Consumer Behavior on Dynamic Pricing When Subjected to Budget Constraints

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    Business Information Management

    Rotterdam School of Management

    Erasmus University Rotterdam

    Master Thesis

    Consumer Behavior on Dynamic

    Pricing when subjected to Budget

    Constraints

    Author:

    Marcel van der Perk348539

    Supervisor:

    Dr. Wolfgang Ketter

    Co-supervisor:Dr. Jan van Dalen

    December 5, 2013

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    Preface

    The author declares that the text and work presented in this Master thesis is original

    and that no other sources than those mentioned in the text and its references have been

    used in creating the Master thesis.

    The copyright of the Master thesis rests with the author: The author is responsible

    for its contents. Rotterdam School of Management (RSM), Erasmus University is only

    responsible for the educational coaching and beyond that cannot be held responsible for

    the content.

    Acknowledgment

    I would like to thank everyone involved in the creation of this Master Thesis. First, I

    would like to thank Dr. Wolfgang Ketter for his inspiring classes that made me chose this

    research direction in the first place. Second, I would like to thank Dr. Jan van Dalen for

    his continuous support, the critical review of my work and providing excellent feedback

    that resulted in this thesis. Special thanks goes out to Dr. Laurens Rook who helped

    me get started during the first few months.

    Finally I would like to thank Wolfgang Schmid & Markus Zanker from the Klagenfurt

    University for providing the tool that I used for my experiment. Without this tool thisexperiment would not have been possible.

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    Table of Contents

    Preface 1

    Acknowledgement 1

    1 Introduction 6

    2 Research Question and Goal 7

    3 Consumer Choice Theory 9

    3.1 Consumer preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

    3.2 Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

    3.3 Income and substitution effect. . . . . . . . . . . . . . . . . . . . . . . . 103.4 Conceptual Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

    4 Data and Methodology 13

    4.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

    4.2 Experimental design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

    4.3 Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

    4.4 Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

    4.5 Sample. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

    4.6 Pilot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

    4.7 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.8 Descriptive analysis of baseline measurement . . . . . . . . . . . . . . . 19

    4.9 Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

    5 Analysis 23

    5.1 Effect of a budget constraint on energy consumption . . . . . . . . . . . 23

    5.2 Effect of price on willingness to shift . . . . . . . . . . . . . . . . . . . . 23

    5.3 Effect of pricing on energy consumption . . . . . . . . . . . . . . . . . . 26

    5.4 Observation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

    6 Discussion and Conclusion 32

    6.1 Main findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

    6.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

    6.3 Managerial implications . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

    6.4 Limitations and further research . . . . . . . . . . . . . . . . . . . . . . 35

    Bibliography 36

    A Experiment 39

    A.1 Opening page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

    2

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    A.2 Page 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

    A.3 Page 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40A.4 Page 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

    A.5 Page 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

    A.6 Page 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

    A.7 Page 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

    A.8 Page 7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

    A.9 Page 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

    B Questionnaire 46

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    List of Figures

    1 Conceptual Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

    2 The income and substitution effect . . . . . . . . . . . . . . . . . . . . . 10

    3 Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

    4 Histogram of baseline measurement in kWh . . . . . . . . . . . . . . . . 20

    5 Total consumption per time slot with flat pricing . . . . . . . . . . . . . 24

    6 Total consumption per time slot with real-time pricing . . . . . . . . . . 25

    7 Correlation between price change and changed consumption . . . . . . . 25

    8 Trend in consumer profiles. . . . . . . . . . . . . . . . . . . . . . . . . . 29

    9 Tutorial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

    10 Baseline measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

    11 Flat pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

    12 Schedule energy consumption with budget . . . . . . . . . . . . . . . . . 43

    13 Real-time pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

    14 Schedule energy consumption with budget under dynamic pricing . . . . 45

    List of Tables

    1 Attribute: price per hour. . . . . . . . . . . . . . . . . . . . . . . . . . . 15

    2 Attribute: budget constraint. . . . . . . . . . . . . . . . . . . . . . . . . 15

    3 Attribute: appliance consumption. . . . . . . . . . . . . . . . . . . . . . 164 Descriptive Statistics of the baseline measurement . . . . . . . . . . . . 20

    5 Demographics of participants . . . . . . . . . . . . . . . . . . . . . . . . 21

    6 Gathered data by budget group . . . . . . . . . . . . . . . . . . . . . . . 22

    7 Consumption behavior with flat tariff . . . . . . . . . . . . . . . . . . . 23

    8 Descriptive Statistics of the Tariff Type . . . . . . . . . . . . . . . . . . 24

    9 Consumption behavior with real-time pricing . . . . . . . . . . . . . . . 26

    10 Consumption profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

    11 Descriptives of Identified Consumer Profiles . . . . . . . . . . . . . . . . 28

    12 Comparison of means between consumer profiles . . . . . . . . . . . . . 29

    13 Change in consumption of appliances. . . . . . . . . . . . . . . . . . . . 31

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    Executive Summary

    Energy is a commodity we all take for granted, yet with changing policies andconsumer demand for more durable energy sources things are about to change.

    Real-time pricing is needed to accommodate for the renewable energy sources that

    produce power intermittently. How will these changes affect the consumption of

    consumers? Previous studies have focused on the aggregate results when it comes

    to introducing dynamic pricing. Therefore the goal of this study is to investigate

    how consumers on a individual level change their behavior with these variable

    prices.

    This research set up an experiment to which 37 people participated. These par-

    ticipants were asked to schedule their weekly energy consumption with five com-

    mon household appliance through a tool that measured their current consumption.

    After this first step they were assigned a budget under flat pricing to see which

    decisions were made. Followed by the introduction of dynamic pricing to see how

    they shifted their energy load, which appliances were used and how each one deals

    with variable prices under a budget.

    The results show that the participants were able repair their purchasing p ower

    inflicted by the budget constraint with dynamic pricing. An interesting find was

    that a large group of participants did not try to consume more, but used these

    effects to save even more money to be spend on other goods.

    Before this study can say for sure that consumers truly benefit from these dy-

    namic prices a bigger sample is needed. The recommendation is to perform a pilot

    with smart meters and smart appliances where an endless stream of accurate con-

    sumer behavior data can be gathered for analysis.

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    1 Introduction

    Energy is indispensable for our daily life. At the same time energy becomes an increas-

    ingly scarce resource. Almost all experts and scholars believe that energy demand will

    continue to grow fast in the future. Generally, there are two ways to deal with the in-

    creased demand from energy (Prindle et al., 2007): investing in new energy sources and

    capabilities; and use energy more efficiently. Both alternatives are explained.

    First, huge investments are made in new energy sources, like, renewable energy includ-

    ing solar, wind and geothermal energy. In the future energy market, there will be an

    increasing proportion of energy from such renewable energy sources (Parliament,2009).

    The current large-scale power delivery infrastructure was designed for a one-way flow

    of energy with relatively few large power plants in the market. Introducing sustainable

    energy forces change to the centralized nature of the grid to accommodate the partici-

    pation of small businesses and households. It is expected that the market will transform

    from a centralized one to a decentralized one (Ketter et al., 2011; Joskow,2008).

    Second, efforts are made to use energy more efficiently. The supply and demand of energy

    can not be balanced easily. Peak demand occurs when all the business machines and

    many household appliances are running. Off-peak hours are times when those equipment

    stop running. However, the electricity supply cannot be adjusted so rapidly according to

    demand. For power plants, it is costly to switch those machines on and off. Renewable

    sustainable energy sources produce energy intermittently and cannot start and stop atwill. Introduction of a smart grid (Amin and Wollenberg, 2005; Ipakchi and Albuyeh,

    2009) where energy can be monitored real-time allows for better management of energy

    consumption and results in a more efficient energy usage. Real-time monitoring allows

    dynamic pricing and can motivate consumers to shift their loads to off-peak period and

    help producers to better dispatch their capacities (Gottwalt et al., 2011; Joskow and

    Tirole, 2006).

    Faruqui and George(2005) shows that peak consumption can be reduced with dynamic

    pricing. Consumers shift their load to times where energy is cheaper. This effect results

    in a lower peak energy demand, a lower load on the current infrastructure, cost reduction

    and an economic efficiency gain by being more conservative about energy consumption(Faruqui and Sergici,2010). On the topic of dynamic pricing to incentivise consumers to

    change, most research has been done by analyzing consumer behavior on a macro-scale.

    They leave unanswered consumer behavior on a micro-scale.

    The goal of this thesis is to research the effects of dynamic energy prices on consumer

    behavior from a micro-economic perspective. A more detailed look at how a consumer

    reacts when confronted with a price increase at peak demand.

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    2 Research Question and Goal

    With the introduction of dynamic pricing consumers have three options; they can use

    less energy, reschedule their energy consumption or a combination of both. On an ag-

    gregate level it has been shown that consumers use less energy on peak times when

    dynamic pricing is introduced and that total consumption of energy decreases (Faruqui

    and Sergici, 2010).

    A group that might run the problems of a set budget constraint could be the less

    wealthy.Faruqui et al.(2010) has shown that the lower income groups will benefit from

    the introduction of dynamic prices. If dynamic prices were introduced today, 65% would

    see a decrease in their energy bill. How will those 35% react that will be confronted with

    a price increase with their current consumption pattern (Faruqui, 2010). What has not

    been modeled is dynamic pricing from the perspective of the consumer. How content

    are they with these changes? Will they be forced to change their consumption as they

    simply cannot afford their current pattern or do they see it as a chance to consume

    more energy at different times? From this the research question gets formulated as:

    what is the consumer behavior on dynamic energy pricing when subjected to a budget

    constraint?

    According to the economic theory it is expected that consumers for a part reduce their

    consumption around peak hours and that another part of their consumption is moved to

    different time slots. Illustration 1shows the conceptual model of the expected consumerbehavior. The questions that this model should be able to solve are how much is shifted

    to different time slots? Will energy not consumed at peak-hours really be consumed at

    different times?

    Figure 1: Conceptual Model of the interactive influence of tariff and budget on energy

    consumption and willingness to shift consumption

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    The answer to these questions explain how consumers behave when dynamic energy

    prices are introduced, and are forced to reschedule their energy consumption around peekhours. The outcome could be used as part of energy policies defined by the government.

    First step is the discussion on the literature on consumer behavior and economic theory

    in the next chapter. Followed by the methodology chapter that discusses the techniques

    used to capture consumer behavior with dynamic energy prices. The analysis chapters

    shows the captured data and the significance of all tests used. Finally the conclusions

    are reported and discussed.

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    3 Consumer Choice Theory

    Consumer choice theory is a set of principles that models consumer behavior. It explains

    the relation between preferences and expenditures and how consumers may maximize

    their utility under budget constraints (Deaton and Muellbauer, 1980). This chapter

    explains how consumers behave and the expected behavior is discussed regarding the

    introduction of real-time pricing.

    In the first section consumer preferences are explained and how these preferences affect

    product selection. These preferences cannot always be fulfilled, the constraints in decision

    making are therefore discussed in the second section. The third section explains the

    economic principles behind consumer selection and how consumers deal with changing

    constraints and budgets. Finally the hypothesis how consumers behave in the future

    energy market are introduced.

    3.1 Consumer preferences

    Consumers have varying preferences. For some, the use of renewable energy sources have

    a higher fulfillment rate than gray energy sources. This shows that not only financial

    aspects are taken into account, even though the provided product is technically the

    same, as is the case with green energy. Consumers are willing to pay a premium for

    green energy (Zarnikau, 2003). This holds for energy as well as for other consumption

    products, like fair-trade coffee (De Pelsmacker et al., 2005). Thus, in theory consumers

    are willing to spend more on green energy or try to minimize their consumption of gray

    energy when green electricity is not available.

    One assumption made is that the preferences of consumers should be complete. This

    means that the consumer knows his or her own preferences. This assumption allows us to

    compare choices made by the same consumer (Varian,2006). The second assumption is

    that goods are available in all quantities. For a consumer to show his or her preferences

    it is irrelevant if the choice for 100% green energy is realistic or not.

    3.2 Constraints

    Choices made by consumers are affected by three constraints; endowment effects, budget

    constraints and time constraints (Nicholson and Stapleton, 2005). Endowment effects are

    the changes in income of consumers; budget constrains the amount consumers can spend;

    and the time constraints of when a certain constraint applies. Time constraints restrict

    consumers in that they can not delay the consumption, of for example energy, by five

    years thinking energy will be cheaper then and thus maximizing utility.

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    Assumed is that consumer preferences exhibit non-satiation. When consumers try not

    to maximize the set budget for energy, it cannot be assumed that they will simply spendless. As the saturation for utility is endless, it can only assume consumers spend their

    savings on other goods or services (Deaton and Muellbauer, 1980).

    3.3 Income and substitution effect

    Faced with budget changes, consumer response reflects two economic effects; the income

    effect and the substitution effect. An illustration of these effects is in illustration 2,

    which shows a diagram of 2 products, energy at time slot 18 and at time slot 19. Energy

    at time slot 19 becomes more expensive and the consumer selects a different bundle.

    Displayed in the move from e1 to e1 along indifference curve 1 (IC1). At the sametime the income of the consumer increases, thus the budget constraint moves parallel

    outward, allowing the consumer to consume more of both products. This new optimum

    is found in e2

    at the crossing point of IC2 and BC2.

    e1e1

    e

    2

    Et18

    Et19

    IC1

    IC2

    BC1

    BC2

    Substitution Effect Income Effect

    Total Effect

    Figure 2: The income and substitution effect

    Income effect The income effect in economics can be defined as the change in con-

    sumption resulting from a change in real income (Sullivan, 2003, p.80). If prices remain

    constant a change in income will lead to a parallel shift of the budget constraint. The

    consumer will simply consume more or less of everything.

    Substitution effect A substitute good is a good that replaces another good when

    its price increases. A price change changes the product bundle the consumer selects.

    Electricity is technically a prefect substitute. Gray or green energy do not differ as a

    product, only the in way it is produced. Consumers do make this distinction and treat

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    gray and green energy as different products. Electricity has to be consumed in the same

    time slot it is produced, as storing energy is expensive and ineffective (Chen et al., 2009).With dynamic pricing, each hour of the day must be treated as a different product. And

    consumption is expected to move to adjacent time slots when the energy price at a

    certain hour increases.

    Slutskys equation shows how every price change can be decomposed into an income

    effect and a substitution effect (Deaton and Muellbauer, 1980). A price increase of energy

    at a certain hour will lead to the substitution of another product in a cheaper time slot

    (the substitution effect). It will also make consumers feel less wealthy, and thus have a

    negative effect on his purchasing power (the income effect). These two effects cannot be

    completely separated as they are intertwined.

    3.4 Conceptual Model

    In figure 1the conceptual model is shown which model the two main effects discussed in

    the literature review, the income and substitution effect. The goal is to research consumer

    behavior when selecting energy tariffs while under a budget constraint. A change in

    this budget will have consequences for both the total amount of energy consumed and

    the willingness to shift consumption across the day. The effects from the budget are

    moderated by the different prices per hour.

    Budget. The budget a consumer has available to spend on energy. Following the in-come effect of economic theory, it is expected that a lower budget will result in a lower

    consumption of energy. A higher budget will result in an increase of total energy con-

    sumption. There will be a maximum to this effect, there is a point where a consumer

    has no need for more energy and will use the remaining budget on other goods.

    Hypothesis 1a A lower budget will result in a lower total consumption of energy.

    Hypothesis 1b A higher budget will result in a higher consumption of energy. This

    will be a non-linear effect that will reach a different maximum for each consumer.

    Consumers can counter the effect of a budget decrease by switching their consumption

    to different time slots. Therefore it is expected that a lower budget will also result in a

    greater amount shifted to different time periods. On the other hand consumers with a

    higher salary have more concerns about the environment and are willing to pay more

    for green energy (Zarnikau, 2003). Despite this fact, it is still expected that they are

    not willing to change their consumption pattern to accommodate the use of renewable

    energy.

    Hypothesis 2 A higher budget will lead to fewer changes in scheduling behavior.

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    Price per hour. The dynamic price that is different for each hour of the day. As en-

    ergy is a technically perfect substitute, it is expected that consumers shift their loads todifferent time slots when the price increases. A higher price will also make the consumer

    less wealthy and thus also reduce their total energy consumption.

    Hypothesis 3a A higher price per hour will shift consumption to different time slots.

    Hypothesis 3b A higher price per hour will decrease total energy consumption

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    4 Data and Methodology

    Measuring consumers behavior requires an experimental design, in which subjects choices

    can be observed in a controlled environment (Bryman and Bell, 2007). Key to designing

    an experiment is to maintain orthogonality between attributes, as this prevents collinear-

    ity between attributes (Toner et al., 1999). Stated Choice experiments are designed to

    determine the influence of different variables on predefined outcomes (Rose and Bliemer,

    2008).

    When it comes to stated choice there are three steps to consider ( Bliemer and Rose,

    2006): (i) specify the model, (ii) generate experimental design, (iii) construct a ques-

    tionnaire. In the next section, the model will be specified. The experimental design will

    be constructed and tested in the following sections.

    The following information is required to measure the behavior of consumers with a

    budget under different circumstances. First, consumers need to disclose their normal

    consumption profile. Second, a budget will be assigned and measured how the participant

    changed his behavior. Third, its measured how content the participant is with his new

    schedule. Fourth, real-time pricing is introduced and measured is the change in behavior.

    How many changes are made to the energy consumption to different time slots and the

    change in total consumption. Fifth, post-survey questions, including the measurement

    to see how content the participant is with his new consumption pattern.

    Baseline

    Measurement

    Reschedule

    consumption

    with assigned

    budget under

    fixed pricing

    Measure

    post-decision

    satisfaction

    Reschedule

    consumption

    with assigned

    budget under

    real-time

    pricing

    Post-survey

    Figure 3: Experimental Design

    4.1 Model

    Consumer decisions need to be captured for each appliance for each time slot to test

    the hypothesis about consumer behavior. Each participant (i) will be assigned a budget

    (mi). This budget is spent on scheduling appliances (n) for each time slot (T). The

    price per kWh (P) can change during each time slot. (C) is the consumption in kWh

    of appliance (n). The delta () is a binary variable that shows whether an appliance is

    active.

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    mi =

    T

    t=1

    n

    j=1

    ijtCjPt (1)

    4.2 Experimental design

    Participants are given the scenario that they are a one-person household and asked to

    schedule their energy consumption through the webbased PowerTac scheduling tool. This

    will be the baseline measurement. In the next step the participant is randomly assigned

    a budget and asked to reschedule his consumption to fit the set budget. After which they

    will be given a short questionnaire about the choices made and how convenient this new

    schedule is. Dynamic pricing is explained to the participant and his schedule is displayedagain. This time with different energy prices per time slot and asked to reschedule his

    consumption again. Finally the participants will be asked to complete a questionnaire to

    determine how satisfied they are with their decisions. Data captured from the scheduling

    in a system and measuring their satisfaction through a paper questionnaire shows if the

    participants are willing to change their energy consumption or are being forced to change

    their consumption pattern.

    Attribute: price per hour. To simulate the effects of dynamic pricing, a different

    tariff is introduced for each hour. The average price for a kw/h is 24,35 cents (Centraal,

    2013). In the first scheduling part the average of 24,35 cents is used as the flat tariff

    price. For the second part, the rescheduling will use the simulated dynamic pricing by

    increasing the price for peak hours and decreasing the price for off-peak hours. The

    interval is determined by analyzing current day consumption. The different prices make

    it possible to compare the behavior of participants within a budget group.

    Table 1is determined for a period of 24 hours.

    Attribute: budget constraintThe budget constraints is determined as a fixed per-

    centage. The actual budget is determined by applying the percentage to the total price in

    the baseline measurement. This relative budget shows how participants react to different

    budgets. The budget percentages are in steps of 10 percent, between -0% to -30%. The

    different intervals allow for comparison of participants between the different budgets.The respective budgets are placed table 2.

    The table shows five rows with the different budgets and their deviation from the aver-

    age budget. The fifth group will most likely be able to maximize their utility without

    reaching their set budget constraint. This will be the control group, which is needed

    to see if consumers are willing to reschedule without being forced to by the budget

    constraint.

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    Scheduling Re-Scheduling

    Time slot Price Change Time slot Price

    1-24 e0,2435 -72.3% 1-24 e0,0674

    25-28 e0,2435 +3.3% 25-28 e0,2516

    29-32 e0,2435 -9.3% 29-32 e0,2208

    33-36 e0,2435 -17.5% 33-36 e0,2008

    37-40 e0,2435 +10.8% 37-40 e0,2699

    41 e0,2435 +8.8% 41 e0,2649

    42-44 e0,2435 +10.8% 42-44 e0,2699

    45 e0,2435 +17.7% 45 e0,286646-48 e0,2435 +10.8% 46-48 e0,2699

    49 e0,2435 -7.6% 49 e0,2249

    50-52 e0,2435 +10.8% 50-52 e0,2699

    53-56 e0,2435 +9.8% 53-56 e0,2674

    57-60 e0,2435 +8.5% 57-60 e0,2641

    61-64 e0,2435 -12.8% 61-64 e0,2124

    65-68 e0,2435 -38.8% 65-68 e0,1491

    69-72 e0,2435 -2.5% 69-72 e0,2374

    73-76 e0,2435 +12.9% 73-76 e0,2749

    77-80 e0,2435 -9.3% 77-80 e0,2208

    81-84 e0,2435 +13.9% 81-84 e0,2774

    85-88 e0,2435 +21.1% 85-88 e0,2949

    89-96 e0,2435 -72.3% 89-96 e0,0674

    Table 1: Attribute: price per hour

    Percentage

    Group 1 -0 %

    Group 2 -10 %

    Group 3 -20 %

    Group 4 -30 %

    Table 2: Attribute: budget constraint

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    Attribute: appliances. The following five appliances can be scheduled during the ex-

    periment namely; consumers electronics, ICT appliances, dishwasher, washing machineand the electric stove. The consumption of these appliances is listed in table 3. A

    household has other appliances that are not scheduled, but do consumer power, such as

    a refrigerator, light etc. The budget constraint is based on an average that does include

    the consumption of these devices. These appliances are not in the experiment because

    they cannot be scheduled. To compensate for the lack of these appliances, a fixed value

    ofe27,- is taken out of the monthly budget.

    Appliance Consumption (kWh)

    Consumer Electronics 0,1

    ICT appliances 0,15Dishwasher 0,53

    Washing machine 0,6

    Stove 1,84

    Table 3: Attribute: appliance consumption.

    Money illusion (Shafir et al., 1997), plays a role. If the numbers are presented for schedul-

    ing one day, participants will see differences in range of a couple of cents and likely be

    indifferent of any cost increase. Therefore, all figures are extrapolated to a consumption

    over a year. The yearly cost allows for participants to easily see the impact it has on

    their current situation. This will give a better representation of the effects of a budget

    constraint.

    4.3 Questionnaire

    Satisfaction needs to be measured to see how content participants are with the outcome.

    This to prevent users from rescheduling energy so they fit their budget constraint and

    are able to continue to the next page, but actually dont support the decisions they

    made. e.g. find the constraints to be impossible to live with. To measure this the post-

    decision making scale ofSainfort and Booske(2000)is used. Items range from 1 (stronglydisagree) to 5 (strongly agree). Participants are asked to answer these questions twice.

    Once after the introduction of a budget with scheduling and a second time after the

    introduction of dynamic pricing.

    The used variables for demographics are age, gender, completed education, their current

    living situation and if they currently are the one who pays the energy bill.

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    4.4 Measures

    Total energy consumptionThe total amount of energy (Ci) a participants schedules

    measured in KW/h. The consumption of a appliance (Cj) and the delta as a state

    variable.

    Ci =

    T

    t=1

    n

    j=1

    ijtCj (2)

    Consumption shifted to a different time slot The total amount of energy shifted

    between the two times the participants are asked to schedule their energy consumption.

    mTis defined in equation2 on page17.

    = Ci C

    i (3)

    Compared to previous work, the fifteen experiments analyzed by Faruqui and Sergici

    (2010) have gathered real data from consumers by using smart meters before and af-

    ter the introduction of real-time pricing. Those experiments only captured the total

    consumption of a household for each time slot. This experiment does not capture the

    real-life consumption of consumers, but tries to capture this information in a simulation.

    An advantage of this approach is that we can measure which appliance is used at which

    time and which decision a consumer makes when shifting his consumption. Also mea-

    surable is the ability to ask each consumer how satisfied they are with their scheduling

    decisions.

    4.5 Sample

    A full fractional design requires results in all possible combination (Rose and Bliemer,

    2009). Energy tariffs, the highest tariff of 27 cents minus the lowest tariff of 6 cents

    results in 21 different energy tariffs. The highest budget ofe61 minus the lowest ofe37

    results in 24 different budget constraints. To account for other influences, 10 participants

    are needed per group. This results in 5040 (21 x 24 x 10) participants needed. Instead a

    fractional design is chosen, where the budget constraint is limited to 4 sets, a set energyprice per time slot, resulting in a more manageable 40 required respondents. The pilot

    has shown that an experiment for each participant takes about 30 minutes. More than

    40 respondents is also impractical considering the time it would require.

    The target group to participate in this experiment are those that have their own house

    or apartment and can identify with the energy bill. As previously determined 40 partic-

    ipants are required. Data will be collected in a supervised fashion, where participants

    will complete the experiment on a laptop provided by the author. The choice for a su-

    pervised fashion is made to assist participants with any questions they might have when

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    scheduling energy. Experience from previous studies using the powertac scheduling tool

    show, that in a supervised fashion the data is more reliable.

    4.6 Pilot

    A pilot study has been held in week 33. Friends and colleagues participated in the

    experiment in a supervised fashion. During which they were guided in how to schedule

    their consumption. After the experiment they were questioned if they found anything

    unclear or discovered any potential issues. The following things were adjusted according

    to their feedback;

    Participants found that by being directly assigned a budget, that they were more fo-

    cused on scheduling energy around the set budget instead of giving a truthful approxi-

    mation of their consumption schedule. To address this issue a baseline measurement is

    introduced by letting participants first schedule their consumption without any budget

    constraints.

    The energy budget included the energy tax return of 385,53 a year. According to the

    participants this made the energy budget unrealistic as this does not match the amount

    they pay every month. The budget has been recalculated without this tax return.

    Some cosmetic changes have been made, such as the budget constraint has been made

    more clear by increasing the font size.

    4.7 Data collection

    The collection took place in the month of October 2013. Friends, colleagues and ac-

    quaintances who had not yet participated in the pilot were asked to participate. It was

    done in a supervised fashion to maintain a high quality of data. To make sure that the

    participants could empathize with the situation, all participants were asked in advance

    if they live on their own or have lived on their own for more than six months. Student

    housing is excluded, because they usually pay a fixed monthly tariff that includes rent,

    electricity, water and they cannot empathize with the situation.During the experiment the supervisor asked the following questions to the participants

    at the end of each step. The questions are listed here to make the entire experiment

    both transparent and to minimize any bias introduced by the supervisor. Without these

    questions, participants schedule the consumption around with the sole purpose of staying

    in their assigned budget, but forget that they would have to live with this schedule.

    Asking these questions makes them review the schedule they filled in more closely.

    Would you make this decision in real life as well?

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    Are you satisfied with this schedule?

    Does this schedule effect your routine of working?

    Additional information was given when participants started the third step (the one where

    real-time pricing was introduced). They were explicitly informed that they also had to

    possibility to consume more energy if their budget allowed it. During the pilot some

    participants were so focused on remaining below their budget, that they considered

    themselves to be done with the experiment when real-time pricing was beneficial to

    them.

    During each experiment the participant gave a detailed description of each decision they

    made. Many times their questions were not about the tool, but the questions sought

    confirmation that their decisions were valid or justifiable. The supervisor tried to avoidanswering these questions and negated them by explaining that there is no right or

    wrong answer and stressed that it is their preference, their choice and that they never

    have to account for the decisions they made. Of course being so focused on scheduling

    energy does create a bias, as most participants normally never pay so much attention

    to when they use which appliance.

    Data from participant number 29 was removed from the dataset, because the participant

    refused to empathize with the situation given in the experiment.

    4.8 Descriptive analysis of baseline measurement

    Its imperative that extreme values are removed before analysis as these values can

    interfere with the outcome (Van Dalen and De Leede,2008).

    First the z-score reveals that participant number 1 has a high score of 3,63 from the

    mean. The second step is to analyze these values in a histogram. A look at the histogram

    in figure4 shows that participant number 1 does not fit the distribution and is removed

    from the dataset before further analysis. The impact of this removal is displayed in table

    4.

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    Figure 4: Histogram of baseline measurement in kWh

    N Minimum Maximum Mean Standard deviation

    Before 36 434.46 2379.00 956.2717 348.63676

    After 35 434.46 1553.29 915.6223 252.75987

    Table 4: Descriptive Statistics of the baseline measurement

    4.9 Demographics

    The sample contains 35 participants available for analysis. Of which 24 are male and

    11 female. The largest group by age with 40% are the 35-55 age group. Followed by the

    age group of 25-34 with 34.29%. These groups are not the same size as they represent

    different stages in life. The group 21-24 contains 20% of the total sample and finally

    the group 55+ is underrepresented with just 5.71%. An overview of all demographics is

    available in table5.

    The education level of the sample group is not equivalent to the average in the Nether-

    lands. According to the CBS, 28% of the population has a bachelors degree or higher

    compared to the sample, where 42.9% have a bachelors degree or higher. The remaining

    sample is difficult to compare to the national average as the CBS registers the type of

    high school to determine the education level. Despite this its safe to say those with an

    average education level are unrepresented with only 20% completed vocational education

    in the sample, 28.6% completed high school and 8.6% not finishing high school.

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    The current living situation of the participants is that the largest group, 60%, live with

    a partner in a house. Followed by 17.1% who still live at home with their parents. Tosee if participants can empathize with the situation, they had to answer the question if

    they are the ones paying the energy bill and 48.6% of sample pays the energy bill and

    the remainder of 51.4% does not.

    Variable Category Frequency Percentage

    Gender Male 24 68.6%

    Female 11 31.4%

    Age 21-24 7 20%

    25-34 12 34.29%

    35-55 14 40%

    55+ 2 5.71%

    Education Did not finish high school 3 8.6%

    High school 10 28.6%

    Vocational Education 7 20%

    Bachelors degree 10 28.6%

    Masters degree or higher 5 14.3%

    Living Alone in a flat/apartment 2 5.7%

    Alone in a house 0 0%

    With parents in a flat/apartment 0 0%

    With parents in a house 6 17.1%With a partner in a flat/apartment 3 8.6%

    With a partner in a house 21 60%

    With other people in a house 2 5.7%

    With other people in a flat/apartment 1 2.9%

    Decision maker yes 17 48.6%

    no 18 51.4%

    Table 5: Demographics of participants

    A comparison of the average consumption in kWh of the baseline measurement to thenational average consumption of a one-person household cannot be conducted directly,

    because in this experiment not all consumption can be scheduled and the use of an

    electric stove is not comparable as most do not have an electric one. The table 4on page

    20shows an average of 915.62 kWh per year that the participants scheduled during this

    experiment. The average for a one-person household is 2010 kWh (Nibud, 2013). During

    the setup of the experiment the use of appliances not in this experiment was estimated to

    be around 1322 kWh a year. The combined consumption of what participants scheduled

    and was assumed totals to 2237.62 kWh a year. A difference of 227,62 kWh that can be

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    explained by the fact that this experiment used an electric stove and not every household

    has one. Consumption wise, the participants in this group are a good reflection of theconsumption of actual one person households.

    The experiment has randomly divided participants into four budget groups. Table 6

    reports the frequencies of participants per group. It was expected that after the experi-

    ment that there would be a uniform distribution, 8 to 9 per group. The randomization

    function appeared to have been not entirely random. Group 1 with a budget of -0% is un-

    derrepresented, while budget groups 2 (-10%) and 3 (-20%) are overrepresented.

    Percentage Participants

    Group 1 -0% 5

    Group 2 -10% 10Group 3 -20% 12

    Group 4 -30% 8

    Table 6: Gathered data by budget group

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    5 Analysis

    This chapter is divided into four sections that give an in-depth analysis to test the

    different hypotheses. The conclusion of this study will be in the next chapter.

    5.1 Effect of a budget constraint on energy consumption

    The impact of a budget constraint on energy consumption is a linear effect where par-

    ticipants can spend less due to the budget. The importance of this test is that it is the

    foundation for the other analysis in the next sections. Table 7 shows the descriptives of

    consumer behavior with a flat tariff under a budget constraint. This behavior is mea-

    sured by looking at the energy consumption in kWh before and after they have beenassigned a budget.

    Before After Paired sample T-test

    Budget group Avg Std Avg Std t Sig.

    -0% 808,954 107,747 808,954 107,747 - -

    -10% 904,536 326,190 781,545 277,638 6,511 0,000

    -20% 1023,983 255,361 802,173 194,337 11,108 0,000

    -30% 833,606 168,184 570,211 114,118 12,968 0,000

    Table 7: Consumption behavior with flat tariff

    The table also holds the results of a paired samples T-test between the before and

    after introduction of the budget. The budget has a significant effect on consumption

    for each group (p 0,001). Except the -0% could not be calculated as the difference

    is exactly zero. A relation between the assigned budget and the delta between energy

    consumption before and after is explored with an ANOVA to determine the effect of

    a budget constraint on energy consumption under a flat tariff. This is found to be

    significant (F = 31,063, p = 0,000). An expected result, because participants were forced

    to reduce consumption and a linear decrease is expected as they have no choice but to

    reduce their consumption under a flat tariff.

    5.2 Effect of price on willingness to shift

    An important relation to analyze is the effect of a time slots price on shifting behavior. A

    higher price for a time slot should move participants away to consume in different time

    slots. First, total consumption of all participants for each time slot has been depicted in

    figure5.After the introduction of dynamic prices the consumption has shifted, which

    is displayed in figure 6. Visible is that the biggest peak has been reduced by 9.7%.

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    The exact numbers are published in table8. Despite the higher price, participants still

    seem to prefer the time slots 70 to 80, probably due to their current lifestyle. Anotherimportant thing to note is the increase in total consumption with 3.52%.

    Table 8: Descriptive Statistics of the Tariff Type

    N Minimum Maximum Mean Std Consumption (kWh)

    Flat Tariff

    Price 96 24,35 24,35 24,35 0,00

    Consumption 96 0,398 44,633 8,175 8,543 784,758

    Real Time Tariff

    Price 96 6,74 29,49 18,68 9,000Consumption 96 0,725 40,703 8,462 9,145 812,378

    Note: This table shows the minum, maximum and average of the price across all time

    slots. Also included is the sum of total consumption for each tariff type.

    Figure 5: Consumption per time slot with flat pricing

    Note: On the left axis the total consumption in kWh is displayed per time slot (bottom

    axis). The line represents the price that is displayed on the right axis. The price is in

    eurocents per kWh.

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    Figure 6: Consumption per time slot with real-time pricing

    Note: On the left axis the total consumption in kWh is displayed per time slot (bottom

    axis). The line represents the price that is displayed on the right axis. The price is in

    eurocents per kWh.

    Figure 7: Correlation between price change and changed consumption

    Note: The blue line on the left axis shows the change in eurocents per kWh with the

    introduction of real-time pricing. The green line on the right axis displays the change

    in consumption for each time slot.

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    Table 10: Consumption profiles

    Consumption in kWh

    Same Higher

    Willing to shift 13 15

    Not willing to shift 0 7

    Note: Displays the number of participants that have been categorized into the four

    groups. Each group is considerd to be a different consumption profile.

    The second group that is analyzed consists of those that have shifted energy and con-

    sumed more energy in kWh. This data shows that this group was able to repair their

    impacted consumption pattern in from the previous step. The lower the budget, the lessparticipants seem to consume extra. A possible explanation is that their budgets are so

    low that they cannot consume energy at their preferred times and instead give up using

    this appliance, as they still have budget remaining. Another explanation might be that,

    because they sacrificed a large part of their original consumption that have grown ac-

    customed to their new consumption profile and do not need/want to spend more energy,

    even when their budget allows it.

    The third group that has been identified has not shifted any energy, but has only in-

    creased consumption. Participants in this group have already scheduled their high con-

    suming appliances in times that were cheaper with dynamic pricing. This is an effect of

    the current dual tariff in the Netherlands. Therefore, they could not shift any consump-

    tion as they already have the cheapest time slots.

    There is not enough data to analyze each subgroup on its own. The average change in

    consumption shows a trend in figure8.

    For each profile we analyzed the relation between the profile and the change in con-

    sumption. The result is significant (F = 7,050, p = 0,003). The relation between the

    profile and the amount shifted to different time slots is significant (F = 8,345, p =

    0,001).

    Further analysis to determine the differences between the different profile groups, a

    Tukey HSD test has been performed. Table 12 shows the results. The change in con-

    sumption seems to only be significant between profile 1 (same (or lower) consumption

    and shifted consumption) and profile 2 (increased consumption and shifted consump-

    tion). The amount shifted to different time slots are significant between all profile 2 and

    3, 3 and 1, but not between 1 and 2.

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    Table 11: Descriptives of Identified Consumer Profiles

    Delta Shift Euro under budget

    Budget N Avg Std Avg Std Avg Std

    Profile 1: same (or lower) consumption and shifted consumption

    -0% 4 -0,155 0,828 16,243 11,817 32,580 16,278

    -10% 5 -4,680 10,465 9,810 6,176 63,98 23,775

    -20% 4 -5,798 11,595 16,893 13,038 97,138 42,909

    -30% 0 - - - - - -

    Profile 2: increased consumption and shifted consumption

    -0% 0 - - - - - -

    -10% 3 160,143 249,578 9,790 2,131 58,947 20,489

    -20% 6 119,218 102,913 15,487 5,991 61,831 26,825-30% 6 93,695 70,963 13,643 7,313 74,190 20,570

    Profile 3: increased consumption and no shift in consumption

    -0% 0 - - - - - -

    -10% 2 44,850 13,789 0,000 - 86,160 54,122

    -20% 2 99,320 72,804 0,000 - 68,265 3,599

    -30% 2 103,920 79,309 0,000 - 80,225 32,718

    Note: This table shows the descriptives of the three different groups. It shows the

    amount participants changed, how much they have shifted to different time slots and

    how many euros they are under budget.

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    Table 12: Comparison of means between consumer profiles

    Dependent Variable Mean Difference (I-J) Std. Error Sig.

    Change in Consumption

    1 2 -120,826* 32,540 ,002

    3 -86,328 42,383 ,120

    2 1 120,826* 32,540 ,002

    3 34,497 41,481 ,687

    3 1 86,328 42,383 ,120

    2 -34,497 41,481 ,687

    Amount shifted

    1 2 ,358 2,842 ,991

    3 13,968* 3,701 ,002

    2 1 -,358 2,842 ,991

    3 13,610* 3,623 ,002

    3 1 -13,968* 3,701 ,002

    2 -13,610* 3,623 ,002

    *. The mean difference is significant at the 0.05 level.

    Note: This table holds the results of the Tukey HSD test to display the differences in

    means between groups.

    Figure 8: Trend in consumer profiles

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    5.4 Observation

    One observation made is that participants, when confronted with their budget first start

    to downsize the consumption of low power consuming appliances, such as consumer

    electronics and ICT. The experiment did not capture the order of changes made to the

    schedule. Only the amount of changes at the end of the stage was captured. This data is

    shown in table13. The different groups cannot be compared amongst each other as the

    baseline measurement is unique per participant. What is visible from these descriptives

    is that a large majority (50%+) of the changes are accounted to consumer electronics

    and ICT, despite these two being the least consuming appliances of the five.

    Participants only considered changing the consumption of the remaining three appliances

    when they realized that giving up on CE and ICT is not enough. If participants forexample were to start with a shorter (dish) washing program they would not have to

    make these relative large changes to CE and ICT.

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    Table 13: Change in consumption of appliances

    Number of Changes Change in kWh

    Budget Appliance Avg Std % %

    -0% CE 0 0 0

    ICT 0 0 0 0

    DW 0 0 0 0WM 0 0 0 0

    STOVE 0 0 0 0

    -10% CE 20,40 34,30 44.25% 2,04 21,56%

    ICT 20,60 26,75 44.68% 3,09 32,66%

    DW 3,10 5,51 6.72% 1,64 17,37%

    WM 0,80 1,75 1.74% 0,48 5,07%

    STOVE 1,20 1,48 2.60% 2,21 23,34%

    Total 46,10 - 1 00,00% 9,46 100,00%

    -20% CE 18,33 29,54 35,77% 1,83 10,74%

    ICT 16,67 13,51 32,53% 2,50 14,65%DW 9,08 8,38 17,71% 4,81 28,19%

    WM 4,25 6,03 8,29% 2,55 14,94%

    STOVE 2,92 2,47 5,70% 5,37 31,48%

    Total 51,25 - 100,00% 17,07 100,00%

    -30% CE 43,50 32,76 53,86% 4,35 21,46%

    ICT 21,75 18,014 26,93% 3,26 16,09%

    DW 7,50 4,75 9,29% 3,98 19,61%

    WM 4,88 3,40 6,04% 2,93 14,44%

    STOVE 3,13 1,727 3,88% 5,76 28,41%

    Total 80,76 - 100,00% 2,.27 100,00%Note: For each budget this table holds the amount of changes participants made for

    each appliance. Each change is measured in a 15 minute time slot. Also displayed is

    the effect of these changes measured in kWh.

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    6 Discussion and Conclusion

    This research has analyzed and explained to some extend what decisions would con-

    sumers make, how they would react, to the introduction of dynamic pricing while under

    a budget constraint. As everyone has a budget a some level it is important to see how each

    individual would react. This chapter will first present the main findings and discusses

    these findings with the literature. Followed by practical implications and concluding

    with the limitations and the next steps for further research.

    6.1 Main findings

    The impact of a budget on consumers decision making has been measured under different

    conditions. The first condition is under flat pricing and the second under dynamic pric-

    ing. This to distinguish between the effect of different tariffs and that of budget.

    The impact of a budget constraint on energy consumption. Under flat pricing it

    is expected according to the income effect of economic theory that consumption reduces

    linear with the budget. This is tested in the first hypothesis as:

    Hypothesis 1aA lower budget will result in a lower total consumption of energy.

    Hypothesis 1b A higher budget will result in a higher consumption of energy.

    This will be a non-linear effect that will reach a different maximum for each

    consumer.

    The statistical analysis is explained in section 5.1, where the change in consumption is

    measured according to the different budget group. Based upon the results, it is found

    that the effect of a budget constraint on the change in energy consumption is significant.

    This simply means that with a lower budget a consumer has to reduce his consumption.

    The opposite, namely increasing the budget does not have an increased linear effect on

    consumption, there is a point where a consumer has no need for more energy and will

    use the remaining budget on other goods.

    The impact of pricing on energy consumption. Consumers can counter the effect

    of a budget decrease by switching their consumption to different time slots. Therefore,

    it is expected that a lower budget will also result in a greater amount shifted to different

    time periods. Leading to the following hypothesis:

    Hypothesis 2A higher budget will lead to fewer changes in scheduling behavior.

    Analyzing consumer behavior with dynamic pricing in section 5.3 has revealed that there

    is not one way of how consumers react to a budget constraint. Measured in the amount

    people shift consumption to different times and how the participants changed total con-

    sumption. Further analyses showed that consumer behavior can be split into 3 distinct

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    from the experiment setup. Dynamic prices could lead to such high prices for certain

    periods that only the wealthy are able to consume energy at their choosing. This fearhas been somewhat prevented by the implementing fairness to reduce the extreme

    variances introduced by real-time pricing, but still providing benefits to both consumers

    and power utility companies (Vuppala et al.,2011).

    Some consumers cannot oversee the consequences of when they use certain appliances.

    e.g. have less experience with budgeting or to plan the use of appliances throughout the

    day.Heath and Soll (1996) shows that consumers sometimes have problems of keeping

    track of their spendings when it revolves around many smaller spendings. This could

    be the case with energy as well. Do all consumers have the mental capability or desire

    to keep track of the consumption rate of each appliance in their home, the constant

    changing energy price every 15 minutes of the day and expected to make an optimal

    schedule throughout the day.

    During this study it is assumed that consumer preferences are complete, meaning that

    the consumer exactly knows his own preferences (Varian,2006). Because of the controlled

    environment in this experiment, all tariff data for each slot has been given in advance

    and participants are forced to critically review their consumption in each stage. It is

    questionable if these conditions are met when dynamic pricing is actually introduced.

    The tariff is unpredictable and has to be communicated to consumers each time the

    tariff changes.

    6.3 Managerial implications

    This research shows that using dynamic energy prices can be used by consumers to

    consume more energy for the same price or consume the same and pay less. For this

    they do lose the ability to consume energy whenever they want. Implementing this kind

    of pricing not only reduces peak-loads on the energy grid and leads to an overall higher

    efficiency on the energy market, but can help people with a tight budget to reduce their

    energy bill (Faruqui, 2010).

    This study further adds that consumers are not just influenced by dynamic pricing

    to reduce consumption at peak hours and that all consumers try to maximize their

    consumption, but that there is a large group of consumers who are actively seeking to

    reduce their energy bill and are willing to shift more. Besides only focusing on reducing

    peak consumption these consumers could be targeted to use energy when there is a

    surplus of energy on the grid. Roughly one third of the participants tried to actively

    maximize the amount they saved. In recent years there have been more trends where

    people see downsizing as a sport or game (Schreurs et al.,2012).

    Another application for these results lie with the government. As shown that every group

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    of participants was below budget and could have spend more on energy if they wanted.

    The introduction of dynamic pricing could trigger or improve the energy conservatism incitizens, reducing total energy consumption, because citizens are confronted when they

    use which appliance at which cost.

    6.4 Limitations and further research

    The biggest limitation is the amount of data available. Gathering data in a supervised

    fashion does guarantee a high quality of data, but limits the amount of participants

    that can participate in a reasonable time frame. A pilot with smart meters and smart

    appliances would solve this problem. This is difficult to set up, but there are two reasons

    to still set this up. First, doing so will remove any bias that is introduced in the currentexperiment by how data is presented, the introduced bias of a supervisor. Secondly, it

    will produce a near endless stream of perfect data when the usage is captured of every

    appliance at every minute of the day for each household. Combined with modern big

    data solutions will allow for any imaginable analysis to be performed.

    The experiment in its current form can be improved by making the dynamic prices based

    on the schedule on the participants and not fixed per time slot. Real-time pricing should

    emulate that each participant pays more for the time slots that he currently uses. This

    prevents that some participants are already on the cheap side when this kind of pricing

    is introduced compared to their baseline schedule.

    The content of the experiment itself can be improved with the introduction of other

    appliances such as electric vehicles. Electric vehicles consume large amounts of energy

    and allowing to schedule this consumption gives an extra dimension to modeling con-

    sumer behavior. Instead of charging these vehicles when the consumer comes home.

    This improved experiment should be able to charge electric vehicles during the night

    when electricity is cheaper. It should even be possible to model the ability to use the

    remaining energy from the vehicle at peak times (Andersson et al.,2010). Adding elec-

    tric vehicles to the experiment has to be presented carefully as most consumers do not

    have experience with a vehicle that requires 8 hours for a full charge ( Binding et al.,

    2010).

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    A Experiment

    The experiment will be displayed in the following sections. The pages have been stripped

    of any layout to make the text more readable on paper.

    A.1 Opening page

    Dear participant,

    This experiment asks you to schedule your energy consumption under different situ-

    ations. It is important that you carefully read each situation and take your time to

    complete these situations as you would in real life.

    Thank you for participating and press begin to start.

    A.2 Page 1

    At the beginning of this part of the study you will be asked to assume a certain role. We

    kindly ask you to read carefullythe instructions and bear in mind the information

    given to you while making decisions during the simulation and filling in the

    questionnaire.

    You live in your own apartment in Rotterdam. You pay the energy bill separately fromthe bills with which you have to deal by yourself. Until now you have been using a flat

    electricity tariff which was re-evaluated once a year so that you either had to pay a bit

    more or got some money back. You did not pay attention to what kind of energy is

    being used, nor did you keep track of when you use the home appliances. Nevertheless,

    you probably have some habitual schedule as for how many times a week and when you

    usually use some of them.

    Please carefully consider the use of the following appliances (stove, dishwasher, washing

    machine, consumer electronics and ICT hardware). You are supposed to use each

    type of appliance at least once per week and to use each day of the week at

    least one appliance.

    Energy cost is represented by a colored graph in the background of the daily schedules.

    The scale is 0 to 40 eurocents/kilowatt-hour.

    Please create a truthful weekly schedule for these appliances based on your own per-

    sonal preferences.

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    A.3 Page 2

    Introduction to the Energy Scheduling Tool

    On the next page we will ask you to schedule your consumption pattern in the Energy

    Scheduling Tool.

    There will be a graph for each day of the week. Below each graph is the hour of the

    day (0-24h), and at the right side the costs per Kilowatt hours (kWh) in eurocents. The

    colored background represents the energy costs.

    Scheduling: At the beginning there is already a typical one household week schedule

    pre-filled. You can customize this default schedule to your situation and preference.

    Extra household appliances can be scheduled with Dragn Drop from the top rightonto the preferred day and hour. You can also alter the duration of an appliance

    by making the bar smaller or larger. Already scheduled appliances can be altered by

    selecting and dragging the bars to a different hour or day. You can remove scheduled

    appliances by dragging the appliance back to the top right on the appliance pool. When

    you are done with scheduling energy, you can confirm by pressing next at the bottom

    of the page.

    Please use the animation below to become familiar with the Energy Scheduling Tool

    before going to the next page.

    Figure 9: Animation showing how to use the scheduling tool.

    Press next to start with the Energy Scheduling Tool.

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    A.4 Page 3

    Figure 10: Baseline measurement

    A.5 Page 4

    Introduction to the Energy Scheduling Tool

    On the next page we will ask you to schedule your consumption pattern in the EnergyScheduling Tool.

    You will be a assigned a monthly budget (that will be displayed at the bottom of the

    page). Please do not schedule more energy than your budget allows.

    Flat Tariff- The price per kWh is the same across the day.

    There will be a graph for each day of the week. Below each graph is the hour of the

    day (0-24h), and at the right side the costs per Kilowatt hours (kWh) in eurocents. The

    colored background represents the energy costs.

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    Figure 11: Flat pricing

    Press next to start with the Energy Scheduling Tool.

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    A.6 Page 5

    Figure 12: Schedule energy consumption with budget

    A.7 Page 6

    Please write down on the paper questionnaire how satisfied youre with the scheduleyou made. e.g. it enforces you to change habits or hinders you to follow social or work

    obligations

    Please write down the following ID; ...

    A.8 Page 7

    Introduction to real time pricing

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    On the next page we will ask you to re-schedule your consumption pattern in the energy

    scheduling tool. This time with real time pricing.Real Time Pricing Tariff - The price per kWh is different for each hour of the

    day. The price pattern will consists of high prices in the afternoon and low prices at

    night.

    Figure 13: Real-time pricing

    There will be a graph for each day of the week. Below each graph is the hour of the

    day (0-24h), and at the right side the costs per Kilowatt hours (kWh) in eurocents. The

    colored background represents the energy costs.

    Press next to start with the Energy Scheduling Tool.

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    A.9 Page 8

    Figure 14: Schedule energy consumption with budget under dynamic pricing

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    B Questionnaire

    Post-Decision Satisfaction (Sainfort and Booske, 2000)

    DS1 My decision is sound

    DS2 I am comfortable with my decision

    DS3 My decision is the right one for this situation

    DS4 I am satisfied with my decision

    DS5* It was difficult to make a choice

    DS6 I had no problem using the information

    DS7 The information was easy to understand

    * reverse coded

    Scales range from 1 (strongly disagree) to 5 (strongly agree)

    Demographics

    Gender Male / Female

    Age

    Education Previous education:

    1 = Did not finish high school

    2 = High school graduate

    3 = Some college

    4 = Bachelors degree

    5 = Masters degree or higher

    Living Type of household:

    1 = Alone in a flat/apartment,

    2 = Alone in a house,

    3 = With parents in a flat/apartment,

    4 = With parents in a house,

    5 = With a partner in a flat/apartment,

    6 = With a partner in a house,

    7 = With other people in a house,

    8 = With other people in a flat/apartment

    Decision making Are you the one who pays the energy bill?

    yes / no