Analysis of Residential Consumers’ Attitudes Toward Electricity Tariff and Preferences for Time-of-Use Tariff in Korea

Abstract

In recent years, a Time-of-Use (TOU) tariff for residential consumers has received much more attention with the increasing deployment of smart meters for residential consumers in Korea. The introduction of the TOU tariff for residential consumers is expected to allow residential consumers to have a choice of electricity rate plans in addition to load management. An analysis of residential consumers’ preferences for the TOU tariff is needed to identify TOU attributes’ levels. This paper analyzes Korean consumers’ attitudes toward current residential electricity tariff and preferences for TOU tariff using demographic characteristics collected by a face-to-face survey. Consumers’ preferences on key attributes of TOU tariff are analyzed using the conjoint analysis, and attitudes toward current residential electricity tariff are estimated by the multiple indicators multiple causes (MIMIC) model. The analysis results of this study show that based on their attitude toward electricity tariff, consumers’ attitudes can be divided into three latent variables, and preferences for TOU tariff are discrete by group rather than continuous by individual consumer.

Full text

Received January 29, 2022, accepted February 28, 2022, date of publication March 8, 2022, date of current version March 15, 2022.
Digital Object Identifier 10.1109/ACCESS.2022.3157615
Analysis of Residential Consumers’ Attitudes
Toward Electricity Tariff and Preferences for
Time-of-Use Tariff in Korea
MINSEOK JANG 1, HYUN CHEOL JEONG 1, TAEGON KIM 1, HYUN-MIN CHUN2,
AND SUNG-KWAN JOO 1, (Member, IEEE)
1School of Electrical Engineering, Korea University, Seongbuk-gu, Seoul 02841, Republic of Korea
2Korea Electric Power Corporation (KEPCO), Naju-si, Jeollanam-do 58322, Republic of Korea
Corresponding author: Sung-Kwan Joo (skjoo@korea.ac.kr)
This work was supported in part by the Korea Institute of Energy Technology Evaluation and Planning (KETEP), in part by the Ministry of
Trade, Industry & Energy (MOTIE) of the Republic of Korea under Grant 20181210301430, and in part by the Basic Research Program
through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT) under Grant
2020R1A4A1019405 and Grant 2020R1F1A1075872.
ABSTRACT In recent years, a Time-of-Use (TOU) tariff for residential consumers has received much
more attention with the increasing deployment of smart meters for residential consumers in Korea. The
introduction of the TOU tariff for residential consumers is expected to allow residential consumers to have
a choice of electricity rate plans in addition to load management. An analysis of residential consumers’
preferences for the TOU tariff is needed to identify TOU attributes’ levels. This paper analyzes Korean
consumers’ attitudes toward current residential electricity tariff and preferences for TOU tariff using
demographic characteristics collected by a face-to-face survey. Consumers’ preferences on key attributes
of TOU tariff are analyzed using the conjoint analysis, and attitudes toward current residential electricity
tariff are estimated by the multiple indicators multiple causes (MIMIC) model. The analysis results of this
study show that based on their attitude toward electricity tariff, consumers’ attitudes can be divided into three
latent variables, and preferences for TOU tariff are discrete by group rather than continuous by individual
consumer.
INDEX TERMS Time-of-use tariff, consumer attitude, consumer preference, structural equation model,
conjoint, mixed-logit model, latent class conditional logit analysis.
I. INTRODUCTION
In 2019, the government of South Korea presented its third
energy master plan to shift to a low-carbon energy system
with focus on renewable energy on the supply side and energy
efficiency on the demand side [1]. From the perspective
of the demand side, demand response and dynamic pricing
will play a key role in moving toward a low-carbon energy
system [2], [3].
The time-based tariff structures can be configured with
static or dynamic form. Time-of-Use (TOU) tariff generally
applies to electricity usage over static time windows of
several hours, where the price for each time window is
typically divided into two or three per day. Critical peak
pricing (CPP), which is combination of static and dynamic,
The associate editor coordinating the review of this manuscript and
approving it for publication was S. Ali Arefifar .
applies a high price for on-peak during a specific period
defined as a critical event. Real time pricing is charged for
electricity usage based on hourly metering, and charged price
is linked to the wholesale electricity market price [4].
Furthermore, a time-based electricity tariff for residential
consumers has received increasing attention in recent years
in Korea as the Korea Electric Power Corporation (KEPCO)
installs more smart meters for residential consumers. Korea
is at the initial stage of implementing a Time-of-Use (TOU)
tariff for residential consumers. The introduction of the
TOU tariff for residential consumers is expected to allow
residential consumers to have a choice of electricity rate plans
in addition to load management. The Korean government
and KEPCO plan to begin with the residential TOU tariff in
Jeju Island in the fourth quarter of the year 2021. Therefore,
analyzing Korean residential consumers’ preferences for the
TOU tariff is needed to identify TOU attributes’ levels.
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M. Jang et al.: Analysis of Residential Consumers’ Attitudes Toward Electricity Tariff and Preferences
A conjoint analysis is one of commonly adopted methods
to investigate customer preferences prior to introducing new
services or products. Conjoint analysis was frequently used in
other fields such as electric vehicles (EVs) [5], [6]. Previous
studies on consumers’ preferences for electricity tariff has
been carried out. Nicolson et al. [7] found that 39% of British
consumers are willing to switch to a TOU tariff. However, this
study for British consumers only investigated if respondents
would switch their current electricity tariff to a TOU tariff
but it did not analyze the various attributes respondents
preferred when selecting their TOU tariff. More recently,
the heterogeneity of preferences for electricity service was
analyzed in Britain [8]. This study for heterogeneity of
preferences only shows the kind of compensation required
for consumers willing to accept dynamic tariffs and did
not examine the preference of the attributes that make
up the TOU tariff. In Switzerland, Kaufmarn et al. [9]
conducted conjoint analysis for attributes composed of a
critical peak price (CPP) tariff. They found that customers
prefer smart metering service if the electricity tariff changes
to CPP and different customer segments exist. However,
the conjoint analysis for attributes of CPP used a small
sample of customers and a bottom-up cluster method using
part-worth sensitivity values, which does not fit consumers’
preferences for CPP appropriately. Also, reference [9] mainly
focuses on the attributes of smart metering. The consumers’
preferences for CPP attributes based on their demographic
characteristics were not analyzed in Switzerland study.
Consumers’ preferences were analyzed by combining the
various tariffs and the amount of carbon dioxide emission
reduction through conjoint analysis [10]. In their study,
results indicated that households tend to accept heating limits
rather than electricity usage, and monetary compensation
can be replaced with carbon dioxide emission reduction
level when customers accept a dynamic tariff. Furthermore,
in Sweden, the acceptance of restrictions on households’
electricity usage was analyzed using a conjoint analysis when
a pro-environmental framing was used [11]. Reference [11]
suggested that a pro-environmental message could influence
the preferences of people who are less engaged in saving
electricity usage.
There are eight principles impacting tariff design [12]. The
primary focus of this study is on a public acceptance, i.e.,
consumers’ preferences. Furthermore, little is known about
consumers’ preferences for TOU tariff options and attributes
based on demographic characteristics of the customer.
Therefore, it is also important to consider a combination of
attributes, which comprise of TOU tariff by demographic
characteristics when analyzing the preferences for TOU
tariffs. The reason is that no matter how economical and
technical a tariff design is, it is of no use unless the consumers
choose tariff.
This study conducted an extensive analysis of Korean
residential consumers. First, this study attempts to analyze
consumers’ preference heterogeneity for attributes, which is
an important factor in selecting the TOU tariff. Peak-times,
month, on-to-off peak ratio, and whether to include weekends
are the factors that vary in accordance with the energy policy
directions and the governments. Analysis of the consumer
preferences on these attributes would be useful information
in designing the effective future TOU tariff. In addition, the
consumer preferences on the on-to-off peak ratio by peak
hours and whether to include weekends would be important
information in establishing strategies for utility companies.
For example, if customers prefer a tariff with shorter peak
hours but a higher on-to-off peak rate ratio, utilities better
design the rate structure within the subscription level targeted
by the utility based on consumers’ preference of each attribute
for consumer acceptability of the TOU tariff. Therefore,
the top-down model, which is latent class conditional logit
model, is used to analyze which attribute is more important,
according to each customer group’s propensity. The empirical
results of analyzing consumers’ preference for TOU tariff are
beneficial in increasing the acceptance of a TOU tariff.
Furthermore, in previous studies on EVs the relationship
between consumers’ demographic characteristic and their
opinion about electric vehicles were investigated using a
structural equation model (SEM) [13], [14]. Little known
about consumers’ attitudes toward electricity tariff based on
demographic characteristic. This study utilizes the multiple
indicators multiple causes (MIMIC) model, which is a type
of structural equation, for analyzing residential consumers’
attitudes for current electricity tariff [15]. Hence, this study
also derives the relationship between residential consumers’
socio-demographic characteristics and their attitude toward
the present electricity tariff. These results about residential
consumers’ attitudes would be helpful for government and
utility to plan energy policy.
These analyses for consumers’ preferences and attitudes
toward tariff are conducted by using STATA.
The remainder of the paper is organized as follows.
Section II introduces the method for analyzing attitudes
toward electricity tariff using the MIMIC model and
consumers’ preferences for TOU using conjoint analysis.
Section III presents the demographic data and survey, the
results of attitudes toward electricity tariff, and the analysis
of preferences for TOU using conjoint analysis. Finally,
Section IV concludes the study.
II. ANALYSIS OF CONSUMERS’ ATTITUDES AND
PREFERENCES FOR ELECTRICITY TARIFF
A. STRUCTURAL EQUATION MODEL FOR CONSUMERS’
ATTITUDES TOWARD ELECTRICITY TARIFF
In this section, the MIMIC model is applied to analyze
Korean consumers’ attitudes toward electricity tariff. It is
assumed in the MIMIC model that a relationship exists
between latent variables and demographic variables. There
is also a relationship between latent variables and indicator
variables. In the first stage, a structural equation is used
to estimate the relationship between latent variables and
consumers’ demographic characteristics. In the second stage,
a measurement equation is used to analyze the relationship
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between latent variables and indicator variables. The two
equations, which are adopted from [15], are given by:
XL,C=βDXD,C+εs(1)
XI,C=βLXL,C+εm(2)
where, XD,Cis a demographic information vectors of
consumer C, which is collected by survey. XL,Cis a latent
variables, which was not observed and indicated consumers’
attitudes toward Koreas’ electricity tariff. εsis a normal
distribution error of a structural equation. XI,Cis an vector
of indicator collected by a consumers’ attitude part in a
survey. εsis a normal distribution error of a measurement
equation. For a measurement equation, if the questions of
consumers’ attitude are asked by a discrete ordered case
which is Likert scale with the scale of M, ordered probit
model could better explain a discrete indicator than linear
regression. An example would be ‘‘Do you believe that
current electricity bill is too expensive?’’: (1) agree, (2)
neutral, (3) disagree. A discrete indicator XI,Cis represented
as follows [16]:
XI,C=















j1if z < τ1
j2if τ1≤z< τ2
· · ·
jM−1if τM−2≤z< τM−1
jMif τM−1≤z
(3)
where, j1,· · · ,jMare discrete values. τ1,· · · ,τMare
parameters which is estimated by measurement equation, and
zis a discrete indicator value which is defined as (2). Then, the
probability of a discrete indicator can be presented as follows:
Pr XI,C=jk=Pr (τk−1≤z< τk)
=F(τk−1)−F(τk)(4)
where Fis the cumulative distribution function (CDF) of
the error term εm. If εmassumed a normal distribution,
equation (4) is called ordered probit.
B. CONJOINT ANALYSIS FOR CONSUMERS’ TOU
ATTRIBUTE PREFERENCES
Currently, there are multiple methods for analyzing con-
sumers’ preferences for goods or services. A conjoint
analysis is a popular multivariate technique that examines
respondents’ preferences for virtual goods or services, which
are composed of the selected number of attributes. This
method surveys respondents’ preferences for a virtual product
that is created using levels constituting each attribute and
analyzes how customers consider the importance of each
attribute [17], [18].
To accurately investigate customer preferences in conjoint
analysis, the following descriptions are important. First,
attributes must be independent of each other, and it is
desirable that attributes do not exceed eight [18]. Each
attribute could be split into several levels. Second, in conjoint
analysis, the main effects of an orthogonal design method
guarantee orthogonality between individual attributes that
are used to separate the impact of individual attributes on
selection behavior. The orthogonal design method overcomes
the widely recognized drawback that a high correlation
between attributes is a problem in conjoint analysis [19].
Finally, consumers’ preferences data are analyzed using
discrete choice models or methods.
The conditional logit model, introduced by
McFadden et al. [20], is relatively easy to estimate and
interpret results but has limitations in that it does not
sufficiently account for the heterogeneity of preferences
among individual consumers. In this study, a mixed-logit
model and a latent class conditional logit model are
introduced to solve the limitation of not accounting for
preference heterogeneity [21], [22]. As preferences toward
electricity vary according to each consumer’s environment,
the preferences, i.e., utilities for factors that make up the
electricity bill are also different. Preference heterogeneity
is divided into systematic preference heterogeneity related
to the observation of respondents and heterogeneity asso-
ciated with unobserved characteristics. By estimating the
distribution of coefficients showing the influence of factors
that affect consumers’ preferences for the attributes of TOU
tariff, the mixed-logit model can explain the heterogeneity
showing individual consumers’ preferences for different
factors. Consumers’ preference consists of a deterministic
part observed through the proposed questionnaire and a
stochastic part related to uncertainty.
The equations of the mixed-logit method are presented
in (5), (6), and (7) as like [5], [21]. If respondent nfaces
a choice set comprising Jalternatives, then the utility of
respondent n for alternative jin the choice set is presented
as:
Unj =β0
nxnj +εnj (5)
where, xnj is a vector of attributes, which respondent n
faces alternative jin choice situation tand βnis a vector
of coefficients of each attribute. εnj is assumed as the
random variables that are independent and have the identical
distribution of extreme type I. The vector of coefficients,
βn, is assumed to be continuous random variable that have
probability density function whose parameter θfollow a
normal or lognormal distribution, i.e., f(β|θ).For a given βn,
the probability Pnj (β)of respondent nchoosing alternative j
in choice situation is modeled as follows:
Pnj =Z eβ0
nxnj
PJ
j=1eβ0
nxnj !f(β|θ)dβ(6)
A model representing the form of (6) is called a mixed-logit
model. This model could approximate any random utility
model depending on f(β|θ). As it is not possible to express
Pnj in a closed form, θmust be estimated by simulation for
estimating the parameter. An approximation of c
Pnj is obtained
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by deriving βvalues as many as Rfrom f(β|θ).
ˆ
Pnj =1
R
R
X
r=1
eβ0
nxnj
PJ
j=1eβ0
nxnj (7)
By contrast, if each respondent is included in an unobserved
class, it is assumed that the preferences within the class are
homogeneous; however, there are heterogeneous preferences
between classes in a latent class conditional logit model [22].
As documented in [22], if the parameter βnof (6) is a discrete
random variable with bsvalues (s =1,...,S), and each
probability is ps(s =1,...,S), Pnj is the probability of a
latent class conditional logit model as follows:
Pnj_latent =
S
X
s=1
ps
ebsxnj
PJ
j=1ebsxnj (8)
where psis the probability that the respondent n will belong
to the class s. ebsxnjt /PJ
k=1ebsxnkt is conditional probability
of choosing an alternative j when a respondent n of class
s in choice t set. A membership likelihood function, which
classifies respondent ninto class s, can be modeled as
Mns =λsZn+ns (9)
where Znis a vector of demographic characteristics and an
attitude variable for the tariff of respondent n.ns is assumed
as random variables that are independent and have identical
distribution of extreme type I. Then, ps, which is defined as
conditional logit model, is given:
ps=eλsZn
PS
s=1eλsZn
(10)
In this study, CAIC (Consistent Akaike’s Information
Criterion) and BIC (Bayesian information criterion) are used
to determine the best model that accounts for preference
heterogeneity among mixed-logit model and latent class
conditional model as follows [23], [24]:
CAIC = −2(ln L)+K(ln M +1)(11)
BIC = −2(ln L)+K ln M (12)
where, ln Lis the maximized sample log likelihood, Kis the
total number of estimated parameters, and Mis the number
of decision makers.
In this study, while investigating the attitude stage, Korean
electricity consumers’ attitudes toward electricity tariff are
analyzed. While investigating the TOU preference stage,
conjoint analysis is performed to analyze attributes that
are important to consumers in selecting a TOU tariff. The
analysis process proposed in this study is shown in Fig 1.
III. EMPIRICAL RESULTS
A. DEMOGRAPHIC DATA AND SURVEY
For accurate screening, face-to-face data collection was per-
formed. Prior to conducting the attitude and conjoint survey
by the end of March 2021, respondents’ demographic char-
acteristics were surveyed to identify heterogeneity among
FIGURE 1. Analysis procedure for residential consumers’ attitudes toward
electricity tariff and preferences for time-of-use (TOU) tariff.
TABLE 1. Summary of demographic information.
TABLE 2. Questions for attitude toward electricity tariff.
1,103 electricity consumers, who currently live in Seoul and
Gyeonggi-do. The summary of collected demographic data is
presented in Table 1.
For the attitude survey, a set of questions were created to
determine consumers’ attitudes toward the electricity tariff.
Eight questions were asked to understand if consumers agree
with them, primarily reflecting their attitude toward the
electricity tariff. Table 2 presents the questions to gauge
consumers’ attitudes toward the electricity tariff.
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TABLE 3. Attributes and levels of TOU in the conjoint survey.
For the conjoint survey, a set of attributes and levels were
created for generating the alternative choice sets describing
the various TOU tariff types. The levels of each attribute
designed in this study are shown in Table 3.
In [12], principles of a desirable rate structure were
presented. Public acceptability of tariff is also identified as an
important principle among the eight principles for a desirable
rate structure. Therefore, unlike previous studies which have
provided a monitoring device for increasing acceptance of
TOU tariff, the increase in consumer acceptance of TOU tariff
is analyzed by reflecting a combination of the design features
of the TOU tariff plan, which are defined as the length of
the peak period, the ratio of the peak to off-peak price, what
months TOU is in applying, and whether to apply weekends.
In this study, the structure of TOU tariff, which is the
combined tier,on-to-off peak rate ratio,month,peak-times,
and whether to include weekends is set as the key attributes
that influence consumers’ TOU preference. Table 3 presents
the attributes and levels with descriptions. The choice sets,
which are designed to affect the probability of respondents’
selection of TOU alternatives, are very important.
This study uses the orthogonal main effects design
that ensures orthogonality of each attribute for composing
simplified alternative cards for the survey. Therefore, four
alternative choice sets comprising four cards each were
generated using the orthogonal main effects design to
separate the impact of each attribute based on respondents’
answers. Alternative choice sets, which are used in the study,
are included in the Appendix.
To validate consumer responses for the conjoint survey
and the analysis models, the following four methods were
performed. For validating the consumers’ information, First,
a relatively large number of surveys were conducted.
Many samples could help the analysis model of consumer
preferences have robustness from outliers. Second, the survey
was conducted in an on-site environment that could prevent
the respondents’ insincere responses as much as possible
TABLE 4. Factor analysis with consumers’ attitudes.
compared to on-line. Lastly, information of consumer’s
replying cards in the same pattern, which is a representative
example of insincere responses, was removed. For validating
the analysis model, the statistical validity of the model
parameter estimation was analyzed using the p-value, which
is a representative statistical power.
B. RESULTS OF INVESTING CONSUMERS’ ATTITUDES
TOWARD THE ELECTRICITY TARIFF
First, factor analysis is performed to find latent variables
that reflected consumers’ attitudes toward electricity tariff
by using the maximum likelihood method with varimax
rotation [14]. As a result of factor analysis, three is
the optimal value for number of factors. Thus, questions
related to consumers’ attitudes toward electricity tariff were
aggregated, as shown in Table 4.
For each question, the attribute is included in the factor
with the largest factor loading. By grouping factors with
the largest factor loading, each factor’s characteristics can
be observed, and a name is assigned to indicate the
characteristics of each factor to make a latent variable.
As questions 5 and 6 referred to the need for reforming the
current electricity tariff and the need for various electricity
tariffs, F1 was designated as a latent variable indicating
consumers’ attitude toward the ‘‘Need for various types of
electricity tariffs.’’ As questions 2 and 3 asked consumers
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M. Jang et al.: Analysis of Residential Consumers’ Attitudes Toward Electricity Tariff and Preferences
FIGURE 2. The MIMIC result of consumers’ attitude for electricity tariff.
TABLE 5. The estimated the structural equation for electricity tariff
attitude.
about the current electricity bill, F2 was designated as a
latent variable indicating the feeling that the ‘‘The current
electricity tariff is too expensive.’’ As questions 1 and 10 have
the common features of saving the electricity tariff, F3
was identified as consumers’ attitude toward ‘‘Saving on
electricity tariff.’’ Based on the factor analysis, three latent
variables are obtained from surveying consumers’ attitude
toward electricity tariff. MIMIC result is presented in Fig 2.
Second, as shown in Fig 2, the structural equation was
estimated using latent variables and consumers’ demographic
information, as presented in Table 5.
Table 5 shows that each latent variable is structured
based on consumers’ demographic characteristics. The effect
on the latent variable can be interpreted according to the
significant coefficient of each demographic characteristic.
To the question ‘‘Need for various types electricity tariffs,’’
female consumers respond more positively to the need for
having different types of electricity tariffs than male. For
TABLE 6. The estimated the measurement equation for electricity tariff
attitude.
the question ‘‘The current electricity tariff is too expensive,’’
male with many family members, high family income and
education level think that the current electricity tariff is
expensive. The number of family members per household has
the greatest influence on the ‘‘The current electricity tariff
is too expensive,’’ and it may show that the more number of
members in a family, the higher the electricity consumption
and more excessive the electricity bills. Residential electricity
consumers with higher education level responded positively
to the item ‘‘Saving on electricity tariff.’’
Finally, the relationship between the latent variables
and attitude indicators was estimated in the measurement
equation, as shown in Table 6. Measurement equations using
the MIMIC model can reduce the dimensionality of the effect
indicators. For the item ‘‘Need for various types of electricity
tariffs,’’ as question 6 is not statistically significant, latent
class variables that require various tariffs can be defined
through question 5. As question 3 is statistically significant
with ‘‘Current electricity tariff is too expensive,’’ consumers
who believe the current electricity bill is very expensive are
paying a lot. As question 10 is statistically significant with
‘‘Saving on electricity tariff,’’ it is presumed that consumers’
attitudes toward saving on electricity is conspicuous if they
are aware of the monthly electricity usage, and the energy
efficiency rating of household appliances is perceptible.
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TABLE 7. Consumers’ preference for TOU tariff using mixed-logit model.
Summing up the the analysis results of consumers’
attitudes, Korean residential consumers’ attitudes toward
electricity tariff are divided into three latent variables:
(i) Need for various types of electricity tariffs,(ii) Current
electricity tariff is too expensive, and (iii) Saving on
electricity tariff. The latent variables are explained through
the demographic characteristics of consumers and indicators
belonging to each group.
C. RESULTS OF INVESTIGATING CONSUMERS’
PREFERENCES FOR TOU TARIFF
Equation (5) can be re-expressed to model the preferences of
residential electricity consumers for TOU as follows:
TOU(Unjt )
=βratez,nxrate z,jt +βmonth,nxmonth,jt +βweekend,nxweekend ,jt
+βpeak−times,nxpeak −times,jt +εnjt (for z =B,...,F)
(13)
where βratez,n,xratez,jt are the vectors of coefficients and
variables indicating preferences for tariff designs. βmonth,n,
xmonth,jt ,βweekend,n,xweekend ,jt ,βpeak −times,n,xpeak−times,jt are
coefficients and variables indicating preferences for month,
weekends, and peak-times. Rate A is assumed to be the
base category. As most previous studies adopted the log-
normal and normal distribution for parametric mixed-logit
estimation [25]. In a log-normal distribution, it is useful
to estimate the expected parametric same sign for all
respondents. At the same time, a normal distribution has no
restrictions. For that reason, parameters are estimated using
the normal distribution in this study. Results indicating the
preference to use the mixed-logit model are shown in Table 7.
The mean of Rate B coefficient is the highest positive
value, whereas the mean of Rate C coefficient is the highest
negative value in the rate design attribute, indicating that
residential consumers prefer a lower on-to-off peak ratio.
In addition, Rate D and Rate E, which are the 3-tier rate
structure, with a lower on-to-off peak ratio have positive
coefficients of means compared to Rate C and Rate F with a
higher on-to-off peak ratio, indicating that consumers prefer
a lower on-to-off peak ratio.
The mean of weekends coefficient is another significantly
positive variable. This implies that consumers could control
TABLE 8. The criterion for optimizing class.
TABLE 9. Consumers’ preference for TOU tariff using latent class
conditional logit.
their electricity usage pattern following the TOU tariff over
weekends. These results of the mixed-logit model were
analyzed assuming that consumers’ preferences have con-
tinuous heterogeneity, suggesting that there are differences
in preferences for TOU tariff among individual customers.
Otherwise, the latent class conditional logit model assumes
that respondents’ heterogeneity has a discrete distribution that
can be endogenously or potentially partitioned. The criterion
for classifying latent class for conditional logit model is
influenced by an individual’s demographic characteristics.
In this study, latent classes are divided using individual
characteristics that affect the utility variable and (8). For
determining the number of classes, both CAIC and BIC are
used.
Since both BIC and CAIC show small values in the 4 class,
it is the optimal one, as shown in Table 8. Therefore, the
latent class conditional logit model is conducted following
the 4 class. Therefore, the latent class conditional logit model
results using 4 classes is presented in Table 9 and Fig 3.
The mixed-logit model and the latent class conditional
logit model do not have the tendency to show that one
model is more superior [26]. However, while explaining
the heterogeneity in consumers’ preferences for the TOU
tariff, both BIC and AIC have smaller values for the latent
class conditional logit model, as shown in Tables 7 and 9.
Therefore, the latent class conditional logit model is well
fitted for this study.
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FIGURE 3. Proportion of each consumer group according to TOU tariff
preferences.
Consumers in class 1 have more numbers of family
members and relatively low-income level. Class 1, referred to
as big-family group, clearly shows a positive preference for
the 2-tier rate structure compared to other groups, and prefers
Rate C to Rate B. As a result, 2-tier rate structure and lower
off-peak rates are preferred in the big-family group. In the
case of other attributes, the big-family group clearly shows
a negative preference for the coefficient of weekends and
peak times but has a positive preference for the coefficient of
month in comparison other groups. Therefore, the big-family
group prefers a longer month period with short peak times
only on weekdays. As customers, females are more likely
to be included in class 2, which is referred to as the female
consumer group. This group prefers Rate B and peak hours
both on weekdays and weekends. Consumers included in the
female consumer group clearly show a negative preference
for longer peak times and months. Consumers in class 3,
which is referred to as the strategy group, clearly prefer
the 3-tier rate structure and higher on-peak rate. There is
no significant demographic difference between the strategy
group and the reference group, which is class 4. Moreover, the
strategy group has a negative preference for all the variables
representing period such as weekends and month. This
implies that the strategy group minimizes the inconvenience
of behavioral changes and tries to obtain maximum benefits
by reducing electricity consumption for a shorter period with
a higher on-peak rate. Relatively, class 4, referred as to the
main group, has more consumers than other groups. The main
group prefers Rate D, which has a lower on-peak rate, a higher
off-peak, and 3-tier rate structure, compared to other rate
options. The main group also prefers the TOU tariff with all
week and a longer month period.
The next phase of this research is to study the consumers’
preference for TOU tariff in combination with clustering of
customers’ load profiles to design a customized electricity
tariff for a targeted group.
IV. CONCLUSION
In this study, an analysis of Korean residential electricity
consumers’ attitudes toward electricity tariff are divided into
three latent variables. The latent variables are explained
through the demographic characteristics of consumers and
their answers for questions about attitudes toward electricity
tariff belonging to each group.
Consumers’ preferences for TOU tariff were analyzed with
rate design,month,weekends, and peak-times, which are
key attributes in composing a TOU tariff. The results of
the mixed-logit model describe the individual consumer’s
preference for attributes of TOU tariff. However, there is a
physical limit to designing tariff plans that are suitable for an
individual consumer based on its personal preference. Hence,
latent class conditional logit model is conducted additionally
using consumers’ demographic characteristics. The results
of the latent class conditional logit model are presented as
four classes: big-family group, females, strategy group, and
main group. These four groups show different preferences
for the TOU tariff, which is according to the demographic
characteristics of each group.
Considering the main group, which accounts for 50%
of the participation among the four groups as shown in
Fig 3, to increase the consumer acceptance of TOU tariff
for residential consumers, the proposed rate structure is
combination of lower peak price, lower on-to-off peak rate
ratio, longer month period, and only mid-peak or off-peak on
weekends. In addition, although the peak-times coefficient is
not statistically significant, it could be seen that consumers
tend to want a longer peak period on the lower peak price.
These results suggest that a tariff option such as rate discount
is possible to increase the acceptability of the TOU tariff as
like previous studies implying that supplying a monitoring
device such as IHD could increase the acceptability of the
TOU rate system. Furthermore, even if a lower price is
applied in peak period, consumers tend to accept a longer
peak period. Therefore, the consumer acceptance of TOU
tariff can be achieved while maintaining the principle of
efficient pricing.
Future research needs to be directed toward the devel-
opment of method that combines residential consumers’
historical data responding to actual TOU tariff and demo-
graphic characteristic. Such the future study will be useful in
designing a customized electricity tariff for a targeted specific
socioeconomic group.
APPENDIX
Tables 10 to 13 show the choice sets of alternative cards com-
posed of attributes proposed in this study. The respondents
choose the most attractive alternative card in each choice set.
TABLE 10. Choice set 1 of TOU tariff in the face-to-face survey.
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TABLE 11. Choice set 2 of TOU tariff in the face-to-face survey.
TABLE 12. Choice set 3 of TOU tariff in the face-to-face survey.
TABLE 13. Choice set 4 of TOU tariff in the face-to-face survey.
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MINSEOK JANG received the B.S. degree in electrical engineering from
Kwangwoon University, in 2014, and the Ph.D. degree in electrical and
computer engineering from Korea University. From 2017 to 2019, he was a
Big Data Platform Engineer with Tmax Data Company.His research interests
include the energy IoT, big data, and machine learning.
HYUN CHEOL JEONG received the B.S. and M.S. degrees in electrical
engineering from Dong-A University, in 2018 and 2020, respectively. He is
currently pursuing the Ph.D. degree in electrical engineering with Korea
University. His research interests include energy big data and machine
learning.
TAEGON KIM received the B.S. degree in electronic engineering from
Korea University, in 2020, where he is currently pursuing the M.S. degree
in electrical engineering. His research interests include the energy IoT, big
data, and machine learning.
HYUN-MIN CHUN received the master’s degree from Pusan National
University, in 1998, the M.B.A. degree from Aalto University, in 2008,
and the Ph.D. degree from Yonsei University. Since 1998, she has been
working with Korea Electric Power Corporation (KEPCO), where she is
currently a Charge Policy Manager. Her research interests include utility
costs, regulatory frameworks, and sustainable pricing function.
SUNG-KWAN JOO (Member, IEEE) received the M.S. and Ph.D. degrees
from the University of Washington, Seattle, WA, USA, in 1997 and 2004,
respectively. From 2004 to 2006, he was an Assistant Professor with
the Department of Electrical and Computer Engineering, North Dakota
State University, Fargo, ND, USA. He is currently a Professor with the
School of Electrical Engineering, Korea University, Seoul, South Korea.
His research interests include multidisciplinary research related to energy
systems, involving information technologies, optimization, and intelligent
systems.
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