Which dynamic pricing rule is most preferred by consumers?—Application of choice experiment
© The Author(s) 2017
Received: 11 October 2015
Accepted: 21 February 2017
Published: 2 March 2017
This study investigates consumers’ preference for dynamic pricing rules using a choice experiment. Among alternative electricity pricing rules, time of use (TOU) is most preferred by consumers, and our estimation results show that TOU has the highest value of WTP among pricing rules. Furthermore, consumers’ characteristics affect their choice of a pricing rule. Our results show that risk preference in particular affects the choice probability of each pricing rule.
KeywordsChoice experiment Dynamic pricing Electricity
In many regions, electricity demand varies significantly based on time of day. The difference in electricity demand between peak and off-peak time periods has increased annually in Japan.1 A decreased load factor implies a high adjustment of electricity supply. Thus, electricity suppliers must increase the electricity price, which might lead to a decrease in consumer welfare. To reduce the demand gap between peak and off-peak time periods, demand-side control of energy use is an important tool to consider. In fact, the Japanese government is considering the introduction of dynamic pricing rules to decrease the electricity demand gap between peak and off-peak time periods.2
Dynamic pricing can reduce electricity demand by increasing the electricity rate when the electricity demand is strong. Electricity rates decrease when electricity demand weakens. The concept of dynamic pricing is based on economic incentives. Many previous studies (for example, Joskow 1975; Chao and Pecks 1996) analyze how to make the effective dynamic pricing rule maximize total welfare. Based on a theoretical analysis, several researchers have implemented field experiments to evaluate the performance of dynamic pricing rules. The empirical results of such field experiments show that dynamic pricing can contribute to decreasing peak-time electricity demand (Matsukawa et al. 2000; Faruqui and George 2005; Herter 2007). In addition, Allcott (2011) indicates that dynamic pricing can contribute to increasing the consumer surplus.
However, there remains an important policy question regarding dynamic pricing: Even if dynamic pricing rules have been theoretically effective, it is unclear whether consumers actually accept dynamic pricing rules. Joskow and Wolfram (2012) note that there are several factors that prevent consumers from accepting dynamic pricing rules. One reason they note is the cost of metering. Metering would be too costly for small commercial consumers. Furthermore, consumers might not easily understand the effectiveness of potentially complex designs of dynamic pricing.
We investigate whether consumers accept dynamic pricing using a choice experiment. We estimate willingness to pay (WTP) from the results of a choice experiment. Then, we analyze the most preferable dynamic pricing rules for consumers. There are several previous studies related to our study. Borenstein (2006) estimated the number of consumers who take risk aversion action. Risk aversion implies the avoidance of an electric pricing rule with high volatility, such as real-time pricing (RTP). Borenstein (2006) found that 77% of consumers investigated were risk averse. In addition, Borenstein (2013) found that consumers tend to remain in the flat-rate fee scheme. He also used field experiment data to analyze what characteristics of consumers lead them to switch to other electricity rates. As a result, he noted that low-income consumers tend to switch from a flat-rate scheme to a dynamic pricing scheme. A dynamic pricing scheme has the possibility to improve several energy problems, such as energy conservation and mitigation of CO2 emissions. But, an effective dynamic pricing scheme requires the joining of several types of consumers (Kurakawa et al. 2016). Therefore, understanding consumer preferences for each dynamic pricing rule is important in actually implementing a dynamic pricing scheme. Furthermore, some researchers have noted that consumers may prefer an energy management system that automatically adjusts electricity usage to a dynamic pricing scheme. Therefore, we add the analysis of the preference for direct load control (DLC), which does not require the risky price change of electricity to consumers; instead, it automatically adjusts the rate based on use.
2 Data and setting of choice experiment
2.1 Data collection
Estimation result of conditional logit
(If respondents choose TOU = 1, if respondents choose other pricing scheme = 0)
(If respondents choose CPP = 1, if respondents choose other pricing scheme = 0)
(If respondents choose RTP = 1, if respondents choose other pricing scheme = 0)
(If respondents choose DLC = 1, if respondents choose other pricing scheme = 0)
(If respondents choose DLCCPP = 1, if respondents choose other pricing scheme = 0)
Willingness to pay for electricity fee per month of each option (Japanese yen)
Willingness to pay for first fixed fee in each option (Japanese yen)
2.2 Choice experiment modeling
In the survey, we used a choice experiment. We presented respondents information about the demand response (for details on the information given to correspondents is shown in Additional file 1: Appendix). Respondents were asked to cast eight decisions during the experiment. Although the contents of options changed each time, option 6 (general pricing rule) remained the same. (Option 6 was the general pricing rule. In short, consumers did not change the pricing rule). The general pricing rule is the most common option in Japan. The order of the choice set was random, and the contents of the choice set were different for each respondent. Each time, the value of the “monthly fee” (the variable name was “month”) and the “initial cost” (the variable name is “first”) was randomly changed. The initial cost for the introduction of the demand response is one important factor that determines whether consumers join new pricing schemes. To control the initial cost effect for decision making, we added the initial cost as the basic attribute to the questionnaire. Respondents chose one option they found optimal. The general pricing rule (option 6) did not change the monthly electricity rate. Before asking the question in the choice experiment, we explained each dynamic pricing rule to respondents (see Additional file 1: Appendix).
Specifically, we asked respondents the following questions: “Among the following six electricity pricing alternatives, please select one you think is most preferable. The electricity rate you pay will be always supposed to satisfy your budget constraints. Please note that you will be able to freely spend the extra money you obtain from choosing the pricing.”
3 Estimation method
3.1 Conditional logit model
3.2 Marginal willingness to pay (MWTP)
4.1 Choice from electricity pricing options and its WTP
Estimation results of marginal willingness to pay
Marginal willingness to pay: MWTP (Japanese yen)
Previous studies note that consumers do not tend to accept the dynamic pricing rule, but our results show that TOU is the most preferable rule for the electricity rate. These results imply that consumers want to adapt the dynamic pricing rule in Japan. In fact, the WTP of RTP is also high, but consumers also want to avoid the risk of large electricity price changes. Thus, the WTP of critical peak pricing (CPP) is lower than that of other pricing schemes. In addition, the WTP of RTP is similar to that of DLC. These results imply that consumers may also accept the DLC for the electricity pricing rule.
4.2 Characteristics of motivating acceptance of dynamic pricing
Estimation results of multinomial logit model (economic incentive rules)
Critical peak price
Estimation results of multinomial logit model (DLC and general rule)
Direct load control
Mixed with DLC and CPP
List of variables
The average electricity rate of spring, summer, fall and winter
(The electricity rate is what respondents are paying)
Respondents’ degree of knowledge about energy-saving behavior (i.e., what respondents suppose is a good way to save energy use). Variables show the number of such questioner choices. Total numbers of such questions are 14
1. Turning off the air conditioner
2. Setting the temperature of the air conditioner to 28 °C
3. Cleaning the air conditioner’s filter frequently
4. Turning off the TV frequently when you are not watching it
5. Putting the refrigerator in an appropriate place
6. Setting the refrigerator’s temperature properly
7. Organizing the contents of the refrigerator
8. Setting the temperature of the air conditioner to 20° in the winter
9. Lowering the temperature setting of the water heater
10. Turning on the heater only when necessary
11. Unplugging electrical products when not in use
12. Not leaving the shower running
13. Setting the temperature of electrical carpet properly
14. Not reheating a bath
Whether respondents live in an all-electric home
Respondents live in an all-electric home = 1
Other = 0
The number of households of respondents
Whether the respondent has a job in an electric power industry
Yes = 1
No = 0
Age of respondent
Gender of respondent
Male = 1
Female = 0
Respondents graduated from
Primary school = 0
Junior high school = 1
High school = 2
National College of Technology = 3
Vocational school = 4
Junior college = 5
University = 6
Master’s degree program = 7
Doctoral degree program = 8
Annual income of respondents
The risk-level preference of respondents
Whether the respondents live in the Tokyo metropolitan area
Respondents lived in the Tokyo metropolitan area = 1
Other = 0
Whether respondents stay at home almost all day
Respondents stay at home almost all day = 1
Other = 0
Whether respondents live a self-owned home
Respondents live in self-owned home = 1
Other = 0
Whether respondents live in a rented house
Respondents live in rented house = 1
Other = 0
Whether respondents live in an apartment
Respondents live in an apartment = 1
Other = 0
Whether households of respondents are composed of only a married couple
Households of respondents are composed only of a married couple = 1
Other = 0
Whether households of respondents are composed of a married couple and a child
Households of respondents are composed of a married couple and a child = 1
Other = 0
Whether households of respondents are composed of three generations
Households of respondents are composed of three generations = 1
Other = 0
Whether respondents live alone
Respondents live alone = 1
Other = 0
Electricity rate per month
Initial cost to introduce the tool for dynamic pricing
Descriptive statistics for multinomial logit model
“All” and “time” show a negative coefficient with the choice probability of all economic incentive schemes. All-electric homes need to manage all energy usage with electricity. In short, people who live in such homes need to use more electricity than others do. Thus, such people dislike the electricity price change. Furthermore, people who spend a considerable amount of time in their home use more electricity than others do. Therefore, they do not tend to choose the dynamic pricing schemes.
“Education” shows a positive coefficient with the choice probability of all dynamic pricing schemes. A high educational background leads to a strong understanding of the merit of dynamic pricing schemes. This result shows the characteristic may increase the choice probability of dynamic pricing schemes. “Risk” shows a positive coefficient with the choice probability of CPP and RTP, but a high-risk preference leads to a decrease in the choice probability of DLC and TOU. These results show that consumers consider the risk of price change. “Single-life” shows a negative coefficient with the choice probability of all dynamic pricing schemes. People who live alone do not want to think about the electricity price change, as their consumption is small. They may think of the switching of the price rule as bothersome. These results imply that the characteristics of people and families affect their choice of a dynamic pricing scheme in the near future.
Estimation results of marginal effects (economic incentive rules)
Critical peak price
Estimation results of marginal effects (DLC and general rule)
Direct load control
Mixed with DLC and CPP
5 Discussion and conclusion
In this paper, we estimate the WTP for each dynamic pricing rule of electricity based on choice experiment. In addition, we analyze what factors influence the choice of each dynamic pricing scheme. As a result, we produce several important findings regarding consumers’ choice of a dynamic pricing rule.
First, TOU is the most preferable pricing rule for consumers. Our estimation results show that TOU has the highest value of WTP of all pricing rules. Second, the characteristics of each consumer affect the choice of a pricing rule. Borenstein (2006) notes that the risk of a pricing change is one of the important factors in choosing a pricing rule. In fact, we find that consumers who have a preference for risk aversion tend not to choose the RTP. In addition, our results show several characteristics influence the choice of a pricing rule. Our estimation results show that household characteristics are important factors in the choice of a dynamic pricing rule (economic incentive schemes and DLC). Regarding personal characteristic, a strong academic record increases the choice probability of economic incentive rules. Borenstein (2013) notes that lower-income consumers tend to prefer dynamic pricing rule, but our results do not show a robust relationship between income and the choice probability of dynamic pricing schemes. Therefore, the relationship between the choice of each pricing rule and income level could be more complex.
Energy conservation is an important policy problem worldwide. In the near future, demand-side control will become a more important policy for countries and regions because dynamic pricing schemes are one of the best tools to control the demand of electricity. However, many consumers need to support the dynamic pricing rule to achieve effective control of energy demand. Therefore, we need to consider not only the direct effect of each pricing rule but also whether consumers want to support such a pricing rule.
According to the Agency for Natural Resources and Energy in Japan, the power demand of the household sector has increased due to the improvement of living standards. In the household sector, which widely uses air-conditioning and electric carpet, power demand is substantial on hot days of summer and cold days of winter.
With the introduction of the demand response, the Ministry of Economy, Trade and Industry has expressed the view that the demand response can bring a new energy-saving mechanism to Japan. In addition, the Ministry of Economy, Trade and Industry believes that this system can prevent black outs of the power supply. For more information, see the following Web site: http://www.meti.go.jp/committee/sougouenergy/shoene_shinene/sho_ene/pdf/006_03_00.pdf.
In this study, we apply the conditional logit estimation method to estimate the parameters. However, conditional logit estimation relies on the assumption of independence from irrelevant alternatives (IIA). Thus, we cannot consider the utilities of each alternative to be correlated in this estimation.
In addition, we calculate the MWTP for initial cost in each pricing scheme. The MWTP for initial cost is calculated by the ratio of the coefficient of each price scheme to that of “First.” Preference ranking of each pricing scheme for initial cost and monthly fee is exactly same. This tells we can understand which dynamic pricing rules better for consumers as both calculation results of MWTP show the same ranking. However, consumers might not clarify well the difference between initial cost and monthly fee.
In this estimation, we define the choice of status quo as the reference.
YY contributed to the design of the web survey and analysis of the data. KT analyzed the data, wrote the manuscript and submitted this manuscript to this journal as corresponding author. SM helped in conception of this research and writing the manuscript. All authors read and approved the final manuscript.
This paper was supported by CREST of Japan Science and Technology (in particular, this funding supports data collection by web survey). Also, this paper was supported by Grant-in-Aid for Specially Promoted Research (26000001) by the Japan Society for the Promotion of Science (in particular, this funding supports our analysis) and S16 by Ministry of Environment (in particular, this funding supports our interpretation of data).
The authors declare that they have no competing interests.
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