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Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

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

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

Contract No. DE-AC36-08GO28308

Grid Integration of Aggregated 

Demand Response, Part 2: 

Modeling Demand Response in 

a Production Cost Model

Marissa Hummon, David Palchak, 

Paul Denholm, and Jennie Jorgenson 

National Renewable Energy Laboratory

Daniel J. Olsen, Sila Kiliccote, Nance Matson, 

Michael Sohn, Cody Rose, Junqiao Dudley, 

and Sasank Goli

Lawrence Berkeley National Laboratory 

Ookie Ma

U.S. Department of Energy

Technical Report

NREL/TP-6A20-58492

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

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

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

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

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Grid Integration of Aggregated 

Demand Response, Part 2: 

Modeling Demand Response in 

a Production Cost Model

Marissa Hummon, David Palchak, 

Paul Denholm, and Jennie Jorgenson 

National Renewable Energy Laboratory

Daniel J. Olsen, Sila Kiliccote, Nance Matson, 

Michael Sohn, Cody Rose, Junqiao Dudley, 

and Sasank Goli 

Lawrence Berkeley National Laboratory 

Ookie Ma 

U.S. Department of Energy

Prepared under Task No. SA12.0384

Technical Report

NREL/TP-6A20-58492

December 2013
NOTICE

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iii

Foreword

This report is one of a series stemming from the U.S. Department of Energy (DOE) Demand 

Response and Energy Storage Integration Study. This study is a multi-national-laboratory effort 

to assess the potential value of demand response (DR) and energy storage to electricity systems 

with different penetration levels of variable renewable resources and to improve our 

understanding of associated markets and institutions. This study was originated, sponsored, and 

managed jointly by the Office of Energy Efficiency and Renewable Energy and the Office of 

Electricity Delivery and Energy Reliability.

Grid modernization and technological advances are enabling resources, such as DR and energy 

storage, to support a wider array of electric power system operations. Historically, thermal 

generators and hydropower in combination with transmission and distribution assets have been 

adequate to serve customer loads reliably and with sufficient power quality, even as variable 

renewable generation like wind and solar power become a larger part of the national energy 

supply. While DR and energy storage can serve as alternatives or complements to traditional 

power system assets in some applications, their values are not entirely clear. This study seeks to 

address the extent to which DR and energy storage can provide cost-effective benefits to the grid 

and to highlight institutions and market rules that facilitate their use.

The project was initiated and informed by the results of two DOE workshops; one on energy 

storage and the other on DR. The workshops were attended by members of the electric power 

industry, researchers, and policymakers, and the study design and goals reflect their 

contributions to the collective thinking of the project team. Additional information and the full 

series of reports can be found at www.eere.energy.gov/analysis/. 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
iv

Acknowledgments

The authors would like to thank the following individuals for their valuable input and comments 

during the analysis and publication process: Mark O’Malley, Brendan Kirby, Elaine Hale, Trieu 

Mai, Nate Blair, Keith Searight, Erik Ela, Chunlian Jin, Di Wu, and Michael Kintner-Meyer. 

Any errors or omissions are solely the responsibility of the authors. 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
v 

Executive Summary

Introduction

Renewable integration studies have evaluated many challenges associated with deploying large 

amounts of variable wind and solar generation technologies. These studies can evaluate 

operational impacts associated with variable generation, benefits of improved wind and solar 

resource forecasting, and trade-offs between institutional changes, including increasing 

balancing area cooperation and technical changes such as installing new flexible generation. 

Demand response (DR) resources present a potentially important source of grid flexibility and 

can aid in integrating variable generation; however, integration analyses have not yet 

incorporated these resources explicitly into grid simulation models as part of a standard toolkit 

for resource planners. 

Part 1 of this report, “Load Availability Profiles and Constraints for the Western 

Interconnection,” examines the potential for different types of commercial and residential 

building, municipal, and industrial non-manufacturing loads to provide bulk power system 

services [1]. Industrial manufacturing loads were examined and quantified by Starke et al. [2]. 

These include energy, capacity, and operating reserves. Their abilities to provide energy and 

operating reserve services are represented by a set of hourly availabilities for each service, 

including some additional resource constraints specific to individual end-use types. The services 

are defined in Table ES-1. Historically, applications for DR have been limited and used primarily 

for emergencies and peak shaving. As such, their capabilities are often assessed only for a few 

select times of the year. This assessment quantifies the DR resource potential throughout the year 

and can thereby be used in scenarios with substantial deviations of electricity supply from 

historical norms, such as cases with high levels of wind and solar generation. 

Table ES-1. Description and Physical Requirements of the Products Modeled in This Study 

Reserves Products Physical Requirements

Product 

Type General Description

How Fast 

to 

Respond

Length of 

Response

Time to 

Fully 

Respond

How Often Called

Energy Shed or shift energy 

consumption over time

5 minutes ≥1 hour 10 

minutes

1–2 times per day 

with 4–8 hour 

notification

Regulation Response to random 

unscheduled deviations in 

scheduled net load

30 

seconds

Energy 

neutral in 

15 

minutes

5 minutes Continuous within 

specified bid period

Flexibility Additional load following 

reserve for large unforecasted wind/solar ramps

5 minutes 1 hour 20 

minutes

Continuous within 

specified bid period

Contingency Rapid and immediate 

response to a loss in supply

1 minute ≤30 

minutes

≤10 

minutes

≤ Once per day

 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
vi 

This report (Part 2) implements DR resources in the commercial production cost model 

PLEXOS. Production cost models are utility planning tools commonly used in renewable 

integration analyses because they can mimic many of the near real-time decisions and conditions 

faced by power system operators. Further, they output a number of useful metrics, such as 

estimates of different types of operational costs and power plant emissions. 

The DR resource assessment detailed in Part 1 has been conducted in a large area across the

western United States. In order to explore the methodology, we model a subset of the western 

United States, the Colorado test system. The Colorado system has been used in several previous 

studies on the performance and value of storage [3] and concentrating solar power [4]. The work

described here demonstrates an approach to modeling DR in production cost models and how its 

availability to power system operators can reduce operational costs and improve generator 

utilization.

Approach

Our approach assumes that DR providers submit their capabilities to power system operators, 

and subsequently are dispatched to minimize the overall cost of generation in a manner similar to 

conventional generators but including the various operational constraints of the individual DR 

resources. It also allows DR resources to provide operating reserves in addition to energy, and 

thereby be fully co-optimized alongside generators in providing bulk power system services. 

We assume that DR can provide energy services by either shedding load or shifting its use 

between different times. In addition to energy, the modeling considers DR resources capable of 

providing operating reserves. Operating reserves include frequency regulation, contingency 

reserve, and flexibility (or ramping) reserve. Frequency regulation manages short-term variations 

in demand due to unpredicted changes in the net of load, wind, and solar generation. 

Contingency reserve addresses large power plant or transmission line failures. Finally, flexibility 

reserve is a proposed service that responds to large and unexpected wind and solar ramp events. 

Figure ES-1. Residential water heating DR in Public Service of Colorado (upper): Hourly capacity 

provision for (a) spring and (b) summer; hourly marginal price of energy and operating 

reserves (lower)

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vii 

The production cost model runs a chronological hourly simulation of the power system to assess 

the operational value of DR. Calculating operational value is approached two ways. First, we 

compare the total costs of production between scenarios. By making incremental changes to the 

system, changes to production costs can be attributed to the addition of new resources or 

differences in operational practices. Changes in production costs represent the total operational 

value to the system as a whole, irrespective of how value is distributed to different entities. 

Second, we examine the short-run marginal costs of production. In regions served by an 

independent system operator or regional transmission organization, marginal costs equate to 

market clearing prices and indicate the expected revenue that resources would earn as market 

participants. Outside these regions, marginal costs relate to a vertically integrated utility’s 

avoided cost for providing the associated service. 

Results

There are significant variations in the availabilities of different types of DR resources in the 

Colorado test system. Table ES-2 shows that the average availability for energy and operating 

reserves as well as the energy available through load shedding during the top 20 load hours 

(which is a rough estimate for the DR capacity value). The assumed DR resource can provide up 

to 113 MW of capacity (roughly 2% of peak load) and shift 135 GWh of energy. It can also meet 

about 33% of the need for frequency regulation, 19% of spinning contingency reserve, and 85% 

of flexibility reserve. These numbers represent the annual average response that could be 

achieved for individual grid services but does not include co-optimization. For DR resources that 

can provide multiple grid services, providing one grid service incurs a lost opportunity for 

providing another grid service, which is accounted for by constraining the simulation of DR in 

the production cost model. 

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viii

Table ES-2. Availability of Demand Response to Provide Energy, Operating Reserves (Regulation, 

Contingency, and Flexibility) in the Colorado Test System, and Capacity During the Top 20 Hours 

of Greatest Demand

Demand Response 

Resource

Energy Operating Reserves Capacity

Mean [MW] % of Requirement

Mean 

[MW]

Max 

[GWh]

Reg Cont Flex Reg Cont Flex Mean [MW]

Residential Cooling 10.9 38.8 10.3 10.3 10.3 8.8 2.5 23.9 55.1

Residential Water 

Heating 1.8 15.7 1.8 1.8 1.8 1.5 0.4 3.4 1.3

Commercial Cooling 2.1 10.7 0.1 1.6 0.2 0.1 0.4 0.6 8.2

Commercial Heating 2.2 3.8 - - - - - - - 

Commercial Lighting - - 1 3 3 0.8 0.7 5.8 -

Commercial Ventilation - - 1.1 3.4 3.4 0.9 0.8 6.6 -

Municipal Pumping 1.7 2.1 - - - - - - 2.1

Wastewater Pumping 1.5 1.6 - - - - - - 1.5

Outdoor Lighting 1.7 - 23.4 23.4 23.4 20.7 5.8 44.2 - 

Refrigerated 

Warehouses 0.2 0.3 - - - - - - 0.3

Agricultural Pumping 17 49.9 0 25.6 0 0 6.3 0 36.6

Data Centers 8 11.7 0 8 0 0 2 0 8 

All DR Resources 45.4 49.9 37.7 77 42.1 32.9 19 84.5 113

Implementing these resources into the Colorado test system leads to $7.9 million in total 

operational savings. This is higher than the $5.4 million in total revenue based on what the 

resources could earn in a market setting. The difference stems largely from price-suppression 

effects, as entry of DR into the market results in lower overall market-clearing prices. Most of 

the revenue comes from the provision of operating reserves, with only 21% coming from energy 

transactions. This is partly due to the fact that energy provision entails a number of constraints 

related to the timing of load recovery (which can occur prior to or following a curtailment). For 

instance, thermal loads must maintain temperature tolerances, limiting the time difference 

between a load shed and a load recovery and limiting energy price arbitrage value.

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ix

Figure ES-2. Average annual revenue (left axis) from the day-ahead market per (a) total enabled 

capacity and (b) annual availability for each type of demand response resource in the Colorado 

test system. Annual DR resource availability (right axis) is expressed as the (a) annual availability 

factor or as (b) total annual availability

The value of these resources is driven by their availabilities and the coincidence of their 

availability to times of high production costs. Some resources have a nearly constant available 

response capacity, while those of others are highly seasonal. For instance, residential cooling 

availability, during peak demand hours, is much greater than that of residential water heating, yet 

the average availability of residential cooling is less than that of residential water heating. 

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x 

The annual availability factor is the sum of the maximum capacity available during each time 

period divided by the peak available capacity times the number of time intervals (i.e., total hours 

per year). This is similar to capacity factor, which is a measure of the power plant actual energy 

production compared to the possible energy production if the plant operated at maximum 

capacity during all time periods. The value of DR, normalized to dollars per peak enabled 

capacity ($/kW), closely tracks the availability factor, as shown in Figure ES-2a. Peak-enabled 

capacity represents the maximum response capability across all hours of the year. 

The value of DR can also be measured by the cumulative availability across the year ($/MW-h), 

as shown in Figure ES-2b. The cumulative availability is the fraction of electricity from an 

aggregation of end uses that is flexible through DR. This metric reflects the correlation of each 

resource’s availability to times of high market prices for operating reserves as well as its ability 

to take advantage of large energy price differences across hours of the day. Those resources with 

lower correlations and more constraints on energy shifting will tend to have lower values on a 

cumulative availability basis. Figure ES-2 only shows the revenue from each grid service, which 

neglects other electricity production cost savings such as avoided start-up costs.

Discussion

This study has investigated the abilities of different types of end-use loads to provide bulk power 

system services, including energy, capacity, and operating reserves in the western United States,

and then implemented them in a Colorado test system model. Parts 1 and 2 of this report series 

provide a framework for incorporating DR in renewable integration analysis. However, 

additional work is necessary to verify many of the study assumptions.

Thus far, the analysis has been conducted sequentially, rather than iteratively. First, the resource 

assessment has been conducted without full understanding of the economic value of different 

resource types. In practice, more valuable resources will have greater incentives to offer their 

capabilities to the power system. Second, the resulting commitment and dispatch of the DR

resources through the production cost modeling has not been tested against detailed examinations 

of the resources’ capabilities. Because we are modeling the aggregate response capabilities of 

many individual providers, it is not clear how many customers need to be enrolled in order to 

meet aggregated response utilization or whether the initial assumptions make efficient use of 

those enrolled. 

Finally, the total operational savings found through the production cost modeling ($7.9 million) 

is comparable in size to what the resources might earn as capacity resources. Capacity marketclearing prices and costs for avoided capacity (i.e., the annual carrying cost of a combustion 

turbine) are in the range of $77/kW-year [5] and $210/kW-year [6]. We assume that DR can 

provide 113 MW of capacity, which has a capacity value between $8.7 million and $23.7 

million. Future efforts will assess the capacity credit of DR and investigate more thoroughly 

capacity value alongside operational value. 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
xi 

Table of Contents

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

2 Production Cost Modeling and Demand Response.......................................................................... 2

2.1 Production Cost Modeling: Energy and Operating Reserves .......................................................... 2

2.2 Modeled Grid Services Provided by Demand Response ................................................................. 4

2.3 Modeling Aggregated Demand Response........................................................................................ 5

2.3.1 Production Cost Modeling Approach .................................................................................. 5

2.3.2 Simulation of Energy Shifting ............................................................................................. 9

2.3.3 Co-Optimization of Energy and Reserves ......................................................................... 11

3 Results................................................................................................................................................. 13

3.1 Colorado Test System.................................................................................................................... 13

3.2 Base Case Results (No DR Resources).......................................................................................... 16

3.3 Demand Response Resources in the Test System.......................................................................... 17

3.4 Examples of Demand Response Performance ............................................................................... 21

3.4.1 Commercial Lighting......................................................................................................... 21

3.4.2 Municipal Wastewater Pumping........................................................................................ 22

3.4.3 Residential Water Heating ................................................................................................. 24

3.5 Operational Value of Demand Response ....................................................................................... 25

3.5.1 System Value ..................................................................................................................... 25

3.5.2 Value of Individual DR Products....................................................................................... 28

4 Conclusion .......................................................................................................................................... 35

References................................................................................................................................................. 37

Appendix.................................................................................................................................................... 40

Additional Examples of Demand Response Performance in the Test System..................................... 40

Residential Cooling........................................................................................................................ 40

Residential Heating........................................................................................................................ 42

Commercial Cooling...................................................................................................................... 43

Commercial Heating ...................................................................................................................... 45

Data Centers................................................................................................................................... 46

Agricultural Pumping..................................................................................................................... 48

Municipal Pumping........................................................................................................................ 49

Refrigerated Warehouse................................................................................................................. 50

Commercial Ventilation................................................................................................................. 51

Outdoor Lighting ........................................................................................................................... 53

Seasonal Daily Average Energy and Reserve Availability and Provision ........................................... 54

Marginal Price Duration Curves for Reserves...................................................................................... 57

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xii 

List of Figures

Figure 1. Example of the (a) unit commitment and (b) economic energy dispatch from a 

production cost model ................................................................................................................3

Figure 2. Enabled demand response capacity is defined as the capacity of the end use that is 

equipped/enrolled to respond to a signal or instruction to reduce load .....................................8

Figure 3. Maximum availability of energy, contingency, and flexibility for demand response 

from commercial cooling resources in Public Service Colorado on August 1–3 ......................9

Figure 4. Example of the (a) total load from an illustrative end use and the portion of that load 

available for demand response activities and (b) the constrained availability of DR 

capacity for charging and generation .......................................................................................10

Figure 5. System price duration curve for (a) energy and (b) operating reserves in the base 

case of the Colorado test system ..............................................................................................16

Figure 6. Comparison of the (left axis) hourly capacity available from DR by sector and (right 

axis) hourly system load DR availability is the maximum available from each DR resource 

across the energy and ancillary service markets ......................................................................21

Figure 7. Commercial lighting DR in PSCo: Mean (a) daily and (b) hourly provision of 

capacity ....................................................................................................................................22

Figure 8. Commercial lighting DR in PSCo (upper): Hourly capacity provision for (a) spring 

and (b) summer; hourly marginal price of operating reserves (lower) ....................................22

Figure 9. Wastewater pumping DR in PSCo: Mean (a) daily and (b) hourly provision of 

capacity ....................................................................................................................................23

Figure 10. Wastewater pumping DR in PSCo (upper): Hourly capacity provision for (a) spring 

and (b) summer; hourly marginal price of energy and operating reserves (lower) .................23

Figure 11. Residential water heating DR in PSCo: Mean (a) daily and (b) hourly provision 

of capacity ................................................................................................................................24

Figure 12. Residential water heating DR in PSCo (upper): Hourly capacity provision for (a) 

spring and (b) summer; hourly marginal price of energy and operating reserves (lower) ......25

Figure 13. Mean increase in the daily load profile for spring and summer when DR provides 

energy. Negative numbers are a reduction in total system load, positive numbers are an 

increase in load. .......................................................................................................................25

Figure 14. Reserve provision (a) regulation, (b) contingency, and (c) flexibility for the base 

case and when DR resources are available in the system for reserves ....................................26

Figure 15. Annual average revenue per unit of generation or reserves for each DR resource ......30

Figure 16. Average annual revenue (left axis) from the day-ahead market per (a) total enabled 

capacity and (b) annual availability for each type of demand response resource in the 

Colorado test system ................................................................................................................32

Figure 17. Illustration of the DR available capacity, scaled by the enabled rated capacity for 

(a) residential cooling and (b) residential water heating in the Colorado test system .............33

Figure 18. Residential cooling demand response in PSCo: Mean (a) daily and (b) hourly 

provision of capacity ................................................................................................................41

Figure 19. Residential cooling demand response in PSCo: (upper) Hourly capacity provision 

for (a) spring and (b) summer and (lower) hourly marginal price of energy and operating 

reserves ....................................................................................................................................41

Figure 20. Residential heating demand response in the Northwest: Average (a) daily and (b) 

hourly provision of capacity; the daily capacity is zero between mid-June and late-August, 

when residences do not use electricity for heating ..................................................................42

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xiii

Figure 21. Residential heating demand response in the Northwest: (upper) Hourly capacity 

provision for April 15–April 18 and (lower) hourly marginal price of energy and operating 

reserves ....................................................................................................................................43

Figure 22. Commercial cooling demand response in PSCo: Mean (a) daily and (b) hourly 

provision of capacity ................................................................................................................44

Figure 23. Commercial cooling demand response in PSCo: (upper) Hourly capacity provision 

for (a) spring and (b) summer and (lower) hourly marginal price of energy and operating 

reserves ....................................................................................................................................44

Figure 24. Commercial heating demand response in WACM: Mean (a) daily and (b) hourly 

provision of capacity ................................................................................................................45

Figure 25. Commercial heating demand response in WACM: (upper) Hourly capacity 

provision for spring and (lower) hourly marginal price of energy and operating reserves 

(x-axis tick marks 12 p.m.) ......................................................................................................46

Figure 26. Data center demand response in PSCo: Mean (a) daily and (b) hourly provision 

of capacity ................................................................................................................................47

Figure 27. Data center demand response in PSCo: (upper) Hourly capacity provision for (a) 

spring and (b) summer and (lower) hourly marginal price of energy and operating reserves .47

Figure 28. Agricultural pumping demand response in PSCo: Mean (a) daily and (b) hourly 

provision of capacity ................................................................................................................48

Figure 29. Agricultural pumping demand response in PSCo: (upper) Hourly capacity provision 

for (a) spring and (b) summer and (lower) hourly marginal price of energy and operating 

reserves ....................................................................................................................................49

Figure 30. Municipal pumping demand response in PSCo: Mean (a) daily and (b) hourly 

provision of capacity ................................................................................................................49

Figure 31. Municipal pumping demand response in PSCo: (upper) Hourly capacity provision 

for (a) spring and (b) summer and (lower) hourly marginal price of energy and operating 

reserves ....................................................................................................................................50

Figure 32. Refrigerated warehouse demand response in PSCo: Mean (a) daily and (b) hourly 

provision of capacity ................................................................................................................51

Figure 33. Refrigerated warehouse demand response in PSCo: (upper) Hourly capacity 

provision for (a) spring and (b) summer and (lower) hourly marginal price of energy and 

operating reserves ....................................................................................................................51

Figure 34. Commercial ventilation demand response in PSCo: Mean (a) daily and (b) hourly 

provision of capacity ................................................................................................................52

Figure 35. Commercial ventilation demand response in PSCo: (upper) Hourly capacity provision 

for (a) spring and (b) summer and (lower) hourly marginal price of energy and operating 

reserves ....................................................................................................................................53

Figure 36. Outdoor lighting demand response in PSCo: Mean (a) daily and (b) hourly provision 

of capacity ................................................................................................................................53

Figure 37. Outdoor lighting demand response in PSCo: (upper) Hourly capacity provision for 

(a) spring and (b) summer and (lower) hourly marginal price of energy and operating 

reserves ....................................................................................................................................54

Figure 38. Seasonal daily average capacity available from demand response for each service ....54

Figure 39. Mean daily allocation of demand response capacity for energy and operating reserves, 

by season ..................................................................................................................................55

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xiv 

Figure 40. Mean daily allocation of demand response capacity for energy and operating reserves, 

by season ..................................................................................................................................55

Figure 41. Average seasonal daily provision of regulation by demand response in the Colorado 

test system ................................................................................................................................56

Figure 42. Average seasonal daily provision of contingency by demand response in the 

Colorado test system ................................................................................................................56

Figure 43. Average seasonal daily provision of flexibility by demand response in the Colorado 

test system ................................................................................................................................57

Figure 44. Price duration curves for test system with and without demand response resources 

providing energy and operating reserves—(top) regulation, (middle) contingency, and 

(bottom) flexibility ...................................................................................................................57

List of Tables

Table 1. Description and Physical Requirements of the Products Modeled in This Study .............5

Table 2. Demand Response Resources Modeled in This Report .....................................................7

Table 3. Demand Response Resource Energy Operation Restrictions, Penalties, and Costs ........11

Table 4. Time Lapse to Full Load Shed for Each Demand Response Resource ...........................12

Table 5. Test System Generator Capacity in 2020 ........................................................................14

Table 6. Summary of Operating Reserves in the Base Case of Test System .................................15

Table 7. Assumed Additional Operating Cost for Units Providing Frequency Regulation 

Service......................................................................................................................................15

Table 8. Availability of Demand Response to Provide Energy in the Colorado Test System 

on an Annual Basis as Well as in the Top 20 Hours of Greatest Demand ..............................18

Table 9. Availability of Demand Response for Operating Reserves (Regulation, Contingency, 

and Flexibility) in the Colorado Test System ..........................................................................19

Table 10. Demand Response Availability Factor for Energy and All Services .............................20

Table 11. Comparison of the Test System Energy Results When Demand Response 

Resources are Available ...........................................................................................................27

Table 12. Production Costs for Base Case and Base Case With Demand Response 

Providing Energy and Operating Reserves ..............................................................................28

Table 13. Annual Demand Response Energy and Reserve Provision (GWh or GW-h) and 

Revenue (M$) ..........................................................................................................................29

Table 14. Marginal Reserve and Energy Price When Demand Response Resources are 

Available in the Test System ...................................................................................................31

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1 

1 Introduction

Demand response (DR) is a source of flexibility for the electric power system, which may 

become more important with increasing use of variable and uncertain renewable generation 

resources. This is a two-part series of reports. Part 1 of this report series estimates that there are 

more than 2.7 GW of commercial, residential, municipal, and industrial non-manufacturing DR 

capacity available for energy and more than 1.7 GW available for frequency regulation reserves, 

representing an average 0.7% of load and 56% of frequency regulation reserve requirements in 

the Western Interconnect [1]. Historically, large commercial and industrial customers have 

participated in interruptible load programs. These resources are typically called upon only during 

emergencies, rather than integrated dynamically into the power system optimization. 

The emergence of smart grid technologies and new market structures could allow a greater 

fraction of electricity consumers to participate in DR programs. A key challenge for system 

planners is to understand the potential operational value of DR. Traditionally, new generation 

resources are evaluated in grid simulation tools that model the operation of the grid and 

determine the cost and feasibility of different generation mixes. However, the traditional 

framework of these modeling tools makes it difficult to evaluate DR resources, and as a result, 

these resources are often not represented or are represented using simplistic approximations [7]. 

This report describes the implementation and performance of multiple DR resources in a 

commercial grid simulation tool. In addition, we demonstrate how this can be used to calculate 

the value of DR, with an example analysis of DR in a test power system. The resource valuation 

process uses hourly DR profiles for a large number of appliance and equipment systems in 

commercial, residential, municipal, and industrial non-manufacturing facilities. These profiles 

were generated by Lawrence Berkeley National Laboratory and discussed in Part 1 of this report 

series [1]. These DR profiles were then implemented in the PLEXOS production cost model 

(PCM) as described in Section 2 of this report. Sections 2.1 and 2.2 introduce production cost 

modeling and the co-optimization of providing energy and operating reserves. Section 2.3

describes our approach to aggregating DR availability and the implications of that approach on 

modeling DR in a PCM. Section 3 demonstrates the performance and impact of DR in a test 

power system, analyzing the impact of all DR on the total production cost and the individual 

revenue streams for each DR resource providing load shifting/arbitrage and three classes of 

operating reserve products: regulation, flexibility, and spinning contingency reserves. The 

appendices have additional data on DR performance. Overall, this analysis demonstrates how DR 

profiles can be simulated in traditional planning tools to assess the value of DR in the bulk 

power system.

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2 

2 Production Cost Modeling and Demand Response

2.1 Production Cost Modeling: Energy and Operating Reserves

Production cost modeling describes the simulation of grid operation using standard tools used by 

utilities and system planners. PCMs1 simulate the commitment (the process of scheduling power 

plant operation) and dispatch (the actual power output) of the power plant fleet to meet load at 

least cost while maintaining system security. The simulations are used to evaluate factors, 

including the operational feasibility of future grid mixes and the cost of system operation. 

PCMs are chronological simulations of the grid, where in each time step (typically 1 hour) the 

objective function of the model is to minimize production cost by dispatching generators in order 

of variable cost (from lowest to highest) where variable cost includes fuel and operation and 

maintenance. The model includes a large number of parameters and constraints that affect the 

dispatch, including power plant efficiency as a function of plant output, plant availability, power 

plant start-up times, ramp rates, and environmental restrictions. The model may also include the 

availability of transmission between generators and load centers. A key parameter to the model is 

ensuring that in each hour, the aggregated set of generators meets the total demand, plus any 

additional operating reserve requirements. Models optimize against a fixed load profile, and 

historically, models do not consider the ability to vary load to address generation capacity 

shortfalls,

2 meet reserve requirements, or reduce the cost of operating the system.

Figure 1 illustrates the result of a PCM for five consecutive days in mid-July for the test system 

described in Section 3.1. Figure 1a shows the unit commitment of the generation fleet, 

demonstrating the total online generator availability to meet both the load and the operating 

reserve requirement. Figure 1b shows the actual economic dispatch of committed units. The 

figures show how many of the units that are committed to meet the peak demand in the middle of 

the day must be “backed down” (reduce output) overnight to accommodate the reduced demand. 

The majority of the variations in load are met by combined cycle (CC) units. These units (along 

with the coal units) cannot be quickly turned on, so often remain online even during periods of 

lower demand. Alternatively, higher operational cost combustion turbines (CTs) can be started 

quickly, so are only committed and dispatched during periods of high demand. 

 

1

 These tools have a number of names, including “production cost” and “security-constrained unit commitment and 

economic dispatch” models. To realistically model the grid, these tools require extensive generator databases and 

include transmission constraints and other elements to capture the challenges of reliably operating the electric grid.

2 Most PCMs place a soft constraint on providing load and reserves to prevent the model from reaching 

solution infeasibility.

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3 

Figure 1. Example of the (a) unit commitment and (b) economic energy dispatch from a production 

cost model

Coal generators (along with nuclear and geothermal plants) are often referred to as “baseload" 

units due to their low variable costs. They are dispatched first (at the “bottom” of the dispatch 

stack) and typically only reduce output during periods of significant reduced demand. Hydro 

dispatch is performed in a somewhat different manner. While it has essentially zero fuel cost, 

hydro dispatch also has limited energy availability based on the availability of water resources 

upstream of the generators and regulation of waterway flow below the generator. Hydro units 

also have the ability to ramp very quickly in response to variation in load. Hydro is therefore 

often dispatched as a load-following and peaking plant, while operating under various 

environmental, recreation, and regulatory constraints of minimum and maximum water levels.3

Wind and solar generators do not fit the normal paradigm of baseload or dispatchable resources. 

These plants act to reduce the net load (load minus solar and wind generation) on the system, 

reducing output from the marginal (highest cost) plant on the dispatch stack. However, these 

plants add additional complexity given their variability and uncertainty, which can add to total 

reserve requirements.

Reserves represent generation capacity that is “held” to ensure reliable system operation. There 

are a number of types of operating reserves held by system operators and also a large number of 

names applied to different reserve products depending on the market [8]. In this study, we 

examine three classes of spinning operating reserve products: regulation, contingency, and 

flexibility. Spinning reserves refer to reserves provided by generators that are on and producing 

power at less than full capacity. To hold spinning reserves, a generator commits readily available 

capacity for power production in real time. Non-spinning reserves, capacity from generators that 

are not scheduled to be producing power, are also procured in the market at nearly zero cost. 

Regulation reserves are designed to meet short-term (seconds to a few minutes) variation in 

demand due to unpredicted changes in load or variable generation resources, such as solar and 

wind power plants. Power plants must be equipped with an automatic generator control (AGC) 

system in order to provide regulation reserves; the AGC receives a regulation control signal and 

 

3 This is an oversimplification. In some locations large hydro resources allow it to meet a large fraction of load, 

including baseload demand. In addition, some “run-of-river” hydro plants are not dispatchable and are essentially 

baseload plants but with an output that varies daily and seasonally.

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4 

automatically adjusts the generator output. Regulation reserve requirements vary depending on 

load characteristics and the contributions from renewable generation. 

Contingency reserves address large power plant or transmission line failures and are often based 

on the single largest contingency that could occur on the system. Contingency reserves are 

independent of wind and solar penetration, assuming no single wind or solar plant (and

associated transmission) becomes the single largest contingency. 

Flexibility (sometimes called ramping) reserves are an emerging type of reserves designed to 

accommodate large, unpredicted changes in generation from solar and wind power plants on time 

scales greater than the regulation time frame. Flexibility reserve products are under development 

in two markets, California Independent System Operator (CAISO) and Midwest Independent 

System Operator (MISO) [9,10]. Detailed discussion of the calculation and provision of reserves 

can be found in references [11] and [12]. 

An important consequence of holding operating reserves is that they impose a cost to the system. 

This cost occurs because of the additional generators are required to be committed and operated 

at minimum levels, which reduces the efficiency of the system dispatch. An extensive discussion 

of reserve costs origins and sensitivities is provided by Hummon et al. [12]. The addition of DR 

providing operating reserves is anticipated to reduce system dispatch inefficiencies.

2.2 Modeled Grid Services Provided by Demand Response 

We use a PCM to consider the potential value of DR to provide energy and operating reserves.

Part 1 of this report series also assesses the ability of DR to provide firm capacity for the system. 

The value of capacity is not included in the production cost of energy and therefore is not 

assessed in this grid-simulation paradigm. DR provides energy services by shedding energy or 

shifting energy demand over time, generally reducing demand during periods of high prices or 

moving demand from periods of high to low prices. This can be analogous to energy storage 

when providing load-leveling or energy arbitrage [3]. We use the term energy to describe the 

reduction in load and the terms re-charge and pre-charge to describe the replacement of that load 

either after or before the DR event, respectively. For operating reserves, we consider the three 

products discussed in Section 3.1. 

The attributes of performance for DR providing various bulk power system services are not 

uniform across regions, nor are DR resources universally allowed to participate in bulk power 

system services [13]. In order to translate the physical capabilities of different DR strategies into 

eligibility for participation, we developed a set of generalized product definitions. These 

products are summarized in Table 1, which include the expected response characteristics of 

resources seeking to provide each type of service to the system.

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5 

Table 1. Description and Physical Requirements of the Products Modeled in This Study 

Reserves Products Physical Requirements

Product 

Type General Description

How 

Fast to 

Respond

Length of 

Response

Time to 

Fully 

Respond

How Often Called

Energy Shed or shift energy 

consumption over time

5 

minutes

≥1 hour 10 

minutes

1–2 times per day 

with 4–8 hour 

notification

Regulation Response to random 

unscheduled deviations 

in scheduled net load

30 

seconds

Energy 

neutral in 

15 

minutes

5 

minutes

Continuous within 

specified bid 

period

Flexibility Additional load following 

reserve for large 

unforecasted wind/solar 

ramps

5 

minutes

1 hour 20 

minutes

Continuous within 

specified bid 

period

Contingency Rapid and immediate 

response to a loss in 

supply

1 minute ≤30 

minutes

≤10 

minutes

≤ Once per day

 

2.3 Modeling Aggregated Demand Response

2.3.1 Production Cost Modeling Approach

This analysis uses a commercial PCM, PLEXOS,

4 to examine the operation of DR resources 

when providing both energy and operating reserves. There are three primary approaches to 

simulating DR: price responsive DR, system optimized load reductions for energy balancing, and 

system optimized load reduction/recovery for both energy imbalance and operating reserve 

provision. Several studies implement DR providing energy services using a supply curve to 

represent price responsive demand [14-16]. Other studies simulated DR energy services by 

scheduling the flexible portion of demand during the optimization [15,17-21]. Negnevitsky et al. 

[19] explicitly modeled load recovery, analogous to our “pre-/re-charge,” of demand-side 

resources, as a constraint on the operation of DR in a PCM. Several studies have examined the 

impact of procuring demand-side resources for operating reserves, including using large 

interruptible loads to increase reliability (decrease in loss of load probability) at least cost [22,23] 

or acquiring aggregated DR in the day-ahead market for load-following and regulation operating 

reserves [24,25]. Other integration studies have included cost-based interruptible load as an 

aspect that Faria et al. [26] implemented in a co-optimization of DR and distributed generation 

on a distribution network. 

We take the approach of co-optimizing DR to provide energy and operating reserves, as well as 

constraining the recovery of load allocated for energy shifting, for each DR resource. A single 

DR resource is represented by an aggregation of the end use over a region; for example, the 

residential cooling DR hourly time series profile for a large balancing authority (BA) area 

 

4 PLEXOS is produced by Energy Exemplar. http://energyexemplar.com. 

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6 

representing most of the load in the State of Colorado represents the hourly sum of all residential 

cooling units expected to be enrolled in the DR program (outfitted with automatic controls) and 

operating during each hour. The profile is scaled based on the anticipated call rate (defined as 

how frequently or length of time the load must reduce demand). For instance, if the residential 

cooling DR call is typically 15 minutes for a unit, the hourly value is one-quarter of the total 

capacity available for any 15-minute period. The simulation in this report evaluates the impact 

on the bulk power system over large areas (such as a utility service territory or balancing area), 

as opposed to local impacts on the distribution network. DR is constrained in a manner similar to 

a conventional generation asset but considers the unique aspects of DR, including load recovery

(i.e., energy shifting is energy-neutral) and constraints on frequent cycling of loads and timevarying response rates. We are unaware of any previous study that considers large-scale and 

highly detailed implementation of DR in a commercial PCM. 

The PCM objective function remains the same: minimize the total production cost of generating 

energy to meet the demand in every time interval and hold required reserves to ensure system 

reliability from the perspective of a central scheduler. DR resources are implemented as virtual 

generators with time series profiles that define their availability for each grid service. The PCM 

DR services have small5

 variable operating costs in our model. This allows for an estimate of the 

potential value of various DR resources, which can then be compared to their implementation 

costs. However, there are significant energy limitations (via constraints limiting hours of 

operation, starts per day, etc.) and co-optimization constraints that limit the total energy 

scheduled from DR. DR resource availability by resource type, grid services, and balancing 

authority were assessed according to Part 1 of this report series [1]. 

Table 2 summarizes the types of DR considered, categorized by end-use and modeling 

parameters (operation/constraint similarity), as well as availability for different services.

 

5 DR variable operating and maintenance (VO&M) costs are small compared to the fuel cost of 

conventional generators.

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7 

Table 2. Demand Response Resources Modeled in This Report 

Product End-Use 

Category Model Category Energy6 Regulation Contingency Flex

Commercial 

Lighting Commercial Reserves Only X X X 

Commercial 

Ventilation Reserves Only X X X 

Commercial 

Cooling

Energy Shifting 

and Reserves Shift X X X 

Commercial 

Heating

Energy Shifting 

and Reserves Shift X X X 

Agricultural 

Pumping

Industrial NonManufacturing

Energy Shifting 

and Contingency 

Reserves

Shift X 

Refrigerated 

Warehouses

Energy Shifting

Only Shift

Data Centers

Energy Shifting 

and Contingency 

Reserves

Shift X 

Wastewater 

Pumping Municipal Energy ShiftingOnly Shift 

Municipal 

Pumping

Energy Shifting

Only Shift

Outdoor 

Lighting Reserves Only X X X 

Residential 

Heating Residential Energy Shedding and Reserves Shed X X X 

Residential 

Cooling

Energy Shifting 

and Reserves Shift X X X 

Residential 

Water Heating

Energy Shifting 

and Reserves Shift X X X 

Enabling DR requires equipping end uses with communication and control equipment to allow 

the load to receive control signals or information from the system operator and act on that 

information to change their demand. The process of aggregating end-use load response for DR is 

discussed in Part 1 of this report series. Figure 2 demonstrates the relative terms for end-use 

capacity: enabled rated, enabled, and available. The enabled rated capacity is the nameplate 

capacity of each end-use unit times the number of units equipped to receive a control signal. For 

instance, in residential cooling, the nominal nameplate capacity (k) of a cooling unit is 2.4 kW, 

and this study estimates participation of approximately 130,000 units (N) in the Colorado test 

system. Therefore, the enabled rated capacity (R) is

R = kunit ∙ N (1) 

which is equal to 310 MW for residential cooling in the Colorado test system (red, dashed line in 

Figure 2). The enabled capacity (E) is the maximum response, non-coincident with load, that is

 

6 Energy “shift” resources require load recovery, discussed in Section 2.3.2. Energy “shed” resources do not require 

load recovery.

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8 

expected from the enabled rated capacity (R) and is calculated from the duty cycle (δ) and 

sheddability (σ):

E = R ∙ δ ∙ σ (2) 

For residential cooling, the duty cycle of cooling units during periods of peak demand is 

estimated to be about 50%. This means that 50% of the cooling units are operating during peak 

demand. The sheddability is the fraction of the enabled rated capacity that is able to be shut 

down for a DR event. Residential cooling is assumed to have a sheddability of 0.7 [1]. Thus, the 

enabled capacity for residential cooling is 109 MW (green, dashed line in Figure 2). The timevarying availability represents the time-varying duty cycle for which residential cooling is 

inversely proportional to the ambient temperature.

Figure 2. Enabled demand response capacity is defined as the capacity of the end use that is 

equipped/enrolled to respond to a signal or instruction to reduce load. The available capacity is 

the fraction of enabled capacity that is “bid” into the wholesale electricity market during each 

24-hour optimization window.

Time series profiles of available capacity were generated for each DR resource and for each grid 

service (energy and operating reserves) that DR resource can provide [1]. Each time series 

profile of a service is input into PLEXOS, which can then co-optimize all DR resources across 

all potential services to minimize production cost. The co-optimization is subject to the 

constraint that the sum of all energy and ancillary service provision during each time interval 

cannot exceed the maximum availability in that interval. Figure 3 shows three days in August, of 

commercial cooling availability by grid service in Colorado. These are the input profiles for the 

PCM. Depending on hourly availability, some combination of energy, contingency, and 

flexibility reserves can be provided up to 70 MW. Commercial cooling is constrained to only 

allow energy to be shifted between 6 a.m. and 6 p.m.; thus, the charging profile is zero overnight 

between 6 p.m. and 6 a.m. The sum of energy and ancillary service provision during each 

interval cannot exceed the capacity available for the largest product—most often the energy 

availability. The following sections describe in detail the implementation of these services in 

the PCM. 

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9 

Figure 3. Maximum availability of energy, contingency, and flexibility for demand response from 

commercial cooling resources in Public Service Colorado on August 1–3 

2.3.2 Simulation of Energy Shifting

PLEXOS models the permitted energy shifting scheduling some DR to reduce load during higher 

cost hours and increase load during lower cost hours. All of the energy shifting DR resources are 

required to replace the displaced load by increasing the load during other times. This energy 

replacement is also called load recovery and is analogous to an energy storage device but where 

the “charging” can occur before or after the discharging (load reduction). DR energy shifting is 

modeled as a 100% efficient storage device.7

 In other words, an equal amount of charge and 

discharge energy is accounted for in each DR resource. The charging and energy capacities are 

constrained by DR profiles. Section 2.3.1 described the DR profiles for energy and operating 

reserve availability. This section describes the DR profile for charging and the implementation of 

that in PLEXOS.

An illustration of the capacity available for generation and charging is shown in Figure 4. The 

total load profile is defined by the solid black line. The fraction of the end use that is available 

for DR activities is shaded dark grey, and the fraction of the end use that is fixed and not 

available for DR is shaded light blue. Figure 4b zooms in on the fraction of end-use load 

available for energy and the corresponding capacity available for DR charging (shown in pink). 

DR can “peak shift,” providing a reduced load during periods of high demand and increased load 

during periods of low demand. Charging for heating and cooling loads is subject to the additional 

constraint that it must be completed during the daytime hours (see Table 3).

 

7 DR pre-charge and re-charge may result in storage efficiencies greater than or less than 100%. Further research is 

needed to determine the appropriate modeling parameters for each type of DR and may vary by climate, season, 

time of day, and implementation.

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10 

Figure 4. Example of the (a) total load from an illustrative end use and the portion of that load 

available for demand response activities and (b) the constrained availability of DR capacity for 

charging and generation

The time series of capacity available for generation is assigned to the discharge of the storage 

device. Similarly, the time series of capacity available for charging is assigned to the load of the 

storage device. In this configuration, DR is essentially a storage device with limitations of when 

and how much the device can provide energy to the system or draw energy from the system.

The operational constraints associated with energy shifting are listed by type of DR in Table 3. 

Energy scheduling is constrained by both the maximum number of hours of operation per day 

and by the schedule for charging. The entire energy-shifting DR is energy neutral over 24 hours. 

This means that the system must either pre-charge or re-charge the DR resource. While some DR 

resources may be capable of shifting energy over longer time periods (greater than 24 hours), the 

modeling horizon in this study is 24 hours. Decision complexity (and thus computation time) 

increases with the length of the optimization horizon.In addition to the challenges associated 

with modeling longer duration load shifting, Sioshansi et al. [27] has demonstrated that most of 

the value of load shifting appears to occur on the diurnal cycle. 

Several DR resources have soft constraints for energy operation with penalty prices for 

constraint violations. Soft constraints are presented in PCMs as a dollar value on the violation of 

that constraint. For instance, municipal pumping resources are constrained to “operate” once per 

day. If this were a hard constraint, there would never be an occasion when that DR resource may 

operate more than once per day. As a soft constraint, the cost of violating the constraint is part of 

the cost minimization function and may occur when it is the least-cost option of meeting load. 

The soft constraint with the least penalty will be violated first. For this reason, it is important that 

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11 

all soft constraint costs are set relative to each other. The penalties are paid to the DR resource 

and are part of the total revenue for DR.8

Table 3. Demand Response Resource Energy Operation Restrictions, Penalties, and Costs 

DR Resource Operation Restrictions Penalties Costs

Charging 

Hours 

(within 24 

hours)

Max 

Hours 

per Day

Max 

Starts 

per Day

Max Hour 

Shed 

(per hour)

Max 

Starts 

(per start)

Variable 

Operating & 

Maintenance

($/MWh)

Residential Heating N/A 1 1 Strictly enforced

Commercial Cooling 6 a.m.–6 p.m.

Commercial Heating 3 a.m.–7 p.m.

Residential Cooling 6 a.m.–6 p.m.

Data Centers Any 4 Strictly enforced

Residential Water 

Heating Any

Wastewater Pumping Any 3 1 $20.00 $50.00

Agricultural Pumping Any 8 1 $20.00 $50.00 $2 

Municipal Pumping Any 2 1 $50.00 $100.00 

Refrigerated 

Warehouses

Any 4 1 Strictly enforced

2.3.3 Co-Optimization of Energy and Reserves

As discussed in Section 2.3.1, our approach to modeling DR in PLEXOS is to enforce energy 

shifting and charging for DR providing energy services and to co-optimize the DR capacity for 

energy shifting with provision of operating reserves. The capability to provide operating reserves 

is largely dependent on the response rate of the load. The response rate (along with other 

characteristics) of DR changes with the mechanisms that control load. The response rate varies 

across DR resources and is defined differently than conventional fuel-burning thermal 

generators. Ramp rates set the maximum change in power output per minute and the capacity 

available for operating reserves. For instance, if a generator has a ramp rate of 5 MW/minute, the 

maximum regulation reserve provision is 25 MW because regulation services have to be fully 

met within 5 minutes. Cooling and heating DR uses both changes to the thermostat set-point or

direct load control to activate and “ramp” the load shed [28]. DR from pumping applications 

often involves human action to change the load consumption of the system [29]. The differences 

between DR service activation and generators necessitated that we model DR resources with 

time-varying and service-varying response time. 

 

8 If the penalty scales with the unit of the violation (e.g., number of hours) rather than the size of the DR capacity, it 

will be evaluated in future DR modeling studies.

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12 

Table 4 defines the “fast” and “slow” time lapse to full load shed for each DR resource. Each DR 

resource could either have a fast actuation or a slow actuation, yet the total capacity available to 

respond is constrained by the resource and not the response mechanism. The fast response time is 

assigned to the contingency and regulation reserve capacity and the slow response time is 

assigned to the energy and flexibility reserve capacity. The grouping of energy/flexibility and 

contingency/regulation resources is based on the response times needed for the product (see 

Section 2.2). The PCM co-optimizes both sets of responses and capacities, while maintaining the 

differing response rates. We convert the time to full load shed to a ramp rate by taking the 

capacity time series for the service type (e.g., contingency reserve) divided by the time to full 

load shed. Our implementation in PLEXOS uses two separate generation objects9 with different 

response rates. One generator represents the energy shifting and flexibility reserve (slow 

response services), while its counterpart provides spinning contingency and regulation reserves 

(fast response services). While the provision of each grid service from DR is limited by the 

individual profile for each grid service, a single constraint overlaps all four grid services to 

enforce that the sum of all DR capacity providing energy and operating reserves cannot exceed 

the maximum of any one grid service profile. 

Table 4. Time Lapse to Full Load Shed for Each Demand Response Resource

Product 

Time Lapse to Full Load Shed

Contingency and 

Regulation 

Energy Shifting 

and Flexibility

Residential Heating 1 min 15 min

Commercial Cooling 1 min 15 min

Commercial Heating 1 min 15 min

Residential Cooling 1 min 15 min

Data Centers 1 min 15 min

Residential Water 

Heating 30 sec 30 sec

Wastewater Pumping 1 min 5 min

Agricultural Pumping 1 min 1 min

Municipal Pumping 1 min 5 min

Refrigerated 

Warehouses 1 min 5 min

Commercial Lighting 30 sec 30 sec

Commercial 

Ventilation 1 min 15 min

Outdoor Lighting 40 sec 40 sec

 

9 In PLEXOS, a generator cannot have separate (unrelated) ramp rates for different services.

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13 

3 Results

3.1 Colorado Test System

To evaluate the impact of DR on an electric power system, we developed a test case composed of 

two balancing areas largely in the State of Colorado. The test system is described extensively in

three sources [3,4,12]. The Colorado test system consists of two balancing areas [PSCo and 

Western Area Colorado Missouri (WACM)] using data derived from the database established by 

the Western Electricity Coordinating Council (WECC) Transmission Expansion Policy Planning 

Committee (TEPPC) model and other publicly available datasets. Transmission is modeled 

zonally, without transmission limits within each BA area. Projected generation and loads were 

derived from the TEPPC 2020 scenario [30]. Hourly load profiles were based on 2006 data and 

scaled to match the projected TEPPC 2020 annual load. Hourly solar and wind power generation 

profiles are time synchronized to the load profiles for the year 2006. 

The system peaks in the summer with a 2020 coincident peak demand of 13.7 GW and annual 

demand of 79.0 TWh. A total of 201 thermal and hydro generators are included in the test 

system, with total capacities listed in Table 5. We adjusted the conventional generator mix to 

ensure the available capacity (after outages) was always at least 9% greater than demand by 

adding a total of 1,450 MW (690 MW of combustion turbines and 760 MW of CC units). This 

adjustment was necessary in part because the simulated system does not include contracted 

capacity from surrounding regions or any capacity contribution from solar and wind resources. 

The base case of the test system assumes a wind and solar penetration of 16% on an energy 

basis. For comparison, Colorado received about 11% of its electricity from wind in 2012 [31].10

Photovoltaic (PV) profiles were generated using the System Advisor Model (SAM) [32] with 

2006 meteorology. Wind data was derived from the Western Wind and Solar Integration Study 

(WWSIS) dataset [33].11 Discrete wind and solar plants were added from the WWSIS datasets 

until the installed capacity produced the targeted energy penetration.12

 

10 Colorado generated 6,045 GWh from wind in 2012 compared to total generation of 53,594 GWh. EIA “Electric 

Power Monthly with Data for December 2012,” February 2012. See [30]. 11 All generation profiles were adjusted to be time synchronized with 2020, which is a leap year. 12 The sites were chosen based on capacity factor and do not necessarily reflect existing or planned locations for 

wind and solar plants.

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14 

Table 5. Test System Generator Capacity in 2020

System Capacity (MW)

 Coal 6,178

 Combined Cycle 3,724

 Gas Turbine/Gas Steam 4,045

 Hydro 773

 Pumped Storage 560

 Wind 3,347 (10.7 TWh)

 Solar PV 878 (1.8 TWh)

 Demand Response 293

 Othera 513

Total 15,793 a Includes oil- and gas-fired internal combustion generators. 

Fuel prices were derived from the TEPPC 2020 database. Coal prices were $1.42/MMBtu for all 

plants. Natural gas prices varied by month and ranged from $3.90/MMBtu to $4.20/MMBtu, 

with an average of $4.10/MMBtu [34]. No constraints or costs were applied to carbon or 

other emissions.

We generated hourly requirements for contingency, regulation, and flexibility reserves.13

Contingency reserves are based on the single largest unit (an 810-MW coal plant) and allocated 

with 451 MW to PSCo and 359 MW to WACM; 50% is met by spinning units.14 Regulation and 

flexibility reserve15 requirements vary over time based on the statistical variability of load, wind, 

and PV, with the methodology described in detail by Ibanez et al. [11]. Hummon et al. [12] 

describes the application of the methodology to the test system. 

The sum of the total operating reserves (met by spinning units) averages 582 MW, which 

corresponds to about 6.4% of average load. Table 6 summarizes the general characteristics of the 

three modeled reserve services. Reserves were modeled as “soft constraints,” meaning the 

system was allowed to not meet requirements if the cost of provision exceeded the threshold 

value shown in Table 6. These penalties were chosen to be high enough so that the least-cost 

decision is likely to be starting a new unit to provide reserves. Unserved load was also modeled 

as a soft constraint, with a penalty price of $10,000/MWh. 

 

13 For additional discussion of these reserves (especially flexibility reserves, which is not yet a well-defined market 

product), see Ela et al. [8]. 14 The PSCo and WACM balancing areas are part of the Rocky Mountain Reserve group, which shares contingency 

reserves based on these values. 

15 For these services only the “upward” reserve requirements were evaluated. The need for downward reserves 

becomes of greater importance at high renewable penetration when conventional thermal generators are operated at 

or near their minimum generation points for more hours of the year. Future work will evaluate the cost and price of 

separate up and down reserve products in these scenarios.

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15 

Table 6. Summary of Operating Reserves in the Base Case of Test System

Operating 

Reserve 

Service

System 

Drivers

Time to 

Respond 

[minutes]

Requirement 

(% of load) 

Mean (min/max)

Penalty 

[$/MW-h]

Regulation PV, wind, load 5 1.33 (1.00/1.71) 9,500

Contingency

Largest 

generator 10 4.54 (2.97/5.95) 9,000

Flexibility PV, wind 20 0.64 (0.13/1.07) 8,500

The availability and constraints of individual generators providing reserves are major drivers for 

the cost of providing reserves and therefore for the value of DR-providing reserves. Not all 

generators are capable of providing regulation reserves based on operational practice or lack of 

necessary equipment to follow a regulation signal. The results reported are for a system that only 

allows a subset of generators to provide regulation and flexibility. We based our assumptions on 

the PLEXOS database established for the CAISO’s 33% Renewable Integration Study [35]. This 

dataset assigns regulation capability to a subset of plants, which is about 60% of total capacity 

within California (as measured by their ramp rate). Similarly, we allowed only 60% of all 

dispatchable generators (i.e., coal, gas CC, dispatchable hydro, and pumped storage) to provide 

regulation.16 Based on feedback from various utilities and system operators, we further restricted 

CTs from providing regulation. We allow all dispatchable plants (including CTs) to provide 

flexibility and contingency reserves. 

An additional cost was assigned to plants providing regulation, associated with additional wear 

and tear and heat rate degradation associated with non-steady-state operation. This is 

functionally equivalent to a generator regulation “bid cost” in restructured markets, discussed in 

PJM Manual 15: Cost Development Guidelines [36]. The assumed regulation costs, by unit type, 

are provided in Table 7. We did not apply a regulation bid cost to DR resources that were 

capable of providing regulation. Essentially this makes DR “first in line” for providing regulation 

services, with the only limitation being co-optimization of the DR resource for load 

shedding/recovery. 

Table 7. Assumed Additional Operating Cost for Units Providing Frequency Regulation Service

Generator Type Cost ($/MW-h)

Supercritical Coal 15

Subcritical Coal 10

Combined Cycle 6

Gas/Oil Steam 4

Hydro 2 

Pumped Storage 2

The PLEXOS simulations performed in this analysis used day-ahead scheduling with a 48-hour 

optimization window, rolling forward in 24-hour increments. The extra 24 hours in the unit 

commitment horizon (for a full 48-hour window) were necessary to properly commit the 

 

16 In practice, there may be far fewer generators that provide regulation services, where information is proprietary, 

and which may affect the marginal price of regulation. 

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16 

generators with high start-up costs and the dispatch of energy storage. All scenarios were run for 

one chronological year using PLEXOS version 6.207 R08, using the Xpress-MP 23.01.05 solver, 

with the model performance relative gap set to 0.5%. 

3.2 Base Case Results (No DR Resources)

Figure 5 summarizes the energy and reserves prices produced in the test system in the base case 

without added DR. Figure 5a shows a price duration curve (PDC) for energy, with an average 

energy price of $32/MWh. Any value associated with load shifting will occur by reducing 

demand during periods of highest price (on the left side of the PDC) and increasing demand 

during periods of lower price (on the right side of the PDC) [12]. Operating reserve price 

duration curves for the base system are provided in Figure 5b. The average price of regulation 

reserves was $14.7/MW-h. For comparison, the average market clearing price for regulation in 

2011 was $11.8/MW-h in New York Independent System Operator (NYISO), $10.8/MW-h in 

MISO, and $16.1/MW-h in CAISO [37]. 

Figure 5. System price duration curve for (a) energy and (b) operating reserves in the base case of 

the Colorado test system

The average price of spinning reserves in the base system across the two balancing areas 

simulated in the test system was $7.0/MW-h.17 The values can be compared to 2011 average 

market clearing prices of $7.4/MW-h in NYISO, $2.8/MW-h in MISO, and $7.2/MW-h in 

CAISO. Of note is the large number of hours where the price of spinning reserves is close to 

zero, which is often observed in the clearing price for spinning reserves in wholesale markets. 

For example, in 2011, the clearing price for spinning contingency reserves in both MISO and 

CAISO was less than $1/MW-h for over 2,000 hours. 

 

17 As with regulation reserves, this excludes hours of extremely high prices driven by internal model penalties.

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17 

The cost of flexibility reserves was very low due to the relatively slow response rate (20 minutes 

compared to 10 minutes for spinning reserves and 5 minutes for regulation) and a small overall 

requirement. However, the actual use of flexibility reserves in real-time dispatch could be more 

costly than our model captures. This reserve service has yet to be implemented in a restructured 

market, and our assumptions regarding requirements and use may be substantially different from 

a flexibility product actually implemented by utilities and system operators. Additional analysis 

of the performance of flexibility reserve capacity deployed in sub-hourly dispatch will be 

required to fully evaluate the potential benefits in terms of system cost savings by allowing 

demand to provide this service. 

3.3 Demand Response Resources in the Test System

As discussed in Section 2.3.1, the capacity available for each DR resource, for each grid service, 

was assessed independently. Table 8 summarizes the availability of each DR resource to provide 

energy on an annual basis as well as during peak demand hours (top 1%) in the Colorado test 

system. Table 9 summarizes the availability of each DR resource to provide operating reserves. 

In addition to available capacity, Table 9 includes the availability of DR resources in terms of the 

average percentage of hourly reserve requirement that could be met by the resource. This 

calculation does not take into account the mutual exclusivity of the resource capacity.

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18 

Table 8. Availability of Demand Response to Provide Energy in the Colorado Test System on an 

Annual Basis as Well as in the Top 20 Hours of Greatest Demand

Demand Response

Resources 

Providing Energya

Annual Top 20 Load Hours

Capacity 

(mean/min/max) 

[MW]b 

Annual 

Available 

Energy with 

Constraints 

[GWh]c

Annual 

Hours 

Availabled

Capacity 

(mean/min/max) 

[MW]

% of Load 

(mean/min/max)

Residential Cooling 10.9 / 0 / 108.7 38.8 5,390 55.1 / 41.3 / 72.2 0.4 / 0.3 / 0.5

Residential Water 

Heating 1.8 / 0.5 / 3.9 15.7 8,784 1.3 / 1.1 / 1.6 0 / 0 / 0

Commercial Cooling 2.1 / 0 / 46.4 10.7 4,338 8.2 / 4.2 / 12.9 0.1 / 0 / 0.1

Commercial Heating 2.2 / 0 / 25.5 3.8 8,390 0 / 0 / 0 0 / 0 / 0

Municipal Pumping 1.7 / 0.4 / 3.8 2.1 8,784 2.1 / 1.7 / 2.7 0 / 0 / 0

Wastewater Pumping 1.5 / 1.5 / 1.5 1.6 8,784 1.5 / 1.5 / 1.5 0 / 0 / 0

Refrigerated 

Warehouses 0.2 / 0 / 0.4 0.3 8,685 0.3 / 0.3 / 0.4 0 / 0 / 0

Agricultural Pumping 17 / 1.7 / 41.2 49.9 8,784 36.6 / 32 / 40 0.3 / 0.2 / 0.3

Data Centers 8 / 8 / 8 11.7 8,784 8 / 8 / 8 0.1 / 0.1 / 0.1

Totale 45.4 / 14.5 / 

227.8 134.6 8,784 113.1 / 91.8 / 137.3 0.8 / 0.7 / 1

a In the test system, residential heating is primarily sourced from natural gas, not electricity. We include the methods 

of modeling residential heating because it is modeled in the western interconnect. See Ref. [1].

b Peak megawatt is the maximum capacity available for that resource (across all products), except for agricultural 

pumping where the maximum capacity available for contingency is 61.9 MW.

c Annual gigawatt-hours of energy availability is calculated by finding the maximum energy available for each day 

constrained by the number of allowable hours. This expresses the maximum energy shifting potential; the 

production cost model optimizes that energy shifting and will always be less than this quantity.

d The year analyzed (2020) is a leap year, hence 8,784 hours. e Total numbers are less than the sum of the end uses due to non-coincident availability.

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19 

Table 9. Availability of Demand Response for Operating Reserves (Regulation, Contingency, and 

Flexibility) in the Colorado Test System

Demand Response

Resource

Mean Capacity 

[MW]

Mean % of Reserve

Requirement 

Available from 

Demand Response

Hours of Average 

or Higher 

Availability 

 Reg Cont Flex Reg Cont Flex

Residential Cooling 10.3 10.3 10.3 8.8 2.5 23.9 2,656

Residential Water 

Heating 1.8 1.8 1.8 1.5 0.4 3.4 4,334

Commercial Cooling 0.1 1.6 0.2 0.1 0.4 0.6 2,052

Commercial Lighting 1.0 3.0 3.0 0.8 0.7 5.8 3,945

Commercial Ventilation 1.1 3.4 3.4 0.9 0.8 6.6 4,890

Outdoor Lighting 23.4 23.4 23.4 20.7 5.8 44.2 4,329

Agricultural Pumping 0.0 25.6 0.0 0.0 6.3 0.0 0

Data Centers 0.0 8.0 0.0 0.0 2.0 0.0 0

Totala 37.7 77.0 42.1 32.9 19.0 84.5 4,985

a Total numbers are less than the sum of the end uses due to non-coincident availability.

Table 8 and Table 9 show that the total capacity available for energy and operating reserves is 

not distributed equally over the DR resource categories. Some resources have a nearly constant 

capacity available, while others are highly seasonal. For instance, residential cooling capacity, 

during peak demand hours, is 50 times greater than residential water heating, yet the annual 

expected energy from residential cooling is less than 3 times greater than residential water 

heating. The annual availability factor (AF) is the sum of the maximum capacity available during 

each time period divided by the peak available capacity times the number of time intervals (i.e., 

total hours per year). This is similar to capacity factor, which is a measure of the power plant 

actual energy production compared to the possible energy production if the plant operated at 

maximum capacity during all time periods. The “energy” AF for a DR resource is calculated 

based on the probable energy production (with operation constraints such as maximum number 

of hours per day; see Table 3) divided by the sum of the peak available capacity over all hours of 

the year. Table 10 provides the availability factors for energy and for all services. AF does not 

capture the time varying value of DR but does convey the difference between seasonal and nonseasonal resources. 

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20 

Table 10. Demand Response Availability Factor for Energy and All Services

Demand Response

Resource

Energy 

Availability 

Factor [%]

Total 

Availability 

Factor [%]

Demand Response

Resource

Energy 

Availability 

Factor [%]

Total 

Availability 

Factor [%]

Residential Cooling 4 10 Municipal Pumping 6 46

Residential Water 

Heating 46 46 Wastewater Pumping 13 100

Commercial 

Cooling 3 5 Outdoor Lighting 0 52

Commercial 

Heating 2 9 Refrigerated Warehouses 10 47

Commercial 

Lighting 0 75 Agricultural Pumping 9 41

Commercial 

Ventilation 0 81 Data Centers 17 100

The time series profiles provide the PCM with the upper bound of availability for each service, 

and we impose a constraint such that the total energy and reserve provision could not exceed the 

availability of any one service. On average, about 0.5% of Colorado test system load is available 

for DR, with a minimum of about 0.1% occurring during early evening hours in the winter and a 

maximum of about 2.3% occurring during late afternoon hours in the summer. 

While we did not perform a detailed capacity credit analysis, some insights can be gained by 

examining the availability of DR during periods of highest demand. Figure 6 shows the hourly 

system load (right axis) and the maximum hourly capacity available from DR (left axis) in the 

four categories: municipal, industrial non-manufacturing, commercial, and residential loads (see

Section 2.3 for the DR resources in each category). During the top 1% of demand hours, about 

115 MW of DR was available to shed or shift load, which is about 2.5 times the average 

DR availability. 

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21 

Figure 6. Comparison of the (left axis) hourly capacity available from DR by sector and (right axis) 

hourly system load DR availability is the maximum available from each DR resource across the 

energy and ancillary service markets. The top 1% of hours, measured by system load, are marked 

with red circles.

3.4 Examples of Demand Response Performance 

Three examples of DR are examined in this section: commercial lighting, wastewater pumping, 

and residential water heating. For each resource, we examine the allocation of capacity via two 

figures. The first figure shows the annual daily average provision of the DR resource across 

energy and operating reserves as well as the annual hourly average provision. The second figure 

shows two snapshots of hourly energy and ancillary service provision data, from spring and 

summer. Additional examples of DR performance can be found in the appendix. 

3.4.1 Commercial Lighting

Commercial lighting (shown in Figure 7 and Figure 8) is able to hold flexibility, regulation, and 

contingency reserves but cannot shift energy. Capacity available for flexibility reserves is the 

largest during every interval and therefore sets the total capacity of commercial lighting DR that 

can provide operating reserves. Commercial lighting is cyclic over the day and week (see 

Figure 7), decreasing overnight and on the weekends. The provision for regulation is near its full 

regulation availability at all times; the remaining capacity is primarily provisioned for 

contingency reserves, with intermittent provision of flexibility reserves, the lowest price service.

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22 

Figure 7. Commercial lighting DR in PSCo: Mean (a) daily and (b) hourly provision of capacity

Figure 8. Commercial lighting DR in PSCo (upper): Hourly capacity provision for (a) spring and (b) 

summer; hourly marginal price of operating reserves (lower)

3.4.2 Municipal Wastewater Pumping

Wastewater pumping can provide energy shifting but is assumed not able to provide operating 

reserves. When many pumps are aggregated together, there is the potential to offer operating 

reserves, but individual facilities are assumed to be too risk-averse to participate in calls of that 

frequency. Part 1 of this report series has a more in-depth discussion on the controllability and 

acceptability factors for wastewater pumping [1]. Wastewater pumping has a constant available 

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23 

capacity, as can be observed in Figure 9a and Figure 9b. Similar to many of the other DR 

resources that can shift energy, Figure 9b shows that the average day shifts energy away from the 

early evening hours to the late evening and early morning hours. 

Figure 9. Wastewater pumping DR in PSCo: Mean (a) daily and (b) hourly provision of capacity

The discrepancy between the maximum energy and the energy utilization (black area) in 

Figure 9a is explained by the constraints placed on the usage of wastewater pumping for DR. 

There is a $50 violation for scheduling the aggregated set of pumps to turn off more than once a 

day and a $20 violation for every 1 hour of utilization over 3 hours. Figure 10 shows the 

operation of these units in the (a) spring and (b) summer. Similar to municipal pumping, the 

violations are more frequent in July when energy prices are higher. 

Figure 10. Wastewater pumping DR in PSCo (upper): Hourly capacity provision for (a) spring and 

(b) summer; hourly marginal price of energy and operating reserves (lower)

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24 

3.4.3 Residential Water Heating

Residential water heating can provide both energy shifting and reserves services. Figure 11a 

shows a seasonal availability pattern, with greater overall demand during the winter. The 

maximum energy and operating reserves available from water heating peak twice a day (morning 

and evening). The generation obtained from varying the electrical load of a water heater must be 

shifted to other hours within the day, just as with other energy shifting DR resources. The 

fraction of load from water heaters offered for DR is such that the temperature of the water 

heater will stay within its operating range. Similar to residential cooling DR, residential water 

heating can hold contingency during charging. The shifted energy is not restricted to a set of 

hours, and thus can be optimized to the hours with the lowest price (i.e., overnight) (see 

Figure 11b). 

Regulation dominates the allocation of reserve provision because it is the most valuable product 

but also because the maximum available capacity of residential water heating for regulation is 

4.5 times greater than the capacity available for contingency. Regulation is energy-neutral over 

15–20 minutes; thus, there is a high potential to use water heater capacity for regulation reserves 

without disturbing the operation of the water heater.

Figure 11. Residential water heating DR in PSCo: Mean (a) daily and (b) hourly provision 

of capacity

Figure 12 shows the hourly allocation of capacity in residential water heating during two 4-day 

periods in the spring and summer, as well as the marginal price of energy and operating reserves. 

In the summer, the residential water heating DR resource is typically used for energy between 

the hours of 1 p.m. and 8 p.m. and shifts that energy to the late evening and overnight hours 

when the price of energy is lowest. This is similar to the operation of an energy storage device 

arbitraging on-/off-peak energy prices. It also holds capacity for contingency events while 

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25 

recovering energy. During all other hours, the residential water heating DR capacity is held 

for regulation.

Figure 12. Residential water heating DR in PSCo (upper): Hourly capacity provision for (a) spring 

and (b) summer; hourly marginal price of energy and operating reserves (lower)

3.5 Operational Value of Demand Response 

3.5.1 System Value

The operational value of DR is derived from two sources. First, when shifting energy, it can 

reduce use of the highest cost generation units. Second, while providing reserves it can reduce 

the use of less efficient partially loaded thermal generators, as well as the variable cost associated 

with providing regulation services from conventional generators. 

Figure 13 shows the impact of DR energy shifting on mean daily load patterns for spring and 

summer. In the spring, DR usually shifts load from daytime to overnight hours. In the summer, 

DR primarily reduces system load between 1 p.m. and 8 p.m., while load recovery occurs 

overnight and into the early morning hours. The shift in load is much more dramatic in the 

summer, swinging between a load shed of 100 MW to a load recovery of 50 MW. 

Figure 13. Mean increase in the daily load profile for spring and summer when DR provides 

energy. Negative numbers are a reduction in total system load, positive numbers are an increase 

in load.

Figure 14 shows the distribution of reserves provided by the different types of generation. On an 

annual basis, DR provides about 27% of regulation reserves and 8% of contingency reserves. DR 

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26 

provides very little flexibility reserves; the sum of all DR providing reserves is limited by the 

largest grid service availability, and generally all of the DR capacity is allocated to energy, 

regulation reserves, and contingency reserves. 

Figure 14. Reserve provision (a) regulation, (b) contingency, and (c) flexibility for the base case 

and when DR resources are available in the system for reserves

The overall impact of the services provided by 293 MW of peak available DR is summarized in

Table 11. DR acts to reduce generation from the highest cost gas-fired generation and shift this 

generation to lower cost resources. In particular, DR reduced the use of the lowest efficiency 

power plants (combustion turbines) by about 10%.

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27 

Table 11. Comparison of the Test System Energy Results When Demand Response Resources 

are Available

 Without Demand 

Response

With Demand 

Response

Increase 

(Absolute / %)

Generation (GWh) 

Coal 45,981 46,111 130 / 0.3%

Gas Combined Cycle 14,741 14,637 -104 / -0.7%

Gas Combustion Turbine 1,199 1,075 -124 / -10.3%

Hydro 3,792 3,792 0 / 0%

Pumped Hydro Storage (PHS) 1,037 1,013 -24 / -2.3%

Winda 10,705 10,705 0 / 0%

PV 1,834 1,834 0 / 0%

Other 92 92 0 / 0%

Demand Response - 117 -

Total Generation (GWh)b 79,381 79,376 -5 / 0%

Fuel Use (1,000 MMBTU) 

Coal 486,467 487,870 1,403 / 0.3%

Gas 128,473 126,195 -2,277 / -1.8%

Total Fuel Use 614,939 614,065 -874 / -0.1%

a Neither wind nor PV experienced curtailment in these cases.

b The difference in generation is associated with the additional losses that occur from changes in the use of the 

pumped hydro storage.

The shift in fuel use resulting from DR deployment translates into annual fuel savings of about 

$7 million, or 0.6%. Table 12 summarizes the changes between the base case and system with 

DR in the four cost categories tracked in the PLEXOS model. The dominant source of benefit is

associated with reduced fuel use; however, there is also a significant savings associated with 

avoided regulation costs. This cost is associated with decreased non-steady state operation of 

conventional generators providing regulation.

The addition of 293 MW of DR in the test system reduced total production cost by about 0.6%. 

Because DR provides a large fraction of the total regulation requirement it also reduces the total 

costs associated with regulation “bid” costs by about 37%. This result will change if DR 

resources bid in an equivalent wear and tear cost associated with DR providing regulation 

services.

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28 

Table 12. Production Costs for Base Case and Base Case With Demand Response Providing 

Energy and Operating Reserves

Production Cost [M$] Base Case Base Case 

with DR

Decrease in 

Cost With DR

(M$ / % of 

Base)

Fuel Cost 1,215.0 1,208.0 7.0 / 0.6%

Variable O&M Cost 151.8 152.2 -0.4 / -0.3%

Start and Shutdown Cost 58.4 58.7 -0.4 / -0.6%

Regulation Reserve Bid Price 4.5 2.9 1.7 / 36.8%

Total Generation Cost 1,429.7 1,421.8 7.9 / 0.6%

3.5.2 Value of Individual DR Products

The value of DR provided in Table 12 represents the aggregated value of all DR products. 

Overall, dividing the $7.9 million in production cost savings by the non-coincident enabled DR 

capacity available, 293 MW, yields a value of $26.91/kW-year of DR available capacity. An 

alternative metric is the value of DR in terms of the annual availability, which is calculated by 

finding the sum of the maximum hourly availability across all DR resources. For the Colorado 

test system, the annual availability is 725 GW-h, and the value of the availability is 

$10.90/MW-h. However, this does not provide any insight into the relative value of different DR 

products or the value in a market setting. Furthermore, DR resources can have both operational 

value and capacity value. The PCM determines only the operational value. Determining the 

operational value of individual DR products would require separate simulations, each comparing 

individual or combinations of various DR. However, some insight can be gained by examining 

the provision of each service and the corresponding price. 

Table 13 summarizes the market revenue for each DR product, under the assumption that the 

marginal energy and reserve prices produced by PLEXOS represent the market-clearing price in 

a restructured market.

18 The revenue of each DR resource is calculated by taking the hourly 

energy schedule or reserve provision times the hourly marginal price for each service. The 

revenue due to energy also accounts for the increase in load at another time; thus, it is the 

revenue from buying energy when the marginal price is low and selling it back to the grid when 

the marginal price of energy is higher. All DR resources have a no-cost bid for operating 

reserves. 

 

18 This implicitly assumes that the marginal prices generated by a PCM are equal to the marginal prices generated in 

a market setting. This is an important and potentially significant limitation when comparing the value of storage in a 

vertically integrated utility and a restructured market. PCMs do not typically include generator bidding and other 

factors that could drive market prices much higher. The results presented here are unlikely to represent the true 

difference between DR value in a market and non-market setting. However, they do represent some of the general 

challenges associated with value capture by DR associated with generator starts and price suppression.

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29 

Table 13. Annual Demand Response Energy and Reserve Provision (GWh or GW-h) and 

Revenue (M$)

DR 

Resources Energy Regulation Contingency Flexibility Total 

Scheduled/Revenue Provision/Revenue (GW-h/M$) Revenue

(GWh/M$) [M$]

Residential 

Cooling 22.4 / 0.037 48.5 / 0.400 23.4 / 0.115 0.1 / 0.000 0.553

Residential 

Water 

Heating

2.6 / 0.038 10.3 / 0.139 2.7 / 0.007 0 / 0.000 0.185

Commercial 

Cooling 6.5 / 0.004 0.2 / 0.002 5.5 / 0.026 0.2 / 0.001 0.032

Commercial 

Heating 0.7 / 0.011 - - - 0.012

Commercial 

Lighting - 8.6 / 0.099 16.3 / 0.098 0.8 / 0.002 0.199

Commercial 

Ventilation - 9.8 / 0.111 18.6 / 0.109 1 / 0.002 0.222

Municipal 

Pumping 1.7 / 0.042 - - - 0.042

Wastewater 

Pumping 2.5 / 0.062 - - - 0.062

Outdoor 

Lighting - 204.6 / 2.073 0.8 / 0.005 - 2.078

Refrigerated 

Warehouses 0.3 / 0.005 - - - 0.005

Agricultural 

Pumping 68.9 / 0.723 - 155.1 / 0.695 - 1.418

Data Center 11.3 / 0.207 - 59 / 0.342 - 0.548

Total DR 116.8 / 1.129 282.1 / 2.824 281.5 / 1.398 2.1 / 0.005 5.355

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30 

Table 13 also shows the annual allocation of each DR resource across energy and operating 

reserves, expressed as either gigawatt-hours for energy or the provision of a gigawatt of reserves 

capacity for a scheduled hour (GW-h). Overall, DR capacity is primarily allocated for reserves; 

17% of the allocated capacity provides energy, 41% provides regulation, 41% provides 

contingency, and less than 1% for flexibility. Outdoor lighting and residential cooling make up 

90% of the DR provision for regulation. Agricultural pumping and data centers make up 76% of 

the DR provision for contingency.19 Regulation services cost about twice as much as 

contingency; thus, the revenue for DR from regulation is about 53% of the total revenue for DR.

Figure 15. Annual average revenue per unit of generation or reserves for each DR resource

Figure 15 shows the annual average revenue for each grid service by each DR resource. The

revenue per unit of reserve provision is fairly constant across DR resources, despite that the 

availability of DR for reserves is highly time varying. The variance in the value of energy from 

DR is much larger. The cooling resources are arbitraged within daytime hours, limiting the net 

 

19 Large industrial loads were not considered in this study but have historically been used to provide spinning 

contingency services.

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31 

revenue. Commercial heating resources are also arbitraged within daytime hours; however, the 

difference between the average highest and lowest price hour during the daytime in the winter is 

$19.0/MWh while the summer difference is $11.8/MWh. The municipal and non-industrial 

manufacturing resources are not constrained by the time of day that the generation must be 

recharged. The average daily difference between the highest and lowest priced hours, across all 

seasons, is $20.0/MWh. 

The total revenue of $5.4 million is less than the $7.9 million in production cost savings largely 

due to the price-suppression effects of DR on the reserves prices.20 This is demonstrated in 

Table 14, where the addition of DR reduces the mean price of regulation by about 23% and the 

mean price of contingency reserve by about 20%. This presents a significant challenge to DR in 

market environments as demonstrated previously by Denholm et al. [3]. See the appendix for 

price duration curves for regulation, contingency, and flexibility reserves. 

Table 14. Marginal Reserve and Energy Price When Demand Response Resources are Available in 

the Test System

Service System 

without DR

System 

with DR

DR Contribution to Annual 

Reserve and Energy 

Median/Mean Requirement 

($/MW-h)

Regulation 13.4 / 14.72 8.14 / 11.31 282.1 GW-h / 26.9%

Contingency 4.34 / 7 2.54 / 5.62 281.5 GW-h / 7.9%

Flexibility 0 / 3.27 0 / 2.75 2.1 GW-h / 0.4%

Energy 28.96 / 31.97 28.98 / 31.39 116.8 GWh / 0.1%

Figure 16a translates the total revenue for the aggregated DR products into a value per kilowatt

of enabled capacity. It includes the various grid service components of this value. In addition to 

the energy and reserves categories, it includes a value associated with penalties—several types of 

DR resource were permitted to exceed their daily operation constraints by incurring a penalty 

cost (see Table 3 for operation constraints and penalties). Agricultural pumping and wastewater 

pumping both exceeded the constrained maximum available energy provision, which increases 

their revenue from energy by about 7% and 35%, respectively.

 

20 In addition to price suppression, DR has the potential to reduce start costs that would typically not be captured in a 

market environment. This is observed and discussed previously by Denholm et al. (2013).

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32 

Figure 16. Average annual revenue (left axis) from the day-ahead market per (a) total enabled 

capacity and (b) annual availability for each type of demand response resource in the Colorado 

test system. Annual DR resource availability (right axis) is expressed as the (a) annual availability 

factor or as (b) total annual availability.

The overall value per kilowatt of each service (Figure 16a) is largely determined by the 

availability factor, which relates to the enabled capacity discussed in Section 2.3.2 and illustrated 

in Figure 2. The scaling factor applied to create the enabled capacity effectively “derates” each 

DR resource by a different amount. This produces the large difference between certain products, 

such as residential cooling and water heating. Dividing by the enabled rated capacity, which 

removes this scaling factor would yield a different set of revenue per kilowatt values. For 

instance, the enabled capacity of residential cooling is 109 MW and the enabled rated capacity is 

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33 

310 MW (see discussion is Section 2.3.2), while the enabled capacity of residential water heaters 

is 4 MW and the enabled rated capacity is 223 MW [see Eq. (1), using a duty cycle of 7% and a 

sheddability of 25%]. This is illustrated in Figure 17, where we show the available capacity 

relative to the enabled rated capacity. The revenue per enabled rated capacity for residential 

cooling and water heating is $3.1/kW-year and $0.7/kW-year, respectively. Alternately, utilities 

often measure DR participation in terms of number of customers or devices that are enabled as 

opposed to the capacity enabled. The 310 MW of residential cooling is captured in 

approximately 130,000 units, while the 223 MW of residential water heating is captured in 

approximately 50,000 units. This yields a value per unit-year of $7.4/unit-year and $3.3/unit-year 

for residential cooling and water heating, respectively. 

Figure 17. Illustration of the DR available capacity, scaled by the enabled rated capacity for (a) 

residential cooling and (b) residential water heating in the Colorado test system

Figure 16b shows the total annual revenue per annual availability. The annual availability is the 

sum of the maximum hourly availability across all of the grid services for each DR resource. The 

annual availability is the fraction of electricity from an aggregation of end uses that is flexible 

through DR. This metric reflects the correlation of each resource’s availability to times of high 

market prices for operating reserves as well as its ability to take advantage of large energy price 

differences across hours of the day. Those resources with lower correlations and more constraints 

on energy shifting will tend to have lower values on a cumulative availability basis. Dividing by 

the annual availability instead of the enabled capacity increases the value of residential cooling 

relative to water heating. This is because cooling resources have a strong seasonal dependence

that correlates well with peak demand and high energy and operating reserve prices, while water 

heating is relatively constant throughout the year (see Figure 17).

Finally, it should be emphasized that these values only consider the operational value of the DR 

device and do not consider capacity value. Many existing DR programs focus primarily on the 

capacity value of DR. For example, Xcel Energy offers $40/year for each residential cooling unit 

with a Saver Switch, which is used to curtail cooling loads during critical peak periods [38]. 

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34 

Wisconsin offers $32/year ($8/month for June, July, and August) for direct load control of 

residential air conditioning and $24/year ($2/month) for direct load control of residential electric 

water heating [39]. The difference in capacity value payment is due to the time-varying 

availability of that capacity: a single residential cooling unit provides more capacity during peak 

load hours than a single residential electric water heater.

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35 

4 Conclusion

We demonstrated a method for modeling an energy-limited DR resource that both explicitly 

accounts for the constraints on the operation DR and co-optimizes DR capacity between 

providing energy and operating reserves. This approach enables DR to participate in multiple 

grid service markets, while price responsive demand curves are limited to providing one grid 

service. Each DR resource model was customized using capacity and ramping availability 

profiles and operating constraints in Part 1 of this report series [1]. Generation from variable and 

uncertain resources continues to grow, increasing the operating reserves necessary to meet intrahour variability and multi-hour forecast error for net load. This report demonstrates that DR

resources can provide a significant fraction of operating reserves and reduce peak demand. 

Our approach yielded production cost savings in the Colorado test system of about $27/kW-year 

for the 293 MW of non-coincident peak DR capacity, or an average value of about $11/MW-h 

for the 725 GW-h of annual availability. Most of this production cost savings came from DR 

displacing natural-gas-fired combustion turbines from providing peak energy and contingency 

reserves. The revenue per kilowatt of enabled DR capacity (annual peak available capacity) 

varies significantly across the resources from less than $1/kW-year to more than $65/kW-year. 

Across all DR resources, only 20% of the revenue came from the energy market, while more 

than 50% of revenue came from the regulation reserve market and the remainder from the 

contingency reserve market. This revenue calculation did not include capacity value, which may 

be of significantly higher value for many DR resources. 

In order to assess the value of DR we used peak-enabled capacity. However, this may not fully 

reflect the value of these resources because it may not capture the cost of enabling the capacity. 

For some DR resources, the capital costs of enabling DR scales with the number of end-use loads 

enabled, not necessarily with the capacity of DR enabled. The value of DR on a per-unit basis 

can be significantly different from the value per kilowatt of available capacity. For instance, in 

the Colorado test system with a 15% renewable penetration, residential cooling and water 

heating have a value per enabled kilowatt of $5/kW-year and $47/kW-year, respectively. 

However, on a per-unit basis, the value is $7.4/unit-year and $3.3/unit-year, respectively. Again, 

these values only reflect their operation in the Colorado test system and neglect the capacity 

value of DR. 

Additional analysis is needed to understand the impact of renewables on several aspects of DR 

operation and value. The provision of down reserves from DR was not evaluated in this study 

and might become more important at higher penetrations of variable renewable generation and 

thus present a source of revenue for DR. Greater wind and solar penetration may also increase 

the actual deployment of regulating and flexibility reserves, changing the operation of DR. This 

analysis only included day-head hourly modeling. Sub-hourly analysis of DR performance is 

needed to evaluate the impact of the DR control signal on end-use performance. For instance, 

under real-time operating conditions should DR be allowed to exceed its scheduled performance 

if the price of energy is higher than expected? Our research is the first step toward evaluating the 

economic potential of DR. A more thoroughly assessment of the potential should be pursued by 

iteratively modeling various DR penetrations to inform the incremental value of additional DR. 

Finally, this analysis points to the need to consider the capacity value of DR. Past work in 

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36 

storage analysis has demonstrated considerable capacity value, and existing programs are 

essentially paying for DR to provide firm capacity during peak operating periods. 

In summary, DR can provide a significant source of operating reserves when co-optimized 

between availability for reserves and energy. Using operating constraints, such as maximum 

operating time and requiring pre-/re-charge for energy use from DR, yields realistic DR 

performance without the use of a price responsive supply curve. These modeling methods can be 

incorporated into renewable penetration integration studies as a source of flexibility for 

the system.

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
37 

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[26] Faria, P.; Vale, Z.; Soares, T.; Morais, H. (2012). “Energy and Reserve Provision Dispatch 

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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
40 

Appendix 

Additional Examples of Demand Response Performance in the Test 

System

Each DR resource has a separate time series that defines the capacity available for energy 

shifting, charging, and each type of ancillary service. This section demonstrates the annual daily 

average availability and performance, as well as two snapshots (spring and summer) of hourly 

performance for each DR resource. The DR resources are organized by their modeling 

characteristics: thermal resources, pumping resources, and resources that only provide operating 

reserves. In most cases we present data for the DR resource installed in the PSCo balancing area. 

Section 3 explores the aggregate effects of DR-providing energy and operating reserves in PSCo 

and WACM.

Residential Cooling

DR from residential cooling end use peaks in the summer and is zero from October to April 

(Figure 18a). The load shed (in order to provide generation) must be recovered in the form of 

pre- or post-increased cooling of the residence between 6 a.m. and 6 p.m. (time is reported in 

PST). The energy use is a smaller fraction of total provision than regulation and is about equal to 

contingency. Two factors influence this co-optimization. First, in the PCM, the energy must be 

recovered (net-zero over a 24-hour period), while capacity held for reserves does not need to be 

“paid back.” Second, the response time to full load shed is 1 minute for contingency and 

regulation and 15 minutes for energy. Therefore, there is more capacity available for contingency 

and regulation reserve provision than there is available for energy. 

The average daily profile (Figure 18b) shows that the residential cooling DR is optimally 

dispatched in a pre-cooling pattern. This resource, as well as all other resources that provide 

contingency as a service, are allowed to hold contingency reserves during charging because the 

additional load could be shed in a contingency event. The residential cooling DR also holds only 

a tiny fraction of its capacity for flexibility reserves. This is likely due to the fact that flexibility 

reserves in the test system can be provisioned by generators at no opportunity cost (and therefore 

has zero marginal price) 40% of the time. This reduces the incentive for other zero marginal cost 

resources to provide flexibility capacity.

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41 

 

Figure 18. Residential cooling demand response in PSCo: Mean (a) daily and (b) hourly provision 

of capacity

Figure 19 shows the hourly allocation of capacity for the residential cooling DR in the spring (at 

the tail of the available capacity window) and summer (two orders of magnitude greater than 

spring, at the peak of the available capacity window), as well as the marginal price of energy and 

operating reserves (lower panels). In the summer the residential cooling DR resource is usually 

used for energy between the hours of 1 p.m. and 5 p.m. and shifts that energy to the late evening 

and early morning hours (when the price of energy is lower). It also holds capacity for 

contingency events while recovering load. During all other hours the residential cooling DR 

capacity is held for regulation.

Figure 19. Residential cooling demand response in PSCo: (upper) Hourly capacity provision for (a) 

spring and (b) summer and (lower) hourly marginal price of energy and operating reserves

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42 

Residential Heating

Residential heating is the only thermal process that does not accrue shedded load that must be 

recharged by the system. It is also the only DR resource that is not available in the test system 

because residential heating is primarily sourced from natural gas rather than electricity. In areas 

where the residential heating is sourced from the grid, the available energy shedding capacity is 

available for one hour per day. The data shown in Figure 20 is from the northwest United States. 

The seasonal availability follows the expected pattern, going to zero over the summer months, 

with slightly more capacity available on the weekends. The energy schedule is largely driven by 

price because there is no charging. The total capacity available for regulation provision is less 

than the maximum availability for energy; when residential heating is not used for energy it is 

nearly always provisioned for regulation. The capacity availability for regulation is less than the 

capacity available for energy.

 

Figure 20. Residential heating demand response in the Northwest: Average (a) daily and (b) hourly 

provision of capacity; the daily capacity is zero between mid-June and late-August, when 

residences do not use electricity for heating

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43 

Figure 21. Residential heating demand response in the Northwest: (upper) Hourly capacity 

provision for April 15–April 18 and (lower) hourly marginal price of energy and operating reserves. 

There is no residential heating capacity available for demand response in the summer.

Commercial Cooling

DR originates from commercial cooling peaks in the summer and is zero from October to April 

(Figure 22a). The load shed for energy must be recovered in the form of pre- or post-cooling of 

the commercial building between 6 a.m. and 6 p.m. The energy use is a smaller fraction of total 

provision than contingency. This is primarily driven by the operation constraint that the energy 

must be recovered, while capacity held for reserves does not need to be re-charged. 

The average daily profile (Figure 22b) shows that the optimized commercial cooling operates 

through pre-cooling. The resource is allowed to hold contingency reserves when recovering load, 

similar to other energy shifting resources. The commercial cooling DR holds only a tiny fraction 

of its total capacity for regulation and flexibility reserves. This is because the capacity available 

for regulation from commercial cooling is about one-tenth the capacity available for 

contingency, and flexibility is largely covered by other generators at no opportunity cost to 

the system.

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44 

Figure 22. Commercial cooling demand response in PSCo: Mean (a) daily and (b) hourly provision 

of capacity

Figure 23 shows the hourly allocation of capacity for the commercial cooling DR in the spring 

(at the tail end of the available capacity window) and summer (two orders of magnitude greater 

than spring, at the peak of the available capacity window), as well as the marginal price of 

energy and operating reserves (lower panels). In the summer the commercial cooling DR 

resource is typically used for energy between the hours of 1 p.m. and 5 p.m. and shifts that 

energy to the late evening and early morning hours when the price of energy is lower. It also 

holds capacity for contingency reserve while paying back energy and during all other hours when 

the load shedding is less than the maximum energy available. 

Figure 23. Commercial cooling demand response in PSCo: (upper) Hourly capacity provision for 

(a) spring and (b) summer and (lower) hourly marginal price of energy and operating reserves

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45 

Commercial Heating

Commercial heating DR is seasonally available, peaking in the winter, and is not available from 

June through September. It can provide energy shifting as well as contingency, flexibility, and 

regulation, although the operating reserves are less than 0.1 MW in all time periods and are 

therefore considered negligible and ignored for our simulations. WACM is used as the example 

here because there was not an adequate commercial electric heating resource identified for the 

PSCo region. Charging is constrained from 3 a.m. through 6 p.m. to be congruent with the 

heating patterns in a typical commercial setting, which decreases the opportunities for energy 

arbitrage. Charging constraints could depend on the types of buildings and vary by region and 

occupant behavior. Commercial heating typically runs for 2.5 hours per day and recovers load 

over 3.5 hours. 

The average daily profile is shown in Figure 24b. The middle of the day offers minimal to no 

opportunities for energy shifting because of the direct effect on the comfort of the occupants. The 

combination of the average day maximum energy constraint shown in Figure 24b, along with the 

charging constraints, explains the relatively infrequent provision of capacity represented in 

Figure 25. There are only a small number of hours that arbitrage makes economic sense given 

the constrained number of capable provision and charging hours. Figure 25 (lower) also shows 

the marginal price of energy and operating reserves, which is the driver of arbitrage for 

the resource.

Figure 24. Commercial heating demand response in WACM: Mean (a) daily and (b) hourly 

provision of capacity

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46 

Figure 25. Commercial heating demand response in WACM: (upper) Hourly capacity provision for 

spring and (lower) hourly marginal price of energy and operating reserves (x-axis tick marks 

12 p.m.) 

Data Centers

Data centers are capable of providing contingency and energy shifting with a relatively 

predictable load and therefore a very uniform provision capability. The data centers are able to 

hold contingency reserves as much as the load profiles will allow, but the energy shifting has the 

added restriction that it can shed load for a maximum of 4 hours in a 24-hour period. This time 

restriction is in place because a large part of the load for a data center is cooling for the servers. 

Limiting the amount of time that the load can be shed keeps the temperature drift within an 

acceptable range. The ramping capability is the same as other thermal DR resources, such as 

commercial and residential cooling, with contingency reserve able to ramp to its maximum in 

one minute through direct load control (e.g., fans and pumps) and thermostat changes, which 

take 15 minutes to reach their maximum energy shifting capability. Figure 26 shows the annual 

provision of energy and contingency as (a) daily averages and (b) the mean hourly provision. 

While the availability of contingency and energy is equal for this resource in PSCo, the time 

restrictions and the required charging cause the average energy to be much lower than its ability 

to hold contingency reserve capacity, as can be seen by the discrepancy between the services in 

Figure 26. 

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47 

Figure 26. Data center demand response in PSCo: Mean (a) daily and (b) hourly provision of 

capacity

Figure 27 shows the DR operation of the data center over a 3-day period for the spring and 

summer, as well as the marginal prices for energy and all operating reserves in those periods. It 

can be gathered from the following figures that energy is often shed during the late 

afternoon/early evening, when prices are typically highest, and that charging is taking place 

during the night, when prices for energy are typically lowest. The energy shifting capability was 

utilized for the full four hour daily maximum 99% of the days in our annual simulation. 

Figure 27. Data center demand response in PSCo: (upper) Hourly capacity provision for (a) spring 

and (b) summer and (lower) hourly marginal price of energy and operating reserves

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48 

Agricultural Pumping

DR from agricultural pumping is different than the rest of the resources because it can provide 

more contingency than energy shifting. This is a reflection of the participation rate that is 

expected from agricultural pumps, which is lower than the less-committing contingency reserve 

[1]. The resource is largely seasonal, as seen in Figure 28a, with the bulk of the capacity 

available in summer due to the growing season cycle. There are no restrictions on the time of day 

that energy can be shifted, which helps to explain the tendency for energy to be shed during the 

high priced hours in the middle of the day, as seen in Figure 28b. 

Figure 28. Agricultural pumping demand response in PSCo: Mean (a) daily and (b) hourly 

provision of capacity

Although there are no restrictions on the time of day that energy can be shifted, there are three 

other restrictions that limit the capabilities of agricultural pumping to shift energy freely. One is 

a $10 fee for every call, which likely requires a person physically turning off the agricultural 

watering pumps. The pumps are also limited by a maximum start of once per day, with a $50 

penalty for a violation, as well as a limit of 8 hours of load shed per day, with a violation fee of 

$20 per hour violated. Both of these “soft constraints” refer only to the energy allocation of the 

resource and are violated during the simulation incurring penalties to the optimization. A start 

limitation can be a deterrent to frequent switching, which could lead to decreased effectiveness 

or increased wear for a mechanical machine like an agricultural pump. The operation of the 

agricultural pumping DR resource proved to be sensitive to the magnitude of the penalty, 

although a more thorough understanding is needed of the pricing of the penalty relative to other 

system costs. Figure 29 shows that energy shifting is utilized more than the 8-hour limit and 

breaks the start violation multiple times. It also shows the expected result—that charging is 

happening during lower price hours and energy is being shed during high price hours. 

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49 

Figure 29. Agricultural pumping demand response in PSCo: (upper) Hourly capacity provision for 

(a) spring and (b) summer and (lower) hourly marginal price of energy and operating reserves

Municipal Pumping

Municipal pumping capacity follows a seasonal pattern as well as a daily pattern that can be 

observed in Figure 30a and Figure 30b. Energy is the only service that municipal pumping can 

provide, which is subject to full charging within 24 hours of energy shed. Figure 30b shows that 

there is a tendency to shift energy away from the early evening hours when energy prices are 

typically highest to the hours when prices are typically lower in the late evening and 

early morning. 

Figure 30. Municipal pumping demand response in PSCo: Mean (a) daily and (b) hourly provision 

of capacity

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50 

The energy shifting capability of municipal pumping is also constrained to 2 hours of energy 

shedding in a day and one start per day. Both of these constraints are “soft,” meaning they can be 

broken with the payment of a fee. Utilizing the energy shifting more than 2 hours results in a $50 

violation penalty per hour, and a call to use the pumps more than once per day results in a $100 

violation penalty per call. There is also a $5 cost for initiating DR from municipal pumping 

(equivalent to a start cost for a generator).

Figure 31 shows examples of operation for the (a) spring and (b) summer, as well as the marginal 

prices for energy and operating reserves for those periods. Energy is typically being shifted away 

from the late evening to the middle of the night, as observed by the average day in Figure 30b. It 

is also clear that some constraints, such as the maximum operating hours of 2 per day, are being 

violated in July but not in April. 

Figure 31. Municipal pumping demand response in PSCo: (upper) Hourly capacity provision for (a) 

spring and (b) summer and (lower) hourly marginal price of energy and operating reserves

Refrigerated Warehouse

Refrigerated warehouses have a seasonal and daily pattern (Figure 31) similar to the other 

“energy only” DR resources. These also tend to shift energy away from the peaking hours in late 

afternoon/early evening to the middle of the night. Similar to other pump-like resources, the 

daily runtime is restricted to 4 hours. There is no option to violate this constraint, which can be 

observed in the limited hours of operation in Figure 33. As expected, the energy is being shifted 

to the lower price times of day in both the (a) spring and (b) summer. 

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Figure 32. Refrigerated warehouse demand response in PSCo: Mean (a) daily and (b) hourly 

provision of capacity

Figure 33. Refrigerated warehouse demand response in PSCo: (upper) Hourly capacity provision 

for (a) spring and (b) summer and (lower) hourly marginal price of energy and operating reserves

Commercial Ventilation

Commercial ventilation and outdoor lighting are DR resources that are not able to offer energy as 

a service; however, their capacity is co-optimized between the three operating reserves. Each DR 

resource has a constrained capacity available for each ancillary service response in each time 

interval. The sum of the capacity allocated to all operating reserves, by each DR resource in each 

time interval, cannot exceed the largest capacity available for any one ancillary service during 

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52 

that time interval. Thus, DR capacity for one ancillary service is provisioned exclusive of 

any other. 

Commercial ventilation has very similar ancillary service capabilities to commercial lighting in 

magnitude and pattern (Figure 34), although the daily profile for commercial ventilation does not 

decrease as much overnight (Figure 35). As a result, commercial ventilation provides a nearly 

constant 1 MW of regulation reserves throughout the year. The majority of the remaining 

capacity is provisioned for contingency reserves.

Figure 34. Commercial ventilation demand response in PSCo: Mean (a) daily and (b) hourly 

provision of capacity

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Figure 35. Commercial ventilation demand response in PSCo: (upper) Hourly capacity provision 

for (a) spring and (b) summer and (lower) hourly marginal price of energy and operating reserves

Outdoor Lighting

Outdoor lighting has a strong daily cycle, with almost no availability during daylight hours and 

seasonal variation due to length of day variations. Outdoor lighting is available to serve both 

regulation and flexibility, which are identical in magnitude. Figure 36 and Figure 37 (upper 

plots) show that outdoor lighting provides regulation reserves over flexibility, which is expected 

from the consistently higher marginal price of regulation (see Figure 37b). 

Figure 36. Outdoor lighting demand response in PSCo: Mean (a) daily and (b) hourly provision 

of capacity

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54 

Figure 37. Outdoor lighting demand response in PSCo: (upper) Hourly capacity provision for (a) 

spring and (b) summer and (lower) hourly marginal price of energy and operating reserves

Seasonal Daily Average Energy and Reserve Availability 

and Provision

Figure 38. Seasonal daily average capacity available from demand response for each service

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Figure 39. Mean daily allocation of demand response capacity for energy and operating reserves, 

by season

Figure 40. Mean daily allocation of demand response capacity for energy and operating reserves, 

by season

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Figure 41. Average seasonal daily provision of regulation by demand response in the Colorado 

test system

Figure 42. Average seasonal daily provision of contingency by demand response in the Colorado 

test system

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Figure 43. Average seasonal daily provision of flexibility by demand response in the Colorado 

test system

Marginal Price Duration Curves for Reserves

Figure 44. Price duration curves for test system with and without demand response resources 

providing energy and operating reserves—(top) regulation, (middle) contingency, and 

(bottom) flexibility

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