The present invention relates to electric power messaging and settlements, and more particularly, to advanced energy settlements, messaging, and applications for electric power supply, load, and/or curtailment and data analytics associated with the same.
Generally, it is known in the prior art to provide electric power systems management, including financial settlements and messaging. However, limited information is available to electric power consumers regarding their past, present, and future projected use of power with sufficient details to make informed choices about types of power supply and pricing alternatives. Furthermore, retail electric providers (REPs) in prior art systems and methods do not have access to data and analytics to provide optimal pricing for power supplied to business and/or residential electricity customers, and do not have the ability to provide advanced energy settlements to provide the lowest pricing for power supplied at predetermined times, due at least in part to costs associated with obtaining power agreements without visibility to the data and analytics that provide a reduced risk of capital and performance associated with the supply and demand sides. Thus, there remains a need for improved information, controls, real-time or near-real-time data on power consumption and production for electric power market participants, REPs, customers, data centers, and microgrid owners, and messaging and management of financial settlement therefor.
The present invention relates to electric power messaging and settlements, and more particularly, to advanced energy settlements, messaging, and applications for electric power supply, load, and/or curtailment and data analytics associated with the same. Systems and methods for data analytics and customer or consumer guidance and controls are provided, and coupled with graphical user interfaces for interactive control and command of grid elements, design, specification, construction, management and financial settlement for data centers and/or microgrids, business and residential power consumption, control, management, messaging and settlements, mobile applications, websites, marketing offers, optimal pricing for comparable energy plans, retail electric provider and direct consumer alternatives, network of power architecture, EnergyNet applications, software development kits, application web-based storefronts, and combinations thereof.
The present invention provides for systems, methods, and graphical user interface (GUI) embodiments for providing electric power usage (past, current, and/or future projected) information, management, financial settlements, and messaging, and applications as described herein.
These and other aspects of the present invention will become apparent to those skilled in the art after a reading of the following description of the preferred embodiment when considered with the drawings, as they support the claimed invention.
Referring now to the drawings in general, the illustrations are for the purpose of describing preferred embodiment(s) of the invention at this time, and are not intended to limit the invention thereto. Any and all text associated with the figures as illustrated is hereby incorporated by reference in this detailed description.
The present invention provides systems and methods for data analysis, messaging, advanced energy settlements, command and control and management of electric power supply, demand, and/or curtailment including graphical user interfaces for consumers, including consumer profiles and alternative pricing programs and/or settlement programs for business and residential applications, including but not limited to graphical user interfaces for interactive control and command of grid elements, design, specification, construction, management and financial settlement for data centers and/or microgrids, business and residential power consumption, control, management, messaging and settlements, mobile applications, websites, marketing offers, optimal pricing for comparable energy plans, retail electric provider and direct consumer alternatives, network of power architecture, EnergyNet applications, software development kits, application web-based storefronts, and combinations thereof. Apparatus embodiments are also provided in accordance with the systems and methods described herein.
Furthermore, novel methods of the present invention provide for consumer guidance and controls that are coupled with graphical user interfaces for mobile applications, websites, and computer displays that provide improved information and controls for consumers for electric power consumption and management of financial settlement therefor. Preferably, the customer sets their preferences through the user interfaces and then the customer's own data, including whether the customer has opted in for direct response participation, is used to make recommendations for grid elements, services, etc., to the end users, and inputs or opt in/out relating to permissions of data use (e.g., meter data aggregator usage).
In the description of the present invention, it will be understood that all EnergyNet embodiments and AES systems and methods descriptions include and incorporate by this reference without regard to individual, specific recitation for each example described, real-time and/or near-real-time data, including revenue grade metrology used for AES financial settlements. Revenue grade metrology data, which a generic computer is incapable of using, is generated by active grid elements in the power grid; measured data is then transformed into settlement grade data for market financial settlement for load and supply. Additionally and similarly, real-time communication, messaging, and data packet transfer is provided over at least one network associated with the systems and methods of the present invention. This requires physical devices, including at least one client device and at least one server, communicating and interacting over the network. The present invention is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks, more specifically, advanced energy settlements, messaging, and applications for electric power supply, load, and/or curtailment and data analytics associated with the same.
This detailed description of the present invention includes energy financial settlements and messaging and/or data packet transfer or transmission, including the following issued patents and/or copending applications by common inventor and/or assignee Causam Energy, Inc.: U.S. Pat. Nos. 8,849,715, 8,583,520, 8,595,094, 8,719,125, 8,706,583, 8,706,584, 8,775,283, 8,768,799, 8,588,991, and 8,761,952, each of which is incorporated by reference in its entirety herein; US Patent Publication Nos. 2014/0180884, 2014/0279326, 2014/0277788, 2014/0039701, 2014/0277786, and 2014/0277787, each of which is incorporated by reference in its entirety herein; and WIPO Publication Nos. WO2014/066087, 2014/0039699, and WO2014/022596, each of which is incorporated by reference in its entirety herein.
The systems and methods of the present invention also provide support and functionality for at least one distribution service provider through the market-based platform to allow communities, municipalities, cooperative power groups, and/or other combinations of persons or entities to be aggregated to form at least one distribution service provider, which may exist within another distribution service provider, transmission/distribution service provider (TDSP), and/or utility. Additionally, a meter data aggregator (MDA) is provided to interface with the distribution service provider and power marketer and/or utility.
The present invention includes a multiplicity of interactive graphical user interfaces (GUIs) for all aspects of AES and/or EnergyNet embodiments. By way of example and not limitation, as illustrated in the figures, at least one GUI is provided for electric power consumption for business or commercial facilities, including information and/or controls wherein the GUI is provided for mobile applications, websites, terminal and/or computer displays, and combinations thereof. For mobile applications, one embodiment includes a mobile communication computer device, such as a smartphone, tablet computer, or other mobile smart interactive communications device (personal/wearable or portable), having an application including software operable on a processor coupled with memory, wherein the mobile communication computer device is constructed and configured for network-based communication within a cloud-based computing system as illustrated in
By way of example, and not limitation, the computing devices 820, 830, 840 are intended to represent various forms of digital computers and mobile devices, such as a server, blade server, mainframe, mobile phone, personal digital assistant (PDA), smartphone, desktop computer, netbook computer, tablet computer, workstation, laptop, and other similar computing devices. The components shown here, their connections and relationships, and their functions are meant to be exemplary only, and are not meant to limit implementations of the invention described and/or claimed in this document
In one embodiment, the computing device 820 includes components such as a processor 860, a system memory 862 having a random access memory (RAM) 864 and a read-only memory (ROM) 866, and a system bus 868 that couples the memory 862 to the processor 860. In another embodiment, the computing device 830 may additionally include components such as a storage device 890 for storing the operating system 892 and one or more application programs 894, a network interface unit 896, and/or an input/output controller 898. Each of the components may be coupled to each other through at least one bus 868. The input/output controller 898 may receive and process input from, or provide output to, a number of other devices 899, including, but not limited to, alphanumeric input devices, mice, electronic styluses, display units, touch screens, signal generation devices (e.g., speakers), or printers.
By way of example, and not limitation, the processor 860 may be a general-purpose microprocessor (e.g., a central processing unit (CPU)), a graphics processing unit (GPU), a microcontroller, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a state machine, gated or transistor logic, discrete hardware components, or any other suitable entity or combinations thereof that can perform calculations, process instructions for execution, and/or other manipulations of information.
In another implementation, shown as 840 in
Also, multiple computing devices may be connected, with each device providing portions of the necessary operations (e.g., a server bank, a group of blade servers, or a multi-processor system). Alternatively, some steps or methods may be performed by circuitry that is specific to a given function.
According to various embodiments, the computer system 800 may operate in a networked environment using logical connections to local and/or remote computing devices 820, 830, 840 through a network 810. A computing device 830 may connect to a network 810 through a network interface unit 896 connected to a bus 868. Computing devices may communicate communication media through wired networks, direct-wired connections, or wirelessly, such as acoustic, RF, or infrared, through an antenna 897 in communication with the network antenna 812 and the network interface unit 896, which may include digital signal processing circuitry when necessary. The network interface unit 896 may provide for communications under various modes or protocols.
In one or more exemplary aspects, the instructions may be implemented in hardware, software, firmware, or any combinations thereof. A computer readable medium may provide volatile or non-volatile storage for one or more sets of instructions, such as operating systems, data structures, program modules, applications, or other data embodying any one or more of the methodologies or functions described herein. The computer readable medium may include the memory 862, the processor 860, and/or the storage media 890 and may be a single medium or multiple media (e.g., a centralized or distributed computer system) that store the one or more sets of instructions 900. Non-transitory computer readable media includes all computer readable media, with the sole exception being a transitory, propagating signal per se. The instructions 900 may further be transmitted or received over the network 810 via the network interface unit 896 as communication media, which may include a modulated data signal, such as a carrier wave or other transport mechanism, and includes any delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics changed or set in a manner as to encode information in the signal.
Storage devices 890 and memory 862 include, but are not limited to, volatile and non-volatile media such as cache, RAM, ROM, EPROM, EEPROM, FLASH memory, or other solid state memory technology; discs (e.g., digital versatile discs (DVD), HD-DVD, BLU-RAY, compact disc (CD), or CD-ROM) or other optical storage; magnetic cassettes, magnetic tape, magnetic disk storage, floppy disk, or other magnetic storage devices; or any other medium that can be used to store the computer readable instructions and which can be accessed by the computer system 800.
It is also contemplated that the computer system 800 may not include all of the components shown in
In one embodiment, the application (e.g., smartphone app) automatically provides information via the GUI associated with the app to indicate to the user (consumer) information about electric pricing plan alternatives, including but not limited to their location, the price for electric power supply on any per unit (e.g., data center, microgrid, building type (commercial or residential), facility, device, grid element, and combinations thereof) basis for a duration and/or at a predetermined time, and combinations thereof. Also, preferably the app GUI provides additional information including marketing and advertising information about any merchants, products, and/or services associated with or related to their profile(s), power usage, activities within the system, and combinations thereof. Also preferably, the app GUI provides an interactive interface allowing inputs to be received for generating at least one account and corresponding profile, advanced energy settlements selections, etc. In one embodiment of the present invention, the received inputs are associated with a consumer or user profile that is stored on the smartphone and/or in a database associated with a server computer and/or cloud-based computing system with at least one server computer and at least one database having remote inputs and outputs via the data and communications network, preferably via secure access and/or secure messaging for authorized users associated with the at least one account.
In a virtualized or cloud-based computing system and methods of the present invention, the following components are provided as illustrated by way of example and not limitation to those described in
The EnergyNet data platform used with AES preferably provides and/or is operable for real-time revenue grade data ingress; stores and organizes packet level information that can be used for forecasting, data mining, revenue extraction, event detection, sophisticated energy management, and enterprise integration purposes; aggregates and stores revenue data into revenue grade settlement blocks (or Power Trading Blocks (PTBs)); connects microgrid and spot market buyers and sellers; provides a fully automated and low latency industry leading settlement process underpinned by a distributed settlement rules engine capable of settling with both distributed and fixed generator market participants; provides an automated payment exchange which supports all advanced billing models (e.g., shared data plan, daily plan, predict & pay); manages payments as single energy bills for customers with EnergyNet responsible for settlement payments between multiple distributed energy generators and the customer's existing energy retailer; provides a real-time energy purchasing solution that matches the customer's real energy consumption against energy currently available within the microgrid or spot market; captures and transforms market data that can provide intelligent analytics by generators for trending, forecasting, planning, and maximizing revenue/investment opportunities; captures and transforms energy data that can provide intelligent analytics for customers' energy management, forecasting, procurement, profiling, bill optimization, and recommendation purposes; and integrates with the existing distributed energy market exchange to allow EnergyNet buyers and sellers to connect and agree prices on distributed generation. As illustrated in
For example, in a microgrid management application the EnergyNet platform is transacting between the market, utility, consumers, REPs, distribution service providers, balancing authorities, etc. The energy management system (EMS) power model includes data for frequency, voltage, VARs, state estimation, SCED, and actual or real-time information. Data associated with the microgrid is communicated over a secure IP network. The GUIs of the present invention allow for monitoring the market conditions or grid stability conditions as signaled by the distribution service providers, utilities, etc.; it also allows for monitoring of normal conditions, reserves, forecast conditions, etc. External triggers for the EMS may include changes in forecast conditions, actual conditions, market conditions, market price, schedule based upon forecast price exceeding operating cost for the microgrid, etc. Software as a Service (SaaS) operable within the systems and methods of the present invention provides for dispatch of load and supply via EMS systems for distributed assets, wherein the microgrid is considered its own balancing area. So the various external triggers, including the market and/or market-based pricing, are operable as inputs to activate the isolation or connection of the microgrid according to the profile associated with the microgrid. In one embodiment, the microgrid is a secure, critical infrastructure (such as, by way of example and not limited to, data centers) and/or a military installation or facility, wherein the microgrid is locally managed in GUIs and software for grid stability and function such that the computer and software that controls the microgrid and its grid elements are located within the geographic footprint of the microgrid to enable it to function as its own balancing authority shielded from any external controls of the electrical power flows within it. A microgrid is considered any sub-grid, power generating asset, or power supplying asset that can island itself from the electric power grid and/or connect or reconnect with the main electric power grid (having external controls from the microgrid).
Buyers and sellers of electric power are connected within the microgrid or spot market associated with AES of the present invention. Sellers can advertise their generated capacity to customers in near real time and customers can make intelligent purchasing decisions based upon actionable real-time data. The Advanced Energy Settlement (AES) process performs all billing, payment, and settlement activities with financial and clearing participants. A configurable market purchasing rules engine ranks and selects energy from the market based on customer preferences such as cost, payment preference, locality, renewability of the energy, market supply, consumption, etc., and may recommend purchasing from one or more suppliers. The suitability of the offering also depends on additional factors, such as any minimum and maximum usage constraints, which requires decisions to be made based upon forecasts derived using historical data and profiles stored within EnergyNet.
The EnergyNet data platform provides distinct graphic user interfaces (GUIs) for various participants of advanced energy settlements. In one embodiment, the GUIs are web-based interfaces. In another embodiment, the GUIs are interfaces of mobile application programs (Apps) for various participants.
The GUI enables simulation and modeling for building demand response resources DERs, microgrids, etc., allowing for a drag and drop that automatically triggers generation of a power model and a pro forma model having at least one generator and/or at least one load device associated with it, and an engineering interconnection based upon location, equipment, grid identifier, geodetic information, attachment point information, etc. The model considers collected data provided by the customer, historical data, and the current environment of the distribution system; it allows any operable attachment point to be an energy settlement and market-based financial settlement point, and provides an interconnection to the attachment point. The model also indicates if devices are added, provides cost information for the devices, lists the attributes of the devices, etc., which are used as inputs to generate a cost curve that determines how much the customer will spend and funds receivable based upon participation in programs (e.g., encouraging sustainable or alternative energy).
The system includes a grid element catalog that includes attributes of the grid elements. Based upon customer inputs, the model indicates options that match or fit the customer's profile. The model also provides information about financing and energy capacity programs as provided by REP, TDSP, independent system operator (ISO), RTO, community, FERC, and/or the governing body of the power grid. Once the customer selects a grid element, the system provides digital contract elements and/or financing terms associated with that grid element and/or corresponding services. For example, installation, service, and maintenance contract terms for generator, solar, etc. The digital contract is a standard form document between suppliers and consumers at wholesale or retail level. Digital contract terms are coordinated through the platform for market participants (e.g., utilities, consumers, and all parties between the utility and consumer). Digital contract terms for a grid element device are presented as part of update messaging and/or programming, through a coordinator or distributed database, or combinations thereof. Contract terms and data, including but not limited to financial settlements for grid elements and their participation on or with any electric power grid, extend through the fields of the template and function as a complex rules engine to be administered vis-à-vis the grid elements and related or corresponding services, distributed architectures, networks, etc.
The GUI shows options for customers based on customer preferences, data generated by the customer, and the results of power modeling. End use customers (residential or commercial) are presented choices for grid elements, OEMs offering grid elements, energy plans, and service and maintenance plans.
The platform makes calculations based upon the reliability of microgrids and/or DERs. These calculations are used to provide recommendations and updated information to users in real time and/or near real time through the GUIs.
Electric vehicles or other mobile power storage devices on the microgrid are part of the platform. The present invention allows for receiving, delivering, and/or discharging power from a mobile power storage device, interrupting the charging of that device, and combinations thereof with a portable market participant platform and corresponding GUI. Grid elements may decouple or couple to any pre-approved attachment point; this provides for dynamic interconnection of the grid element having mobile power storage. The platform dynamically updates the model for the grid upon confirmation of location or geo-detection of that grid element. The platform also contains predictive analytics that show locations in need of power inputs. Required components associated with the mobile storage device or electric vehicle include at least a meter for revenue grade metrology sufficient for market-based financial settlement and at least one pre-approved attachment point for the interconnect; the mobile storage device or electric vehicle must also be registered with the platform. Pre-approved interconnection zones are thus provided for mobile grid elements; these zones and/or their aggregation further provide for logical nodes for controlling or inputting power or load, demand response, etc. The zones may further function as balancing areas.
Utility Operator Interface
A utility operator interface provides a utility view for control room staff to control DERs with transparency. Maps, tables, and charts are applied for illustration and view in regional or smaller areas. Regional control scenario algorithm and detail view control for specific premise or units are applied for real-time behavior or run-mode adjustments to support grid operations.
Interconnection Processing Interface
Vendor/Aggregator View Interface
Marketplace View Interface
Financial Settlement View Interface
Tiers or Levels within the EnergyNet Platform
One embodiment of the present invention is a system of an advanced energy network, comprising a platform communicatively connected to at least one distributed computing device operable for providing inputs from at least one energy user, wherein the platform is operable to: create a user profile for the at least one energy user; collect energy usage data for the at least one energy user; associate the energy usage data with the user profile corresponding to the at least one energy user; aggregate the energy usage data; estimate projected energy usage for the at least one energy user; predict energy consumption data based on the energy usage data and the projected energy usage data; and store the energy usage data, the projected energy usage data, and the predicted energy consumption data in a database. In Level 0 (L0) of the present invention, the user or consumer is engaged in the platform by providing verified information on actual energy usage to the platform. In Level 1 (L1) of the present invention, the user may provide additional information to the system and/or additional information may be gathered from public sources. In Level 2 (L2) of the present invention, the user may add grid elements to their user profile. In Level 3 (L3) of present invention, the utility, grid element vendors, meter data aggregators, etc. may identify sales opportunities based on data in the database and provide marketing for products and/or service offerings to consumers (consumer users) or commercial users with profiles within the EnergyNet platform. In Level 4 (L4) of the present invention grid elements operable for providing electric power supply (by way of example and not limitation, solar power generation, fuel cell or battery power storage devices, wind generation, back-up power generators, etc.) that are properly constructed and configured, modeled, and connected with revenue grade metrology acceptable for energy settlement and market-based financial settlement within the energy market, are introduced after being registered and profile created within the EnergyNet platform.
In one embodiment, for level 0 (L0) the actual energy usage data documented within a utility bill is uploaded to the platform by an energy user having a profile or creating a profile on the EnergyNet platform. The actual energy usage data is uploaded and communicated over at least one network to at least one computer or server associated with the platform, which automatically recognizes the format of the utility bill based upon prior utility bill(s) uploaded by at least one user. For example, if a first user uploads a utility bill to the platform and selects the relevant information from the utility bill, the platform may automatically recognize the format of utility bills for subsequent users who have the same service provider. Also or alternatively, the energy user inputs indication of which data to capture from the utility bill for automatic association with that user's profile. The system also provides options for the energy user to selectively redact information on the utility bill, such as customer name, account number, and PIN number. The platform may automatically populate the database based on the data on actual energy usage in the utility bill. The platform is further operable to collect at least one of real-time or near real-time data from grid elements and data from smart meters associated with the at least one user.
The embodiments disclosed make use of the “user profiles” concept. The user profile includes, but is not limited to, the following: (1) energy user name; (2) service address; (3) electric provider; (4) building type; (5) historical and current bill dates; and (6) historical and current charges for electrical service. The user profile may further include information regarding geodetic location; meter ID; customer programs (possibly including program history); device information, including device type and manufacturer/brand; user energy consumption patterns; and connection and disconnection profile. The connection/disconnection profile can include service priority (i.e., elderly, police, etc.) and disconnection instructions.
In other embodiments, additional data called “variability factors” may be captured by the system as part of the user profile, including, but not limited to, the following: (1) outside temperature, (2) sunlight, (3) humidity, (4) wind speed and direction, (5) elevation above sea level, (6) orientation of the service point structure, (7) duty duration and percentage, (8) set point difference, (9) current and historic room temperature, (10) size of structure, (11) number of floors, (12) type of construction (brick, wood, siding etc.) (13) color of structure, (14) type of roofing material and color, (15) construction surface of structure (built on turf, clay, cement, asphalt etc.), (16) land use (urban, suburban, rural), (17) latitude/longitude, (18) relative position to jet stream, (19) quality of power to devices, (20) number of people living in and/or using structure, (21) age of structure, (22) type of heating, (23) lot description, (24) type of water, (25) other square footage, and (26) other environmental factors. Additional data that may be stored by the system include vacancy times, sleep times, and times in which control events are permitted. User profiles may also include whether a swimming pool is located at the service address.
In level 1 (L1) of the present invention, the user may provide additional information to the system and/or additional information may be gathered from public sources to further populate the user profile. Information regarding the plurality of variability factors may obtained from public sources. For example, information regarding weather (e.g., outside temperature, sunlight, humidity, wind speed and direction) may be obtained from publicly available weather services. Additionally, information regarding size of structure (e.g., square footage), number of floors or stories, type of roofing material, type of construction, age of structure, type of heat, etc. may be found on publicly available websites (e.g., county or state records, Zillow, and Trulia). Users may be given incentives to provide additional information for their user profile.
The user profile may further contain information regarding user preferences, wherein the user preferences comprise at least one of automatic uploading of utility bills, contact preferences, payment preferences, privacy preferences, renewability of energy sources, grid element preferences, rate plans, consumption, cost, locality, and market supply.
The platform uses information in the user profile to generate more accurate predictive consumption data. For example, if one energy user uploads a utility bill, that utility bill may be used to generate predictive consumption data for similar structures or similar geographic locations (e.g., houses in the same neighborhood). If additional energy users upload utility bills, the aggregated data from the utility bills may be used to generate more accurate predictive consumption data. With additional information, such as variability factors, the platform is able to increase the accuracy of the prediction. For example, a house with a pool and an electric vehicle would be expected to use more electricity than a house in the same neighborhood without a pool or electric vehicle. Additionally, a larger house or multi-story house would have a larger predictive energy consumption than a smaller house or single-story house in the same neighborhood. Also, typically older houses have lower energy efficiency, due to factors affecting energy consumption, e.g., older HVAC equipment that is less efficient than modern equipment, and/or factors affecting the leakage of conditioned air, e.g., less insulation, older windows and doors, etc. Variability factors may be added to the system by users or obtained from public sources of data.
The platform is further operable to display a map of the predicted energy consumption as shown in
In Level 2 (L2) of the present invention, the system receives user inputs that associate at least one grid element with their corresponding user profile. The grid elements include but are not limited to power transfer switches, wind meters, utility meters, battery discharge controllers, tenant sub-meters, solar meters, power distribution units (PDUs), appliance switches, electric vehicle charging stations, distributed energy resources (DERs), transfer switches, electric vehicle batteries, inverters, controllable loads, weather stations, and/or HVAC environments. For example, the system may receive an indication or selection inputs from a user regarding a present or future interest in, or action for installing and operating of, solar panels to their roof for the location associated with their corresponding user profile; this change and the user's preferences or profile regarding the solar panels is saved in the database.
In Level 3 (L3) of present invention, the at least one utility or market participant and its partners (e.g., vendors) utilize the EnergyNet platform to identify sales opportunities based on data in the database. Data that is anonymized or permission-based access to data from user profiles may be used to provide insights on inefficient devices, defective devices, or devices that require updating to meet current standards. User profile data may also be used to identify related sales opportunities. For example, if energy consumption patterns suggest that the user may be very interested in personal energy conservation, then sales efforts could be directed toward that individual concerning products related to that lifestyle. This information can be used by the utility or its partners to provide incentives to users to buy newer, updated devices, or obtain maintenance for existing devices. The user is given the option to opt out of having his user profile used for sales and marketing efforts, or for regulating energy conservation. The user profile makes use of open standards (such as the CPExchange standard) that specify a privacy model with the user profile. The use of consumption patterns in this manner is governed by national, state, or local privacy laws and regulations.
A further embodiment of using user profiles to identify sales opportunities involves the use of device information to create incentives for users to replace inefficient devices. By identifying the known characteristics and/or behavior of devices within a service point, the invention identifies those users who may benefit from replacement of those devices. The invention estimates a payback period for replacement. This information is used by the utility or its partners to create redemptions, discounts, and campaigns to persuade users to replace their devices.
Users may be grouped by geography or some other common characteristics. For example, groups might include “light consumers” (because they consume little energy), “daytime consumers” (because they work at night), “swimmers” (for those who have a pool and use it), or other categories. Categorizing users into groups allows the utility or its partners or market participants to target sales and marketing efforts to relevant users.
EnergyNet Graphs
Financial Model Visualization Interface
A financial model visualization interface allows at least one utility or market participant, to run Monte Carlo simulations for adding new meters to the market, energy usage distribution, and/or energy generation distribution. Adjusting the simulation parameters (e.g., mean, standard deviation, skewness) provides for minimizing or managing risk for decision-making and investment related to the electric power grid, and to better predict outcomes.
The following are incorporated herein by reference in their entirety: the NY REV order, CAL ISO rules and proposed rules and subsequent order for DER marketplace, ERCOT presentation stakeholder concerns, and terms and their definitions: telemetry light, telemetry medium, etc.
Certain modifications and improvements will occur to those skilled in the art upon a reading of the foregoing description. For example, Software as a Service (SaaS) or Platform as a Service (PaaS) may be provided in embodiments of the present invention. Also, updated communications such as 5G and later alternatives are considered within the scope of the present invention. The above-mentioned examples are provided to serve the purpose of clarifying the aspects of the invention and it will be apparent to one skilled in the art that they do not serve to limit the scope of the invention. All modifications and improvements have been deleted herein for the sake of conciseness and readability but are properly within the scope of the present invention.
The present invention is related to and claims priority from the following U.S. patent documents: this application is a continuation of U.S. patent application Ser. No. 15/273,088, filed Sep. 22, 2016, which claims priority from U.S. Provisional Patent Application No. 62/222,470, filed Sep. 23, 2015, each of which is incorporated herein by reference in its entirety.
Number | Name | Date | Kind |
---|---|---|---|
3906242 | Stevenson | Sep 1975 | A |
4023043 | Stevenson | May 1977 | A |
4589075 | Buennagel | May 1986 | A |
4799059 | Grindahl et al. | Jan 1989 | A |
4819180 | Hedman et al. | Apr 1989 | A |
4819229 | Pritty et al. | Apr 1989 | A |
5237507 | Chasek | Aug 1993 | A |
5361982 | Liebl et al. | Nov 1994 | A |
5388101 | Dinkins | Feb 1995 | A |
5481546 | Dinkins | Jan 1996 | A |
5502339 | Hartig | Mar 1996 | A |
5544036 | Brown et al. | Aug 1996 | A |
5560022 | Dunstan et al. | Sep 1996 | A |
5570002 | Castleman | Oct 1996 | A |
5592491 | Dinkins | Jan 1997 | A |
5640153 | Hildebrand et al. | Jun 1997 | A |
5644173 | Elliason et al. | Jul 1997 | A |
5675503 | Moe et al. | Oct 1997 | A |
5696695 | Ehlers et al. | Dec 1997 | A |
5721936 | Kikinis et al. | Feb 1998 | A |
5926776 | Glorioso et al. | Jul 1999 | A |
5973481 | Thompson et al. | Oct 1999 | A |
6018690 | Saito et al. | Jan 2000 | A |
6078785 | Bush | Jun 2000 | A |
6102487 | Oevreboe | Aug 2000 | A |
6107693 | Mongia et al. | Aug 2000 | A |
6112136 | Paul et al. | Aug 2000 | A |
6115580 | Chuprun et al. | Sep 2000 | A |
6115676 | Rector et al. | Sep 2000 | A |
6154859 | Norizuki et al. | Nov 2000 | A |
6216956 | Ehlers et al. | Apr 2001 | B1 |
6233327 | Petite | May 2001 | B1 |
6254009 | Proffitt et al. | Jul 2001 | B1 |
6286021 | Tran et al. | Sep 2001 | B1 |
6296612 | Mo et al. | Oct 2001 | B1 |
6301528 | Bertram et al. | Oct 2001 | B1 |
6304552 | Chapman et al. | Oct 2001 | B1 |
6327541 | Pitchford et al. | Dec 2001 | B1 |
6366217 | Cunningham et al. | Apr 2002 | B1 |
6374101 | Gelbien | Apr 2002 | B1 |
6437692 | Petite et al. | Aug 2002 | B1 |
6512966 | Lof et al. | Jan 2003 | B2 |
6519509 | Nierlich et al. | Feb 2003 | B1 |
6529839 | Uggerud et al. | Mar 2003 | B1 |
6535797 | Bowles et al. | Mar 2003 | B1 |
6577962 | Afshari | Jun 2003 | B1 |
6583521 | Lagod et al. | Jun 2003 | B1 |
6601033 | Sowinski | Jul 2003 | B1 |
6621179 | Howard | Sep 2003 | B1 |
6622097 | Hunter | Sep 2003 | B2 |
6622925 | Carner et al. | Sep 2003 | B2 |
6633823 | Bartone et al. | Oct 2003 | B2 |
6671586 | Davis et al. | Dec 2003 | B2 |
6681154 | Nierlich et al. | Jan 2004 | B2 |
6687574 | Pietrowicz et al. | Feb 2004 | B2 |
6732055 | Bagepalli et al. | May 2004 | B2 |
6747368 | Jarrett | Jun 2004 | B2 |
6778882 | Spool et al. | Aug 2004 | B2 |
6784807 | Petite et al. | Aug 2004 | B2 |
6826267 | Daum et al. | Nov 2004 | B2 |
6832135 | Ying | Dec 2004 | B2 |
6834811 | Huberman et al. | Dec 2004 | B1 |
6836737 | Petite et al. | Dec 2004 | B2 |
6850557 | Gronemeyer | Feb 2005 | B1 |
6862498 | Davis et al. | Mar 2005 | B2 |
6865450 | Masticola et al. | Mar 2005 | B2 |
6868293 | Schurr et al. | Mar 2005 | B1 |
6879059 | Sleva | Apr 2005 | B2 |
6891838 | Petite et al. | May 2005 | B1 |
6897931 | Lee et al. | May 2005 | B2 |
6900556 | Provanzana et al. | May 2005 | B2 |
6904336 | Raines et al. | Jun 2005 | B2 |
6906617 | Van der Meulen | Jun 2005 | B1 |
6909942 | Andarawis et al. | Jun 2005 | B2 |
6914533 | Petite | Jul 2005 | B2 |
6914893 | Petite | Jul 2005 | B2 |
6934316 | Cornwall et al. | Aug 2005 | B2 |
6944555 | Blackett et al. | Sep 2005 | B2 |
6961641 | Forth et al. | Nov 2005 | B1 |
6978931 | Brobeck | Dec 2005 | B2 |
6990593 | Nakagawa | Jan 2006 | B2 |
7003640 | Mayo et al. | Feb 2006 | B2 |
7019667 | Petite et al. | Mar 2006 | B2 |
7035719 | Howard et al. | Apr 2006 | B2 |
7039532 | Hunter | May 2006 | B2 |
7053756 | Mollenkopf et al. | May 2006 | B2 |
7053767 | Petite et al. | May 2006 | B2 |
7085739 | Winter et al. | Aug 2006 | B1 |
7088014 | Nierlich et al. | Aug 2006 | B2 |
7103511 | Petite | Sep 2006 | B2 |
7111018 | Goodrich et al. | Sep 2006 | B1 |
7123994 | Weik et al. | Oct 2006 | B2 |
7133750 | Raines et al. | Nov 2006 | B2 |
7141321 | McArthur et al. | Nov 2006 | B2 |
7142949 | Brewster et al. | Nov 2006 | B2 |
7177728 | Gardner | Feb 2007 | B2 |
7181320 | Whiffen et al. | Feb 2007 | B2 |
7184861 | Petite | Feb 2007 | B2 |
7200134 | Proctor et al. | Apr 2007 | B2 |
7206350 | Korobkov et al. | Apr 2007 | B2 |
7206670 | Pimputkar et al. | Apr 2007 | B2 |
7209804 | Curt et al. | Apr 2007 | B2 |
7209840 | Petite et al. | Apr 2007 | B2 |
7233843 | Budhraja et al. | Jun 2007 | B2 |
7263073 | Petite et al. | Aug 2007 | B2 |
7263450 | Hunter | Aug 2007 | B2 |
7274975 | Miller | Sep 2007 | B2 |
7282921 | Sela et al. | Oct 2007 | B2 |
7289887 | Rodgers | Oct 2007 | B2 |
7295128 | Petite | Nov 2007 | B2 |
7305282 | Chen | Dec 2007 | B2 |
7313465 | O'Donnell | Dec 2007 | B1 |
7337153 | Peljto et al. | Feb 2008 | B2 |
7343341 | Sandor et al. | Mar 2008 | B2 |
7345998 | Cregg et al. | Mar 2008 | B2 |
7346463 | Petite et al. | Mar 2008 | B2 |
7366164 | Habib et al. | Apr 2008 | B1 |
7397907 | Petite | Jul 2008 | B2 |
7406364 | Rissanen et al. | Jul 2008 | B2 |
7412304 | Uenou | Aug 2008 | B2 |
7424268 | Diener et al. | Sep 2008 | B2 |
7424527 | Petite | Sep 2008 | B2 |
7440871 | McConnell et al. | Oct 2008 | B2 |
7451019 | Rodgers | Nov 2008 | B2 |
7468661 | Petite et al. | Dec 2008 | B2 |
7480501 | Petite | Jan 2009 | B2 |
7486681 | Weber | Feb 2009 | B2 |
7502698 | Uenou et al. | Mar 2009 | B2 |
7528503 | Rognli et al. | May 2009 | B2 |
7536240 | McIntyre et al. | May 2009 | B2 |
7541941 | Bogolea et al. | Jun 2009 | B2 |
7565227 | Richard et al. | Jul 2009 | B2 |
7609158 | Banting et al. | Oct 2009 | B2 |
7650425 | Davis et al. | Jan 2010 | B2 |
7697492 | Petite | Apr 2010 | B2 |
7711796 | Gutt et al. | May 2010 | B2 |
7715951 | Forbes et al. | May 2010 | B2 |
7738999 | Petite | Jun 2010 | B2 |
7739378 | Petite | Jun 2010 | B2 |
7747165 | Emery et al. | Jun 2010 | B2 |
7844370 | Pollack et al. | Nov 2010 | B2 |
7890436 | Kremen | Feb 2011 | B2 |
7925552 | Tarbell et al. | Apr 2011 | B2 |
7940901 | Paraskevakos et al. | May 2011 | B2 |
7949435 | Pollack et al. | May 2011 | B2 |
8010812 | Forbes et al. | Aug 2011 | B2 |
8032233 | Forbes et al. | Oct 2011 | B2 |
8032461 | Winter et al. | Oct 2011 | B2 |
8045660 | Gupta | Oct 2011 | B1 |
8060259 | Budhraja et al. | Nov 2011 | B2 |
8068938 | Fujita | Nov 2011 | B2 |
8095233 | Shankar et al. | Jan 2012 | B1 |
8145361 | Forbes et al. | Mar 2012 | B2 |
8260468 | Ippolito et al. | Sep 2012 | B2 |
8260470 | Forbes et al. | Sep 2012 | B2 |
8305215 | Markhovsky et al. | Nov 2012 | B2 |
8307225 | Forbes et al. | Nov 2012 | B2 |
8311483 | Tillman et al. | Nov 2012 | B2 |
8315717 | Forbes et al. | Nov 2012 | B2 |
8315743 | Sackman et al. | Nov 2012 | B2 |
8359124 | Zhou et al. | Jan 2013 | B2 |
8359215 | Robbins et al. | Jan 2013 | B1 |
8364609 | Ozog | Jan 2013 | B2 |
8407252 | Bennett et al. | Mar 2013 | B2 |
8417569 | Gross | Apr 2013 | B2 |
8428752 | Bennett et al. | Apr 2013 | B2 |
8442917 | Burke | May 2013 | B1 |
8457802 | Steven et al. | Jun 2013 | B1 |
8467353 | Proctor | Jun 2013 | B2 |
8473111 | Shankar et al. | Jun 2013 | B1 |
8521337 | Johnson | Aug 2013 | B1 |
8565811 | Tan et al. | Oct 2013 | B2 |
8571930 | Galperin | Oct 2013 | B1 |
8583520 | Forbes | Nov 2013 | B1 |
8583799 | Podila | Nov 2013 | B2 |
8588991 | Forbes | Nov 2013 | B1 |
8639392 | Chassin | Jan 2014 | B2 |
8684266 | Bennett et al. | Apr 2014 | B2 |
8704678 | Wang et al. | Apr 2014 | B2 |
8761051 | Brisebois et al. | Jun 2014 | B2 |
8761952 | Forbes | Jun 2014 | B2 |
8818283 | McHenry et al. | Aug 2014 | B2 |
10295969 | Forbes, Jr. | May 2019 | B2 |
20010030468 | Anderson et al. | Oct 2001 | A1 |
20010038343 | Meyer et al. | Nov 2001 | A1 |
20020019758 | Scarpelli | Feb 2002 | A1 |
20020019802 | Malme et al. | Feb 2002 | A1 |
20020035496 | Fukushima et al. | Mar 2002 | A1 |
20020036430 | Welches et al. | Mar 2002 | A1 |
20020084655 | Lof et al. | Jul 2002 | A1 |
20020091626 | Johnson et al. | Jul 2002 | A1 |
20020109607 | Cumeralto et al. | Aug 2002 | A1 |
20020138176 | Davis et al. | Sep 2002 | A1 |
20020143693 | Soestbergen et al. | Oct 2002 | A1 |
20020161648 | Mason et al. | Oct 2002 | A1 |
20020198629 | Ellis | Dec 2002 | A1 |
20030006613 | Lof et al. | Jan 2003 | A1 |
20030009705 | Thelander et al. | Jan 2003 | A1 |
20030036820 | Yellepeddy et al. | Feb 2003 | A1 |
20030074244 | Braxton | Apr 2003 | A1 |
20030083980 | Satake | May 2003 | A1 |
20030144864 | Mazzarella | Jul 2003 | A1 |
20030149937 | McElfresh et al. | Aug 2003 | A1 |
20030160595 | Provanzana et al. | Aug 2003 | A1 |
20030167178 | Jarman et al. | Sep 2003 | A1 |
20030171851 | Brickfield et al. | Sep 2003 | A1 |
20030176952 | Collins | Sep 2003 | A1 |
20030198304 | Sugar et al. | Oct 2003 | A1 |
20030204756 | Ransom et al. | Oct 2003 | A1 |
20030220864 | Peljto et al. | Nov 2003 | A1 |
20030225483 | Santinato et al. | Dec 2003 | A1 |
20030229572 | Raines et al. | Dec 2003 | A1 |
20030233201 | Horst et al. | Dec 2003 | A1 |
20040006439 | Hunter | Jan 2004 | A1 |
20040024483 | Holcombe | Feb 2004 | A1 |
20040044571 | Bronnimann et al. | Mar 2004 | A1 |
20040088083 | Davis et al. | May 2004 | A1 |
20040095237 | Chen et al. | May 2004 | A1 |
20040107025 | Ransom et al. | Jun 2004 | A1 |
20040117330 | Ehlers et al. | Jun 2004 | A1 |
20040128266 | Yellepeddy et al. | Jul 2004 | A1 |
20040138834 | Blackett et al. | Jul 2004 | A1 |
20040153170 | Santacatterina et al. | Aug 2004 | A1 |
20040158417 | Bonet | Aug 2004 | A1 |
20040158478 | Zimmerman | Aug 2004 | A1 |
20040162793 | Scott et al. | Aug 2004 | A1 |
20040193329 | Ransom et al. | Sep 2004 | A1 |
20040203826 | Sugar et al. | Oct 2004 | A1 |
20040206813 | Brobeck | Oct 2004 | A1 |
20040225514 | Greenshields et al. | Nov 2004 | A1 |
20040230533 | Benco | Nov 2004 | A1 |
20050021397 | Cui et al. | Jan 2005 | A1 |
20050033481 | Budhraja et al. | Feb 2005 | A1 |
20050055432 | Rodgers | Mar 2005 | A1 |
20050065742 | Rodgers | Mar 2005 | A1 |
20050080772 | Bem | Apr 2005 | A1 |
20050096856 | Lubkeman et al. | May 2005 | A1 |
20050096857 | Hunter | May 2005 | A1 |
20050096979 | Koningstein | May 2005 | A1 |
20050097204 | Horowitz et al. | May 2005 | A1 |
20050125243 | Villalobos | Jun 2005 | A1 |
20050127680 | Lof et al. | Jun 2005 | A1 |
20050131583 | Ransom | Jun 2005 | A1 |
20050138432 | Ransom et al. | Jun 2005 | A1 |
20050144437 | Ransom et al. | Jun 2005 | A1 |
20050192711 | Raines et al. | Sep 2005 | A1 |
20050192713 | Weik et al. | Sep 2005 | A1 |
20050197742 | Scott et al. | Sep 2005 | A1 |
20050216302 | Raji et al. | Sep 2005 | A1 |
20050216580 | Raji et al. | Sep 2005 | A1 |
20050227625 | Diener | Oct 2005 | A1 |
20050234600 | Boucher et al. | Oct 2005 | A1 |
20050240314 | Martinez | Oct 2005 | A1 |
20050240315 | Booth et al. | Oct 2005 | A1 |
20050246190 | Sandor et al. | Nov 2005 | A1 |
20050267642 | Whiffen et al. | Dec 2005 | A1 |
20050276222 | Kumar et al. | Dec 2005 | A1 |
20050288954 | McCarthy et al. | Dec 2005 | A1 |
20060020544 | Kaveski | Jan 2006 | A1 |
20060020596 | Liu et al. | Jan 2006 | A1 |
20060022841 | Hoiness et al. | Feb 2006 | A1 |
20060025891 | Budike | Feb 2006 | A1 |
20060031934 | Kriegel | Feb 2006 | A1 |
20060038672 | Schoettle | Feb 2006 | A1 |
20060064205 | Ying | Mar 2006 | A1 |
20060069616 | Bau | Mar 2006 | A1 |
20060106635 | Ulrich et al. | May 2006 | A1 |
20060119368 | Sela et al. | Jun 2006 | A1 |
20060142900 | Rothman et al. | Jun 2006 | A1 |
20060142961 | Johnson et al. | Jun 2006 | A1 |
20060161310 | Lal | Jul 2006 | A1 |
20060161450 | Carey et al. | Jul 2006 | A1 |
20060168191 | Ives | Jul 2006 | A1 |
20060190354 | Meisel et al. | Aug 2006 | A1 |
20060195334 | Reeb et al. | Aug 2006 | A1 |
20060212350 | Ellis et al. | Sep 2006 | A1 |
20060224615 | Korn et al. | Oct 2006 | A1 |
20060241244 | Soeda et al. | Oct 2006 | A1 |
20060241314 | Sullivan et al. | Oct 2006 | A1 |
20060271244 | Cumming et al. | Nov 2006 | A1 |
20060271314 | Hayes | Nov 2006 | A1 |
20060276938 | Miller | Dec 2006 | A1 |
20060282328 | Gerace et al. | Dec 2006 | A1 |
20070021874 | Rognli et al. | Jan 2007 | A1 |
20070026857 | Kotzin | Feb 2007 | A1 |
20070038563 | Ryzerski | Feb 2007 | A1 |
20070058453 | Shaffer et al. | Mar 2007 | A1 |
20070058629 | Luft | Mar 2007 | A1 |
20070067132 | Tziouvaras et al. | Mar 2007 | A1 |
20070070895 | Narvaez | Mar 2007 | A1 |
20070085702 | Walters et al. | Apr 2007 | A1 |
20070091900 | Asthana et al. | Apr 2007 | A1 |
20070100503 | Balan et al. | May 2007 | A1 |
20070100961 | Moore | May 2007 | A1 |
20070124026 | Troxell et al. | May 2007 | A1 |
20070150353 | Krassner et al. | Jun 2007 | A1 |
20070156621 | Wright et al. | Jul 2007 | A1 |
20070156887 | Wright et al. | Jul 2007 | A1 |
20070174114 | Bigby et al. | Jul 2007 | A1 |
20070192333 | Ali | Aug 2007 | A1 |
20070203722 | Richards et al. | Aug 2007 | A1 |
20070204176 | Shaffer et al. | Aug 2007 | A1 |
20070213878 | Chen | Sep 2007 | A1 |
20070214118 | Schoen et al. | Sep 2007 | A1 |
20070214132 | Grubb et al. | Sep 2007 | A1 |
20070255457 | Whitcomb et al. | Nov 2007 | A1 |
20070260540 | Chau et al. | Nov 2007 | A1 |
20070276547 | Miller | Nov 2007 | A1 |
20070286210 | Gutt et al. | Dec 2007 | A1 |
20070291644 | Roberts et al. | Dec 2007 | A1 |
20070293171 | Li et al. | Dec 2007 | A1 |
20070299562 | Kates | Dec 2007 | A1 |
20080010212 | Moore et al. | Jan 2008 | A1 |
20080015976 | Sander et al. | Jan 2008 | A1 |
20080039979 | Bridges et al. | Feb 2008 | A1 |
20080039980 | Pollack et al. | Feb 2008 | A1 |
20080039989 | Pollack et al. | Feb 2008 | A1 |
20080040223 | Bridges et al. | Feb 2008 | A1 |
20080040295 | Kaplan et al. | Feb 2008 | A1 |
20080040296 | Bridges et al. | Feb 2008 | A1 |
20080040479 | Bridge et al. | Feb 2008 | A1 |
20080046387 | Gopal et al. | Feb 2008 | A1 |
20080052145 | Kaplan et al. | Feb 2008 | A1 |
20080091580 | Kremen | Apr 2008 | A1 |
20080091581 | Kremen | Apr 2008 | A1 |
20080091590 | Kremen | Apr 2008 | A1 |
20080091625 | Kremen | Apr 2008 | A1 |
20080091626 | Kremen | Apr 2008 | A1 |
20080104026 | Koran | May 2008 | A1 |
20080109387 | Deaver et al. | May 2008 | A1 |
20080130673 | Cregg et al. | Jun 2008 | A1 |
20080133604 | Kim | Jun 2008 | A1 |
20080147465 | Raines et al. | Jun 2008 | A1 |
20080154801 | Fein et al. | Jun 2008 | A1 |
20080165714 | Dettinger et al. | Jul 2008 | A1 |
20080172312 | Synesiou et al. | Jul 2008 | A1 |
20080177423 | Brickfield et al. | Jul 2008 | A1 |
20080177678 | Di Martini et al. | Jul 2008 | A1 |
20080186202 | Vaswani et al. | Aug 2008 | A1 |
20080195462 | Magdon-Ismail et al. | Aug 2008 | A1 |
20080209117 | Kajigaya | Aug 2008 | A1 |
20080224892 | Bogolea et al. | Sep 2008 | A1 |
20080231114 | Tolnar et al. | Sep 2008 | A1 |
20080238710 | Tolnar et al. | Oct 2008 | A1 |
20080249832 | Richardson et al. | Oct 2008 | A1 |
20080255899 | McConnell et al. | Oct 2008 | A1 |
20080263025 | Koran | Oct 2008 | A1 |
20080270223 | Collins et al. | Oct 2008 | A1 |
20080272934 | Wang et al. | Nov 2008 | A1 |
20080281473 | Pitt | Nov 2008 | A1 |
20080306824 | Parkinson | Dec 2008 | A1 |
20080306830 | Lasa et al. | Dec 2008 | A1 |
20080313632 | Kumar et al. | Dec 2008 | A1 |
20080319893 | Mashinsky et al. | Dec 2008 | A1 |
20090012996 | Gupta et al. | Jan 2009 | A1 |
20090018884 | McConnell et al. | Jan 2009 | A1 |
20090024718 | Anagnostopoulos et al. | Jan 2009 | A1 |
20090040029 | Bridges et al. | Feb 2009 | A1 |
20090043519 | Bridges et al. | Feb 2009 | A1 |
20090043520 | Pollack et al. | Feb 2009 | A1 |
20090045804 | Durling et al. | Feb 2009 | A1 |
20090046625 | Diener et al. | Feb 2009 | A1 |
20090055031 | Slota et al. | Feb 2009 | A1 |
20090055032 | Rodgers | Feb 2009 | A1 |
20090062970 | Forbes et al. | Mar 2009 | A1 |
20090063228 | Forbes | Mar 2009 | A1 |
20090063680 | Bridges et al. | Mar 2009 | A1 |
20090066287 | Pollack et al. | Mar 2009 | A1 |
20090088907 | Lewis et al. | Apr 2009 | A1 |
20090111463 | Simms et al. | Apr 2009 | A1 |
20090112701 | Turpin | Apr 2009 | A1 |
20090112758 | Herzig | Apr 2009 | A1 |
20090119039 | Banister et al. | May 2009 | A1 |
20090124241 | Krishnaswamy et al. | May 2009 | A1 |
20090125462 | Krishnaswamy et al. | May 2009 | A1 |
20090135836 | Veillette | May 2009 | A1 |
20090138362 | Schroedl et al. | May 2009 | A1 |
20090157529 | Ehlers et al. | Jun 2009 | A1 |
20090157545 | Mobley | Jun 2009 | A1 |
20090177548 | Eisenlohr | Jul 2009 | A1 |
20090187284 | Kreiss et al. | Jul 2009 | A1 |
20090187344 | Brancaccio et al. | Jul 2009 | A1 |
20090187499 | Mulder et al. | Jul 2009 | A1 |
20090195349 | Frader-Thompson et al. | Aug 2009 | A1 |
20090198384 | Ahn | Aug 2009 | A1 |
20090200988 | Bridges et al. | Aug 2009 | A1 |
20090207950 | Tsuruta et al. | Aug 2009 | A1 |
20090228335 | Niyogi et al. | Sep 2009 | A1 |
20090240381 | Lane | Sep 2009 | A1 |
20090240677 | Parekh et al. | Sep 2009 | A1 |
20090281673 | Taft | Nov 2009 | A1 |
20090281674 | Taft | Nov 2009 | A1 |
20090313034 | Ferro et al. | Dec 2009 | A1 |
20090313103 | Ambrosio et al. | Dec 2009 | A1 |
20090319415 | Stoilov et al. | Dec 2009 | A1 |
20090326726 | Ippolito et al. | Dec 2009 | A1 |
20100023337 | Case | Jan 2010 | A1 |
20100076835 | Silverman | Mar 2010 | A1 |
20100094981 | Cordray et al. | Apr 2010 | A1 |
20100100250 | Budhraja et al. | Apr 2010 | A1 |
20100106332 | Chassin et al. | Apr 2010 | A1 |
20100106342 | Ko et al. | Apr 2010 | A1 |
20100106575 | Bixby | Apr 2010 | A1 |
20100106641 | Chassin et al. | Apr 2010 | A1 |
20100138452 | Henkin et al. | Jun 2010 | A1 |
20100146599 | Padmanabha et al. | Jun 2010 | A1 |
20100163634 | Klein et al. | Jul 2010 | A1 |
20100164749 | Hope et al. | Jul 2010 | A1 |
20100169175 | Koran | Jul 2010 | A1 |
20100179862 | Chassin et al. | Jul 2010 | A1 |
20100191862 | Forbes et al. | Jul 2010 | A1 |
20100198535 | Brown et al. | Aug 2010 | A1 |
20100217452 | McCord et al. | Aug 2010 | A1 |
20100217549 | Galvin et al. | Aug 2010 | A1 |
20100217550 | Crabtree et al. | Aug 2010 | A1 |
20100217642 | Crubtree et al. | Aug 2010 | A1 |
20100228601 | Vaswani et al. | Sep 2010 | A1 |
20100235008 | Forbes et al. | Sep 2010 | A1 |
20100250590 | Galvin | Sep 2010 | A1 |
20100255794 | Agnew | Oct 2010 | A1 |
20100259998 | Kwon et al. | Oct 2010 | A1 |
20100274407 | Creed | Oct 2010 | A1 |
20100293045 | Burns et al. | Nov 2010 | A1 |
20100306033 | Oved et al. | Dec 2010 | A1 |
20100324748 | Voysey | Dec 2010 | A1 |
20100325719 | Etchegoyen | Dec 2010 | A1 |
20100328849 | Ewing et al. | Dec 2010 | A1 |
20110007824 | Bridges et al. | Jan 2011 | A1 |
20110010016 | Giroti | Jan 2011 | A1 |
20110015799 | Pollack et al. | Jan 2011 | A1 |
20110022242 | Bukhin et al. | Jan 2011 | A1 |
20110025556 | Bridges et al. | Feb 2011 | A1 |
20110029655 | Forbes et al. | Feb 2011 | A1 |
20110040666 | Crabtree et al. | Feb 2011 | A1 |
20110055036 | Helfan | Mar 2011 | A1 |
20110060474 | Schmiegel et al. | Mar 2011 | A1 |
20110060476 | Iino et al. | Mar 2011 | A1 |
20110080044 | Schmiegel | Apr 2011 | A1 |
20110090939 | Diener et al. | Apr 2011 | A1 |
20110106321 | Cherian et al. | May 2011 | A1 |
20110106729 | Billingsley et al. | May 2011 | A1 |
20110115302 | Slota et al. | May 2011 | A1 |
20110130982 | Haag et al. | Jun 2011 | A1 |
20110133655 | Recker et al. | Jun 2011 | A1 |
20110137763 | Aguilar | Jun 2011 | A1 |
20110145061 | Spurr et al. | Jun 2011 | A1 |
20110161250 | Koeppel et al. | Jun 2011 | A1 |
20110172837 | Forbes, Jr. | Jul 2011 | A1 |
20110172841 | Forbes, Jr. | Jul 2011 | A1 |
20110185303 | Katagi et al. | Jul 2011 | A1 |
20110196546 | Muller et al. | Aug 2011 | A1 |
20110196547 | Park et al. | Aug 2011 | A1 |
20110202418 | Kempton et al. | Aug 2011 | A1 |
20110204717 | Shaffer | Aug 2011 | A1 |
20110204719 | Sackman et al. | Aug 2011 | A1 |
20110208365 | Miller | Aug 2011 | A1 |
20110208366 | Taft | Aug 2011 | A1 |
20110208367 | Sackman et al. | Aug 2011 | A1 |
20110231028 | Ozog | Sep 2011 | A1 |
20110235656 | Pigeon | Sep 2011 | A1 |
20110251730 | Pitt | Oct 2011 | A1 |
20110254269 | Kaiser | Oct 2011 | A1 |
20110257809 | Forbes et al. | Oct 2011 | A1 |
20110258022 | Forbes et al. | Oct 2011 | A1 |
20110267202 | Efthymiou et al. | Nov 2011 | A1 |
20110270454 | Kreiss et al. | Nov 2011 | A1 |
20110270457 | Kreiss et al. | Nov 2011 | A1 |
20110270550 | Kreiss et al. | Nov 2011 | A1 |
20110270682 | Valin | Nov 2011 | A1 |
20110282511 | Unetich | Nov 2011 | A1 |
20110288905 | Mrakas | Nov 2011 | A1 |
20120004872 | Oh et al. | Jan 2012 | A1 |
20120029720 | Cherian et al. | Feb 2012 | A1 |
20120029897 | Cherian et al. | Feb 2012 | A1 |
20120059532 | Reifenhaeuser et al. | Mar 2012 | A1 |
20120078427 | Jang et al. | Mar 2012 | A1 |
20120089263 | Park et al. | Apr 2012 | A1 |
20120095830 | Contreras Delpiano et al. | Apr 2012 | A1 |
20120095841 | Luckerman et al. | Apr 2012 | A1 |
20120101652 | Shin et al. | Apr 2012 | A1 |
20120131100 | Van Olst et al. | May 2012 | A1 |
20120146799 | Bell et al. | Jun 2012 | A1 |
20120153888 | Jung | Jun 2012 | A1 |
20120154171 | Hurri et al. | Jun 2012 | A1 |
20120196482 | Stokoe | Aug 2012 | A1 |
20120205977 | Shin et al. | Aug 2012 | A1 |
20120221162 | Forbes | Aug 2012 | A1 |
20120223840 | Guymon et al. | Sep 2012 | A1 |
20120226384 | Forbes | Sep 2012 | A1 |
20120230214 | Kozisek et al. | Sep 2012 | A1 |
20120232816 | Oh et al. | Sep 2012 | A1 |
20120239218 | Forbes | Sep 2012 | A1 |
20120245753 | Forbes | Sep 2012 | A1 |
20120246392 | Cheon | Sep 2012 | A1 |
20120253540 | Coyne et al. | Oct 2012 | A1 |
20120259760 | Sgouridis et al. | Oct 2012 | A1 |
20120282942 | Uusitalo et al. | Nov 2012 | A1 |
20120296482 | Steven et al. | Nov 2012 | A1 |
20120296799 | Playfair et al. | Nov 2012 | A1 |
20120303553 | LaFrance | Nov 2012 | A1 |
20120310760 | Phillips | Dec 2012 | A1 |
20120310800 | Xia et al. | Dec 2012 | A1 |
20120316691 | Boardman et al. | Dec 2012 | A1 |
20120316697 | Boardman et al. | Dec 2012 | A1 |
20130006435 | Berrios et al. | Jan 2013 | A1 |
20130020992 | Wu et al. | Jan 2013 | A1 |
20130023285 | Markhovsky et al. | Jan 2013 | A1 |
20130031201 | Kagan et al. | Jan 2013 | A1 |
20130035802 | Khaitan et al. | Feb 2013 | A1 |
20130038468 | Wang et al. | Feb 2013 | A1 |
20130054036 | Cherian | Feb 2013 | A1 |
20130079939 | Thomas et al. | Mar 2013 | A1 |
20130079943 | Darden | Mar 2013 | A1 |
20130110297 | Reichmuth et al. | May 2013 | A1 |
20130123998 | King et al. | May 2013 | A1 |
20130124320 | Karner | May 2013 | A1 |
20130144768 | Rohrbaugh | Jun 2013 | A1 |
20130173360 | Thatcher | Jul 2013 | A1 |
20130178990 | Kayton et al. | Jul 2013 | A1 |
20130191260 | Michael | Jul 2013 | A1 |
20130231793 | Elliott et al. | Sep 2013 | A1 |
20130242792 | Woodings | Sep 2013 | A1 |
20140018969 | Forbes | Jan 2014 | A1 |
20140025486 | Bigby et al. | Jan 2014 | A1 |
20140039699 | Forbes, Jr. | Feb 2014 | A1 |
20140039701 | Forbes | Feb 2014 | A1 |
20140039703 | Forbes | Feb 2014 | A1 |
20140114829 | Forbes, Jr. | Apr 2014 | A1 |
20140114844 | Forbes, Jr. | Apr 2014 | A1 |
20140163309 | Bernhard et al. | Jun 2014 | A1 |
20140222698 | Potdar et al. | Aug 2014 | A1 |
20140277788 | Forbes, Jr. | Sep 2014 | A1 |
20140278851 | Kopanati | Sep 2014 | A1 |
20140279711 | Angelis et al. | Sep 2014 | A1 |
20140304025 | Steven et al. | Oct 2014 | A1 |
20140351010 | Kong | Nov 2014 | A1 |
20150094968 | Jia et al. | Apr 2015 | A1 |
20150160672 | Hakim et al. | Jun 2015 | A1 |
20150278968 | Steven et al. | Oct 2015 | A1 |
20160055507 | Patil | Feb 2016 | A1 |
20170025893 | Forbes, Jr. | Jan 2017 | A1 |
20170083989 | Brockman et al. | Mar 2017 | A1 |
20180343339 | Lotter et al. | Nov 2018 | A1 |
Number | Date | Country |
---|---|---|
1729223 | Dec 2006 | EP |
2159749 | Mar 2010 | EP |
2000078748 | Mar 2000 | JP |
2001306839 | Nov 2001 | JP |
20040180412 | Jun 2004 | JP |
2004248174 | Sep 2004 | JP |
2006060911 | Mar 2006 | JP |
2007132553 | May 2007 | JP |
20050045272 | May 2005 | KR |
20060036171 | Apr 2006 | KR |
20070008321 | Jan 2007 | KR |
100701298 | Mar 2007 | KR |
20070098172 | Oct 2007 | KR |
20080112692 | Dec 2008 | KR |
20090033299 | Apr 2009 | KR |
2007136456 | Nov 2007 | WO |
2008073477 | Jun 2008 | WO |
2008125696 | Oct 2008 | WO |
2011079235 | Jun 2011 | WO |
2012008979 | Jan 2012 | WO |
2012015507 | Feb 2012 | WO |
2012015508 | Feb 2012 | WO |
2012058114 | May 2012 | WO |
Entry |
---|
“Aman Saima, Simmhan Yogesh, Prasanna Viktor, Energy Management Systems: State of the Art and Emerging Trends, Jan. 2013, IEEE Communications Magazine” (Year: 2013). |
“Molderink Albert, Bakker Vincent, Bosman Maurice, Hurink Johann, Smith Gerard, Sep. 2010, IEEE Transactions on Smart Grid vol. 1 No. 2” (Year: 2010). |
“Adika Christopher, Wang Lingfend, Autonomous Appliance Scheduoing for Household Energy Management, Mar. 2014, IEEE Transactions on Smart Grid, vol. 1 No. 5” (Year: 2014). |
Automated power exchange. (2000). Energy Markets, 19. Retrieved from http://search.proquest.com/docview/228731930?accountid=14753. |
B.J. Kirby, Spinning Reserve from Responsive Loads, Oak Ridge National Laboratory, United States Dept. of Energy, Mar. 2003 (54 pages). |
Byers J. Risk Management and Monetizing the Commodity Storage Option. Natural Gas & Electricity [serial online]. Jul. 2005; 21 (12):1-8. Available from: Business Source Complete, Ipswich, MA. |
C.W. Gellings and W.M. Smith, Integrating Demand-Side Management into Utility Planning, Proceedings of the IEEE, vol. 77, Issue: 6, Jun. 1989, pp. 908-918 (Abstract only). |
Cazalet, E. G. & Samuelson, R. D. 2000, “The power market: E-commerce for all electricity products”, Public Utilities Fortnightly, vol. 138, No. 3, pp. 42-47. |
Ercot Settlement Metering Operating Guide. Dec. 2010. http://www.ercot.com/mktrules/guides/settlement/201 0/index. |
Eric Hirst and Brendan Kirby, Opportunities for Demand Participation in New England Contingency-Reserve Markets, New England Demand Response Initiative, Feb. 2003 (15 pages). |
Eric Hirst and Richard Cowart, Demand Side Resources and Reliability, New England Demand Response Initiative, Mar. 20, 2002 (32 pages). |
Galvin Electricity Institute: Frequently Asked Questions, printed Apr. 23, 2014, same page available through archive.org unchanged Mar. 1, 2008. |
GE Digital Energy Residential Electrical Metering Brochure. Sep. 12, 2012. https://web.archive.org/web/20120912144353/http://www.gedigitalenergry.com/products/brochures/1210-Family.pdf. |
IDC Energy I. IDC Energy Insights Forecasts 27% Worldwide Growth in the Commercial Smart Building Systems Market. Business Wire (English) [serial online]. 4: Available from: Regional Business News, Ipswich, MA. |
Illinois General Assembly: Public Act 094-0977, Effective Date: Jun. 30, 2006. |
Kathleen Spees and Lester B. Lave, Demand Response and Electricity Market Efficiency, The Electricity Journal, vol. 20, Issue 3, Apr. 2007 (online Mar. 27, 2007), pp. 69-85 (Abstract only). |
L.T. Anstine, R.E. Burke, J.E. Casey, R. Holgate, R.S. John, and H.G. Stewart, Application of Probability Methods to the Determination of Spinning Reserve Requirements for the Pennsylvania-New Jersey-Maryland Interconnection; IEEE Transactions on Power Apparatus and Systems, vol. 82, Issue 68, Oct. 1963, pp. 726-735 (Abstract only). |
Lobsenz G. Maryland Regulators Reject BG&E Smart Grid Proposal. Energy Daily [serial online]. Jun. 23, 2010; (118): 3. Available from: Business Source Complete, Ipswich, MA. |
M. Rashidi-Nejad, Y.H. Song, and M.H. Javidi-Dasht-Bayaz, Operating Reserve Provision in Deregulated Power Markets, IEEE Power Engineering Society Winter Meeting, vol. 2, 2002, pp. 1305-1310 (Abstract only). |
Michael Ahlheim and Friedrich Schneider; “Allowing for Household Preferences in Emission Trading, A Contribution to the Climate Policy Debate”; Environmental and Resource Economics, vol. 21, pp. 317-342; Kluwer Academic Publishers; The Netherlands; 2002. |
Moeller, Mar. 15, 2011, NERC, 116 pages. |
Olivier Rousse; “Environmental and economic benefits resulting from citizens' participation in CO.sub.2 emissions trading: An efficient alternative solution to the voluntary compensation of CO.sub.2 emissions”, Energy Policy 36 (2008), pp. 388-397; Oct. 29, 2007 (online). |
Pablo A. Ruiz and Peter W. Sauer, Valuation of Reserve Services, IEEE Proceedings of the 41 .sup.st Hawaii International Conference on System Sciences, 2008 (9 pages). |
Paul Darbee, Insteon Compared, SmartLabs, Inc., Jan. 2, 2006, 69 pages. |
Paul Darbee, Insteon The Details, Smarthome, Inc., Aug. 11, 2005, 68 pages. |
Thomas, K. 2000, “Energy e-commerce takes off”, Energy Markets, vol. 5, No. 4, pp. 26. |
Woolf, Tim, Demand Response Compensation in Organized Wholesale Energy Markets, May 4, 2010, NARUC, 34 pages. |
Zhu Jinxiang, G. Jordan, and S. Ihara, The Market for Spinning Reserve and Its Impacts on Energy Prices, IEEE Power Engineering Society Winter Meeting, vol. 2, 2000, pp. 1202-1207 (Abstract Only). |
Chicco, Gianfranco. Load Pattern-Based Classification of Electricity Customers, May 2004, IEEE Transactions on Power Systems, vol. 19, No. 2 (Year: 2004). |
Valero Verdu, Sergio. Classification, Filtering, and Identification of Electrical Customer Load Patterns Through the Use of Self-Organizing Maps, Nov. 2006, IEEE Transactions on Power Systems, vol. 21, No. 4 (Year: 2006). |
Number | Date | Country | |
---|---|---|---|
20200098056 A1 | Mar 2020 | US |
Number | Date | Country | |
---|---|---|---|
62222470 | Sep 2015 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 15273088 | Sep 2016 | US |
Child | 16678208 | US |