Valuing Residential Energy Efficiency:
Analysis for a PrototypicalSoutheastern Utility
Andy Satchwell | Berkeley Lab
Ryan Hledik | The Brattle Group
Oleksandr Kuzura | The Brattle GroupOluwatobi Adekanye | NREL
Chioke Harris | NRELJULY 2022
This work was funded by the U.S. Department of Energy Building Technologies Office under Lawrence Berkeley National Laboratory Contract No. DE-AC0205CH11231 and National Renewable Energy Laboratory Contract No. DE-AC36-08GO28308.

Disclaimer
This document was prepared as an account of work sponsored by the United States Government.
While this document is believed to contain correct information, neither the United States Government
nor any agency thereof, nor The Regents of the University of California, nor any of their employees,
makes any warranty, express or implied, or assumes any legal responsibility for the accuracy,completeness, or usefulness of any information, apparatus, product, or process disclosed, orrepresents that its use would not infringe privately owned rights. Reference herein to any specificcommercial product, process, or service by its trade name, trademark, manufacturer, or otherwise,
does not necessarily constitute or imply its endorsement, recommendation, or favoring by the UnitedStates Government or any agency thereof, or The Regents of the University of California. The views andopinions of authors expressed herein do not necessarily state or reflect those of the United States
Government or any agency thereof, or The Regents of the University of California.
Ernest Orlando Lawrence Berkeley National Laboratory is an equal opportunity employer.
COPYRIGHT NOTICEThis manuscript has been authored by authors at Lawrence Berkeley National Laboratory underContract No. DE-AC02-05CH11231 and National Renewable Energy Laboratory under Contract No. DEAC36-08GO28308 with the U.S. Department of Energy. The U.S. Government retains, and the publisher,by accepting the article for publication, acknowledges, that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of thismanuscript, or allow others to do so, for U.S. Government purposes1

Contents

1

Introduction

2

Approach Overview

3

Findings

4

Conclusion

A Technical Appendix describing
key modeling inputs, assumptions,and methodology is available at:https://emp.lbl.gov/publications/v
aluing-residential-energyefficiency

Contents

1

Introduction

2

Approach Overview

3Findings

4Conclusion

Study Purpose
In the context of an evolving U.S. power system, it is important to develop a morenuanced understanding of the value of energy efficiency (EE) as a system resource.
The purpose of this study is to identify how
much cost-effective residential EE remains,which new program and policy initiatives
could access it, and the value it would bringto the power system.
The benefits of residential EE to the powersystem are estimated in a forward-looking
system planning and valuation analysis.

The analysis focuses on a variety ofresidential EE measures that could be
deployed by a prototypical summer-peaking
utility in the Southeastern U.S.
Findings are based on NREL’s hourly ResStockEE savings profiles and Berkeley Lab’s Cost ofSaving Electricity database. The modeling wasdone using Brattle’s GridSIM capacity
expansion platform.
4Key study boundaries
THIS ANALYSIS DOES…

THIS ANALYSIS DOES NOT…

Identify the amount of each EE measurethat is cost-effective from the utility’sperspective.

Identify cost-effective EE from theparticipating or non-participating
customer perspective.

Assess a range of common standalone
residential EE measures and a packaged
grouping that may be offered by utilities.

Model numerous EE measures withdifferent performance assumptions andgrouped into many possible packages forprogram delivery.

Characterize a vertically-integrated utility
intended to be generally representative ofSoutheastern loads and resources.

Evaluate specific Southeastern utilities,
alternative future grid scenarios, anduncertainty in EE assumptions.

5Contents

1Introduction

2

Approach Overview

3

Findings

4Conclusion

Analysis framework
The analysis uses a capacity expansion
model (Brattle’s GridSIM model) toquantify the prototypical Southeasternutility’s total cost of serving electricity
demand for a 20-year planning horizon(i.e., 2021-2040).
We analyze the impact of EE additions
under business-as-usual (BAU) conditions,and then quantify the extent to which
cost-effective residential EE deployment
would increase under various programand policy initiatives.
Note: See Technical Appendix for EE measure assumptions andmodeling methodology.

The modeling simulation selects the costeffective EE portfolio by allowing it to“compete” with supply side resources.
 EE benefits are represented by avoided system

resource costs (i.e., avoided capacity, energy,and ancillary services).
 EE costs are represented by participation
incentives (i.e., share of incremental equipment& installation cost), administrative costs, andmarketing costs.

Energy savings from the EE measures(except smart thermostats, which aremodeled as demand flexibility) aremodeled as hourly-interval annualshapes.
7

Scenarios
The analysis quantifies achievable, cost-effective EE potential under several scenarios, toexplore how new policy initiatives can unlock greater EE value.
Modeling scenarios: Business-As-Usual (BAU) Achievable Potential:

Utility cost-effective, achievable EE potential under
average assumptions about measure costs, customeradoption, and grid characteristics.
 EE Cost Reduction: Relative to the BAU assumptions,
EE measure costs are reduced by 40% (e.g., throughtechnological advancements).
 Grid Cost Increase: Relative to the BAU assumptions,power system costs are higher (e.g., due to higherthan-anticipated fuel prices or labor costs, ormaterials shortages).
 Higher EE Adoption: Relative to the BAU
Note: Further detail about each scenario is provided in the discussion of findings.

assumptions, maximum customer adoption of EE
measures is higher (e.g., due to improved
marketing/awareness campaigns).
 Clean Energy Standard: A clean energy standard isintroduced that ramps up to 40% of generation fromcarbon-free sources by 2040.
 Packaged Measures: Measures are offered tocustomers as a package rather than a la carte.
 Technical Potential: Provided only for context ofupper-bound EE potential, assumes eventual 100%
participation among eligible customers in allmeasures modeled.

8The GridSIM model is used to simulate long-term power system
operations and the impact of energy efficiency
INPUTS
Supply

Existing resources
Fuel prices
Investment/fixed costs
Variable costs
New resources (incl. EE)

GridSIM OPTIMIZATION ENGINE
Objective Function
 Minimize NPV of investment & operational costs

With dynamic
selection ofcost-effective
EE measuresDemand Representative day hourly demand Capacity needs
 Operational reserves requirements

Energy Efficiency
 Hourly load reduction parameters
 Measure interactions

Regulations, Policies, Market Design
 State energy policies and renewablegeneration procurement mandates

Constraints

OUTPUTS
Annual Investments
and Retirements (incl. EE)
Hourly Operations
Supplier Revenues
System and Customer Costs

 Market design and co-optimized operations

Capacity
EnergyAncillary services

 Regulatory & policy constraints
 Resource operational constraints
 EE adoption constraints

Note: Further detail about the GridSIM platform and the optimization formulation is provided in the Technical Appendix.

PricesEmissions and Clean
Energy Additions
9Characterizing the prototypical Southeastern utility
The analysis uses a prototypicalSoutheastern utility to illustrate
the power system value of energy
efficiency.
 The prototypical utility is definedusing the characteristics of a varietyof Southeastern utilities.
 Gas generation accounts for most of
the system generation and capacity
expansion.
 Some solar capacity is added as solar
costs decline by 2030 and beyond.

MODELED GENERATION MIX EVOLUTION WITHOUT EEGeneration (TWh)7060

Solar5040Nuclear

30Gas
NG_CC

20

Hydro

100Coal2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039

MODELED CAPACITY MIX EVOLUTION WITHOUT EEInstalled Capacity (GW)
181614121086420

2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039

Note: See the Technical Appendix for details on assumed utility system
conditions.

WindWindSolar
Nuclear
Gas
NG_CC
Hydro
Coal10Energy efficiency measures analyzed
EE measure savings shapes are based on Duke Energy Carolinas weather locations inResStock toensure consistency in the weather driving utility loads and EE shapes.
Smart thermostats are included as DR measures in the analysis.
EE Measures
AirSealing

AtticInsulation

WindowsEE Heat
Pump Water DR ThermostatHeaters

EE CAC& ASHP*Cool
Roofs

Envelope
Water Heating

EE Packages

Thermostat DRHVAC
HVAC + Envelope +Water Heating +Tstat* CAC = Central air conditioning, ASHP = air-source heat pumps
Note: See the Technical Appendix for additional EE measure detail, performance assumptions,discussion on EE measure selection and package definition, and a description of the NREL ResStock tool.

11EE energy impacts
EE savings profiles are represented on an hourly basis. In some hours, the EE packages increase
consumption relative to the baseline (though all packages decrease usage over the year).
SUMMER

Envelope

SHOULDER

Water Heating

HVAC

WINTER

HVAC + Envelope + Water Heating

Note: Energy savings profiles shown are a per-household average for all days of the season. Summer is June through August, winter is December
through February, and shoulder is all other months.
Thermostat hourly modifiers not shown because the measure is modeled as a dispatchable DR resource.

Contents1Introduction

2

Approach Overview

3Findings

4Conclusion

Business-as-usual achievable potential
ACHIEVABLE REDUCTION IN ANNUAL
SYSTEM ENERGY SALESHistorical Savings

 In 2019, Southeastern utility energy efficiency programs deployedover the prior 20 years provided total annual system energy
savings of 1.3%.
 This calculation is based on EIA-861 data. It sums incremental EE
savings over 20 years, and expresses this sum as a portion of total
2019 Southeastern utility sales.

Business-as-usual savings potential

 Under business-as-usual (BAU) conditions, EE potential that could

be achieved through utility programs is almost 3x higher than what
has been achieved historically. As discussed previously in thisreport, this savings potential reflects a forward-looking estimate ofcost-effective levels of EE development in a utility resource
planning framework under typical system conditions.
14Defining the EE Cost Reduction scenario
The “EE Cost Reduction” scenario captures theimpact of a reduction in EE program costs that could
be attributed to developments such as technologicalbreakthroughs, economies of scale, or initiativessuch as increased research and development activity.

AVERAGE EE PROGRAM COST, BY REGION, 2009-2015

We assume a 40% reduction in EE program costs,
based on a review of Berkeley Lab’s analysis ofenergy efficiency program costs.

 The cost of saving electricity (CSE) varied significantly

among U.S. Census regions ranging from a low of$0.015/kWh in the Midwest to $0.033/kWh in theNortheast.
 We assumed a 40% reduction in costs representing theapproximate regional difference from the South (ourregion of focus) to the Midwest (lowest).
 While regional differences in EE costs could be driven inpart by a different mix of adopted measures across theregions, we use this comparison as general support forthe magnitude of cost reduction assumed in thisscenario.

Source: Hoffman, I., Goldman, C., Murphy, S., Frick, N., Leventis, G., Schwartz, L. The Cost of Saving
Electricity Through Energy Efficiency Programs Funded by Utility Customers: 2009–2015. 2018.
https://emp.lbl.gov/publications/cost-saving-electricity-through15

Impact of the EE Cost Reduction scenario
ACHIEVABLE REDUCTION IN ANNUAL SYSTEM ENERGY SALES

The 40% reduction in EE program costs results in EE becoming amore cost-competitive resource. Annual cost-effective energysavings potential increases to 5.5% as a result.

16

Defining the Grid Cost Increase scenario
The “Grid Cost Increase” scenario captures theimpact of an increase in power system costs,
which could be due to factors such as higherthan-anticipated fuel prices or labor costs, ormaterials shortages.

NREL ATB SOLAR CAPITAL COST PROJECTIONS, BY CASE Wind and solar capital cost: Increased costs

consistent with the NREL Annual TechnologyBaseline (ATB) “Conservative” case assumptions(see figure at right).

 Fossil generation capital cost: Capital cost of new

fossil generation is increased by 10%.

 Fixed O&M cost: Increased by 10% for allgeneration.

 Natural gas price: Natural gas fuel costs are

increased by 20%.

17

Impact of the Grid Cost Increase scenario
ACHIEVABLE REDUCTION IN ANNUAL SYSTEM ENERGY SALESEnergy efficiency becomes more economicallycompetitive due to the increased cost of generation
resources. The increase in cost-effective EE is smaller
than in the EE Cost Reduction scenario, primarily
because the modeled EE cost reduction is larger thanthe modeled generation cost increase.

18

Defining the Higher EE Adoption scenario
The “Higher EE Adoption” scenario captures the impactof increased EE adoption at a given participation
incentive level, which could be due to initiatives such as
improved marketing and awareness campaigns.

MAXIMUM ADOPTION RATES, BY EE PACKAGE

BAU

Higher EEAdoption

 This scenario assumes that, all else equal, an increased share

Envelope

30%50%

 For example, whereas up to 30% of customers were assumed

Water Heating

60%

80%

Thermostat DR

30%

50%

HVAC

50%

70%

Envelope + HVAC + Water Heating

60%

80%of eligible customers will adopt the EE measures.

to adopt the envelope package at the highest available
incentive level* in the BAU scenario, 50% would adopt theenvelope package at that highest incentive level in theHigher EE Adoption scenario.

 The assumed increase in participation is derived fromassumptions developed for DOE’s A National Roadmap for
Grid-Interactive Efficient Buildings, based on a review ofvarious market research studies and EE potential studies.

Source: U.S. Department of Energy, A National Roadmap for Grid-Interactive Efficient
Buildings, prepared by Berkeley Lab, The Brattle Group, Energy Solutions, and Wedgemere
Group, May 2021.

* Refer to the Technical Appendix for more information on incentiveassumptions.
19

Impact of the Higher EE Adoption scenario
ACHIEVABLE REDUCTION IN ANNUAL SYSTEM ENERGY SALES

Expanding the base of engaged customers
through innovative means other thanfinancial incentives (e.g., awarenesscampaigns, targeted marketing) cansignificantly increase overall EE impacts.

20Defining the Clean Energy Standard scenario
The “Clean Energy Standard” scenario captures the role that EE can play in satisfying a policy requirement forclean energy resources.
 We assume that a new clean energy standard is introduced, ramping up to 40% of generation from carbon-free sources by2040 (which is consistent with the requirements of clean energy standards in some other U.S. states). EE savings countdirectly toward satisfying the requirement.
 While state-level RPS requirements are uncommon in the Southeastern U.S., many Southeastern utilities have announceddeep decarbonization goals.
SoutheastStateRPS Policy

Is EE
Eligible?

Example Utility or State Decarbonization Goals

Alabama

None-Florida

None

-

Southern Company net-zero GHG emissions by 2050
NextEra 67% reduction in CO2 emissions from 2005 levels by 2025

Georgia

None-

Southern Company net-zero GHG emissions by 2050

Louisiana

None-Entergy net-zero carbon emissions by 2050

Mississippi

None-North Carolina

11.88% by 2021 (weighted-averaged of IOU and POU obligations)

YesSouthern Company net-zero GHG emissions by 2050
Duke Energy net-zero CO2 emissions by 2050

South Carolina

None-Dominion net-zero carbon emissions by 2050

Tennessee

None-TVA net-zero carbon emissions by 2050

Texas

MW requirement amounting to ~5% of electricity sales.

NoAEP, Entergy, Xcel net-zero carbon emissions by 2050

Virginia

100% by 2050

No

Dominion net-zero carbon emissions by 2050
21

Impact of the Clean Energy Standard scenario
ACHIEVABLE REDUCTION IN ANNUAL SYSTEM ENERGY SALES

Allowing EE savings to count directly
toward the clean energy standard’s
requirements is a key driver of thesignificant impact of this scenario.

22Defining the Packaged Measures scenario
The “Packaged Measures” scenario captures the potential for bundling anddiscounting EE packages to boost program participation and capture
installation efficiencies.
 We used ResStock data to account for the energy savings interactions

between HVAC, envelope, and water heating measures when combined intoa package (e.g., envelope measures improve thermal efficiency and reducethe incremental impacts of HVAC measures when considered incombination).
 To capture the maximum potential benefit of measure packaging, we
assumed that the package would be adopted at the highest achievableparticipation assumed for any of the individual measures.
 We assumed that the levelized cost of saved electricity for the package isequal to the lowest of any individual measure, to account for the cost savings
associated with installing the measures together.
 In general, there is limited empirical information available on the costs,
benefits, and customer interest in EE packages; this area needs furtherresearch.
23

Impact of the Packaged Measures scenario
ACHIEVABLE REDUCTION IN ANNUAL SYSTEM ENERGY SALESWhile interactions can decrease
the energy savings contributionsof individual measures in apackage, cost savings and/orincreased adoption potentiallycould more than offset thiseffect, resulting in a net increasein system energy savings.

Note: Cumulative EE reduction in system annual sales may be different from component values due to rounding.

24

Achievable reduction in annual system energy sales
For a prototypical Southeastern utility, residential EE only
Under business-as-usual
conditions, achievable EE
potential is almost 3x
higher than what has
been achieved
historicallyWith new program and policy
initiatives, residential EE could
increase the historical savings
rate by over 7x, resulting in a
9.5% reduction in annual systemsales by 2040

Additional savings
potential still
remains

20%
50%
19%
11%

Note: Cumulative EE reduction in system annual sales may be different from component values due to rounding.

Four-fifths oftechnical potential
could be captured if
all new program andpolicy initiatives
were implemented25

Energy efficiency “scorecard”
MODELED RESIDENTIAL ENERGY EFFICIENCY IMPACTS, 2021-2040

Business-as-Usual Scenario

With All Policy Initiatives

Energy savings

3.8% reduction in total system sales(11.3% of residential sales)

10.4% reduction in total system sales(30.6% of residential sales)

Avoided CO2 emissions

17 million tons
(4.9% of total power sector emissions)

86 million tons*(24.9% of total power sector emissions)

Retired coal capacity

2,532 MW
(About five medium-sized coal plants)

2,532 MW*
(About five medium-sized coal plants)

Power generation costsavings$1,025 million, NPV(4.8% of total power system cost)$4,759 million, NPV†
(17.7% of total power system cost)* Effects attributable to combined impact of energy efficiency and renewable generation required by clean energy standard. All coal capacity isretired in BAU scenario, so there are no further increases in coal retirements associated with additional policy initiatives.
† Reference generation cost includes grid cost increases and clean energy standard.

26

Contents

1

Introduction

2

Approach Overview

3

Findings

4

Conclusion

Key insights
EE is a cost-effective resource For the prototypical Southeastern utility analyzed in this study, a limited portfolio of residential EE

measures could reduce system energy consumption by 3.8% per year, and save roughly $1 billion (NPV) inresource costs under BAU cost and participation assumptions by 2040.
 Implementing all the program and policy initiatives considered would increase annual energy savings 2.7x
and deliver ~$3.7 billion (NPV) in additional power system cost savings.

Reducing utility EE program costs and/or higher program participation are among the most impactful
initiatives
 Initiatives that lower EE costs (e.g., R&D activities) and increase program participation (e.g., customer

education, more effective contractor channels) resulted in the largest increase in cost-effective residentialEE.
 The two initiatives may complement each other as lower EE costs are likely to drive greater program
participation.

Utility resource planning tools can be used to assess the value of residential EE to the utility powersystem Publicly available tools, like Berkeley Lab's Time-Sensitive Value (TSV) Calculator, can be used for initial

screening of high value EE measures that are then modeled within resource planning tools.
 Additional analysis outside of the traditional resource planning framework can account for additional
incremental EE benefits such as deferred investment in distribution system upgrades

Packaging EE measures is under-explored While many of the interactions with energy savings can be estimated, there is little empirical evidence toinform whether measure packages are cheaper and/or more likely to be adopted than standalone EE
measures.

28

Acknowledgements
This work was funded by the U.S. Department of Energy (DOE) Building Technologies
Office under Lawrence Berkeley National Laboratory Contract No. DE-AC0205CH11231 and National Renewable Energy Laboratory Contract No. DE-AC3608GO28308. We would like to especially thank Dale Hoffmeyer for his support ofthis work. For comments and input on this analysis, we also thank Marc LaFrance
and Maddy Salzman (DOE), Ross Beppler (Southern Company), Margaret Pigman
and Natalie Mims Frick (Berkeley Lab), and Julie Michals (E4TheFuture). Roger
Lueken and Jesse Cohen (Brattle) provided additional research support. A portion ofthe research was performed using computational resources sponsored by theDepartment of Energy's Office of Energy Efficiency and Renewable Energy andlocated at the National Renewable Energy Laboratory. Any remaining errors oromissions are our own.
The views and opinions of authors expressed herein do not necessarily state orreflect those of the United States Government or any agency thereof, The Regents of
the University of California, or the Alliance for Sustainable Energy, LLC.

29