In Japan, since the earthquake and tsunami on 11 March 2011 precipitated a disastrous accident at the Fukushima Daiichi Nuclear Power Station, much attention has been given to the promotion of renewable energy as an important component of Japan’s future energy mix. While it is practical to address the problem created by the shutdown of nuclear power plants by maximizing the usage of existing thermal power plants, it is not possible to immediately increase the supply of electricity. In addition, increased emissions of greenhouse gases, especially CO2, are also a major issue. Even though renewable energies show potential in reducing CO2 emissions, they have a very low capacity value, which leads to supply uncertainty. Therefore, this study shifts the focus to the perspective of electric energy demand management. To be specific, lowering electric energy consumption can help not only by reducing the costs of serving energy but also by balancing and making the supply-demand system more efficient. Consumers are expected to decrease their electric energy consumption by receiving incentives through dynamic pricing and real-time information via smart meters.
Why was Nushima Island chosen to be the experimental target? Nushima Island is a remote island located in the south of Hyogo prefecture, where electric energy needs rely on supply provided by the Kansai electric power company (Osaka, Japan) (Figure 1
]. The island had an estimated population of about 527 people (statistics as of 2012, Hyogo Prefecture (Hyogo, Japan) [2
]). At present, the small island confronts a situation in which the Japanese government introduced electric power market liberalization to the general household-level in April 2016 (Agency for Natural Resources and Energy (Tokyo, Japan) [3
]). Although the new market system can create great advantages for consumers, such as by lowering the prices by empowering them to choose their electric power supplier, it may cause supply volatility by dissolving the market-stabilizing responsibilities that used to be taken on by the regional electric power monopoly suppliers. Consequently, the communities that are not only far away from the mainland, but also difficult to deliver electric energy to, like Nushima, may no longer be a target of electric power companies. In Japan, more than 6500 islands that have the same characteristics of Nushima will be confronted with electric energy shortage issues.
The aim of the dynamic pricing experiment in Nushima is to assess the possibility of the community’s self-control of its electric energy demand through dynamic pricing. According to the results of this experiment, this paper may establish a smart-energy community’s model that is both environmentally friendly and resistant to the electric power market’s instability.
The term “dynamic pricing” used in this research is basically a concept that extended from the concept of time-of-use (TOU) rates. TOU is an advanced extension of a multiple tariff, which includes a critical peak price as well as a peak and off-peak price. The idea of these prices is to shift consumption to off peak when the cost of generating and procuring electric energy is lower than in peak period. Additionally, extreme weather conditions may also require a critical peak price, which would reflect the cost of procuring energy when demand is highest. This kind of extreme weather we might encounter in summer at the height of holidays, or in the most frozen days of winter. For another example of TOU rate, an electric power supplier may offer a tariff that differentiates between several time periods throughout the weekday or weekends [4
]. In this research, dynamic pricing interventions are prompted based on weather forecasts.
The effects of dynamic pricing in electric energy demand response have been researched mainly in developed countries such as the United States, Canada, Japan, etc. Especially since the energy crisis of 2000–2001 in the western United States, dynamic pricing’s impact on electric energy consumption constraint has been receiving more attention. Faruqui and Sergici [5
] summarized in total 15 experimental studies related to dynamic pricing programs in the past. Many of those experiments were conducted in the United States. Almost all of those experimental studies are not fully based on Randomized Controlled Trials (RCT), and use TOU rates and critical peak pricing (CPP) tariffs to stimulate consumers to lower their usage. According to Faruqui and Sergici [5
], TOU rates and CPP tariffs were reported to bring about a drop in peak demand from 3% to 6% and from 13% to 20%, respectively. Most of the preceding studies applied panel data to analyze results, and the effects of dynamic pricing were observed by the difference-in-differences (DID) estimator, utilizing the treatment group with the dynamic pricing rate, and the control group with existing rates. Even when applying the DID estimator, unless the participants are divided into two groups at random to receive interventions, the experiment has a quasi-experimental design where “the cause is manipulable and occurs before the effect is measured” (Shadish et al. [6
], p. 14). Regarding dynamic pricing rates, the previous studies tended to adopt multiple price points, which allow predicting not only the impact of one given rate in the study but also other rates, rather than a single time-varying rate.
The recent field experiment of Wolak [7
] is one of the RCT experimental studies which have a strong impact. According to his finding, while CPP tariffs were reported to bring about a 13% reduction of electric energy consumed at peak period, critical peak rebate (CPR) tariffs reduced the consumption by only 5.3%.
Nonetheless, a big question of whether energy-saving effects persist or not still remains. According to Frey and Rogers [8
], persistence is caused by forming psychological habits, changing the way people think, changing future costs, and utilizing external reinforcement. With regard to this issue, starting with the Home Energy Reports (HERs) of the Sacramento Municipal Utility District Electricity Company (SMUD, Sacramento, CA, USA), there have been dozens of experiments conducted. Summarizing the transition of HERs, Khawaja and Stewart [9
] found that if it is assumed that the load curtailing effect of the last year of a three-year experiment was 100%, the effect of the first year was 57%, and that of the second was 87%.
Allcott and Rogers [10
] analyzed the persistence of HERs’ effect in detail, based on the data of three demand response programs. All these programs had a population comprising approximately 70,000–80,000 households, who were comparatively heavy energy users. The population was randomly appointed to treatment and control groups. The treatment group was also randomly assigned to several groups which received reports with difference frequencies. The estimation results disclosed that for the group which stopped receiving reports after two years of the experiment, the effect was gradually diminishing. However, the effect still retained its persistence during the 2 years following the withdrawal.
Apparently, the ideal approach to verify the persistence of a demand response program is to lengthen experimental periods, yet due to limits of budget and difficulties in acquiring approvals of projects, it is arduous work to keep an experiment running for a long time. To tackle this issue, this study makes a new approach, which is explained at the end of this section.
In Japan, demand response programs are still in their foundation stage. There are several experimental studies on electric demand response and dynamic pricing that have been carried out lately, such as in Kyoto, Yokohama, Toyota, and Kitakyushu, the four major smart-community projects currently being sponsored by the government. The impact of dynamic pricing is tested through smart meters and visualization systems. In some projects, the visualization websites are connected through tablets provided to participants in order to receive notification of critical peaks. These systems give the participating households an opportunity to confirm their point balances. The participants are given a certain amount of points, which are then subtracted according to their peak hour demand. They are then paid an incentive payment based on the remaining point balance after the experiment period. One of the most considerable experiments is the smart city operational experiment in Keihanna city (Kyoto) during 2012–2013. In the experiment, a combination of TOU rates and CPP was used in both the summer and winter. That study demonstrated that in comparison with summer, the winter consumption reduction through TOU improved from 8.2% to 14.9%, but diminished from 14.1% to 6.4% through CPP (Japan Smart City Report [11
On the other hand, there is another experimental study conducted in Keihanna city by Ito et al. [12
]. In the study, they focused on the comparison between CPP tariffs and non-monetary “nudges” (or moral suasion). The result suggested that while CPP tariffs had a treatment effect of approximately 16%, non-monetary incentives only helped reduce consumption by 3%. The authors assessed the durability of moral suasion and economic incentives by comparing the effects of repeated interventions. Moreover, to investigate the persistence of the experiment, they compared the consumption of treatment and control groups during post-experimental period. However, there is a limitation in their estimation. Although they applied an excellent design of a random controlled trial, their regression model did not appear to have sufficient components of the difference in differences method. Their main empirical equation is the following:
Ln xit = αMit + βEit + θi +λt + μit
In this equation, Mit equals 1 if the household is in the moral suasion group and receives the treatment; Eit equals 1 if the household is in the economic incentive group and receives the treatment. In other words, these two variables are the interaction terms of the treatment group and experimental period dummies, which should have also been included in the regression model.
There is also a study by Shimada et al. [13
] that evaluated the impacts of real-time feedback (“nudges”) and dynamic pricing on management by using the daily electric energy consumption data in Nushima. In this study, the authors used a quasi-experimental research method, which used pre- and post-test design. They compared the difference between the before and after experiment periods to evaluate the effects of dynamic pricing. They found that real-time feedback and dynamic pricing were effective in controlling electric energy demand. Specifically, when the frequency of access to tablet PCs reached three times per day, the estimated reductions of feedback and dynamic pricing were 20% and 2% respectively. However, a query on the method of calculating dynamic pricing effects can be raised, since those effects were estimated as the difference between the reduction rates of the period applying feedback—pricing and of the period which applied only feedback. Precisely, these two periods’ characteristics are different from each other (much like spring and summer electric energy consumption behaviors are absolutely dissimilar). In addition, according to Torgerson and Torgerson [14
], pre- and post-test studies have some limitations, since they will over-estimate any benefit of a policy because of “regression to the mean effects” and “temporal changes”.
This study is an analysis which extends the study of Shimada et al. [13
] to investigate the impacts of dynamic pricing more accurately, through adopting a control group and a treatment group. Although this study inherits the originality of establishing tariffs based on the weather (sunny, cloudy, rainy), which is supposed to dominate the solar photovoltaic generation system, it focuses on the effects of pricing based on the premise that visualization’s effects were already excluded. Therefore, this study picks up the short periods when the subtraction rates are applied to compare with the same-length-period right before and after them, and conduct analysis based on hourly data.
The participants were divided into control and treatment groups by random selection. Nevertheless, unlike previous studies, this study attempts a new approach to examine the persistence or habit formation in post-experimental period by making an inverse change between the two groups in the winter’s experiment.
This study also adds hourly effects as life-style factors on demand constraint. Moreover, during the experiment the fact that there are households who do not pay attention to the pricing system at all was noticed, and thus the study also assesses the frequency of access’s marginal effect, which has not been done in previous studies. The following hypothesis can be raised: both frequency of access and dynamic pricing have negative impacts on electric energy consumption, and these impacts are persistent over long periods of time.
4. Policy Implications
At the hypothesizing stage of this study, there are two kinds of policy that would have potential for electric energy demand management at the household level, in remote islands where solar photovoltaic generation is considered for utilization. One policy is adjusting marginal price in accordance with energy-generating capacity, which fluctuates due to the weather or solar radiation of a day. The other policy is having consumers form energy-saving habits by letting them undertake a treatment, which provides an incentive payment for a while. In fact, this experiment is the combination of these two policies over a short time of two weeks.
On one hand, habit formation which is confirmed in the post-experimental periods hints at an approach to turn energy efficiency and conservation into consumers’ daily life styles. This policy requires an initial investment in smart meters and feedback technologies, and incentive payments during trial periods. However, since the energy-saving habits once formed are sustainable, this policy can be applied for a small and remote community like Nushima Island, where electric energy supply is volatile due to the market liberalization.
On the other hand, it still remains unexplained what kind of dynamic pricing design is appropriate for the price elasticity of electric energy demand. This kind of intervention should be a short-term means to provide a hook of habit formation, since consumers cannot curtail consumption when it comes to their limit. Once habit formation is confirmed, the intervention should be cancelled or be loosened. Nevertheless, whether habit formation occurs even after withdrawing interventions or not also depends on the culture and characteristics of the consumers and regions. For instance, since a major proportion of the households in Nushima use only electricity as their domestic energy, they have a strong incentive to reduce their electric energy usage. These characteristics may not apply to other areas. Therefore, the determination of the intervention’s duration should be taken discreetly in accordance with other regional characteristics.
Although the so-called dynamic pricing through real-time information feedback has been studied in several field experiments so far, this study explores its potential using the perspective of remote islands which attempt to utilize solar resources as a basis for energy policy.
As discussed in the theoretical framework, a demand response system helps not only electric energy suppliers but also their consumers to achieve better ends, while promoting renewable energy and energy conservation to mitigate global warming. The regression results of Section 5
also suggest that the dynamic pricing is effective in constraining the energy demand of consumers by reducing their electric energy usage by 13.8%. However, in opposition to the expectation that the more frequently households access the visualization system, the more strongly the demand-constrained effect is stimulated, the result is that households tend to increase their consumption by 9.2%–10.7% per time of access.
Using the new approach to verify habit formation or persistence of dynamic pricing by demonstrating an inverse change between the control and treatment groups in the second trial, the results confirm that the intervention is persistent and energy-saving habits were firmly formed among participants of the initial treatment group. Since the two groups had an inverse change between each other, the insignificant coefficient of DID implies that even though the winter treatment group underwent the treatment, they could not curtail consumption by less than the former treatment group’s consumption; thus the former treatment group still retains their energy-saving habits.
Regarding the responses of the households to change their deduction rates, the result is not consistent with the hypothesis of demand elasticity to price. The regression results suggest that the consumers respond to an average final incentive payment as a whole, rather than to marginal changes of deduction rate.
The findings on dynamic pricing effects, habit formation and households’ reaction toward deduction rates shed some light on policy implications for remote islands. Specifically, this load-curtailing intervention, which is accompanied with incentive payments, has potential in creating a policy shock that leads to consumers forming energy-serving habit.