# Patent

## Title
Electric power system control with measurement of energy demand and energy efficiency

## Abstract

A method, apparatus, system and computer program is provided for controlling an electric power system, including implementation of voltage measurement using paired comparison analysis applied to calculating a shift in average usage per customer from one time period to another time period for a given electrical use population where the pairing process is optimized using a novel technique to improve the accuracy of the measurement.

## Background
This application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application Ser. No. 62/109,322 filed on Jan. 29, 2015, which is hereby incorporated by reference in its entirety. This application is also a continuation-in-part to U.S. patent application Ser. No. 14/193,980, filed Feb. 28, 2014, which claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application Ser. No. 61/789,085, filed Mar. 15, 2013, which are each hereby incorporated by reference in their entirety.


BACKGROUND
The present disclosure relates to a method, an apparatus, a system and a computer program for controlling an electric power system, including measuring the effects of optimizing voltage, conserving energy, and reducing demand using t distributions. More particularly, the disclosure relates to a novel implementation of electrical demand and energy efficiency improvement measurement using a paired samples t-test to compare the population demand and energy usage over a specific time period. This method enables the direct statistical measurement of energy and demand changes between two time periods for an energy use population. This comparison can be used as a basis to accurately quantify energy efficiency and demand reduction values for savings resulting from implementation of a modification to the electric power system.
Electricity is commonly generated at a power station by electromechanical generators, which are typically driven by heat engines fueled by chemical combustion or nuclear fission, or driven by kinetic energy flowing from water or wind. The electricity is generally supplied to end users through transmission grids as an alternating current signal. The transmission grids may include a network of power stations, transmission circuits, substations, and the like.
The generated electricity is typically stepped-up in voltage using, for example, generating step-up transformers, before supplying the electricity to a transmission system. Stepping up the voltage improves transmission efficiency by reducing the electrical current flowing in the transmission system conductors, while keeping the power transmitted nearly equal to the power input. The stepped-up voltage electricity is then transmitted through the transmission system to a distribution system, which distributes the electricity to end users. The distribution system may include a network that carries electricity from the transmission system and delivering it to end users. Typically, the network may include medium-voltage (for example, less than 69 kV) power lines, electrical substations, transformers, low-voltage (for example, less than 1 kV) distribution wiring, electric meters, and the like.
The following, the entirety of which is herein incorporated by reference, describe subject matter related to power generation or distribution: Power Distribution Planning Reference Book, Second Edition, H. Lee Willis, 2004; Estimating Methodology for a Large Regional Application of Conservation Voltage Reduction, J. G. De Steese, S. B. Merrick, B. W. Kennedy, IEEE Transactions on Power Systems, 1990; Implementation of Conservation Voltage Reduction at Commonwealth Edison, IEEE Transactions on Power Systems, D. Kirshner, 1990; Conservation Voltage Reduction at Northeast Utilities, D. M. Lauria, IEEE, 1987; Green Circuit Field Demonstrations, EPRI, Palo Alto, Calif., 2009, Report 1016520; Evaluation of Conservation Voltage Reduction (CVR) on a National Level, PNNL-19596, Prepared for the U.S. Department of Energy under Contract DE-ACO5-76RL01830, Pacific Northwest National Lab, July 2010; Utility Distribution System Efficiency Initiative (DEI) Phase 1, Final Market Progress Evaluation Report, No 3, E08-192 (July 2008) E08-192; Simplified Voltage Optimization (VO) Measurement and Verification Protocol, Simplified VO M&V Protocol Version 1.0, May 4, 2010; MINITAB Handbook, Updated for Release 14, fifth edition, Barbara Ryan, Brian Joiner, Jonathan Cryer, Brooks/Cole-Thomson, 2005; Minitab Software, http://www.minitab.com/en-US/products/minitab/; Statistical Software provided by Minitab Corporation.
Further, U.S. patent application Ser. No. 61/176,398, filed on May 7, 2009 and US publication 2013/0030591 entitled VOLTAGE CONSERVATION USING ADVANCED METERING INFRASTRUCTURE AND SUBSTATION CENTRALIZED VOLTAGE CONTROL, the entirety of which is herein incorporated by reference, describe a voltage control and energy conservation system for an electric power transmission and distribution grid configured to supply electric power to a plurality of user locations.
SUMMARY
Various embodiments described herein provide a novel method, apparatus, system and computer program for controlling an electric power system, including implementation of voltage measurement using paired t statistical analysis applied to calculating a shift in average usage per customer from one time period to another time period for a given electrical use population where the pairing process is optimized using a novel technique to improve the accuracy of the statistical measurement.
According to an aspect of the disclosure, the energy validation process (EVP) measures the level of change in energy usage for the electrical energy delivery system (EEDS) that is made up of an energy supply system (ESS) that connects electrically to one or more energy usage systems (EUS). A modification is made to the operation of the EEDS or to an energy usage device (EUD) at some electrical point on an electrical energy delivery system (EEDS) made up of many energy usage devices randomly using energy at any given time during the measurement. The purpose of the energy validation process (EVP) is to measure the level of change in energy usage for the EEDS. The electrical energy supply to the electrical energy delivery system (EEDS) is measured in watts, kilowatts (kw), or Megawatts (MW) (a) at the supply point of the ESS and (b) at the energy user system (EUS) or meter point. This measurement records the average usage of energy (AUE) at each of the supply and meter points over set time periods such as one hour.
The test for the level of change in energy use is divided into two basic time periods: The first is the time period when the modification is not operating, i.e., in the “OFF” state. The second time period is when the modification is operating, i.e., in the “ON” state. Because electrical energy usage is not constant but varies with other independent variables such as weather and ambient conditions, weather and ambient variation as well as other independent variables must be eliminated from the comparison of the “OFF” state to the “ON” state. The intent is to leave only the one independent variable being measured in the comparison of average energy usage from the “OFF” to the “ON” condition.
To eliminate the effect of the ambient and/or weather conditions a pairing process is used to match energy periods with common ambient and/or weather conditions using a pairing process. As an example, temperature, heating degree, cooling degree and other weather conditions are recorded for each energy measurement over the set time periods. These periods are paired if the temperature, heating degree, cooling degree and other weather conditions match according to an optimization process for selecting the most accurate pairs.
To eliminate other independent variables not being measured that will cause variation in the measurement, an EEDS of a near identical energy supply system and near identical energy usage system that is located in the same ambient and/or weather system is used. To eliminate the other independent variables, the changes in energy in the EEDS of a near identical energy supply system are subtracted from the changes measured by the EEDS under test. This method corrects the test circuit for the effects of the other remaining independent variables.
The measurement process consists of first pairing intervals of average energy usage data from the “OFF” state to the “ON” state. The first step is to eliminate significant outliers that are easily identified as not being associated with the independent variable. As an example, if the expected (based on experience or otherwise) load shift resulting from a modification is a maximum of 2 kw and the data shows a population member with an load shift of 10 MW, this element can be excluded. Exclusion has to be done consistently across the population not to destroy the population normality.
The second step is to set the limits of the pairing process. The limits may be set based, at least in part, on the accuracy desired. The accuracy also depends on the number of data points used. As an example, for temperature difference, a limit might be chosen to be one degree Fahrenheit (F). With this choice of limits, a time period type is chosen over which data measurements are examined. Choice of the time period may depend on what EEDS operating environment conditions are relevant for a chosen analysis. For example, a 24-hour time period may be chosen to include the variation of the measured data over a full day. As another example, a four-hour time period in the evening may be chosen to include the variation of measured data over a peak evening electricity usage period.
During the time period, data is collected from a set of sensors in a portion of the EEDS with the modification in the “ON” state. During the same type time period (which may or may not run concurrently with time period for collection in the “ON” state), data is collected from a group of sensors that are potential pairs to the set from a portion of the EEDS with the modification in the “OFF” state. The pairs are reviewed to assure that the best match of temperature levels between the match is chosen. This process may be repeated for other variables. Once the best group of pairs is identified, a standard process of paired t is applied to determine the average change in energy usage from the “OFF” state to the “ON” state using a t distribution for the group of pairs identified. This process can determine, within a confidence level, the actual range of change in energy use from the “OFF” state to the “ON” state for this population. For this process, measurements can be made at the electrical energy delivery system (EEDS) meter point(s) or at the energy usage systems (EUS) meter point(s) or with the energy usage device (EUD) meter points or any combination of EEDS, EUS and EUD meter points.
The resulting change in energy usage may then be used to control the electric energy delivery system. For example, components of the EEDS may be modified, adjusted, added or deleted, including the addition of capacitor banks, modification of voltage regulators, changes to end-user equipment to modify customer efficiency, and other control actions.
According to a further aspect of the disclosure, the energy validation process (EVP) measures the level of change in energy usage for the electrical energy delivery system (EEDS) that is made up of an energy supply system (ESS) that connects electrically to one or more energy usage systems (EUS). This is similar to the aspect described above, however multiple modifications are made to EEDS operation or to energy usage devices (EUD) at electrical point(s) on an electrical energy delivery system (EEDS) made up of many energy usage devices randomly using energy at any given time during the measurement. The purpose of the energy validation process (EVP) is to measure the level of change in energy usage for the EEDS with combined modifications and with each of the individual modifications. The electrical energy supply to the electrical energy delivery system (EEDS) is measured in watts, kw, or MW (a) at the supply point of the ESS and (b) at the energy user system (EUS) or meter point. This measurement records the average usage of energy (AUE) at each of the supply and meter points over set time periods such as one hour.
The test for the level of change in energy use improvement is divided into two basic time periods: The first is the time period when the modification is not operating, i.e., in the “OFF” state. The second time period is when the modification is operating, i.e., in the “ON” state. Because electrical energy usage is not constant but varies with other independent variable such as weather and ambient conditions, weather and ambient variation as well as other independent variables must be eliminated from the comparison of the “OFF” state to the “ON” state. The intent is to leave only the independent variables being measured in the comparison of average energy usage from the “OFF” to the “ON” condition.
To eliminate the effect of the ambient and/or weather conditions a pairing process is used to match energy periods with common ambient and/or weather conditions using a pairing process. As an example temperature, heating degree, cooling degree and other weather conditions are recorded for each energy measurement over the set time periods. These periods are paired if the temperature, heating degree, cooling degree and other weather conditions match according to an optimization process for selecting the most accurate pairs.
To eliminate other independent variables not being measured that will cause variation in the measurement, an EEDS of a near identical energy supply system and near identical energy usage system that is located in the same ambient and/or weather system is used. To eliminate the other independent variables, the changes in energy in an EEDS of a near identical energy supply system are subtracted from the changes measured by the EEDS under test. This method corrects the test EEDS for the effects of the other remaining independent variables.
The measurement process consists of first pairing intervals of average energy usage data from the “OFF” state to the “ON” state. The first step is to eliminate significant outliers that are easily identified as not being associated with the independent variable. As an example, if the expected load shift for a modification is a maximum of 2 kw and the data shows a population member with a load shift of 10 MW, this element can be excluded. Exclusion has to be done consistently across the population not to destroy the population normality.
The second step is to set the limits of the pairing process. As an example for temperature difference a limit might be chosen to be one degree F. With this choice of limits, similar to the preceding described aspect, a time period is chosen over which data measurements shall be or have been taken from a set of sensors with the modification in the “ON” state, and from a group of sensors that are potential pairs to the set, with the modification in the “OFF” state. The pairs are reviewed to assure that the best match of temperature levels between the match is chosen. This is repeated for other variables and once the best group of pairs is identified, a standard process of paired t is applied to determine the average change in energy usage from the “OFF” state to the “ON” state using a t distribution for the group of pairs identified. This process can determine within a confidence interval the actual range of change in energy use from the “OFF” state to the “ON” state for this population. For this process, measurements can be made at the electrical energy delivery system (EEDS) meter point(s) or at the energy usage systems (EUS) meter point(s) or with the energy usage device (EUD) meter points or any combination of EEDS, EUS and EUD meter points.
The resulting change in energy usage may then be used to control the electric energy delivery system. For example, components of the EEDS may be modified, adjusted, added or deleted, including the addition of capacitor banks, modification of voltage regulators, changes to end-user equipment to modify customer efficiency, and other control actions.
The energy validation process (EVP) may further contain a second independent variable such as humidity that affects the energy usage. The EVP is then used to provide a second pairing variable that is secondary to the first pairing variable. The process pairs the first variable as close as possible with the population “OFF” to “ON” values for the chosen energy intervals. The matching second variable is already matched to the first variable for the interval. A weighed scoring of the pairs is implemented based on the relative slopes of the linear relationship between the energy and the respective independent variable. This produces an optimized selection of pairs to most closely match the two population points. This linear optimal matching provides the best pairing of the data for t-distribution evaluation. This method allows multiple values to be optimally paired for calculating average energy changes using the t-distribution.
The energy validation process (EVP) may further contain an electrical energy delivery system (EEDS) that is made up of an energy supply system (ESS) that connects electrically to one or more energy usage systems (EUS) that has three phases of power. The EVP will then perform all power and independent variable calculations by phase values in all combinations of EEDS, ESS, EUS, and EUDs to calculate the energy changes due to modifications in the energy systems. Thus calculations may be performed separately using data for sensed properties specific to each of one of the three phases. In this way, the effects of the modifications to the EEDS for one or more phases may be compared to its effects for the other phase(s).
The energy validation process (EVP) may further contain a second independent variable such as voltage where the ratio of the average change in voltage to average change in energy is being calculated or the conservation voltage reduction factor (CVRF). This factor measures the capacity of the EEDS, EUS and EUD's to change energy usage in response to the independent variable of voltage. The EVP calculates the CVRF first by pairing two energy states from the “OFF” state to the “ON” state as already described. Second the ratio of the percent change in energy divided by the percent change in voltage for the sample is calculated between the two states for each sample in the population. Optimal pairing matches the closest samples for evaluation using a t-distribution to determine the confidence interval for the average value of the CVRF.
The energy validation process (EVP) may further contain multiple independent variables such as voltage and circuit unbalance where the ratio of the average change in voltage and circuit unbalance to average change in energy is being calculated or the energy reduction factor (ERF). This factor measures the capacity of the EEDS, EUS and EUD's to change energy usage in response to multiple independent variables. The EVP calculates the ERF first by pairing two energy states from the “OFF” state to the “ON” state as already described. Second the ratio of the change in energy divided by the change in combined % change of the multiple variables for the sample is calculated between the two states for each sample in the population. Optimal pairing matches the closest samples for evaluation using a t-distribution to determine the confidence interval for the average value of the ERF.
The energy validation process (EVP) may further contain an electrical energy delivery system (EEDS) that is made up of an energy supply system (ESS) that connects electrically to one or more energy usage systems (EUS). The EVP evaluation time period (or interval) can be developed in multiple levels. This is useful to categorize the connected EUD's using a linear regression technique. As a starting point the interval could use the standard interval of 24 hours to capture the effects of load cycling over multiple hours. But in some cases not all loads will be connected during the full 24 hours and the energy measurements may not be consistent over the total period. To address this, for example, evaluations are separated into seasons to represent the different loads, such as air conditioning and heating between the summer and winter seasons respectively. In the fall and spring these loads may not exist under mild weather conditions, so they are evaluated separately as well. In addition each season is evaluated by using linear regression to represent the multiple variables that affect the loads for each hour, such as heating degree level, cooling degree level, day type (weekend, weekday or holiday), humidity, growth in load, and others. The hours are then grouped by the regression factor ranges to match the general characteristics of the load. This regression results in dividing each season into hour ranges for each 24 hour period that can be independently compared to determine their separate characteristics of energy performance in the population. The EVP will then perform all power and independent variable calculations by phase values, by season, by hourly ranges in all combinations of EEDS, ESS, EUS, and EUDs to calculate the energy changes due to modifications in the energy systems.
Additional features, advantages, and embodiments of the disclosure may be set forth or apparent from consideration of the detailed description and drawings. Moreover, it is to be understood that both the foregoing summary of the disclosure and the following detailed description are exemplary and intended to provide further explanation without limiting the scope of the disclosure as claimed.



BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are included to provide a further understanding of the disclosure, are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the detailed description serve to explain the principles of the disclosure. No attempt is made to show structural details of the disclosure in more detail than may be necessary for a fundamental understanding of the disclosure and the various ways in which it may be practiced. In the drawings:
FIG. 1 shows an example of an EEDS made up of an electricity generation and distribution system connected to customer loads, according to principles of the disclosure;
FIG. 2 shows an example of a voltage control and conservation (VCC) system being measured at the ESS meter point and the EUS made up of Advanced Metering Infrastructure (AMI) measuring Voltage and Energy, according to the principles of the disclosure;
FIG. 3 shows an example of an Energy Validation Process (EVP) according to principles of the disclosure;
FIG. 4 shows an example of an Energy Validation Process (EVP) data base structure according to principles of the disclosure;
FIG. 5 shows an example of general outlier analysis to determine population measurements that are outside of normal operation, according to principles of the disclosure;
FIG. 6 shows an example of voltage outlier analysis to determine if independent variables such as voltage measurements are outside of normal operation, according to principles of the disclosure;
FIG. 7 shows examples of graphs of a voltage histograms of “OFF to ON” comparisons for determining the characteristics of the independent variables, according to principles of the disclosure;
FIG. 8 shows examples of graphs of sample points by weather and season in the “ON” and “OFF” conditions to view the characteristics of the weather and seasonal shifts in each sample and sample pair;
FIG. 9 shows an example of the high level pairing process for matching the weather, day type, and humidity for a population sample, according to the principles of the disclosure;
FIG. 10 shows an example of the results of breaking the load data into groups by season and by hourly groups with similar characteristics, according to the principles of the disclosure;
FIG. 11 shows an example of a process map of the optimal pairing process, according to the principles of the disclosure;
FIG. 12 shows an example of a histogram of the data pairing process to determine the CVR factor for the EEDS, according to principles of the disclosure;
FIG. 13 shows an example of an application of a paired test analysis process determining the change in usage per customer. The top histogram represents the pairing results and the bottom scatter plot demonstrates the results of the pairing values, according to principles of the disclosure;
FIG. 14 shows examples of histograms of the data pairing process to determine the CVR factor for the EEDS, one with a control EEDS to remove other independent variables, and one without the control EEDS, according to principles of the disclosure;
FIG. 15 shows an example of a summary chart for the data shown in previous Figures on CVR factor and Energy savings per customer, according to principles of the disclosure;
FIG. 16 shows an example of a pairing process meeting filter criteria according to principles of the disclosure;
FIG. 17 shows an example of a scatterplot of the paired population according to principles of the disclosure; and
FIG. 18 shows an example of the histogram of the data from an example CVR factor pairing calculation according to principles of the disclosure.



The present disclosure is further described in the detailed description that follows.
DETAILED DESCRIPTION OF THE DISCLOSURE
The disclosure and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments and examples that are described and/or illustrated in the accompanying drawings and detailed in the following description. It should be noted that the features illustrated in the drawings are not necessarily drawn to scale, and features of one embodiment may be employed with other embodiments as the skilled artisan would recognize, even if not explicitly stated herein. Descriptions of well-known components and processing techniques may be omitted so as to not unnecessarily obscure the embodiments of the disclosure. The examples used herein are intended merely to facilitate an understanding of ways in which the disclosure may be practiced and to further enable those of skill in the art to practice the embodiments of the disclosure. Accordingly, the examples and embodiments herein should not be construed as limiting the scope of the disclosure. Moreover, it is noted that like reference numerals represent similar parts throughout the several views of the drawings.
A “computer”, as used in this disclosure, means any machine, device, circuit, component, or module, or any system of machines, devices, circuits, components, modules, or the like, which are capable of manipulating data according to one or more instructions, such as, for example, without limitation, a processor, a microprocessor, a central processing unit, a general purpose computer, a super computer, a personal computer, a laptop computer, a palmtop computer, a notebook computer, a desktop computer, a workstation computer, a server, or the like, or an array of processors, microprocessors, central processing units, general purpose computers, super computers, personal computers, laptop computers, palmtop computers, notebook computers, desktop computers, workstation computers, servers, or the like.
A “server”, as used in this disclosure, means any combination of software and/or hardware, including at least one application and/or at least one computer to perform services for connected clients as part of a client-server architecture. The at least one server application may include, but is not limited to, for example, an application program that can accept connections to service requests from clients by sending back responses to the clients. The server may be configured to run the at least one application, often under heavy workloads, unattended, for extended periods of time with minimal human direction. The server may include a plurality of computers configured, with the at least one application being divided among the computers depending upon the workload. For example, under light loading, the at least one application can run on a single computer. However, under heavy loading, multiple computers may be required to run the at least one application. The server, or any if its computers, may also be used as a workstation.
A “database”, as used in this disclosure, means any combination of software and/or hardware, including at least one application and/or at least one computer. The database may include a structured collection of records or data organized according to a database model, such as, for example, but not limited to at least one of a relational model, a hierarchical model, a network model or the like. The database may include a database management system application (DBMS) as is known in the art. At least one application may include, but is not limited to, for example, an application program that can accept connections to service requests from clients by sending back responses to the clients. The database may be configured to run the at least one application, often under heavy workloads, unattended, for extended periods of time with minimal human direction.
A “communication link”, as used in this disclosure, means a wired and/or wireless medium that conveys data or information between at least two points. The wired or wireless medium may include, for example, a metallic conductor link, a radio frequency (RF) communication link, an Infrared (IR) communication link, an optical communication link, or the like, without limitation. The RF communication link may include, for example, WiFi, WiMAX, IEEE 802.11, DECT, 0G, 1G, 2G, 3G or 4G cellular standards, Bluetooth, and the like.
The terms “including”, “comprising” and variations thereof, as used in this disclosure, mean “including, but not limited to”, unless expressly specified otherwise.
The terms “a”, “an”, and “the”, as used in this disclosure, means “one or more”, unless expressly specified otherwise.
Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more intermediaries.
Although process steps, method steps, algorithms, or the like, may be described in a sequential order, such processes, methods and algorithms may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps be performed in that order. The steps of the processes, methods or algorithms described herein may be performed in any order practical. Further, some steps may be performed simultaneously.
When a single device or article is described herein, it will be readily apparent that more than one device or article may be used in place of a single device or article. Similarly, where more than one device or article is described herein, it will be readily apparent that a single device or article may be used in place of the more than one device or article. The functionality or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality or features.
A “computer-readable medium”, as used in this disclosure, means any medium that participates in providing data (for example, instructions) which may be read by a computer. Such a medium may take many forms, including non-volatile media, volatile media, and transmission media. Non-volatile media may include, for example, optical or magnetic disks and other persistent memory. Volatile media may include dynamic random access memory (DRAM). Transmission media may include coaxial cables, copper wire and fiber optics, including the wires that comprise a system bus coupled to the processor. Transmission media may include or convey acoustic waves, light waves and electromagnetic emissions, such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.
Various forms of computer readable media may be involved in carrying sequences of instructions to a computer. For example, sequences of instruction (i) may be delivered from a RAM to a processor, (ii) may be carried over a wireless transmission medium, and/or (iii) may be formatted according to numerous formats, standards or protocols, including, for example, WiFi, WiMAX, IEEE 802.11, DECT, 0G, 1G, 2G, 3G or 4G cellular standards, Bluetooth, or the like.
According to one non-limiting example of the disclosure, a voltage control and conservation (VCC) system 200 is provided (shown in FIG. 2) and the EVP is being used to monitor the change in EEDS energy from the VCC. The VCC, which includes three subsystems, including an energy delivery (ED) system 300, an energy control (EC) system 400 and an energy regulation (ER) system 500. The VCC system 200 is configured to monitor energy usage at the ED system 300 and determine one or more energy delivery parameters at the EC system (or voltage controller) 400. The EC system 400 may then provide the one or more energy delivery parameters CED to the ER system 500 to adjust the energy delivered to a plurality of users for maximum energy conservation. The energy validation process (EVP) system 600 monitors through communications link 610 all metered energy flow and determines the change in energy resulting from a change in voltage control at the ER system. The EVP system 600 also reads weather data information through a communication link 620 from an appropriate weather station 640 to execute the EVP process 630.
The VCC system 200 is also configured to monitor via communication link 610 energy change data from EVP system 600 and determine one or more energy delivery parameters at the EC system (or voltage controller) 400. The EC system 400 may then provide the one or more energy delivery parameters CED to the ER system 500 to adjust the energy delivered to a plurality of users for maximum energy conservation. Similarly, the EC system 400 may use the energy change data to control the electric energy delivery system 700 in other ways. For example, components of the EEDS 700 may be modified, adjusted, added or deleted, including the addition of capacitor banks, modification of voltage regulators, changes to end-user equipment to modify customer efficiency, and other control actions.
The VCC system 200 may be integrated into, for example, an existing load curtailment plan of an electrical power supply system. The electrical power supply system may include an emergency voltage reduction plan, which may be activated when one or more predetermined events are triggered. The predetermined events may include, for example, an emergency, an overheating of electrical conductors, when the electrical power output from the transformer exceeds, for example, 80% of its power rating, or the like. The VCC system 200 is configured to yield to the load curtailment plan when the one or more predetermined events are triggered, allowing the load curtailment plan to be executed to reduce the voltage of the electrical power supplied to the plurality of users.
FIG. 1 is similar to FIG. 1 of US publication 2013/0030591, with overlays that show an example of an EEDS 700 system, including an EUS system 900 and an ESS system 800 based on the electricity generation and distribution system 100, according to principles of the disclosure. The electricity generation and distribution system 100 includes an electrical power generating station 110, a generating step-up transformer 120, a substation 130, a plurality of step-down transformers 140, 165, 167, and users 150, 160. The electrical power generating station 110 generates electrical power that is supplied to the step-up transformer 120. The step-up transformer steps-up the voltage of the electrical power and supplies the stepped-up electrical power to an electrical transmission media 125. The ESS 800 includes the station 110, the step-up transformer 120, the substation 130, the step-down transformers 140, 165, 167, the ER 500 as described herein, and the electrical transmission media, including media 125, for transmitting the power from the station 110 to users 150, 160. The EUS 900 includes the ED 300 system as described herein, and a number of energy usage devices (EUD) 920 that may be consumers of power, or loads, including customer equipment and the like.
As seen in FIG. 1, the electrical transmission media may include wire conductors, which may be carried above ground by, for example, utility poles 127 and/or underground by, for example, shielded conductors (not shown). The electrical power is supplied from the step-up transformer 120 to the substation 130 as electrical power EIn(t), where the electrical power EIn in MegaWatts (MW) may vary as a function of time t. The substation 130 converts the received electrical power EIn(t) to ESupply(t) and supplies the converted electrical power ESupply(t) to the plurality of users 150, 160. The substation 130 may adjustably transform the voltage component VIn(t) of the received electrical power EIn(t) by, for example, stepping-down the voltage before supplying the electrical power ESupply(t) to the users 150, 160. The electrical power ESupply(t) supplied from the substation 130 may be received by the step-down transformers 140, 165, 167 and supplied to the users 150, 160 through a transmission medium 142, 162, such as, for example, but not limited to, underground electrical conductors (and/or above ground electrical conductors).
Each of the users 150, 160 may include an Advanced Meter Infrastructure (AMI) 155, 169. The AMI 155, 169 may be coupled to a Regional Operations Center (ROC) 180. The ROC 180 may be coupled to the AMI 155, 169, by means of a plurality of communication links 175, 184, 188, a network 170 and/or a wireless communication system 190. The wireless communication system 190 may include, but is not limited to, for example, an RF transceiver, a satellite transceiver, and/or the like.
The network 170 may include, for example, at least one of the Internet, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a personal area network (PAN), a campus area network, a corporate area network, the electrical transmission media 125, 135 and transformers 140, 165, 167, a global area network (GAN), a broadband area network (BAN), or the like, any of which may be configured to communicate data via a wireless and/or a wired communication medium. The network 170 may be configured to include a network topology such as, for example, a ring, a mesh, a line, a tree, a star, a bus, a full connection, or the like.
The AMI 155, 169 may include any one or more of the following: A smart meter; a network interface (for example, a WAN interface, or the like); firmware; software; hardware; and the like. The smart meter may be configured to determine any one or more of the following: kilo-Watt-hours (kWh) delivered; kWh received; kWh delivered plus kWh received; kWh delivered minus kWh received; interval data; demand data; voltage; current; phase; and the like. If the smart meter is a three phase meter, then the low phase voltage may be used in the average calculation, or the values for each phase may be used independently. If the meter is a single phase meter, then the single voltage component will be averaged.
The AMI 155, 169 may further include one or more collectors (shown in FIG. 2) configured to collect smart meter data from one or more smart meters tasked with, for example, measuring and reporting electric power delivery and consumption at one or more of the users 150, 160. Alternatively (or additionally), the one or more collectors may be located external to the users 150, 160, such as, for example, in a housing holding the step-down transformers 140, 165, 167. Each of the collectors may be configured to communicate with the ROC 180.
VCC System 200
FIG. 2 shows an example of the VCC system 200 with the EVP system 600 monitoring the change in energy resulting from the VCC controlling the EEDS in the more efficient lower 5% band of voltage, according to principles of the disclosure. The VCC system 200 includes the ED system 300, the EC system 400 and the ER system 500, each of which is shown as a broken-line ellipse. The VCC system 200 is configured to monitor energy usage at the ED system 300. The ED system 300 monitors energy usage at one or more users 150, 160 (shown in FIG. 1) and sends energy usage information to the EC system 400. The EC system 400 processes the energy usage information and generates one or more energy delivery parameters CED, which it sends to the ER system 500. The ER system 500 receives the one or more energy delivery parameters CED and adjusts the electrical power ESupply(t) supplied to the users 150, 160 based on the received energy delivery parameters CED. The EVP system 600 receives the weather data and the energy usage data and calculates the energy usage improvement from the VCC.
The VCC system 200 minimizes power system losses, reduces user energy consumption and provides precise user voltage control. The VCC system 200 may include a closed loop process control application that uses user voltage data provided by the ED system 300 to control, for example, a voltage set point VSP on a distribution circuit (not shown) within the ER system 500. That is, the VCC system 200 may control the voltages VSupply(t) of the electrical power ESupply(t) supplied to the users 150, 160, by adjusting the voltage set point VSP of the distribution circuit in the ER system 500, which may include, for example, one or more load tap changing (LTC) transformers, one or more voltage regulators, or other voltage controlling equipment to maintain a tighter band of operation of the voltages VDelivered(t) of the electric power EDelivered(t) delivered to the users 150, 160, to lower power losses and facilitate efficient use of electrical power EDelivered(t) at the user locations 150 or 160.
The VCC system 200 controls or adjusts the voltage VSupply(t) of the electrical power ESupply(t) supplied from the EC system 500 based on smart meter data, which includes measured voltage VMeter(t) data from the users 150, 160 in the ED system 300, and based on validation data from the EVP system 600. The VCC system 200 may adjust the voltage set point VSP at the substation or line regulator level in the ER system 500 by, for example, adjusting the LTC transformer (not shown), circuit regulators (not shown), or the like, to maintain the user voltages VMeter(t) in a target voltage band VBand-n, which al may include a safe nominal operating range.
The VCC system 200 is configured to maintain the electrical power EDelivered(t) delivered to the users 150, 160 within one or more voltage bands VBand-n. For example, the energy may be delivered in two or more voltage bands VBand-n substantially simultaneously, where the two or more voltage bands may be substantially the same or different. The value VBand-n may be determined by the following expression [1]:

VBand-n=VSP+ΔV  [1]

where VBand-n is a range of voltages, n is a positive integer greater than zero corresponding to the number of voltage bands VBand that may be handled at substantially the same time, VSP is the voltage set point value and ΔV is a voltage deviation range.

For example, the VCC system 200 may maintain the electrical power EDelivered(t) delivered to the users 150, 160 within a band VBand-1 equal to, for example, 111V to 129V for rural applications, where VSP is set to 120V and ΔV is set to a deviation of seven-and-one-half percent (+/−7.5%). Similarly, the VCC system 200 may maintain the electrical power EDelivered(t) delivered to the users 150, 160 within a band VBand-2 equal to, for example, 114V to 126V for urban applications, where VSP is set to 120V and ΔV is set to a deviation of five (+/−5%).
The VCC system 200 may maintain the electrical power EDelivered(t) delivered to the users 150, 160 at any voltage band VBand-n usable by the users 150, 160, by determining appropriate values for VSP and ΔV. In this regard, the values VSP and ΔV may be determined by the EC system 400 based on the energy usage information for users 150, 160, received from the ED system 300.
The EC system 400 may send the VSP and ΔV values to the ER system 500 as energy delivery parameters CED, which may also include the value VBand-n. The ER system 500 may then control and maintain the voltage VDelivered(t) of the electrical power EDelivered(t) delivered to the users 150, 160, within the voltage band VBand-n. The energy delivery parameters CED may further include, for example, load-tap-changer (LTC) control commands.
The EVP system 600 may further measure and validate energy savings by comparing energy usage by the users 150, 160 before a change in the voltage set point value VSP (or voltage band VBand-n) to the energy usage by the users 150, 160 after a change in the voltage set point value VSP (or voltage band VBand-n), according to principles of the disclosure. These measurements and validations may be used to determine the effect in overall energy savings by, for example, lowering the voltage VDelivered(t) of the electrical power EDelivered(t) delivered to the users 150, 160, and to determine optimal delivery voltage bands VBand-n for the energy power EDelivered(t) delivered to the users 150, 160.
ER System 500
The ER system 500 may communicate with the ED system 300 and/or EC system 400 by means of the network 170. The ER system 500 is coupled to the network 170 and the EC system 400 by means of communication links 510 and 430, respectively. The EC system 500 is also coupled to the ED system 300 by means of the power lines 340, which may include communication links.
The ER system 500 includes a substation 530 which receives the electrical power supply EIn(t) from, for example, the power generating station 110 (shown in FIG. 1) on a line 520. The electrical power EIn(t) includes a voltage VIn(t) component and a current IIn(t) component. The substation 530 adjustably transforms the received electrical power Eat) to, for example, reduce (or step-down) the voltage component Vat) of the electrical power EIn(t) to a voltage value VSupply(t) of the electrical power ESupply(t) supplied to the plurality of smart meters 330 on the power supply lines 340.
The substation 530 may include a transformer (not shown), such as, for example, a load tap change (LTC) transformer. In this regard, the substation 530 may further include an automatic tap changer mechanism (not shown), which is configured to automatically change the taps on the LTC transformer. The tap changer mechanism may change the taps on the LTC transformer either on-load (on-load tap changer, or OLTC) or off-load, or both. The tap changer mechanism may be motor driven and computer controlled. The substation 530 may also include a buck/boost transformer to adjust and maximize the power factor of the electrical power EDelivered(t) supplied to the users on power supply lines 340.
Additionally (or alternatively), the substation 530 may include one or more voltage regulators, or other voltage controlling equipment, as known by those having ordinary skill in the art, that may be controlled to maintain the output the voltage component VSupply(t) of the electrical power ESupply(t) at a predetermined voltage value or within a predetermined range of voltage values.
The substation 530 receives the energy delivery parameters CED from the EC system 400 on the communication link 430. The energy delivery parameters CED may include, for example, load tap coefficients when an LTC transformer is used to step-down the input voltage component VIn(t) of the electrical power EIn(t) to the voltage component VSupply(t) of the electrical power ESupply(t) supplied to the ED system 300. In this regard, the load tap coefficients may be used by the ER system 500 to keep the voltage component VSupply(t) on the low-voltage side of the LTC transformer at a predetermined voltage value or within a predetermined range of voltage values.
The LTC transformer may include, for example, seventeen or more steps (thirty-five or more available positions), each of which may be selected based on the received load tap coefficients. Each change in step may adjust the voltage component VSupply(t) on the low voltage side of the LTC transformer by as little as, for example, about five-sixteenths (0.3%), or less.
Alternatively, the LTC transformer may include fewer than seventeen steps. Similarly, each change in step of the LTC transformer may adjust the voltage component VSupply(t) on the low voltage side of the LTC transformer by more than, for example, about five-sixteenths (0.3%).
The voltage component VSupply(t) may be measured and monitored on the low voltage side of the LTC transformer by, for example, sampling or continuously measuring the voltage component VSupply(t) of the stepped-down electrical power ESupply(t) and storing the measured voltage component VSupply(t) values as a function of time t in a storage (not shown), such as, for example, a computer readable medium. The voltage component VSupply(t) may be monitored on, for example, a substation distribution bus, or the like. Further, the voltage component VSupply(t) may be measured at any point where measurements could be made for the transmission or distribution systems in the ER system 500.
Similarly, the voltage component VIn(t) of the electrical power EIn(t) input to the high voltage side of the LTC transformer may be measured and monitored. Further, the current component ISupply(t) of the stepped-down electrical power ESupply(t) and the current component IIn(t) of the electrical power EIn(t) may also be measured and monitored. In this regard, a phase difference φIn(t) between the voltage VIn(t) and current IIn(t) components of the electrical power EIn(t) may be determined and monitored. Similarly, a phase difference φSupply(t) between the voltage VSupply(t) and current ISupply(t) components of the electrical energy supply ESupply(t) may be determined and monitored.
The ER system 500 may provide electrical energy supply status information to the EC system 400 on the communication links 430 or 510. The electrical energy supply information may include the monitored voltage component VSupply(t). The electrical energy supply information may further include the voltage component VIn(t), current components IIn(t), ISupply(t), and/or phase difference values φIn(t), φSupply(t), as a function of time t. The electrical energy supply status information may also include, for example, the load rating of the LTC transformer.
The electrical energy supply status information may be provided to the EC system 400 at periodic intervals of time, such as, for example, every second, 5 sec., 10 sec., 30 sec., 60 sec., 120 sec., 600 sec., or any other value within the scope and spirit of the disclosure, as determined by one having ordinary skill in the art. The periodic intervals of time may be set by the EC system 400 or the ER system 500. Alternatively, the electrical energy supply status information may be provided to the EC system 400 or ER system 500 intermittently.
Further, the electrical energy supply status information may be forwarded to the EC system 400 in response to a request by the EC system 400, or when a predetermined event is detected. The predetermined event may include, for example, when the voltage component VSupply(t) changes by an amount greater (or less) than a defined threshold value VSupplyThreshold (for example, 130V) over a predetermined interval of time, a temperature of one or more components in the ER system 500 exceeds a defined temperature threshold, or the like.
ED System 300
The ED system 300 includes a plurality of smart meters 330. The ED system 300 may further include at least one collector 350, which is optional. The ED system 300 may be coupled to the network 170 by means of a communication link 310. The collector 350 may be coupled to the plurality of smart meters 330 by means of a communication link 320. The smart meters 330 may be coupled to the ER system 500 by means of one or more power supply lines 340, which may also include communication links.
Each smart meter 330 is configured to measure, store and report energy usage data by the associated users 150, 160 (shown in FIG. 1). Each smart meter 330 is further configured to measure and determine energy usage at the users 150, 160, including the voltage component VMeter(t) and current component IMeter(t) of the electrical power EMeter(t) used by the users 150, 160, as a function of time. The smart meters 330 may measure the voltage component VMeter(t) and current component IMeter(t) of the electrical power EMeter(t) at discrete times ts, where s is a sampling period, such as, for example, s=5 sec., 10 sec., 30 sec., 60 sec., 300 sec., 600 sec., or more. For example, the smart meters 330 may measure energy usage every, for example, minute (t60 sec), five minutes (t300 sec), ten minutes (t600 sec), or more, or at time intervals variably set by the smart meter 330 (for example, using a random number generator).
The smart meters 330 may average the measured voltage VMeter(t) and/or IMeter(t) values over predetermined time intervals (for example, 5 min., 10 min., 30 min., or more). The smart meters 330 may store the measured electrical power usage EMeter(t) including the measured voltage component VMeter(t) and/or current component IMeter(t) as smart meter data in a local (or remote) storage (not shown), such as, for example, a computer readable medium.
Each smart meter 330 is also capable of operating in a “report-by-exception” mode for any voltage VMeter(t), current IMeter(t), or energy usage EMeter(t) that falls outside of a target component band. The target component band may include, a target voltage band, a target current band, or a target energy usage band. In the “report-by-exception” mode, the smart meter 330 may sua sponte initiate communication and send smart meter data to the EC system 400. The “report-by-exception” mode may be used to reconfigure the smart meters 330 used to represent, for example, the lowest voltages on the circuit as required by changing system conditions.
The smart meter data may be periodically provided to the collector 350 by means of the communication links 320. Additionally, the smart meters 330 may provide the smart meter data in response to a smart meter data request signal received from the collector 350 on the communication links 320.
Alternatively (or additionally), the smart meter data may be periodically provided directly to the EC system 400 (for example, the MAS 460) from the plurality of smart meters, by means of, for example, communication links 320, 410 and network 170. In this regard, the collector 350 may be bypassed, or eliminated from the ED system 300. Furthermore, the smart meters 330 may provide the smart meter data directly to the EC system 400 in response to a smart meter data request signal received from the EC system 400. In the absence of the collector 350, the EC system (for example, the MAS 460) may carry out the functionality of the collector 350 described herein.
The request signal may include, for example, a query (or read) signal and a smart meter identification signal that identifies the particular smart meter 330 from which smart meter data is sought. The smart meter data may include the following information for each smart meter 130, including, for example, kilo-Watt-hours (kWh) delivered data, kWh received data, kWh delivered plus kWh received data, kWh delivered minus kWh received data, voltage level data, current level data, phase angle between voltage and current, kVar data, time interval data, demand data, and the like.
Additionally, the smart meters 330 may send the smart meter data to the meter automation system server MAS 460. The smart meter data may be sent to the MAS 460 periodically according to a predetermined schedule or upon request from the MAS 460.
The collector 350 is configured to receive the smart meter data from each of the plurality of smart meters 330 via the communication links 320. The collector 350 stores the received smart meter data in a local storage (not shown), such as, for example, a computer readable medium. The collector 350 compiles the received smart meter data into a collector data. In this regard, the received smart meter data may be aggregated into the collector data based on, for example, a geographic zone in which the smart meters 330 are located, a particular time band (or range) during which the smart meter data was collected, a subset of smart meters 330 identified in a collector control signal, and the like. In compiling the received smart meter data, the collector 350 may average the voltage component VMeter(t) values received in the smart meter data from all (or a subset of all) of the smart meters 330.
The EC system 400 is able to select or alter a subset of all of the smart meters 330 to be monitored for predetermined time intervals, which may include for example 15 minute intervals. It is noted that the predetermined time intervals may be shorter or longer than 15 minutes. The subset of all of the smart meters 330 is selectable and can be altered by the EC system 400 as needed to maintain minimum level control of the voltage VSupply(t) supplied to the smart meters 330.
The collector 350 may also average the electrical power EMeter(t) values received in the smart meter data from all (or a subset of all) of the smart meters 330. The compiled collector data may be provided by the collector 350 to the EC system 400 by means of the communication link 310 and network 170. For example, the collector 350 may send the compiled collector data to the MAS 460 (or ROC 490) in the EC system 400.
The collector 350 is configured to receive collector control signals over the network 170 and communication link 310 from the EC system 400. Based on the received collector control signals, the collector 350 is further configured to select particular ones of the plurality of smart meters 330 and query the meters for smart meter data by sending a smart meter data request signal to the selected smart meters 330. The collector 350 may then collect the smart meter data that it receives from the selected smart meters 330 in response to the queries. The selectable smart meters 330 may include any one or more of the plurality of smart meters 330. The collector control signals may include, for example, an identification of the smart meters 330 to be queried (or read), time(s) at which the identified smart meters 330 are to measure the VMeter(t), IMeter(t), EMeter(t) and/or φMeter(t) (φMeter(t) is the phase difference between the voltage VMeter(t) and current IMeter(t) components of the electrical power EMeter(t) measured at the identified smart meter 330), energy usage information since the last reading from the identified smart meter 330, and the like. The collector 350 may then compile and send the compiled collector data to the MAS 460 (and/or ROC 490) in the EC system 400.
EC System 400
The EC system 400 may communicate with the ED system 300 and/or ER system 500 by means of the network 170. The EC system 400 is coupled to the network 170 by means of one or more communication links 410. The EC system 400 may also communicate directly with the ER system 500 by means of a communication link 430.
The EC system 400 includes the MAS 460, a database (DB) 470, a distribution management system (DMS) 480, and a regional operation center (ROC) 490. The ROC 490 may include a computer (ROC computer) 495, a server (not shown) and a database (not shown). The MAS 460 may be coupled to the DB 470 and DMS 480 by means of communication links 420 and 440, respectively. The DMS 480 may be coupled to the ROC 490 and ER SYSTEM 500 by means of the communication link 430. The database 470 may be located at the same location as (for example, proximate to, or within) the MAS 460, or at a remote location that may be accessible via, for example, the network 170.
The EC system 400 is configured to de-select, from the subset of monitored smart meters 330, a smart meter 330 that the EC system 400 previously selected to monitor, and select the smart meter 330 that is outside of the subset of monitored smart meters 330, but which is operating in the report-by-exception mode. The EC system 400 may carry out this change after receiving the sua sponte smart meter data from the non-selected smart meter 330. In this regard, the EC system 400 may remove or terminate a connection to the de-selected smart meter 330 and create a new connection to the newly selected smart meter 330 operating in the report-by-exception mode. The EC system 400 is further configured to select any one or more of the plurality of smart meters 330 from which it receives smart meter data comprising, for example, the lowest measured voltage component VMeter(t) and generate an energy delivery parameter CED based on the smart meter data received from the smart meter(s) 330 that provide the lowest measured voltage component VMeter(t).
The MAS 460 may include a computer (not shown) that is configured to receive the collector data from the collector 350, which includes smart meter data collected from a selected subset (or all) of the smart meters 330. The MAS 460 is further configured to retrieve and forward smart meter data to the ROC 490 in response to queries received from the ROC 490. The MAS 460 may store the collector data, including smart meter data in a local storage and/or in the DB 470.
The DMS 480 may include a computer that is configured to receive the electrical energy supply status information from the substation 530. The DMS 480 is further configured to retrieve and forward measured voltage component VMeter(t) values and electrical power EMeter(t) values in response to queries received from the ROC 490. The DMS 480 may be further configured to retrieve and forward measured current component IMeter(t) values in response to queries received from the ROC 490. The DMS 480 also may be further configured to retrieve all “report-by-exception” voltages VMeter(t) from the smart meters 330 operating in the “report-by-exception” mode and designate the voltages VMeter(t) as one of the control points to be continuously read at predetermined times (for example, every 15 minutes, or less (or more), or at varying times). The “report-by-exception voltages VMeter(t) may be used to control the EC 500 set points.
The DB 470 may include a plurality of relational databases (not shown). The DB 470 includes a large number of records that include historical data for each smart meter 330, each collector 350, each substation 530, and the geographic area(s) (including latitude, longitude, and altitude) where the smart meters 330, collectors 350, and substations 530 are located.
For instance, the DB 470 may include any one or more of the following information for each smart meter 330, including: a geographic location (including latitude, longitude, and altitude); a smart meter identification number; an account number; an account name; a billing address; a telephone number; a smart meter type, including model and serial number; a date when the smart meter was first placed into use; a time stamp of when the smart meter was last read (or queried); the smart meter data received at the time of the last reading; a schedule of when the smart meter is to be read (or queried), including the types of information that are to be read; and the like.
The historical smart meter data may include, for example, the electrical power EMeter(t) used by the particular smart meter 330, as a function of time. Time t may be measured in, for example, discrete intervals at which the electrical power EMeter magnitude (kWh) of the received electrical power EMeter(t) is measured or determined at the smart meter 330. The historical smart meter data includes a measured voltage component VMeter(t) of the electrical energy EMeter(t) received at the smart meter 330. The historical smart meter data may further include a measured current component IMeter(t) and/or phase difference φMeter(t) of the electrical power EMeter(t) received at the smart meter 330.
As noted earlier, the voltage component VMeter(t) may be measured at a sampling period of, for example, every five seconds, ten seconds, thirty seconds, one minute, five minutes, ten minutes, fifteen minutes, or the like. The current component IMeter(t) and/or the received electrical power EMeter(t) values may also be measured at substantially the same times as the voltage component VMeter(t).
Given the low cost of memory, the DB 470 may include historical data from the very beginning of when the smart meter data was first collected from the smart meters 330 through to the most recent smart meter data received from the smart meter 330s. 
The DB 470 may include a time value associated with each measured voltage component VMeter(t), current component IMeter(t) phase component φMeter(t) and/or electrical power EMeter(t), which may include a timestamp value generated at the smart meter 330. The timestamp value may include, for example, a year, a month, a day, an hour, a minute, a second, and a fraction of a second. Alternatively, the timestamp may be a coded value which may be decoded to determine a year, a month, a day, an hour, a minute, a second, and a fraction of a second, using, for example, a look up table. The ROC 490 and/or smart meters 330 may be configured to receive, for example, a WWVB atomic clock signal transmitted by the U.S. National Institute of Standards and Technology (NIST), or the like and synchronize its internal clock (not shown) to the WWVB atomic clock signal.
The historical data in the DB 470 may further include historical collector data associated with each collector 350. The historical collector data may include any one or more of the following information, including, for example: the particular smart meters 330 associated with each collector 350; the geographic location (including latitude, longitude, and altitude) of each collector 350; a collector type, including model and serial number; a date when the collector 350 was first placed into use; a time stamp of when collector data was last received from the collector 350; the collector data that was received; a schedule of when the collector 350 is expected to send collector data, including the types of information that are to be sent; and the like.
The historical collector data may further include, for example, an external temperature value TCollector(t) measured outside of each collector 350 at time t. The historical collector data may further include, for example, any one or more of the following for each collector 350: an atmospheric pressure value PCollector(t) measured proximate the collector 350 at time t; a humidity value HCollector(t) measured proximate the collector 350 at time t; a wind vector value WCollector(t) measured proximate the collector 350 at time t, including direction and magnitude of the measured wind; a solar irradiant value LCollector(t) (kW/m2) measured proximate the collector 350 at time t; and the like.
The historical data in the DB 470 may further include historical substation data associated with each substation 530. The historical substation data may include any one or more of the following information, including, for example: the identifications of the particular smart meters 330 supplied with electrical energy ESupply(t) by the substation 530; the geographic location (including latitude, longitude, and altitude) of the substation 530; the number of distribution circuits; the number of transformers; a transformer type of each transformer, including model, serial number and maximum Megavolt Ampere (MVA) rating; the number of voltage regulators; a voltage regulator type of each voltage regulator, including model and serial number; a time stamp of when substation data was last received from the substation 530; the substation data that was received; a schedule of when the substation 530 is expected to provide electrical energy supply status information, including the types of information that are to be provided; and the like.
The historical substation data may include, for example, the electrical power ESupply(t) supplied to each particular smart meter 330, where ESupply(t) is measured or determined at the output of the substation 530. The historical substation data includes a measured voltage component VSupply(t) of the supplied electrical power ESupply(t), which may be measured, for example, on the distribution bus (not shown) from the transformer. The historical substation data may further include a measured current component ISupply(t) of the supplied electrical power ESupply(t). As noted earlier, the voltage component VSupply(t), the current component ISupply(t), and/or the electrical power ESupply(t) may be measured at a sampling period of, for example, every five seconds, ten seconds, thirty seconds, a minute, five minutes, ten minutes, or the like. The historical substation data may further include a phase difference value φSupply(t) between the voltage VSupply(t) and current ISupply(t) signals of the electrical power ESupply(t), which may be used to determine the power factor of the electrical power ESupply(t) supplied to the smart meters 330.
The historical substation data may further include, for example, the electrical power EIn(t) received on the line 520 at the input of the substation 530, where the electrical power EIn(t) is measured or determined at the input of the substation 530. The historical substation data may include a measured voltage component VIn(t) of the received electrical power EIn(t), which may be measured, for example, at the input of the transformer. The historical substation data may further include a measured current component IIn(t) of the received electrical power EIn(t). As noted earlier, the voltage component VIn(t), the current component IIn(t), and/or the electrical power EIn(t) may be measured at a sampling period of, for example, every five seconds, ten seconds, thirty seconds, a minute, five minutes, ten minutes, or the like. The historical substation data may further include a phase difference φIn(t) between the voltage component VIn(t) and current component IIn(t) of the electrical power EIn(t). The power factor of the electrical power EIn(t) may be determined based on the phase difference φIn(t).
According to an aspect of the disclosure, the EC system 400 may save aggregated kW data at the substation level, voltage data at the substation level, and weather data to compare to energy usage per smart meter 330 to determine the energy savings from the VCC system 200, and using linear regression to remove the effects of weather, load growth, economic effects, and the like, from the calculation.
In the VCC system 200, control may be initiated from, for example, the ROC computer 495. In this regard, a control screen 305 may be displayed on the ROC computer 495, as shown, for example, in FIG. 3 of US publication 2013/0030591. The control screen 305 may correspond to data for a particular substation 530 (for example, the TRABUE SUBSTATION) in the ER system 500. The ROC computer 495 can control and override (if necessary), for example, the substation 530 load tap changing transformer based on, for example, the smart meter data received from the ED system 300 for the users 150, 160. The ED system 300 may determine the voltages of the electrical power supplied to the user locations 150, 160, at predetermined (or variable) intervals, such as, e.g., on average each 15 minutes, while maintaining the voltages within required voltage limits.
For system security, the substation 530 may be controlled through the direct communication link 430 from the ROC 490 and/or DMS 480, including transmission of data through communication link 430 to and from the ER 500, EUS 300 and EVP 600.
Furthermore, an operator can initiate a voltage control program on the ROC computer 490, overriding the controls, if necessary, and monitoring a time it takes to read the user voltages VMeter(t) being used for control of, for example, the substation LTC transformer (not shown) in the ER system 500.
FIG. 3 shows the energy validation process 600 for determining the amount of conservation in energy per customer realized by operating the VCC system in FIGS. 1-2. The process is started 601 and the data the ON and OFF periods is loaded 602 by the process manager. The next step is to collect 603 the hourly voltage and power (MW) data from the metering data points on the VCC system from the DMS 480 which may be part of a supervisory control and data acquisition (SCADA) type of industrial control system. Next the corresponding weather data is collected 604 for the same hourly conditions. The data is processed 605, 606, 607, 608 to improve its quality using filters and analysis techniques to eliminate outliers that could incorrectly affect the results, as describe further below. If hourly pairing is to be done the hourly groups are determined 609 using the linear regression techniques. The next major step is to determine 611, 612, 613, 614, 615, 616, 617 the optimal pairing of the samples, as described further below.
FIG. 4 shows an example of the database structure where the initial data for analysis is kept. This relational data base allows for fast processing of the data and marking of data that is not to be used because of the anomalies. The efficient storage of the data for continued analysis is useful to provide the evaluation performance for the EVP.
FIG. 5 shows an example of an application of the data quality reviews of the data before processing. Using the database the values are scanned for out of range levels in all categories, such as the zero MW readings 622 and the very low voltage readings 623. These are identified and removed before processing. Second, it shows repeated values, such as the repeated voltage reading of 122.331 volts in the data box 621, that are indicative of bad measurements and would severely degrade the calculation of the energy change. These are also removed from the future calculations. Known anomalies, including missing records due to daylight savings time changes 624, are removed as well.
FIG. 6 shows an example of a frequency plot of voltage at hourly intervals. It is expected that most of the data will follow a normalized form when analyzed. This makes it easier to spot poor quality data in the outlier data as shown here. Data are reviewed using the frequency plots and the outliers are reviewed for consistency with normal operating conditions on the system. The outliers, such as voltages 623, can be eliminated if they fall outside of predetermined bands. This is an example of analysis applied to all of the variables.
FIG. 7 shows an example of histogram plots of the “OFF” to “ON” data comparisons for both voltage and MW. As is seen on the top two diagrams the voltage for the “ON” state has a significantly wider deviation that the “OFF” data. This is also a concern when the standard deviation of the comparison data does not match. Data sets having out of range or non-matching standard deviations may be filtered out. In contrast the MW data had very consistent standard deviations and very little differences in the “ON” to “OFF” state population characteristics.
FIG. 8 shows an example of the comparison of scatterplots of the “OFF” (black points on the scatterplots) to “ON” (red points on the scatterplots) populations by season and by group. These plots are useful for reviews of the level of the sampling across the entire performance levels. As can be seen from these examples there are a number of areas 624, 625 where there are no “ON” samples for large areas of the “OFF” performance levels. This means that more sampling will be needed to accurately represent these conservation performance zones. The quick review of the scatterplots can give a significant knowledge of the sample size and adequacy for this type of measurement.
FIG. 9 shows an example of the high level pairing process which is based on a well-established statistical comparison technique called paired t. The purpose of this calculation is to compare two samples of data to determine the average shift in a variable mean from one sample set to the other. Documentation of the details of paired t analysis can be found in a number of standard statistic publications and is readily available in standard software packages. FIG. 9 is a high level description of the process being applied to the Substation 530 Transformer and ED 300 circuit MW and Voltage data. The value being calculated is the CVR factor which establishes the ratio of (a) the percent power (watts) change from sample 1 (P1) to sample 2 (P2) to (b) the percent voltage (volts) change from sample 1 (V1) to sample 2 (V2). The CVR factor=((P1−P2)/P1)/((V1−V2)/V1). Sample 1 is take from the MW and Voltage data at the meter when the CVR control system is “OFF” and Sample 2 is taken from the data when CVR is “ON”. A larger CVR factor indicates more power savings from reduction in voltage, with common observed CVR factors for some CVR systems being observed in the range of about 0.2 to 1.2.
Sets of samples are paired using the rules of FIG. 9. Records 1 and 2 in FIG. 9 are for Samples 1 and 2 respectively. For Samples 1 and 2: the immediately upstream transformer (TX #1) must be the same (=); the Status (e.g., whether CVR is OFF or ON) must be different (< >), the Day Type (e.g., workday, weekend or holiday) must be equal (=); Cooling degree days and heating degree days (CDD/HDD) should each be matched within plus or minus one degree day (±1 DD), and relative humidity should be matched within plus or minus five percent (±5% RH). This matching of two samples from the “OFF” and “ON” states creates one pair of samples. Once paired, the volts and power from the Samples 1 and 2 may be used for the CVR factor calculation. At least 30 of these pairs are required for the calculation of the average difference between the two sample sets to have statistical significance (about a 95% confidence level).
There are three features of the paired t analysis for the illustrated embodiment. First the paired samples are independent. This requires that for each sample taken from a data set, whether for sample 1 (OFF state) or sample 2 (ON state), the values from the sample can only be used and paired one time in the analysis. Once they are used, the samples are removed from the data sets to choose the next pair. The second feature is that the data sets are normal data sets. This is checked statistically for each analysis. Normality is checked using the Anderson-Darling normality test. Third, the number of paired t samples are greater than about 30 to be statistically significant. This calculation will be shown for each set of analysis. Once these three features are present, the paired t analysis is implemented and the average difference is determined within a confidence interval determined by the variation of the paired samples. The illustrated embodiment uses 95% confidence level for the CVR analysis.
FIG. 10 shows an example of a method used for decreasing the variation in the calculation by separating the samples into consistent groups. For the MW and Voltage data this is done by grouping the sample data into like hours that are consistent with each other. This may be done with a linear regression technique. Using linear regression, the consistency of the variables is checked. Samples taken in the same hour of the day (hours 0 through 23 in the heading of the table of FIG. 10) are grouped and are noted in the same column in FIG. 10. Sample hours that represent like data are determined by using the linear regression constants to check consistency between hours that are grouped together. In addition each data set is grouped into a seasonal grouping as well. The result of this grouping process is to first break the sample data up into the seasonal groups of winter, spring, summer and fall. Then using the linear regression break the hours for each seasonal day (0 to 23) into like groups for paired t testing. This technique will lessen the variation in the paired t calculation for average difference from one sample group to another. The table in FIG. 10 is an example of this type of process.
FIG. 11 shows the detailed pairing process for a multiple variable example of the VCC pairing both HDD/CDD along with humidity. The process creates a total list of possible matched pairs in all combinations. Each pair is scored based on a linear optimization method to weigh the independent variable appropriately based on its energy effect and use the linearization to form the optimal scoring for the pair including both independent variables of HDD/CDD and humidity using linear regression constants. For example, if the energy effect (e.g., change in CVR factor) for HDD/CDD is five times the energy effect for humidity, a difference in HDD/CDD between samples is weighted five times as much as the difference in humidity between samples.
Once this process is complete the list is reviewed for the best score. These are paired and removed from the pairing list. The process is repeated for each of the remaining pairs until all pairs have been optimally matched for variables within the tolerance levels as shown in the process diagram of FIG. 11. In this way the pairing is optimized to the population giving the best accuracy for the data available, according to the illustrated embodiment with the given criteria.
FIG. 12 shows an example of the histogram of the data from the CVR factor pairing calculation. It is noted that the pairing is normalized and fits the characteristics of the t-distribution. With this information the data can be used to evaluate the range of average values of the CVR factor for the circuit during the time period the data was taken. This data can be calculated for a data set of 30 or more and will produce an accurate representation of the range of the CVR factor. Each data set requires a one-day time period. Normally the 95% confidence interval is used to determine a usable range for the CVR factor. This statistical factor is specifically for the circuit under evaluation and provides ongoing evaluation of the performance of the circuit down to a minimum of 30 data sets and thus a 30-day interval.
FIG. 13 shows an example of the histogram and the scatterplot of the energy saving per customer over the interval from the same paired t analysis. The top graph is a measure of the kW/customer change and has the same type of normalized characteristic that is compatible with the t-distribution confidence interval analysis. The scatterplot of the paired population plotted in an “OFF” to “ON” state give a quick intuitive evaluation of the paired data. In general if the majority of the pairs are below the red line the VCC system is improving the conservation, if they are equally spaced on either side of the line it is not having any effect and if they are on average above the line it is having the reverse effect. In this case it is easy to see that the samples are clearly showing improvement in conservation of energy.
FIG. 14 shows an alternative example of the CVR factor analysis for another circuit. The graph on the right of FIG. 14. demonstrates the characteristics for a measurement done without the control circuit being used to compensate for the other independent variables. The results show a non-normal population with a much higher CVR savings. In this case there was a substantial decrease in load because of lower electricity demand due to the downturn in the economy, thus making the CVR factor look abnormally high. The graph on the left of FIG. 14 is with the control circuit and uses the circuit to remove the non-normal effects of the negative economic growth. The CVR factor using this control circuit does decrease but the normality becomes very strong and the data is back in a normal range for the VCC control system to be the only independent variable controlling the effects.
FIG. 15 shows an example of the final calculations on both the CVR factor and the savings in energy derive from the optimal pairing of the VCC system energy. This results in a direct calculation of the capacity of the circuit to reduce energy as stated in the CVR factor. This capacity is its ability to conserve energy by reducing voltage in the lower operating band. The VCC system executes this type of control and the EVP independently calculates the capacity of the circuit to continue to conserve as other modifications to the voltage performance are implemented.
FIG. 15 also shows an example of the final calculations for energy savings during the measurement time under study. This energy savings is a continuous reporting of the circuit's ability to continue to sustain the conservation savings that were calculated for the VCC system. This ability to continuously track the performance instead of having to do repeated one time testing of the circuit to estimate the performance represents a major step forward in the technology. Existing systems are based on one time tests that greatly reduce the efficiency performance just to estimate the CVR factor and the energy saving performance. In addition they must be repeated on regular intervals to determine if the saving is being sustained. This EVP system provides a major step forward in being able to generate near metered savings without reducing the efficiency of the VCC system.
In one example embodiment a paired t approach blends a CVR modification ON-OFF approach with filters that leverage the benefits of a regression analysis. This blended approach may provide meaningful results in a shorter period of time than an unblended approach and allows users of the blended approach to have little to no down time after beginning an implementation of a CVR program. The end result is a CVR factor which represents the percent change in energy for a percent change in voltage. This CVR factor can then be used with hourly load and voltage data to sum energy savings during a CVR deployment.
One goal of a measurement and verification analysis is a clean view of performance with and without voltage reduction in service. In one example, a system can be configured to calculate savings at a CVR node (e.g. a substation transformer or circuit) level using substation electrical energy supplied (MW) and bus voltage (line voltage) to compare energy usage before (OFF) and after (ON) voltage reduction. A CVR node may include, for example, a substation transformer or circuit or other location where CVR is to be calculated, which can be at any location within a power generation, transmission, and distribution system.
In one embodiment, the data may be from the same season to avoid skewing due to load changes between heating, cooling and other factors. The data analysis can be performed across time (e.g. summer 2013 vs. summer 2014) or within a season (e.g. July 2014 vs. August 2014), or it can accommodate a structured day on—day off test run for a minimum time period, for example about two weeks.
In one example embodiment, a process can include: inputting ON-OFF data; applying filters, such as filtering for weather and outliers; creating pairs and generating a CVR factor; and using the CVR factor to calculate savings.
Further to the description discussing FIGS. 8 and 10, in another example embodiment, data collected from the “ON” state and data collected from the “OFF” state can be from the same season to avoid skewing due to load changes between heating, cooling, and other seasonal factors. The data can be analyzed across time, for example summer 2013 versus summer 2014; within a season, for example July 2014 versus August 2014; or across a structured day on-day off test for a minimum time period, for example about two weeks.
In an example embodiment using an hourly pairing, hours in an ON period can be compared to hours in an OFF period, for example every hour in an ON period can be compared to every hour in an OFF period. In one example embodiment, once all filters, which will be discussed in more detail below, have been applied, a pool of acceptable pairs is generated. As will be discussed further below, an outlier filter may be applied before or after a pool of acceptable pairs is generated. The pairs can be rank ordered based on one or more criteria, for example quality of temperature match; quality of humidity match; hour type match; and any other custom filter, and can be followed by a tie-breaker criteria, which in one example embodiment can be time elapsed between records. Once records have been rank-ordered, a selection of pairs, for example the best pairs, can be chosen until a desired number of pairs have been matched, for example all possible pairs have been matched.
Filters can be used to refine an ON-OFF pairing process. One or more filters can be used on a set of data to achieve paired records and may function as a modified regression method for pairing records. In one example, these filters ensure that the primary difference between records is the voltage change. In one embodiment, one or more filters can be used on a set of data to achieve paired records within specific tolerances, or nearly identical. In one example, this method compares ON and OFF records that are nearly identical in terms of weather and therefore in terms of expected load.
The following are example filters that can be employed alone or in combination with any number of the other filters: a Temperature Filter; a first prior time filter, for example, a prior 6 Hours Filter that averages the temperature, or other measurement, over the prior 6 hours; a second prior time filter, for example, a prior 72 Hours Filter that averages the temperature, or other measurement, over the prior 72 hours; a Humidity Filter; a 168-Hourly Index Filter; a MW Change Filter; a Voltage Change Filter; a Custom Filter; and an Outlier Filter. Any suitable combination of filters may be chosen. FIG. 16 shows an example embodiment employing a Temperature Filter, a Prior 6 Hours Filter, a Prior 72 Hours Filter, a Humidity Filter, a 168-Hourly Index Filter, a MW Change Filter; a Voltage Change Filter; a Custom Filter, and an Outlier Filter, along with example specific tolerances of each filter.
In one example embodiment of the Temperature Filter, the Temperature Filter may exclude records that are not within a specified temperature difference. Any suitable temperature difference may be used. FIG. 16 shows one example embodiment where the temperature difference is limited to 1 degree Fahrenheit.
In one example embodiment of the Prior 6 Hour Filter, the Prior 6 Hour Filter may exclude records that are not within a specified average temperature difference for the 6 hours immediately preceding the time when the record was taken. Any suitable average temperature difference may be used. FIG. 16 shows one example embodiment where the average temperature difference is limited to 1 degree Fahrenheit.
In one example embodiment of the Prior 72 Hour Filter, the Prior 72 Hour Filter may exclude records that are not within a specified average temperature difference for the 72 hours immediately preceding the time when the record was taken. Any suitable average temperature difference may be used. FIG. 16 shows one example embodiment where the average temperature difference is limited to 1 degree Fahrenheit.
In one example embodiment of the Relative Humidity Filter, the Relative Humidity Filter may exclude records that are not within a specified humidity difference. Any suitable humidity difference may be used. In one example embodiment, the difference is limited to less than or equal to about two percentage points. In another example embodiment, the difference is limited to less than or equal to about five percentage points. FIG. 16 shows one example embodiment where the difference is limited to less than or equal to one percentage point.
In one example embodiment of the 168 Hourly Index Filter, the 168 Hourly Index Filter can be analyzed with a statistical regression to determine the loading impact of an hour, so that similar hours can be paired. For the 168 Hourly Index, each hour in a seven day time period is given an index number (i.e., from 1 to 168) corresponding to the chronological occurrence of that hour. In an example embodiment using hourly pairing, it is preferable for the hours to have similar loading characteristics. The 168 hours in a particular week are analyzed with a statistical regression to determine the loading impact of the hours independent of the temperature and humidity impacts, so that similar hours can be paired. It can be appreciated that other suitable index windows may be employed. FIG. 16 shows an example embodiment where the 168 Hourly Index is configured to filter records that are not within one index number.
In one example embodiment of the MW Filter, the MW Filter may exclude data points that demonstrate large changes in energy consumption. A certain reduction is expected, but large swings may indicate that external forces are at contributing. Any suitable percent change may be used. In one example embodiment, the percent change can be limited to less than or equal to 3%. FIG. 16 shows another example embodiment where the percent change can be limited to less than or equal to 5%.
In one example embodiment of the Voltage Filter, the Voltage Filter may ensure that the two records do not have voltages within a specified voltage range. Any suitable voltage range may be used. FIG. 16 shows one example embodiment where the records must be at least about 1 volt apart.
In one example embodiment of the Custom Filter, the Custom Filter may allow users to create a custom filter of their choice that may be unique to their service territory. Any suitable custom filter may be used. FIG. 16 shows one example embodiment where the custom filter is set to a linear value correlation.
In one example embodiment of the Outlier Filter, the Outlier Filter may be applied with the other filters or can be applied after the records have been paired, and can be used to analyze the resulting pairs for outliers. Any suitable outlier filtering process may be used. In one example embodiment, the Outlier Filter uses Median Absolute Deviation (MAD) to filter out resulting pairs that are extreme outliers. FIG. 16 shows one example embodiment using MAD, wherein the Outlier Filter is limited to a MAD of plus or minus 5.
Further to the description discussing FIG. 13, FIG. 17 shows an example of a scatterplot of the paired population. Each dot represents a paired set of hours at their respective loads. The paired population plotted in an “OFF” to “ON” state gives a quick intuitive evaluation of the paired data. The data points below the line indicate the ON record had a lower load than the OFF record in the pair, indicating a load reduction for the matched pair. In general, if the majority of the pairs are below the line, the VCC system is improving the conservation, if they are equally spaced on either side of the line it is not having any effect, and if they are on average above the line it is having the reverse effect. In the example embodiment of FIG. 17, the scatterplot shows that in most cases shown, the ON record had lower load than its corresponding OFF record, showing an improvement in conservation of energy.
Further to the description of FIG. 12, FIG. 18 shows an example of the histogram of the data from the CVR factor pairing calculation. It is noted that the pairing is normalized and fits the characteristics of the t-distribution. With this information the data can be used to evaluate the range of average values of the CVR factor for the circuit during the time period the data was taken. The mean CVR factor across all pairs can be used as the CVR factor for the time period analyzed. In one example embodiment, this data can be calculated for a data set of 30 or more and will produce an accurate representation of the range of the CVR factor. Normally the 95% confidence interval is used to determine a usable range for the CVR factor. This statistical factor is specifically for the circuit under evaluation and provides ongoing evaluation of the performance of the circuit down to a minimum of 30 data sets.
Once a CVR factor has been established, the savings calculation becomes a function of comparing the actual hourly voltages to a baseline voltage. The resulting percent reduction in voltage, combined with the CVR factor, gives a percent reduction in energy usage. In one configuration, the system may use the hourly energy savings computation to sum all hours and give a monthly or seasonal energy savings value. The resulting electric power control system produces a statistically accurate, repeatable energy savings measurement while preserving energy savings.
In one example embodiment using a paired t approach, a process for calculating savings at a CVR node may include the following steps: collecting data when a modification to the electric power system is in the OFF state; collecting data when a modification to the electrical power system is in the ON state; collecting other common ambient and/or weather condition data on for each ON state and OFF state data point, said other common ambient and/or weather conditions comprising one or more of temperature, heating degree, cooling degree, humidity, prior 6 hour average, prior 72 hour average, 168 hourly index, electrical energy, volts, custom conditions, and outliers; applying filters; creating pairs; generating a CVR factor; and using the CVR factor with hourly load and voltage data to sum energy savings during the CVR deployment.
While the disclosure has been described in terms of exemplary embodiments, those skilled in the art will recognize that the disclosure can be practiced with modifications in the spirit and scope of the appended claims. These examples are merely illustrative and are not meant to be an exhaustive list of all possible designs, embodiments, applications or modifications of the disclosure.

## Claims

1. A control system for an electric power grid configured to supply electric power from a supply point to a plurality of consumption locations, the system comprising:
a plurality of sensors, wherein each sensor is located at a respective one of a plurality of distribution locations on the distribution grid at or between the supply point and at least one of the plurality of consumption locations, and wherein each sensor is configured to sense a component of the supplied electric power received at the respective distribution location and to generate measurement data based on the sensed component of the power;
a controller configured to receive measurement data from the plurality of sensors, and to operate the electric power grid in a modification-on state or in a modification-off state; wherein the controller generates an energy delivery parameter based on the measurement data;
a component adjusting device configured to adjust a component of the electric power grid in response to the energy delivery parameter;
wherein the controller is configured to determine, using an energy validation process, the change in energy characteristics between the modification-on state and the modification-off state, using a paired comparison, wherein the paired comparison includes a pairing process and the pairing process includes applying at least one filter selected from the group consisting of:
a temperature filter configured to not pair records that are not within a predetermined temperature difference,
a prior time filter configured to not pair records that are not within a predetermined average temperature difference for a predetermined prior time period immediately preceding the time when the records were taken,
a relative humidity filter configured to not pair records that are not within a predetermined humidity difference,
an interval index filter configured to order sub-interval periods by a loading impact of each sub-interval period and to not pair records that are not within a predetermined index offset value,
MW filter configured to not pair records that not within a predetermined MW difference,
a voltage filter configured to not pair records that not within a predetermined voltage difference,
a linear value correlation filter configured to not pair records that not within a predetermined linear correlation factor, and
an outlier filter configured to exclude paired records greater than a predetermined Median Absolute Deviation (MAD).

2. The system of claim 1, wherein the controller generates an energy delivery parameter based on the measurement data when the controller is in the modification-on state, but not when the controller is in the modification-off state.

3. The system of claim 1, wherein the component of the supplied electric power is measured by the sensors on an interval basis.

4. The system of claim 1, wherein the component of the supplied electric power is voltage.

5. The system of claim 4, wherein the component of the electric power grid adjusting device comprises: a load tap change transformer that adjusts the voltage of the electric power supplied at the supply point based on a load tap change coefficient; or a voltage regulator that adjusts the voltage of the electric power supplied at the supply point based on the energy delivery parameter.

6. The system of claim 5, wherein the load tap change transformer is located at a substation and at least one sensor is located at the substation.

7. The system of claim 1, wherein the modification is conservation voltage reduction (CVR).

8. The system of claim 7, wherein the change in energy characteristic is determined at a a CVR node.

9. The system of claim 1, wherein the energy characteristic is the conservation voltage reduction factor.

10. The system of claim 1, wherein the energy characteristic is the energy savings.

11. The system of claim 1, wherein the paired comparison includes a pairing process for selecting pairs of data to compare.

12. The system of claim 11, wherein the paired comparison includes a paired t measurement.

13. The system of claim 1, wherein the paired comparison comprises an additional process that breaks the paired comparison process into measurements by season and uses linear regression constants to determine the blocks of hours where consistent loads exist and paired t comparisons can be calculated within predetermined limits.

14. The system of claim 1, wherein the predetermined prior time of the prior time filter is about six hours.

15. The system of claim 1, wherein the predetermined prior time of the prior time filter is about seventy two hours.

16. The system of claim 1, wherein the at least one filter includes the outlier filter and the outlier filter is configured to exclude paired records greater than a predetermined Median Absolute Deviation (MAD).

17. The system of claim 1, wherein the controller is configured to use the paired t p-factor to eliminate data having values outside of corresponding predetermined normalized ranges of values to determine measurement accuracy.

18. The system of claim 1, wherein the controller is configured to determining the change in energy characteristic based on a first variable.

19. The system of claim 18, wherein the first variable is season, grouped hour, or customer type.

20. The system of claim 18, wherein the controller is configured to provide a second pairing variable that is secondary to the first pairing variable, to pair the first variable values to the closest modification-off to modification-on values, and determining a weighed scoring of the pairs based on the relative slopes of the linear relationship between the first and second respective variables.

21. The system of claim 1, wherein the controller is configured to exclude data that is affected by non-efficiency variables.

22. A method for controlling electrical power supplied to a plurality of distribution locations located at or between a supply point and at least one consumption location, each of the plurality of distribution locations including at least one sensor configured to sense a voltage of the supplied electric power received at the respective distribution location and generate measurement data based on the sensed voltage, the method comprising:
controlling the electric power grid in a modification-on state or in a modification-off state; wherein a controller generates an energy delivery parameter based on the measurement data when the controller is in the modification-on state, but not when the controller is in the modification-off state;
operating an component adjusting device configured to adjust a component of the electric power grid in response to the energy delivery parameter;

measuring the component of the supplied electric power with the sensors on an interval basis using an energy validation process, and determining the change in energy characteristics between the voltage conservation-voltage-reduction-on state and the conservation-voltage-reduction-off being using a paired comparison, wherein the paired comparison includes a pairing process and the pairing process includes applying at least one filter selected from the group consisting of:
a temperature filter configured to not pair records that are not within a predetermined temperature difference,
a prior time filter configured to not pair records that are not within a predetermined average temperature difference for a predetermined prior time period immediately preceding the time when the records were taken,
a relative humidity filter configured to not pair records that are not within a predetermined humidity difference,
an interval index filter configured to order sub-interval periods by a loading impact of each sub-interval period and to not pair records that are not within a predetermined index offset value,
a MW filter configured to not pair records that not within a predetermined MW difference,
a voltage filter configured to not pair records that not within a predetermined voltage difference,
a linear value correlation filter configured to not pair records that not within a predetermined linear correlation factor, and
an outlier filter configured to exclude paired records greater than a predetermined Median Absolute Deviation (MAD).

23. The method of claim 22, wherein the component of the supplied electric power is voltage.

24. The method of claim 22, wherein the modification is conservation voltage reduction (CVR).

25. The method of claim 22, wherein the component of the electric power grid adjusting device comprises: a load tap change transformer that adjusts the voltage of the electric power supplied at the supply point based on a load tap change coefficient; or a voltage regulator that adjusts the voltage of the electric power supplied at the supply point based on the energy delivery parameter.

26. The method of claim 22 wherein the energy characteristic is the conservation voltage reduction factor.

27. The method of claim 22, wherein the energy characteristic is the energy savings.

28. The method of claim 22, wherein the paired comparison includes a pairing process for selecting pairs of data to compare.

29. The method of claim 28, wherein the paired comparison includes a paired t measurement.

30. The method of claim 22, wherein the paired comparison comprises an additional process that breaks the paired comparison process into measurements by season and uses linear regression constants to determine the blocks of hours where consistent loads exist and paired t comparisons can be calculated accurately, within predetermined limits.