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Energy Research & Social Science
journal homepage: www.elsevier.com/locate/erss
Original research article
Convenience before coins: Household responses to dual dynamic price
signals and energy feedback in Sweden
Isak Öhrlund
⁎
, Åse Linné, Cajsa Bartusch
Uppsala University, Department of Engineering Sciences, Division of Industrial Engineering and Management, Lägerhyddsvägen 1, Box 534, 752 21 Uppsala, Sweden
ARTICLE INFO
Keywords:
Demand response
Demand charge
Real-time pricing
Real-time feedback
ABSTRACT
To enable and cope with an increase of intermittent electricity generation, the power industry and regulators are
trying to incentivise users to use electricity at certain times through dynamic pricing and feedback. The effects of
such interventions on patterns of electricity use have been extensively studied, yet little is known about how and
why householders do or do not respond to such interventions. Using interviews and activity-based diaries, this
study provides a qualitative exploration of how and why householders who are subject to a demand-based time-
of-use distribution tariff, real-time retail pricing and real-time feedback, do or do not respond to such inter-
ventions in their daily lives. We find that the householders have adapted a range of existing practices and have
started to engage in new ones that aim to reduce peak demand during peak hours, partly without the support of
feedback. Drawing on theories of practice, we challenge common preconceptions about how and why price
signals work by demonstrating how the size of the financial incentive that a price signal provides does not have
that much influence on householders’ willingness to engage in demand response. We argue that price signals
work by providing new meanings to practices that use electricity, that feedback can mediate these meanings, and
that what matters for householders’ willingness to engage in demand response is that the changes they undertake
do not cause them any inconvenience by limiting the temporal flexibility of other doings in their daily lives.
1. Introduction
The balance between electricity supply and demand is becoming
increasingly difficult to manage as the amount of weather-dependent
generation and the electrification of products is increasing while eco-
nomically viable storage solutions are not readily available at the
market just yet [1,2]. To meet these new challenges, regulators are
pushing for new market structures to promote new business models,
services and technologies that can increase residential electricity users’
contribution to the balancing of the grid. Householders are thus in-
creasingly expected to provide the flexibility that the electricity in-
dustry is slowly losing.
A common approach to induce demand response (DR) is through
dynamic pricing (i.e. time-varying rates or price-based demand re-
sponse programs), which entails that at least part of the price that users
pay for electricity varies over time — the price for the supply of elec-
tricity, the distribution of electricity, or both. Although rarely men-
tioned in the literature on dynamic pricing, the latter scenario implies
that users may face more than one dynamic price signal at a time,
possibly contrasting and coming from different actors [3–6]. It all de-
pends on the structure of the electricity market in question. In markets
that have come far in their liberalisation process and have genuine
retail competition (i.e. customer choice) in place, users are free to buy
their electricity from any retailer (i.e. supplier or provider) of their
choice, while having it delivered by a given monopolistic distribution
system operator (DSO). This state of affairs opens up for dynamic pri-
cing at more than one end. For this to happen in practice, retail and
distribution costs must be properly separated and communicated
transparently [3]. Dynamic retail contracts, whose existence more or
less require that DSOs are sufficiently unbundled (i.e. separated from
any former supply business), must be available, and a sufficient number
of retailers need to exist in a market in order for it to be genuinely
competitive [7]. Technical systems such as “smart meters” and reliable
communication networks must also be in place to allow for dynamic
pricing. Sweden has come a long way in these respects, resulting in that
some users are already facing dual dynamic price signals. As we know
very little about how users respond to such scenarios and as other
countries are expected to follow Sweden’s example, the findings of this
study should be relevant to any electricity market already being de-
regulated or moving in that direction.
A number of different dynamic pricing schemes have been devel-
oped and tested around the globe, such as time-of-use pricing (TOU or
https://doi.org/10.1016/j.erss.2019.02.008
Received 3 July 2018; Received in revised form 8 February 2019; Accepted 13 February 2019
⁎
Corresponding author.
E-mail address: isak.ohrlund@angstrom.uu.se (I. Öhrlund).
Energy Research & Social Science 52 (2019) 236–246
2214-6296/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/BY-NC-ND/4.0/).
T
TOUP), critical or direct peak pricing (CPP or DPP), critical peak or
peak time rebates (CPR or PTR), variable peak pricing (VPP) and real-
time pricing (RTP). The main difference between them is how fre-
quently the price varies and how the price variations are communicated
(see, for example Darby and McKenna [2] and Albadi and El-Saadany
[8] for overviews). Tariffs may consist of several rate components and
the time-varying component(s) may be energy and/or demand-based,
meaning that the unit of cost is per kWh and/or per kW. Energy-based
tariffs are the most commonly used today, although the popularity of
tariffs including demand charges is increasing as DSOs strive toward
increasing the cost-reflectiveness of tariffs [3,9–11]. In addition, dy-
namic pricing schemes are often combined with “enabling” or “smart”
technologies such as in-home displays (IHDs) or online applications to
communicate prices and/or to provide feedback on consumption — all
with the aim of increasing DR even further.
The effects, in terms of demand response, of different dynamic
pricing schemes (primarily in the form of single energy-based tariffs,
with and without enabling technologies) on householders’ electricity
use in quantitative terms have been studied by many, but the numbers
vary widely between studies (see for example [12–15]). Some have
argued that the variations can be largely explained by differences in
conditions such as the peak to off-peak price ratio and/or access to
enabling technologies [14,16,17]. Others have argued that house-
holders’ responses to dynamic price signals are more complex, and that
we must look beyond the numbers to understand the socio-cultural
processes that determine how and why people use energy [18–27].
Doing so will not only help in understanding the variability between
studies, but more importantly, to improve the design of future demand-
side interventions. We will now provide an overview of important
works that have addressed the issue of whether, how and why house-
holders respond to dynamic pricing (with and without feedback) before
presenting our study. Given the nature of these research questions, this
account primarily covers qualitative studies.
1.1. Studies on how and why householders respond to dynamic pricing
Strengers [19] was one of the first to suggest that dynamic pricing
schemes work by creating new “social and cultural meanings” to ev-
eryday practices. Her analysis was based on interviews with 23
households (12 with an IHD) that took part in a trial of an energy-based
DPP scheme. She concluded that the householders had shifted the
majority of practices such as laundering, cooking, vacuuming, clothes
drying and ironing to off-peak hours in response to peak price events.
However, she found that their motive was rarely financial, but rather a
perceived social responsibility to act. This led Strengers to suggest that
there seem to be non-financial motivations behind householders’ re-
sponses to dynamic pricing.
Powells et al. [1] explored the practices of householders during peak
hours and the relative flexibility of those practices in response to TOU
pricing. Similar to Strengers [19], they found that “laundry, household
chores [including tumble drying, ironing and vacuum cleaning] and dish
washing practices were performed differently as a result of the introduction
of the tariff”, whereas other practices, such as cooking and watching TV
were not. Powells et al. [1] concluded that the householders had
changed the performance of a number of practices that were not spe-
cifically tied to any socially conventional times that would otherwise
constrain their temporal flexibility. Although certain practices were
identified as being more flexible than others, the authors noted con-
siderable variation between households in how these practices were
performed, and thus in how flexible they were. Torriti [28] also high-
lighted the “timing of energy-related practices” and pointed out that
cooking was the least flexible practice. In their investigation of 21 UK
households’ perceptions of TOU pricing, Murtagh, Gatersleben and
Uzzell [21] also found that the timing of cooking practices seemed to be
particularly difficult to change due to the temporal rhythm of society.
Similar to Strengers [19], they also found that householders expressed
non-financial motivations for wanting to respond to dynamic pricing,
such as doing something for the general good or because of a feeling of
shared responsibility.
Through interviews with eight Danish households involved in a trial
of electric vehicles (EVs), an energy-based TOU distribution tariff and
an energy-based RTP retail contract, Friis and Haunstrup Christensen
[29] explored householders’ responses to dynamic pricing. They found
that the householders had time-shifted laundry, dishwashing and EV-
charging practices in response to the TOU tariff. However, the house-
holders “found the real-time pricing scheme too complicated and time-
consuming to follow”, leading the authors to conclude that “[the RTP]
scheme did not affect the households’ electricity consumption.” [29]. In line
with previous studies, the authors found that “all households also ex-
perienced time constraints related to changing the timing of their daily do-
ings”, again suggesting that the temporal rhythm of society limit
householders’ ability to engage in DR.
Hargreaves, Nye and Burgess [22] provided one of the first analyses
of how householders respond to real-time feedback from IHDs through
interviews with 15 UK households. Although the householders in their
study were not subject to dynamic pricing, the topic was discussed with
the householders. In contrast to what Strengers [19] found, the
householders said they would “require significant financial incentives (…)
before consider[ing] changing the times of certain practices” — timings
which they had little control over anyway due to the temporal rhythm
of society [22]. Given that the householders had reduced their overall
electricity consumption in response to feedback from the IHDs, these
statements suggest that DR is viewed separately from energy saving,
because dynamic pricing introduces temporal restrictions that may
conflict with peoples’ everyday lives.
Barnicoat and Danson [30] also used IHDs in their study of how
elderly use electricity and their willingness and capacity to shift elec-
tricity use in time. The participants paid attention to the IHDs which
provided hourly feedback on electricity consumption and costs and
expressed interest in changing the timing of activities “to realise savings”
[30], but the IHDs themselves were not sufficient to bring about such
changes. Their feelings about dynamic pricing were mixed. Some were
willing to adapt to a TOU pricing scheme if it was easy enough, while
RTP was perceived as more problematic due to its time requirements
and temporal incompatibility with household routines.
1.2. Identified research gaps and the aim of our study
The previous studies have provided a number of insights into how
and why householders do or do not (or would or would not) respond to
dynamic pricing. Householders who were asked about their willingness
to respond to dynamic pricing in theory seemed to be rather unwilling
to respond, whereas householders who were subject to dynamic pricing
in real life had adapted a number of practices. Hence what house-
holders think they would do in response to dynamic pricing in theory
may not reflect what they would actually do if being exposed to it. This
calls for further studies on householders who have actually been ex-
posed to dynamic pricing. We must also recognise that the reviewed
studies had various ways of capturing the householders’ response to
dynamic pricing and feedback, which may have contributed to their
different results (c.f. Darby et al. [31]). Our review shows that motives
for responding to dynamic pricing vary and may not always be related
to financial gain, but also to what is percieved as being socially and
culturally expected. Furthermore, temporal constraints seem to be an
important barrier to DR and some practices seem to be more flexible
than others.
A number of questions do however remain unanswered and deserve
further attention. Firstly, even though a few studies have been carried
out in empirical contexts where both dynamic pricing and feedback has
been present, very little has been said about the specific interaction
between the two. As feedback is an essential part of dynamic pricing,
and both dynamic pricing and feedback may have an individual effect
I. Öhrlund, et al. Energy Research & Social Science 52 (2019) 236–246
237
on householders’ electricity use, we believe that it is important to ex-
plore how these two elements are taken into consideration by house-
holders who engage in DR. Secondly, none of the studies have examined
how householders respond to dual dynamic price signals. As further
liberalisation of electricity markets is encouraged worldwide [7,32,33]
and as regulators and market actors continuously strive to increase DR,
such scenarios may become more common, and since different types of
dynamic price signals seem to induce different degrees of response
[12–15], it is important to explore how householders deal with having
more than one price signal at a time. Thirdly, none of the studies have
studied the effects of demand-based tariffs, which are becoming in-
creasingly common as regulators and the industry strive toward more
cost-reflective pricing [3,9–11,34]. Since demand-based tariffs aim to
reduce individual consumption peaks rather than overall electricity
usage, they are more sensitive to how and when electricity is consumed.
Therefore, users’ responses to such tariffs might differ from their re-
sponse to energy-based tariffs.
Our study aims to expand and elaborate on the existing knowledge
about householders’ responses to dynamic pricing and to address these
gaps. We do so by providing a qualitative exploration of how and why
householders in Sweden, who are subject to a mandatory demand-based
TOU distribution tariff, real-time retail pricing and real-time feedback,
do or do not consider and respond to such interventions in their daily
lives.
1.3. Theoretical lens
As stressed in a number of the above studies, electricity use is an
outcome of what people do, and those doings are perceived to be more
or less negotiable depending on how they interconnect with the spatio-
temporal reality of domestic doings and society at large. These insights
and observations fit well with theories of practice in which both social
order and individuality are seen to result from “practices” that are
“temporally unfolding and spatially dispersed nexus[es] of doings and say-
ings” [35]. Practices as “entities” are actualised and sustained as people
or “carriers of practice” perform these doings and sayings [35,36], and
they “emerge, persist and disappear [over time] as links between their de-
fining elements are made and broken. “[36]. In other words, theories of
practice may help to explain why certain doings that require electricity
are performed in the way they are with regards to their constituting
elements and how they interconnect with other practices in space and
time.
As there are a number of different accounts of theories of practice
(for overviews, see e.g. [37,38]), there are also a number of different
ways in which the elements of practice are defined. In the account of
Shove et al. [36], which has been used, e.g. by Stengers [39], to explain
how and why people engage in demand response, the elements are
defined as (i) materials such as “objects, infrastructures, tools, hardware
and the body itself”, (ii) competences such as “practical consciousness,
cultivated skill (…) or shared understandings of good or appropriate per-
formance in terms of which specific enactments are judged” and (iii)
meanings such as “social and symbolic significance of participation at any
one moment” [36]. In the context of practices that use electricity, ma-
terials of relevance may include appliances, enabling technologies and
electricity itself; competences may include awareness about one’s own
electricity use, practical knowledge about how to change one’s own
electricity use and/or shared understandings of how we use electricity;
and meanings may include meanings associated with electricity itself
[39].
As electricity demand is an outcome of social practices, and dy-
namic pricing seeks to induce a temporal shift in the performances of
these practices, it is also important to consider the temporal dynamics
of practices [40,41]. In this context, the rhythms of practices, i.e. the
“patterns in the routinised or habituated doing of practices in similar ways at
similar times (…) and/or a functional coordination of different practices
into connected sequences” [40] are of particular importance. Dynamic
pricing seeks to create moments of “arrhythmia” or “disturbances to the
pre-existing rhythms of performance of practices and the possibility of the
emergence of new ones.” [1].
In this study we are inspired by Shove et al.’s [36] account of a
theory of practice, and Strengers’ [39] application of that account, in
analysing and discussing the possible explanations for why house-
holders do or do not alter the timing of the performances of practices
that use electricity in response to dynamic price signals and feedback.
As it is problematic to observe how householders actually perform
electricity-using practices in their everyday lives, we have chosen to
base our analysis on the householders’ statements about how and why
they perform electricity-using practices the way they do given in in-
terviews and diaries. Although some might not agree, Hitchings [42]
argues that talking to respondents about how they perform practices is
a pragmatic and valid approach.
We now continue with a description of our empirical context and
methods (Section 2), empirical observations (Section 3), analysis and
discussion (Section 4), and conclusions (Section 5).
2. Empirical context and methods
2.1. The sample, dynamic pricing schemes and research interventions
In contrast to the majority of studies on dynamic pricing, we did not
study householders who volunteered to take part in a time-limited
dynamic pricing trial. We carried out our study in the distribution area
of the DSO Sala-Heby Energi Elnät AB in Sweden, where all residential
users with a fuse size of 16–25 A (i.e. single-family households) have
been subject to a mandatory two-part TOU distribution tariff consisting
of a demand charge (SEK/kW, or actually SEK/kWh/h) and a fixed
access charge (SEK/year) depending on fuse size since 2009. Previous
explorations of the effects of this particular tariff suggest that it has
brought about a substantial decrease in peak demand during peak hours
which has been sustained over time [43,44]. The area is situated about
120 km from Stockholm and has about 13 500 end users. The users that
are subject to the tariff pay for the average of their five highest hourly
peaks, during peak hours, every month (Table 1). Consequently, the
tariff primarily provides an incentive to avoid hourly peaks, but also to
reduce overall electricity use, during peak hours.
Sweden has had a liberalised electricity market since 1996 with
retail competition and unbundled DSOs. Consequently, householders
pay separately for the distribution and supply of electricity to DSOs and
retailers, respectively. Thus, the households in the study area pay se-
parately for the supply of electricity (in addition to the TOU
Table 1
The design and price levels of the two-part TOU distribution tariff applied to users with a fuse size of 16–25 A.
Period Price (including 25 % VAT)
Peak (7 a.m. – 7 p.m. weekdays except public holidays) April–October: 40 SEK/kW (4.3 EUR / 5.1 USD)*
November–March: 98.5 SEK/kW (10.6 EUR / 12.7 USD)
a
Off-peak (all other times) 0 SEK/kW
a
The average exchange rate for 1 SEK during the time the final sample of households participated in the study was ≈ 0.11 EUR or
I. Öhrlund, et al. Energy Research & Social Science 52 (2019) 236–246
238
distribution tariff) to a retailer of their choice. In Sweden, there are
about 130 retailers offering a number of different retail contracts that
householders can choose from, including energy-based RTP contracts.
However, by 2014, only about 8 600 of the 5.3 million users in
Sweden with a fuse size below 63 A had chosen a RTP retail contract
[45], where the price of electricity changes hourly to reflect the market
price established at the day-ahead market of the Nord Pool power
market. The vast majority of users still have a contract where the price
per kWh is fixed based on the average monthly (49 percent), yearly (13
percent) or even longer-term (16 percent) price [46]. Thus, there are
still very few households that are subject to dynamic pricing at both the
distribution and retail level, but the retailer Sala-Heby Energi AB which
has a high presence in the study area has been proactive in the pro-
motion of RTP retail contracts, which provided a good opportunity to
find a sufficient number of households with RTP retail contracts for our
study.
The DSO provided us with the contact details of their users and in
2014 we began contacting single-family households, initially via email
and if necessary by phone. They were asked whether they wanted to
participate in a study in which they would have access to real-time
feedback on their electricity consumption and associated costs and
thereby help to contribute to an increased understanding of the use-
fulness of such. 60 households accepted — 19 with RTP retail contracts,
17 with average-monthly-price contracts, 13 with average-annual-price
contracts and five with other types of contracts. Each household re-
ceived a real-time electricity meter that sent consumption data wire-
lessly to a web-based feedback interface that could be reached from any
Internet-connected device. 40 of the 60 households also received an
IHD, which could be used for continuous display of the feedback in-
terface anywhere in their home where they had access to Wi-Fi. About 6
months after the last household was up and running, the householders
were invited to a meeting where we explained the function and purpose
of the TOU distribution tariff and RTP retail contracts, and how they
could make use of these dynamic pricing schemes to lower their elec-
tricity expenses.
As in most studies on dynamic pricing, our sample of households
suffered from “volunteer selection bias” as well as “intervention selec-
tion bias”, in that the householders chose to participate in our study,
and some to have RTP retail contracts — two biases that are likely to
skew findings toward the “positive” end [17,47]. However, instead of
seeing these biases as weaknesses, we decided to see them as an op-
portunity to do a qualitative assessment of a best-case scenario in terms
of householders’ willingness and ability to engage in DR; i.e. to study
how householders who are particularly interested in the subject and
have access to personal guidance respond to dynamic pricing and
feedback in their daily lives. With this in mind, it is also worth re-
cognising that single-family homes generally consume more electricity
than other housing categories, both in terms of energy (kWh) and
power (kW), and therefore have higher electricity expenses. They also
have full authority over home investments. Hence, householders living
in single-family homes are suitable respondents with regards to our
aim.
To make sure that our householders were in fact particularly in-
terested in participating, we asked them a second time whether they
wanted to continue to participate for another year after having parti-
cipated for 10–15 months, while also committing to do as much as they
could and found reasonable to adapt their electricity use. They were
also offered a one-year subscription to an energy-monitoring service
provided by the DSO for free (worth 300 SEK). This resulted in 14
households participating in the project (including one that had not
participated earlier) for a comparatively long period of time, thus also
allowing us to capture long-term effects. During the remainder of the
project three households dropped out — leaving 11 households in the
final sample from which we collected our data (Table 2).
We note that the final sample consists of middle-aged and elderly
couples. It is not entirely surprising that the average age is high, as
younger people may not want, and often cannot afford, to live in a
single-family home. However, we still believe that the sample is skewed
toward the upper end of the age scale. This in itself may not be a
concern with regards to the generalizability of the results, but age is
correlated with occupation, and occupation may be of concern. People
in Sweden that are 65 years and older are usually retired, possibly
meaning that they have more temporal flexibility in their lives.
Although this study aims at assessing a best-case scenario, this needs to
be kept in mind when interpreting results. To ease interpretation, each
quote is followed by the sex, age and household affiliation of each re-
spondent.
2.2. Feedback design
The feedback interface that was provided had been developed to-
gether with a smaller group of householders from the same electricity
distribution area prior to the study. These householders were identified
by the DSO and invited to attend workshops with the aim of developing
a real-time feedback interface for electricity use and associated costs.
The households were briefed about the TOU distribution tariff and the
rationale behind RTP retail contracts to ensure that they had the ne-
cessary knowledge. Their preferences regarding the feedback interface
were discussed and used as input for a first version of the interface, to
which they were given access and tested for some time. The same
procedure was repeated until the interface was considered complete
and ready to be used in our study. The main page of the final feedback
interface is illustrated in Fig. 1.
Except for information on electricity consumption and associated
costs in different formats, the householders requested that colours be
used to indicate the state of the different parameters in the interface to
ease interpretation. Traffic light colour indicators were implemented in
the top three boxes on the main page (Fig. 1) as well as in the two bar
charts reachable from the bottom menu showing the historical elec-
tricity use and the hourly retail price. In the “Right now” box, the
colour would go from green to red if the cost for the current hour was in
danger of becoming high — either due to a relatively high retail price or
because it might become one of the top five peak hours of the month, or
both. Similarly, the colour of the “Grid cost” box would go from green
to red if the consumption in current hour might become one of the top
five peaks of the month. In the “Current electricity price” box, the six
cheapest hours during the current day would be indicated in green, the
six most expensive hours in red, and the rest in yellow. The same logic
applied to the bar chart showing the hourly retail price on the current
and coming day. Finally, the traffic light colours were also used to in-
dicate different levels of consumption in the bar chart showing histor-
ical usage.
For the sake of our discussion later, it is important to note that the
colours of the “Right now” and “Current electricity price” boxes were
based on the relative retail price within each day, rather than fixed
price levels. This means that a power consumption of 1 kW could be
represented by green or red and that a retail price of 1 SEK/kWh could
also be represented by green, yellow or red, depending on the retail
price in that hour relative to the rest of the day. Worth noting is also the
fact that the boxes showing the grid and retail costs at times may dis-
play different colours due to the different nature of the two price sig-
nals, possibly meaning that they convey contradictory messages to the
user about whether it is costly or not to use electricity at that point in
time.
2.3. Data collection
Nine months after the households had agreed to remain in the
project for another year, the householders in the final sample (Table 2)
were asked to fill in personal diaries every day during a week of their
choice, and to participate in face-to-face interviews. The diaries were
semi-structured around four questions that aimed to capture their views
I. Öhrlund, et al. Energy Research & Social Science 52 (2019) 236–246
239
on what constitutes an electricity-using activity, what materials were
involved, if and how they reflected on the price of electricity, and
whether they did anything to try to adapt to that price. The house-
holders were asked to not do anything differently that week, as we
wanted the diary to reflect their regular everyday lives. In total, 14
householders filled in the diary, representing nine of the eleven
households in Table 2.
Semi-structured face-to-face interviews were conducted after the
diary week. Similar to Bourgeois et al. [48], we used a number of
documents and visualisations during the interviews to guide our ques-
tions and facilitate the discussion. These were (i) the diaries, (ii) ex-
cerpts from the feedback interface showing graphs of their electricity
use each month since the start of their participation in the project, their
hourly electricity use and the hourly retail price on each day of the
diary week, and (iii) four different non-household-specific scenarios
from the main page of the feedback interface, illustrating different
consumption and price levels and their associated colours. The docu-
ments were used to determine whether the householders had under-
stood and could explain how their doings related to their use of elec-
tricity and the hourly retail price, and to investigate how they had used
and interpreted the feedback interface.
All household members were asked to participate in the interviews,
and in most cases the two adults in each home did. The interviews took
1.5 h on average and revolved around four themes: (i) their perceived
impact of their participation in the project on their daily lives; (ii) their
perceived reasons to change their electricity use in general; (iii) their
perceived impact of the real-time feedback, and (iv) their perceived
possibilities to adapt in response to the price signals and the feedback.
Each interview was recorded and transcribed verbatim. Two of the
authors of this study participated in the analysis of the transcripts,
which entailed reading them through to identify similarities and dif-
ferences between the householders’ statements regarding how and why
they had or had not considered and responded to the price signals and
the feedback. The transcripts were read through several times to
identify and verify common themes across interviews, as well as to
ensure that we did not to miss anything and that the householders’
statements had been interpreted correctly. Finally, the most evident
findings with regard to their contribution to our understanding of how,
why and why not householders respond to dynamic pricing and feed-
back were summarised for the preparation of this article.
3. Empirical observations
In this section, we present our empirical observations based on the
interviews regarding how and why the householders did or did not
respond to the price signals and the feedback in their everyday lives.
We begin with a description of what they claim to have done in Section
3.1, followed by a more detailed exposition of what specific aspects of
the price signals and the feedback that they considered or did not
consider in their doings, in Section 3.2–3.4, with the aim of addressing
the identified research gaps. More specifically, Section 3.2 focuses on
how the householders considered the different price signals and the
interaction between them, Section 3.3 focuses on if and how the feed-
back facilitated their response to the price signals, and Section 3.4 fo-
cuses on how they used, interacted with, perceived and interpreted the
feedback itself.
3.1. Adaptions of the performance of everyday practices
We found that all householders (except one whose knowledge about
the TOU tariff was uncertain) considered themselves to have made a
number of changes in their daily lives. These included recurring tem-
poral adaptions of the performance of both frequent and less frequent
practices, new ways of temporally coordinating the use of appliances, as
well as one-time changes to the schedules of automated appliances.
The majority of the changes were said to be, or were clearly in re-
sponse to the TOU distribution tariff. Most commonly, the householders
claimed that they regularly shifted the timing of dishwashing and
laundry practices from peak to off-peak hours. Other practices such as
showering, tumble drying, vacuum cleaning, bubble bathing and sauna
bathing were also mentioned as having been shifted from peak to off-
peak hours on several occasions. One of the more technically interested
householders avoided running the garage heating fan when working in
the garage during peak hours and even experimented with manual
Table 2
The final sample of households.
Family Sex and age of
household members
Retail contract Feedback via Months of participation
1M 47, F 48, F 17, F 15 RTP Online portal +IHD 26
2M 48, F 54, M 15 RTP Online portal + IHD 24
3M 57, F 52, M 15 RTP Online portal + IHD 20
4M 70, F 70 RTP Online portal + IHD 20
5M 73, F 74 RTP Online portal + IHD 19
6M 79, F 78 RTP Online portal 20
7M 60, F 55 RTP Online portal 3
8M 58, F 54, M 20 Yearly fixed Online portal + IHD 23
9M 53, F 62 Yearly fixed Online portal + IHD 20
10 M 66, F 63 Yearly fixed Online portal + IHD 19
11 M 70, F 69 Yearly fixed Online portal 21
Fig. 1. The default page of the real-time feedback interface at a certain time for
a particular household with direct English translations added within square
brackets for the purpose of this article. The "Right now" box shows the power
consumption in real time. "Current electricity price" shows the current retail
price of electricity. “Grid cost” shows the expected cost of distribution given
that the household continues to use the same amount of power throughout the
hour and “Electricity retail cost" shows the corresponding exptected retail cost.
Similarly, “Cost for the hour” shows the total expected cost for both distribution
and retail. A menu at the bottom of the interface (not visible here) provided
access to further information such as historical consumption on an hourly/
daily/monthly/yearly basis and the hourly retail price for the current and
coming day.
I. Öhrlund, et al. Energy Research & Social Science 52 (2019) 236–246
240
operation of the heat pump to see if it was possible to reduce its power
consumption during peak hours without causing any major temperature
changes in the house.
”[We would say:] No you can’t turn on the dishwasher because it is
not 7 p.m. yet”
(F 54, household 2)
“We can say: should we turn on the dishwasher? [No] let’s wait for
half an hour until 7 p.m. That kind of consciousness has grown into
our electricity use by now”
(M 79, household 6)
A few householders also described how they had figured out how to
avoid consumption peaks during peak hours by temporally co-
ordinating the use of appliances. They had spread the use of appliances
across adjacent hours by turning them on in the middle rather than at
the beginning of an hour, as well as by avoiding running more than one
appliance at the same time. These new ways of coordinating the use of
appliances are evidently a result of the demand charge component of
the TOU tariff, as it (in contrast to a conventional volumetric charge
component) not only incentivises householders to reduce their overall
electricity use during peak hours, but also to avoid hourly consumption
peaks during peak hours.
“[Male:] Yes, but is it better if so to say, …if you use a lot of power,
…is it better to operate… so to say… from half past twelve to half
past one, so that you get [your consumption] on two different hours,
or? [Female:] That’s right!”
(M 70 & F 70, household 4)
“But we know that if one is using the oven and… you don’t have to
run a lot of other stuff at the same time. So I can warm up the oven
one hour and run the hotplate the other hour if I start the oven 10
[minutes] to [the whole hour]. When it has been warmed up it does
not use so much energy. Then you can turn on the hotplate the next
hour, so that way you spread it [the use] across two hours.”
(M 57, household 3)
Roughly half of the householders who had RTP retail contracts also
described how they, in addition to the TOU tariff, took the hourly retail
price into account when shifting the timing of the performance of
common practices such as dishwashing, laundering and tumble drying,
as well as when setting the timer on automated appliances such as
dehumidifiers and garage heating fans. Fig. 2 visually summarises all
the changes reported by the householders.
3.2. Dealing with dual dynamic price signals
Given that this study is one of the first qualitative studies carried out
in a setting where householders are subject to dual dynamic price sig-
nals, and that the price signals are different and may sometimes provide
contradictory incentives [6], we wanted to know how the householders
had dealt with that and considered the price signals in their doings.
Not all householders seemed to have a full understanding of the
distinction between the two price signals. However, the majority who
did seemed to give the TOU tariff precedence over the RTP, arguing that
they had the impression that the TOU-associated costs accounted for
most of their bills. Two of the householders that had RTP retail con-
tracts who said that they did not adapt to the RTP also seemed to reason
along those lines when arguing for why they chose not to adapt. The
reason that they had chosen RTP retail contracts was that they were
under the impression that it would be cheaper compared to other types
of contracts.
“You have to run the washing machine sometime. If you run it here
[pointing at a relatively low retail price during peak-hours in graph]
then you risk to increase the TOU costs. If you run it here [pointing
at a relatively high retail price during off-peak hours] then it will
cost you two öre [Swedish currency; a wording meaning “very
little”] extra [in retail costs] or something like that, but then you
avoid getting it [the peak] on the TOU tariff peak hours”
(M 57, household 3)
“Since the distribution tariff is based on the five highest values right
(?), that is what you want to keep low. Because if you mess up there,
it quickly shoots away. And during winter it is expensive as well.”
(M 73, household 5)
“Yes, you can say that [it is the TOU tariff that we consider firstly
rather than the retail price], yes that's the way it is. Because as my
wife says it [the TOU tariff] really breaks through [in terms of
costs].”
(M 79, household 6)
3.3. The interaction between dynamic pricing and feedback
Since dynamic pricing and feedback go hand in hand and have been
shown to have effects on their own, we wanted to know if and how the
feedback had facilitated the householders’ responses to the price sig-
nals.
We found that all of the householders considered themselves to have
learned to adapt to the TOU tariff years before the project started, de-
spite not having access to feedback on the TOU-associated costs of their
actions in real time. None of the householders said that the feedback
itself had had an additional impact on their adaptions to the TOU tariff.
Their claims were supported by the fact that no householder said they
had used the feedback on the expected costs for the current hour
Fig. 2. A summary of all the different changes that the
householders mentioned they had done. Temporal adaptions
of existing practices are represented in blue, new ways of
temporally coordinating the use of appliances in green and
one-time changes to the schedules of automated appliances in
orange. The more householders that mentioned a particular
change, the bigger the bubble.
I. Öhrlund, et al. Energy Research & Social Science 52 (2019) 236–246
241
provided by the feedback interface — neither the expected TOU costs,
RTP costs or the total costs. Similar to the findings of Hargreaves et al.
[22], the reason for this was that the estimates were perceived as dif-
ficult to understand because of their sensitivity to sudden shifts in the
current power consumption which sometimes led to seemingly un-
realistic numbers.
Even though the householders claimed to have responded to the
TOU tariff without the support of feedback, the feedback had played a
role for those who also claimed to have adapted to the RTP. One of the
households told us how they, in addition to waiting for the TOU off-
peak period, waited for the retail price to come down after 7 p.m. be-
fore using the dishwasher or washing machine. However, other
householders claimed to have found a daily pattern in the retail price
which they used to schedule their response instead of following and
responding to the actual retail price. The most enthusiastic household
always ran their tumble dryer at 4 a.m., arguing that the retail price was
usually at its lowest at that time. They also ran a dehumidifier and their
winter garage heating during the night to take advantage of the com-
bination of a relatively low retail price and the off-peak hours of the
TOU tariff.
“[Male:] Sometimes it [the boxes in the interface] can be totally red,
even though it is after 7 p.m. (…) [Female:] Then we’ll wait a bit,
because later it is usually green”
(M 70 & F 70, household 4)
“Then we found that between about 4–5 a.m. [the electricity price
was at its lowest]. And when I realised that it was almost always like
that, we just stuck to that… So now we do not look any more, now
we just assume that it is then when it is cheapest”
(M 57, household 3)
“If you look there [on the hourly electricity price in the feedback
interface] for a couple of days, you'll see how it looks…, that it is a
kind of pattern, and then it was pretty clear. (…) So based on that, it
may not be very interesting to have contact with the electricity
meter [the real-time feedback interface] when the patterns are as
stable as they are.”
(M 60, household 7)
3.4. The use of feedback
Although the feedback seemed to have facilitated a response to the
hourly retail price for a number of householders with RTP retail con-
tracts, most of the reported changes were made in response to the TOU
tariff without the use of feedback. Others have made similar observa-
tions, such as Murtagh, Gatersleben and Uzzell [49] who observed how
householders who engaged in conservation efforts had done so before
having access to energy feedback. That being said, all householders in
our study still had access to the real-time feedback for a long period of
time, which paved the way for an in-depth discussion around how they
had used, interacted with, perceived and interpreted the feedback.
In line with what a number of other studies have found [22,23,50],
the majority of the householders claimed that the feedback had in-
creased their interest in and general awareness of their electricity
consumption. Most householders also said they had used the real-time
feedback to explore how much different appliances consumed, and that
it had sometimes surprised them to see how much or how little it was.
Just as others have observed [21,22], the householders described how
their interest in the feedback was greater at the beginning of the pro-
ject.
As the main parts of the feedback interface that the householders
said to have used were colour-coded, we were interested in knowing
how they had perceived and potentially made use of that feature. We
found that almost all householders had looked for and relied on the
colours when assessing the situation, rather than the numerical in-
formation provided by the interface. As opposed to absolute numbers
on consumption, prices and costs — which many said that they could
not relate to — the colours were perceived to be easy to spot and in-
terpret, and quickly gave meaning to the numbers they saw and the
situation they were in. Hargreaves et al. [22] also describe how their
interviewees showed little interest in absolute numbers and considered
them unhelpful in contrast to simple visualisations. This tendency has
also been observed in larger surveys, where householders have been
asked to rate the usefulness of different feedback features [51], as well
as in feedback design studies, where colours in particular have been
shown to be perceived as quick and intuitive means of providing in-
formation on electricity use [52]. Not only do householders seem to
prefer simple visualisations over numbers, but visual prompts as simple
as an orb glowing in different colours to represent different price levels
have been shown to boost householders’ responses to RTP considerably
[2].
“No, it is the colours [that I look for in the box showing the current
retail price]. Because the number doesn’t tell me much. I do not
know what it [the number] is. It is more like: Oh, it is red!”
(F 48, household 2)
4. Analysis and discussion
Having presented our empirical observations of the householders'
doings and their considerations of the price signals and feedback in
those doings, we turn now to an analysis and discussion of these ob-
servations.
4.1. How do price signals “work”?
As dynamic pricing schemes provide financial incentives to engage
in DR, it is natural to presume that any changes made in response to
such incentives are made to save money. Indeed, when we asked the
householders themselves, saving money was the primary motive for
them wanting to change their electricity use, which is in line with the
findings of several studies [22,23,49]. Furthermore, the majority of
householders who gave the TOU tariff precedence over the RTP said
they did so because the TOU-associated costs were higher, suggesting
that the size of the potential savings is an important determinant of
whether householders respond to dynamic pricing schemes or not. On
the surface, these findings seem to resonate well with theories of eco-
nomic rationality and claims such as that the size of the financial in-
centive is the main determinant of householders’ response to dynamic
pricing [14,16,53]. These claims imply that householders simply will
not engage in DR to any significant extent if electricity is not expensive
enough, as has been claimed to be the case in Sweden [53]. However, a
number of our observations lead us to question the importance of fi-
nancial savings.
The fact that the householders claimed to have adapted to the TOU
tariff years before having access to feedback suggests that the magni-
tude of the savings due to their actions has not mattered to them that
much in their everyday decisions. Without feedback, the householders
could not possibly have known the TOU-associated cost of their actions,
as the hourly costs resulting from a demand-based tariff depend on the
size of the peak in the current hour in relation to past peaks during a
month. In fact, when we asked the householders directly, none of the
householders had any idea about how much money they saved by re-
sponding to the price signals. Nevertheless, all householders assumed
that they saved some money and said that they thought it was unwise
not to adapt if one could.
“No [we haven’t tried to estimate the savings], but it must have [an
effect] (…) if it does not affect your life too much then you can do it.
Even if you only earn a few [Swedish] krona.”
(F 69, household 11)
“I know that we earn from it [adapting]. […] I guess it [the financial
I. Öhrlund, et al. Energy Research & Social Science 52 (2019) 236–246
242
saving] is relatively little… The electricity is pretty cheap after all”
(M 57, household 3)
“No [I have not tried to estimate what I would save] I don’t think
that… it [responding] will render that much [savings] but… it is
probably just because it could work [to program the heat pump to
switch on and off automatically]”
(M 60, household 7)
Given that dynamic pricing schemes provide financial incentives to
engage in DR and that some research suggests that the size of the fi-
nancial incentive really matters [14,16,53], one may wonder about the
significance of our observations. However, we are not the first to have
made these kinds of observations. Murtagh, Gatersleben and Uzzell
found that financial savings were considered to be an important factor
by householders who engaged in energy conservation, but that “all
savings however small were important” and that they “appeared to serve a
symbolic as well as a monetary function” [49]. Strengers [19] noted that
households in a control group of a DPP trial who received peak event
information without any financial incentive to respond reduced their
peak event demand by 11–13%. Similarly, in discussing the findings of
the largest TOU trial in Ireland, Darby and McKenna noted that “it
appeared as though the main factor affecting customer response was the
existence of time-varying prices, rather than the actual figures involved.”
[2].
This leads us to suggest that it is not the actual financial savings that
determine whether householders choose to respond to dynamic pricing
schemes. So, what is it then? Strengers [39] suggest that price signals
can convey new meanings about electricity that have the ability to
'rematerialise' it, turning it something largely intangible to a material
element of practice, and thus making it come to matter to practices that
use it. In this way, price signals have the power to reconfigure the way
in which practices that use electricity are performed and, as our ob-
servations suggest, even give rise to new ways of temporally co-
ordinating the performance of practices that use electricity when de-
mand charges are involved.
Strengers suggests that these new meanings may be meanings of
“frugality and finiteness [that come to life] without challenging broader
meanings of abundance and availability” [39]. Her suggestion makes
sense with regards to householders who claim to be motivated by fac-
tors other than financial savings, such as a perceived civic or moral
responsibility to act [19,21] or a desire to save energy [49], but does it
also apply to householders, such as those in this study, who claim to be
motivated by financial savings and willing to make changes as long as
they save something? In this context, is it not possible that price signals
simply convey meanings of costliness that render the enactment of
certain practices, and the use of certain appliances, at certain times as
wasteful or unnecessary? If so, is it possible that these meanings are
sustained by the fact that the true costs (or gains) are actually unknown
to the householders? We cannot say based solely on this study, but we
believe that these questions deserve further debate.
Furthermore, as pointed out by Strengers “prices need to be inter-
preted to give them meaning, much like language or other symbolic systems
which convey social and cultural values as well as economic ones” [39],
which brings us to the role of feedback in conveying meanings about
practices that use electricity. In addition to our observation that the
householders relied on the colour indicators rather than the numerical
information provided by the feedback interface, we also observed that
the colours had pre-inscribed symbolic meanings for the householders
that seemed to have an effect on their perception and interpretation of
prices, costs and consumption levels. To them, the colour green meant
that everything was fine, whereas the colour red signalled that some-
thing undesirable was going on that required their attention, which is in
line with what others have found [20,31,52].
“I probably look mostly at the colour. Because green means OK.
That’s how I think.”
(F 48, household 1)
”There [pointing at a red-coloured box showing current retail elec-
tricity price] you can see that you shouldn’t turn on things. The price
is extra high at the moment”
(M 57, household 3)
”Kronor and öre [Swedish currency; roughly meaning “euros and
cents”], I don’t think about that too much. When they display is
green then it is OK, something like that”
(M 53, household 9)
Even though the colours could have been programmed to mean
many different things, none of the householders had seriously reflected
on how the colours corresponded to the numbers; they simply trusted
their “correctness”. A red-coloured number, irrespective of its size, was
interpreted as “bad” (i.e. high). This means that a red-coloured price
level of, e.g. 0.5 SEK/kWh or a consumption level of e.g. 1 kW, could be
interpreted as being high even though the numbers themselves may be
low in absolute terms or in comparison to normal levels over longer
periods of time. This illustrates how feedback may act not only as a
passive conveyer of information, but also as an active ingredient in the
crafting of meanings associated with practices that use electricity —
both by mediating the meanings provided by price signals and by
“translating” intangible numbers describing consumption, prices and
costs into something meaningful.
4.2. What determines whether and how far householders are willing to
adapt?
Given that price signals “work” through the meanings they convey
(both with and without feedback) about practices that use electricity,
rather than through the actual financial incentive they provide, what
determines the level to which householders are willing to adapt?
In talking to the householders about which changes they considered
themselves to have made and why, they said they were willing to make
any change as long as it did not cause them any inconvenience in their
daily lives. If we understand convenience as being “associated with the
capacity to shift, juggle and reorder episodes and events” in time as sug-
gested by Shove [54], this can be understood as a willingness to change
as long as it does not limit the temporal flexibility of other doings in
their everyday lives.
Dishwashing and doing the laundry were practices that the house-
holders mentioned as being easy to shift in time without interfering too
much with the temporal rhythm of their lives. Several others have also
found that householders seem willing to shift these particular practices
in time [1,19,30]. However, Friis and Haunstrup Christensen concluded
that “households generally experienced the extra doings and loss of control
[associated with time-shifting of dishwashing and laundering] as
stressful and inconvenient, particularly during weekdays” [30]. Apart from
the findings of Higginson, Thomson and Bhamra [55], previous re-
search [1,21] also support our finding that householders are unwilling
to change the timing of cooking and eating practices. For householders
who work and/or have children, cooking and eating practices are
strongly connected to the temporal organisation of other practices such
as going to work, taking children to school, doing homework, watching
television, etc. [56]. However, even the householders who were retired
did not seem willing to shift the timing of their meals, suggesting that
the apparent inflexibility of cooking and eating practices has to do with
bodily needs as well as temporality.
“It [using the dishwasher on off-peak hours] is so simple that you do
not even have to think about it”
(M 60, household 7)
“It must not get inconvenient, and I mean to eat […] having regular
meals is important.”
(M 73, household 5)
I. Öhrlund, et al. Energy Research & Social Science 52 (2019) 236–246
243
“No, it [the adaptions] should not go to extremes, we're supposed to
be able live”
(F 78, household 6)
When discussing the diary week with the householders, they all felt
that they had done everything in their power to adapt without causing
inconvenience, and said that no further adaptions than the ones they
had already made would have been possible. This could help to explain
why the householders did not consider the introduction of the real-time
feedback to have had any additional impact on their adaptions to the
TOU tariff, i.e. they had already done everything they were willing and
able to do without causing inconvenience.
“…you cannot ONLY save electricity, you have to put it in relation
to wanting a worthy life”
(M 66, household 10)
“When we were done with that [the adaptions we had already
made] we were satisfied, so after that we haven’t, or at least I ha-
ven’t cared.”
(M 57, household 3)
“I don’t think that you can get any lower [in electricity use] than
that. Then you will change your life to the worse in some way.”
(M 66, household 10)
So how is it then that the feedback seemed to facilitate a response to
the RTP if the householders had already done everything they were
willing and/or able to do? Well, the fact is that the real-time feedback
did not lead to any further adaptions than those already made in re-
sponse to the TOU tariff, but rather it led to that the householders took
the RTP into account as well. That is, not only did they shift the per-
formances of practices from peak to off-peak hours, but they also shifted
the performances to times of low retail prices. This illustrates how the
introduction of a second dynamic price signal may affect the timing of
the performance of practices that are already temporally negotiated,
while the limit of which practices that are considered negotiable with
regard to their impact on convenience remain the same.
The importance of convenience was not only evident with regards to
how far the householders were willing to adapt to the price signals, but
also to the way in which the householders used the feedback interface.
The fact that a number of householders with RTP had identified a re-
curring retail price pattern that they used as a rule of thumb when
timing the performance of certain practices suggests that the house-
holders valued predictability before correctness, in that it allowed them
to respond to the RTP without causing inconvenience. This new mental
retail price signal that they had created was not only predictable, but it
also aligned the temporal demands that each price signal put on the
householders, thus allowing them to adapt to both signals simulta-
neously without having to bother about their hourly interaction.
These observations also provide clues that could help to explain the
general decrease of the householders’ interest in the feedback over
time. Similar to Hargreaves et al. [22], we suggest that the house-
holders quickly learned what they needed to know in order to make the
changes they could and were willing to make without causing any in-
convenience. This naturally eliminated the need for further feedback,
even though they were subject to two different dynamic price signals.
Consistent with this suggestion, almost none of the original 60 house-
holders that accepted to take part in the study wanted to keep the real-
time feedback for a discounted price after the study. As such, we feel
inclined to question the claim of Buchanan, Russo and Anderson that “If
feedback is to maintain its effectiveness over time (…) consumers [must]
continue to engage with [it]” [23].
Furthermore, even though the householders reported that they had
reacted strongly to the colours provided by the feedback interface in
real time, their reaction did not seem to translate into immediate ac-
tion. When confronted with red colours in real time, their immediate
reaction was to try to understand what was going on, but once they had
found a satisfying explanation (which they most often did), they did not
perform an immediate action to try to lower their electricity use in
response, because most often, doing so would cause them incon-
venience. A few householders said that they had considered doing
things differently the next time when having been confronted with red
colours, but only if it would not cause them inconvenience.
“When we have a red power consumption, we are often doing
something. Then we’re busy. […] If you’re cooking food it [the
feedback interface] might display something else [as in red], but
then we aren’t looking at this [the feedback interface]”
(M 57, household 3)
“When you see that the display shines red you understand that – OK,
this was a bit high, high consumption here and this is also expensive,
so next time we won’t do it like this, we’ll do it like that instead. If
there is a possibility to change [what we do], it is not always pos-
sible, but IF it is.”
(F 54, household 2)
”[Male:] No [we don’t turn things off] because it happens sometimes
that it gets like this [red] during daytime when you’re cooking or
[using the] oven, stuff like that. Then it can become like this
[pointing at red-coloured box], that all is red. It happens. [Female:]
It happens. [Male:] And then you get a bit frightened, because that’s
not how you want it, but you can’t do much about it. [Female:] You
get a bit angry, but you continue [to do what you’re doing] anyway”
(M 70 & F 70, household 4)
To summarise, many of our observations suggest that the impact on
convenience is at least as important as the size of the financial incentive
for determining how far householders are willing to go in response to
dynamic pricing and real-time feedback. Before concluding, we want to
raise a few issues regarding why certain practices may be portrayed as
being “more flexible” often than others. Firstly, in thinking about which
performances of practices householders are willing to shift in time and
why, we must keep in mind that different practices are more or less
common across households to start with. Practices that a majority of
householders in a given study claim to shift in time are not necessarily
practices that are particularly easy to shift in time in comparison to
others. They might simply be more common than other practices that
exhibit the same kind of temporal flexibility. For example, in Sweden,
all single-family homes have a washing machine, and most also have a
dishwasher, meaning that doing the laundry and the dishes are prac-
tices that are carried out in most households. However, not all house-
holds have tumble dryers, garage heaters, bubble baths or saunas, etc.
This means that practices that are associated with these less common
appliances are naturally less likely to pop up as practices that house-
holders report to have adapted in response to demand-side interven-
tions.
Furthermore, we noted that the practices that the householders said
they had adapted were practices associated with appliances that use a
lot of power (wattage). Some of these practices might also be particu-
larly easy to shift in time, but since other practices that we believe hold
a similar degree of temporal flexibility are missing from the list (e.g.
charging of mobile devices), we believe that there is more to this than
mere coincidence. We find it likely that the householders’ knowledge
(i.e. “competences”) of how much power different appliances use has an
influence on which practices they consider being relevant to change
when given an incentive to do so. This may not seem as controversial,
but it has consequences for the discussion on the temporal flexibility of
the performance of practices, because it means that practices that hold
similar temporal flexibility may be considered to be quite different in
terms of their negotiability, depending on how much power and/or
energy (i.e. kWh) those practices are perceived to use. As competences
are shaped by the social and cultural context wherein householders live,
their education, etc., paying attention to this might help to explain
differences between studies carried out in different socio-cultural
I. Öhrlund, et al. Energy Research & Social Science 52 (2019) 236–246
244
contexts with regards to what practices householders are willing to
adapt the performance of in response to demand-side interventions.
This also highlights the potential role of feedback in influencing
householders’ response to dynamic pricing in more ways than we have
discussed here, as it seems likely that feedback may be involved in the
crafting of householders’ competencies.
5. Conclusions
This study aimed at increasing our knowledge about how and why
householders do or do not respond to different dynamic price signals
and real-time feedback. To explore just how far householders may be
willing and/or able to go, we chose to study householders living in
single-family homes who were subject to dynamic pricing and showed a
particular interest in taking part in a research project where they would
receive feedback on their electricity use and associated costs. As it
turned out, the final sample of householders consisted mainly of
middle-aged and elderly couples. Whether this is a sign of this parti-
cular group of citizens being more interested in taking part in research
projects or more motivated to engage in demand response is unknown.
Hence, readers should keep the characteristics of the sample in mind
when assessing the generalizability of the results. Although exploring
these aspects has not been a part of the purpose our study, we recognise
that householders of different gender may respond differently to dy-
namic price signals and feedback, e.g. due to how different household
chores are divided between individuals, and we therefore encourage
readers to keep this in mind as well.
As part of fulfilling the overall aim, this study set out to explore
three research gaps, one being the interaction between dynamic pricing
and feedback. Our observations suggest that for demand response
purposes, feedback has a limited role to play, and that is mainly in the
communication of hourly retail prices, in the short run. The house-
holders had not used the feedback to respond to the demand-based TOU
distribution tariff. They had already understood the concept of peak
and off-peak hours and made the changes they were willing and able to
make without causing themselves any inconvenience. The householders
with RTP retail contracts initially used the feedback, but most of them
lost their interest in it once they had learnt when the retail price was
usually high and how its recurring daily pattern interacted with the
TOU tariff’s peak and off-peak hours. In the long run, this meant that
householders with RTP relied on their understanding of how the retail
price usually varied over a day when engaging in demand response,
rather than the actual retail price on a daily basis. Our observations of
how the householders had interpreted the feedback interface do how-
ever suggest that simple design elements, such as colours, can change a
householder’s perception of what constitutes a high price and/or a high
power consumption.
Another research gap that we wanted to explore was how house-
holders respond to dual dynamic price signals — in this case a combi-
nation of a demand-based TOU distribution price signal and a RTP retail
signal. When confronted with both price signals, householders tended
to give precedence to the TOU signal, and as explained above, to
translate the hourly retail price into a “TOU retail price signal”. This
mentally constructed price signal was not only convenient and pre-
dictable in comparison to the hourly retail price, but it helped the
householders to align the temporal demands that the two price signals
put on them, allowing them to respond to both signals simultaneously
without having to take their hourly interaction into account.
A question related to the third research gap that we wanted to ex-
plore was whether householders’ response to demand-based tariffs
differ from their response to conventional energy-based tariffs as they
incentivize different usage patterns; cutting hourly peaks versus low-
ering overall electricity usage. Most householders did not respond dif-
ferently, but limited their efforts to recurring temporal shifts of the
performance of practices and made one-time changes to the schedules
of automated appliances to lower their overall electricity use during
peak hours. A limited few of the householders did however report that
they had also engaged in temporal coordination of their use of appli-
ances to cut their hourly peak demand during peak hours. This ob-
servation adds to the limited literature on how householders’ respond to
demand-based tariffs in comparison to conventional energy-based tar-
iffs.
We naturally presume that any changes that householders make in
response to price signals are made to save money, yet we observed that
the householders’ knowledge about how much money they were actu-
ally saving was non-existent. Drawing on theories of practice and pre-
vious research, we hypothesize that dynamic price signals “work” by
bringing new meanings to practices that use electricity which en-
courage householders to shift the performance of those practices in
time. In line with observations made in a number of previous studies,
we suggest that the mere existence of a price signal may therefore be
enough to induce demand response, and that the degree to which
householders respond may largely be determined by the degree to
which responding causes inconvenience, rather than only by the
amount of money they are saving.
In summary, our findings have a number of possible policy im-
plications. Firstly, as the success of a price-based demand response
program may not only depend on the size of the financial incentive it
provides, it might be more effective to inform users of how they can
adapt by changing the timing of the performance of certain practices
and of the use of appliances, rather than providing them with details
about tariff structures and cost calculation examples. Secondly, if price
signals get too complex and/or too demanding to follow and respond to,
individually or together, householders might either ignore them or
simplify them to the extent that they no longer provide the incentive
that was intended. If the aim is to reduce overall usage during hours of
peak demand and/or peak prices, TOU tariffs might therefore be more
effective than highly dynamic pricing schemes such as RTP in getting
the message across. Separate billing of distribution and retail costs may
also entail that the financial incentive to respond is perceived as smaller
than it is. This may be another reason to avoid combining different
dynamic price signals. Thirdly, with demand-based tariffs, users may
engage in “time-coordinating practices” during peak hours that lower
their costs, but not necessarily their strain on the grid, as they might
simply be shifting the timing rather than the magnitude of their peaks.
Adding a volumetric charge component to a TOU tariff and/or short-
ening the billing time interval may help to alleviate such unintended
effects. Lastly, real-time feedback seems to play a limited role in facil-
itating householders’ response to dynamic pricing in the long run. It
might be more effective to spend money on educating users on how to
respond to price signals, rather than on complex feedback systems. The
role of feedback may however change as users start to produce and/or
store their own electricity as such developments will change the con-
ditions under which real-time feedback and dynamic pricing operate
today. In which ways and to what extent the requirements and pre-
ferences of consumers and prosumers might change in response are
research questions that need to be further explored.
Declarations of interest
None.
Funding
This work was supported by the Swedish Energy Agency [grant
number 37589-1].
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