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Conference Paper

NREL/CP-5500-77423

September 2020

Modeling the Interaction Between 

Energy Efficiency and Demand 

Response on Regional Grid Scales

Preprint

Brian F. Gerke,1 Cong Zhang,1 Andrew Satchwell,1

Samanvitha Murthy,1 Mary Ann Piette,1 Elaina Present,2

Eric Wilson,2 Andrew Speake,2 and Rajendra Adhikari2

1 Lawrence Berkeley National Laboratory 

2 National Renewable Energy Laboratory

Presented at the 2020 ACEEE Summer Study on Energy Efficiency in 

Buildings

August 17-21,2020
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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.

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Conference Paper

NREL/CP-5500-77423

September 2020

Modeling the Interaction Between 

Energy Efficiency and Demand 

Response on Regional Grid Scales

Preprint

Brian F. Gerke,1 Cong Zhang,1 Andrew Satchwell,1

Samanvitha Murthy,1 Mary Ann Piette,1 Elaina Present,2

Eric Wilson,2 Andrew Speake,2 and Rajendra Adhikari2

1 Lawrence Berkeley National Laboratory 

2 National Renewable Energy Laboratory

Suggested Citation

Gerke, Brian F., Cong Zhang, Andrew Satchwell, Samanvitha Murthy, Mary Ann Piette, 

Elaina Present, Eric Wilson, Andrew Speake, and Rajendra Adhikari.2020. Modeling the 

Interaction Between Energy Efficiency and Demand Response on Regional Grid Scales: 

Preprint. Golden, CO: National Renewable Energy Laboratory. NREL/CP-5500-77423. 

https://www.nrel.gov/docs/fy20osti/77423.pdf. 
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Modeling the Interaction Between Energy Efficiency and Demand Response 

on Regional Grid Scales

Brian F. Gerke, Cong Zhang, Andrew Satchwell, Samanvitha Murthy, Mary Ann Piette, 

Lawrence Berkeley National Laboratory

Elaina Present, Eric Wilson, Andrew Speake, Rajendra Adhikari, National Renewable Energy 

Laboratory

ABSTRACT

With increasing penetration of intermittent renewable generation at grid and distributed scales, 

flexible building loads can provide significant system value and support the evolving needs of 

the grid. The growing value of load flexibility may complicate the traditional separation between 

energy efficiency (EE) and demand response (DR). EE measures may compete in some cases

with a building’s DR capabilities but complement one another in other cases. EE can also

increase or decrease the need for DR at the system level and change the availability of DR to 

meet system needs. In this study we present a bottom-up approach to modeling interactive effects 

between EE and DR in buildings within two regions of the US electricity grid. From a library of 

building simulation models for different buildings and climates, we synthesize system-level 

demand profiles and the impacts of potential future EE portfolios. Coupling the underlying 

building models with a database of DR-enabling technologies, we then compute the quantity of 

DR that can be delivered in each scenario. The results show that EE and DR interactions are 

largely driven by the timing of EE savings that are measure-specific and the coincidence with 

system peak demand that is region-specific. We also find that perspective of the impacts matters 

– for instance that some EE measures reduce the system need for DR but also reduce the DR 

potential. Our results imply that utility EE and DR programs developed without considering 

interactive effects may lead to increased grid-management challenges over the long term.

Introduction

Energy efficiency (EE) and demand response (DR) resources provide important utility 

system and ratepayer benefits. At the same time, the rapid change in the amount of variable 

renewable energy (VRE), like solar and wind, is reshaping the role and economic value of EE 

and DR. For example, the proliferation of distributed generation has reduced electricity sales and 

changed the timing of net peak demand in certain regions (e.g., California). Additionally, the 

diurnal patterns and volatility of wholesale prices are changing due, in part, to large increases in 

utility-scale VRE resources with zero marginal costs (Seel et al., 2018). These changes will 

likely affect time-dependent valuation of EE and DR measures (Boomhower and Davis, 2017).

Utilities are increasingly interested in integrating EE and DR measures and technologies (as well 

as other distributed energy resources) as a strategic approach to improve their collective costeffectiveness and performance (Potter et al., 2018; York et al., 2019). However, little is 

understood about the specific EE and DR performance characteristics that may be best 

integrated, the interplay between changing EE and DR resource potential, and the resulting 

utility system impacts.

In this study we present a bottom-up approach to modeling interactive effects between 

residential EE and DR in two regions of the US electricity grid, California and Texas. Within 

each region we combine building simulation data with data on building stock and hourly

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electricity system demand and generation. We focus the analysis on interactive effects in 

residential buildings due mostly to limited publicly available commercial end-use savings 

shapes, though a planned future study will model EE and DR interaction in commercial buildings 

as well. We focus on one commonly implemented type of DR product, namely “shed” DR1used 

to manage utility system peak loads (typically occurring on hot summer days in these regions) 

and reduce the need for peaking generation resources. The study describes EE and DR 

interactions in terms of changes in hourly load (at the building and system level) arising from EE 

interventions; it does not explore EE-induced changes in customer DR participation rates (e.g., 

arising from improved capabilities), customer economics (e.g., bill savings) or utility costs (e.g., 

avoided capacity costs). Future work will consider these topics to paint a more complete picture 

of EE-DR interaction.

This paper is organized as follows. To frame and contextualize our modeling and analysis 

efforts, we begin with a summary of a broad conceptual framework2for thinking about 

interactions between EE and DR from a variety of perspectives. We then lay out our approach to 

modeling hourly electricity demand from buildings, scaling these up to the utility system level, 

and modifying the resulting building and system load shapes to simulate different EE adoption 

scenarios. We develop three different portfolios of EE measures, intended to illustrate different 

modes of EE-DR interactions, and we define two metrics for assessing changes in the potential 

DR resource and in the need for DR at the system level. Finally, we use these metrics to explore 

the interactive effects between EE and DR for our defined EE portfolios, and we discuss 

implications.

A Conceptual Framework for EE/DR Interactions

This study relies on a conceptual framework (Satchwell et al., forthcoming) to organize 

and identify EE and DR attributes, technological factors, and system conditions that are likely to 

drive interactions between EE and DR in commercial and residential buildings. EE and DR differ 

from each other in ways that are important for understanding how EE and DR interact, so clear 

definitions will be prerequisite to developing a conceptual framework. We define EE as a

persistent and maintained reduction in energy consumption required to provide a fixed level of 

service.3 By contrast, we define DR as an active modification4in energy demand or consumption 

on a limited-time basis, in response to an incentive or command signal, which may result in a 

reduced level of service.5 The framework incorporates another concept, demand flexibility (DF), 

which we define as the capability associated with a building to modify energy consumption in 

response to utility grid needs.6 Like DR, DF is characterized by active load management on 

 

1 This is as distinct from other, more novel types of DR considered in the literature (e.g., Alstone et al., 2017), such 

as “shift” DR for load shifting, or “shimmy” for ancillary services (e.g., frequency reserves). These may also have 

important interactions with EE, which we plan to explore in future work

2 See Satchwell et al. (forthcoming) for a more detailed description of the conceptual framework, its qualitative 

application to several residential and commercial EE measures, and identification of key attributes driving EE and 

DR interactions.

3 Consistent with other definitions of EE in the literature (e.g., York and Kushler, 2005; Goldman et al., 2010).

4 E.g., reduction, increase, shift, or modulation.

5

It is worth noting that this definition, which encompasses response to time-varying tariffs, is broader than what is 

sometimes used in other contexts and studies, which focus specifically on short-term load reductions.

6 See recent literature on grid-interactive efficient buildings (GEB) (Eckman et al., 2019; Neukomm et al., 2019).

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timescales consistent with utility system and grid needs. Unlike EE and DR, DF is not a resource 

in the traditional sense, but a potential that the utility or system operator can utilize to provide 

reliable electricity service. From the system operator’s perspective, EE and DR are what you 

have in your portfolio and DF is what you can do with the resources you have.

The framework describes the interactive effects of a “change” at the building level, 

defined by the point at which an EE or DR investment is made.7 EE and DR investments will 

change the end-use consumption on a temporal basis in different ways. When updating an end 

use with a more efficient technology, the load shape following the EE investment may represent 

an overall percent reduction, maintaining a similar shape. In other cases, the load shape may look 

quite different on an hourly or sub-hourly basis when an EE or DR investment includes controls 

technology, thermal improvements, or different operational strategies.

The framework is comprised of two levels, each of which is subdivided further into two 

sublevels exploring distinct interactions. The first level assesses changes that occur at the 

building and the second level aggregates buildings to represent utility-scale changes and 

describes interactions in terms of the utility system need for DR and availability of DR. Each 

sublevel is defined by an analytical question and metric by which the interaction between EE and 

DR is measured. Table 1 describes the conceptual framework, including the perspective, change 

metric, and definition of competitive, complementary, and neutral EE and DR interactions for 

each level and sublevel.

The present study focuses on EE and DR interactions at levels 1a, 2a, and 2b.8 Level 1a is 

focused on the change in the building-level DF due to an EE or DR investment and asks, in the 

presence of a more efficient measure, what is the change in technical potential and capability to 

shed, shift, or modulate the affected load? Whether EE and DR compete with or complement 

each other at level 1a is a function of two distinct changes to DF: (1) the change in technical 

potential, as defined by the change in passive load shape, reflects whether and how the 

underlying load shape changes following an EE or DR investment to change the total load (e.g., 

if the load is lower in all hours, there is less load technically available to participate in demand 

response), as well as its coincidence with system need for DR; and (2) the change in capability 

reflects whether and how the building is more or less able to reliably9 provide a responsive or 

flexible load when needed by the utility.

Level 2a is focused on the change in need for DR resources in the utility system and asks,

what is the change in likelihood that the system needs incremental demand response resources?

Level 2a specifically considers whether the EE investments made by many customers or building 

owners have, in aggregate, increased or decreased the likelihood of needing DR resources to 

address utility system conditions. At this level of the framework, an increase in system need 

reflects competition, and a decrease in system need reflects complementarity between EE and 

DR. Interactions between EE and DR at level 2a are almost entirely driven by the coincidence of 

the energy/demand savings at the building level and the net load driving system conditions.10

 

7 By a residential customer or commercial building owner (or an aggregator operating across multiple buildings).

8 Level 1b interactions encompass changes in participation drivers (e.g., customer comfort, financial incentives) 

that are highly uncertain, and specific to program implementation, and beyond the scope of this study. 

9 The concept of reliability is represented here by automation or remote controllability that increases DF without 

changing anything about how much load can be controlled. EE may also affect reliability through other means, 

including changes in thermal inertia or the effectiveness of pre-cooling, but these are not explored in this study.

10 The need for DR may also be driven by system conditions other than net load (e.g., generator unit outage). 

However, the probability of such contingency events would not change with a change in EE or DR resources.

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The presence of controls capabilities is also an important driver, as they may increase or decrease 

the coincidence of energy/demand savings with system load. This depends on the specifics of 

how building controls are implemented.

Table 1. Conceptual framework levels, change metrics, and definitions.

Level Perspective Change Metric Competition Complement Neutral

1a Building Demand 

flexibility (DF)

Less load able to 

shed, shift, or 

modulate

More load able to 

shed, shift, or 

modulate

No change in load 

able to shed, shift, 

or modulate

1b Building DF 

participation 

fraction

Lower fraction of 

DF participating as 

a demand response 

resource

Higher fraction of 

DF participating as 

a demand response 

resource

No change in the 

fraction of DF 

participating as a 

demand response 

resource

2a Utility system Demand 

response (DR) 

need

Increased 

likelihood of 

needing DR 

resources to meet 

utility system 

conditions

Decreased 

likelihood of 

needing DR 

resources to meet 

utility system 

conditions

No change in the 

likelihood of 

needing DR 

resources to meet 

utility system 

conditions

2b Utility system DR availability Reduced 

availability of DR 

resources to meet 

specific system 

need or condition

Increased 

availability of DR 

resources to meet 

specific system 

need or condition

No change in the 

availability of DR 

resources to the 

system operator

Finally, level 2b is focused on the change in the availability of DR in the utility system 

and asks, what is the change in the quantity of DR that is available to meet specific system 

needs? Dispatchable resources, like DR, are used by utility system operators to meet system 

conditions and needs to maintain electricity reliability and service levels. EE may interact with 

DR by increasing or decreasing the amount of DR that is available to utility system operators. 

Whether EE and DR compete with or complement each other at level 2b depends largely on the 

net effect of levels 1a (change in building DF) and 1b (change in building DF participation 

fraction). Specifically, this depends on the interplay between the change in passive load shape, 

addition of capabilities (e.g., via controls or operational strategies), and change in participation.11

 

11 The quantity assessed at this sublevel could be considered similar to the market potential in DR potential studies 

that measure the technical capabilities of end uses to provide DR and the propensity of customers to participate in 

DR programs.

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Analytical Approach

Our study of load interactions between EE and DR focuses on two regions of the US 

electricity grid, corresponding approximately to California and Texas.12 These regions were 

selected to represent different regimes of particular interest for studying EE-DR interactions in 

the context of VRE growth: a region with high penetration of solar electricity generation, shifting 

system peak loads into evening hours (California); and a region with a strong cooling load-driven 

peaks and high penetration of intermittent wind generation (Texas). In this study we model the 

contributions of residential to the system-level load, using weather and system-level demand data 

from calendar year 2016.13

Simulating the building contribution to system load

The building-level changes in EE and DR are based on simulations, weighted to reflect 

the present-day building stock. The simulations used 2016 weather data for 10–15 representative 

locations within or nearby our two modeled grid regions, selected to span the full range of 

climate zones14 found within each region. Because these are the results of detailed building 

simulations, the representative load shapes are disaggregated into a large number of individual 

electrical end uses, allowing us to consider the impacts of specific EE and DR measures on the 

building-level demand and demand flexibility. 

To represent residential buildings, we use the ResStock simulation tool (Wilson et al., 

2016, 2017) from the National Renewable Energy Laboratory (NREL). ResStock computes a 

large number of individual building simulations, covering a wide diversity of building types, 

vintages, envelope characteristics, equipment configurations, and occupant behavior, to represent 

the housing stock in each location with high fidelity. We combine these simulations to yield load 

shapes that represent the average energy consumption, by end use, for several residential 

building types (i.e., single-family, multi-family, and mobile homes) in each representative 

location. We simulate these average building load shapes for a baseline scenario, representing 

the present-day building stock, and for several EE scenarios in which portfolios of EE measures 

are applied to the building stock

Future iterations of this work will represent commercial load using NREL’s ComStock 

model, which provides similar functionality to ResStock for commercial buildings. At the time 

of this study, ComStock was still under development, so, as a placeholder, we used a public set 

of commercial building simulations for various locations in the US (EERE, 2013). These singlebuilding simulations cannot accurately represent the full commercial building stock in detail, so

we focus this analysis on the residential sector, where the ResStock simulations can yield better 

accuracy. 

The representative building-level load shapes are then aggregated up to the utility system 

level. To support this, we match a commercially available dataset on US real estate parcels, to 

 

12 More specifically, we model system-level loads within the CAMX and ERCT grid regions defined in the 

electricity markets module (EMM) of the National Energy Modeling System (NEMS) from the US Energy 

Information Administration (EIA), as of 2016 (EIA, 2017). The EMM region definitions have been updated for 

2020, but the changes do not impact our region boundaries in this study. 

13

 Assuming a different year would result in higher or lower impacts than in the present study, but we would expect 

qualitatively similar interactive effects.

14 We use the climate zone definitions from ASHRAE standard 169-2013 in this study.

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the building-simulation locations.

15 The result is a set of geographic subregions, each of which 

maps to one of the representative weather locations, and an accounting of the total building 

floorspace, by building type, in each subregion. We represent the load for those buildings by the 

appropriate average load shapes for each location. Weighting the representative load shapes by 

the total building floor space and summing the results yields the hourly contribution to total 

system demand from commercial and residential buildings within each region. Figure 1 shows a 

schematic diagram of the procedure for aggregating representative building load shapes to a 

system-level contribution, for the example of small commercial office buildings. 

Figure 1. Schematic diagram showing the approach to aggregating building load shapes for representative cities to a 

system-level contribution. The example shown is for small office buildings in California; this procedure is repeated 

for all modeled building types and in both regions.

Last, we account for the contribution of non-building loads (e.g., industrial or agricultural 

loads) to ensure that the aggregated building simulations give an accurate representation of the 

overall demand from each sector. We augment our bottom-up model with top-down constraints 

based on actual system demand data, namely the hourly system-level demand in 2016 for each 

region, as reported by the EIA Hourly Electric Grid Monitor (EIA, n.d.-a), and the 2016

electricity sales (in kWh) by sector in each region, as reported in EIA Form 861 (EIA, n.d.-b). 

The Form 861 data allows us to compute the fraction of electricity consumption attributable to 

each sector in 2016 (e.g., in California, the residential fraction is 31%). By comparing our 

simulated baseline system-level building consumption to the actual system consumption, we can 

compute an overall calibration factor for each sector. We apply identical calibration factors to the 

 

15 In a small number of cases representing sparsely populated areas, there was no suitably representative weather 

data available for a particular climate zone within a particular region. In those cases, the model simply uses the 

geographically nearest representative location, regardless of climate zone.

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baseline and EE scenarios as to the baseline, to ensure that all scenarios receive the same 

adjustment and that we do not inadvertently alter the EE savings.

With these calibrations in place, we have estimates of the contribution from residential 

and commercial buildings to total system load. In the baseline scenario, we can then infer that the 

difference between the actual 2016 system load and our simulated building load represents the 

non-building loads that we have not simulated. In each EE scenario, we hold this load fixed and 

add it to the simulated building loads to yield the system-level load after EE measures have been 

applied. In the baseline scenario, this approach ensures that our simulated system load is exactly 

equal to the actual 2016 system load. Figure 2 shows the resulting hourly average load over the 

course of a day from residential, commercial, and other loads in California and Texas. The total 

load is identical to the actual system-level load that occurred in 2016, while the building 

contributions are simulated. 

Figure 2. Simulated system-level 2016 load curves, by sector, for an average day in our modeled regions. 

Simulated EE scenarios

We selected several EE measures and combined them into different portfolios for 

modeling in ResStock. To develop a focused set of portfolios we considered the ways in which 

different kinds of EE measures may interact with DR, drawing on key interactive attributes from 

Satchwell et al. (forthcoming). We modeled three portfolios, each of which has different 

implications for EE and DR interactions in the framework:

1. An equipment-only portfolio, made up of efficiency upgrades to equipment across 

nearly all end-uses, including heating and cooling, appliances, electronics, and lighting.

Equipment upgrades tend to reduce the amount of flexible load at the building level, 

(because of reduced overall load), but they may also relieve system-level need for DR.

2. A controls-only portfolio, consisting of a programmable or connected thermostat with set 

points manipulated to reduce consumption during overnight and unoccupied daytime 

hours. Controls-based EE measures will tend to increase DF at the building level;

depending on the control strategies employed, they may also modify the need for DR at 

the system level.

3. An envelope-only portfolio, made up of envelope improvements including insulation, air

sealing, and improved windows. Similar to equipment upgrades, envelope measures 

reduce the amount of flexible load at the building level; however, because they also 

increase thermal inertia, they tend to increase the flexibility of the remaining demand. 

They may also reduce the need for DR at the system level by decreasing peak loads.

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Table 2 lists the specific EE measures in each portfolio, including EE metrics where 

appropriate. To apply these portfolios in ResStock, we treat each measure as a minimum 

efficiency level: for each measure, a building is upgraded if its relevant end use in the baseline 

scenario was less efficient than the measure. To ensure significant impacts on the total system 

load, the EE measures chosen were quite aggressive, upgrading all buildings to the best available 

technologies on today’s market and yielding large (tens of percentage points) reductions in 

energy consumption for the affected end uses. While these EE scenarios are not particularly 

realistic near-term achieved savings levels, they represent stylized impacts on grid-level demand 

chosen to be large enough to drive readily apparent EE-DR interactions. 

Table 2. Summary of detailed measures used to model the EE measure portfolios in this study. EE metric acronyms 

are defined in a note at bottom.

Portfolio Affected building element Upgrade

Equipment 

only

Central air conditioner Replace with efficient two-speed air conditioner (SEER 18)

Electric furnace or air 

source heat pump Replace with efficient air source heat pump (SEER 22, HSPF 10)

Electric baseboard heating Replace with efficient mini-split heat pump (SEER 29.3, HSPF 14)

Electric water heater Replace with electric heat pump water heater (EF 2.3)

Pool pump 25% reduction in energy consumption

Dishwasher Replace with efficient unit (199 kWh/yr)

Clothes washer Replace with efficient unit (IMEF 2.92)

Electric clothes dryer Replace with ventless heat pump unit (CEF 4.5)

Lighting Upgrade to 100% LED lighting

Refrigerator Replace with efficient unit (EF 22.2)

Electronics 50% reduction in energy consumption

Controls 

only

Thermostat settings (for 

homes with no existing 

thermostat offsets)

All homes with no existing offsets:

Cooling nighttime setup: 4 oF, 10 PM to 6 AM,

Heating nighttime setback: 8 oF, 10 PM to 6 AM

Homes with no existing offsets AND unoccupied on weekdays:

Cooling daytime setup: 7 oF, 8 AM to 6 PM (weekdays only)

Heating daytime setback: 8 oF, 8 AM to 6 PM (weekdays only)

(offsets based on ENERGY STAR recommendations)

Envelope 

only

Wall insulation Upgrade all walls to R-33 (R-13 cavity plus R-20 external XPS)

Attic insulation Upgrade unfinished attic/ceiling insulation to R-49

Air sealing 25% reduction in ACH50

Windows

Upgrade windows to U-0.17 (R-5.9), SHGC 0.25 to 0.49 (climate 

dependent)

Basement/crawlspace 

insulation

Upgrade insulation (R-13 to R-30 depending on climate and 

construction)

Acronyms—SEER: seasonal EE ratio; HSPF: heating seasonal performance factor; EF: energy factor; IMEF: integrated modified 

energy factor; CEF: combined energy factor; ACH50: air changes per hour at 50 Pascals; SHGC: solar heat gain coefficient.

Estimating DR technical potential with DR-Path

To estimate the potential DR resource in each EE scenario, we use the resulting building 

and system-level loads from the above procedure as inputs into LBNL’s DR-Path model. DRPath was developed and refined to support analysis of DR potential for the California Public 

Utilities Commission (Alstone et al. 2016, 2017; Gerke et al. 2020). Based on the hourly system 

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load shape, the model estimates the likelihood that DR will be needed in each hour;16 applying 

this to each representative load shape, it computes the weighted-average quantity of DR that is 

technically available from each building type and end use. The model then couples the buildinglevel DR technical potential with a database of DR measures having different performance levels 

and costs, and a model for customer participation, to analyze future pathways to enabling DR. 

In this study, we focus on the DR technical potential (i.e., excluding effects from measure 

cost and performance or from customer participation) and examine how it changes under various 

EE scenarios. Limitations from the enabling technology and customer willingness to participate 

in DR with a particular end use will constrain the quantity of DR that can be delivered in 

practice, but by examining changes in the technical potential we can explore how changes in the 

load shape alone impact the available DR resource. In particular, we examine how EE changes 

the technical potential to provide DR at the building level, representing impacts at level 1a of the 

framework, and then we aggregate this up to the system level to explore changes in the DR 

technical potential at the utility scale, representing impacts at level 2b.17

Assessing changes in the system need for DR

We assess how the EE upgrades change the need for DR in the electricity system (level 

2a of the framework) by examining changes to the annual peak demand after first subtracting the 

actual VRE generation that occurred in each region in 2016.18 Specifically, in each of our 

scenarios and regions, we examine changes in the shape of the net load duration curve within the 

top 3% of hours of the year. We focus on the top 3% of hours because these have been identified 

elsewhere (Alstone et al., 2016) as the most probable for DR utilization.19 Extreme peak hours 

such as these will need to have their demand met either by DR or by peak generation resources,

such as natural gas combustion turbines, which tend to have both high cost (due to low 

utilization), as well as low efficiency and high emissions intensity relative to other resources. A 

large portion of DR’s value to the grid stems from its ability to eliminate the need for peaking 

generation capacity, thus bringing down overall system costs and emissions (Faruqui et al., 

2007).

Figure 3 illustrates the metrics we use to assess changes in the system need for DR. The 

figure shows net load duration curves for an illustrative baseline scenario (blue) and EE scenario 

(red), with the 97th percentile in demand indicated for each scenario. The EE scenario, in this 

example, yields a reduction in load in all hours of the year. Relative to the baseline scenario, the 

maximum demand in the EE scenario is reduced by an amount Δpeak (see the inset panel of 

Figure 3). If the stack of generation resources on the grid is held fixed, as would be the case in 

the short-term immediately following a region-wide EE upgrade, a reduction in system peak 

demand indicates a reduction in the system need for DR, since there is less demand that need to 

be met by peaking generation resources and DR resources. Thus, the change in peak load, Δpeak,

can be used as a metric for the short-term impacts of EE on the system need for DR.

 

16 In general for load-shedding DR, this will be significant only in the top few percent of hours in terms of system 

load each year.

17 In addition to these effects, EE and DR interactions may affect how the cost and capability of DR-enabling 

technology (level 1a) are affected, how the cost of securing customer participation (level 1b) changes, and how these 

changes impact the DR supply curve (level 2b). We leave exploration of these interactions to future work.

18 As published by the relevant ISOs and balancing authorities.

19 Indeed, this represents a fairly generous accounting of the potential DR hours. Other studies have considered the 

top 1% of hours (Faruqui et al, 2007) or even fewer (Satchwell et al., 2013).

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Figure 3. Schematic diagram of our approach to estimating changes in the system-level need for DR 

in response to EE upgrades, via examining the top 3% of hours in an illustrative load duration curve. 

Over the long term, however, the generation stack will evolve through retirement and 

construction of new generation resources that will be selected based on their specific economics 

of operation in the context of expected future load. In the illustrative EE scenario, compared to 

the baseline, the 97th percentile of load has fallen by an amount Δ97 (see the inset panel of

Figure 3). In that case, certain generation resources that would have been economical to operate 

in the baseline scenario, by serving load in more than 3% of hours, may become uneconomical in 

the EE scenario due to reduced utilization. Then the top 3% of hours will still need to be served 

by expensive peaking capacity or by DR resources. Thus, a useful long-term indicator of system 

need for DR is the difference in demand between the maximum and the 97th percentile. We refer 

to this as the “peakiness” of the system, denoted by A and illustrated in the inset panel of Figure 

3 for both the baseline and the EE scenario. If the EE savings occur preferentially during peak 

hours, then the peak will fall farther than the 97th percentile (Δpeak > Δ97), and AEE will be 

smaller than Abase, indicating EE and DR complementarity in the form of reduced long-term

need for DR. If the savings occur preferentially outside the top 3% of hours, AEE may exceed

Abase, indicating EE and DR competition. Therefore, we use the change in peakiness,

ΔA = Abase − AEE, as a metric for long-term EE and DR interaction at level 2a. 

Results and Discussion

The EE measures applied in each scenario modify the load shapes of individual homes, 

which changes the quantity of load that is available to provide DR and drives EE and DR 

This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications. 10
interactions at level 1a of the framework. Figure 4 shows the change in technical DR potential

estimated by DR-Path, in each EE scenario relative to the baseline, for average single-family 

detached homes in selected representative locations.

20 The equipment-only and envelope-only

scenarios both lead to reductions in building-level DR potential, reflecting a reduction in 

available load during system peak hours and indicating competition at level 1a. The controlsonly scenario, by contrast, slightly increases building-level DR potential, which indicates 

complementarity at level 1a. This is primarily because the programmable thermostat settings 

used in this scenario (see Table 2) suppress cooling energy consumption during daytime and 

overnight hours, but must increase it to achieve the desired set point in the evening, which 

coincides with the typical daily system peak in both regions.21 There is notable geographical

variation in the interactions for all scenarios, which reflects a strong climate dependence in the 

baseline loads and in the EE load impacts, as well as variation in the building stock.

Figure 4. Bars show the change in an average home’s technical potential to provide load-shedding DR, 

for each of our EE scenarios relative to the baseline, for single-family detached homes in selected 

representative locations within each of our modeled grid regions. 

Figure 5 shows EE and DR interactions at level 2b of the framework as the technical DR 

potential from residential buildings, in the base scenario and the EE scenarios, aggregated to the 

system level in each region and broken down by end use (stacked bars). Figure 5 also shows the 

fractional change in annual residential electricity consumption for each of the scenarios (red 

dots) to demonstrate the EE savings achieved. Overall, Texas has a higher technical DR potential 

from residential loads in the base case (~26 GW) than does California (~16 GW), owing 

primarily to Texas’s much larger residential cooling load during peak hours. In each EE scenario, 

there is a reduction in total electricity consumption, which varies by scenario.

 

20 Shreveport, LA, is used as a representative weather location for certain areas of Texas near the Louisiana border.

21 The increase in DR potential may be even larger when one considers the increased DF capability that these 

controls enable, an effect that will be considered in future work. 

This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications. 11
Figure 5. Bars show the absolute system-level technical potential for shed-type DR (i.e., total 

load in sheddable end-uses, on average during peak hours), by end use, in each of our 

analyzed regions, for the base scenario and each EE scenario. Red dots show the total 

electrical energy consumption from residential buildings, relative to the baseline scenario. 

In the equipment-only scenario, DR potential is smaller for all end uses, and the reduction 

is roughly proportional to the reduction in residential energy consumption, since all end uses 

have reduced load arising from improved device efficiency. This represents the most 

straightforward type of EE and DR interaction at level 2b: because there is less load within each 

end use, the quantity of DR that can be delivered in principle is smaller and EE and DR are in 

competition. The envelope-only scenario also shows a smaller DR potential, but in this case the 

decline is notably larger than the reduction in energy consumption. This occurs because the 

decline in both quantities is driven by reductions in cooling load, which occur disproportionately 

during peak periods (in these summer-peaking systems), yielding competition between EE and 

DR at level 2b.22 The situation is more complicated in the controls-only scenario. Although 

there is less residential energy consumption overall, the DR potential is larger than in the 

baseline scenario. As we saw at the building level in Figure 4, this arises from the increased onpeak load needed to offset daytime thermostat setups, though the setups save energy on balance. 

This interplay yields a modest complementarity between EE and DR at level 2b.

 

22 An offsetting consideration is that envelope improvements increase thermal inertia, which may improve the 

capability of a building to shed cooling load, either more deeply or for a longer period of time, while maintaining 

occupant comfort. Thus, the overall competition at level 2b for envelope measures may be smaller than shown here.

This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications. 12
Figure 6 presents EE and DR interactions at level 2a of the framework, relating to the 

system need for DR. Specifically, we show changes in the peak and peakiness metrics, Δpeak and 

ΔA, expressed as percentages of the base-case quantity. All metrics are computed with respect to 

the system load net VRE generation. California shows a fairly straightforward EE-DR interaction 

in the equipment-only scenario, with the EE upgrades reducing the system peak and peakiness, 

indicating both a short-term and long-term level-2a complementarity. In Texas, however, while 

equipment upgrades reduce system peak load, there is little impact on the peakiness (i.e., top 3% 

of load). This likely reflects the centrality of residential cooling to daily peak load in Texas, such 

that equipment EE improvements yield proportional reductions in load across the top 3% of 

hours. In both states, the envelope-only scenario shows a clear EE and DR complementarity at 

level 2a, with reductions in both system peak and peakiness arising from reductions in on-peak 

cooling load. In contrast, at levels 1a and 2b, the equipment-only and envelope-only portfolios 

led to competition, demonstrating how EE and DR interactions can have different effects at 

different levels. 

Figure 6. Metrics for the change in DR need at the system level, in each region and for each EE 

scenario. All metrics are expressed as a percentage of the relevant baseline quantity and computed 

with respect to the net system load. Negative numbers represent a decrease in the quantity being 

assessed, with respect to the baseline scenario, and positive numbers represent an increase

The controls-only scenario yields mixed EE and DR interactions at level 2a. In 

California, the system peakiness increases significantly, with a small increase in system peak. 

This reflects the fact that California’s solar-driven net load peaks consistently in the evening 

hours, coincident with the increased cooling load that is needed to offset energy-saving daytime 

thermostat setups. By contrast, Texas sees a large decrease in peakiness and a moderate 

reduction in peak load, likely because the large and variable wind generation resource shifts net 

load peaks away from the evening hours on some days. These results highlight the importance of 

developing controls strategies that are appropriate for the specific system and need being 

addressed. In California, for instance, the energy savings achieved through daytime thermostat 

setups have exacerbated peak-management challenges on the system level. A thermostat strategy 

that instead employed pre-cooling to reduce evening peaks could mitigate the same gridThis report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications. 13
management challenges, but this might come at the expense of increased energy consumption,23

which is another form of EE and DR competition. It is also worth noting that the available 

system-level DR resource has increased in this scenario (see Figure 5), and that complementarity 

at level 2b could offset the competition at level 2a.

Conclusion

We constructed a bottom-up simulation of the system-level demand from buildings in 

two regions of the electricity grid (California and Texas) that exhibit a high need for shed DR. In 

the context of a conceptual framework for understanding EE and DR interactive effects, we 

applied various illustrative portfolios of EE measures to the underlying building simulations to 

explore interaction between EE measure portfolios and the need for and technical availability of 

DR in each region. We find that interactions between EE and DR can be either competitive or 

complementary, depending on the framework level being considered, and the outcome is driven 

by the coincidence of EE savings (that are measure-specific) with system peak demand (that is 

region specific). For example, the equipment-only portfolio curtails energy consumption across 

several multiple end-uses that coincide strongly with system peak demand; this reduces buildinglevel DR technical potential (level 1a competition), reduces the system need for DR (level 2a 

complementarity), and reduces the system-level DR resource (level 2b competition). EE and DR 

interactions also depend strongly on the specifics of the power system, particularly at level 2a. 

For instance, the controls-only portfolio increases system need for DR (level 2a competition) in 

California, where the solar-driven net load peaks at the same time as the thermostat settings 

increase load. But the same strategy reduces system need for DR (level 2a complementarity) in 

Texas where high wind penetration often shifts the net load peak out of these hours.

Our findings suggest three key considerations for EE and DR program design and utility 

planners: 1) it is important to consider how EE changes both the quantity of DR that is available 

and the need for DR in the system when considering integrated approaches to EE and DR; 2) any 

EE measure has interactions with DR that are nuanced and measure specific, and these can be 

revealed by hourly load shape analysis; and 3) the same EE measure in one system may have 

different DR interactions than in a different system, and controls-based measures in particular 

should be developed in a way that reflects system-specific needs. In future work we will consider 

a broader range of measure portfolios and interactive effects, including investigating 

combinations of equipment, controls, and envelope measures and exploring how EE and DR 

interact via changes in customer capability (level 1a) and willingness (level 1b) to provide DR. 

By considering interactive effects from a range of perspectives across our framework we will

explore how to ensure that utility planners can evaluate interactive effects of EE and DR 

programs to help minimize overall system costs and emissions.

Acknowledgments. The work described in this study was funded by the U.S. Department of 

Energy’s Building Technologies Office under Lawrence Berkeley National Laboratory Contract 

No. DE-AC02-05CH11231 and National Renewable Energy Laboratory Contract No. DE-AC36-

08GO28308. We thank Monica Neukomm for supporting this study. We also thank Sydney 

Forrester (LBNL) and Maggie Molina (ACEEE) for input and comments on earlier drafts. Any 

remaining errors or omissions are our own.

 

23 Resulting impacts on system costs and emissions would be limited if the increased consumption occurs during 

high renewable generation periods, however.

This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications. 14
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