HK1163899B

HK1163899B - Methods and systems for analyzing energy usage

Info

Publication number: HK1163899B
Application number: HK12104418.2A
Authority: HK
Inventors: G．勒罗克斯; Y．索伊尔米; S．库尔斯; J．M．阿克里德; T．霍伊姆; S.马瑟
Original assignee: 埃森哲环球服务有限公司
Priority date: 2010-04-26
Filing date: 2012-05-07
Publication date: 2017-09-29

Abstract

Systems and methods consistent with the present invention allow an energy consumption index to be generated from DR response data and influencer data. The energy consumption index may indicate the energy consumption of a consumer before receiving a DR signal, the change in the consumption after the consumer receives a DR signal, and the consumer's propensity to respond to a DR signal. Systems and methods consistent with the present invention also allow energy providers to monitor, forecast, and plan for changes in consumer demand for energy. Various energy planning tools may facilitate an energy provider's ability to monitor, forecast, and plan for such changes.

Description

Method and system for analyzing energy usage

Technical Field

Methods and systems relating to analyzing energy consumption by energy users and using such analyses are described.

Background

The first electrical energy distribution systems designed a century ago featuring centralized generation and unidirectional power flow. Problems associated with early power distribution systems include the risk of dc power, the isolated nature of each distribution network, the difficulty of predicting demand, the possibility of isolated damage leading to cascading failures, and inefficient power transmission over long distances.

Some of these problems have subsequently been successfully solved. For example, switching to ac power makes long distance power transmission safer and more efficient, and new grid topologies make power distribution less susceptible to catastrophic failures. However, some of these same problems currently remain in the industry.

In particular, the measurement, prediction and planning of changes in the consumer's demand for energy has proven difficult from the outset, and so far remains. Despite this difficulty, accurately determining demand is important to power companies because imbalances between production and consumption can result in voltage drops and even outages. For the short period between production and consumption, these imbalances will become catastrophic almost as soon as they are detected. Power companies are also forced to maintain the amount of power generation and distribution to meet peak loads, even if such loads occur infrequently. Therefore, a delicate balance between overproduction and underproduction must be achieved to avoid grid faults.

Disclosure of Invention

According to one aspect of the invention, a computer-implemented method of analyzing energy usage is provided. The method comprises the following steps: receiving demand response DR response data from a first group of energy users; associating the DR response data with impact data, the impact data stored in a database, relating to the first energy user group; determining an energy consumption index for the first group of energy users, wherein the energy consumption index comprises: transmitting a value of energy consumption before DR data to devices corresponding to the first energy user group; a propensity of the first group of energy users to change energy consumption in response to DR data; and a change in energy consumption after transmitting DR data to devices corresponding to the first energy user group; and establishing an energy consumption index for a second energy user group based on the energy consumption index for the first energy user group.

According to some embodiments of the invention, the method further comprises: establishing an energy consumption index for a third energy user group based on the energy consumption index for the second energy user group.

According to some embodiments of the invention, the first energy consumer group comprises a sample group of consumers that are representative of a customer base of the energy provider.

According to some embodiments of the invention, establishing an energy consumption index for the second group of energy users comprises: correlating the energy consumption behavior of the first energy consumer group with the energy consumption behavior of the second energy consumer group.

According to some embodiments of the invention, the energy consumption index for the first group of energy users further comprises a time period for which the energy change lasts.

According to some embodiments of the invention, the second group of energy users is a subset of the first group of energy users.

According to some embodiments of the invention, the DR response data includes a value of energy consumption on a per device basis.

According to some embodiments of the invention, the impact data comprises at least one of: climate conditions, location, customer attributes, and occupancy type of energy consumers in the first energy consumer group.

According to some embodiments of the invention, the energy consumption index for the first group of energy users is displayed in a star pattern.

According to some embodiments of the invention, the energy consumption index for the first group of energy users is displayed in a three-dimensional cube format.

According to some embodiments of the invention, the DR response data is transmitted between devices corresponding to the first energy consumer group and a third party.

According to another aspect of the present invention, a system for managing energy usage by utilizing demand response DR data is provided. The system comprises: a first database storing DR response data received from the user devices corresponding to the first energy user group; a second database storing impact data relating to the first energy user group; a computer processor coupled to the first database and the second database, the computer processor associating the DR response data with the impact data, the computer processor further determining an energy consumption index for a first group of energy users and establishing an energy consumption index for a second group of energy users based on the energy consumption index for the first group of energy users, wherein the energy consumption index for the first group of energy consumers comprises: transmitting a value of energy consumption before DR data to a using device corresponding to the first energy user group; a propensity of the first group of energy users to change energy consumption in response to DR data; and a change in energy consumption after transmitting DR data to a using device corresponding to the first energy user group.

According to some embodiments of the invention, the system further comprises: a computer server coupled to the first database and programmed to receive DR response data from the user devices corresponding to the first energy user group.

According to some embodiments of the invention, the first database and the second database comprise the same database file and are located in the same hardware unit.

According to some embodiments of the invention, the DR response data is transmitted between the user devices corresponding to the first energy consumer group and the third party.

According to yet another aspect of the invention, a computer-implemented method of managing energy supply is provided. The method comprises the following steps: determining energy planning criteria for a group of energy users, wherein the energy planning criteria comprise: a forecast of the computer-created propensity for the group of energy users to change energy consumption; a forecast of energy consumption of the group of energy users created using a computer; and a determination of an actual energy consumption of the group of energy users; determining an optimal level of factors related to energy supply based on the energy planning criteria; and determining a signal to be transmitted to a receiving device of a target energy user group to achieve the optimal level of the factor related to the energy supply.

According to some embodiments of the invention, determining the signal to be transmitted to the receiving device of the target group of energy users further comprises: determining a type of DR signal to be transmitted to receiving devices of the target group of energy users; determining a target group of energy users to send the DR signal to their receiving devices; and determining a timing scheme for transmitting the DR signal to the receiving devices of the target group of energy users.

According to some embodiments of the invention, the method further comprises: simulating an effect of transmitting the determined type of DR signal type to the receiving devices of the determined target group of energy users according to the determined timing scheme.

According to some embodiments of the invention, the method further comprises: transmitting the determined type of DR signal to the receiving devices of the determined target group of energy users according to the determined timing scheme; receiving response data from the receiving devices of the target energy user group; and determining whether energy consumption of the target energy user group changes in response to the determined type of DR signal transmitted to the receiving device of the target energy user group.

According to some embodiments of the invention, the factor relating to energy supply is an energy price charged to the group of energy users.

According to some embodiments of the invention, determining the optimal level of the factor related to energy supply further comprises: determining price elasticity for the group of energy users.

According to some embodiments of the invention, determining the optimal level of the factor related to energy supply further comprises: determining demand resiliency for the group of energy users.

According to some embodiments of the invention, determining the optimal level of the factor related to energy supply further comprises: the effect of introducing items of DR signals to new markets is simulated.

According to some embodiments of the invention, determining the optimal level of the factor related to energy supply further comprises: determining the existence of a problem in the energy supply network; determining a type of DR signal to send to the receiving devices of the target energy user group to mitigate the problem in the energy supply network.

According to some embodiments of the invention, determining the type of DR signal to be transmitted further comprises: determining a minimum level of energy load to be routed from a first area in the energy supply network to a second area in the energy supply network.

According to some embodiments of the invention, determining the optimal level of the factor related to energy supply further comprises: the negative watt capacity of the energy provider is determined.

According to some embodiments of the invention, the negative watt capacity is graphically represented as a function of cost.

Various other embodiments are also disclosed. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description, serve to explain the principles of the invention.

Drawings

FIG. 1A depicts an exemplary arrangement for sending and receiving a demand response ("DR") signal and a DR response signal.

Fig. 1B depicts an exemplary system for communicating DR signals and DR response signals between a server and a device capable of receiving DR signals.

FIG. 2 depicts a flow diagram generally illustrating one exemplary embodiment of a process for determining an energy consumer's response to DR data.

FIG. 3 depicts an exemplary system for storing DR response data in a database.

FIG. 4 shows several exemplary types of impact data.

FIG. 5A depicts an exemplary system for associating DR response data with impact data.

FIG. 5B illustrates an exemplary process of logically associating DR response data with impact data.

FIG. 6 depicts an exemplary data structure for an energy consumption index and various impact data.

Fig. 7 depicts an exemplary data table including energy consumption index indicator data, wherein several data fields are missing.

FIG. 8 depicts an exemplary process for partitioning consumers by consumer type.

Fig. 9 depicts one exemplary data arrangement following the partitioning process, showing energy consumption indicators and various augmented DR response data.

Fig. 10 shows an exemplary diagram of a process of determining an energy consumption index for a group of energy consumers outside the sample group.

FIG. 11 depicts one exemplary data structure for utilizing consumption data, consumption forecast data, and DR capability forecast data, as well as various dimensional data.

FIG. 12 depicts an exemplary embodiment of graphically representing data.

FIG. 13 depicts an exemplary hierarchy of dimension data.

FIG. 14A depicts one exemplary embodiment of a graphical representation of energy consumption data in a present day forecasting tool.

FIG. 14B depicts an exemplary embodiment of a graphical representation of energy consumption data in a present day forecasting tool.

FIG. 15 depicts an exemplary embodiment of a strategic energy pricing tool.

Fig. 16 depicts an exemplary embodiment of an emergency management tool.

FIG. 17 is a diagram of an exemplary pivot table arrangement interface for viewing negative tile (negawatt) capacity data.

Detailed Description

Reference will now be made in greater detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. Wherever convenient, the same reference numbers will be used throughout the drawings to refer to the same or like parts.

Smart grid energy technologies may include the ability of energy suppliers and consumers to communicate about the provision and consumption of energy. The communication may be accomplished by installing or retrofitting an electric meter device at the location of the energy consumer, and utilizing a communication link (e.g., internet, telephone, radio frequency, satellite, television, email, text message, etc.) between the consumer and the electric utility.

One type of device that consumers may use to support such communications is a smart meter. In contrast to conventional energy meter devices, smart meters may have the ability to receive signals from energy suppliers regarding energy prices, current demand levels, requests to adjust energy consumption, etc. Such signals received by a consumer's smart meter or other device may be referred to as demand response ("DR") signals. Smart meters may also have the ability to send back signals (commonly referred to as "DR response" signals) to the energy provider related to the energy usage of the consumer.

A utility company or third party may utilize smart meters to affect demand and maintain grid stability. Methods of affecting demand or otherwise maintaining grid stability may be referred to as "demand response" projects.

The demand response item may take several forms, such as communicating with the energy consumer regarding: price of energy, supply of energy, upcoming events related to energy pricing or supply, energy demand, upcoming holidays for energy consumers, etc. For example, pricing-based demand response items may be operated such that energy prices may be increased during periods of high demand and prices may be decreased during periods of low demand. Such pricing adjustments may be beneficial, particularly when the means available to store energy is limited when it has been generated for later use. Thus, appropriate energy pricing adjustments may help avoid energy over and under conditions. However, the ability to properly adjust the energy prices depends on the ability to accurately measure, predict, and plan consumer demand, rather than the ability to react to consumer demand.

In a region, smart grids may be implemented to varying degrees. For example, a smart grid including smart meters in communication with an energy provider (or third party) may be deployed in a country, state, city, neighborhood, or even a single building. As discussed further below, individual energy consumers may be associated with one or more smart meters, and the smart meters may communicate directly with the energy provider or a third party (e.g., a consulting company).

Energy suppliers strive to make accurate measurements, predictions, and plans for consumer demand. The task of efficiently and effectively directing the operation of energy supply can be complex, where there are various types of large energy consumption data to process. In addition, the measurement and prediction of energy demand can be difficult to understand and integrate into the operation of energy suppliers. In particular, in view of the high fixed costs faced by energy suppliers (e.g., the costs of infrastructure and maintenance), and the corresponding conflicts of suppliers with risks and large-scale changes, it may be important to integrate demand measurements and forecasts to the energy supplier's operations, but doing so in a cost-effective manner is challenging.

The method and system described herein allow measuring, representing and forecasting energy consumption, energy demand and the possibility of energy reduction or energy conversion in a beneficial way. The various methods and systems described herein allow energy providers to better understand the factors that drive energy consumption and make adjustments to their operation (if needed). The methods and systems described herein may indicate to the energy provider the manner in which attempts are made to affect energy consumption, or the manner in which plans are made for expected changes in energy consumption, among other benefits.

Referring to FIG. 1A, an exemplary arrangement 100A for communicating a demand response ("DR") signal 102 and a DR response signal 105 is depicted. In some embodiments, an energy provider 101 (e.g., a provider of electricity, natural gas, fuel oil, liquefied gas, etc.) may send a DR signal 102 to some or all of its energy consumers 104. The energy consumer 104 may be a residential consumer, a commercial consumer, a government consumer, or any other type or combination of energy consumers. The DR signal 102 from the energy provider 101 may include or otherwise indicate various types of information related to energy consumption, such as a current or future price of energy, a current or future demand level, a temperature of a heating or cooling system, a request to adjust energy consumption, and/or a request to automatically adjust energy consumption.

Fig. 1B depicts an exemplary system 100B for communicating DR signals 102 and DR response signals 105 between a server 106 and a device 107 capable of receiving DR signals. The device 107 that receives the DR signal 102 may be located at the location of the energy consumer 104 (e.g., in the consumer's home or business, or fixed on an exterior wall of such a building). In some implementations, the DR signal 102 can be received remotely by the energy consumer 104 (e.g., via a cellular telephone, pager, or computer). The device 107 may be a smart meter or a DR gateway device, or other device capable of receiving the DR signal 102. The device 107 may include a graphical display, a light emitting diode display, indicator lights, dials, etc., that indicate aspects of the operation of the device. For example, the device 107 may include a graphical display that indicates that the device 107 is on, connected (e.g., connected to a wireless network, wired internet connection, etc.), and successfully communicates with the energy provider 101. As another example, the device 107 may include indicator lights corresponding to various apparatuses 109 in the consumer's home, where the indicator lights indicate whether the device 107 successfully communicates with the apparatuses 109. As yet another example, the device 107 may include a dial that indicates the energy consumption level (e.g., in kilowatts (kW), kilowatt-hours (kWh)). In some implementations, the device 107 may be connected to a consumer's television, computer, or other video display, and information related to the DR signal 102 may be displayed on the television, computer, or video display.

In some embodiments, multiple devices 107 receiving DR signal 102 may be used, for example, in combination with device 107, where devices 107 are located at different levels in a consumer's home or business, or at the location of individual apparatuses 109 in a user's home or business. In embodiments comprising multiple devices 107 receiving the DR signal 102, a network scheme may be employed in which a central device 107 receives the DR signal 102 and distributes the signal 102 to satellite devices 107 located in the consumer's home or business as needed. In some embodiments, the device 107 may be integrated with a consumer's circuit breaker box, thermostat, or other energy exchange device, such that the device 107 may both receive the DR signal and conveniently control the power levels and status of different areas, rooms, or individual devices 109 in the consumer's home or business.

The DR signal 102 may be transmitted to the energy consumer 104 via any one or more of a variety of communication mediums 103. The communication medium 103 may include, for example, the internet, telephone, radio frequency, satellite, television, text message, email, pager, etc. In some implementations, the energy provider 101 can include a computer server 106 in communication with a device 107, where the device 107 can be located at the location of the energy consumer 104. DR signal 102 may be sent to consumer 104, for example, by a "push" or "pull" operation or at predetermined intervals. The server 106 may use the database 108 to store and organize information, such as the DR response signal 105 and impact data (as discussed further below). The server 106 may also use a computer processor (e.g., a microprocessor, a microcontroller, a personal computer, etc.) for processing data received by the server 106 and other processing operations.

Once the device 107 associated with the energy consumer 104 receives the DR signal 102 from the energy provider 101, the consumer 104 may choose to respond by adjusting its energy consumption. In some embodiments, the DR signal 102 may automatically adjust the energy consumption of the consumer without any confirmation action by the consumer 104.

As an example of a "manual" DR response activity, if consumer 104 receives DR signal 102 at 3:00 PM indicating that the energy price will rise by a certain amount at 5:00 PM or indicating that local demand may be increasing at 5:00 PM, consumer 104 may elect to reduce its energy consumption at 5:00 PM or around 5:00 PM, for example, by turning off or down device 109 such as an electric light, television, computer, heating system, etc. In some embodiments, the DR signal 102 may be received by the device 107, and in some embodiments, the DR signal 102 may be received by a device of the energy consumer 104 other than the device 107.

As one example of an "automatic" DR response behavior, if the consumer 104 receives a DR signal 102 at 3:00 PM indicating that the energy price or demand level will rise above a threshold level selected by the consumer 104 at 5:00 PM, some or all of the consumer's energy consuming devices 109 may be automatically turned off or turned down at 5:00 PM or around 5:00 PM. This "automatic" type of energy regulation can be realized, for example, by: the energy consumption device 109 capable of receiving the DR data 102 is controlled using a wireless network or a circuit. The controller may compare the information contained in the DR data 102 to threshold data and set the device to "on", "off", or "turn down" based on whether the threshold is met. For example, the consumer 104 may set a threshold price level for electricity such that when the electricity price exceeds the threshold level, some or all of the consumer's energy consuming devices 109 are turned off or turned down. In embodiments where energy adjustments are made automatically after receiving DR data 102, consumers 104 may have an override option whereby they may prevent automatic adjustments of energy consumption from occurring. Additionally, the consumer 104 may employ a hybrid approach, where some energy consuming devices 109 automatically respond to the DR signal 102, while other devices 109 rely on manual DR response behavior.

In some embodiments, after consumer 104 receives DR data 102 from energy provider 101, DR response signal 105 may be sent from consumer 104 to energy provider 101. In other embodiments, the DR response signal 105 may be sent without the consumer 104 first receiving the DR signal 102 from the energy provider 101.

The DR response signal 105 may include information related to the energy consumer's response to the DR signal 102. For example, DR response signal 105 may indicate the extent to which consumer 104 decreases or increases energy consumption (e.g., in kW, kWh, or as a percentage of consumption change) in response to DR signal 102. In some embodiments, the DR response signal 105 may be specific to individual devices 109 in the consumer's home, and in some embodiments, the DR response signal 105 may include various other data, such as time and date information, local weather information, indoor temperature information, identification information about the consumer 104, and so forth.

The DR response signal 105 from the energy consumer 104 may be sent on an automatic, semi-automatic, or manual basis, for example. The DR response signal 105 may be transmitted using the same communication medium 103 as the DR signal 102 or via another medium. In some embodiments, the DR response signal 105 may be transmitted on a continuous or near-continuous basis, while in other embodiments, the DR response data 105 may be transmitted at fixed intervals. For example, a customer's smart meter or DR gateway 107 may be configured to send the DR response signal 105 hourly. In other embodiments, the consumer 104 may manually decide when to send the DR response data 105, or the energy provider 101 may request the DR response data 105 from the consumer 104. In some embodiments, the DR response signal 105 includes information about a particular energy consuming device 109 (e.g., a particular washing machine, television, etc.) of the consumer 104.

The DR response signal 105 may be received by the energy provider 101 or a third party (such as a counseling or outsourcing company). In various embodiments, the DR response data 105 may be received by the energy provider 101 and then may be transmitted to a third party, or may be received by a third party and then transmitted to the energy provider 101, and so on. As discussed in further detail below, the energy provider 101 or a third party may analyze and apply the DR response data 105 to optimize various aspects of energy supply.

The server 106 may be a typical Web server capable of communicating with Web clients, may be specifically designed for communicating with a specific device 107 capable of receiving the DR signal 102 and transmitting the DR response signal 105, may be designed for receiving signals from, for example, a cellular telephone or satellite device, or the likeA server of the number. The server 106 may run server software, such as ApacheWeblogicWebObjectsOracleCaudium, and the like. The server software enables the server 106 to send and receive the DR signal 102 and DR response signal 105 to transmit signals to a database 108 or other data storage medium or the like. Database 108 may run database software, which may be written in SQL, QL, CQL, COQL, XPath, MDX, QUEL, DMX, etc. Other data storage media that may be used to store the DR signal 102 and DR response signal 105 include service area networks, network attached storage, more temporary forms of storage devices, and the like. In some embodiments, multiple databases 108 may be used to store the DR signal 102, DR response signal 105, impact data, and the like; while in other embodiments, the DR signal 102, the response signal 105, the influence data, etc. are stored in one database 108 (e.g., in the same database file or in separate database files within the same database hardware unit). The server 106 and database 108 may be operated by an energy provider, a third party (e.g., a consulting company), combinations thereof, and the like. In some embodiments, the server 106 comprises a server farm, whereby a plurality of server hardware units receive a large number of DR signals 102 in a coordinated manner. Such a server farm may include load balancing devices that act as initial contact points for incoming DR signals 102 and distribute DR signals 102 to server hardware units as appropriate.

FIG. 2 represents a flow diagram generally depicting an exemplary embodiment of a process 200 for determining an energy consumer's response to demand response data. The process 200 may include step 201: DR response data is received from a sample group of energy consumers. In some embodiments, the sample group of consumers may represent a portion of a customer base or an entire customer base. For example, consumers in the sample group may be selected by their geographic location, home/building type, number of residents, annual income, number of cars, age, characteristics of the DR response data, and so forth. The sample group of consumers may include only consumers with the ability to receive DR signals and send DR response signals, a combination of such consumers and consumers without such consuming ability, and so forth.

In some embodiments of process 200, step 201 may include: DR response data received from a sample group of consumers is stored in a database or other similar data storage means. For example, fig. 3 depicts an exemplary system 300 for storing DR response data in a database 303. In some embodiments, data representing the energy consumption of the consumer before receiving DR signal 301A and data representing the consumption after receiving DR signal 301B may be stored in database 303. Data 301A and 301B may include, among other things, a customer ID identifying the consumer, a timestamp, a weather type, climate information associated with the location of the consumer, the consumer's overall energy consumption or energy consumption per appliance, the consumer's overall or appliance-specific consumption changes after receiving the DR signal, whether the consumer responded to the DR signal, and what type of DR signal was sent or included, among other types of information.

In some embodiments, the DR response signal may be sent from the energy consumer to the energy provider or a third party (e.g., a consulting company) without the consumer first receiving the DR signal. Such signals may help the energy provider or third party understand the consumer's consumption behavior without DR signals, historical data, etc. In some embodiments, such signals may allow an energy provider or third party to develop an energy consumption baseline for an energy consumer. For example, if the energy provider concludes a baseline for consumption without a DR signal, the energy provider may be able to determine the effect that the introduction of items of DR signal may have on consumption. Such data may be captured at different times, for example, a day, a week, a month, and a year, in order to obtain a wide view of the consumer's consumption behavior. Additionally, these types of signals may include similar information as the DR response signal. These signals may be received by server 302 and stored in database 303 with signals 301A, 301B, and may be associated with impact data, as described in detail below.

Referring again to fig. 2, process 200 may further include step 202: the DR response data is associated with the impact data. The influence data may indicate conditions and factors related to energy consumption. For example, FIG. 4 shows several exemplary types of impact data 400, such as climate conditions 401, location 402, time 403, home type 404, energy provider characteristics 405, customer attributes 406, and appliance attributes 407. The impact data 400 may be obtained from a consumer's DR response signal, a consumer survey, census data, third party research data, observable data, and the like.

FIG. 5A depicts an exemplary system 500A for associating DR response data 501 with impact data 503. In some embodiments, DR response data 501 and impact data 503 may be stored in relational database 502 or other structured data sources. In other embodiments, DR response data 501 and impact data 503 may be stored in separate databases, storage area networks, network attached storage, more temporary storage, and the like. A computer processor 504 (e.g., a central processing unit, microprocessor, microcontroller, etc.) may be used to process DR response data 501 and/or impact data 503.

In particular embodiments, DR response data 501 may be associated with impact data 503 using keywords or common elements. For example, FIG. 5B illustrates an exemplary process of logically associating DR response data 501 with impact data 503. As shown in fig. 5B, the client ID may serve as a common element between the DR response data 501 and the influence data 503. The process of associating DR response data 501 with influence data 503 may be referred to as "augmenting" DR response data. For example, while the DR response data 501 may include data such as energy consumption of consumers before and after receiving the DR signal, by associating the DR response data 501 with impact data 503 such as address location of customers, home/building type, climate conditions, occupancy level, etc., the DR response data 503 is augmented to have additional or different details related to energy consumption.

Referring to fig. 2, process 200 may further include step 203: an energy consumption index for a sample group of energy consumers is determined. In some embodiments, the energy consumption index may represent a consumer's energy consumption prior to receiving the DR signal, a propensity to respond to the DR signal, a change in consumption after receiving the DR signal, a period of time during which the consumer's consumption change persists, and/or the like.

The energy consumption prior to receiving the DR signal may be expressed in various ways, including, for example, kilowatts (kW) or kilowatt-hours (kWh). The propensity to respond to DR signals may also be expressed in a number of ways, such as a percentage of the frequency with which a consumer has responded to a particular DR signal by decreasing or increasing energy consumption, a measure of the frequency with which a consumer has changed in a sufficient amount to do so, the likelihood that a particular consumer will respond to a particular DR signal by adjusting the energy consumption level of the consumer, and so forth. The average change in consumption may also be expressed in various ways, including, for example, kW, kWh, or a unitless ratio of consumption before receipt of the DR signal compared to consumption after receipt of the DR signal. Also, the period of time during which the consumer's consumption changes may be expressed in several ways. For example, the duration of consumption change may be expressed in terms of time (e.g., minutes, hours, days, etc.), or in terms of both time and energy (e.g., a measure of energy reduction per hour or day).

The energy consumption index may be represented in a three-dimensional space, where dimensions represent various forms of impact data and other information. For example, FIG. 6 depicts an exemplary data structure for processing energy consumption index 600 and impact data 602. For example, the energy consumption index 600 may include indicators 601 related to DR tendency, load before receiving a DR signal, and load after receiving a DR signal. The indicators 601 may be associated with the impact data 602 using common data attributes or "keywords" (e.g., customer ID). In some embodiments, the energy consumption index 600 may be represented graphically, for example, as a hypercube or an online analytical processing ("OLAP") cube. Other multidimensional formats may also be used. As one example, an OLAP cube may be constructed using indicator data 601 as a fact table, and impact data 602 may serve as a dimension. FIG. 6 depicts a star schema type of data organization, however other types may be used, such as snowflake schema. Other possible ways of processing multidimensional data, such as the energy consumption index 600, are discussed further below.

In some cases, the energy expenditure index may lack data. Reasons for missing data may include, for example: the consumer turns off their DR gateway, errors in data transmission between the consumer and the energy provider, or data received by the energy provider is corrupted. Fig. 7 depicts a data table 700 comprising energy consumption index data 702, 703, 704, wherein data is missing in several data fields. In this example, for "customer type 1" with DR signal type "price up 15-19%", the trend data 702, the load 703 before receiving the DR signal, and the load change 704 after receiving the DR signal each have missing entries. In such a case, it may be desirable to generate an estimate for the missing data field. For example, the presumed values for the missing fields may be derived using multidimensional interpolation and/or extrapolation methods. In doing so, field 701, which indicates the constructed data, may be marked as "yes" to indicate that the inferred value is derived. In this manner, gaps in data table 700 may be reduced. In some embodiments, a record of how the estimate is calculated may be generated so that the operator may then determine the feasibility of the energy consumption index and make changes to it (if needed). Such records should be stored in metadata associated with the data table 700 or in a separate data file.

FIG. 8 depicts an exemplary process 800 for partitioning consumers by customer type 802. In conjunction with determining the consumer energy index of the consumer, the consumers within the sample group may be divided. For example, consumers may be divided by factors related to energy consumption such that consumers in one divided section may have similar energy consumption behavior. Another purpose of the partitioning may be to group together consumers for which specific impact data or augmented data 801 is found to accurately predict future energy consumption.

Each customer type 802 may be based on statistical correlation of the augmented data 801 among consumers. For example, the "family type" client type 802 may specify a family or business relationship between occupants or owners of a family or building. Other customer types 802 may include, for example, the capacity of a home or building, the number and type of cars, the level of natural shadows provided by trees or other homes or buildings, and various other factors.

In some embodiments, the client type 802 may replace the "client" field from the augmented DR response data 801 as a result of the partitioning process. For example, fig. 9 depicts one exemplary data arrangement 900 after the partitioning process, showing an energy consumption index 901 and various augmented DR response data 902, including customer types. The data arrangement 900 includes index values 901 that change for DR trends for various customer types, load before receiving a DR signal, and load after receiving a DR signal, based on impact data 902. As discussed further below, the partitioning of consumers within a sample group may support the prediction of energy consumption data for a broader customer group.

Referring again to fig. 2, process 200 may further include step 204: an energy consumption index for a group of energy consumers outside the sample group is determined. In some embodiments, this other energy consumer group may comprise the rest of the energy provider's customer base, or it may comprise only a portion of the customer base. In other embodiments, the other energy consumer groups may include energy consumers other than the energy consumer served by the energy provider. Additionally, in some embodiments, both energy consumer groups may participate in the DR program (e.g., they may send and receive DR signals and DR response signals); while in other embodiments, some or all of the users in the group may not participate in the DR project. In additional embodiments, process 200 may include another step: an energy consumption index for a group of energy consumers other than the group of consumers described above is determined. For example, the process 200 may operate recursively in the following manner: use an aspect of the energy consumption index to apply to a group of energy consumers and use the energy consumption index from the group of energy consumers to apply to a group of other energy consumers, and so on.

In some embodiments, referring again to fig. 2, the step 204 of determining an energy consumption index for a group of energy consumers other than the energy consumer in the sample group may include comparing an attribute of one group of consumers with an attribute of another group of consumers. For example, when dividing consumers as discussed above with reference to FIG. 8, consumers in one group may be compared in terms of customer type 802 with consumers in another group. In some embodiments, the group of consumers for which DR response data and impact data are collected, the energy consumption index is determined, and the customer type 802 is determined may include a sample group of consumers. In such embodiments, consumers outside of the sample group may include the rest of the energy provider's customer base, a portion of such customer base, consumers not served by the energy provider, and so forth. In some implementations, multiple customer types 802 may be used to represent a consumer group.

For example, an exemplary sample group may include 10,000 consumers outside of a customer group of 1,000,000 consumers served by an energy provider. From the 10,000 consumers within the sample group, 500 consumers may be determined to fall within the customer type 802 of energy efficiency level based on their similar energy consumption behavior. For example, the 500 consumers may be considered to fall between 90 and 95 percent of energy efficiency based on their use of energy saving or recycling equipment. The 10,000 consumers from the sample group may be compared to the remaining 990,000 consumers within the energy provider's customer group and a correlation between the two groups may be determined. For example, the customer type 802 may be determined for the remaining 990,000 customers in the customer population, and correlations between those customers within the customer type 802 for the energy efficiency level and 500 customers within the same customer type 802 from the sample group may be found.

Fig. 10 shows an exemplary diagram of a process 1000 of determining an energy consumption index for a group of energy consumers other than the energy consumer in the sample group. Process 1000 may include source system data 1001, index propagation engine 1002, and data model 1003. The source system data 1001 may include various types of data, such as data identifying a customer, DR response data, power meter data, weather data, and the like.

The process 1000 may include the steps of: source system data is obtained 1001 for consumers outside the sample group for which extended DR response data and energy expenditure indices have not been determined. In some implementations, the impact data may be available to consumers outside of the sample group. Some or all of the source system data 1001 may be used by the index propagation engine 1002. For example, the index propagation engine 1002 may compare the source system data 1001 with data for consumers within a sample group. In some embodiments, the index propagation engine may use database software, which may be written in SQL,. QL, CQL, COQL, XPath, MDX, QUEL, DMX, and the like. Based on the source system data 1001 and comparable data for consumers within the sample group, as described above, correlations between consumers outside the sample group and one or more consumers within the sample group may be determined.

Indicators (e.g., trends to respond to DR signals, load changes after receiving DR signals, etc.) may be assigned by the propagation engine 1002 to consumers outside the sample group based on the indicators of consumers within the sample group. The generated indicators for consumers outside the sample group can then be used in the data model 1003 to represent consumption forecasts, DR capability forecasts, actual consumption data, etc. for the consumer.

In some implementations, the data model can be presented as one or more fact tables with various dimensions. For example, in the embodiment shown in fig. 10, the consumption forecast, DR capability forecast, and actual consumption data may represent fact data 1005, which may be used as a fact table in a data schema. The customer, location, DR signal, appliance, weather, time of day, and time of year data may represent dimension data 1004 and may be used in a data schema as dimension data around fact tables. The structure of such data patterns may take several forms, for example, in the form of a star or snowflake pattern. The structure of such schema and the manner in which it contains data may be represented graphically (e.g., as an OLAP cube), as described in detail below.

Once the energy consumption index for consumers within the cohort outside the sample cohort is determined, the index can then be used to forecast energy consumption, the impact of DR signals on energy consumption, and so on. For example, based on the specific impact data and the energy consumption index, various scenarios of energy supply may be forecasted. Various forms of impact data and energy consumption indices may enable energy suppliers to answer questions such as: what will the average DR response to DR signal "X" in 11 months for all locations and all consumers? Which consumer group is most sensitive to price in summer, and what is its average price threshold? During the weekend of 2 months, which consumers have the most negative watt (i.e., power saving) possibilities? If the DR item "Y" was the most effective item in the last week, then which item will be the most effective during the afternoon of the next week, and what is the similarity between the consumers who signed up for that item? What kind of DR signal should be sent out during the next 4 hours, and to which consumers? What energy efficiency level or negative watt likelihood can be achieved between a group of non-DR users based on changes in energy consumption associated with the energy users who send and receive the DR signal and the DR response signal?

Fig. 11 depicts one exemplary data structure 1100 for using consumption data 1101, consumption forecast data 1102, and DR capability forecast data 1103, as well as various dimensions 1104 (such as weather, location, DR signal, appliance, time of day, time of year, and customer). The consumption data 1102, the consumption forecast data 1102, and the DR-capability forecast data 1103 may represent fact tables in the structure 1100. In some embodiments, the consumption forecast data 1102 and the DR capability forecast data 1103 may be populated using the energy consumption index discussed above, while in some embodiments, the consumption data 1101 may be obtained from the consumer's DR gateway, from utility records, or the like.

DR capability forecast data 1103 may indicate the propensity of a given consumer to respond to a particular DR signal. In some embodiments, the granularity of DR capability forecast data 1103 may be on a per transaction basis, with one row for each logical intersection with the various dimensions 1104. The values of the DR-capability forecast data 1103 may range from 0 to 1 (e.g., 0.00-1.00), which may be expressed as a percentage, or otherwise expressed.

The consumption forecast data 1102 may include information related to the forecasted energy consumption of the consumer. The granularity of consumption forecast data 1102 may be on a per transaction basis, with one row for each logical intersection with the dimensions 1104. The consumption forecast data 1102 may be expressed in terms of energy (e.g., kWh), average load (e.g., kW), and so forth.

The consumption data 1101 may include information about the actual consumption by the consumer at any given moment. The granularity may be on a per transaction basis, with one row corresponding to each consumption change. The consumption data 1101 may be expressed in terms of energy (e.g., kWh), average load (e.g., kW), or in other forms.

Some or all of the dimensions 1104 shown in FIG. 11 may be used in the data structure 1100, or it may be used in combination with other dimension data. For example, the DR signal dimension may describe various DR signals, information for both automatic and manual DR signals, and various types of possible DR signals (e.g., price, load shedding, reliability, etc.). Appliance dimensions may represent various appliances (e.g., home appliances, HVAC systems, water heaters, etc.) in a consumer's home or building. The customer dimension may include information describing, for example, the customers being supplied by a given energy provider. Attributes of a consumer may be broad, such as age, gender, education level, occupation status, income level, number of appliances, number of occupants in the home, time and length of commute, number of vehicles, use of electric vehicles ("EVs"), average occupancy (e.g., hours at home, etc.), hours of work, DR project status, room type (e.g., isolation type, exterior wall, etc.), home area or capacity, occupancy location (e.g., country, region, city, street, etc.), and whether the owner or tenant status. The time dimension in a year may indicate a timeline that extends into the past or future, and it may represent attributes such as year, month, week, day, date, business day indicator, and weekend. The time of day dimension may indicate information about the time of day (e.g., in terms of minutes or hours) with attributes like hour and day divisions (morning, afternoon, evening, night, etc.). The weather indicators may describe various weather types and temperatures (e.g., degrees celsius or degrees fahrenheit), including barometric pressure and wind levels, and various types and magnitudes of precipitation. The location dimension may indicate a continent, country, region, city, or street of the consumer.

The data from the structure 1100 may be analyzed and graphically represented in various ways. One such approach of an OLAP cube is discussed above with respect to an energy consumption index. As shown in fig. 12, the graphical representation 1200 may be created using OLAP cubes 1201, 1202, and 1203. Using an OLAP cube, a user can query a data source (e.g., data structure 1100 from FIG. 11) quickly and in a multi-dimensional manner. Various hierarchies (e.g., hierarchies of months, weeks, days, hours, etc.) can be developed for the data sources, thereby allowing each of the elements or attributes of the dimension to be analyzed (e.g., "weather").

In some implementations, the dimension data can be structured to support management of the data. For example, FIG. 13 depicts one exemplary hierarchy 1300 of dimension data 1301. The dimensions 1301 may have one or more association levels 1302 that specify the hierarchical relationship between the dimension data 1301. Other hierarchies may also be used to manage user-selected dimensional data and user goals for analyzing the data.

In some embodiments, the OLAP cube may be incorporated into business software. For example, a dashboard may be created for an area of interest to the user. The dashboard may contain charts or dials associated with energy consumption data and may include hyperlinks that a user may follow to analyze the data in a more in-depth manner. In other embodiments, the OLAP cube may be integrated with a user interface (e.g., a graphical user interface accessible via an internet or intranet connection) so that a user may interact with the cube. For example, a user may select the dimensions that they are interested in analyzing, and filter the data as needed. The user may store preferred settings for the interface for preconfigured access to the interface the next time they use the interface. In some implementations, the custom report can be generated based on data accessible in the interface. In other embodiments, the scheduled report may be run based on data accessible in the interface, thereby allowing the energy provider or a third party to create and archive snapshots of the data.

Various tools may be developed to assist energy suppliers based on the data types described above. The tools may be software-based and may be implemented in one or more of a variety of programming languages, such as C, C + +, C #, Java, Lisp, Visual Basic, Python, Perl, F #, etc., or in a computer program such as Microsoft ExcelAnd the like. The tools may be based on the data types described above, such as DR response data, indicator data, extended DR response data, energy consumption index, DR capacity, and consumption forecast.

One such tool is a demand planning tool, which can provide a highly granular (e.g., per transaction) view of energy consumption data. Such tools may receive real-time or near real-time DR response data as well as augmented DR response data from a consumer. Based on energy consumption index factors such as load before receiving the DR signal, load change after receiving the DR signal, and tendency to respond to the DR signal, consumer demand may be monitored and forecasted as described above.

Another planning tool is the current day forecasting tool. The tool may receive real-time or near real-time DR response data, as well as augmented DR response data, from a consumer. One function of the tool is to provide an energy consumption forecast for a given day. Based on the energy consumption index for each consumer, the tool can predict energy consumption within a day in a particular geographic location, thereby allowing the energy provider to adjust the price or supply of energy to meet a target consumption level. Forecasts can be created on a per user, per division, or overall consumer group basis, by customer type, location, time, DR signal, weather conditions, and the like.

FIG. 14A depicts one exemplary embodiment of a graphical representation 1400A of energy consumption data in a present day forecasting tool. The graphical representation 1400A may include both the current day forecast data 1401 and the next day forecast data 1402. The current day forecast data 1401 may represent a consumption forecast for a given day, and the next day forecast data 1402 may represent a consumption forecast for the next day. Vertical line 1403 may represent the current time and may move to the right in real time. The x-axis or "time" axis may be expressed in hours or other time intervals, while the y-axis or "consumption" axis may be expressed as a percentage of output capacity, or as an actual load level (e.g., kW). The graphical representation 1400A depicted in FIG. 14 indicates: from 9:30am to 2:30am on the next day, the consumption will be higher on the current day than on the next day. Among other things, the graphical representation 1400A may assist the user in planning the actual energy supply to be consistent with the forecasted energy consumption.

FIG. 14B depicts an exemplary embodiment of a graphical representation 1400B of energy consumption data in a present day forecasting tool. In addition to the current day forecast data 1401 and the next day forecast data 1401, the graphical representation 1400B may also include a subsequent day DR forecast data 1404. The following day DR forecast data 1404 may be the result of a simulation of energy consumption conditions, which may be run when the optimize DR signal button 1405 is pressed. For example, the optimized DR signal button 1405 may trigger a simulation software routine in which the optimized DR signal to be sent to the consumer is determined by predicting the expected impact of various types of DR signals on energy consumption. In some embodiments, a DR signal that obtains the best approximation to the current-day forecast data 1401 or the following-day DR forecast data 1404 of the next-day forecast data 1402 may be selected as the optimal DR signal to be transmitted. Additionally, view DR details button 1406 may present the user with specific actions corresponding to a given optimal DR signal, such as the type of DR signal, the recipient of the DR signal, the time at which the DR signal should be sent, the amount of DR signal (e.g., by price or request to reduce consumption), etc. The optimization of the DR signal may be based on various types of data, such as weather conditions, cost curves associated with energy supply, revenue for energy providers, and the like.

Another planning tool is a strategic energy pricing tool. The tool may support energy providers in optimizing pricing for entire customer base or parts thereof. One function of the strategic energy pricing tool may be to analyze the price or demand elasticity of the consumer and determine the optimal pricing level to maximize revenue at any given moment. For example, the tool may use variables such as price or time to estimate energy demand in each consumer segment, allowing the energy provider to determine the price elasticity of the consumer. By determining the price elasticity and appropriate pricing levels for the consumer, the energy provider can more effectively balance incentives to adjust prices based on factors such as customer loyalty or customer friction. Additionally, given a particular energy demand level, the tool can determine the effect that DR signal items may have on the relationship between consumption and cost. The energy provider may also predict both the effectiveness of static and dynamic pricing schemes as part of each DR signal item. Additionally, by better measuring and planning consumer price elasticity, energy suppliers may be able to sign more competitive energy contracts that reflect strategic pricing schemes.

FIG. 15 depicts one exemplary embodiment of a graphical representation 1500 of a price and consumption graph in a strategic energy pricing tool. Graphical representation 1500 shows the daily energy consumption of a consumer partition having a "Foxtrot" customer type, which is a function of price. The price elasticity curve 1501 may intersect a particular consumption level and a particular price level such that revenue is maximized at the intersection 1502. Using the tool, an energy provider can analyze the sensitivity of various consumer segments to energy prices and determine an optimal pricing level for each segment.

Another planning tool is a DR extension tool. The tool can model potential expansion effects in the DR project or the introduction of a new DR project in a new geographic location. Among other things, the tool may help the energy provider determine the extent to which DR extension will cause a consumer to change their energy consumption in response to a DR signal. For example, in particular embodiments, consumers currently participating in a DR program may be divided, as described above, and an energy consumption index for the consumers may be determined. Consumers in potential new service areas that have not implemented DR projects may be described based on various impact data and also partitioned as described above. Based on the partitioning of the two consumer sets and the correlation of consumption behaviors found between them, the energy provider may be able to predict the energy consumption behaviors of consumers in potential new service areas. For example, based on the correlation of impact data between existing consumers and potential new consumers, the energy provider may determine that the potential new consumers will be very acceptable for the DR signal and will exhibit significant changes in energy consumption from the DR signal.

Another planning tool is an emergency management tool. One function of the emergency management tool may be to determine optimal DR-related activities to be performed to prevent or mitigate emergency situations in the energy supply (e.g., outage, equipment failure, overproduction, etc.). The tool may also include features related to DR signals that may help the energy provider to restore networking to the energy grid after a failure.

Fig. 16 depicts an exemplary embodiment of an emergency management tool 1600 that includes transmission link monitoring data 1601 and a DR signal recommendation function 1602. Based on the transmission link monitoring data 1601, the emergency management tool 1600 indicates: for distribution node 1a, link IDs 1001, 1002, 1004, and 1005 operate under normal conditions, whereas link ID1003 experiences a failure (e.g., a loss of power) and link ID 1006 experiences a power overload. Based on tool 1600 and the rerouted link data, energy from link ID 1006 may be diverted to link 1003 to at least partially solve the problem experienced by both link IDs. In addition, DR signal recommendation 1602 indicates: a DR signal of "offload and future refund" may be sent to the consumer to mitigate overloading of link ID 1006. The planned load shedding amount is 35% and the signal can be sent immediately until the root cause of the overload problem is resolved. In some embodiments, the destination of the DR signal is also indicated by the DR signal recommendation function 1602, which in fig. 16 is the customer served by node 2 e. In some embodiments, an execute button 1603 is provided which, when pressed, may cause one or more DR signals to be transmitted according to the DR signal recommendation function 1602.

Another planning tool is a planned event management tool. The tool may be operable to assist an energy provider in planning network maintenance or equipment replacement. In some embodiments, the tool indicates when the consumer demand is expected to reach its lowest point (e.g., during a 5-month weekday evening). Based on such indications, the energy provider may plan to perform network maintenance or equipment replacement that affects the availability of energy at the time in order to minimize disruption of energy supply and minimize lost revenue from network downtime. In some embodiments, the energy provider may send a DR signal to the consumer indicating that the energy supply may be interrupted or limited during such maintenance or replacement activities.

An additional planning tool is the negative watt capacity tool. Negative watts of power is an expression for power saved or power not generated. The tool can provide a high granularity negative watt capacity forecast at each customer or aggregate level. The tool may, among other things, plan the energy provider's ability to reduce energy consumption by signaling a particular DR to a particular consumer. For example, based on the energy consumption index of a consumer, or based on consumption forecasts or DR capacity data, an energy provider may calculate a predicted change in energy consumption that a particular consumer may have in sending a particular DR signal to. One benefit of the negative watt capacity tool is that it may help energy providers understand their ability to reduce energy consumption in the current and future, whereby energy providers may address current or future load imbalances in the energy network, may support trading of energy-based goods or securities, and may help to achieve higher efficiency levels of energy supply. The tool may measure the negative watt capacity of the energy provider in terms of energy (e.g., kW), as a percentage (e.g., a percentage of total output), or the like.

In some implementations, the negative watt capacity tool can present a graphical display of the power company's negative watt capacity and associated costs. The cost may be a cost saved for an energy provider by reducing energy consumption, a cost of supplying energy (e.g., a cost of production and transportation), a cost of supplying energy in another energy market (e.g., an adjacent or remote geographic area), etc. In such embodiments, the user may determine what impact the change in negative tile implementation may have on the cost. By allowing the user to plot negative watt capacity versus cost, the energy provider can make cost effective decisions as to where to supply energy, the level of energy supplied, when to change energy supply projects, and the like. The tool may enable an energy provider to effectively engage in energy arbitrage operations or participate in energy spot markets.

FIG. 17 is a diagram of an exemplary pivot table arrangement interface 1700 for viewing negative watt capacity data. The pivot table arrangement interface 1700 may be based on Microsoft ExcelOr similar data management program. The user may select various pivot fields 1701, filter the data through specific fields 1703, and view the resulting pivot 1702 of the data. In the embodiment shown in fig. 17, the user may view the negative watt capacity for the state of illinois during 11 months of 2009 on a city-by-city basis. In some implementations, as shown in FIG. 17, the cost associated with a particular negative watt capacity (e.g., lost revenue due to loss of sales) may be indicated in the pivot table 1702. Pivot table 1702 may be created based on a particular DR item (e.g., a scheme for type, content, and DR signaling). For example, in fig. 17, the DR item "C" is indicated as one of the filter elements 1703. In some implementations, the pivot table arrangement interface 1700 can include a button 1604 for graphically representing data in the pivot table 1702.

Another planning tool is a full DR browser tool. Such tools may allow energy providers to achieve a comprehensive and unified perspective view about all their DR initiations. In some embodiments, the tools may include each of the tools described above, or may include links to the tools. The full DR browser facility may be presented as one or more Internet or intranet pages, or may be presented as software running locally or over a network by a user.

Other embodiments will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true spirit and scope of the invention being indicated by the following claims. It is further intended that the embodiments described above may be combined as appropriate so that features of one embodiment may be used in another embodiment.

Claims

1. A computer-implemented method of analyzing energy usage, comprising:

receiving demand response DR response data from a first group of energy users;

associating the DR response data with impact data, the impact data stored in a database, relating to the first energy user group;

determining, using a computer processor, an energy consumption index for the first group of energy users, wherein the energy consumption index comprises:

transmitting a value of energy consumption before DR response data to devices corresponding to the first energy user group;

a propensity of the first group of energy users to change energy consumption in response to DR response data; and

a change in energy consumption after transmitting DR response data to devices corresponding to the first energy user group; and

establishing an energy consumption index for a second energy user group based on the energy consumption index for the first energy user group.

2. The method of claim 1, further comprising: establishing an energy consumption index for a third energy user group based on the energy consumption index for the second energy user group.

3. The method of claim 1, wherein the first energy consumer group comprises a sample group of consumers that are representative of a customer base of an energy provider.

4. The method of claim 1, wherein establishing an energy consumption index for the second group of energy users comprises: correlating the energy consumption behavior of the first energy consumer group with the energy consumption behavior of the second energy consumer group.

5. The method of claim 1, wherein the energy consumption index for the first energy user group further comprises a period of time for which energy changes are sustained.

6. The method of claim 1, wherein the second group of energy source users is a subset of the first group of energy source users.

7. The method of claim 1, wherein the DR response data includes a value of energy consumption on a per device basis.

8. The method of claim 1, wherein the impact data comprises at least one of: climate conditions, location, customer attributes, and occupancy type of energy consumers in the first energy consumer group.

9. The method of claim 1, wherein the energy consumption index for the first group of energy users is displayed in a star pattern.

10. The method of claim 1, wherein the energy consumption index for the first group of energy users is displayed in a three-dimensional cube format.

11. The method of claim 1, wherein the DR response data is communicated between devices corresponding to the first energy consumer group and a third party.

12. A system for managing energy usage by utilizing demand response DR response data, comprising:

a first database storing DR response data received from the user devices corresponding to the first energy user group;

a second database storing impact data relating to the first energy user group;

a computer processor coupled to the first database and the second database, the computer processor associating the DR response data with the impact data, the computer processor further determining an energy consumption index for a first group of energy users and establishing an energy consumption index for a second group of energy users based on the energy consumption index for the first group of energy users, wherein the energy consumption index for the first group of energy consumers comprises:

transmitting a value of energy consumption before DR response data to a using device corresponding to the first energy user group;

a change in energy consumption after transmitting DR response data to a using device corresponding to the first energy user group.

13. The system of claim 12, further comprising: a computer server coupled to the first database and programmed to receive DR response data from the user devices corresponding to the first energy user group.

14. The system of claim 12, wherein the first database and the second database comprise the same database file and are located in the same hardware unit.

15. The system of claim 12, wherein the first energy consumer group comprises a sample group of consumers that are representative of a customer base of an energy provider.

16. The system of claim 12, wherein the energy consumption index for the first energy user group further comprises a period of time for which energy changes are sustained.

17. The system of claim 12, wherein the second group of energy users is a subset of the first group of energy users.

18. The system of claim 12, wherein establishing an energy consumption index for the second group of energy users comprises: correlating the energy consumption behavior of the first energy consumer group with the energy consumption behavior of the second energy consumer group.

19. The system of claim 12, wherein the DR response data includes a value of energy consumption on a per device basis.

20. The system of claim 12, wherein the impact data comprises at least one of: climate conditions, location, customer attributes, and occupancy type of energy consumers in the first energy consumer group.

21. The system of claim 12, wherein the energy consumption index for the first group of energy users is displayed in a star pattern.

22. The system of claim 12, wherein the energy consumption index for the first group of energy users is displayed in a three-dimensional cube format.

23. The system of claim 12, wherein the DR response data is communicated between the user devices corresponding to the first group of energy users and a third party.

24. A computer-implemented method of managing energy supply, comprising:

determining energy planning criteria for a group of energy users, wherein the energy planning criteria comprise:

a forecast of the computer-created propensity for the group of energy users to change energy consumption;

a forecast of energy consumption of the group of energy users created using a computer; and

determining an actual energy consumption of the group of energy users;

determining an optimal level of factors related to energy supply based on the energy planning criteria;

determining a signal to be transmitted to a receiving device of a target group of energy users to achieve the optimal level of the factor related to the energy supply, wherein determining the signal to be transmitted comprises:

determining a type of Demand Response (DR) signal to be transmitted to the receiving devices of the target group of energy users;

determining a target group of energy users to send the DR signal to their receiving devices; and

determining a timing scheme for transmitting the DR signals to the receiving devices of the target group of energy users;

transmitting the determined type of DR signal to the determined receiving devices of the target energy user group according to the determined timing scheme;

receiving response data from the receiving devices of the target energy user group; and

determining whether energy consumption of the target energy user group changes in response to the determined type of DR signal sent to the receiving device of the target energy user group.

25. The method of claim 24, further comprising: simulating an effect of transmitting the determined type of DR signal type to the receiving devices of the determined target group of energy users according to the determined timing scheme.

26. The method of claim 24, wherein the factor related to energy supply is an energy price charged to an energy user group.

27. The method of claim 24, wherein determining an optimal level of a factor related to energy supply further comprises: determining price elasticity for the group of energy users.

28. The method of claim 24, wherein determining an optimal level of a factor related to energy supply further comprises: determining demand resiliency for the group of energy users.

29. The method of claim 24, wherein determining an optimal level of a factor related to energy supply further comprises: the effect of introducing items of DR signals to new markets is simulated.

30. The method of claim 24, wherein determining an optimal level of a factor related to energy supply further comprises:

determining the existence of a problem in the energy supply network;

determining a type of DR signal to send to the receiving devices of the target energy user group to mitigate the problem in the energy supply network.

31. The method of claim 30, wherein determining the type of DR signal to be transmitted further comprises: determining a minimum level of energy load to be routed from a first area in the energy supply network to a second area in the energy supply network.

32. The method of claim 24, wherein determining an optimal level of a factor related to energy supply further comprises: the negative watt capacity of the energy provider is determined.

33. The method of claim 32, wherein the negative watt capacity is represented graphically as a function of cost.