US008406933B2
`
`(12) United States Patent
`Nagel et a1.
`
`(10) Patent N0.:
`(45) Date of Patent:
`
`US 8,406,933 B2
`Mar. 26, 2013
`
`(54)
`
`SYSTEMS AND METHODS FOR
`ESTIMATING THE EFFECTS OF A REQUEST
`TO CHANGE POWER USAGE
`
`(75)
`
`Inventors: Paul E. Nagel, Draper, UT (US);
`William B. West, Sandy, UT (U S)
`
`(73)
`
`Assignee: Control4 Corporation, Salt Lake City,
`UT (U S)
`
`(*)
`
`Notice:
`
`Subject to any disclaimer, the term of this
`patent is extended or adjusted under 35
`U.S.C. 154(b) by 233 days.
`
`(21)
`
`App1.No.: 12/858,199
`
`(22)
`
`Filed:
`
`Aug. 17, 2010
`
`(65)
`
`Prior Publication Data
`
`US 2011/0046806 A1
`
`Feb. 24, 2011
`
`Related US. Application Data
`
`(60)
`
`Provisional application No. 61/234,963, ?led on Aug.
`18, 2009.
`
`(51)
`
`(52)
`(58)
`
`Int. Cl.
`(2006.01)
`G06F 19/00
`US. Cl. ...................................... .. 700/286; 713/300
`
`Field of Classi?cation Search ................ .. 700/286,
`700/291, 295; 713/300, 320; 719/318
`See application ?le for complete search history.
`
`(56)
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`
`1/2011 McConnell et a1. ........ .. 702/188
`7,877,235 B2 *
`2003/0094610 A1* 5/2003 Aoki et a1. .................... .. 257/48
`2003/0229900 A1 12/2003 Reisman
`2007/0053513 A1
`3/2007 Ho?berg
`2007/0239317 A1* 10/2007 Bogolea et a1. ............. .. 700/276
`2007/0244604 A1 * 10/2007 McNally ...... ..
`700/291
`2008/0147465 A1* 6/2008 Raines et a1. ................... .. 705/7
`2008/0177678 A1
`7/2008 Di Martini et a1.
`OTHER PUBLICATIONS
`ZigBee Alliance, “ZigBee Speci?cation,” ZigBee Document
`053474r17, Jan. 2008.
`Leeds, David J ., “The Smart Grid in 2010: Market Segments, Appli
`cations and Industry Players,” GTM Research, Jul. 2009.
`International Search Report issued for International Patent Applica
`tion No. PCT/US2010/045913 on Oct. 6,2010.
`International Preliminary Report on Patentability issued for Interna
`tional Patent Application No. PCT/US2010/045913 on Mar. 1, 2012.
`
`* cited by examiner
`
`Primary Examiner * Kidest Bahta
`(74) Attorney, Agent, or Firm * Austin Rapp & Hardman
`(57)
`ABSTRACT
`Systems and methods for estimating the effects of a request to
`change poWer usage are described. Device data about one or
`more devices may be received. User behavior data about past
`and anticipated user behavior may be received. Effects of a
`request to change poWer usage on a poWer grid may be esti
`mated using the device data and the user behavior data.
`Whether to send the request to change poWer usage may be
`determined based on the estimated effects.
`
`44 Claims, 15 Drawing Sheets
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`US 8,406,933 B2
`
`1
`SYSTEMS AND METHODS FOR
`ESTIMATING THE EFFECTS OF A REQUEST
`TO CHANGE POWER USAGE
`
`RELATED APPLICATIONS
`
`This application is related to and claims priority from US.
`Provisional Patent Application Ser. No. 61/234,963, ?led
`Aug. 18, 2009, for “Systems and Methods for Estimating the
`Effects of a Request to Change PoWer Usage,” With inventors
`Paul E. Nagel and William B. West.
`
`TECHNICAL FIELD
`
`The present disclosure relates generally to electricity gen
`eration. More speci?cally, the present disclosure relates to
`estimating the effects of a request to change poWer usage.
`
`BACKGROUND
`
`In recent years, the price of electronic devices has
`decreased dramatically. In addition, the types of electronic
`components that can be purchased have continued to
`increase. For example, DVD players, large screen TVs, multi
`carousel CD and DVD players, MP3 players, video game
`consoles, and similar consumer electronic items have become
`more Widely available While continuing to drop in price.
`The decreasing prices and increasing types of electronic
`components have packed today’ s homes and businesses With
`modern conveniences. As more of these components are sold,
`the average household poWer consumption also increases.
`Typical homes and businesses noW include more poWer-con
`suming devices than ever before. With the increasing
`demands for poWer, at times poWer consumption may
`approach the limit on the capacity to generate poWer. If the
`consumption gets too close to the upper limit on poWer gen
`eration capacity, poWer outages and/or disruptions, such as
`blackouts and broWnouts, may occur.
`To avoid such poWer disruptions, a region may build infra
`structure to increase poWer generation. HoWever, increasing
`poWer generation for a geographic region is often very expen
`sive. Thus, it may be more cost effective to determine Ways to
`decrease consumption at certain times. As such, there is a
`need for improved systems and methods for decreasing poWer
`consumption While limiting the adverse effects as much as
`possible.
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`BRIEF DESCRIPTION OF THE DRAWINGS
`
`FIG. 1A is a chart illustrating one con?guration of a system
`using the directive model;
`FIG. 1B is a chart illustrating one con?guration of a system
`using the objective model;
`FIG. 1C is a block diagram illustrating one con?guration of
`a system for estimating the effects of a demand response;
`FIG. 1D is a chart illustrating one con?guration of a system
`in Which the present systems and methods may be used;
`FIG. 2 is a block diagram illustrating another con?guration
`of a system for estimating the effects of a demand response;
`FIG. 3 is a block diagram illustrating a con?guration of a
`home area netWork (HAN);
`FIG. 4 is a block diagram illustrating one con?guration of
`a poWer management console;
`FIG. 5 is a block diagram illustrating one con?guration of
`a screenshot on the poWer management console;
`FIG. 6 is a How diagram illustrating a method for estimat
`ing the effects of a demand response;
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`FIG. 7 is a How diagram illustrating another method for
`estimating the effects of demand responses;
`FIG. 8 is a block diagram of a HAN controller;
`FIG. 9 is a How diagram illustrating a method for control
`ling a device using a HAN controller;
`FIG. 10 is a How diagram illustrating a method for adjust
`ing the control of a device using a HAN controller;
`FIG. 11 is a block diagram illustrating multiple con?gura
`tions of a screenshot on the HAN controller;
`FIG. 12 is a block diagram ofa HAN device; and
`FIG. 13 is a block diagram illustrating various components
`that may be utiliZed in a computing device/ electronic device.
`
`DETAILED DESCRIPTION
`
`A method for estimating the effects of a request to change
`poWer usage is disclosed. Device data about one or more
`devices is received from a home area netWork. User behavior
`data about the devices is received. Effects on a poWer grid of
`a request to change poWer usage are estimated using the
`device data and the user behavior data. It is determined
`Whether to send the request to change poWer usage based on
`the estimated effects.
`In one con?guration, poWer data about poWer generation in
`the poWer grid may be received. Effects on the poWer grid of
`a request to change poWer usage may be estimated using the
`device data, the user behavior data, and the poWer data. Cus
`tomer preferences about the devices may also be received.
`Effects on the poWer grid of a request to change poWer usage
`may be estimated using the device data, the user behavior
`data, and the customer preferences.
`The user behavior data may include one or more of the
`folloWing: past device responses to requests to change poWer
`usage, typical behavior of the devices as a function of time,
`suggestions for reduction in poWer consumption, typical
`loads of the devices, typical loads of the house in Which the
`devices reside, and anticipated poWer consumption of the
`devices.
`The request to change poWer usage may not be sent based
`on a determination that the request to change poWer usage
`should not be sent. Alternatively, the request to change poWer
`usage may be sent using one or more of the folloWing:
`Z-Wave by Zensys, ZigBee Smart Energy (ZigBee SE), Zig
`Bee Home Automation (ZigBee HA), Global System for
`Mobile communications (GSM), any of the HomePlug stan
`dards, Broadband over PoWer Lines (BPL), and PoWer Line
`Communication (PLC).
`The device data may include one or more of the folloWing:
`device type, geographic location, poWer consumption, cur
`rent status, and anticipated poWer consumption. The poWer
`data may include one or more of the folloWing: an amount of
`poWerbeing generated in the poWer grid, sources of the poWer
`being generated, sources of stored poWer, current Weather
`conditions, and the forecasted Weather conditions.
`The request to change poWer usage may be a request for
`reduced poWer consumption of one or more devices. The
`request to change poWer usage may be sent to the home area
`netWork controller for the home area netWork controller to
`send to one or more devices. Furthermore, the request to
`change poWer usage may be sent to a utility meter that sends
`it to the home area netWork controller that sends it to the
`devices.
`In one con?guration, the device data and the poWer data
`may be stored. Third parties may be alloWed to access this
`stored device data and stored poWer data. Reports may be
`generated based on the device data in order to assess the
`health of the devices.
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`In one con?guration, estimating the effects of the request to
`change power usage may include estimating the change in
`revenue associated with the request to change power usage.
`Estimating the effects of the request to change power usage
`may also include estimating the change in power consump
`tion associated with the request to change power usage.
`An apparatus for estimating the effects of a request to
`change power usage is also disclosed. The apparatus includes
`a processor and memory in electronic communication with
`the processor. Executable instructions are stored in the
`memory. The instructions are executable to receive device
`data about one or more devices from a home area network
`controller. The instructions are also executable to receive user
`behavior data about the devices. The instructions are also
`executable to estimate effects of a request to change power
`usage on a power grid using the device data and the user
`behavior data. The instructions are also executable to deter
`mine whether to send the request to change power usage
`based on the estimated effects.
`A computer-readable medium is also disclosed. The com
`puter-readable medium includes instructions. The instruc
`tions are executable for receiving device data about one or
`more devices from a home area network controller. The
`instructions are also executable for receiving user behavior
`data about the devices. The instructions are also executable
`for estimating effects of a request to change power usage on a
`power grid using the device data and the user behavior data.
`The instructions are also executable for determining whether
`to send the request to change power usage based on the
`estimated effects.
`A method for managing a device based on a request to
`change power usage is also disclosed. A request to change
`power usage is received. It is determined how to adjust man
`agement of the device based on the request to change power
`usage, device data, and customer preferences. The manage
`ment of the device is adjusted based on the determining.
`Information about the adjustment is stored.
`The device data may include one or more of the following:
`device type, geographic location, power consumption, and
`current status. The request to change power usage may be a
`request for reduced power consumption by the device. The
`request to change power usage may be received using one or
`more of the following: Z-Wave by Zensys, ZigBee Smart
`Energy (ZigBee SE), ZigBee Home Automation (ZigBee
`HA), Global System for Mobile communications (GSM), any
`of the HomePlug standards, Broadband over Power Lines
`(BPL), and Power Line Communication (PLC). The adjusting
`may include one or more of the following: taking no action,
`changing a setting on the device, or turning the device off
`based on the device data.
`An apparatus for managing a device based on a request to
`change power usage is also disclosed. The apparatus includes
`a processor and memory in electronic communication with
`the processor. Executable instructions are stored in the
`memory. The instructions are executable to receive a request
`to change power usage. The instructions are also executable
`to determine how to adjust management of the device based
`on the request to change power usage, device data, and cus
`tomer preferences. The instructions are also executable to
`adjust the management of the device based on the determin
`ing. The instructions are also executable to store information
`about the adjustment.
`A computer-readable medium is also disclosed. The com
`puter-readable medium includes instructions. The instruc
`tions are executable for receiving a request to change power
`usage. The instructions are also executable for determining
`how to adjust management of the device based on the request
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`to change power usage, device data, and customer prefer
`ences. The instructions are also executable for adjusting the
`management of the device based on the determining. The
`instructions are also executable for storing information about
`the adjustment.
`The terms “power” and “energy” may be used interchange
`ably herein. It is to be understood that “power” generally
`refers to a rate of consumption and anything measured in
`watts, while “energy” generally refers to a unit of work mea
`sured in kWh and similar units of energy. However, the term
`“power” may be used herein to refer to both. Therefore the
`term “power” as used herein may refer to a rate of transfer,
`use, or generation of electrical energy as well as electrical
`energy itself.
`As the demand for power approaches the capacity to gen
`erate power, it may be desirable to either increase generation
`capacity, reduce consumption, or some combination of the
`two. Since increasing generation capacity may be prohibi
`tively expensive, an increasing amount of focus is now on
`intelligently reducing consumption without affecting lif
`estyle. One way this problem has been approached has been
`to use a directive model, where a power generation facility
`sends a directive to a home to perform a very speci?c action.
`For example, the thermostat in a home may receive a mes sage
`from a power facility requesting that the setting on the home’ s
`thermostat be raised by four degrees on a hot day in order to
`save power. The thermostat may then follow this directive and
`change the programmed setting. However, identical mes
`sages received by different thermostats may produce incon
`sistent power savings. In other words, these directives may
`produce different results in different homes, e. g., a home with
`shade may warm up slower on a hot day than a home with no
`shade. When the directive has been accomplished (raising the
`inside temperature by four degrees), then the program may
`proceed as usual. Therefore, the exact duration and amount of
`reduction in power consumption may be unknown before a
`directive is actually sent in this model.
`Another way to intelligently reduce power consumption
`may be an objective model. In this model, a power generation
`facility may send an objective to a home that is more general,
`e.g., reduce power consumption. This means that rather than
`simply sending a speci?c task, as in the directive model, the
`objective model allows some type of decision based logic in
`the home to determine how to accomplish the objective. For
`example, if the objective is to reduce power consumption by
`the heating and cooling system by ?ve percent over the next
`hour, a Home Area Network controller within the home may
`determine and implement appropriate settings for the heating
`and cooling system. This objective approach may provide for
`better power reduction with limited lifestyle adjustments. In
`other words, the systems and methods disclosed herein may
`provide bene?ts to both a utility provider, by allowing for the
`avoidance of power consumption during peak demand, as
`well as the utility recipient, by saving money with minimal
`discomfort and inconvenience.
`FIG. 1A is a chart illustrating one con?guration of a system
`implementing the present systems and methods using the
`directive model. FIG. 1B is a chart illustrating one con?gu
`ration of a system implementing the present systems and
`methods using the objective model. In other words, FIGS. 1A
`and 1B may further illustrate the distinction between the
`directive model and the objective model. The solid lines 115
`may represent the state of the heating and cooling system as a
`function of time, e.g., ON or OFF. The dotted lines 117 may
`represent the temperature inside a home as a function of time.
`The dashed lines 119 may represent the outside temperature
`as a function of time.
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`In FIG. 1A, the home may have received a directive to raise
`the set point of the heating and cooling system to 78 degrees
`Fahrenheit. In the illustrated con?guration, the outside tem
`perature exceeds 90 degrees. Therefore, the directive may be
`complied with very quickly. In other words, the heating and
`cooling system may turn OFF for only one half of an hour,
`thus resulting in minimal power reduction. In such a con?gu
`ration, a power provider may have estimated more reduction
`in power consumption from the directive, and therefore be
`required to send more directives to achieve the desired power
`reduction it requires. This may be inef?cient and costly.
`In FIG. 1B, however, the home may have received an
`objective to reduce power consumption by 20 percent from
`2:00 pm to 6:00 pm. A Home Area Network controller may
`use decision logic based on a user’s preferences and choose to
`cycle the heating and cooling system in order to comply with
`the objective. In the illustrated con?guration, the heating and
`cooling system may turn ON for a short period then OFF for
`a longer period during the speci?ed time period. This may
`result in slightly higher temperatures during this period, but
`also vastly reduced power consumption compared to the
`directive model in FIG. 1A. Therefore, the objective model
`may provide better power reduction with minimal lifestyle
`discomfort because it allows decision logic within the home
`to determine and implement the best way to achieve desired
`power reduction based on gathered data, e.g., user prefer
`ences, current home settings, etc.
`The improved power reduction resulting from using the
`objective model may have several advantages. First, it may
`allow a utility provider, such as a power company, to more
`accurately avoid peak demand. As will be discussed below,
`utility providers may be required to keep a certain percentage
`of power generation capacity available for critical services,
`e.g., hospitals, emergency responders, etc. Thus, at peak peri
`ods, like midday, the utility provider may be able to send
`objectives to reduce power consumption in order to avoid
`peak demand and avoid having to buy more power generation
`from other providers.
`The objective model may also bene?t power consumers by
`saving them money through ef?cient reduction in power con
`sumption. For example, a power company may determine the
`rates charged for power by taking the peak consumption
`period over a de?ned time period, e.g., the highest day’s
`consumption in the previous month. Therefore, the higher a
`consumer’s peak, the higher the rate charged for the entire
`month. Under this billing structure, a consumer may wish to
`limit their peak periods of power consumption in order to
`reduce their monthly rate. Likewise, a power company may
`charge a higher ?at rate during peak hours than during non
`peak hours. Under this billing structure, a consumer may wish
`to limit consumption during the period with the highest rate.
`Likewise, a power company may charge a ?at rate that
`changes every hour. Under this billing structure, a consumer
`may wish to limit their power consumption when they are
`informed of a high rate for the upcoming hour. Thus, ef?cient
`reduction of power consumption may lower a consumer’s
`cost of power under any rate structure, e.g., tiered pricing, ?at
`rate, hourly variable, etc.
`Despite the advantages of the objective model, it is still not
`ideal. More speci?cally, the exact power reduction in
`response to an objective may not be known because the vari
`ous states/con?gurations and preferences of the homes to
`which the objective is sent are not known. For example, if a
`cooling system in a home was not running, an objective to
`reduce heating and cooling consumption would not result in
`any reduction. Likewise, a home may not comply with this
`type of request. It may be inef?cient and time-consuming for
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`a power facility to achieve a speci?c load reduction by trial
`and error. Therefore, it may be desirable to estimate the
`effects of a request from a power company or utility system to
`decrease power consumption (a “demand response”) before
`the request is sent.
`FIG. 1C is a block diagram illustrating one con?guration of
`a system 100 for estimating the effects of a demand response.
`The system 100 may include a utility system 102 that may
`include a utility management console 104. The utility system
`102 may be any system capable of producing, distributing,
`monitoring, or collecting a desired resource or services. This
`may include one or more servers, workstations, web sites,
`databases, etc. The utility system 102 may be centrally
`located or distributed across several facilities. Examples of a
`utility system 102 include electricity generation facilities,
`natural gas distribution facilities, telephone service facilities,
`etc. The utility system 102 may include a utility management
`console 104 that may allow the utility system 102 to estimate
`the effect of a demand response before a demand response is
`sent. As used herein the term “demand response” refers to a
`request from the utility system 102 for decreased consump
`tion. A more general request than a demand response is a
`request to change power usage in some way. The utility man
`agement console 104 may estimate the effect of a demand
`response by collecting data from one or more home area
`networks (HAN) 108.
`A HAN 108 may be a group of controlled devices operating
`in the same environment. Examples of devices in a HAN 108
`include, without limitation, a thermostat, a light switch, a
`washer, a dryer, a fumace, an air conditioner, a pool control
`ler, etc. The HAN 108 may communicate with the utility
`system 102 through a network 106. The network 106 may
`represent the Internet, one or more wide area networks
`(WANs), one or more local area networks (LANs), etc. Addi
`tionally, the network 106 may represent communication
`using power transmission lines. The network 106 may be
`implemented using wired and/or wireless communication
`technologies and may use any available protocols to pass data
`between the utility system 102 and the HAN 108. These
`protocols may include, but are not limited to, hypertext trans
`fer protocol (HTTP), ?le transfer protocol (FTP), secure ?le
`transfer protocol (SFTP), Z-Wave by Zensys, ZigBee Smart
`Energy (ZigBee SE), ZigBee Home Automation (ZigBee
`HA), Global System for Mobile communications (GSM), any
`of the HomePlug standards, Broadband over Power Lines
`(BPL), Power Line Communication (PLC), proprietary serial
`protocols, etc.
`FIG. 1D is a chart illustrating one con?guration of a system
`100 in which the present systems and methods may be used.
`The solid line 103 may represent power generation at the
`utility system 102 in terms of MegaWatt hours (MWh) as a
`function of time. The dashed line 105 may represent the
`threshold power generation in terms of MegaWatt hours
`(MWh) as a function of time. The dotted line 107 may repre
`sent the power consumption of a power grid in terms of
`MegaWatt hours (MWh) as a function of time. The power in
`the system may be generated using a number of techniques,
`e.g., nuclear, wind, solar, coal ?red, geothermal, etc.
`The threshold 105 may de?ne a power generation buffer
`that should be maintained. The buffer may represent line loss
`in delivering the power as well as capacity that should be kept
`available for critical services, e.g., hospitals, emergency
`responders, etc. In other words, the utility system 102 may be
`required to maintain the gap between the power generation
`1 03 and the threshold 1 05. In order to maintain this buffer, the
`power consumption 1 07 may not increase above the threshold
`105. As mentioned earlier, a utility system 102 may increase
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`power generation 103, Which can be very costly and time
`delayed, or decrease power consumption 107. One Way of
`reducing poWer consumption 107 may be to send a demand
`response.
`In one con?guration, the utility system 102 may monitor
`the poWer consumption 107 in the system 100 to identify
`trends 109 of increased consumption that may indicate that
`poWer consumption 111 Will exceed the threshold 105. When
`a trend 109 is identi?ed, the utility system 102 may send a
`demand response that causes the poWer consumption 113 to
`stay beloW the threshold 105. In other Words, a demand
`response may prevent the poWer consumption 111 that
`exceeds the threshold 105, and, instead, keep poWer con
`sumption 113 beloW the threshold 105. The present systems
`and methods may enable utility systems 102 to estimate the
`effects of a demand response in order to preserve the buffer
`betWeen the threshold 105 and the poWer generation 103.
`FIG. 2 is a block diagram illustrating another con?guration
`of a system 200 for estimating the effects of a demand
`response. There may be a poWer system 202 that may include
`a poWer management console 204 capable of estimating the
`effects of a demand response. The poWer system 202 may
`communicate With one or more HANs 208 through one o