Demand response management system

Information

  • Patent Grant
  • 8782190
  • Patent Number
    8,782,190
  • Date Filed
    Wednesday, February 2, 2011
    14 years ago
  • Date Issued
    Tuesday, July 15, 2014
    10 years ago
Abstract
A demand response management system which may be implemented with demand response logic. The system may be used by utilities, independent system operators, intermediaries and others to manage operations of demand response programs relative to customers, clients, participants, and users of outputs from the utilities, independent system operators, and the like. Demand response logic of the demand response management system may provide demand signal propagation and generation from demand response events.
Description
BACKGROUND

The present disclosure pertains to utility resources and particularly to assessment and distribution of the resources. More particularly, the invention pertains to beneficial management of resources and their loads.


SUMMARY

The disclosure reveals a demand response management system which may be implemented with demand response logic. The system may be used by utilities, independent system operators, intermediaries and others to manage operations of demand response programs relative to customers, clients, participants, and users of outputs from the utilities, independent system operators, and the like. Demand response logic of the demand response management system may provide demand signal propagation and generation from demand response events.





BRIEF DESCRIPTION OF THE DRAWING


FIG. 1 is a diagram of an interaction between a utility and/or independent system operator and a demand response resource;



FIG. 2 is a diagram of a classification hierarchy of the types of demand response interactions and signals that may be used relative to a demand response resource;



FIG. 3 is a diagram of how demand response logic may transform high level grid conditions into eventual load control commands;



FIGS. 4
a, 4b and 4c are diagrams illustrating cases where some or virtually all of demand response logic is implemented by a demand response management system which may reside within a utility and/or independent system operator or an intermediary entity;



FIG. 5 is a diagram showing a consolidated scenario in which demand response signals generated by a demand response management system may be delivered to either an energy management control system or directly to a load control mechanism within a customer's facility;



FIG. 6 is a diagram of where one or more demand response resources may be an entity that have a relationship with a utility and/or independent service operator or and intermediary relative to being a participant in a demand response event;



FIG. 7 is a diagram showing a demand response management system for generating a demand response signal, which may be adjusted, for a particular client, customer or participant, relative to a demand response event; and



FIG. 8 is a diagram of a table being a way of representing quantities and/or rules allowing them to be easily edited for adjusting a demand response signal for a particular customer, client or participant, relative to a demand response event.





DESCRIPTION

The present disclosure reveals an implementation of demand response (DR) logic within a demand response management system (DRMS). The system and associated software may be effected and operated with one or more computers/controllers (controllers) and connections. The DRMS is a system that may be used by utilities and independent system operators (ISO's) to manage the operation of DR programs. A focus of the DRMS may be on the operational aspects of managing the selection, signaling and monitoring of the DR resources that are participating in DR programs. The DRMS may be specifically designed to manage the operations of automated DR programs. The DR logic components of the DRMS are noted herein.


There may be various types of interactions that might occur between the utility/ISO and a DR resource as part of a DR program. FIG. 1 is a diagram of an interaction between a utility/ISO 11 and a DR resource (customer) 12. There may be DR signals 13 going from utility/ISO to DR resource 12. There may be load measurement signals 14 going from DR resource 12 to utility/ISO 11.


Customer, client, user, participant, and like terms, may be used, interchangeably or distinct from one another, depending on a context of a pertinent portion of a description or a claim.


A description of DR Signals 13 may be noted. At the highest level, there may virtually always be some sort of grid condition, be it economic or grid reliability in nature, which triggers a so called DR event that requires some sort of interaction between the utility/ISO and its customers. This interaction may eventually trigger some sort of load control taking place at a customer's facility. The interaction between the utility/ISO 11 and the customer 12 may be mediated by a so called DR signal 13 that represents a communication between the utility/ISO 11 and the customer 12. It is the information contained within the DR signal 13 that may dictate where much of the decision making takes place in how the initial grid condition that triggered the DR event results in the eventual load control.


There may be a classification hierarchy of the types of DR interactions and signals that may be used as illustrated by a diagram in FIG. 2. Three classes of interactions that may occur incorporate a supply state 16, DR resource instructions 17, and load controller commands 18.


A supply 16 state may refer to information about conditions concerning the supply of electricity that may affect DR resource's 12 load profile. The conditions may incorporate prices of electricity, sources of generation (e.g., hydro versus coal), carbon content, reliability of supply or grid conditions, and other conditions.


The nature of this information may be such that it does not necessarily include any specific instructions for how the load profile of the DR resource should change. Virtually all decisions as to what the desired load profile should be in response to the information within a DR signal 13 may be within the DR resource 12. A very typical example of this type of DR signal 13 may be real-time or dynamic electricity prices that may be sent to a DR resource 17.


DR resource instructions may refer to information that specifies what the load profile of a DR resource 12 should be as a result of receiving a DR signal 13. Examples of this information may incorporate specific consumption levels (which can be either up or down), dispatch instructions, and load profile specifications.


This type of information may be more specific than information of the supply state 16 in that it indicates what the load profile of DR resource 12 should be. The information does not necessarily indicate how individual loads of DR resource 12 should be controlled and thus the intelligence for determining how to control individual loads may be virtually all within DR resource 12. The information may be about load shifting or shedding, and the certainty or predictability of a load shape change.


Typical examples of such information may incorporate dispatch instructions that may be sent from an ISO 11 to an aggregator. Such dispatch instructions may often be in a form of an amount of load that DR resource 12 is expected to provide.


Load controller commands 18 may refer to specific load control commands sent to a controller of a load that specifies the state that the load should be in. Examples may incorporate existing DR programs such as AC cycling in which air conditioners within residences are turned on and off. This information may be used for DLC (direct load control).


DR logic 21 may support supply state 16 and the DR resource instructions 17. DR logic 21 may be a part of or provided by a computer/controller (computer) at a place where the logic 21 is situated. The computer may incorporate one or more inputs, a processor, a user interface with a keyboard and display, a memory, external connections such as an internet, one or more outputs, and so forth. The computer may be utilized with virtually all items in and pertinent to FIGS. 1-8.


A specification for the DR logic 21 may be necessary to support load controller commands (direct load control). DR logic 21 may transform high level grid conditions 22 into eventual load control commands 23 as indicated a diagram of FIG. 3. DR logic 21, associated with a computer, may be instantiated within a single entity or may be distributed across different systems as entities. While there may be “use cases” where no DR logic 21 is implemented within an entity that interacts with the customer facility 27 (i.e., utility/ISO 11 or intermediary 25), one may note the use cases where at least some of the DR logic 21 is implemented by a DRMS 24 which resides within the utility/ISO 11 or an intermediary entity 25 as shown in FIGS. 4a-4c. A last use case of FIG. 4c in which virtually all of the DR logic 21 may be embedded within the utility/ISO 11 or intermediary entity 25 may be considered as providing direct load control (DLC) as indicated by commands 34 which go to load and DER 28 of customer facility 27 via gateway 35.


In FIG. 4a, a first use case shows a scenario wherein some of the DR logic 21 may reside within an energy management and control system (EMCS) 26 within a customer facility 27. EMCS 26 may be an actual device or a software module running inside a larger system with a computer such as customer facility 27. Upon receiving a DR signal 13, EMCS 26 may be responsible for processing the information within the DR signal 13 into some sort of facility wide load profile objectives and/or load control commands. There may be an interaction between EMCS 26 and load and DER 28.


In FIG. 4b, the second use case shows a scenario wherein the load and DER 28 (e.g., an appliance, thermostat, or the like), having DR logic 21, may interact directly with the DRMS 24 via gateway 35 to receive the DR signal 13. In FIG. 4c, as noted herein, the DRMS 24 may provide direct load commands 34 directly to load and DER 28. It may be that virtually all of the DR logic 21 concerning how to respond to a DR signal is embedded directly in a load controller.


There may be scenarios which are a combination of the first and second use cases in which some of the DR logic 21 is embedded within an EMCS 26 and some of the DR logic 21 is embedded within the load controller.


The present approach may deal with DR logic 21 that is instantiated within the DRMS 24. It may be assumed that the nature of the DR signals 13 which are being delivered by the DRMS 24 are of either a grid state or a DR resource instruction category. DR signals 13 may conform to an existing standard or specification such as the OpenADR.


In FIG. 5, a diagram shows a consolidated scenario in which DR signals 13 generated by the DRMS 24 may be delivered to either an EMCS 26 or directly to a load controller within the customer's facility 27.


Main functions of DR logic 21 within the DRMS 24 of utility/ISO 11 or intermediary 25 may incorporate 1) DR signal 13 propagation and 2) DR signal 13 translation (generation). A notion of a DR event 31 (FIG. 6) may be further defined. A DR event may be initiated by the utility/ISO 11 or intermediary 25 which is responsible for ultimately generating a DR signal 13 for the customer 12 or customer facility 27. A main function of DR logic 21 may be to take a DR event 31 and generate an appropriate DR signal 13.


A DR event 31 may have several attributes. One attribute may be a schedule for the various periods which is associated with the DR event. Two very significant periods may be 1) the so-called notification period before an event and 2) the period when the event itself is active. Another attribute may be a set of information or instructions which is associated with the DR event. This information may be particularly specific to a DR program and be a main instrument that the utility/ISO 11 uses to interact with the customer 12 during DR events. Examples of information or instructions may incorporate prices, shed levels and device commands. This information may fall into one of the three categories of supply state 16, DR resource instructions 17 and load controller commands 18, as indicated in FIG. 2.


DR Signal 13 propagation of DR logic 21 within DRMS 24, may be noted. One of the functions of the DR logic 21 may be to take a DR event and a specification as to which DR resources (customers) 12 are to receive signals, and from that determine an appropriate set of customers and their devices that need to receive DR signals 13. This may be referred to as propagating the DR signal 13.


In a diagram of FIG. 6, a DR resource 12 may be an entity (typically a customer or participant) that has a relationship with the utility/ISO 11 or intermediary 25 and represents the entity that the utility/ISO 11 or intermediary 25 interacts with. This means that if the utility/ISO 11 wants to issue a DR event 31, then the DR resources 12 may be the entities that utility/ISO 11 calls upon to participate in the event 31.


DR resource 12 management and signal propagation may be noted. Each customer (i.e., DR resource 12) may manage a set of so-called clients 32 (EMCS or device) that it uses to manage its interactions with the DRMS 24. It may be the clients 32 that receive DR signals 13 from the DRMS 24. Thus, as shown in FIG. 6, each DR resource 12 may have associated with it multiple clients 32. Each of the clients may receive its own version of a DR signal 13 corresponding to a DR event 31.


Each DR resource 12 and set of associated clients 32 may have a set of attributes such as customer name, geographic location and grid location. Furthermore, DR resources 12 and clients 32 may be associated with groups that can be used for aggregation purposes. When a DR event 31 is issued by the utility/ISO 11, there may be additional data that specify who is to receive the DR signals 13. As recipients of DR signals 13, the data may indicate specific customers, aggregated group identifiers, resource groups, geographic regions and/or locations, and/or grid locations. These data or specifications may be used by the DRMS 24 to determine which DR resources 12 and which clients 32 of the DR resources 12 are to receive the DR signals 13.


Participation rules may be noted for DR signal 13 propagation. In addition to the attributes, each DR resource 12 may have associated with it a set of business rules that dictate the schedule constraints of when the DR resource 12 will participate in DR events 31. Such rules may incorporate: 1) Blackout periods having specific date/time periods, time of day, days of week, and/or days of month; 2) A maximum number of events for a year, month, week, and/or consecutive days; 3) Maximum and minimum durations of events; and/or 4) Maximum and minimum notification times for upcoming events.


If an event is issued that violates any of the constraints or rules, then the DRMS 24 may be configured such that it will not propagate a DR signal 13 to the DR resource 12 or its clients 32. Thus, the constraints or rules may also be a mechanism to control how DR signals 13 are propagated to the clients 32 of DR resources 12.


DR signal 13 generation (translation) of DR logic 21 within DRMS 24 may be noted. One of the functions of DR logic 21 may be to translate whatever information is associated with a DR event 31 into an appropriate DR signal 13 that can be sent to the customer or resource 12. In some cases, there may be very little, if any, translation or transformation necessary, but in some cases the DR event 31 information (e.g., prices, shed levels, and so forth) may be translated into a form that is specific for the customer 12.


The following are general transformations (or translations) that may take place to generate a DR signal 13. One may be to customize the DR event 31 schedule for the customer 12. A second transformation may be to customize the DR event 31 information specifically for the customer 12. For example, a particular DR program may use prices as the main instrument in a DR signal 13, but each individual customer 12 may receive a different price based upon a bid that the customer has placed. Thus, the price within the DR signal 13 may be customized for each customer 12.


A third transformation may be to translate the information in the DR event 31 to an alternate form which makes it easier for the customer's automation equipment to consume it. Examples of transformations of this type may include the simple DRAS (demand response automation server) client signal levels that are described in the OpenADR (open automated demand response (communications specification)). The simple levels in OpenADR may be specifically designed to allow more complex information such as prices and shed amounts to be translated into one of a finite set of simple levels such as NORMAL, MODERATE, and HIGH. These simple levels may then be more easily translated into specific load control commands using DR logic 21 within the customer's facility 27.


A fourth transformation may be to use feedback from the customer 12 (e.g., real-time usage information) to modify the DR event 31 and associated signal information. A fifth transformation may be to translate the information in a DR event 31 into actual load control commands, e.g., direct load control commands 34 of FIG. 4c. The fifth transformation may be described in U.S. patent application Ser. No. 12/834,841, filed Jul. 12, 2010, and entitled “A System for Providing Demand Response Services”, which is hereby incorporated by reference.


A diagram of FIG. 7 shows the DR signal 13 generation (translation) process of a DR management system. DR logic 21 (FIG. 5) and one of its main functions, DR signal generation, may be noted. The mechanism of the Figure described herein may allow for DR event 31 information to be translated, but it may also be possible to translate the timing parameters of the DR event 31 by using the participation rules indicated herein. If the rules defined for a particular customer 12 or participant, do not match the event 31 schedule, then it may be possible for the DRMS to be configured such that the DR signal 13 which is generated can be adjusted to match the business rules.


For example, one may assume that a participation rule has been defined for a particular participant, such as “Can participate in DR events from 3 pm to 6 pm.” Now one may assume that a DR event 31 is issued with a schedule, such as “Event active from 4 pm to 7 pm.” Since there is a period of the DR event 31 in which the participant has specified that it will not participate, it may be possible for the DRMS to be configured to generate a DR signal 13 that spans from 4 pm to 6 pm in order to match the participant's rule, by eliminating the 6 pm to 7 pm period in which the participant cannot participate. This way, the participant may be present for the whole schedule of event 31. Likewise, the DRMS may also have been configured to reject the DR event 31 and generate no DR signal 13 for that participant in accordance with the signal propagation rules as described herein.


Implementation of DR logic 21 for signal generation, that of another main function of the DR logic 21 (FIG. 5) may be noted. A portion or more of DR logic 21 being used to generate DR signals 13 may be described in several steps as shown FIG. 7. A first step 41 may entail customizing or modifying the schedule of the DR event 31 to match user participation rules 36 of the DR resource 12. A second step 42 may entail customizing or modifying DR event 31 information in view of bids 37. Bids may be just one example of information or variable. One or more of other items, such as shed levels, prices, demands, usage, and so forth, may be used apart from bids 37 or in combination with bids. A third step 43 may entail translating DR event 31 information in view of user preferences and translation rules 38.


The second step 42 and the third step 43 may entail actually modifying the information that is encapsulated within DR signal 13. Steps 41, 42 and 43 may be modeled as a set of Boolean equations which relate the following quantities: 1) Start time may be when the condition first becomes valid; 2) End time may be an end of when the condition is valid; 3) Variable may be either an input variable associated with the event 31 (e.g., price, shed level, bid, usage, and/or so forth), or it may be a telemetry variable that is fed back via real-time telemetry 45 to steps 42 and/or 43 from a client or customer's facility 27 synch as a usage or device state (for instance, the variables may typically be fixed and set by the definition of the DR program); 4) Condition may be logical Boolean operation that relates the one or more variables to some user defined value or values (for instance, this may be a typical sort of Boolean operations such as greater than, less than, equal, and so forth); and 5) Operations may be what is to be done if the rule is TRUE.


One way of representing these quantities or rules in a way that allows them to be easily edited by users may be in the form of an example table 40 as shown in FIG. 8. Table 40 may consist of a collection of rules. The top row may list headings indicating a rule, start time, end time, variable, condition, value, “operation 1: set simple level” and “operation 2: set price”. Table 40 shows just one instance of rules as these rules may be edited or there could be other rules which are different in type, kind, and/or number. Each rule number (for instance, 1, 2, 3 or 4) may represent a set of conditions, such as start time, end time, variable, condition, and value, in that when AND'd together they result in a TRUE, which will execute the specified operation, such as an operation 1 of setting a simple level or an operation 2 of setting a price, as for instance in the example of table 40.


In table 40, there may be multiple types of operations other than those shown as examples, which can be executed, and rules, with conditions which are related to a particular operation, that may be logically grouped together so that the first rule, which relates to a particular operation, that is TRUE in terms of its conditions, is the rule which applies. In the example table, the conditions are “AND'd”; however, there may be rules where the conditions are logically “OR'd” in determining if rule is true for purpose of implementing a designated operation. The conditions may be connected in a combination of being logically “AND'd” and “OR'd” a truth determination of the respective rule and or the operation to be effected.


In rule one of table 40, for example, the price level may be set to the “BID” level and thus this may be a representation of how a price level for a DR event 31 may be customized for a particular client 32. In an actual deployment, this type of operation may probably be fixed by default by the business rules of the DR program and would not necessarily appear as an editable table to the user.


In rules 2-4 of table 40, the operation may be set to the simple level of the DR signal 13. This simplification may represent a type of signal translation in which the information associated with the DR event 31 (e.g., price) can be converted into a simpler representation. This does not necessarily imply that the price information is not also sent with the DR signals 13, but it may mean that an alternate form (i.e., simple level) is also sent. This may give a great deal of flexibility to the client 32 in how it consumes the information and is supported by DR signal's specifications such as OpenADR.


There may be separate collections of rules for each client 32 and a DR program that client 32 is participating in. The nature of the DR program may define the variables that are associated with the program. For example, one program might use price while another might use shed level. Furthermore, feedback variables may be used in the rules as a way to modify the DR signals 13 in real time during the DR event 31 as could be shown as an instance in rule three.


The items in table 40 noted herein may be effected with a computer. Table 40 may be meant to allow for easy modification by end users and be just one representation of a set of rules that could be applied to converting a DR event 31 to a DR signal 13. There may be other representations. There may be other ways of specifying the rules which relate the various variables and user specified conditions to values that appear in DR signal 13. Sets of rules may be created using free form equations that are interpreted, or even some sort of programming language such as Java may be used.


An application which is relevant to the present application is U.S. patent application Ser. No. 12/834,841, filed Jul. 12, 2010, and entitled “A System for Providing Demand Response Services”, which claims the benefit of U.S. Provisional Patent Application No. 61/271,084, filed Jul. 17, 2009. U.S. patent application Ser. No. 12/834,841, filed Jul. 12, 2010, is hereby incorporated by reference. U.S. Provisional Patent Application No. 61/271,084, filed Jul. 17, 2009, is hereby incorporated by reference.


An application which is relevant to the present application is U.S. patent application Ser. No. 12/245,560, filed Oct. 3, 2008, and entitled “Critical Resource Notification System and Interface Device”, which claims the benefit of U.S. Provisional Patent Application No. 60/977,909, filed Oct. 5, 2007. U.S. patent application Ser. No. 12/245,560, filed Oct. 3, 2008, is hereby incorporated by reference. U.S. Provisional Patent Application No. 60/977,909, filed Oct. 5, 2007, is hereby incorporated by reference.


In the present specification, some of the matter may be of a hypothetical or prophetic nature although stated in another manner or tense.


Although the present system and/or approach has been described with respect to at least one illustrative example, many variations and modifications will become apparent to those skilled in the art upon reading the specification. It is therefore the intention that the appended claims be interpreted as broadly as possible in view of the prior art to include all such variations and modifications.

Claims
  • 1. A method for generating a demand response signal, comprising: providing a source of a demand response (DR) event;obtaining the DR event from the source;customizing a schedule of the DR event for a customer;customizing information about the DR event for the customer;providing the schedule and information about the DR event as a DR signal to the customer;providing a computer to assist in performing the present method; andwherein:customizing the schedule of the DR event for a customer comprises customizing timing parameters of the DR event to remove schedule constraints relative to business rules of the customer;customizing information about the DR event for the customer comprises customizing price for each customer; and/ora DR signal generated from the DR event is configured to match business rules of the customer; andthe DR signal generated for the customer is adjusted to match the business rules of the customer.
  • 2. The method of claim 1, further comprising translating information of the DR event to a form of a DR signal appropriate for the customer.
  • 3. The method of claim 1, further comprising receiving feedback from the customer for further customizing information about the DR event and schedule of the DR event as needed or requested by the customer.
  • 4. The method of claim 1, further comprising translating information about a DR event into actual load control commands to the customer.
  • 5. The method of claim 1, further comprising a table of rules for customizing a schedule of the DR event for a customer, for customizing information of the DR event for the customer, and for translating information of the DR event to a form appropriate for the customer.
  • 6. The method of claim 5, wherein the rules for customizing and translating are in a form of Boolean equations.
  • 7. The method of claim 6, wherein if conditions of a rule are true, then a predetermined operation is effected.
  • 8. The method of claim 1, wherein at least one of the customizing a schedule and customizing information steps is performed before DR event information is provided to the customer.
  • 9. The method of claim 2, wherein at least one of the customizing a schedule and customizing information steps is performed by a utility, an independent system operator, or an intermediary between the customer and the utility or independent system operator.
Parent Case Info

This application claims the benefit of U.S. Provisional Patent Application No. 61/301,123, filed Feb. 3, 2010, and entitled “Demand Response Management System”. U.S. Provisional Patent Application No. 61/301,123, filed Feb. 3, 2010, is hereby incorporated by reference. This application is a continuation-in-part of U.S. patent application Ser. No. 12/834,841, filed Jul. 12, 2010, and entitled “A System for Providing Demand Response Services”, which claims the benefit of U.S. Provisional Patent Application No. 61/271,084, filed Jul. 17, 2009. U.S. patent application Ser. No. 12/834,841, filed Jul. 12, 2010, is hereby incorporated by reference. U.S. Provisional Patent Application No. 61/271,084, filed Jul. 17, 2009, is hereby incorporated by reference.

US Referenced Citations (151)
Number Name Date Kind
4110827 Shavit Aug 1978 A
4130874 Pai Dec 1978 A
4153936 Scmitz et al. May 1979 A
4419667 Gurr et al. Dec 1983 A
4850010 Stanbury et al. Jul 1989 A
4937760 Beitel et al. Jun 1990 A
5341142 Reis et al. Aug 1994 A
5500561 Wilhelm Mar 1996 A
5566084 Cmar Oct 1996 A
5572438 Ehlers et al. Nov 1996 A
5598349 Elliason et al. Jan 1997 A
5719854 Choudhury et al. Feb 1998 A
5822553 Gifford et al. Oct 1998 A
5892758 Argyroudis Apr 1999 A
6026375 Hall et al. Feb 2000 A
6195367 Jakobik et al. Feb 2001 B1
6209018 Ben-shachar et al. Mar 2001 B1
6252950 Duty et al. Jun 2001 B1
6259723 Miyashita Jul 2001 B1
6278717 Arsenault et al. Aug 2001 B1
6289384 Whipple et al. Sep 2001 B1
6366926 Pohlmann et al. Apr 2002 B1
6446136 Pohlmann et al. Sep 2002 B1
6519509 Nierlich et al. Feb 2003 B1
6566926 Patterson May 2003 B1
6574581 Bohrer et al. Jun 2003 B1
6832249 Ciscon et al. Dec 2004 B2
6865685 Hammond et al. Mar 2005 B2
6985087 Soliman Jan 2006 B2
7010700 Foss et al. Mar 2006 B1
7039532 Hunter May 2006 B2
7069309 Dodrill et al. Jun 2006 B1
7260616 Cook Aug 2007 B1
7333880 Brewster et al. Feb 2008 B2
7337237 Salahshoor et al. Feb 2008 B2
7346467 Bohrer et al. Mar 2008 B2
7392115 Schindler Jun 2008 B2
7401086 Chorafakis et al. Jul 2008 B2
7528503 Rognli et al. May 2009 B2
7565227 Richard et al. Jul 2009 B2
7650289 Cooper et al. Jan 2010 B2
7676657 Lindholm et al. Mar 2010 B2
7702424 Cannon et al. Apr 2010 B2
7742953 King et al. Jun 2010 B2
7797009 Kiiskila et al. Sep 2010 B2
7806845 Arm et al. Oct 2010 B2
7845576 Siddaramanna et al. Dec 2010 B2
7865252 Clayton Jan 2011 B2
7873441 Synesiou et al. Jan 2011 B2
7885718 Yano et al. Feb 2011 B2
7886166 Shnekendorf et al. Feb 2011 B2
7925384 Huizenga Apr 2011 B2
7941528 Hicks, III et al. May 2011 B2
7954726 Siddaramanna et al. Jun 2011 B2
7958229 Conway Jun 2011 B2
8023410 O'Neill Sep 2011 B2
8073558 Koch et al. Dec 2011 B2
8091794 Siddaramanna et al. Jan 2012 B2
8140658 Gelvin et al. Mar 2012 B1
8163276 Hedrick et al. Apr 2012 B2
8170774 Forte et al. May 2012 B2
8183995 Wang et al. May 2012 B2
8199773 Aubin et al. Jun 2012 B2
8232745 Chemel et al. Jul 2012 B2
8234876 Parsonnet et al. Aug 2012 B2
8260468 Ippolito et al. Sep 2012 B2
8260650 Miller Sep 2012 B2
8327024 Pattison et al. Dec 2012 B2
8352094 Johnson et al. Jan 2013 B2
8373547 Benya et al. Feb 2013 B2
8417391 Rombouts et al. Apr 2013 B1
20030016237 Hickey Jan 2003 A1
20030033230 Mccall Feb 2003 A1
20030233064 Arm et al. Dec 2003 A1
20040034484 Solomita, Jr. et al. Feb 2004 A1
20040137897 Teixeira Jul 2004 A1
20040203649 Cashiola Oct 2004 A1
20050027636 Gilbert et al. Feb 2005 A1
20050152694 Chown Jul 2005 A1
20050172304 Tavares et al. Aug 2005 A1
20050194456 Tessier et al. Sep 2005 A1
20050229220 Fisher et al. Oct 2005 A1
20050262026 Watkins Nov 2005 A1
20060047369 Brewster et al. Mar 2006 A1
20070005195 Pasquale et al. Jan 2007 A1
20070222295 Wareham et al. Sep 2007 A1
20080011864 Tessier et al. Jan 2008 A1
20080046715 Balazs et al. Feb 2008 A1
20080167931 Gerstemeier et al. Jul 2008 A1
20080172312 Synesiou et al. Jul 2008 A1
20080173280 Hou Jul 2008 A1
20080177678 Di Martini et al. Jul 2008 A1
20080262848 Shienbrood et al. Oct 2008 A1
20090001180 Siddaramanna et al. Jan 2009 A1
20090001181 Siddaramanna et al. Jan 2009 A1
20090046625 Diener et al. Feb 2009 A1
20090092062 Koch et al. Apr 2009 A1
20090187499 Mulder et al. Jul 2009 A1
20090198384 Ahn Aug 2009 A1
20090204977 Tavares et al. Aug 2009 A1
20090248217 Verfuerth et al. Oct 2009 A1
20090267540 Chemel et al. Oct 2009 A1
20090271255 Utter et al. Oct 2009 A1
20090281674 Taft Nov 2009 A1
20090295594 Yoon Dec 2009 A1
20090297488 Fraser et al. Dec 2009 A1
20090313083 Dillon et al. Dec 2009 A1
20090319090 Dillon et al. Dec 2009 A1
20090326726 Ippolito et al. Dec 2009 A1
20100057480 Arfin et al. Mar 2010 A1
20100076615 Daniel et al. Mar 2010 A1
20100076835 Silverman Mar 2010 A1
20100088261 Montalvo Apr 2010 A1
20100106342 Ko et al. Apr 2010 A1
20100106543 Marti Apr 2010 A1
20100106982 Castelli et al. Apr 2010 A1
20100114340 Huizenga et al. May 2010 A1
20100138363 Batterberry et al. Jun 2010 A1
20100168924 Tessier et al. Jul 2010 A1
20100274377 Kaufman et al. Oct 2010 A1
20100283606 Tsypin et al. Nov 2010 A1
20100324962 Nesler et al. Dec 2010 A1
20110016200 Koch Jan 2011 A1
20110040550 Graber et al. Feb 2011 A1
20110040666 Crabtree et al. Feb 2011 A1
20110046805 Bedros et al. Feb 2011 A1
20110093493 Nair et al. Apr 2011 A1
20110113068 Ouyang May 2011 A1
20110172836 Boss et al. Jul 2011 A1
20110172838 Pai et al. Jul 2011 A1
20110196539 Nair et al. Aug 2011 A1
20110196546 Muller et al. Aug 2011 A1
20110199209 Siddaramanna et al. Aug 2011 A1
20110212700 Petite Sep 2011 A1
20110231320 Irving Sep 2011 A1
20110258049 Ramer et al. Oct 2011 A1
20110301774 Koch Dec 2011 A1
20120066397 Koch et al. Mar 2012 A1
20120066686 Koch Mar 2012 A1
20120093141 Imes et al. Apr 2012 A1
20120109399 Tran May 2012 A1
20120136915 Koch et al. May 2012 A1
20120173030 Taft Jul 2012 A1
20120197456 Walter et al. Aug 2012 A1
20120197457 Walter et al. Aug 2012 A1
20120197458 Walter et al. Aug 2012 A1
20120245968 Beaulieu et al. Sep 2012 A1
20120271473 Koch Oct 2012 A1
20120277920 Koch Nov 2012 A1
20130035992 Silverman Feb 2013 A1
20130079931 Wanchoo et al. Mar 2013 A1
Foreign Referenced Citations (7)
Number Date Country
WO 2009006133 Jan 2009 WO
WO 2009020606 Feb 2009 WO
WO 2009023230 Feb 2009 WO
WO 2009085610 Jul 2009 WO
WO 2011065007 Jun 2011 WO
WO 2013025565 Feb 2013 WO
WO 2013055551 Apr 2013 WO
Non-Patent Literature Citations (43)
Entry
“Demand Response Measurement and Verification Literature Review,” 29 pages, prior to Nov. 29, 2012.
“Smart Demand Response: A Discussion Paper,” Energy Networks Association, energyuk, 44 pages, prior to Nov. 29, 2012.
International Search Report for PCT ApplicationSerial No. pct/us2012/058537, International Filing Date Oct. 3, 2012.
U.S. Appl. No. 13/689,551, filed Nov. 29, 2012.
Hunt, “Automated Demand Response System and Advanced End-Use Services Platform,” Optimal Technologies, 31, pages, Sep. 24, 2004.
Olson, “New Approaches in Automating and Optimizing Demand Response to Solve Peak Load Management Problems,” Building IQ brochure, 8 pages, 2011.
Schisler et al., “The Role of Demand Response in Ancillary Services Markets,” IEEE, 3 pages, 2008.
Violette et al., “DRR Valuation and Market Analysis vol. II: Assessing the DRR Benefits and Costs,” Summit Blue Consulting, 112 pages, Jan. 6, 2006.
Zaidi et al., “Load Recognition for Automated Demand Response in Microgrids,” IEEE, pp. 2436-2439, 2010.
U.S. Appl. No. 12/895,640, filed Sep. 30, 2010.
U.S. Appl. No. 13/272,086, filed Oct. 12, 2011.
Cruz, “Tutorial on GPU Computing with an Introduction to CUDA,” 37 pages, prior to Nov. 17, 2011.
Holmberg, “Facility Interface to the Smart Grid,” National Institute of Standards and Technology, 7 pages, printed 2012.
Honeywell, “Automated Demand Response—Southern California Program,” 2 pages, printed Aug. 1, 2011.
Honeywell, “The Perfect Response to Peak Events,” 4 pages, Nov. 2010.
http://en.wikipedia.org/wiki/Demand—response, “Demand Response,” 10 pages, printed Feb. 3, 2012.
https://buildingsolutions.honeywell.com/Cultures/en-US/Markets/Utilities/DemandResponse/, 1page, printed Feb. 3, 2012.
Kiliccote et al., “Open Automated Demand Response Communications in Demand Response for Wholesale Ancillary Services,” Lawrence Berkeley National Laboratory, Report No. LBNL-2945E, 13 pages, Nov. 2009.
Koch et al., “Direct Versus Facility Centric Load Control for Automated Demand Response,” Lawrence Berkeley National Laboratory, Report No. LBNL-2905E, 11 pages, Nov. 2009.
Koch et al., “Scenarios for Consuming Standardized Automated Demand Response Signals,” Lawrence Berkeley National Laboratory, Report No. LBNL-1362E, 10 pages, Nov. 2008.
Koch, “The Demand Response Automation Server (DRAS),” Building Performance, http://www.akuacom.com/assets/pdf/ASHRAE—2008—Ed—Koch.pdf, 18 pages, prior to Nov. 17, 2011.
U.S. Appl. No. 13/621,195, filed Sep. 15, 2012.
Abdullah et al., “Demand-Side Energy Management Performed Using Direct Feedback via Mobile Systems: Enables Utilities to Deploy Consumer Based Demand Response Programs,” 2010 IEEE International Energy Conference and Exhibition, pp. 172-177, 2010.
European Search Report for Related Application No. EP 12169650.4, Dated Nov. 22, 2012.
U.S. Appl. No. 13/016,181, filed Jan. 28, 2011.
U.S. Appl. No. 13/016,265, filed Jan. 28, 2011.
U.S. Appl. No. 13/016,306, filed Jan. 28, 2011.
U.S. Appl. No. 13/298,706, filed Nov. 17, 2011.
U.S. Appl. No. 13/299,716, filed Nov. 18, 2011.
Kiliccote et al., “Findings from Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings,” Lawrence Berkeley National Laboratory, Report No. LBNL-3643E, May 2010.
Coughlin et al., “Estimating Demand Response Load Impacts: Evaluation of Baseline Load Models for Non-Residential Buildings in California,” Lawrence Berkeley National Laboratory, Report No. LBNL-63728, 33 pages, Jan. 2008.
Koch et al., “Architecture Concepts and Technical Issues for an Open, Interoperable Automated Demand Response Infrastructure,” Berkeley National Laboratory, Report No. LBNL-63664, 7 pages, Oct. 2007.
Koch et al., “Open Automated Demand Response for Small Commercial Buildings,” Lawrence Berkeley National Laboratory, Report No. LBNL-2195E, 104 pages, Jul. 2009.
Piette et al., “Automated Critical Peak Pricing Field Tests: Program Description and Results,” Berkeley National Laboratory, Report No. LBNL-59351, 67 pages, Apr. 2006.
Piette et al., “Automated Critical Peak Pricing Field Tests: 2006 Pilot Program Description and Results,” Berkeley National Laboratory, Report No. LBNL-62218, 67 pages, Aug. 2007.
Piette et al., “Design and Implementation of an Open, Interoperable Automated Demand Response Infrastructure,” Berkeley National Laboratory, Report No. LBNL-63665, 6 pages, Oct. 2007.
Piette et al., “Findings From Seven Years of Field Performance Data for Automated Demand Response in Commercial Buildings,” Berkeley National Laboratory, Report No. LBNL-3643E, 15 pages, May 2010.
Piette et al., “Findings From the 2004 Fully Automated Demand Response Tests in Large Facilities,” Lawrence Berkeley National Laboratory, Report No. LBNL-58178, 197 pages, Sep. 2005.
Piette et al., “Linking Continuous Energy Management and Open Automated Demand Response,” Lawrence Berkeley National Laboratory, Report No. LBNL-1361E, 9 pages, Nov. 2008.
Piette et al., “Open Automated Demand Response Communications Specification,” Version 1.0, CEC-500-2009-063, 214 pages, Apr. 2009.
Piette et al., “Participation through Automation: Fully Automated Critical Peak Pricing in Commercial Buildings,” Berkeley National Laboratory, Report No. LBNL-60614, 14 pages, Aug. 13-18, 2006.
Watson et al., “Machine to Machine (M2M) Technology in Demand Responsive Commercial Buildings,” Berkeley National Laboratory, Report No. LBNL-55087, 18 pages, Aug. 2004.
Yin et al., “Auto-DR and Pre-Cooling of Buildings at Tri-City Corporate Center,” Lawrence Berkeley National Laboratory, Report No. LBNL-3348, 140 pages, Nov. 2008.
Related Publications (1)
Number Date Country
20110125542 A1 May 2011 US
Provisional Applications (2)
Number Date Country
61301123 Feb 2010 US
61271084 Jul 2009 US
Continuation in Parts (1)
Number Date Country
Parent 12834841 Jul 2010 US
Child 13019943 US