EEWeb Pulse - Issue 73 by EEWeb Magazines

INTERVIEW

EEWeb Issue 73

November 20, 2012

Matt Rogers

Founder and VP of Engineering NEST TECHNICAL ARTICLE

Nest Learning Thermostat Efficiency Simulation TECHNICAL ARTICLE

Distribution Systems Automation and Optimization

Electrical Engineering Community

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TABLE OF CONTENTS

Matt Rogers NEST Interview with Matt Rogers - Founder and VP of Engineering

Featured Products

Nest Learning Thermostat Efficiency Simulation: Data from First Three Months

BY NEST LABS A look into the back-end technology that goes into Nestâ&#x20AC;&#x2122;s game-changing thermostat and the energy savings in a variety of locations after the first three months of use.

Distribution Systems: Automation and Optimization - Part 1

BY NICHOLAS ABI-SAMRA WITH QUANTA TECHNOLOGY Why Distribution Automation (DA) is considered to build upon in developing the Smart Grid as it transforms the distribution network towards more automation.

RTZ - Return to Zero Comic

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The Nest thermostat is pioneering the next generation of energy efficient and cost-effective home cooling systems. Known as “the Learning Thermostat,” it can remember your personal settings based on user patterns and can also be controlled entirely from your iPod or iPad. We spoke with Matt Rogers, the CEO and Co-Founder, about how his time at Apple inspired the Nest thermostat, the complex algorithms behind the simple and stunning display and user interface and his plans for the company’s long-term growth.

Matt NESTR 4

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INTERVIEW

tRogers Visit www.eeweb.com

EEWeb PULSE Why don’t you tell us a little bit about your background? I did my undergrad and graduate work at Carnegie Mellon in the computer engineering field. At school, I worked on a mix of computer architecture, silicon, robotics and embedded systems software. Between my senior year and graduate year, I got an internship at Apple in the iPod area where I was a part of the iPod firmware team. After the internship, I got an offer to come back after I finished school, which was the best opportunity for a young college guy. When I joined the team, I began to work my way up—starting from firmware and software driver work through operating system work to leading the team on entire software efforts and when I left Apple in 2010, I was managing about half of the software engineers doing iPod and iPhone work. How did you come up with idea for Nest? I had always wanted to start my own company. It was all about a matter of

“One thing led to another and we both thought that we could do this; we built the iPod, so we could certainly build a thermostat.” what the right idea was and when the right time to do it was. I was having lunch with Tony, my co-

founder, in October of 2009, and we were talking about life and where we were. I was talking about starting my own company in the smart home space, and I brought it up to Tony and he thought it was a terrible idea - that a company should stem from a product idea. Coincidentally, he’d been toying with the idea of starting a company as well. He was building a green home in Lake Tahoe and was frustrated with the thermostat options. After talking a bit, we agreed that there was nothing that met our standards of what a thermostat - which controls about half of your home’s energy should be. One thing led to another and we both thought that we could do this; we built the iPod, so we could certainly build a thermostat. We founded the company and started hiring our close friends and building the first idea and the first product.

syndicate of vendors that could help us provide for this new platform. We had an idea of what we wanted to do, but the key was that we wanted to be low-cost, low-power and we wanted to provide enough volume out of the

Did you get some help in the beginning or was it self-funded? For the first six months or so, we were self-funded. We went for VC money when it made sense to the company and when we needed to go to that next level of growth. What was the experience of developing this product? The first two or three months were basically Tony and me meeting with a lot of the vendors we worked with before in terms of silicon, sensors and battery technology. We basically wanted to put together a

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gate, so nothing too bleeding-edge in terms of silicon. We basically toured around the Valley and put together the first platform.

INTERVIEW Did the UI of the thermostat evolve with the product or was there an initial layout for it? The first idea of what we wanted the UI for the thermostat to be was a big circle with a big number the target

time and temperature and the tics on the wheel, evolved over the first year of development. We wanted it to be clear to people that it is a thermostat. Most people use it to tell what the temperature is in the room and to change the temperature, so we tried to make that interface as easy as possible. Tell us a little about the company’s growth. Did it grow quickly? How big is the company today? We are well over 100 today. When we started in May 2010, it was just the two of us and we’ve been growing pretty steadily since then. We’ve hired over 100 people in two years. In terms of backgrounds by way of career, we’ve hired people from Apple, Google, Microsoft, and some of the newer companies like Twitter and Facebook—a really great DNA from all of these companies around the Valley that have built great hardware and software products. What do you look for when hiring new employees?

temperature - in the middle. That’s what we started with—a big circle with a 72 in the middle. From there, we evolved to basically where we are today. We learned a lot along the way. A lot of the details in terms of all the different features of the UI, like

When we hire people, we look for two core things. One is the passion—that passion and drive and emotional attachment to what we are doing and the mission to save energy and develop great products. The other is a very deep technical confidence. We are looking for people who know their area like the back of their hand, who are not just great designers on the surface, but go really deep on details because we are a very detailoriented team on all areas of our

design. For example, our electrical engineering team has done a lot of work to power the device. We had to devise a method to sip power off of

“The goal here is for the device to sleep 99% of the time and only wakes up when it needs to, in order to keep it extremely low power.“ the wires for the heater or cooler without triggering the equipment, but enough to charge the battery. It’s very deep technology and requires a diligence that we’ve not found in most companies. This is one of the reasons why this technology has never been deployed in mass by thermostats. The goal here is for the device to sleep 99% of the time and only wakes up when it needs to, in order to keep it extremely low power. Why don’t you tell us a little about the features and technology behind the thermostat? When we call it the “Learning Thermostat,” a lot of things go into that. We have this feature called Auto-Away, which turns down the temperature to your desired away temperatures when you aren’t home. We use an infrared sensor, like the kind you’d find in a security system, with added software and algorithms on top to detect and to know when you’re not home. We also have

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“We are also a culture that really appreciates design, so we are willing to add deep value to the product to create that better consumer experience.” Airwave on the iPhone a feature called Auto-Schedule. Instead of having to program your schedule, which most people don’t do because it’s such a complex process, our system basically learns from your inputs. Basically, as you turn the dial, we learn from your patterns. When you turn it to 72 at 7 AM, we learn that you like it at 72 at 7 AM. It’s that simple. Over the course of the first week after installation, we learn your patterns. We also have a feature called Airwave, which is very useful in the summer time. We learn from the system in your home when we could turn off the compressor early and just run the fan to keep your house cool. What that does is dramatically cut down on the amount of cycle time from your very expensive air conditioner by using the fan instead to blow that cold air off of the coils in your air conditioner. It’s a great efficiency feature and only Nest has this today. Is it a microcontroller-based product? It’s

actually

combination.

A traditional thermostat is microcontroller based. We actually have two independent sub-systems. We have the base, which is the part that attaches to the wall where the wires go, which is microcontroller based. The display unit that attaches to the base actually has a very powerful CPU, much like you’d find in an iPhone 4.

802.11 radio in there that connects to basically every wireless router. When you install the product, you just enter the password and it acts as a safe dial mechanism where you turn and click on each of the letters in your password and it connects and automatically gets software updates over the web and you can control it remotely from your iPhone.

What challenges do you foresee in the market, and where do you see Nest headed in the next few years?

In terms of future products—you don’t put a team like this together to build one product. Tony and I have a deep background in building amazing consumer products and most of our team has done the same.

We are very much focused on improving our customer experience. We’ve already released seven software updates since we’ve launched. Those are for a variety of features, like adding Airwave and a monthly energy report we e-mail our customers every month showing them how much energy they’ve used and how they could save more. We are continuously adding great new features to the product. We have an

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How big is the thermostat market? Residential market in the U.S. is about 10 million units a year, based on our data, but being the industry that it is, it’s very difficult to get that deep data. If you include commercial, it’s probably about 50% larger than that.

INTERVIEW

Thermostat on the iPhone

Thermostat on the iPad How would you describe the work environment at Nest? We are located in Palo Alto, California just off the main strip where a lot of the prominent startups are. We are a very detailoriented culture, which runs across all of the different disciplines—the hardware design, user interface, electrical engineering, services and marketing. All those things matter and when you start to miss some of the details, you lose the vision of the product. We are also a culture that really appreciates design, so we are willing to add deep value to the product to create that better

consumer experience. Where a lot of companies are looking to cut costs here and there, we are looking to add value, which is very much the opposite. You’d think it would be hard to recruit people from great companies like Apple and Google to go build a thermostat, but it’s actually been pretty easy. We have a great mission and we have a very passionate team on board to complete that mission. A great thing about the team is that a lot of these people have worked together before, so it’s kind of like a reunion where people know that they have worked well together in the past, so let’s try something new.

A really great technology-related story is that our VP of technology is actually my robotics professor from Carnegie Mellon, Yoky Mastuoka. When I went to go found this company, I knew I needed someone with a robotics background. To build a learning thermostat, I needed someone who was the best at AI and learning. So I called Yoky, who was then at Google doing their selfdriving cars and their new Google X initiative and she runs all of our deep technology, analytics and algorithms. All of the “learning” in the “Learning Thermostat” comes from Yoky. It’s interesting because Yoky taught me pretty much everything I know about software. ■

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PROJECT

Nest™ Learning Thermostat Efficiency Simulation Update Using Data from First Three Months The energy savings described herein are primarily

gained by changing the thermostat setpoint temperature while occupants are away from the home and when they are asleep. For the vast majority of occupants who have no setback schedule programmed, the greatest savings are realized simply by generating a setback schedule that suits their daily routines. Further energy savings can be gained through (1) Auto--Away, or reducing heating, cooling and air conditioning (HVAC) usage during extended absences from the home (e.g. while on vacation), and (2) making very small changes in the setpoint temperature (e.g. by changing the setpoint by one degree Fahrenheit), perhaps in response to learnings from the Nest Leaf. The goal of this white paper is to demonstrate that certain features of the Nest® Learning Thermostat™ are expected to result in energy savings, based on the described simulations.

Using the simulation findings and weighting them across all U.S. zip codes, it’s estimated that the Nest Learning Thermostat can save an average of $173 per year, before applying any of Nest’s additional energy-saving features such as Auto-Away™ and the Leaf. Modifications of the simulations will also be undertaken as needed. It is important to note that the energy savings described herein are expected without resorting to any optimized control of the HVAC system components. It is also important to note that the strategy of the Nest Learning Thermostat is not solely energy savings. The Nest Learning Thermostat places a high priority on the user’s comfort. This white paper makes assumptions of households with mild to moderate energy consciousness in mind.

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EEWeb PULSE List of Features Simulated Auto-Away. The Nest Learning Thermostat’s “autoaway” feature automatically detects non--occupancy events, whether they last for several hours or multiple days (e.g. a vacation). The Auto-Away feature is based on algorithms that interpret occupancy sensor data and provide a confidence determination of whether or not the occupants are away from the home. When the confidence level is high that occupants are away, the Auto-Away feature makes a decision to override the existing schedule to save additional energy. During relatively short periods of non-occupancy, from a few hours to a few days, the setpoint is set to a value where substantial efficiency gains can be realized. Auto-Schedule. The Nest Learning Thermostat automatically learns a user’s preferred temperature as well as schedule. The learning algorithm is based on the user’s manual temperature selection on the device. Through the proprietary automatic learning algorithm, the thermostat replays a setback schedule, greatly benefitting the vast majority who train the thermostat wisely. Note that the learning algorithm simulated herein provides energy savings without substantially impacting the user’s comfort. The Nest Leaf. Another way the Nest Learning Thermostat encourages users to select efficient temperatures is the display of a green leaf icon known as the “Nest Leaf.” The Nest Leaf displays when the person controlling the thermostat has chosen an energy--efficient setting. The Leaf appears at different temperatures in different households, based on (1) the calculated efficiency of the household, (2) the HVAC system model and (3) the user’s prior behavior.

becomes more familiar with the HVAC system, which Installation There are HVAC–system compatib Time to target temperature. in turn leads to more economical and environmentally A local professional can help you install Nest in minutes, or Works with 95% of 24V s several ways Nest Learning Thermostat encourages friendly use of energy. you can do it yourself using our step-by-step guides, videos heat pump & radiant. users to select an efficient setpoint temperature. One and support. • Heating: 1, 2 and 3 stag 3. Methods of these features is called “time to temperature.” The • Cooling: 1 and 2 stages n time-to-temperature feature calculates and displays, Product Specifications • Heat pump: with auxilia Is 3.1. Simulation Setup W x D):to83 x 83 x 31.6 mm in real-time, an indication ofDimensions how long (Lit xtakes • Fan (G) t Weight: 8.6 oz / 244 g (C, Rh, Rc) reach the current setpoint temperature. Many people General Setup: The thermostat energy simulation • Power Humidifier a • based onor dehumidif use an exaggerated temperature setting hoping to model is a custom-made dynamic model Warranty cool or heat the house more quickly. This behavior principles of heat transfer and HVAC equipment l 2-year limited hardware. Common (C)on wire not requ is both ineffective and inefficient. The Nest Learning performance incorporating state-of-the-art research l Thermostat’s time-to-temperature feature shows building and equipment performance. The following a Connectivity Go to nest.com/works to the user that an exaggerated setpoint temperature simulations assume a 1,800 sq. ft. single-family home you’ll need Nest Concierg t Wi-Fi recommended—required for remote control, weather Energy Reports this and software takes much longer to reach, info, which discourages with anupdates. average efficiency level. The building envelope Dual fuel systems (heat • i Wireless Security: AES-128 ,heat SSL/TLS, WEP, WPA/WPA2. wasteful behavior. By providing real-time feedback transfer model begins with a standard U*A*dT humidifier • Whole-home o temperatures, the user model, where U is the heat transfer coefficient; A is when selecting manual input

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bility

PROJECT the surface area of the house, and dT is the difference between the indoor and outdoor temperatures. The model also accounts for thermal mass that exchanges directly with the inside and outside environments. Weather: A typical year data set of hourly values of solar radiation and meteorological elements is used. The third and latest typical meteorological year (“TMY3”) data is used, which was developed by the National Renewable Energy Laboratory for the climates simulated. Solar gain through windows is modeled based on hourly solar data from TMY3. Other Aspects of the Model. A number of details are employed in the simulation in an effort to account for important system dynamics that could have an impact on various thermostat control strategies. Some of these details include: - Air infiltration is based on a detailed infiltration model that includes wind and stack effects using hourly wind speeds and indoor and outdoor temperatures.

systems: gas, electric, oil, forced air,

- Heating and cooling equipment is modeled to include

ges (W1, W2,transient W3) start-up effects, and interactions with thermal s (Y1, Y2) mass and distribution systems. ary and emergency heat (O/B, AUX, E)

an indoor air humidity model that balances the effects of moisture loads from occupants and air infiltration with the dynamic moisture removal capacity of the air conditioner. Other Parameters. Following are a list of conditions used in the simulation: - The heating maintenance band is 1.4°F wide, with 1°F below and 0.4°F above the setpoint temperature. (E.g. if the setpoint is 70°F, the system maintains the room temperature between 69.0°F and 70.4°F). - The cooling maintenance band is 2.0F wide, with 0.7°F below and 1.3°F above the setpoint temperature. (E.g. if the setpoint is 80°F, the system maintains the room temperature between 81.3°F and 79.3°F). - Heating and cooling temperature is monitored for overshoot. If the temperature in the room (without solar effect) overshoots, then the maintenance band is adjusted to keep the room temp fluctuation as close to the maintenance band as possible. - The air conditioner system has a minimum cycle time (both on and off) of 5 minutes (unless manually commanded), and the forced-air gas heating system has a minimum cycle time of 3 minutes. - A simple model of user’s window usage is assumed. If the room temperature suggests that the HVAC system should turn on, but the outside temperature is at least 5°F in the favorable direction, the user is assumed to open the windows and not turn on the HVAC system. For example, if the air conditioner set point is 80°F, and the house internal temperature is 83°F then normally the air conditioning system should activate. However, if the outside temperature is 72°F (cooler than 80°F – 5°F = 75°F), then the HVAC system is not activated and instead windows are opened. 3.2. Experimental Conditions

- The heating equipment is assumed to be forced--air gas furnace. For both heating and cooling, single-fier (HUM, DEHUM) stage systems are assumed. In future simulations, we plan to add heat pump systems, non--forced-air uired in 99% of installations. systems, as well as multi-stage heating and cooling systems into thefind model. o check compatibility and out if

Geographic Locations: We simulated a sample of cities in the continental U.S. with at least one city from each of nine U.S. climate zones seen in Figure 1. The cities included in the simulation are: Atlanta, Boston, Chicago, Dallas, Denver, Houston, Miami, Minneapolis, Phoenix, San Diego, San Francisco, and Spokane.

latent (i.e., humidity-related) capacity modeled using

Among the list of cities, we have included four cities in which the Nest Learning Thermostat should be available in store at launch: Miami, Dallas, Houston,

ge professional installation for: - Air conditioner capacity and power draw are modeled t pump with furnace) as a function of indoor and outdoor conditions with rs and dehumidifiers

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Figure 1: Climate Zones of the Continental United States and San Francisco (Bay Area). Accordingly, there are some climate zones in Figure 1 that are represented with more than one city in the simulation. The simulation assumes all houses to have heating as well as cooling HVAC systems. This means that it ignores the fact that air-conditioning is rarely needed (or equipped in homes) in San Francisco and Spokane, and heating is used infrequently in Phoenix and Miami. Occupant Types: For purposes of the simulation, the household occupants are classified into one of two types:

that the Nest Learning Thermostat automatically learns for the Type 1 and Type 2 occupants. It is also assumed that the same program schedule is used on weekdays and weekends. Temperature ranges are selected to match mild to moderate energy conscious households. The heating and cooling schedules and setpoint temperatures simulated are as follows: - Type 1 heating: 6am to 10pm, setpoint = 70°F 10pm to 6am, setpoint = 62°F - Type 1 cooling: 8am to 6pm, setpoint = 79°F

- Type 1 (75%): Do not have long and regular mid-day non-occupied periods. Examples of Type 1 occupants include: a family with young children; a retired couple; and households in which at least one member works at home.

6pm to 8am, setpoint = 76°F

- Type 2 (25%): Have predictable, long periods of non-occupancy in the middle of the day. Examples of Type 2 occupants include: a working couple having no children; a working couple having children in allday daycare; and a family with older children who are out of home during the day.

6pm to 10pm, setpoint = 70°F

Program Schedules: Assumptions are made about the schedules and setpoint temperatures

- “Hold” (no program): Heating setpoint = 70°F

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- Type 2 heating: 6am to 8am, setpoint = 70°F 8am to 6pm, setpoint = 62°F

10pm to 6am, setpoint = 62°F - Type 2 cooling: 8am to 6pm, setpoint = 82°F 6pm to 8am, setpoint = 76°F

Cooling setpoint = 76°F

PROJECT The “Hold” setpoint temperatures for heating and cooling represent those who either (1) do not own a programmable thermostat; or (2) own a programmable thermostat but do not program the thermostat. As discussed above, approximately 92% of the population falls into one of these two categories, unless they adjust the temperature manually constantly. Thus, it is assumed that due to the Nest Learning Thermostat features such as learning, time-to-temp, and the Nest Leaf, about 92% of households move from no program schedule to either the Type 1 or Type 2 schedules shown. More accurate values of behavior observed in field trials will be used in future simulations for both baseline condition and learned condition. For example, baseline condition will be changed from “hold” to actual recorded temperature/behavior prior to Nest Learning Thermostat installation. In addition, the learned schedule times and setpoint temperatures will vary from city to city, and schedules may be different on weekends versus weekdays. However, we believe that the simulation using the values shown provides a good starting point. The simulation results are calculated for both Type 1 and Type 2 occupants, for each of three cases: - Learned schedule and setpoint: Type 1 and Type 2 schedules as shown above. These results show expected energy and costs savings simply by adopting the Nest Learning Thermostat without any away time (e.g. vacations).

City Atlanta Boston Chicago Dallas Denver Houston Miami Minneapolis Phoenix San Diego San Francisco Spokane

4. Results

- 1°F carving: Each of the Type 1 and Type 2 occupant types are simulated assuming the user changed his/her behavior based on Nest Leaf display to use 1°F less (for heat) or more (for cool) as their setpoint from the learned schedule. In other words, the setpoint temperatures are all adjusted by 1°F to increase efficiency. - Auto-away: Auto-away that occurs a few hours at a time on daily basis is not simulated here. A wide range of daily auto-away usage is expected, and field trial data will provide an anchoring point for future simulation. For this white paper, both Type 1 and Type 2 occupant types are simulated with two periods of non-occupancy for two weeks at a time (i.e. four weeks of non-occupancy annually). Temperature ranges are selected to match mild to moderate energy conscious households. One two-week absence is simulated in December with safety temperature for heating set to 45°F, and the other two-week absence is simulated in August with safety temperature for cooling set to 95°F. While results shown below reflect these assumptions, simulations with significantly milder temperature choices (62°F for December time away, and 79°F for August time away) are also simulated and commented where appropriate. Energy Costs: The cost of the energy varies from city to city. The average or most recent energy cost from US Energy Information Administration was used. The values used are in Table below.

kWh cost (dollars) 0.10 0.15 0.10 0.11 0.10 0.11 0.12 0.10 0.10 0.15 0.15 0.08

Therms cost (dollars) 1.56 1.47 0.94 1.01 0.81 1.01 1.81 0.87 1.59 0.98 0.98 1.20

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EEWeb PULSE 4. Results Figure 2 shows a summary of simulated annual cost savings for Type 1 and Type 2 occupants with the setpoints as described in Section 3.2, along with savings for the 1°F carving and for the auto-away periods. The costs reflect the calculated savings when compared to the non-programmed “hold” set points. As can be seen, some cities such as Boston and Spokane have much greater savings than others, such as San Diego and San Francisco. This is largely due to the fact that the overall usage and associated costs vary widely by geographic location. As expected, the Type 2 occupants, who are absent from the house for long predictable periods during the day, benefit the most from the Nest Learning Thermostat. Additionally, the 1°F carving in set point temperatures leads to substantial savings in all cases. Finally, the Auto-away feature allows for even greater cost savings when the occupants are away during the two-week periods each year. 5. Summary of Simulation Findings Average annual savings over the twelve modeled cities for those whose prior practice was not to program their thermostats is $116 for occupants adopting a single set back (Type 1) and $227 for occupants adopting a dual setback (Type 2) both without any vacation period accounted for. If two 2-week away periods are simulated, the average annual savings raises to $184 for Type 1 occupants and $285 for Type 2 occupants. With milder two 2-week away period temperatures the average annual savings is $133 for Type 1 occupants and $235 for Type 2 occupants. For occupants taking 1°F off (lowering set point by 1°F for heating and raising the set point by 1°F for cooling) but without any vacation period, the average savings is $176 for Type 1 occupants, and $281 for Type 2 occupants. Using the simulation findings and weighting them across all U.S. zip codes, it’s estimated that the Nest Learning Thermostat can save an average of $173 per year, before applying any of Nest’s additional energy-saving features such as Auto-Away™ and the Leaf. Single versus Dual Setback: On average, about twice as much cost saving ($112, 97% increase in savings) is realized when moving from a single setback (Type 1) to a dual setback (Type 2) for occupants who do not have a vacation absence. The increase in savings from a single to dual setback is less both in dollars and percentage increase when

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4. Results

600

Total Annual C (heating an

500 400 300 200 100 0

Figure 2: Total nnual Cost Cost Figure 2: TotalAAnnual Sa

the occupants have already accepted to use 1-degree less for setpoint ($105, 60% increase in savings) or have four weeks of away time accounted for ($101, 55% increase in savings). 1°F Setpoint Change: Average increased annual savings for accepting a 1°F change in setpoint values is $60 or 52% savings increase for occupants having a single setback (Type 1), and $54 or 24% savings increase for occupants having a dual setback (Type 2). The smaller amount of increased savings for dual setback households makes sense since those occupants use less energy to begin with. Auto-away savings: Average increased annual savings for accounting for two 2-week absences from the home (one in August and one in December) is $68 or 59% savings increase for occupants having a single setback (Type 1), and $58 or 25% savings increase for occupants having dual setbacks (Type 2). With milder temperature choices, $17 or 15% savings increase

PROJECT

Cost Savings nd cooling) Type 1 Type 2

Type 1 (-‐ 1°F) Type 2 (-‐ 1°F)

Type 1 + Auto Away Type 2 + Auto Away

t Savings (heating and cooling) avings (heating and cooling)

for occupants having a single setback (Type 1), and $8 or 4% savings increase for occupants having dual setbacks (Type 2). Similar to the case above, the smaller amount of increased savings for dual setback households makes sense since those occupants use less energy to begin with.

To see a full summary of the findings, view the original white paper at Nest’s Website:

People who already program their thermostats: Occupants who already have a suitable programmed thermostat (about 8% of households) will still benefit from features such as the 1-degree carving, and auto-away detection. The average savings over the basic program for the 1-degree change in set back is $60 (6%), and $54 (7%) for single and dual setback occupants respectively. The average saving over the basic program for the two 2-week absences is $68 and $58 for single and dual setback occupants, respectively. For both single and dual setback occupants, the auto-away feature leads to a 7% average cost saving. ■

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Distributio Systems Automation & Optimization Part 1

Nicholas Abi-Samra

Vice President, President ofAsset Quanta Technologies- Quanta Technology Vice Management

Present day Distribution Automation (DA) goes beyond reducing manual procedures. DA makes distribution systems more controllable and flexible based on accurate data for decision-making applications. This is accomplished through a set of intelligent sensors, processors and fast communications to remotely monitor and coordinate distribution assets. DA is considered a foundation to build upon in developing the Smart Grid as it transforms the distribution network towards more automation.

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TECH ARTICLE

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EEWeb PULSE Distribution Automation History and Initial Concepts Over 30-40% of the total investments in the electrical sector go to distribution systems, yet, they have not received the technological impact in the same manner as the generation and transmission systems. Up until recently, most of the distribution networks have worked with minimum monitoring systems, mainly with local and manual control of capacitors, sectionalizing switches and voltage regulators and, without adequate computation support for the system’s operators. This is now changing, with the trend increasingly moving to automate distribution systems to improve their reliability, efficiency and service quality. Over the years, Distribution Automation took many shapes, form local automation with no communication requirements, to more advanced, two-way communication, as shown in Table 1. The concept of automation in distribution systems has been around for many decades, but had a ripple in in the 1970’s, albeit at a sporadic pace, for the improvement of distribution system operating performance. Early automation applications included capacitor switching, voltage regulation and limited feeder reconfiguration. From the 1990’s distribution networks started to come under pressure to improve the quality and reliability of the delivered power. Efforts to make the power distribution systems ‘smarter’ started to get hold and traditional distribution automation (DA) was born.

Type Communications Requirements

Local Automation No Communications

Monitoring & Control One-Way Communications (Limited Bandwidth)

Advanced Distribution Automation Advanced Two-Way Communications

During those years, the use of reclosers and automatic switches to reduce outage times became more widespread. In addition, due to deregulation, distribution systems also came under cost pressure for optimization of operation and maintenance practices. In the 2000’s, the above pressures increased along with new ones such as the ever increasing occurrences of distributed generation in many forms in MV and LV networks. These requirements are pushing further the need for monitoring, automation, control and protection of distribution systems. DA applications have been related with the deployment of SCADA (Supervisory Control and Data Acquisition) technology in the distribution circuits and substations.

Example Applications/ Automation Level

• Sectionalizers: Automated fault restoration via pre-programmed sequencing. • Voltage regulators: Automated voltage regulation for long feeders • Remote control of capacitors and feeder reclosers • Messages from short circuit indicators control center for fast fault location • Distribution line monitoring • Power Quality (Harmonic content) measurements • Fault detection and restoration • Automatic reconfiguration of feeders • Voltage regulation and reactive power control for: ° Reduction of line voltage during peak load conditions, or for energy conservation ° Reduction of system losses ° Buck/boost bus voltages in case of abnormal situations, such as threat of voltage collapse, back-feeding, etc. • Distribution underground network grid monitoring and control • Supporting Distributed Energy Resources (DER) and microgrids • Complementing AMI • Interacting with transmission system

Distribution Automation (DA) Distribution Automation (ADA)?

Advanced

Some like to divide the terminology used for DA and ADA, in the sense that the former is concerned with automated control of basic distribution circuit switching functions, while the latter, ADA, is concerned with complete automation of all the controllable equipment and functions in the distribution system. In this document, the term DA is chosen to automation applied to the distribution system, with regard to the above distinction. The number of DA projects at the different utilities is

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increasing, with different approaches. Many of these projects encompass a large area of a distribution system. It is unlikely that any one approach of DA will be the sole preferred technique for utilities. There are too many differences between various utilities -- and even within an individual utility -- to justify a universal solution.

Benefits of Distribution Automation The benefits of DA can be grouped into three bins: operational, customer-related and financial: Operational Benefits

• Better fault detection, isolation and restoration • Reduced outage duratiion • Improved voltage profile and reactive power (VAR) management/optimization • Better visibility into the grid and more accurate data informaation for system operation and planning • Better component loading

Customer Benefits

• Better quality of supply and service reliability • More customer choice

Financial Benefits

• Less manual labor • Decreased interruption costs2 • Improved utilization of system capacity • Better customer retention for improved quality of supply

I. THEN AND NOW: THE DISTRIBUTION POWER SYSTEM, TWO DIFFERENT ENVIRONMENTS The increasing penetration of residential and municipal solar generation, and the distributed generation in general, impose challenges on the existing distribution infrastructure and the system operator. New flow patterns may require changes to the protection and control strategies, enhanced distribution automation and microgrid capabilities, capabilities, voltage and VAR management, and over all enforcement of distribution grid infrastructure. The changes are best depicted in the Figures 1 and 2.

TECH ARTICLE

Distribution Circuit Congestion Most distribution systems in the United States were designed decades ago based on the loading analysis performed at the time. These were based on historical load profiles, statistical analysis with some assumed diversity factors. Many distribution circuits have been operating close to their operating limits, and additional load may push them above their emergency operating limits. Several electric vehicles (EV) plugged into the same circuit could cause a localized overload on the distribution circuit and transformers while these are subjected to variations in demand due to normal customer activities. The unbalanced conditions created by such loads are on top of imbalance due to the large number of unequal single-phase and double-phase loads. The above could result in degradation of customer power quality, congestion on certain feeders, voltage concerns on longer feeders and increased line losses. The major changes in load types, levels and load patterns may now require upgrades to the transformers and other equipment or shifting loads between transformers.

II. FEEDER AUTOMATION Feeder automation is an important part of distribution automation and has received considerable attention over the last few years. Many approaches have been proposed and implemented in power utilities worldwide. Progress in large scale distribution automation has been slow due to the massive investments needed, but funding by the federal government for utilities implementing smart grids has accelerated deployment of these technologies. Feeder automation is implemented either based on a centralized approach or a distributed one. A centralized approach is capable of providing complete FA functions but requires large scale implementation. A distributed approach is simpler, more flexible, can be implemented in a small scale but can only provide limited FA functionalities.

III. FEEDER RECONFIGURATION 2

Over the next 10 years, individual utilities may use various combinations of DA approaches across their service areas to create reliability tiers to maximize customer and utility value. The creation of reliability tiers could introduce new utility revenue models and would allow commercial and industrial customers to choose between “higher-grade” utility power or expanded uses of back-up generators, to lower consumers’ overall energy costs.

Distribution systems are normally configured radially for effective coordination of their protective devices. Two types of switches are generally found in the system for both protection and configuration management: 1. Sectionalizing switches (normally closed switches) Visit www.eeweb.com

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Power Flow

Generating Plant

Transmission System

Unidirectional Power Flow

Circuit Breakers

Unidirectional Power Flow

Sub-transmission System Voltage Regulator

Feeders Sectionalizing switch

Distribution System

Capacitor bank

Lateral feeder

Figure 1: Then...Simplified Power Systems Power Flow

Generating Plant

Transmission System Circuit Breakers

Sub-transmission System DG Voltage Regulator

Sectionalizing switch

Distribution System

By changing the status of the sectionalizing and tie switches, the configuration of distribution system is varied and loads are transferred among the feeders while the radial configuration format of electrical supply is preserved and all load points are not interrupted. Feeder reconfiguration entails the modification of the topology of an electrical system by closing or opening tie and sectionalizing switches, in order to obtain a better performance of the system. It has been used to improve voltage regulation balance, feeder loading, as well as reducing system losses. Examples of objectives of feeder reconfiguration include: real power loss reduction, equipment (e.g., transformer and feeder) load balancing, phase balancing, system restoration, bus voltage profile improvement, increasing reliability and power quality improvement.

Real Power Loss Reduction

Home

Feeders

2. Tie switches/breakers (normally opened switches).

Capacitor bank

Lateral feeder

Under normal operating conditions, the network is reconfigured to reduce the systemâ&#x20AC;&#x2122;s losses. One method which can be used to achieve this is through an explicit formula for determining the variations in system losses, three-phase line flows and voltages in terms of system and network data, with respect to variations in control devices, network components and connections. Transformer losses can be minimized if the substation transformers are loaded in proportion to their capacity. The reconfiguration of the system for reliability and loss reduction can be accomplished in an automated mode using the same sectionalizers which are used for fault isolation and service restoration. From a practical point of view, reconfiguration once every few hours would be sufficient for loss reduction. The additional benefit of more frequent reconfiguration is very minimal.

Equipment Feeder Load Balancing Home

Figure 2: Now...Excepted Changes in Flows of Electric Power

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Feeder reconfiguration may be used to avoid over loading of critical transformers (and/or feeders) resulting from load variations. In order to keep the system reliable, a part of the load from the

overloaded feeder must be transferred to an adjacent transformer feeder that is relatively lightly loaded. Similarly main transformer overloading problem can be addressed by identifying the appropriate feeder causing the overload and transferring a part of load from that feeder load to an adjacent transformer which is lightly loaded. This redistribution of load among feeders and transformers makes the system more balanced and the risk of overloading is reduced thereby increasing the reliability of a system. The network can be reconfigured to balance load in feeders and/or to avoid the overloading of critical transformers and feeders resulting from load variations. This redistribution of load among feeders and transformers makes the system more balanced and the risk of overloading is reduced thereby increasing the reliability of a system. One method this could be accomplished is through monitoring certain electrical parameters (current, voltage etc.) in the system and initiating breaker trips based on hitting threshold values.

Reconfiguration in Case of a Fault By the use of remote interconnect switching; utilities can restore power to as many consumers as possible during the time of multiple faults. Under conditions of permanent failure, the network is reconfigured to restore the service, minimizing the zones without power.

Reconfiguration for Reliability Predictive reliability models and schemes can be used to compute reliability indices for the distribution system in order to apply algorithms to reconfigure the system to achieve optimum reliability. Thus feeder reconfiguration presents electric utilities with an opportunity to boost reliability without the addition of new components.

Equipment Loading and Voltage Drop Criteria System reconfigurations should not violate equipment loading and voltage drop criteria; hence, a power flow for each system configuration needs to be performed to identify voltage and capacity violations.

Optimization Techniques Heuristic techniques have been proposed to reach a near optimal solution for feeder reconfiguration in a short period. Other approaches have been in which the optimal configuration was achieved by opening the branches with lowest current in the optimal load flow solutions for the configuration with all switches closed.

TECH ARTICLE

Fuzzy logic and the combinatorial optimization-based methods have also been used.

Special Considerations Reconfiguration with Weighted Objectives Reconfiguration of the system may be defined in terms of maximum reliability or minimum losses, or a combination of these two. The task of finding the optimal balance between them is approached as a multicriteria/multiobjective optimization problem. On one hand, we have the customersâ&#x20AC;&#x2122; reliability demands for power delivery and on the other hand we have the losses and their economic impact on the system. In the optimization total customer interruption cost is used as the measure of system reliability performance from the customer perspective. The losses costs are closely related to the analyzed network, its components, structure and available resources. It is possible to extend the multiobjective approach by studying every feeder as an individual objective instead of the total system. Furthermore, with more objectives, the solution space quickly becomes difficult to grasp with the increasing number of load points. It is interesting to note that the two objectives do not entirely point the solution in two different directions.

Reconfiguration with Unbalanced Conditions The actual distribution feeders are primarily unbalanced in nature due to various reasons, for example, unbalanced consumer loads, presence of single, double, and three-phase line sections, and existence of asymmetrical line sections. The inclusion of system unbalances increases the dimension of the feeder configuration problem because all three phases have to be considered instead of a single phase balanced representation. Consequently, the analysis of distribution systems necessarily required a power flow algorithm with complete three-phase model. Potential unbalanced conditions created by such loads could cause problems on main feeders and other laterals.

Feeder Reconfiguration with Distributed Generation Recent development in DG technologies such as wind, solar, fuel cells, hydrogen, and biomass has drawn attention for utilities to accommodate DG units in their systems. The introduction of DG units brings a number of technical issues to the system since the distribution network with DG units is no longer passive.

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EEWeb PULSE IV. VOLTAGE AND REACTIVE POWER (VAR) CONTROL AND OPTIMIZATION As energy demands increase, and power-hungry new technologies such as electric vehicles proliferate, utilities will need to find ways to meet peak-load requirements. Volt-VAR optimization, which reduces losses from transmission and distribution, can free up much needed capacity to help meet future demand. For that, the industry has progressed from fixed capacitor banks to one way controlled devices, and now capacitor banks managed by two-way communications and fully intelligent controls that operate based on existing conditions and handle reactive-power loads throughout the distribution system.

Conventional Voltage Control Conventional voltage control is intended to maintain acceptable voltage profile along a distribution feeder in accordance with locally available measurements. Though this often leads to sensible control actions taken at the local level, this could be suboptimal when it comes to voltage and reactive power (var) control on a larger scale. In addition, utilities continually face system losses from reactive load, or “VAR,” created by large customer load devices such as washing machines, air conditioning units, etc. To address these losses, utilities have implemented methods to regulate and reduce the amount of VAR on their systems through “Volt/VAR control” (a general term used to describe different approaches to regulating voltage and VAR on distribution feeders). By optimizing voltage and reactive power, great efficiencies can be realized on the distribution system. The primary goal of Volt/VAR control is to minimize the amount of VARs generated by centralized generation and shipped via transmission or distribution systems and, in turn, helping utilities achieve greater system efficiency and increased system capacity.

Conservation Voltage Reduction (CVR) The most common smart distribution voltage control function is Conservation Voltage Reduction (CVR) to intentionally lower the voltage on the distribution feeder to the lowest acceptable voltage value to reduce demand and energy consumption. The ROI for a VVO project could be as short as two years as a result of cost savings from reduced losses and reduced generation costs.

Ideally, information should be collected form all voltage and VAR control devices and acted upon to obtain optimal consistency with optimized control objectives. This approach is commonly referred to as integrated VVO. VVO is an advanced application that runs periodically or in response to operator demand and uses two-way communication infrastructure. VVO makes it possible to optimize the energy delivery efficiency on distribution systems using real-time information without causing voltage/current violations. VVO should work in various system design and operating conditions.

Technical Challenges The control variables available to VVO are the control settings for switchable capacitors and tap changers of voltage regulating transformers. VVO is basically an optimization problem due to the following challenges:

Load Sensitivity to the voltage profile Work by this author has shown that customer load is sensitive to the voltage profile of the system and that the load must be modeled accurately to quantify the impacts and benefits of Volt/var measures. “Discrete” (integer) or binary decision variables

Many possible solutions are not continuous, but rather discrete. Examples: For a single switchable capacitor bank, the control variable is binary (Out: 0 or In: 1) For a typical tap changer, the control variable is an integer that varies from -16 to +16

Nonlinear objective

Energy loss is a non-linear function

High dimension nonlinear constraints

Thousands of powerflow equations

Large search space and control variables

Optimization algorithms need to be efficient and robust for large problems

However, improving the voltage profile (e.g., with capacitors) can result in an increase in load that may exceed the loss reduction. Some conventional loads do not accurately model changes to the system resulting from changes in the voltage profile.

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TECH ARTICLE

Real-time VVO

VVO Requirements

New generation of automation control, more robust bidirectional communication, and a new range of linesensing solutions to enable centralized and distributed control schemes hold a lot of promise for real-time voltVAR control for reducing line losses and peak-demand shaving. By aggregating and analyzing volt and reactive power real-time data from across the distribution grid operators can monitor the reliability of the system as load-profile shifts occur (thus making long-established power-flow models obsolete). Volt-VAR optimization programs can also provide another opportunity to boost returns from installed assets, such as advanced meters. Closed-loop control schemes, based on real-time data collection, can enable utilities to dynamically manage power quality. Utilities can prevent harmful voltage excursions that inevitably damage and/or reduce the useful life of equipment. The latest technology enhancements offered through advanced volt-VAR controls determine whether devices are turned off or on by taking real-time measurements and analyzing the associated VAR flows. This allows utilities to optimize the system across all feeders served by a substation, eliminating a situation in which one feeder has a leading power factor and another has a lagging power factor but in which the substation bus has met the target power factor. Innovations in volt-VAR management technology are enabling the industry to move closer to maintaining a consistent power factor across all operating conditions

For VVO to operate properly, it is necessary to assure that the optimal quantity, sizing and placement of capacitors and regulators across individual feeders. Intelligent controls and communications, as well as central analytical software are then added to into the system.

Centralized and Distributed VVO Intelligence Centralized intelligence allows the management of the grid on an overall substation level to maximize efficiency. Centralized intelligence can be layered over distributed intelligent controls. Such an approach eliminates vulnerability to a single point of failure, such a communication failure which may cause the system to lose all functionality. In a layered system, and in the event that communications are lost, the system would continue to function as a result of the distributed intelligence, albeit, at less optimal level.

VVO with Distributed Generation Advanced volt-VAR control systems are needed to manage the effects that renewable energy resources, plug-in EVs and photovoltaics on the grid. These have the potential of dramatically changing a systemâ&#x20AC;&#x2122;s voltage profile, affecting the quality of service. Having analytics and sensing and a number of voltage monitoring points will create a real-time view of a systemâ&#x20AC;&#x2122;s voltage profile on the system with such devices. Because voltage is managed within tight ANSI norms, the accuracy of the sensing data is important . Communication bandwidth and low latency are also vital factors for obtaining quality data in real time for correct control decisions. It is also important to have sufficient voltage-regulation devices on the feeders, whether these are capacitor banks or line voltage regulators to deal with the intermittency of some of the devices. About the Author Nicholas Abi-Samra has been actively involved in IEEE for more than 35 years. As Vice President of Asset Management at Quanta Technology, he and his team help utilities better manage and modernize their assets at lower total lifecycle cost. He was both General Chair and overall Technical Program Coordinator for the 2012 IEEE Power & Energy Society General Meeting.

Part 1 of a 3-part series...

VVO Coordination with Other DA Technologies Volt-VAR can be layered in with self-healing and distributed energy management systems. This could to provide addition layers of intelligence that will improve voltage and VAR support under different operating conditions and system topology changes.

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Low Voltage ORing FET Controller ISL6146

Features

The ISL6146 represents a family of ORing MOSFET controllers capable of ORing voltages from 1V to 18V. Together with suitably sized N-channel power MOSFETs, the ISL6146 increases power distribution efficiency when replacing a power ORing diode in high current applications. It provides gate drive voltage for the MOSFET(s) with a fully integrated charge pump.

• ORing Down to 1V and Up to 20V with ISL6146A, ISL6146B, ISL6146D and ISL6146E

The ISL6146 allows users to adjust with external resistor(s) the VOUT - VIN trip point, which adjusts the control sensitivity to system power supply noise. An open drain FAULT pin will indicate if a conditional or FET fault has occurred. The ISL6146A and ISL6146B are optimized for very low voltage operation, down to 1V with an additional independent bias of 3V or greater. The ISL6146C provides a voltage compliant mode of operation down to 3V with programmable Undervoltage Lock Out and Overvoltage Protection threshold levels The ISL6146D and ISL6146E are like the ISL6146A and ISL6146B respectively but do not have conduction state reporting via the fault output. TABLE 1. KEY DIFFERENCES BETWEEN PARTS IN FAMILY PART NUMBER

KEY DIFFERENCES

ISL6146A

Separate BIAS and VIN with Active High Enable

ISL6146B

Separate BIAS and VIN with Active Low Enable

ISL6146C

VIN with OVP/UVLO Inputs

ISL6146D

ISL6146A wo Conduction Monitor & Reporting

ISL6146E

ISL6146B wo Conduction Monitor & Reporting

VOLTAGE DC/DC (3V - 20V)

VIN GATE VOUT BIAS ADJ ISL6146B FLT GND

Q2 +

VOLTAGE DC/DC (3V - 20V)

VIN GATE VOUT VOUT BIAS ADJ ISL6146B FLT GND

FIGURE 1. TYPICAL APPLICATION

October 5, 2012 FN7667.3

• VIN Hot Swap Transient Protection Rating to +24V • High Speed Comparator Provides Fast <0.3µs Turn-off in Response to Shorts on Sourcing Supply • Fastest Reverse Current Fault Isolation with 6A Turn-off Current • Very Smooth Switching Transition • Internal Charge Pump to Drive N-channel MOSFET • User Programmable VIN - VOUT Vth for Noise Immunity • Open Drain FAULT Output with Delay - Short between any two of the ORing FET Terminals - GATE Voltage and Excessive FET VDS - Power-Good Indicator (ISL6146C) • MSOP and DFN Package Options

Applications • N+1 Industrial and Telecom Power Distribution Systems • Uninterruptable Power Supplies • Low Voltage Processor and Memory • Storage and Datacom Systems

• Programmable Voltage Compliant Operation with ISL6146C

C O M M O N P O W E R

GATE FAST OFF, ~200ns FALL TIME ~70ns FROM 20V TO 12.6V ACROSS 57nF GATE OUTPUT SINKING ~ 6A

B U S +C O M M O N P O W E R B U S

FIGURE 2. ISL6146 GATE HIGH CURRENT PULL-DOWN

Intersil (and design) is a registered trademark of Intersil Americas Inc. Copyright Intersil Americas Inc. 2011, 2012 All Rights Reserved. All other trademarks mentioned are the property of their respective owners.

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