2015-2
The Effect of Demand Response on
Electricity Consumption in Japan
Fumitoshi Mizutani Takuro Tanaka
Eri Nakamura

The Effect of Demand Response on Electricity Consumption in Japan

Fumitoshi Mizutani
Kobe University
Graduate School of Business Administration
2-1 Rokkodai, Nada-ku, Kobe 657-8501 Japantoshi@kobe-u.ac.jp

Takuro Tanaka
Kobe University
Graduate School of Economics
2-1 Rokkodai, Nada-ku, Kobe 657-8501 Japan
takuro_0710@yahoo.co.jp

Eri Nakamura
Kobe University
Graduate School of Business Administration
2-1 Rokkodai, Nada-ku, Kobe 657-8501 Japanenakamura@person.kobe-u.ac.jp

Correspondent Author: Fumitoshi Mizutani
toshi@kobe-u.ac.jp1

The Effect of Demand Response on Electricity Consumption in Japan[Abstract]: The main purpose of this study is to investigate, by using regressions analysis, the DR
effect on households’ electricity consumption. We employ three kinds of estimation models: a pooled
OLS model, a random effect model, and a fixed effect model. Major results are as follows. First, theDR scheme clearly reduces electricity consumption. As the peak-time price of electricity increases
by 20 yen/kWh in the form of TOU and CPP, electricity consumption decreases by about 8.1% atsample mean. However, consumption after DR tends to increase, most likely due to the reboundeffect.

Second, the reduction effects of the DR scheme can be strengthened as households’ income

becomes higher.

In contrast, as more people stay at home during the daytime and the temperaturerises, the reduction effects of the DR scheme may become weaker. Third, electricity price, household
characteristics, and external conditions are significant factors affecting electricity consumption.
Fourth, the effects of some DR schemes such as requests to save electricity, TOU, and CPP, candiffer largely according to household characteristics and external conditions.

[Key Words]: Demand Response, Electricity Consumption, Time of Use, Critical Peak Pricing[JEL Classification]: L4, L5, L9

1. Introduction
Reducing electricity consumption has become an increasingly important issue in terms ofresource conservation.

One of the most commonly used ways to encourage consumers to saveelectricity is “demand response (DR),” a system by which consumers control their own level ofconsumption.

A DR scheme can be implemented in many ways, such as Time of Use (TOU),

Critical Peak Pricing (CPP), and Variable Peak Pricing (VPP).

TOU sets a fixed price forelectricity according to season and time, CPP sets a high price only during peak time in order toreduce consumption, and VPP is a variation on CPP in that the price during peak time is adjusted
according to what demand was on the previous day.

Researchers such as Faruqui and George(2005) and Herter et al. (2007) have studied the effect of the DR scheme on electricity consumption,
but their analyses have limitations which will be addressed in this paper.
The purposes of this study are to examine the effect of the DR scheme on electricity
consumption while considering the characteristics of households, and to simulate the differences inthe effect of DR among various households.

Our paper makes the following three contributions.

First, we simulate the effect of DR based on different types of households and make clear its effectin various situations.

For example, we examine to what degree households reduce electricity
2

consumption under a DR scheme, in what conditions (e.g. temperature and income) households are
more sensitive to a DR scheme, and the extent of a DR scheme’s influence in the most and least
effective cases.

Previous studies have rarely simulated these situations.

Second, we analyze the effect of DR scheme while controlling for households’
characteristics, such as the number of people at home in the daytime.

Although electricity

consumption can differ largely depending on these characteristics, previous studies have notconsidered this fact.

Whether family members stay at home in the daytime has not been consideredin any previous work, but our results suggest that when more members stay at home in the daytime,the effect of the DR scheme shifts from reducing electricity consumption to increasing it. These
results suggest the importance of controlling for these factors in the analysis of the DR scheme.
Third, we define the DR scheme as an ordered variable with five levels in the estimation.
Moreover, our definition of a DR scheme integrates three types of different DR schemes into one
variable according to the strength of each scheme on demand levels.

Previous studies mostly

define the DR scheme as a dummy variable expressing merely whether the DR scheme isimplemented or not (e.g. Faruqui et al. 2014; Jessoe and Rapson 2014). By defining the DR
scheme as an ordered variable, we can obtain less biased results than with an estimation using adummy variable.
This paper consists of five sections after the introduction.
studies on the DR scheme and electricity consumption.

Section 2 reviews previous

Section 3 explains the model, data, andvariables used in the estimation.

Section 4 shows the estimation results by regressions and Section

5 shows the simulation results.

And finally Section 6 summarizes the conclusions.

2. Previous Studies
In this section, we summarize the major previous studies on DR and electricity
consumption, focusing especially on empirical analysis. Major previous studies are summarized inTable 1.

**************
Table 1**************

Previous studies on DR and electricity consumption have certain common characteristics.
First, most studies conclude that DR has a significant effect on electricity consumption butthat the impact of DR is different depending on the situation.

For example, Herter and Wayland(2010) argue that DR surely reduces electricity consumption during the implementation time of DR,
3while it increases consumption on the previous and subsequent days. Jessoe and Rapson (2014)indicate that the effect of DR can change whether the households are provided information about theamount of their electricity consumption.

Households provided more information consume lesselectricity, and households able to check their information on home displays more often can reduce
their consumption through the learning effect.

Similarly, Faruqui et al. (2014) show that ecologicalfacilities can enhance the reduction effect of DR on electricity consumption.

They indicate alsothat households are more sensitive than company users to DR.
Second, most studies use data only about individual households.

For example, Jessoe andRapson (2014) use data on households in Connecticut, and Faruqui and George (2005), Herter et al.
(2007), and Herter and Wayland (2010) use data from California. Faruqui and Sergici (2011) use
data about individual households in Baltimore, and Ida et al. (2013) use household data from Kyoto
and Kitakyushu in Japan. While these studies examine the DR from the perspective of demand forelectricity among households, rarely have studies examined the issue of companies’ electricity
consumption, though Faruqui et al. (2014) include data on corporate electricity consumers in
addition to data on households.
Third, previous empirical studies have included certain variables, such as ecological
technology, temperature, and the use of certain appliances, and evaluated the effect of thesevariables requiring large amounts of electricity as determinants of electricity consumption. For
example, Herter et al. (2007), Faruqui and Sergici (2011), and Faruqui et al. (2014) include thevariable of ecological facilities at home, and Herter and Wayland (2010), Faruqui and Sergici (2011),
and Faruqui et al. (2014) include the temperature variable in their estimation.
Types of DR examined in previous studies cover a wide range: Time of Use (TOU),
Critical Peak Pricing (CPP), Real Time Price (RTP), Peak Time Rebate, and Variable Peak Pricing(VPP).

Faruqui and George (2005) compare the effects of CPP, VPP, and TOU, and conclude that

VPP is the most effective, CPP is the second best, and TOU is the third best.

Faruqui et al. (2014)

investigate TOU, CPP, and PTR, while Ida et al. (2013) examine TOU and CPP. Faruqui and Sergici(2011) focus on PTR, while Herter et al. (2007), Faruqui and Sergici (2011), and Herter and Wayland(2011) focus on CPP. It is worth noting that most of these previous studies include the DR variable in
the form of a dummy variable.
In addition to the above-mentioned studies, there have been other kinds of studies: policy
evaluation studies such as Nishimura’s (2014), which evaluates previous DR schemes in the US,
France, etc., and discusses the possibility of a future DR scheme in Japan. There have also beentheoretical studies, such as that by Chao and DePillis (2013).
However, previous studies leave certain issues unresolved. The most important task now is
to use adequate variables in the estimation in order to control for conditional factors influencing
electricity consumption and to simulate the effect of DR based on estimation results using these4adequate variables.

For example, although electricity consumption is determined by individual

households’ characteristics, previous studies tend to control only experimental conditions such as
price, group, temperature, and facilities at home.

Some studies such as Ida et al. (2013) and Jessoe

and Rapson (2014) include the characteristics of households as a form of individual effect of a fixedpanel data model.

However, this method is not useful in making clear the effects of household

characteristics on electricity consumption and thus does not allow us to pinpoint what kind ofhouseholds are more sensitive to the DR scheme or which is the most effective DR scheme forhouseholds with different characteristics.

Thus, we specify the variables expressing households’

characteristics and based on the estimation results considering these characteristics we simulate theeffects of the DR scheme.

3. Empirical Analysis
3.1 Model
Our model reflects the assumption that electricity consumption is determined by electricity
price, DR schemes, characteristics of family, appliances, and house, and other external conditions, as
shown in equation (1).
,is electricity consumption,

,

,

,

,

is electricity price,

vector of characteristics of a household,
vector of housing conditions, and(1)is the vector of DR effect,is theis the vector of appliances in a household,is theis the vector of the other external conditions. Based onequation (1), we specify three empirical models: (i) Pooled OLS model, (ii) Random effect model1,(iii) Fixed effect model2.

(Pooled OLS Model):1

In the random effect model,
is the unobserved individual effect. The covariances ofandexplanatory variables are assumed to be zero.
2
The fixed effect model of panel data controls the individual effect of a household. The merit ofthis model is that we do not need to specify the variables of individual effect in the form of the above,,, andvariables, which can reduce the misspecification bias. Thus, in equation
(4),
,
, and
variables are not explicitly defined, since the variables which do notchange during the experimentation period cannot be identified in the fixed effect model. These
individual effects are included in . The covariances ofand explanatory variables are assumed
not to be zero.
5

Σ
Σ

Σ

,

Σ

.

∙

,

∙

,

Σ

,

Σ

,

Σ

,

,

∙

,

,

,

(2)
(Random Effect Model):
Σ
Σ

Σ

,

Σ

.
,

∙

,

∙

Σ

,

Σ

,

Σ

,

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(3)
(Fixed Effect Model):
Σ
Σ

,

Σ

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∙

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∙

∙

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(4)
,

,

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,
,

,
,

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,

,

,
,

is the electricity consumption of household per hour,,.

is the electricity price,is thetreatment variable of the implementation of DR, which takes 0 to 5 according to the strength of the
DR scheme.

is the treatment variable expressing the implementation time of DR, that is,is the treatment variable expressing 7 hours before thethe peak time of 13:00 to 16:00,

is the treatment variable expressing 7 hours after the

implementation of DR, andandimplementation of DR.

are included because, as Herter and Wayland (2010)

argue, there can be a rebound effect of DR that increases electricity consumption before and after the
implementation of DR.
A household’s characteristics include the household’s income (), and the number of residents in a household during thefamily members per household (daytime ().

refrigerators at home (The appliance conditions of a household include the number of) and the number of air conditioners at home (housing conditions include house age ((), floor size in the house ().

), September dummy (The), and type of house), and the dummy of whether the facilities at home are fully electrificated (other external conditions include the temperature measured at every hour (dummy (), the number of

).

The

), the weekend), and 23 time dummies for every hour ().

Since our data used here consist of panel data of household and time, the error terms
6within a household might have a serial correlation. Therefore, we estimate the standard errors basedon cluster variances.

3.2 Data
We use data from a social experiment on the effect of DR on households’ electricity
consumption, called Keihanna Ecological City Next Generation’s Energy and Social System
Experimental Project (Keihanna Ekoshithi Jisedai d Enerugi Syakai Shisutemu Jissho Purojekuto).
This project was carried out in Kyoto prefectures in Japan between 23 July and 28 September in2012.

This experiment was planned by the Ministry of Economy, Trade and Industry andimplemented by Keihanna Eco-City Promotion Council, an organization consisting of local
government, energy-related private companies, universities, and various research institutions, withthe purpose of demonstrating an energy system for a smart community in the above areas.
Participants in this experiment were 681 households, divided into four groups, with GroupA as the control group (with no treatment) and Groups B, C and D as treatment groups. Second,
among treatment groups, Group B was requested to save electricity before the implementation day of
CPP (Critical Peak Pricing).

Furthermore, Group C and D were treatment groups under TOU(Time of Use) and CPP. Although groups C and D were, respectively, assigned to the winterexperimentation, we recognize these two groups as the same in this analysis since there were nodifferences between them during the summer experimentation.

3.3 Variables
In this section, we will explain the definition of variables used in this study. A summary ofstatistics is shown in Table 2.

**************
Table 2**************First, Q is the amount of electricity hourly consumed by a household.
Second, P is the price for a household’s consumption, defined as follows.

Because theexperiment data do not include electricity price, we must glean these data from other sources3. The
value of P is equal to 20 yen/kWh if the monthly accumulated consumption of a household is equal
to or smaller than 120 kWh.

The value of P is equal to 25 yen/kWh if consumption is larger than

120 kWh and smaller than 200kWh.

The value of P is equal to 26 yen/kWh if consumption is

3

These values are based on the variable price A in the areas of Kansai Electric Power in August and
September 2012.
7

equal to or larger than 300 kWh. In the estimation, P is included as the form of the logarithm, lnP.
In our study, three types of DR are examined: (i) a request for electricity saving, (ii) TOU(Time of Use), and (iii) CPP (Critical Peak Pricing). “A request for electricity saving” means that
consumers are simply requested to reduce electricity consumption voluntarily, with no obligation to
do so. “TOU” adds 20 yen/kWh to the ordinary price for the peak hours of 13:00~16:00 only.
“CPP” sets a higher price if the temperature will exceed 30 degrees according to the previous day’s
weather forecast. Of these three types of DR, TOU is stronger than a request for electricity saving,and CPP is stronger than TOU.

Moreover, our CPP has three levels: CPP40, CPP60, and CPP80.

In CPP40, CPP60, and CPP80, 40 yen/kWh, 60 yen/kWh, and 80 yen/kWh are added, respectively,
as peak pricing to the ordinary price.

Thus, our DR scheme has five levels in ascending order: arequest for electricity saving, TOU, CPP40, CPP60, and CPP80.
In the analysis, the value of DR at the peak time (DRpeak) is defined in Table 3. For
example, DRpeak is equal to one, if the group is under a request for electricity saving (Group B), andthe group is under a request for electricity saving on the date, and the time is between 13:00 and
16:00.

As another example, the value of DRpeak is equal to two, if the group is any treatment group(Group C or D), the treatment day is under TOU, and the time is between 13:00 and 16:00.
Furthermore, the value of DRpeak is equal to three, if the group is any treatment group, the day isunder CPP40, and the time is between 13:00 and 16:00. Thus, as DRpeak becomes larger, thetreatment of DR scheme as price becomes higher.

**************
Table 3**************

Demand response by individual users is also affected before and after the DR scheme isimplemented. We set up the variable of DR 7 hours before the plan is announced (DRpre) and thevariable of DR 7 hours after the plan is announced (DRpos). The variables DRpre and DRpos aredefined in a way similar to DRpeak.

That is, the value of DRpre (DRpos) is equal to one, if the groupis B, the day is under the request for electricity saving, and the time is 7 hours before (after) the DR
implementation, and so on.
Household characteristics have an important effect on demand for electricity. We also
include three kinds of household characteristics. First, FAMincome is the income level of a household.
This variable is an index from 1 to 12 categories. 1 is the lowest category (less than 2 million yen) ,2 is the second lowest (2 to 3 million yen) and 12 is the highest category (more than 15 million yen).
Second, FAMnumber is the number of people per household. Third, we also include daytime residence.
FAMdaytime is the number of residents present in a household during the daytime.
8Appliance conditions and housing conditions are also important factors determining
demand for electricity. As for these conditions, first, APPrefrig is the number of refrigerators at home
and APPair is the number of air conditioners at home.

Second, HSEage is house age and HSEsize isthe floor area in a house. HSEtype is the dummy variable which takes a value of one if the house is acommunal building, otherwise zero.

HSEelec is the dummy variable which takes a value of one if

the facilities at home are fully electrificated, otherwise zero.
Finally, we include five kinds of other external conditions. First, OTHtemp is thetemperature measured every hour. Second, OTHweek is the weekend dummy variable. This takes avalue of one if the day is a weekend day, otherwise zero. Third, OTHsep is the dummy variable on adate in September. This takes a value of one if the day is in September, otherwise zero.

Finally, we

include temperature variable of the day, OTHtemp. In this study, we take this variable as the form of
the logarithm, lnOTHtemp.

4. Estimation Results
Table 4 shows the estimation results of equations (2) to (4), which are the pooled OLS
model, the random effect model and the fixed effect model. As this table shows, most coefficientsshow a reasonable sign. Furthermore, the coefficients of significant variables are relatively stable
among these models, which suggests that the results are robust in terms of model specification inpanel data models (the fixed effect and the random effect models). Among these three models, test
results show that the fixed effect model is considered as best and the random effect model as second
best4. Therefore, we base our discussion on the results of the fixed effect model.

**************
Table 4**************

As we mentioned before, the results seem reasonable because important coefficients show
the expected signs.

For example, the coefficient of electricity price (lnP) is negative with statisticalsignificance, which indicates that higher price leads to less consumption of electricity. Since themagnitude of the coefficient is between -0.059 and -0.074, as the price increases by one hundred
percent, the electricity consumption decreases by 5.9% to 7.4%.
Next, we will explain the results of the effect of the DR. First, the most important result is
4First, according to the Breusch-Pagan test, the random effect model is better than the pooled OLS
model. Second, according to the robust F test, the fixed effect model is better than the pooled OLS
model. Third, according to the robust Hausman test, the fixed effect model is better than the
random effect model.
9that DRpeak has a negative sign with strong statistical significance, which means that DR surely
reduces electricity consumption.

As the peak-time price of electricity increases by 20 yen/kWh in

the form of TOU and CPP, electricity consumption decreases by about 8.1%. This value of change
rate is calculated based on the interpretation of the semi-log linear model.

The change rate in thesemi-log linear model, which we specify here as DRpeak variable and cross-terms, can be calculated
approximately
,

∑

as
∙

,

1.

,

∙

,

∑

,

∙

,

Thus, in the fixed effect model, since values for DRpeak variable

and cross-terms are about -0.084, the value of exp(-0.084)-1 is about -0.081, that is, about -8.1%5.
It is worth noting that the results of the random effect model are similar to those of the fixed effectmodel6.
As for consumption 7 hours after and before DR implementation, the following results are
obtained. First, since the coefficient of DRpos is positive with statistical significance, theconsumption after DR time tends to increase. Presumably this result reflects a rebound effect. Thisis consistent with the result of Herter and Wayland (2010), which shows that DR reduces electricity
consumption during the implementation time, but increases it after the DR time.

Our results alsosuggest that consumers shift electricity consumption from the DR time (the peak time of 13:00 to
16:00) to non-DR time (the off-peak time of 16:00 to 23:00).

However, second, the coefficient ofDRpre is not statistically significant. Consumption before the DR time would not change.
Some cross-terms of DRpeak with other factors are statistically significant. For example,
DRpeak・FAMincome shows the negative sign with statistical significance. This result shows that the
reducing effects of DR would be larger as households’ income becomes higher.

In contrast, thecoefficients of DRpeak・FAMdaytime and DRpeak・lnOTHtemp show a positive relationship with statisticalsignificance. These results mean that as more people stay at home in the daytime and the weather
becomes hotter, the reducing effects of DR could diminish.
As stated above, the negative coefficient of lnP indicates that higher price leads to lesselectricity consumption.

Since the magnitude of the coefficient is between -0.059 and -0.074, as

the price increases by one percent, electricity consumption decreases by 5.9% to 7.4%.
The coefficients of most other variables are also reasonable.

For example, since

FAMnumber APPrefrig and APPair are positive, as the family becomes larger or has more refrigerators
and air conditioners, electricity consumption would be larger.

As the number of householdincreases by one, electricity consumption increases by about 11.5%. As the number of refrigerators
increases by one, electricity consumption increases by 7.9%.
5

As the number of air conditioners

In this case, we calculate values at sample mean for DRpeak variable and the cross terms withstatistical significance. From Table 4, we obtain results as follows:
∙
∙
∙
0.084
.
Therefore, the value of exp(-0.084)-1 is about -0.081.
6
Results in the case in the random effect model, obtained in a similar way, are about -7.8%.
10

increases by one, electricity consumption increases by 11.6%. On the other hand, the coefficients of
FAMincome and FAMdaytime show a positive sign but the magnitudes are very small: the effects of anincrease in income or in the number of residents present in the daytime are only 1.1% and 3.7%,
respectively. Similarly, the coefficients of HSEage, HSEsize, and HSEelec have positive signs. These
results indicate that, as the house becomes older or larger, or the house is fully electrificated,
electricity consumption is larger.
Moreover, since lnOTHtemp and OTHweek show the positive sign with statistical significance,electricity consumption tends to increase on a hot day or during the weekend.

As the temperature

increases by one percent, electricity consumption also increases by about 33%. Thus, electricity
consumers are more sensitive to changes in the weather than to changes in price.

In contrast, thecoefficients of OTHsep show the negative sign so that households consume less electricity if the day
is in September.

Variables such as HSEtype, DRpeak・FAMnumber, DRpeak・APPair, DRpeak・APPrefrig, andDRpre are not statistically significant.

5. Simulation Results
In this section, based on the regression results, we simulate how much electricity
consumption would be changed by the implementation of DR. When households need a large
amount of electricity, DR is inadequate to bring about electricity saving, as DR works least
effectively in severe situations such as “when the weather is hot, a household has a low income, andpeople stay at home during the daytime.” Table 5 shows the effect of each DR on electricity
consumption. In this case, we take three different scenario cases: (i) the least effective case, (ii) thecase of average effectiveness, and (iii) the most effective case.

**************
Table 5**************

The combination of the conditions such that “temperature is 34.1℃, income category is 3,
and the number of residents during the daytime is 2” is the case in which DR is least effective. In
contrast, households can control their electricity consumption largely by DR when they face afavorable situation.

The combination of conditions such that

“temperature is 27.5℃, income

category is 10, and the number of residents during daytime is 0” is the most effective case.

The

case such that “temperature is 31.4℃, income category is 5, and the number of people at home in thedaytime is 1” is the average effective case.
Based on these calculations, Table 5 shows the following results. First, the effect on11electricity consumption of a request for electricity saving is -0.7% at the least and -11.6% at the most.
This means that only showing the quantitative indicators for consumers to save electricity, if it works
most effectively, can reduce electricity consumption more than in the case of TOU working
averagely.

Second, the TOU can reduce by between 1.4% and 21.9%.

The most effective case of

TOU is nearly the same as the averagely effective case of CPP80.

Third, CPP can reduce

electricity consumption by 2.1% to 41.0%.

If the effect of CPP80 were to be maximized, electricity

consumption could be reduced nearly by a half. Thus, the effect of each DR can differ largely
depending on household characteristics and external conditions.
Because electricity consumption increases immediately subsequent to a period of DR
implementation, it is important to see the net effect of the scheme. As stated in the previous section,
while DR surely reduces electricity consumption, it also induces a rebound effect in that a household
consumes more electricity than usual after the DR time.

Next, in Table 6, we see the simulation

results of the net consumption of electricity during and after DR.

**************
Table 6**************The left side of Table 6 shows the effect of this rebound, and the right side shows the net
effect of DR in a day, which is calculated by taking the difference between the reduction rate during
DR and the increase rate after DR.

In this table, we consider three scenario cases: (i) the least

effective case, (ii) the case of average effectiveness, and (iii) the most effective case. These are thesame as in Table 5.
In Table 6, there are cases where the rebound effect outweighs the reduction effect of DR
on consumption.
consumption.

The positive sign of the change rates means that DR increases electricity

In the cases where DR works least effectively, net consumption increases by at least

1.0% and at most 5.4%.

However, if DR works at least averagely, it can reduce electricity

consumption as a net effect over the course of a day.
Last, Table 7 shows the simulation results of the DR effect depending on temperature,income, and the number of people at home in the daytime.

**************
Table 7**************

The case on the left has temperature changing, the other two factors being fixed as
12

“income category of 5 and with one person staying at home in the daytime.”

The case in the

middle has income changing, the temperature being fixed at 31.4℃ and with one person staying at
home in the daytime.

The case on the right has the number of people at home in the daytimechanging, with the income category being 5 and the temperature 31.4℃.

As more people stay athome during the daytime, as the temperature becomes higher, or as the income level becomes lower,
DR loses its reducing effect.

When the household income category is at 10, CPP80 can reduce

electricity consumption by 32.5%.

Higher income leads to a greater reduction rate of electricity use

perhaps because high-income households generally use more electricity than lower income
households and thus have more room in which to reduce consumption.

6. Conclusions
By using regression analysis and simulating how much electricity consumption can besaved through DR schemes, this paper investigates the DR effect on households’ electricity
consumption. We employ three kinds of estimation models: a pooled OLS model, a random effectmodel, and a fixed effect model. The main results of our analyses are as follows.
First, the DR scheme clearly reduces electricity consumption.

As the peak-time price ofelectricity increases by 20 yen/kWh in the form of TOU and CPP, electricity consumption decreases
by about 8.1% depending on the other factors specified.
increase, probably due to the rebound effect.

However, consumption after DR tends toThis suggests that consumers shift their electricity

consumption from DR time (the peak time of 13:00 to 16:00) to non-DR time (the off-peak time of
16:00 to 23:00).
Second, the effect of the DR scheme is related to the other factors.

For example, thereduction effects of the DR scheme can be strengthened as households’ income becomes higher.

In

contrast, as more people stay at home during the daytime and the temperature rises, the reduction
effects of DR scheme can diminish.
Third, electricity price, households’ characteristics, and external conditions are significant
factors related to electricity consumption.
consumption.

A higher price surely leads to less electricity

When the price increases by one hundred percent, electricity consumption decreases

by 5.9% to 7.4%. The household with more family members, more air conditioners and morerefrigerators, consumes more electricity.

Electricity consumption tends to increase on hot days oron the weekend.
Fourth, the effects of certain DR schemes such as a request for electricity saving, TOU,
and CPP, can largely differ depending on household characteristics and external conditions.

At itsmost effective, the mere practice of showing consumers quantitative indicators to motivate them tosave electricity can reduce electricity consumption more than in the case where TOU works
13averagely.
Fifth, there are some cases in which the rebound effect outweighs the reduction effect of
the DR scheme on electricity consumption.

In the cases where the DR scheme works least

effectively, the net electricity consumption increases by 1.0% to 5.4%.

However, if DR works atleast averagely, it can reduce electricity consumption as a net effect over the course of a day. The net
reduction of electricity is between 3.4 and 32.2%.
Last, the effect of the DR scheme depends on the number of residents present during
daytime, temperature, and income. A higher number of people at home in the daytime, a higher
temperature, and a lower income level lead to a lower reduction effect created by the DR scheme.

Acknowledgements
For this study, data were provided by Mitsubishi Heavy Industries, Ltd (MHI). We would like tothank MHI, especially Mr. Tomoyuki Enomoto and Mr. Yasushi Imori, for providing data and initialcomments for our study.

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Wholesale Electricity Markets,” Journal of Regulatory Economics, Vol.43, pp.265-283.
Faruqui, A. and S. George (2005) “Quantifying Customer Response to Dynamic Pricing,” Electricity
Journal, Vol.18, pp.53-63.
Faruqui, A. and S. Sergici (2010) “Household Response to Dynamic Pricing of Electricity: A Surveyof 15 Experiments,” Journal of Regulatory Economics, Vol.38, pp.193-225.
Faruqui, A. and S. Sergici (2011) “Dynamic Pricing of Electricity in the Mid-Atlantic Region:
Econometric Results from the Baltimore Gas and Electric Company Experiment,” Journal ofRegulatory Economics, Vol.40, pp.82-109.
Faruqui, A., S. Sergici, and L. Akaba (2014) “The Impact of Dynamic Pricing on Residential andSmall

Commercial

andIndustrial

Usage:

New

Experimental

Evidence

fromConnecticut,” Energy Journal, Vol.35, pp.137-160.
Herter, K. (2007) “Residential Implementation of Critical-Peak Pricing of Electricity,” Energy Policy,
Vol.32, pp.2121-2130.
Herter, K., P. McAuliffe, and A. Rosenfeld (2007) “An Exploratory Analysis of California
Residential Customer Response to Critical Peak Pricing of Electricity,” Energy, Vol.32,
14

pp.25-34.
Herter, K. and S. Wayland (2010) “Residential Response to Critical-Peak Pricing of Electricity:
California Evidence” Energy, Vol.35, pp.1561-1567.
Ida, T., K. Ito, and M. Tanaka (2013) “Using Dynamic Electricity Pricing to Address Energy Crises
Evidence from Randomized Field Experiments,” Kyoto University, Working Paper.
Jessoe, K. and D. Rapson (2014) “Knowledge is (Less) Power Experimental Evidence from
Residential Energy Use,” American Economic Review, Vol.104, pp.1417-1438.
Joskow, P. and C. Wolfram (2012) “Dynamic Pricing of Electricity,” American Economic Review,
paper and proceedings, Vol.102, pp.381-385.
Kim J. H. and A. Shchervakova (2011) “Common Failures of Demand Response,” Energy, Vol.36,
pp.873-880.
Nishimura, K. (2014) “Prospects for Demand Response to Capacity Value in Japan (Wga Kuni ni
Okeru Dhimando Risuponsu no Genjo to Tenbo),” Journal of Public Utility Economics(Koeki Jigyo Kenkyu), Vol.66, No.2, pp. 57-63 (in Japanese).

15

Table 1 Previous Studies
Study
Faruqui and George(2005)

Herter (2007)

Data and Method
Data: California, US; Household andBusiness; July, 2003 – December, 2004
Method:No
explanation(probably
regression analysis)
Data: California, US; Household; 2003 2004
Method: Matching estimation

Herter et al. (2007)

Data: California, US; Household; July 2003
- September 2004
Method: Matching estimation

Faruqui and Sergici(2010)

Summarize 15 DR social experiments in the
US.
Method: Survey research
Summarize the existing contribution of DR
resources in U.S. electric power markets.
Data: California, US; Household, July and
September 2004
Method: OLS
Summarize obstacles to DR scheme success

Capper et al. (2010)
Herter and Wayland(2010)
Kim andShchervakova (2011)

Faruqui and Sergici(2011)

Data: Baltimore, US; Household, 2008 and2009

Major Results-Effect of electricity consumption saving through DR,-All DR schemes (VPP, CPP, TOU) are effective: VPP＞CPP>TOU．

-CPP can reduce household electricity consumption.
-High-use customers respond significantly more in kW reduction than do
low-use customers, while low-use customers save significantly more inpercentage reduction of annual electricity bills than do high-use customers.
-CPP can reduce household electricity consumption.
-Households with automated air-conditioning controls can reduce electricity
consumption more than those without the device through the CPP scheme.
-CPP can reduce electricity consumption.
-Households with automatic controlling devices can reduce electricity
consumption more than those without the device through the CPP scheme.
-The currently existing DR resource contribution, in terms of potential peakload reduction, has increased since 2006 by about 10%.
-Effect on electricity consumption per hour
-Negative effect: Peak-time of DR day (-)-Positive effect: Next day of DR day (+), Previous day of DR day (+)-Consumer barriers: (i) customer knowledge of DR scheme, (ii) availability oftechnology, (iii) information feeds, (iv) response fatigue, (v) technology
cost and financing, (vi) potential saving of electricity, (vii) sacrificing
behavior in switching patterns-Producer barriers: (i) investment recovery, (ii) promotional responsibility,(iii) managerial incentives-Structural barriers: (i) DR program structure, (ii) regulatory process andpolicy support-Model 1: effect on peak-off peak ratio of electricity consumption per day;Price ratio・THI Difference (-), THI Difference (+), Price ratio・THI
16

Joskow and Wolfram(2012)

Ida et. al. (2013)

Faruqui et al. (2014)

Jessoe and Rapson.
(2014)

Method: SUR, Fixed effect model,
Simulation by PRISM
Summarize lessons of DR schemes based on
previous studies

Data: Kyoto and Kita-Kyushu, Japan;
Household; July – September 2012
Method: Fixed effect model
Data: Connecticut, US; Household andsmall business; June-August 2009
Method: SUR, Fixed effect model,
Simulation by PRISM
Data: Connecticut, US; Household; July August 2011
Method: OLS for ITT, 2SLS for TOT, Fixed
effect model

Difference・PTR (+), Price ratio・THI Difference・Tech (-)-Model 2: effect on average electricity consumption per day; Price・THI (-)-Merits of DR schemes: (i) saving electricity consumption at peak time, (ii)saving excess investment in facilities, (iii) suppression of electric powercompany’s market control-Possibility of implementation of DR schemes: (i) technology innovation, (ii)public opinion on deregulations, (iii) aging of existing facilities, (iv)promotion of renewable energy-Problems of DR schemes: (i) installation cost of meters, (ii) increase of costof firms due to complexity of price system, (iii) lack of understanding byconsumers, (iv) redistribution of income-Electricity consumption per 30 minutes-Negative effect: Warning (-), Warning・income (-), CPP (-)-Positive effect: CPP・income (+), CPP・Mean Usage (+)-Model 1: effect on peak-off peak ratio of electricity consumption per day;Price ratio・THI Difference (-), THI Difference (+), Price ratio・THI
Difference・PTR (+), Price ratio・THI Difference・Tech (-)-Model 2: effect on average electricity consumption per day; Price・THI (-)-Effect on electricity consumption per 15 minutes-Price (-), Price+IHD(-), (Price+IHD) ・ (number of seeing IHD) (-),
(Price+IHD) ・ (number of confirming DR announcement) (-),
(Price+IHD)・post DR (+)

17

Table 2 Summary of Statistics

Variable

Mean

Std. Dev.

Min

Max

Q (Hourly electricity consumption: kwh)

0.565

0.592

0.000

13.800P (Electricity price)23.3882.605

20.00026.000DRpeak (Demand response at peak hours)

0.168

0.699

0.000

5.000

DRpre (Demand response at pre-hours)

0.296

0.905

0.000

5.000

DRpos (Demand response at post-hours)

0.296

0.905

0.000

5.000

FAMincome (Income category: 1 - 12)

5.915

2.715

1.000

12.000FAMdaytime (Residence at home during daytime)

1.298

0.965

0.000

4.000

FAMnumber (Number of people per household)

3.191

1.198

1.000

7.000

APPrefrig (Number of refrigerators per household)

1.136

0.432

0.000

3.000

APPair (Number of air conditioners per household)

3.576

1.705

0.000

10.000HSEage (House age: year)

3.156

1.616

1.000

6.000

HSEsize (Floor area per house: m2)

4.306

1.057

1.000

7.000

HSEtype (Type of house: collective housing = 1, otherwise = 0)

0.773

0.419

0.000

1.000

HSEelec (Dummy of fully-electrificated house: fully-electrificated house=1, otherwise=0)

0.312

0.463

0.000

1.000

OTHtemp (Temperature measured every hour : C degree)

26.6354.074

14.90035.800OTHweek (Weekend dummy: weekend=1, otherwise=0)

0.288

0.453

0.000

1.000

OTHsep (September dummy: September=1, otherwise=0)

0.475

0.499

0.000

1.000

(Note) The number of the observations is 698,088 for each variable.

18

Table 3 Definition of Variable of DRpeak

Variable of DRpeak

Group

DRpeak = 1

Treatment

Date
group

(Group B)
DRpeak = 2

Treatment

Date

Time
of

the

requestfor

13:00 to 16:00

electricity saving
group

Date of TOU

13:00 to 16:00

Date of CPP40

13:00 to 16:00

Date of CPP60

13:00 to 16:00

Date of CPP80

13:00 to 16:00(Group C or D)
DRpeak = 3

Treatment

group(Group C or D)
DRpeak = 4

Treatment

group(Group C or D)
DRpeak = 5

Treatment

group(Group C or D)
DRpeak = 0

otherwise

19

Table 4 Estimation Results
Pooled
OLS
0.897***(0.070)RandomEffect-0.059**(0.023)

Fixed
Effect-0.074***(0.023)

DRpeak

-1.007***(0.114)-0.745***(0.101)-0.741***(0.102)

DRpre

0.001
(0.006)0.002(0.004)0.002(0.004)

DRpos

0.017**(0.007)0.017***(0.004)0.017***(0.005)

FAMincome

0.010*(0.005)0.011*(0.006)

FAMdaytime

0.033**(0.016)

0.036**(0.017)

FAMnumber

0.099***(0.012)0.109***-0.013

APPrefrig

0.068***(0.012)0.076***(0.013)

APPair

0.108***(0.038)0.110***(0.040)HSEage0.021**(0.009)0.023**(0.010)HSEsize0.035**(0.017)0.038**(0.019)HSEtype0.0266(0.0440)0.0326(0.0488)

HSEelec

0.287***(0.031)0.329***(0.034)lnOTHtemp

0.718***(0.034)0.343***(0.020)0.337***(0.020)OTHweek

0.0521***(0.008)0.051***(0.006)

0.051***(0.006)OTHsep-0.016**(0.007)-0.084***(0.006)-0.085***(0.006)-0.006**(0.003)-0.005**(0.002)-0.005**(0.002)0.016*(0.009)0.018**(0.007)0.018**(0.007)0.002(0.006)-0.004(0.005)-0.004(0.005)0.003(0.004)0.001(0.004)0.001(0.004)Model
lnP

DRpeak・FAMincome
DRpeak・FAMdaytime
DRpeak・FAMnumber
DRpeak・APPrefrig

20DRpeak・APPair
DRpeak・lnOTHtemp
Constant

NR2
log likelihood
Number of households

0.016(0.016)0.017(0.013)0.017(0.013)0.274***(0.030)-7.068***(0.311)0.204***(0.023)-2.957***(0.140)0.202***(0.023)-1.665***(0.116)6934170.276-686987-6934170.259
493

6934170.137-597149
493(Note) Robust standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

Table 5 Simulation Results of the Effects of DR Policy
DR Type

Least effective case

Average effective
case

Most effective caseRandom Effect Model
Request for Electricity

-0.7%

-5.1%

-11.6%

TOU

-1.4%

-9.9%

-21.9%

CPP40

-2.1%

-14.5%-30.9%

CPP60

-2.8%

-18.8%-38.9%

CPP80

-3.5%

-22.9%-41.0%

-0.7%

-5.0%

-11.6%

TOU

-1.3%

-9.8%

-21.8%

CPP40

-2.0%

-14.4%-30.8%

CPP60

-2.7%

-18.7%-38.8%

SavingFixed Effect Model
Request for Electricity
Saving

CPP80-3.3%-22.8%-40.9%(Note)(1) The least effective case is temperature of 34.1℃, income category of 3, daytime residence of 2.
(2) Average effective case is temperature of 31.4℃, income category of 5, daytime residence of 1.
(3) The most effective case is temperature of 27.5℃, income category of 10, daytime residence of 0.

21

Table 6 Net Effect of DR by Simulation
Increasing rate
Net effect of DR
of electricity
DR Typeconsumption
Least effective
Average
Most effective
after 7 hours ofcaseeffective casecaseDRRandom Effect Model
Request of Electricity Saving
1.7%
1.0%
-3.4%
-9.9%
TOU
3.4%
2.0%
-6.5%
-18.5%
CPP40
5.1%
3.0%
-9.3%
-25.8%
CPP60
6.9%
4.1%
-11.9%-32.0%
CPP80
8.7%
5.3%
-14.2%-32.3%
Fixed Effect Model
Request of Electricity Saving
1.7%
1.0%
-3.4%
-9.9%
TOU
3.4%
2.1%
-6.4%
-18.4%
CPP40
5.1%
3.1%
-9.2%
-25.7%
CPP60
6.9%
4.3%
-11.8%-31.9%
CPP80
8.7%
5.4%
-14.１%-32.2%(Note)(1) Net effect is calculated by taking the difference between the reduction rate during DR and theincrease rate after DR.
(2) The least effective case is temperature of 34.1℃, income category of 3, daytime residence of 2.
(3) Average effective case is temperature of 31.4℃, income category of 5, daytime residence of 1.
(4) The most effective case is temperature of 27.5℃, income category of 10, daytime residence of 0.

22

Table 7 DR Effects on Temperature, Income, and the Number of Residents of Households
Income Category

Temperature (℃)

Number of Residence of Household

27.1

31.4

34.5

3

5

10

0

1

2

Request for Electricity Saving

-7.6%

-5.1%

-3.5%

-4.1%

-5.1%

-7.6%

-6.7%

-5.1%

-3.4%

TOU

-14.6%

-9.9%

-6.8%

-8.0%

-9.9%

-14.6%-13.0%

-9.9%

-6.7%

CPP40

-21.1%-14.5%-10.1%-11.7%-14.5%-21.0%-18.9%-14.5%

-9.8%

CPP60

-27.1%-18.8%-13.2%-15.3%-18.8%-27.0%-24.4%-18.8%-12.9%

CPP80-32.7%-22.9%-16.2%-18.8%-22.9%-32.5%-29.5%-22.9%-15.8%

Request for Electricity Saving

-7.6%

-5.0%

-3.4%

-4.0%

-5.0%

-7.5%

-6.7%

-5.0%

-3.3%

TOU

-14.5%

-9.8%

-6.8%

-7.9%

-9.8%

-14.5%-13.0%

-9.8%

-6.6%

CPP40

-21.0%-14.4%-10.0%-11.6%-14.4%-20.9%-18.8%-14.4%

-9.7%

CPP60

-27.0%-18.7%-13.1%-15.2%-18.7%-26.9%-24.2%-18.7%-12.7%

CPP80-32.5%-22.8%-16.1%-18.6%-22.8%-32.4%-29.3%-22.8%-15.6%

Random Effect Model

Fixed Effect Model[2015.2.5 1187]

23