WO2015033419A1 - Information provision device and information provision method - Google Patents

Information provision device and information provision method Download PDF

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Publication number
WO2015033419A1
WO2015033419A1 PCT/JP2013/073920 JP2013073920W WO2015033419A1 WO 2015033419 A1 WO2015033419 A1 WO 2015033419A1 JP 2013073920 W JP2013073920 W JP 2013073920W WO 2015033419 A1 WO2015033419 A1 WO 2015033419A1
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WO
WIPO (PCT)
Prior art keywords
power
information
predetermined
consumer
distribution network
Prior art date
Application number
PCT/JP2013/073920
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French (fr)
Japanese (ja)
Inventor
明久 石田
昭博 伊藤
Original Assignee
株式会社日立製作所
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Publication date
Application filed by 株式会社日立製作所 filed Critical 株式会社日立製作所
Priority to PCT/JP2013/073920 priority Critical patent/WO2015033419A1/en
Publication of WO2015033419A1 publication Critical patent/WO2015033419A1/en

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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
    • H02J13/00034—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for the elements or equipment being or involving an electric power substation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/12—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation
    • Y04S10/123—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation the energy generation units being or involving renewable energy sources
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/221—General power management systems
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Definitions

  • the present invention relates to an information providing apparatus and an information providing method.
  • power generation devices owned by consumers are mainly assumed to use renewable energy such as sunlight and wind power.
  • the power generation amount depends on the external environment such as the amount of solar radiation and the wind speed, the output is likely to fluctuate and is difficult to control. Electricity is difficult to accumulate. For this reason, in order to effectively use the electricity generated by the consumer, it is necessary for the consumer to use it or to make it available (sold).
  • Patent Documents 1 and 2 methods for allocating the amount of electric power that can be sold to consumers who wish to sell electric power have been studied.
  • JP 2012-175895 A Japanese Unexamined Patent Publication No. 2011-91983
  • Patent Document 1 proposes a method in which each power consumer can obtain power sales opportunities evenly in a situation where a large number of power consumers desire to sell power for a limited amount of power that can be sold. .
  • this method when multiple power consumers connected to a common transformer wish to sell power at the same time, the power available period is divided into unit times, and each unit time is evenly distributed to individual power consumers. Allocate. Thereby, each electric power consumer can obtain the power sale opportunity equally.
  • each consumer cannot know in advance how often a power sale possible period is allocated. For this reason, each consumer has no choice but to throw away the surplus power as long as power can not be stored, even if the power sale period is not allocated in spite of planning to sell power.
  • Patent Document 2 proposes a technique that enables a plurality of power consumers to simultaneously sell power.
  • this method when a plurality of power consumers connected to a common transformer wish to sell power at the same time, the voltage value that each power consumer flows through the grid is adjusted. Thereby, electric power can be supplied to the system and sold.
  • each consumer cannot know the amount of power that can be sold in advance, and therefore may not be able to sell the planned amount of power.
  • the conventional method when the amount of power that can be sold is limited to a consumer who wants to sell power, a sale opportunity is allocated, but if the opportunity is lost, surplus power can be used effectively It is not possible, and the convenience for consumers is low.
  • the object of the present invention is to provide power consumers with predetermined information indicating the possibility of power supply from the power consumer to the power grid, thereby making it possible to promote the use of surplus power in the power consumer and improve usability.
  • An object of the present invention is to provide an information providing apparatus and an information providing method that are made possible.
  • an information providing apparatus that provides information related to power supply to a power system to a power consumer capable of supplying power to the power system.
  • a communication interface unit that communicates with a power management device provided in the house and a predetermined sensor group provided in the power system, first information acquired from the power management device, second information acquired from the predetermined sensor group, and a predetermined computer
  • predetermined information indicating the possibility of power supply from the power consumer to the power system is generated based on the first information and the second information.
  • an arithmetic processing unit that provides predetermined information to the power consumer by transmitting the predetermined information to the power management apparatus via the communication interface unit.
  • the predetermined information may be information indicating an expectation of electric power that can be sold from an electric power consumer to an electric power company that operates or uses the electric power system.
  • Explanatory drawing which shows the structure of the power management system containing a power sale prospect presentation apparatus.
  • Configuration explanatory diagram showing the hardware configuration and software configuration of the power sale prospecting presentation device
  • the data regarding a consumer are shown,
  • (a) is the structure of the data which shows a customer's equipment,
  • (b) is the structure of the data which shows the log
  • (c) is the structure of the data which shows a consumer's position. Is shown.
  • Data relating to the distribution network is shown.
  • (A) is a data structure indicating the equipment of the distribution network section.
  • (B) is a data structure indicating a history of the status of the distribution network section.
  • (C) Data indicating the position of the distribution network section. The structure of is shown.
  • (A) shows the configuration of data relating to the power generation environment
  • (b) shows the configuration of data for classifying consumers
  • (c) shows the configuration of data for classifying distribution network classification.
  • the data structure obtained by statistically processing data obtained from the customer and the distribution network is shown, (a) is the data structure indicating the statistics of the customer history data, and (b) is the statistics of the customer environment data.
  • (C) shows the structure of the data indicating the statistics of the distribution network history data, and (d) shows the structure of the data indicating the statistics of the environmental data of the distribution network.
  • Explanatory drawing which shows a mode that the historical data of customer equipment is statistically processed.
  • the flowchart of the process which sets the label which classifies a power distribution network division.
  • the flowchart of the process which calculates a power sale prospect.
  • the pattern of the method of presenting electric power sales prospect to a consumer is shown.
  • the pattern of the other method of showing electric power sales prospect to a consumer is shown.
  • the information providing apparatus calculates and presents the possibility that surplus power generated by the consumer can be supplied to the power grid for a power consumer (hereinafter referred to as a consumer) connected to the power grid.
  • the surplus power is sold to a business operator who uses or manages the power system and is consumed by other consumers. Therefore, the information providing apparatus of the present embodiment is an apparatus that presents a power sale prospect to a consumer.
  • the information providing apparatus can reduce or prevent waste of electricity generated by a consumer when there is a possibility that the consumer's desired power sale amount may exceed the power sale possible amount.
  • the information providing apparatus can support estimation of whether or not the facility operation plan can actually be performed, such as power generation, power storage, and power use by consumers.
  • the information providing apparatus presents the prospect of whether the power can be sold in a certain time zone to the consumer.
  • the feasibility is presented.
  • the equipment for presenting electric power sales to the consumer includes the equipment data of the electrical equipment possessed by the consumer, the history data of the electrical equipment possessed by the consumer, the location data of the consumer, the equipment data for each power grid section, the power grid section History data, distribution network location data, and environmental data corresponding to the location data are collected and stored.
  • the distribution network section which is an example of the “predetermined section”, is a part of the distribution network, and is obtained by dividing the topology of the distribution network for each transformer or substation.
  • the information providing apparatus of the present embodiment presents the prospect of power sale to the consumer in the following procedure.
  • the order of execution is not limited as long as the power sale likelihood can be calculated.
  • Cluster customers using equipment data, history data, and environmental data corresponding to customer location data (2)
  • the distribution network section is clustered using the facility data, the history data, the environmental data corresponding to the position data of the distribution network section, and the ratio of each customer cluster connected to the distribution network section.
  • Machine learning is performed using history data and environmental data for each cluster in the distribution network section, and the state of the distribution network section in the date, time, and environmental conditions is obtained as a probability density function.
  • Machine learning is performed using historical data and environmental data of connected consumers for each cluster in the distribution network section, and the power generation amount of the consumers at the date, time, and environmental conditions is obtained as a probability density function.
  • the state of the distribution network section is predicted from the result of the machine learning in (3) above and the predicted distribution of environmental data obtained from the date and time, weather forecast, and the like.
  • the power generation amount of the customer connected to a certain distribution network section is predicted from the result of the machine learning in (4) above and the predicted distribution of environmental data obtained from the date and time, weather forecast, and the like.
  • the information providing apparatus provides information related to the amount of purchased power (amount of power sold) to the consumer before the consumer actually sells surplus power.
  • amount of power sold amount of power sold
  • the customer can make an effort in advance to avoid loss, and the usability and reliability are improved.
  • the expected value of the amount of power purchased from the entire consumer at a certain date and time is presented in advance as prospect information “n% of the total power generation amount of the entire consumer”. This allows consumers to store excess power that exceeds the amount of power purchased, for example, storing hot water in tanks, producing ice with an ice maker, or fully charging an electric vehicle battery. Can be prepared for consumption or storage. Or when there is little prospect of a power sale, a consumer can also stop a power generator and can perform maintenance.
  • FIG. 1 is a block diagram of a power management system including a power sale prospecting presentation device 10 as an “information providing device”.
  • the power sale prospecting presentation device 10 is a device that presents the prospect of whether power can be sold in a certain time zone to a consumer. The configuration and operation of the power sale prospect presentation device 10 will be described later.
  • the customer 20 is an abstract representation of various electric power users such as ordinary households, factories, and buildings.
  • the customer 20 includes a power generation device 22, a power consumption device 23, and a power storage device 24 corresponding to the respective characteristics.
  • Examples of the power generation device 22 include a solar power generation device, a wind power generation device, a geothermal power generation device, a solar thermal power generation device, and a gas cogeneration system. Many of the power generation devices 22 use renewable energy, and thus the amount of power generation is unstable. However, the power generation device 22 such as a gas cogeneration system has a capability of generating power as planned.
  • Examples of the power consuming device 23 include an air conditioner, various home appliances, a lighting device, a man conveyor device, and a manufacturing facility.
  • Examples of the power storage device 24 include a lead storage battery, a lithium ion battery, a nickel metal hydride battery, an air zinc battery, and a sodium sulfur battery. A configuration may be used in which a plurality of types of storage batteries having different properties are used in combination.
  • the power storage device 24 is not limited to one that is fixedly installed inside or outside the customer 20, and may be a movable power storage device. Examples of the movable power storage device include a battery of an electric vehicle that can be fed from an electric wiring in the consumer 20.
  • the EMS 21 manages and controls the devices 22, 23, and 24 of the customer 20.
  • the EMS 21 is connected to the power sale prospecting presentation apparatus 10 via the communication network 40 so as to be capable of two-way communication, and transmits information regarding each device 22, 23, 24 to the power sale prospecting presentation apparatus 10. Further, the EMS 10 acquires a power sale prospect from the power sale prospect presentation device 10 via the communication network 40, and displays the obtained power sale prospect on the display unit of the EMS 21.
  • the EMS 21 can use a television device that is physically provided separately from the EMS 21 as a display unit for EMS.
  • the EMS 21 is also communicably connected to the environment sensor 25 and transmits information detected by the environment sensor 25 to the power sale prospecting presentation device 10 via the communication network 40.
  • the environmental sensor 25 is a sensor that measures and outputs “information about the power generation environment”. Examples of information relating to the power generation environment include solar radiation, temperature, and wind speed. In the present embodiment, description will be given mainly by taking the amount of solar radiation and the temperature as examples.
  • the distribution network 30 which is a part of the power system is for supplying electricity to the customer 20, and a plurality of distribution network sections 31 are logically set.
  • the distribution network section 31 is a part of the distribution network obtained by dividing the topology of the distribution network 30 for each transformer or substation.
  • Each distribution network section 31 is provided with a distribution network section state sensor 32 (state sensor 32 in the figure) and a distribution network section environment sensor 33 (environment sensor 33 in the figure).
  • the distribution network partition state sensor 32 is a sensor that measures and outputs the state (voltage, current) of the distribution network partition.
  • the distribution network section environment sensor 33 is a sensor that measures and outputs, for example, the amount of solar radiation and the temperature as information on the power generation environment of the distribution network section.
  • the power sale prospecting presentation apparatus 10 classifies each customer under management according to a predetermined standard (11) and connects the consumers.
  • the distribution network sections are also classified according to other predetermined criteria (12).
  • the power sale prospecting presentation device 10 learns the operating status of the facilities 22 to 24 owned by the consumer (13), and similarly learns the status of the distribution network for each distribution network section 31 (14).
  • the power sale prospecting presentation device 10 predicts the state of the customer facility under a certain date and time and a predicted environment based on the learning result of the customer facility (15). Similarly, the power sale prospecting presentation device 10 predicts the state of the distribution network section under a certain date and time, a certain time, and the predicted environment based on the learning result of the distribution network section (16).
  • the power sale prospecting presentation device 10 calculates a power sale prospect from the prediction result about the situation of the customer facility and the prediction result about the situation of the distribution network section (17), and uses the calculated power sale prospect to the EMS 21 of the consumer. (18).
  • FIG. 2 is a block diagram showing a hardware configuration and a software configuration of the power sale prospecting presentation device 10.
  • the power sale prospecting presentation device 10 is configured as a computer device including a microprocessor (hereinafter, CPU: Central : Processing Unit) 101, a memory 102, an auxiliary storage device 103, and a communication interface unit 104, for example.
  • CPU Central : Processing Unit
  • the CPU 101 realizes predetermined functions F10 to F12 by executing a computer program stored in the memory 102.
  • the memory 102 stores various data T10 to T20 and various programs (programs that are functions F10 to F12).
  • the auxiliary storage device 103 is a relatively large-capacity storage device such as a hard disk drive or a flash memory device, and stores various computer programs, operating systems, and the like.
  • the communication interface unit 104 communicates with devices on the customer 20 side and devices on the power distribution network 30 side via the communication network 40.
  • the auxiliary storage device 103 may be eliminated and all the computer programs and data may be stored in the memory 102.
  • the power sale prospect presentation device 10 includes a history data collection unit F10 and an analysis unit F11 as processing execution units.
  • the power sale prospecting presentation apparatus 10 includes, as a database, customer facility data T10, customer history data T11, customer location data T12, distribution network facility data T13, distribution network history data T14, distribution network location data T15, and environment data T16. , Customer classification data T17, distribution network section classification data T18, various statistics T19, and various cumulative probability density distributions T20.
  • the process execution units F10 and F11 communicate with external devices (the customer side device and the distribution network side device) via the communication unit F12.
  • the history data collection unit F10 which is an example of the “information collection unit”, collects data from the EMS 21 of each consumer and the sensors 32 and 33 in the distribution network section via the communication unit F12, the communication network 40, and the like.
  • the history data collection unit F10 stores the collected data in a predetermined database.
  • the analysis unit F12 Based on the data collected by the history data collection unit F10, the analysis unit F12 performs a process described later to calculate a power sale prospect and present it to the consumer.
  • the communication unit F12 communicates with the external devices 21, 32, and 33 using the communication interface unit 104.
  • FIG. 3 shows data related to the customer 20.
  • the customer facility data T10 shown in FIG. 3 (a) manages data related to customer facilities.
  • the customer ID C100, the maximum power generation amount C101, the maximum power consumption C102, and the maximum power storage amount C103 are associated with each other.
  • the customer ID C100 is information for identifying the customer 20.
  • the maximum power generation amount C101 indicates the maximum output power of the power generation device 22 that the customer 20 has.
  • the maximum power consumption C102 is the maximum value of power that can be consumed by the power consuming device 23 of the consumer 20.
  • the maximum power storage amount C103 is the maximum value of electricity that can be stored in the power storage device 24 of the consumer 20.
  • Each entry shown in the customer facility data T10 corresponds to the EMS 21 held by the customer 20, and when the EMS 21 is registered in the power sale prospecting presentation device 10, a corresponding entry is created. When the registration is deleted from the power sale prospecting presentation device 10, the entry corresponding to the deleted customer is also deleted.
  • FIG. 3B shows customer history data T11 for managing the history of operation data of the electric devices 22 to 24 held by the customer.
  • the customer history data T11 manages, for example, a customer ID C110, a date C111, a time C112, a power generation amount C113, a power consumption amount C114, and a power storage amount C115 in association with each other.
  • Customer ID C110 is information for identifying a customer.
  • the date C111 and the time C112 are the date and time when the operation data of the facilities 22 to 24 is acquired from the EMS 21.
  • the operation data of the electric devices 22 to 24 owned by the customer is represented by three of the power generation amount C113, the power consumption amount C114, and the storage amount C115, and is managed in association with the set of the customer ID C110, the date C111, and the time C112. Is done. Each entry of this data is added every time operation data is sent from the EMS 21.
  • FIG. 3C shows customer position data T12 for managing data relating to a place where the customer 20 is located.
  • the customer position data T12 manages, for example, a customer ID C120, a longitude C121, a latitude C122, and an ID C123 of the distribution network section 31 to which the customer is connected.
  • Customer ID C120 is information for identifying a customer.
  • the longitude C121 and the latitude C122 indicate the place where the customer is located.
  • the connected distribution network section ID C123 is identification information for specifying the distribution network section of the part where the customer is connected to the distribution network.
  • Each entry of this data corresponds to the EMS 21 of the consumer 20, and an entry corresponding to the entry of the EMS 21 of the consumer 20 is created in the electricity sales prospecting device 101. When the registration is deleted, the corresponding entry is also deleted.
  • FIG. 4 shows a configuration example of data related to the distribution network section.
  • FIG. 4A is data for managing the distribution network equipment data.
  • the distribution network facility data T13 manages, for example, a distribution network section ID C130, a maximum allowable current C131, and a rated voltage C132 in association with each other.
  • the distribution network section ID C130 is information for identifying the distribution network section.
  • the facility data for each distribution network section is represented by two of the maximum allowable current C131 and the rated voltage C132, and is managed in association with the distribution network section ID C130.
  • the values in this table are basically fixed values unless they are changed due to construction of the distribution network.
  • FIG. 4B is data for managing the history of the state data of the distribution network.
  • the distribution network history data T14 manages, for example, a distribution network section ID C140, a date C141, a time C142, a current C143, and a voltage C144 in association with each other.
  • the distribution network section ID is information for identifying the distribution network section.
  • the date C141 and the time C142 are the date and time when the state data of the power distribution network is acquired from the state sensor 32.
  • the state data for each distribution network section is represented by two of current C143 and voltage C144, and is managed in association with a set of distribution network section ID C140, date C141, and time C142. Each entry of this data is added each time status data is sent from the distribution network partition status sensor 32.
  • FIG. 4C shows distribution network position data T15 for managing data related to the location where the distribution network section is located.
  • the distribution network section ID C150 is information for identifying the distribution network section.
  • the longitude C151 and the latitude C152 indicate the location where the distribution network section is located.
  • the value of this data is basically a fixed value except that it is changed by the construction of the distribution network.
  • FIG. 5 shows environmental data T16, customer classification data T17, and distribution network section classification data T18.
  • FIG. 5 (a) is an environmental data table for managing environmental data regarding locations where consumers and distribution network sections are located.
  • the environment data table T16 manages, for example, longitude C160, latitude C161, date C162, time C163, temperature C164, and solar radiation amount C165 in association with each other.
  • Longitude C160 and longitude C161 indicate locations where environmental data is measured. Date C162 and time C163 indicate the date and time when the environmental data was measured.
  • the environmental data is represented by two values of temperature C164 and solar radiation amount C165.
  • An entry in the environmental data table T16 is added each time environmental data is sent from the EMS 21 and the distribution network section environment sensor 33.
  • FIG. 5 (b) shows data for managing the classification results of consumers.
  • the customer classification data T17 is managed by associating the customer ID C170 and the customer classification label C171.
  • the customer ID C170 is information for identifying a customer.
  • the customer classification label C171 represents to which cluster a consumer is classified.
  • Each entry of this data corresponds to the EMS 21 and is created when the EMS 21 is registered in the power sale prospecting presentation device 10, and when the registration is deleted, the corresponding entry is deleted.
  • Fig. 5 (c) shows data for managing the classification result of the distribution network section.
  • the distribution network section classification data T18 manages, for example, a distribution network section ID C180 and a distribution network section classification label C181 in association with each other.
  • the distribution network section ID C180 is information for identifying the distribution network section.
  • the distribution network section classification label C181 indicates to which cluster the distribution network section is classified. Each entry of this data corresponds to each distribution network section.
  • FIG. 6 shows a configuration example of various statistics T19.
  • the statistics calculated from the customer history data T11, the customer environment data, the distribution network history data T14, and the distribution network environment data are the statistics T19a of the customer history data T11 and the statistics of the customer environment data, respectively. It is stored in the form of T19b, statistics T19c of distribution network history data T14, and statistics T19d of environment data of the distribution network.
  • FIG. 7 shows an example of various cumulative probability density distributions T20 that store probability density functions obtained according to a predetermined algorithm.
  • the probability density distribution is stored in the form of a cumulative probability density distribution.
  • FIG. 7 shows, as an example, the cumulative probability density distribution of the power generation amount of consumers classified into the customer label 1 when the temperature is 20-25 ° C. and the solar radiation amount is 3.9-4.0 kwh. Is.
  • Various probability density distributions are stored in such a format.
  • FIG. 8 is a flowchart showing a process for calculating a customer classification label.
  • the subject of the operation of the flowchart may be any one of the expected power sale presentation device 10, the CPU 101 of the expected power sale presentation device 10, the computer program executed by the CPU 101, and the analysis unit F11.
  • the analysis unit F11 will be described as an operation subject.
  • the analysis unit F11 calculates a statistic T19a of the customer history data T11 (S10).
  • the analysis unit F11 quantizes the customer history data T11 in units of one hour, and in the range of the time quantized in units of one hour and the date including three days before and after the processing target date, Acquire power generation, power consumption, and power storage. Then, the analysis part F11 calculates
  • step S10 will be described in detail with reference to FIG.
  • the measured value of the power generation amount is as shown in FIG. It is specifically to quantize the data in the 11:00 range of “January 4 (1/4)” in 1-hour units and then take statistics of power generation in the range of dates including the three days before and after.
  • the average value is obtained as shown in FIG. 9B, and the solid line of the average (statistic A) in the range
  • the data is stored in the cells surrounded by, and further, as shown in FIG. 9C, the median is obtained and stored in the cells surrounded by the solid line of the median (statistic B) within the range, The same processing is shown.
  • the data of 11 o'clock on January 5 (1/5) is also quantized in units of one hour, and taking the statistics of the power generation amount within the date range including 3 days before and after, 9A shows that the same processing as described above is performed on the data in the range surrounded by the dotted line of the measurement value of the power generation amount in FIG. The same processing is performed in the subsequent processing for obtaining statistics.
  • the analysis unit F11 calculates a statistical amount T19b of the customer's environmental data (S11).
  • the analysis unit F11 acquires environmental data having the closest distance from the longitude and latitude of each customer. With respect to the environmental data, the time is quantized in units of one hour, and the date includes a date including three days before and after the date to be processed. Within this range, the average value, median value, and mode value of air temperature and solar radiation are obtained as statistics.
  • the analysis unit F11 clusters customers (S12).
  • the analysis unit F11 uses the customer facility data T10, the statistic T19a of the customer history data T11 obtained in step S10, and the statistic T19b of the customer environmental data obtained in step S11 as feature quantities, and the distance Clustering is performed using a clustering technique such as a k-means method as a predetermined algorithm using Euclid distance or the like.
  • the analysis unit F11 defines customer classification labels such as “1”, “2”, and “3” for each customer cluster obtained in step S12.
  • the analysis unit F11 assigns the value of the defined label to the customer classification label C171 (FIG. 5B) corresponding to the customer classified into the cluster.
  • FIG. 10 is a flowchart of processing for calculating a distribution network section classification label.
  • the analysis unit F11 calculates a statistic T19c of the distribution network history data T14 (S20).
  • the analysis unit F11 quantizes the distribution network history data T14 on an hourly basis.
  • the analysis unit F11 acquires the current and voltage of each distribution network section in the range of the date including the time quantized in units of one hour and the three days before and after the processing target date.
  • the analysis part F11 calculates
  • the analysis unit F11 calculates a statistic T19d of the distribution network environment data (S21).
  • the analysis unit F11 acquires corresponding environmental data from the longitude and latitude of each distribution network section.
  • the analysis unit F11 quantizes the environmental data in units of one hour, and sets the date including three days before and after the processing target date as a range.
  • the analysis part F11 calculates
  • the analysis unit F11 calculates the ratio of the customer classification labels of the consumers connected to the distribution network section (S22).
  • the analysis unit F11 acquires a consumer list connected to each distribution network section based on the consumer position data T12.
  • the analysis part F11 calculates the ratio of the consumer classification label of the consumer connected to each distribution network section from the consumer list and the consumer classification data T17.
  • the analysis unit F11 executes clustering of distribution network sections (S23).
  • the analysis unit F11 includes the distribution network facility data T13, the statistics T19c of the distribution network history data T14 obtained in step S20, the statistics T19d of the distribution network environmental data obtained in step S21, and the distribution obtained in step S22.
  • Clustering is performed by using the ratio of the customer classification labels of the customers connected to the network section as the feature amount, using the Euclid distance or the like as the distance, and using a clustering technique such as the k-means method as the predetermined algorithm.
  • the analysis unit F11 stores the clustering result (S24).
  • the analysis unit F11 defines a label for each cluster of the distribution network section obtained in step S23.
  • the analysis unit F11 substitutes the value of the defined label into the distribution network section label C181 corresponding to the distribution network section classified into the cluster.
  • FIG. 11 is a flowchart of a process for calculating the expected power sale.
  • the analysis unit F11 learns the state of the distribution network (S20).
  • the analysis unit F11 performs machine learning on the basis of history and environmental data for each distribution network partition classification by a method such as Bayesian estimation, and calculates the state under the date, time, and environmental conditions as a probability density function. Asking.
  • the probability density function obtained as a learning result is an example of “second processed information”.
  • the analysis unit F11 learns the equipment operation status of the customer connected to the distribution network section (S21). For each distribution network section, the analysis unit F11 performs machine learning using a method such as Bayesian estimation of power generation amount, power storage amount, and power consumption amount based on historical data and environmental data of consumers connected to the distribution network section. Thus, the power generation amount, power storage amount, and power consumption amount under the date, time and environmental conditions are obtained as a probability density function.
  • the probability density function obtained as a learning result is an example of “first processed information”.
  • the analysis unit F11 predicts the state of the distribution network (S22). Based on the result of machine learning in step S20, the analysis unit F11 predicts the state of the distribution network section from the predicted distribution of the environmental data obtained from the date and weather forecast, and the distribution network section at a certain date and time. The state prediction is obtained as a probability distribution.
  • the analysis unit F11 predicts the equipment operation status of the customer (S23).
  • the analysis unit F11 predicts the equipment operation status of the customer connected to each distribution network section from the predicted distribution of environmental data obtained from the date and time and weather forecast based on the result of step S21, and at a certain date and time. Obtained as a probability distribution of the amount of power generation, the amount of electricity stored, and the amount of power consumption However, if the customer's equipment operation status is provided in advance by the customer, a probability distribution may be created using that value.
  • the analysis unit F11 calculates the expected power sales based on the state prediction of the distribution network and the facility operation state prediction of the customer (S24). Based on the results of steps S22 and S23, the analysis unit F11 makes an estimate of the amount of power that can be supplied from the consumer to the power system (if the view is changed, the amount of power that can be purchased by the power company).
  • the maximum power generation capacity of a power generation device 22 of a customer connected to a certain distribution network section is 100 kw
  • the power generation device is 80% or more in a certain temperature and solar radiation forecast at a certain date and time. It is assumed that the probability of driving at an output of 90% is calculated. In addition, it is assumed that it is calculated from the state prediction of the distribution network that there is no problem even if power supply of 20 kw / h is performed from all customers in a certain distribution network section. In that case, the analysis unit F11 indicates to the customer's EMS 21 connected to the distribution network section that the electric power company can supply up to 25% of the power generation amount at the customer's date and time. The probability of being able to buy is 90% ".
  • information including a power sale prospect is transmitted to a plurality of consumers at regular intervals or at regular intervals. That is, the power sale prospects are simultaneously presented to the EMS 21 of a plurality of consumers by a method similar to broadcasting. As a result, it is possible to notify a plurality of consumers of the expected power sale all at once.
  • the EMS 21 in response to an inquiry from a customer, information including a power sale prospect is transmitted to the EMS 21.
  • the EMS 21 requests the power sale prospect presentation device 10 to transmit information including the power sale prospect with the customer ID clearly specified.
  • the power sale prospecting presentation apparatus 10 transmits information including the power sale prospect for the consumer to the requesting EMS 21. Thereby, the electric power sale prospect can be shown individually.
  • FIG. 13 (a) shows a pattern in which information including a power sale prospect is transmitted at regular time intervals or at regular time intervals in accordance with consumer distribution registration.
  • a consumer who wishes to present information including a power sale prospect registers a customer ID in the power sale prospect presentation apparatus 10 in advance.
  • the power sale prospect presentation device 10 transmits information including the power sale prospect to the EMS 21 having the registered consumer ID. Thereby, the electric power sale prospect can be shown in multiple times at the timing which a consumer desires.
  • FIG. 13B shows a pattern for providing information including a power sale prospect that can be a reference for the consumer to the EMS 21 of the consumer that is not registered in the power sale prospect presentation device 10. Even if the customer does not transmit the facility data and the history data to the power sale prospect presentation device 10 in advance, the consumer can obtain information about the power sale prospect.
  • the EMS 21 of the unregistered customer transmits the facility information (information on the power generation device, the power consumption facility, and the storage facility) to the power sale prospecting presentation device 10 and requests the presentation of information including the power sale prospect.
  • the power sale prospecting presentation apparatus 10 searches for consumers having facilities similar to the facility information of unregistered consumers, and uses the power sale prospects for similar customers as reference information to the EMS 21 of unregistered consumers. Send.
  • the power sale prospecting presentation device 10 can give information on the prospect of power sale to the consumer before the consumer sells surplus power. Therefore, the consumer can know in advance the possibility of selling surplus power, and can formulate and execute an action plan for not wasting surplus power as much as possible. Therefore, the convenience of consumers can be improved, the fairness of the power purchase system can be increased, and the reliability can be enhanced.
  • 10 Electricity sales prospect presentation device
  • 20 Electric power consumer
  • 21 EMS
  • 22 Power generation device
  • 23 Electric power consumption facility
  • 24 Electric storage facility
  • 25 Environmental sensor
  • 30 Distribution network
  • 31 Distribution network section
  • 32 Status sensor
  • 33 Environmental sensor
  • 40 Communication network
  • F10 History data collection unit
  • F11 Analysis unit
  • F12 Communication unit
  • 101 Microprocessor
  • 102 Memory
  • 103 Auxiliary storage device
  • 104 Communication Interface part

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  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Remote Monitoring And Control Of Power-Distribution Networks (AREA)

Abstract

The objective of the present invention is to enable the prior presentation of forecasted electricity selling in a manner such that the surplus electrical power of consumers is not wasted. An electricity selling forecast presentation device (10) as an information provision device is provided with: a communication interface unit that communicates with a predetermined sensor group (32, 33) provided to a power grid (30) and a power management device (21) provided to a power consumer (20); a storage unit that stores first information acquired from the power management device (21), second information acquired from the predetermined sensor group (32, 33), and a predetermined computer program; and a computation processing unit that, on the basis of the first information and second information and by means of executing the predetermined computer program, generates (17) predetermined information indicating the likelihood of supplying electrical power from the power consumer (20) to the power grid (30), and provides (18) the predetermined information to the power consumer by means of transmitting to the power management device (21) via the communication interface unit.

Description

情報提供装置および情報提供方法Information providing apparatus and information providing method
　本発明は、情報提供装置および情報提供方法に関する。 The present invention relates to an information providing apparatus and an information providing method.
　近年、太陽光発電装置、風力発電装置、ＮＡＳ電池（ナトリウム・硫黄電池）、電気自動車などが、一般家庭、ビルディング、工場などの電力需要家に普及しつつある。スマートグリッドと呼ばれる新たな枠組みでは、発電装置、蓄電池、および空調装置などの各種電気機器を通信ネットワークで接続し、それら電気機器機器の稼働状況を遠隔監視し、電力の過不足分を融通し合う。これにより、スマートグリッドでは、地域内での電力使用量の効率化を実現する。 In recent years, solar power generation devices, wind power generation devices, NAS batteries (sodium / sulfur batteries), electric vehicles, and the like are becoming popular in power consumers such as ordinary households, buildings, and factories. In a new framework called smart grid, various electrical devices such as power generators, storage batteries, and air conditioners are connected via a communication network, and the operation status of these electrical devices is monitored remotely, and the excess or deficiency of power is interchanged. . As a result, the smart grid realizes efficient use of electric power in the region.
　スマートグリッドにおいて、需要家が持つ発電装置は主に太陽光、風力といった再生可能エネルギーを用いたものが想定されている。このような発電装置は発電量が日射量、風速といった外部環境に依存するため、出力が変動しやすく制御が難しい。また電気は蓄積することが難しい。そのため需要家が発電した電気を有効に利用するためには、需要家自身が利用するか、または、外部へ融通する（売る）必要がある。 In smart grids, power generation devices owned by consumers are mainly assumed to use renewable energy such as sunlight and wind power. In such a power generation device, since the power generation amount depends on the external environment such as the amount of solar radiation and the wind speed, the output is likely to fluctuate and is difficult to control. Electricity is difficult to accumulate. For this reason, in order to effectively use the electricity generated by the consumer, it is necessary for the consumer to use it or to make it available (sold).
　しかし、需要家の余剰電力を他の需要家に融通できない場合も考えられる。電力を融通するためには電力需要が必須だが、再生可能エネルギーで発電している場合、売電を希望する需要家の近隣は殆ど似た外部環境下にあると考えられる。 However, there may be cases where the surplus power of the customer cannot be accommodated by other customers. Power demand is indispensable in order to accommodate electricity, but when generating electricity with renewable energy, the neighborhood of consumers who want to sell power is considered to be in a similar external environment.
　そのため売電を希望する需要家の近隣でも同様に電気が余っており、近隣に電力需要がなく、電気を融通できない場合が想定される。また、融通しようとする電力量が配電網のキャパシティを超えてしまうケースが考えられる。その場合も需要家は電気を融通することができない。 Therefore, it is assumed that there is a surplus of electricity in the vicinity of the customer who wants to sell electricity, and there is no demand for electricity in the vicinity and electricity cannot be accommodated. In addition, there may be a case where the amount of power to be interchanged exceeds the capacity of the distribution network. Even in this case, consumers cannot exchange electricity.
　現在、総発電量に対し、ダム等の大規模水力を除いた再生可能エネルギー由来の発電量が占める割合は数％未満であり、発電量の変動を配電網で吸収できているため、配電網のキャパシティ不足による電力融通の制限という事態は起こっていない。 Currently, the proportion of power generation derived from renewable energy excluding large-scale hydropower such as dams accounts for less than a few percent of total power generation, and fluctuations in power generation can be absorbed by the power distribution network. There has been no situation of limited power interchange due to lack of capacity.
　しかし今後は、再生可能エネルギー由来の発電量の割合が数十％まで増大するとの試算があり、その場合に先ほど挙げた事態が起こりうる。そのため、売電希望の需要家に対して売電可能量を割り振るための手法が研究されている（特許文献１、２）。 However, in the future, there is a trial calculation that the ratio of the amount of power generated from renewable energy will increase to several tens of percent. In that case, the situation mentioned above may occur. Therefore, methods for allocating the amount of electric power that can be sold to consumers who wish to sell electric power have been studied (Patent Documents 1 and 2).
特開２０１２-１７５７９５号公報JP 2012-175895 A 特開２０１１－９１９８３号公報Japanese Unexamined Patent Publication No. 2011-91983
　特許文献１では、限られた売電可能量に対して多数の電力需要家が売電を希望する状況下において、各電力需要家が売電機会を均等に得らる手法を提案している。この手法は、共通の変圧器に接続する複数の電力需要家が同時に売電を希望した時、売電可能期間を単位時間ごとに分割し、それぞれの単位時間を個々の電力需要家に均等に割り振る。これにより、各電力需要家が売電機会を均等に得られる。しかし、この手法では、各需要家は、どの程度の頻度で売電可能期間が割り振られるかを事前に知ることができない。このため、各需要家では、売電を計画したにも関わらず売電可能期間が割り振られない場合には、蓄電できない限りその余剰電力を捨てるしかない。 Patent Document 1 proposes a method in which each power consumer can obtain power sales opportunities evenly in a situation where a large number of power consumers desire to sell power for a limited amount of power that can be sold. . In this method, when multiple power consumers connected to a common transformer wish to sell power at the same time, the power available period is divided into unit times, and each unit time is evenly distributed to individual power consumers. Allocate. Thereby, each electric power consumer can obtain the power sale opportunity equally. However, with this method, each consumer cannot know in advance how often a power sale possible period is allocated. For this reason, each consumer has no choice but to throw away the surplus power as long as power can not be stored, even if the power sale period is not allocated in spite of planning to sell power.
　特許文献２では、複数の電力需要家が同時に売電を行うことを可能とする手法を提案している。この手法では、共通の変圧器に接続する複数の電力需要家が同時に売電を希望した時、各電力需要家が系統に流す電圧値を調整する。これにより、同時に系統に電力を供給して売電を行うことができる。しかし、この手法でも、各需要家は、売電可能な量を事前に知ることができないため、計画した量の電力を売電できない可能性がある。
このように、従来手法では、売電希望の需要家に対して売電可能量が限られている際に、売却機会を割り振るが、機会を失った場合に余剰電力を有効に活用することができず、需要家の使い勝手も低い。 Patent Document 2 proposes a technique that enables a plurality of power consumers to simultaneously sell power. In this method, when a plurality of power consumers connected to a common transformer wish to sell power at the same time, the voltage value that each power consumer flows through the grid is adjusted. Thereby, electric power can be supplied to the system and sold. However, even with this method, each consumer cannot know the amount of power that can be sold in advance, and therefore may not be able to sell the planned amount of power.
In this way, in the conventional method, when the amount of power that can be sold is limited to a consumer who wants to sell power, a sale opportunity is allocated, but if the opportunity is lost, surplus power can be used effectively It is not possible, and the convenience for consumers is low.
　本発明の目的は、電力需要家から電力系統への電力供給の可能性を示す所定の情報を電力需要家に提供することで、電力需要家での余剰電力の活用を促進でき、使い勝手を向上できるようにした情報提供装置および情報提供方法を提供することにある。 The object of the present invention is to provide power consumers with predetermined information indicating the possibility of power supply from the power consumer to the power grid, thereby making it possible to promote the use of surplus power in the power consumer and improve usability. An object of the present invention is to provide an information providing apparatus and an information providing method that are made possible.
　上記課題を解決すべく、本発明に係る情報提供装置は、電力系統に電力を供給可能な電力需要家に対して電力系統への電力供給に関する情報を提供する情報提供装置であって、電力需要家に設けられる電力管理装置と電力系統に設けられる所定のセンサ群とに通信する通信インターフェース部と、電力管理装置から取得する第１情報と所定のセンサ群から取得する第２情報と所定のコンピュータプログラムとを記憶する記憶部と、所定のコンピュータプログラムを実行することで、第１情報および第２情報に基づき、電力需要家から電力系統への電力供給の可能性を示す所定の情報を生成して、所定の情報を通信インターフェース部を介して電力管理装置に送信することで電力需要家に提供する演算処理部と、を備える。 In order to solve the above problems, an information providing apparatus according to the present invention is an information providing apparatus that provides information related to power supply to a power system to a power consumer capable of supplying power to the power system. A communication interface unit that communicates with a power management device provided in the house and a predetermined sensor group provided in the power system, first information acquired from the power management device, second information acquired from the predetermined sensor group, and a predetermined computer By generating a storage unit that stores the program and a predetermined computer program, predetermined information indicating the possibility of power supply from the power consumer to the power system is generated based on the first information and the second information. And an arithmetic processing unit that provides predetermined information to the power consumer by transmitting the predetermined information to the power management apparatus via the communication interface unit.
　所定の情報は、電力需要家から電力系統を運営または使用する電気事業者に売却可能な電力についての見込みを示す情報であってもよい。 The predetermined information may be information indicating an expectation of electric power that can be sold from an electric power consumer to an electric power company that operates or uses the electric power system.
売電見込み提示装置を含む電力管理システムの構成を示す説明図。Explanatory drawing which shows the structure of the power management system containing a power sale prospect presentation apparatus. 売電見込み提示装置のハードウェア構成およびソフトウェア構成などを示す構成説明図Configuration explanatory diagram showing the hardware configuration and software configuration of the power sale prospecting presentation device 需要家に関するデータを示し、（ａ）は需要家の設備を示すデータの構成、（ｂ）は需要家の設備の履歴を示すデータの構成、（ｃ）は需要家の位置を示すデータの構成を、示す。The data regarding a consumer are shown, (a) is the structure of the data which shows a customer's equipment, (b) is the structure of the data which shows the log | history of a customer's equipment, (c) is the structure of the data which shows a consumer's position. Is shown. 配電網に関するデータを示し、（ａ）は配電網区画の設備を示すデータの構成、（ｂ）は配電網区画の状態の履歴を示すデータの構成、（ｃ）配電網区画の位置を示すデータの構成を、示す。Data relating to the distribution network is shown. (A) is a data structure indicating the equipment of the distribution network section. (B) is a data structure indicating a history of the status of the distribution network section. (C) Data indicating the position of the distribution network section. The structure of is shown. （ａ）は発電環境に関するデータの構成、（ｂ）は需要家を分類するデータの構成、（ｃ）は配電網区分を分類するデータの構成を、示す。(A) shows the configuration of data relating to the power generation environment, (b) shows the configuration of data for classifying consumers, and (c) shows the configuration of data for classifying distribution network classification. 需要家および配電網から得たデータを統計処理して得られるデータの構成を示し、（ａ）は需要家履歴データの統計量を示すデータの構成、（ｂ）は需要家環境データの統計量を示すデータの構成、（ｃ）は配電網履歴データの統計量を示すデータの構成、（ｄ）は配電網の環境データの統計量を示すデータの構成を、示す。The data structure obtained by statistically processing data obtained from the customer and the distribution network is shown, (a) is the data structure indicating the statistics of the customer history data, and (b) is the statistics of the customer environment data. (C) shows the structure of the data indicating the statistics of the distribution network history data, and (d) shows the structure of the data indicating the statistics of the environmental data of the distribution network. 或るラベルに分類される需要家において、特定の日時および特定の環境条件下での発電量の累積確率密度分布を示すデータ。Data indicating the cumulative probability density distribution of the amount of power generation at a specific date and time and a specific environmental condition in a consumer classified into a certain label. 需要家を分類する処理を示すフローチャート。The flowchart which shows the process which classifies a consumer. 需要家設備の履歴データを統計処理する様子を示す説明図。Explanatory drawing which shows a mode that the historical data of customer equipment is statistically processed. 配電網区画を分類するラベルを設定する処理のフローチャート。The flowchart of the process which sets the label which classifies a power distribution network division. 売電見込みを算出する処理のフローチャート。The flowchart of the process which calculates a power sale prospect. 需要家に売電見込みを提示する方法のパターンを示す。The pattern of the method of presenting electric power sales prospect to a consumer is shown. 需要家に売電見込みを提示する他の方法のパターンを示す。The pattern of the other method of showing electric power sales prospect to a consumer is shown.
　以下、図面に基づいて、本発明の実施の形態を説明する。本実施形態の情報提供装置は、電力系統に接続されている電力需要家（以下、需要家）に対し、その需要家で生じる余剰電力を電力系統に供給できる可能性を算出し、提示する。余剰電力は、電力系統を使用または管理する事業者に売却され、他の需要家により消費される。従って、本実施形態の情報提供装置は、売電見込みを需要家に提示する装置である。 Hereinafter, embodiments of the present invention will be described with reference to the drawings. The information providing apparatus according to the present embodiment calculates and presents the possibility that surplus power generated by the consumer can be supplied to the power grid for a power consumer (hereinafter referred to as a consumer) connected to the power grid. The surplus power is sold to a business operator who uses or manages the power system and is consumed by other consumers. Therefore, the information providing apparatus of the present embodiment is an apparatus that presents a power sale prospect to a consumer.
　本実施形態の情報提供装置は、売電可能量に対し需要家の売電希望量が上回る可能性がある場合に、需要家が発電した電気の無駄を軽減または防止できるようにする。また、本実施形態の情報提供装置は、需要家の発電、蓄電、電力使用といった、設備稼働計画が実際に行えるかどうかの見積もりも支援できる。 The information providing apparatus according to the present embodiment can reduce or prevent waste of electricity generated by a consumer when there is a possibility that the consumer's desired power sale amount may exceed the power sale possible amount. In addition, the information providing apparatus according to the present embodiment can support estimation of whether or not the facility operation plan can actually be performed, such as power generation, power storage, and power use by consumers.
　そこで、本実施形態の情報提供装置は、需要家に対し、ある時間帯で売電が行えるかの見込みを提示する。また、需要家がある時間帯での売電を含む機器の稼働計画を問い合わせた際、その実行可能性を提示する。 Therefore, the information providing apparatus according to the present embodiment presents the prospect of whether the power can be sold in a certain time zone to the consumer. In addition, when a customer inquires about an operation plan of equipment including power sale in a certain time zone, the feasibility is presented.
　需要家への売電見込提示装置は、例えば、需要家が持つ電気機器の設備データ、需要家が持つ電気機器の履歴データ、需要家の位置データ、配電網区画ごとの設備データ、配電網区画ごとの履歴データ、配電網区画の位置データ、位置データに対応する環境データ、を収集し蓄積する。「所定区画」の一例である配電網区画とは、配電網の一部分であり、配電網のトポロジを変圧器ないしは変電所ごとに分割することで得られる。 For example, the equipment for presenting electric power sales to the consumer includes the equipment data of the electrical equipment possessed by the consumer, the history data of the electrical equipment possessed by the consumer, the location data of the consumer, the equipment data for each power grid section, the power grid section History data, distribution network location data, and environmental data corresponding to the location data are collected and stored. The distribution network section, which is an example of the “predetermined section”, is a part of the distribution network, and is obtained by dividing the topology of the distribution network for each transformer or substation.
　そして、本実施形態の情報提供装置は、下記の手順で、需要家に対し売電の見込みを提示する。但し、売電見込みを算出できるのであれば、実行の順番は問わない。 Then, the information providing apparatus of the present embodiment presents the prospect of power sale to the consumer in the following procedure. However, the order of execution is not limited as long as the power sale likelihood can be calculated.
　（１）需要家を、設備データと、履歴データと、需要家の位置データに対応する環境データとを用いてクラスタリングする。
　（２）配電網区画を、設備データと、履歴データと、配電網区画の位置データに対応する環境データと、配電網区画に接続する需要家のクラスタごとの割合とを用いてクラスタリングする。
　（３）配電網区画のクラスタごとに履歴データと環境データを用いて機械学習を行い、日にち、時刻、環境条件における配電網区画の状態を確率密度関数として得る。
　（４）配電網区画のクラスタごとに、接続している需要家の履歴データと環境データを用いて機械学習を行い、日にち、時刻、環境条件における需要家の発電量を確率密度関数として得る。
　（５）上記（３）の機械学習の結果と、日時、天気予報などから得られる環境データの予測分布とから、配電網区画の状態を予測する。
　（６）上記（４）の機械学習の結果と、日時、天気予報などから得られる環境データの予測分布とから、ある配電網区画に接続している需要家の発電量を予測する。
　（７）各配電網区画ごとの売電見込を求める。 (1) Cluster customers using equipment data, history data, and environmental data corresponding to customer location data.
(2) The distribution network section is clustered using the facility data, the history data, the environmental data corresponding to the position data of the distribution network section, and the ratio of each customer cluster connected to the distribution network section.
(3) Machine learning is performed using history data and environmental data for each cluster in the distribution network section, and the state of the distribution network section in the date, time, and environmental conditions is obtained as a probability density function.
(4) Machine learning is performed using historical data and environmental data of connected consumers for each cluster in the distribution network section, and the power generation amount of the consumers at the date, time, and environmental conditions is obtained as a probability density function.
(5) The state of the distribution network section is predicted from the result of the machine learning in (3) above and the predicted distribution of environmental data obtained from the date and time, weather forecast, and the like.
(6) The power generation amount of the customer connected to a certain distribution network section is predicted from the result of the machine learning in (4) above and the predicted distribution of environmental data obtained from the date and time, weather forecast, and the like.
(7) Estimate power sales for each distribution network section.
　本実施形態の情報提供装置は、需要家に対し、需要家が余剰電力を実際に売電する前に、電力買取量（売電量）に関する情報を提供する。売電量の予測を需要家に提示することで、需要家は、損失を避けるための努力を事前に行うことができ、使い勝手および信頼性が向上する。 The information providing apparatus according to the present embodiment provides information related to the amount of purchased power (amount of power sold) to the consumer before the consumer actually sells surplus power. By presenting the prediction of the amount of power sold to the customer, the customer can make an effort in advance to avoid loss, and the usability and reliability are improved.
　例えば、ある日時における需要家全体からの電力買取量の期待値を、「需要家全体の総発電量のｎ％」という見込み情報として事前に提示しておく。これにより需要家は、例えばタンクに高温の湯を溜めたり、製氷機で氷を製造したり、電気自動車のバッテリを満充電にしたりする等のように、電力買取量を超えた分の余剰電力を消費または蓄電するための準備を行うことができる。または、売電見込みが少ない場合、需要家は、発電装置を停止させてメンテナンスを行うこともできる。 For example, the expected value of the amount of power purchased from the entire consumer at a certain date and time is presented in advance as prospect information “n% of the total power generation amount of the entire consumer”. This allows consumers to store excess power that exceeds the amount of power purchased, for example, storing hot water in tanks, producing ice with an ice maker, or fully charging an electric vehicle battery. Can be prepared for consumption or storage. Or when there is little prospect of a power sale, a consumer can also stop a power generator and can perform maintenance.
　図１～図１１を参照して第１実施例を説明する。図１は、「情報提供装置」としての売電見込み提示装置１０を含む電力管理システムのブロック図である。 The first embodiment will be described with reference to FIGS. FIG. 1 is a block diagram of a power management system including a power sale prospecting presentation device 10 as an “information providing device”.
　売電見込み提示装置１０は、需要家に対し、ある時間帯で売電が行えるのかの見込みを提示する装置である。売電見込み提示装置１０の構成および動作については、さらに後述する。 The power sale prospecting presentation device 10 is a device that presents the prospect of whether power can be sold in a certain time zone to a consumer. The configuration and operation of the power sale prospect presentation device 10 will be described later.
　需要家２０は、例えば、一般家庭、工場、ビルディングなどの各種電力使用者を抽象的に表現したものである。需要家２０は、それぞれの特性に応じた、発電装置２２、電力消費装置２３、蓄電装置２４を有する。 The customer 20 is an abstract representation of various electric power users such as ordinary households, factories, and buildings. The customer 20 includes a power generation device 22, a power consumption device 23, and a power storage device 24 corresponding to the respective characteristics.
　発電装置２２としては、例えば、太陽光発電装置、風力発電装置、地熱発電装置、太陽熱発電装置、ガスコジェネレーションシステムなどがある。発電装置２２の多くは、再生可能エネルギーを利用しているため、発電量が不安定であるが、ガスコジェネレーションシステムのような発電装置２２は、計画通りに発電する能力を備えている。 Examples of the power generation device 22 include a solar power generation device, a wind power generation device, a geothermal power generation device, a solar thermal power generation device, and a gas cogeneration system. Many of the power generation devices 22 use renewable energy, and thus the amount of power generation is unstable. However, the power generation device 22 such as a gas cogeneration system has a capability of generating power as planned.
　電力消費装置２３としては、例えば、空調装置、各種家庭電化製品、照明装置、マンコンベア装置、製造設備などを挙げることができる。 Examples of the power consuming device 23 include an air conditioner, various home appliances, a lighting device, a man conveyor device, and a manufacturing facility.
　蓄電装置２４としては、例えば、鉛蓄電池、リチウムイオン電池、ニッケル水素電池、空気亜鉛電池、ナトリウム硫黄電池などがある。性質の異なる複数種類の蓄電池を組み合わせて使用する構成でもよい。蓄電装置２４は、需要家２０の内部または外部に固定設置されるものに限らず、移動可能な蓄電装置であってもよい。移動可能な蓄電装置としては、例えば、需要家２０内の電気配線から給電可能な電気自動車のバッテリなどを挙げることができる。 Examples of the power storage device 24 include a lead storage battery, a lithium ion battery, a nickel metal hydride battery, an air zinc battery, and a sodium sulfur battery. A configuration may be used in which a plurality of types of storage batteries having different properties are used in combination. The power storage device 24 is not limited to one that is fixedly installed inside or outside the customer 20, and may be a movable power storage device. Examples of the movable power storage device include a battery of an electric vehicle that can be fed from an electric wiring in the consumer 20.
　ＥＭＳ（Energy Management System）２１は、需要家２０の有する各機器２２、２３、２４を管理し制御する。ＥＭＳ２１は、売電見込み提示装置１０と通信ネットワーク４０を介して双方向通信可能に接続されており、各機器２２、２３、２４に関する情報を売電見込み提示装置１０に送信する。また、ＥＭＳ１０は、売電見込み提示装置１０から通信ネットワーク４０を介して売電見込みを取得し、取得した売電見込みをＥＭＳ２１の有する表示部に表示する。なお、ＥＭＳ２１は、ＥＭＳ２１とは物理的に別々に設けられているテレビジョン装置を、ＥＭＳ用の表示部として使用することもできる。 The EMS (Energy Management System) 21 manages and controls the devices 22, 23, and 24 of the customer 20. The EMS 21 is connected to the power sale prospecting presentation apparatus 10 via the communication network 40 so as to be capable of two-way communication, and transmits information regarding each device 22, 23, 24 to the power sale prospecting presentation apparatus 10. Further, the EMS 10 acquires a power sale prospect from the power sale prospect presentation device 10 via the communication network 40, and displays the obtained power sale prospect on the display unit of the EMS 21. Note that the EMS 21 can use a television device that is physically provided separately from the EMS 21 as a display unit for EMS.
　ＥＭＳ２１は、環境センサ２５とも通信可能に接続されており、環境センサ２５の検出した情報を通信ネットワーク４０を介して売電見込み提示装置１０に送信する。環境センサ２５は、「発電環境に関する情報」を計測して出力するセンサである。発電環境に関する情報としては、例えば、日射量、気温、風速などがある。本実施例では、主に日射量と気温を例に挙げて説明する。 The EMS 21 is also communicably connected to the environment sensor 25 and transmits information detected by the environment sensor 25 to the power sale prospecting presentation device 10 via the communication network 40. The environmental sensor 25 is a sensor that measures and outputs “information about the power generation environment”. Examples of information relating to the power generation environment include solar radiation, temperature, and wind speed. In the present embodiment, description will be given mainly by taking the amount of solar radiation and the temperature as examples.
　電力系統の一部である配電網３０は、需要家２０に電気を供給するためのものであり、複数の配電網区画３１が論理的に設定されている。配電網区画３１とは、配電網３０のトポロジを変圧器または変電所ごとに分割して得られる配電網の一部分である。各配電網区画３１には、配電網区画状態センサ３２（図中、状態センサ３２）と、配電網区画環境センサ３３（図中、環境センサ３３）とが設けられている。 The distribution network 30 which is a part of the power system is for supplying electricity to the customer 20, and a plurality of distribution network sections 31 are logically set. The distribution network section 31 is a part of the distribution network obtained by dividing the topology of the distribution network 30 for each transformer or substation. Each distribution network section 31 is provided with a distribution network section state sensor 32 (state sensor 32 in the figure) and a distribution network section environment sensor 33 (environment sensor 33 in the figure).
　配電網区画状態センサ３２は、配電網区画の状態（電圧、電流）を計測して出力するセンサである。配電網区画環境センサ３３は、配電網区画の発電環境に関する情報として、例えば日射量と気温を計測して出力するセンサである。 The distribution network partition state sensor 32 is a sensor that measures and outputs the state (voltage, current) of the distribution network partition. The distribution network section environment sensor 33 is a sensor that measures and outputs, for example, the amount of solar radiation and the temperature as information on the power generation environment of the distribution network section.
　売電見込み提示装置１０の動作の概要を先に簡単に説明すると、売電見込み提示装置１０は、管理下にある各需要家を所定の基準で分類し（１１）、需要家の接続している配電網の区画も他の所定の基準に従って分類する（１２）。売電見込み提示装置１０は、需要家の有する設備２２～２４の稼働状況を学習し（１３）、同様に、配電網の状態を配電網区画３１ごとに学習する（１４）。 The outline of the operation of the power sale prospecting presentation apparatus 10 will be briefly described first. The power sale prospecting presentation apparatus 10 classifies each customer under management according to a predetermined standard (11) and connects the consumers. The distribution network sections are also classified according to other predetermined criteria (12). The power sale prospecting presentation device 10 learns the operating status of the facilities 22 to 24 owned by the consumer (13), and similarly learns the status of the distribution network for each distribution network section 31 (14).
　売電見込み提示装置１０は、需要家の設備の学習結果に基づいて、ある日時、ある時刻および予測された環境下での需要家設備の状況を予測する（１５）。同様に、売電見込み提示装置１０は、配電網区画の学習結果に基づいて、ある日時、ある時刻および予測された環境下での配電網区画の状態を予測する（１６）。 The power sale prospecting presentation device 10 predicts the state of the customer facility under a certain date and time and a predicted environment based on the learning result of the customer facility (15). Similarly, the power sale prospecting presentation device 10 predicts the state of the distribution network section under a certain date and time, a certain time, and the predicted environment based on the learning result of the distribution network section (16).
　売電見込み提示装置１０は、需要家設備の状況についての予測結果と配電網区画の状況についての予測結果とから、売電見込みを算出し（１７）、算出した売電見込みを需要家のＥＭＳ２１に提示する（１８）。 The power sale prospecting presentation device 10 calculates a power sale prospect from the prediction result about the situation of the customer facility and the prediction result about the situation of the distribution network section (17), and uses the calculated power sale prospect to the EMS 21 of the consumer. (18).
　図２は、売電見込み提示装置１０のハードウェア構成およびソフトウェア構成を示すブロック図である。 FIG. 2 is a block diagram showing a hardware configuration and a software configuration of the power sale prospecting presentation device 10.
　売電見込み提示装置１０は、例えば、マイクロプロセッサ（以下、ＣＰＵ：Central Processing Unit）１０１と、メモリ１０２と、補助記憶装置１０３と、通信インターフェース部１０４とを備えるコンピュータ装置として構成される。 The power sale prospecting presentation device 10 is configured as a computer device including a microprocessor (hereinafter, CPU: Central ： Processing Unit) 101, a memory 102, an auxiliary storage device 103, and a communication interface unit 104, for example.
　ＣＰＵ１０１は、メモリ１０２に格納されるコンピュータプログラムを実行することで所定の機能Ｆ１０～Ｆ１２を実現する。メモリ１０２は、各種データＴ１０～Ｔ２０、各種プログラム（機能Ｆ１０～Ｆ１２の基となるプログラム）を記憶する。補助記憶装置１０３は、例えばハードディスクドライブ、フラッシュメモリデバイスなどのように比較的大容量の記憶装置であり、各種コンピュータプログラムやオペレーティングシステムなどを格納する。通信インターフェース部１０４は、通信ネットワーク４０を介して需要家２０側の機器および配電網３０側の機器と通信する。なお、補助記憶装置１０３を廃止し、メモリ１０２に全てのコンピュータプログラムおよびデータ類を記憶してもよい。 The CPU 101 realizes predetermined functions F10 to F12 by executing a computer program stored in the memory 102. The memory 102 stores various data T10 to T20 and various programs (programs that are functions F10 to F12). The auxiliary storage device 103 is a relatively large-capacity storage device such as a hard disk drive or a flash memory device, and stores various computer programs, operating systems, and the like. The communication interface unit 104 communicates with devices on the customer 20 side and devices on the power distribution network 30 side via the communication network 40. The auxiliary storage device 103 may be eliminated and all the computer programs and data may be stored in the memory 102.
　売電見込み提示装置１０のソフトウェアなどに関する構成を説明する。売電見込み提示装置１０は、処理実行部として、履歴データ収集部Ｆ１０と分析部Ｆ１１を有する。売電見込み提示装置１０は、データベースとして、需要家設備データＴ１０、需要家履歴データＴ１１、需要家位置データＴ１２、配電網設備データＴ１３、配電網履歴データＴ１４、配電網位置データＴ１５、環境データＴ１６、需要家分類データＴ１７、配電網区画分類データＴ１８、各種統計量Ｔ１９、各種累積確率密度分布Ｔ２０を持つ。処理実行部Ｆ１０、Ｆ１１は、通信部Ｆ１２を介して外部の機器（需要家側の機器および配電網側の機器）と通信する。 The configuration of the software etc. of the power sale prospecting presentation device 10 will be described. The power sale prospect presentation device 10 includes a history data collection unit F10 and an analysis unit F11 as processing execution units. The power sale prospecting presentation apparatus 10 includes, as a database, customer facility data T10, customer history data T11, customer location data T12, distribution network facility data T13, distribution network history data T14, distribution network location data T15, and environment data T16. , Customer classification data T17, distribution network section classification data T18, various statistics T19, and various cumulative probability density distributions T20. The process execution units F10 and F11 communicate with external devices (the customer side device and the distribution network side device) via the communication unit F12.
　「情報収集部」の一例である履歴データ収集部Ｆ１０は、通信部Ｆ１２および通信ネットワーク４０等を介して、各需要家のＥＭＳ２１および配電網区画の各センサ３２、３３からデータを収集する。履歴データ収集部Ｆ１０は、収集したデータを所定のデータベースに格納する。分析部Ｆ１２は、履歴データ収集部Ｆ１０が集めたデータに基づいて、後述の処理を実行することで、売電見込みを算出し、需要家に提示する。通信部Ｆ１２は、通信インターフェース部１０４を用いて、外部装置２１、３２、３３と通信する。 The history data collection unit F10, which is an example of the “information collection unit”, collects data from the EMS 21 of each consumer and the sensors 32 and 33 in the distribution network section via the communication unit F12, the communication network 40, and the like. The history data collection unit F10 stores the collected data in a predetermined database. Based on the data collected by the history data collection unit F10, the analysis unit F12 performs a process described later to calculate a power sale prospect and present it to the consumer. The communication unit F12 communicates with the external devices 21, 32, and 33 using the communication interface unit 104.
　図３～図７を参照して、売電見込み提示装置１０で管理する各種データの構成例を説明する。図３は、需要家２０に関するデータを示す。 A configuration example of various data managed by the power sale prospecting presentation device 10 will be described with reference to FIGS. FIG. 3 shows data related to the customer 20.
　図３（ａ）に示す需要家設備データＴ１０は、需要家の設備に関するデータを管理するもので、例えば、需要家ＩＤ　Ｃ１００、最大発電量Ｃ１０１、最大消費電力Ｃ１０２、最大蓄電量Ｃ１０３を対応付けて管理する。需要家ＩＤ　Ｃ１００は、需要家２０を識別するための情報である。最大発電量Ｃ１０１は、需要家２０の有する発電装置２２の最大出力電力を示す。最大消費電力Ｃ１０２は、需要家２０の有する電力消費装置２３で消費可能な電力の最大値である。最大蓄電量Ｃ１０３は、需要家２０の有する蓄電装置２４で蓄電可能な電気の最大値である。 The customer facility data T10 shown in FIG. 3 (a) manages data related to customer facilities. For example, the customer ID C100, the maximum power generation amount C101, the maximum power consumption C102, and the maximum power storage amount C103 are associated with each other. Manage. The customer ID C100 is information for identifying the customer 20. The maximum power generation amount C101 indicates the maximum output power of the power generation device 22 that the customer 20 has. The maximum power consumption C102 is the maximum value of power that can be consumed by the power consuming device 23 of the consumer 20. The maximum power storage amount C103 is the maximum value of electricity that can be stored in the power storage device 24 of the consumer 20.
　需要家設備データＴ１０に示す各エントリは、需要家２０が持つＥＭＳ２１と対応しており、売電見込提示装置１０にＥＭＳ２１が登録される際に、対応するエントリが作成される。売電見込み提示装置１０から登録が削除されると、削除された需要家に対応するエントリも削除される。 Each entry shown in the customer facility data T10 corresponds to the EMS 21 held by the customer 20, and when the EMS 21 is registered in the power sale prospecting presentation device 10, a corresponding entry is created. When the registration is deleted from the power sale prospecting presentation device 10, the entry corresponding to the deleted customer is also deleted.
　図３（ｂ）は、需要家が持つ電気機器２２～２４の稼働データの履歴を管理するための需要家履歴データＴ１１を示す。需要家履歴データＴ１１は、例えば、需要家ＩＤ　Ｃ１１０、日付Ｃ１１１、時刻Ｃ１１２、発電量Ｃ１１３、消費電力量Ｃ１１４、蓄電量Ｃ１１５を対応付けて管理する。 FIG. 3B shows customer history data T11 for managing the history of operation data of the electric devices 22 to 24 held by the customer. The customer history data T11 manages, for example, a customer ID C110, a date C111, a time C112, a power generation amount C113, a power consumption amount C114, and a power storage amount C115 in association with each other.
　需要家ＩＤ　Ｃ１１０は需要家を識別する情報である。日付Ｃ１１１および時刻Ｃ１１２は、設備２２～２４の稼働データをＥＭＳ２１から取得した日付および時刻である。需要家が持つ電気機器２２～２４の稼働データは、発電量Ｃ１１３、消費電力量Ｃ１１４、蓄電量Ｃ１１５の３つで表され、需要家ＩＤ　Ｃ１１０と日付Ｃ１１１および時刻Ｃ１１２の組と紐付けて管理される。このデータの各エントリは、ＥＭＳ２１から稼働データが送られてくるたびに追加される。 Customer ID C110 is information for identifying a customer. The date C111 and the time C112 are the date and time when the operation data of the facilities 22 to 24 is acquired from the EMS 21. The operation data of the electric devices 22 to 24 owned by the customer is represented by three of the power generation amount C113, the power consumption amount C114, and the storage amount C115, and is managed in association with the set of the customer ID C110, the date C111, and the time C112. Is done. Each entry of this data is added every time operation data is sent from the EMS 21.
　図３（ｃ）は、需要家２０が位置する場所に関するデータを管理するための需要家位置データＴ１２を示す。需要家位置データＴ１２は、例えば、需要家ＩＤ　Ｃ１２０、経度Ｃ１２１、緯度Ｃ１２２、需要家の接続している配電網区画３１のＩＤ　Ｃ１２３を対応付けて管理する。 FIG. 3C shows customer position data T12 for managing data relating to a place where the customer 20 is located. The customer position data T12 manages, for example, a customer ID C120, a longitude C121, a latitude C122, and an ID C123 of the distribution network section 31 to which the customer is connected.
　需要家ＩＤ　Ｃ１２０は需要家を識別する情報である。経度Ｃ１２１および緯度Ｃ１２２は、需要家が位置している場所を示す。接続している配電網区画ＩＤ　Ｃ１２３は、需要家が配電網と接続している部分の配電網区画を特定するための識別情報である。このデータの各エントリは、需要家２０が持つＥＭＳ２１と対応しており、売電見込装置１０１に需要家２０のＥＭＳ２１が登録される際に対応するエントリが作成され、売電見込み提示装置１０から登録が削除されると、対応するエントリも削除される。 Customer ID C120 is information for identifying a customer. The longitude C121 and the latitude C122 indicate the place where the customer is located. The connected distribution network section ID C123 is identification information for specifying the distribution network section of the part where the customer is connected to the distribution network. Each entry of this data corresponds to the EMS 21 of the consumer 20, and an entry corresponding to the entry of the EMS 21 of the consumer 20 is created in the electricity sales prospecting device 101. When the registration is deleted, the corresponding entry is also deleted.
　図４は、配電網区画に関するデータの構成例を示す。図４（ａ）は、配電網の設備データを管理するデータである。配電網設備データＴ１３は、例えば、配電網区画ＩＤ　Ｃ１３０、最大許容電流Ｃ１３１、定格電圧Ｃ１３２を対応付けて管理する。 FIG. 4 shows a configuration example of data related to the distribution network section. FIG. 4A is data for managing the distribution network equipment data. The distribution network facility data T13 manages, for example, a distribution network section ID C130, a maximum allowable current C131, and a rated voltage C132 in association with each other.
　配電網区画ＩＤ　Ｃ１３０は、配電網区画を識別するための情報である。配電網区画ごとの設備データは、最大許容電流Ｃ１３１と定格電圧Ｃ１３２の２つで表され、配電網区画ＩＤ　Ｃ１３０と紐付けて管理される。この表の値は、配電網の工事等によって変更される場合以外は基本的に固定値である。 The distribution network section ID C130 is information for identifying the distribution network section. The facility data for each distribution network section is represented by two of the maximum allowable current C131 and the rated voltage C132, and is managed in association with the distribution network section ID C130. The values in this table are basically fixed values unless they are changed due to construction of the distribution network.
　図４（ｂ）は、配電網の状態データの履歴を管理するデータである。配電網履歴データＴ１４は、例えば、配電網区画ＩＤ　Ｃ１４０、日付Ｃ１４１、時刻Ｃ１４２、電流Ｃ１４３、電圧Ｃ１４４を対応付けて管理する。 FIG. 4B is data for managing the history of the state data of the distribution network. The distribution network history data T14 manages, for example, a distribution network section ID C140, a date C141, a time C142, a current C143, and a voltage C144 in association with each other.
　配電網区画ＩＤは、配電網区画を識別する情報である。日付Ｃ１４１および時刻Ｃ１４２は、配電網の状態データを状態センサ３２から取得した日付および時刻である。配電網区画ごとの状態データは、電流Ｃ１４３と電圧Ｃ１４４との２つで表され、配電網区画ＩＤ　Ｃ１４０、日付Ｃ１４１、および時刻Ｃ１４２の組と紐付けて管理される。このデータの各エントリは、配電網区画状態センサ３２から状態データが送られてくるたびに追加される。 The distribution network section ID is information for identifying the distribution network section. The date C141 and the time C142 are the date and time when the state data of the power distribution network is acquired from the state sensor 32. The state data for each distribution network section is represented by two of current C143 and voltage C144, and is managed in association with a set of distribution network section ID C140, date C141, and time C142. Each entry of this data is added each time status data is sent from the distribution network partition status sensor 32.
　図４（ｃ）は、配電網区画が位置する場所に関するデータを管理する配電網位置データＴ１５を示す。配電網区画ＩＤ　Ｃ１５０は、配電網区画を識別する情報である。経度Ｃ１５１および緯度Ｃ１５２は、配電網区画が位置する場所を示す。このデータの値は、配電網の工事等によって変更される以外は基本的に固定された値である。 FIG. 4C shows distribution network position data T15 for managing data related to the location where the distribution network section is located. The distribution network section ID C150 is information for identifying the distribution network section. The longitude C151 and the latitude C152 indicate the location where the distribution network section is located. The value of this data is basically a fixed value except that it is changed by the construction of the distribution network.
　図５は、環境データＴ１６、需要家分類データＴ１７、配電網区画分類データＴ１８を示す。 FIG. 5 shows environmental data T16, customer classification data T17, and distribution network section classification data T18.
　図５（ａ）は、需要家や配電網区画が位置する場所についての環境データを管理するための環境データテーブルである。環境データテーブルＴ１６は、例えば、経度Ｃ１６０、緯度Ｃ１６１、日付Ｃ１６２、時刻Ｃ１６３、気温Ｃ１６４、日射量Ｃ１６５を対応付けて管理する。 FIG. 5 (a) is an environmental data table for managing environmental data regarding locations where consumers and distribution network sections are located. The environment data table T16 manages, for example, longitude C160, latitude C161, date C162, time C163, temperature C164, and solar radiation amount C165 in association with each other.
　経度Ｃ１６０および経度Ｃ１６１は環境データを測定した場所を示す。日付Ｃ１６２および時刻Ｃ１６３は環境データを測定した日付および時刻を示す。環境データは気温Ｃ１６４および日射量Ｃ１６５の２つの値で表される。環境データテーブルＴ１６のエントリは、ＥＭＳ２１および配電網区画環境センサ３３から環境データが送られてくるたびに追加される。 Longitude C160 and longitude C161 indicate locations where environmental data is measured. Date C162 and time C163 indicate the date and time when the environmental data was measured. The environmental data is represented by two values of temperature C164 and solar radiation amount C165. An entry in the environmental data table T16 is added each time environmental data is sent from the EMS 21 and the distribution network section environment sensor 33.
　図５（ｂ）は、需要家の分類結果を管理するデータを示す。需要家分類データＴ１７は、需要家ＩＤ　Ｃ１７０と、需要家分類ラベルＣ１７１を対応付けて管理する。需要家ＩＤ　Ｃ１７０は、需要家を識別する情報である。需要家分類ラベルＣ１７１は、需要家がどのクラスタに分類されるかを表すものである。このデータの各エントリは、ＥＭＳ２１と対応しており、ＥＭＳ２１が売電見込提示装置１０に登録される際に作成され、登録が削除されると対応するエントリが削除される。 FIG. 5 (b) shows data for managing the classification results of consumers. The customer classification data T17 is managed by associating the customer ID C170 and the customer classification label C171. The customer ID C170 is information for identifying a customer. The customer classification label C171 represents to which cluster a consumer is classified. Each entry of this data corresponds to the EMS 21 and is created when the EMS 21 is registered in the power sale prospecting presentation device 10, and when the registration is deleted, the corresponding entry is deleted.
　図５（ｃ）は、配電網区画の分類結果を管理するデータを示す。配電網区画分類データＴ１８は、例えば、配電網区画ＩＤ　Ｃ１８０と、配電網区画分類ラベルＣ１８１を対応付けて管理する。配電網区画ＩＤ　Ｃ１８０は、配電網区画を識別する情報である。配電網区画分類ラベルＣ１８１は、配電網区画がどのクラスタに分類されるかを表す。このデータの各エントリは各配電網区画と対応している。 Fig. 5 (c) shows data for managing the classification result of the distribution network section. The distribution network section classification data T18 manages, for example, a distribution network section ID C180 and a distribution network section classification label C181 in association with each other. The distribution network section ID C180 is information for identifying the distribution network section. The distribution network section classification label C181 indicates to which cluster the distribution network section is classified. Each entry of this data corresponds to each distribution network section.
　図６は、各種統計量Ｔ１９の構成例を示す。需要家履歴データＴ１１、需要家の環境データ、配電網履歴データＴ１４、配電網の環境データから算出された統計量は、それぞれ需要家履歴データＴ１１の統計量Ｔ１９ａ、需要家の環境データの統計量Ｔ１９ｂ、配電網履歴データＴ１４の統計量Ｔ１９ｃ、配電網の環境データの統計量Ｔ１９ｄ、の形式で格納される。 FIG. 6 shows a configuration example of various statistics T19. The statistics calculated from the customer history data T11, the customer environment data, the distribution network history data T14, and the distribution network environment data are the statistics T19a of the customer history data T11 and the statistics of the customer environment data, respectively. It is stored in the form of T19b, statistics T19c of distribution network history data T14, and statistics T19d of environment data of the distribution network.
　図７は、所定アルゴリズムに従って求めた確率密度関数を保存する各種累積確率密度分布Ｔ２０の一例を示す。確率密度分布は、累積確率密度分布の形で保存する。図７は、気温が２０－２５℃で、日射量が３.９－４.０kwhの場合での、需要家ラベル１に分類される需要家の発電量の累積確率密度分布を一例として示したものである。このような形式で各種確率密度分布を保存する。 FIG. 7 shows an example of various cumulative probability density distributions T20 that store probability density functions obtained according to a predetermined algorithm. The probability density distribution is stored in the form of a cumulative probability density distribution. FIG. 7 shows, as an example, the cumulative probability density distribution of the power generation amount of consumers classified into the customer label 1 when the temperature is 20-25 ° C. and the solar radiation amount is 3.9-4.0 kwh. Is. Various probability density distributions are stored in such a format.
　図８は、需要家分類ラベルの算出処理を示すフローチャートである。フローチャートの動作の主体は、売電見込み提示装置１０、売電見込み提示装置１０のＣＰＵ１０１、ＣＰＵ１０１の実行するコンピュータプログラム、分析部Ｆ１１、のいずれでもよい。ここでは、分析部Ｆ１１を動作主体として説明する。 FIG. 8 is a flowchart showing a process for calculating a customer classification label. The subject of the operation of the flowchart may be any one of the expected power sale presentation device 10, the CPU 101 of the expected power sale presentation device 10, the computer program executed by the CPU 101, and the analysis unit F11. Here, the analysis unit F11 will be described as an operation subject.
　分析部Ｆ１１は、需要家履歴データＴ１１の統計量Ｔ１９ａを算出する（Ｓ１０）。分析部Ｆ１１は、需要家履歴データＴ１１について１時間単位で量子化し、１時間単位で量子化した時間と、処理対象の日付の前後３日分を含めた日付との範囲で、各需要家の発電量、消費電力量、蓄電量を取得する。その後、分析部Ｆ１１は、そのデータの範囲で、発電量、消費電力量、蓄電量の平均値、中央値、最頻値を統計量として求める。 The analysis unit F11 calculates a statistic T19a of the customer history data T11 (S10). The analysis unit F11 quantizes the customer history data T11 in units of one hour, and in the range of the time quantized in units of one hour and the date including three days before and after the processing target date, Acquire power generation, power consumption, and power storage. Then, the analysis part F11 calculates | requires the electric power generation amount, power consumption amount, the average value of storage amount, the median value, and the mode value as a statistic within the range of the data.
　ステップＳ１０の処理を図９を用いて詳しく説明する。例として、発電量の測定値が図９（ａ）で示すような形であったとする。「１月４日（１／４）」の１１時台のデータについて１時間単位で量子化し、更に前後３日分を含めた日付の範囲で発電量の統計量を取るという事が具体的にどういう処理を示すことなのか説明する。それは、図９（ａ）に示す発電量の測定値における実戦で囲まれた領域のデータについて、図９（ｂ）に示すように平均値を求めて範囲内の平均（統計量Ａ）の実線で囲まれたセルにデータを格納し、さらに、図９（ｃ）に示すように、中央値を求めて範囲内の中央値（統計量Ｂ）の実線で囲まれたセルに格納し、以下同様に行うという処理を示している。 The process of step S10 will be described in detail with reference to FIG. As an example, it is assumed that the measured value of the power generation amount is as shown in FIG. It is specifically to quantize the data in the 11:00 range of “January 4 (1/4)” in 1-hour units and then take statistics of power generation in the range of dates including the three days before and after. Explain what processing is shown. For the data of the region surrounded by the actual battle in the measured value of power generation shown in FIG. 9A, the average value is obtained as shown in FIG. 9B, and the solid line of the average (statistic A) in the range The data is stored in the cells surrounded by, and further, as shown in FIG. 9C, the median is obtained and stored in the cells surrounded by the solid line of the median (statistic B) within the range, The same processing is shown.
　同様に、１月５日（１／５）の１１時台のデータについても１時間単位で量子化し、更に前後３日分を含めた日付の範囲で発電量の統計量を取るという事は、図９（ａ）の発電量の測定値の点線で囲まれた範囲のデータについて、上記同様の処理を行うことを示している。以降の統計量を求める処理においても、同様の処理を行う。 Similarly, the data of 11 o'clock on January 5 (1/5) is also quantized in units of one hour, and taking the statistics of the power generation amount within the date range including 3 days before and after, 9A shows that the same processing as described above is performed on the data in the range surrounded by the dotted line of the measurement value of the power generation amount in FIG. The same processing is performed in the subsequent processing for obtaining statistics.
　図８に戻る。続いて分析部Ｆ１１は、需要家の環境データの統計量Ｔ１９ｂを算出する（Ｓ１１）。分析部Ｆ１１は、各需要家の経度および緯度から、最も距離が近い環境データを取得する。その環境データについて、時刻については１時間単位で量子化し、日付は処理対象の日付の前後３日分を含めた日付を範囲とする。この範囲で気温、日射量の平均値、中央値、最頻値を統計量として求める。 Return to FIG. Subsequently, the analysis unit F11 calculates a statistical amount T19b of the customer's environmental data (S11). The analysis unit F11 acquires environmental data having the closest distance from the longitude and latitude of each customer. With respect to the environmental data, the time is quantized in units of one hour, and the date includes a date including three days before and after the date to be processed. Within this range, the average value, median value, and mode value of air temperature and solar radiation are obtained as statistics.
　分析部Ｆ１１は、需要家をクラスタリングする（Ｓ１２）。分析部Ｆ１１は、需要家設備データＴ１０と、ステップＳ１０で求めた需要家履歴データＴ１１の統計量Ｔ１９ａと、ステップＳ１１で求めた需要家の環境データの統計量Ｔ１９ｂとを特徴量とし、距離としてEuclid距離等を用い、所定のアルゴリズムとしてのk-means法などのクラスタリング手法を用いてクラスタリングを行う。 The analysis unit F11 clusters customers (S12). The analysis unit F11 uses the customer facility data T10, the statistic T19a of the customer history data T11 obtained in step S10, and the statistic T19b of the customer environmental data obtained in step S11 as feature quantities, and the distance Clustering is performed using a clustering technique such as a k-means method as a predetermined algorithm using Euclid distance or the like.
　分析部Ｆ１１は、ステップＳ１２で求めた需要家クラスタごとに、例えば「１」、「２」、「３」等の需要家分類ラベルを定義する。分析部Ｆ１１は、そのクラスタに分類された需要家に対応する需要家分類ラベルＣ１７１（図５（ｂ））に、定義したラベルの値を代入する。 The analysis unit F11 defines customer classification labels such as “1”, “2”, and “3” for each customer cluster obtained in step S12. The analysis unit F11 assigns the value of the defined label to the customer classification label C171 (FIG. 5B) corresponding to the customer classified into the cluster.
　図１０は、配電網区画分類ラベルを算出する処理のフローチャートである。分析部Ｆ１１は、 配電網履歴データＴ１４の統計量Ｔ１９ｃを算出する（Ｓ２０）。分析部Ｆ１１は、配電網履歴データＴ１４について１時間単位で量子化する。分析部Ｆ１１は、次に１時間単位で量子化した時間と、処理対象の日付の前後３日分を含めた日付の範囲で、各配電網区間の電流、電圧を取得する。その後、分析部Ｆ１１は、そのデータの範囲で、電流、電圧の平均値、中央値、最頻値を統計量として求める。 FIG. 10 is a flowchart of processing for calculating a distribution network section classification label. The analysis unit F11 calculates a statistic T19c of the distribution network history data T14 (S20). The analysis unit F11 quantizes the distribution network history data T14 on an hourly basis. Next, the analysis unit F11 acquires the current and voltage of each distribution network section in the range of the date including the time quantized in units of one hour and the three days before and after the processing target date. Then, the analysis part F11 calculates | requires the average value, median value, and mode value of an electric current and a voltage as a statistic within the range of the data.
　分析部Ｆ１１は、配電網の環境データの統計量Ｔ１９ｄを算出する（Ｓ２１）。分析部Ｆ１１は、各配電網区画の経度および緯度から、対応する環境データを取得する。分析部Ｆ１１は、その環境データについて、１時間単位で量子化し、処理対象の日付の前後３日分を含めた日付を範囲とする。分析部Ｆ１１は、この範囲で、気温および日射量の、平均値と中央値と最頻値とを統計量として求める。 The analysis unit F11 calculates a statistic T19d of the distribution network environment data (S21). The analysis unit F11 acquires corresponding environmental data from the longitude and latitude of each distribution network section. The analysis unit F11 quantizes the environmental data in units of one hour, and sets the date including three days before and after the processing target date as a range. The analysis part F11 calculates | requires the average value, median value, and mode value of air temperature and solar radiation amount as a statistic within this range.
　続いて分析部Ｆ１１は、配電網区画に接続している需要家の需要家分類ラベルの割合を算出する（Ｓ２２）。分析部Ｆ１１は、需要家位置データＴ１２を元に、各配電網区画ごとに接続している需要家リストを取得する。分析部Ｆ１１は、その需要家リストと、需要家分類データＴ１７とから、各配電網区画に接続している需要家の需要家分類ラベルの割合を算出する。 Subsequently, the analysis unit F11 calculates the ratio of the customer classification labels of the consumers connected to the distribution network section (S22). The analysis unit F11 acquires a consumer list connected to each distribution network section based on the consumer position data T12. The analysis part F11 calculates the ratio of the consumer classification label of the consumer connected to each distribution network section from the consumer list and the consumer classification data T17.
　分析部Ｆ１１は、配電網区画のクラスタリングを実行する（Ｓ２３）。分析部Ｆ１１は、配電網設備データＴ１３と、ステップＳ２０で求めた配電網履歴データＴ１４の統計量Ｔ１９ｃと、ステップＳ２１で求めた配電網の環境データの統計量Ｔ１９ｄと、ステップＳ２２で求めた配電網区画に接続している需要家の需要家分類ラベルの割合とを特徴量とし、距離としてEuclid距離等を用い、所定のアルゴリズムとしてk-means法等のクラスタリング手法を用いて、クラスタリングを行う。 The analysis unit F11 executes clustering of distribution network sections (S23). The analysis unit F11 includes the distribution network facility data T13, the statistics T19c of the distribution network history data T14 obtained in step S20, the statistics T19d of the distribution network environmental data obtained in step S21, and the distribution obtained in step S22. Clustering is performed by using the ratio of the customer classification labels of the customers connected to the network section as the feature amount, using the Euclid distance or the like as the distance, and using a clustering technique such as the k-means method as the predetermined algorithm.
　分析部Ｆ１１は、クラスタリング結果を保存する（Ｓ２４）。分析部Ｆ１１は、ステップＳ２３で求めた配電網区画のクラスタごとにラベルを定義する。分析部Ｆ１１は、そのクラスタに分類された配電網区画に対応する配電網区画ラベルＣ１８１に、定義したラベルの値を代入する。 The analysis unit F11 stores the clustering result (S24). The analysis unit F11 defines a label for each cluster of the distribution network section obtained in step S23. The analysis unit F11 substitutes the value of the defined label into the distribution network section label C181 corresponding to the distribution network section classified into the cluster.
　図１１は、売電見込を算出する処理のフローチャートである。分析部Ｆ１１は、配電網の状態を学習する（Ｓ２０）。分析部Ｆ１１は、各配電網区画分類ごとに、履歴と環境データとを元に、配電網区画の状態をベイズ推定といった手法で機械学習し、日付、時刻、環境条件下における状態を確率密度関数として求める。学習結果として得られる確率密度関数は「第２処理済み情報」の一例である。 FIG. 11 is a flowchart of a process for calculating the expected power sale. The analysis unit F11 learns the state of the distribution network (S20). The analysis unit F11 performs machine learning on the basis of history and environmental data for each distribution network partition classification by a method such as Bayesian estimation, and calculates the state under the date, time, and environmental conditions as a probability density function. Asking. The probability density function obtained as a learning result is an example of “second processed information”.
　続いて分析部Ｆ１１は、配電網区画に接続している需要家の設備稼働状況を学習する（Ｓ２１）。分析部Ｆ１１は、配電網区画ごとに、そこに接続している需要家の履歴データと環境データとを元に、発電量、蓄電量、電力消費量の値をベイズ推定といった手法で機械学習し、これにより日付、時刻、環境条件下における発電量、蓄電量、電力消費量を確率密度関数として求める。学習結果として得られる確率密度関数は「第１処理済み情報」の一例である。 Subsequently, the analysis unit F11 learns the equipment operation status of the customer connected to the distribution network section (S21). For each distribution network section, the analysis unit F11 performs machine learning using a method such as Bayesian estimation of power generation amount, power storage amount, and power consumption amount based on historical data and environmental data of consumers connected to the distribution network section. Thus, the power generation amount, power storage amount, and power consumption amount under the date, time and environmental conditions are obtained as a probability density function. The probability density function obtained as a learning result is an example of “first processed information”.
　分析部Ｆ１１は、配電網の状態を予測する（Ｓ２２）。分析部Ｆ１１は、ステップＳ２０での機械学習の結果を元に、日時と天気予報などから得られる環境データの予測分布から配電網区画の状態を予測し、ある日付とある時刻における配電網区画の状態予測を確率分布として求める。 The analysis unit F11 predicts the state of the distribution network (S22). Based on the result of machine learning in step S20, the analysis unit F11 predicts the state of the distribution network section from the predicted distribution of the environmental data obtained from the date and weather forecast, and the distribution network section at a certain date and time. The state prediction is obtained as a probability distribution.
　分析部Ｆ１１は、需要家の設備稼働状況を予測する（Ｓ２３）。分析部Ｆ１１は、各配電網区画に接続する需要家の設備稼働状況を、ステップＳ２１の結果を元に、日時と天気予報などから得られる環境データの予測分布から予測し、ある日付、時刻における発電量、蓄電量、電力消費量の確率分布として求める。但し、需要家の設備稼働状況が事前に需要家から提供されるなら、その値を用いて確率分布を作成しても良い。 The analysis unit F11 predicts the equipment operation status of the customer (S23). The analysis unit F11 predicts the equipment operation status of the customer connected to each distribution network section from the predicted distribution of environmental data obtained from the date and time and weather forecast based on the result of step S21, and at a certain date and time. Obtained as a probability distribution of the amount of power generation, the amount of electricity stored, and the amount of power consumption However, if the customer's equipment operation status is provided in advance by the customer, a probability distribution may be created using that value.
　分析部Ｆ１１は、配電網の状態予測と需要家の設備稼働状況予測とに基づいて、売電見込を算出する（Ｓ２４）。分析部Ｆ１１は、ステップＳ２２およびＳ２３の結果を元に、需要家から電力系統へ供給可能な電力量（見方を変えれば、電力事業者が買い取り可能な電力量）の見込を立てる。 The analysis unit F11 calculates the expected power sales based on the state prediction of the distribution network and the facility operation state prediction of the customer (S24). Based on the results of steps S22 and S23, the analysis unit F11 makes an estimate of the amount of power that can be supplied from the consumer to the power system (if the view is changed, the amount of power that can be purchased by the power company).
　例えば、ある配電網区画に接続されている需要家の発電装置２２の最大発電能力が１００ｋｗであり、その発電装置が、ある日付のある時刻に、ある気温および日射量の予測において、８０%以上の出力で運転する確率が９０%であると算出されたとする。また、配電網の状態予測から、ある配電網区画の需要家全体から２０ｋｗ／ｈの電力供給を行なっても問題ないと算出されたとする。その場合、分析部Ｆ１１は、その配電網区画に接続された需要家のＥＭＳ２１に対し、その日付とその時刻における売電見込として、「需要家での発電量の２５%までを電気事業者が買い取ることのできる確率は９０％である」と提示する。 For example, the maximum power generation capacity of a power generation device 22 of a customer connected to a certain distribution network section is 100 kw, and the power generation device is 80% or more in a certain temperature and solar radiation forecast at a certain date and time. It is assumed that the probability of driving at an output of 90% is calculated. In addition, it is assumed that it is calculated from the state prediction of the distribution network that there is no problem even if power supply of 20 kw / h is performed from all customers in a certain distribution network section. In that case, the analysis unit F11 indicates to the customer's EMS 21 connected to the distribution network section that the electric power company can supply up to 25% of the power generation amount at the customer's date and time. The probability of being able to buy is 90% ".
　図１２および図１３を用いて、売電見込みを含む情報を需要家に提示する方法の例を幾つか説明する。 12 and 13, some examples of a method for presenting information including a power sale prospect to a consumer will be described.
　図１２（ａ）に示す提供パターンでは、複数の需要家に対して、一定時間おき、あるいは一定時刻に売電見込みを含む情報を送信する。つまり、放送に類似した方法で、複数の需要家のＥＭＳ２１に向けて一斉に売電見込みを提示する。これにより、複数の需要家に一斉に売電見込みを知らせることができる。 In the provision pattern shown in FIG. 12 (a), information including a power sale prospect is transmitted to a plurality of consumers at regular intervals or at regular intervals. That is, the power sale prospects are simultaneously presented to the EMS 21 of a plurality of consumers by a method similar to broadcasting. As a result, it is possible to notify a plurality of consumers of the expected power sale all at once.
　図１２（ｂ）に示す提供パターンでは、需要家からの問い合わせに対し、売電見込みを含む情報をＥＭＳ２１に送信する。ＥＭＳ２１は、需要家ＩＤを明示して売電見込みを含む情報の送信を売電見込み提示装置１０に要求する。売電見込み提示装置１０は、その需要家ＩＤが登録されている場合、その需要家についての売電見込みを含む情報を、要求元のＥＭＳ２１に送信する。これにより、売電見込みを個別に提示できる。 In the provision pattern shown in FIG. 12 (b), in response to an inquiry from a customer, information including a power sale prospect is transmitted to the EMS 21. The EMS 21 requests the power sale prospect presentation device 10 to transmit information including the power sale prospect with the customer ID clearly specified. When the customer ID is registered, the power sale prospecting presentation apparatus 10 transmits information including the power sale prospect for the consumer to the requesting EMS 21. Thereby, the electric power sale prospect can be shown individually.
　図１３（ａ）は、需要家の配信登録にしたがって、一定時間おき、あるいは一定時刻に、売電見込みを含む情報を送信するパターンを示す。売電見込みを含む情報の提示を希望する需要家は、事前に需要家ＩＤを売電見込み提示装置１０に登録しておく。売電見込み提示装置１０は、需要家の指定する所定タイミングが到来すると、登録済みの需要家ＩＤを有するＥＭＳ２１に向けて、売電見込みを含む情報を送信する。これにより、需要家の望むタイミングで売電見込みを複数回提示できる。 FIG. 13 (a) shows a pattern in which information including a power sale prospect is transmitted at regular time intervals or at regular time intervals in accordance with consumer distribution registration. A consumer who wishes to present information including a power sale prospect registers a customer ID in the power sale prospect presentation apparatus 10 in advance. When the predetermined timing designated by the consumer arrives, the power sale prospect presentation device 10 transmits information including the power sale prospect to the EMS 21 having the registered consumer ID. Thereby, the electric power sale prospect can be shown in multiple times at the timing which a consumer desires.
　図１３（ｂ）は、売電見込み提示装置１０に登録されていない需要家のＥＭＳ２１に対して、その需要家にとって参考となり得る売電見込みを含む情報を提供するパターンを示している。需要家は、売電見込み提示装置１０に事前に設備データおよび履歴データを送信していなくても、売電見込みについての情報を得ることができる。 FIG. 13B shows a pattern for providing information including a power sale prospect that can be a reference for the consumer to the EMS 21 of the consumer that is not registered in the power sale prospect presentation device 10. Even if the customer does not transmit the facility data and the history data to the power sale prospect presentation device 10 in advance, the consumer can obtain information about the power sale prospect.
　未登録の需要家のＥＭＳ２１は、設備情報（発電装置、電力消費設備、蓄電設備の情報）を売電見込み提示装置１０に送信し、売電見込みを含む情報の提示を求める。売電見込み提示装置１０は、未登録の需要家の設備情報に類似した設備を有する需要家を検索し、類似する需要家についての売電見込みを参考情報として、未登録の需要家のＥＭＳ２１に送信する。 The EMS 21 of the unregistered customer transmits the facility information (information on the power generation device, the power consumption facility, and the storage facility) to the power sale prospecting presentation device 10 and requests the presentation of information including the power sale prospect. The power sale prospecting presentation apparatus 10 searches for consumers having facilities similar to the facility information of unregistered consumers, and uses the power sale prospects for similar customers as reference information to the EMS 21 of unregistered consumers. Send.
　このように構成される本実施例によれば、売電見込み提示装置１０は、需要家が余剰電力を売電する前に、売電の見込みについての情報を需要家に与えることができる。従って、需要家は、余剰電力の売却の可能性を事前に知ることができ、余剰電力をできるだけ無駄にしないための行動計画を立案し、実行することができる。従って、需要家の使い勝手が向上し、さらに電力買い取りシステムに対する公平性を増し、信頼感を高めることができる。 According to this embodiment configured as described above, the power sale prospecting presentation device 10 can give information on the prospect of power sale to the consumer before the consumer sells surplus power. Therefore, the consumer can know in advance the possibility of selling surplus power, and can formulate and execute an action plan for not wasting surplus power as much as possible. Therefore, the convenience of consumers can be improved, the fairness of the power purchase system can be increased, and the reliability can be enhanced.
　なお、本発明は、上述した実施例に限定されない。当業者であれば、本発明の範囲内で、種々の追加や変更等を行うことができる。 In addition, this invention is not limited to the Example mentioned above. A person skilled in the art can make various additions and changes within the scope of the present invention.
　１０：売電見込み提示装置、２０：電力需要家、２１：ＥＭＳ、２２：発電装置、２３：電力消費設備、２４：蓄電設備、２５：環境センサ、３０：配電網、３１：配電網区画、３２：状態センサ、３３：環境センサ、４０：通信ネットワーク、Ｆ１０：履歴データ収集部、Ｆ１１：分析部、Ｆ１２：通信部、１０１：マイクロプロセッサ、１０２：メモリ、１０３：補助記憶装置、１０４：通信インターフェース部 10: Electricity sales prospect presentation device, 20: Electric power consumer, 21: EMS, 22: Power generation device, 23: Electric power consumption facility, 24: Electric storage facility, 25: Environmental sensor, 30: Distribution network, 31: Distribution network section, 32: Status sensor, 33: Environmental sensor, 40: Communication network, F10: History data collection unit, F11: Analysis unit, F12: Communication unit, 101: Microprocessor, 102: Memory, 103: Auxiliary storage device, 104: Communication Interface part

Claims (10)

  1. 　電力系統に電力を供給可能な電力需要家に対して前記電力系統への電力供給に関する情報を提供する情報提供装置であって、
    　前記電力需要家に設けられる電力管理装置と前記電力系統に設けられる所定のセンサ群とに通信する通信インターフェース部と、
    　前記電力管理装置から取得する第１情報と前記所定のセンサ群から取得する第２情報と所定のコンピュータプログラムとを記憶する記憶部と、
    　前記所定のコンピュータプログラムを実行することで、前記第１情報および前記第２情報に基づき、前記電力需要家から前記電力系統への電力供給の可能性を示す所定の情報を生成して、前記所定の情報を前記通信インターフェース部を介して前記電力管理装置に送信することで前記電力需要家に提供する演算処理部と、
    を備える情報提供装置。     An information providing device for providing information related to power supply to the power system to a power consumer capable of supplying power to the power system,
    A communication interface unit that communicates with a power management device provided in the power consumer and a predetermined sensor group provided in the power system;
    A storage unit for storing first information acquired from the power management apparatus, second information acquired from the predetermined sensor group, and a predetermined computer program;
    By executing the predetermined computer program, based on the first information and the second information, generating predetermined information indicating the possibility of power supply from the power consumer to the power system, the predetermined information An arithmetic processing unit that provides the power consumer by transmitting the information to the power management device via the communication interface unit;
    An information providing apparatus comprising:
  2. 　前記所定の情報は、前記電力需要家から前記電力系統を運営または使用する電気事業者に売却可能な電力についての見込みを示す報である、
    請求項１に記載の情報提供装置。     The predetermined information is a report indicating a prospect of electric power that can be sold from the electric power consumer to an electric power company that operates or uses the electric power system.
    The information providing apparatus according to claim 1.
  3. 　前記電力系統には複数の電力需要家が接続されており、
    　前記演算処理部は、前記所定のコンピュータプログラムを実行することで、前記第１情報および前記第２情報を収集する情報収集部と、前記情報収集部の収集した情報を分析する分析部とを実現し、
    　前記情報収集部は、前記電力管理装置から前記電力需要家の位置、発電、電力消費、蓄電および発電環境に関する情報を前記第１情報として収集し、前記所定のセンサ群から前記電力系統に設定した複数の所定区画毎に位置、電流、電圧、発電環境に関する情報を前記第２情報として取得し、
    　前記分析部は、
    　　前記第１情報から得られる所定の特徴に基づいて、前記複数の電力需要家を複数の需要家ラベルのいずれかに分類し、
    　　前記複数の電力需要家の分類結果と前記第２情報から得る他の所定の特徴とに基づいて、前記複数の所定区画を複数の区画ラベルのいずれかに分類し、
    　　前記複数の需要家ラベルごとに前記第１情報を学習処理して得られる第１処理済み情報を用いて発電量、電力消費量および蓄電量を予測し、
    　　前記複数の区画ラベルごとに前記第２情報を学習処理して得られる第２処理済み情報を用いて電流および電圧を予測し、
    　　前記予測した発電量、電力消費量および蓄電量と前記予測した電流および電圧とに基づいて、前記複数の電力需要家が前記電力事業者に売却可能な電力についての可能性を示す値を確率として算出し、前記算出した確率を用いて前記所定の情報を生成し、前記通信インターフェース部を介して前記電力管理装置に送信する、
    請求項２に記載の情報提供装置。     A plurality of power consumers are connected to the power system,
    The arithmetic processing unit implements an information collecting unit that collects the first information and the second information and an analysis unit that analyzes the information collected by the information collecting unit by executing the predetermined computer program And
    The information collection unit collects information on the position of the power consumer, power generation, power consumption, power storage, and power generation environment from the power management apparatus as the first information, and sets the information to the power system from the predetermined sensor group For each of a plurality of predetermined sections, information on position, current, voltage, power generation environment is acquired as the second information,
    The analysis unit
    Classifying the plurality of power consumers into one of a plurality of consumer labels based on a predetermined feature obtained from the first information;
    Based on the classification results of the plurality of power consumers and other predetermined characteristics obtained from the second information, the plurality of predetermined sections are classified into one of a plurality of section labels,
    Predicting power generation amount, power consumption amount and power storage amount using first processed information obtained by learning processing the first information for each of the plurality of consumer labels,
    Predicting current and voltage using second processed information obtained by learning the second information for each of the plurality of section labels;
    Based on the predicted power generation amount, power consumption amount and storage amount, and the predicted current and voltage, a value indicating the possibility of the power that the plurality of power consumers can sell to the power company as a probability Calculating, generating the predetermined information using the calculated probability, and transmitting to the power management device via the communication interface unit,
    The information providing apparatus according to claim 2.
  4. 　前記分析部は、
    　　前記区画ラベルごとに前記第１情報の履歴と前記発電環境に関する情報とを用い、所定のアルゴリズムで機械学習を行うことで、前記第２処理済み情報を所定の日時および所定の環境条件ごとの確率密度関数として算出し、
    　　前記所定区画ごとに、当該所定区画に接続している電力需要家についての前記第１情報の履歴と前記発電環境に関する情報とを用い、所定のアルゴリズムで機械学習を行うことで、前記第１処理済み情報を所定の日時および所定の環境条件ごとの確率密度関数として算出し、
    　　前記複数の需要家ラベルごとに前記第１処理済み情報を用いて発電量、電力消費量および蓄電量を確率分布として予測し、
    　　前記複数の区画ラベルごとに前記第２処理済み情報を用いて電流および電圧を予測する、
    請求項３に記載の情報提供装置。     The analysis unit
    Using the history of the first information and the information related to the power generation environment for each section label and performing machine learning with a predetermined algorithm, the second processed information is determined as a probability for each predetermined date and predetermined environmental condition. As a density function,
    For each of the predetermined sections, the first processing is performed by performing machine learning with a predetermined algorithm using the history of the first information about the power consumer connected to the predetermined section and the information regarding the power generation environment. Calculated as a probability density function for each predetermined date and time and predetermined environmental conditions,
    Predicting power generation amount, power consumption amount and power storage amount as probability distribution using the first processed information for each of the plurality of consumer labels,
    Predicting current and voltage using the second processed information for each of the plurality of partition labels;
    The information providing apparatus according to claim 3.
  5. 　前記情報収集部が、前記電力管理装置から自装置の担当する電力需要家についての発電量、電力消費量および蓄電量のいずれか一つまたは全部の情報を事前に取得できる場合、前記分析部は、前記電力需要家についての予測は行わずに、前記事前に取得した情報を使用して前記確率を算出する、
    請求項４に記載の情報提供装置。     When the information collecting unit can acquire in advance any one or all of the information on the power generation amount, the power consumption amount, and the power storage amount for the power consumer in charge of the device itself from the power management device, the analysis unit, The probability is calculated using the information acquired in advance without performing prediction for the power consumer.
    The information providing apparatus according to claim 4.
  6. 　前記所定の情報は、前記通信インターフェース部を介して、複数の電力需要家の前記電力管理装置に一斉に送信される、
    請求項１に記載の情報提供装置。     The predetermined information is transmitted simultaneously to the power management devices of a plurality of power consumers via the communication interface unit.
    The information providing apparatus according to claim 1.
  7. 　複数の電力需要家のうち送信を要求する電力管理装置に対して、前記所定の情報を送信する、
    請求項１に記載の情報提供装置。     The predetermined information is transmitted to a power management device that requests transmission among a plurality of power consumers.
    The information providing apparatus according to claim 1.
  8. 　複数の電力需要家のうち事前に配信を希望する旨の登録を行った電力需要家の電力管理装置に前記所定の情報を送信する、
    請求項１に記載の情報提供装置。     The predetermined information is transmitted to the power management device of the power consumer who has registered to request distribution in advance among a plurality of power consumers.
    The information providing apparatus according to claim 1.
  9. 　前記情報収集部は、当該情報収集部に登録されていない未登録の電力管理装置から前記第１情報を取得した場合、前記未登録の電力管理装置から取得した前記第１情報と所定範囲内で一致する他の第１情報を検索し、前記他の第１情報に基づいて得られる前記所定の情報を前記未登録の電力管理装置に送信する、
    請求項１に記載の情報提供装置。     When the information collection unit acquires the first information from an unregistered power management apparatus that is not registered in the information collection unit, the information collection unit is within a predetermined range with the first information acquired from the unregistered power management apparatus. Search for other matching first information, and transmit the predetermined information obtained based on the other first information to the unregistered power management device.
    The information providing apparatus according to claim 1.
  10. 　電力系統に電力を供給可能な電力需要家に対して前記電力系統への電力供給に関する情報をコンピュータ装置を用いて提供する情報提供方法であって、
    　前記コンピュータ装置は、前記電力需要家に設けられる電力管理装置と前記電力系統に設けられる所定のセンサ群とに通信する通信インターフェース部と、前記電力管理装置から取得する第１情報と前記所定のセンサ群から取得する第２情報と所定のコンピュータプログラムとを記憶する記憶部と、演算処理部とを備え、
    　前記演算処理部は、
    　　前記所定のコンピュータプログラムを実行し、
    　　前記第１情報および前記第２情報に基づき、前記電力需要家から前記電力系統への電力供給の可能性を示す所定の情報を生成し、
    　　前記所定の情報を前記通信インターフェース部を介して前記電力管理装置に送信することで前記電力需要家に提供する、
    情報提供方法。     An information providing method for providing information related to power supply to the power system to a power consumer capable of supplying power to the power system using a computer device,
    The computer device includes a communication interface unit that communicates with a power management device provided in the power consumer and a predetermined sensor group provided in the power system, first information acquired from the power management device, and the predetermined sensor. A storage unit that stores second information acquired from the group and a predetermined computer program, and an arithmetic processing unit;
    The arithmetic processing unit includes:
    Executing the predetermined computer program;
    Based on the first information and the second information, generate predetermined information indicating the possibility of power supply from the power consumer to the power system,
    Providing the predetermined information to the power consumer by transmitting the predetermined information to the power management apparatus via the communication interface unit;
    Information provision method.
PCT/JP2013/073920 2013-09-05 2013-09-05 Information provision device and information provision method WO2015033419A1 (en)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005185028A (en) * 2003-12-22 2005-07-07 Tm T & D Kk Monitoring system of low-voltage power distribution system
JP2006121790A (en) * 2004-10-20 2006-05-11 Toyota Motor Corp Power interconnection system
JP2012130096A (en) * 2010-12-13 2012-07-05 Panasonic Corp Power control apparatus and power control system using the same
JP2013132105A (en) * 2011-12-20 2013-07-04 Panasonic Corp Power measurement device

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005185028A (en) * 2003-12-22 2005-07-07 Tm T & D Kk Monitoring system of low-voltage power distribution system
JP2006121790A (en) * 2004-10-20 2006-05-11 Toyota Motor Corp Power interconnection system
JP2012130096A (en) * 2010-12-13 2012-07-05 Panasonic Corp Power control apparatus and power control system using the same
JP2013132105A (en) * 2011-12-20 2013-07-04 Panasonic Corp Power measurement device

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