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Household Preference for New Electricity Service
Plans: the Role of Information Feedback
The 40th IAEE International Conference,
Singapore, June 21, 2017
Jaewoong Lee, Soyoung Yoo, Jiyong Eom
KAIST College of Business
Introduction
 Enabling technologies (AMI/IHDs) improve utility services
through two-way information exchange:
• Promoting cost-effectiveness of dynamic pricing (DP) plans or price-based approaches to
demand response (DR): CPP → TOU/RTP
• Assisting consumers in shifting demand based on simple, easy-to-understand usage/cost
information via IHDs (Faruqui et al., 2010; Ivanov et al., 2013; Kahn & Wolak, 2013)
• ‘Flexing’ electricity demand by enhancing price salience and helping identify the household
production function in the long run (Jessoe & Rapson, 2014)
<#AMI/AMRs installed in U.S.> (source: EIA)
<2015 DR Potential (MW) in U.S.>
70,000,000
Residential
60,000,000
50,000,000
40,000,000
Commercial
Industrial
Transportation
30,000,000
20,000,000
10,000,000
0
2007
2008
2009
2010
2011
2012
2013
2014
Research Motivations and Questions
 Utilities are to make substantial AMI investments while consumer
subscription of new dynamic pricing plans are uncertain:
• Smart meters are indispensable but the returns are hard to expect
• Payback periods might be long: 13 years for smart grid (GTM, 2013)
• Little is known about consumer preference for dynamic pricing plans that
might interact with information feedback provided by enabling technologies
 Three research questions:
1. What is the household preference for time-of-use (TOU) plans and
associated information feedback on electricity use?
2. Whether or not the information feedback promote consumers to adopt
new TOU plans?
3. How can the adoption of new TOU plans be maximized?
3/12
Literature Review
 Information Systems research on energy use (Watson et.al. 2010)
• Information Systems for Environmental Sustainability (Merville, 2010 )
• Absence of real-time information on marginal price resulting in consumer response
to average price (Kahn and Wolak, wp, 2013)
• Providing proper information being effective intervention strategy (Abrahamse, 2005)
• Issue addressed only by a few studies (Watson et.al. 2010; Kossal et al. 2012)
 Economic studies on dynamic pricing with(out) information feedback
• Impact of dynamic pricing in field experimental settings
(Ito et al., 2013; Wolak 2011; Allcott 2011; Jessoe and Rapson, 2014)
• Information from smart thermostats for reducing peak load (Ivanov et al. 2013)
• Information from smart meters and IHDs under dynamic pricing resulting in net
conservation (habit formation) and peak load reductions (Jessoe & Rapson, 2014;
Faruqui, 2010; 2011)
 Challenges exist in transferability of findings to other pricing plans in
different context.
4/12
Literature Review
 Discrete choice experiments used in eliciting households’ stated
preferences for electricity service plans (ESP) & durables purchase:
• Preference for the attributes of time-of-use ESP on households’ subscription
decision making under the default flat rate (EPRI, 2015)
• Preference for durables purchase, tradeoff between upfront costs and savings in
operating costs, and the effect of EnergyGuide labeling (Newell & Siikamaki, 2014)
(Source: EPRI, 2015)
(Source: Newell, & Siikamaki, 2014)
Contributions
 Investigating the potential role of information feedback in
household adoption of new ESPs:
• The value of feedback information for households, particularly as means to
mitigate risks associated with high peak prices
• Tradeoff between information feedback and design features of TOU plans
 Segmenting electricity customers based on WTP for different
levels of information feedback
• Effect of sociodemographic characteristics (Newell & Siikamaki, 2014) on the
adoption of new ESPs w/ and w/o information feedback
• Optimal configuration of ESP portfolios (EPRI, 2015) and the influence of
information feedback
• Implications for utilities’ upfront investment decisions for enabling
technologies, such as AMIs and IHDs
6/12
Method: Setting and Process
 Choice experiment on Korean residential electricity consumers
• Korean households in Seoul with interval meters and with household heads aged over 20s
• 100 households in April 2017 (pilot) and 1,500 in July 2017 (main)
• Pilot survey: face-to-face guided survey by Gallup: local recruit & tablet PC-assisted survey
 Survey components and data quality controls
• [Part A] home characteristics incl. owned/rent, floor area
• [Part B] household characteristics incl. electricity bill in Summer/Winter 2016, family members
• [Part C] ESP choice experiment (starting with explanation of current rate scheme (IBT), TOU rates,
and 13 choice sets to follow)
• [Part D] Appliance use information: energy efficiency and hours of usage
• [Post treatment] Untrustworthy answers excluded: final N=46
(e.g., respondents with the same alternatives for all choice situations)
7/12
Method: Choice Experiment
 In total, 13 choice sets each with 3 ESPs varying in TOU and feedback
attributes:
• Each set with two TOUs and the default flat rate option
• 13 choice sets drawn from 4 TOU rate design and 2 feedback attributes
• 3 different types of choice sets implemented to maximize D-efficiency
[Figure] Sample Choice Set
230 KRW/kWh
pm1~pm5
(4 hours)
90 KRW/kWh
[Table] Choice Attributes/Levels
Levels
2
Attributes
#Lvs
Peak length (hrs.)
2
2
4
Timing of peak
2
AM
PM
300 KRW/kWh
am10~pm12
(2 hours)
Off-peak price
(% of flat rate)
3
40%
50%
60%
50 KRW/kWh
Peak price
(% of flat rate)
3
150%
200%
250%
Information
feedback
3
none at peak
Cost of feedback
(USD/month)
3
1
0
$5
3
real-time
usage
$10
Results: fixed coefficient model (N=46)
Socio-demographics
 Household behavior represented by
the nested logit model in sequence: (i)
the decision to subscribe new service
plans (upper nest) and (ii) the choice of
an alternative maximizing the utility
(lower nest)
 Sociodemographic variables only
concerns the first decision making.
𝑃𝑖𝑗𝑘 = 𝑃𝑖𝑘 ∙ 𝑃𝑖𝑗 | 𝑘
𝑃𝑖𝑘
𝑃𝑖𝑗 | 𝑘
New ESPs
Option 1
…
Current Plan
(outside)
Option 𝐽
Estimates
Per area cooling demand
-13.234
Per area heading demand
-6.244
Dummy: Owned (vs. Rent)
0.519
Dummy: Jeonse (vs. Rent)
0.128
Number of family generations
-0.308
***
Per family Income
-0.094
***
ESP Attributes
***
Estimates
Off-peak price
-2.101
***
Peak price
-0.935
***
Constant: new ESPs
0.878
***
Peak: 4 hours (vs. 2 hours)
-0.051
Peak: Morning (vs. Afternoon)
-0.199
**
Cost of feedback ($/month)
-0.161
***
Dummy: Feedback (Peak)
0.010
Dummy: Feedback (Realtime)
-0.016
: robust over 10 distinct initial values
Summary & Discussion
 Average households do seem to prefer new ESPs:
• Especially new ESPs with low off-peak prices and the cost of information feedback
• Homeowners tend to subscribe new ESPs particularly when they are small families
 Household does not seem to perceive benefits from information
feedback through instant messages in choosing new ESPs:
• Positive information effects shown in previous field studies can be reversed.
 Why consumers might think they could be worse off with feedback?
• Mental/searching costs of identifying cost savings opportunities
• Incentives diminishing with fixed service cost provision (>50% choosing the default)
• Fear for “double” changes---new to dynamic prices and feedback (“IT takes time”)
 Non-positive a priori preference for information feedback calls
for households’ experimentation before introducing ESPs:
• Test period of smart meters and IHDs (Jessoe & Rapson, 2014)
• ’Shadow Billing’ to encourage positive selection (Borenstein, 2013)
10/12
Future Work
 Characterizing individual heterogeneity (Newell & Siikamaki, 2014)
• Which sociodemographic variables are related to positive willingness-to-pay to the
information feedback?
• Welfare analysis with policy simulations using the model results
 Under which ESP portfolio would social welfare and the acceptance of households be maximized?
 What is the role of information feedback in terms of welfare improvement?
[Formula]:
Random coefficient
WTP-space model
(McFadden & Train
2005; Newell &
Siikamaki, 2014)
𝑈𝑖𝑗 = 𝜆𝑖 𝑇𝑒𝑐ℎ𝐶𝑜𝑠𝑡𝑗 + 𝛼𝑑𝑃𝑒𝑎𝑘𝐴𝑙𝑒𝑟𝑡 + 𝛽𝑑𝑅𝑒𝑎𝑙𝑈𝑠𝑒 + 𝛾
𝑝𝑃𝑒𝑎𝑘
+ 𝜂𝑋𝑗 + 𝜀𝑖𝑗
𝑝𝑂𝑃
𝐾
𝜆𝑖 = 𝜆0 +
𝜆𝑘 𝑍𝑖𝑘 ,
𝜆0 ~𝐿𝑁 − (𝜇𝜆 , 𝜎𝜆2 )
𝑘=1
𝑈𝑖𝑗 : utility of consumer 𝑖 obtained from the ESP alternative 𝑗
𝑇𝑒𝑐ℎ𝐶𝑜𝑠𝑡𝑖𝑗 : the cost of information feedback in the ESP alternative 𝑗
𝑝𝑃𝑒𝑎𝑘 𝑝𝑂𝑃 : peak-to-offpeak price ratio (≥ 1, with 1 at the outside option)
𝑑𝑃𝑒𝑎𝑘𝐴𝑙𝑒𝑟𝑡 , 𝑑𝑅𝑒𝑎𝑙𝑈𝑠𝑒 : dummy variables representing information feedback in the ESP 𝑗
𝑋𝑗 : vector of other TOU alternatives; peak duration, timing of peak in the ESP 𝑗
𝑍𝑖𝑘 : sociodemographic variables of consumer 𝑖 (𝑘 = 1, … , 𝐾)
11/12
Thanks for your time!
Q&A