WO2014168606A1 - Appareil et procédé de gestion de consommation électrique - Google Patents

Appareil et procédé de gestion de consommation électrique Download PDF

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Publication number
WO2014168606A1
WO2014168606A1 PCT/US2013/035629 US2013035629W WO2014168606A1 WO 2014168606 A1 WO2014168606 A1 WO 2014168606A1 US 2013035629 W US2013035629 W US 2013035629W WO 2014168606 A1 WO2014168606 A1 WO 2014168606A1
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WO
WIPO (PCT)
Prior art keywords
machine
energy
economic
rules
energy usage
Prior art date
Application number
PCT/US2013/035629
Other languages
English (en)
Inventor
Daniel Halvard MILLER
Original Assignee
Ge Intelligent Platforms, Inc .
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ge Intelligent Platforms, Inc . filed Critical Ge Intelligent Platforms, Inc .
Priority to PCT/US2013/035629 priority Critical patent/WO2014168606A1/fr
Publication of WO2014168606A1 publication Critical patent/WO2014168606A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling

Definitions

  • the subject matter disclosed herein relates to determining an optimum energy usage of machines and, more specifically, to determining an optimum usage based upon the usage of machine energy signatures.
  • an assembly line may include various types of machines that perform different types of functions.
  • the different machines draw electrical power.
  • the sequencing of the machines that make up the assembly line affects the peak power consumption of the line.
  • the varying times and loads of power drawn by the machines can result in inefficiencies in the operation of the assembly line, wasted power, and higher costs for operating the assembly line.
  • sub-tasks that operate in a particular machine typically have the same limitations.
  • machines in a factory setting are typically started by operators when the operator arrives for work for a shift. In many cases, this results in the machines all starting operation at the same time or at approximately the same time. This situation can result in a large electrical load being drawn as, for example, motors in the machines are started simultaneously, or in another example, heaters in the machines are activated
  • the approaches described herein optimize energy usage for different machines, pieces of equipment, or portions of these machines and equipment, where the machines are used together (or are disposed in proximity together), for example in a factory or plant.
  • the present approaches allow customers to save money on their electrical bill while at the same time not reducing the output of the factory or plant.
  • the present approaches allow the operator to manage the plant output with respect to higher electrical prices.
  • machines are described as being part of a factory or plant environment. However, it will be realized that the machines may be disposed in any type of environment such as in an office building, airport, shopping area, school, university, to mention a few examples. Further, as described herein machine operation as between separate machines is optimized. However, it will be appreciated that the approaches described herein can be applied to portions of machines.
  • machine and is used herein, it is meant any apparatus that performs a function and that can include one or more sub-portions.
  • a machine may be implemented as hardware, computer software, or combinations of hardware and software.
  • a manufacturing plant is charged for both the base load and peak electricity consumed in a given time period.
  • the machines have a machine energy signature, which relates to the consumption of energy over time and/or as compared to cycles or modes of machine operation (e.g., start-up mode).
  • the present approaches utilize these machine energy signatures to sequence the machine operations to minimize the total peak energy consumption and actively manage and reduce peak energy consumption.
  • the present approaches actively manages energy consumption to reduce peak energy consumption.
  • the machines would perform a similar task within the machine as is done plant- wide.
  • the approaches described herein understand the machine energy signature as it relates to specific tasks/modes of operation and uses this information to minimize energy consumption. For example a machine that raises and lowers a mass could sequence the lowering of the mass (a sequence that generates electricity) such that while the mass was lowered another process in the machine utilized this energy.
  • data that relates to and describes a raw energy usage of a machine is received. Based upon the energy usage data, a machine energy signature for the machine is determined. Rules that relate to activation of the machine are stored. Based upon the machine energy signature of the machine and the rules, the operation of the machine and/or other machines is optimized.
  • an economic model that models economic usage of the machine is stored.
  • Data from external sources e.g., economic information
  • the model is stored.
  • Data from external sources e.g., economic information
  • the data from external sources relates to time of day energy costs.
  • the optimizing uses the economic result.
  • the machine is selected from an assembly line, an individual machine, or portions of machines. Other examples of machines are possible.
  • the rules involve activation of the machine during peaks and valleys of energy usage. Other examples are possible.
  • the interface includes an input and an output and the input is configured to receive data that relates to and describes a raw energy usage of a machine.
  • the memory device including stored rules.
  • the processor is coupled to the interface and the memory, and is configured to, based upon the energy usage data, determine a machine energy signature for the machine.
  • the processor is further configured to, based upon the machine energy signature of the machine and the rules, transmit a control signal at the output that optimizes the operation of this or other machines.
  • FIG. 1 comprises a block diagram of a system for optimizing energy usage according to various embodiments of the present invention
  • FIG. 2 comprises a block diagram of a system for optimizing energy usage according to various embodiments of the present invention
  • FIG. 3 comprises a flow chart of an approach for optimizing energy usage according to various embodiments of the present invention.
  • FIG. 4 comprises a block diagram of an apparatus for optimizing energy usage according to various embodiments of the present invention.
  • the approaches described herein optimize energy usage for different machines, pieces of equipment, or portions of these machines and equipment.
  • the present approaches allow customers to save money on their electrical bill while at the same time not reducing the output of the factory or plant.
  • the present approaches allow the operator to manage the plant output with respect to electrical prices.
  • the energy signatures for the various machines in the plants are captured or obtained. An optimization approach is then utilized to determine to sequence activation of various machines such that peak electrical consumption is minimized.
  • a machine or supervisory controller when machine operators requested to start various machines simultaneously, a machine or supervisory controller would understand the energy signatures for the machines in question and then an operational/activation sequence for the machine startup is formed to minimize peak electrical consumption.
  • the supervisory controller may be
  • the supervisory controller would have the ability to either delay or advance machine operation such that the energy signatures of all the machines are optimized to reduce peak energy consumption.
  • These approaches could be implemented to control modes, hardware, control logic, software, or functions within the machine itself. In other words, individual sequences/functions in the machine itself could be optimized to reduce peak energy consumption.
  • the different modes and activation sequences can be utilized to not only reduce plant electrical consumption during normal operations but also can be utilized to respond to requests from the electrical generation facilities when they need to reduce the total load on the entire external electrical supply grid. Thus, operation of the supervisory control would be tied into the electrical supply grid.
  • the supervisory controller performs optimizations to sequence electrical power consumption to minimize peak consumption with a goal of peak power reduction. Additionally, this operation extends to machine operational modes where the total plant power consumption would be reduced as well.
  • the system includes a machine or supervisory controller 102.
  • the machine controller 102 may be implemented as a general processing device (e.g., a microprocessor) that executes programmed computer instructions.
  • the machine controller 102 receives rules 104.
  • the rules 104 specify a target energy usage for a specific set of parameters. For example, the rules may specify that only one machine can use a specified high amount of usage for a given period of time.
  • the rules 104 may be implemented using any appropriate software data structure or computer instructions, and may be stored in memory.
  • the machine controller 102 also receives data from external sources 106.
  • the data from external sources 106 may be economic data.
  • the data from external sources 106 may show that energy usage costs more at certain times of the day compared to other times of the day.
  • the data from external sources 106 may be implemented according to any format or contained in any appropriate software data structure.
  • the machine controller 102 also receives data 108 from a machine 110.
  • the machine controller 102 takes the data 108 and determines a machine energy signature 1 15.
  • the machine energy signature 115 shows energy usage as factored against time and/or machine cycle. For example, energy usage may be high at time 1 in cycle 1, but low at time 2 in cycle 2 and still lower at time 3 (that is still within cycle 3).
  • the machine controller 102 applies the machine energy signature to the rules 104 to create a control signal 112 that optimizes the energy usage of a machine 114.
  • the cycles may be related to aspects of machine operation such as a start-up cycle to name one example.
  • the machine controller 102 also uses an economic model 117.
  • the economic model may specify the costs involved in making a product.
  • the data from external sources 106 is applied against the economic model 117 to further optimize the energy usage. For example, based upon the economic model 117 and energy costs at certain times of the day, it may be determined to increase energy usage at certain times of the day.
  • the results of the economic model 117 may be combined with the results of the machine energy signature 115 to determine the control signal 112. For example, different weighting factors may be used to determine the information in the control signal 112.
  • the control signal 112 specifies when, for how long, and/or how much energy the machine 114 can use for a given time.
  • the data 108 that relates to and describes a raw energy usage of the machine 110 is received.
  • the machine energy signature 115 for the machine is determined.
  • the machine energy signature 115 may describe the amount of energy usage over time (e.g., see graph shown in FIG. 2) and this may be implemented according to any appropriate data structure.
  • Rules 104 that relate to activation of the machine are stored. Based upon the machine energy signature of the machine and the rules, the operation of other machines is optimized using the control signal 112.
  • the economic model 117 that models economic usage of the machine is stored in an appropriate memory device at the machine controller 102.
  • Data from external sources 106 e.g., economic information such as pricing information
  • the data from external sources 106 relates to time of day energy costs.
  • the optimizing uses the economic result.
  • the machines 110 and 114 are selected from an entire assembly line, individual machines on the assembly line, or portions of machines. Other examples of machines are possible.
  • the rules 104 involve activation of the machine during peaks and valleys of energy usage. Other examples of rules 104 are possible.
  • a machine controller 202 is coupled to a first machine 204, a second machine 206, a third machine 208, and a fourth machine 210.
  • the machine controller 202 receives data 212 from the first machine 204.
  • the data 212 describes the energy usage of the machine 204 over time and/or as related to cycles of the machine 204.
  • the machine controller 202 may be implemented as a general processing device (e.g., a microprocessor) that executes programmed computer instructions.
  • the data 212 is used to form a machine energy signature 214 that is stored in a memory 216.
  • the memory 216 also stores rules 218 and an economic model 221.
  • the memory 216 may be any type of memory storage device.
  • the machine energy signature 214 includes a high energy usage at time 1, but lower energy usages at time 2, time 3, and time 4.
  • the rules 218 specify a target energy usage for a specific set of parameters.
  • the rules 218 may specify that only one machine can use a specified high amount of usage for a given period of time.
  • the rules 218 may be implemented using any appropriate software data structure and may be stored in memory 216.
  • the machine controller 202 applies the rules 218 to the machine energy signature
  • first control signal 226 may operate the second machine 206 to operate at time 2 (and then deactivate); the second control signal 228 may activate the third machine 208 at time 3 (and then turn off); and the third control signal 230 may operate the fourth machine 210 at time 4 (and then turn off).
  • the machine controller 202 may also receive data from external sources 220 from an external source 222.
  • the external source 222 may be another system such as an accounting system.
  • the data from external sources 220 may be economic data. For example, the data from external sources 220 may show that energy usage costs more at certain times of the day than at other times of the day.
  • the data from external sources 220 may be implemented according to any format or contained in any appropriate software data structure.
  • the machine controller 202 also uses the economic model 221 that is stored in the memory 216.
  • the economic model 221 may specify the costs involved in making a product.
  • the data from external sources 220 is applied against the economic model 221 to further optimize the energy usage. For example, based upon the economic model and energy costs at certain times of the day, it may be determined to increase energy usage at certain times of the day. Consequently, the control signals 226, 228, and 230 maybe determined by both using the machine energy signature 214/rules 218 and the economic model 221 /data from external sources 220.
  • step 302 data that relates to and describes a raw energy usage of a machine is received.
  • step 304 based upon the energy usage data, a machine energy signature for the machine is determined.
  • step 306 rules that relate to activation of the machine are stored.
  • step 308 based upon the machine energy signature of the machine and the rules, the operation of other machines is optimized.
  • the interface 402 includes an input 408 and an output 410 and the input 408 is configured to receive data that relates to and describes a raw energy usage of a machine.
  • the memory 404 includes stored rules 407.
  • the processor 406 is coupled to the interface 402 and the memory 404, and is configured to, based upon the energy usage data, determine a machine energy signature 409 for the machine.
  • the processor 406 is further configured to, based upon the machine energy signature of the machine and the rules 407, transmit a control signal at the output 410 that optimizes the operation of other machines and/or the machine related to the machine energy signature 409.

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Abstract

La présente invention concerne la réception de données d'usage d'énergie qui concernent et décrivent un usage d'énergie brute d'une machine. Les données d'usage d'énergie sont utilisées pour déterminer une signature d'énergie de machine pour la machine. Des règles relatives à l'activation de la machine sont stockées. La signature d'énergie de machine de la machine et les règles sont utilisées pour optimiser le fonctionnement d'autres machines.
PCT/US2013/035629 2013-04-08 2013-04-08 Appareil et procédé de gestion de consommation électrique WO2014168606A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/US2013/035629 WO2014168606A1 (fr) 2013-04-08 2013-04-08 Appareil et procédé de gestion de consommation électrique

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2013/035629 WO2014168606A1 (fr) 2013-04-08 2013-04-08 Appareil et procédé de gestion de consommation électrique

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WO2014168606A1 true WO2014168606A1 (fr) 2014-10-16

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Non-Patent Citations (1)

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