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Customer Data Access – Valuing Feedback: A Strategy for Customer Engagement
NARUC Webinar
March 15, 2012
LBNL Smart Grid Technical Advisory Project
March 15, 2012
Chuck Goldman, Staff Scientist
Electricity Markets and Policy Group
Lawrence Berkeley National Laboratory
Roger Levy, Levy Associates
Lead Consultant,
Smart Grid Technical Advisory Project
4/25/2012 1
Karen Herter
Herter Energy Research Solutions
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� "...the inauguration of smart meters with grudging and involuntary
exposure of millions to billions of human beings to pulsed microwave
radiation should immediately be prohibited..." 18
� “Smart meters have no value to the customer and the customer knows
that”. (19)
� “The general public has no idea how much they pay for electricity or
Is there a customer engagement problem?
LBNL Smart Grid Technical Advisory Project
� “The general public has no idea how much they pay for electricity or
how to use less, undermining the central premise of smart meters and
hindering their adoption”.(20)
� “..most people do not know what devices in the home consume the
most or least energy, and they do not understand their electricity
bill.”(21)
� “…people have absolutely no clue how to go about saving energy as a
result, most of their actions are not geared toward long-term,
sustainable actions to lower their energy footprint.(22)
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1. What is “data access” and how can it be
structured to provide the “feedback” to
support short and long-term changes in
customer energy usage?
2. What guidance does prior research or
Webinar Objectives
LBNL Smart Grid Technical Advisory Project
2. What guidance does prior research or
experience provide in answering this first
question?
4/25/2012 3
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“… feedback is proving a critical first step in
engaging and empowering consumers to
The Purpose of Customer Data Access is to Provide Feedback
What is Customer Data Access ?
LBNL Smart Grid Technical Advisory Project
engaging and empowering consumers to
thoughtfully manage their energy resources.” 1
“Feedback ….making energy more visible and
more amenable to understanding and control.6
4/25/2012 4
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Why is Customer Data Access Important?
Customer education and engagement is critical to achieve
smart grid efficiency, demand response, and renewable
integration benefits.
� Prior research and existing pilots emphasize short-term behavior change by focusing on meter data access and in-
LBNL Smart Grid Technical Advisory Project
behavior change by focusing on meter data access and in-home displays.
� Feedback to address the long-term infrastructure changes and investment necessary to make major, permanent changes in usage is not being addressed.
� The emphasis on short-term feedback creates unreasonable expectations and misdirects policy regarding hardware investment and customer education.
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In home displays (IHD’s) are the most
important vehicle for providing
customers with data access.
Studies show that customers with
IHD’s have been shown to reduce
energy use 5% to 15%.
Myth vs. Fact
LBNL Smart Grid Technical Advisory Project 64/25/2012
energy use 5% to 15%.
Studies have shown that the rate, bill
design, and frequency of billing
influence IHD impacts.
Residential customers with access to
near real-time meter data reduce
usage more than customers with
next day access.
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In home displays (IHD’s) are the most
important vehicle for providing
customers with data access.?
Studies show that customers with
IHD’s have been shown to reduce
energy use 5% to 15%.
We are not aware of any studies that have examined this issue.
Myth vs. Fact
LBNL Smart Grid Technical Advisory Project 74/25/2012
energy use 5% to 15%.
Studies have shown that the rate, bill
design, and frequency of billing
influence IHD impacts.
Residential customers with access to
near real-time meter data reduce
usage more than customers with
next day access.
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In home displays (IHD’s) are the most
important vehicle for providing
customers with data access.?
Studies show that customers with
IHD’s have been shown to reduce
energy use 5% to 15%.
TF
We are not aware of any studies that have examined this issue.
Multiple studies report this finding, however with few exceptions, most research is short-term and anecdotal.
Myth vs. Fact
LBNL Smart Grid Technical Advisory Project 84/25/2012
energy use 5% to 15%.
Studies have shown that the rate, bill
design, and frequency of billing
influence IHD impacts.
Residential customers with access to
near real-time meter data reduce
usage more than customers with
next day access.
research is short-term and anecdotal.
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In home displays (IHD’s) are the most
important vehicle for providing
customers with data access.?
Studies show that customers with
IHD’s have been shown to reduce
energy use 5% to 15%.
TF
We are not aware of any studies that have examined this issue.
Multiple studies report this finding, however with few exceptions, most research is short-term and anecdotal.
Myth vs. Fact
LBNL Smart Grid Technical Advisory Project 94/25/2012
energy use 5% to 15%.
Studies have shown that the rate, bill
design, and frequency of billing
influence IHD impacts.
Residential customers with access to
near real-time meter data reduce
usage more than customers with
next day access.
?Few studies and questionable results.
research is short-term and anecdotal.
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In home displays (IHD’s) are the most
important vehicle for providing
customers with data access.?
Studies show that customers with
IHD’s have been shown to reduce
energy use 5% to 15%.
TF
We are not aware of any studies that have examined this issue.
Multiple studies report this finding, however with few exceptions, most research is short-term and anecdotal.
Myth vs. Fact
LBNL Smart Grid Technical Advisory Project
energy use 5% to 15%.
Studies have shown that the rate, bill
design, and frequency of billing
influence IHD impacts.
F
Residential customers with access to
near real-time meter data reduce
usage more than customers with
next day access.
?
Most studies ignore these variables.
Few studies and questionable results.
research is short-term and anecdotal.
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“The research literature
shows that in-home displays
…achieving savings in the
“The results show that the
initial savings in of 7.8% after 4
months could not be sustained
IHD’s are the Solution
Feedback Expectations
IHD’s are not the Solution
LBNL Smart Grid Technical Advisory Project
…achieving savings in the
range of 5–15%..” 3
“Consumers could cut their
household electricity use as
much as 12 percent …if U.S.
utilities use feedback tools ..4
in the medium- to long-term. “ 5
Real time monitors “ may not
be suitable tools to decrease
consumption unless
homeowners are presented
with more information on how
to conserve or a cost incentive
such as TOU pricing.” 6
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� Data. “A behavior must be measured, captured, and stored. “
� Relevance. “The information must be relayed to the individual,
not in the raw-data form in which it was captured but in a
context that makes it emotionally resonant. “
Feedback: Four Stages3
Customer Data Access - Framework
LBNL Smart Grid Technical Advisory Project
context that makes it emotionally resonant. “
� Consequence. “The information must illuminate one or more
paths ahead. “
� Action. “There must be a clear moment when the individual can
recalibrate a behavior, make a choice, and act. “
Key Question: What approaches provide the content consistent with this framework?
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1. What information influences customer energy usage?
2. What does the research tell us?
a) Research studies
Webinar Agenda
LBNL Smart Grid Technical Advisory Project
a) Research studies
b) Ongoing pilots
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1. What information influences customer energy usage
LBNL Smart Grid Technical Advisory Project4/25/2012 14
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Customer Feedback Policy Objectives
Behavior Change
• Program thermostat
Turn off lights
Adaptation
• Plant shade trees
Weather strip
Infrastructure Change
• High-efficiency
appliances
What are you trying to accomplish?
LBNL Smart Grid Technical Advisory Project
• Turn off lights
• Shorter showers
• Fewer wash loads
• Unplug electronics
• Weather strip
• Install CFL lights
• Install timers
• Programmable
Thermostat options
appliances
• Replace windows
• Insulate walls
• Insulate ceilings
• Install Solar PV
Short –term, low cost,
quick decisions, real-
time feedback.
Near–term, medium
cost, lengthy decisions,
multiple info sources.
Long–term, high cost,
protracted decisions,
multiple info sources.
4/25/2012 15
Price Automation Subsidies, Incentives
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Matching Feedback to Support Customer Infrastructure Decisions
99
Life in Years*
Dishwasher
Dryer, Electric
Freezer
Microwave Oven
10 20
1313
1111
99
??
LBNL Smart Grid Technical Advisory Project
Range, Electric
Refrigerator, Standard
Washer
Water Heater, Electric
Air Conditioner, Room
Air Conditioner, Central
Heat Pump
1313
1313
1010
1111
1010
1515
1616
* Study of Life Expectancy of Home Components, National Association of Home Builders, February 2007, http://www.nahb.org/fileUpload_details.aspx?contentID=99359
?
Source: Opower, http://opower.com/uploads/library/file/15/xrds_opower.pdf
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What are the research options?
LBNL Smart Grid Technical Advisory Project4/25/2012 17
* Figure 1. Information Options, DOE Smart Grid Investment Grant, Technical Advisory Group Guidance Document #2, Non-Rate Treatments in Consumer
Behavior Study Designs, August 6, 2010.
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What do customers need?
Customers have to understand how they use energy before they can make rational decisions to improve
efficiency and change their usage patterns.
Customers have to understand how they use energy before they can make rational decisions to improve
efficiency and change their usage patterns.
1. What information do customers need to make rational energy decisions?
What
LBNL Smart Grid Technical Advisory Project
to make rational energy decisions?
2. Which behavioral and infrastructure decisions best support the consumer value function?
3. What is the best form and medium to present the information to support these decisions?
What
Information ?
Which
Decisions ?
Which Delivery
Channel ?
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What to Measure
� What to measure – electricity, gas, water, carbon?
� What level of measurement – whole house or end-use?
� What type of measurement – real-time, near real-time, actual data, historical data, or social normative
LBNL Smart Grid Technical Advisory Project
� What capability – monitoring only or management too?
� What medium – stand alone, web, PC/phone applications?
� What time frame – days, months, years?
� What information – energy, demand, price, cost, technology availability, saving measures, other?
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1
Standard Billing
monthly,
2
Enhanced Billing
Info and advice,
3
Estimated Feedback
Web-based audits,
4
Daily /Weekly Feedback
Usage
5
Real-time Feedback
In home displays,
6
Real-time Plus
HANs, appliance
EPRI Feedback Delivery Mechanism Spectrum 5
EPRI: Customer Information Continuum
LBNL Smart Grid Technical Advisory Project
monthly,
bi-monthlyInfo and advice,
household specific
Web-based audits,
billing analysis,
appliance
disaggregation
Usage
measurements by
mail, email, self-
metered
In home displays,
pricing signal
HANs, appliance
disaggregation,
control
“Indirect” Feedback (provided after usage)“Direct Feedback –
(provided during usage)
Information availability
Cost to implementLow High
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EPRI Feedback Delivery Mechanism Spectrum 5
1
Standard Billing
monthly,
bi-monthly
2
Enhanced Billing
Info and advice,
household specific
3
Estimated Feedback
Web-based audits,
billing analysis,
4
Daily /Weekly Feedback
Usage
measurements by
5
Real-time Feedback
In home displays,
pricing signal
6
Real-time Plus
HANs, appliance
disaggregation,
EPRI: Customer Information Continuum
LBNL Smart Grid Technical Advisory Project
Action Items
Social Media
Goals (Benchmarks)
Control Signals
Price, Event Signals
Scenario Analysis
Rate Options
appliance
disaggregation
mail, email, self-
metered
control
“Indirect” Feedback (provided after usage)“Direct Feedback –
(provided during usage)
Online modeling, Instructional videos, Worksheets Online modeling, Instructional videos, Worksheets Digital Machine-to-MachineDigital Machine-to-Machine
Long-term - historical and comparative short-term - current
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2. What does the research tell us?
� Meta studies
LBNL Smart Grid Technical Advisory Project
� Utility pilots
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� EPRI – Residential Electricity Use Feedback: A Research Synthesis
and Economic Framework (2009).5
� ACEEE – Advanced Metering Initiatives and Residential Feedback
Programs: A Meta-Review for Household Electricity Saving
Opportunities (2010).1 *
� Darby - The Effectiveness of Feedback on Energy Consumption: A
Key Meta Studies
LBNL Smart Grid Technical Advisory Project
� Darby - The Effectiveness of Feedback on Energy Consumption: A
Review for DEFRA of the Literature on Metering, Billing and Direct
Displays (2009).6
� Fischer: Historical Feedback Studies
� VaasaETT [Empower Demand] - The potential of smart meter
enabled programs to increase energy and system efficiency: a
mass pilot comparison (2011)13
� Brattle: Recent Feedback Studies
4/25/2012 23
* See Reference #24 for updated ACEEE review of real-time feedback studies, February 2012.
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10%
15%
20%
Co
nserv
ati
on
Eff
ect
Europe
EPRI: Electricity Use Feedback5
“…one shortcoming of some past research is it does not
impose sufficient structure on the initial sample design
to test for differences in feedback effect among
customers with different housing, demographic, and
electricity pricing circumstances.”5
“…one shortcoming of some past research is it does not
impose sufficient structure on the initial sample design
to test for differences in feedback effect among
customers with different housing, demographic, and
electricity pricing circumstances.”5
LBNL Smart Grid Technical Advisory Project4/25/2012
-10%
-5%
0%
5%
0 500 1000 1500 2000 2500
Co
nserv
ati
on
Eff
ect
Participation Levels
Europe
Japan
North America
Figure 3-1. Range of study participation levels
24
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“..these estimates are
dominated by studies with
small sample sizes and
short duration: further
“..these estimates are
dominated by studies with
small sample sizes and
short duration: further
ACEEE Meta Review
ACEEE: Feedback Effectiveness2
LBNL Smart Grid Technical Advisory Project
studies with large sample
sizes and longer duration
are needed before
conclusions can be
drawn.” 2
studies with large sample
sizes and longer duration
are needed before
conclusions can be
drawn.” 2
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Darby: Behavior Change7
What do we know about measured savings from feedback studies?
Savings
[studies N=]
Direct Feedback Studies
N=21
Indirect Feedback Studies
N=13
Studies 1987-2000
N=21
Studies 1975-2000
N=38
20%+ 3 3 3
LBNL Smart Grid Technical Advisory Project
20% peak 1 3
15-19% 1 1 1 3
10-14% 7 6 5 13
5-9% 8 6 9
0-4% 2 3 4 6
unknown 3 1 3
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Fischer: Historical Feedback Studies9
LBNL Smart Grid Technical Advisory Project 274/25/2012
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VaasaETT : Empower Demand 13
Table 12. Duration of IHD pilots and energy conservation.
8.68%IHD (n=30)
6.00%
5.94%
Other (n=14)
Detailed Invoice (n=23)
LBNL Smart Grid Technical Advisory Project4/25/2012
Figure 4. Overall consumption reduction as per feedback pilot type
5.13%
6%
11%
9%
(n=23)
Webpage (n=7)
1-6 months (n=11) 7-12 months (n=8) >12 months (n=11)
Length of Trial
28
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Brattle: Recent Feedback Studies 12
10%
12%
14%
16%
18%
20%
Co
nse
rva
tio
n I
mpact (%
)
IHD-Only Impacts IHD and Prepayment
Impacts
IHD and Time-Varying Rates
Impacts
LBNL Smart Grid Technical Advisory Project
0%
2%
4%
6%
8%
10%
Co
nse
rva
tio
n I
mpact (%
)
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� Customers who actively used an IHD in the pilots reduced their electricity consumption by about 7%
The Bottom Line
Brattle: Recent Feedback Studies 12
LBNL Smart Grid Technical Advisory Project
� When customers both used an IHD and were on some type of electricity pre-payment system, they reduced their electricity consumption by about 14%
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� ARRA Consumer Behavior Pilots (In process)
� Commonwealth Edison
� Oklahoma Gas & Electric
Key Pilot Research Studies
LBNL Smart Grid Technical Advisory Project
� Oklahoma Gas & Electric
� SMUD Residential Information and Controls
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Sierra
Pacific
Nevada
Power OG&E MMLD CVPS VEC
MN
Power CIC SMUD DECo Total
Rate Treatments
TOU � � � 3
CPP � � � � � � � � 8
DOE-SGIG Consumer Behavior Pilots
150,000 customers are expected to “participate” as treatment or control customers in ~10 DOE SGIG-funded projects involving AMI, dynamic pricing
and consumer behavior studies
150,000 customers are expected to “participate” as treatment or control customers in ~10 DOE SGIG-funded projects involving AMI, dynamic pricing
and consumer behavior studies
LBNL Smart Grid Technical Advisory Project
CPP � � � � � � � � 8
CPR � � 2
VPP � � 2
Non-Rate Treatments
Education � � 2
Cust. Service � 1
IHD � � � � � � � � � 9
PCT � � � 3
DLC � 1
Features
Bill Protection � � � � 4
Experimental Design
Opt In � � � � � � � � � 9
Opt Out � � 2
Within � 1
4/25/2012 32
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Commonwealth Edison – Pilot Results
Numbers Rates
Offer Acquire Implement Acquire Implement
Customers Provided with Free IHD’s
L5. Basic IHD 485 485 163 100% 34%
Table 4-1. Acquisition and Implementation of Free and Purchased Technology4
LBNL Smart Grid Technical Advisory Project
485 485 163 100% 34%
L6. Advanced IHD 205 205 26 100% 13%
Customers Given Option to Purchase IHD’s
L5b. Basic IHD 211 5 4 2% 1%
L6b. Advanced IHD 205 4 4 2% 1%
Notes:
• Basic IHD: linked to meter, continuous usage with historical comparison
• Advanced IHD: combines usage data with access to data via internet, also combined with PCT, not fully described.
• For row L5 the 34% represents the number of customers provided free IHD’s that actually installed and initialized the device.
For row L5b, only 2% (5/211) of the customers chose to purchase an IHD and then only 80% (4/5) of those were installed. IHD
usage
4/25/2012 33
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25%
30%
35%
40%
45%
50%
Co
nse
rva
tio
n I
mpact (%
)
PCT Only IHD Only Web Only
29.9% 29.2%32.6%
26.2% 25.6%28.3%
PCT, IHD, Web
TOUTOU--CPCP
VPPVPP--CP LowCP Low
VPPVPP--CP MedCP Med
Weekdays
OG&E: 2010 Demand Response Study17
LBNL Smart Grid Technical Advisory Project 344/25/2012
0%
5%
10%
15%
20%
25%
Co
nse
rva
tio
n I
mpact (%
)
11.1%
16.5%
11.1%8.0%
10.6% 10.9%
3.7%
8.3%
11.8% 12.4%
VPPVPP--CP HighCP High
� Pre-assigned cells
� Online self-enrollment
� Best Bill Guarantee
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� Feedback study impacts are oversold, creating unrealistic expectations
� Pilots focus on IHD hardware rather than information
� Rate design and pricing are ignored but essential for creating a customer value function
What are the issues and limitations
LBNL Smart Grid Technical Advisory Project
� Billing information is needed to reinforce the value function
� IHD’s support short-term behavior change, not long-term infrastructure change
� Research is searching for a single solution where the market will probably require a dynamic mix of multiple treatments over extended time frames.
4/25/2012 35
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Behavioral Change
AdaptationInfrastructure
Change
Policy Options
What policies should you consider ?
LBNL Smart Grid Technical Advisory Project
• Data Access
• Understandable
Rates
• Dispatchable Prices
• Clear Bills
------------
• Privacy
• Evaluation Tools
• Rebates
• Open Markets for
Technology
• Standards
• Building Standards
• Appliance Standards
• Financial Incentives
• Rate simplification and
stability
• Billing clarity and
customization
4/25/2012 36
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3. SMUD Residential Information and Controls Study
LBNL Smart Grid Technical Advisory Project4/25/2012 37
Karen Herter, Ph.D.
Project Design
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Research Team and Funding
• Research Team
– Herter Energy Research Solutions
– Sacramento Municipal Utility District (SMUD)
• Funding
– Sacramento Municipal Utility District (SMUD)
LBNL Smart Grid Technical Advisory Project 384/25/2012
– California Energy Commission Public Interest Energy Research via the Demand Response Research Center at Lawrence Berkeley Lab
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Study Goals
� Build on what we already know
� TOU rates are effective for shifting load every day
� Dynamic rates are effective for shedding load during events
� Thermostat automation enhances both of these effects
� Answer some new questions
� Does real-time energy data enhance energy and/or peak savings?
LBNL Smart Grid Technical Advisory Project 394/25/2012
� Is there added value in providing real-time appliance energy data?
� Combine rates, automation, real-time data and enhanced customer
support to…
� capture synergies between program variables
� provide as realistic an experience as possible
� define results that can be translated to the real world
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What we already know
Results of residential pricing studies in Ontario, California, Puget Sound, Florida,
Australia, Illinois, Missouri, New Jersey, Maryland, Connecticut, Washington DC
LBNL Smart Grid Technical Advisory Project 404/25/2012
Q: Might real-time data from new smart meters provide additional value?
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Residential Information & Controls Study
� Phase 1: 2009 Simulation Research
� 450+ SMUD participants
� Simulated home environment w/ TOU-CPP rate
� Findings
� Home data: No savings
LBNL Smart Grid Technical Advisory Project 414/25/2012
� Home data: No savings
� Appliance data: 6% savings
� Phase 2: 2012 Summer Solutions Pilot
� 265 residential SMUD participants
� Equipment installations in Sacramento and Folsom
� Treatments
� Real-time data: Home vs. Appliance
� Incentives: Dynamic rate vs. Load control
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Research Design N=265 residential customers
A. Information Treatments - randomly assigned
A. Baseline (88)A. Baseline (88)
LBNL Smart Grid Technical Advisory Project 424/25/2012
4
Control only (81)Control only (81)
Neither (49)Neither (49)
Rate only (44)Rate only (44)
Rate + Control (91)Rate + Control (91)
B. Dynamic Rate and AC Load Control - customer chosen
C. Appliance Data (88)C. Appliance Data (88)B. Home Data (89)B. Home Data (89)
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Research Design N=265 residential customers
A. Information Treatments - randomly assigned
A. Baseline (88)A. Baseline (88)
LBNL Smart Grid Technical Advisory Project 434/25/2012
Control only (81)Control only (81)
Neither (49)Neither (49)
Rate only (44)Rate only (44)
Rate + Control (91)Rate + Control (91)
B. Dynamic Rate and AC Load Control - customer chosen
C. Appliance Data (88)C. Appliance Data (88)B. Home Data (89)B. Home Data (89)
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Information System A - Baseline
Communicating Thermostat
RCSZwave
LBNL Smart Grid Technical Advisory Project 444/25/2012
Gateway provides OpenADR event notification
Zwave
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Information System B – Home data
Communicating Thermostat with Energy Information Display
RCS
Sit
e D
ata D
ata
Sto
rag
e &
Pre
sen
tatio
n
Zwave
Whole-house sub-meter
LBNL Smart Grid Technical Advisory Project 454/25/2012
Gateway with Information Display via Computer
RCS
Da
ta S
tora
ge
& P
rese
nta
tion
Zwave
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Information System C – Appliance data
Communicating Thermostat with Energy Information
Display
RCS
Sit
e D
ata D
ata
Sto
rag
e &
Pre
sen
tatio
n
Zwave
Whole-house sub-meter
LBNL Smart Grid Technical Advisory Project 464/25/2012
110V sub-meter
HVAC sub-meter
Gateway with Information Display via Computer
RCS
Da
ta S
tora
ge
& P
rese
nta
tion
220V sub-meter
Ap
pli
an
ce D
ata
ZwaveZwave
Zwave
Zwave
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User Interface (Appliance data)
LBNL Smart Grid Technical Advisory Project 474/25/2012
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User Interface (Appliance data)
LBNL Smart Grid Technical Advisory Project 484/25/2012
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Research Design N=265 residential customers
A. Information Treatments - randomly assigned
A. Baseline (88)A. Baseline (88)
C. Appliance Data (88)C. Appliance Data (88)B. Home Data (89)B. Home Data (89)
LBNL Smart Grid Technical Advisory Project 494/25/2012
B. Dynamic Rate and AC Load Control - customer chosen
C. Appliance Data (88)C. Appliance Data (88)B. Home Data (89)B. Home Data (89)
Control only (81)Control only (81)
Neither (49)Neither (49)
Rate only (44)Rate only (44)
Rate + Control (91)Rate + Control (91)
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Recruitment – Program Choices
• Rate
– TOU-CPP rate, a.k.a. the “Summer Solutions rate”
– Customer determines response to high-price events
...of customers offered a dynamic Rate and/or AC Control
Rate +
Neither,
13%
Control
only, 13%
LBNL Smart Grid Technical Advisory Project 504/25/2012
events
– 12 events
• Control
– 4°set point raise during events
– One override allowed
– Same 12 events as TOU-CPP rate
All participants receive one of the three randomly assigned equipment configurations, no matter their program choices
N=238
Rate +
Control,
49%
Rate only,
25%
13%
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Optional TOU-CPP Rate
LBNL Smart Grid Technical Advisory Project 514/25/2012
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Hypotheses
• For all participants
� Energy use is lower
� Weekday peak demand is lower
� Peak demand on event days is lower
� Electricity bills are lower
• Savings are better for customers:
LBNL Smart Grid Technical Advisory Project 524/25/2012
� (a) with more information
� (b) who chose more program options
� (c) on the dynamic rate, compared to direct load control
� (d) with higher energy use
� (e) with certain self-reported behaviors
� (f) with certain dwelling characteristics
� (g) with certain demographic characteristics
� (h) with higher satisfaction levels
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LBNL Smart Grid Technical Advisory Project 534/25/2012
Field Test & Findings
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Field Study: Education and Outreach
� Installers assisted with thermostat settings
o Encouraged all participants to automate response to critical
events
� Quick Start Guide and equipment user guides
� Websites with information, tips, discussion board
LBNL Smart Grid Technical Advisory Project 544/25/2012
� Websites with information, tips, discussion board
� On-site energy assessments with personalized recommendations
� Summer Solutions Rate magnet
� SS rate vs. Standard bill comparison
� 24-hour advance notification of events
o via email, thermostats, text message, phone
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Events - Overview
� Twelve events from July to September
� Notify Participants
o Email – including recommendations for participant action
o Thermostat display – blinking light and message
o Computer energy display – ACTIVE event status displayed
LBNL Smart Grid Technical Advisory Project 554/25/2012
o Computer energy display – ACTIVE event status displayed
o Special requests: Phone calls or text message
� Notify Equipment
o OpenADR to gateway
o ZWave from gateway to thermostat
o Thermostat initiates Automatic Temperature Control (4°F) or
customer-programmed response to events
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2011 Temperatures and Events
LBNL Smart Grid Technical Advisory Project 564/25/2012
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2.5
3.0
pa
nt 2010 Weekday (weather-corrected baseline)
2011 Non-Event Weekday
Load Impacts - 100° day
Daily
weekday
impactBaseline
LBNL Smart Grid Technical Advisory Project
0.0
0.5
1.0
1.5
2.0
2.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Av
g.
kW
pe
r p
ar
cip
Hour
2011 Non-Event Weekday
2011 Event
574/25/2012
Event impact
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Information Effects
LBNL Smart Grid Technical Advisory Project 584/25/2012
Values in bold indicate a statistically significant difference from “Baseline information”
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Rate and Control Effects
LBNL Smart Grid Technical Advisory Project 594/25/2012
Values in bold indicate a statistically significant difference from “Neither option”
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Billing Impacts
LBNL Smart Grid Technical Advisory Project 604/25/2012
Note: These bill savings are in addition to those associated with energy savings
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Customer Satisfaction
� 86% = Excellent or Good
o All groups were equally satisfied
� 90% signed up again for Summer Solutions 2012
o 5% dropped out, 5% unreachable
LBNL Smart Grid Technical Advisory Project 614/25/2012
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Hypotheses
• For all participants
� Energy use is lower: YES
� Weekday peak demand is lower: YES
� Peak demand on event days is lower: YES
� Electricity bills are lower: YES
• Savings are better for customers:
LBNL Smart Grid Technical Advisory Project 624/25/2012
� (a) with more information: MIXED
� (b) who chose more program options: YES
� (c) on the dynamic rate, compared to direct load control: YES
� (d) with higher energy use: YES
� (e) with certain self-reported behaviors: YES (pre-cooling, peak offset)
� (f) with certain dwelling characteristics: YES (swimming pools)
� (g) with certain demographic characteristics: NO (age, education, income)
� (h) with higher satisfaction levels: MIXED (no savings for dropouts)
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Recommendations
1) Dynamic Rate + Advanced Thermostat
� Offer at least one dynamic rate option, e.g. TOU-CPP
� Display rate and event status on thermostat
� Allow customers to automate precooling + peak offsets
� Real-time energy data nice, but not necessary
LBNL Smart Grid Technical Advisory Project 634/25/2012
� Real-time energy data nice, but not necessary
2) Enhanced Customer Service
� Educated customer support staff
� Free home energy assessments for participants
� Rate calculator with scenario testing
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References - 1
Title Link
1
Advanced Metering Initiatives and Residential Feedback Programs: A Meta-
Review for Household Electricity-Saving Opportunities, ACEEE, Martinez,
Donnelly, Laitner, June 2010
https://www.burlingtonelectric.com/ELBO/assets/smartgrid/ACEEE%20report%20on%20smart%20grid.pdf
2The Persistence of Feedback-Induced Energy Savings in the Residential
Sector: Evidence from a Meta-Review
http://www.stanford.edu/group/peec/cgi-bin/docs/events/2010/becc/presentations/3D_KarenEhrhardt-Martinez.pdf
3 Harnessing the Power of Feedback Loops, Wired, TGoetz, June 19, 2011.http://www.wired.com/magazine/2011/06/ff_feedbackloop/
LBNL Smart Grid Technical Advisory Project
3
4The Effect on Electricity Consumption of the Commonwealth Edison
Customer Application Program Pilot: Phase 1, April 2011.http://www.smartgridinformation.info/pdf/3273_doc_1.pdf
5Residential Electricity Use Feedback: A Research Synthesis and Economic
Framework, EPRI, Neenan, Figure 2-2, February 2009.
http://opower.com/uploads/library/file/4/residential_electricity_use_feedback.pdf
6Exploring consumer preferences for home energy display functionality,
August 2009.http://www.cse.org.uk/pdf/consumer_preferences_for_home_energy_display.pdf
4/25/2012 64
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Title Link
7 Making it obvious: designing feedback into energy consumption, http://www.bluelineinnovations.com/documents/makingitobvious.pdf
8Direct Energy Feedback Technology Assessment for Southern California Edison
Company, March 2006http://sedc-coalition.eu/wp-content/uploads/2011/05/Stein-In-Home-Displays-March-2006.pdf
9Feedback on household electricity consumption: a tool for saving energy, Energy
Efficiency, Corinna Fischer, 2008.http://www.mendeley.com/research/feedback-household-electricity-consumption-tool-saving-energy/
References - 2
LBNL Smart Grid Technical Advisory Project
10Some consideration on the (in)effectiveness of residential energy feedback
systems, Carnegie Mellon University, August 2010http://www.paulos.net/papers/2010/ineffective_energy.pdf
11U.S.Department of Energy Smart Grid Investment Grant Technical Advisory
Group Guidance Document #2, Non-Rate Treatments in Consumer Behavior
Study Designs, August 6, 2010.
http://www.smartgrid.gov/sites/default/files/pdfs/cbs-guidance-doc2-cbsp-outline.pdf
12The Impact of Informational Feedback on Energy Consumption – A Survey of the
Experimental Evidence, The Brattle Group, May 20, 2009http://www.brattle.com/_documents/uploadlibrary/upload772.pdf
4/25/2012 65
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Title Link
13The potential of smart meter enabled programs to increase enerty and system
efficiency: a mass pilot comparison, vaasa ett, October 2011http://www.esmig.eu/press/filestor/empower-demand-report/
14Energy Conservation and Carbon Reduction, Energywatch Smart Metering
Seminar, S.Darby, September 2005
http://www.powershow.com/view/fdc0b-YTM4Z/Energy_conservation_and_carbon_reduction_flash_ppt_presentation
15The Impact of In-Home Displays on Energy Consumption, Colorado Public
Service Commission, A. Faruqui, S.Serguci, June 7, 2010
http://www.dora.state.co.us/puc/presentations/InformationMeetings/SmartGrid/06-07-10CIM_Smart-Grid_ImpactIn-homeDisplays(Brattle%20Group).pdf
References - 3
LBNL Smart Grid Technical Advisory Project
16A Policy Framework for the 21st Century Grid: Enabling Our Secure Energy
Future, Executive Office of the President of the United States, June 2011, p.45.
http://www.whitehouse.gov/sites/default/files/microsites/ostp/nstc-smart-grid-june2011.pdf
17Oklahoma Gas & Electric 2010 Demand Response Study , Interim Results &
Lessons Learned, M.Farrell, March 30, 2011.
http://www.linkedin.com/news?viewArticle=&articleID=879569178&gid=2610610&type=member&item=78093355&articleURL=http%3A%2F%2Fwww%2Eraabassociates%2Eorg%2Fmain%2Froundtable%2Easp%3Fsel%3D109&urlhash=IlpS&goback=%2Egde_2610610_member_78093355
18Olle Johansson, PhD, Department of Neuroscience, Karolinska Institute
(Sweden)
http://www.facebook.com/SmartMeterRevolt#!/SmartM
eterRevolt?v=info#info_edit_sections
19Customer ‘education’ draws fire, Intelligent Utility Magazine, P.Carson,
September 1, 2011
http://www.intelligentutility.com/article/11/09/consumer
-education-draws-fire
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References - 4
Title Link
20A Problem for Smart Meters: People Don’t Understand Electricity, Fast
Company, A. Schwartz, August 29, 2011. http://www.fastcompany.com/1776357/a-problem-for-smart-meter-projects-people-dont-understand-electricity-pricing
22Energy Conservation and the Consumer Dilemma, SPARK E-
Newsletter, K.Ashton, January 10, 2011. http://www.fortnightly.com/exclusive.cfm?o_id=513
23Americans are Clueless on Saving Energy, Study Finds,
greentechenterprise, K.Tweed, August 19, 2010. http://www.greentechmedia.com/articles/read/americans-are-cluless-on-saving-energy-study-finds/
LBNL Smart Grid Technical Advisory Project
24Results from Recent Real-Time Feedback Studies, Report Number
B122, ACEEE, B.Foster, S.Mazur-Stommen, Feburary 2012
5
6
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Contact Information
� Chuck Goldman
Lawrence Berkeley National Laboratory
510 486-4637
� Roger Levy
Smart Grid Technical Advisory Project
LBNL Smart Grid Technical Advisory Project68
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� Karen Herter
Herter Energy Research Solutions
www.HerterEnergy.com
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