View
2.530
Download
1
Category
Tags:
Preview:
DESCRIPTION
Presented by Ed Carroll and Mark Brown of Franklin Energy during Conservation Improvement Program (CIP) discussion hosted by the Minnesota Office of Energy Security on July 21st, 2009
Citation preview
Research to Inform Design of Residential Energy Use Behavior
Change PilotChange Pilot
Conservation Improvement ProgramConservation Improvement Program Discussion Hosted by the Minnesota Office
of Energy Security
July 21st, 2009
Ed CarrollMark Brown
Flow of DiscussionFlow of DiscussionWhy Consider Behavior Change?Project Overview and Research ApproachBehavioral Change Interventions OverviewLit t R iLiterature ReviewUtility Experiences with Behavior Change Pilot ProgramsCost Effectiveness and Applicability to MNCost Effectiveness and Applicability to MNKey Lessons Learned – Utility Manager PerspectivesPilot Models to ConsiderQ&A
Why Consider Behavior Change?Valuable tool to help you meet your CIP program goals
Utility experiences show that interventions can lead to significant energy savings:energy savings:
2% to 7% savings for program participants in the first year
Traditional product-based prescriptive and custom incentive programsTraditional product based prescriptive and custom incentive programs may be inadequate and provide diminishing returns:
“We realized we would be unable to meet our energy savings targets for the residential sector with purely a product basedtargets for the residential sector with purely a product-based approach.” – Program Manager, Seattle City Light
“The lighting program we have here is so successful that we are ll i t t f t ti l i th t f It i b freally going to run out of potential in the next few years. It is by far
the program that results in the biggest savings. It is sort of like the linchpin will be gone.” – Program Manager, SMUD
Cost effectiveness as low as 3¢ per kWh in first-year savings
Project OverviewOBJECTIVE: to provide the Minnesota Office of Energy Security (OES) and utilities a solid plan for piloting residential energy use behavior change programs as part of their CIP efforts
GOAL: to help Minnesota utilities better understand how to accelerate energy savings resulting from changes in residential energy-use behavior
ACTIVITIES: (completed Nov. 2008 to Apr. 2009):
Collected data and analysis from available published research
Interviewed experienced program managers, consultants, and researchers
Id tifi d b t ti d l l d f t di dIdentified best practices and lessons learned from studies and pilots aimed at addressing consumer behavior
Developed a report providing recommendations for utility pilot p p p g y pprograms applicable to Minnesota’s residential market
Information ResourcesInterview Respondents Published Research ResourcesInterview Respondents
Utilities/Administrators:• Austin Utilities (K. Lady)• Baltimore Gas & Electric (R. Kiselewich)
Published Research Resources
ACEEEEPRI
Baltimore Gas & Electric (R. Kiselewich)• BC Hydro (A. Korteland)• Connexus Energy, MN (B. Sayler)• City Utilities, MO (C. Shaefer)• Energy Trust of Oregon (K. Youngblood)• Pacific Gas & Electric (J Medvitz)
Precourt Institute for Energy Efficiency (Stanford Univ.)Environmental Change Institute (UK)BEHAVE Program (Europe)• Pacific Gas & Electric (J. Medvitz)
• Silicon Valley Power (L. Brown)• SMUD, Sacramento (A. Crawford)
Consultants/Vendors:
BEHAVE Program (Europe)CIEE (California Institute for Energy and Environment)
J l P bli ti• Comverge/ComEd (K. Papadimitriu)• The Brattle Group (A. Faruqui)• Paragon Consulting (B. Jackson)• Positive Energy (A. Laskey)• Van Denburgh Consulting (E. Van Denburgh)
Journal Publications:• Energy Efficiency• Energy Policy• Journal of Environmental Psychology• Journal of Environmental Systemsg g ( g )
Researchers:• Energy Center of Wisconsin (I. Bensch)• FSEC - Florida (D. Parker)
y
• Org. for Energy and the Environment – the Netherlands (H. van Elburg)
Interventions Overview
Behavior Change TheoryBehavior Change Impact of Feedback
Realize that there is a problem
Behavior ChangeDecision Making
Impact of Feedback
• Identifies cost of behavior or deviation from peers
Habitual Behavior Realize relevance of behavior to problem
R li ibiliti t
from peers
• Indicates the impact of specific behavior changes
• Turning on/off lights• Use of appliances
S tti th th t t
Realize possibilities to influence problem
Weigh motives:• Personal norms
changes
• Setting the thermostat Personal norms• Social norms• Other motives (e.g., comfort)
Evaluate conflicting motiveElectricity:
E bli d t
• Can frame behavior in terms of cost ($) or impact on the environment
Challenges
Evaluate conflicting motive
Take action
• Enabling product• Low-involvement• Intangible• Dissatisfier
• Repetitive prompts help to form new persistent habits
• Low cost priorityhabits
Categories of InterventionsWe see three distinct behavioral change program categories:
MonitorsReportsRatesWe see three distinct behavioral change program categories:
In-home devices and displays providing feedback• Real-time feedback on energy use and costs• Devices interface with utility electric meter or through CT
clips installed at electric panel• Examples: PowerCost Monitor, Kill-A-Watt, TED
Customized, regular feedback delivered to consumers• Processed feedback via mailed reports or online interface• Opportunity to incorporate comparative data/feedback
D i i i / t d i ( t t i )
pp y p p• Examples: Positive Energy, BC Hydro’s Team Power
Smart
Dynamic pricing / rate designs (e.g., smart metering)• Protocols that allow for different rates to be charged
based on time of use• Enabled by advanced metering infrastructure and two• Enabled by advanced metering infrastructure and two-
way communication between the utility and customer
Monitors (Direct Feedback)Pros:
PowerCost Monitorfrom Blue Line Innovations
• Users able to receive real-time feedback from their meter via a mobile monitor.
• Real-time feedback allows users to experiment and see the impact of their behaviorof their behavior
• Multiple utilities have demonstrated the savings achieved by customers using these devices
• NSTAR: 3% annual energy savings in ongoing pilot
Click to Launch Video• Hydro One: 6.5% annual savings in a 500-home pilot• Dominion: 6% saving in non-electric water heat homes
Cons:Opt in nature of programs (e g soliciting customers to install
The Energy Detectivefrom Energy, Inc.
• Opt-in nature of programs (e.g., soliciting customers to install devices) leads to low adoption rates and limited scale
• Low willingness to pay relative to device cost• Significant drop-out rates among participants as the novelty of the
device wears off, monitors are put away, or batteries die• Questions about persistence of savings and cost effectiveness of
the $130+ devices• Data capture reliability and resolution raises concerns
Click to Launch Video #2
• Data capture reliability and resolution raises concerns• Compatibility issues with meter/panel designs and interface
Reports (Indirect Feedback)Pros:Pros:• Opt-out (vs. opt-in) nature allows utilities to design and conduct
rigorous large-scale pilots and implementation for entire populations in desired segmentsP id ti f db k h i t ’ f• Provide comparative feedback, showing a customer’s performance relative to their neighbors; power of social norms
• Customized reports based on housing, demographic, and psychographic factors to maximize appeal
• Can operate with or without in-home devices and AMI• Cost effectiveness of savings achieved:
• 3¢ per kilowatt hour in first year (Positive Energy at SMUD)
Cons:• Will not match the real-time and (unless coupled with AMI-enabled
technology) use-specific feedback that in-home devices providegy) p p• Utilities must be careful in targeting and crafting their messaging in
order to minimize potential negative effects:• Small minority of customers offended by comparative feedback
C t d id t i th i ti• Customer may decide to increase their energy consumption
Rates (Dynamic Pricing)Pros:Pros:• Dynamic pricing provides direct monetary incentives for consumers• Utilities are better able to match prices to energy production and/or
purchase costs• Flexibility in rate design (e.g., time-of-use, real-time, critical-peak).• Solutions typically require in-home displays that provide feedback:
• Real-time and cumulative cost/energy consumption info and associated energy savings impactsassociated energy savings impacts
• Advantage of permanent installation/use
Cons:• Costly infrastructure investment requiring substantial resources to
install meters and develop integrated IT platforms• Programs costs are typically justified by returns from operational
efficiency and capacity (i e peak load) management and savingsefficiency and capacity (i.e., peak load) management and savings• Energy efficiency/conservation savings are typically secondary
benefits and not primary drivers
Literature Review
Literature Review
Major review studies: Key FindingsFeedback leads to energy gysavings:
• Direct: 5 to 15%• Indirect: 0 to 10%
Characteristics of effective feedback:
• Given frequently• Involves interaction• Involves interaction• Involves appliance-specific
information• Given over longer period• Presented in a user-
friendly formatConcern with experimental rigor of studiesrigor of studies
Findings from Literature Review
Covers 5 review studies and
(C. Fischer, 2008)
21 original studies across 10 countriesConcludes that feedback
i l istimulates energy savings –‘usual savings of 5% to 12%’Characteristics of effective feedback:feedback:
• Given frequently• Involves interaction• Involves appliance-specific pp p
information• Given over longer period• Presented in an
understandable and appealingunderstandable and appealing way
Findings from Literature Review
Savings from direct feedback – average from 5-15%
(S. Darby, 2006)
Savings from indirect feedback (e.g., billing) - range from 0-10%.
High energy users may respond more than low users to direct feedbackfeedback
Persistence of energy savings created from feedback when individuals develop new habits or invest in efficiency measures
Useful display features include instantaneous usage, expenditure, and historic feedback
Indirect feedback can be most helpful for evaluating heating load andIndirect feedback can be most helpful for evaluating heating load and the impact of investments in insulation/new major appliances
Direct feedback is better for understanding the impact of smaller end-uses and the significance of moment to moment behavior
Findings from Literature Review
Reviews thirty eight field studies from 1977 to 2004 aimed at
(Abrahamse et al, 2005)
Reviews thirty-eight field studies from 1977 to 2004 aimed at encouraging households to reduce energy consumption
Identifies that much of the research on energy conservation i t ti h l k d th i t i t l diti (interventions has lacked the appropriate experimental conditions (e.g., significant sample size, appropriate control groups) to validate findings and draw definitive conclusions
The large majority of studies addressing feedback find it to be an effective means to generate energy savings, with more frequent feedback leading to greater effectiveness
Rewards for energy conservation may influence behavior, but the effects are found to be short-lived
Using inter entions in combination is fo nd to ha e an impro ed effectUsing interventions in combination is found to have an improved effect
Utility Experiences with Behavior Change ProgramsChange Programs
Illustrative Case StudiesIllustrative Case StudiesDirect feedback via display devices:• Hydro One (Ontario, Canada)• NSTAR (Massachusetts)• Recent Findings Update:
• Dominion (Virginia)• Seattle City Lighty g• Energy Trust of Oregon
Indirect feedback• Positive Energy/SMUD
• Update on savings validation (Summit Blue)
BC Hydro• BC Hydro
Case Study: Hydro One - PowerCost Monitor Pilot
Study Findings
6.5% aggregate reduction in electricity (kWh) consumptiony ( ) p8% reduction in non-electrically heated homes
5% reduction in non-electric
Pilot Program Methodology
Study period >1 year
heat/hot water homes16% reduction in non-electric heat homes w/ electric hot water
400+ participantsSample across wide variation of climate and geography
1% reduction in electrically heated homes; load “completely overwhelms”
11% of homes have electric heat in area
“income and demographic factorsImpact measured based on historical comparisonPowerCost Monitor (Blue Line Innovations) used by participants
income and demographic factors had no impact on the responsiveness to the monitor”60% of participants felt the monitor Innovations) used by participants made a difference in their homes
Source: Summary: The Impact of Real-Time Feedback on Residential Electricity Consumption: The Hydro One Pilot, March 2006
Case Study: NSTAR - PowerCost Monitor PilotStudy Findings
2.9% savings for customers who used the monitor (~$64/year)
Pilot Program Methodology
Pilot began May 2008
66%-75% installation rate33% of initial users stopped usingthe monitor during the study period
3,100+ units soldMedia coverage (TV, print) coincided with significant rise in sales
63% of participants indicate behavior change60% noticed savings in their bill
Offering Unit Price Adoption RateDirect install during energy audit Free 95%
Offering previous audit customers free PCM Free 14%PCMRetailOffering previous audit customers free PCM Free 14%
Direct Mail Solicitation/Media Promotion
$9.99 6%$29.99 5%$49 99 0 3%
RetailPrice:~$140
$49.99 0.3%
Source: 2008 BECC Conference Presentation: Power Cost Monitor Pilot, David MacLellan, NSTAR, November 2008
Recent In-Home Display Pilots: Dominion Virginia Power
Pilot Program Methodology
Study Findings
6% kWh energy savings in homes without electric hot waterg gy
Free PowerCost Monitor, pre-programmed with rateEnrolled 1 000 users from 4 600
19% kWh savings in homes with electric hot waterSavings estimates based on weather-Enrolled 1,000 users from 4,600
solicitations13-month study; began Nov. 2007GoodCents used as vendor to
gnormalized billing analysis comparing historical consumption53% of respondents reported technical difficultiesGoodCents used as vendor to
execute pilot30% response to mailed survey soliciation, with pre-paid return and
i ti f f lti k
technical difficultiesBattery lifeSensor water damage
coupon incentive for free multipack of CFLsIn process of completing post-study survey
Plan to structure full rollout with $25 user payment for the meter; to achieve “skin in the game”Using Blue Line PCM monitors thatsurvey Using Blue Line PCM monitors that have AMI compatibility
Source: AESP Webinar Presentation: Managing an In-Home Energy Display Pilot Project, July 2009
Recent In-Home Display Pilots: Seattle City Light Study Findings
2-3% average electricity savings in comparison with control group
Pilot Program Methodology
Goal of 33 home energy monitors installed
No significant variation in savings achieved across different monitors evaluatedS h t di i t d ithinstalled
Randomly chosen participantsSingle family homes only
3 types of meters installed
Somewhat disappointed with results to date;
Expected greater savings based on manufacturer claims and3 types of meters installed
PowerCost MonitorThe Energy Detective
on manufacturer claims and previously published studies
Difficulty in convincing customers to participate in the study
Cent-a-Meter8-month test period
Survey company found themselves having to sell hard
Logistics issues for electrical permits, installation scheduling for panelinstallation scheduling for panel devices
Source: AESP Webinar Presentation: Managing an In-Home Energy Display Pilot Project, July 2009
Recent In-Home Display Pilots: Energy Trust of Oregon Study Findings
Pil t P M th d l
Study Findings
Preliminary findings indicate “monitors did not have a significant impact on energy usePilot Program Methodology
Over 350 monitors deployed:164 sold via Web site @ $29 99
significant impact on energy use for either cohort”Six month response rates:
57% of Early Adopters164 sold via Web site @ $29.99 to “Early Adopters” (EA)201 installed as part of a Home Energy Review (HER)
57% of Early Adopters55% of HER cohort
66% of EA and 64% of HER using device after 6 months
Savings evaluation involved control groups with random stratified sample with adequate regional and home vintage representation
Two thirds of non-users report monitor no longer functionalLighting, space heating, and clothes dryers most often attributed as
g pSurveys conducted within 3 weeks of installation and at 6 months afterFirst installations in Jan of 2008
dryers most often attributed as savings sourceWhile never significant, point estimates of energy savings were gy ghighest at 3 months, and declined at 6-9 months
Source: AESP Webinar Presentation: Managing an In-Home Energy Display Pilot Project, July 2009
Indirect Feedback Programs
Case Study: Positive Energy @ SMUDStudy Findings (Ongoing)
Pilot Program Methodology
Study Findings (Ongoing)
2% savings achieved on average for treatment group (~250kWh p.a.)g gy
Program launched April 200835,000 customer treatment group(non-targeted)
3¢ per kWh savings cost averageSignificantly higher savings among:
• Higher energy consumers(non-targeted)• 25,000 homes receiving monthly • 10,000 homes receiving quarterly
55 000 t t l
• Greenergy (renewable energy) customers
• Monthly vs. quarterly recipients55,000 customer control groupRandom sampling to create representative populationR t id ‘h ’ h
Indication of correlation of higher savings for lower income population800 of 35,000 decided to opt out
Reports provide a ‘here’s how you compare to your neighbors’ message customized to the home (type, size, location)
<1% of 35,000 opted to set personal goalPositive customer feedback
Customized energy savings tips provided along with report
Few very negative reactions
Source: Interviews with President of Positive Energy and Program Manager at SMUD
Verification Analysis for Impact of Positive Energy at SMUDSummit Blue Consulting - May 2009
“The estimate of annual savings from each of the three methods ranged from 2.1% to 2.2% showing strong robustness of results. The range around each of these estimates is tight, providing good reliability and precision…The strength of these estimates rests on the clean design of the experimentand the very large sample sizes that were used. It is often difficult to accurately assess a program savings of 2% from billing analysis because of the wide range of variability in
t bill b t th l l f thi i t ll d
Source: Impact Evaluation of Positive Energy SMUD Pilot Study, May 26, 2009
customer bills, but the large scale of this experiment allowed for accurate assessment of savings from this program.”
Behavioral Science Research – Normative FeedbackSeminal research published in 2007 by Nolan J M Schultz P WSeminal research published in 2007 by Nolan, J. M.. Schultz, P. W., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V.Used doorhanger messages to test response to four conservation messages among California residents:messages among California residents:
(1) they could save money by conserving energy(2) they could save the earth’s resources by conserving energy(3) they could be socially responsible citizens by conserving energy(3) they could be socially responsible citizens by conserving energy(4) the majority of their neighbors tried regularly to conserve energy
Only the social norming message produced significant savings
Source: "Normative Social Influence is Underdetected," J.M. Nolan, P.W. Schultz, R. B. Cialdini, N.J. Goldstein, and V. Griskevicius, Personality and Social Psychology Bulletin (July 2008).
Positive Energy – Home Electricity Report Example
Source: Positive Energy
Positive Energy – Home Electricity Report Example
Source: Positive Energy
Positive Energy – Home Electricity Report Example
Source: Positive Energy
Positive Energy – Home Electricity Report ExampleCustomized TipsCustomized Tips
Driven By:
HousingSi• Size
• Age• Fuel type• Pool etc• Pool, etc.
Consumption• Amount• PatternPattern
Demographics• Income
A• Age• Length of
residenceDIY• DIY
• Green
Source: Positive Energy
Positive Energy – Home Electricity Report Example
Source: Positive Energy
Case Study: BC Hydro Behavior Change Market Test
Pilot Program Methodology
Study Findings
Reduction target had significant impact on recruitment success5% h d i ifiPilot Program Methodology
1-Year pilot launched early 2007Recruited employees of BC Hydro’s l t t
5% target had significant freeridership problem10% goal found to be optimalCash rewards more appealing thanlargest customer
Employees encouraged to participate:
• Commit to a given electricity
Cash rewards more appealing than prize draw rewardseNewsletter drove online visitsMore frequent visitors to online t l hi d hi h l t i it• Commit to a given electricity
reduction target• Use online tool to track/compare
consumption
tool achieved higher electricity savingsReported behavior changes
• Turning off lights• Participants received cash rebate for
achieving target (e.g. 5% electricity rebate for achieving
4 Different incentive rewards tested
u g o g ts• Changing laundry habits• Shorter showers• Unplugging chargers• Turning down the thermostat• Turning down the thermostat
Source: BC Hydro
BC Hydro – Team Power Smart
Online tools allow anyone in BC to enroll by committing to use 10% less energy over one year
Track consumptionTrack consumptionCompare consumption to similar householdsVisibility to community rivalry and promotion of “Pride of Province”
M b b fit i l d i l ff d t iti t iMembers benefits include special offers and opportunities to win prizes in drawings and contestsProgram supported by a roster of Team Power Smart Leaders including celebrity athletes and community leaderscelebrity athletes and community leadersExpected results among participants (~4% to 5% total savings):
17% become Achievers – average savings of 21%24% become Savers – average savings of 4%59% become Non-Achievers – no savings on average
Currently 74,000 members (4% of customers) enrolled toward goal of
Source: BC Hydro
Currently 74,000 members (4% of customers) enrolled toward goal of 210,000 by 2010
BC Hydro – Team Power Smart
Source: BC Hydro
Psychographic Segmentation
Targeting “StumblingTargeting Stumbling Proponents” with Team Power Smart
Cross references utility-focused categories (e.g., home heating, appliances, and lighting) with emotive categories:emotive categories:
• Health+Wellness• Food+Drink• Life+Leisure• Family+Friends
G• Home+Garden• Gadgets+Tech.
Uses survey data and demographic/housingdemographic/housing parameters to target customized messages most likely to be received positively to a given audience
Source: BC Hydro
given audience.
Cost Effectivenessand Applicability to MNand Applicability to MN
Examining Program Cost EffectivenessHome Energy Reports – Positive Energy @ SMUD (N=35,000)
250 kWh (~2%) first-year savings in non-targeted householdsFirst year cost of conserved energy (i e assumes no persistence):First-year cost of conserved energy (i.e., assumes no persistence):
3¢ per kWh (<$8 per household per year variable cost)In-Home Direct Feedback – PowerCost Monitor
Device Cost: ~$140 (without installation)Requires utility subsidy of ~$100+ to spur adoption (e.g. NSTAR)Likely savings potential of 3% (NSTAR) to 7% (Hydro One)Likely savings potential of 3% (NSTAR) to 7% (Hydro One)Cost of conserved energy @ $100/household program cost:
1 YearAssumed Savings Persistence Horizon
1 Year'first-year' 2 Years 5 Years 10 Years 20 Years
Savings scenario:3% - 330kWh $0.32 $0.16 $0.07 $0.04 $0.027% - 770 kWh $0.14 $0.07 $0.03 $0.02 $0.01
(cost of conserved energy: $ per kWh)
(Assumes: $100 program cost, 11,000 kWh average consumption, 5% discount rate)
Benchmarking First-Year Costs of Energy Savings
Source: Summit Blue Consulting, 2008
Importance of Opt-In vs. Opt-OutNSTAR’s PCM pilot sought to evaluate customer willingness to pay
Findings indicate the utility would have to subsidize nearly $100 of the device cost in order to reach a significant population (>1%)
Limited participation in opt-in programs has significant implications to achievable program savings:
Even if device programs could yield 10% savings (as per literature), if only 5% participate a utility would be limited to a 0 5% population impact5% participate, a utility would be limited to a 0.5% population impactConversely, a program like Positive Energy, saving only 2% among participants (possibly all customers), could have 4X the population impact
NSTAR Pil t Fi di C t Willi t P
95%FreeDirect install during energy auditAdoption RateUnit PriceOffering
95%FreeDirect install during energy auditAdoption RateUnit PriceOffering
NSTAR Pilot Findings – Customer Willingness to Pay
6%$9.99Direct Mail Solicitation/
Media Promotion5%$29.99
14%FreeOffering previous audit customers free PCM6%$9.99
Direct Mail Solicitation/Media Promotion
5%$29.99
14%FreeOffering previous audit customers free PCM
Media Promotion0.3%$49.99
Media Promotion0.3%$49.99
Source: NSTAR
Regional Energy IntensityIntensity in West North Central States matches national average
Factors that can influence regional differences:Climate – associated HVAC energy useAge distribution of the housing stock – associated appliance and weatherization efficiencyPopulation’s attitude toward conservationAmount of resources going toward EE and conservation programs
Source: Energy Information Administration
Residential Electricity End Use Consumption DriversAverage % of Household kWh by Region
30%
35%
Climate and fuel source
20%
25%
sum
ptio
n .
differences reflect relative share of electricity
consumption by region
15%
20%
Elec
tric
ity C
on
5%
10%
% o
f
0%
U.S. Avg. 16.0% 10.1% 5.0% 26.7% 9.1% 8.8% 7.2% 6.7% 2.5% 7.7%
West North Central 14.7% 8.2% 6.1% 29.2% 7.7% 8.6% 7.0% 8.1% 2.1% 8.3%
Air Conditioning
Space Heating
HVAC Appliances
Kitchen Appliances
Water Heating Lighting Home
ElectronicsLaundry
AppliancesOther
EquipmentOther End
Uses
Source: Energy Information Administration
New England 6.6% 6.6% 4.5% 32.6% 8.0% 13.2% 11.3% 8.6% 4.6% 4.1%
South Atlantic 21.4% 10.4% 4.2% 23.2% 12.4% 6.8% 5.7% 6.1% 2.4% 7.3%
Residential Electricity End Use Consumption DriversAverage Household kWh by Region
3,500
4,000
2,500
3,000
t Hou
rs
1,500
2,000
Ann
ual K
ilow
att
Uniformity exists in usesinvolving frequent behavioral interaction
500
1,000
A
-
U.S. Avg. 1,837 1,159 574 3,065 1,045 1,010 827 769 287 884
West North Central 1,689 942 701 3,356 885 988 805 931 241 954
Air Conditioning
Space Heating
HVAC Appliances
Kitchen Appliances
Water Heating Lighting Home
ElectronicsLaundry
AppliancesOther
EquipmentOther End
Uses
Source: Energy Information Administration
New England 491 491 334 2,423 595 981 840 639 342 305
South Atlantic 3,150 1,531 618 3,415 1,825 1,001 839 898 353 1,075
Evaluation of Miscellaneous Electric Loads (MELs)( )
Source: Energy Information Administration
Residential Electricity Consumptionby End Usey
Source: Energy Information Administration
Average Household Consumption by MEL
Source: Energy Information Administration
MELs in the Context of Total Energy Use
Source: Energy Information Administration
Water Heater Fuel Source by Region
F l Oil90%
100%
Gas
Gas
Gas
Fuel Oil
70%
80%
ds Gas
Gas
Gas
40%
50%
60%
of H
ouse
hold
Electric39%
Electric63%
Electric20%
30%
40%
% o
39% Electric26% Electric
20%
Electric29%
0%
10%
U S W t Mid t S th N th t
Source: Energy Information Administration
U.S. West Midwest South Northeast
Water Heater Fuel Sourceby Population Densityy p y
Fuel Oil
Other90%
100%
Gas Gas
Gas
Fuel Oil
70%
80%
ds Gas
40%
50%
60%
of H
ouse
hold
Electric Electric
Electric63%
20%
30%
40%
% o
35% 35% Electric26%
0%
10%
Citi T S b b R l
Source: Energy Information Administration
Cities Town Suburbs Rural
Average Household Energy Spendingby End Use and Region - (All Fuel Sources)
$2 388
$3,000
useh
old
All Other$739
$1,885$1,634
$1,823 $1,788
$2,388
$2,000
$2,500
ding
per
Hou
A/CWater Heat Water Heat W t H t
Water HeatRefrig.
RefrigRefrig. Refrig.
Refrig.All Other$647 All Other
$642
All Other$596
All Other$635
$1,634
$1,000
$1,500
Ene
rgy
Spe
n
Heating Heating Heating
HeatingA/C
A/C
A/CA/C
Water HeatWater Heat
Water Heat Water HeatRefrig.
Heating$500
,
ge A
nnua
l E
$0Total West Midwest South NortheastA
vera
10%: $190 $160 $180 $180 $2405% $95 $80 $90 $90 $120
Source: Energy Information Administration
5%: $95 $80 $90 $90 $1202%: $38 $33 $36 $36 $28
Key Lessons Learned –Utility Manger PerspectivesUtility Manger Perspectives
Motivation is the essential ingredient
Program Manager Perspectives: Key Lessons Learned
Motivation is the essential ingredient
Upfront customer input is invaluable• “Don’t design a project within your own four walls.”
Taking an iterative approach ensures consistency with goals and avoids technical issues• “Know your goals at the outset.”
A cross functional pilot team helps to ensure success
It is important to be sensitive to customer satisfactionIt is important to be sensitive to customer satisfaction impacts
Leveraging peer utility experience improves likelihoodLeveraging peer utility experience improves likelihood of success
Pre pilot surveys establish a baseline for analysis
Program Manager Perspectives: Key Lessons Learned
Pre-pilot surveys establish a baseline for analysis
Incorporate a control group
Novelty of the feedback will wear off
Meter interface can present barriersp
IHDs can be hampered by low installation rates
S l ti t b ll it d t th tSolution must be well suited to the customer population
“There probably isn’t going to be a silver bullet ”• There probably isn t going to be a silver bullet.
Tailoring messaging to specific segments can ensure messages resonate with your audiencemessages resonate with your audience
Program Models to Consider
Program Models to ConsiderProgram Models
Model 1:In-Home Energy Use
Monitor
Model 2:Indirect/Comparative Feedback on Home
Energy Use
Model 3:Hybrid Approach –
Comparative and Direct Feedback
Participants receive regular
Program Basics
Participants receive a monitor that provides real-
time feedback on home energy use in order to track and experiment with their
Participants receive regular reports in the mail that will compare their energy use with neighbors in similar homes. Targeted energy
i ti ill l b
p gcomparative feedback
reports and energy tips. Participants will be
encouraged to make use of real-time power monitors th t b h da d e pe e t t t e
energy use behavior saving tips will also be communicated.
that can be purchased or borrowed for several
months at a time.
Customer Engagement Method Opt-in Opt-out Opt-out (reports)
Opt-in (in-home device)
Targeted participant household savings(as % of total kWh)
5%(mid of 3% to 7% range)
Valid among self-selected participant population
2%Average in total customer
population; targeted segments would have
significantly higher savings
2%+Average in total customer
population; targeted segments would have
significantly higher savings participant population (e.g., in the 5% to 10% range)
(e.g., in the 5% to 10% range)
Big Advantage Real-time feedback for participants
Cost effective approach with broader reach
Hybrid approach maximizes savings potential
Significantl higher cost per Req ires integration ith Greater comple it /
Source: Energy Information Administration
Big Disadvantage Significantly higher cost per kWh saved
Requires integration with system data
Greater complexity/ resource requirements
Program Considerations: Model 3Points of Emphasis
Program Objective • Give customers the ability to compare energy-use with their neighbors• Provide opportunity for the utilization of in-home monitors, possibly on a temporary basis
Target Customer • Broad reach of the opt-out home energy report across geographic, housing, demographic strataTarget Customer Market • Use data from indirect feedback program to identify customer segments with the greatest
potential to benefit from direct feedback
Program Logistics
• Need internal IT system for report generation or contract third-party services• Detailed data on houses and homeowners may need to be obtained from third-party/proprietary
sources• Consider subsidized purchase for feedback devices or model to provide on a temporary basis
Customer Education
• Utilize energy use reports as a platform for education about conservation ideas and promotion of the direct feedback program
Enhancements • Raise awareness and promote associated devices to aid in customer behavior changes
Trade Ally Plan • Evaluate need for technical/installation assistance for feedback devices
Savings and Goals Assumptions
• Anticipated savings of 2% in indirect feedback population; additional savings from device group• Ongoing measurement is necessary to establish baselines for long-term savings persistence
Marketing and • If a temporary device lending program is ruled out, subsidies for customer device purchases Marketing and Incentive Strategy would be necessary, promoted through the indirect feedback reports
• Evaluate the incorporation of customer goal setting and commitments as a motivator
Quality Control Plan
• Having adequate pilot scale, duration, and measurement systems will ensure accurate cost effectiveness quantification
Program Budget
Source: Energy Information Administration
Program Budget Considerations • Evaluate available internal resources, third-party service costs, and need for device subsidies
Example: Behavior Change Pilot Program Plan - Model 3Process Step Inputs Actions Outputs
Critical Success Factors(Application of LessonsProcess Step Inputs Actions Outputs (Application of Lessons
Learned)
Identify Team/
Available internal resources
Potential
Identify required program pilot team with cross functional (operational, finance, technical, customer service) capabilities to address all aspects of program execution and business case assessment
Project team Project plan Define pilot
program outcome A diverse pilot team helps to Team/ Objectives
Potential implementation partners
business case assessment Define project timeline and specific pilot
learning objectives (e.g., quantify savings potential and $/kWh for program)
Quantify resource and budget requirements
program outcome measures
Pilot program budget
p pensure success
Review work of peer utilities; engage in di l
Determination of t Taking an iterative approach
Prepare for Customer Engagement
Identification of potential program partners (e.g., Positive Energy)
dialog Engage program partners (if
necessary/desired) Develop IT integration plan to enable
generation of home energy use reports Develop list of items on which to collect
customer input
program partner engagement
Identified challenges to report generation
Identified device preferences
Taking an iterative approach to piloting solutions ensures consistency with goals
Leveraging the experience of peer utilities improves chances of success
Validating the functionality of customer input Obtain real-time feedback devices and test
internally
preferences Customer input
objectives
new technology can avoid headaches down the road
Solicit customer engagement Collect feedback from a focus group (or
survey) Collect feedback on key aspects of program
Identified customer concerns with reports
Upfront customer input provides invaluable guidance for successful
Collect Customer Input
Small customer (e.g., focus group) population
Customer input objectives
Collect feedback on key aspects of program marketing and execution:
o Receptivity to comparative feedbacko Desired report information elements,
format/graphicso Attitudes toward conservationo Interest in real-time feedback devices
reports Key themes to
incorporate in customer targeting and messaging
Identified barriers to user acceptance
guidance for successful program design
Ensure the solution is well suited to customer population
Interfacing with meters for in-home devices can
Source: Energy Information Administration
o Interest in device distribution/rentalarrangements
pof device present barriers
Process Step Inputs Actions Outputs Applicable Lessons Learned
Establish desired customer segments on which to d t i i t
Define Parameters f C t
Available data on customer energy use and segmentation parameters:
o Level of
determine program impact Calculate required program sample size (in each
population) to allow for adequate precision/confidence in program outcomes measurement*
Establish a control group of (at least) similar size for comparison that is representative of the
Necessary program treatment and control group size
Identified customer segment representation
Incorporating a control group that representative of the underlying population and sufficiently large allows for the necessary precision andfor Customer
Comparison
o Level of energy use
o Ageo Incomeo Home
size/type/age
for comparison that is representative of the treatment group
Develop customer education plans to maximize awareness and satisfaction
Determine means/parameters to group customer homes for energy use comparisons (e.g., 100 homes of similar size in neighborhood)
representation desired in pilot group
Customer education plan
Program budget
the necessary precision and confidence to draw conclusions about specific sub-segments of the population
Determine program budget
*Note: See Appendix 1 for discussion of sample size determination. Control and treatment groups should be defined to observe impact of indirect feedback. The selection bias of device user population requires historical data comparison to evaluate savings.
Develop
Develop energy use reports to communicate customer energy use in comparison to neighbors and historical consumption
Template for home energy use report
Motivation is the essential ingredientDevelop
Energy Report Content
Customer segmentation data
and historical consumption Develop/obtain comprehensive lists of energy
savings measures to potentially recommend Establish means to select customized energy
savings tips for customers based on known segmentation parameters
gy p Means to determine
customized savings tips to include (may come from program partner)
g Look beyond traditional
customer segmentation models to find messages that resonate with particular groups
Id if l f d i l di / lDevelop Real-Time Feedback Device Distribution Model
Device preferences
Identified barriers to user acceptance of device
Identify plan for device lending/rental program (e.g. distribution through mail, library checkout, etc.)
Purchase adequate number of devices to support pilot
Develop necessary customer education materials to facilitate device lending program
Device lending program resources
Real-time feedback gives users the opportunity to experiment in finding energy saving behaviors
Source: Energy Information Administration
to facilitate device lending program
Process Step Inputs Actions Outputs Lessons Learned
Customer focus
Define survey to capture:o Home characteristics (e.g., appliances)o Demographics
E b h i / tt
Baseline profile of customer h t i ti d
Conduct Pre-Pilot Survey
Customer focus group feedback
Example surveys from past programs and other utilities
o Energy use behaviors/patternso Attitudes toward conservationo History of participation in utility energy
efficiency programs (e.g., rebates, etc.) Select pilot treatment and control groups (likely
random/stratified sample) Collect feedback from customers across treatment
characteristics and attitudes
Confirmation that treatment and control samples represent the underlying population
Pre-pilot surveys can establish baselines for analysis
Collect feedback from customers across treatment, control, and total customer populations
population
Selected treatment population
Resources to support report
Distribute customer education materials describing program/reports
Regularly generate and distribute home energy use reports to treatment group customers
o More frequent feedback has been shown to Pilot program
Execute Pilot Study
support report generation and distribution
Device distribution/ collection model
Resource to field
o More frequent feedback has been shown to lead to greater energy savings
Promote opportunities for participants to obtain real-time feedback devices to aid in their efforts to save energy
Facilitate distribution and collection of real-time feedback devices
gparticipation
Addressed customer concerns
Demand for real-time feedback devices
Motivated and educated participants
Ensure pilot execution allows for measurement of cost effectiveness
customer calls, questions, issues
Customer communications
Assist/respond to customer questions/issues with device installation/operation
Consider offerings customer the opportunity to establish an energy reduction goal
educated participants
Develop survey instruments to evaluate:oPerceptions of home energy use reports/devices Ability to adjust
Collect Participant Feedback
Pilot program participation
oPerceptions of home energy use reports/devicesoImpact on motivationoBehavior changes madeoInvestments madeoParticipation in other utility energy efficiency programs (e.g., rebates/incentives) – Important for savings adjustments/avoid double-counting
Ability to adjust savings for concurrent efficiency program participation
Survey data/feedback on participant experience and
Be sensitive to program’s impact on customer satisfaction
Source: Energy Information Administration
oConservation attitudes Collect feedback from pilot treatment/control groups
satisfaction
Process Step Inputs Actions Outputs Lessons Learned
Energy
Measurement of participant energy savings
Opt-out nature of program allows for
lt t bEvaluate Program Results/Savings Cost Effectiveness
Energy consumption data
Quantification of pilot program costs
Data from participant
Obtain measures of actual consumption over treatment period for treatment, control (if any), and population (sample)
Compare to normalized historical consumption and control group data to determine impact of the feedback intervention on energy conservation
savings Determination of
program cost effectiveness ($ per kWh of savings)
Determination of differences across
results to be more reasonably extended to potential for savings in entire population
Specific customer segments (e.g., higher energy users) are likelyfeedback survey segments (e.g.,
savings for high energy users)
energy users) are likely to see different levels of savings
Conduct ongoing monitoring
Pilot program participation
Execute customer surveys and data collection to determine persistence of energy savings and customer involvement
Data on device use pattern
Data on savings
[Limited data exists on persistence of savings from utility programs]monitoring involvement persistence from utility programs]
Source: Energy Information Administration
Note on Sample Size DeterminationOpt-in device program inherently prohibit simple control group determination due to the self-selected nature of the treatment groupOpt-out programs lend themselves to easier control group definition
Avoids problems that can come from using historical consumption data beyond the need for weather normalization
Economic conditionsMedia messagingIndividual household factors:
Tenant changesOccupancyRenovations
Alternative approaches to evaluation of savingsConfidence interval around the meanConfidence interval around the % change from prior periodLinear regression and differenced linear fixed effects modelsg
Source: Author’s calculations
Note on Sample Size DeterminationThe required sample size for a study aimed at verifying savings performance is a function of several parameters:
Hypothesized magnitude of energy saving to detect (μ0-μ1)Standard deviation of energy consumption across households (σ)Standard deviation of energy consumption across households (σ)Desired confidence (1-α) and power (1-β): tolerance for making a wrong conclusion
Sample size to test the difference in two population meansRule of thumb for 95% Confidence, 80% Power:
2
10
212/12
zz
n 210
16
n
1% 2% 5% 10%
100 kWh 200 kWh 500 kWh 1000 kWh
Hypothesized Annual Energy Savings (to Test)
100 kWh 200 kWh 500 kWh 1000 kWh1000 kWh 1,600 400 64 16
2000 kWh 6,400 1,600 256 64
3000 kWh 14,400 3,600 576 144
4000 kWh 25,600 6,400 1,024 256
Std. Dev. of Annual Energy
Cons mption
Source: Author’s calculations
4000 kWh 25,600 6,400 1,024 256
5000 kWh 40,000 10,000 1,600 400Consumption
Thank you!y
Questions?Ed Carroll: ecarroll@franklinenergy.com
608-310-6910
Mark Brown: mbrown@franklinenergy.com612-237-8268
Behavior Change Through Rate Design
20
25
30
kWh
Average Customer
Rate D20
25
30
kWh
Average Customer
Rate D20
25
30
kWh
Average Customer
Rate D20
25
30
kWh
Average Customer
20
25
30
kWh
Average Customer
Rate D
Studies have shown that as much as a 6%
i
10
15
20
Cen
ts /
k
Rate C10
15
20
Cen
ts /
k
Rate C10
15
20
Cen
ts /
k
Rate C10
15
20
Cen
ts /
k10
15
20
Cen
ts /
k
Rate C
energy savings can be achieved from inclining block rates that take
0
5
0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 2 000
Rate A
Rate B Existing Flat Rate
0
5
0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 2 000
Rate A
Rate B Existing Flat Rate
0
5
0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 2 000
Rate A
Rate B
0
5
0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 2 0000
5
0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 2 000
Rate A
Rate B Existing Flat Rate
advantage of price elasticity in consumer demand.
200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000kWh / Month
200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000kWh / Month
200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000kWh / Month
200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000kWh / Month
200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000kWh / Month
Avg Percent Change in UsagePrice Elasticity Rate A Rate B Rate C Rate DPrice Elasticity Rate A Rate B Rate C Rate D
Short Run Mean -5.9% -2.2% -1.0% -0.5%Std Dev 2.0% 0.8% 0.3% 0.2%
Long Run Mean -18.4% -6.7% -3.1% -0.7%gStd Dev 6.5% 2.4% 1.1% 0.4%
Inclining Block Rate Bill ImpactsI li i bl k t ld b d i d th t l th hi h t fInclining block rate would be designed so that only the highest users of electricity would see billing increases.
Simulated Distribution of Bill Impacts
10%
20%
30%
l
Tier 1 Cutoff
Original Break-even Point
-20%
-10%
0%
10%
n M
onth
ly B
il
-50%
-40%
-30%
Cha
nge
in
No Price Elasticity
Break-even Point w/Price Elasticity
-70%
-60%
100
200
300
400
500
600
700
800
900
1,00
0
1,10
01,
200
1,30
01,
400
1,50
01,
600
1,70
0
1,80
01,
900
2,00
0
With Price Elasticity
Customer Size (kWh/month)
Recommended