Changing user behavior with home electricity use to reduce and shift the demand on the electric grid

Changing User Behavior with Home Electricity Use to Reduce and Shift the

Demand on the Electric Grid

by

Saluka Amarasinghe B.S. Mechatronics Engineering

University of Waterloo, 2014

Submitted to the Integrated Design and Management Program in partial fulfillment of the requirements of the degree of

MASTER OF SCIENCE IN ENGINEERING AND MANAGEMENT AT THE

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

JUNE 2019

@2019 Saluka Amarasinghe. All rights reserved.

The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created.

Signature of Author:

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Saluka Amarasinghe Integrated Design & Management Program May 15, 2019

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certified by:

Tony Hu Academic Director, Integrated Design & Management Program Thesis Supervisor Accepted by: MASSACHUSETTS INSTITUTE OFT ECHNOLOGY

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7 2019

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Matthew S. K essy Executive Director, Integrated Design & Management Pro ram

Changing User Behavior with Home Electricity Use to Reduce and Shift the

Demand on the Electric Grid

by

Saluka Amarasinghe

Submitted to the program of Integrated Design and Management on May 10, 2019 in Partial Fulfillment of the Requirements for the Degree of Master of Science in Engineering and

Management at the Massachusetts Institute of Technology

Abstract

Most household consumers in the US are unaware of their electrical usage or the price of electricity until they receive their monthly bill. However, they are concerned about being "wasteful" when it comes to electricity use. But most consumers have no idea what being

wasteful means. What if there was a way to show residential consumers their real-time electrical usage in terms of price or in terms of environmental emissions from non-renewable power plants? Would this appeal to their concerns about wastefulness and cause them to change their behavior with household appliance use?

To test this question an experiment was designed, and multiple prototypes were built. The experiment consisted of a prototype showing a traffic light color pattern of two lights. The first light indicated the price/emissions metric chosen based on user allegiance. The second light indicated the usage of electricity in their home. After running this experiment, the key takeaway was that that consumers will change their electrical usage behavior based on a metric that matters to them but will not compromise comfort or convenience over price or emissions. Electric energy trading in the US is a complicated system and fundamentally a business. Electrical energy is predicted and traded the day before, generally using clean energy sources in the US. However, if there is a surge in demand on the predicted day, dirty power is turned on.

A dirty power plant is classified as being harmful to the environment by burning coal or oil. Dirty

power plants are also expensive to the consumer and inefficient in the electric grid but can be turned on instantly in times of need. Because of this, the trading system is designed to minimize the use of dirty power. The electricity trading models follow a principle called the "duck curve". The duck curve is a graph of power production over the course of a day that shows the timing imbalance between peak demand and renewable energy production. Generally, grid usage follows the duck curve.

Thesis Supervisor: Tony Hu

Acknowledgements

I would first like to thank my thesis advisors, Gian Pangaro, Creative Director at the IDEO

CoLab in Cambridge and Tony Hu from the Integrated Design and Management at MIT. I appreciate how available you were as advisors to help me think through the research and structure of this thesis. Above all, I thank you for your insightful comments, patience, and encouragement to apply academic rigor and disciplined methods to explore a compelling thesis topic.

I would like to thank Matthew Kressy, for believing in me and opening doors I never knew

existed. The same gratitude is expressed to Andy Macinnis, Melissa Parrillo and the rest of the IDM administrative team.

In addition, I would also like to thank Professor Reid Bailey from the University of Virginia for being a valuable team member in the initial stages of this research. I was constantly impressed

by his capacity to inspire and incredible work ethic to complete the tasks of this study. I would like to acknowledge the IDEO CoLab organization who first inspired me to tackle this

project and provided me with the resources and knowledge to create something amazing. While

I had a good understanding of design research and empathy through the IDM program, my time at the CoLab helped hone my skills and learn so much more about the human centered design process and how to apply it this real-world problem. Most of the work in this thesis occurred during my fellowship at the CoLab and could not have been completed without the support of everyone on that team.

Finally, being at MIT was a dream that I couldn't even imagine, yet alone a possibility, but somehow, I ended up here. There have been many people who helped me get here, but none have done more for me than my mother and father. For that, I am eternally grateful and hope I have made you proud.

Intellectual Property

While this document and the work performed during this research belong to the author, the original problem statement of understanding consumer behavior and how it relates to the electric grid belongs to IDEO CoLab. This includes the name SaveWatt used to identify the prototypes that were built during the research. The author does not claim the rights to that name. This also includes the work done by the author during the time at the IDEO CoLab facility. In addition, IDEO CoLab provided resources and participants to the author that helped solve the stated problem through the design process.

Table of Contents

1 . In tro d u c tio n ... 1 3 1 .1 T h e P ro b le m ... 14 1.2 Background Research ... 14 1.2.1 Behavior Change ... 14 1.2.2 W allpaper Theory... 16

1.2.3 The Electric Grid System ... 17

1.2.4 The Residential Customer... 21

1.2.5 The Duck Curve ... 22

1 .3 A p p ro a c h ... 2 3 2. Design Research ... 25 2 .1 U s e r R e s e a rc h ... 2 5 2 .1 .1 P a rtic ip a n ts ... 2 5 2.1.2 Research Questions... 26 2 .2 R e s e a rc h In s ig h ts ... 2 6 2 .3 Id e a G e n e ra tio n ... 2 7 2 .4 Id e a tio n R e s u lts ... 2 9 3. Experiment Design & Testing ... 31

3.1 Experimental Questions... 31 3 .2 E x p e rim e n t S e tu p ... 3 2 3.2.1 Subject Recruiting ... 33 3 .2 .2 D a ta R e c o rd ing ... 3 3 3.2.3 Subject Interviews ... 36 3.2.4 Experiment Timeline ... 40 3.3 Prototype Development ... 40

3.3.1 The SaveW att Light... 41

3.3.2 The SaveW att Appliance Sensor...43

3.3.1 Hardware Design ... 43

3 .3 .2 S o ftw a re D e s ig n ... 4 6 4 . E x p e rim e n t R e s u lts ... 4 9 4.1 Data Analysis & Results ... 49

4.1.1 Quantitative Results ... 50

4.1.2 Qualitative Results ... 53

4 .2 K e y In s ig h ts ... 5 6 4 .3 Id e a G e n e ra tio n ... 58

4.3.1 Preliminary Sketches ... 58

4.3.2 Detailed Renders & Tests ... 60

4.3.3 Final Form Factor... 62

5 . F in a l P ro to ty p e ... 6 4 5.1 Criteria, Needs & Objectives...64

5.2 Hardware Design...65

5.2.1 Hardware Component Description ... 65

5.2.2 Battery Life Calculation ... 68

5.3 Software Design ... 69

5.3.1 Prototype Firmware... 69

5.3.1 Smartphone Application ... 70

5.4 User Tests of the Final Prototype ... 75

6 . C o n c lu s io n s ... 8 0 6.1 Review of Research ... 80

6.2 Research Conclusion...81

6.3 Areas of Opportunity for Future Studies...82

7 . W o rk s C ite d ... 8 4 8 . A p p e n d ix ... 8 6 8.1 Katie's Interview Package... 86

8.2 Katie's 3-Day Log Book ... 95

8.3 Final Prototype Firmware... 99

8.4 Final Prototype Mechanical CAD ... 102

List of Tables

Table 1. U.S. residential sector electricity consumption by major end uses in 2018 ... 21

Table 2. Participant list for initial user research ... 25

Table 3. Stakeholder research participants - energy trading and infrastructure...26

Table 4. Five major themes summarized from the user research ... 26

Table 5. Participants of the experiment ... 33

Table 6. Data being recorded during the experiment...34

Table 7. Description of the Grid Metric light and Usage light on the SaveWatt prototype. ... 41

Table 8. Description of each component in the software design... 47

Table 9. Summary of interview and logbook recordings from each participant. ... 54

Table 10. Key quotes from participants that re-inforce the idea behind the prototype...55

Table 11. Key insights from the prototype testing with participants... 56

Table 12. Description of components in the SaveWatt Sticker ... 66

List of Figures

Figure 1. Areas in which a nudge is likely to be most effective is indicated with a YES ... 15

Figure 2. 5 minute shower product designed to save water...17

Figure 3. Energy consumption in the United States from all sources... 18

Figure 4. The three electrical power grids and their balancing authorities in the US]...19

Figure 5. The key players in getting energy from the supplier (generator) to your home ... 20

Figure 6. Electrical pricing auction process by PJM ISO ... 20

Figure 7. Real-time pricing data from Coined's website ... 21

Figure 8. The duck curve shown in orange...23

Figure 9. Human centered design approach used in this research...24

Figure 10. Low fidelity prototypes of ideas generated from user research...28

Figure 11. Prototyping board used to test our prototypes with users ... 29

Figure 12. Sample Logbook page for the participant's manual entry of data...35

Figure 13. Pre and exit interview package for each participant used internally for analysis. ... 38

Figure 14. Participant information package given to the participant including the log-book. ... 39

Figure 15. Timeline for each participant's participation in the experiment...40

Figure 16. Experiment prototypes ready for deployment ... 41

Figure 17. T he S aveW att Light... 42

Figure 18. The SaveWatt Appliance Sensors... 43

Figure 19. CAD model of the SaveWatt Light box that is 3D printed...45

Figure 20. Off-the-shelf box that was modified to act as the SaveWatt Appliance Sensor box ..45

Figure 21. Laser cut covers for the SaveWatt Light Box showing two available grid metrics ... 46

Figure 22. Software design block diagram for server side and Photon firmware... 47

Figure 23. Participants of the experiment holding and using the prototype...49

Figure 24. Data collection of Katie's appliances use and real-time price light...50

Figure 25. Data collection of Sonja's appliances use and emissions light...51

Figure 26. Data collection of Gabe's appliances use and emissions light...52

Figure 27. Data collection of Carolyn's appliances use and emissions light. ... 53

Figure 28. Form factor ideation sketches for the high-fidelity version of the SaveWatt light...59

Figure 29. Three participant chosen form factor concepts for high fidelity prototyping...60

Figure 30. 3D renders and context photo of the glow cube. ... 61

Figure 31. 3D renders and context photo of the duck curve pebble... 61

Figure 33. Participant research notes for form factor decision... 63

Figure 34. System block diagram of the SaveWatt Sticker Prototype...66

Figure 35. Three SaveWatt Sticker lights were built for multiple user tests...68

Figure 36. Front and back view of the SaveWatt Sticker light components...68

Figure 37. Grid metric and usage settings screen for the smartphone app...71

Figure 38. Current and historical waste score for the smartphone app...72

Figure 39. Usage from Sense or Tendril API being shown on the SaveWatt smartphone app...73

Figure 40. Historical, real-time and predicted price/emissions data on the smartphone app...74

Figure 41. The SaveWatt sticker mounted on a magnetic wall at Filipp's apartment...76

Figure 42. A SaveWatt sticker on a clothes dryer during the final prototype test. ... 77

Figure 43. The sticker mounted on the refrigerator at Amanda K's home. ... 78

Figure 44. The sticker mounted on the wall by the kitchen light switch at Priyantha's home...78

1. Introduction

The energy industry is complicated. Most US consumers are charged a near-constant retail price for electricity, despite substantial hourly variation in the wholesale market price. Because electricity is very costly to store, wholesale prices vary from day to day and often fluctuate by an order of magnitude between low-demand nighttime hours and high-demand afternoons.

Nearly all retail consumers, however, are charged some average price that does not reflect the wholesale price at the time of consumption. In theory, economists have long recognized that this creates allocative inefficiencies, and there is a long literature on "peak load pricing" and "real-time pricing". In practice, the implications of correcting this inefficiency fundamentally depends on how price elastic consumers are (Alcott).

So why study real-time pricing and how does this connect with user behavior? Real-time pricing is one of the central issues in an important industry. In 2007, the electric power sector

accounted for 2.5 percent of United States GDP, or $326 billion in retail sales per year (Alcott).

Most US households currently have electricity meters that simply record the total consumption of electricity since installation, meaning that the consumer cannot be charged prices that vary from hour to hour. Furthermore, the only way for the electric utility to observe household's consumption is to send a worker to read the meter, a costly and potentially error-prone process (Alcott).

The "Smart Grid" is a set of emerging electric power information technologies that include, among other things, household energy management devices and technologies that facilitate communication between electricity retailers and consumers. From the utility's perspective, improvements in these technologies offer reduced meter reading and administrative costs and the potential for real-time metering of electricity use. Furthermore, by allowing households to more easily observe prices and consumption, and even to automate how air conditioners and other appliances turn on and off in response to real-time prices, Smart Grid technologies can increase consumers' price elasticity of demand (Alcott).

The primary hypothesis of this paper is that if consumers are shown the price of electricity they are consuming in real-time, then they would change their habits in using electrical devices within the home. This change of behavior will then affect how energy is generated and how energy demand is utilized in order to keep using clean generation methods.

1.1 The Problem

This research document explores two ideas. The first idea deals with people and the way they behave. The second idea explores how the energy infrastructure in the US is built. The

combination of the two ideas is the essence of this research topic. So, in short, to describe the problem that requires solving it would be: How might we change a consumer's behavior with

residential electricity use to reduce or shift the demand on the electric grid during peak hours?

In order to answer the above question prior research work needs to be done to understand exactly how both the ideas work. The next few sections of this introductory chapter will explain these concepts in further detail.

1.2 Background Research

1.2.1 Behavior Change

What motivates someone to change their behavior? There are three core concepts when it comes to behavior change. Nudging, framing and conditioning. Each is described in detail

below.

Nudging means carefully guiding people's behavior in desirable directions without using either a carrot or whip. Instead when nudging, one arranges the choice situation in a way that makes the desirable outcome the easiest or the most attractive option.

Nudging can also be described as a method that aims to shape our routine decisions, choices or behavior without actively changing our values. We know that almost half of our daily

decisions and actions are based on habits and routines, which we barely reflect upon.

Some everyday examples of nudging include:

* _{Using default options in situations with complex information, e.g. pension funds or} financial services

* _{Simplifying and framing complex information making key information more salient} -energy labelling, displays

* _{Making changes in the physical environment making preferable options more convenient} for people - e.g. changing layouts and functions, showing with steps and signs, giving remainders and warnings of different kinds to individuals

* Using social norms - showing information on what others are doing such as recommendations.

Perceived complexity: HIGH

YES YES

LOW involvement HIGH

decision/ involvement

Habitual behaviour decision

YES _?

Perceived complexity: LOW

Figure 1. Areas in which a nudge is likely to be most effective is indicated with a YES

Framing is a cognitive bias where people decide on options based on whether the options are

presented with positive or negative semantics; e.g. as a loss or as a gain. People tend to avoid risk when a positive frame is presented but seek risks when a negative frame is presented. Gain and loss are defined in the scenario as descriptions of outcomes. This concept of framing was first described by Daniel Kahneman in his book Thinking Fast and Slow.

Finally, conditioning can be classified into two subcategories, classical conditioning and operant conditioning. Classical conditioning is like Pavlov's Dog, if you play a bell each time food is near a dog, then later when you play the bell the dog will salivate. Operant conditioning, when person does X you give them a treat and when they do Y you punish them.

In the context of behavior change with residential energy use we can think of the following characteristics and examples:

* Much of residential energy use is habitual, a side effect of other tasks. Does this mean we can provide a means to simplify and frame electricity use? Examples include informative metering and displays, energy bill information, labelling on appliances.

* Feedback with energy use is most effective when it is frequent, involves interaction and choice, and includes a breakdown of consumption.

* Changes to the physical environment, can we design the home to promote sustainable energy behavior or show prompts of better behavior? Should the proximity of these prompts matter?

* What about changing behavior based on the status quo bias? People are likely to continue a course of action since it has been traditionally been the one pursued, even if it is not in their best interest.

* Can we nudge consumers based on the herd mentality, where people are heavily influenced by the actions of others?

Given that we have some basic information about how to change user behavior critical success factors in nudging or conditioning for home energy use should be in general:

1. Part of a larger package combining several instruments

2. Identify behaviors you want to change and the factors influencing them

3. Focus nudging on behaviors it can change such as habitual/automatic behavior.

1.2.2 Wallpaper Theory

Wallpaper theory is a phenomenon that is primarily observed with goods or services that attempt to change user behavior but fail. The concept here is that when a good or service is purchased by a consumer, they use it for a certain period. But after that learning period, the consumer is now automatically programmed to predict the outcome of using that good. Hence, they stop using it because they have learned the patterns and lost interest or engagement with the item. This product now ends up becoming wallpaper and simply blends into the background and is completely forgotten about. While this concept might be ideal for certain goods or

services, when it comes to behavior change it is a black hole. A great example of wallpaper theory is the 5-minute shower product shown below.

Figure 2. 5 minute shower product designed to save water

While the 5-minute shower product successfully prompts the consumer to save water, eventually the consumer becomes programmed to the length of their showers and no longer uses the product to keep time. Hence the product is now always in the shower, blending in with the wall and never being used.

When trying to change consumer energy behavior it is important that whatever solution is created not succumb to the perils of wallpaper theory where instead of training the user to reduce electricity, they default back to their preferred methods of wasting energy. This is simply because no one can predict the price of electricity at any given time. It is always changing, and users cannot rely on prior training or a false understanding of how the grid works in order to reduce or shift their energy consumption.

1.2.3 The Electric Grid System

Now that we understand a bit about our consumers and how we might be able to change their behavior, let's analyze the second idea of this research paper by understanding the electric grid. In order to simplify this understanding, we will focus only on the United States grid

infrastructure.

To get a scale of the opportunity here, 4.18 trillion kWh of electricity were generated at utility-scale electricity generation facilities in the United States. About 63% of this electricity generation was from fossil fuels (coal, natural gas, petroleum, and other gases). About 20% was from nuclear energy, and about 17% was from renewable energy sources.

The following chart below shows overall how much energy is wasted in the US. But if we focus on just electrical generation, we see that 66% of electrical energy is wasted and only 34% is

used by residential, commercial, industrial and transportation outputs. It is also clear from this chart the residential category is the highest consumer of electrical energy.

Estimated U.S. Energy Consumption in 2018: 101.2 Quads Net Eecltirity 0.03 0..61 Im ot 0.15 2.67 5.15 0.52 . 3.6 0.02 0.1 C 1.22 - -. 121

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28.3

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Figure 3. Energy consumption in the United States from all sources

There are three separate grids that come together to create the United States complex full network. There is the Eastern Grid, the Western Grid, and the Texas (ERCOT) Grid, with the Eastern Grid being the largest of the three. While all three of these grids are connected, they are also operated independently by entities known as the balancing authorities.

A balancing authority ensures, in real time, that power system demand and supply are finely

balanced. This balance is needed to maintain the safe and reliable operation of the power system. If demand and supply fall out of balance, local or even wide-area blackouts can result. The figure below shows the three separate grids as well the balancing authorities per region in the US.

U.S. electric power regions 0,0 CC) 0 Interconnections Eastern O ERCOT Western

Circles represent the 66

balancing authorities

Figure 4. The three electrical power grids and their balancing authorities in the US]

When it comes to pricing, there are three major components that influence electricity prices:

1. Electrical generation (Supplier): These are the power plants that produce the electricity.

They can come from various renewable (clean) or non-renewable sources (dirty). 2. Transmission and distribution (substations and transmission lines): These are the

independent service operators (ISO) that deliver the generated electricity to your home or business.

3. Regulation (energy marketers): These are the regulatory bodies that control the pricing

of electricity. Electricity is bought and sold as a trading commodity with various ISOs and energy distributors.

The image below shows how the three components described above work together to get electricity to your home.

Your business SupluBSubstadom Supper Cu or nftbwe Your home lr C ELECTRICITY SUPPLY Open to competition

LET IITIY IDELIVEKY

Regulated

Figure 5. The key players in getting energy from the supplier (generator) to your home

The key idea to pricing is that electricity is bought in advance in bulk by ISO's in a market style auction. Below is a figure showing a typical cycle for how energy is purchased for an operating day by one ISO known as PJM (responsible for most of New England electricity distribution).

- Throughout the operating day PJM continually re-evaluates and sends out individual

generation schedule updates, as required. Market closed until

The iddng rocss s a eve-da widowmidnight fr the

next operating day

Re-bidding begins PJM Posts LMP Market closed

Bidding process taking place

a.m. p~m. p.m.

1.2.4 The Residential Customer

With all the intricate details of energy generation, it is important to understand how it boils down for a residential customer. If energy is bought in bulk the day before, and there is no direct

connection between the energy generation and the customer use, how can we have real-time pricing? Fortunately, the ISO's including PJM, Corned, and Exelon break down their bulk pricing into real-time pricing based on the real-time usage which is easily tracked using the metering system implemented nationwide. This real-time price of electricity is easily viewed on their website as shown in the image below.

E powerIngvusIs HOME ABOUT PRICES FAQS ENERGYTIPS TOOLS CONTACT

M Hourl Ii P nmg Account

2.1c 2.Ot 2.2e

W Year iue Prrs Tm rrPcs Pricing Table

REM-TME HOURLY PRICES FOR APRIL 2MK11, 219 3.5

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AW 20 2am. 4am Lam 8m 4oa 12 pm 211. 44M 64m 84. 10pm

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Figure 7. Real-time pricing data from Comed's website

In addition to the price, it is also important to understand how a residential consumer uses their electricity within the home. This is because not all appliances consume pure electrical energy. For example, a stove might be powered using natural gas which is not a topic of this report. The table below shows the most common uses of pure electricity in the home.

Table 1. U.S. residential sector electricity consumption by major end uses in 2018

End use Billion kWh Share of total

Space cooling 214 15%

Space heating 207 14%

Water heating 174 12%

Lighting 91 6%

Televisions and related equipment1 62 4%

Clothes dryers 60 4%

Computers and related equipment2 26 2%

Furnace fans and boiler circulation pumps 25 2%

Freezers 20 1%

Cooking 16 1%

Clothes washers3 10 10%

Dishwashers3 1%

Other uses4 _{460 31%}

1. Includes televisions, set-top boxes, home theater systems, DVD players, and video game consoles.

2. Includes desktop and laptop computers, monitors, and networking equipment. 3. Does not include water heating.

4. Includes small electric devices, heating elements, exterior lights, outdoor grills, pool and spa heaters, backup electricity generators, and motors not listed above. Does not include electric vehicle charging.

What this table is showing us is that if we could somehow change the behavior of consumers regarding their use of HVAC then we can make dramatic changes to the grid demand.

1.2.5 The Duck Curve

So how does changing user behavior and understanding real-time pricing reduce or shift

demand on the electric grid? This is where the duck curve idea comes into play. The duck curve is a graph of power production over the course of a day that shows the timing imbalance

between peak demand and renewable energy production. In many energy markets the peak demand occurs after sunset, when solar power is no longer available. In locations where a substantial amount of solar electric capacity has been installed, the amount of power that must be generated from sources other than solar or wind displays a rapid increase around sunset and peaks in the mid-evening hours, producing a graph that resembles the silhouette of a duck. Without any form of energy storage, after times of high solar generation generating companies must rapidly increase power output around the time of sunset to compensate for the loss of solar generation, a major concern for grid operators.

30000 25000 20000-15000 10000 5000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour

-Total load - Load -solar -wind (Duck Curve) - Solar output

Figure 8. The duck curve shown in orange

In most cases, to compensate for the loss of solar, grid operators turn on dirty non-renewable sources of power like oil, coal or natural gas. These sources create massive air pollutants and are also extremely expensive to run but can be turned on in an instant. In addition, there is research that shows there is a growing gap between morning and evening hours prices relative to midday hours prices.

The goal of this research is twofold. The first is to see if we can reduce the widening gap

of the electricity price during mid-day hours by shifting demand by residential

customers. The second is to see if we can reduce the consumption of electricity during peak demand by residential customers.

1.3 Approach

In order to solve the problem statement in 1.1 The Problem, we will employ a human centered

process flow of the approach is shown in the figure below and is like the popular design thinking framework.

Figure 9. Human centered design approach used in this research.

There are five key stages to this approach as shown in the figure above:

1. Explore - Develop empathy for the residential consumer by understanding their needs

and how they perceive the current state of their electrical use.

2. Express - How do we solve the larger problem of connecting residential electrical use to a lower or shifting demand on the electric grid?

3. Create - Create multiple low fidelity prototypes that fulfill the user needs and can

potentially solve the problem statement.

4. Testing & Experiments - Test the above low fidelity prototypes in an iterative fashion with real users and create one works-like high fidelity prototype.

5. Implement - Create a final prototype that incorporates learnings from the experiment and

is a possible product-ready version of the solution to the problem.

Since this study is about changing user behavior human centered design is an appropriate methodology.

2. Design Research

2.1 User Research

The goal of user research is to gain a deep understanding of user needs. It's important to go in with no assumptions and a blank slate about the status quo.

2.1.1 Participants

For this study, ten participants were interviewed in person to understand various aspects about their electricity consumption and their understanding of the electric grid infrastructure. The participants were chosen to have a combination of renters, homeowners and landlords. Below is a table of the ten individuals. It is important to note that none of the participants were classified as lower socio-economic status participants. There is a confounding variable that if participants were of lower income status the results could change dramatically.

Table 2. Participant list for initial user research

Russell 48/M Working professional Graduate Student professional professional Working professional Working professional Working professional Location Atlanta, GA -I-Boston, MA Boston, MA Boston, MA Boston, MA Newton, MA Nashville, TN -I-Roanoke, VA Roanoke, VA Charlottesville, VA Notes Renter Lany's wife (Renter) Anna's husband (Renter) Renter Rienter Landlord Homeowner Rhesa's husband (homeowner) Russell's wife (homeowner) Landlord

In addition to the ten user participants, 2 stakeholder participants were also interviewed. The below table is a list of stakeholders researched to gather more information about the energy industry.

Name Age/Gender Occupation

Jack 23/M Graduate Student

Anna 29/F Graduate Student

31/M 29/F Lany Jennifer

Xmanda

Bruce Laura 30/F Working 88/M Retiree 54/F Working Rhesa Oliver 47/F 35/M

1.

Table 3. Stakeholder research participants - energy trading and infrastructure

Organization Occupation Location

Constellation (Exelon) Energy Trader Baltimore, MD

Director of Energy Procurement Columbia, MD

2.1.2 Research Questions

The questions for the users were divided into three parts:

" Part A was a step in getting to know the user: where they live, their occupation', age and other key elements of their story in order to develop a relationship.

" Part B was to get insights into their home energy use. This was further broken down into: o Understanding what electrical appliances, they use in their house/apartment. o Getting their feedback on what appliances they thought consumed the most

electricity.

o Getting their perspective on their electrical consumption.

o Do they have processes in place to actively management their electricity use? o When it comes to electricity use, what do they care about the most

(cost/environment/capacity)?

o If they could track usage on certain electrical appliances which ones would they

be and why?

o Do they use any sort of energy augmentation or storage like batteries or solar panels?

* Part C was to get their understanding of the electric industry and how the grid works.

2.2 Research Insights

After analyzing the raw interview transcripts of the user research there are some that emerge between them. The table below will identify five themes, summarize provide quotes or examples from the users that support the themes.

Theme The idea of wastefulness

clear themes the theme and

Table 4. Five major themes summarized from the user research

Description Example Quotes

This is an idea that most "Price doesn't motivate me in the participants relate to in terms of just least bit, it's just the right thing to do." waste in general. None of them liked

Name Francesca

There is always a negotiation I'm not wasteful, but others are Convenience and comfort are king Is that how electricity works?

to waste and it is a characteristic trait they grew up with.

In most family or relationship situations, a decision about appliance use or electricity use is almost always an agreement between two or more parties within the home. It is rarely an individual choice (unless you live alone). This was an especially interesting idea in that most participants thought they were very good with their electricity consumption and that it wasn't them but other people causing issues with electrical waste.

Convenience and comfort always trumped usage. If it was too hot in the house, the AC was on

regardless of how expensive it was.

Most respondents have heard of the

duck curve or just peak demand in general, but they were misinformed

of the length of time the duck curve covers or when it happens.

"I was raised to always turn off the light when I leave the room.

"We have very different levels of comfort so there's always a daily argument about how hot or cold the house is."

"I wouldn't use a clothes dryer or

dishwasher if it weren't for my spouse"

"I think we use energy well. If a

program could get others to be less wasteful, I am all for it."

One user saw ads on TV to use less energy but said "that applies to other people who are not already paying attention."

"Don't mess with my comfort, but I wouldn't mind delaying stuff like laundry"

"I care for the environment with

recycling and turning off lights but only when it's convenient."

"I didn't know that the price was so

high for so long. I thought it was only a few minutes"

"Why is it so much cheaper during

the day? I figured it would be most expensive during the day."

2.3 Idea Generation

Based on the themes and insights from the initial research questions with the participant pool, multiple possible ideas were generated. These ideas were tested with users to see how they reacted in terms of solving the problem and with behavior change. All the initial low fidelity ideas

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Figure 10. Low fidelity prototypes of ideas generated from user research

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The goal of these ideas is to come up with possible solutions that our user research group could look at and see if they resonate with them or if they solve our problem. Another important goal is to see if we can narrow down the themes from our user research insights. It is often too difficult to design for multiple themes, so the best way is to reduce it to ideally less than three themes from the five that are identified. Three are chosen because it is harder to create a solution that fits five different themes. Focus is a better approach. The prototypes help with narrowing the themes down because they easily eliminate ideas that users don't resonate with.

2.4 Ideation Results

This section will discuss the results from showing the various low fidelity ideas to the same ten users from 2.1.1 Participants. The goal of this section is narrow down our core research themes from 5 insights to 2 major insights and 1 possible minor insight.

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Figure 11. Prototyping board used to test our prototypes with users

A summary of the user tests can be written as:

* Most users resonated with the idea of being wasteful. While price or carbon emissions were a factor, they felt like the impact they can have on price or emissions was minimal. But if they were told they were being wasteful, then they had an adverse reaction to that sentiment.

" Most users believed that they were good with their electricity use. They felt that it was other people who could benefit from a solution to show how much electricity they were wasting. For example, the neighborhood heat maps resonated well with almost all

participants. In addition, they mentioned things like "if you could show how much electricity my roommate was using that would prove I'm better at conserving".

* Finally, many users didn't live alone so for them electricity use was a social process in that multiple individuals were involved such as a significant other or a roommate.

To sum up the results, we can narrow down the research insights into three major categories:

1. Triggering wastefulness

2. "Not my problem"

3. A social process

Moving forward, these three themes will be used in the design and building of a higher fidelity prototype to use in an actual experiment with real people. This performed experiment is outlined in section 3.

3. Experiment Design & Testing

3.1 Experimental Questions

The primary research question that needs to be addressed by the experiment must be: How can we best shape the experiment to see if we can create a prototype that affects user behavior in terms of electrical use? Given this primary question, there are some secondary follow up questions that also need to be addressed before the experiment has started:

* What prototype should we make that addresses our three themes discovered in user research?

* How should we collect data and record user behavior and decisions? * What electric grid variables should we show to users?

Keep in mind that the experiment is not a final version of a solution to our primary question but more so a step in the process towards developing that final solution where that final solution is a design of a product.

Let's try to answer these three questions below. Think of the answers to these three questions as an initial needs list for the experiment prototype:

1. What prototype should we make that addresses our three themes discovered in user

research?

The prototype we build must be able to show a user's usage of electricity within the home and show some electrical grid metric/variable. The prototype must be simple to understand at a glance and be located centrally so multiple users can interact with it. The usage of electricity is measured by looking at the binary status (on/off) of a variable use appliance. A variable use appliance is something that is controlled by the user at any given time. For example, a dishwasher or clothes dryer is turned on and off by the user. But a water heater or heating furnace is automatically controlled using a thermostat.

2. How should we collect data and record user behavior and decisions?

The prototype must be able to record the usage of the variable use appliance and store it in a location where comparisons can be made to the grid metric. This is particularly critical in situations where a lack of user action is signaled. For example, if the grid metric was real-time price of electricity, and it was at a given time extremely expensive, then if the user made a conscious decision to not turn on the appliance, it would be difficult to record this decision

without asking the user. To solve the data recording problem there are three aspects to be implemented in the prototype and testing:

* The prototype must automatically record usage of variable use appliances.

" The prototype must record the values of the real-time grid metric.

" The user must fill out a daily log of their behavior with the prototype, including moments of inaction.

3. What electric grid variables should we show to users?

This is a critical question to ask, because during the user research it was clear that real-time price did not resonate with every user. Some users highly valued knowing real-time emissions. Therefore, it is important for the prototype to allow the user to choose which grid metric to communicate. In this case, the two options are:

* Real-time price (price/kWh)

* Real-time emissions (emission/kWh)

3.2 Experiment Setup

The experiment involved four users running the prototype for a minimum of three days. This will allow three days of data to be recorded as well as enough time to see a user change their behavior based on the prototype. Four users were chosen because of material constraints for the prototype.

Below is the process of how the experiment was setup and performed:

1. Four users were pre-selected based on criteria described in section Error! Reference source not found..

2. The prototypes were designed and built.

3. Each prototype was installed in the user's home.

4. The users participated in a pre and post experiment survey.

5. The users participated in a logging activity for every night they have the prototype. 6. After three days, the prototype was removed, and usage data was analyzed along with

3.2.1 Subject Recruiting

The following is a list of criteria that was followed when selecting the four users to test the prototype:

* Must pay for their electrical use.

" Must own appliances that are variable use (ex: AC, dishwasher, clothes dryer).

* Must live with multiple people to test the social dynamic of decision making (ex: family or roommates).

" Must live locally so that the prototype can be installed quickly.

" Must be willing to participate in a pre-survey, post-survey and daily logging activity. Using these criteria, the table below shows the four participants that were selected, the electric grid metric they wanted to be shown and the appliance to be recorded.

Table 5. Participants of the experiment

Name Chosen Grid Metric Chosen Appliance to Measure Household Katie Real-time Price Living Room AC Family home

Kitchen AC

Sonja Emissions Living Room AC Family home

Bedroom AC

Gabriel Emissions Living Room AC Family home Bedroom AC

Caroln Emissions Living Room AC Spouse apartment

Dishwasher

3.2.2 Data Recording

For the experiment there were two types of data being recorded:

* Automatic data which is data recorded by our system automatically without any user input.

* Manual data which is data recorded manually by the user every night in the form of a log book entry.

Table 6. Data being recorded during the experiment

Data Type Automatic

Convenience factor

Variable Name

Real time price of electricity

Real time emissions

Usage of both appliances

Lack of action

Below is an image of the log-book page that was filled out every day of the study by the participant. There are three pages in the log-book that the participant must fill out. They fill out one page for each of the three days of the experiment.

Description

Measured in kWh and is the current price of electricity.

Measured in carbon emissions per kWh and is a measure of how many carbon emissions are being put into the air.

A measurement of the two appliances being

measured in the participant's home. If both appliances are on, the value is 2, 1 if one is on, and 0 if neither is on.

This metric is user recorded and asks if the user saw the price/emissions of electricity was high and decided not to turn on an appliance. This metric is user recorded and asks if the user saw the price/emissions of electricity was high and decided to turn on an appliance anyway.

SAVEWATT PARTICIPANT PACKAGE

COLAB

DAY 1 SURVEY

QUESTION 1

Do you remember deciding to not turn something on due to the Save Watt lights? Was it one of the measured appliances or another appliance? If so, tell us more about any time this happened.

QUESTION 3

What colors did you see the peak demand light today? One check per row.

Didn't see this color Only once Not often, but more than once Most of the time

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Yellow

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QUESTION 4

Do you remember seeing a yellow or red peak light and deciding to turn on an appliance anyway? If so, tell us more?

QUESTION 5

Do you intentionally check the SaveWatt lights or do you mainly just happen to see it in your normal pattern ofactivities?

Summer 2018 @ IDEO 2017 ALL RIGHTS RESERVED

Figure 12. Sample Logbook page for the participant's manual entry of data

QUESTION 2

3.2.3 Subject Interviews

In order to ensure that the experiment was run properly and that all possible insights and data were gathered we also be interviewed the four participants. Keep in mind that these four

participants were entirely different participants than the ones from the user research study in section 2.1.1 Participants.

The interviews took the following format:

* Recruiting email sent to notify the participant of being selected and to schedule a time for the in-person experiment installation.

* During the in-person experiment installation, an interview was also conducted. This was the pre-experiment interview. We also scheduled the exit interview and device removal at this stage. The participant was also provided with a participant package that included:

o A description of the study and an introduction to the examiner (Sal Amarasinghe)

o A technical description of the prototype

o A description of the electric grid metrics (price/emissions)

o Their log-book for recording daily activity

o A timeline and time commitment (installation, experiment days, removal)

o Their responsibilities in the experiment o Any anticipated risks

o A confidentiality agreement and disclaimer form

This interview and installation usually lasted about 1 hour and 30 minutes.

* During the experiment, every night at 9PM the participant received a SMS text reminding them to complete their daily log-book page.

* After the 3 days of running the experiment the device was removed from the participant's home and an exit interview was performed. The participant received a $50 Amazon gift card for their time and if they wished they could see the results of the experiment later. This exit interview usually lasted about 1 hour and 30 minutes.

The Pre-experiment interview had the following questions and answers recorded:

1. Participant name, their Wi-Fi credentials to connect with our prototype, the appliances

they want to connect to, and finally the electric grid metric they preferred to see on the prototype.

2. Questions around the current management of the two appliances being measured. What is the normal use of the device? When is it turned on/off? How is it decided when to turn the appliance on/off? Who is involved in the making of the decision to turn it on/off? 3. Questions around why they chose a particular electric grid metric (emissions/price). Which measure do you think would mean the most to you and others in your house?

The exit interview had the following questions and answers recorded:

1. A brief overview of the overall experience with the prototype with questions such as how

it made them feel, if they noticed it or interacted with it and most importantly did it change any behavior with electrical usage.

2. An understanding of intra-house dynamics. Did the dynamic between people change with respect to operating the measured appliance? Were new people involved? Did it help align people or do the opposite?

3. Their opinion on the form factor of the prototype: Did it look like you expected it to look?

What would you change about the design? Did you have to go out of their way to see it, or was it a part of your normal routine? How much mental effort was required to

understand the prototype?

4. User expectations and behavior: What did you wish it did differently? Did you want to change your electric grid metric? Did you ever wonder if it was working properly?

Below are some images of the interview packages and participant packages. The participant never saw the interview packages and that was strictly used for internal research analysis.

INTERVIEWER PACKAGE

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SAVEWATT INTERVIEWER PACKAGE

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CURRENT MANAGEMENT OF THE TWO APPLIANCES

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3.2.4 Experiment Timeline

Below is a figure showing the timeline for the experiment including when each participant is received the prototype as well as their interview schedules.

While there were three functional prototypes built, only two were given to the participants which is why the experiment was staggered for the four participants. One prototype was kept internally for testing and ensuring the experiment was functioning as intended.

Before the experiment was deployed the prototype was tested multiple times inhouse to ensure that there were no technical issues. This was important since the devices are installed remotely with no access to technical support if something were to go wrong.

Monday Theday Wednesday Thursday Friday Saturday Sunday Monday Tuesday Wednesday Thursday Frkay Jul 23 Jul 24 Jul 25 Jul 26 Jul 27 Jul 20 Jul 29 Jul 30 Jul 31 Aug i Aug 2 Aug 3 Katde Peltd ay y2 B;y3

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Figure 15. Timeline for each participant's participation in the experiment

3.3 Prototype Development

The prototype was developed by understanding multiple aspects of the experiment. The following list can be classified as the primary needs list of the experiment prototype:

" _{Design a system that considers the three major insights from user research}

* Design a system that is of medium fidelity, more "works-like" than "looks-like" * Design a system that can be built quickly using readily available rapid prototyping

capabilities (e.g.: laser cutter/3d printer/free cloud computing)

* Design a system that can work remotely and is self-sufficient (power/connectivity)

" _{Design a system that considers the data requirements outlined in section 3.2.2 Data}

Recording.

Given these primary needs, the following figures and paragraphs detail what was built for the experiment. The name of the prototype was called SaveWatt. This name was chosen to provide a reference for our participants and for them to form a connection to the prototype.

Figure 16 is an image of all three sets of SaveWatt prototypes that were built. What is seen in the image is three SaveWatt Lights and 6 SaveWatt Appliance Sensors. Each participant is

given a kit consisting of one SaveWatt Light and two SaveWatt Appliance Sensors. Each of these devices is discussed in the following sections.

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Figure 16. Experiment prototypes ready for deployment

3.3.1 The SaveWatt Light

The SaveWatt Light is a small light box that shows two lights, a grid metric light and an

appliance usage light. The usage light indicates the total usage of the two measured appliances. The grid metric light shows the level of the peak demand metric that was chosen.

The lights are simple RGB LEDs that change into three colors, red, yellow and green.

Light Color Green

Yellow

Red

Grid Metric Light

Very low average price of electricity or lower than average carbon emissions Average level price of electricity or carbon emissions

Higher than average level price of electricity or carbon emission.

Usage Light

Neither appliance is currently in use indicating low usage.

Only one of the two appliances is currently in use.

Both appliances are in use indicating high use.

The SaveWatt Light is connected to the internet to perform three functions:

1. Connect to a real-time pricing/real-time emissions API from an electric ISO.

2. Read usage data from the SaveWatt Appliance sensors.

3. Record "automatic" data to a private server as defined in section 3.2.2 Data Recording

APPLIANCE USAGE LIGHT

Shows a red, yellow or green

light based on the usage ofthe two appliances our system is

connected to.

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Figure 17. The SaveWatt Light

3.3.2 The SaveWatt Appliance Sensor

The SaveWatt Appliance Sensor is simply a measurement device that non-invasively attaches to an appliance that the participant wants to measure the current consumption of. Two are provided per participant to measure two different variable use appliances. The sensor can accurately measure how much energy is being used when the appliance is turned on. It feeds this information to the SaveWatt Light via the internet. Once installed, the participant will have no interaction with the SaveWatt Appliance Sensor.

APPLIANCE SENSOR x2

This black box is connected to the appliance being measured and detects its use. It is also

connected to the cloud. 0

Usage

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Figure 18. The SaveWatt Appliance Sensors

3.3.1 Hardware Design

The electrical and mechanical design of both the SaveWatt Light and SaveWatt Appliance Sensor is relatively simple. Essentially the entire prototype is an Internet of Things (loT) system. Because of the IoT nature, the electrical design primarily leverages the use of the Particle Photon due to its WiFi capabilities built into the microcontroller (MCU). The figure below is a high level electrical block diagram of both the loT devices (Light and Sensor). A description of