 

0% 9% 14% 19% 

Oregon 

Pennsylvania 

0% 3% 9% 14% 

7% 10% 15% 19% 

Rhode Island 

South Carolina 

7% 10% 13% 16% 

4% 9% 17% 23% 

South Dakota 

Tennessee 

1% 6% 12% 17% 

4% 8% 17% 24% 

Texas 

Utah 

1% 8% 15% 21% 

7% 12% 18% 23% 

Vermont 

Virginia 

7% 8% 11% 13% 

1% 6% 11% 16% 

Washington 

West Virginia 

0% 4% 9% 12% 

3% 10% 13% 18% 

Wisconsin 

Wyoming 

1% 5% 8% 11% 

0% 6% 9% 12% 

82 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

Alabama State Profile 

Key drivers of Alabama’s demand response potential estimate include: higher-than-average residential 

CAC saturation of 62 percent, a customer mix that has an above average share of peak demand in the 

Small C&I class (31%), a moderate amount of existing Interruptible Tariffs for the Large C&I class, 

and the potential to deploy AMI at a faster-than-average rate. Enabling technologies and DLC are cost 

effective for all customer classes in the state. Most of the growth potential in demand response comes 

from the Residential class. 

BAU: Alabama’s existing demand response comes primarily from a large Interruptible Tariff program 

for Large C&I customers. 

Expanded BAU: Growth in demand response impacts is driven primarily through two sources. There 

is the addition of Other DR programs for the Large C&I class, which currently do not exist in the state. 

In addition, there is a lot of growth potential for DLC in the Residential class due higher-than-average 

residential CAC saturation. 

Achievable Participation: High CAC saturation in the Residential class drives a significant increase in 

demand response potential through dynamic pricing with and without enabling technologies. Large 

C&I demand response potential is not significantly higher than in the Expanded BAU scenario due to 

smaller per-customer impacts from pricing with technology relative to Other DR. 

Full Participation: Similar to the Achievable Participation scenario, high CAC saturation in the 

Residential class drives the increase in impacts. The growth in impacts from the base BAU scenario are 

dominated by pricing with enabling technologies, which are cost-effective for all customer classes. 

0 

500 

1,000 

1,500 

2,000 

2,500 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 23.3 GW) 

Alabama DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 83 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Alabama, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

16 0.1% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

16 0.1% 

0 0.0% 

42 0.2% 

559 2.4% 

0 0.0% 

0 0.0% 

601 2.6% 

825 3.5% 

453 1.9% 

145 0.6% 

0 0.0% 

0 0.0% 

1,422 6.1% 

1,929 8.3% 

130 0.6% 

16 0.1% 

0 0.0% 

0 0.0% 

2,074 8.9% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

13 0.1% 

0 0.0% 

0 0.0% 

15 0.1% 

312 1.3% 

17 0.1% 

3 0.0% 

0 0.0% 

0 0.0% 

333 1.4% 

730 3.1% 

9 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

739 3.2% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

8 0.0% 

9 0.0% 

33 0.1% 

0 0.0% 

50 0.2% 

110 0.5% 

73 0.3% 

4 0.0% 

33 0.1% 

0 0.0% 

221 0.9% 

322 1.4% 

36 0.2% 

0 0.0% 

33 0.1% 

0 0.0% 

391 1.7% 

0 0.0% 

10 0.0% 

0 0.0% 

1,224 5.2% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

1,234 5.3% 

0 0.0% 

10 0.0% 

0 0.0% 

1,311 5.6% 

271 1.2% 

1,592 6.8% 

58 0.2% 

105 0.5% 

0 0.0% 

1,311 5.6% 

112 0.5% 

1,586 6.8% 

169 0.7% 

136 0.6% 

0 0.0% 

1,311 5.6% 

0 0.0% 

1,616 6.9% 

0 0.0% 

10 0.0% 

16 0.1% 

1,224 5.2% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

1,250 5.4% 

0 0.0% 

61 0.3% 

581 2.5% 

1,345 5.8% 

271 1.2% 

2,258 9.7% 

1,305 5.6% 

648 2.8% 

152 0.6% 

1,345 5.8% 

112 0.5% 

3,562 15.3% 

3,150 13.5% 

310 1.3% 

16 0.1% 

1,345 5.8% 

0 0.0% 

4,821 20.6% 

Alabama System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

84 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

Alaska State Profile 

Key drivers of Alaska’s demand response potential estimate include: very low residential CAC 

saturation, a customer mix that has an above average share of peak demand in the Small and Medium 

C&I classes (26% and 34%, respectively), a small amount of existing demand response, and the 

expectation that it will deploy AMI at a lower-than-average rate. Enabling technologies are cost 

effective for all C&I classes in the state, but not for the residential class. 

BAU: Alaska’s existing demand response comes from two sources. In the Residential class, there is a 

small amount of non-air conditioning DLC, and in the Medium C&I class, there is a small amount of 

Other DR. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of 

Interruptible Tariffs programs for the Large C&I class, which currently do not exist in the state. Within 

the Large C&I class, demand response is split between Interruptible Tariffs and Other DR. The only 

other substantial growth in demand response comes from Interruptible Tariffs in the Medium C&I class. 

Achievable Participation: A significant increase in demand response potential comes from dynamic 

pricing with and without enabling technology. However, for the Large C&I class specifically, demand 

response potential does not change significantly from Expanded BAU scenario due to smaller percustomer impacts from pricing relative to Other DR. Since enabling technology did not prove to be 

cost-effective in the Residential sector, all of the pricing impacts are without enabling technology. 

Full Participation: Similar to the Achievable Participation scenario, a significant increase in demand 

response potential comes from dynamic pricing. The majority of the statewide impacts come from 

pricing with enabling technologies, which are cost-effective for all customer classes except Residential. 

0 

5 

10 

15 

20 

25 

30 

35 

40 

45 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 1.7 GW) 

Alaska DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 85 
Total Potential Peak Reduction from Demand Response in Alaska, 2019 

Small Med Large Residential Small Med. Large Total Residential C&I C&I C&I Total (% of C&I C&I C&I (% of (MW) (% of (% of (% of (MW) system) (MW) (MW) (MW) system) system) system) system) 

 

BAU 

Pricing with Technology 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0%

Pricing without Technology 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0%

Automated/Direct Load Control 4 0.2% 0 0.0% 0 0.0% 0 0.0% 4 0.2%

Interruptible/Curtailable Tariffs 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0%

Other DR Programs 0 0.0% 0 0.0% 0 0.0% 3 0.2% 3 0.2%

Total 4 0.2% 0 0.0% 0 0.0% 3 0.2% 7 0.4%

 

Expanded BAU 

Pricing with Technology 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0%

Pricing without Technology 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.0%

Automated/Direct Load Control 4 0.2% 1 0.1% 1 0.1% 0 0.0% 6 0.4%

Interruptible/Curtailable Tariffs 0 0.0% 0 0.0% 4 0.2% 9 0.5% 12 0.7%

Other DR Programs 0 0.0% 0 0.0% 0 0.0% 6 0.4% 6 0.4%

Total 4 0.3% 1 0.1% 5 0.3% 15 0.9% 26 1.5%

 

Achievable Participation 

Pricing with Technology 0 0.0% 10 0.6% 11 0.7% 1 0.1% 23 1.4%

Pricing without Technology 22 1.3% 1 0.0% 9 0.5% 2 0.1% 34 2.0%

Automated/Direct Load Control 4 0.2% 0 0.0% 1 0.0% 0 0.0% 5 0.3%

Interruptible/Curtailable Tariffs 0 0.0% 0 0.0% 4 0.2% 9 0.5% 12 0.7%

Other DR Programs 0 0.0% 0 0.0% 0 0.0% 3 0.2% 3 0.2%

Total 26 1.6% 11 0.7% 24 1.4% 15 0.9% 77 4.6%

 

Full Participation 

Pricing with Technology 0 0.0% 24 1.5% 33 2.0% 4 0.2% 61 3.7%

Pricing without Technology 29 1.8% 0 0.0% 5 0.3% 3 0.2% 38 2.3%

Automated/Direct Load Control 4 0.2% 0 0.0% 0 0.0% 0 0.0% 4 0.2%

Interruptible/Curtailable Tariffs 0 0.0% 0 0.0% 4 0.2% 9 0.5% 12 0.7%

Other DR Programs 0 0.0% 0 0.0% 0 0.0% 3 0.2% 3 0.2%

Total 33 2.0% 25 1.5% 42 2.5% 19 1.1% 119 7.1%

Alaska System Peak Demand Forecasts by Scenario 

1,300 

1,350 

1,400 

1,450 

1,500 

1,550 

1,600 

1,650 

1,700 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

Appendix A – State Profiles 

86 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

Arizona State Profile 

Key drivers of Arizona’s demand response potential estimate include: higher-than-average residential 

CAC saturation of 87 percent, a customer mix that has an above average share of peak demand in the 

Residential and Small C&I classes (54% and 26%, respectively), a small amount of existing demand 

response, and the potential to deploy AMI at a faster-than-average rate. Enabling technologies and 

DLC are cost effective for all customer classes in the state. This cost-effectiveness, high residential 

CAC saturation and a large proportion of customers in the Residential and Small C&I sectors means 

that control of CAC load will be the key driver of demand response growth in Arizona. 

BAU: Arizona’s existing demand response comes primarily from a small Interruptible Tariffs program 

for large C&I customers. Note that Arizona has the largest residential TOU program in the U.S., but for 

reasons described previously in the report, TOU rates are excluded as a demand response option in this 

analysis. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of DLC 

programs for the Residential class, which currently do not exist in the state. This growth is due to 

Arizona’s high share of Residential load and high CAC saturation rate. 

Achievable Participation: High CAC saturation in the Residential sector drives a significant increase 

in demand response potential through dynamic pricing with and without enabling technologies. 

Full Participation: Similar to the Achievable Participation scenario, high CAC saturation combined 

with a large share of load in the Residential sector drives the increase in impacts. The impacts are 

dominated by pricing with enabling technologies, which are cost-effective for all customer classes. 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

3,500 

4,000 

4,500 

5,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 22.4 GW) 

Arizona DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 87 
 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Arizona, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

4 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

4 0.0% 

0 0.0% 

114 0.5% 

636 2.8% 

0 0.0% 

0 0.0% 

750 3.3% 

2,003 8.9% 

913 4.1% 

166 0.7% 

0 0.0% 

0 0.0% 

3,082 13.7% 

4,685 20.9% 

67 0.3% 

4 0.0% 

0 0.0% 

0 0.0% 

4,755 21.2% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

2 0.0% 

6 0.0% 

0 0.0% 

0 0.0% 

7 0.0% 

254 1.1% 

17 0.1% 

1 0.0% 

0 0.0% 

0 0.0% 

273 1.2% 

595 2.7% 

11 0.1% 

0 0.0% 

0 0.0% 

0 0.0% 

606 2.7% 

0 0.0% 

0 0.0% 

0 0.0% 

6 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

6 0.0% 

0 0.0% 

11 0.1% 

6 0.0% 

55 0.2% 

0 0.0% 

74 0.3% 

119 0.5% 

91 0.4% 

3 0.0% 

55 0.2% 

0 0.0% 

269 1.2% 

349 1.6% 

58 0.3% 

0 0.0% 

55 0.2% 

0 0.0% 

462 2.1% 

0 0.0% 

0 0.0% 

0 0.0% 

184 0.8% 

30 0.1% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

214 1.0% 

0 0.0% 

4 0.0% 

0 0.0% 

220 1.0% 

112 0.5% 

336 1.5% 

24 0.1% 

44 0.2% 

0 0.0% 

220 1.0% 

47 0.2% 

334 1.5% 

70 0.3% 

57 0.3% 

0 0.0% 

220 1.0% 

30 0.1% 

377 1.7% 

0 0.0% 

0 0.0% 

4 0.0% 

189 0.8% 

30 0.1% 

Total 

(MW) 

Total 

(% of 

system) 

223 1.0% 

0 0.0% 

130 0.6% 

648 2.9% 

275 1.2% 

112 0.5% 

1,166 5.2% 

2,400 10.7% 

1,065 4.8% 

170 0.8% 

275 1.2% 

47 0.2% 

3,957 17.7% 

5,698 25.4% 

193 0.9% 

4 0.0% 

275 1.2% 

30 0.1% 

6,200 27.7% 

Arizona System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

88 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

Arkansas State Profile 

Key drivers of Arkansas’s demand response potential estimate include: average residential CAC 

saturation of 55 percent, a customer mix that has an above average share of peak demand in the small 

and Large C&I classes (21% and 31%, respectively), a small amount of existing demand response, and 

the expectation that it will deploy AMI at a slightly lower-than-average rate. Enabling technologies and 

DLC are cost effective for all customer classes in the state. 

BAU: Arkansas’s existing demand response comes from all customer classes, but none of these 

programs are that large. DLC in all but the Large C&I class contributes the majority of the total. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of 

Other DR programs and Interruptible Tariffs for the Large C&I class. This high growth is due to 

Arkansas’s high share of Large C&I load. 

Achievable Participation: CAC saturation in the Residential sector drives a significant increase in 

demand response potential through dynamic pricing with and without enabling technologies. Large 

C&I demand response potential is slightly lower than in the Expanded BAU scenario due to smaller 

per-customer impacts from pricing with technology relative to Other DR. 

Full Participation: Similar to the Achievable Participation scenario, CAC saturation drives the 

increase in impacts. The impacts are dominated by pricing with enabling technologies, which are costeffective for all customer classes. Interruptible Tariffs in the Large C&I sector remain a significant 

portion of overall impacts and a key source of growth from BAU. 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 12 GW) 

Arkansas DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 89 
 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Arkansas, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

69 0.6% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

69 0.6% 

0 0.0% 

13 0.1% 

202 1.7% 

0 0.0% 

0 0.0% 

215 1.8% 

418 3.5% 

246 2.0% 

69 0.6% 

0 0.0% 

0 0.0% 

733 6.1% 

978 8.1% 

87 0.7% 

69 0.6% 

0 0.0% 

0 0.0% 

1,134 9.4% 

0 0.0% 

0 0.0% 

120 1.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

120 1.0% 

0 0.0% 

0 0.0% 

120 1.0% 

0 0.0% 

0 0.0% 

120 1.0% 

106 0.9% 

6 0.0% 

120 1.0% 

0 0.0% 

0 0.0% 

232 1.9% 

248 2.1% 

3 0.0% 

120 1.0% 

0 0.0% 

0 0.0% 

371 3.1% 

0 0.0% 

0 0.0% 

13 0.1% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

13 0.1% 

0 0.0% 

1 0.0% 

13 0.1% 

9 0.1% 

0 0.0% 

23 0.2% 

29 0.2% 

19 0.2% 

13 0.1% 

9 0.1% 

0 0.0% 

70 0.6% 

85 0.7% 

9 0.1% 

13 0.1% 

9 0.1% 

0 0.0% 

117 1.0% 

0 0.0% 

0 0.0% 

0 0.0% 

79 0.7% 

13 0.1% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

92 0.8% 

0 0.0% 

5 0.0% 

0 0.0% 

536 4.5% 

643 5.3% 

1,184 9.8% 

57 0.5% 

104 0.9% 

0 0.0% 

536 4.5% 

262 2.2% 

960 8.0% 

167 1.4% 

135 1.1% 

0 0.0% 

536 4.5% 

13 0.1% 

852 7.1% 

0 0.0% 

0 0.0% 

202 1.7% 

79 0.7% 

13 0.1% 

Total 

(MW) 

Total 

(% of 

system) 

295 2.4% 

0 0.0% 

19 0.2% 

336 2.8% 

545 4.5% 

643 5.3% 

1,543 12.8% 

611 5.1% 

375 3.1% 

202 1.7% 

545 4.5% 

262 2.2% 

1,996 16.6% 

1,479 12.3% 

235 2.0% 

202 1.7% 

545 4.5% 

13 0.1% 

2,474 20.6% 

Arkansas System Peak Demand Forecasts by Scenario 

0 

2,000 

4,000 

6,000 

8,000 

10,000 

12,000 

14,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

90 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

California State Profile 

Key drivers of California’s demand response potential estimate include: lower-than-average residential 

CAC saturation of 41 percent, a customer mix that has an above average share of peak demand in the 

Medium and Large C&I classes (50% combined), a large amount of existing demand response, and the 

potential to deploy AMI at a faster-than-average rate. DLC is cost effective for all customer classes in 

the state. Enabling technologies are not cost effective for the Small C&I class. 

BAU: California’s existing demand response comes from three major sources – Interruptible Tariffs 

and Other DR in the Large C&I class and DLC in the Residential class. In addition, there is moderate 

demand response in place in the Small and Medium C&I classes, as well as some dynamic pricing. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of 

Other DR programs for the Large C&I class. This is due to California’s high share of Large C&I load, 

which would also allow for significant growth in the existing Interruptible Tariff. Demand response 

potential in the Large C&I class is nearly the same as in the BAU scenario. 

Achievable Participation: Dynamic pricing with technology in the Residential class drives a 

significant increase in demand response potential. Large C&I demand response potential is slightly 

higher than in the Expanded BAU scenario. 

Full Participation: Similar to the Achievable Participation scenario, dynamic pricing with technology 

in the Residential sector drives a significant increase in demand response potential. Demand response 

potential in the Large C&I class is nearly the same as in the Achievable Participation scenario. 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

7,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 69.6 GW) 

California DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 91 
 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in California, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

391 0.6% 

970 1.4% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

1,361 2.0% 

0 0.0% 

391 0.6% 

970 1.4% 

0 0.0% 

0 0.0% 

1,361 2.0% 

1,931 2.8% 

1,400 2.0% 

970 1.4% 

0 0.0% 

0 0.0% 

4,302 6.2% 

4,518 6.5% 

757 1.1% 

970 1.4% 

0 0.0% 

0 0.0% 

6,245 9.0% 

0 0.0% 

21 0.0% 

36 0.1% 

0 0.0% 

31 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

88 0.1% 

0 0.0% 

21 0.0% 

42 0.1% 

0 0.0% 

31 0.0% 

94 0.1% 

0 0.0% 

29 0.0% 

36 0.1% 

0 0.0% 

31 0.0% 

96 0.1% 

0 0.0% 

38 0.1% 

36 0.1% 

0 0.0% 

31 0.0% 

105 0.2% 

0 0.0% 

108 0.2% 

45 0.1% 

25 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

177 0.3% 

0 0.0% 

108 0.2% 

152 0.2% 

233 0.3% 

1 0.0% 

494 0.7% 

500 0.7% 

382 0.5% 

67 0.1% 

233 0.3% 

1 0.0% 

1,182 1.7% 

1,462 2.1% 

243 0.3% 

45 0.1% 

233 0.3% 

0 0.0% 

1,983 2.8% 

0 0.0% 

13 0.0% 

0 0.0% 

1,626 2.3% 

1,012 1.5% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

2,651 3.8% 

0 0.0% 

36 0.1% 

0 0.0% 

1,626 2.3% 

1,012 1.5% 

2,674 3.8% 

205 0.3% 

372 0.5% 

0 0.0% 

1,626 2.3% 

1,012 1.5% 

3,215 4.6% 

598 0.9% 

482 0.7% 

0 0.0% 

1,626 2.3% 

1,012 1.5% 

3,719 5.3% 

0 0.0% 

532 0.8% 

1,050 1.5% 

1,651 2.4% 

1,043 1.5% 

Total 

(MW) 

Total 

(% of 

system) 

4,276 6.1% 

0 0.0% 

556 0.8% 

1,163 1.7% 

1,859 2.7% 

1,044 1.5% 

4,622 6.6% 

2,636 3.8% 

2,184 3.1% 

1,072 1.5% 

1,859 2.7% 

1,043 1.5% 

8,795 12.6% 

6,578 9.4% 

1,521 2.2% 

1,050 1.5% 

1,859 2.7% 

1,043 1.5% 

12,052 17.3% 

California System Peak Demand Forecasts by Scenario 

0 

10,000 

20,000 

30,000 

40,000 

50,000 

60,000 

70,000 

80,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW)

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

92 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

Colorado State Profile 

Key drivers of Colorado’s demand response potential estimate include: lower-than-average residential 

CAC saturation of 47 percent, a customer mix that has an above average share of peak demand in 

Medium and Large C&I (57% combined), a moderate amount of existing demand response, and the 

expectation that it will deploy AMI at a slightly lower-than-average rate. DLC is cost effective for all 

customer classes in the state. Enabling technologies are not cost effective for the Small C&I class. 

BAU: Colorado’s existing demand response comes primarily from DLC for Residential and Medium 

C&I customers. An Interruptible Tariff program for Large C&I customers also contributes significantly 

to the total. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of 

Other DR programs for the Large C&I class. In addition, the Medium C&I class provides some 

Interruptible Tariffs demand response. 

Achievable Participation: The Residential class and a large proportion of customers in the Medium 

C&I sector drive a significant increase in demand response potential through dynamic pricing with and 

without enabling technologies. Large C&I demand response potential is slightly lower than in the 

Expanded BAU scenario due to smaller per-customer impacts from pricing with technology relative to 

Other DR. 

Full Participation: Similar to the Achievable Participation scenario, customers in the Residential and 

Medium C&I sectors drive the increase in impacts. The impacts are dominated by pricing with 

enabling technology for Residential and Medium C&I customers. 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 13.2 GW) 

Colorado DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 93 
 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Colorado, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

114 0.9% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

114 0.9% 

0 0.0% 

15 0.1% 

145 1.1% 

0 0.0% 

0 0.0% 

159 1.2% 

409 3.1% 

273 2.1% 

114 0.9% 

0 0.0% 

0 0.0% 

796 6.0% 

958 7.3% 

128 1.0% 

114 0.9% 

0 0.0% 

0 0.0% 

1,200 9.1% 

0 0.0% 

1 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

2 0.0% 

0 0.0% 

1 0.0% 

7 0.1% 

0 0.0% 

0 0.0% 

7 0.1% 

0 0.0% 

3 0.0% 

2 0.0% 

0 0.0% 

0 0.0% 

5 0.0% 

0 0.0% 

4 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

5 0.0% 

0 0.0% 

0 0.0% 

177 1.3% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

177 1.3% 

0 0.0% 

8 0.1% 

177 1.3% 

52 0.4% 

0 0.0% 

237 1.8% 

159 1.2% 

122 0.9% 

177 1.3% 

52 0.4% 

0 0.0% 

510 3.9% 

465 3.5% 

77 0.6% 

177 1.3% 

52 0.4% 

0 0.0% 

772 5.8% 

0 0.0% 

11 0.1% 

0 0.0% 

104 0.8% 

20 0.2% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

135 1.0% 

0 0.0% 

11 0.1% 

0 0.0% 

104 0.8% 

140 1.1% 

255 1.9% 

29 0.2% 

53 0.4% 

0 0.0% 

104 0.8% 

57 0.4% 

244 1.8% 

86 0.6% 

69 0.5% 

0 0.0% 

104 0.8% 

20 0.2% 

278 2.1% 

0 0.0% 

12 0.1% 

292 2.2% 

104 0.8% 

20 0.2% 

Total 

(MW) 

Total 

(% of 

system) 

428 3.2% 

0 0.0% 

34 0.3% 

329 2.5% 

156 1.2% 

140 1.1% 

659 5.0% 

598 4.5% 

451 3.4% 

293 2.2% 

156 1.2% 

57 0.4% 

1,555 11.8% 

1,509 11.4% 

279 2.1% 

292 2.2% 

156 1.2% 

20 0.2% 

2,256 17.1% 

Colorado System Peak Demand Forecasts by Scenario 

0 

2,000 

4,000 

6,000 

8,000 

10,000 

12,000 

14,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW)

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

94 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

Connecticut State Profile 

Key drivers of Connecticut’s demand response potential estimate include: lower-than-average 

residential CAC saturation of 27 percent, a customer mix that has an above average share of peak 

demand in the Residential and Large C&I classes (45% and 31%, respectively), a large amount of 

existing demand response in the Medium and Large C&I sectors (especially Other DR), and the 

expectation that it will deploy AMI at a slightly lower-than-average rate. DLC is cost effective for all 

customer classes in the state. Enabling technologies are not cost effective for the Small and Large C&I 

classes. 

BAU: Connecticut’s existing demand response comes primarily from Other DR for Medium and Large 

C&I customers, the bulk of which is in the ISO New England forward capacity market. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of 

Interruptible Tariffs for the Large C&I class, which currently do not exist in the state. This high growth 

is due to Connecticut’s large share of Large C&I load. 

Achievable Participation: The Residential class drives a significant increase in demand response 

potential through dynamic pricing with and without enabling technologies. Large C&I demand 

response potential is slightly higher than in the Expanded BAU scenario. 

Full Participation: Similar to the Achievable Participation scenario, a large share of load in the 

Residential class drives the increase in impacts. Since CAC saturation is lower than average, the 

growth the Residential sector is not as much as is seen in hotter states for this scenario. The Large C&I 

class does not experience any growth in pricing with enabling technology because it is not cost effective 

for that class. Overall, the incremental increase in potential is small relative to the BAU. 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 8.5 GW) 

Connecticut DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 95 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Connecticut, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

7 0.1% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

7 0.1% 

0 0.0% 

9 0.1% 

104 1.2% 

0 0.0% 

0 0.0% 

113 1.3% 

195 2.3% 

154 1.8% 

27 0.3% 

0 0.0% 

0 0.0% 

376 4.4% 

457 5.4% 

93 1.1% 

7 0.1% 

0 0.0% 

0 0.0% 

557 6.5% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

3 0.0% 

0 0.0% 

0 0.0% 

3 0.0% 

0 0.0% 

3 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

4 0.0% 

0 0.0% 

4 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

4 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

130 1.5% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

130 1.5% 

0 0.0% 

2 0.0% 

4 0.1% 

9 0.1% 

130 1.5% 

146 1.7% 

29 0.3% 

22 0.3% 

2 0.0% 

9 0.1% 

130 1.5% 

193 2.3% 

84 1.0% 

15 0.2% 

0 0.0% 

9 0.1% 

130 1.5% 

239 2.8% 

0 0.0% 

0 0.0% 

0 0.0% 

3 0.0% 

1,229 14.4% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

1,233 14.5% 

0 0.0% 

3 0.0% 

0 0.0% 

303 3.6% 

1,229 14.4% 

1,536 18.0% 

0 0.0% 

75 0.9% 

0 0.0% 

303 3.6% 

1,229 14.4% 

1,608 18.9% 

0 0.0% 

125 1.5% 

0 0.0% 

303 3.6% 

1,229 14.4% 

1,658 19.5% 

0 0.0% 

0 0.0% 

7 0.1% 

3 0.0% 

1,360 16.0% 

Total 

(MW) 

Total 

(% of 

system) 

1,369 16.1% 

0 0.0% 

14 0.2% 

111 1.3% 

313 3.7% 

1,360 16.0% 

1,798 21.1% 

224 2.6% 

255 3.0% 

29 0.3% 

313 3.7% 

1,360 16.0% 

2,181 25.6% 

541 6.4% 

237 2.8% 

7 0.1% 

313 3.7% 

1,360 16.0% 

2,458 28.9% 

Connecticut System Peak Demand Forecasts by Scenario 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

7,000 

8,000 

9,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW)

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

96 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

Delaware State Profile 

Key drivers of Delaware’s demand response potential estimate include: average residential CAC 

saturation of around 55 percent, a customer mix that has an above average share of peak demand in the 

Small C&I class (36%), a moderate amount of existing demand response in the Large C&I class though 

Other DR, and the potential to deploy AMI at a faster-than-average rate. DLC and enabling 

technologies are cost effective for all customer classes in the state. 

BAU: Delaware’s existing demand response comes primarily from a large Other DR program for 

Large C&I customers. In addition, there is a moderate amount of DLC in the Residential class. Small 

and Medium C&I have any demand response. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of DLC 

programs for the Residential class and Interruptible Tariffs for the Large C&I class, which currently do 

not exist in the state. Although Delaware has a large share of Small C&I load, there is not much growth 

in that customer class in this scenario. 

Achievable Participation: CAC saturation in the Residential class drives a significant increase in 

demand response potential through dynamic pricing with enabling technology. The Small C&I class 

shows some growth through dynamic pricing with enabling technology. 

Full Participation: Similar to the Achievable Participation scenario, residential CAC saturation 

combined with a large share of load in the Small C&I class drives the increase in impacts. Medium and 

Large C&I also show an increase due to pricing with enabling technology. 

0 

50 

100 

150 

200 

250 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 2.9 GW) 

Delaware DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 97 
 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Delaware, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

18 0.6% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

18 0.6% 

0 0.0% 

5 0.2% 

44 1.5% 

0 0.0% 

0 0.0% 

50 1.7% 

84 2.9% 

52 1.8% 

18 0.6% 

0 0.0% 

0 0.0% 

154 5.3% 

196 6.7% 

22 0.8% 

18 0.6% 

0 0.0% 

0 0.0% 

235 8.1% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

41 1.4% 

2 0.1% 

0 0.0% 

0 0.0% 

0 0.0% 

44 1.5% 

96 3.3% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

97 3.4% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

3 0.1% 

0 0.0% 

4 0.1% 

9 0.3% 

6 0.2% 

0 0.0% 

3 0.1% 

0 0.0% 

17 0.6% 

25 0.9% 

3 0.1% 

0 0.0% 

3 0.1% 

0 0.0% 

30 1.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

100 3.4% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

100 3.4% 

0 0.0% 

1 0.0% 

0 0.0% 

48 1.7% 

100 3.4% 

150 5.2% 

7 0.2% 

13 0.5% 

0 0.0% 

48 1.7% 

100 3.4% 

169 5.8% 

21 0.7% 

17 0.6% 

0 0.0% 

48 1.7% 

100 3.4% 

187 6.4% 

0 0.0% 

0 0.0% 

18 0.6% 

0 0.0% 

100 3.4% 

Total 

(MW) 

Total 

(% of 

system) 

118 4.1% 

0 0.0% 

7 0.3% 

46 1.6% 

51 1.8% 

100 3.4% 

204 7.0% 

141 4.8% 

74 2.5% 

18 0.6% 

51 1.8% 

100 3.4% 

384 13.2% 

338 11.6% 

43 1.5% 

18 0.6% 

51 1.8% 

100 3.4% 

550 18.9% 

Delaware System Peak Demand Forecasts by Scenario 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

3,500 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

98 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

District of Columbia Profile 

Key drivers of the District of Columbia’s demand response potential estimate include: average 

residential CAC saturation of around 55 percent, a customer mix that has an above average share of 

peak demand in the Large C&I class (52%), a moderate amount of existing demand response in the 

Large C&I sector due to Other DR programs, and the potential to deploy AMI at a faster-than-average 

rate. DLC is cost effective for all customer classes in the state. Enabling technologies are not cost 

effective for the Residential class. 

BAU: The District of Columbia’s existing demand response comes entirely from Other DR for Large 

C&I customers. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of 

Interruptible Tariffs for the Large C&I class, which currently do not exist in the state. Other DR 

expands substantially as well. This high growth is due to the District of Columbia’s large share of 

Large C&I load. 

Achievable Participation: Large C&I demand response potential is lower than in the Expanded BAU 

scenario due to smaller per-customer impacts from pricing with technology relative to Other DR. This 

leads to lower demand response potential even though the other classes increase in demand response 

potential. 

Full Participation: Similar to the Expanded BAU scenario, a large share of load in the Large C&I 

sector drives the increase in impacts. Since enabling technologies are not cost-effective for the 

Residential sector, the growth the Residential sector is not as much as is seen in other states for this 

scenario. C&I demand response potential is slightly higher than in the Achievable Participation 

scenario because of growth in pricing with and without enabling technology, which is cost-effective for 

all C&I sectors. 

0 

50 

100 

150 

200 

250 

300 

350 

400 

450 

500 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 2.8 GW) 

District of Columbia DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 99 
 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in District of Columbia, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

0 0.0% 

0 0.0% 

3 0.1% 

26 0.9% 

0 0.0% 

0 0.0% 

29 1.0% 

0 0.0% 

41 1.5% 

7 0.2% 

0 0.0% 

0 0.0% 

47 1.7% 

0 0.0% 

54 1.9% 

0 0.0% 

0 0.0% 

0 0.0% 

54 1.9% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

13 0.5% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

14 0.5% 

31 1.1% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

32 1.1% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.1% 

1 0.0% 

4 0.1% 

0 0.0% 

6 0.2% 

14 0.5% 

9 0.3% 

0 0.0% 

4 0.1% 

0 0.0% 

27 1.0% 

40 1.4% 

4 0.2% 

0 0.0% 

4 0.1% 

0 0.0% 

48 1.7% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

209 7.5% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

209 7.5% 

0 0.0% 

4 0.1% 

0 0.0% 

124 4.5% 

347 12.5% 

475 17.1% 

19 0.7% 

34 1.2% 

0 0.0% 

124 4.5% 

209 7.5% 

386 13.8% 

54 2.0% 

44 1.6% 

0 0.0% 

124 4.5% 

209 7.5% 

431 15.5% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

209 7.5% 

Total 

(MW) 

Total 

(% of 

system) 

209 7.5% 

0 0.0% 

8 0.3% 

27 1.0% 

128 4.6% 

347 12.5% 

511 18.3% 

46 1.6% 

84 3.0% 

7 0.3% 

128 4.6% 

209 7.5% 

474 17.0% 

125 4.5% 

103 3.7% 

0 0.0% 

128 4.6% 

209 7.5% 

565 20.3% 

District of Columbia System Peak Demand Forecasts by 

Scenario 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW)

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

100 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

Florida State Profile 

Key drivers of Florida’s demand response potential estimate include: very high residential CAC 

saturation of 91 percent, a customer mix that has an above average share of peak demand in the 

Residential class (59%), a large existing residential DLC program, and the potential to deploy AMI at a 

faster-than-average rate. DLC is cost effective for all customer classes in the state. Enabling 

technologies are not cost effective for the Small C&I class. Florida’s demand response potential is 

highly dependent on recruiting participants from the Residential class, as is shown in the Achievable 

and Full Participation scenarios. 

BAU: Florida’s existing demand response comes primarily from DLC in the Residential class and an 

Interruptible Tariffs program for Large C&I customers. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of DLC 

for the Residential class. This is due to Florida’s high share of Residential load. There is also growth in 

the Large C&I class due to Other DR. 

Achievable Participation: High CAC saturation in the Residential class drives a significant increase in 

demand response potential through dynamic pricing with and without enabling technologies. Large 

C&I demand response potential is slightly lower than in the Expanded BAU scenario due to smaller 

per-customer impacts from pricing with technology relative to Other DR. 

Full Participation: Similar to the Achievable Participation scenario, high CAC saturation combined 

with a large share of load in the Residential class drives the increase in impacts. The impacts are 

dominated by pricing with enabling technologies, which are cost-effective for all customer classes 

except Small C&I. 

0 

2,000 

4,000 

6,000 

8,000 

10,000 

12,000 

14,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 62.5 GW) 

Florida DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 101 
 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Florida, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

42 0.1% 

1,622 2.6% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

1,665 2.7% 

0 0.0% 

227 0.4% 

3,091 4.9% 

0 0.0% 

0 0.0% 

3,318 5.3% 

4,494 7.2% 

2,037 3.3% 

1,622 2.6% 

0 0.0% 

0 0.0% 

8,154 13.1% 

10,513 16.8% 

133 0.2% 

1,622 2.6% 

0 0.0% 

0 0.0% 

12,269 19.6% 

0 0.0% 

0 0.0% 

73 0.1% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

73 0.1% 

0 0.0% 

1 0.0% 

73 0.1% 

0 0.0% 

0 0.0% 

74 0.1% 

0 0.0% 

16 0.0% 

73 0.1% 

0 0.0% 

0 0.0% 

89 0.1% 

0 0.0% 

21 0.0% 

73 0.1% 

0 0.0% 

0 0.0% 

94 0.2% 

0 0.0% 

0 0.0% 

0 0.0% 

24 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

24 0.0% 

0 0.0% 

34 0.1% 

125 0.2% 

187 0.3% 

0 0.0% 

346 0.6% 

432 0.7% 

288 0.5% 

52 0.1% 

187 0.3% 

0 0.0% 

958 1.5% 

1,264 2.0% 

139 0.2% 

0 0.0% 

187 0.3% 

0 0.0% 

1,590 2.5% 

0 0.0% 

0 0.0% 

0 0.0% 

1,163 1.9% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

1,163 1.9% 

0 0.0% 

18 0.0% 

0 0.0% 

1,242 2.0% 

574 0.9% 

1,833 2.9% 

123 0.2% 

223 0.4% 

0 0.0% 

1,242 2.0% 

238 0.4% 

1,825 2.9% 

358 0.6% 

289 0.5% 

0 0.0% 

1,242 2.0% 

0 0.0% 

1,889 3.0% 

0 0.0% 

42 0.1% 

1,695 2.7% 

1,187 1.9% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

2,924 4.7% 

0 0.0% 

280 0.4% 

3,289 5.3% 

1,428 2.3% 

574 0.9% 

5,571 8.9% 

5,049 8.1% 

2,564 4.1% 

1,747 2.8% 

1,428 2.3% 

239 0.4% 

11,026 17.7% 

12,135 19.4% 

582 0.9% 

1,695 2.7% 

1,428 2.3% 

0 0.0% 

15,841 25.4% 

Florida System Peak Demand Forecasts by Scenario 

0 

10,000 

20,000 

30,000 

40,000 

50,000 

60,000 

70,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

102 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

Georgia State Profile 

Key drivers of Georgia’s demand response potential estimate include: higher-than-average residential 

CAC saturation of 82 percent, a customer mix that has an above average share of peak demand in the 

residential and Large C&I classes (50% and 25%, respectively), a moderate amount of existing demand 

response, and the potential to deploy AMI at a faster-than-average rate. Enabling technologies and 

DLC are cost effective for all customer classes in the state. 

BAU: Georgia’s existing demand response comes primarily from one of the largest RTP tariffs in the 

country for large C&I customers. An interruptible tariff program also contributes significantly to the 

total. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of 

Other DR programs for the Large C&I class, which currently do not exist in the state. This is due to 

Georgia’s high share of Large C&I load, which would also allow for significant growth in the existing 

interruptible tariff. DLC also exhibits additional incremental potential in the Residential class as it is 

cost effective to implement. 

Achievable Participation: High CAC saturation in the residential sector drives a significant increase in 

demand response potential through dynamic pricing with enabling technologies. Large C&I demand 

response potential is slightly lower than in the Expanded BAU scenario due to smaller per-customer 

impacts from pricing with technology relative to Other DR. 

Full Participation: Similar to the Achievable Participation scenario, high CAC saturation combined 

with a large share of load in the residential sector drives the increase in impacts. The impacts are 

dominated by pricing with enabling technologies, which are cost-effective for all customer classes. 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 34.7 GW) 

Georgia DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 103 
 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Georgia, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

130 0.4% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

130 0.4% 

0 0.0% 

95 0.3% 

1,146 3.3% 

0 0.0% 

0 0.0% 

1,241 3.6% 

2,062 5.9% 

974 2.8% 

296 0.9% 

0 0.0% 

0 0.0% 

3,332 9.6% 

4,823 13.9% 

114 0.3% 

130 0.4% 

0 0.0% 

0 0.0% 

5,066 14.6% 

0 0.0% 

2 0.0% 

63 0.2% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

65 0.2% 

0 0.0% 

2 0.0% 

63 0.2% 

0 0.0% 

0 0.0% 

65 0.2% 

155 0.4% 

9 0.0% 

63 0.2% 

0 0.0% 

0 0.0% 

227 0.7% 

363 1.0% 

5 0.0% 

63 0.2% 

0 0.0% 

0 0.0% 

431 1.2% 

0 0.0% 

0 0.0% 

2 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

2 0.0% 

0 0.0% 

14 0.0% 

35 0.1% 

58 0.2% 

0 0.0% 

106 0.3% 

190 0.5% 

127 0.4% 

14 0.0% 

58 0.2% 

0 0.0% 

389 1.1% 

557 1.6% 

61 0.2% 

2 0.0% 

58 0.2% 

0 0.0% 

678 2.0% 

0 0.0% 

628 1.8% 

0 0.0% 

332 1.0% 

22 0.1% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

982 2.8% 

0 0.0% 

628 1.8% 

0 0.0% 

1,290 3.7% 

844 2.4% 

2,761 8.0% 

143 0.4% 

628 1.8% 

0 0.0% 

1,290 3.7% 

353 1.0% 

2,414 7.0% 

419 1.2% 

628 1.8% 

0 0.0% 

1,290 3.7% 

22 0.1% 

2,358 6.8% 

0 0.0% 

630 1.8% 

196 0.6% 

332 1.0% 

22 0.1% 

Total 

(MW) 

Total 

(% of 

system) 

1,179 3.4% 

0 0.0% 

739 2.1% 

1,244 3.6% 

1,348 3.9% 

844 2.4% 

4,174 12.0% 

2,550 7.4% 

1,737 5.0% 

374 1.1% 

1,348 3.9% 

353 1.0% 

6,363 18.4% 

6,161 17.8% 

807 2.3% 

196 0.6% 

1,348 3.9% 

22 0.1% 

8,534 24.6% 

Georgia System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

30,000 

35,000 

40,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

104 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

Hawaii State Profile 

Key drivers of Hawaii’s demand response potential estimate include: very low CAC saturation of 17.6 

percent, a customer mix that has an above average share of peak demand in the Large C&I (35%), a 

minimal amount of existing demand response, and the potential to deploy AMI at a faster-than-average 

rate. Enabling technologies and DLC are cost effective for all the C&I customer classes, however not 

for the Residential class. 

BAU: Hawaii’s existing demand response comes from DLC participation in the Residential class and 

Interruptible Tariff participation in the Large C&I class. 

Expanded BAU: Growth in demand response impacts is driven primarily by the Large C&I class. 

There is a significant increase in Interruptible Tariffs and the addition of Other DR programs. This is 

due to Hawaii’s high share of Large C&I load. 

Achievable Participation: Though the Residential class is limited by a low CAC saturation and a lack 

of enabling technology, there is still growth in potential through pricing programs. Large C&I demand 

response potential is slightly lower than in the Expanded BAU scenario due to smaller per-customer 

impacts from pricing with technology relative to Other DR, while there is moderate growth in the Small 

and Medium C&I classes. 

Full Participation: Similar to the Achievable Participation scenario, there is growing potential across 

the classes in dynamic pricing, though it is limited in the Residential class due to a lack of enabling 

technology. Finally, the Large C&I class still exhibits strong potential in Interruptible Tariffs. 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 2.1 GW) 

Hawaii DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 105 
 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Hawaii, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

20 0.9% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

20 0.9% 

0 0.0% 

2 0.1% 

20 0.9% 

0 0.0% 

0 0.0% 

22 1.0% 

0 0.0% 

37 1.8% 

20 0.9% 

0 0.0% 

0 0.0% 

57 2.7% 

0 0.0% 

49 2.3% 

20 0.9% 

0 0.0% 

0 0.0% 

69 3.3% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

12 0.6% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

13 0.6% 

28 1.3% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

29 1.4% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.1% 

2 0.1% 

5 0.2% 

0 0.0% 

8 0.4% 

15 0.7% 

11 0.5% 

1 0.0% 

5 0.2% 

0 0.0% 

31 1.5% 

43 2.0% 

7 0.3% 

0 0.0% 

5 0.2% 

0 0.0% 

54 2.6% 

0 0.0% 

0 0.0% 

0 0.0% 

24 1.1% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

24 1.1% 

0 0.0% 

2 0.1% 

0 0.0% 

27 1.3% 

50 2.4% 

78 3.7% 

11 0.5% 

19 0.9% 

0 0.0% 

27 1.3% 

21 1.0% 

78 3.7% 

31 1.5% 

25 1.2% 

0 0.0% 

27 1.3% 

0 0.0% 

83 3.9% 

0 0.0% 

0 0.0% 

20 0.9% 

24 1.1% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

44 2.1% 

0 0.0% 

5 0.2% 

23 1.1% 

32 1.5% 

50 2.4% 

109 5.2% 

37 1.8% 

68 3.2% 

21 1.0% 

32 1.5% 

21 1.0% 

179 8.5% 

102 4.8% 

82 3.9% 

20 0.9% 

32 1.5% 

0 0.0% 

235 11.2% 

Hawaii System Peak Demand Forecasts by Scenario 

0 

500 

1,000 

1,500 

2,000 

2,500 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

106 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

Idaho State Profile 

Key drivers of Idaho’s demand response potential estimate significant residential CAC saturation of 

66.5 percent, a customer mix that has an above average share of peak demand in the Medium C&I 

classes (39%), a minimal amount of existing demand response, and the potential to deploy AMI at a 

faster-than-average rate. Enabling technologies and DLC are cost effective for all customer classes in 

the state except for the Medium C&I segment. 

BAU: Idaho’s existing demand response comes from DLC programs in the Residential and Medium 

C&I classes. 

 Expanded BAU: With a unique customer mix weighted towards the Residential and Medium C&I 

segments, growth in demand response impacts is spread across these two classes as well as in the Large 

C&I class. DLC potential has increased for the Residential class, while Interruptible Tariffs and Other 

DR make up the increase in potential found in the Medium and Large C&I classes. 

Achievable Participation: High CAC saturation in the Residential sector drives a significant increase 

in demand response potential through dynamic pricing with and without enabling technologies. The 

size of the Medium C&I class contributes to the larger role that it plays in the state’s total potential. 

Full Participation: In the Full Participation scenario, the Residential class exhibits the most potential 

in dynamic pricing. The Medium and Large C&I classes have moderate increases from the same 

pricing programs, with potential from Other DR in the Large class dropping off due to an assumption 

that these customers would instead be enrolled in pricing programs. Potential from the Medium C&I 

class would be higher, but is mitigated by the lack of enabling technology for dynamic pricing. 

0 

100 

200 

300 

400 

500 

600 

700 

800 

900 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 6 GW) 

Idaho DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 107 
 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Idaho, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

31 0.5% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

31 0.5% 

0 0.0% 

16 0.3% 

123 2.0% 

0 0.0% 

0 0.0% 

139 2.3% 

323 5.3% 

170 2.8% 

32 0.5% 

0 0.0% 

0 0.0% 

526 8.7% 

757 12.5% 

41 0.7% 

31 0.5% 

0 0.0% 

0 0.0% 

829 13.7% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

14 0.2% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

15 0.3% 

33 0.5% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

33 0.6% 

0 0.0% 

0 0.0% 

37 0.6% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

37 0.6% 

0 0.0% 

6 0.1% 

37 0.6% 

25 0.4% 

0 0.0% 

69 1.1% 

0 0.0% 

108 1.8% 

37 0.6% 

25 0.4% 

0 0.0% 

171 2.8% 

0 0.0% 

180 3.0% 

37 0.6% 

25 0.4% 

0 0.0% 

243 4.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

2 0.0% 

0 0.0% 

84 1.4% 

59 1.0% 

144 2.4% 

13 0.2% 

23 0.4% 

0 0.0% 

84 1.4% 

24 0.4% 

144 2.4% 

37 0.6% 

30 0.5% 

0 0.0% 

84 1.4% 

0 0.0% 

150 2.5% 

0 0.0% 

0 0.0% 

68 1.1% 

0 0.0% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

68 1.1% 

0 0.0% 

24 0.4% 

161 2.7% 

109 1.8% 

59 1.0% 

354 5.9% 

350 5.8% 

302 5.0% 

69 1.1% 

109 1.8% 

24 0.4% 

855 14.1% 

826 13.7% 

252 4.2% 

68 1.1% 

109 1.8% 

0 0.0% 

1,255 20.8% 

Idaho System Peak Demand Forecasts by Scenario 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

7,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

108 A National Assessment of Demand Response Potential 
 

 

 

Appendix A – State Profiles 

Illinois State Profile 

Key drivers of Illinois’s demand response potential estimate include: higher-than-average residential 

CAC saturation of 75 percent, a customer mix that has an above average share of peak demand in the 

Large C&I class (42%), a moderate amount of existing demand response, and the potential to deploy 

AMI at a slightly faster-than-average rate. Enabling technologies are cost-effective only for the Small 

and Large C&I classes. DLC technology is cost-effective for all customer classes in the state. 

BAU: Illinois’s existing demand response comes primarily from its Large C&I class, namely in the 

Other DR category. The Residential class contributes minimally with DLC participation. 

Expanded BAU: Growth in demand response impacts is driven primarily through the Other DR 

programs and Interruptible Tariffs for the Large C&I class. Residential DLC exhibits small growth in 

the existing DLC program. 

Achievable Participation: High CAC saturation in the residential sector implies significant demand 

response potential through pricing programs, but this is realized without enabling technology as it is not 

cost-effective in this class in Illinois. It is, however, cost-effective for the Small and Large C&I classes, 

and this is reflected in the results. Large C&I demand response potential is slightly higher than in the 

Expanded BAU scenario due to higher assumed participation in pricing programs. 

Full Participation: Potential increases relative to the Achievable Participation scenario due to impacts 

from pricing programs, limited somewhat by the lack of cost-effective enabling technology in the 

Residential and Medium C&I classes. The Large C&I class maintains strong potential from 

Interruptible Tariffs and Other DR as well. 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

3,500 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 35.9 GW) 

Illinois DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 109 
 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Illinois, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

1 0.0% 

178 0.5% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

179 0.5% 

0 0.0% 

39 0.1% 

369 1.0% 

0 0.0% 

0 0.0% 

407 1.1% 

0 0.0% 

1,131 3.1% 

178 0.5% 

0 0.0% 

0 0.0% 

1,309 3.6% 

0 0.0% 

1,508 4.2% 

178 0.5% 

0 0.0% 

0 0.0% 

1,686 4.7% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

10 0.0% 

0 0.0% 

0 0.0% 

11 0.0% 

210 0.6% 

13 0.0% 

3 0.0% 

0 0.0% 

0 0.0% 

225 0.6% 

492 1.4% 

8 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

499 1.4% 

0 0.0% 

0 0.0% 

0 0.0% 

10 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

10 0.0% 

0 0.0% 

2 0.0% 

9 0.0% 

15 0.0% 

0 0.0% 

26 0.1% 

0 0.0% 

45 0.1% 

4 0.0% 

15 0.0% 

0 0.0% 

63 0.2% 

0 0.0% 

74 0.2% 

0 0.0% 

15 0.0% 

0 0.0% 

89 0.2% 

0 0.0% 

0 0.0% 

0 0.0% 

134 0.4% 

1,883 5.2% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

2,017 5.6% 

0 0.0% 

19 0.1% 

0 0.0% 

243 0.7% 

2,329 6.5% 

2,592 7.2% 

192 0.5% 

349 1.0% 

0 0.0% 

243 0.7% 

1,883 5.2% 

2,667 7.4% 

561 1.6% 

452 1.3% 

0 0.0% 

243 0.7% 

1,883 5.2% 

3,139 8.7% 

0 0.0% 

1 0.0% 

178 0.5% 

144 0.4% 

1,883 5.2% 

Total 

(MW) 

Total 

(% of 

system) 

2,206 6.1% 

0 0.0% 

61 0.2% 

387 1.1% 

258 0.7% 

2,329 6.5% 

3,036 8.5% 

402 1.1% 

1,537 4.3% 

184 0.5% 

258 0.7% 

1,883 5.2% 

4,265 11.9% 

1,052 2.9% 

2,042 5.7% 

178 0.5% 

258 0.7% 

1,883 5.2% 

5,414 15.1% 

Illinois System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

30,000 

35,000 

40,000 

2009

2010

201120122013

2014

2015

2016

2017

20182019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

110 A National Assessment of Demand Response Potential 
 

 

 

Appendix A – State Profiles 

Indiana State Profile 

Key drivers of Indiana’s demand response potential estimate include: higher-than-average residential 

CAC saturation of 74 percent, a customer mix that has an above average share of peak demand in the 

Large C&I class (35%), a moderate amount of existing demand response, and the potential to deploy 

AMI at an average rate. Enabling technologies and DLC are cost effective for all customer classes in 

the state. 

BAU: Indiana’s existing demand response comes primarily from the Large C&I class. BAU demand 

response for this class is split between Interruptible Tariffs and Other DR. 

Expanded BAU: Demand response potential for the Large C&I class remains largely unchanged. 

However, due to the high Residential CAC saturation, DLC potential in this class has grown 

significantly. 

Achievable Participation: High CAC saturation in the residential sector drives a significant increase in 

demand response potential through dynamic pricing with and without enabling technologies. This is 

bolstered by the gains across the C&I classes due to pricing programs. 

Full Participation: Continuing the trend from the Achievable Participation scenario, high CAC 

saturation in the residential sector and cost-effective enabling technology drive the increases in impacts 

from dynamic pricing programs. Potential in the C&I classes grows slightly as pricing program 

participation increases relative to the other scenarios. 

0 

500 

1,000 

1,500 

2,000 

2,500 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 26.5 GW) 

Indiana DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 111 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Indiana, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

116 0.4% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

116 0.4% 

0 0.0% 

25 0.1% 

512 1.9% 

0 0.0% 

0 0.0% 

537 2.0% 

852 3.2% 

431 1.6% 

131 0.5% 

0 0.0% 

0 0.0% 

1,414 5.3% 

1,994 7.5% 

85 0.3% 

116 0.4% 

0 0.0% 

0 0.0% 

2,195 8.3% 

0 0.0% 

0 0.0% 

23 0.1% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

23 0.1% 

0 0.0% 

0 0.0% 

23 0.1% 

0 0.0% 

0 0.0% 

23 0.1% 

96 0.4% 

6 0.0% 

23 0.1% 

0 0.0% 

0 0.0% 

125 0.5% 

225 0.8% 

3 0.0% 

23 0.1% 

0 0.0% 

0 0.0% 

252 0.9% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

7 0.0% 

24 0.1% 

47 0.2% 

0 0.0% 

78 0.3% 

141 0.5% 

113 0.4% 

10 0.0% 

47 0.2% 

0 0.0% 

311 1.2% 

413 1.6% 

77 0.3% 

0 0.0% 

47 0.2% 

0 0.0% 

538 2.0% 

0 0.0% 

29 0.1% 

0 0.0% 

549 2.1% 

621 2.3% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

1,199 4.5% 

0 0.0% 

29 0.1% 

0 0.0% 

575 2.2% 

621 2.3% 

1,225 4.6% 

128 0.5% 

232 0.9% 

0 0.0% 

575 2.2% 

621 2.3% 

1,556 5.9% 

373 1.4% 

301 1.1% 

0 0.0% 

575 2.2% 

621 2.3% 

1,870 7.0% 

0 0.0% 

29 0.1% 

139 0.5% 

549 2.1% 

621 2.3% 

Total 

(MW) 

Total 

(% of 

system) 

1,338 5.0% 

0 0.0% 

61 0.2% 

559 2.1% 

622 2.3% 

622 2.3% 

1,863 7.0% 

1,218 4.6% 

782 2.9% 

163 0.6% 

622 2.3% 

622 2.3% 

3,407 12.8% 

3,006 11.3% 

467 1.8% 

139 0.5% 

622 2.3% 

621 2.3% 

4,855 18.3% 

Indiana System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

30,000 

2009

2010

201120122013

2014

2015

2016

2017

20182019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

112 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

Iowa State Profile 

Key drivers of Iowa’s demand response potential estimate include: higher-than-average residential CAC 

saturation of 70 percent, a customer mix that has an above average share of peak demand in the Large 

C&I class (34%), a small amount of existing demand response, and the potential to deploy AMI at a 

slightly faster-than-average rate. Enabling technologies are cost effective for all customer classes. 

BAU: Iowa’s existing demand response comes primarily from Interruptible Tariff and Pricing program 

participation in the Large C&I class. There is small DLC participation in the Residential and Medium 

C&I classes as well. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of 

Other DR programs and growth in Interruptible Tariffs participation for the Large C&I class, with slight 

growth in the Residential and Medium C&I classes contributing as well. 

Achievable Participation: High CAC saturation in the residential sector drives a significant increase in 

demand response potential through dynamic pricing. The Small and Medium C&I classes show some 

potential, mainly through dynamic pricing. Large C&I demand response potential is slightly lower than 

in the Expanded BAU scenario due to smaller per-customer impacts from pricing with technology 

relative to Other DR. 

Full Participation: Similar to the Achievable Participation scenario, growth in the Residential class is 

driven by pricing with enabling technology. The Small and Medium C&I classes also exhibit an 

increase in dynamic pricing potential. With pricing making up a larger percentage of assumed 

participation in the Large C&I class, Other DR does not factor into the total impacts. 

0 

100 

200 

300 

400 

500 

600 

700 

800 

900 

1,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 11.6 GW) 

Iowa DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 113 
 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Iowa, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

76 0.7% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

76 0.7% 

0 0.0% 

13 0.1% 

129 1.1% 

0 0.0% 

0 0.0% 

142 1.2% 

323 2.8% 

171 1.5% 

76 0.7% 

0 0.0% 

0 0.0% 

571 4.9% 

755 6.5% 

43 0.4% 

76 0.7% 

0 0.0% 

0 0.0% 

875 7.6% 

0 0.0% 

0 0.0% 

2 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

2 0.0% 

0 0.0% 

0 0.0% 

6 0.1% 

0 0.0% 

0 0.0% 

6 0.1% 

40 0.3% 

2 0.0% 

2 0.0% 

0 0.0% 

0 0.0% 

44 0.4% 

93 0.8% 

1 0.0% 

2 0.0% 

0 0.0% 

0 0.0% 

97 0.8% 

0 0.0% 

0 0.0% 

19 0.2% 

10 0.1% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

30 0.3% 

0 0.0% 

4 0.0% 

19 0.2% 

25 0.2% 

0 0.0% 

49 0.4% 

59 0.5% 

47 0.4% 

19 0.2% 

25 0.2% 

0 0.0% 

151 1.3% 

173 1.5% 

32 0.3% 

19 0.2% 

25 0.2% 

0 0.0% 

250 2.2% 

0 0.0% 

0 0.0% 

0 0.0% 

510 4.4% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

510 4.4% 

0 0.0% 

5 0.0% 

0 0.0% 

510 4.4% 

230 2.0% 

745 6.4% 

49 0.4% 

88 0.8% 

0 0.0% 

510 4.4% 

94 0.8% 

742 6.4% 

142 1.2% 

115 1.0% 

0 0.0% 

510 4.4% 

0 0.0% 

767 6.6% 

0 0.0% 

0 0.0% 

97 0.8% 

521 4.5% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

618 5.3% 

0 0.0% 

23 0.2% 

154 1.3% 

536 4.6% 

230 2.0% 

942 8.1% 

471 4.1% 

309 2.7% 

97 0.8% 

536 4.6% 

94 0.8% 

1,507 13.0% 

1,164 10.1% 

191 1.6% 

97 0.8% 

536 4.6% 

0 0.0% 

1,988 17.2% 

Iowa System Peak Demand Forecasts by Scenario 

0 

2,000 

4,000 

6,000 

8,000 

10,000 

12,000 

14,000 

2009

2010

201120122013

2014

2015

2016

2017

20182019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

114 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

Kansas State Profile 

Key drivers of Kansas’s demand response potential estimate include: higher-than-average residential 

CAC saturation of 83.7 percent, a customer mix that has a significant share of peak demand in the 

Residential and Large C&I classes (44% and 31%, respectively), a small amount of existing demand 

response, and the potential to deploy AMI at a slower-than-average rate. Enabling technologies are cost 

effective for all customer classes in the state except for the Large C&I class. DLC technology is costeffective across all classes. 

BAU: Kansas’s existing demand response comes primarily from Interruptible tariffs in the Large C&I 

class and minimal DLC participation in the Residential and Small C&I classes. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of 

Other DR programs for the Large C&I class, which currently do not exist in the state, as well as growth 

in the Large C&I class’s Interruptible Tariff programs and the Residential class’s DLC programs. 

Achievable Participation: High CAC saturation in the residential sector drives a significant increase in 

demand response potential through dynamic pricing programs. Large C&I demand response potential is 

slightly lower than in the Expanded BAU scenario due to smaller per-customer impacts from pricing 

without technology relative to Other DR and Interruptible Tariffs. 

Full Participation: High CAC saturation combined with a large share of load in the Residential sector 

drives the increase in impacts. With enabling technology being cost-effective for all but the Large C&I 

class, there are significant impacts in this category for the Small and Medium C&I classes. The Large 

C&I class contributes significantly through Interruptible Tariffs and pricing without enabling 

technology. 

0 

200 

400 

600 

800 

1,000 

1,200 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 10.3 GW) 

Kansas DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 115 
 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Kansas, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

15 0.1% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

15 0.1% 

0 0.0% 

9 0.1% 

226 2.2% 

0 0.0% 

0 0.0% 

236 2.3% 

466 4.5% 

219 2.1% 

57 0.6% 

0 0.0% 

0 0.0% 

742 7.2% 

1,089 10.6% 

24 0.2% 

15 0.1% 

0 0.0% 

0 0.0% 

1,127 11.0% 

0 0.0% 

0 0.0% 

19 0.2% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

19 0.2% 

0 0.0% 

0 0.0% 

19 0.2% 

0 0.0% 

0 0.0% 

19 0.2% 

75 0.7% 

5 0.0% 

19 0.2% 

0 0.0% 

0 0.0% 

98 1.0% 

176 1.7% 

3 0.0% 

19 0.2% 

0 0.0% 

0 0.0% 

197 1.9% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

4 0.0% 

7 0.1% 

0 0.0% 

11 0.1% 

20 0.2% 

16 0.2% 

1 0.0% 

7 0.1% 

0 0.0% 

43 0.4% 

58 0.6% 

11 0.1% 

0 0.0% 

7 0.1% 

0 0.0% 

75 0.7% 

0 0.0% 

0 0.0% 

0 0.0% 

211 2.0% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

211 2.0% 

0 0.0% 

3 0.0% 

0 0.0% 

211 2.1% 

229 2.2% 

443 4.3% 

0 0.0% 

113 1.1% 

0 0.0% 

211 2.1% 

93 0.9% 

417 4.1% 

0 0.0% 

188 1.8% 

0 0.0% 

211 2.1% 

0 0.0% 

399 3.9% 

0 0.0% 

0 0.0% 

33 0.3% 

211 2.0% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

244 2.4% 

0 0.0% 

13 0.1% 

248 2.4% 

218 2.1% 

229 2.2% 

708 6.9% 

560 5.5% 

352 3.4% 

78 0.8% 

218 2.1% 

93 0.9% 

1,300 12.6% 

1,322 12.9% 

225 2.2% 

33 0.3% 

218 2.1% 

0 0.0% 

1,798 17.5% 

Kansas System Peak Demand Forecasts by Scenario 

0 

2,000 

4,000 

6,000 

8,000 

10,000 

12,000 

2009

2010

201120122013

2014

2015

2016

2017

20182019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

116 A National Assessment of Demand Response Potential 
 

 

Appendix A – State Profiles 

Kentucky State Profile 

Key drivers of Kentucky’s demand response potential estimate include: higher-than-average residential 

CAC saturation of 76 percent, a fairly typical customer mix with significant load in the Medium C&I 

class (30%), a minimal amount of existing demand response, and the potential to deploy AMI at a 

slightly slower-than-average rate. Enabling technologies and DLC are cost effective for all customer 

classes in the state. 

BAU: Kentucky’s existing demand response comes from the Residential and Large C&I classes. DLC 

in the Residential class and an Interruptible Tariff in the Large C&I class make up most of the existing 

demand response, with Other DR in the Large C&I class also contributing. 

Expanded BAU: Growth in demand response impacts is driven primarily through an increase in Other 

DR programs for the Large C&I class and growth in DLC for the Residential class. The Medium C&I 

class also gains demand response potential split mainly from an Interruptible Tariff. 

Achievable Participation: High CAC saturation in the residential sector drives a significant increase in 

demand response potential through dynamic pricing with enabling technologies. Large C&I demand 

response potential is slightly lower than in the Expanded BAU scenario due to smaller per-customer 

impacts from pricing with technology relative to Other DR. There is also significant growth in demand 

response for the Small and Medium C&I classes driven by dynamic pricing programs 

Full Participation: Residential class potential increases due to dynamic pricing. Overall, high CAC 

saturation across the Residential, Small C&I and Medium C&I classes drives the significant dynamic 

pricing potential, with the Large C&I class exhibiting significant potential in Interruptible Tariff 

programs. 

0 

500 

1,000 

1,500 

2,000 

2,500 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 22.6 GW) 

Kentucky DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 117 
 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Kentucky, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

116 0.5% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

116 0.5% 

0 0.0% 

18 0.1% 

377 1.7% 

0 0.0% 

0 0.0% 

395 1.7% 

759 3.4% 

377 1.7% 

116 0.5% 

0 0.0% 

0 0.0% 

1,251 5.5% 

1,774 7.9% 

67 0.3% 

116 0.5% 

0 0.0% 

0 0.0% 

1,957 8.7% 

0 0.0% 

0 0.0% 

6 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

6 0.0% 

0 0.0% 

0 0.0% 

6 0.0% 

0 0.0% 

0 0.0% 

6 0.0% 

164 0.7% 

9 0.0% 

6 0.0% 

0 0.0% 

0 0.0% 

179 0.8% 

383 1.7% 

5 0.0% 

6 0.0% 

0 0.0% 

0 0.0% 

394 1.7% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

8 0.0% 

12 0.1% 

69 0.3% 

0 0.0% 

89 0.4% 

227 1.0% 

151 0.7% 

5 0.0% 

69 0.3% 

0 0.0% 

452 2.0% 

664 2.9% 

73 0.3% 

0 0.0% 

69 0.3% 

0 0.0% 

806 3.6% 

0 0.0% 

0 0.0% 

0 0.0% 

155 0.7% 

56 0.2% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

211 0.9% 

0 0.0% 

4 0.0% 

0 0.0% 

437 1.9% 

272 1.2% 

713 3.2% 

59 0.3% 

108 0.5% 

0 0.0% 

437 1.9% 

110 0.5% 

715 3.2% 

174 0.8% 

140 0.6% 

0 0.0% 

437 1.9% 

56 0.2% 

807 3.6% 

0 0.0% 

0 0.0% 

122 0.5% 

155 0.7% 

56 0.2% 

Total 

(MW) 

Total 

(% of 

system) 

332 1.5% 

0 0.0% 

30 0.1% 

394 1.7% 

506 2.2% 

272 1.2% 

1,202 5.3% 

1,209 5.4% 

645 2.9% 

126 0.6% 

506 2.2% 

111 0.5% 

2,596 11.5% 

2,995 13.3% 

285 1.3% 

122 0.5% 

506 2.2% 

56 0.2% 

3,963 17.5% 

Kentucky System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

2009

2010

201120122013

2014

2015

2016

2017

20182019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

118 A National Assessment of Demand Response Potential 
 

 

Appendix A – State Profiles 

Louisiana State Profile 

Key drivers of Louisiana’s demand response potential estimate include: higher-than-average residential 

CAC saturation of 75.5 percent, an average customer mix, no existing demand response programs, and 

the potential to deploy AMI at a slightly slower-than-average rate. Enabling technologies and DLC are 

cost effective for all customer classes in the state. 

BAU: A review of the available data did not identify any existing demand response programs in 

Louisiana. 

Expanded BAU: Growth in demand response impacts under this scenario are driven primarily through 

the addition of Other DR programs and Interruptible Tariffs for the Large C&I class, and a DLC 

program for the Residential class. The Residential class has much potential for DLC and dynamic 

pricing due to its high CAC saturation. 

Achievable Participation: High CAC saturation in the Residential sector drives a significant increase 

in demand response potential through dynamic pricing with and without enabling technologies. Large 

C&I demand response potential is slightly lower than in the Expanded BAU scenario due to smaller 

per-customer impacts from pricing with technology relative to Other DR. 

Full Participation: Similar to the Achievable Participation scenario, high CAC saturation combined 

with a significant share of load in the Residential sector drives the increase in impacts. The impacts are 

dominated by pricing with enabling technologies, which are cost-effective for all customer classes. 

Lastly, an Interruptible Tariff in the Large C&I class contributes significantly to Louisiana’s demand 

response potential under this scenario. 

0 

500 

1,000 

1,500 

2,000 

2,500 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 20 GW) 

Louisiana DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 119 
 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Louisiana, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

0 0.0% 

0 0.0% 

24 0.1% 

356 1.8% 

0 0.0% 

0 0.0% 

380 1.9% 

837 4.2% 

417 2.1% 

91 0.5% 

0 0.0% 

0 0.0% 

1,345 6.7% 

1,959 9.8% 

74 0.4% 

0 0.0% 

0 0.0% 

0 0.0% 

2,033 10.2% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

4 0.0% 

0 0.0% 

0 0.0% 

5 0.0% 

168 0.8% 

9 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

179 0.9% 

394 2.0% 

5 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

399 2.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

7 0.0% 

38 0.2% 

49 0.2% 

0 0.0% 

94 0.5% 

163 0.8% 

109 0.5% 

15 0.1% 

49 0.2% 

0 0.0% 

336 1.7% 

477 2.4% 

53 0.3% 

0 0.0% 

49 0.2% 

0 0.0% 

579 2.9% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

4 0.0% 

0 0.0% 

342 1.7% 

244 1.2% 

589 3.0% 

51 0.3% 

93 0.5% 

0 0.0% 

342 1.7% 

99 0.5% 

585 2.9% 

150 0.7% 

121 0.6% 

0 0.0% 

342 1.7% 

0 0.0% 

612 3.1% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

0 0.0% 

0 0.0% 

35 0.2% 

398 2.0% 

391 2.0% 

244 1.2% 

1,068 5.4% 

1,220 6.1% 

628 3.1% 

107 0.5% 

391 2.0% 

100 0.5% 

2,445 12.3% 

2,979 14.9% 

252 1.3% 

0 0.0% 

391 2.0% 

0 0.0% 

3,622 18.1% 

Louisiana System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

2009

2010

201120122013

2014

2015

2016

2017

20182019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

120 A National Assessment of Demand Response Potential 
Maine State Profile 

Key drivers of Maine’s demand response potential estimate include: lower than average residential 

CAC saturation of 14%, above average share of peak demand (34%) in the Large C&I classes, and a 

large amount of existing demand response. Pricing with enabling technologies are only cost effective 

for the Large C&I class. DLC is cost effective for all classes. 

BAU: Maine’s existing demand response comes predominantly from the Large C&I class through 

participation in the ISO New England forward capacity market. These impacts account for over 60% of 

the total impacts under all scenarios, resulting in smaller incremental differences between BAU and the 

potential scenarios in comparison to most states. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of 

interruptible tariffs for the Large C&I class. This is due to Maine’s above average share of Large C&I 

load, which would also allow for some growth in the Other DR category. 

Achievable Participation: The increase in demand response potential comes primarily from dynamic 

pricing without enabling impacts. Dynamic pricing with enabling technology, which is cost effective for 

the Large C&I class, contributes additional potential for that customer group. 

Full Participation: Similar to the Achievable Participation scenario, the impacts are dominated by 

pricing without enabling technologies for all customer classes. For the Large C&I class, pricing with 

enabling technology also contributes to the total potential. 

0 

100 

200 

300 

400 

500 

600 

700 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 3.2 GW) 

Maine DR Potential in 2019, by Scenario 

Appendix A – State Profiles 

Federal Energy Regulatory Commission 121 
 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Maine, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

18 0.6% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

18 0.6% 

0 0.0% 

2 0.1% 

18 0.6% 

0 0.0% 

0 0.0% 

20 0.6% 

0 0.0% 

53 1.7% 

18 0.6% 

0 0.0% 

0 0.0% 

72 2.2% 

0 0.0% 

71 2.2% 

18 0.6% 

0 0.0% 

0 0.0% 

89 2.8% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

5 0.2% 

5 0.2% 

0 0.0% 

12 0.4% 

0 0.0% 

23 0.7% 

2 0.1% 

5 0.2% 

0 0.0% 

31 1.0% 

0 0.0% 

39 1.2% 

0 0.0% 

5 0.2% 

0 0.0% 

45 1.4% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

492 15.4% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

492 15.4% 

0 0.0% 

1 0.0% 

0 0.0% 

78 2.5% 

492 15.4% 

571 17.9% 

12 0.4% 

21 0.7% 

0 0.0% 

78 2.5% 

492 15.4% 

603 18.9% 

34 1.1% 

28 0.9% 

0 0.0% 

78 2.5% 

492 15.4% 

631 19.8% 

0 0.0% 

0 0.0% 

18 0.6% 

0 0.0% 

492 15.4% 

Total 

(MW) 

Total 

(% of 

system) 

510 16.0% 

0 0.0% 

4 0.1% 

25 0.8% 

83 2.6% 

492 15.4% 

604 19.0% 

12 0.4% 

99 3.1% 

21 0.7% 

83 2.6% 

492 15.4% 

706 22.2% 

34 1.1% 

139 4.4% 

18 0.6% 

83 2.6% 

492 15.4% 

766 24.1% 

Maine System Peak Demand Forecasts by Scenario 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

3,500 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

122 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

Maryland State Profile 

Key drivers of Maryland’s demand response potential estimate include: higher-than-average residential 

CAC saturation of 78%, above average share of peak demand (48%) in the residential class, a large 

amount of existing demand response, and the potential to deploy AMI at a faster-than-average rate. 

Pricing with enabling technologies are cost effective for all customer classes, except for the Medium 

C&I class. DLC is cost effective for all customer classes. 

BAU: Maryland’s existing demand response comes primarily from residential DLC and Other DR 

programs for Large C&I customers. The large impacts for Other DR are a due to participation in PJM 

demand response programs. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of 

interruptible tariffs for the Large C&I class. The rest of the increase in potential comes from dynamic 

pricing without enabling technology. Overall, the incremental increase relative to the BAU scenario is 

small because the state is already achieving significant impacts from non-pricing programs. 

Achievable Participation: High CAC saturation in the residential sector drives a significant increase in 

demand response potential through dynamic pricing with enabling technologies. Growth in dynamic 

pricing with enabling technologies occurs for all C&I customers except for Medium C&I, as this is the 

only class for which the option is not cost effective. 

Full Participation: Relative to the Achievable Participation scenario, high CAC saturation combined 

with a large share of load in the residential sector drives the increase in impacts. The impacts are 

dominated by pricing with enabling technologies for all customer classes except for Medium C&I 

customers. 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 15.7 GW) 

Maryland DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 123 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Maryland, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

502 3.2% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

502 3.2% 

0 0.0% 

54 0.3% 

502 3.2% 

0 0.0% 

0 0.0% 

556 3.5% 

933 5.9% 

459 2.9% 

502 3.2% 

0 0.0% 

0 0.0% 

1,894 12.0% 

2,182 13.9% 

76 0.5% 

502 3.2% 

0 0.0% 

0 0.0% 

2,760 17.5% 

0 0.0% 

0 0.0% 

13 0.1% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

13 0.1% 

0 0.0% 

1 0.0% 

20 0.1% 

0 0.0% 

0 0.0% 

21 0.1% 

173 1.1% 

10 0.1% 

13 0.1% 

0 0.0% 

0 0.0% 

196 1.2% 

405 2.6% 

5 0.0% 

13 0.1% 

0 0.0% 

0 0.0% 

423 2.7% 

0 0.0% 

0 0.0% 

0 0.0% 

9 0.1% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

9 0.1% 

0 0.0% 

2 0.0% 

5 0.0% 

11 0.1% 

0 0.0% 

19 0.1% 

0 0.0% 

34 0.2% 

2 0.0% 

11 0.1% 

0 0.0% 

47 0.3% 

0 0.0% 

56 0.4% 

0 0.0% 

11 0.1% 

0 0.0% 

68 0.4% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

1,143 7.3% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

1,143 7.3% 

0 0.0% 

8 0.1% 

0 0.0% 

334 2.1% 

1,143 7.3% 

1,485 9.4% 

50 0.3% 

91 0.6% 

0 0.0% 

334 2.1% 

1,143 7.3% 

1,618 10.3% 

146 0.9% 

118 0.7% 

0 0.0% 

334 2.1% 

1,143 7.3% 

1,741 11.1% 

0 0.0% 

0 0.0% 

515 3.3% 

9 0.1% 

1,143 7.3% 

Total 

(MW) 

Total 

(% of 

system) 

1,667 10.6% 

0 0.0% 

65 0.4% 

528 3.4% 

345 2.2% 

1,143 7.3% 

2,081 13.2% 

1,156 7.3% 

593 3.8% 

517 3.3% 

345 2.2% 

1,143 7.3% 

3,755 23.8% 

2,733 17.4% 

255 1.6% 

515 3.3% 

345 2.2% 

1,143 7.3% 

4,991 31.7% 

Maryland System Peak Demand Forecasts by Scenario 

0 

2,000 

4,000 

6,000 

8,000 

10,000 

12,000 

14,000 

16,000 

18,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

124 A National Assessment of Demand Response Potential 
 

 

 

Appendix A – State Profiles 

Massachusetts State Profile 

Key drivers of the Massachusetts demand response potential estimate include: significantly lower-thanaverage residential CAC saturation of 12.7 percent, a customer mix that has an above average share of 

peak demand in the Large C&I class, a moderate amount of existing Other DR, and an AMI deployment 

schedule that is anticipated to be slower-than-average. Enabling technologies are cost effective for all 

classes except the Medium C&I class; DLC technology is cost effective across all customer classes. 

BAU: Massachusetts’ existing demand response comes entirely from the Large C&I class, which 

currently has significant enrollment in Other DR, particularly ISO-NE programs. 

Expanded BAU: The Expanded BAU scenario includes the addition of an interruptible tariff for the 

Large C&I class, which can have significant impact due to the high share of Large C&I peak demand in 

the customer mix. DLC program participation by the Residential class also contributes to 

Massachusetts’ Expanded BAU scenario. 

Achievable Participation: Low CAC saturation in the residential sector limits dynamic pricing 

potential. Furthermore, with enabling technology only cost effective in the Small and Large C&I 

classes, Other DR in the Large C&I class is still the dominant source of demand response potential. 

Full Participation: The Full participation scenario is similar to the Achievable Participation scenario, 

with incremental increases in dynamic pricing potential. The relatively low incremental difference 

between the BAU scenario and the Full Participation scenario is driven primarily by low CAC 

saturation and limited cost-effectiveness for enabling technology. 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 14.4 GW) 

Massachusetts DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 125 
 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Massachusetts, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

0 0.0% 

0 0.0% 

4 0.0% 

85 0.6% 

0 0.0% 

0 0.0% 

90 0.6% 

121 0.8% 

179 1.2% 

22 0.2% 

0 0.0% 

0 0.0% 

322 2.2% 

283 2.0% 

169 1.2% 

0 0.0% 

0 0.0% 

0 0.0% 

452 3.1% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

1 0.0% 

0 0.0% 

1 0.0% 

7 0.0% 

0 0.0% 

0 0.0% 

8 0.1% 

111 0.8% 

7 0.0% 

2 0.0% 

0 0.0% 

0 0.0% 

120 0.8% 

260 1.8% 

4 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

264 1.8% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

8 0.1% 

7 0.1% 

0 0.0% 

16 0.1% 

0 0.0% 

31 0.2% 

3 0.0% 

7 0.1% 

0 0.0% 

42 0.3% 

0 0.0% 

52 0.4% 

0 0.0% 

7 0.1% 

0 0.0% 

60 0.4% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

990 6.9% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

990 6.9% 

0 0.0% 

3 0.0% 

0 0.0% 

371 2.6% 

990 6.9% 

1,364 9.5% 

56 0.4% 

101 0.7% 

0 0.0% 

371 2.6% 

990 6.9% 

1,518 10.6% 

163 1.1% 

131 0.9% 

0 0.0% 

371 2.6% 

990 6.9% 

1,655 11.5% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

990 6.9% 

Total 

(MW) 

Total 

(% of 

system) 

991 6.9% 

0 0.0% 

8 0.1% 

101 0.7% 

379 2.6% 

990 6.9% 

1,478 10.3% 

288 2.0% 

319 2.2% 

27 0.2% 

379 2.6% 

990 6.9% 

2,002 13.9% 

706 4.9% 

357 2.5% 

0 0.0% 

379 2.6% 

990 6.9% 

2,432 16.9% 

Massachusetts System Peak Demand Forecasts by Scenario 

0 

2,000 

4,000 

6,000 

8,000 

10,000 

12,000 

14,000 

16,000 

2009

2010

201120122013

2014

2015

2016

2017

20182019 

Peak Demand (MW) 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

126 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

Michigan State Profile 

Key drivers of Michigan’s demand response potential estimate include: above average residential CAC 

saturation of 57%, above average share of peak demand (37%) in the Large C&I classes, a large amount 

of existing demand response, and the potential to deploy AMI at a faster-than-average rate. Pricing with 

enabling technologies are cost effective for all customer classes, except for the residential class. DLC is 

cost effective for all customer classes. 

BAU: Michigan’s existing demand response comes predominantly from interruptible tariffs for the 

Large C&I class and represents one of the largest interruptible loads in the country. Interruptible tariffs 

account for at least 30% of the total potential under all other scenarios. The state is also one of the few 

states that has a significant portion of price induced demand response. 

Expanded BAU: Significant growth in Other DR is due to Michigan’s above average share of Large 

C&I load. The rest of the impacts come from Pricing without technology and DLC for the other 

customer segments. 

Achievable Participation: The increase in demand response potential comes primarily from dynamic 

pricing without enabling impacts. Dynamic pricing with enabling technology which is cost effective for 

all classes except for the residential sector, contributes additional potential for the C&I customers. 

Large C&I demand response potential is slightly lower than in the Expanded BAU scenario due to 

smaller per-customer impacts from pricing relative to Other DR. The movement of participants in Other 

DR to pricing also contributes to this effect. 

Full Participation: Similar to the Achievable Participation scenario, the impacts are dominated by 

dynamic pricing without enabling technologies for all customer classes. The lower potential for Large 

C&I than in the other scenarios is due to participation changes within the different demand response 

options. 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 27.5 GW) 

Michigan DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 127 
 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Michigan, 2019 

Residential 

(MW) 

Residential 

(% of 

system) 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

Total 

(MW) 

Total 

(% of 

system) 

BAU 

Pricing with Technology 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

0 

570 

0 

0 

0.0% 

2.1% 

0.0% 

0.0% 

6 

69 

0 

0 

0.0% 

0.3% 

0.0% 

0.0% 

0 

0 

2 

0 

0.0% 

0.0% 

0.0% 

0.0% 

77 

0 

1,339 

86 

0.3% 

0.0% 

4.9% 

0.3% 

83 

639 

1,341 

86 

0.3% 

2.3% 

4.9% 

0.3% 

Total 570 2.1% 75 0.3% 2 0.0% 1,502 5.5% 2,149 7.8% 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

0 

37 

570 

0 

0 

0.0% 

0.1% 

2.1% 

0.0% 

0.0% 

0 

6 

69 

0 

0 

0.0% 

0.0% 

0.3% 

0.0% 

0.0% 

0 

7 

18 

42 

0 

0.0% 

0.0% 

0.1% 

0.2% 

0.0% 

0 

77 

0 

1,339 

1,245 

0.0% 

0.3% 

0.0% 

4.9% 

4.5% 

0 

127 

657 

1,380 

1,245 

0.0% 

0.5% 

2.4% 

5.0% 

4.5% 

Total 607 2.2% 75 0.3% 67 0.2% 2,661 9.7% 3,409 12.4% 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

0 

801 

570 

0 

0.0% 

2.9% 

2.1% 

0.0% 

160 

10 

69 

0 

0.6% 

0.0% 

0.3% 

0.0% 

88 

70 

7 

42 

0.3% 

0.3% 

0.0% 

0.2% 

105 

190 

0 

1,339 

0.4% 

0.7% 

0.0% 

4.9% 

352 

1,071 

647 

1,380 

1.3% 

3.9% 

2.4% 

5.0% 

Other DR Programs 0 0.0% 0 0.0% 0 0.0% 516 1.9% 516 1.9% 

Total 1,371 5.0% 238 0.9% 207 0.8% 2,149 7.8% 3,965 14.4% 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

0 

1,068 

570 

0 

0 

0.0% 

3.9% 

2.1% 

0.0% 

0.0% 

373 

6 

69 

0 

0 

1.4% 

0.0% 

0.3% 

0.0% 

0.0% 

256 

48 

0 

42 

0 

0.9% 

0.2% 

0.0% 

0.2% 

0.0% 

306 

246 

0 

1,339 

86 

1.1% 

0.9% 

0.0% 

4.9% 

0.3% 

935 

1,368 

639 

1,380 

86 

3.4% 

5.0% 

2.3% 

5.0% 

0.3% 

Total 1,638 6.0% 448 1.6% 345 1.3% 1,977 7.2% 4,409 16.0% 

Michigan System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

30,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

128 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

Minnesota State Profile 

Key drivers of Minnesota’s demand response potential estimate include: a substantial amount of 

existing demand response, above average share of peak demand (30%) in the Large C&I classes and a 

large residential base. Pricing with enabling technologies is not cost effective for all customer classes, 

except for the Medium C&I class. DLC is cost effective for all customer classes. 

BAU: Minnesota’s existing demand response comes primarily from interruptible tariffs and Other DR 

programs for Medium and Large C&I customers. The savings from interruptible tariffs account for at 

least 40% of the total impacts under all scenarios, resulting in smaller incremental differences between 

BAU and the potential scenarios in comparison to most states. The rest of the existing potential comes 

from direct load control programs for residential and Small and Medium C&I customers. 

Expanded BAU: DLC and dynamic pricing without enabling technology account for the growth in 

potential. Since current participation levels in interruptible tariffs is substantially high, there is not much 

scope for growth in this program. 

Achievable Participation: The increase in demand response potential comes primarily from dynamic 

pricing without enabling impacts. Dynamic pricing with enabling technology which is cost effective for 

the Medium C&I class contributes additional savings. 

Full Participation: Similar to Achievable Participation, the incremental impacts come from dynamic 

pricing. 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

2,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 17.8 GW) 

Minnesota DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 129 
 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Minnesota, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

304 1.7% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

304 1.7% 

0 0.0% 

15 0.1% 

428 2.4% 

0 0.0% 

0 0.0% 

443 2.5% 

0 0.0% 

492 2.8% 

304 1.7% 

0 0.0% 

0 0.0% 

796 4.5% 

0 0.0% 

656 3.7% 

304 1.7% 

0 0.0% 

0 0.0% 

959 5.4% 

0 0.0% 

0 0.0% 

170 1.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

170 1.0% 

0 0.0% 

0 0.0% 

170 1.0% 

0 0.0% 

0 0.0% 

170 1.0% 

0 0.0% 

3 0.0% 

170 1.0% 

0 0.0% 

0 0.0% 

173 1.0% 

0 0.0% 

4 0.0% 

170 1.0% 

0 0.0% 

0 0.0% 

174 1.0% 

0 0.0% 

0 0.0% 

11 0.1% 

38 0.2% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

49 0.3% 

0 0.0% 

7 0.0% 

27 0.2% 

61 0.3% 

0 0.0% 

96 0.5% 

127 0.7% 

102 0.6% 

11 0.1% 

61 0.3% 

0 0.0% 

302 1.7% 

372 2.1% 

69 0.4% 

11 0.1% 

61 0.3% 

0 0.0% 

514 2.9% 

0 0.0% 

1 0.0% 

0 0.0% 

1,290 7.2% 

242 1.4% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

1,533 8.6% 

0 0.0% 

5 0.0% 

0 0.0% 

1,290 7.2% 

242 1.4% 

1,537 8.6% 

0 0.0% 

121 0.7% 

0 0.0% 

1,290 7.2% 

242 1.4% 

1,653 9.3% 

0 0.0% 

202 1.1% 

0 0.0% 

1,290 7.2% 

242 1.4% 

1,734 9.7% 

0 0.0% 

1 0.0% 

485 2.7% 

1,329 7.4% 

242 1.4% 

Total 

(MW) 

Total 

(% of 

system) 

2,056 11.5% 

0 0.0% 

27 0.2% 

626 3.5% 

1,352 7.6% 

242 1.4% 

2,247 12.6% 

127 0.7% 

718 4.0% 

485 2.7% 

1,352 7.6% 

242 1.4% 

2,924 16.4% 

372 2.1% 

931 5.2% 

485 2.7% 

1,352 7.6% 

242 1.4% 

3,381 19.0% 

Minnesota System Peak Demand Forecasts by Scenario 

0 

2,000 

4,000 

6,000 

8,000 

10,000 

12,000 

14,000 

16,000 

18,000 

20,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

130 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

Mississippi State Profile 

Key drivers of Mississippi’s demand response potential estimate include: above average residential 

CAC saturation of 75% and a customer mix that has an above average share of peak demand in the 

residential and Large C&I classes (47% and 30%, respectively). Pricing with enabling technologies and 

DLC are cost effective for all customer classes in the state. 

BAU: Mississippi’s existing demand response comes solely from interruptible tariffs for the Large C&I 

class. 

Expanded BAU: Growth in demand response impacts is driven through the addition of Other DR 

programs for the Large C&I class and DLC for the residential class. Growth in the existing interruptible 

tariffs accounts for the remaining portion. 

Achievable Participation: Dynamic pricing with enabling impacts accounts for almost 50% of the 

increase in potential. Dynamic pricing without enabling technology contributes additional savings. 

Large C&I demand response potential is slightly lower than in the Expanded BAU scenario due to 

smaller per-customer impacts from pricing relative to Other DR. The movement of participants in Other 

DR to pricing also contributes to this effect. 

Full Participation: Similar to the Achievable Participation scenario, the impacts are dominated by the 

dynamic pricing options for all customer classes. Dynamic pricing with enabling represents over 75% 

of the potential under this scenario. This has the effect of reducing or eliminating the potential from all 

of the other demand response options, in particular, DLC and Other DR. 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 12.1 GW) 

Mississippi DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 131 
 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Mississippi, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

0 0.0% 

0 0.0% 

17 0.1% 

230 1.9% 

0 0.0% 

0 0.0% 

247 2.0% 

557 4.6% 

277 2.3% 

59 0.5% 

0 0.0% 

0 0.0% 

892 7.4% 

1,303 10.8% 

49 0.4% 

0 0.0% 

0 0.0% 

0 0.0% 

1,351 11.2% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

5 0.0% 

0 0.0% 

0 0.0% 

5 0.0% 

114 0.9% 

6 0.1% 

1 0.0% 

0 0.0% 

0 0.0% 

122 1.0% 

268 2.2% 

3 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

271 2.2% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

2 0.0% 

0 0.0% 

3 0.0% 

6 0.0% 

4 0.0% 

0 0.0% 

2 0.0% 

0 0.0% 

11 0.1% 

17 0.1% 

2 0.0% 

0 0.0% 

2 0.0% 

0 0.0% 

20 0.2% 

0 0.0% 

0 0.0% 

0 0.0% 

75 0.6% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

75 0.6% 

0 0.0% 

5 0.0% 

0 0.0% 

315 2.6% 

262 2.2% 

581 4.8% 

55 0.5% 

100 0.8% 

0 0.0% 

315 2.6% 

107 0.9% 

577 4.8% 

161 1.3% 

130 1.1% 

0 0.0% 

315 2.6% 

0 0.0% 

605 5.0% 

0 0.0% 

0 0.0% 

0 0.0% 

75 0.6% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

75 0.6% 

0 0.0% 

22 0.2% 

236 2.0% 

316 2.6% 

262 2.2% 

836 6.9% 

732 6.1% 

387 3.2% 

60 0.5% 

316 2.6% 

107 0.9% 

1,602 13.3% 

1,748 14.5% 

183 1.5% 

0 0.0% 

316 2.6% 

0 0.0% 

2,247 18.6% 

Mississippi System Peak Demand Forecasts by Scenario 

0 

2,000 

4,000 

6,000 

8,000 

10,000 

12,000 

14,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

132 A National Assessment of Demand Response Potential 
 

 

Appendix A – State Profiles 

Missouri State Profile 

Key drivers of Missouri’s demand response potential estimate include: above average residential CAC 

saturation of 87%, above average share of peak demand (51%) in the residential class, and a moderate 

amount of existing demand response. Pricing with enabling technologies and DLC are cost effective for 

all customer classes. 

BAU: Missouri’s existing demand response comes predominantly from interruptible tariffs for the 

Large C&I class. Direct load control programs for the other classes account for the remainder. 

Expanded BAU: Significant growth in DLC impacts is due to Missouri’s above average share of 

residential load. Growth for the Large C&I class in Other DR and interruptible tariffs account for the 

remaining portion. 

Achievable Participation: The increase in demand response potential comes primarily from dynamic 

pricing with enabling impacts which is cost effective for all classes. Dynamic pricing without enabling 

technology contributes additional potential for all customers. Large C&I demand response potential is 

slightly lower than in the Expanded BAU scenario due to smaller per-customer impacts from pricing 

relative to Other DR. The movement of participants in Other DR to pricing also contributes to this 

effect. 

Full Participation: Similar to the Achievable Participation scenario, the impacts are dominated by the 

dynamic pricing with enabling option for all customer classes. This has the effect of reducing or 

eliminating the potential from all of the other demand response options, in particular, DLC and Other 

DR. 

0 

500 

1,000 

1,500 

2,000 

2,500 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 21.1 GW) 

Missouri DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 133 
 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Missouri, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

29 0.1% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

29 0.1% 

0 0.0% 

30 0.1% 

809 3.8% 

0 0.0% 

0 0.0% 

840 4.0% 

977 4.6% 

450 2.1% 

207 1.0% 

0 0.0% 

0 0.0% 

1,634 7.7% 

2,285 10.8% 

38 0.2% 

29 0.1% 

0 0.0% 

0 0.0% 

2,352 11.1% 

0 0.0% 

0 0.0% 

29 0.1% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

29 0.1% 

0 0.0% 

0 0.0% 

29 0.1% 

0 0.0% 

0 0.0% 

29 0.1% 

93 0.4% 

6 0.0% 

29 0.1% 

0 0.0% 

0 0.0% 

127 0.6% 

217 1.0% 

3 0.0% 

29 0.1% 

0 0.0% 

0 0.0% 

249 1.2% 

0 0.0% 

0 0.0% 

5 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

5 0.0% 

0 0.0% 

6 0.0% 

13 0.1% 

39 0.2% 

0 0.0% 

58 0.3% 

117 0.6% 

93 0.4% 

5 0.0% 

39 0.2% 

0 0.0% 

254 1.2% 

341 1.6% 

64 0.3% 

5 0.0% 

39 0.2% 

0 0.0% 

449 2.1% 

0 0.0% 

0 0.0% 

0 0.0% 

219 1.0% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

219 1.0% 

0 0.0% 

6 0.0% 

0 0.0% 

638 3.0% 

328 1.6% 

972 4.6% 

69 0.3% 

126 0.6% 

0 0.0% 

638 3.0% 

134 0.6% 

966 4.6% 

202 1.0% 

163 0.8% 

0 0.0% 

638 3.0% 

0 0.0% 

1,002 4.7% 

0 0.0% 

0 0.0% 

63 0.3% 

219 1.0% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

282 1.3% 

0 0.0% 

43 0.2% 

851 4.0% 

677 3.2% 

328 1.6% 

1,899 9.0% 

1,255 5.9% 

674 3.2% 

241 1.1% 

677 3.2% 

134 0.6% 

2,982 14.1% 

3,045 14.4% 

268 1.3% 

63 0.3% 

677 3.2% 

0 0.0% 

4,052 19.2% 

Missouri System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

134 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

Montana State Profile 

Key drivers of Montana’s demand response potential estimate include: a higher than average share of 

peak demand (53%) in the Small C&I class and a moderate CAC saturation of 42%. Pricing with 

enabling technologies and DLC are cost effective for all customer classes in the state. 

BAU: Montana’s existing demand response comes solely from interruptible tariffs for the Large C&I 

class. 

Expanded BAU: Growth in demand response impacts is driven through the addition of Other DR 

programs for the Large C&I class and DLC for the residential and Small C&I classes. Growth in the 

interruptible tariffs accounts for the remaining portion. 

Achievable Participation: Dynamic pricing with enabling impacts accounts for over 50% of the 

increase in potential, with 20% of this increase due to the potential from Small C&I. Dynamic pricing 

without enabling technology contributes additional savings. 

Full Participation: Similar to the Achievable Participation scenario, the impacts are dominated by 

the dynamic pricing options for all customer classes. Dynamic pricing with enabling represents almost 

80% of the potential under this scenario. This has the effect of reducing or eliminating the potential 

from all of the other demand response options, in particular, DLC and Other DR. 

0 

50 

100 

150 

200 

250 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 3.7 GW) 

Montana DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 135 
 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Montana, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

0 0.0% 

0 0.0% 

2 0.0% 

51 1.4% 

0 0.0% 

0 0.0% 

52 1.4% 

84 2.3% 

63 1.7% 

13 0.3% 

0 0.0% 

0 0.0% 

160 4.3% 

196 5.3% 

35 1.0% 

0 0.0% 

0 0.0% 

0 0.0% 

232 6.3% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

2 0.1% 

0 0.0% 

0 0.0% 

2 0.1% 

69 1.9% 

5 0.1% 

1 0.0% 

0 0.0% 

0 0.0% 

74 2.0% 

160 4.4% 

3 0.1% 

0 0.0% 

0 0.0% 

0 0.0% 

163 4.4% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

2 0.1% 

0 0.0% 

3 0.1% 

6 0.2% 

5 0.1% 

0 0.0% 

2 0.1% 

0 0.0% 

13 0.4% 

18 0.5% 

3 0.1% 

0 0.0% 

2 0.1% 

0 0.0% 

24 0.6% 

0 0.0% 

0 0.0% 

0 0.0% 

7 0.2% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

7 0.2% 

0 0.0% 

0 0.0% 

0 0.0% 

53 1.4% 

27 0.7% 

80 2.2% 

6 0.2% 

10 0.3% 

0 0.0% 

53 1.4% 

11 0.3% 

80 2.2% 

16 0.4% 

13 0.4% 

0 0.0% 

53 1.4% 

0 0.0% 

83 2.2% 

0 0.0% 

0 0.0% 

0 0.0% 

7 0.2% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

7 0.2% 

0 0.0% 

2 0.1% 

53 1.4% 

55 1.5% 

27 0.7% 

137 3.7% 

164 4.5% 

82 2.2% 

14 0.4% 

55 1.5% 

11 0.3% 

326 8.9% 

391 10.7% 

55 1.5% 

0 0.0% 

55 1.5% 

0 0.0% 

501 13.6% 

Montana System Peak Demand Forecasts by Scenario 

2,700 

2,800 

2,900 

3,000 

3,100 

3,200 

3,300 

3,400 

3,500 

3,600 

3,700 

3,800 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

136 A National Assessment of Demand Response Potential 
 

 

Appendix A – State Profiles 

Nebraska State Profile 

Key drivers of Nebraska’s demand response potential estimate include: higher-than-average residential 

CAC saturation of 83%, a customer mix that has a moderate share of peak demand in the residential and 

Medium C&I classes (40% and 27%, respectively) and a substantial amount of existing demand 

response. Pricing with enabling technologies are cost effective for all customer classes, except for the 

Large C&I class. DLC is cost effective for all customer classes. 

BAU: Nebraska’s existing demand response comes predominantly from interruptible tariffs for Large 

C&I customers. The impacts from this option represent at least 30% of the total impacts under all 

scenarios. DLC for Small & Medium C&I accounts for the remaining portion of existing demand 

response. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of 

Other DR for the Large C&I class and DLC for the residential class. 

Achievable Participation: High CAC saturation in the residential sector drives a significant increase 

in demand response potential through dynamic pricing with enabling technologies. Large C&I demand 

response potential is slightly lower than in the Expanded BAU scenario due to smaller per-customer 

impacts from pricing relative to Other DR. The movement of participants in Other DR to pricing also 

contributes to this effect. 

Full Participation: Similar to the Achievable Participation scenario, high CAC saturation combined 

with a moderate share of load in the residential sector drives the increase in impacts. The impacts are 

dominated by pricing with enabling technologies for all customer classes except for the Large C&I 

customers. The pricing options have the effect of reducing or eliminating the potential from all of the 

other demand response options, in particular, DLC and Other DR. 

0 

100 

200 

300 

400 

500 

600 

700 

800 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 7.3 GW) 

Nebraska DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 137 
 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Nebraska, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

1 0.0% 

0 0.0% 

4 0.1% 

172 2.4% 

0 0.0% 

0 0.0% 

176 2.4% 

284 3.9% 

135 1.9% 

44 0.6% 

0 0.0% 

0 0.0% 

462 6.3% 

664 9.1% 

17 0.2% 

1 0.0% 

0 0.0% 

0 0.0% 

681 9.3% 

0 0.0% 

0 0.0% 

30 0.4% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

30 0.4% 

0 0.0% 

0 0.0% 

30 0.4% 

0 0.0% 

0 0.0% 

30 0.4% 

43 0.6% 

3 0.0% 

30 0.4% 

0 0.0% 

0 0.0% 

76 1.0% 

100 1.4% 

2 0.0% 

30 0.4% 

0 0.0% 

0 0.0% 

132 1.8% 

0 0.0% 

0 0.0% 

15 0.2% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

15 0.2% 

0 0.0% 

1 0.0% 

15 0.2% 

19 0.3% 

0 0.0% 

35 0.5% 

57 0.8% 

46 0.6% 

15 0.2% 

19 0.3% 

0 0.0% 

137 1.9% 

167 2.3% 

31 0.4% 

15 0.2% 

19 0.3% 

0 0.0% 

232 3.2% 

0 0.0% 

0 0.0% 

0 0.0% 

625 8.6% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

625 8.6% 

0 0.0% 

1 0.0% 

0 0.0% 

625 8.6% 

75 1.0% 

701 9.6% 

0 0.0% 

37 0.5% 

0 0.0% 

625 8.6% 

30 0.4% 

693 9.5% 

0 0.0% 

61 0.8% 

0 0.0% 

625 8.6% 

0 0.0% 

687 9.4% 

0 0.0% 

0 0.0% 

46 0.6% 

625 8.6% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

671 9.2% 

0 0.0% 

6 0.1% 

217 3.0% 

645 8.8% 

75 1.0% 

943 12.9% 

384 5.3% 

220 3.0% 

89 1.2% 

645 8.8% 

30 0.4% 

1,367 18.8% 

931 12.8% 

111 1.5% 

46 0.6% 

645 8.8% 

0 0.0% 

1,732 23.8% 

Nebraska System Peak Demand Forecasts by Scenario 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

7,000 

8,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

138 A National Assessment of Demand Response Potential 
 

 

 

Appendix A – State Profiles 

Nevada State Profile 

Key drivers of Nevada’s demand response potential estimate include: a very high residential CAC 

saturation of 87%, and a customer mix that has an above average share of peak demand in the 

residential sector. The rate of AMI deployment is likely to be at a lower-than-average rate. Dynamic 

pricing with enabling technology and DLC are cost effective for all customer classes in the state. 

Control of residential air-conditioning load is the key driver of demand response potential in Nevada. 

BAU: Nevada’s existing demand response comes primarily from residential DLC programs. However, 

current participation levels are low and there exists scope for significant growth in potential. 

Expanded BAU: Growth in demand response impacts is driven primarily through substantial expansion 

in residential DLC programs due to very high levels of CAC saturation in the state. Impacts also grow 

due to large C&I participation in ‘Interruptible’ and ‘Other DR’ programs. 

Achievable Participation: High CAC saturation in the residential sector drives a significant increase in 

demand response potential through pricing programs. Large C&I demand response potential is slightly 

lower than in the Expanded BAU scenario due to smaller per-customer impacts from pricing with 

technology relative to Other DR. 

Full Participation: Similar to the Achievable Participation scenario, high CAC saturation combined 

with a large share of residential load leads to substantial increase in impacts. The impacts are dominated 

by pricing with enabling technologies. Small and medium C&I potential from pricing programs 

increase. Large C&I potential is lower than in the Achievable scenario. This is because customers 

choose dynamic pricing over ‘Other DR’ programs, leading to a lower level of impacts caused by 

smaller per-customer impacts from pricing programs relative to ‘Other DR’. 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 9.2 GW) 

Nevada DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 139 
 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Nevada, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

22 0.2% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

22 0.2% 

0 0.0% 

12 0.1% 

356 3.9% 

0 0.0% 

0 0.0% 

368 4.0% 

682 7.4% 

313 3.4% 

90 1.0% 

0 0.0% 

0 0.0% 

1,085 11.8% 

1,596 17.4% 

25 0.3% 

22 0.2% 

0 0.0% 

0 0.0% 

1,642 17.9% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

4 0.0% 

0 0.0% 

0 0.0% 

4 0.0% 

94 1.0% 

6 0.1% 

1 0.0% 

0 0.0% 

0 0.0% 

102 1.1% 

221 2.4% 

4 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

225 2.4% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

2 0.0% 

7 0.1% 

0 0.0% 

10 0.1% 

23 0.2% 

17 0.2% 

1 0.0% 

7 0.1% 

0 0.0% 

49 0.5% 

67 0.7% 

11 0.1% 

0 0.0% 

7 0.1% 

0 0.0% 

85 0.9% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

2 0.0% 

0 0.0% 

259 2.8% 

186 2.0% 

447 4.9% 

39 0.4% 

71 0.8% 

0 0.0% 

259 2.8% 

75 0.8% 

444 4.8% 

113 1.2% 

91 1.0% 

0 0.0% 

259 2.8% 

0 0.0% 

464 5.1% 

0 0.0% 

0 0.0% 

22 0.2% 

0 0.0% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

22 0.2% 

0 0.0% 

14 0.2% 

363 3.9% 

267 2.9% 

186 2.0% 

830 9.0% 

838 9.1% 

407 4.4% 

92 1.0% 

267 2.9% 

75 0.8% 

1,679 18.3% 

1,996 21.7% 

131 1.4% 

22 0.2% 

267 2.9% 

0 0.0% 

2,416 26.3% 

Nevada System Peak Demand Forecasts by Scenario 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

7,000 

8,000 

9,000 

10,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

140 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

New Hampshire State Profile 

Key drivers of New Hampshire’s demand response potential estimate include: a higher than average 

share of large C&I peak load (33%) and large base of existing load participation in the ISO-NE market. 

It has a lower than national average residential CAC saturation at 13%, thereby limiting load reduction 

potential from DLC programs. Dynamic pricing with enabling technology is cost-effective only for 

residential and small C&I customers. DLC is cost-effective for all customer classes. 

BAU: New Hampshire’s existing demand response is primarily derived from ‘Other DR’ programs, 

due to large C&I load participation in the ISO-NE market. 

Expanded BAU: Growth in demand response impacts is driven primarily through the growth of 

Interruptible programs for large C&I customers. This is due to Rhode Island’s high share of large C&I 

load, which allow for growth in Interruptible programs. Potential for growth in ‘Other DR’ programs is 

limited due to current high participation levels. Load reductions from residential DLC programs also 

grow in this scenario. 

Achievable Participation: Growth in impacts in this scenario is driven by the potential derived through 

‘pricing without technology’ option, primarily from residential and large C&I customers. Growth in 

impacts from ‘pricing with technology’ comes from both residential and small C&I customers. ‘Other 

DR’ program potential remains at current high levels. 

Full Participation: Similar to the Achievable Participation scenario, increase in residential and small 

C&I customer participation in pricing options drive increase in impacts. Contribution from ‘Other DR’ 

and Interruptible programs continues to dominate for large C&I customers. 

0 

50 

100 

150 

200 

250 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 2.9 GW) 

New Hampshire DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 141 
 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in New Hampshire, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

2 0.1% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

2 0.1% 

0 0.0% 

2 0.1% 

21 0.7% 

0 0.0% 

0 0.0% 

23 0.8% 

32 1.1% 

45 1.6% 

5 0.2% 

0 0.0% 

0 0.0% 

82 2.9% 

76 2.6% 

41 1.4% 

2 0.1% 

0 0.0% 

0 0.0% 

119 4.1% 

0 0.0% 

0 0.0% 

3 0.1% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

3 0.1% 

0 0.0% 

0 0.0% 

3 0.1% 

0 0.0% 

0 0.0% 

3 0.1% 

25 0.9% 

2 0.1% 

3 0.1% 

0 0.0% 

0 0.0% 

30 1.0% 

58 2.0% 

1 0.0% 

3 0.1% 

0 0.0% 

0 0.0% 

62 2.2% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

2 0.1% 

0 0.0% 

0 0.0% 

0 0.0% 

2 0.1% 

0 0.0% 

3 0.1% 

0 0.0% 

0 0.0% 

0 0.0% 

3 0.1% 

0 0.0% 

9 0.3% 

0 0.0% 

0 0.0% 

87 3.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

95 3.3% 

0 0.0% 

9 0.3% 

0 0.0% 

74 2.6% 

124 4.3% 

206 7.2% 

0 0.0% 

26 0.9% 

0 0.0% 

74 2.6% 

87 3.0% 

186 6.5% 

0 0.0% 

43 1.5% 

0 0.0% 

74 2.6% 

87 3.0% 

203 7.1% 

0 0.0% 

9 0.3% 

5 0.2% 

0 0.0% 

87 3.0% 

Total 

(MW) 

Total 

(% of 

system) 

101 3.5% 

0 0.0% 

11 0.4% 

24 0.9% 

74 2.6% 

124 4.3% 

233 8.1% 

57 2.0% 

74 2.6% 

9 0.3% 

74 2.6% 

87 3.0% 

300 10.4% 

134 4.7% 

88 3.0% 

5 0.2% 

74 2.6% 

87 3.0% 

387 13.5% 

New Hampshire System Peak Demand Forecasts by Scenario 

2,200 

2,300 

2,400 

2,500 

2,600 

2,700 

2,800 

2,900 

3,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

142 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

New Jersey State Profile 

Key drivers of New Jersey’s demand response potential estimate include: high levels of large C&I load 

participation in the PJM market, a customer mix with almost 48% of the load from residential 

customers and 26% of the load from large C&I customers, and the potential to deploy AMI at a fasterthan-average rate. CAC saturation is at a moderate level of 55%. ‘Pricing with technology’ is costeffective for all customer classes. DLC is also cost effective for all customer classes in the state. 

BAU: New Jersey’s existing demand response comes primarily from large C&I load participation in 

the PJM market. The remaining comes from residential DLC programs. 

Expanded BAU: Increase in impacts for this scenario is primarily due to expansion in residential DLC 

programs and Interruptible programs for large C&I customers, driven by large share in load for these 

two customer classes. Also, the potential associated with large C&I participation in ‘Other DR’ 

programs grows. 

Achievable Participation: A high share of residential load in the total drives a substantial increase in 

impacts for residential customers through participation in pricing programs. In this scenario, impacts 

from residential DLC go back to current levels as customers choose pricing over DLC. For C&I 

customers, additional load reduction is obtained through pricing programs. 

Full Participation: Similar to the Achievable Participation scenario, high impacts in this scenario are 

largely driven by a high level of residential load participating in pricing programs. Also, load reduction 

from C&I customers participating in pricing programs increases. Large C&I load participation in ‘Other 

DR’ programs continues at current high participation levels. 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

2,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 20 GW) 

New Jersey DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 143 
 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in New Jersey, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

108 0.5% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

108 0.5% 

0 0.0% 

29 0.1% 

401 2.0% 

0 0.0% 

0 0.0% 

430 2.2% 

709 3.5% 

381 1.9% 

108 0.5% 

0 0.0% 

0 0.0% 

1,198 6.0% 

1,659 8.3% 

100 0.5% 

108 0.5% 

0 0.0% 

0 0.0% 

1,867 9.3% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

7 0.0% 

0 0.0% 

0 0.0% 

8 0.0% 

164 0.8% 

10 0.1% 

2 0.0% 

0 0.0% 

0 0.0% 

176 0.9% 

384 1.9% 

6 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

390 2.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

2 0.0% 

3 0.0% 

11 0.1% 

0 0.0% 

17 0.1% 

34 0.2% 

26 0.1% 

1 0.0% 

11 0.1% 

0 0.0% 

73 0.4% 

99 0.5% 

17 0.1% 

0 0.0% 

11 0.1% 

0 0.0% 

127 0.6% 

0 0.0% 

0 0.0% 

0 0.0% 

8 0.0% 

601 3.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

609 3.0% 

0 0.0% 

9 0.0% 

0 0.0% 

112 0.6% 

933 4.7% 

1,054 5.3% 

78 0.4% 

142 0.7% 

0 0.0% 

112 0.6% 

601 3.0% 

932 4.7% 

227 1.1% 

183 0.9% 

0 0.0% 

112 0.6% 

601 3.0% 

1,124 5.6% 

0 0.0% 

0 0.0% 

108 0.5% 

8 0.0% 

601 3.0% 

Total 

(MW) 

Total 

(% of 

system) 

717 3.6% 

0 0.0% 

41 0.2% 

411 2.1% 

123 0.6% 

933 4.7% 

1,508 7.5% 

985 4.9% 

559 2.8% 

111 0.6% 

123 0.6% 

601 3.0% 

2,379 11.9% 

2,369 11.9% 

307 1.5% 

108 0.5% 

123 0.6% 

601 3.0% 

3,508 17.5% 

New Jersey System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

144 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

New Mexico State Profile 

Key drivers of New Mexico’s demand response potential estimate include: a customer mix that has an 

above average share of peak demand for medium C&I customers (50%), and a large share of residential 

(86%) in the total number of customer accounts. New Mexico has a low level of existing demand 

response with significant potential for growth across all rate classes. Dynamic pricing with enabling 

technology is cost-effective for all customer classes. Also, DLC is cost effective for all customer 

classes. 

BAU: The state’s existing demand response comes primarily from large C&I participation in 

Interruptible programs. 

Expanded BAU: Growth in demand response potential under this scenario is derived through 

residential participation in DLC programs, and large C&I load participation in Interruptible and ‘Other 

DR’ programs. The potential for expansion is significant, given the low level of existing demand 

response. 

Achievable Participation: The potential increase in this scenario is primarily realized through 

residential pricing programs. The increase in impacts from the residential class is significant, given its 

high share in the total account population. Load reduction potential from C&I customers grow due to 

increased participation in pricing programs. Some of the large C&I customers participating in ‘Other 

DR’ programs choose to participate in the pricing programs. 

Full Participation: Similar to the Achievable Participation scenario, a very high share of residential 

accounts in the total number of customer accounts drive increase in impacts from residential pricing 

programs. For the small and medium C&I classes, impacts are dominated by pricing with enabling 

technology. However, for the large C&I customers, impacts are dominated by participation in 

Interruptible programs. 

0 

50 

100 

150 

200 

250 

300 

350 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 5.7 GW) 

New Mexico DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 145 
 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in New Mexico, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

0 0.0% 

0 0.0% 

4 0.1% 

40 0.7% 

0 0.0% 

0 0.0% 

44 0.8% 

120 2.1% 

88 1.6% 

10 0.2% 

0 0.0% 

0 0.0% 

218 3.8% 

280 4.9% 

49 0.9% 

0 0.0% 

0 0.0% 

0 0.0% 

329 5.8% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

2 0.0% 

0 0.0% 

0 0.0% 

3 0.0% 

32 0.6% 

2 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

34 0.6% 

74 1.3% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

76 1.3% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

2 0.0% 

7 0.1% 

15 0.3% 

0 0.0% 

24 0.4% 

46 0.8% 

35 0.6% 

3 0.0% 

15 0.3% 

0 0.0% 

100 1.8% 

135 2.4% 

23 0.4% 

0 0.0% 

15 0.3% 

0 0.0% 

173 3.0% 

0 0.0% 

0 0.0% 

0 0.0% 

41 0.7% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

41 0.7% 

0 0.0% 

1 0.0% 

0 0.0% 

157 2.8% 

93 1.6% 

251 4.4% 

19 0.3% 

35 0.6% 

0 0.0% 

157 2.8% 

38 0.7% 

249 4.4% 

57 1.0% 

46 0.8% 

0 0.0% 

157 2.8% 

0 0.0% 

259 4.6% 

0 0.0% 

0 0.0% 

0 0.0% 

41 0.7% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

41 0.7% 

0 0.0% 

7 0.1% 

50 0.9% 

172 3.0% 

93 1.6% 

322 5.7% 

217 3.8% 

161 2.8% 

14 0.2% 

172 3.0% 

38 0.7% 

601 10.6% 

546 9.6% 

119 2.1% 

0 0.0% 

172 3.0% 

0 0.0% 

837 14.7% 

New Mexico System Peak Demand Forecasts by Scenario 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

146 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

New York State Profile 

Key drivers of New York’s demand response potential estimate include: a very high level of load 

participating in NYISO demand response Programs, a customer mix with almost 40% of the load from 

residential customers, and the potential to deploy AMI at a faster-than-average rate. New York has a 

lower than average residential CAC saturation at 16.7%. ‘Pricing with technology’ and DLC are cost 

effective for all customer classes in the state. 

BAU: New York’s existing demand response comes primarily from large C&I load participation in the 

NYISO market. This dominates the potential estimated across all scenarios. 

Expanded BAU: Since current participation levels in NYISO demand response programs are 

substantially high, there is not much scope for growth in this program. Increase in impacts for this 

scenario is primarily derived from an expansion in residential DLC programs and Interruptible 

programs for large C&I customers. 

Achievable Participation: A moderately high share of residential load in the total drives a significant 

increase in demand response potential through pricing programs. For the C&I sector too, additional load 

reduction is derived through participation in pricing programs. However, impacts from ‘Other DR’ 

programs continue to dominate due to persistently high large C&I participation levels in NYISO 

market. 

Full Participation: Higher participation of residential and C&I load (primarily small and medium 

C&I) in pricing programs drive potential increase in this scenario, as compared to the ‘Achievable 

Participation’ scenario. However, the impacts are dominated by high level of large C&I participation in 

NYISO programs. 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

3,500 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 37.4 GW) 

New York DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 147 
 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in New York, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

21 0.1% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

21 0.1% 

0 0.0% 

21 0.1% 

387 1.0% 

0 0.0% 

0 0.0% 

408 1.1% 

443 1.2% 

485 1.3% 

99 0.3% 

0 0.0% 

0 0.0% 

1,026 2.7% 

1,035 2.8% 

392 1.0% 

21 0.1% 

0 0.0% 

0 0.0% 

1,448 3.9% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

25 0.1% 

0 0.0% 

0 0.0% 

25 0.1% 

272 0.7% 

17 0.0% 

6 0.0% 

0 0.0% 

0 0.0% 

295 0.8% 

636 1.7% 

10 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

647 1.7% 

0 0.0% 

0 0.0% 

10 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

10 0.0% 

0 0.0% 

11 0.0% 

35 0.1% 

71 0.2% 

0 0.0% 

117 0.3% 

215 0.6% 

168 0.4% 

14 0.0% 

71 0.2% 

0 0.0% 

467 1.2% 

627 1.7% 

111 0.3% 

10 0.0% 

71 0.2% 

0 0.0% 

819 2.2% 

0 0.0% 

0 0.0% 

0 0.0% 

104 0.3% 

2,668 7.1% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

2,772 7.4% 

0 0.0% 

7 0.0% 

0 0.0% 

164 0.4% 

2,668 7.1% 

2,839 7.6% 

82 0.2% 

149 0.4% 

0 0.0% 

164 0.4% 

2,668 7.1% 

3,063 8.2% 

240 0.6% 

193 0.5% 

0 0.0% 

164 0.4% 

2,668 7.1% 

3,265 8.7% 

0 0.0% 

0 0.0% 

31 0.1% 

104 0.3% 

2,668 7.1% 

Total 

(MW) 

Total 

(% of 

system) 

2,803 7.5% 

0 0.0% 

39 0.1% 

447 1.2% 

235 0.6% 

2,668 7.1% 

3,389 9.1% 

1,011 2.7% 

818 2.2% 

120 0.3% 

235 0.6% 

2,668 7.1% 

4,852 13.0% 

2,538 6.8% 

706 1.9% 

31 0.1% 

235 0.6% 

2,668 7.1% 

6,179 16.5% 

New York System Peak Demand Forecasts by Scenario 

28,000 

29,000 

30,000 

31,000 

32,000 

33,000 

34,000 

35,000 

36,000 

37,000 

38,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

148 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

North Carolina State Profile 

Key drivers of North Carolina’s demand response potential estimate include: above average residential 

CAC saturation of 84%, an above average share of peak demand (51%) in the residential class, and a 

moderate amount of existing demand response. Pricing with enabling technologies and DLC are cost 

effective for all customer classes in the state. 

BAU: North Carolina’s existing demand response comes primarily from residential DLC and 

interruptible tariffs for the Medium and Large C&I classes. The state is also one of the few states with a 

significant portion of price induced demand response. Other DR for the Large C&I class accounts for 

the remaining portion. 

Expanded BAU: Growth in demand response impacts is driven through the growth of Other DR 

programs for the Large C&I class and DLC for the residential class. Growth in dynamic pricing and 

existing interruptible tariffs account for the remaining portion. 

Achievable Participation: Dynamic pricing with enabling impacts accounts for almost 50% of the 

increase in potential. Dynamic pricing without enabling technology contributes additional savings. 

Large C&I demand response potential is slightly lower than in the Expanded BAU scenario due to 

smaller per-customer impacts from pricing relative to Other DR. The movement of participants in Other 

DR to pricing also contributes to this effect. 

Full Participation: Similar to the Achievable Participation scenario, the impacts are dominated by 

dynamic pricing with enabling technologies for all customer classes. This option represents over 75% of 

the potential in this scenario. The lower potential for Large C&I than in the other scenarios is due to 

participation changes within the different demand response options. 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 32.6 GW) 

North Carolina DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 149 
 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in North Carolina, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

547 1.7% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

547 1.7% 

0 0.0% 

67 0.2% 

1,022 3.1% 

0 0.0% 

0 0.0% 

1,089 3.3% 

2,038 6.2% 

952 2.9% 

547 1.7% 

0 0.0% 

0 0.0% 

3,537 10.8% 

4,768 14.6% 

98 0.3% 

547 1.7% 

0 0.0% 

0 0.0% 

5,413 16.6% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

14 0.0% 

0 0.0% 

0 0.0% 

15 0.0% 

203 0.6% 

11 0.0% 

4 0.0% 

0 0.0% 

0 0.0% 

218 0.7% 

476 1.5% 

6 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

482 1.5% 

0 0.0% 

0 0.0% 

0 0.0% 

93 0.3% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

93 0.3% 

0 0.0% 

11 0.0% 

12 0.0% 

108 0.3% 

0 0.0% 

132 0.4% 

226 0.7% 

150 0.5% 

5 0.0% 

108 0.3% 

0 0.0% 

488 1.5% 

659 2.0% 

73 0.2% 

0 0.0% 

108 0.3% 

0 0.0% 

840 2.6% 

0 0.0% 

62 0.2% 

0 0.0% 

608 1.9% 

79 0.2% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

749 2.3% 

0 0.0% 

62 0.2% 

0 0.0% 

707 2.2% 

1,134 3.5% 

1,902 5.8% 

94 0.3% 

171 0.5% 

0 0.0% 

707 2.2% 

464 1.4% 

1,436 4.4% 

275 0.8% 

222 0.7% 

0 0.0% 

707 2.2% 

79 0.2% 

1,283 3.9% 

0 0.0% 

62 0.2% 

547 1.7% 

701 2.1% 

79 0.2% 

Total 

(MW) 

Total 

(% of 

system) 

1,388 4.3% 

0 0.0% 

140 0.4% 

1,048 3.2% 

814 2.5% 

1,134 3.5% 

3,137 9.6% 

2,561 7.9% 

1,285 3.9% 

555 1.7% 

814 2.5% 

465 1.4% 

5,680 17.4% 

6,178 18.9% 

399 1.2% 

547 1.7% 

814 2.5% 

79 0.2% 

8,017 24.6% 

North Carolina System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

30,000 

35,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

150 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

North Dakota State Profile 

Key drivers of North Dakota’s demand response potential estimate include: an above average share of 

peak demand (27%) in the Small C&I class and a moderate CAC saturation of 51%. Pricing with 

enabling technologies and DLC are cost effective for all customer classes in the state. 

BAU: North Dakota’s existing demand response comes primarily from DLC programs for all classes, 

except for the Large C&I class. Price induced demand response for the Large C&I class accounts for the 

remaining portion. 

Expanded BAU: Growth in demand response impacts is driven through the addition of Other DR 

programs and interruptible tariffs. Growth in the existing residential DLC programs accounts for the 

remaining portion. 

Achievable Participation: Dynamic pricing with enabling impacts accounts for approximately 40% of 

the increase in potential, with 10% of this increase due to the potential from Small C&I. Dynamic 

pricing without enabling technology contributes additional savings. Large C&I demand response 

potential is slightly lower than in the Expanded BAU scenario due to smaller per-customer impacts 

from pricing relative to Other DR. The movement of participants in Other DR to pricing also 

contributes to this effect. 

Full Participation: Similar to the Achievable Participation scenario, the impacts are dominated by 

dynamic pricing with enabling technologies for all customer classes. This option represents almost 70% 

of the potential in this scenario. The pricing options have the effect of reducing or eliminating the 

potential from all of the other demand response options, in particular, Other DR for the Large C&I 

class. 

0 

50 

100 

150 

200 

250 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 3 GW) 

North Dakota DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 151 
 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in North Dakota, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

13 0.4% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

13 0.4% 

0 0.0% 

2 0.1% 

42 1.4% 

0 0.0% 

0 0.0% 

43 1.4% 

70 2.3% 

45 1.5% 

13 0.4% 

0 0.0% 

0 0.0% 

128 4.2% 

163 5.4% 

20 0.7% 

13 0.4% 

0 0.0% 

0 0.0% 

196 6.5% 

0 0.0% 

0 0.0% 

5 0.2% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

5 0.2% 

0 0.0% 

0 0.0% 

5 0.2% 

0 0.0% 

0 0.0% 

5 0.2% 

28 0.9% 

2 0.1% 

5 0.2% 

0 0.0% 

0 0.0% 

35 1.2% 

65 2.2% 

1 0.0% 

5 0.2% 

0 0.0% 

0 0.0% 

71 2.4% 

0 0.0% 

0 0.0% 

5 0.2% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

5 0.2% 

0 0.0% 

0 0.0% 

5 0.2% 

4 0.1% 

0 0.0% 

10 0.3% 

12 0.4% 

9 0.3% 

5 0.2% 

4 0.1% 

0 0.0% 

30 1.0% 

34 1.1% 

6 0.2% 

5 0.2% 

4 0.1% 

0 0.0% 

50 1.7% 

0 0.0% 

4 0.1% 

0 0.0% 

0 0.0% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

4 0.1% 

0 0.0% 

4 0.1% 

0 0.0% 

57 1.9% 

41 1.4% 

102 3.4% 

9 0.3% 

16 0.5% 

0 0.0% 

57 1.9% 

17 0.6% 

97 3.2% 

25 0.8% 

20 0.7% 

0 0.0% 

57 1.9% 

0 0.0% 

102 3.4% 

0 0.0% 

4 0.1% 

23 0.8% 

0 0.0% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

28 0.9% 

0 0.0% 

7 0.2% 

52 1.7% 

61 2.0% 

41 1.4% 

160 5.3% 

118 3.9% 

72 2.4% 

23 0.8% 

61 2.0% 

17 0.6% 

290 9.7% 

287 9.6% 

48 1.6% 

23 0.8% 

61 2.0% 

0 0.0% 

419 13.9% 

North Dakota System Peak Demand Forecasts by Scenario 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

3,500 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

152 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

Ohio State Profile 

Key drivers of Ohio’s demand response potential estimate include: a relatively high number of 

residential accounts at 5 million, higher-than-average residential CAC saturation of 63%, and a 

customer mix that has an above average share of peak demand in the large C&I class at 30%. AMI 

deployment is likely to take place at a lower-than-average rate for the state. ‘Pricing with technology’ is 

cost-effective for all customer classes. DLC is cost-effective for all customer classes. 

BAU: Ohio’s existing demand response comes primarily from large C&I load participation in ‘Other 

DR’ programs. Current demand response from DLC and ‘Interruptible’ programs is low. 

Expanded BAU: Growth in demand response impacts is driven primarily through participation in 

‘Interruptible’ and ‘Other DR’ programs for large C&I customers. Also, there is a significant growth in 

impacts coming from residential DLC programs. This is due to Ohio’s high level of residential accounts 

with a higher than average CAC saturation. 

Achievable Participation: High residential customer participation in dynamic pricing options drives 

the increase in demand response potential for this scenario. C&I customers participate in ‘pricing with 

technology’ that also leads to an increase in impacts. Large C&I demand response potential is lower 

than in the Expanded BAU scenario due to smaller per-customer impacts from pricing programs relative 

to ‘Other DR’ program impacts. 

Full Participation: Similar to the Achievable Participation scenario, increase in potential is driven by 

a high level of residential and C&I customer participation in ‘pricing with technology’ option. 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

3,500 

4,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 38.6 GW) 

Ohio DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 153 
 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Ohio, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

10 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

10 0.0% 

0 0.0% 

32 0.1% 

747 1.9% 

0 0.0% 

0 0.0% 

779 2.0% 

1,095 2.8% 

615 1.6% 

190 0.5% 

0 0.0% 

0 0.0% 

1,900 4.9% 

2,562 6.6% 

190 0.5% 

10 0.0% 

0 0.0% 

0 0.0% 

2,761 7.1% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

11 0.0% 

0 0.0% 

0 0.0% 

12 0.0% 

258 0.7% 

16 0.0% 

3 0.0% 

0 0.0% 

0 0.0% 

277 0.7% 

605 1.6% 

9 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

614 1.6% 

0 0.0% 

0 0.0% 

2 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

2 0.0% 

0 0.0% 

7 0.0% 

21 0.1% 

53 0.1% 

0 0.0% 

83 0.2% 

160 0.4% 

128 0.3% 

9 0.0% 

53 0.1% 

0 0.0% 

350 0.9% 

468 1.2% 

87 0.2% 

2 0.0% 

53 0.1% 

0 0.0% 

610 1.6% 

0 0.0% 

13 0.0% 

0 0.0% 

8 0.0% 

450 1.2% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

471 1.2% 

0 0.0% 

13 0.0% 

0 0.0% 

1,492 3.9% 

1,891 4.9% 

3,396 8.8% 

156 0.4% 

284 0.7% 

0 0.0% 

1,492 3.9% 

772 2.0% 

2,704 7.0% 

457 1.2% 

369 1.0% 

0 0.0% 

1,492 3.9% 

450 1.2% 

2,768 7.2% 

0 0.0% 

13 0.0% 

11 0.0% 

8 0.0% 

451 1.2% 

Total 

(MW) 

Total 

(% of 

system) 

483 1.2% 

0 0.0% 

54 0.1% 

779 2.0% 

1,546 4.0% 

1,891 4.9% 

4,270 11.1% 

1,670 4.3% 

1,043 2.7% 

202 0.5% 

1,546 4.0% 

772 2.0% 

5,231 13.5% 

4,091 10.6% 

655 1.7% 

11 0.0% 

1,546 4.0% 

451 1.2% 

6,753 17.5% 

Ohio System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

30,000 

35,000 

40,000 

45,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

154 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

Oklahoma State Profile 

Key drivers of Oklahoma’s demand response potential estimate include: higher-than-average residential 

CAC saturation of 84%, and a customer mix that has an above average share of peak demand in the 

residential class (50%). The level of existing demand response is low. ‘Pricing with technology’ is costeffective for all customers, except for the small C&I class. DLC is cost effective for all customer classes 

in the state. 

BAU: Oklahoma’s existing demand response comes primarily from load enrolled in ‘Interruptible’ and 

‘Other DR’ programs for C&I customers. 

Expanded BAU: The residential sector has a high potential for growth due to high CAC saturation 

level, coupled with a low base of existing programs. In this scenario, growth in demand response 

impacts is driven primarily through the addition of residential DLC programs and through increase in 

large C&I load participation in ‘Interruptible’ and ‘Other DR’ programs. 

Achievable Participation: High CAC saturation in the residential sector drives a significant increase in 

demand response potential through ‘pricing with technology’ option. Large C&I demand response 

potential is slightly lower than in the Expanded BAU scenario due to smaller per-customer impacts 

from pricing programs relative to Other DR. 

Full Participation: Similar to the Achievable Participation scenario, high CAC saturation combined 

with a large share of load in the residential sector drives increase in impacts. Increase in impacts is 

dominated by ‘pricing with technology’, which is cost-effective for all customer classes. Large C&I 

potential decreases, due to smaller per-customer impacts from pricing programs relative to Other DR. 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

2,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 14.2 GW) 

Oklahoma DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 155 
 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Oklahoma, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

0 0.0% 

0 0.0% 

21 0.1% 

351 2.5% 

0 0.0% 

0 0.0% 

372 2.6% 

746 5.3% 

350 2.5% 

90 0.6% 

0 0.0% 

0 0.0% 

1,185 8.3% 

1,744 12.3% 

38 0.3% 

0 0.0% 

0 0.0% 

0 0.0% 

1,782 12.6% 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

1 0.0% 

0 0.0% 

0 0.0% 

5 0.0% 

0 0.0% 

0 0.0% 

6 0.0% 

0 0.0% 

5 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

7 0.0% 

0 0.0% 

7 0.1% 

1 0.0% 

0 0.0% 

0 0.0% 

8 0.1% 

0 0.0% 

0 0.0% 

0 0.0% 

3 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

3 0.0% 

0 0.0% 

4 0.0% 

13 0.1% 

12 0.1% 

0 0.0% 

29 0.2% 

101 0.7% 

67 0.5% 

5 0.0% 

12 0.1% 

0 0.0% 

185 1.3% 

295 2.1% 

33 0.2% 

0 0.0% 

12 0.1% 

0 0.0% 

339 2.4% 

0 0.0% 

0 0.0% 

0 0.0% 

8 0.1% 

10 0.1% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

18 0.1% 

0 0.0% 

4 0.0% 

0 0.0% 

258 1.8% 

605 4.3% 

867 6.1% 

50 0.4% 

91 0.6% 

0 0.0% 

258 1.8% 

247 1.7% 

646 4.5% 

146 1.0% 

118 0.8% 

0 0.0% 

258 1.8% 

10 0.1% 

532 3.7% 

0 0.0% 

0 0.0% 

1 0.0% 

11 0.1% 

10 0.1% 

Total 

(MW) 

Total 

(% of 

system) 

22 0.2% 

0 0.0% 

30 0.2% 

369 2.6% 

270 1.9% 

605 4.3% 

1,273 9.0% 

896 6.3% 

514 3.6% 

96 0.7% 

270 1.9% 

247 1.7% 

2,023 14.2% 

2,185 15.4% 

196 1.4% 

1 0.0% 

270 1.9% 

10 0.1% 

2,662 18.7% 

Oklahoma System Peak Demand Forecasts by Scenario 

0 

2,000 

4,000 

6,000 

8,000 

10,000 

12,000 

14,000 

16,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

156 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

Oregon State Profile 

Key drivers of Oregon’s demand response potential estimate include: a moderate residential base with 

1.6 million accounts, a customer mix that has an above average share of peak demand in the medium 

C&I class (35%), and the potential to deploy AMI at a faster-than-average rate. Dynamic pricing with 

enabling technology and DLC are cost effective for all customer classes in the state. Oregon has a 

moderate residential CAC saturation value of 38%. 

BAU: Oregon has a low level of existing demand response, primarily associated with large C&I 

participation in ‘Other DR’ programs for one of the IOUs in the region. Dominance on hydro power for 

generation in the Pacific Northwest region has historically led to low levels of demand response 

resources. 

Expanded BAU: Growth in demand response impacts is driven primarily through the addition of DLC 

programs for residential customers, and through C&I load participation in ‘Interruptible’ and ‘Other 

DR’ programs. The potential for growth is significant, since existing demand response is at a very low 

level. 

Achievable Participation: The increase in impacts is primarily associated with pricing programs. 

Participation of residential customers in ‘Pricing with technology’ option drives a significant increase in 

demand response potential. Also, impacts from ‘pricing without technology’ increase across all 

customer classes. 

Full Participation: Similar to the Achievable Participation scenario, impacts are dominated by 

‘pricing with enabling technology’. Residential impacts grow substantially due to significantly higher 

participation in pricing programs. Among the three C&I rate classes, medium C&I impacts dominate 

due to its high share in the overall peak load. 

0 

200 

400 

600 

800 

1,000 

1,200 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 12.8 GW) 

Oregon DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 157 
 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Oregon, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

2 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

2 0.0% 

0 0.0% 

18 0.1% 

168 1.3% 

0 0.0% 

0 0.0% 

187 1.5% 

336 2.6% 

275 2.2% 

43 0.3% 

0 0.0% 

0 0.0% 

654 5.1% 

786 6.2% 

173 1.4% 

2 0.0% 

0 0.0% 

0 0.0% 

961 7.5% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

4 0.0% 

0 0.0% 

0 0.0% 

5 0.0% 

52 0.4% 

3 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

57 0.4% 

122 1.0% 

2 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

124 1.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

8 0.1% 

14 0.1% 

39 0.3% 

0 0.0% 

61 0.5% 

119 0.9% 

91 0.7% 

6 0.0% 

39 0.3% 

0 0.0% 

254 2.0% 

347 2.7% 

58 0.5% 

0 0.0% 

39 0.3% 

0 0.0% 

444 3.5% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

3 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

3 0.0% 

0 0.0% 

2 0.0% 

0 0.0% 

143 1.1% 

26 0.2% 

171 1.3% 

21 0.2% 

39 0.3% 

0 0.0% 

143 1.1% 

11 0.1% 

214 1.7% 

63 0.5% 

51 0.4% 

0 0.0% 

143 1.1% 

3 0.0% 

259 2.0% 

0 0.0% 

0 0.0% 

2 0.0% 

0 0.0% 

3 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

5 0.0% 

0 0.0% 

29 0.2% 

187 1.5% 

182 1.4% 

26 0.2% 

424 3.3% 

528 4.1% 

408 3.2% 

50 0.4% 

182 1.4% 

11 0.1% 

1,179 9.2% 

1,318 10.3% 

284 2.2% 

2 0.0% 

182 1.4% 

3 0.0% 

1,788 14.0% 

Oregon System Peak Demand Forecasts by Scenario 

0 

2,000 

4,000 

6,000 

8,000 

10,000 

12,000 

14,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

158 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

Pennsylvania State Profile 

Key drivers of Pennsylvania’s demand response potential estimate include: a relatively high level of 

load participation in the PJM market, a high residential population base with 50% CAC saturation, 

customer mix that has an above average share of peak demand for large C&I customers, and the 

potential to deploy AMI at a faster-than-average rate. Pricing with enabling technology and DLC are 

cost-effective for all customer classes. 

BAU: Pennsylvania’s existing demand response comes primarily from large C&I load participation in 

the PJM market. A portion of the existing demand response potential also comes from legacy 

interruptible programs in the state, along with residential DLC program. 

Expanded BAU: Growth in demand response impacts is driven primarily through the increase of 

‘Other DR’ programs for the large C&I class (due to higher load participation in the PJM market), and 

the expansion of DLC programs for residential customers. Load reduction potential associated with 

interruptible programs also grows, due to Pennsylvania’s high share of large C&I load. 

Achievable Participation: For this scenario, growth in residential impacts is associated with the 

pricing options. C&I customer participation in ‘pricing with technology’ cause a growth in potential. 

‘Other DR’ programs continue to dominate the load reduction potential for large C&I customers. 

Full Participation: Similar to the Achievable Participation scenario, high residential and C&I 

customer participation in the pricing options (primarily ‘pricing with technology’) drives the increase in 

impacts. ‘Other DR’ programs for large C&I customers maintain their large share in the total potential. 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

3,500 

4,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW) 

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 36.5 GW) 

Pennsylvania DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 159 
 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Pennsylvania, 2019 

Residential 

(MW) 

Residential 

(% of 

system) 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

Total 

(MW) 

Total 

(% of 

system) 

BAU 

Pricing with Technology 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

0 

108 

0 

0 

0.0% 

0.3% 

0.0% 

0.0% 

0 

0 

0 

0 

0.0% 

0.0% 

0.0% 

0.0% 

0 

0 

0 

0 

0.0% 

0.0% 

0.0% 

0.0% 

0 

0 

338 

1,969 

0.0% 

0.0% 

0.9% 

5.4% 

0 

108 

338 

1,969 

0.0% 

0.3% 

0.9% 

5.4% 

Total 108 0.3% 0 0.0% 0 0.0% 2,307 6.3% 2,415 6.6% 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

0 

46 

641 

0 

0 

0.0% 

0.1% 

1.8% 

0.0% 

0.0% 

0 

1 

12 

0 

0 

0.0% 

0.0% 

0.0% 

0.0% 

0.0% 

0 

10 

27 

43 

0 

0.0% 

0.0% 

0.1% 

0.1% 

0.0% 

0 

16 

0 

916 

1,969 

0.0% 

0.0% 

0.0% 

2.5% 

5.4% 

0 

73 

679 

958 

1,969 

0.0% 

0.2% 

1.9% 

2.6% 

5.4% 

Total 687 1.9% 13 0.0% 79 0.2% 2,901 7.9% 3,680 10.1% 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

887 

582 

166 

0 

2.4% 

1.6% 

0.5% 

0.0% 

253 

16 

3 

0 

0.7% 

0.0% 

0.0% 

0.0% 

129 

101 

11 

43 

0.4% 

0.3% 

0.0% 

0.1% 

129 

235 

0 

916 

0.4% 

0.6% 

0.0% 

2.5% 

1,398 

934 

180 

958 

3.8% 

2.6% 

0.5% 

2.6% 

Other DR Programs 0 0.0% 0 0.0% 0 0.0% 1,969 5.4% 1,969 5.4% 

Total 1,635 4.5% 272 0.7% 283 0.8% 3,250 8.9% 5,439 14.9% 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

2,075 

266 

108 

0 

0 

5.7% 

0.7% 

0.3% 

0.0% 

0.0% 

592 

10 

0 

0 

0 

1.6% 

0.0% 

0.0% 

0.0% 

0.0% 

377 

66 

0 

43 

0 

1.0% 

0.2% 

0.0% 

0.1% 

0.0% 

378 

305 

0 

916 

1,969 

1.0% 

0.8% 

0.0% 

2.5% 

5.4% 

3,422 

647 

108 

958 

1,969 

9.4% 

1.8% 

0.3% 

2.6% 

5.4% 

Total 2,450 6.7% 602 1.6% 486 1.3% 3,568 9.8% 7,105 19.5% 

Pennsylvania System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

30,000 

35,000 

40,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

160 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

Rhode Island State Profile 

Rhode Island has a higher than average share of large C&I peak load (29%). The state’s demand 

response potential is driven by large C&I load participation in the ISO-NE market. Rhode Island has a 

lower than average residential CAC saturation at 12%. Dynamic pricing with enabling technology is 

cost-effective only for residential and large C&I customers, thereby restricting the potential that can be 

derived from this option. DLC is cost-effective for all customer classes. It has a lower than average 

AMI deployment rate. 

BAU: Rhode Island’s existing demand response is derived from ‘Other DR’ programs, due to large 

C&I load participation in the ISO-NE market. 

Expanded BAU: Growth in demand response impacts is driven primarily through the growth of 

Interruptible programs for large C&I customers. This is due to Rhode Island’s high share of large C&I 

load, which allow for growth in Interruptible programs. Also, there is a potential for growth in 

residential DLC programs. 

Achievable Participation: Growth in impacts in this scenario is driven by the potential derived from 

pricing options, primarily from residential customers and to a smaller extent from medium C&I 

customers. Since ‘pricing with technology’ is cost-effective only for residential and large C&I 

customers, there is a low growth in potential associated with this option. Potential through large C&I 

load participation in the ISO-NE market dominates overall other types of demand response programs. 

Full Participation: Similar to the Achievable Participation scenario, increase in customer participation 

in pricing options, primarily for residential and medium C&I customers, drives the increase in impacts. 

Similar to the other scenarios, large C&I load maintains high participation levels in the ISO-NE market. 

0 

50 

100 

150 

200 

250 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 2 GW) 

Rhode Island DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 161 
 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Rhode Island, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

14 0.7% 

0 0.0% 

0 0.0% 

15 0.7% 

19 0.9% 

28 1.4% 

4 0.2% 

0 0.0% 

0 0.0% 

50 2.5% 

44 2.2% 

26 1.3% 

0 0.0% 

0 0.0% 

0 0.0% 

70 3.4% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

3 0.2% 

4 0.2% 

0 0.0% 

7 0.4% 

0 0.0% 

16 0.8% 

1 0.1% 

4 0.2% 

0 0.0% 

20 1.0% 

0 0.0% 

26 1.3% 

0 0.0% 

4 0.2% 

0 0.0% 

30 1.5% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

140 6.9% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

140 6.9% 

0 0.0% 

0 0.0% 

0 0.0% 

44 2.2% 

140 6.9% 

183 9.1% 

7 0.3% 

12 0.6% 

0 0.0% 

44 2.2% 

140 6.9% 

201 10.0% 

19 0.9% 

15 0.8% 

0 0.0% 

44 2.2% 

140 6.9% 

218 10.8% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

140 6.9% 

Total 

(MW) 

Total 

(% of 

system) 

140 6.9% 

0 0.0% 

1 0.1% 

18 0.9% 

47 2.3% 

140 6.9% 

206 10.2% 

25 1.2% 

56 2.8% 

5 0.2% 

47 2.3% 

140 6.9% 

273 13.5% 

63 3.1% 

68 3.4% 

0 0.0% 

47 2.3% 

140 6.9% 

318 15.7% 

Rhode Island System Peak Demand Forecasts by Scenario 

1,500 

1,600 

1,700 

1,800 

1,900 

2,000 

2,100 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

162 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

South Carolina State Profile 

Key drivers of South Carolina’s demand response potential estimate include: higher-than-average 

residential CAC saturation of 84 percent and a moderate amount of existing demand response. An 

expectation for AMI deployment that slightly lags the national average could lead to less potential 

demand response. Enabling technologies and DLC are cost-effective for all customer classes in the 

state. 

BAU: South Carolina’s existing demand response comes primarily from an interruptible tariff program 

for both Medium and Large C&I classes. A small amount comes from pricing without technology for 

the Large C&I class. 

Expanded BAU: Growth in demand response impacts are driven through the addition of Other DR 

programs for the Large C&I class, which currently do not exist in the state. Significant growth also 

results from residential participation in DLC programs and large C&I customer participation in 

Interruptible tariffs. 

Achievable Participation: High CAC saturation in the Residential sector drives a significant increase 

in demand response potential through dynamic pricing, with the majority of customers increasing 

impacts through the use of enabling technologies. Medium C&I demand response potential is slightly 

increased through the addition of dynamic pricing. Large C&I demand response potential is slightly 

lower than in the Expanded BAU scenario due to smaller per-customer impacts from pricing with 

technology relative to Other DR. 

Full Participation: Residential potential demand response increases dramatically due to dynamic 

pricing with technology reaching more customers. Again, high CAC saturation leads to large demand 

response potential for the residential sector. Dynamic pricing with technology modestly increases the 

demand response potential for the remaining sectors. 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 20.8 GW) 

South Carolina DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 163 
 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in South Carolina, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

5 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

5 0.0% 

0 0.0% 

27 0.1% 

343 1.6% 

0 0.0% 

0 0.0% 

370 1.8% 

1,086 5.2% 

506 2.4% 

87 0.4% 

0 0.0% 

0 0.0% 

1,679 8.1% 

2,541 12.2% 

50 0.2% 

5 0.0% 

0 0.0% 

0 0.0% 

2,596 12.5% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

5 0.0% 

0 0.0% 

0 0.0% 

6 0.0% 

147 0.7% 

8 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

156 0.8% 

344 1.7% 

4 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

348 1.7% 

0 0.0% 

0 0.0% 

0 0.0% 

423 2.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

423 2.0% 

0 0.0% 

5 0.0% 

5 0.0% 

489 2.3% 

0 0.0% 

499 2.4% 

129 0.6% 

86 0.4% 

2 0.0% 

489 2.3% 

0 0.0% 

706 3.4% 

377 1.8% 

42 0.2% 

0 0.0% 

489 2.3% 

0 0.0% 

907 4.4% 

0 0.0% 

76 0.4% 

0 0.0% 

307 1.5% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

383 1.8% 

0 0.0% 

76 0.4% 

0 0.0% 

563 2.7% 

394 1.9% 

1,034 5.0% 

83 0.4% 

150 0.7% 

0 0.0% 

563 2.7% 

161 0.8% 

957 4.6% 

242 1.2% 

195 0.9% 

0 0.0% 

563 2.7% 

0 0.0% 

1,000 4.8% 

0 0.0% 

76 0.4% 

5 0.0% 

730 3.5% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

811 3.9% 

0 0.0% 

109 0.5% 

353 1.7% 

1,052 5.1% 

395 1.9% 

1,909 9.2% 

1,445 6.9% 

750 3.6% 

91 0.4% 

1,052 5.1% 

161 0.8% 

3,498 16.8% 

3,503 16.8% 

291 1.4% 

5 0.0% 

1,052 5.1% 

0 0.0% 

4,851 23.3% 

South Carolina System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

164 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

South Dakota State Profile 

Key drivers of South Dakota’s demand response potential estimate include: higher-than-average 

residential CAC saturation of 71 percent and a small amount of existing demand response. Enabling 

technologies are cost-effective for all C&I classes and Residential customers. Also, AMI deployment 

that potentially lags the national average could lead to slower realized demand response potential. 

BAU: South Dakota’s existing demand response comes primarily from direct load control for both the 

Residential and Small C&I classes. A small amount of demand response comes from the Large C&I 

class, in the form of interruptible tariffs. 

Expanded BAU: Growth in demand response is driven equally through an interruptible tariff program 

and other demand response programs for the Large C&I class. The other category of demand response 

programs does not currently exist in the state. Residential DLC contributes to increased demand 

response potential, as well. 

Achievable Participation: Increases in this scenario result from dynamic pricing programs, both with 

and without enabling technology, primarily through participation of residential and small C&I 

customers in these pricing programs. 

Full Participation: Demand response potential is further realized through increases in both dynamic 

pricing programs. Large C&I customers that were in other demand response programs have shifted in 

to both dynamic pricing programs, with the majority enrolling in the with technology option. Again, 

higher-than-average CAC saturation results in the Residential class having the largest amount of 

potential demand response, with a very large fraction coming in the form of dynamic pricing with 

enabling technologies. 

0 

50 

100 

150 

200 

250 

300 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 2.7 GW) 

South Dakota DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 165 
 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in South Dakota, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

13 0.5% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

13 0.5% 

0 0.0% 

2 0.1% 

41 1.5% 

0 0.0% 

0 0.0% 

43 1.6% 

100 3.7% 

53 2.0% 

13 0.5% 

0 0.0% 

0 0.0% 

165 6.2% 

234 8.7% 

13 0.5% 

13 0.5% 

0 0.0% 

0 0.0% 

259 9.6% 

0 0.0% 

0 0.0% 

13 0.5% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

13 0.5% 

0 0.0% 

0 0.0% 

13 0.5% 

0 0.0% 

0 0.0% 

13 0.5% 

33 1.2% 

2 0.1% 

13 0.5% 

0 0.0% 

0 0.0% 

48 1.8% 

76 2.8% 

1 0.0% 

13 0.5% 

0 0.0% 

0 0.0% 

90 3.4% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

1 0.0% 

0 0.0% 

2 0.1% 

2 0.1% 

2 0.1% 

1 0.0% 

1 0.0% 

0 0.0% 

6 0.2% 

7 0.3% 

1 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

9 0.3% 

0 0.0% 

0 0.0% 

0 0.0% 

4 0.2% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

4 0.2% 

0 0.0% 

0 0.0% 

0 0.0% 

67 2.5% 

33 1.2% 

100 3.7% 

7 0.3% 

13 0.5% 

0 0.0% 

67 2.5% 

14 0.5% 

100 3.7% 

20 0.8% 

16 0.6% 

0 0.0% 

67 2.5% 

0 0.0% 

103 3.8% 

0 0.0% 

0 0.0% 

26 1.0% 

4 0.2% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

30 1.1% 

0 0.0% 

3 0.1% 

55 2.0% 

67 2.5% 

33 1.2% 

158 5.9% 

142 5.3% 

69 2.6% 

26 1.0% 

67 2.5% 

14 0.5% 

318 11.8% 

337 12.6% 

31 1.2% 

26 1.0% 

67 2.5% 

0 0.0% 

462 17.2% 

South Dakota System Peak Demand Forecasts by Scenario 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

166 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

Tennessee State Profile 

Key drivers of Tennessee’s demand response potential estimate include: higher-than-average residential 

CAC saturation of 81 percent and a moderate amount of existing demand response. Dynamic pricing 

with enabling technologies are cost-effective for all customer classes. AMI deployment that potentially 

lags the national average could lead to slower realized demand response potential. Large C&I 

represents a significantly smaller-than-average share of peak (6%), resulting in a smaller state-wide 

impact for this class. 

BAU: Tennessee has existing demand response for Medium and Large C&I classes, through 

participation in Interruptible tariffs. A smaller impact comes from Large C&I due to this class 

representing a smaller portion of overall peak. 

Expanded BAU: Demand response potential increase is driven by DLC for Residential customers. 

Smaller increases result Interruptible and ‘Other DR’ programs, for the remaining classes. 

Achievable Participation: Significant potential comes from the two pricing programs, mostly for the 

residential class of customers. Residential potential demand response is driven by high CAC saturation, 

leading to this class representing a large share of system peak. 

Full Participation: Demand response potential increases are driven mostly by pricing with enabling 

technology, for all customer classes. This is most pronounced for the residential customers who switch 

from DLC programs in to pricing with technologies. Again, high CAC saturation drives most of the 

potential impact for this class of customers. 

0 

500 

1,000 

1,500 

2,000 

2,500 

3,000 

3,500 

4,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 27.6 GW) 

Tennessee DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 167 
 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Tennessee, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

0 0.0% 

0 0.0% 

30 0.1% 

586 2.1% 

0 0.0% 

0 0.0% 

617 2.2% 

1,515 5.5% 

717 2.6% 

149 0.5% 

0 0.0% 

0 0.0% 

2,381 8.6% 

3,544 12.8% 

85 0.3% 

0 0.0% 

0 0.0% 

0 0.0% 

3,629 13.1% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

9 0.0% 

0 0.0% 

0 0.0% 

10 0.0% 

282 1.0% 

16 0.1% 

2 0.0% 

0 0.0% 

0 0.0% 

300 1.1% 

660 2.4% 

8 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

668 2.4% 

0 0.0% 

0 0.0% 

0 0.0% 

809 2.9% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

809 2.9% 

0 0.0% 

8 0.0% 

13 0.0% 

930 3.4% 

0 0.0% 

951 3.4% 

262 0.9% 

174 0.6% 

5 0.0% 

930 3.4% 

0 0.0% 

1,370 5.0% 

765 2.8% 

84 0.3% 

0 0.0% 

930 3.4% 

0 0.0% 

1,779 6.4% 

0 0.0% 

0 0.0% 

0 0.0% 

425 1.5% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

425 1.5% 

0 0.0% 

2 0.0% 

0 0.0% 

488 1.8% 

137 0.5% 

627 2.3% 

29 0.1% 

52 0.2% 

0 0.0% 

488 1.8% 

55 0.2% 

624 2.3% 

83 0.3% 

67 0.2% 

0 0.0% 

488 1.8% 

0 0.0% 

639 2.3% 

0 0.0% 

0 0.0% 

0 0.0% 

1,234 4.5% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

1,234 4.5% 

0 0.0% 

41 0.1% 

608 2.2% 

1,418 5.1% 

137 0.5% 

2,204 8.0% 

2,087 7.6% 

959 3.5% 

156 0.6% 

1,418 5.1% 

56 0.2% 

4,676 16.9% 

5,053 18.3% 

245 0.9% 

0 0.0% 

1,418 5.1% 

0 0.0% 

6,715 24.3% 

Tennessee System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

30,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

168 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

Texas State Profile 

Key drivers of demand response potential in Texas include: higher-than-average residential CAC 

saturation of 80 percent and very little existing demand response. Enabling technologies are costeffective for all customer classes, except for small C&I customers. Also, potential AMI deployment 

significantly leads the national average and could lead to faster realization of potential demand 

response. 

BAU: The majority of Texas’s current demand response comes from the Large C&I class, through 

participation in Interruptible tariffs and ‘Other DR’ programs in the ERCOT market. The state has a 

small amount of direct load control for the other customer classes. 

Expanded BAU: High CAC saturation leads to growth in residential demand response potential 

through direct load control. Most of the remaining growth in potential comes from the Large C&I class, 

through participation in Interruptible and ‘Other DR’ programs. 

Achievable Participation: High CAC saturation coupled with faster-than-average AMI deployment 

lead to significant potential acceptance of dynamic pricing for the Residential class. Some residential 

growth results from customers shifting from DLC programs in to the two dynamic pricing programs. 

Small increases in demand response potential result from medium and large C&I customers enrolling in 

both dynamic pricing programs. 

Full Participation: Significant demand response potential comes from the Residential class, driven 

primarily by high CAC saturation and a faster-than-average AMI penetration rate. Both Medium and 

Large C&I classes show growth in demand response through increased enrollment in dynamic pricing 

with enabling technology. 

0 

2,000 

4,000 

6,000 

8,000 

10,000 

12,000 

14,000 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 88.6 GW) 

Texas DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 169 
 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Texas, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

79 0.1% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

79 0.1% 

0 0.0% 

236 0.3% 

2,371 2.7% 

0 0.0% 

0 0.0% 

2,607 2.9% 

4,758 5.4% 

2,289 2.6% 

614 0.7% 

0 0.0% 

0 0.0% 

7,661 8.6% 

11,129 12.6% 

318 0.4% 

79 0.1% 

0 0.0% 

0 0.0% 

11,525 13.0% 

0 0.0% 

0 0.0% 

39 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

39 0.0% 

0 0.0% 

1 0.0% 

39 0.0% 

0 0.0% 

0 0.0% 

40 0.0% 

0 0.0% 

27 0.0% 

39 0.0% 

0 0.0% 

0 0.0% 

66 0.1% 

0 0.0% 

37 0.0% 

39 0.0% 

0 0.0% 

0 0.0% 

75 0.1% 

0 0.0% 

0 0.0% 

48 0.1% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

48 0.1% 

0 0.0% 

70 0.1% 

190 0.2% 

280 0.3% 

3 0.0% 

543 0.6% 

925 1.0% 

615 0.7% 

79 0.1% 

280 0.3% 

1 0.0% 

1,900 2.1% 

2,703 3.1% 

298 0.3% 

48 0.1% 

280 0.3% 

0 0.0% 

3,330 3.8% 

0 0.0% 

0 0.0% 

0 0.0% 

232 0.3% 

413 0.5% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

645 0.7% 

0 0.0% 

35 0.0% 

0 0.0% 

2,218 2.5% 

1,640 1.9% 

3,894 4.4% 

250 0.3% 

454 0.5% 

0 0.0% 

2,218 2.5% 

680 0.8% 

3,602 4.1% 

730 0.8% 

588 0.7% 

0 0.0% 

2,218 2.5% 

413 0.5% 

3,949 4.5% 

0 0.0% 

0 0.0% 

166 0.2% 

232 0.3% 

413 0.5% 

Total 

(MW) 

Total 

(% of 

system) 

810 0.9% 

0 0.0% 

343 0.4% 

2,599 2.9% 

2,498 2.8% 

1,643 1.9% 

7,083 8.0% 

5,932 6.7% 

3,386 3.8% 

732 0.8% 

2,498 2.8% 

681 0.8% 

13,230 14.9% 

14,562 16.4% 

1,241 1.4% 

166 0.2% 

2,498 2.8% 

413 0.5% 

18,880 21.3% 

Texas System Peak Demand Forecasts by Scenario 

0 

10,000 

20,000 

30,000 

40,000 

50,000 

60,000 

70,000 

80,000 

90,000 

100,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

170 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

Utah State Profile 

Key drivers of Utah’s demand response potential estimate include: lower-than-average residential CAC 

saturation of 42 percent and a fair amount of existing demand response. Enabling technologies are costeffective for all customer classes. The state has a smaller-than-average Residential class and AMI 

deployment that potentially lags the national average, potentially leading to slower realized demand 

response potential. The state is characterized by a larger-than-average Medium C&I class that has 

significant amounts of existing demand response. 

BAU: Utah’s existing demand response is characterized by a large interruptible tariff program for the 

Medium C&I class. The rest of the existing demand response is through direct load control programs 

for the Residential and Medium C&I classes. 

Expanded BAU: The majority of the growth in demand response potential is driven by interruptible 

tariffs and other demand response for the Large C&I class. 

Achievable Participation: Demand response potential for this scenario comes mostly through the two 

dynamic pricing programs, with the majority utilizing enabling technologies. Enabling technologies are 

cost-effective for all customer classes. 

Full Participation: Under this scenario, dynamic pricing with enabling technology continues to grow 

for all customer classes. Demand response potential for the Large C&I class decreases slightly, as 

customers switch from other demand response programs to the dynamic pricing programs, which have 

smaller per-customer impacts. 

0 

100 

200 

300 

400 

500 

600 

700 

800 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 7 GW) 

Utah DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 171 
 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Utah, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

75 1.1% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

75 1.1% 

0 0.0% 

4 0.1% 

115 1.6% 

0 0.0% 

0 0.0% 

119 1.7% 

190 2.7% 

136 1.9% 

75 1.1% 

0 0.0% 

0 0.0% 

401 5.7% 

444 6.3% 

72 1.0% 

75 1.1% 

0 0.0% 

0 0.0% 

591 8.5% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

2 0.0% 

0 0.0% 

0 0.0% 

2 0.0% 

27 0.4% 

2 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

30 0.4% 

64 0.9% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

65 0.9% 

0 0.0% 

0 0.0% 

102 1.5% 

347 5.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

449 6.4% 

0 0.0% 

2 0.0% 

102 1.5% 

347 5.0% 

0 0.0% 

451 6.4% 

65 0.9% 

50 0.7% 

102 1.5% 

347 5.0% 

0 0.0% 

564 8.1% 

191 2.7% 

32 0.5% 

102 1.5% 

347 5.0% 

0 0.0% 

671 9.6% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

148 2.1% 

107 1.5% 

256 3.7% 

22 0.3% 

40 0.6% 

0 0.0% 

148 2.1% 

43 0.6% 

254 3.6% 

65 0.9% 

52 0.7% 

0 0.0% 

148 2.1% 

0 0.0% 

266 3.8% 

0 0.0% 

0 0.0% 

177 2.5% 

347 5.0% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

524 7.5% 

0 0.0% 

7 0.1% 

219 3.1% 

495 7.1% 

107 1.5% 

828 11.8% 

304 4.4% 

228 3.3% 

178 2.5% 

495 7.1% 

43 0.6% 

1,249 17.9% 

763 10.9% 

158 2.3% 

177 2.5% 

495 7.1% 

0 0.0% 

1,593 22.8% 

Utah System Peak Demand Forecasts by Scenario 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

7,000 

8,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

172 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

Vermont State Profile 

Key drivers of Vermont’s demand response potential estimate include: significantly lower-than-average 

CAC saturation of 7 percent and enabling technologies that are cost-effective for only the Medium and 

Large C&I classes. Vermont’s potential AMI deployment could lead the national average and result in 

faster realized demand response potential. However, the key driver of this state’s demand response 

potential is very low residential CAC saturation and enabling technologies not being cost-effective for 

this class, leading to fairly small incremental potential relative to the BAU scenario. 

BAU: Vermont has a large amount of existing demand response for the Large C&I class, through 

interruptible tariffs and other demand response. 

Expanded BAU: Small demand response potential increases occur for the Large C&I class, through 

interruptible tariffs and other demand response. The Residential class shows a small amount of 

potential demand response through participation in DLC programs. 

Achievable Participation: Residential and Medium and Large C&I classes show slight increases in 

dynamic pricing programs. The residential class has a much smaller-than-average demand response 

potential due to very low CAC saturation and enabling technologies not being cost-effective for this 

class. 

Full Participation: Small increases in potential demand response result for all classes of customers. 

Overall the state shows a small amount incremental demand response potential driven primarily by low 

CAC saturation and enabling technologies not being cost-effective for both Residential and Small C&I 

classes. 

0 

20 

40 

60 

80 

100 

120 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 1.2 GW) 

Vermont DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 173 
 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Vermont, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.1% 

6 0.5% 

0 0.0% 

0 0.0% 

7 0.5% 

0 0.0% 

23 1.8% 

1 0.1% 

0 0.0% 

0 0.0% 

24 1.9% 

0 0.0% 

30 2.4% 

0 0.0% 

0 0.0% 

0 0.0% 

30 2.4% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.1% 

0 0.0% 

0 0.0% 

1 0.1% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.1% 

0 0.0% 

1 0.1% 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.1% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.1% 

2 0.2% 

0 0.0% 

4 0.3% 

6 0.5% 

5 0.4% 

0 0.0% 

2 0.2% 

0 0.0% 

13 1.1% 

18 1.4% 

3 0.3% 

0 0.0% 

2 0.2% 

0 0.0% 

23 1.8% 

0 0.0% 

2 0.2% 

0 0.0% 

30 2.4% 

57 4.6% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

89 7.2% 

0 0.0% 

2 0.2% 

0 0.0% 

30 2.4% 

57 4.6% 

89 7.2% 

4 0.4% 

8 0.6% 

0 0.0% 

30 2.4% 

57 4.6% 

99 8.0% 

13 1.0% 

10 0.8% 

0 0.0% 

30 2.4% 

57 4.6% 

110 8.9% 

0 0.0% 

2 0.2% 

0 0.0% 

30 2.4% 

57 4.6% 

Total 

(MW) 

Total 

(% of 

system) 

89 7.2% 

0 0.0% 

3 0.3% 

8 0.6% 

32 2.6% 

57 4.6% 

100 8.1% 

10 0.8% 

36 2.9% 

2 0.2% 

32 2.6% 

57 4.6% 

137 11.1% 

30 2.5% 

44 3.6% 

0 0.0% 

32 2.6% 

57 4.6% 

163 13.2% 

Vermont System Peak Demand Forecasts by Scenario 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

174 A National Assessment of Demand Response Potential 
Key drivers of South Dakota’s DR potential estimate include: higher-than-average residential CAC 

saturation of 71 percent and a small amount of existing DR. Enabling technologies are cost-effective 

for all C&I classes, but not for residential customers. Also, AMI deployment that lags the national 

average could lead to slower realized DR potential. 

BAU: South Dakota’s existing DR comes primarily from direct-load control for both the residential 

and small C&I classes. A small amount of DR comes from the large C&I class, in the form of 

interruptible tariffs. 

Expanded BAU: Growth in DR is driven equally through an interruptible tariff program and other DR 

programs for the large C&I class. The other category of DR programs does not currently exist in the 

state. Residential DLC contributes to increased DR potential, as well. 

Achievable Participation: Increases in this scenario result from dynamic pricing programs, both with 

and without enabling technology. As mentioned above, enabling technologies are not cost-effective for 

the residential class, resulting in no DR potential from pricing with technologies. 

Full Participation: DR potential is further realized through increases in both dynamic pricing 

mic pricing with out enabling technologies. 

programs. Large C&I customers that were in other DR programs have shifted into both dynamic 

pricing programs, with the majority enrolling in the with technology option. Again, higher-than-average 

CAC saturation result in the residential class having the largest amount of potential DR, with almost all 

of it coming in the form of dyna

Appendix A – State Profiles 

Virginia State Profile 

Key drivers of Virginia’s demand response potential include lower-than-average residential CAC 

saturation (50 percent) and a small amount of existing demand response. Enabling technologies are 

cost-effective for all customer classes. Also, potential AMI deployment slightly leads the national 

average. A Large C&I class with a higher than average share of the system peak results in the class 

representing a significant amount of the state’s overall demand response potential. 

BAU: Virginia’s small amount of existing demand response comes from DLC programs for residential 

customers and large C&I customer participation in ‘Other DR’ programs. 

Expanded BAU: Growth in potential demand response is the result of higher than average peak 

demand in the large C&I class, resulting in large impacts from both interruptible tariffs and other 

demand response. The Residential class has a significant growth in load reduction coming from DLC 

programs. 

Achievable Participation: Enabling technologies are cost-effective for all customer classes, resulting 

in large dynamic pricing potential growth from these technologies. The Residential and Small C&I 

classes show customers enrolling in to the two dynamic pricing programs rather than in DLC programs. 

Full Participation: The cost-effectiveness of enabling technology leads to significant growth in 

dynamic pricing for all classes, especially residential customers. The Residential and Large C&I 

classes account for most of the peak load, resulting in the majority of the demand response potential 

coming from these two classes. 

0 

500 

1,000 

1,500 

2,000 

2,500 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 27.4 GW) 

Virginia DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 175 
 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Virginia, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

68 0.2% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

68 0.2% 

0 0.0% 

32 0.1% 

439 1.6% 

0 0.0% 

0 0.0% 

471 1.7% 

861 3.1% 

550 2.0% 

112 0.4% 

0 0.0% 

0 0.0% 

1,523 5.6% 

2,015 7.4% 

238 0.9% 

68 0.2% 

0 0.0% 

0 0.0% 

2,321 8.5% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

8 0.0% 

0 0.0% 

0 0.0% 

8 0.0% 

100 0.4% 

5 0.0% 

2 0.0% 

0 0.0% 

0 0.0% 

107 0.4% 

233 0.9% 

3 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

236 0.9% 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

1 0.0% 

0 0.0% 

7 0.0% 

14 0.1% 

37 0.1% 

0 0.0% 

57 0.2% 

137 0.5% 

91 0.3% 

6 0.0% 

37 0.1% 

0 0.0% 

270 1.0% 

400 1.5% 

44 0.2% 

0 0.0% 

37 0.1% 

0 0.0% 

480 1.8% 

0 0.0% 

0 0.0% 

0 0.0% 

2 0.0% 

189 0.7% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

191 0.7% 

0 0.0% 

11 0.0% 

0 0.0% 

625 2.3% 

519 1.9% 

1,154 4.2% 

117 0.4% 

213 0.8% 

0 0.0% 

625 2.3% 

212 0.8% 

1,167 4.3% 

342 1.2% 

276 1.0% 

0 0.0% 

625 2.3% 

189 0.7% 

1,431 5.2% 

0 0.0% 

0 0.0% 

68 0.2% 

3 0.0% 

189 0.7% 

Total 

(MW) 

Total 

(% of 

system) 

260 1.0% 

0 0.0% 

50 0.2% 

461 1.7% 

662 2.4% 

519 1.9% 

1,691 6.2% 

1,215 4.4% 

859 3.1% 

120 0.4% 

662 2.4% 

212 0.8% 

3,068 11.2% 

2,990 10.9% 

560 2.0% 

68 0.2% 

662 2.4% 

189 0.7% 

4,468 16.3% 

Virginia System Peak Demand Forecasts by Scenario 

0 

5,000 

10,000 

15,000 

20,000 

25,000 

30,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

176 A National Assessment of Demand Response Potential 
 

 

Appendix A – State Profiles 

Washington State Profile 

Key drivers of Washington’s demand response potential estimate include: lower-than-average 

residential CAC saturation of 29 percent and no existing demand response. Enabling technologies are 

cost-effective for all classes. Also, the state’s potential AMI deployment slightly leads the national 

average. Low CAC saturation and non-existent demand response are the key drivers for the state. 

BAU: Currently, the state has no demand response. Historically, low energy prices and a surplus of 

hydro capacity have made demand response seemingly less attractive in this region. 

Expanded BAU: The majority of the potential demand response is from Large C&I, through 

interruptible tariffs and other demand response. Some Residential demand response potential comes 

from DLC and dynamic pricing. 

Achievable Participation: Demand response potential is driven by dynamic pricing with and without 

enabling technology. Many of the residential customers enrolled in DLC programs under the EBAU 

scenario would instead be expected to enroll in dynamic pricing with enabling technology under this 

scenario. Relative to the EBAU scenario, Large C&I customers would be enrolled more heavily in 

dynamic pricing than in interruptible tariff and other demand response programs. 

Full Participation: Dynamic pricing programs dominate the demand response potential for this 

scenario, primarily those utilizing enabling technologies. The largest amount of load reduction can be 

potentially derived from residential customers. Enabling technologies are cost-effective for all customer 

classes. Some interruptible tariff demand response remains for both Medium and Large C&I. 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 22.6 GW) 

Washington DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 177 
 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Washington, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

0 0.0% 

0 0.0% 

21 0.1% 

118 0.5% 

0 0.0% 

0 0.0% 

139 0.6% 

424 1.9% 

457 2.0% 

30 0.1% 

0 0.0% 

0 0.0% 

911 4.0% 

991 4.4% 

365 1.6% 

0 0.0% 

0 0.0% 

0 0.0% 

1,357 6.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

8 0.0% 

0 0.0% 

0 0.0% 

9 0.0% 

118 0.5% 

8 0.0% 

2 0.0% 

0 0.0% 

0 0.0% 

128 0.6% 

275 1.2% 

5 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

280 1.2% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

7 0.0% 

12 0.1% 

41 0.2% 

0 0.0% 

60 0.3% 

127 0.6% 

97 0.4% 

5 0.0% 

41 0.2% 

0 0.0% 

270 1.2% 

370 1.6% 

62 0.3% 

0 0.0% 

41 0.2% 

0 0.0% 

473 2.1% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

5 0.0% 

0 0.0% 

381 1.7% 

271 1.2% 

657 2.9% 

57 0.3% 

104 0.5% 

0 0.0% 

381 1.7% 

111 0.5% 

652 2.9% 

167 0.7% 

134 0.6% 

0 0.0% 

381 1.7% 

0 0.0% 

682 3.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

0 0.0% 

0 0.0% 

33 0.1% 

138 0.6% 

422 1.9% 

271 1.2% 

864 3.8% 

725 3.2% 

665 2.9% 

37 0.2% 

422 1.9% 

111 0.5% 

1,960 8.7% 

1,803 8.0% 

567 2.5% 

0 0.0% 

422 1.9% 

0 0.0% 

2,792 12.4% 

Washington System Peak Demand Forecasts by Scenario 

17,000 

18,000 

19,000 

20,000 

21,000 

22,000 

23,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

178 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

West Virginia State Profile 

Key drivers of West Virginia’s demand response potential estimate include: a CAC saturation of 50 

percent and a moderate amount of existing demand response, and a larger-than-average Large C&I class 

(32%). Enabling technologies are cost-effective for all classes of customers. Also, potential AMI 

deployment slightly leads the national average. The larger-than-average Large C&I class, with 

significant existing demand response, is the primary driver for the state. 

BAU: West Virginia has a significant amount of existing demand response for the Large C&I class, but 

none for the remaining classes. 

Expanded BAU: Demand response potential comes primarily from the Residential and Large C&I 

classes. Residential demand response potential is in DLC programs, while the incremental increase in 

Large C&I potential is in interruptible tariff and ‘Other DR’ programs. 

Achievable Participation: The main driver of demand response potential in this scenario is through 

dynamic pricing, with a significant amount of impact coming from the use of enabling technologies. 

Enabling technologies are cost-effective for all customer classes. The Large C&I class continues to 

dominate demand response potential because of its larger-than-average share of system peak load. 

Full Participation: Demand response potential from dynamic pricing with enabling technology is 

largest under this scenario, with all customer classes exhibiting incremental increases in demand 

response potential relative to the other scenarios. For large C&I customers, potential from Interruptible 

tariffs and ‘Other DR’ programs continue to dominate. 

0 

100 

200 

300 

400 

500 

600 

700 

800 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 8.2 GW) 

West Virginia DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 179 
 

 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in West Virginia, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

0 0.0% 

0 0.0% 

7 0.1% 

104 1.3% 

0 0.0% 

0 0.0% 

111 1.4% 

192 2.3% 

123 1.5% 

27 0.3% 

0 0.0% 

0 0.0% 

342 4.2% 

450 5.5% 

54 0.7% 

0 0.0% 

0 0.0% 

0 0.0% 

504 6.2% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

3 0.0% 

0 0.0% 

0 0.0% 

3 0.0% 

50 0.6% 

3 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

54 0.7% 

118 1.4% 

1 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

119 1.5% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

2 0.0% 

5 0.1% 

13 0.2% 

0 0.0% 

19 0.2% 

42 0.5% 

28 0.3% 

2 0.0% 

13 0.2% 

0 0.0% 

84 1.0% 

121 1.5% 

13 0.2% 

0 0.0% 

13 0.2% 

0 0.0% 

147 1.8% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

250 3.1% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

250 3.1% 

0 0.0% 

3 0.0% 

0 0.0% 

238 2.9% 

431 5.3% 

672 8.2% 

36 0.4% 

65 0.8% 

0 0.0% 

238 2.9% 

250 3.1% 

589 7.2% 

104 1.3% 

84 1.0% 

0 0.0% 

238 2.9% 

250 3.1% 

677 8.3% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

250 3.1% 

Total 

(MW) 

Total 

(% of 

system) 

250 3.1% 

0 0.0% 

12 0.1% 

112 1.4% 

251 3.1% 

431 5.3% 

806 9.8% 

320 3.9% 

219 2.7% 

29 0.4% 

251 3.1% 

250 3.1% 

1,069 13.1% 

794 9.7% 

153 1.9% 

0 0.0% 

251 3.1% 

250 3.1% 

1,448 17.7% 

West Virginia System Peak Demand Forecasts by Scenario 

0 

1,000 

2,000 

3,000 

4,000 

5,000 

6,000 

7,000 

8,000 

9,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

180 A National Assessment of Demand Response Potential 
Appendix A – State Profiles 

Wisconsin State Profile 

Key drivers of Wisconsin’s demand response potential estimate include: a significant level of CAC 

saturation at 62 percent and a small amount of existing demand response. Enabling technologies are 

cost-effective for all C&I classes, but not for the Residential class. Also, a potential AMI deployment 

schedule that leads the national average could lead to faster realized demand response potential. 

BAU: Wisconsin has existing demand response for Large C&I through an interruptible tariff program. 

DLC programs are in place for the remaining customer classes, with the Residential class exhibiting the 

largest impacts. 

Expanded BAU: The Large C&I class exhibits significant demand response potential, which is driven 

by enrollment in new interruptible tariff and other demand response programs. Dynamic pricing plays a 

very small role relative to DLC impacts for Residential customers in this scenario 

Achievable Participation: The majority of the incremental increase in demand response potential is 

due to dynamic pricing. Pricing with enabling technologies appears for all classes, except for the 

Residential class for which it is not cost effective. Still, the Residential class exhibits significant 

potential through participation in dynamic pricing programs without enabling technology. Total 

potential demand response decreases for the Large C&I class as a result of customers shifting to 

dynamic pricing programs, which produce smaller per-customer impacts. 

Full Participation: Potential demand response continues to grow through increased enrollment in 

dynamic pricing programs. Large C&I customers are more heavily enrolled in dynamic pricing 

programs, slightly decreasing potential impacts from this class. 

0 

100 

200 

300 

400 

500 

600 

700 

800 

900 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 18.4 GW) 

Wisconsin DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 181 
 

 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Wisconsin, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

135 0.7% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

135 0.7% 

0 0.0% 

21 0.1% 

151 0.8% 

0 0.0% 

0 0.0% 

172 0.9% 

0 0.0% 

487 2.6% 

135 0.7% 

0 0.0% 

0 0.0% 

621 3.4% 

0 0.0% 

649 3.5% 

135 0.7% 

0 0.0% 

0 0.0% 

784 4.3% 

0 0.0% 

0 0.0% 

24 0.1% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

24 0.1% 

0 0.0% 

0 0.0% 

24 0.1% 

0 0.0% 

0 0.0% 

24 0.1% 

63 0.3% 

4 0.0% 

24 0.1% 

0 0.0% 

0 0.0% 

90 0.5% 

147 0.8% 

2 0.0% 

24 0.1% 

0 0.0% 

0 0.0% 

173 0.9% 

0 0.0% 

0 0.0% 

33 0.2% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

33 0.2% 

0 0.0% 

9 0.0% 

33 0.2% 

37 0.2% 

0 0.0% 

79 0.4% 

111 0.6% 

89 0.5% 

33 0.2% 

37 0.2% 

0 0.0% 

270 1.5% 

324 1.8% 

61 0.3% 

33 0.2% 

37 0.2% 

0 0.0% 

455 2.5% 

0 0.0% 

0 0.0% 

0 0.0% 

40 0.2% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

40 0.2% 

0 0.0% 

9 0.0% 

0 0.0% 

244 1.3% 

331 1.8% 

583 3.2% 

70 0.4% 

128 0.7% 

0 0.0% 

244 1.3% 

137 0.7% 

579 3.1% 

205 1.1% 

166 0.9% 

0 0.0% 

244 1.3% 

0 0.0% 

615 3.3% 

0 0.0% 

0 0.0% 

191 1.0% 

40 0.2% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

231 1.3% 

0 0.0% 

39 0.2% 

207 1.1% 

281 1.5% 

331 1.8% 

858 4.7% 

244 1.3% 

707 3.8% 

191 1.0% 

281 1.5% 

137 0.7% 

1,560 8.5% 

677 3.7% 

878 4.8% 

191 1.0% 

281 1.5% 

0 0.0% 

2,027 11.0% 

Wisconsin System Peak Demand Forecasts by Scenario 

0 

2,000 

4,000 

6,000 

8,000 

10,000 

12,000 

14,000 

16,000 

18,000 

20,000 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

182 A National Assessment of Demand Response Potential 
 

Appendix A – State Profiles 

Wyoming State Profile 

Key drivers of Wyoming’s demand response potential estimate include: lower-than-average residential 

CAC saturation of 42 percent and no existing demand response. Enabling technologies are costeffective for all C&I classes and for residential customers. Also, potential AMI deployment that lags 

the national average could lead to slower realized demand response potential. The larger-than-average 

Large C&I class (36%) is the main driver of demand response in the state. 

BAU: Currently, Wyoming has no existing demand response. 

Expanded BAU: The Large C&I class represents the vast majority of demand response potential in the 

state, through enrollment in both interruptible tariff and other demand response programs. A moderate 

amount of demand response potential exists in residential DLC programs. 

Achievable Participation: Impacts from dynamic pricing are relatively small compared to demand 

response potential in Other DR and Interruptible tariffs. All classes adopt enabling technologies. Total 

demand response potential decreases slightly for the Large C&I class due to customers shifting from 

other demand response programs in to pricing programs, which have smaller per- customer peak 

impacts. 

Full Participation: Incremental demand response potential is highest for the residential, small, and 

medium C&I classes under this scenario. The Large C&I class drives total potential demand response 

in the state. 

0 

50 

100 

150 

200 

250 

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large

Residential

Small

Medium

Large 

Potential Peak Reduction (MW)

Pricing w/Tech Pricing w/o Tech DLC Interruptible Tariffs Other DR 

BAU Expanded BAU 

Achievable 

Participation 

Full 

Participation 

(2019 System Peak = 4 GW) 

Wyoming DR Potential in 2019, by Scenario 

Federal Energy Regulatory Commission 183 
 

 

 

 

 

 

 

 

 

Appendix A – State Profiles 

Total Potential Peak Reduction from Demand Response in Wyoming, 2019 

BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Expanded BAU 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Achievable Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

Full Participation 

Pricing with Technology 

Pricing without Technology 

Automated/Direct Load Control 

Interruptible/Curtailable Tariffs 

Other DR Programs 

Total 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Residential 

(MW) 

Residential 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

26 0.7% 

0 0.0% 

0 0.0% 

27 0.7% 

38 0.9% 

28 0.7% 

7 0.2% 

0 0.0% 

0 0.0% 

72 1.8% 

88 2.2% 

15 0.4% 

0 0.0% 

0 0.0% 

0 0.0% 

104 2.6% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Small 

C&I 

(MW) 

Small 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

49 1.2% 

3 0.1% 

0 0.0% 

0 0.0% 

0 0.0% 

53 1.3% 

115 2.9% 

2 0.1% 

0 0.0% 

0 0.0% 

0 0.0% 

117 3.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Med. 

C&I 

(MW) 

Med 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

0 0.0% 

1 0.0% 

3 0.1% 

0 0.0% 

5 0.1% 

11 0.3% 

8 0.2% 

1 0.0% 

3 0.1% 

0 0.0% 

23 0.6% 

31 0.8% 

5 0.1% 

0 0.0% 

3 0.1% 

0 0.0% 

40 1.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Large 

C&I 

(MW) 

Large 

C&I 

(% of 

system) 

0 0.0% 

0 0.0% 

1 0.0% 

0 0.0% 

129 3.2% 

93 2.3% 

222 5.6% 

19 0.5% 

35 0.9% 

0 0.0% 

129 3.2% 

37 0.9% 

220 5.5% 

56 1.4% 

45 1.1% 

0 0.0% 

129 3.2% 

0 0.0% 

230 5.8% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

0 0.0% 

Total 

(MW) 

Total 

(% of 

system) 

0 0.0% 

0 0.0% 

2 0.0% 

29 0.7% 

132 3.3% 

93 2.3% 

256 6.4% 

117 2.9% 

74 1.9% 

8 0.2% 

132 3.3% 

37 0.9% 

368 9.3% 

291 7.3% 

68 1.7% 

0 0.0% 

132 3.3% 

0 0.0% 

491 12.4% 

Wyoming System Peak Demand Forecasts by Scenario 

2,800 

3,000 

3,200 

3,400 

3,600 

3,800 

4,000 

4,200 

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 

Peak Demand (MW 

System Peak 

(without DR) 

BAU 

Expanded BAU 

Achievable 

Participation 

Full Participation 

184 A National Assessment of Demand Response Potential 
APPENDIXB.	 LESSONSLEARNEDIN

DATADEVELOPMENT

Development of state-level data for a bottom-up demand response potential assessment is a complex and 

challenging task. Data had to be developed for each state and D.C. by type of end-use customer, by type 

of demand response program, and by demand response potential estimation scenario, with an analysis 

timeframe spanning 2009-2019. The data development process drew upon information from a variety of 

different sources. There were challenges faced in compiling information, often not uniformly available 

across sources, to arrive at data required for potential estimation for all states. This section briefly 

discusses some of the challenges related to data development and scope for future improvements that are 

likely to address these challenges. It is intended to serve as a guide for making future updates to the 

analysis. 

Nature of utility data reporting 

Challenges 

In developing some of the key data items required for potential estimation, utility data was aggregated 

to come up with state level estimates. Very often, it was found that for utility companies with service 

territories across multiple states, the reporting of data is at the operating company or entity level and 

is not disaggregated at the state level for these companies. This posed difficulties in directly 

aggregating the data to come up with state level estimates. Examples of data items where this 

difficulty was encountered are: state level estimates that show number of accounts by rate class, sales 

by rate class, and state peak load forecasts. In such cases, entity level data was disaggregated to the 

state based on other utility-level parameters reported by the operating company. 

Scope for future improvements 

Alteration of the nature of utility data reporting for those with service territories across multiple states 

is likely to address this problem. If utilities report data at the operating state level, instead of 

aggregate data at the entity level, it will make state level estimations easier. 

Incomplete and non-uniform information availability for key data items 

Challenges 

Difficulties were posed by lack of availability of information related to key data items for potential 

estimation. Also, often when information was available, it was available from a wide variety of 

sources, and thus not uniformly characterized. 

Examples of key data inputs where such challenges were faced are CAC saturation for residential 

customers and unit impact estimation for residential DLC programs. In the case of residential CAC 

saturation estimation across states, there were only very few states where information was available 

from statewide saturation surveys and other similar sources. Often, it was necessary to compile 

individual utility-level information and use that as the basis for arriving at state level estimates. There 

were a few states where data was available from housing surveys for certain metropolitan areas in the 

state. Also, data availability was for different years. Additionally, there were some states where it was 

difficult to source the data directly from any state level estimate. In such cases, regional values from 

appliance saturation surveys (e.g. Residential Appliance Saturation Survey conducted by EIA) were 

used to derive the state estimate. 

Federal Energy Regulatory Commission 185 
 

 

Appendix B – Lessons Learned in Data Development 

Scope for future improvements 

Development of comprehensive databases for key items with uniform level of information availability 

is likely to address this problem. For example, for residential CAC saturation data, a central 

repository of information from different sources to arrive at state level estimates could be compiled 

and periodically updated. 

Data coverage by utility ownership 

Challenges 

During the process of developing aggregate state level estimates from utility data, there were 

difficulties due to lack of data from non-IOUs in the states. For example, FERC Form No.1 data 

reporting served as the basis for developing distribution of C&I customers by rate class (small, 

medium, and large). 71 But the FERC Form No. 1 data is available only for Investor-Owned Utilities 

(IOUs). In the absence of any such similar data reporting from non-IOUs, it was assumed that the 

distribution for IOUs was applicable to the non-IOUs in the state as well. Also, there were cases 

where data was not available for all IOUs in the state. Therefore, in all such cases, the estimations 

from the limited utility data set were assumed to be representative for the state. 

Scope for future improvements 

Systematic and uniform data collection from all utilities, across different ownership types, is likely to 

address this problem. 

Level of data availability 

Challenges 

In developing some of the data items, it was necessary to apply regional estimates as proxy for state 

level data, wherever information at the state level was difficult to obtain. In cases where regional 

estimations could not be directly applied, the regional data was disaggregated to provide state level 

estimates based on related data available by state. For example, system peak load forecast from 

NERC was available only at the NERC regional level, which had to be disaggregated to arrive at state 

level system peak values. The methodological framework for doing that is described under the ‘Data 

Development’ section in the Appendix. Another example is application of regional estimates for 

growth rate in C&I accounts for all states in a particular census region, since variation by state for this 

particular item was difficult to estimate. 

Scope for future improvements 

Wherever information is available only at the regional level, future efforts could be directed towards 

systematically developing information at the state level by encouraging relevant agencies to report 

state-level information. 

Difficulties related to data development by C&I rate classes 

Challenges 

A key challenge in developing data related to demand response potential estimation was in 

developing data for the three rate classes (small, medium, and large) for the C&I sector. Almost all 

key data inputs for potential estimation had to be developed at the rate class level. However, there 

was no source from where the information could be directly procured for the commercial rate classes. 

FERC Form No. 1 data, where individual utilities (IOUs only) report information by rate schedule, 

was used as the primary basis for developing data by rate class. But use of the FERC Form No. 1 data 

71 FERC Form-1 data was the best, most recently available information among possible data sources, including EIA, USDA Rural 

Utilities Service (RUS) and other entities that compile databases, etc. 

186 A National Assessment of Demand Response Potential 
Appendix B – Lessons Learned in Data Development 

for data estimation by rate class, in turn posed challenges that was inherent to the nature of the FERC 

Form No. 1 data availability and reporting requirements. 

For example: FERC Form No.1 data is reported only by IOUs, and therefore the distribution of C&I 

customers by rate class applied only to IOUs. In the absence of similar data availability for non-IOUs, 

we applied the distribution from FERC Form No. 1 to all utilities in a state to arrive at the state level 

distribution, thereby assuming that the distribution of C&I customers by rate class for IOUs applies to 

non-IOU utilities as well. For utilities that operate in multiple states, it was necessary to assume that 

the same mix of C&I customers applies to all states in which a utility operates. In addition, FERC 

Form No. 1 data was not available for all IOUs across states. 

Scope for future improvements 

Data availability from utilities, which indicates the classification of customers by peak load, is likely 

to address this problem. This will enable categorizing C&I customers into different peak load size 

ranges. Also, information should be available from utilities across different ownership types. 

Federal Energy Regulatory Commission 187 

 

 

 

 

 

 

APPENDIXC.	 DETAILONBARRIERS

ANALYSIS

A number of barriers are preventing demand response from reaching its full potential in the United States. 

Some of these barriers are regulatory in nature, stemming from existing policies and practices that are not 

designed to facilitate the use of demand response as a resource. These barriers exist in both wholesale 

and retail markets. Other barriers are economic in nature. Finally, certain technological limitations are 

also standing in the way. In total, there are some 24 barriers to demand response. This appendix includes 

a discussion of existing demand response barriers, organized into four categories: (1) Regulatory barriers 

(general, retail and wholesale), (2) economic barriers, (3) technological barriers, and (4) other barriers. 

Regulatory Barriers 

Regulatory barriers are impediments to demand response caused by a particular regulatory regime, market 

design, market rules, or the demand response programs themselves. 72 Regulatory barriers constitute the 

largest group of barriers in this analysis. Below is a summary of the major regulatory barriers, divided 

into three sub-categories: general, retail-level, and wholesale-level. 

General Regulatory Barriers 

Retail and Wholesale Price Disconnect 

Principal among the regulatory barriers is the lack of a direct connection between retail and wholesale 

electricity prices. This refers specifically to the lack of dynamic pricing in retail markets. 73 Simply put, 

most of today’s retail tariffs do not reflect the time variation in the cost of electricity supply. As a result, 

customers are not provided with the appropriate price signals to promote efficient electricity consumption 

and may over-consume power during expensive peak periods and under-consume power during 

inexpensive off-peak periods. 

Retail customers are essentially provided a full requirements contract in which suppliers bear all the 

volumetric and price risk. Such fully hedged rates dominate the marketplace, particularly for residential 

customers. Dynamic pricing rates are not provided as universal service rates nor are they offered as the 

default service to residential customers of any utility in the US. Indeed, in most parts of the country, 

dynamic pricing rates are not even available on an elective basis to residential customers. One often cited 

reason is that the necessary metering technology is not widely deployed to this class of customers. 74 But 

there are other reasons as well, including a perception that customers do not like price volatility. 

While it is true that time-of-use (TOU) rates are the default rate for large commercial and industrial (C&I) 

customers at some utilities, these rates do not fully reflect the dynamics of power markets or electricity 

supply costs. Larger C&I customers in restructured power markets such as Connecticut, Illinois, 

Maryland, Michigan, New England, New Jersey and New York commonly face default real-time pricing 

72 Barriers related to customer attitudes, which sometimes fall into this category, are addressed in the “Other Barriers” section. 73 In this discussion, we distinguish between two types of time-varying pricing, dynamic and static. Traditional TOU rates, in which 

prices typically vary by rate period, day of week and season, have higher prices during all peak rate periods and lower prices 

during all off-peak rate periods. Since TOU price levels and the timing of the periods are known with certainty, they are static 

time varying prices. Dynamic prices have some degree of uncertainty associated with them, either concerning when certain 

prices are in effect, or what price levels are in each time period. Critical peak pricing is a dynamic rate in which the prices on 

certain days are known ahead of time, but the days on which those prices occur are not known until the day before or day of. 

Real time pricing is another form of dynamic pricing, in which prices in effect in each hour are not known ahead of time. 74 However, time-varying rates are an option for some residential customers. For example, Arizona Public Service and Salt River 

Project offer widely-adopted residential TOU rates. Georgia Power offers a residential critical peak pricing (CPP) rate. 

Federal Energy Regulatory Commission 189 
 

 

 

Appendix C – Detail on Barriers Analysis 

(RTP) rates. 75 However, even these rates typically do not reflect the full time variation in supply costs, as 

they do not incorporate long-run capacity costs in peak period prices. 

In July 2008, there was a decision by the California Public Utilities Commission (CPUC) to make 

dynamic pricing the default rate offering for all customer classes in the state. 76 , 77 

Measurement and Verification (M&V) Challenges 

To accurately assess the benefits of demand response, it is necessary to have standardized practices for 

quantifying demand reductions. Currently, these practices are often unclear, inaccurate, and inconsistent 

across utilities, states and ISOs. This has negative impacts on three specific areas: demand response 

contract settlement, operational planning, and long term resource planning. To date, the focus has 

generally been on developing M&V practices for settlement purposes, and determining the appropriate 

level of demand response that should be compensated. However, operational and long term planning 

have not been key factors in that development process. Both deserve more attention. Operational 

methods need to be developed to better predict the short term (i.e. day-ahead) impacts of demand 

response resources. M&V is important to the long-term planning process to the extent that it will 

influence generation, transmission, and distribution investment decisions. 

In April of this year, the California Public Utilities Commission adopted a set of load impact protocols 

that California’s IOUs must use to develop both ex post and ex ante impact estimates for all of their 

demand response programs. 78 These protocols are designed primarily to support long term resource 

planning and to asses progress toward meeting resource adequacy requirements in California. They set 

minimum requirements in terms of the type of information that must be provided for each demand 

response resource (e.g., impact estimates for each hour on a typical event day) and the factors that must 

be taken into consideration when developing impact estimates (e.g., ex ante impact estimates must be 

developed for weather conditions representing 1-in-2 and 1-in-10 weather years). Each year, California’s 

utilities are required to produce ex post impact estimates for each program for the prior year and to update 

ex ante impact estimates for the subsequent five year period. The protocols were used by each of 

California’s three major IOUs in their recent demand response program applications. 79 In conjunction 

with these applications, thousands of Excel spreadsheets were filed with the CPUC showing ex post and 

ex ante impact estimates for roughly a dozen different types of demand response resources and various 

customer segments. These tables are good examples of the type of information resource that can be 

developed in the industry when regulators and other stakeholders establish good M&V standards and 

protocols. 

Another example of useful work in the M&V area is represented by recent work being done by the North 

American Electric Reliability Corporation (NERC) initiated an effort to improve its data collection 

process for evaluating existing demand response resources at the NERC region level. 80 The effort will 

specifically focus on expanding and more accurately defining the sources of demand response that are 

reported, as well as improving the methodology that utilities will use to collect and report data on their 

demand-side management (DSM) programs. 

Much of NERC’s initiative will be coordinated with work that is being done by the North American 

Energy Standards Board (NAESB) to create M&V standards for wholesale markets. This work will focus 

75 FERC, “2007 Assessment of Demand Response and Advanced Metering,” September 2007. 76 Decision adopting dynamic pricing timetable and rate design guidance for Pacific Gas & Electric Company, D. 08-07-045, July 31,
 

2008. 77 The residential class is an exception, where legislation (Assembly Bill 1X) freezes the rates for 130 percent of baseline usage until 

the power purchase contracts that were signed by the state during the energy crisis of 2001 have expired. 78 CPUC D.08-04-050 issued on April 28, 2008 with Attachment A. 79 See, for example, Stephen S. George, Josh Bode and Josh Schellenberg. Load Impact Estimates for Southern California 

Edison’s Demand Response Program Portfolio, September 25, 2008. Filed in conjunction with SCE’s Demand Response 

Program Application for 2009-2011. 80 NERC, “Data Collection for Demand-Side Management for Quantifying Its Influence on Reliability: Results and 

Recommendations,” December 2007. 

190 A National Assessment of Demand Response Potential 
 

Appendix C – Detail on Barriers Analysis 

on developing voluntary demand response standards that would have both wholesale and retail 

components. 81 Meetings are currently being held to bring industry leaders together to focus on specific 

recommendations for these standards. 

A related barrier to measurement and verification – disagreement on cost-effectiveness analysis – is 

discussed in the “Retail Regulatory Barriers” section. 

Shared State and Federal Jurisdiction 

Another barrier to demand response is that of shared state and federal jurisdictions. State commissions 

regulate retail sales in their own jurisdictions, but do not regulate wholesale markets or transmission. 

FERC, on the other hand, regulates wholesale markets, but has no direct control over retail tariffs. 82 To 

the extent that these regulatory bodies have conflicting policy objectives, lack of a coordinated effort can 

pose a serious barrier to demand response. This concept can also be extended to include state-level 

interactions with RTOs and ISOs, where a coordinated effort across multiple states is needed to maximize 

the reliability value of utility-operated demand response programs. At the recent FERC Technical 

Conference on demand response in organized markets, a representative from Dominion Electric 

Cooperative cited this as a major barrier to their demand response efforts, specifically indicating that no 

consensus for the demand response “end game” has been reached, and that a single roadmap is needed to 

move forward and address the “intertwining between federal and state jurisdictions.” 83 

Perception of Gaming 

The perception that some participants in demand response programs will “game” the system has become a 

barrier for demand response programs that require the estimation of a participant’s baseline consumption 

level. This can apply at both the wholesale and retail levels. For example a large industrial customer that 

is bidding demand reductions into a wholesale demand response program would have the incentive to 

increase its baseline in order to appear to provide larger demand reductions. A similar incentive would 

exist in retail programs such as peak-time rebates (PTR) for residential customers, where customers are 

paid based on how much they lower their usage with reference to an unobserved baseline. RTOs such as 

PJM are currently examining methods for reducing the ability of participants to artificially inflate their 

baselines. 

Considerable attention was paid to this topic at the FERC Technical Conference on demand response in 

organized markets. Participants identified ongoing efforts to address the baseline gaming issue in both 

California and PJM. Further, ISO New England (ISO-NE) and New York ISO (NYISO) were identified 

as discussing a new proposed method of estimating baselines. 84 A number of suggestions were proposed 

for addressing this issue, including using different estimation methods for different customer types (e.g., 

making a distinction between weather-responsive and non-weather-responsive customers) and relying on 

an entire season of historical load data. 85 

Lack of Sufficient Real Time Information Sharing Between ISOs and Utilities 

When responding to an emergency event on the system, ISOs are not always aware of how much of a 

particular demand response resource is available, or even when it has been called by the utilities. This 

lack of real time communication among ISOs, utilities, and aggregators limits the value of demand 

response to ISOs for operational planning purposes and potentially leaves valuable demand response 

resources sitting idle at a time when they are needed most. According to the FERC 2007 Demand 

Response Assessment, this was found to be an issue during heat waves in the summer of summer 2006 in 

both California and the Midwest ISO. 86 

81 NAESB comments to FERC Technical Conference on Demand Response in Wholesale Markets, April 2007. 82 An exception to this is ERCOT which is not subject to FERC jurisdiction because it is wholly contained within the state of Texas 

and only has asynchronous transmission connections with other states. 83 Proceedings to FERC Technical Conference on Demand Response in Organized Electric Markets, May 21, 2008, p. 136. 84 Proceedings to FERC Technical Conference on Demand Response in Organized Electric Markets, May 21, 2008, p. 17. 85 Ibid., p. 65. 86 FERC, “2007 Assessment of Demand Response and Advanced Metering,” September 2007. 

Federal Energy Regulatory Commission 191 
 

Appendix C – Detail on Barriers Analysis 

Lack of Reliability and Predictability of Demand Response 

For demand response to be valuable as a resource, it must be dependable and predictable. In other words, 

when a program operator “pushes the button” they need to know that they will get the amount of demand 

reduction that they are expecting. Today, there are concerns that demand response is not as reliable as a 

supply-side resource. This is largely due to a lack of historical evidence (or at least data) showing 

consistent impacts from demand response resources or estimates of what demand response resources will 

provide under various event conditions. This is particularly true for economic programs such as dynamic 

pricing, for which there have been many robust pilots that have quantified the impacts, but for which 

there is not yet a significant history of full scale deployment. This shortcoming should decline over time 

as more empirical evidence is developed and made available to the industry, such as the load impacts 

recently filed by California’s IOUs that were referenced above. 

At the wholesale level, in ISO-NE the results of a small pilot showed that the aggregate performance of 

demand response assets varied from 30 percent to 90 percent of the expected reduction from one demand 

response event to the next. Efforts are underway to expand the size of these pilots and develop more 

robust results. 87 

This barrier may be derived partly from the voluntary nature or many demand response programs. These 

programs do not require that enrolled customers provide peak reductions during critical events – they 

simply offer payments if the customers respond. By putting control of the program in the hands of the 

participant, there is no guarantee that the load reduction will be provided. However, a noteworthy 

counterargument to be made is that while a specific customer may or may not respond to an event on any 

given day, what matters is the aggregate response from all customers enrolled in a program. To the extent 

that this aggregate number is statistically predictable, then the program does serve as a reliable resource. 

Retail Regulatory Barriers 

Policy Restrictions on Demand Response 

One of the single biggest barriers to demand response at the retail level is policy restrictions that have the 

unintended consequence of limiting or even prohibiting certain types of demand response. This most 

commonly occurs in the form of restrictions on rate design. One such example is California’s Assembly 

Bill 1X, which has been interpreted by the CPUC as a rate freeze for the first two tiers of each residential 

customer’s usage. 88 This effectively prohibits utilities from offering time-of-use or dynamic rates to 

residential customers on a default basis because they would raise prices in the first two tiers for peak 

periods. Because of this constraint, the utilities in California have proposed the use of Peak Time Rebates 

(PTR) for all residential customers. A PTR is a “carrot only,” pay for performance program that pays 

customers a certain amount for each kWh reduced during peak periods on high demand days. 

Utilities in New York currently face a similar problem. In New York, state law prohibits utilities from 

placing residential customers on mandatory or default time-of-use-rates, forcing them to provide these 

rates on an opt-in basis and effectively reducing the participation rate. In Maine, current restrictions on 

the form of Standard Offer Service that can be offered through regulated utilities significantly inhibit 

(some have argued prevent) the ability to offer peak time rebates or critical peak prices to customers that 

do not switch to competitive suppliers. 

Ineffective Demand Response Program Design 

Ineffective demand response program design can lead to low enrollment and/or low impacts for demand 

response programs. One such example is the Puget Sound TOU pricing pilot of 2002. 89 The pilot tested a 

TOU rate with a very small peak-to-off peak price differential. Due to this design, customers who shifted 

significant amounts of load from the peak period to the off peak period saw only small bill savings, and 

87 Proceedings to FERC Technical Conference on Demand Response in Organized Electric Markets, May 21, 2008, p. 132. 88 See, for example, CPUC Decision 04-04-020. 89 Ahmad Faruqui and Stephen S. George, “Demise of PSE’s TOU Program Imparts Lessons,” Electric Light and Power, January
 

2003. 

192 A National Assessment of Demand Response Potential 
 

Appendix C – Detail on Barriers Analysis 

lost interest in participating as a result. As large numbers of customers exited the program, it created a 

public relations problem for the utility and the program was shut down. 

Another characteristic of poor demand response program design is a short expected program life. When 

programs are implemented as trial programs, there can be hesitancy on the part of customers to invest in 

the equipment, systems, and training necessary to make the program a success. Other characteristics of 

the program must also be designed with the intent to balance operational needs with customer ability to 

respond. For example, if the lead time to respond to demand response events is short (e.g., day-of) and 

customers are not equipped with enabling technologies to automate load reductions, then their ability to 

respond will be limited. 90 The duration and frequency of demand response events will also influence the 

participation level of customers. Ultimately, demand response programs must be designed to find an 

attractive balance between the reward that customers receive and the inconvenience (or cost) that they 

incur by participating. 

Other examples of ineffective program design include disconnects between event triggers and operational 

needs (e.g., calling CPP events too late in the day to influence day ahead bids and dispatch schedules), 

telemetry requirements that may not be relevant for demand resources, and paying incentives that are 

significantly lower than avoided capacity costs and therefore limiting program participation. 

Financial Disincentives for Utilities 

Without certain regulatory mechanisms in place, utilities generally have a disincentive to pursue programs 

that will reduce sales. While this problem is most pronounced with energy efficiency programs, it is also 

present with programs to encourage demand response. Ultimately, the reduction in sales that results from 

demand response programs will cause the utility to fall short of recovering the fixed revenue requirement 

that would otherwise be recovered in the absence of the sales reduction. 

The lost revenue disincentive associated with demand response is particularly relevant with respect to 

TOU rates and dynamic pricing. These rates are designed to be revenue neutral assuming no change in 

the pattern of energy use, but they ultimately are expected to change the pattern of use. If customers are 

on TOU pricing, revenue is expected to fall as a result of the change in consumption. With dynamic 

pricing there is also an issue that a significant amount of revenue is being collected through prices during 

the peak periods of a few “critical” days. To the extent that critical events are not triggered on those days 

and the critical prices are not dispatched, the utility would fall short of its revenue requirement. 

To address this, some states have regulatory incentives in place to either remove this disincentive, or 

provide a financial incentive to pursue demand-side programs. The regulatory mechanisms fall into three 

categories: 

•	 Direct cost recovery: This is the most common form of regulatory incentive. It allows utilities to 

recover the DSM program implementation costs in a timely manner. It is also the weakest of the 

three mechanisms for promoting DSM. 

•	 Fixed cost recovery: This category includes “decoupling.” Essentially, the link between sales 

and revenue is removed and utilities are allowed to true-up their rates between rate cases to 

recover the lost revenues associated with the decreased electricity sales. 

•	 Shareholder incentives: This includes all models that are designed to provide utilities with a 

financial incentive above and beyond their normal rate of return on investments. A recent 

example is California’s Shared Savings model, which shares the net benefits of DSM impacts 

between the utility and the consumer. The Duke Save-a-Watt model is another such example, 

although it has not yet been adopted. 

90 This has recently been observed in the ComEd residential RTP program. 

Federal Energy Regulatory Commission 193 
 

 

Appendix C – Detail on Barriers Analysis 

Many states have adopted various forms of these regulatory incentive mechanisms, as illustrated in Figure 

C-1. 

Figure C-1: Regulatory Mechanisms for Promoting DSM at Electric Utilities 

However, it is important to note that some of these regulatory mechanisms only apply to energy 

efficiency measures and do not include impacts from demand response. 

Disagreement on Cost-Effectiveness Analysis 

Accurate estimation of the financial value of peak reductions induced by demand response is essential to 

understanding and quantifying demand response benefits. Currently, there is significant disagreement as 

to what should and should not be included in such benefits assessments. For example, wholesale 

electricity price reductions are widely cited as a benefit of increased demand response efforts. However, 

as this is often considered a short-term benefit, it is unclear as to the time horizon over which these 

benefits should be included. Further, others argue that this benefit is simply a transfer of wealth from 

generators to consumers and should not be included as a benefit of demand response at all. This was the 

topic of a recent workshop sponsored by the Mid-Atlantic Distributed Resources Initiative (MADRI). 91 

In addition to which types of benefits should be included in an accurate cost effectiveness assessment, 

there are also issues concerning the valuation of avoided costs. For example, one major source of 

financial benefit from demand response is avoided generating capacity cost. However, there is significant 

disagreement over what should be used as the avoided capacity price. Utilities in California have agreed 

that the full cost of a peaking plant should be derated to account for revenues that it will earn through 

sales to the market, as well as to account for a lack of certainty that a demand response program will 

effectively reduce demand at the time of system peak. However, there is disagreement as to how this 

adjustment should be calculated. Further disagreement arises as to the level of avoided transmission and 

distribution (T&D) capacity that should be accounted for by demand response. Some cost-effectiveness 

tests have been developed in California, although no standard has yet been set. The issue is being 

91 Newell, Sam and Frank Felder, “Quantifying Demand Response Benefits in PJM,” Study Report Prepared for PJM 

Interconnection, LLC and the Mid-Atlantic Distributed Resources Initiative (MADRI), January 29, 2007. 

194 A National Assessment of Demand Response Potential 
 

 

 

Appendix C – Detail on Barriers Analysis 

examined in an ongoing CPUC proceeding (R.07-01-041). Standards for cost effectiveness are also the 

topic of the previously mentioned NAESB effort in this area. 92 

Lack of Retail Competition 

According to some analysts, lack of retail competition is another barrier to demand response. In regions 

without significant competition at the retail level, providers of demand response programs may not have 

the same incentive to minimize costs and offer services that are as robust as if there were firms offering 

competing services. Increased competition from third party aggregators could be a way of introducing 

innovative program designs and marketing channels. In fact, FERC issued its Wholesale Competition 

Final Rule (or Order No. 719) which addresses this issue. 93 Order No. 719 requires all RTOs and ISOs to 

permit aggregators of retail customers to bid demand response on behalf of retail customers directly into 

the organized energy market, unless the law or regulations of the relevant electric retail regulatory 

authority do not permit a retail customer to participate. 94 

Wholesale Regulatory Barriers 

Market Structures Oriented Toward Accommodating Supply Side Resources 

Supply-centric market structures limit the participation of demand response resources in several ways. 

These limitations can include demand response not being allowed to participate in certain markets or 

overly restrictive market rules that make participation prohibitively expensive or otherwise extremely 

difficult, restrictions on who can bid demand response into the market, restrictions on suppliers of 

standard offer service to provide demand response, and lack of a capacity payment for demand response. 

Wholesale electricity markets have reliability rules that are specific to the limitations of generators, but 

not necessarily to demand response resources. For example, rules such as minimum run times would 

apply to supply side resources, but there are not also maximum run time rules(bidding parameters, as that 

term is used in Order No. 719), which would accommodate demand response resources. 95 

Accommodating these limitations and developing more robust market rules could increase demand 

response participation in wholesale markets. FERC addressed this issue in its Order No. 719 in requiring 

each RTO or ISO to accept bids from demand response resources, on a basis comparable to any other 

resources, for ancillary services that are acquired in a competitive bidding process, if the demand 

response resources: (1) are technically capable of providing the ancillary service and meet the necessary 

technical requirements; and (2) submit a bid under the generally-applicable bidding rules at or below the 

market-clearing price, unless the laws or regulations of the relevant electric retail regulatory authority do 

not permit a retail customer to participate. 96 Indeed, growing participation of demand response resources 

in ancillary services markets has been observed, particularly in ISO-NE. 97 

There is also sometimes confusion as to who can actually participate in wholesale markets as a provider 

of demand response. Andrew Ott of PJM recently indicated that this is a particular barrier to demand 

response in PJM. Specifically, he noted that “there’s really no established process in the PJM tariff today 

to allow us to determine whether end users within its jurisdiction in certain customer classes should or 

should not be able to participate significantly in PJM’s wholesale market. There’s ambiguity.” 98 

There are other markets where demand response is not allowed to compete at all. 99 For example, demand 

response is not allowed to bid in the operating reserve markets of ISO-NE. This was cited as a major 

92 Draft Agenda to NAESB DSM-EE meeting on October 3, 2008. 93 Wholesale Competition in Regions with Organized Electric Markets, Order No. 719, 73 Fed. Reg. 64, 100 (Oct. 28, 2008), FERC
 

Stats. & Regs. ¶ 61,071 (2008), p. 3 and 154-164. 94 Id. at p. 3 and 154-164. 95 FERC, “2006 Assessment of Demand Response and Advanced Metering,” August 2006, p. 117 - 118. 96 Order No. 719, FERC Stats. & Regs. ¶ 61,071, p. 3 and 47. 97 FERC, “2007 Assessment of Demand Response and Advanced Metering,” September 2007. 98 Proceedings to FERC Technical Conference on Demand Response in Organized Electric Markets, May 21, 2008, p. 127. 99 A summary of the markets in which demand response can and cannot compete is provided in the Policy Options section of this 

memorandum. 

Federal Energy Regulatory Commission 195 
 

 

 

Appendix C – Detail on Barriers Analysis 

barrier to demand response adoption in wholesale markets by Eric Woychik of Comverge in the FERC 

Technical Conference on demand response in organized markets. 100 

The full value of demand response should be recognized. For example, demand response has an “option” 

value in the sense that, regardless of whether it is used, it can be depended upon for reliability and 

planning purposes. As a result, it should be allowed to compete with supply side resources in planning 

processes. In regions without a capacity market, or where demand response cannot participate in capacity 

markets, this can pose a challenge and lead to undervaluing the resource. ISO-NE is an example of a 

market that allows demand response to compete in its Forward Capacity Market (FCM) up to a limit. In 

the past four auctions, 2,500 MW of demand response have cleared the market representing roughly nine 

percent of the resource base in 2010. 101 In fact, Henry Yoshimura of ISO-NE recently indicated that 

“demand resources are no longer facing barriers in the capacity markets.” 102 PJM also allows demand 

response in its capacity market, and 7,047 MW of demand response cleared in its auction held for 

2012/2013. 103 

Economic Barriers 

Economic barriers refer to situations where the financial incentive for utilities or aggregators to offer 

demand response programs, and for customers to pursue these programs, is limited. These barriers are 

described below. 

Inaccurate Price Signals 

Inaccurate prices are a barrier to programs in which demand responds to price signals. An inaccurate 

price could cause a resource to reduce demand when the underlying energy value is low, or raise it when 

the value is high, which would impair the economic efficiency of the energy market. FERC recognized in 

Order No. 719 that prices that fail to accurately reflect the value of energy may inhibit and deter entry of 

demand response and thwart innovation. 104 

Lack of Sufficient Financial Incentives to Induce Participation 

For some customers, demand response programs may not provide a sufficient financial incentive to 

participate. If customers place a high enough value on being able to consume as much electricity as they 

want, when they want it, then the financial incentives to participate in demand response programs may not 

be large enough to justify their participation. Of course, higher payments are likely to result in increased 

participation. For example, Southern California Edison (SCE) offers one of the most financially attractive 

residential air conditioner direct load control (DLC) programs, with an annual payment of between $100 

and $200105 for participants who sign up for 100% cycling and unlimited interruptions. This is likely one 

of the factors that has led to enrollment of over 325,000 residential customers in SCE’s program, with 

almost 90% of them selecting the 100% cycling option. 

Additionally, dynamic rates by definition will result in some customers experiencing bill increases due to 

their peakier-than-average consumption patterns, and these customers may not opt-in to such a rate if it is 

only offered on a voluntary basis. However, when accounting for moderate shifting of load from peak to 

off-peak periods, such rates could become financially attractive for a larger segment of customers. 

Further, it has been argued that there is a hedging cost implicit in a flat retail electricity rate, and that by 

100 Proceedings to FERC Technical Conference on Demand Response in Organized Electric Markets, May 21, 2008, p. 13. 101 Ibid., p. 130. 102 Ibid., p. 131. 103 2012/2013 Base Residual Auction Report Document, http://www.pjm.com/~/media/markets-ops/rpm/rpm-auction-info/2012-13­

base-residual-auction-report-document-pdf.ashx. 104 Wholesale Competition in Regions with Organized Electric Markets, Order No. 719, 73 Fed. Reg. 61,400 (Oct. 28, 2008), FERC 

Stats. & Regs. ¶ 31,281 (2008), reh’g pending, at P 192. 105 The payment varies depending on the number of tons of air conditioning a customer has. For more details, see Stephen S. 

George, Josh Bode and Josh Schellenberg. Load Impact Estimates for Southern California Edison’s Demand Response 

Program Portfolio, September 25, 2008. 

196 A National Assessment of Demand Response Potential 
 

 

 

Appendix C – Detail on Barriers Analysis 

passing price volatility through to customers in the form of dynamic pricing, electric utilities would avoid 

this cost and, as a result, should be able to reduce rates by the amount of the risk premium. 106 Accounting 

for this would further expand the share of customers for whom dynamic rates would be financially 

attractive. Commonwealth Edison has taken this approach in order to increase consumer interest in its 

residential RTP program. 

Alternatively, some utilities have had success with programs that offer no financial incentive but simply 

appeal to the customer’s desire to help avoid large scale brownouts or blackouts or to improve the 

environment. A respondent to a recent survey on the barriers to demand response indicated that some of 

his large commercial customers were happy to respond to phone calls on critical days by reducing load, 

even without any financial incentives. At the same time, they did not want to formally participate in a 

demand response program because the paperwork and other requirements were very costly and the 

savings were not proportionately large. 107 PG&E’s air conditioning load control program is another 

example of how consumers are willing to help out in emergencies for little financial remuneration, and a 

significant contrast to SCE’s program. PG&E has enrolled roughly 75,000 customers in its Smart AC air 

conditioning cycling program based on a one-time payment of $25 and an appeal indicating that 

participation would be “doing one small thing” that would “actually help prevent power interruptions and 

protect the environment.” 108 

Technological Barriers 

Potential technological barriers to rapid implementation of demand response include the need for new 

types of metering equipment, metering standards, or communications technology. 

Lack of Advanced Metering Infrastructure (AMI) 

The lack of AMI poses a very significant barrier to implementing price-based demand response. 

Currently, there is only one utility in the United States (PPL) that has the metering capability and meter 

data management systems (MDMS) in place that are necessary to put all of its customers on default 

dynamic pricing. While there are many millions of meters currently installed that can be read remotely by 

fixed network, automated meter reading (AMR) systems (which actually transmit data quite frequently), 

the vast majority of these systems would require significant upgrades to support daily delivery of billing 

quality, interval data and extensive investment in MDMS and billing systems to support large scale 

participation in dynamic pricing tariffs. Even in places where a commitment to full interval metering and 

data management exists, such as California, we are still several years away from being able to place large 

numbers of customers on default dynamic pricing. 

However, progress has been made in terms of developing plans for AMI deployment. In addition to 

California’s decision to equip customers with AMI, the state of Connecticut passed a bill requiring 

utilities to begin to deploy AMI by 2009. Texas regulators are also moving toward mandatory AMI 

metering for all customers. Many utilities are planning AMI deployment, or actively analyzing it, 

including Portland General Electric in Oregon, Central Vermont Public Service in Vermont and Baltimore 

Gas & Electric, to name just a few. Northeast Utilities is developing a pilot to test the potential impacts 

of rates that the new smart meters will enable them to provide. Additionally, there are currently ongoing 

dynamic pricing pilots in Maryland (BGE) and Washington, DC (Pepco). With the requirement in the 

Energy Policy Act of 2005 (EPAct 2005) that all states investigate time-based metering, costeffectiveness analyses have been conducted by many other utilities across the US as well. 

106 The lower cost can be estimated by using a well-known formula, which expresses the “risk premium” as an exponential function 

of retail load volatility, wholesale price volatility and retail load-wholesale price correlation. Monte Carlo simulations under a 

variety of plausible assumptions yield a median value of 6 percent. See “Rethinking Rate Design,” prepared for the Demand 

Response Research Center, August 2007. 107 Ahmad Faruqui and Ryan Hledik, “The State of Demand Response in California,” prepared for the California Energy 

Commission, April 2007. 108 Quote taken from the PG&E direct mail offer letter. 

Federal Energy Regulatory Commission 197 
 

 

Appendix C – Detail on Barriers Analysis 

Lack of Cost-Effective Enabling Technologies 

There is a diverse menu of technologies that can improve customers’ ability to provide demand response, 

but these technologies are not yet all cost-effective. Examples of enabling technologies include smart 

thermostats that respond to high prices with an automated adjustment to their setting, whole house 

gateway systems that allow multiple devices to be similarly made price sensitive, advanced energy 

management systems in commercial buildings and process control systems in industrial facilities that can 

reduce load when needed. Customer awareness of these technologies is low and given the low level of 

market penetration, the cost of the technologies is high, creating a Catch-22 situation. 109 It has also been 

argued that the marketing infrastructure (the value chain from the equipment manufacturer to the retailer 

and the installing contractor) is in its infancy. A “market transformation” initiative akin to that pursued in 

the energy efficiency business may be needed to allow rapid penetration of smart (price sensitive) control 

technologies in customer premises that would allow them to see the full benefits of demand response. 

Concerns about Technological Obsolescence and Cost Recovery 

Despite increasing investment in AMI, some regulators and decision makers still have concerns about the 

useful life of smart meters, as well as the risks that the technology could shortly be replaced with 

something better. 110 Concerns about technological obsolescence also extend to the previously described 

enabling technologies, many of which are still in the development phase. Ultimately, these concerns 

contribute to doubts about the ability to recover the cost of these investments before they need to be 

replaced. As there is uncertainty surrounding whether state commissions will allow the cost of AMI or 

enabling technologies to be rate-based, this poses a barrier to increased investment. 111 

Lack of Interoperability and Open Standards 

Interoperability and open standards refer to the manner in which various technologies, such as meters and 

in-home enabling technologies, communicate. If advanced meters contain communication chips based on 

open communication standards, such as ZigBee, it might be possible for consumers to purchase in-home 

control and information devices that would automatically communicate with their meter and that, in turn, 

would help automate or otherwise increase demand response. Open standards might also reduce costs by 

encouraging competition among technology providers to obtain large scale meter and other technology 

contracts. A number of jurisdictions and/or utilities are building open communications standards into the 

functional specifications for AMI systems that they will consider. On the other hand, some have 

questioned whether the meter should serve as the gateway to Home Area Networks (HAN) and other 

devices, because this might allow utilities to control the technology and access to meter data by third 

parties could be limited. 

The need for appropriate technical protocols and standards was a key issue at a recent PJM Symposium 

on Demand Response. The symposium identified a number of topics requiring further development, 

including region-wide communications protocols, meter data reporting standards, and open access to 

meter data. 112 More recently, the National Institute of Standards and Technology has contracted with 

EPRI to develop an interim road map that will serve as a guide to inventory existing standards, and 

identify the need to resolve differences in standards or create new standards entirely. These standards are 

scheduled to be submitted to FERC by the end of 2009. 

Other Barriers 

Some additional barriers do not fall into the categories described above. These barriers are summarized 

here. These are generally related to customer perceptions of demand response programs and a resulting 

limited willingness to enroll. 

109 However, the cost of the technologies is rapidly decreasing. The cost of smart thermostats in particular has fallen to less than 

one-third of the price three years ago. 110 FERC, “2007 Assessment of Demand Response and Advanced Metering,” September 2007. 111 Ibid., p. 128-129. 112 Energetics Incorporated, “Proceedings to PJM Symposium on Demand Response,” June 8, 2007. 

198 A National Assessment of Demand Response Potential 
 

 

 

Appendix C – Detail on Barriers Analysis 

Lack of Customer Awareness and Education 

A deficiency of customer education regarding demand response and its benefits has served as a major 

barrier to further participation in demand response programs. To a large extent, this is evident in a lack of 

simple customer awareness of demand response programs, which was cited by Toronto Hydro Energy 

Services as a market transformation barrier for demand response. 113 Inertial behavior also contributes to 

low participation rates in voluntary programs. Part of this disparity reflects the challenge of creating 

customer awareness about options, part reflects inertia and still another part reflects uncertainty about the 

potential benefits of selecting each option. Due to limited customer experience with price-based demand 

response, and limited utility experience with marketing these programs, a focus on customer education 

and customer awareness will be key in overcoming this barrier. 

Risk Aversion 

A significant barrier to customer participation in dynamic pricing options is risk aversion. The 

Momentum Market Intelligence study cited above also showed that, when selecting a pricing option, 

customers focus more on the downside risk that their bills might go up if they go on the rate, than on the 

upside potential that they can save money either by virtue of having a favorable load shape already or by 

reducing or shifting load from high cost to low cost periods, or both. This risk aversion is one of the 

primary reasons why default pricing options will lead to much higher customer enrollment than will optin enrollment. Research also shows that customers who experience time varying rates have high levels of 

satisfaction and, when offered the option of staying on such rates, most will do so and will also 

recommend such tariffs to their friends. 114 Combined, the above research suggests strongly that default, 

time-based pricing could not only lead to very high participation in such tariffs, but high satisfaction, 

which is quite contrary to the fears that many express when such notions are suggested. 

Fear of Customer Backlash 

This has been cited as a concern by some utilities who feel that heavily-used dynamic pricing could cause 

customer fatigue, cause them to feel exploited if bill savings were small, or trigger a “revolt” in response 

to the higher critical peak prices. However, others feel that a well designed program, coupled with 

effective marketing and educational efforts, could prevent this from becoming a significant barrier. 115 

The research cited above also strongly suggests that such fears are largely unfounded. 

Perceived Lack of Ability to Respond 

Some customers feel that they have already done all they can do to become efficient consumers of 

electricity. This is particularly true in states with highly successful energy efficiency programs. In 

California, large customers on mandatory TOU rates feel they have already shifted as much of their peak 

usage to off-peak periods as they can, given the constraints of their business. If these customers were 

enrolled in a dynamic rate or an additional demand response program, the argument is that they would not 

know what to do to further reduce peak demand. This is another issue of customer education, where 

information on cost-effective means for further reducing peak load could facilitate participation in 

demand response programs for these customers. This barrier is also related to the issue of determining the 

appropriate financial incentive – given a high enough payment, it could become cost-effective for these 

customers to curtail consumption for certain end-uses that they otherwise would not do. 116 

Concern Over Environmental Impacts 

There is some concern that demand response programs could shift load to off peak hours when coal plants 

are on the margin, resulting in an increase in emissions. This depends both on the capacity mix in the 

region and on the impact of demand response on customer consumption patterns. For example, in a 

113 Toronto Hydro Energy Services. Development of an Electricity Demand Management and Demand Response Program for 

Commercial Buildings: Report on Design Charette. November 28, 2003. 114 See Dean Schultz and David Lineweber, Real Mass Market Customers React to Real Time-Differentiated Rates: What Choices 

Do They Make and Why? 16th National Energy Services Conference. San Diego, CA. February 2006. See also Momentum 

Market Intelligence, Statewide Pricing Pilot: End-of-Pilot Participant Assessment. December 2004. 115 Ahmad Faruqui and Ryan Hledik, “The State of Demand Response in California,” prepared for the California Energy 

Commission, April 2007, p. 28 116 Ibid., p. 27. 

Federal Energy Regulatory Commission 199 
Appendix C – Detail on Barriers Analysis 

region where natural gas plants are almost always the “marginal” units, or for demand response programs 

that simply reduce consumption during peak periods (without shifting load to off peak periods), negative 

environmental impacts should not be a concern. However, in a region where, say, a natural gas-fired 

plant is the marginal unit during peak periods and a coal plant is the marginal unit during off peak 

periods, if a customer were to respond to a demand response program by shifting load from the peak 

period to the off-peak period, the net result would be an increase in generation from a plant with higher 

emissions levels. 

Perceived Temporary Nature of Demand Response Impacts 

Often, demand response impacts are seen as a deferral of supply side investments rather than as a 

substitute. In other words, the peak demand reductions from a demand response program could delay 

necessary investment in, say, a new transmission line, but to the extent that there is still load growth in the 

region, the transmission line will ultimately need to be built. There may be an expectation that once the 

transmission line is built, the demand response program will no longer be necessary and will be dropped. 

This perceived temporary nature of the demand response program could limit willingness of a utility to 

invest in it, or willingness of customers to participate in it. 

200 A National Assessment of Demand Response Potential 
 

APPENDIXD.	 DATABASEDEVELOPMENT

DOCUMENTATION

This appendix provides a detailed summary of the data development process that was used to create the 

model inputs for the demand response potential assessment. Figure D-1 illustrates how the different data 

elements were developed. The straight arrows depict relationships between the model inputs, while the 

dashed arrows show key data sources used in determination of the data elements. 

The data elements developed for the assessment and described in this appendix can be broadly classified 

into two categories: 

1.	 Market characteristics data 

a) Number of customer accounts by rate class by state 

b) Electricity sales by rate class by state 

c) System peak load forecast by state 

d) Average peak load per customer by rate class by state 

e) Growth rate in per customer peak load 

f) Central Air Conditioning (CAC) market saturation data 

g) Advanced Metering Infrastructure (AMI) deployment schedule by state 

2.	 Demand response program related data 

a) Business-As-Usual (BAU) Demand Response Potential estimation 

b) Current participation in demand response programs 

c) Impacts from non-pricing programs 

d) Impacts from pricing programs 

e) Cost-effectiveness analysis 

This section describes how each of the data elements listed here was developed. 

Federal Energy Regulatory Commission 201 
Appendix D – Database Development Documentation 

Figure D-1: Data Development for Model Inputs – Relationships Between Data Elements and Key Information S

202 A National Assessment 
 

 

Appendix D – Database Development Documentation 

Market Characteristics Data 

a) Number of Customer Accounts by Rate Class by State 

Four rate classes were considered in the model: 

1.	 Residential, 

2.	 Small commercial and industrial (C&I), 

3.	 Medium C&I, and 

4.	 Large C&I. 

State-level data, published by EIA117 , provides the number of customers and electricity sales for the 

residential, commercial, and industrial sectors. Therefore, the number of residential accounts in each state 

was readily available from the EIA database. However, since the EIA only reports values for the 

commercial and industrial sectors as a whole, further analysis using FERC Form No. 1118 data was 

required in order to determine the breakdown of small, medium, and large C&I accounts for each state. 

The following steps describe the process undertaken to estimate the number of C&I accounts by rate class 

for each state. 

1.	 Electricity Sales by Rate Schedule: FERC Form No. 1 data provides the number of accounts and 

corresponding electricity sales for customers on different rate schedules. FERC Form No. 1 is 

reported only by IOUs. These data were evaluated and used to calculate electricity sales per customer 

for each rate schedule. 

2.	 Initial Customer Classification into residential and C&I customers: Customers were then classified 

into the residential and C&I segments based on the label of the rate schedule provided in FERC Form 

No. 1. To the extent possible, rate schedule descriptions from utility tariff books were obtained to 

validate the classifications. 

3.	 Further C&I Customer Classification: The next step was to apply average load factors by rate class to 

estimate peak load per customer for each rate schedule. The average load factors assumed for the 

three C&I rate classes were: 

•	 Load factor for small C&I: 0.6, 

•	 Load factor for medium C&I: 0.7, and 

• Load factor for large C&I: 0.7. 119
 

These load factors were applied to the electricity sales per customer (Step 1) for each C&I rate
 

schedule in FERC Form No. 1 (Step 2) to estimate peak load per customer. Based on the calculated 

value of peak load per customer, the C&I rate schedules were grouped into the three C&I rate classes:
 

small, medium, and large. The classification was based on the following ranges for peak load: 

•	 Small: 0 to 20 kW; 

•	 Medium: Greater than 20 to 200 kW; and 

• Large: Greater than 200 kW. 

For each utility that reported FERC Form No. 1 data, these first three steps provided an estimation of
 

the percentage of total C&I customers falling into each of the three C&I rate classes. 

4.	 C&I Adjustments for Multi-State Operation by Utilities: Adjustments were then made to C&I data for 

utilities that had operations in multiple states. For these utilities, the FERC Form No. 1 data on the 

number of customers and sales were apportioned to all states in which the utilities operate using 

117 http://www.eia.doe.gov/cneaf/electricity/epa/epa_sprdshts.html 118 FERC Form 1 Database - Electric Utility Annual Report; survey data collected from FERC Form 1 – “Annual Major Electric 

Utilities, Licensees, and Others.” http://www.ferc.gov/docs-filing/eforms/form-1/viewer-instruct.asp 119 The load factor assumptions are based on the team’s extensive experience working with load shape data and undertaking load 

shape analysis. 

Federal Energy Regulatory Commission 203 
Appendix D – Database Development Documentation 

information reported in EIA Form-861. Thus, it was possible to disaggregate the multi-state, utilityreported FERC Form No. 1 data into information for each state in which a given utility operates. This 

provided a more accurate representation of the number of C&I accounts and sales for each rate class 

by utility and by state. 

5.	 State-Level Aggregation of Utility Data for C&I Accounts: Multiple utility data for each state were 

aggregated to arrive at the distribution of small, medium, and large C&I accounts for each state. This 

assumed that the distribution obtained from FERC Form No. 1 is representative for the state as a 

whole, with the implicit assumption that the distribution applies to IOUs and non-IOUs as well. 

Nebraska was the only state for which FERC Form No. 1 data were not available. Since Nebraska’s 

characteristics were assessed to be similar to that of Idaho, Idaho’s data were used as a proxy for 

assuming the C&I distribution for Nebraska. 

6.	 Number of C&I Accounts by Rate Class and State: The final step in estimating the number of C&I 

accounts by rate class was to apply the percentage distribution for account population by rate class 

(derived from the previous steps) to the total number of C&I accounts by state (obtained from EIA 

Form-861 state-level data). This provided the number of C&I accounts by rate class for each state. 

Table D-1 lists the resulting number of accounts by state for the residential, small C&I, medium C&I, and 

large C&I rate classes. 

204 A National Assessment of Demand Response Potential 
Appendix D – Database Development Documentation 

Table D-1: Number of Accounts by Rate Class 

State Number of accounts by rate class 

Residential Small C&I Medium C&I Large C&I 

Alabama 2,077,677 362,448 12,354 3,801 

Alaska 266,671 45,183 3,270 62 

Arizona 2,567,749 280,527 15,965 1,381 

Arkansas 1,301,517 199,604 6,629 3,442 

California 12,971,924 1,567,550 301,662 17,772 

Colorado 2,068,055 282,139 88,021 1,531 

Connecticut 1,449,983 141,998 11,261 8,044 

Delaware 390,239 47,323 1,475 374 

District of Columbia 206,047 24,506 1,842 1,229 

Florida 8,615,249 921,368 224,874 9,195 

Georgia 4,039,005 483,576 66,628 11,363 

Hawaii 409,581 55,808 7,482 632 

Idaho 647,581 65,923 55,692 928 

Illinois 5,054,895 541,263 26,791 21,435 

Indiana 2,734,788 286,888 65,468 8,038 

Iowa 1,320,241 183,320 30,471 3,507 

Kansas 1,213,189 221,809 10,962 7,594 

Kentucky 1,918,247 272,458 27,771 3,050 

Louisiana 1,870,160 196,805 89,052 3,192 

Maine 693,400 75,666 13,927 1,065 

Maryland 2,187,996 230,938 17,496 4,054 

Massachusetts 2,631,568 367,459 22,605 4,510 

Michigan 4,336,390 485,729 44,172 10,836 

Minnesota 2,283,083 189,477 75,091 10,044 

Mississippi 1,222,047 228,202 1,565 2,228 

Missouri 2,670,172 347,394 25,739 4,651 

Montana 456,112 103,892 890 238 

Nebraska 787,312 178,123 10,854 2,889 

Nevada 1,079,306 145,469 4,497 1,963 

New Hampshire 600,399 102,868 831 1,875 

New Jersey 3,414,289 461,304 10,998 10,375 

New Mexico 829,100 122,560 16,755 1,296 

New York 6,855,544 958,009 66,351 5,265 

North Carolina 4,128,231 619,832 29,169 3,277 

North Dakota 310,222 54,365 2,211 699 

Ohio 4,908,791 569,999 59,607 13,010 

Oklahoma 1,629,818 243,831 30,398 3,097 

Oregon 1,610,829 220,262 36,132 1,521 

Pennsylvania 5,217,010 618,439 75,656 10,577 

Rhode Island 432,307 48,623 8,614 864 

South Carolina 2,028,361 326,244 15,666 2,327 

South Dakota 355,714 66,375 658 875 

Tennessee 2,660,110 428,663 30,312 3,735 

Texas 9,397,317 1,269,490 411,961 5,756 

Utah 911,744 103,864 16,754 791 

Vermont 310,842 46,230 3,075 313 

Virginia 3,170,126 369,208 32,352 7,886 

Washington 2,762,275 345,256 26,145 3,568 

West Virginia 855,919 135,823 11,181 1,199 

Wisconsin 2,581,840 290,192 44,419 4,518 

Wyoming 245,648 61,758 3,587 585 

Total 118,473,006 15,108,276 2,159,118 223,764 

b) Electricity Sales by Rate Class by State 

The distribution of electricity sales by rate class was determined using the same approach as described 

above for estimating the number of accounts by rate class. As before, the electricity sales data were 

readily available for residential accounts from EIA. However, the small, medium, and large C&I sales 

data had to be developed from FERC Form No. 1 data. FERC Form No. 1 data contains electricity sales 

data by rate schedule along with number of accounts for IOUs. An analogous estimation methodology to 

the one already outlined for the number of accounts (see steps 1-6 in the previous section) was used to 

develop the C&I sales data. The result was state-level aggregate sales data for each of the four rate 

classes. 

Federal Energy Regulatory Commission 205 
Appendix D – Database Development Documentation 

Table D-2 lists the resulting electricity sales by state for the residential, small C&I, medium C&I, and 

large C&I rate classes. 

Table D-2: Electricity Sales by Rate Class 

State Electricity Sales by Rate Class (GWh) 

Residential Small C&I Medium C&I Large C&I 

Alabama 32,870 26,023 13,385 19,534 

Alaska 2,204 1,575 2,030 524 

Arizona 33,897 20,352 13,897 7,493 

Arkansas 17,788 8,510 3,427 18,824 

California 93,402 28,440 73,061 73,567 

Colorado 17,752 2,745 20,932 9,767 

Connecticut 13,204 2,903 4,336 11,843 

Delaware 4,330 3,794 1,118 2,544 

District of Columbia 1,853 1,214 1,750 6,509 

Florida 119,013 13,879 54,139 45,492 

Georgia 55,433 12,525 22,410 46,961 

Hawaii 3,309 1,373 2,189 3,944 

Idaho 8,438 1,232 9,729 4,051 

Illinois 47,145 22,662 4,851 71,030 

Indiana 32,818 9,432 20,575 45,653 

Iowa 13,723 4,039 8,854 17,897 

Kansas 13,886 7,095 2,808 17,045 

Kentucky 26,425 14,356 28,538 20,483 

Louisiana 29,304 14,262 19,889 17,188 

Maine 4,432 915 2,715 4,537 

Maryland 27,356 15,727 3,369 17,477 

Massachusetts 19,988 12,250 3,494 21,148 

Michigan 35,192 15,783 12,829 47,081 

Minnesota 22,531 3,252 19,154 23,629 

Mississippi 18,612 9,582 693 18,651 

Missouri 34,841 8,667 16,457 24,274 

Montana 4,602 6,871 858 1,890 

Nebraska 9,557 4,182 8,313 5,967 

Nevada 12,544 7,982 2,766 12,326 

New Hampshire 4,482 2,601 163 4,126 

New Jersey 29,594 17,322 5,143 29,307 

New Mexico 6,293 3,071 6,164 6,514 

New York 50,072 28,910 32,902 30,992 

North Carolina 53,736 16,586 27,852 31,033 

North Dakota 3,962 2,776 1,737 3,076 

Ohio 52,221 25,608 23,471 56,129 

Oklahoma 22,610 4,793 12,616 17,143 

Oregon 19,731 5,389 16,687 7,474 

Pennsylvania 53,550 26,874 19,677 48,843 

Rhode Island 3,064 731 1,724 2,475 

South Carolina 29,017 11,640 14,959 26,889 

South Dakota 4,166 3,290 351 2,524 

Tennessee 41,565 22,932 31,213 9,555 

Texas 132,220 24,047 115,175 85,287 

Utah 8,621 2,587 8,523 7,374 

Vermont 2,182 729 1,109 1,921 

Virginia 43,624 8,633 16,925 39,493 

Washington 35,806 12,788 18,393 20,241 

West Virginia 11,199 4,419 5,191 12,151 

Wisconsin 22,138 6,646 17,059 25,843 

Wyoming 2,585 4,822 1,428 6,490 

Total 1,388,887 518,818 757,030 1,092,209 

206 A National Assessment of Demand Response Potential 
 

 

Appendix D – Database Development Documentation 

c) System Peak Load Forecast by State 

System peak demand forecast values are readily available from NERC at the regional level. 120 The NERC 

peak demand forecast is provided for eight NERC Regions (excluding Alaska and Hawaii) and several 

sub-regions for four of the NERC regions. Only data for New York are available at the (sub-region) state 

level. Because the model in the study requires state-level forecast values to serve as a reference point for 

the demand response impacts, the NERC regional data had to be segmented by state. 

The NERC forecast was divided among the states (except for New York, Alaska and Hawaii) according 

to the percentage of total electric sales for each state 121 . This methodology helps establish consistency 

between the state system peak forecast and the bottom-up aggregated peak load estimate for a state using 

customer class data by rate class for number of accounts and average peak load per customer. NERC peak 

demand data for New York was used directly since it was reported at the state level 122 . Since NERC des 

not provide values for Alaska and Hawaii, summer peak values for these states were obtained from EIA 

Form-861 data. 

There were limited data sources available for benchmarking the state values. Where available, state values 

were compared and modified to reflect state filings and planning studies. We also benchmarked national 

level estimates with data from other sources. 123 

Table D-3 provides the system peak load forecast by state for the time horizon being considered in this 

study. 

120 2008 Long Term Reliability Assessment 2008-2017, North American Electric Reliability Corporation, October 2008 121 Electric Sales, Revenue, and Price, Table 2. Sales to Bundled and Unbundled Consumers by Sector, Census Division, and 

State, 2006, Energy Information Administration, 

 http://www.eia.doe.gov/cneaf/electricity/esr/esr_sum.html 

122 Comparing the NERC peak demand data for New York with that obtained using the same approach followed for other states 

reveals that the NERC data is about 10% higher. Nevertheless, it was deemed more accurate to use the NERC data directly in 

this case. 

EIA Form-861 provides utility reported peak values. This EIA data was used to arrive at a national estimation of peak load 

by aggregating the utility reported peak values in the database. The EIA data was also used to arrive at state peak values 

by aggregating utility peak data for a state. A comparison of the peak values at the national level showed that the peak 

value estimated from the EIA data was significantly higher than the total peak load forecast reported by NERC. A 

comparison of peak estimates at the state level across the two datasets revealed differences. There were some states 

where peak load estimation using EIA data came close to the NERC forecasted values. But for other states, the peak 

values from EIA and NERC forecast were different. Differences in state peak estimates can be explained by the nature of 

utility data reporting in EIA. In the EIA database, utilities with service territories across multiple states, report their peak 

loads only against one particular state (most likely the state of their mailing address) and do not provide the state-level 

break-up of their peak. This leads to an inaccurate estimation of the state level peak by simply aggregating the utility 

reported data. 

Federal Energy Regulatory Commission 207 
 

Appendix D – Database Development Documentation 

Table D-3: Peak Demand Forecast by State: 124 

State Peak demand forecast by state (MW) 

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 (projected) 

Alabama 19,000 19,410 19,817 20,191 20,544 20,921 21,344 21,751 22,140 22,536 22,939 

Alaska 1,417 1,438 1,459 1,481 1,503 1,526 1,549 1,572 1,596 1,620 1,644 

Arizona 18,456 18,862 19,219 19,585 19,964 20,324 20,676 21,030 21,380 21,721 22,067 

Arkansas 9,875 10,089 10,296 10,479 10,652 10,836 11,038 11,236 11,426 11,622 11,821 

California 57,137 58,395 59,479 60,606 61,814 62,930 64,052 65,183 66,326 67,404 68,500 

Colorado 10,837 11,076 11,281 11,495 11,724 11,936 12,149 12,363 12,580 12,785 12,992 

Connecticut 7,524 7,658 7,785 7,905 8,016 8,116 8,202 8,277 8,343 8,401 8,459 

Delaware 2,503 2,545 2,593 2,630 2,661 2,698 2,734 2,768 2,804 2,836 2,869 

District of 

Columbia 

2,403 2,443 2,489 2,524 2,554 2,589 2,625 2,657 2,691 2,723 2,754 

Florida 49,453 50,296 51,242 52,470 53,721 54,909 55,952 57,217 58,498 59,788 61,106 

Georgia 28,215 28,824 29,428 29,984 30,508 31,068 31,696 32,300 32,878 33,466 34,064 

Hawaii 1,790 1,816 1,844 1,871 1,899 1,928 1,957 1,986 2,016 2,046 2,077 

Idaho 4,962 5,072 5,166 5,264 5,369 5,465 5,563 5,661 5,760 5,854 5,949 

Illinois 30,465 31,019 31,631 32,120 32,552 33,043 33,553 34,033 34,517 34,980 35,449 

Indiana 22,890 23,266 23,709 24,043 24,328 24,664 25,000 25,311 25,635 25,933 26,236 

Iowa 9,169 9,607 9,945 10,176 10,357 10,527 10,705 10,877 11,045 11,221 11,400 

Kansas 8,630 8,820 8,990 9,127 9,256 9,395 9,535 9,678 9,821 9,971 10,124 

Kentucky 18,889 19,251 19,637 19,963 20,259 20,588 20,941 21,275 21,605 21,929 22,258 

Louisiana 16,332 16,686 17,031 17,341 17,634 17,947 18,293 18,629 18,953 19,283 19,619 

Maine 2,812 2,862 2,909 2,954 2,996 3,033 3,065 3,093 3,118 3,140 3,161 

Maryland 13,583 13,806 14,069 14,267 14,436 14,636 14,835 15,020 15,212 15,389 15,568 

Massachusetts 12,695 12,922 13,134 13,337 13,525 13,693 13,839 13,966 14,077 14,175 14,273 

Michigan 23,292 23,820 24,351 24,739 25,063 25,422 25,786 26,127 26,475 26,808 27,144 

Minnesota 14,123 14,798 15,318 15,674 15,952 16,214 16,489 16,753 17,013 17,284 17,559 

Mississippi 9,835 10,047 10,258 10,451 10,634 10,829 11,048 11,259 11,460 11,665 11,874 

Missouri 17,362 17,739 18,102 18,424 18,728 19,053 19,408 19,755 20,090 20,434 20,783 

Montana 2,991 3,075 3,143 3,206 3,268 3,326 3,385 3,443 3,502 3,559 3,616 

Nebraska 5,771 6,047 6,260 6,405 6,519 6,626 6,738 6,846 6,952 7,063 7,175 

Nevada 7,538 7,704 7,847 7,996 8,155 8,303 8,451 8,600 8,751 8,893 9,038 

New Hampshire 2,539 2,585 2,627 2,668 2,705 2,739 2,768 2,794 2,816 2,835 2,855 

New Jersey 17,273 17,559 17,889 18,143 18,361 18,613 18,862 19,092 19,329 19,547 19,768 

New Mexico 4,671 4,774 4,863 4,953 5,050 5,139 5,230 5,321 5,413 5,500 5,589 

New York 33,809 34,167 34,444 34,768 35,112 35,475 35,807 36,133 36,436 36,762 37,091 

North Carolina 26,548 27,120 27,689 28,212 28,706 29,232 29,823 30,392 30,936 31,489 32,051 

North Dakota 2,379 2,493 2,581 2,641 2,687 2,732 2,778 2,822 2,866 2,912 2,958 

Ohio 33,238 33,799 34,443 34,931 35,351 35,843 36,335 36,794 37,270 37,715 38,165 

Oklahoma 11,919 12,183 12,418 12,606 12,784 12,976 13,170 13,367 13,565 13,772 13,983 

Oregon 10,476 10,706 10,905 11,112 11,333 11,538 11,744 11,951 12,160 12,358 12,559 

Pennsylvania 31,488 32,007 32,616 33,075 33,467 33,930 34,392 34,820 35,265 35,676 36,092 

Rhode Island 1,785 1,817 1,847 1,875 1,902 1,926 1,946 1,964 1,979 1,993 2,007 

South Carolina 16,947 17,312 17,675 18,009 18,324 18,660 19,037 19,400 19,747 20,100 20,460 

South Dakota 2,128 2,229 2,308 2,361 2,403 2,443 2,484 2,524 2,563 2,604 2,645 

Tennessee 22,475 22,960 23,441 23,884 24,302 24,747 25,248 25,729 26,190 26,658 27,134 

Texas 72,723 74,203 75,734 77,169 78,381 79,898 81,259 82,637 83,881 85,433 87,014 

Utah 5,742 5,868 5,977 6,090 6,212 6,324 6,437 6,550 6,665 6,773 6,884 

Vermont 1,085 1,099 1,112 1,125 1,139 1,152 1,165 1,178 1,192 1,205 1,218 

Virginia 22,412 22,882 23,357 23,785 24,186 

Washington 18,538 18,946 19,298 19,663 20,055 20,417 20,782 21,149 21,519 21,869 22,225 

Wisconsin 14,845 15,458 15,951 16,292 16,562 16,825 17,099 17,362 17,622 17,887 18,157 

Total 793,121 809,926 826,192 840,838 854,547 868,879 883,359 897,672 911,725 925,880 940,267 

24,617 25,097 25,557 26,000 26,447 26,902 

West Virginia 6,916 7,042 7,181 7,295 7,396 7,510 7,630 7,744 7,857 7,967 8,078 

Wyoming 3,236 3,326 3,401 3,469 3,536 3,599 3,662 3,725 3,789 3,850 3,912 

124 The peak load numbers are based on the NERC report titled ‘2008 Long Term Reliability Assessment 2008-2017’, October 2008. 

The NERC report provides the peak demand forecast for eight NERC regions (excluding Alaska and Hawaii). Peak demand 

values for Alaska and Hawaii were obtained from EIA Form-861 data and added to the NERC total to arrive at the total peak 

demand estimates for the whole U.S. 

208 A National Assessment of Demand Response Potential 
 

Appendix D – Database Development Documentation 

d) Average Peak Load per Customer by Rate Class by State 

One of the key inputs to demand response potential estimation is average electricity use per customer per 

hour during time periods when demand response programs are likely to be used but before any demand 

response occurs. We refer to the time period representing when demand response has a high probability 

of being used as the “peak period” on a “typical event day” and represent that period by the hours 

between 2 and 6 pm on the top 15 system load days in each state. Note that average energy use across the 

top 15 system load days will produce demand response load impact estimates that are significantly lower 

than if they were based on the single hour of system peak or based on fewer than the top 15 system load 

days. Utility and/or ISO system load data were used to identify top system load days in each state. 

Hourly load data are not available for all utilities and states or for all customer segments within states. 

Indeed, no data at all were found that distinguished between residential customers with and without 

central air conditioning. Fortunately, hourly load data were available on a large enough cross section of 

utilities that it was possible to use regression analysis to estimate normalized load shapes for each relevant 

customer segment and to use these models to develop load shapes for all other states and customer 

segments. Table D-4 summarizes utilities from which hourly load data was used by state and customer 

segment. Following Table D-4 is a list of the data sources for each utility. 

Table D-4: Summary of Utility Data Used in Regression Analysis 

Summary of Utility Data Used in Regression Analysis 

State Residential Small C&I Medium C&I Large C&I 

California PG&E, SCE & SDG&E PG&E, SCE & SDG&E PG&E, SCE & SDG&E PG&E, SCE & SDG&E 

Connecticut 

United Illuminating 

Company 

United Illuminating 

Company 

United Illuminating 

Company 

United Illuminating 

Company 

District of Columbia Pepco Pepco Pepco Pepco 

Idaho Idaho Power Idaho Power Idaho Power Idaho Power 

Illinois Amaren, ComEd Amaren Amaren Amaren 

Indiana Duke Energy Duke Energy Duke Energy Duke Energy 

Massachusetts National Grid National Grid National Grid National Grid 

Maryland Pepco, BG&E Pepco, BG&E Pepco Pepco 

Maine Central Maine Power Central Maine Power Central Maine Power Central Maine Power 

Michigan Detroit Edison Detroit Edison Detroit Edison Detroit Edison 

Missouri Amaren Amaren Amaren Amaren 

North Carolina Duke Energy Duke Energy Duke Energy Duke Energy 

New Hampshire National Grid National Grid National Grid National Grid 

New Jersey JCPL, PSEG JCPL JCPL JCPL, PSEG 

New York National Grid National Grid National Grid National Grid 

Ohio Duke Energy Duke Energy Duke Energy Duke Energy 

Pennsylvania MetEd, Penelec MetEd, Penelec MetEd, Penelec MetEd, Penelec 

Rhode Island National Grid National Grid National Grid National Grid 

South Carolina Duke Energy Duke Energy Duke Energy Duke Energy 

Texas Ercot Ercot Ercot Ercot 

Vermont Burlington Electric Burlington Electric Burlington Electric Burlington Electric 

Federal Energy Regulatory Commission 209 
Appendix D – Database Development Documentation 

Utilities List with Sources 

•	 PG&E: http://www.pge.com/nots/rates/tariffs/energy_use_prices.shtml 

•	 SCE: http://www.sce.com/AboutSCE/Regulatory/loadprofiles/loadprofiles.htm 

•	 SDGE: FSC Internal 

•	 United Illuminating Company: 

http://www.uinet.com/uinet/connect/UINet/Top+Navigator/About+UI/Doing+Business+With+UI 

/Suppliers+-+Aggregators/Load+Profiles/ 

•	 Pepco: https://suppliersupport.pepco.com/suppliersupport/suppliersupportframe.htm 

•	 Idaho Power: GEP Internal 

•	 Ameren: http://www.ameren.com/IlChoice/adc_cc_profile_select.asp 

•	 ComEd: FSC Internal 

•	 Duke Energy: FSC Internal 

•	 National Grid MA: https://www.nationalgridus.com/masselectric/energy_supplier/index.asp 

•	 National Grid RI: https://www.nationalgridus.com/narragansett/energy_supplier/index.asp 

•	 National Grid NH: https://www.nationalgridus.com/granitestate/energy_supplier/index.asp 

•	 National Grid NY: 

http://www.nationalgridus.com/niagaramohawk/energy_supplier/elec_load_profile.asp 

•	 BG&E: http://supplier.bge.com/LoadProfiles_EnergySettlement/historicalloaddata.htm 

•	 Central Maine Power: FSC Internal 

•	 Detroit Edison: 

http://www.suppliers.detroitedison.com/internet/infocenter/custdata/loadprofiles/profiles.jsp 

•	 JCPL: http://www.firstenergycorp.com/supplierservices/New_Jersey/Load_Profiles.html 

•	 PSEG: http://www.pseg.com/customer/energy/energy_profiles.jsp 

•	 Penelec: http://www.firstenergycorp.com/supplierservices/Pennsylvania/Met-Ed_and_Penelec/M 

E_and_PN_Load_Profile.html 

•	 MetEd: http://www.firstenergycorp.com/supplierservices/Pennsylvania/Met-Ed_and_Penelec/ME 

_and_PN_Load_Profile.html 

•	 Ercot: http://www.ercot.com/mktinfo/loadprofile/ 

•	 Burlington Electric: FSC Internal 

Data from the utilities identified in Table D-4 were used to estimate regression models that relate 

normalized hourly load to a variety of variables that influence load in each hour, including weather, 

central air conditioning saturation and seasonal, monthly, day-of-week and hourly usage patterns. This 

statistical analysis was used to separate weather sensitive and non-weather sensitive load for residential 

customers. The normalized load shapes were then combined with estimates of average annual energy use 

and central air conditioning saturation by customer segment for each state and state-specific weather data 

to produce hourly load estimates for each customer segment and state. The average, hourly energy use 

between 2 and 6 pm on the top 15 system load days was used as the basis for estimating load impacts for 

price-based demand response options for each customer segment. The outcome of this estimation process 

is summarized in Table D-5. 

210 A National Assessment of Demand Response Potential 
 

 

 

 

 

 

 

 

 

 

 

Appendix D – Database Development Documentation 

Table D-5: Average Energy Use per Hour (2 - 6 pm) on Top 15 System Peak Days 

Average Energy Use per Hour Between 2 and 6 pm on Top 15 System Peak Days 

(kWh/hr) 

State Residential No CAC Residential with 

CAC 

Small C&I 

(<20kW) 

Medium C&I 

(20-200kW) 

Large C&I 

(>200kW) 

Alabama 1.88 4.29 15.06 192.09 747.82 

Alaska 0.89 0.94 4.48 79.82 1029.24 

Arizona 1.56 4.18 16.88 165.50 822.41 

Arkansas 1.62 3.80 9.09 92.64 800.59 

California 0.83 1.79 3.18 37.63 555.49 

Colorado 1.01 2.11 1.91 40.05 901.10 

Connecticut 1.01 3.35 3.89 63.37 205.70 

Delaware 1.27 2.53 15.17 125.09 951.18 

District of Columbia 1.03 2.10 9.52 158.33 744.54 

Florida 1.64 3.21 2.90 40.13 695.77 

Georgia 1.63 3.73 5.44 59.68 601.70 

Hawaii 0.89 1.49 4.20 45.07 841.74 

Idaho 1.54 3.56 3.95 30.98 636.35 

Illinois 1.07 1.84 7.31 28.29 449.82 

Indiana 1.38 2.78 6.32 52.36 798.43 

Iowa 1.19 2.27 4.11 47.31 709.25 

Kansas 1.38 3.07 6.38 43.67 317.52 

Kentucky 1.64 3.45 10.51 176.00 958.65 

Louisiana 1.86 3.99 14.62 38.52 771.32 

Maine 0.71 1.71 2.04 29.65 570.85 

Maryland 1.43 2.92 13.09 32.10 606.14 

Massachusetts 0.84 2.42 5.98 24.48 641.85 

Michigan 0.93 1.85 6.18 48.07 608.74 

Minnesota 1.13 2.17 3.21 41.64 327.42 

Mississippi 1.81 4.10 8.76 78.15 1214.63 

Missouri 1.55 3.33 5.03 110.37 748.32 

Montana 1.19 2.27 12.28 156.79 1100.58 

Nebraska 1.40 2.82 4.55 127.93 291.10 

Nevada 1.38 3.39 12.07 112.40 930.63 

New Hampshire 0.83 2.65 4.73 32.07 305.90 

New Jersey 0.95 3.24 7.11 77.16 394.96 

New Mexico 0.90 1.78 4.81 61.23 707.20 

New York 0.80 2.65 5.67 81.14 819.98 

North Carolina 1.55 3.48 5.57 168.27 1373.15 

North Dakota 1.47 2.85 9.65 129.07 614.41 

Ohio 1.22 2.43 8.53 65.09 603.86 

Oklahoma 1.67 3.61 3.84 69.80 777.67 

Oregon 1.45 2.68 4.52 74.62 679.85 

Pennsylvania 1.18 2.32 8.18 42.74 644.24 

Rhode Island 0.78 2.29 2.72 31.84 392.99 

South Carolina 1.70 4.01 7.64 171.79 1696.41 

South Dakota 1.35 2.57 9.28 87.11 401.63 

Tennessee 1.86 4.41 11.51 185.85 376.15 

Texas 1.69 3.71 3.73 47.23 2086.24 

Utah 1.11 2.34 4.91 86.14 1322.23 

Vermont 0.78 2.12 2.19 48.54 772.87 

Virginia 1.58 3.32 4.58 88.22 708.11 

Washington 1.53 2.60 6.50 109.94 771.14 

West Virginia 1.50 3.13 6.34 78.07 1431.48 

Wisconsin 0.99 1.72 4.08 60.61 781.90 

Wyoming 1.24 2.46 14.86 65.71 1550.80 

Federal Energy Regulatory Commission 211 
 

 

 

Appendix D – Database Development Documentation 

The statistical models underlying the estimates in Table D-5 were estimated using panel regressions. 

Each load profile represented an individual panel (broken down by utility, region, state and customer 

class). Each panel contained data in hourly form, for at least one consecutive year’s worth of data (8,760 

hourly observations), with some panels containing several years of data. The regression models were 

designed to accurately predict normalized hourly load for electricity customers nationwide given the time 

of day, day of week, and month, with a focus primarily on the accuracy of the predictions in the months 

and hours of the day when a demand response event is likely to be called. Hourly loads were estimated 

for the four customer classes: Residential and Small, Medium and Large commercial and industrial. 

Separate models were estimated for residential customers in the New England states and non-New 

England states. This segmentation was intended to reflect inherent differences in the housing stock. 

Homes in the New England states are typically older, smaller and have a much lower CAC penetration 

due to a lack of centralized vents. This also typically results in a much higher concentration of room air 

conditioners, a variable for which there is no reliable data source. With the effect of the temperaturebased variables in the model scaled directly by CAC penetration, segmenting the residential class ensures 

appropriate coefficients for these variables. Without the segmentation, the model produced biased 

estimates at the low end of the saturation of central air conditioning due to the bias in the New England 

states. 

For each customer segment, functional form was closely considered and then several specifications were 

tested using a fixed-effects panel regression model. This approach controls for auto-correlation in the 

errors and ensures correct standard errors. The selection of the final regression model was based on its 

accuracy under normal and extreme weather conditions, and on its theoretical consistency. The same 

specification was used for all customer segments, with the main difference being that CAC penetration 

varies in the residential segment, while it is held constant for the C&I segments. With C&I load much 

less dependent on CAC load, and variation in CAC penetration significantly lower in these segments, this 

is a valid approach. 

The final models depict normalized energy use for customers across states and classes as a function of 

variables that capture typical load shapes associated with operational schedules, and, for the residential 

model, variables designed to capture central air conditioning load based on central air conditioning 

penetration and cooling-degree-hours. The dependent variable in each regression consisted of normalized 

hourly energy use, and the explanatory variables for the residential model were: 

•	 Hourly binary variables to define the typical load profile for a day; 

•	 Monthly binary variables to capture seasonal variation; 

•	 Day-of-week binary variables to capture variation in energy use throughout the week; 

•	 A weekend & holiday binary variable interacted with hourly binary variables to capture the 

different hourly load profile typically found on weekends or holidays; 

•	 A Monday or Friday binary variable interacted with hourly binary variables to capture the 

different hourly load profiles found on Mondays and Fridays; 

•	 Cooling-Degree-Hours * Central Air Conditioning Penetration interacted with hour binary 

variables to capture the impact of air conditioning load across the hours; 

Mathematically, the regressions can be expressed by: 9 7	 24 24 normalizedkWxt = ax +∑ bi ⋅ Monthi +∑ ck ⋅ Dayofweekk +∑ d j ⋅ Hourj +∑ ej ⋅ Hourj ⋅Weekendholiday + i=5 k =1 j=1 j =1
 

24 24 ∑ f j ⋅ Hourj ⋅ MondayOrFriday +∑ g j ⋅ Hourj ⋅CoolingDegreeHours⋅CACpenetration +Uxt j =1	 j =1 

In this equation, 

normalizedkWxt represents the normalized hourly usage for state or utility x at time t; 

212 A National Assessment of Demand Response Potential 
 

Appendix D – Database Development Documentation 

a - g are estimated parameters; 

Monthi is a dummy variable for month i;
 

Dayofweekk is a dummy variable for day of week k; 

Hourj is a dummy variable for hour j; 

Weekendholiday is a dummy variable specifying the day as either a weekend or holiday; 

MondayOrFriday is a dummy variable specifying the day as either a Monday or Friday; 

CoolingDegreeHours is the cooling degree hours measured as the maximum of 0 or temperature - 

65 

Uxt is the error term;
 

The accuracy of the models’ predictions across all the states hinges on the amount of variation in the load 

profiles used as inputs. As indicated in Table D-4, load data underlying the regressions span a wide range 

of geographic regions and include hot and cold climates, humid and dry climates, and a wide variation in 

central air conditioning saturation. 

An analysis of the Predicted vs. Actual loads shows that the models predict well for all customer classes 

across various metrics. Figure D-2 shows the predicted vs. actual results for the commercial and 

industrial classes. Model accuracy is excellent even at the high end of the temperature spectrum and 

across all hours of the day during peak (top 15) system load days. 

Federal Energy Regulatory Commission 213 
Small C&I Predicted vs. Actual: Peak System Days Small C&I Predicted vs. Actual: by Temperature 

0.00025 0.00025 

d 

0.0002 

ao Lylru0.00015 o Hdeliz

0.0001 

amroN0.00005 

0.0002 

ad oy Llru0.00015 Hoedzi

0.0001 

almrNo 0.00005 

Actual Predicted Actual Predicted 

0 0 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 45 

48 

51 

54 

57 

60 

63 

66 

69 

72 

75 

78 

81 

84 

87 

90 

93 

Hour Temperature (F) 

Medium C&I Predicted vs. Actual: Peak System Days Medium C&I Predicted vs. Actual: by Temperature 

0.00018 0.00018 

0.00016 0.00016 

d a

0.00014 

oLy 0.00012 rlu

0.0001 

 Hoedz0.00008 malir

0.00006 

No

0.00004 

d a

0.00014 

o Lly0.00012 ruo

0.0001 

 Hde0.00008 liza

0.00006 

mroN0.00004 

0.00002 0.00002 Actual Predicted Actual Predicted 0 0 

5 

8 

4 

7 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 4

4

51 

5

5

60 

63 

66 

69 

72 

75 

78 

81 

84 

87 

90 

93 

Hour Temperature (F) 

Large C&I Predicted vs. Actual: Peak System Days Large C&I Predicted vs. Actual: by Temperature 

0.00016 0.00016 

0.00014 0.00014 

d a

ad o

o

0.00012 0.00012 

 L

y L

ly

lr

r

0.0001 0.0001 u

u

o H

o

0.00008 

 H

0.00008 ded lize

0.00006 

zi

0.00006 

al

ma

m

.00004 r

ro

0 o

0.00004 

N

N 0.00002 0.00002 Actual Predicted Actual Predicted 0 0 

0 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 5

53 

56 

59 

62 

65 

68 

1 

4 

7 

0 

7

3 

7

6 

9 

7

2 

8

5 

8

8 

8

8

9

9

9

Hour Temperature (F) 

Figure D-2: Predicted vs. Actual Results for Commercial and Industrial Classes 

Appendix D – Database Development Documentation 

Figures D-3 and D-4 compare predicted and actual values for the residential model. As with the C&I 

models, the residential models predict well across the temperature spectrum. When comparisons are 

made for states grouped according to CAC saturation, it is evident that even with the segmented models, 

the predicted values are low at high temperature values for states with lower CAC saturations. Indeed, 

the average under-prediction across all states for the peak period on the top 15 system load days is 8.5 

percent. While not ideal, this under prediction means that the price-based, demand response potential 

estimates are conservative. Furthermore, predictions are very accurate for the higher CAC quadrants, 

even at high temperatures, which is where the majority of residential demand response potential will 

come from. 

214 A National Assessment of Demand Response Potential 
 

Appendix D – Database Development Documentation 

Figure D-3: Residential Actual vs. Predicted by Temperature 

Residential Predicted vs. Actual: by Temperature 

0 

0.00005 

0.0001 

0.00015 

0.0002 

0.00025 

0.0003 

45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99 102 105 108 

Temperature (F) 

Normalized Hourly Load 

Actual Predicted 

Figure D-4: Residential Actual vs. Predicted by Temperature; CAC Quadrant 

Residential Predicted vs. Actual: by Temperature 

1st CAC Quadrant 

0.00006 

0.00008 

0.0001 

0.00012 

0.00014 

0.00016 

0.00018 

0.0002 

Normalized Hourly Load 

Residential Predicted vs. Actual: by Temperature 

2nd CAC Quadrant 

0.0001 

0.00015 

0.0002 

0.00025 

Normalized Hourly Load 

0 

0.00002 

0.00004 

45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 

Temperature (F) 

Actual Predicted 

0 

0.00005 

45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 

Temperature (F) 

Actual Predicted 

Residential Predicted vs. Actual: by Temperature Residential Predicted vs. Actual: by Temperature 

3rd CAC Quadrant 4th CAC Quadrant 

0.0003 0.0003 

0.00025 0.00025 

0.0001 

0.00015 

0.0002 

Normalized Hourly Load 

0.0001 

0.00015 

0.0002 

Normalized Hourly Load 

0.00005 0.00005 

Actual Predicted Actual Predicted 

0 0 

45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99 102 106 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 105 

Temperature (F) Te mpe rature (F) 

Table D-6 provides the average per-customer peak load by state. 

Federal Energy Regulatory Commission 215 
 

Appendix D – Database Development Documentation 

Table D-6: Average Per-Customer Peak Load by Rate Class 

Average peak load per customer (kW) 

State Residential Small C&I Medium C&I Large C&I 

Alabama 3.4 15.1 192 748 

Alaska 0.9 4.5 80 1,029 

Arizona 3.8 16.9 165 822 

Arkansas 2.8 9.1 93 801 

California 1.2 3.2 38 555 

Colorado 1.5 1.9 40 901 

Connecticut 1.6 3.9 63 206 

Delaware 1.9 15.2 125 951 

District of Columbia 1.6 9.5 158 745 

Florida 3.1 2.9 40 696 

Georgia 3.4 5.4 60 602 

Hawaii 1.0 4.2 45 842 

Idaho 2.9 3.9 31 636 

Illinois 1.7 7.3 28 450 

Indiana 2.4 6.3 52 798 

Iowa 1.9 4.1 47 709 

Kansas 2.8 6.4 44 318 

Kentucky 3.0 10.5 176 959 

Louisiana 3.5 14.6 39 771 

Maine 0.8 2.0 30 571 

Maryland 2.6 13.1 32 606 

Massachusetts 1.0 6.0 24 642 

Michigan 1.5 6.2 48 609 

Minnesota 1.7 3.2 42 327

Mississippi 3.5 8.8 78 1,215 

Missouri 3.1 5.0 110 748 

Montana 1.6 12.3 157 1,101 

Nebraska 2.6 4.5 128 291 

Nevada 3.1 12.1 112 931 

New Hampshire 1.1 4.7 32 306 

New Jersey 2.2 7.1 77 395 

New Mexico 1.3 4.8 61 707 

New York 1.3 5.7 81 820 

North Carolina 3.2 5.6 168 1,373 

North Dakota 2.2 9.7 129 614 

Ohio 2.0 8.5 65 604 

Oklahoma 3.3 3.8 70 778 

Oregon 1.9 4.5 75 680 

Pennsylvania 1.7 8.2 43 644 

Rhode Island 1.0 2.7 32 393 

South Carolina 3.6 7.6 172 1,696 

South Dakota 2.2 9.3 87 402 

Tennessee 3.9 11.5 186 376 

Texas 3.3 3.7 47 2,086 

Utah 1.6 4.9 86 1,322 

Vermont 0.9 2.2 49 773 

Virginia 2.5 4.6 88 708 

Washington 1.8 6.5 110 771 

West Virginia 2.3 6.3 78 1,431 

Wisconsin 1.4 4.1 61 782 

Wyoming 1.7 14.9 66 1,551 

216 A National Assessment of Demand Response Potential 
 

Appendix D – Database Development Documentation 

 e) Growth Rate in per Customer Peak Load 

In estimating the growth rate in peak l oad per custome r, the analysis started with base year values for the 

following items: 

1. Growth rate in number of accounts by rate class, 

2. Average peak load per customer account by rate class, and 

3. State peak forecast. 

In orderto estimate the g rowth rate in critical peak per customer, it is first necessary to estimate the 

growth rate in account population by rate class. For the re sidential sector, the populationfore cast for each 

state was readily obtained from the U.S. Census Bureau and this was assu med to apply directly to the 

growth rate of residential accounts. In order to estimate the growth rate in accounts for all C&I rate 

classes, growth rates in 'Commercial sq.ft.' were used as a proxy (obtained from Supplemental Tables to 

the Annual Ene rgy Outlook 2008 that provides projections on Commercial Sq.ft. by census division) 125 , 

since better estimates were not available. 

The overall peak load for a particular rate cla ss is arrived at by aggregating the product of critical peak 

load per account and the number of accounts by rate class. It is assumed that the overall peak load for 

each rate class grows at the same rate as the system peak, obtained from NERC forecast values (as 

explained in the previous section). Therefore, in the final step, the underlying assumptions related to 

growth rate in number of accounts by rate class on the growth in aggreg ate peak load by rate class were 

used to ascertain th e implicit critical peak growth rates per customer by rate class. 

Table D-7 lists thepopulation and critical peak growth rate values for each state. 

125 ‘Supplemental Tables to the Annual Energy Outlook 2008” – http://www.eia.doe.gov/oiaf/archive/aeo08/supplement/index.html 

Federal Energy Regulatory Commission 217 
Appendix D – Database Development Documentation 

Table D-7: Growth Rate in Population and Critical Peak Load by Rate Class 

State Population growth rate (%) Critical peak growth rate (%) 

Residential Small C&I Medium C&I Large C&I Residential All C&I 

Alabama 0.3 1.3 1.3 1.3 1.6 0.6 

A ska la 1.1 1.3 1.3 1.3 0.4 0.2

Ar zoni a 1.6 1.7 1.7 1.7 0.2 0.1 

Arkan sas 0.6 1.5 1.5 1.5 1.2 0.3

California 1.1 1.5 1.5 1.5 0.8 0.4 

Colora do 0.9 1.7 1.7 1.7 0.9 0.1

Connecti ut c 0.3 0.8 0.8 0.8 0.9 0.4 

Delaware 0.9 1.3 1.3 1.3 0.4 0.1 

District of 

Columbia 

-0.1 1.3 1.3 1.3 1.5 0.1 

Florida 2.0 1.6 1.6 1.6 0.2 0.6 

Georgia 1.3 1.6 1.6 1.6 0.6 0.3 

Hawaii 0.6 1.3 1.3 1.3 0.9 0.2 

Idaho 1.4 1.7 1.7 1.7 0.4 0.1 

Illinois 0.3 1.1 1.1 1.1 1.3 0.4 

Indiana 0.4 1.1 1.1 1.1 1.0 0.3 

Io wa 0.6 1.1 1.1 1.1 1.6 1.1

Kansas 0.3 1.1 1.1 1.1 1.3 0.5 

Kentucky 0.4 1.3 1.3 1.3 1.3 0.4 

Louisiana 0.3 1.5 1.5 1.5 1.6 0.4 

Maine 0.4 0.8 0.8 0.8 0.7 0.4 

Maryland 1.0 1.3 1.3 1.3 0.4 0.1 

Massachusetts 0.3 0.8 0.8 0.8 0.8 0.4 

Michigan 0.3 1.1 1.1 1.1 1.2 0.4 

M innesota 0.9 1.1 1.1 1.1 1.3 1.1

Mississippi 0.3 1.3 1.3 1.3 1.6 0.6 

M issouri 0.5 1.1 1.1 1.1 1.3 0.7

Montana 0.6 1.7 1.7 1.7 1.3 0.2 

Nebraska 0.6 1.1 1.1 1.1 1.6 1.1 

Nevada 1.6 1.7 1.7 1.7 0.2 0.1 

New Hampshire 1.0 0.8 0.8 0.8 0.2 0.4 

New Jersey 0.5 0.7 0.7 0.7 0.8 0.7 

New Mexico 0.6 1.7 1.7 1.7 1.2 0.1 

New York 0.1 0.7 0.7 0.7 0.8 0.3 

North Carolina 1.4 1.6 1.6 1.6 0.5 0.3 

North Dakota 0.6 1.1 1.1 1.1 1.6 1.1 

Ohio 0.1 1.1 1.1 1.1 1.3 0.3 

Oklahoma 0.4 1.5 1.5 1.5 1.2 0.1 

Oregon 1.1 1.5 1.5 1.5 0.7 0.4 

Pennsylvania 0.2 0.7 0.7 0.7 1.2 0.7 

Rhode Island 0.4 0.8 0.8 0.8 0.8 0.4 

South Carolina 0.9 1.6 1.6 1.6 1.0 0.3 

South Dakota 0.6 1.1 1.1 1.1 1.6 1.1 

Tennessee 0.9 1.3 1.3 1.3 1.0 0.6 

Texas 1.5 1.5 1.5 1.5 0.3 0.3 

Utah 1.4 1.7 1.7 1.7 0.4 0.1 

Vermont 0.6 0.8 0.8 0.8 0.6 0.4 

Virginia 1.1 1.6 1.6 1.6 0.7 0.3 

Washington 1.2 1.5 1.5 1.5 0.6 0.4 

West Virginia -0.1 1.5 1.5 1.5 1.6 0.1 

Wisconsin 0.5 1.1 1.1 1.1 1.5 0.9 

Wyoming 0.3 1.7 1.7 1.7 1.6 0.2 

f) Central Air Conditioning (CAC) Market Saturation Data 

As a first step in determining the saturation of CAC equipment in the residential sector, CAC saturation 

values were compiled from a combination of primary and secondary information sources for each state. 

These multiple sources included EIA Regional Energy Consumption Survey (RECS) data, American 

Housing Survey data, utility reports, specific reports on state-level appliance saturation surveys, and 

information obtained from utilities through direct contacts (indicated in Table D-8). 

218 A National Assessment of Demand Response Potential 
 

Appendix D – Database Development Documentation 

For states with data from multiple sources, professional judgment was used to determine the data that 

provided the closestapproximation to the state level value in order to estimate the default saturation value 

for each state. The esti matio n approach varied by state; som etimes a si ngle best source value was used as 

the default estimate, while a t other times CAC saturation values were obtained from multiple sources. T he 

specific methodology used for estimating the defau lt value for each state is indicated in Table D-8. 

For the C&I sector, CAC saturation values were obtained from the Commercial Building Energy 

IA. 126 Consumption Survey (CBECS) data provided by E 

Table D-8 summarizes the residential CAC saturation values and how they were derived. 

126 Please refer to Table B41, ‘Cooling Equipment Floorspace for Non-Mall Buildings”. 

http://www.eia.doe.gov/emeu/cbecs/cbecs2003/detailed_tables_2003/2003set8/2003excel/b41.xls, published by EIA. The table 

provides cooling saturation by building floorspace for the four census regions. We assume that small C&I buildings have floor 

space less than 25,000 sq.ft., For medium C&I customers, we assume that the floor space area ranges bet ween 25,000 to 

250,000 sq.ft. This data is available only at the Census region level. All states falling within a censu s region are assumed to 

have the same saturation value. 

Federal Energy Regulatory Commission 219 
Residential 

Default CAC CAC 

Saturation Derivation of Default CAC Saturation 

State Value Saturation Value Value Detailed reference 

Alabama 62.0% 

Used higher value based on CDD compared to other states in census division. 

55.1% 2005 RECS data from EIA for East South Central Division, Tab62.0% Southern Company Residential Saturation Survey, Dec. 20 07. 

Alaska 2.5% 

One data source 0.0% Information from Todd Hoener at Golden Valley Electric Assn. Average of two sources 5% BC Hydro 2003 Residential End Use Study (Northern Region) 

Arizona 86.8% Used value obtained from APS -more current than AHS data 86.8% 

Information from Jim Wantor at Arizona Public Service (AP S). 

of residential customers are in the desert and 99% 

of them ha

are not in the desert and half of them have CAC. 

92.1% American Housing Survey (AHS) for the Phoenix Metropolit an 

Arkansas 54.6% One data source 54.6% 

Association of Electric Cooperatives of Arkansas (AECC): App

that in 2006 approximately 54.6% of the electric cooperatives’ 

had electric central air conditioning. 

California 41.0% RASS data was used as the default data source 

41.0% California Statewide Residential Appliance Saturation Study (R

79.9% American Housing Survey (AHS) for the Sacramento, CA Bureau. Met47.4% American Housing Survey (AHS) for the Santa Ana, CA Bureau. Met

38.7% American Housing Survey (AHS) for the Los A ngeles, CA Me

Bureau. 

­

70.5% American Housing Survey (AHS) for the San Bernardino U.S. Census Bureau. On

34.5% American Housing Survey (AHS) for the San Diego, CA Bureau. Met45.0% 2005 RECS data from EIA. 

Colorado 47.2% 

Average of PSC and AHS values. Tri-state data seems low compared to other values. 

45.0% Information from Bruce Nielson at the Public Service Co (P 

SC 

is for 2006. 

49.5% American Housing Survey (AHS) for the Denver Me tropolitan A

22.6% Tri-State: Jim Spiers provided data for Tri-State's 4 states f

survey of our 44 Members in Colorado, Wyoming, Nebraska an

Connecticut 26.9% One data source 26.9% American Housing Survey (AHS) for the Hartford Metropolitan Delaware 53.0% One data source 53.0% PHI AMI business case filing District of Colum bia 56.0% One data source 56.0% PHI AMI business case filing Florida 91.0% RECS data 93.0% Information from John Haney at FPL. 

84.9% American Housing Survey (AHS) for the Miami/Ft Lauderda

Census Bureau. 

Appendix D – Database Development Documentation Table D-8: Residential CAC Saturation Values by State 220 A National Assessment 
Residential 

Default CAC CAC 

Saturation Derivation of Default CAC Saturation 

State Value Saturation Value Value Detailed reference 

91.0% 2005 RECS data from EIA. 68.0% Southern Company Residential Saturation Survey, Dec. 2007. 

Georgia 82.2% Average o f all data - SoCo is current, but low compared to AHS value. 91.5% American Housing Survey (AHS) for the Atlanta Metropolitan A

73.0% Southern Company (SoCo) Residential Saturation Survey, Dec

22.5% Hawaiian Electric Co.: 2007 REEPs. 

Hawaii 17.6% Weighted average based on number of households for each utility 4.1% Maui Electric Co.: Residential Appliance Survey, 7/03. 

1.2% Hawaii Electric Light Co.: 2007 Residential Appliance Saturatio

Idaho 66.5% One data source 66.5% 

Information from P. Werner at Idaho Power Co. According to survey of Idaho Power's service territory (not the state) in including heat pumps was 60.6%. The current saturation is an 

Illinois 75.0% 

Average of AHSand Xcel Energy value - including MEEA data raises average to 81% which seems out of line compared to other states in census division. 

60.0% American Housing Survey (AHS) for the Chicago Metropolitan 90.0% Midwest Residential Market Assessment and DSM Potential St

94.3% Claire Saddler, ComEd, wrote that MEEA conducted 309 SF h

found that 94.3% of those sampled had central A/C. 

Indiana 74.4% 

Average of all data - factors in the more current Xcel Energy value and the AHS data for the Indianapolis area. 

82.8% American Housing Survey (AHS) for the Indianapolis Metro

Bureau. 

66.0% Midwest Residential Market Assessment and DSM Potential St

Iowa 70.0% One data source 70.0% IPL Energ y Efficiency Plan Vol. II Appendix D (Iowa Utility As

Study 8/2/07). 

Kansas 83.7% One data source 83.7% American Housing Su rvey (AHS) for the Kansas Bureau. City MetroKentucky 76.0% One data source 76.0% Midwest Residential Market Assessment and DSM Potential St

Louisiana 75.5% One data source 75.5% American Housing Survey Bureau. (AHS) for the New Orleans Metr

Maine 14.0% One data source 14.0% Data obtained from FSC. Maryland 78.0% One data source 78.0% BGE AMI business case filing 

Massachusetts 12.7% One data source 12.7% 2005 RECS data (New England Division); Table HC11.6. Michigan 57.2% Average of all data - values are fairly close 

56.0% Electric Demand Comparison, Consumers Energy 6/22/06 (20060.9% American Housing Survey (AHS) for the Detroit Metropolitan A

52.0% Consumers Energy Demand Response program plan 

60.0% Midwest Residential Market A ssessment and DSM Potential St

Minnesota 51.2% Average of all data - values are fairly close 48.3% Great River Energy Planning Study, 2003. 54.0% Midwest Residential Market Assessment and DSM Potential St

Appendix D – Databas

Federal Energy Regulatory Commission 
State 

Default CAC 

Saturation 

Value 

Derivation of Default CAC 

Saturation Value 

Residential 

CAC 

Saturation 

Value Detailed reference 

Mississippi 74.7% 

Average of all data - SoCo data is more current, but low compared to AHS data. 

81.4% American Housing Survey ( AHS) for the Memphis Metropolitan 

AR, MS), U.S. Census Bureau. 

68.0% Southern Company (SoCo) Residential Saturation Survey, Dec

Missouri 87.5% Average of all data - values are fairly close 

92.0% 2006 Missouri Statewide Residential Lighting and Appliance Ef85.0% Midwest Residential Market Assessment and DSM Potential St

85.5% American Housing Survey (AHS) for the St. Louis Metr opolitan 

IL), U.S. Census Bureau. 

Montana 42.1% One data source 42.1% 2005 RECS data (Mountain Division); Table HC14.6. Nebraska 82.8% 

Used NPPD data - Tri-State d ata seems low compared to other midwest saturation rates. 

82.8% Information from Joel Young at Nebraska Public Power Distric

Central A/C penetration in NPPD’s service area was 82.8% in 2

22.6% Tri-State: Jim Spiers provided data for Tri-State's 4 states f

survey of our 44 Members in Colorado, Wyoming, Nebraska an

Nevada 86.8% Assume same as AZ based on CDD 42.1% 2005 RECS data (Mountain Division); Table HC14.6. New Hampshire 12.7% One data source 12.7% 2005 RECS data (New England Division), Table HC1 1.6.New Jersey 55.0% Used Brattle data - more current 

55.0% Atlanta City Electric AMI business case filing 

45.7% American Housing Survey (AHS) for the Northern New Jers

Census Bureau. 

New Mexico 42.0% One data source 42.0% 2005 RECS data (Mountain Division), Table HC14.6. New York 16.7% Average of all data 

12.0% Source: Knowledge Networks, 2007 Electric Forecasting 

Summer 2007, Prepared for ConEdison, p. 19. 

23.6% American Housing Survey (AHS) for the Buffalo, NY Metro

Bureau. 

16.4% American Housing Survey (AHS) for the NY City Metropolitan A15.0% 2005 RECS data from EIA .

North Carolina 84.4% One data source 84.4% American Housing Survey (AH S) for the Charlotte Metropolita

SC), U.S. Census Bureau. 

North Dakota 51.0% 

Average of RECS and Minnesota CAC % - using only RECS data 

seems high compared to CDDs. Minnes ota has similar CDDs data. 

70.9% 2005 RECS data (West North Central Division), Table HC12.6. 

Ohio 62.9% Average of all data - factors in all 

values given the range of values. 

51.3% American Housing Survey (AHS) for the Cleveland Metropolita

75.3% American Housing Survey (AHS) for the Bureau. Columbus, OH Met

Appendix D – Database Development Documentation 222 A National Assessment 
Residential 

Default CAC CAC 

Saturation Derivation of Default CAC Saturation 

State Value Saturation Value Value Detailed reference 

62.0% Midwest Residential Market Assessment and DSM Potential St

Oklahoma 84.2% One data source 84.2% American Housing Survey (AHS) for the Oklahoma Metro

Bureau. 

Oregon 38.0% Used PGE Customer Data current - more 28.0% American Housing Survey (AHS) for the Portland, OR Metr

Bureau. 38.0% PGE Customer Data 2007. 

Pennsylvania 49.8% Weight ed average based on housing stock from each area AHS 48.4% American Housing Survey (AHS) for the Philadelphia Metro

Bureau. 52.3% American Housing Survey (AHS) for the Pittsburg Metropolitan 

Rhode Island 12.5% One data source 12.5% American Housing Survey (AHS) for the Providence, Pawtu

1998, U.S. Census Bureau. 

South Carolina 84.4% One data source 84.4% American Housing Survey (AHS) for the Charlotte Metropolita

SC), U.S. Census Bureau. 

South Dakota 70.9% One data source 70.9% 2005 RECS data (West North Central Division); Table HC12.6. 

Tennessee 81.4% One data source 81.4% American Housing Survey (AHS) for the Memphis Me tropolitan 

AR, MS), U.S. Census Bureau. 

Texas 80.0% RECS data 

77.9% American Housing Survey (AHS) for the San Antonio Metro

Bureau. 

92.1% American Housing Survey (AHS) for the Dallas, TX Metro

Bureau. 

87.0% American Housing Survey (AHS) for the Arlington, T X Metr

Bureau. 

80.0% 2005 RECS data from EIA. 

Utah 42.1% One data source 42.1% 2005 RECS data (Mountain Division), Table HC14.6 Vermont 7.2% One data source 7.2% FSC study 

Virginia 50.2% One data source 50.2% 2005 RECS data (South Atlantic Division), Table HC13.6. 

Washington 28.6% 

Average of all data - Northwest Energy value is for the entire NW area, not by state. 

50.0% Single Family Residential E xisting Stock Assessment, Northw

2007. 

7.2% American Housing Survey (AHS) for the Seattle-Everett Met

Bureau. 

West Virginia 50.2% One data source 50.2% 2005 RECS data (South Atlantic Division), Table HC13.6. Wisconsin 62.0% 

Average of all data - factors in all values given the range of values. CDDS in WI is on the low end. 

72.0% Central Air Conditioning in Wisconsin, Energy Center of Wisco51.0% Midwest Residential Market Assessment and DSM Potential St

53.1% American Housing Survey (AHS) for the Milwaukee, WI Met

Bureau. 

72.0% Information from Harvey Dorn at Alliant Energy. Note that this d

Appendix D – Databas

Federal Energy Regulatory Commission 
Residential 

Default CAC CAC 

Saturation Derivation of Default CAC Saturation 

State Value Saturation Value Value Detailed reference 

Wyoming 42.0% One data source 42.0% 2005 RECS data (Mountain Division), Table HC14.6. 

Appendix D – Database Development Documentation 224 A National Assessment 
Appendix D – Database Development Documentation 

g) AMI Deployment Schedule by State 

Advanced metering is a necessary technology to support price-responsive demand response for massmarket customers. However, having advanced meters is a necessary but not sufficient condition to 

support price-responsive demand response—a utility also needs a meter data management system 

(MDMS) and billing system that will support price-responsive demand response options. Quite often, 

utilities install meters that qualify as advanced meters in that they gather hourly or sub-hourly data daily, 

but use them as an AMR system to produce monthly meter reads—they do not install the MDMS and 

billing systems needed to support wide scale price-responsive demand response. A notable example is 

PPL, which completed its AMI deployment around 2004 and, until recently, had the only large scale AMI 

system in the country that was generating hourly data on all customers on a daily basis. However, it 

wasn’t until 2008 that the company installed an MDMS system capable of supporting widespread use of 

price-responsive demand response. Similarly, many small cooperatives and municipalities have AMR or 

AMI meter systems that can deliver hourly data (although not necessarily daily) but, currently, these 

systems are almost universally being used only to support monthly meter reads. Without an MDMS 

system designed to clean and manager hourly data, these small installations can not support wide spread 

use of price-responsive demand response. The AMI deployment scenarios described below recognize that 

more than just metering is needed to support price-responsive demand response. The deployment time 

lines for each scenario are based on the understanding that only systems that have MDMS and billing 

systems are considered AMI for purposes of supporting demand response potential. 

Two AMI deployment scenarios were developed for each state. 

•	 The “Full Deployment” scenario is used to support the Achievable Participation and Full 

Participation demand response scenarios and assumes that all utilities will have AMI meters in 

place for all customers, along with the MDMS and billing systems required to support pricebased demand response, by the end of the forecast horizon, 2019. Deployment timing is based on 

a set of assumptions described below, and varies significantly across states based on current 

plans, the mix of utilities in each state, and other factors. 

•	 The “Partial Deployment” scenario is used to support the Expanded BAU potential scenario and 

includes AMI deployment plans for each state based largely on a continuation of current trends. 

It includes utilities that already have or are currently deploying AMI systems and other utilities 

that, based on a variety of data sources summarized below, have expressed interest in or are 

believed to have a higher probability of installing such systems over the next ten years. 

These two alternative scenarios should not be considered forecasts of actual AMI meter and system 

deployment. The full deployment scenario is predicated on the assumption that all customers will have 

smart meters by the end of the ten-year forecast horizon. This assumption is combined with a variety of 

information and assumptions that drive the likely sequence of installations across utilities in a state and 

across st ates that are described below. The partial deployment scenario is probably closer to what might 

actually occur, but it is not a true forecast, since a true forecast would require conducting business cases 

on hundreds or perhaps thousands of utilities and an assessment of the likely political and other barriers to 

deployment in each state. Such work is significantly beyond the scope of this analysis. Even if such 

work could be completed, it would be subject to change frequently due to some of the factors outlined 

below. The AMI deployment scenarios presented here should be considered a reasonable starting point 

for each state based on expert judgment and publicly available information about plans and interest. The 

demand response potential model has been intentionally set up so that alternative deployment scenarios 

can easily be substituted. 

In addition to limited time and money, one of the primary reasons why the demand response potential 

estimates are based on AMI deployment scenarios rather than forecasts is that the experience over the last 

five years illustrates well how difficult it is to forecast AMI activity. The rate of AMI investment 

depends on a wide variety of factors that are constantly in flux, including federal tax and grant policy, 

Federal Energy Regulatory Commission 225 
Appendix D – Database Development Documentation 

state regulatory policy, technology evolution and testing, and fundamental business case economics,

among others. The key forecasting challenges include, but are not limited to: 

1.	 Actual deployment of AMI systems depends importantly on state regulatory policy. Unless regulated 

utilities anticipate that AMI investments will reduce overall revenue requirements, they will be 

reluctant to undertake those investments without firm indications from state regulators that such 

investments will be considered prudent. Thus, regulatory commissions can retard or advance the 

deployment of AMI within a state by the prudence and clarity they provide. However, forecasting 

state regulatory commission viewpoints on AMI is extremely difficult because most states have not 

formulated firm policy and because policy goals within each state are evolving, causing regulatory 

positions to fluctuate. 

2.	 Federal policy can and does operate to change the basic revenue requirements of AMI. The 2007 

change in tax code to identify AMI assets as ten-year, instead of twenty-year, property for tax 

purposes had a significant impact on improving business case economics. By authorizing funds to 

support up to 50 percent matching investment funds for Smart Grid and AMI projects, the Federal 

government has provided further stimulus in the recent American Recovery and Reinvestment Act of 

2009. However, future federal AMI initiatives are tied to the economic situation, policies toward 

greenhouse gas emissions, and transmission grid management policies, and are extremely difficult to 

anticipate in the future years leading up to 2019. 

3.	 There are a variety of AMI technologies available in the mark et place, but some of the features and 

dimensions of these technologies are currently evolving. This on-going evolution makes it difficult 

for utilities that want to see a fully-deployed system in operation to make a decision to proceed, even 

if their interest is strong. For example, home-area-networking to support in-home displays and 

integrated under-glass service disconnection switches are of increasing interest to utilities, but largescale deployments of these AMI capabilities are not yet observable. Consequently, translating utility 

stated interests into expected AMI deployment dates is very difficult and depends on specific utility 

risk profiles. 

4.	 The fundamental economics of AMI deployment varies significantly from utility to utility. Some of 

the key factors that influence the cost-effectiveness of demand response are: 

a. The higher the current meter reading costs, the more likely utilities are to adopt AMI, but 

current meter reading costs vary significantly from utility to utility as a result of aut omation 

capital currently invested (e.g., drive-by or fixed network AMR), the presence or absence of 

associated natural gas meters, the prevailing wage levels in the service territory, and the 

observed meter density (meters per square mile) in the service territory. 

b. Some utilities have substantial field activity related to off-cycle billing reads and service 

connections and disconnections, while other utilities have minimal field activities in these 

areas. AMI systems can create dramatic cost savings in these areas. Thus, this activity can 

be extremely significant in creating benefits to offset AMI costs so it creates important 

variability in the business case analysis. 

c. Theft of service can be a major consideration for some utilities, and AMI can be very helpful 

in identifying and reducing theft. For these utilities where theft is important, and where AMI 

can be used to reduce theft, the cost/benefit calculation will be much more positive, raising 

the chances thatAMI will be implemented. 

d. All utilities seek to reduce estimated and delayed bills, and AMI can help with this goal in a 

very significant way. However, the number and percentage of estimated and delayed bills 

varies significantly from utility to utility, as does the importance of reducing them, so that it 

can be very difficult to predict specific utilities that will gain the most from AMI. 

226 A National Assessment of Demand Response Potential 
Appendix D – Database Development Documentation 

Because the benefits and costs of AMI can be so utility-specific, it is difficult to forecast where positive 

business cases will be found without detailed, utility-specific analysis, which in turn makes it difficult to 

forecast which utilities will proceed to implement AMI first. The alternative approach taken here 

involves the following steps: 

1.	 Six data sources were obtained and examined to determine the most current status of or interest in 

AMI by hundreds of utilities in the United States. The data sources are listed below: 

a.	 In a report to the GridWise Alliance (The U.S. Smart Grid Revolution: KEMA’s 

Perspectives for Job Creation, December 23, 2008), KEMA summarized their assessment 

of major AMI projects and their respective deployment schedules. 

b.	 In a 2008 survey of utilities, FERC asked a series of questions designed to identify 

current installations and future interest and plans for installing AMI. 

c.	 In a January 2008 evaluation of AMI initiatives Utilipoint compiled a list of utilities 

either implementing or in the process of implementing AMI. 

d.	 The Enernex Smart Meter Data for the California Energy Commission is a compilation of 

utilities with active projects or intere st in AMI, using a map database format created by 

the Energy Retail Association. 

e.	 FERC's annual staff reports on Demand Response and AMI identify particular utilities 

with plans to deploy AMI systems (Assessment of Demand Response and Advanced 

Metering 2007, September, 2007, and Assessment of Demand Response and Advanced 

Metering, December, 2008). 

f.	 The Institute for Electric Energy Efficiency has released their recent survey of Smart 

Meter Deployment, Utility-Scale Deployment of Smart Meters, April, 2009. 

2.	 Relevant information from all six data sources was merged into the Form EIA-861, File 2 

database, which essentially provides a complete census of all utilitie s in the country and a 

mapping of utilities into states. The File 2 data were also used to categorize utilities into size 

strata and to identify any utilities where no information about AMI status or interest was 

contained in any of the other data sources. 

3.	 Themerged data from step 2 provided a profile of the AMI status of each utility and also a 

convenient way of identifying situations where different data sources pr ovided contradictory 

information. In situations where there were internal contradictions, the expert knowledge of the 

team was used to judge which data was likely to be most accurate. 

4.	 Based on the information above, each utility was assigned to one of the eight classification groups 

described in Table D-9. 

Federal Energy Regulatory Commission 227 
Table D-9: Classification of Utilities by AMI Status 

Classification of Utilities by AMI Status 

Category Utilities Customers 

Utilities with more than 100,000 customers (or their affiliates) that appear to be 5 2.9 million committed to deploying AMI within two years 

Utilities with more than 100,000 customers (or their affiliates) that appear to be 32 34.9 million committed to deploying AMI over the next five years. 

Utilities w ith more than 100,000 customers (or their affiliates) that have fixed­ 28 15.6 million network AMR systems in place 

Utilities with more than 100,000 customers (or their affiliates) that appear to have 63 39.6 million some interest in deploying AMI 

Utilities with more than 100,000 customers that have given no indication of having 85 20.9 million interest in deploying AMI 

Utilities with 10,000-100,000 customers that indicated interest in AMI in the FERC 122 3.9 million 

survey 

Utilities w ith 10,000 – 100,000 customers that did not indicate interest in AMI in 660 17.5 million the FERC su rvey 

Utilities with less than 10,000 customers 2,540 5.8 million 

All Categories 3,535 140.0 Million 

Appendix D – Database Development Documentation 

5.	 The final step in the process involved producing judgmental assessments of the likelihood that

eachut lity will deploy A i MI and the time period over which it is likely to be deployed in each of 

the two deployment scenarios. The probabilities and deployment schedules for each category are 

summarized in Table D-10. 

228 A National Assessment of Demand Response Potential 
Table D-10: Assumed Probability and Schedule for Utilities Underlying Each AMI Deployment Scenario 

Assumed Probability and Schedule for Utilities 

Underlying Each AMI Deployment Scenario 

Partial Deployment Deployment Category Full Deployment Deployment Start End 

Utilities with more than 100,000 

customers (or their affiliates) that 100% 100% 2009 2011 appear to be committed to deploying 

AMI within two years 

Utilities with more than 100,000 

customers (or their affiliates) that 100% 100% 2009 2013 appear to be committed to deploying 

AMI over the next five years. 

Utilities with more than 100,000 

customers (or their affiliates) that have 100% 67% 2014 2019

fixed-network AMR systems in place 

Utilities with more than 100,000 

customers (or their affiliates) that 100% 50% 2014 2019 appear to have som e interest in 

deploying AMI 

Utilities with more than 100,0 00 

customers that h ave given no indication 100% 25% 2014 2019

of having interest in deploying AMI 

U tilities with 10,000-100,000 customers 

t hat indicated interest in AMI in the 100% 50% 2014 2019

FERCsurvey 

Utilitie s with 10,000 – 100,000 

customers that did not indicate interest 100% 25% 2016 2019

in AMI in the FERC survey 

Utilities with less than 10,000 100% 5% 2017 2019 customers 

For utilities with automated meter reading systems in place, we assigned a start year and an end year for 

AMI deployment specific to each utility, based on the age of the automated meter reading system 

currently in place. 

The information and assumptions summarized above lead to different meter deployments for each state 

and different rates of deployment nationally across the scenarios. Table D-11 shows the annual and 

cumulative deployment for each forecast year for the two scenarios. Figure D-5 shows the percent of 

meters in each state that would be AMI meters by the by the end of the forecast period for the partial 

deployment scenario. 

Appendix D – Database Development Documentation 

Federal Energy Regulatory Commission 229 
Table D-11: Annual and Cumulative Deployment for Each Forecast Year under EBAU and AP/FP Scenarios 

Partial Deployment Scenario 

(Used in Expanded BAU Potential) 

Full Deployment Scenario 

(Used in Achievable & Full Participation 

Scenarios) 

Year Annual Installations Cumulative 

Installations Annual Installations Cumulative Installations 

2009 7,949,249 7,949,249 7,949,249 7,949,249 

2010 8,157 ,557 16,106,806 8,260,157 16,209,405 

2011 8,157,557 24,264,363 8,260,157 24,469,562 

2012 8,197,899 32,462,262 8,796,464 33,266,026 

2013 8,197,899 40,660,160 8,796,464 42,062,490 

2014 6 ,180,478 46,840,638 13,241,914 55,304,404 

2015 6,039 ,977 52,880,615 13,032,212 68,336,616 

2016 6,23 1,172 59,111,787 16,053,354 84,389,970 

2017 6,895,117 66,006,904 19,005,862 103,395,832 

2018 7,002,218 73,009,122 18,846,010 122,241,842 

2019 6,827,310 79,836,432 18,744,805 140,986,647 

Appendix D – Database Development Documentation 

Figur e D-5: Percent of Meters in State That Are AMI Meters in 2019 

rs te e Mft o cen reP

 

100% 

90% 

80% 

70% 

60% 

50% 

40% 

30% 

20% 

10% 

0% 

AK

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AR

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CA

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CT

DC

DE

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KSKY

LA

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WVWY 

230 A National Assessment of Demand Response Potential 
 

Appendix D – Database Development Documentation 

Demand Response Program Related Data 

a) Business-As-Usual (BAU) Demand Response Pot ential Estimation 

The demand response potential estimation for the Busin ess-As-U sual (BAU) scenar io was develop ed with 

2008 FERC Demand Response Survey data 127 , using the fol lowing f our steps: 

1. Classification of Programs: Th e first s tep was to cla ssify pr ograms report ed in the FERC Survey 

database to the program categories being considere d in est imation of the potential: Direct Loa d 

Control (DLC) P rograms, Interruptible Programs, Pricing Programs, an d 'Other Types of DR 

Programs'. This classification was based on informa tion provided in the FERC survey related to 

'Program Name' and 'Program Descrip tion'. 

2. Assignment of P rograms to C&I Rate Classes: The next step was to a ssign demand response 

programs targeted toward C&I customers to the small, medium, and large rate classes. The survey 

database indicated whether a demand response program was being offered to commercial and/or 

industrial customers. For all such programs being offered to C&I customers, peak load per 

customer was estimated using program enrollment data in the survey. The survey database 

reported the ‘Number of customers enrolled’ and the ‘Load enrolled’ for each demand response 

program. This data was used to calculate the load enrolled per customer. If the per customer load 

enrolled was les s than or equal to 20 kW, the program was assigned to th e small C&I rate class. 

For medium C&I customers, the enrolled load per customer ranged between 20-200 kW, while 

for large C&I customers the value was greater than 200 kW. 

3.	 Aggregation of Survey Data: The Survey database provides data on ‘No. of Customers Enrolled', 

'Load Enrolled', and 'Potential Load Reduction' for demand response programs reported by 

utilities. Data for these items were aggregated to the state level to come up with estimates for 

these items by rate class and program type 128 . Certain adjustments were made to the 2008 Survey 

data to obtain the BAU estimate of the load reduction potential. These adjustments are described 

in Chapter III of the report in a sidebar titled ‘Benchmarking the BAU Estimate’. In addition, the 

total load reduction potential reported by ISO-NE and PJM in the FERC Survey database had to 

be allocated to the states served by these entities. 129 

The BAU potential estimation results are included in ‘Table 5- Known DR Participation’, which appears 

in the ‘Inputs Database’ worksheet of the Demand Response Potential Estimation model. 

127 For details related to the FERC 2008 Demand Response Survey, please refer to the FERC Staff Report titled ‘2008 Assessment 

of Demand Response and Advanced Metering’. It should be noted that only those programs that reported a positive 'Potential 

Load Reduction' in the database were included in developing the BAU forecast. 128 It should be noted that only those programs that reported a positive 'Potential Load Reduction' in the database were included in 

developing the BAU forecast. 129 In the FERC survey database, ISO-NE and PJM reported their entire load reduction potential only against a particular state. ISONE reported its entire potential against Connecticut, while PJM reported its entire potential against DC. For ISO-NE, the 

potential reported was allocated across all states falling under ISO-NE’s jurisdiction, based on actual data obtained from ISONE. For PJM, the load reduction potential was distributed across all states served by PJM, in the proportion of load served by 

PJM for these states. 

Federal Energy Regulatory Commission 231 
Appendix D – Database Development Documentation 

b) Current Participation in Demand Response Programs 

The methodology for determining current participation rates in demand response programs varied by type 

of program. For estimating participation rates in Residential Direct Load Control p rograms (CAC cycling 

only), a distinct approach was used as compared to what was followed for the rema ining demand response 

program types considered in our analysis. 

The bullet points below describe the approaches used for: 1) residential DLC programs; 2) other 

rema n i ing demand response programs. 

•	 Participation Rate Estimation for Residential DLC Programs: The FERC survey database was used as 

the primary source of information for estimating current participation rates in residential DLC 

programs (for the case of CAC only). For each state, the total ‘Number of Customers Enrolled’ for a 

particular demand response program type was obtained by aggregating utility data for the state. This 

was then divided by the ‘Total Number of Customers’ developed for each state by rate class to arrive 

at participation rate estimates by program type and rate class for each state. 

An assessment was also carried out to determine how representative the FERC survey data were for 

estimating 'Participation Rate' for the entire state. If more than 50% of the state's residential 

population was being covered by the FERC survey, the FERC survey data were considered to be 

representative of the state. On the other hand, if less than 50% of the residential customer population 

wasrepresented, information from outside sources was obtained to arrive at 'best' estimates for a 

state. Outside information sources included utility websites, utility program reports and regulatory 

filings, and direct contact with utilities. 

•	 Participation Rate Estimation for all other Demand Response Programs: The estimation of 

participation rates for all other demand response programs relied on FERC survey data, wherever 

information was available on number of customers enrolled in different demand response programs.

Theparticipation rate was estimated both as ‘percentage of customers’ as well as ‘percentage of 

load’. Participation rate as ‘percentage of customers’ was obtained by aggregating ‘No. of Customers 

Enrolled’ data from the FERC survey for a particular type of demand response program and dividing

thatby the corresponding ‘Total No. of Customers’ in the state by rate class. Similarly, participation 

rateas ‘percentage of load’ was obtained by aggregating ‘Total load enrolled’ data from the FERC 

survey for a particular type of demand response pr gr o am and dividing that by the corresponding 

‘Total Load’ in the state by rate class. 

Participation rate estimations by demand response program type and by Rate Class appe ar in the ‘Inputs 

Database’ worksheet of the Demand Response Potential Estimation model. 

c) Impacts from Non-pricing Programs 

The methodology used to estimate impacts of demand response programs varied by the type of program. 

The bullet points below describe the approaches used for: 1) DLC programs (CAC control only); 2) 

Interruptible and ‘Other DR’ programs; 

1) Impact Estimation for DLC Programs (CAC control only): For arriving at ‘best estimates’ of unit load 

reduction impacts for residential DLC programs, a combination of information sources was employed. 

The sources included FERC survey database information, which was used for estimating impacts by 

dividing the ‘Potential Load Reduction’ value by the ‘Number o f Customers Enrolled’. In addition, 

specific estimates from utility programs outside the FERC survey database were obtained along with 

DLC program evaluation reports. For states where information was missing, a default value of 1 kW 

232 A National Assessment of Demand Response Potential 
 

 

 

Appendix D – Database Development Documentation 

reduction per customer was assumed. Per-customer load reduction im pacts for C&I customers from DLC 

programs were estimated by applying a multiplier to the per customer impact for residential customers. 130 

2) Impact Estimation for Interruptible and ‘Other DR’ programs: For these programs, the FERC survey 

database information was used for arriving at load reduction estimates. The 'Potential Load Reduction' as 

a percentage of the 'Enrolled Load' by demand response program type was used to estimate demand 

response program impacts. 

d) Impacts from Pricing Programs 

The Achievable Participation and Full Participation potential estimates rely heavily on price-based 

demand response options, specifically on dynamic tariffs that deliver high price signals on relatively few 

high-demand days when demand response benefits are greatest. Estimates of the load impact associated 

with pricing options are based on variables known as price elasticities. Economists define the “own” 

price elasticity as the percentage change in the quantity purchased of a good or service divided by the 

percentage change in the price of that good or service. There is a similar concept, known as the elasticity 

of substitution, which summarizes the relationship of two goods or services that are substitutes for each 

other. The elasticity of substitution is equal to the percentage change in the ratio of the quantities 

purchased of two goods to the ratio of the prices of the two goods. Put another way, the elasticity of 

substitution summarizes the rate at which consumers substitute one good for another based on the relative 

prices of the two goods. 

In the case of electricity demand, if prices are higher at one time of day relative to another, consumers 

may be willing to shift their load from the h igh priced to the low priced period. An example would be a 

consumer shifting the timing of their laundry from the peak to the off peak period. Alternatively, or in 

addition, a consumer might just forgo some energy use during the high price period. An example would 

be switching off lights during high priced periods—consumers don’t use more lighting during low priced 

periods because they used less during high priced periods. 

One approach to estimating how electricity demand would change in response to time varying prices 

involves estimating a two-equation demand system, where one equation determines the rate at which 

consumers substitute off-peak energy use for peak-period energy use and the second equation estimates 

the overall demand for energy. In combination, the two equations can predict the change in energy use in

each time period as consumers move from non-time varying to time-varying prices. This is the approach 

that underlies the estimates of time-based price response in the demand response potential model. 

A variety of pricing experiments and other studies have been conducted that allow for estimation of 

demand models and price elasticities such as those described above. These studies show that price 

responsiveness for residential customers varies across regions based in part on differences in the use of air 

conditioning. Climate differences can also impact price responsiveness, as can the presence or absence of 

enabling technology such as programmable communicating thermostats and other load control devices. 

Price responsiveness also differs between residential and non-residential customers with residential 

customers generally being more price responsive than non-residential customers. These factors have been 

taken into account in developing estimates of price response that reflect variation in the characteristics of 

customers across states. The remainder of this section summarizes how state-specific estimates of price 

response were developed in this project. 

Residential 

The California Statewide Pricing Pilot (SPP) produced estimates of price elasticity for residential 

customers that captured variations in customer price responsiveness across four different climate zones in 

the state. These estimates were codified in the Pricing Impact Simulation Model (PRISM) which allows 

130 This multiplier was based on estimations of the number of cycling switch devices required for Direct Load Control for C&I 

customers. 

Federal Energy Regulatory Commission 233 
60% 

50% 

duct ion 40% ad Re

30% 

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10% 

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49.0% 

25.4% 

22.8% 21.6% 21.5% 20.1% 19.6% 

15.0% 15.3% 13.0% 14.0% 

12.0% 11.2% 11.8% 11.0% 

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Appendix D – Database Development Documentation 

price elasticities to vary as a function of a zone’s saturation of central air condition (CAC) equipment and 

weather conditions. 131 Specifically, it was found that zones with higher CAC saturation (which were also 

the hotter climate zones) were more price elastic than zones with low CAC saturations (which were also 

the milder climate zones). CAC saturation was found to be a key driver of differences in price 

responsiveness across the zones. These findings made it possible to express price elasticity as a function 

of CAC saturation, allowing the PRISM results to be projected to other regions of the country. 

However, this projection needs be interpreted as the first step in a two-step process. Dynamic pricing 

pilots have been conducted in several lo cations and when the results of PRISM, calibrated to the CAC 

saturations were compared with those ofpilots conducted in those regions, it was found that PRISM did 

not explain all the variation in pilot results. Figure D-6 summarizes a comparison for nine recent 

residential dynamic pricing pilots. 132 

Figure D-6: Comparison of Impacts from Recent Pricing Pilots to Calibrated PRISM Simulations 

It is apparent that, even when accounting for CAC saturation and the price ratio tested in a given pilot, 

PRISM does not exactly replicate the pilot’s results. Given the state-specific nature of this study, it is 

necessary to capture these regional differences. However, while each of the pilots in Figure D-6 draws 

some valuable conclusions about customer price response, some judgment must be exercised in 

determining whether to extrapolate their findings to a larger population beyond the participants of the 

pilot. The details of each pilot were carefully reviewed to determine which should be considered when 

adjusting the PRISM simulated impacts to account for regional differences. Ultimately, six of these nine 

 

131 The experiment also identified the relationship between price elasticity and average temperature. However, the effect of 

temperature on price response is much less significant than that of CAC saturation. For the purposes of this study, the 

temperature effect is held constant across regions. 132 For more information on the key findings of recent dynamic pricing pilots, see Ahmad Faruqui and Sanem Sergici, "Household 

response to dynamic pricing of electricity: A survey of the experimental evidence," January 10, 2009. 

http://www.hks.harvard.edu/hepg/Papers/2009/The%20Power%20of%20Experimentation%20_01-11-09_.pdf. 

234 A National Assessment of Demand Response Potential 
pilots were excluded from the analysis. Reasons for excluding these pilots are summarized in Table D12. 

Table D-12: Pilot Impacts Excluded from Assessment 

Pilot Reason for Exclusion 

California (Anaheim Public Utilities) Results of the more comprehensive California SPP are being used 

for California, and the Anaheim impacts are very similar 

Colorado (Xcel) The study identifies issues with self-selection bias which potentially 

result in an overstatement of the impacts 

Idaho (Idaho Power) The mix of pilot participants was not considered to be representative 

of the larger population of utility customers 

Illinois (ComEd) Impacts are based on a residential RTP rate and there is not enough 

available data to accurately determine the impact on average critical 

peak consumption (results presented in Figure D-6 are during the 

single highest hour of peak demand) 

Ontario (Hydro Ottawa) For the purposes of this state-specific study, pilots are limite d to 

those conducted in the United States; this pilot could be included in 

studies of a broader geographical scope but the large standard 

errors reported in the pilot may preclude extrapolation of results to 

other regions 

Washington (PSE) The pilot tested a non-dynamic, traditional TOU and that too with a 

very low peak-to-existing price ratio (1.17), preventing the results to 

be used in this study 

Appendix D – Database Development Documentation 

Based on this review, impacts from three of the nine pilots on which data were available were used to 

further refine the simulations derived from PRISM. Those pilots were conducted in Maryland by BGE, in 

Missouri by Ameren, and in New Jersey by PSE&G. In each of these pilots, actual customer price 

response was found to be lower than that simulated by PRISM. A likely explanation for this is that 

PRISM does not account for the effect of humidity. The California SPP was conducted across zones with 

a wide range of average temperatures but all the zones lay in a state with relatively low humidity. As a 

result, the model results would not reflect the likely conclusion that customers in more humid regions 

would be less responsive to dynamic pricing given the higher loss of comfort that they would experience 

by turning down their air conditioner on hot summer days. 

In Maryland, Missouri, and New Jersey, PRISM-simulated peak demand reductions were scaled back to 

equal the lower impacts that were observed in these three pilots. In addition, adjustments were made for 

all states east of the Rocky Mountains to account for the humidity effect observed in the three pilots. 

PRISM-simulated residential impacts for these states were derated by 20 percent, which is the 

approximate midpoint of the difference between the California SPP impacts and that of the three 

previously described pilots. 

PRISM allows separate impact estimates to be developed for customers who are offered dynamic pricing 

in conjunction with enabling technologies. Specifically, for the purposes of the Achievable and Full 

Participation demand response scenarios, it is assumed that residential customers would be offered a 

programmable communicating thermostat whenever the incremental effect of this enabling technology is 

likely to be large enough to make such a device cost effective. The California SPP captured the price 

elasticity of customers who were both enrolled in dynamic pricing and equipped with programmable 

communicating thermostats. As a result, these elasticities were used in California and in states west of 

the Rockies. The PRISM simulations were scaled back for states east of the Rocky Mountains in the 

same manner as for those customers who did not have the enabling technologies. 

Small and Medium C&I 

Price elasticities for Small and Medium C&I customers were also estimated during the California SPP. 

Small C&I customers provide peak reductions of less than one percent even at high price ratios. Medium 

C&I customers were found to be somewhat more responsive, but less so than residential customers. 

There are no results from other studies upon which to base any regional variation in these im pacts and so 

the California SPP results were held constant across the states. Price elasticity with enabling technology 

Federal Energy Regulatory Commission 235 
 

Appendix D – Database Development Documentation 

also comes directly from the California SPP. For both the Small and Medium C&I classes, customers are 

assumed to be offered programmable communicating thermostats. 

Large C&I 

Large C&I customers were not included in theCalifornia SPP nor are they included in any other pricing 

pilots. Therefore, price elasticity data for this customer class is limited to a few full-s cale 

implementations in the Northeastern U.S. Much of this information was summarized in a recent st udy 

carried out by the Demand Response Resea rch Center. 133 According to this study, the elasticity of 

substitution could be as high as -0.15 and thedaily elasticity could be as high as -0.20. Both estima tes 

varied greatly by sector and rate offering. There is a significant amount of uncert ainty in these estimat es 

and they are based on a limited number of participants, so for the purposes of the Assessment they ha ve 

been scaled down to avoid potentially overstati ng the impacts. 134 This is an area in which further res earch 

is warranted. 

There is very limited information on the potential for demand response when customers in this class are 

equipped with enabling technologies. For the purposes of the Assessment, it is assumed that t hese 

customers would be offered automated demand respo nse, a technology that would allow for a 

coordinated, autom ated curtailment of electricity consumption at a number of customer end uses. The 

best available information on the potential im pacts of automated demand response comes from a rec ent 

study by the Demand Response Research Center. 135 Large C&I customers at all three of California’s 

investor-owned utilities were equipped with the technology, and on average the incremental additional 

reduction in peak demand was found to be at least 13 percent, or an 86 percent increase over the 

anticipated response to dynamic pricing in the absence of the technology. It is this incremental increase 

of 86 percent that was used to represent the incremental impact of enabling technology for the Large C&I 

class in the Assessment. 

It should be noted that, while the DRRC study represents the best available information on this topic, the 

findings are based on a technology demonstration project rather than on the results of a scientific 

experiment. As a result, there is significantly more uncert ainty in these estimates. This is also an area 

where further research is warranted. 

Assumed Elasticities 

The final elasticities used in the Assessment are presented in Table D-13. 

Table D-13: Assumed Elasticities by Customer Class 

Type of Elasticity Res (No CAC) Res (CAC) Small C&I Medium C&I Large C&I 

Without 

Enabling 

Technology 

Critical Day Substitution -0.0472 -0.1383 0.0000 -0.0412 -0.0500 

Critical Day 

Daily Elasticity -0.0330 -0.0487 0.0000 -0.0250 -0.0200 

Normal Weekday Substitution -0.0425 -0.1336 0.0000 -0.0493 -0.0500 

Normal Weekday Daily 

Elasticity -0.0354 -0.0511 0.0000 -0.0250 -0.0200 

Weekend 

Daily Elasticity -0.0354 -0.0511 0.0000 -0.0250 -0.0200 

With 

Enabling 

Technology 

Critical Day Substitution -0.0472 -0.3523 -0.0892 -0.0815 N/A 

Critical Day 

Daily Elasticity -0.0330 -0.0677 -0.0250 -0.0250 N/A 

133 Goldman, C., Hopper, N., B harvirkar, R., Neenan, Cappers, P. August 2007. A Methodology for Estimating Large-Customer 

Demand Response Market Potential, Lawrence Berkeley National Laboratory Report No. LBNL-63346, presented at: IEPEC 

Conference, Chicago. 134 These elasticities were recently used in a study for the Demand Response Research Center and are further discussed in: 

Ahmad Faruqui, Ryan Hledik, and John Tsoukalis, “The Power of Dynamic Pricing,” The Electricity Journal, April 2009. 135 See “Automated Demand Response for Commercial and Industrial Facilities: A Progress Report to the CPUC,” prepared by the 

Demand Response Research Center, December 2007. Also, Wikler, G., et. al., “Enhancing Price Response through Auto-DR: 

California’s 2007 Implementation Experience,” Proceedings of the ACEEE Summer Study on Energy Efficiency in Buildings, 

August 2008 

236 A National Assessment of Demand Response Potential 
Appendix D – Database Development Documentation 

For states east of the Rockies, residential impacts derived from PRISM with and without technology are 

scaled back by 20 percent. Impacts for Maryland, Missouri, and New Jersey are scaled back by seven 

percent, 34 percent, and 39 percent, respectively, to equal results determined by pilots in those states (see 

discussion above). Large C&I impacts are increased by 86 percent to represent the impacts of automated 

demand response. 

The price elasticities summarized above for residential customers produce quite different percent 

reductions across states as a function of the variation in climate and air conditioning saturations. There 

are also differences in the estimated percent reduction in peak period energy use based on differences in 

the assumed ratio of prices during the peak period. The percent reduction in peak period energy use for 

residential customers for each state and two price ratios are shown in Table D-14. Note that the 

relationship between price and energy use is not linear. That is, while the price ratio doubles going from 

4to 1 to 8 to 1, the percent reduction in peak demand increases by less than 100 percent. For example, 

the doubling of the price ratio in California leads to a 57 percent decrease in peak period energy use. 

Federal Energy Regulatory Commission 237 
Appendix D – Database Development Documentation 

Table D-14: Percent Reduction in Peak Period Energy Use for the Average Residential Customer 

State CAC Saturation Percent Peak Period 

Reduction for 4 to 1 Price 

Ratio 

Percent Peak Period 

Reduction for 8 to 1 Price 

Ratio 

Alabama 62.00% 9.67% 15.18% 

Alaska 2.50% 6.64% 10.57% 

Arizona 86.80% 14.28% 22.33% 

Arkansas 54.60% 9.15% 14.38% 

California 41.00% 10.25% 16.13% 

Colorado 47.24% 10.79% 16.97% 

Connecticut 26.91% 7.20% 11.37% 

Delaware 53.00% 9.04% 14.20% 

District of Columbia 56.00% 9.25% 14.53% 

Florida 91.00% 11.72% 18.32% 

Georgia 82.25% 11.10% 17.37% 

Hawaii 17.55% 6.55% 10.36% 

Idaho 66.50% 12.49% 19.58% 

Illinois 75.00% 10.59% 16.59% 

Indiana 74.39% 10.55% 16.52% 

Iowa 70.00% 10.24% 16.05% 

Kansas 83.68% 11.20% 17.53% 

Kentucky 76.00% 10.66% 16.70% 

Louisiana 75.49% 10.62% 16.64% 

Maine 14.00% 6.30% 9.98% 

Maryland 78.00% 12.56% 19.66% 

Massachusetts 12.70% 6.20% 9.83% 

Michigan 57.22% 9.34% 14.66% 

Minnesota 51.15% 8.91% 14.00% 

Mississippi 74.72% 10.57% 16.56% 

Missouri 87.50% 9.46% 14.80% 

Montana 42.10% 10.34% 16.28% 

Nebraska 82.80% 11.14% 17.43% 

Nevada 86.80% 14.28% 22.33% 

New Hampshire 12.70% 6.20% 9.83% 

New Jersey 55.00% 7.00% 11.00% 

New Mexico 42.00% 10.33% 16.26% 

New York 16.75% 7.32% 11.56% 

North Carolina 84.35% 11.25% 17.60% 

North Dakota 51.00% 8.90% 13.99% 

Ohio 62.86% 9.74% 15.27% 

Oklahoma 84.16% 11.24% 17.58% 

Oregon 38.00% 9.98% 15.72% 

Pennsylvania 49.75% 8.81% 13.85% 

Rhode Island 12.49% 6.19% 9.81% 

South Carolina 84.35% 11.25% 17.60% 

South Dakota 70.90% 10.30% 16.14% 

Tennessee 81.44% 11.04% 17.29% 

Texas 80.00% 10.94% 17.13% 

Utah 42.10% 10.34% 16.28% 

Vermont 7.20% 5.82% 9.24% 

Virginia 50.20% 8.84% 13.90% 

Washington 28.62% 9.16% 14.45% 

West Virginia 50.20% 8.84% 13.90% 

Wisconsin 62.03% 9.68% 15.18% 

Wyoming 42.00% 8.27% 13.01% 

e) Cost effectiveness analysis 

For the purposes of economic screening, the five demand response programs being considered in the 

analysis can be divided into two broad categories – those that do not require an enabling technology for 

participation and those that do. The demand response options that do not require an enabling technology 

for participation were deemed to be cost-effective for all states. For the demand response options that do 

require an enabling technology for participation, a measure-level economic screen was conducted to 

238 A National Assessment of Demand Response Potential 
 

 

Appendix D – Database Development Documentation 

assess their cost-effectiveness in each state. The purpose of this preliminary analysis is to determine 

which states have the critical peak customer loads which would justify the initial costs of en abling 

technology irrespective of participant rates. The two types of options for which an economic screen wa s 

conducted are: 1) Dynamic Pricing with Enabling Technology, and 2) Direct Load Control . This section 

describes the methodology and the results associated with the economic screening of these two types of

demand response options. 

Methodology 

The econom ic screen uses a simple version of the Total Resource Cost (TRC) Test that compares the 

lifetime benefits of the demand response option (i.e., avoidedcapacity costs) relative to the associated

costs toenable each option (i.e., costs related to technology ad option, implementation and delive ry, etc.) 

on a per-participant basis. Inputs f or the e conomic screen include impact estimates per participant by 

state, capacity costs, equipment costs and i mplementation costs, as well as economic parameters such as 

discount and cost escalation rates. T he bene fits are obtained by multiplying the unit demand reduction for 

each technology by avoided capacity costs ($/kW) over the ten year time horizon and discounting the 

dollar savings to a present value equivalent basis. The costs a re equal to the equipment and 

implementation costs per participan t. 136 If the benefit-cost rati o is 1.0 0 or greater, the demand response 

option is considered cost-effective and is included in the state’s Full Participation potential results. 

To determinecost-effectiveness associatedwith the two demand response options, the impact estimates 

already developed as part of demand response potential estimation were used. The Dynamic Pricing 

Option wit hout enabling technologyis deem ed to be cost-effect ive. He nce this analysis considers only the 

benefits and costs attributable to the technology component. The enabling technologies include d in the 

analysis are: 

•	 Programmable Communicating Thermostats and remotely-controlled switches for the small and 

medium load customers, and 

•	 Automa ted Demand Response technologies for the large load customers. 

The equipment type and associated costs aresummarized in Table D-15 for the two demand response 

options by customer class. An additional 15% was added to the equipment costs to represent up-front 

costs for program development and ongoingcosts for implementation and delivery. 137 

138 Table D -15: Enablin g Technology Equi pment C osts 

Customer 

Type 

Dynamic Pricing Direct Load Control 

Equipment Unit Cost Equipment Cost 

Residential PCT $200 Switch $200 

Small C&I PCT $350 Switch $350 

Medium C&I PCT $1,050 RAuto-D $1,050 

Large C&I Auto-DR $13,500 N/A 139 

N/A 

An avoided capacity cost of $75 per kW (re presenting the investment cost of a gas-fired combustion 

turbine-generator) was used to derive the avoided cost benefits. This value was escalated at 3% per year 

for each year beyond 2009. The projected avoided costs were d iscoun ted to present value equivalents 

using a discount rate of 5%. 140

136 The cost-effectiveness is not performed at the program-level, therefore the effects of incentives and participation rates are not 

included in this analysis. 137 This percentage is commonly used for these types of studies and it based on benchmark experience from actual demand 

response program implementation nationwide. 138 The costs are based on vendor estimates and utility program cost data for programs with similar demand response options. 139 Note that Direct Load Control for large C&I customers was not considered in the analysis. 140 The assumptions related to avoided capacity costs, cost escalation rates, and discount rates represent commonly accepted 

estimates for similar analyses conducted in the industry. 

Federal Energy Regulatory Commission 239 
Appendix D – Database Development Documentation 

Summary of Results 

A demand response option with enabling technology is cost-effective and as such passes the economic 

screen if the benefit-cost (B/C) ratio is 1.00 or higher. Summary results of the economic screen are 

included in Tables 16 and 17 for Dynamic Pricing with Enabling Technology and Direct Load Control, 

respectively. The tables list the B/C ratios for each state and indicate the states where the demand 

response options are cost-effective. 

The economic screening results show that Dynamic Pricing with Enabling Technology is a cost-effective 

option for the majority of states. However, there are a number of states for which it fails the economic 

screen. The results vary by customer type. Dynamic Pricing with Enabling Technology for residential 

customers is cost-effective for 42 states (84% of states). The option for small C&I customers is costeffective for 40 states (80% of states) as well as for the District of Columbia. For the medium C&I 

customers, the option is cost-effective for 43 states (86% of states) and the District of Columbia, while for 

the large C&I category it is cost-effective for 45 states (90% of states) and the District of Columbia. The 

results indicate that Dynamic Pricing with Enabling Technology is cost-effective primarily for those 

states with high critical peak loads associated with large cooling or other end-use requirements. In 

particular, this option is highly cost-effective in Arizona and Nevada. 

Notable results and observations from the Dynamic Pricing with Enabling Technology screen: 

•	 A state not passing the cost-effectiveness screen does not suggest these programs should not be 

pursued in that state. The estimates are based on price response using class-average load shapes. 

Many of the states that did not pass in fact have varying weather characteristics that would lead to 

different impacts. Some regions might have higher impacts and thus these programs may indeed 

be cost-effective. 

•	 As the customer class size increases and approaches the large C&I class (starting with the small 

C&I), more states become cost-effective. 

•	 These trends suggest that as dynamic pricing tariffs are introduced across the country, utilities 

that are considering adopting one of their own might consider startin g with the larger customer 

classes and gradually introduce the tariffs to the smaller classes once more information is 

available. 

•	 Careful attention should be given to the economic analysis for these types of programs, 

particularly when looking at the residential class, which in some reg ions of the country may not 

provide the needed level of savings to justify the cost of enablement technologies such as 

programmable communicating thermostats and au tomated demand response. 

Direct Load Control is a cost-effective demand respon se option for most states bec ause of the higher per 

participant savings associated with this option. The analysis showed that Direct Load Control is costeffective for residential customers in 48 states (9 6%) an d the District of Columbia. The only states for 

which it is not cost-effective for residential customers are Alaska and Hawaii. Among both small and 

medium C&I customers, Direct Load Control is cost-effective for all states and the District of Columb ia. 

Notable results and observations from the Direct Load Control with Enabling Technology screen: 

•	 Most states passed the economic screen. However, for those states that failed the screen, methods 

of direct load control other than air conditioning might be viable. 

•	 Methods to control water heating and pumping loads may be more viable in these regions. 

240 A National Assessment of Demand Response Potential 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix D – Database Development Documentation 

Table D-16: Economic Screen Results for Dynamic Pricing with Enabling Technology 

Dynamic Pricing with Enabling Technology 

Residential Small C&I Medium C&I Large C&I 

Pass B/C Fail B/C Pass B/C Fail B/C Pass B/C Fail B/C Pass B/C Fail B/C 

AL 1.93 AK 0.53 AK 1.13 CA 0.80 AK 2.46 ID 0.96 AK 3.04 CT 0.61 

AR 1.71 DC 0.95 AL 3.81 CO 0.48 AL 5.93 IL 0.87 AL 2.21 KS 0.94 

AZ 2.35 HI 0.67 AR 2.30 CT 0.98 AR 2.86 MA 0.76 AR 2.36 MN 0.97 

CA 1.01 IL 0.83 AZ 4.27 FL 0.73 AZ 5.11 MD 0.99 AZ 2.43 NE 0.86 

CO 1.18 ME 0.77 DC 2.41 ME 0.52 CA 1.16 ME 0.91 CA 1.64 NH 0.90 

CT 1.51 MI 0.83 DE 3.84 MN 0.81 CO 1.24 NH 0.99 CO 2.66 

DE 1.14 MN 0.98 GA 1.38 OK 0.97 CT 1.96 RI 0.98 DC 2.20 

FL 1.44 VT 0.96 HI 1.06 RI 0.69 DC 4.89 DE 2.81 

GA 1.68 WI 0.77 IA 1.04 TX 0.95 DE 3.86 FL 2.05 

IA 1.02 ID 1.00 VT 0.55 FL 1.24 GA 1.78 

ID 2.00 IL 1.85 GA 1.84 HI 2.48 

IN 1.25 IN 1.60 HI 1.39 IA 2.09 

KS 1.38 KS 1.62 IA 1.46 ID 1.88 

KY 1.55 KY 2.66 IN 1.62 IL 1.33 

LA 1.80 LA 3.70 KS 1.35 IN 2.36 

MA 1.09 MA 1.51 KY 5.43 KY 2.83 

MD 1.53 MD 3.31 LA 1.19 LA 2.28 

MO 1.24 MI 1.56 MI 1.48 MA 1.89 

MS 1.84 MO 1.27 MN 1.28 MD 1.79

MT 1.28 MS 2.22 MO 3.41 ME 1.69 

NC 1.57 MT 3.11 MS 2.41 MI 1.80

ND 1.28 NC 1.41 MT 4.84 MO 2.21 

NE 1.27 ND 2.44 NC 5.19 MS 3.59 

NH 1.19 NE 1.15 ND 3.98 MT 3.25 

NJ 1.11 NH 1.20 NE 3.95 NC 4.05 

NM 1.00 NJ 1.80 NJ 2.38 ND 1.81 

NV 1.91 NM 1.22 NM 1.89 NJ 1.17 

NY 1.19 NV 3.06 NV 3.47 NM 2.09 

OH 1.10 NY 1.44 NY 2.50 NV 2.75 

OK 1.62 OH 2.16 OH 2.01 NY 2.42 

OR 1.51 OR 1.14 OK 2.15 OH 1.78 

PA 1.04 PA 2.07 OR 2.30 OK 2.30 

RI 1.03 SC 1.93 PA 1.32 OR 2.01 

SC 1.80 SD 2.35 SC 5.30 PA 1.90 

SD 1.16 TN 2.91 SD 2.69 RI 1.16 

TN 1.98 UT 1.24 TN 5.74 SC 5.01 

TX 1.67 VA 1.16 TX 1.46 SD 1.19 

UT 1.32 WA 1.65 UT 2.66 TN 1.11 

VA 1.49 WI 1.03 VA 2.72 TX 6.16

WA 1.46 WV 1.60 VT 1.50 UT 3.90 

WV 1.41 WY 3.76 WA 3.39 VA 2.09 

WY 1.11 WI 1.87 VT 2.28 

WV 2.41 WA 2.28 

WY 2.03 WI 2.31 

WV 4.23 

WY 4.58 

Federal Energy Regulatory Commission 241 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix D – Database Development Documentation 

Table D-17: Economic Screen Results for Direct Load Control 

Direct Load Control 

Residential Small C&I Medium C&I 

Pass B/C Fail B/C Pass B/C Fail B/C Pass B/C Fail B/C 

AL 5.41 AK 0.00 AK 3.87 AK 3.87 

AR 3.37 HI 0.93 AL 6.18 AL 6.18 

AZ 3.11 AR 3.85 AR 3.85 

CA 1.49 AZ 3.55 AZ 3.55 

CO 1.71 CA 4.90 CA 4.90 

CT 3.30 CO 4.15 CO 4.15 

DC 2.98 CT 3.77 CT 3.77 

DE 2.50 DC 3.41 DC 3.41 

FL 4.11 DE 2.86 DE 2. 86

GA 3.85 FL 4.70 FL 4.70 

IA 1.67 GA 4.40 GA 4. 40

ID 3.14 HI 2.48 HI 2. 48

IL 1.20 IA 6. 15 IA 6. 15

IN 3.06 ID 3. 59 ID 3. 59

KS 1 2.7 IL 3. 23 IL 3. 23

K 3 Y 3.1 IN 3. 50 IN 3. 50

LA 1 3.1 KS 3. 10 KS 3. 10

MA 1 3.1 KY 3. 57 KY 3. 57

MD 8 2.3 LA 3. 55 LA 3. 55

ME 5 1.5 MA 3. 55 MA 55 3. 

MI 1.43 MD 2. 72 MD 72 2. 

MN 5 3.1 ME 3. 55 ME 55 3. 

MO 4.18 MI 3.90 MI 3.90 

MS 1 3.1 MN 3. 60 MN 60 3. 

MT 1 3.1 MO 4. 78 MO 78 4. 

NC 1 3.2 MS 3. 55 MS 55 3. 

ND 1 3.1 MT 3. 55 MT 3. 55

NE 1 3.1 NC 3. 67 NC 3. 67

NH 1 3.1 ND 3. 55 ND 3. 55

NJ 8 2.5 NE 3. 55 NE 3. 55

NM 8 1.3 NH 3. 55 NH 3. 55

NV 2 4.0 NJ 2. 95 NJ 2. 95

N 5 Y 4.2 NM 3. 55 NM 55 3. 

OH 4 3.0 NV 4. 60 NV 4. 60

OK 1 3.1 NY 4. 86 NY 4. 86

OR 3.11 OH 3.47 OH 3.47 

PA 1 3.1 OK 3. 55 OK 3. 55

RI 3.11 OR 3. 55 OR 55 3. 

SC 1 2.3 PA 3. 55 PA 3. 55

SD 0 1.9 RI 3. 55 RI 3. 55

TN 1 3.1 SC 2. 64 SC 2. 64

TX 4 3.4 SD 6. 04 SD 6. 04

UT 1 3.1 TN 3. 55 TN 3. 55

VA 1 3.1 TX 3. 93 TX 3. 93

VT 1 3.1 UT 3. 55 UT 3. 55

WA 5 1.6 VA 3. 55 VA 3. 55

WI 4 1.1 VT 3. 55 VT 3. 55

WV 1 3.1 WA 4. 26 WA 26 4. 

W 3.11 Y WI 3.43 WI 3.43 

WV 3.55 WV 553.

WY 55 3. W 55 Y 3. 

242 A National Assessment of Demand Response Potential 
APPENDIXE.UNCERTAINTYANALYSIS

The data and assumptions in this Assessment are based on the results of a detailed survey of demand 

response programs and a comprehensive review of previous research on demand response potential. 

However, as with any forward-looking assessm e nt, the data and assumptions are uncertain. To represent 

the magnitude of the impact of this uncertainty, sensitivity analysis has been conducted on the variables 

that are the key drivers of the potential estimates. 

A number of factors contribute to the overall potential for demand response. However, at the highest level 

the calculation of potential boils down to the following simple equation: 

Total demand response potential = # of customers participating in demand response pro g rams x peak

reduction per participant 

Thus, to develop an understanding of the level of uncertainty in the potential estimates in this 

Assessment, the two components on the right-hand side of the above equation were chosen as the 

variables to be tested through sensitivity analysis. For each of the five categories of demand response 

programs, a high and a low value were chosen for the assumed per-customer impacts and the participation 

rates. In total, this amounts to twenty new assumptions to be run through the model: 5 program types x 2 

values (high and low) x 2 variables (impacts and participation). 

To determine the high and low values, each of the model inputs described above were increased by 50 

percent (representing the high value) and decreased by 50 percent (representing the low value). This 

allowed for a consistent comparison across each of the variables in assessing their relative contributions 

to the uncertainty in the overall potential estimate. The one exception to this is the assumption regarding 

dynamic pricing participation. Because dynamic pricing is a newly developing program and does not yet 

have an established history of participation like the other demand response program types, a wider range 

of uncertainty was used. In the Achieva ble P articipation scen a rio, the high value for participation was 

assumed to be 100 percent (representing a scenario where dynamic pricing is the universal rate) and the 

low value was assumed to be five percent (representing a scenario where dynamic pricing is voluntary 

and few customers choose to enroll). 

The 20 sensitivity assumptions were each run through the model one-at-a-time, while holding all other 

modeling assumptions constant. The analysis was only conducted for the Achievable Participation 

scenario, but the approach could be expanded to apply to the other scenarios as well. The results of the 

model runs can be summarized in a “tornado diagram” as illustrated in Figure E-1. 

243 A National Assessment of Demand Response Potential 
Appendix E – Uncertainty Analysis 

Figure E-1: Results of U n certainty Analysis for the Achievable Potential Scenario in 2019 

Dynamic Pricing Participation 

Dynamic Pricing Impacts 

Enabling Technology Participation 

Interruptible Tariff Participation 

Interruptible Tariff Impacts 

Enabling Technology Impacts 

Other DR Particpation 

Other DR Impacts 

DLC Impacts 

DLC Participation 

-53.4 26.1 

-35.0 35.0 

-11.5 11.5 

-7.2 11.9 

-8.1 6.2 

-5.1 5.1 

-2.4 2.7 

-2.5 1.6 

-1.6 1.8 

-1.6 1.8 

-60 -50 -40 -30 -20 -10 0 10 20 30 40 

Achievable Participation Potential = 138 GW 

U.S. Peak Demand in 2019 = 955 GW 

Change in Demand Response Potential, Achievable Scenario (GW) in 2019 

As expected, Figure E-1 shows that dynamic pricing assumptions contribute the most heavily to 

uncertainty in potential demand response impacts under the Achievable Participation scenario. With low 

participation in dynamic pricing, the Achievable Participation potential of 138 GW would be reduced by 

53 GW to 85 GW, representing a redu ction of 39 percent. Higher participation could increase the impacts 

by 26 GW. The assumed customer response to dynamic pricing also contributes significantly to the 

overall uncertainty. If customers were found to be more or less responsive to dynamic rates than was 

assumed in this analysis, total demand response potential could increase or decrease significantly. At the 

low end, direct load control and Other DR programs do not contribute as significantly to the overall 

uncertainty. 

To put the results of the uncertainty analysis in context it is helpful to know the share of total Achievable 

Participation potential that is held by each demand response program type. This is illustrated in Figure E2. It is generally the case that those programs with a larger share of the potential also contribute a large 

share of the uncertainty. 

244 A National Assessment of Demand Response Potential 
19 GW, 

14% 

48 GW, 

35% 

27 GW, 

20% 

10 GW, Pricing w/ Enabling Tech 

7% 33 GW, Pricing w/o Enabling Tech 

24% Direct Load Control 

Interruptible Tariffs 

Other DR 

Appendix E – Uncertainty Analysis 

Figure E-2: Share of Achievable Participation Potential for Each Demand Response Program Type, 2019 

Federal Energy Regulatory Commission 245 

APPENDIXF.	 ENERGYINDEPENDENCE

ANDSECURITYACTOF

2007,SECTION529

Energy Independence and Security Act of 2007 

TITLE V—ENERGY SAVINGS IN GOVERNMENT AND PUBLIC INSTITUTIONS 

Subtitle C—Energy Efficiency in Federal Agencies 

SEC. 529. ELECTRICITY SECTOR DEMAND RESPONSE. 

(a) IN GENERAL.—Title V of the National Energy Conservation Policy Act (42 U.S.C. 8241 et seq.) 

is amended by adding at the end the following: 

“PART 5—PEAK DEMAND REDUCTION 

“SEC. 571. NATIONAL ACTION PLAN FOR DEMAND RESPONSE. 

“(a) NATIONAL ASSESSMENT AND REPORT.—The Federal Energy Regulatory Commission 

(‘Commission’) shall conduct a National Assessment of Demand Response. The Commission shall, 

within 18 months of the date of enactment of this part, submit a report to Congress that includes each of 

the following: 

“(1) Estimation of nationwide demand response potential in 5 and 10 year horizons, including 

data on a State-by-State basis, and a methodology for updates of such estimates on an annual basis. 

“(2) Estimation of how much of this potential can be achieved within 5 and 10 years after the 

enactment of this part accompanied by specific policy recommendations that if implemented can 

achieve the estimated potential. Such recommendations shall include options for funding and/or 

incentives for the development of demand response resources. 

“(3) The Commission shall further note any barriers to demand response programs offering 

flexible, non-discriminatory, and fairly compensatory terms for the services and benefits made 

available, and shall provide recommendations for overcoming such barriers. 

“(4) The Commission shall seek to take advantage of preexisting research and ongoing work, and 

shall insure that there is no duplication of effort. 

“(b) NATIONAL ACTION PLAN ON DEMAND RESPONSE.—The Commission shall further develop a 

National Action Plan on Demand Response, soliciting and accepting input and participation from a broad 

range of industry stakeholders, State regulatory utility commissioners, and non-governmental groups. The 

Commission shall seek consensus where possible, and decide on optimum solutions to issues that defy 

consensus. Such Plan shall be completed within 1 year after the completion of the National Assessment 

of Demand Response, and shall meet each of the following objectives: 

“(1) Identification of requirements for technical assistance to States to allow them to maximize 

the amount of demand response resources that can be developed and deployed. 

“(2) Design and identification of requirements for implementation of a national communications 

program that includes broad-based customer education and support. 

“(3) Development or identification of analytical tools, information, model regulatory provisions, 

model contracts, and other support materials for use by customers, States, utilities and demand 

response providers. 

“(c) Upon completion, the National Action Plan on Demand Response shall be published, together 

with any favorable and dissenting comments submitted by participants in its preparation. Six months after 

publication, the Commission, together with the Secretary of Energy, shall submit to Congress a proposal 

to implement the Action Plan, including specific proposed assignments of responsibility, proposed budget 

amounts, and any agreements secured for participation from State and other participants. 

Federal Energy Regulatory Commission 247 
Appendix F – Energy Independence and Security Act of 2007, Section 529 

“(d) AUTHORIZATION.—There are authorized to be appropriated to the Commission to carry out 

this section not more than $10,000,000 for each of the fiscal years 2008, 2009, and 2010.”. 

(b) TABLE OF CONTENTS.—The table of contents for the National Energy Conservation Policy 

Act (42 U.S.C. 8201 note) is amended by adding after the items relating to part 4 of title V the following: 

“PART 5—PEAK DEMAND REDUCTION 

“Sec. 571. National Action Plan for Demand Response.”.
 

248 A National Assessment of Demand Response Potential 
APPENDIXG.GLOSSARYOFTERMS
 

•	 Ancillary Service Programs: Customers bid load curtailments into various ancillary services markets 

and agree to be on standby if their bid is accepted. They receive a payment if they are called by the 

ISO/RTO. 

•	 Capacity Programs: Customers offer load curtailments as a replacement to existing generation in the 

market. They are generally notified during the day when curtailment is needed. Large penalties are 

often assessed in the event of non-compliance. 

•	 Critical Peak Pricing: Prices vary by time-of-day and are known to the customer for all pricing 

periods except that the customer does not know when prices in the critical-peak period may be called. 

These prices are called on a day-ahead or day-of basis. 

•	 Demand Bidding/ Buyback (Day-ahead): Customers bid load curtailments in the day-ahead market in 

competition with supply-side resources. 

•	 Demand Bidding/ Buyback (Day-of): Customers bid load curtailments in the day-of market in 

competition with supply-side resources. 

•	 Direct Load Control: In return for a financial incentive, customers agree to have their end-uses such 

as air conditioners and water heaters to be controlled by the utility via switches or programmable 

communicating thermostats. 

•	 Demand Response through Load Aggregators: Load aggregators combine the load reductions of 

smaller participants and submit these reductions to capacity or other emergency or economic demand 

response programs. 

•	 Emergency Demand Response Program: Emergency demand response programs provide incentive 

payments to customers for reducing their loads during reliability-triggered events, but curtailment is 

voluntary. 

•	 Economic Demand Response Program: Economic demand response programs provide incentive 

payments to customers for reducing their loads during economic-triggered events, but curtailment is 

voluntary. 

•	 Emergency Generation: When system’s reliability is threatened, system operator may automatically 

dispatch the generation source at customer’s site. 

•	 Interruptible General Service: Customers pay a lower rate in return for agreeing to interrupt their 

processes to a pre-specified level. This program requires the specification of a baseline or normal 

usage. 

•	 Load curtailment (a nominated load or a contracted firm demand): Customers are paid a specified 

amount per MWh curtailed in response to a call that is made on a day-of basis. This requires the 

specification of a baseline or normal usage. 

•	 Peak Time Rebate: Customers receive a cash rebate for each kWh of load that they reduce below their 

baseline usage during the event hours instead of paying higher rates during the critical event hours. 

Federal Energy Regulatory Commission 249 
Appendix G – Glossary of Terms 

•	 Peak Shed Programs: Peak shed programs are generally implemented through automating 

technologies to reduce the load from certain end-use devices and reduce demand charges that will be 

paid by the customer. 

•	 Peak Shaving via Owned Generation: This is similar to the interruptible/curtailable rate programs 

except that when the load is curtailed or interrupted, it is replaced by the power from own generation 

resources. 

•	 Peak Day Credit: program provides qualifying customers with bill credits on all on-peak charges in 

exchange for an average load reduction of a pre-determined level in consumption across all critical 

event days within a billing cycle. 

•	 Prepay Programs: Customers prepay for their electricity and have in-home displays that provide 

information on consumption. While not a demand response program per se, it’s observed that prepay 

programs increase the effectiveness of time-varying rates. 

•	 Real Time Pricing (Day-ahead): Prices may vary on an hourly and sometime on a semi-hourly basis. 

Customers are provided the prices on a day-ahead basis. 

•	 Real Time Pricing (Day-of): Prices may vary on an hourly and sometime on a semi-hourly basis. 

Customers are provided the prices on an hour-ahead basis. 

•	 Thermal Storage Program: In this program, customers have electric thermal storage units installed on 

electric heaters which operate during off peak hours and agree to curtail electric heat during on peak 

winter periods. 

•	 Time-of-Use Pricing: Prices vary by time-of-day and are known to the customers. 

•	 Utility Controlled Interruptible Rates: Customers pay lower rates in return for agreeing to their 

service being interrupted by the utility. 

250 A National Assessment of Demand Response Potential