Petition Exhibit 1004
`
`U.S. Patent No. 7,142,949 to Brewster et al. issued
`on November 28, 2006
`
`
`
`
`
`
`
`
`
`
`
`
`
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`
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`
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`
`
`Opower, Inc.
`Exhibit 1004
`
`

`

`111111111111111111111111111111111111111111111111111111111111111111111111111
`US007142949B2
`
`c12) United States Patent
`Brewster et al.
`
`(10) Patent No.:
`(45) Date of Patent:
`
`US 7,142,949 B2
`Nov. 28, 2006
`
`(54) AGGREGATION OF DISTRIBUTED
`GENERATION RESOURCES
`
`(75)
`
`Inventors: David B. Brewster, Boston, MA (US);
`Timothy G. Healy, Boston, MA (US)
`
`(73) Assignee: EnerNOC, Inc., Boston, MA (US)
`
`( *) Notice:
`
`Subject to any disclaimer, the term of this
`patent is extended or adjusted under 35
`U.S.C. 154(b) by 401 days.
`
`(21) Appl. No.: 10/314,920
`
`(22) Filed:
`
`Dec. 9, 2002
`
`(65)
`
`Prior Publication Data
`
`US 2004/0111226 Al
`
`Jun. 10, 2004
`
`(51)
`
`Int. Cl.
`H02J 3138
`(2006.01)
`(52) U.S. Cl. ....................... 700/286; 700/295; 700/297
`(58) Field of Classification Search ................ 700/286,
`700/291, 295, 22, 297; 705/412; 702/61
`See application file for complete search history.
`
`(56)
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`6,157,874 A *
`6,269,287 B1 *
`6,522,955 B1 *
`6,625,520 B1 *
`6,633,823 B1 *
`6,691,065 B1 *
`
`.............. 700/295
`12/2000 Cooley et a!.
`7/2001 March ........................ 700/286
`2/2003 Colborn ...................... 700/286
`................. 700/286
`9/2003 Chen et a!.
`10/2003 Bartone et al .............. 700/295
`2/2004 Hayashi et a!. ............... 700/22
`
`6,785,592 B1 *
`6,853,930 B1 *
`6,915,185 B1 *
`
`8/2004 Smith et al ................. 700/291
`2/2005 Hayashi et a!. ............. 700/287
`7/2005 Yamamoto et al .......... 700/286
`
`FOREIGN PATENT DOCUMENTS
`
`EP
`wo
`wo
`wo
`wo
`wo
`wo
`
`1263108 A1
`WOOl/06612 A1
`WOOl/61820 A1
`W001/71881 A2
`WOO 1/98851 A1
`W002/15365 A2
`W003/056681 A1
`
`12/2002
`1/2001
`8/2001
`9/2001
`12/2001
`2/2002
`7/2003
`
`OTHER PUBLICATIONS
`
`Sonderegger, Robert C., "Distributed Generation Architecture and
`Control", £-Vision 2000 Conference, 'Online 2000, pp. 292-301.
`* cited by examiner
`Primary Examiner-Leo Picard
`Assistant Examiner--Charles Kasenge
`(74) Attorney, Agent, or Firm-Bromberg & Sunstein LLP
`
`(57)
`
`ABSTRACT
`
`A method and system are associated with distributed gen(cid:173)
`eration of electric power. Power demand data of at least one
`electric power consumer is monitored over time. Power
`supply data of a regional power distribution system is also
`monitored over time. The power demand data and the power
`supply data are analyzed to coordinate control of at least one
`distributed generation system associated with the electric
`power consumer.
`
`56 Claims, 4 Drawing Sheets
`
`Opower, Inc.
`Exhibit 1004
`
`

`

`106
`
`10
`
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`POWER
`MARKETS
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`
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`Opower, Inc.
`Exhibit 1004
`
`

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`Opower, Inc.
`Exhibit 1004
`
`

`

`~ NOC Vtew Gene1ator Info Cuuent Energy Llfo ConttoLISummary
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`Opower, Inc.
`Exhibit 1004
`
`

`

`If!.!. EnerNOC 24/7 -(Control and Savings Summary)
`jrJ~ file
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`~li:l UI:Cl&r ~ NOC Vtew Generator Info Current Energy Info ControL'Surnmary
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`DG Optimizotion Energy Sovings
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`Opower, Inc.
`Exhibit 1004
`
`

`

`US 7,142,949 B2
`
`1
`AGGREGATION OF DISTRIBUTED
`GENERATION RESOURCES
`
`FIELD OF THE INVENTION
`
`The invention generally relates to generation and distri(cid:173)
`bution of electric power, and specifically, to aggregation of
`distributed generation resources.
`
`BACKGROUND ART
`
`Businesses and industry continue to require and consume
`increased amounts of electric power. One reflection of this
`trend is growing interest in self-generation of electric power,
`either to replace or to supplement that delivered by load(cid:173)
`serving entities and utilities over the existing electric power
`distribution grid. The employment of small-scale power
`generation capability at a local commercial or industrial
`facility has become known as distributed generation (DG).
`Most owners and operators of DG systems lack sophis(cid:173)
`ticated controls and functional software to optimize the
`performance of their systems. This usually results in under(cid:173)
`utilization of DG assets and unfavorable economics for DG
`projects. In addition, most end-users of electric power do not
`want to become experts in microgeneration. While the 25
`number of DG assets increases, much of these sit idle, and
`owners lack the capability to access wholesale power mar(cid:173)
`kets or sell this excess generation capacity back to the
`electric power distribution grid.
`
`SUMMARY OF THE INVENTION
`
`2
`determining cost-effective fuel purchase orders for one or
`more distributed generation systems based on the analysis of
`the power demand data and power supply data.
`Embodiments of the present invention also include vari(cid:173)
`ous user interfaces for monitoring one or more distributed
`generation system and/or demand data associated with one
`or more facilities. In one embodiment, the interface includes
`a power demand section for displaying power demand data
`associated with at least one electric power consumer, a
`10 power usage section for displaying power usage data asso(cid:173)
`ciated with the electric power consumer including power
`usage data associated with at least one distributed generation
`system associated with the electric power consumer, and a
`power cost section for displaying power cost data associated
`15 with the power usage data.
`In such an embodiment, the power demand data may
`include thermal load data associated with the electric power
`consumer. The power usage section may display power
`usage data according to an effective cost rate. The power
`20 cost section may display power cost data according to an
`effective cost rate. In addition, the data displayed may be
`periodically updated, such as at intervals of fifteen minutes
`or less. The power demand data, power usage data, and
`power cost data may include current data and historical data.
`Another embodiment is a user interface for monitoring at
`least one distributed generation system. The interface
`includes a meter section for displaying parametric data
`associated with at least one distributed generation system,
`and an alarms section for displaying a visual warning
`30 indicative of an abnormal operating condition associated
`with the distributed generation system.
`An embodiment also includes another user interface hav(cid:173)
`ing a present control thresholds section for displaying
`present threshold data indicating existing threshold condi-
`35 tions at which at least one distributed generation system
`automatically commences generation of electrical power,
`and a historical thresholds section for displaying historical
`threshold data associated with the distributed generation
`system.
`In such an embodiment, the present threshold data may be
`organized by cost rate and/or by time period. It may also
`include a savings section for displaying cost savings data
`associated with the distributed generation system.
`
`A representative embodiment of the present invention
`includes a method and system associated with distributed
`generation of electric power. Power demand data of at least
`one electric power consumer is monitored over time. Power
`supply data of a regional power distribution system is also
`monitored over time. The power demand data and the power
`supply data are analyzed to coordinate control of at least one
`distributed generation system associated with the electric 40
`power consumer.
`In a further such embodiment, the power demand data
`includes thermal load data associated with the electric power
`consumer. The method may also include determining sav(cid:173)
`ings resulting from the coordinated control.
`In a further embodiment, an optimal control threshold
`condition for the operation of a distributed generation sys(cid:173)
`tem is determined. This may further include automatically
`commencing generation of electric power for the electric
`power consumer when the threshold condition occurs. It 50
`may also include providing an override capability to allow
`for a subsequent override command to prevent the distrib(cid:173)
`uted power generation system from automatically com(cid:173)
`mencing generation of electric power for the electric power
`consumer when the threshold condition occurs.
`The optimal control threshold may be based upon incre(cid:173)
`mental operating time periods for the distributed generation
`system such as 15-minute or one-hour increments. The
`optimal control threshold condition may be determined
`periodically, such as weekly. The optimal control threshold 60
`condition may be based upon a peak load condition, or a
`power consumption cost rate.
`Embodiments of the present invention also include coor(cid:173)
`dinating sales of the electric power generated by one or more
`distributed generation systems to the regional power distri(cid:173)
`bution system, and/or initiating a load curtailment process to
`reduce demand from the grid at strategic times, and/or
`
`45
`
`55
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`FIG. 1 is a functional block diagram of one specific
`embodiment of the present invention.
`FIG. 2 is a screen shot of one embodiment showing a
`display of real-time and historical distributed generation
`system information.
`FIG. 3 is another screen shot of an embodiment showing
`a display of current and historical facility-specific energy
`consumption data.
`FIG. 4 is another screen shot of an embodiment showing
`a display of real-time and historical energy savings data.
`
`DETAILED DESCRIPTION OF SPECIFIC
`EMBODIMENTS
`
`Embodiments of the present invention are directed to
`energy service infrastructure focused on various aspects of
`distributed generation (DG) of electric power including
`monitoring, alarming, control, aggregation, billing, data
`65 management, and reporting. The objectives include genera(cid:173)
`tion control and building energy management and control
`systems that are optimized for peak shaving and demand
`
`Opower, Inc.
`Exhibit 1004
`
`

`

`US 7,142,949 B2
`
`3
`response activities, and which facilitate automation of vari(cid:173)
`ous load curtailment-related strategies at the end-use level.
`Multiple DG systems are networked in real-time within a
`single user interface for optimal control and verification.
`This creates an enabling technology system for facilitating
`customer or end-user participation in day-ahead or real-time
`markets for power, and optimized utilization of distributed
`generation equipment.
`More specifically, embodiments enable end-use electric
`power consumers and networked third parties to optimally
`aggregate and control distributed generation (DG) capacity.
`An economic optimization engine formulates advanced con(cid:173)
`trol strategies for DG systems. In one embodiment, the
`optimization engine periodically determines various deci(cid:173)
`sion rules such as optimal control thresholds for minimizing
`demand charges (peak shaving) and optimal operating peri(cid:173)
`ods to access existing wholesale and other market opportu(cid:173)
`nities. Extensive historical and real-time data resources are
`provided to the optimization engine, including, for example,
`building energy use, fuel costs, asset operation and mainte(cid:173)
`nance costs, local and regional operating constraints (noise,
`other emission restrictions), weather data, existing service
`and rate contracts, and local distribution system conditions.
`The resulting system allows for management of required
`load and distributed generation equipment in response to
`facility conditions, electric system or grid conditions, retail
`market prices, and wholesale market prices.
`FIG. 1 shows a functional block diagram of one specific
`embodiment of the present invention. Multiple DG asset
`nodes 101-103 are in communication with and monitored by
`a network operations center (NOC) 104. The NOC 104 is
`also in communications with the actual electric power
`distribution grid 105, and the grid owners and operators
`(generally an Independent System Operator (ISO) or
`Regional Transmission Organization (RTO)) denoted as
`electric power markets 106, and a fuel infrastructure 107 that
`provides fuel for DG systems. Optimally, there might be one
`NOC 104 per major power market. The NOC 104 also
`maintains and utilizes a database 112 of information gath(cid:173)
`ered from the various blocks it communicates with.
`Within each DG node 101-103 is a microprocessor(cid:173)
`controlled local controller 108 in communications with the
`NOC 104. The local controller 108 may include serial port,
`wireless, and/or Ethernet connection capability. For
`example, in one embodiment, the local controller 108 trans(cid:173)
`lates incoming communications in various protocols such as
`RS232, RS485, Modbus, LONWorks, etc. into a specified
`communications protocol such as Ethernet. In some embodi(cid:173)
`ments, the local controller 108 uses wireless communica(cid:173)
`tions to communicate with the NOC 104 and other equip(cid:173)
`ment within the DG node. In some embodiments, multiple
`communications channels are maintained to be available for
`communications between the NOC 104 and each node
`101-103, and within each node. Such multiple channels
`facilitate more timely and effective responses than tele- 55
`phone-only approaches previously relied upon.
`The local controller 108 controls and co-ordinates the
`operation of the DG assets 109 including transfer switches
`(which in some embodiments may be physically separate
`from the DG assets 109, and thus deserving of a separate
`block), various electric sensors 110 (meters) associated with
`the physical plant serviced by the DG system and the DG
`system itself, as well as various thermal sensors 111 asso(cid:173)
`ciated with the physical plant serviced by the DG system. In
`other words, the local controller 108 determines whether and
`when to dispatch the DG assets 109 that it controls according
`to the various decision rules received and stored from the
`
`4
`NOC 104. In some embodiments, the control of the DG
`assets 109 by the local controller 108 is complete and
`automatic, while in other embodiments, the process can be
`controlled by a human facility manager, who simply needs
`to respond to or ignore the recommended action of the local
`controller 108.
`The electric sensors 110 and thermal sensors 111 may be,
`for example, commercially available "smart meters" to
`meter and monitor facility thermal and electrical loads, i.e.,
`10 industrially-hardened devices that enable real-time, continu(cid:173)
`ous, and accurate remote monitoring of electric and thermal
`characteristics of interest. To provide operating data to the
`local controller 108, older DG units may also require exter(cid:173)
`nal "smart meters" similar to the meters used for facility
`15 loads, while newer DG units generally already have such
`data available at a communications port.
`The facility and DG data generated by the sensors typi(cid:173)
`cally are sent in real-time to the local controller 108 where
`it is generally stored at the DG node for later transfer to the
`20 network operations center 104 and its database 112. This
`data includes distributed generation equipment operating
`information, and facility load data such as real-time and
`historical electric and thermal load data. Typically, the NOC
`104 automatically uploads this data at regular intervals, for
`25 example, once a week, for storage in the centralized data(cid:173)
`base 112. In addition, the sensor data may be uploaded
`responsive to a polling query from the NOC 104.
`The NOC 104 together with the local controller 108 at
`each node 101-103 form a system of distributed intelligence
`30 that represents a shift from previous centralized or non(cid:173)
`existent intelligence models designed for the management of
`distributed power generation systems at end-use customer
`facilities. Each local controller 108 possesses enough intel(cid:173)
`ligence to process the information it receives in order to
`35 determine whether or not to dispatch the DG assets 109 that
`it controls based on the various decision rules it has received
`from the NOC 104. This distributed intelligence system also
`provides redundant data collection, information storage, and
`basic microprocessing capability.
`The NOC 104 contains the core system software: the
`more rigorous and complicated optimization engine that
`formulates the decision rules that carry out the facility(cid:173)
`specific and network control strategies. The NOC 104 uses
`the data gathered from the various other blocks in the
`45 network and stored in its database to determine threshold
`controls for turning on and off the DC assets 109 at the
`various nodes. A threshold command may be, for example,
`a simple on/off command, which tells a generator to operate
`to keep peak kilowatt (kW) demand from exceeding a
`50 pre-set value. Such threshold commands may be updated at
`various intervals and may control the DC assets 109 in
`blocks of time. For example, one specific embodiment sets
`hourly thresholds once per week, for the entire week, for
`each DG asset 109 in the network.
`This threshold setting is inherently difficult. Among other
`things, conventional rate structures are based on both con(cid:173)
`sumption charges and peak demand charges over a billing
`period, which makes calculation of instantaneous "next
`kWh" costs difficult. Specific embodiments use a NOC
`60 algorithm that utilizes the information in the database 112
`(including facility load profiles, DG equipment operating
`characteristics, grid conditions, weather, utility rates, and
`other signals from within customers' facilities and from
`external sources) in a series of parametric calculations to
`65 determine exactly when to trigger DG operation for each
`period of the billing cycle (e.g., per quarter hour or hourly).
`The goal of such an algorithm is to minimize a facility's
`
`40
`
`Opower, Inc.
`Exhibit 1004
`
`

`

`US 7,142,949 B2
`
`5
`overall energy costs by identifying optimal tradeoffs
`between electricity and DG fuel prices. Artificial intelli(cid:173)
`gence (genetic algoritlnns and fuzzy logic) can enable the
`NOC algoritlnn to get better at predicting facility loads,
`becoming "smarter" over time and continually increasing its
`usefulness.
`Once the NOC 104 calculates the threshold controls for an
`upcoming period of time, such as the next week, these may
`be sent via a communications network, such as the Internet
`or wireless system to the local controllers 108 at each node. 10
`The threshold controls are stored in the local controllers 108
`and automatically trigger DG operation based on readings
`from the site's electric and thermal meters 110 and 111. In
`some embodiments, the NOC 104 and/or the individual DG
`nodes 101-103 may have the ability to override these stored
`commands in real-time in response to grid (spot) prices,
`operating constraints, unpredicted facility loads, and other
`signals. Control of the DG assets 109 by the NOC 104
`requires development of command and control software for
`each specific transfer switch and DG make/model. Such
`commands are communicated via public networks (e.g., the
`Internet) or wireless networks to the local controllers 108 at
`each node, and subsequently to the DG assets 109 via serial
`port connections (newer DG systems), dry-contact relay
`(older DG systems), or wireless communications systems.
`The NOC 104 also determines and communicates real-time
`commands to the DG nodes to take advantage of load
`curtailment and grid sellback opportunities.
`Typically, the NOC 104 provides network oversight and
`management of DG assets 24 hours a day, seven days a
`week. The NOC 104 stores and retrieves data from customer
`sites and external sources in its database 112. Facility data
`and key DG parameters are communicated periodically, for
`example, every 15 minutes or less, while optimal control
`thresholds and other signals are broadcast over the network
`to multiple DG nodes.
`Embodiments are adaptable to different DG technologies,
`facility characteristics, rate structures, and control strategies.
`The optimization engine is based on neural networks and
`genetic algoritlnns possessing artificial intelligence that con(cid:173)
`tinually learns more about a facility's consumption patterns,
`DG system performance, and market opportunities. Over
`time, the system evolves into greater efficiency and effec(cid:173)
`tiveness at predicting facility loads. The resulting system is
`an enabling technology with a Web-based component that
`serves as an energy information tool to facilitate decision(cid:173)
`making through real-time access to load data, baseline data,
`historical data, and market activity.
`Moreover, while each individual DG node may be admin(cid:173)
`istered and controlled by the NOC 104 independently of 50
`other DG nodes, in other embodiments, the NOC 104 may
`coordinate the management of multiple DG nodes to obtain
`further benefits. For example, the production capacity and
`fuel sources of multiple nodes can be taken into account in
`determining optimal control thresholds, and excess DG 55
`capacity when a given DG asset is operating may be made
`available to other nodes, depending on specific circum(cid:173)
`stances including specifics of the relevant electric power
`distribution infrastructure.
`The various data gathered by the NOC 104 from each 60
`local controller 108 may be usefully presented in one or
`more user interfaces, such as those shown in FIGS. 2 and 3.
`FIG. 2 allows monitoring of facility energy demand and
`consumption, including, for example, a 15-minute interval
`data section 21 that includes overall electric demand, overall 65
`thermal demand, percent electricity from the grid and from
`the DG assets, and percent useful heat from the site boiler
`
`6
`and from the DG assets. A facility rate information section
`22 identifies the specific electric utility provider, rate sched(cid:173)
`ule, rate period, seasonal period, current consumption
`charge rate, and current demand charge rate. A day's usage
`and cost section 23 summarizes on-peak usage and cost,
`semi-peak usage and cost, off-peak usage and cost, and total
`usage and cost. Applicable peak demand 24 may also be
`displayed.
`FIG. 3 shows an interface for continuously monitoring
`and recording interval data from each DG unit. A DG
`equipment meters section 31 provides displays of DG
`parameters such as battery voltage, oil pressure, engine
`speed, coolant temperature, and power output. This section
`or a similar one could also be used to display fuel level,
`15 ambient temperature, and atmospheric pressure. The process
`of configuring meters to read key operating parameters from
`older DG units requires customization and a slightly differ(cid:173)
`ent approach for each DG make/model. Newer DG instal(cid:173)
`lations are capable of transmitting key operating parameters
`20 via serial port or Ethernet. A run-time data section 32
`displays the current month's run-time, year-to-date run-time,
`maximum armual run-time, and DG operations cost rate.
`Out-of-tolerance alarms 33 can be displayed as a warning
`light indication for various DG failure modes and condi-
`25 tions, and these alarms can further be set to trigger pager and
`email alarms.
`FIG. 4 shows an example of one user interface report
`presented to show the current DG operating plan in combi-
`30 nation with reporting of the effects of the DG optimization
`achieved by a specific embodiment of the invention. A
`current thresholds section 41 has an off-peak row 411, a
`semi-peak row 412, and a peak row 413. Each row corre(cid:173)
`sponds to a different utility supply rate structure period, the
`35 exact times for which may also be displayed as shown in
`FIG. 4. For each rate row 411-413, the optimized on/off
`power demand thresholds are displayed as determined by the
`NOC 104. When power demand on a given day at the local
`DG node reaches the predetermined on-threshold, the local
`40 DG asset 109 at that node will commence operating and
`supplying power to the node in excess of the threshold, until
`power demand falls below the off-threshold, at which point
`the local DG asset 109 ceases operating.
`The user interface report in FIG. 4 also has a thresholds
`45 demand effects section 42 that shows the accumulated
`effects of such optimized operation of DG assets 109 in
`terms of total power consumption, power supplied by the
`DG asset 109 vs. power consumed from the power distri(cid:173)
`bution grid 105, and resulting savings. An optimization
`energy savings section 43 provides further detail regarding
`the current savings attributable to the optimized DG opera(cid:173)
`tion.
`Other specific applications of the strategies developed by
`this system include peak load reduction, load curtailment
`programs, and grid sellback opportunities. For example,
`some organizations can reduce a significant component of
`their annual energy expenses by as much as 33% by reduc(cid:173)
`ing the top 100 hours of peak energy costs. Among the
`benefits conferred by such embodiments, are significant
`energy savings (typically greater than 12% of total energy
`costs) with coordinated use of DG resources. Important
`real-time information is available to enable DG equipment
`to respond quickly to market opportunities and to optimize
`the value of available energy assets. Reports are produced to
`inform customers about the savings resulting from such
`optimization strategies and to help improve system manag(cid:173)
`ers' understanding of their site's or sites' energy usage.
`
`Opower, Inc.
`Exhibit 1004
`
`

`

`US 7,142,949 B2
`
`7
`Other benefits include improved reliability of DG systems
`by regulating their operation, better return on investment
`including opportunities to capture new revenue streams,
`improved utility contracts based on aggregation of energy
`consumption and negotiation of bulk rates, and improved
`supply availability to power grids thereby improving sys(cid:173)
`tem-wide reliability. It is not necessary that energy con(cid:173)
`sumption behavior be changed, thereby offering a non(cid:173)
`intrusive alternative to other demand or load management
`strategies. Outsourcing DG and other energy management 10
`services to networked third parties enables optimal genera(cid:173)
`tion management activities that can be almost undetectable
`to customers.
`Embodiments of the invention may be implemented in
`any conventional computer programming language. For 15
`example, preferred embodiments may be implemented in a
`procedural progrming language (e.g., "C") or an object
`oriented programming language (e.g., "C++"). Alternative
`embodiments of the invention may be implemented as
`pre-programmed hardware elements, other related campo- 20
`nents, or as a combination of hardware and software com(cid:173)
`ponents.
`Embodiments can be implemented as a computer program
`product for use with a computer system. Such implementa(cid:173)
`tion may include a series of computer instructions fixed 25
`either on a tangible medium, such as a computer readable
`medium (e.g., a diskette, CD-ROM, ROM, or fixed disk) or
`transmittable to a computer system, via a modem or other
`interface device, such as a communications adapter con(cid:173)
`nected to a network over a medium. The medium may be 30
`either a tangible medium (e.g., optical or analog communi(cid:173)
`cations lines) or a medium implemented with wireless
`techniques (e.g., microwave, infrared) or other transmission
`techniques. The series of computer instructions embodies all
`or part of the functionality previously described herein with 35
`respect to the system. Those skilled in the art should
`appreciate that such computer instructions can be written in
`a number of progrming languages for use with many
`computer architectures or operating systems. Furthermore,
`such instructions may be stored in any memory device, such 40
`as semiconductor, magnetic, optical, or other memory
`devices, and may be transmitted using any communications
`technology, such as optical, infrared, microwave, or other
`transmis