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Bejan, Ioana; Jensen, Carsten Lynge; Andersen, Laura Mørch; Hansen, Lars
Gårn
Working Paper
The hidden cost of real time electricity pricing
IFRO Working Paper, No. 2019/03
Provided in Cooperation with:
Department of Food and Resource Economics (IFRO), University of Copenhagen
Suggested Citation: Bejan, Ioana; Jensen, Carsten Lynge; Andersen, Laura Mørch; Hansen,
Lars Gårn (2019) : The hidden cost of real time electricity pricing, IFRO Working Paper, No.
2019/03, University of Copenhagen, Department of Food and Resource Economics (IFRO),
Copenhagen
This Version is available at:
http://hdl.handle.net/10419/204432
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The hidden cost of real time
electricity pricing
Ioana Bejan
Carsten Lynge Jensen
Laura M. Andersen
Lars Gårn Hansen
2019 / 03
IFRO Working Paper 2019 / 03
The hidden cost of real time electricity pricing
Authors: Ioana Bejan, Carsten Lynge Jensen, Laura M. Andersen, Lars Gårn Hansen
JEL-classification: L51, L94, C93, Q41
Published: May 2019
See the full series IFRO Working Paper here:
www.ifro.ku.dk/english/publications/ifro_series/working_papers/
Department of Food and Resource Economics (IFRO)
University of Copenhagen
Rolighedsvej 25
DK 1958 Frederiksberg DENMARK
www.ifro.ku.dk/english/
The hidden cost of real time electricity pricing *
Ioana Bejana,
Carsten Lynge Jensena
Laura M. Andersena
Lars Gårn Hansena
a
University of Copenhagen, Institute of Food and Resource Economics, Rolighedsvej 23, DK1958 Frederiksberg C, Denmark (cpz597@alumni.ku.dk, clj@ifro.ku.dk, lma@ifro.ku.dk,
lgh@ifro.ku.dk).
April, 2019
Abstract
In theory real time pricing ensures more efficient electricity markets than time of use pricing.
However, people are prone to habits and regularity, so real time pricing may impose a greater
cost of reacting on consumers. In a randomized field experiment we compared the cost of
reacting to incentives under these two pricing regimes. We utilized smart-metered hourly power
consumption to unobtrusively measure treatment effects. We found that real time pricing reduces
consumer surplus from reacting to incentives by half, compared to reacting under a
corresponding time of use pricing regime. This suggests a substantial economic value to
households of the regularity and predictability provided by time of use pricing.
JEL Classifications: L51, L94, C93, Q41,
Keywords: real time electricity pricing, time of use electricity pricing, field experiment,
household cost of reacting.
*We are grateful to Innovation Fund Denmark and the University Of Copenhagen for supporting the research
leading to this paper, and to the SE Power Company for making it possible to conduct the field experiment.
Declarations of interest: none.
I. Introduction
Imperfections of the regulated electricity markets have been studied by many economists
beginning in the early 1950’s (e.g. Houthakker 1951, Boiteux 1960) theoretically and later
empirically. Allcott (2013) points out that distortions in the electricity markets are often caused
by the way in which regulated utilities price their services. In the long run, traditional flat rate
pricing leads to overinvestment in capacity if high peak supply is to be ensured (Borenstein
2005). Economic theory (e.g. Borenstein, Jaske, and Rosenfeld 2002) suggests that this excess in
investment of capacity could be avoided if utilities used real time pricing (RTP) in retail markets.
High peak prices would create an incentive for consumers to shift usage to lower price periods.
Real time pricing would also help to accommodate variable supply sources such as wind and
solar power generation, benefitting the environment and human health (De Jonghe, Hobbs, and
Belmans 2011). In addition, a price responsive demand would drive overall costs down and
discourage generators from exercising market power during high demand periods, as these
instances would be reduced (Borenstein, Jaske, and Rosenfeld 2002).
The underlying assumption on which RTP theory is based is that the consumer costs of reacting
to dynamic price variation is low, implying that consumers react to varying prices and that it is
efficient to expose them fully to the power systems real time price variation. The theoretical and
simulation work of Borenstein and Holland (2005) found that under this assumption, pricing
schemes that are not allowed to vary over time for all consumers according to wholesale market
cost fail to achieve first-best, even second-best price and investment level optimality. Allowing
all consumers to see real time prices leads to a Pareto efficient allocation in the short-run and to
long-run efficiency of capacity markets.
However, if consumers incur transaction costs in connection with observing prices, planning and
implementing their reactions to price changes, then fully dynamic pricing may not be efficient. In
fact, there is substantial evidence that we are prone to regularity, routine habits in our daily lives1
and e.g. Macey and Brown 1983, Ehrhardt-Martinez 2011, Allcott and Rogers, 2014, Ito et al.,
2018, suggest that this is also the case for activities that use power. If this is so, it may be costlier
for consumers to react to power price changes that occur irregularly and that cannot be predicted
compared to changes that occur regularly and can be predicted. It is the size of these transaction
costs from reacting to RTP compared to time of use (ToU) pricing that we investigated2. In this
1
This literature includes transportation choice (e.g. Aarts, Verplanken and Knippenberg 1998, Thogersen 2006,
Gardner 2009), shopping behavior (Sheth and Venkatesan 1968, Chiu et al. 2012), recycling (e.g. Tonglet et al.,
2004), exercise (Charness and Gneezy, 2009) water conservation (Ferraro et al., 2011).
2
In addition, RTP exposes consumers to risk which implies a welfare cost if consumers are risk averse. There are
also concerns about the wealth redistribution that RTP may cause (Borenstein, Jaske, and Rosenfeld 2002). We leave
these concerns aside.
2
paper we quantify consumers’ reaction to ToU compared to RTP tariffs and from this we derive
the resulting relative consumer utility loss.
We designed a field experiment that allows us to compare reactions to ToU and RTP tariffs. All
participating households were given incentives in the form of rebates, either to increase or
decrease electricity consumption during the same specific 3-hour time slot each day. The only
difference between the two treatments was how the direction of the incentivized consumption
change was allocated. In the first treatment, hereafter called the ToU treatment, half of the
households got the rebate to increase consumption; while the other half of the households got the
same rebate to decrease consumption during the same time slot. In the second treatment, hereafter
named the RTP treatment, the same rebate was applied during the same time slot, but the
instruction to increase or decrease consumption changed randomly from one day to another.
As a result of the randomized allocation, households in either treatment had the same range of
possibilities of response to incentives (e.g. by changing the timing or scale activities that use
power, such as doing the laundry, dish washing, cooking, watching TV, etc.). Intuitively, the key
difference between the two treatments was that households in the first treatment could plan
ahead, adjust their habits and possibly their appliances because they knew that they would get the
same incentive every day for the whole duration of the program. For households in the second
treatment this was more difficult because incentives arrived in an unpredictable, randomized way
as is the case with RTP incentives. Keeping in mind that the time of treatment for all the
households was the same, the experiment design hence enabled us to investigate whether the
households in the two groups had the same transaction costs for the reaction to the treatment.
By utilizing smart meters, we unobtrusively measured power consumption in our treatments. This
allowed us to estimate the effect of the the two tariff structures. During the first two months of
the experiment, we found that the RTP treatment induced half of the response compared to that of
the ToU treatment. Assuming standard functional forms, this translates into a 50% reduction in
consumer surplus under ToU tariffs when consumers instead are subject to RTP tariffs where
habit adjustment and predictability is impeded. The advantage of RTP tariffs is that marginal
costs of electricity production are precisely signaled to consumers. In theory RTP tariffs make it
possible to generate a welfare gain from reallocating power production and consumption that can
be shared between electricity consumers and utility owners. The hypothesis tested is that there
may be important downside in the form of greater consumer transaction costs from reacting RTP
tariffs that should be taken into account. Our result suggests that the optimal tariff structure is
likely a combination of ToU and RTP tariffs, with ToU pricing possibly being the cornerstone of
such a tariff system. Our results are, of course, particular for the electricity supply area and field
setting we investigated. Nevertheless, the incentives in our experiment potentially affected the
broad set of repeated household activities that use power such as cooking, doing the dishes,
3
laundry, watching TV, etc. Thus, if seen in the broader context of inducing changes in household
behavior, what we found for southern Denmark suggests that electricity consumers generally
could have substantial added transaction costs from reacting to RTP tariffs.
Compared to the substantial body of literature on the impact of electricity pricing experiments3,
the innovation in our design is that we combine fixed and dynamic tariffs in a randomized trial so
as to be able to control for confounders, making it possible to identify the cost to households for
reacting to RTP vis-à-vis ToU tariffs. To our knowledge, this has not been attempted before.
In the following section we discuss the reasons for expecting increased transaction costs of
reacting to RTP, compared to ToU pricing and how we will estimate the effects on consumers’
utility to switching tariff systems. The third section provides a description of the field
experiment. The fourth section presents and discusses our results and the fifth section draws the
conclusions of the paper.
II. The cost of reacting to RTP tariffs
Habits allow us to expend less cognitive effort on life’s smaller decisions, thereby reducing the
conscious decision-making costs associated with that behavior. We often do the same things in
the same way without thinking much about it. It may, for example, be efficient to start the
washing machine in the morning before going to work and then loading the dryer first thing when
returning from work. If this way of doing the laundry becomes a habitual part of the day’s
activities, it’s done without much planning or deliberation and with a small risk of forgetting.
Thus, habits imply lower cognitive costs of repeated behavior in connection with household
production; but probably also imply reduced day-to-day responsiveness to changing conditions
around the activity governed by a habit. However, when permanent changes in the conditions are
perceived (e.g. a higher price of electricity in the peak load period), habits can presumably be reoptimized to take them into account. Verplanken et al., (2008) argues that new habits are formed
after a disruptive change in the context in which the habits used to be activated (e.g. the birth of a
child in the household, moving home). Allcott and Rodgers (2014) study suggests that such
disruptive context changes needn’t be dramatic. They find that introducing a nudge in the forms
of the O-power energy report feedback to home owners, affects behavior through a combination
of changes in energy utilization habits and changes to physical capital stock. Ito et al., 2018, who
finds that changes in electricity rates can induce changes in habits associated with electricity
consumption.
3
Studies investigating reaction to time of use pricing are numerous (see, e.g. Faruqui and Sergici, 2010 for a
comprehensive review of electricity pricing experiments) and more recently, a number of experimental investigations
using real time pricing have been published (see e.g. Wolak, 2011, Kessels et al., 2016).
4
In terms of the habit model of Becker and Murphy (1988), it may be possible for a household to
invest in a re-optimization of its stock of habits when electricity prices change. However, such a
re-optimization is costly (in terms of effort) and so it is more likely to be worthwhile if the
change in conditions is long lasting, than if the change is temporary. This implies that the
marginal cost of reacting to a change in the conditions relevant for a repeated power using
activity, likely depends on whether the change is anticipated to be temporary or permanent. When
reacting to a temporary change in the electricity price under a RTP- tariff system, habits may not
be re-optimized. If the household is to react to the price change, it must deviate from the
consumption pattern for which habits are optimized. This entails the use of cognitive resources to
evaluate, plan and execute the deviation each time a price change is announced, which may result
in a smaller reaction to the price change. When reacting to a price change resulting from
introducing ToU tariffs, the change in price is permanent (or at least long term). Re-optimizing
habits to take the changed conditions into account may be worthwhile. If habits are re-optimized,
this reduces the marginal cognitive cost and therefore increases the net benefits of reacting to the
price change. This implies a stronger reaction to a given change in price, if it is implemented
under a ToU tariff system compared to the same price change under a RTP tariff system.
We illustrate this in
Figure 1, where electricity demand (i) during a given peak period of the day is depicted along the
x-axis and marginal utility in monetary units along the y-axis. The current flat rate price of
electricity which also applies during peak time is p and 𝑖0𝑃 indicates the scale of peak time power
use at this price, for which we assume the household’s habits are also optimized. Let U’(i,h) be
the marginal utility from power consumption when habits are optimized for consuming h kWh of
electricity during the peak. The broken curve U’(i,𝑖0𝑃 ) in Figure 1 is the falling marginal utility of
power consumption as peak consumption increases when habits are optimized for consuming 𝑖0𝑃 .
The solid line curve U’(i,i) in Figure 1 is the marginal utility curve that applies when habits are
adjusted to the current scale of peak time consumption. By definition, the curves cross at i and
intuitively since adjusting consumption up or down from i is easier when habits are also adjusted
the U’(i,i) curve is flatter.
5
Figure 1: Reactions and utility gain under ToU and RTP pricing.
Consider electricity consumption on a day after the introduction of a simple ToU tariff that
lowers the peak time price to p-r (while the keeping the off-peak electricity price unchanged at
p). Because ToU tariffs allow planning and habits to adjust, consumer reactions follow the flatter
U’(i,i)-curve and electricity consumption increases to 𝑖+𝑇 . Now consider electricity consumption
on a day after the introduction of a simple RTP tariff that sometimes lowers the peak time price
to p-r (while the keeping the off-peak electricity price unchanged at p). Because RTP tariffs do
not allow planning and habits to adjust, consumer reactions follow the steeper U’(i,𝑖0𝑃 )-curve and
so only increase consumption to 𝑖+𝑅 . Thus, the consumer surplus derived from implementing
correct peak electricity prices using ToU tariffs is the sum of the light grey and dark grey areas
(denoted 𝑈𝑇 (r)), while the consumer surplus derived from using RTP tariffs is only the light grey
area (denoted 𝑈𝑅 (r)). Thus, the utility value of being allowed to adjust habits to the new benefit
curve is the dark gray area (that is 𝑈𝑇 (𝑟)-𝑈𝑅 (𝑟)). The corresponding symmetric social welfare
value for implementing correct high peak prices is also illustrated. A useful measure is the
proportion of the welfare gain of using RTP tariffs (the light grey area), to welfare gain from ToU
tariffs (the light and the dark grey area) when the same incentives are implemented:
U R (r ) / U T (r )
(1)
We call this the relative consumer value of RTP because it indicates the proportion of consumer
surplus welfare lost when implanting incentives using RTP instead of ToU. In order to identify
relative consumer value of RTP, we undertook a field experiment where we induced an
6
increase/decrease in the price of electricity of r per kWh electricity consumption. As an
approximation we measured:
iR iR
iT iT
(2)
This approximation holds exactly if we assume that habit adjustment implies a proportional
increase in the scale of reaction to any given price incentive (see Appendix 1). Given that we
don’t know the underlying functional forms this seems a reasonable approximation. In our
experiment, iR iR is the treatment effect estimated in the RTP treatment and iT iT is the
treatment effect estimated in the ToU treatment.
III. Description of the experiment
The field experiment was conducted in Denmark by the electricity utility company SE 4. A sample
of 2,625 households was randomly selected5 from SE’s customer database and invited to join the
MovePower program6. All customers received an invitation E-mail (see Appendix 2) during April
2014 asking whether they would be willing to participate in the program. In this E-mail, all
households received the same general information about MovePower (that they would receive
suggested time slots for moving their power use through text messages (SMSs)). After receiving
their formal consent to participate, the households were randomly allocated into treatments and
only then given more specifically detailed information about each treatment (see Appendix 3 and
Appendix 4).
In total, 93 customers agreed to participate in the MovePower program7. Two households were
removed from the analyzed sample ex post due to faulty remote measurement meters8. Both
treatment groups were sent SMSs requesting them to increase or decreases some of their daily
power consumption in the same 3-hour time slot (between 20:00 and 23:00). The first text
messages were sent on the 27th of May 2014. We analyzed the households’ reaction to the
experiment up until the 31st July 2014. Both treatment groups were given the same types of
incentives in the form of rebates. The proportion of text messages with rebates for increasing
consumption during the 3-hour time slot and for decreasing consumption was the same in ToU
4
More information about the utility company SE can be found on their website: https://www.se.dk/.
Prior to randomization, businesses and seasonal dwellings were excluded.
6
SE’s database of customers who give SE permission to contact them contains 40,490 of SE’s more than 247,010
customers in Southern Denmark. Of the 2,625 invited households, 1,175 received the invitation by e-mail, while
1,345 received the invitation by letter.
7
In total, 131 households signed up, but of these, 38 were allocated to treatments not reported in this paper.
8
Including them in the analysis with the 18 days and 20 days out of 92 days observation periods where remote
measurements reported power consumption has a negligible effect on the results. We chose to remove from the
analysis because meter errors that result in report fallout of this magnitude might also corrupt data when meters are
reporting. We have no reason to believe that metering errors are affected by treatment allocation.
5
7
and RTP treatments (50% in each direction). Treatment groups differed, however, in the
proportion of increase/decrease instructions given to the individual household and in the
frequency with which the instructions were given. In the ToU treatment group, every household
received instructions to shift power use in the same direction on all days. Households in this
treatment were randomly allocated to one of two sub-groups (50% of households in each): one
sub-group would always receive a rebate for decreasing power usage in the time slot (ToU1 subgroup), while the other group would always receive the rebate to increase power usage in the time
slot (ToU2 sub-group). Households in the ToU treatment received one SMS per week reminding
them of their incentive. In the RTP treatment, each household also received a rebate each day if
they shifted power use during this time slot, but we randomly varied whether they should
increase or decrease consumption in order to receive the rebate. All households in this treatment
received one SMS daily.
The exact pricing incentives were varied across households, but we ensured in our randomization
equal allocation of incentives in all treatments9. Finally, an equal proportion of households in
each treatment were also notified that SE would increase sustainable electricity production in
proportion to the amount of electricity they moved within the treatment period (Table 1).
Table 1: Summary statistics on monetary and environmental incentives
Treatment
No. of
households
Average rebate awarded on
treatment days
(DKK per kWh moved)
Proportion of households also told
that moving power would increase
sustainable electricity production
ToU
47
1.02
38%
RTP
44
1.07
75%
To check the randomization,
9
Some households received a rebate (0.50 DKK, 1.00 DKK or 1.50 DKK) per kWh power moved in accordance
with the text messages. 1 DKK is approximately equivalent to 0.15 USD (average exchange rate during April 2019)
8
Table 2 presents various summary statistics on power consumption before the treatment and the
dwelling type for the two treatment groups.
9
Table 2: Summary statistics on demographics and power consumption for treatment groups.
Treatment
group no. &
type
No. of
households
Average daily
power
consumption*
(in kWh)
Average
power consumption:
hours 20:00 – 23:00*
(in kWh)
Average
birth
year**
Type of
dwelling
Share of
house
ToU
47
4.386
1.551
1959
96%
RTP
44
4.491
1.587
1957
94%
* Before treatment, during the period of the 1st May 2014 to the 26th May 2014.
** Birth year of the person in the household who signed the household up to the MovePower program.
The resulting differences after randomization are small (and statistically insignificant), which
suggests that the randomization had been successful. When estimating the treatment effects
below, we control for remaining differences before treatment power consumption and variation in
power use over time by including household and time-specific fixed effects.
Figure 2 presents daily observations of mean consumption for each of the two treatments during
the relevant time slot before and during the experiment. If randomization has been successful, we
would like these to follow each other closely both before the experiment, which we see is the
case. If the effect of incentives given to decrease and to increase power consumption within each
treatment net out, the plotted mean power demand for each treatment group should also follow
each other during the experiment, which we see is also the case.
Figure 2: Daily mean power consumption (during the hours 20:00 – 23:00) ToU and RTP
treatments
2,5
kWh at hours 20-23
2
1,5
ToU
RTP
1
0,5
0
1-May
26-May
20-Jun
15-Jul
10
IV. Estimation and results
Before the estimation results, we present graphs depicting the performance of our treatment
groups before and after the start of the experiment. In Figure 3, we present the daily mean
consumption during the peak for each of the two sub-treatments in the ToU treatment. We see
that mean consumptions in the two ToU sub-groups follow each other reasonably well during the
pre-experiment period, indicating that randomization of households into ToU1 and ToU2 subgroups was successful. Further, mean consumption then deviates in the expected direction after
the start of the experiment with households in ToU1 increasing consumption and the households
in ToU2 treatment decreasing consumption, in accordance with the incentives they got during the
experiment.
Figure 3: Daily mean power consumption (during the hours 20:00 – 23:00) of the two ToU
treatments
2,5
kWh at hours 20-23
2
1,5
ToU (high peak)
ToU (low peak)
1
0,5
0
1-May
26-May
20-Jun
15-Jul
In Figure 4, we present the daily mean of peak time consumption for the RTP treatment
households. Before the experiment, the mean is taken across all households. During the
experiment, each days observations are divided into two: those for households that on that day
received an incentive to increase consumption (low peak), and those for households that on that
day received an incentive to decrease consumption (high peak). We see some indication that
11
consumption deviates in the expected direction, though much less so than for the ToU
treatment10.
Figure 4: Daily mean power consumption (during the hours 20:00 – 23:00) of the RTP
treatments
2,5
kWh at hours 20-23
2
1,5
RTP (high peak)
RTP (low peak)
1
0,5
0
1-May
26-May
20-Jun
15-Jul
10
We also see a substantial increase in consumption variance at the start of the experiment. This is because the preexperiment sample is twice as large as the two sub-treatments, post-experiment which increases variance of the
mean.
12
Table 3 presents the average power consumption in the time slot of treatment for the 2-months
treatment period, and for the 1-month just prior to households receiving the invitation to join the
Movepower program11. For the RTP treatment group and the two ToU treatment sub-groups, the
average power consumption during the treatment time slot is calculated for the pre-treatment
period (third column). For the treatment period, the average consumption during the treatment
time slot is calculated for days and households receiving incentives to increase/decrease power
usage separately (the fifth column). Finally, in the last column, we present a raw calculation of
the diff-in-diff treatment effect (the difference between the two incentives directions are
calculated as the difference between pre and during treatment power consumption).
The period before treatment runs from 1st May 2014 to 26th May 2014. The treatment period is from 27th May 2014
to 31st July 2014.
11
13
Table 3: Raw treatment effect result
Treatment
Before treatment
Daily average
power consumption
during 20:00 –
23:00 (in kWh)
ToU2
1.517
(27 households)
During treatment
Daily average power
consumption during 20:00 –
23:00 (in kWh)
low peak
1.557
high peak
No obs.
low peak
No obs.
high peak
1.334
low peak
1.595
high peak
1.480
Raw
Difference in
Difference*
0.302
ToU1
1.596
(20 households)
RTP
(44 households)
1.500
*The difference in difference figure is given by: (mean kWhlow peak,during
kWhlow peak,during treatment – mean kWhbefore treatment ).
treatment
0.115
– mean kWhbefore
treatment)
- (mean
14
Table 3, we see that power consumption before and during the treatment is similar, indicating that
seasonal variation has little influence. For each treatment group, the average consumption during
low peak treatment is higher than for that during the high peak treatment, which was expected.
Finally, the row presenting the difference in difference treatment effect for the ToU treatment is
0.302 kWh, while the corresponding treatment effect for the RTP group is 0.115 kWh, indicating
a substantially larger treatment effect for the ToU group. These treatment effects are, however,
not corrected for household-specific effects or seasonal variation. This limitation is addressed in
the following.
To estimate the treatment effects, the data is organized into a panel of 91 households with daily
observations of power use before and during the treatment period. The dependent variable in each
observation is the household’s power consumption in kWh during the 20:00 – 23:00 timeslot
normalized by the household’s sample mean consumption one month before the intervention,
during the same timeslot. The explanatory variables are fixed effects for each household and for
each day, a dummy indicating whether there was any treatment (high peak or low peak) on that
day and a dummy indicating whether the treatment on that day was low peak. The normalization
implies that daily fixed effects, and stochastic variations as well as the treatment effects are
assumed to be proportional to mean household consumption. The following specification was
estimated for each of the treatment groups in order to identify the treatment effect:
15
yid i d 0 TREATid 1 LOWid id
(3)
Where
yid
is normalized power consumption during the hours 20:00 – 23:00 of ith household on day d,
i is a fixed effect for each household,
d
is a fixed effect for each day,
TREATid
LOWid
is a dummy for days the ith household is given either low peak or high peak treatment,
is a dummy for days the ith household is given low peak treatment (the treatment effect),
0 is the estimated parameter to TREAT for each treatment,
1 is the estimated parameter to LOW for each treatment,
id is a stochastic error term.
We include household-specific fixed effects in our estimation to control for sampling variation
across treatments, and date-specific fixed effects to control for weekly and seasonal variation.
The parameter 1 indicates the difference in power use on days with low peak and high peak
notifications - this is the treatment effect we want to estimate. The size of this treatment effect for
the ToU and RTP treatments corresponds to i1T i0T and i1R i0R respectively in equation (2).
The estimation is conducted using the OLS procedure in STATA, using cluster-robust standard
errors. It is likely that the error terms are correlated across time for the same household, which
the cluster-robust standard errors correct for when we cluster on individual households (Cameron
and Trivedi 2010).
16
Table 4: Estimation results
Treatments
LOW treatment days,
1
ToU
0.351***
(0.086)
RTP
0.182***
(0.051)
-0.304
(0.210)
-0.157
(0.150)
Yes
Yes
Yes
Yes
4321
4045
47
44
0.110
0.105
ALL treatment days, 0
Fixed effect day, d
Fixed effect household, i
Observations
No. of households
R-squared
H:
1
ToU
1
RTP
(Two tailed t-test)
1
1
H: ToU
(One tailed t- test)
RTP
Standard errors are reported in parentheses.
p= 0.088 *)
P= 0.044 **)
*** indicates that the parameter is significant at 1% level.
**) t-test on hypothesis cannot be rejected at a 5% level.
*) t-test on hypothesis cannot be rejected at a 10% level.
As presented in
17
Table 4, both estimated treatment effects ( 1 ) are significant at the 1% level. In addition, the
ToU treatment effect is significantly greater than the RTP treatment effect at the 5% level (one
sided test). We may use (2) to calculate:
Relative consumer value of RTP=
iR iR 0.182
UR
0.52
UT
iT iT 0.351
This implies that impeding planning and adjustment of habits are relevant for the power using
activities when using RTP tariffs instead of ToU tariffs, all other things being equal, reduces the
consumer surplus of implementing correct prices by half. This is a substantial reduction. This
reduction may be an underestimate because of the slightly greater incentives (see Table 1) and
reminder frequency in the RTP treatment. On the other hand, the estimate excludes the one-time
investment costs that households in the ToU treatment incurred when they adjusted their habits to
the new tariff structure at the start of the experiment. However, if changes in the utility pricing
system are rare then these costs are negligible.
18
V. Conclusion
In this paper, we used a randomized field experiment to quantify cost of reacting to a RTP tariff
structure compared to ToU pricing. We found that implementing more transparent incentives
through RTP instead of ToU tariffs reduces the estimated consumer surplus by half - when the
investment cost of adjusting habits at the beginning of the experiment for the ToU group is
disregarded. The implication is that stability and predictability of the environments in which
power consuming activities are undertaken is important for consumer surplus and welfare. This is
an important drawback of RTP tariffs which must be weighed against the advantage of RTP
tariffs in allowing utilities to more precisely signaling the marginal cost of electricity production
to consumers. Our result suggests that the optimal tariff structure likely is a combination of ToU
and RTP tariffs and that ToU-tarrifs may be the cornerstone in the optimally combined system.
Looking forward, the need to integrate higher shares of renewable energy sources, especially
wind power in the Danish market and broader intermittent renewable sources in other energy
markets will require greater flexibility of consumers. A wide adoption of smart metering, inhouse automation and enabling technologies that can reduce transaction costs, seem relevant
avenues of future research.
19
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22
Appendix 1: Proportionality
In this appendix we show that assuming proportionality of demand reactions to a given price
change, under RTP and ToU tariffs, implies the same proportionality of the two consumer
surpluses generated from these reactions.
Consider a low peak initially subject to a uniform electricity price p, where a price reduction r, is
implemented through RTP tariffs. The first order condition for optimal household electricity
demand becomes:
p-r = U’(i,i)= U’(i)
(A.1)
This implicitly defines optimal power use as a function of the price reduction, i.e.:
i = f(r)= U’-1(p-r)
(A.2)
Thus, the increase in utility that results from a price decrease of r under a ToU tariff is:
U T (r)
r
( f (q) i )dq
0
(A.3)
q 0
In the same way, we define g(r) = U’-1(p+r, i0P ), whereby the increase in utility that results from
RTP implementation of a price reduction of r is:
U R (r)
r
(g(q, i ) i )dq
0
0
(A.4)
q 0
Now assume that habit adjustment implies a proportional increase in the scale of reaction to any
given price incentive r so that:
g(r) i0 ( f (r) i0 )
(A.5)
Inserting (A.5) in (A4) and then (A.4) and (A.3) we have that:
U R (r) U T (r)
(A.6)
implying that generated consumer surplus from reacting increases with the same proportion.
Since we measure iR- g(r) and iT- f (r) in our experiment we can identify:
iR- i0
iT- i0
(A.7)
In the same way we can identify from a corresponding price increase as:
i0 iR+
i0 iT+
(A.8)
23
Adding (A.7) and (A.8) and rearranging we get.
iR iR+
iT iT+
(A.9)
Thus, assuming proportionality of price effects implies the same proportionality of the resulting
consumer surpluses.
24
Appendix 2: Invitation letter
Three versions of the invitation were used. One promised participation in a lottery for an iPad,
the second asked for help with the transmission to green energy in Denmark, while the third did
both. Invitations were randomized across invited customers and the customers who were invited
by each invitation were randomized over treatments after recruitment. The invitation letter
(combined version) is presented below.
25
Win an
iPad
Read
the newsletter online
Become a test pilot - win an iPad* and help us with a
transition to more green energy in Denmark
Dear Customer
We need your help. Become a test pilot and help us find new ways to accelerate the transition to
green energy and at the same time take part in a draw to win an iPad to the value of 3,699
DKK*. As a test pilot, during the test period, you will receive text messages that tell you when it
is best to use power and what it will mean if you choose to move your consumption. For
example, you can change the time for when you wash clothes / turn on the dishwasher, etc. Of
course, it doesn’t mean that you have to cook roast pork at 3am. But moving the timing of your
power consumption just a little has many advantages. We call the trial MovePower.
Among SE's customers, there is widespread desire to promote green energy, which is
something we would like to satisfy. In Denmark, promoting a green transition through wind
energy seems the obvious thing to do, but wind power is difficult to use because there are large
fluctuations in production during the day. Therefore, it is important to get private households to
play a role so we can exploit wind energy better. Initially, only a limited number of customers will
be asked to take part in the pilot test. You have been selected as representative of a number of
our customers. It is important for us to gather as many different customers’ experiences as
possible. Therefore, your participation means a lot to us.
What is MovePower?
During the test period, you will receive text messages that tell you at what time of day it
is best to use power.
It is completely up to you whether you decide to change the timing of your electricity
consumption based on the information you receive or not.
MovePower will not affect your current electricity contract.
The extra service in MovePower is free.
MovePower also includes an additional offer of remote control. This is free, and you can
decide for yourself whether it's something for you.
Initially, it will be in the form of a pilot test, which last 1 year and only involves selected
Se customers.
26
If you would like to participate in MovePower, you can sign up on the following website:
www.se.dk/testpilot
Use Code: TWELVE DIGIT CODE1
If you don’t want to participate, let us know by sending a message on the following homepage:
www.se.dk/besked
Use code: TWELVE DIGIT CODE2
Kind regards
SE
P.S. If you have any questions about MovePower, you are welcome to contact us on the following
number: 7011 5095
*The winner will be drawn at the start of May 2014 and will be contacted directly. The
prize consists of an iPad Air 16GB and WiFi to the value of 3,699 DKK, which can be
exchanged for cash. Employees of SE are not allowed to participate-
SE | E-mail: se@se.dk | Edison Park 1 | DK-6715 Esbjerg N | Telefon +45 7011 5000 | Fax 7011 5001
27
Appendix 3: Terms of MovePower
Incentives were varied across households, but are randomized across treatments to ensure equal
incentives in all treatments. Some households (Group 1) received a rebate (0.50 DKK, 1.00 DKK
or 1.50 DKK12) per kWh power moved in accordance with the text messages. Other households
(Group 2) in addition to the monetary incentive received an environmental incentive where SE
promised to increase wind power generation by 0.50 kWh, 1.00 kWh or 1.50 kWh per kWh
power moved in accordance with the text messages. Below are the terms of MovePower for the
0.50 DKK and 0.50 kWh incentives.
Here are the terms of MovePower:
As a pilot test, during the test period, you will receive text messages that tell you at what time of day
it is best to use power.
(Group 1) If you choose to follow the recommendations you receive in the text messages and move
the timing of your electricity consumption, you will earn 0.50 DKK in discount for each kWh you
move. In this way, you will save money.
(Group 2) If you choose to follow the recommendations you receive in the text messages and move
the timing of some of your electricity consumption, you will earn 0.50 DKK in discount for each
kWh you move, SE will move 0.5 kWh of conventional electricity production to wind-based
electricity production for each kWh you move. At the same time, SE will move 0.5 kWh of
conventional electricity production to wind-based electricity production for each kWh you move. In
this way, you will save money and help to reduce the environmental impact.
If you choose not to react to the information you receive, nothing will happen. Whether you decide
to make use of the information and move the timing of your consumption is entirely up to you.
To give you an overview, once a month you will receive a text message telling you how many kWh
of your electricity consumption you have moved - as suggested in the text messages.
We calculate how many kWh you have moved through a comparison with your average power
consumption from the previous year.
(Group 1) If you earn a discount, you will receive the money when MovePower ends in one year.
(Group 2) If you earn a discount and a reduction in environmental impact, you will receive the
money and SE will increase the wind turbine capacity when MovePower ends in one year.
12
1 DKK is approximately equivalent to 0.15 USD.
28
MovePower is a limited pilot project and you will receive notification when the project starts and
ends.
If you would like to know more about how you can move the timing of your energy consumption, go to:
www.se.dk/FlytStroem
29
Appendix 4: Information given to subjects about how to move power
MovePower
In Denmark, promoting a green transition through wind energy seems the obvious thing to do,
but wind power is difficult to use because there are large fluctuations in production during the
day. Therefore, it is important to get private households to play a role, so we can exploit wind
energy better. This is what we are testing with MovePower, which encourages you to move the
timing of your consumption to when wind energy is available.
When should I move the timing of electricity consumption?
As part of MovePower, during the test period, you will receive text messages that tell you at
what time of day it is best to use power and what it will mean if you choose to move the timing
of your consumption.
The information in the text messages will sometimes be sent at short notice, but at other times
it will be sent several hours in advance, so how much time you have before you need to move
the timing of your electricity consumption will vary.
How to move the timing of your electricity consumption
To determine how to move the timing of your electricity consumption, it’s a good idea to think
about what would be easiest for you. For example, moving the start time of the:
• dishwasher,
• washing machine,
• tumble dryer,
or you could use the automatic timing for, e.g. the dishwasher. You can also postpone or
speed up the charging of electronic appliances, e.g. PC/iPads/mobile phones, etc. We hope
this has inspired you to move the timing of your electricity consumption.
30