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Contract No. DE-AC36-08GO28308

Technical Report

NREL/TP-6A20-70630

December 2018

Potential Roles for Demand Response 

in High-Growth Electric Systems with 

Increasing Shares of Renewable 

Generation

Elaine Hale, Lori Bird, Rajaraman Padmanabhan, 

and Christina Volpi

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

Office of Energy Efficiency & Renewable Energy

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

Contract No. DE-AC36-08GO28308

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15013 Denver West Parkway

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Technical Report

NREL/TP-6A20-70630

December 2018

Potential Roles for Demand Response 

in High-Growth Electric Systems with 

Increasing Shares of Renewable 

Generation

Elaine Hale, Lori Bird, Rajaraman Padmanabhan, 

and Christina Volpi

National Renewable Energy Laboratory

Suggested Citation

Hale, Elaine, Lori Bird, Rajaraman Padmanabhan, and Christina Volpi. 2018. Potential 

Roles for Demand Response in High-Growth Electric Systems with Increasing Shares of 

Renewable Generation. Golden, CO: National Renewable Energy Laboratory.

NREL/TP-6A20-70630. https://www.nrel.gov/docs/fy19osti/70630.pdf.
NOTICE

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable 

Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding 

provided by U.S. Department of Energy Office of International Affairs. The views expressed herein do not 

necessarily represent the views of the DOE or the U.S. Government.

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Acknowledgements

The authors are grateful for early reviews from Jaquelin Cochran and Paul Denholm, National 

Renewable Energy Laboratory (NREL); country-specific reviews from Lehlogonolo Chiloane

and Peter Mukoma, Council for Scientific and Industrial Research (CSIR), and from Ella Zhou 

and David Palchak (NREL); and the insightful comments and suggestions received from Peter 

Cappers, Lawrence Berkeley National Laboratory (LBNL), Ookie Ma, U.S. Department of 

Energy (DOE), Brady Stoll and Daniel Steinberg (NREL). We thank Jennifer Daw (NREL) for 

project management support, and Michael Meshek (NREL) for editing this report. Funding was 

provided by the DOE’s Office of International Affairs under Field Work Proposal No. IA-GTG 

India SMART Cities DR-FY16-01, “GTG India Demand Response Analysis.” We would 

particularly like to thank Daniel Noll and Kevin Quinlan (DOE) for their roles in supporting this 

work. Any errors or omissions are the sole responsibility of the authors. This research was 

funded under contract number DE-AC36-08GO28308.
iv

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List of Abbreviations and Acronyms

C&I commercial and industrial

CPP critical peak pricing

CPR critical peak rebate

CSIR Council for Scientific and Industrial Research

DER distributed energy resource

DLC direct load control

DOE Department of Energy

DSM demand-side management

DRAM Demand Response Auction Mechanism

EPRI Electric Power Research Institute

ERCOT Electric Reliability Council of Texas

EV electric vehicle

FERC Federal Energy Regulation Commission

FP&L Florida Power & Light

HVAC heating, ventilation, and air conditioning

ISO independent system operator

LBNL Lawrence Berkeley National Laboratory

MISO Midcontinent Independent System Operator, Inc.

MWh megawatt-hour

NE New England

NREL National Renewable Energy Laboratory

NYISO New York Independent System Operator

PURPA Public Utilities Regulatory Policy Act

PV photovoltaic

REIPPPP Renewable Energy Independent Power Producer 

Procurement Programme

REV Reforming the Energy Vision

RTP real-time pricing

ToU time-of-use

TSO transmission system operator

VPP variable peak pricing
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Abstract

Many countries around the world are experiencing rapid economic development while 

simultaneously deploying more wind and solar generators. This report explores potential roles 

for demand response—the modification of electricity load operations to provide grid services—

to support these countries’ development goals through enhancing grid reliability and assisting 

with renewable energy integration in the power sector. After reviewing current demand response 

programs types, which include wholesale and retail market offerings, as well as broader 

distributed energy resource (DER) aggregation programs, we reflect on the historical trajectory 

of demand response as a grid resource and on recent findings from renewable integration studies. 

The reviewed literature suggests that some types of demand response programs are likely to be

more supportive of high-economic growth, high-renewable contexts than others. We conclude by 

outlining best practices for the design of new demand response programs that build on the 

historical lessons learned and are well suited to accommodate expected future changes in 

generation and load patterns.
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Table of Contents

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

2 Roles for Demand Response............................................................................................................... 2

2.1 Demand Response in Wholesale Electricity Markets ................................................................... 3

Demand Response in Energy Markets .......................................................................................... 3

Demand Response in Ancillary Service Markets.......................................................................... 4

Demand Response in Capacity Markets........................................................................................ 5

2.2 Retail Demand Response Programs.............................................................................................. 6

2.3 Distributed Resource Aggregations Providing Demand Response ............................................. 10

2.4 Summary ..................................................................................................................................... 11

3 Demand Response in High-Growth Power Systems with Increasing Shares of Renewable 

Generation: Current Trajectories and Aspirations.......................................................................... 16

3.1 China ........................................................................................................................................... 16

3.2 India............................................................................................................................................. 18

3.3 South Africa ................................................................................................................................ 19

4 Future of Demand Response in High-Growth, Power Systems with Increasing Shares of 

Renewable Generation....................................................................................................................... 20

4.1 Comparison of High-Growth Power Systems with Increasing Shares of Renewable Generation 

with Conditions in the United States When Demand Response Programs Were Originally 

Introduced.................................................................................................................................... 20

4.2 The Potential for Demand Response to Facilitate Renewable Integration and Emissions 

Reductions................................................................................................................................... 21

4.3 Framework for Selecting Demand Response Programs.............................................................. 23

5 Conclusions ........................................................................................................................................ 27

References................................................................................................................................................. 29
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List of Figures

Figure 1. Global demand response market forecast (Feldman and Lockhart 2014) ..................................... 2

Figure 2. Comparison of different dynamic pricing schemes..................................................................... 10

Figure 3. CO2 emissions associated with adding a 300-MW energy-only storage plant to power system 

models with a total conventional capacity of 15.8 GW and a varying amount of wind and 

solar capacity (Denholm et al. 2013) ..................................................................................... 23

Figure 4. Template of the demand response planning process ................................................................... 26

List of Tables

Table 1. Comparison of Demand Response Participation in Different Capacity Markets............................ 5

Table 2. Description of Retail Demand Response Programs Based on Time-Varying Tariffs..................... 6

Table 3. Case Studies of Retail Dynamic Pricing Programs......................................................................... 8

Table 4. Case Studies of Retail Load Control Programs .............................................................................. 9

Table 3. Characteristics of Demand Response Programs ........................................................................... 13

Table 4. Overview of Demand Response Pilot Projects in China............................................................... 17

Table 5. Demand Response Pilot Programs in India................................................................................... 19

Table 6. Demand Response Objectives and Corresponding Demand Response (DR) Options.................. 24
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1 Introduction 

Demand response, the adjustment of electrical load operations to provide grid services, has been 

used for decades to make utility operations and planning more efficient and less capital intensive.

However, the role of demand response has changed with experience, technological 

improvements, and power market restructuring. For example, in recent years demand response 

resources have begun to actively bid energy, capacity, and ancillary services into wholesale 

power markets in the United States (Murtaugh et al. 2017; ERCOT 2017; ISO-NE IMM 2017; 

Potomac Economics 2017; Patton, LeeVanSchaick, and Chen 2017; McAnany 2017; Lee et al. 

2016). In retail markets, dynamic pricing programs have been piloted and in some cases made 

permanent (EPRI 2012). Both of these methods for eliciting demand response are aided by recent 

and still emerging advances in communications and control technology (gtmresearch 2017). 

Historically, demand response programs have been initiated during high energy growth periods 

to help manage system peaks (Hurley, Peterson, and Whited 2013). This and technological 

changes that have enabled a variety of automatic, dependable demand response control types and 

their integration into power system operations imply that demand response resources could be a 

low-cost, flexible, and efficient source of electricity services in emerging economies. 

Furthermore, relative to new generation and transmission resources, demand response can be 

deployed quickly. For example, after accepting responses to its request for proposals in January 

2004, Independent System Operator (ISO) New England (NE) was able to award a demand 

response contract for Southwestern Connecticut that was operational by June 2004 (Electric 

Light & Power 2008; ISO-NE 2004). The flexibility of demand response can act as a 

complement to wind and solar photovoltaic (PV) generation (Cappers et al. 2011), which is 

rapidly being adopted around the world in response to carbon reduction goals and falling costs

(REN21 2017).

In this report, we explore the potential for demand response to mitigate costs associated with 

rapid growth in load, and with renewable energy integration. First, we discuss the variety of roles 

demand response has fulfilled in wholesale and retail markets both within the United States and 

internationally. Second, we describe a set of pilot and fully-implemented demand response 

programs and in China, India, and South Africa—markets that are all experiencing rapid load 

growth and expanded use of renewable energy generation. The report concludes with a 

discussion of key considerations for the design and implementation of new demand response 

programs in wholesale and retail markets. 
2

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2 Roles for Demand Response

Historically, demand response was largely used to provide peak load management in the US—

specifically, load reduction during contingency events. However, with electricity market 

restructuring, regulatory reform, and the accelerating adoption of advanced metering and other 

enabling technologies, demand response in several United States jurisdictions has recently 

evolved to provide additional system services; in some regions demand response provides a full 

suite of energy, capacity, and ancillary services. These new services are helping to address 

operational challenges associated with aging transmission and distribution infrastructure, 

retirement of older thermal generating units, and increasing penetrations of variable generation. 

In the future, such services could be provided in other jurisdictions or expanded to provide 

additional flexibility to aid in the integration of additional variable resources and new 

technologies such as electric vehicles (Feldman and Lockhart 2014). 

The rest of the world has not relied as much on demand response resources as has the United 

States, even for peak-load management. Given the cost-effectiveness of this traditional role, as 

well as the maturation of a wider variety of demand response control and service types that can 

respond to the system needs outlined above, there are new market opportunities for demand 

response. Feldman and Lockhart (2014) estimate that the global market for demand responseenabling technologies could grow to $9.7 billion by 2023 (Figure 1). The Asia-Pacific region in 

particular is expected to see rapid growth as its electricity markets allow load participation in the 

provision of energy and ancillary services.

Figure 1. Global demand response market forecast (Feldman and Lockhart 2014)

Depending on context, demand response programs may be implemented in wholesale or retail 

markets. There are also leading jurisdictions conscientiously working to construct nextgeneration power systems with an emphasis on variable generation, load flexibility, and other 

supporting technologies. Utilities and system operators seeking to integrate demand-side 

resources for the first time or in new ways can benefit from learning about the wide variety of 

approaches and experiences that have come before.

0

50,000

100,000

150,000

200,000

250,000

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023

North America

Europe

Asia Pacific

Latin America

Middle East and

Africa

Projected Demand Response 

Capacity (MW)
3

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2.1 Demand Response in Wholesale Electricity Markets 

Demand response has been implemented in wholesale electricity markets through regulations 

and market rules that allow demand response resources to participate side-by-side with supplyside resources in energy, ancillary service, and capacity markets. In these markets, demand 

response has been implemented primarily by allowing load aggregators, that is, traditional loadserving entities or third-party companies focused on providing demand response solutions, to 

submit load modification or other grid service offers directly into the wholesale market on behalf 

of their customers. Large customers can also offer load resources directly into markets in some 

regions, but this practice is less common.

Demand Response in Energy Markets

There are two potential routes for commercial and industrial (C&I) facilities to reconcile their 

marginal value of electricity consumption with market conditions. Very large commercial and 

industrial (C&I) facilities may be able to directly bid their load into the wholesale electricity 

markets; some utilities and competitive retail providers offer real-time pricing (RTP) via

contracts that directly pass through the wholesale electricity price to C&I customers. The latter 

option, when available, is often the only option for smaller C&I customers, because of either 

explicit market rules on participant size or implicit barriers related to market participant costs. 

Australia’s National Electricity Market is an example of a market in which no direct bidding of 

large C&I load is allowed, but utilities do offer RTP contracts (AEMC 2009; Crossley 2011). 

Currently, the rules for demand bidding in energy markets vary markedly from market to market. 

In some energy markets, demand resource bidding is restricted to the day-ahead market (NYISO 

2017). In others, demand resources are allowed to participate in both day-ahead and intraday 

markets (PJM 2017). ISO New England is going through a transitional phase where demand 

response resources are allowed to place load reduction bids in response to day-ahead locational 

marginal prices and are then settled at those prices for their bids and real-time prices for any 

deviations (ISO-NE IMM 2017). In this case, demand response has no effect on the day-ahead 

prices but does impact the real-time load forecast.

The recent experiences of the California ISO and PJM Interconnection demonstrate how demand 

bidding in energy markets is still being worked out in practice as a complement to other demand 

response services rather than as a primary driver. In 2016, the California ISO appeared to 

experience a shift from most demand response bidding being in the day-ahead market to most 

being in the real-time market offered at or near the market cap of $1,000 per megawatt-hour 

(MWh) (Murtaugh et al. 2017). In PJM Interconnection, demand response participating in the 

PJM energy markets (so-called “economic” demand response) comprised less than 5% of all 

demand response revenues for each of the fiscal years 2014-2016.

1 Thus demand response based 

solely on the economics of buying energy (as opposed to energy bundled with some form of 

capacity payment) tends to be smaller than what demand response provides in terms of ancillary 

services and capacity (McAnany 2016, 2017).

 1 In 2015, total economic demand response revenues were about $8 million out of a total demand response market 

size of $800 million. In 2016, they were about $3 million out of total market of around $650 million. Economic 

demand response made up a larger proportion of 2014 revenue, but that still amounted to less than 5% of the total.
4

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Demand Response in Ancillary Service Markets

Several wholesale electricity markets in North America, Europe, Singapore, and New Zealand 

allow demand response resources to participate in the provision of ancillary services. Such 

markets allow demand response resources to directly compete with supply-side resources to 

provide contingency reserves and frequency regulation services. Demand response resources

provide a substantial portion of reserves in several of these markets; for instance, demand 

response consistently comprises over 50% of the responsive (i.e., spinning) reserves in the 

Electric Reliability Council of Texas (ERCOT) market—the maximum allowed by market rules

(Potomac Economics 2016). 

Compensation for demand response providing contingency reserves varies from market to 

market. In some markets, demand response resources receive capacity payments based on their 

participation in contingency reserve markets. In others, demand response resources are required 

to participate in contingency reserve/emergency load shedding markets based on their status as a 

capacity resource that has cleared a capacity market. The Loads as a Resource (LaaR) program in 

ERCOT (ERCOT 2006) and the U.K. National Grid system operator’s Short Term Operating 

Reserve program (Curtis 2015) are examples of the former; PJM’s capacity performance 

products are examples of the latter.

Although less common than the inclusion of demand response in contingency resources, a 

number of system operators (e.g., ERCOT, the Alberta Electric System Operator, and systems in 

the U.K. and Australia) have programs for under-frequency load shedding, which automatically 

disconnects select loads when a minimum frequency threshold is violated (ERCOT 2017; Ritter 

2011; Heffner et al. 2007). This is sometimes a standalone ancillary service, similar to generator

governor controls, that operates through fast automatic control rather than market dispatch. In the 

case of ERCOT, under-frequency load shedding is the mechanism through which many load 

resources participate in the ancillary service markets (ERCOT 2006, 2017).

In general, demand-side resources are more likely to provide something akin to spinning 

contingency reserves rather than regulation reserves. In ERCOT in August of 2017, for example, 

of the 28,970 MW-h of ancillary services provided by demand response, 28,219 MW-h were for 

responsive reserves (the spinning reserve product mentioned above) and only 751 MW-h were 

for regulation reserves.2 Similarly, in PJM in 2016 demand response provided 67,801 MW-h of 

regulation reserve, compared to 827,091 MW-h of synchronous (contingency) reserve. However, 

a wide variety of equipment types are registered to provide regulation services in PJM—water

heaters, batteries, HVAC, manufacturing, and refrigeration—which demonstrates an increasing 

level of sophistication in the requisite communication and control technology (McAnany 2017).

 2 See “Monthly ERCOT Demand Response from Load Resources” 

(http://mis.ercot.com/misapp/GetReports.do?reportTypeId=13242&reportTitle=Monthly%20ERCOT%20Demand%

20Response%20from%20Load%20Resources&showHTMLView=&mimicKey), which is available from “Load 

Resource Participation in the ERCOT Markets,” ERCOT, http://www.ercot.com/services/programs/load/laar. 

The Responsive Reserve Service is listed as RRS, regulation appears as RegUp and RegDown.
5

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Demand Response in Capacity Markets

The main role of demand response in wholesale markets is as a capacity resource (Lee et 

al. 2016). In formal capacity markets, demand response resources receive capacity payments for 

being available for curtailment on request. Capacity auctions that allow demand response 

participation are regularly held by in the United States by PJM, ISO-NE, New York Independent 

System Operator (NYISO), and the Midcontinent Independent System Operator, Inc. (MISO).

3

Capacity auctions were held for the second time in the U.K. in 2015 (National Grid 2015). Table 

1 summarizes various capacity markets in the United States and the U.K. along with the results 

for demand response resources in recent auctions (Cappers, Goldman, and Kathan 2010; SEDC 

2015; Indo-German Energy Programme 2015). 

Table 1. Comparison of Demand Response Participation in Different Capacity Markets

System 

Operator

Capacity Market 

Name and Type

Recent Capacity 

Auction Results

Demand 

Response 

Cleared

Remuneration

PJM (U.S.) Reliability Pricing 

Model (RPM); 

Capacity obligation

167,305.9 MW for 

the 2019–2020a

delivery period

10,348 MW Capacity payment 

based on performance 

(fast responsiveness), 

with severe penalties 

MISO (U.S.) Resource Adequacy

Requirement (RAR); 

Capacity obligation

135,483 MW for 

the delivery period 

2016–2017a

5,819 MW Capacity payment 

based on auction 

clearing price

ISO-NE 

(U.S.)

Forward Capacity 

Market (FCM); 

Capacity auction

35,567 MW

for the 2019–2020**

delivery period

2,746 MW Monthly capacity 

payment based on 

auction clearing price

National 

Grid (U.K.)

Electricity Market 

Reform (EMR);

Capacity auction

46,350 MW for the 

2019–2020 delivery 

period

450 MW

(8 MW proven 

demand 

response)

Monthly capacity 

payment based on 

auction clearing price

a The delivery period is from June 1–May 31 of the specified years.

Several locations (e.g., Western Australia, France, Ireland, Northern Ireland, Italy, Spain, 

Greece, and South Korea) have capacity markets in place that do not seem to have explicit 

demand-side provisions (Spees and Newell 2014; RTE France 2014; Indo-German Energy 

Programme 2015; SEDC 2015). Capacity market mechanisms are emerging in the Nordic 

countries,

4 and transmission system operators (TSOs) in different European countries are looking 

at different procurement mechanisms for demand-side participation in the long term. Taking the 

grid-interconnected Nordic countries as an example, Finland and Sweden manage capacity with 

strategic reserve or peak load reserve strategies, which are similar to contingency reserves and

involve holding a certain amount of capacity that is activated only when the energy market, 

Elspot, demonstrates a capacity shortage. Denmark has recently initiated a similar strategy.

 3 In light of recent rules implemented at PJM that require all cleared capacity to be part of a new capacity 

performance product available year-round, participation of demand response resources could be affected in future 

auctions. Demand response aggregators can combine different seasonal demand response resources to create a yearround capacity resource; it is unclear how much demand response capacity will be unmatchable in this new scheme. 4 Excluding Iceland, which has a unique, islanded power system consisting of hydro and geothermal generators.
6

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Norway manages capacity needs through a tertiary balancing market that provides capacity 

through seasonal and weekly auctions. All these mechanisms allow demand-side participation,

but increasing the share of demand response may be difficult because of performance 

requirements and low electricity prices. For example, Sweden initially had a goal to replace the 

strategic reserve with an all demand-side market mechanism by 2020, but has since dropped the 

goal of phasing out generators from providing that service (THEMA Consulting Group 2015).

2.2 Retail Demand Response Programs 

Many countries that have not undergone electric market restructuring and do not operate 

wholesale electricity markets nevertheless have utilities that offer demand response opportunities

to residential, commercial, and industrial consumers at the retail level, mostly through timebased tariffs, dynamic pricing programs, interruptible load tariffs, and direct load control

programs.

Time-based tariffs and dynamic pricing programs incent customers to shift electricity use from 

on-peak to off-peak times by exposing them to time-varying price signals. Dynamic pricing 

programs especially come in a variety of forms. Altogether, the major time-varying price 

schemes are time-of-use (ToU) tariffs, real-time pricing (RTP), critical peak pricing (CPP), 

critical peak rebates (CPR), and variable peak pricing (VPP) (Table 2). 

Table 2. Description of Retail Demand Response Programs Based on Time-Varying Tariffs

Pricing Mechanism Description

Time-of-use pricing As one of the earliest pricing techniques used for decades, ToU pricing 

consists of a stepped rate structure that includes a peak rate, an off-peak rate 

(and perhaps a shoulder-peak rate) for predetermined blocks of time. The 

definition of peak times and the associated rates often varies seasonally.

Real-time pricing Real-time pricing is a dynamic pricing scheme that reflects the variation of 

wholesale electricity prices. Customers enrolled in RTP schemes are exposed 

to the actual cost of energy for each hour of the day as determined in a

wholesale day-ahead market, in a utility’s day-ahead unit commitment 

algorithms, or (less commonly) in a wholesale real-time market. 

Critical peak pricing Critical peak pricing is a tariff that adds a time-dependent rate to the normal 

rate (or ToU rate) based on day-ahead demand forecasts so that the energy 

prices are sufficiently high during actual peak demand hours to induce reduced 

consumption. The number of days in which CPP rates are applied is usually 

limited, and the activation of these rates can range from one day ahead to a 

few minutes prior to the peak load event.

Critical peak rebate With a critical peak rebate program, the peak demand periods or emergency 

events are anticipated by the utility are relayed to their customers, and the 

provision of load curtailments by customers during those periods are 

remunerated pro rata at a predefined rate offered by the utility.

Variable peak pricing Variable peak pricing is a combination of ToU and RTP where the on-peak and 

off-peak periods are defined in advance (based on anticipated peak demand 

periods), but the on-peak prices vary based wholesale market prices or 

system lambdas.

These mechanisms differ with regards to the amount of and frequency with which information 

must be exchanged between utilities and customers. For a long time, RTP schemes have been 
7

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held up as the ideal for achieving alignment between customer incentives and system needs so as 

to reduce overall electricity costs for non-participants as well as participants (Borenstein 2005; 

FERC et al. 2009). However, implementing an RTP requires constant communication between 

system operators and customers. In contrast, ToU tariffs are calculated and communicated well 

ahead of time; and CPP, CPR, and VPP only require communication during peak load times. 

These mechanisms therefore carry much lower informational burdens that can be satisfied with 

messages conveyed to customers via mail, e-mail, or text message and interval metering of 

electricity use.

Table 3 summarizes several recent implementations of time-varying pricing programs by 

utilities. We choose to highlight examples that engaged fairly large proportions of customers 

within the affected utility service territories. Italy instituted ToU pricing for all customers after 

it rolled out smart meters in 2011, but it experienced relatively modest customer load shifting 

because of a relatively modest difference in peak and off-peak pricing. This was exacerbated 

by substantial growth in PV during early implementation (nearly 17 gigawatts [GW] of PV were 

installed by 2013), which further drove down the price difference between on-peak and off-peak 

prices. The Baltimore Gas & Electric critical peak rebate program in Maryland resulted in 

significant shifts in consumer demand (on the order of 30% reduction in load) during emergency 

events. A study of the Commonwealth Edison (ComEd) program showed maximum hourly 

prices were lower in the market after ComEd implemented their real-time pricing pilot. These 

examples demonstrate that there are a range of options for implementing programs and success 

can depend on implementation and design details. 
8

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Table 3. Case Studies of Retail Dynamic Pricing Programs

Case Study Program Background Consumer Response and Impact

ToU Rates: Italy After completing the rollout of smart 

meters to all customers in 2011, Italy 

introduced a mandatory ToU tariff for 

nearly 20 million residential customers 

with on-peak prices from 8:00 a.m. to 

7:00 p.m. on weekdays, and lower offpeak prices at other times. The 

difference between on- and off-peak 

prices was modest. The rapid increase in 

adoption of PV at this time contributed to 

the lack of substantial price difference 

(Maggiore et al. 2013)

The energy cost savings achieved by all 

residential customers from July 2010 to 

June 2012 was estimated at 6.45 million 

euros (~1 Euro cents(c€) per customermonth). A monthly average load shifting 

of less than 1% was observed from peak 

to off-peak hours. No significant 

difference between average load shifting 

occurred between on-peak and off-peak 

hours (Maggiore et al. 2013). Another 

study of the program, focused on the 

Trentino province, found that the 

introduction of the two-part ToU tariffs 

(tariff bioraria) exacerbated the peak 

load and increased energy consumption 

by nearly 14% (Torriti 2012).

RTP: 

Commonwealth 

Edison (ComEd), 

Illinois 

After a three-year pilot program, in 2007, 

ComEd offered a voluntary RTP program 

to all residential customers. The hourly 

pricing program allows customers to 

have access to hourly energy prices and 

the ability to adjust usage accordingly. 

Varying factors such as weather, market 

conditions, and usage habits determine 

the customer’s monthly electricity bill

(Commonwealth Edison Company 

2017).

More than 10,000 customers enrolled in 

the full program. Customers saved $9–

$12/month on average (about 15%–20% 

of their monthly bill) by shifting energy 

use to low price hours. During the pilot 

program (2003–2006), the maximum 

hourly price was 12 cents per kilowatthour (kWh), compared to 38 cents/kWh 

during the Illinois energy crisis 2000–

2002 (Star, Isaacson, and Kotewa 2014).

Critical Peak 

Rebate Program:

Baltimore Gas & 

Electric (BG&E), 

Maryland

BG&E’s Smart Energy Rewards CPR 

program is designed to encourage 

residential load shifting during peak 

hours on selected Energy Savings days. 

The program informs customers of a 

peak load event the day before it occurs 

and offers a rebate for reducing demand. 

BG&E submits load curtailment offers to 

the PJM wholesale market and the 

incentives are shared with participants. 

The average rebate offered at program 

launch in 2013 was $1.25/kWh, or nearly 

10 times the average residential 

electricity price. Residential customers 

with smart meters are defaulted into the 

program (Harbaugh 2015). 

In its pilot from 2008 to 2011, the CPR 

program saw an average load reduction 

between 20%–30% during emergency 

events, and customers earned between 

$6–$10 per day during these events

(Faruqui and Sergici 2011; EPRI 2012; 

Harbaugh 2015). In 2013, the program 

had 300,000 participants who 

contributed to an average load reduction 

of 15% during the four emergency 

events in that year. BG&E expanded its 

program to all its residential customers,

and as of 2015, nearly one million 

residential customers were enrolled and 

many saved $5–$8 on each Energy 

Savings day (Harbaugh 2015).
9

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Direct load control and interruptible load tariffs are also important ways utilities obtain demand 

response service through retail markets. Table 4 summarizes two examples of direct load control 

programs recently implemented by utilities.5 First, Florida Power & Light (FP&L) offers its 

residential, commercial, and industrial customers incentives for granting FP&L direct control

over air conditioners, water heaters, space heaters, pool pumps, and miscellaneous C&I 

equipment deemed suitable and acceptable during a site visit.

6 Under this program and others 

like it, participants get a yearly payment in return for the utility being allowed to exercise control

over enrolled equipment during times of peak load, with contractual limitations on event 

frequency and duration. Second, Table 4 describes a pilot project in which BMW and Pacific 

Gas & Electric teamed to encourage groups of electric vehicle (EV) chargers to shift their 

demand in response to utility needs. Because of the success of the first phase of the EV 

program, a second phase is currently being planned. 

Table 4. Case Studies of Retail Load Control Programs

Program Background Consumer Response and Impact

Full-Scale Direct Load Control Program, Florida Power & Light (FP&L), Florida

FP&L maintains large direct load control programs for all 

customers. It provides incentives for residential customers to 

allow the utility to temporarily shut off or modify the 

operations of electric water heaters, air conditioners, space 

heaters, and pool pumps during system emergencies. 

Commercial customers can enroll air conditioners in a similar

program. The utility will also install direct load controllers on 

equipment types determined during site visits as part of their 

C&I demand reduction program, which is incented on a 

dollars-per-kW basis (FPSC 2015).

FP&L estimates taken altogether, their 

energy efficiency and demand 

response programs have superseded 

about 6,000 MW of capacity, of which 

about a third is attributable to demand 

response (FPL 2016).

Electric Vehicle Direct Load Control Program, Pacific Gas and Electric (PG&E), California

The ChargeForward pilot program established by BMW and

PG&E explores EV charging demand response. Phase I of 

the pilot ran from July 2015 to December 2016, and Phase II 

will run from 2018 to 2020. During Phase I, PG&E sent 

signals to BMW requesting a load reduction of up to 100 kW. 

In response, BMW selected participating vehicles to delay 

charging. One hundred BMW i3 owners participated and 

were paid $1,000 up front plus an additional payment up to 

$540 based on the level of participation. Customers were 

notified by BMW of the charging delay, and the customer 

could participate or opt-out. BMW is also using a back-up 

storage supply to respond to PG&E demand response 

requests (BMW Group and Pacific Gas and Electric 

Company 2017). 

Phase I of the ChargeForward program 

resulted in nearly 200 demand 

response events taking place over the 

18-month period, 94% of which 

reached the grid reduction target of 100 

kW. From July 2015 to August 2016,

more than 19,000 kWh of load was 

shifted from at-home EV charging to 

compensate for the grid demand. And, 

92% of participants indicated they were 

satisfied with the program (Pacific Gas 

and Electric Company 2016). 

 5 A running list of such programs in the United States is maintained at “Residential Demand Response Programs,” 

ClearlyEnergy, https://www.clearlyenergy.com/residential-demand-response-programs, last updated October 

10, 2016. 6 In general, C&I traditional interruptible load tariffs are evolving to resemble the newer direct load control 

programs with regards to being implemented via reliable, automated controls.
10

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Overall, these tariff and incentive programs vary in the extent to which they can align customer 

prices with system costs and in the amount of complexity and risk experienced by individual 

customers. Figure 2 illustrates these trade-offs. Although RTP appears to have the most potential 

to align customer and system incentives, at a minimum its implementation requires advanced 

metering infrastructure and it has the potential to expose customers to a high degree of price risk. 

At the other extreme, ToU tariffs are much simpler to implement and less risky to customers, but 

only provide a very blunt (season and time-of-day) and static (determined a year or more ahead 

of time) price signal that may not be sufficient to alleviate the worst peak load conditions. 

Interruptible tariffs, direct load control (DLC) programs, and programs aimed directly at peak 

load conditions (i.e., CPP/CPR and VPP) represent an interesting middle ground.

Figure 2. Comparison of different dynamic pricing schemes

2.3 Distributed Resource Aggregations Providing Demand Response

In the United States, the states of California and New York have recently developed initiatives to 

promote demand response programs that include distributed energy resource participation in 

wholesale markets to better facilitate renewable energy resource integration and to manage 

power flows in the distribution system (CPUC 2015; NYPSC 2016). These efforts are designed 

to support distributed energy resource deployment and bring more demand-side aggregations 

into the wholesale markets for effective side-by-side participation with supply resources. New 

York has an additional goal of facilitating the engagement of customers in energy transactions. 

California’s efforts are more directly focused on supporting their greenhouse gas reduction goals, 

which have already had significant impacts on the state’s generation mix (Lyons and Kassakhlan 

2016).

California’s Demand Response Auction Mechanism (DRAM) (CPUC 2015), which began 

accepting bids in late 2015, is one such initiative where load aggregations greater than 100 kW 

are procured by the three large investor-owned utilities in the state (Pacific Gas & Electric, San 

Diego Gas & Electric, and Southern California Edison) to provide increased flexibility for the 

system. California’s DRAM is intended to expand the demand response markets by allowing 

the aggregation of distributed energy resources that can provide load response (e.g., smart 

thermostats, EV charging, behind-the-meter batteries, and commercial and industrial responsive 

loads). Under the program, load aggregators can make offers (of at least 100 kW) into the 

California ISO day-ahead market through the Proxy Demand Resource Program. The revenue for 

load curtailments in the DRAM mechanism will be entirely shared with participating customers. 

The initial DRAM pilot auction had a minimum target of 22 MW (10 MW each for Pacific Gas 
11

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& Electric and Southern California Edison, and 2 MW for San Diego Gas & Electric), and at 

least 20% of the capacity must come from the residential sector. In its opening auction in 2015, 

the three investor-owned utilities procured more than 40 MW of total capacity, exceeding their 

minimum targets. A second auction in 2016 yielded another 82 MW of capacity (St. John 2016). 

The second round of auction participants will be able to participate in both day-ahead and realtime energy markets in 100-kW increments, as well as bid 500 kW increments into the real-time 

Reliability Demand Response Resource program (St. John 2016). Several load aggregator 

startups and new markets for smart thermostats and other smart devices also emerged as a result 

of this initiative.

New York has been implementing electric sector reforms under its Reforming the Energy Vision 

(REV) initiative, which is designed to empower customers and facilitate distributed energy 

resources and the integration of clean energy resources into the electric system. The state has 

a renewable portfolio standard goal of 50% renewable penetration by 2030. 

In line with the REV objectives, the New York Independent System Operator (NYISO) initiated 

a road-mapping process and a pilot program to allow aggregations of distributed energy 

resources (DERs) to participate in the wholesale electricity market. The resulting roadmap called 

for the aggregation rules to be technology agnostic, so that a DER aggregation could consist 

of different technologies (e.g., storage, distributed generation, and demand reduction) that are 

collectively able to follow dispatch instructions. The aggregations will be at least 100 kW in size 

and must be comprised of resources that are connected at the same transmission node. The 

aggregations can be coordinated by a customer, a distribution system platform provider, or a 

third-party. DERs could potentially have dual participation in retail and wholesale markets

(Pigeon 2017). The NYISO is in the process of implementing a pilot program that is scheduled 

for 2018 to demonstrate the dispatchability of the DERs and ensure they can meet performance 

standards (Yung 2017). 

2.4 Summary

There are many types of demand response programs distinguished by the markets in which they 

operate, whether participation is obtained through direct incentives or time-dependent pricing, 

and their sectoral or end-use specificity. The effectiveness of most demand response programs 

also depends on the degree to which response is automated. Demand response programs are 

starting to grapple with their compatibility with increasing amounts of wind and solar generation; 

California and New York are explicitly working to ensure demand response supports their 

renewable energy goals, and Italy has had the experience of a ToU structure quickly becoming 

out of date because of a rapid increase in PV capacity.

In Table 3, we qualitatively summarize the characteristics of different demand response program 

types with regard to:

• The quality of the response they can provide in terms of certainty, magnitude, and speed

• How robust they are to changing conditions (e.g,, increases in wind and solar penetration)

• How difficult they are to establish in terms of infrastructure requirements, time to establish, 

and cost to establish
12

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• How acceptable they are likely to be to customers based on intrusiveness and customer price 

risk

This qualitative assessment was assembled based on the examples described above, as well as 

other relevant reviews, including EPRI (2012), Cappers et al. (2011), Faruqui, Sergici, and Sharif 

(2010), Faruqui and Sergici (2010), and Barbose, Goldman, and Neenan (2004).
13

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Table 3. Characteristics of Demand Response Programs
14

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While old-style demand response programs (i.e., interruptible tariffs not assisted by direct load 

control implementation, and ToU tariffs) are generally easy and inexpensive to implement, they 

do not provide responses that are as large, certain, or fast as those assisted by automatic controls 

and/or enforced by market settlement processes. Time-of-use tariffs especially, and any contract 

terms that limit events to typical peak (net) load times, are not robust to changes in solar and 

wind penetration, as the time of peak net load may shift (in the case of more solar) or become 

more volatile (in the case of more wind). Time-of-use rates can be designed with solar power 

specifically in mind, as in California’s new matinee energy pricing pilots, which will offer lower 

prices to commercial, industrial, and agricultural customers midday, correlated with times of 

high production from PV plants (CPUC 2016); however, the variability of wind in particular may 

preclude the reliable design of a robust ToU rate for high wind penetration systems.

These deficiencies generally point to following the trend for demand response programs that are 

more formal and automated. With an aggregation or direct load control contract, both the utility 

or aggregator and the customer are clear about what service is being purchased and at what price, 

especially as the program ages and experience accumulates. A much-discussed alternative to that 

paradigm is the real-time pricing tariff, but there are two potential issues to be considered. First, 

effective response to a real-time pricing signal requires automated controls. Without them, 

response requires continually monitoring the price of electricity, which is either (1) not worth the 

time if the utility bill is a small portion of expenses or (2) a stressful and risky burden if energy 

costs are a large part of the household or company budget. Second, if automated response to realtime prices becomes widespread enough, it becomes imperative for that response to be accounted 

for in the price formation process. If this is not done, system balancing can become unstable

(Roscoe and Ault 2010; Roozbehani, Dahleh, and Mitter 2010). 

On the commercial and industrial side, automated control and metrology is generally required 

for modern demand response programs. These C&I demand response programs vary in the 

services provided, from emergency or contingency response, to economic dispatch, to regulation 

reserve. The utilities or system operators generally propose, launch, and revise the programs over 

time, depending on system needs, load characteristics, enrollment numbers, and performance 

levels.

7 These programs certainly do not take as long to go from conception to maturity as 

building new transmission lines does, but the process does typically take several years (Zarnikau 

2010). Challenges remain regarding the integration of demand response into markets and utility 

operations. For example, energy-shifting demand response is likely mis-incentivized in current 

systems because the make-up energy needed for energy shifting is not factored into day-ahead 

unit commitment processes.

Automated demand response programs in the residential sector typically take the form of 

direct load control of appliances such as air conditioners and water heaters. More recently, 

aggregations of programmable controlled thermostats have been added to the mix, and control 

of EV charging has been piloted and could be scaled up over the coming decades. All these 

controllers are operated similarly to modern C&I programs, with either the utility or an 

aggregator issuing control signals (on/off, setpoint changes, or price signals that can then be 

factored into user preferences). In the case of third-party aggregators, they may work directly 

with a vertically integrated utility or bid their aggregated capacity into a wholesale market. These 

 7 “Demand Response,” PJM, http://www.pjm.com/markets-and-operations/demand-response.aspx
15

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programs have the advantage of being able to respond appropriately to actual power system 

conditions. On the potentially negative side, they do come with significant hardware and 

communications requirements, and with the potential to feel intrusive to home owners. 

Critical peak pricing programs are interesting as a potential compromise between old-style and 

newer-style demand response programs. If customers have interval meters, no additional 

infrastructure is needed to implement a critical peak pricing program, as the utility can declare a 

critical peak pricing event a day ahead of time through basic e-mail or text messages. Then, if 

such events are infrequent enough and the incentives large enough, customers may be willing to 

manually adjust the amount of electricity they use during the declared times. However, a caution 

similar to that regarding including the response to real-time prices in price formation applies—

if enough people participate in a critical peak pricing program, the result may be to simply shift 

the peak to right before or right after the event window. 
16

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3 Demand Response in High-Growth Power Systems

with Increasing Shares of Renewable Generation: 

Current Trajectories and Aspirations

Demand response could play a larger role in countries and regions that are experiencing rapid 

load growth. In fact, new demand response programs are emerging in Asia, Africa, and other 

regions for reasons such as growing energy demand, grid congestion, aging infrastructure, and an 

interest in deferring generation and transmission investments. Goals to increase the use of clean 

energy resources could drive further interest in the use of demand response in these regions 

going forward. The following case studies explore efforts to implement demand response and the 

factors driving emerging programs in China, India, and South Africa.

3.1 China 

As a large industrial nation with a forecasted peak load of about one terawatt by 2020 (Lin, Liu, 

and Karl 2016), China is a potentially large market for demand response. Also, the country has 

experienced chronic shortages in power availability because of rapid economic growth, a

situation that has ebbed in recent years. To manage this mismatch in electricity demand and 

supply, large industrial customers were instructed to undertake administratively rationed, 

uncompensated load reductions to reduce peak demand. For example, in 1998, the State Grid 

Corporation of China established the Demand Response Management Center in the Jiangsu 

Province, which promoted demand-side management through both energy efficiency and 

demand response activities. Demand response measures included ToU rate structures, 

interruptible load programs, and the deployment of energy storage devices (particularly 

thermal storage for power plants) (Dong, Xue, and Li 2016).

In April 2002, Jiangsu became the first Chinese province to issue its own demand-side 

management regulations and activate a pilot demand response project that consisted of ToU rate 

structures, interruptible tariffs, voluntary load shifting, and deployment of storage devices to 

facilitate load curtailment. With the aim of deferring investments for transmission expansion and 

the construction of new power plants, the Natural Resources Defense Council conducted 

demand-side management pilot programs from 2013 to 2015 in four cities in China: Beijing, 

Tangshan, Foshan, and Suzhou. The main features of these pilot programs are summarized in 

Table 4 (Stern 2015). Also, Honeywell partnered with the Tianjin Economic-Technological 

Development Area in 2012 to implement China’s first automated demand response (ADR)

project (Navigant 2013; Samad, Koch, and Stluka 2016).
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Table 4. Overview of Demand Response Pilot Projects in China

Suzhou, Jiangsu Beijing Foshan, 

Guangdong

Tangshan, Hebei

Programs 

Offered

Interruptible load 

programs (realtime and contract 

demand 

response)

a

Interruptible load 

and peak load 

pricingb

Cooling storage

pricingc

Interruptible load 

programsd

Load 

Curtailment 

Target

(2013–2015)

1,000 MW 800 MW 450 MW 400 MW

Targeted 

Consumers

Industries and

municipal facilities

Industrial, 

commercial, and 

municipal facilities

Industries and

municipal facilities Industries

Types of 

Projects

Nearly 400

facilities connected 

to a DSM-service 

platform for peak 

load management

131 projects which 

targeted 45 

enterprises for 

dynamic pricing

80 energy

efficiency projects

for industries and 

30 projects for 

peak demand 

shaving

35 energy

efficiency projects 

for power plants

Actual 

Response in 

2015e

2716 customers, 

total 2037 MW

across Jiangsu 

Province

74 customers, 

71 MW

129 customers, 

176 MW NA

a Suzhou City Electric Demand Side Management City Pilot Leading Group Office (2015)

b Beijing Finance Bureau, Beijing Development and Reform Commission (2013)

c Foshan Economic Information Committee (2015)

d Tangshan Finance Bureau (2013)

e Songsong (2017)

Because the current challenges in China’s power system concern over-supply and environmental 

issues rather than the previous challenges of under-supply, there have been shifts in policy since 

2015, starting with State Council Document No. 9 (Pollitt, Yang, and Chen 2017; Dupuy 2016). 

These shifts include an increased focus on integrated planning and market-based dispatch 

mechanisms. In accordance with this trend, the government is also encouraging demand-side 

management pilot regions to employ voluntary price-based mechanisms in place of the 

conventional quantity-rationing approach (IEA 2017; Liu et al. 2015; Zhao et al. 2015). 
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3.2 India

Several factors have created potential demand response opportunities in India, including its rapid 

growth in energy consumption, non-remunerated supply disruptions, and its goals to shift toward 

cleaner energy sources. Demand response could also help Indian utilities better cover their costs 

if residential and agricultural consumers shift loads to more favorable times. In recent years, 

India has faced challenges with supply shortages, including a nearly 3.3% peak load deficit and 

an energy shortage of 2.1% in fiscal year 2015 (Central Electricity Authority 2016). In addition 

to rapid growth, changes in its generation mix may also encourage adoption of demand response. 

India has a capacity target of 175 GW renewable energy by 2022 that expands to 40% non-fossil 

energy production by 2030. 

Currently, time-varying pricing is not widely used in India; time-of-day tariffs are offered to 

large commercial and industrial customers in some states. Otherwise, electricity is typically

supplied at a predetermined tiered tariff structure, with subsidies for agricultural and residential 

customer classes that often result in financial losses for the distribution utilities (Badiani, Jessoe, 

and Plant 2012; Komives et al. 2005). Demand response may be able to mitigate these financial 

pressures by reducing the per-unit cost of electricity, especially if the demand response is 

incented from the subsidized sectors. Remuneration for loss of service is also more consistent 

with the idea of universal electricity access, which is now an explicit goal of the Indian 

government (Singh 2017).

Since 2012, India has conducted a few commercial and industrial demand response pilot 

programs. Table 5 presents the features of two pilot studies conducted in Mumbai and New 

Delhi. The project in New Delhi was India’s first OpenADR demonstration. That pilot study 

involved 144 customers with a total coincident peak load of about 25 MW responding to a total 

of 17 events. By building type, the 75th percentile of responses ranged from 3% in educational 

buildings to 62% in pumping facilities, with an overall 75th percentile of responses at 10% of 

total load (Ghatikar et al. 2015). Honeywell estimates that if similar projects were deployed in 

the commercial and industrial sectors across all of India, the projects could potentially decrease 

the country’s peak electricity demand by around 7.5 GW, or 5% of total peak demand (Poojary 

et al. 2015).
19

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Table 5. Demand Response Pilot Programs in India

Program Features Mumbai New Delhi

Electric Utility Tata Power Mumbai Tata Power Delhi Distribution 

Limited

Demand response provider Customized Energy Solutions Honeywell

Type of program Interruptible load services, energy 

shifting programs (incentivized) Interruptible load services

Other features

Interruptible load implemented by 

reducing air-conditioning load and 

using thermal storage to shift 

energy consumption in industrial

process-heating applications

OpenADR 2.0b, the latest 

software version in the United 

States, was used in tandem with 

advanced metering infrastructure

to communicate with customers 

and dispatch curtailment requests.

Peak load 

reduction potential 18 MW (2014) 12 MW (2016)

Target customers Commercial and industrial 

consumers (>500 kW capacity)

Commercial and industrial 

consumers (> 300 kW capacity)

3.3 South Africa

In 2008, South Africa started to experience widespread power outages and increases in electricity 

prices because of deficiencies in long-term infrastructure planning and investment in prior years. 

In addition to generally needing to manage for population and economic growth, Eskom, which 

manages nearly 95% of South Africa’s demand, was working with aging assets with 

deteriorating performance. This resulted in increased outage rates for generators and 

transmission lines. Distribution performance was also poor (Newbery and Eberhard 2008).

In addition to embarking on a program of refurbishment and new builds, Eskom partnered with 

demand response provider Comverge in 2011 to pilot test a demand response market in South 

Africa. In this program, Comverge managed the first open demand response market in South 

Africa, which led to the procurement of 500 MW of commercial and industrial responsive load.

Comverge also procured and managed 300 MW of this resource itself as a curtailment service 

provider. The demand response pilot program delivered nearly 15,260 MWh of load reduction 

over its seven months duration, which included 549.5 event hours (Comverge 2014). 

Since that time, Eskom has successfully built new capacity and improved plant availability rates. 

The South African Department of Energy Renewable Energy Independent Power Producer 

Procurement Programme (REIPPPP) awarded support to 3,915 MW of renewable capacity over 

three auction windows (Eberhard, Kolker, and Leigland 2014). Also, economic growth has not 

been as fast as what was anticipated in 2008. As a result, today Eskom has a surplus of capacity 

and little to no emphasis on demand response (Eskom 2017). 
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4 Future of Demand Response in High-Growth, Power 

Systems with Increasing Shares of Renewable 

Generation

As shown above, demand response can, has, and could play many roles in power systems. In this 

section, we consider potential roles for demand response in the context of high-growth systems

that also have clean energy goals. Such systems are perhaps best exemplified by India and China.

4.1 Comparison of High-Growth Power Systems with Increasing 

Shares of Renewable Generation with Conditions in the United 

States When Demand Response Programs Were Originally 

Introduced

In the United States, demand response programs were initially introduced by vertically integrated 

utilities in the 1970s, during that period’s general energy crisis and when several important 

factors were at work. Perhaps foremost, the oil embargo in 1973 led to a global increase in 

energy prices, which, coupled with local intermittent difficulties in purchasing reasonably priced 

new generators, made the historic electricity demand growth rates of 8%–9% unsustainable, both 

economically and socially (Bhatnagar and Rahman 1986; U.S. EIA 2016; Hurley, Peterson, and 

Whited 2013). Centralized air conditioning was widely adopted starting in the late 1960s and 

early 1970s, which increased peak summer load in many regions. These new load characteristics 

coupled with the introduction of integrated resource planning in the late 1970s clarified the 

potential benefits of demand-side management with regard to providing peak capacity and 

enhanced reliability (Cappers, Goldman, and Kathan 2010). New legislation related to these 

issues, especially the National Energy Act of 1978, which contained as one of its five statutes the 

Public Utilities Regulatory Policy Act (PURPA), was instrumental in encouraging more demandside control, including energy conservation and energy efficiency measures (Alliance to Save 

Energy 2013). 

Programs initiated during that time and into the 1980s were primarily comprised of interruptible, 

curtailable, or ToU tariffs for large commercial and industrial loads. However, the overall 

effectiveness of these programs may have been marginal. In some jurisdictions, interruptible and 

curtailable loads were called on so rarely (e.g., less than once per year) that the tariffs functioned 

more as an unofficial economic incentive than as peaking capacity. Similarly, the vast majority 

of ToU customers (approximately 75%), at least as of the early 2000s, did not actually shift load 

in response to their ToU price structure (Fryer et al. 2002).

8 More recent ToU studies also show 

mixed results, with multiple pilot programs and natural experiments yielding no or very small (< 

5% peak load reductions and elasticities of substitution less than 0.05) impacts (Jessoe and 

Rapson 2015; EPRI 2012). The old-style interruptible and curtailable rates have at this time

largely been replaced by more-reliable alternatives.

Several aspects of the United States electricity industry in the early 1970s, especially the high 

growth rates and rapid adoption of air-conditioning technologies, are relatable to current 

conditions in India, China, and similar locales. However, the energy landscape is much different. 

 8 Relatedly, Borenstein (2005) finds that under the assumption of equal elasticities (and not accounting for crosselasticities), time-of-use rates can only be expected to capture 20% of the benefits of real-time rates.
21

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Rather than high fuel costs, in the global energy markets low prices are currently the norm; and 

nations are planning to shift to use more renewable energy in their electricity sectors. Also, 

developing nations do not have to forge a completely new path but can build on the experiences 

of others with demand response and different types of energy markets; they can cooperatively 

learn with others how to integrate what may turn out to be the 21st century equivalent of the air 

conditioner: the electric vehicle. 

Thus, some of the same drivers for implementing demand response are present, including

difficulties building infrastructure fast enough to keep up with increasing demand, the potential 

for peakier loads driven by air-conditioning, and a desire to provide more reliable service; but,

this is against a different energy backdrop of low fuel costs, more variable renewable generators, 

and the potential for widespread EV adoption. Both the high fuel costs of the 1970s and the clean 

energy goals of today incentivize less fossil fuel use, but while the implication of the former is 

basic energy efficiency and reducing peak loads, the latter implies the need to provide flexibility 

across multiple timescales in support of increasing deployments of wind and solar PV. The 

demand response innovations of the last several decades, and the potential for emerging 

technologies such as electric vehicles to be operated flexibly, represent an opportunity to quickly 

transition to providing reliable electricity service with significantly less pollution intensity by 

ensuring demand response is implemented to serve as a flexible complement to wind and solar 

PV generation.

4.2 The Potential for Demand Response to Facilitate Renewable 

Integration and Emissions Reductions

As numerous grid integration studies have shown, operational flexibility is an important 

ingredient to successfully adding more variable generation to the supply mix (Brinkman et al. 

2015; Paul Denholm et al. 2016; Bloom et al. 2016; Hale, Stoll, and Novacheck 2018). The most 

important timescales for flexibility are system-dependent (Mills and Seel 2015). Flexibility pinch 

points can be reduced by enlarging geographic areas of cooperation (Brinkman et al. 2015) and 

by reducing minimum generation levels and allowing wind and solar generators to provide 

reserves (Denholm et al. 2016).

As a potential source for supplying increased flexibility, demand response is particularly 

attractive because of the smaller capital investment required to enable it. However, demand 

response is not a single homogeneous resource but rather the ability of many kinds of electrical 

loads to adjust their operations to perform various grid services. This complexity is both an 

advantage and a disadvantage when it comes to assessing the ability of demand response to 

integrate more renewables into power systems. It is an advantage because by harnessing multiple 

types of electrical load, flexibility needs at multiple timescales may be addressed (Barooah, Buic, 

and Meyn 2015). It is a disadvantage because developing reliable, time-varying, and 

geographically specific estimates of the technical and economic potential of various end-uses 

to provide particular grid services is a large effort requiring significant input data. To date, only 

rough short- to medium-term estimates have been compiled (Olsen et al. 2013, 1; Gils 2014).

Importing demand response potential estimates, imperfect as they are, into large-scale production 

cost models used for grid integration studies has provided some initial insights into potential 

roles for demand response in systems with increasing penetrations of wind and solar PV. 
22

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First, adding demand response as a resource into any cost-minimizing model of power system 

operations will always reduce costs, but it will not always reduce emissions, especially if 

emissions are not factored into dispatch decisions. For example, the case study considered in 

Hummon et al. (2013, 2), when combined with average coal and natural gas emissions rates,

9

shows that demand response increased carbon emissions compared to the system without 

demand response. This is in line with what has been seen for storage technologies10 and has 

recently been corroborated under a wider variety of conditions simulated for the Florida power 

system (Hale, Stoll, and Novacheck 2018). For example, Figure 3 shows that for the system 

studied, adding a 300-MW storage plant increases carbon emissions when wind and solar 

penetrations are below about 45% on an annual generation basis but decreases them once 

penetrations are sufficiently high. The mechanism involved is a switch from storage 

predominantly enabling coal generation at the expense of natural gas generation at low 

penetrations to storage reducing the curtailment of enough wind and solar generation to 

eventually decrease overall emissions at high penetrations (Denholm et al. 2013). Demand 

response has a similar ability to reduce curtailment in high penetration systems, although its 

capabilities are generally more constrained (Denholm and Margolis 2016). The exact extent 

to which demand response can reduce curtailment remains an active area of research.

Another role for demand response in integrating renewables is in the supply of additional reserve 

products needed to support the variability and uncertainty of wind and solar. Such value stacking 

is clearly shown in Hummon et al. (2013, 2) and Ma et al. (2016). However, it must be cautioned 

that the amount of reserves needed to integrate wind and solar are not so sizable, such that a 

large number of new entrants has the potential to quickly saturate ancillary service markets, even 

at high wind and solar penetrations (Milligan et al. 2010; Hummon et al. 2013; Baker 2016). 

 9 “Frequently Asked Questions: How much carbon dioxide is produced when different fuels are burned?” EIA,

https://www.eia.gov/tools/faqs/faq.cfm?id=73&t=11

10 Many types of demand response can be interpreted as, or are mathematically equivalent to, electricity storage. 

For one example, dynamically adjusting heating and cooling set points to change the power draw of a building relies 

on the building’s inherent thermal storage. For another example, scheduling loads such as clothes washing or EV 

charging achieves a 100% efficient shift as compared to the less-than-perfect shift achievable by pumped hydro 

or battery storage.
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Figure 3. CO2 emissions associated with adding a 300-MW energy-only storage plant to power 

system models with a total conventional capacity of 15.8 GW and a varying amount of wind and 

solar capacity (Denholm et al. 2013)

4.3 Framework for Selecting Demand Response Programs 

From the discussion thus far, it is apparent that demand response is currently used to meet 

several power system needs and that it may be further called on in the future to help integrate 

wind and solar. These objectives for demand response are summarized in Table 6, which further 

describes the characteristics needed to meet each objective as well as the types of programs that 

meet those requirements.
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Table 6. Demand Response Objectives and Corresponding Demand Response (DR) Options

DR Objectives DR Role/Benefits DR Characteristics Needed DR Options

Peak load shifting DR can help shift 

load at peak 

periods.

Customers willing to participate

several to dozens of times per 

year

Financial viability

Reliable response

Bidding load

Aggregation

Direct load control 

Interruptible tariff

ToU, CPPa, and RTP

Regulation 

reserves

DR can provide 

fast load changes 

up or down for 

balancing.

Fast response

Binding commitments to provide 

response

Automated control to follow 

regulation signal

Bidding load

Aggregation and 

advanced controllers

Direct load control

Contingency 

event (emergency 

response)

DR can respond to

infrequent

contingency 

signals.

Need to ensure certainty, 

speed, and continuous 

response until the event clears

Binding commitments to provide 

response

Bidding load 

Aggregation 

Direct load control

Interruptible tariff

Frequency sensitive 

relays

Managing load 

growth and 

capacity needs

DR can be included 

in capacity markets 

or long-term 

planning.

Relatively rapid program 

implementation

Need to ensure certainty, 

speed, and continuous 

response during peak times

Binding commitments

Bidding load 

Aggregation 

Direct load control

Interruptible tariff

ToU, CPP, and RTP

Ramping reserves DR can help 

address uncertainty 

in net-load ramps.

Fast response

Binding commitments to provide 

response

Automated control preferred

Bidding load 

Aggregation

Direct load control 

RTP

Virtual energy 

storage

DR can help shift 

load to periods of 

excess generation

(e.g., midday for 

high-solar 

systems.)

Customers willing or required to 

participate

Reliable response

Bidding load 

Aggregation

Direct load control 

ToU and RTP

a Whenever CPP is listed, any of CPP, CPR or VPP would be appropriate.
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Utilities and regulators serving high-growth systems with increasing shares of wind and solar 

generation would do well to keep most if not all these objectives in mind, and to go through a 

detailed planning process to discern which types of demand response might be most valuable to 

their systems, when, and where. As shown in Figure 4, for the development of effective demand 

response programs, understanding local conditions is particularly important. Which grid services 

are most needed? Which loads are significant in size and are (potentially) controllable? The 

answers to both these questions may change over time. Capacity and peak load reduction have 

typically been the most important services provided by demand response, but energy shifting, 

ramping reserve, and regulation may become more important as wind and solar penetrations 

increase. For now, in many places, applying direct load control to residential air conditioners

might not yet make sense, but that could change in coming years. In some areas, focusing on 

industrial demand response is a natural choice, while in others, commercial loads are dominant. 

Once the flexible loads and grid service needs have been identified, potential demand response 

programs can be listed and screened as to whether they are a good fit for providing the services 

needed with the loads available. When that list is stabilized, it is time to plan for the time it takes 

to test and revise those potential programs, and to embark on the processes of pilot program 

design, recruitment, rollout, and analysis. All along the way, there are many examples to study, 

colleagues to reach out to, and, increasingly, vendors to consider working with to help ensure the 

establishment of successful programs.
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Figure 4. Template of the demand response planning process

Based on the characteristics of various demand response programs and power systems with high 

penetrations of wind and solar, the types of programs most likely to meet systems’ needs for 

reliable responses under changing conditions include:

• Automated, incentive-based aggregation and direct load control programs

• Real-time prices, or other time-varying prices that are well aligned with system needs, for 

which the demand-side response is facilitated by automatic control

• Critical peak pricing programs.

Aggregation and direct load control are relatively well understood, reliable when done well, 

and can be tailored to fit a variety of needs. Real-time pricing programs coupled with automated 

controls hold promise but have yet to be fully proven, especially with regard to including pricedriven responses in the price formation process as is needed to produce reliable grid operations at 

high penetration. Critical peak pricing programs may be especially useful in the short term, as 

they can produce significant response at peak times with smaller infrastructure investments, 

especially for utilities already undertaking advanced metering initiatives.
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5 Conclusions

A variety of demand response program types have been used or piloted around the world. On the 

retail side, interruptible and curtailable tariffs were a common starting point, but they have given 

way to more robust demand response programs based on direct load control or pay-forperformance. Time-of-use rates are widely available options that do affect some load shifting

and peak load reductions but not always as much as one would expect. Recently, other types of 

dynamic pricing, such as critical peak pricing and real-time pricing have been piloted or 

introduced as long-term options. Wholesale demand response programs are more recent, but are 

rapidly expanding to provide a variety of services in several markets.

Traditionally, and even today, demand response is most commonly used to provide capacity that 

counts toward planning reserves and long-term reliability, and the attendant reductions in load at 

times near system peak or in emergency conditions as required by such a resource. Demand 

response thus contributes to both long-term and short-term system reliability, and as a local 

resource, enhances energy security. Especially with the opening of wholesale markets to demand 

response, additional services, specifically economic energy shifting, regulation reserve signal 

following, and other active forms of load balancing are becoming more common in more places.

These types of services typically provide additional economic value or cost savings to the 

system.

Electricity systems with high demand growth and clean energy goals may want to consider 

accelerating the development of demand response programs specifically tailored to providing 

additional reliability in the short to medium term, and the ability to help integrate variable 

generation in the medium to long term. To that end, modern, verifiable forms of emergency 

demand response from large commercial and industrial customers may be a natural place to 

start. Residential critical peak pricing programs can also be effective, and they do not require 

much communication infrastructure. Static ToU tariffs, which already have a mixed record as 

to how effective they are at shifting load away from peak times, are likely to become even less

effective as additional variable generation makes net load curves less predictable and more 

correlated with weather rather than schedules. Instead, in preparation for deeper forms of 

demand response on the residential side, utilities and regulators may want to consider which 

types of appliances and other loads might be able to provide useful services in the future, and 

what the most effective means of tapping into those resources might be. Advanced metering 

might be desirable if real-time pricing plus automatic controllers are anticipated, or if the 

responses of third-party aggregators are to be verified through official meter readings. If 

other forms of control or verification are anticipated, for instance, direct load control of 

select appliances plus verification based on a sample of participants, or automatic control 

of programmable communicating thermostats, widespread investments in advanced meters 

may be unnecessary.

The ability of demand response to help integrate variable renewable generation has not yet been 

precisely quantified, but there are two general trends. First, the impact of demand response on 

emissions is highly system-dependent. For systems at low penetrations of wind and PV and 

with inexpensive, inflexible coal capacity available, the addition of demand response is likely 

to increase rather than decrease emissions to the extent that it reduces the operation of natural 

gas generators in favor of coal generators. However, this trend reverses once wind and PV 
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penetrations increase far enough past the point of requiring system-balancing curtailments. 

In that case, demand response is used more to reduce renewable energy curtailment, which has a 

net impact on reducing emissions. Second, demand response can be used to provide regulation 

and other system balancing reserves that are needed at greater magnitudes as more wind and 

solar PV are added to the system. However, even in high wind and solar cases, system balancing

needs are likely to stay in the range of a few to a dozen percentage points of load. This modest 

need combined with ancillary services generally being about an order of magnitude less 

expensive to provide than energy means demand response resources will typically be unable 

to justify themselves based solely on regulation and other balancing service revenue streams, 

especially with emerging technologies such as battery storage also competing to provide these 

services. For this reason, the role of demand response as a capacity resource is likely to remain 

important.

Future research should provide a better idea of which types of demand response are most 

valuable when and where. In the meantime, system operators everywhere can investigate how 

demand response programs might be able to provide additional reliability for their systems, with 

an eye toward programs that will be robust under swiftly changing net load patterns.
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