WO2018066260A1 - Système de planification de réponse à une demande, procédé de planification de réponse à une demande et programme de planification de réponse à une demande - Google Patents

Système de planification de réponse à une demande, procédé de planification de réponse à une demande et programme de planification de réponse à une demande Download PDF

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WO2018066260A1
WO2018066260A1 PCT/JP2017/030456 JP2017030456W WO2018066260A1 WO 2018066260 A1 WO2018066260 A1 WO 2018066260A1 JP 2017030456 W JP2017030456 W JP 2017030456W WO 2018066260 A1 WO2018066260 A1 WO 2018066260A1
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demand response
data
power data
power
baseline
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PCT/JP2017/030456
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English (en)
Japanese (ja)
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和正 本田
和明 草清
茂雄 松澤
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株式会社東芝
東芝エネルギーシステムズ株式会社
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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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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
    • 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
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or 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
    • 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
    • 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

  • Embodiments of the present invention relate to a demand response planning system, a demand response planning method, and a demand response planning program.
  • the demand response system is a system that issues a demand response (DR) to a consumer when it is predicted in advance that power supply and demand will be tight, and promotes reduction of power consumption.
  • DR demand response
  • a predetermined incentive is given to a customer who has achieved the power reduction target.
  • the predicted value of the amount of power reduced by the consumer is determined based on a predetermined baseline.
  • the baseline is a predicted value of the power consumption consumed by the consumer when it is assumed that there is no demand response. Ideally, the baseline is calculated after all the power data consumed by the consumer is available.
  • the customer is notified of the amount of power reduction after the demand response is implemented.
  • the problem to be solved by the present invention is to provide a demand response planning system, a demand response planning method, and a demand response planning program capable of reducing an error in a baseline value calculated when a demand response is issued. It is.
  • the block diagram which shows the system configuration
  • the flowchart which shows the flow of a process of the demand response planning system of embodiment.
  • the demand response planning system of the embodiment has a power data loss determination unit, a loss power data complementation unit, and a prediction baseline calculation unit.
  • the power data loss determination unit obtains power data in which a customer's past power usage state is recorded based on an external request for notifying the customer of execution of a demand response to promote power supply and demand suppression, It is determined whether or not the power data is defective.
  • the missing power data complementing unit complements the missing data determined to have occurred by the power data missing determining unit based on a predetermined complementing method.
  • the prediction baseline calculation unit calculates a prediction baseline to be notified to the customer when the demand response is issued based on the complementary power data in which the deficit is complemented by the missing power data complementing unit.
  • Demand response is a mechanism that provides incentives to customers who have reduced power when demand for power supply is tight, and suppresses power consumption to ensure a stable supply of power when demand response is requested.
  • the demand response planning system issues a demand response to the customer when the power supply and demand is predicted to be tight in advance, and notifies the customer of the target power reduction amount.
  • FIG. 1 is a block diagram showing a system configuration of the demand response planning system 1.
  • the demand response planning system 1 includes, for example, a first power database 100, a second power database 110, a baseline calculation unit 200, a predicted baseline value database 150, a confirmed baseline value database 151, and consumers.
  • An incentive calculation unit 310, an incentive calculation unit 311 with an electric power company, a demand response receiving unit 400, and a demand response issuing unit 410 are provided.
  • the first power database 100 stores past power usage data (power data) of each customer.
  • the second power database 110 stores data (DR power data) as a result of power reduction performed by each customer after issuing a demand response.
  • the baseline calculation unit 200 receives a demand response instruction from the electric power company received by the demand response reception unit 400, the baseline calculation unit 200 calculates a baseline based on past power consumption data.
  • the baseline is a predicted value of the power usage that becomes a reference when calculating the power reduction amount.
  • the baseline calculation unit 200 is realized, for example, when a processor such as a CPU (Central Processing Unit) executes a baseline calculation program. Also, some of the functional units in the baseline calculation unit 200 may be realized by hardware such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), or FPGA (Field-Programmable Gate Array). Software and hardware may cooperate. In addition, the functional units other than the baseline calculation unit 200 may be similarly realized by a processor and a program, may be realized by hardware such as an LSI, an ASIC, and an FPGA, or the software and hardware cooperate. You may do.
  • LSI Large Scale Integration
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • the baseline calculation unit 200 includes, for example, a prediction baseline calculation processing unit 210 and a confirmed baseline calculation processing unit 220.
  • the prediction baseline calculation processing unit 210 calculates a prediction baseline calculated after the demand response is issued.
  • the prediction baseline calculation processing unit 210 includes, for example, a first power data deficiency determination unit 211, a first deficient power data complement unit 212, and a prediction baseline calculation unit 213.
  • the above-described functional blocks cooperate to calculate a prediction baseline.
  • the first power data deficiency determination unit 211 is based on an external request for notifying a customer of execution of a demand response for encouraging power supply and demand suppression, among power data stored in the first power database 100, First power data used for calculation of a prediction baseline is acquired.
  • the request from the outside is information on the issuance of demand response by the electric power company received by the demand response receiving unit, and includes information received automatically and information received manually.
  • Information on the issuance of demand response includes, for example, information that is automatically transmitted when occurrence of tightness in power supply and demand is predicted, and information that is transmitted manually.
  • the power data is, for example, data collected from each customer's power meter (such as a smart meter) via a wired or wireless line.
  • the first power data loss determination unit 211 determines whether or not a first loss occurs in the first power data.
  • the first loss power data complementation unit 212 complements the first loss based on a predetermined complementation method.
  • the predetermined complementing method will be described in detail later.
  • the prediction baseline calculation unit 213 calculates a prediction baseline value notified to the customer based on the first power data (complementary power data) supplemented by the first missing power data supplementing unit 212.
  • the prediction baseline calculation unit 213 calculates a prediction baseline based on the calculated prediction baseline value.
  • the prediction baseline is obtained by assigning a prediction baseline value for each time zone within the demand response execution time zone.
  • the prediction baseline is used to calculate a power reduction target value and a customer incentive value.
  • the customer incentive value is an index indicating an incentive given to the customer according to the amount of power reduced by the customer.
  • the prediction baseline calculation unit 213 stores the prediction baseline in the prediction baseline value database 150.
  • the demand response issuing unit 410 issues a demand response to the customer, and notifies the customer of the target power reduction amount based on the predicted baseline.
  • the confirmed baseline calculation processing unit 220 includes, for example, a second power data deficiency determination unit 221, a second deficient power data supplement unit 222, and a confirmed baseline calculation unit 223.
  • the confirmed baseline calculation processing unit 220 calculates a confirmed baseline value after the demand response is performed.
  • the confirmed baseline value is calculated in a state where the first power data is complemented after a lapse of a certain period after the demand response is performed.
  • the confirmed baseline calculation processing unit 220 calculates a confirmed baseline based on the calculated confirmed baseline value.
  • the confirmed baseline is obtained by assigning a confirmed baseline value for each time slot within the demand response execution time slot.
  • the confirmed baseline calculation processing unit 220 includes, for example, a second power data loss determining unit 221, a second missing power data complementing unit, and a 222 confirmed baseline calculation unit 223.
  • the above-described functional blocks cooperate to calculate a fixed baseline.
  • the second power data loss determination unit 221 refers to the first power database 100 after the elapse of a predetermined period after execution of the demand response, and stores the second power data (complementary power data) in which the first loss of the first power data is complemented. ) To get.
  • the second power data is complemented by collecting the first deficient data generated in the first power data from each customer's power meter. However, the second power data is not complete, and some of the data may be missing.
  • the second power data loss determination unit 221 determines whether a second loss occurs in the second power data. Since the second power data is data in which the first loss of the first power data is complemented, the second loss of the second power data is smaller than the first loss of the first power data. Therefore, the data amount of the second power data is larger than the data amount of the first power data.
  • the second missing power data complementing unit 222 also needs to newly complement the second missing and calculate a confirmed baseline.
  • the second missing power data complementing unit 222 complements the second missing determined by the second power data missing determining unit 221 based on a predetermined complementing method.
  • Various methods can be used as the complementing method here. For example, a method according to the complementing method described later may be used. Which method is used depends on the contract with the customer.
  • the confirmed baseline calculation unit 223 calculates a confirmed baseline value notified to the customer based on the supplemented second power data.
  • the confirmed baseline calculation unit 223 calculates a confirmed baseline based on the confirmed baseline value.
  • the fixed baseline value is used to calculate the fixed power reduction amount and business incentive value.
  • the business incentive value is an index indicating the incentive given by the business according to the amount of power reduction.
  • the confirmed baseline calculation unit 223 stores the calculated confirmed baseline in the confirmed baseline value database 151.
  • the power reduction amount is determined so as to be obtained in advance from the predicted baseline value when contracting with the customer.
  • the incentive calculation unit 310 with the customer calculates a customer incentive value.
  • the incentive calculation unit 310 with the customer calculates a customer incentive value based on the prediction baseline and the DR power data related to the power reduction amount stored in the second power database 110.
  • the customer incentive value is notified to the customer by, for example, a notification means (not shown) after the demand response is performed.
  • the incentive calculation unit 311 with the electric power company calculates the power reduction amount and the business incentive based on the confirmed baseline value and the DR power data stored in the second power database 110 after a certain period of time has elapsed since the demand response was performed. Calculate the value.
  • the business incentive value is notified after the elapse of a predetermined period after execution of the demand response. The difference between the business incentive value and the customer incentive value is profit in the demand response business.
  • the prediction baseline is used for calculating the customer incentive value, but a confirmed baseline that is determined after the demand response is performed can also be used.
  • the customer is notified of the incentive value based on the prediction baseline.
  • the type of notification depends on the contract with the customer.
  • the incentive value based on the prediction baseline is notified, it is important to bring the prediction baseline closer to the confirmed baseline.
  • FIG. 2 is a diagram illustrating a calculation example of a prediction baseline according to the first method.
  • the first method is to compare the power data on the implementation date of demand response with the past power data before the implementation date, and based on the difference between the power data on the implementation date and the past power data, the demand response This is a method to calculate the predicted baseline after the announcement.
  • the first method will be described below.
  • past data for the upper 4 days is selected excluding 5/13 data with the lowest average power consumption.
  • Average value data for each time zone (9: 00-12: 00, 13: 00-15: 00) is calculated from the selected past data for 4 days (2).
  • the time zone from 9:00 to 12:00 is set as a correction time zone (A) on the day, and the time zone from 13:00 to 15:00 is set as a demand response execution time zone (B).
  • the time correction adjustment time zone (A) is a time zone in which data is referred to in order to calculate a prediction baseline in the demand response execution time zone (B) of the day. Since the demand for power is increasing on the demand response implementation date, the value of the power data is higher than the past data.
  • the forecast baseline is calculated according to the trend of the high power demand on the day instead of simply using the average of the past data. Therefore, in calculating the prediction baseline of the demand response execution time zone, a difference between the average value of the power data measured on the day of the demand response execution date and the average value of the past data for four days is calculated.
  • the current day data of 6 units (1 unit is 30 minutes, for example) 4 hours to 1 hour (9: 00-12: 00) before the demand response execution start time (13:00) It is recorded.
  • each data of the difference for 6 units of each average for 6 units of the day data and each average value for 6 units of the same time zone of the past data for 4 days is calculated (3).
  • an average value (150 kWh / unit) of difference data for 6 units is calculated, and this value is set as the correction amount for the day (4).
  • the estimated baseline value (kWh) is calculated by adding the current day correction adjustment amount to each hourly average value data in the demand response execution time zone.
  • the predicted baseline value is allocated to each hour of the demand response execution time zone to obtain a predicted baseline (5).
  • FIG. 3 is a diagram illustrating a calculation example of a prediction baseline according to the second method.
  • the second technique is to calculate an expected baseline using the power data of the day that is pre-measured in the previous time zone from when the demand response is issued. For example, the case where a demand response announcement notice is performed 10 minutes before the time (9:40) when the demand response is performed (9:30) is shown.
  • the prediction baseline b is determined by setting the average value of the power data a in a certain time zone before the demand response is issued as the prediction baseline value and allocating the prediction baseline value for each time zone in which the demand response is performed. .
  • the average value of power data from 9:00 to 9:30 is set as the predicted baseline value.
  • the average value of the power data in the baseline calculation target time zone (6 units between 9:00 and 9:30: 1 unit is, for example, 5 minutes) is the predicted baseline value.
  • the prediction baseline b is determined by allocating the prediction baseline value (523 kWh) for each time zone in the demand response execution time (9: 40-10: 40).
  • FIG. 4 is a diagram illustrating power data complementation. As shown in the upper part of FIG. 4, there is a deficiency in the past power data in the time zone (9: 00-11: 0) in which the demand response is performed. For example, the first missing power data complementing unit 212 compensates for missing past power data by the following processing.
  • FIG. 5 is a diagram illustrating power data complementation.
  • two data (80, 90) of the time zone before and after the data of the time zone of 6: 00-6: 30 5: 30-6: 00, 6: 30-7: 00) ) Is used.
  • FIG. 6 is a diagram illustrating power data complementation.
  • a part of the 11/17 predicted baseline calculation target time zone (A) (8: 20-8: 50) (C) (8: 30-8: 35) The power data is missing.
  • two data (520, 560) in the time zone (8: 25-8: 30, 8: 35-8: 40) before and after the data in the time zone (C) are used for complementation. Used in the calculation of
  • the predicted baseline is an average value of 6 units (1 unit is 5 minutes) from 30 minutes before the demand response announcement notice to the demand response announcement notice.
  • the predicted baseline b is determined by arranging the predicted baseline value 530 (kWh) for each time zone of the demand response execution time (B) (9: 40-10: 40).
  • the first deficient power data complementing unit 212 complements the first deficit by calculating the average value of the data stored in the time zones before and after the time zone in which the first deficiency occurs.
  • the data may be complemented by an interpolation method using a function such as a least square method or a spline interpolation, in addition to a method of calculating data loss as an average value.
  • FIG. 7 is a flowchart showing a processing flow of the demand response planning system 1.
  • the power supply and demand is tight, and the demand response receiving unit 400 receives a demand response issue from the power company.
  • the prediction baseline calculation processing unit 210 starts calculation of the prediction baseline value at the time when the demand response is issued (step S100).
  • the first power data loss determination unit 211 determines whether there is a loss in the power data (first power data acquired from the first power database) necessary for the baseline calculation (step S101).
  • the first loss power data complementing unit 212 uses a predetermined complement method for the first loss of the first power data. Based on the above, complement or predict (step S102).
  • the prediction baseline calculation unit 213 calculates a prediction baseline at the time when the demand response is issued based on the complemented first power data (step S103). If the power data loss determination unit 211 determines in step S101 that there is no loss in the first power data, step S103 is executed.
  • the demand response issuing unit 410 issues a demand response to the customer (step S104), and simultaneously notifies the power reduction amount based on the predicted baseline.
  • the customer performs the demand response (step S105).
  • the confirmed baseline meter processing calculation unit 220 starts calculating the confirmed baseline.
  • the second power data loss determination unit 221 acquires the second power data from the first power database 100 after a certain period of time has elapsed since the demand response has been performed.
  • the second power data loss determination unit 221 determines whether there is a loss in the power data (second power data) necessary for the baseline calculation (step S106).
  • the second power loss data supplement unit 222 supplements the second loss based on a predetermined complement method.
  • the confirmed baseline calculation unit 223 calculates a confirmed baseline based on the supplemented second power data (step S108).
  • the incentive calculation unit 311 calculates a power reduction amount due to power saving performed by the customer based on the confirmed baseline and the DR power data (step S109).
  • the incentive calculation unit 310 derives a power saving result of each demand response based on the DR power data and the prediction baseline stored for each past demand response. Then, the incentive calculation unit 311 derives the result of the demand response for each execution of the demand response actually performed by the customer based on the DR power data and the confirmed baseline value stored for each past demand response execution. (Step S110).
  • the incentive calculation unit 311 calculates a customer incentive value from the power saving result for each demand response execution. Then, the incentive calculation unit 311 calculates a reward amount (business incentive value) for each demand response execution from the power saving result (step S111).
  • the demand response planning system 1 it is possible to calculate a prediction baseline that serves as a reference for the amount of power that should be reduced by the customer when a demand response is issued.
  • the demand response planning system 1 can calculate the prediction baseline by complementing even if there is a deficiency in the power data used for calculating the prediction baseline. Thereby, the demand response planning system 1 can notify the customer of the target power reduction amount based on the prediction baseline when the demand response is issued.
  • the deficient data is complemented by calculating the average value of the data before and after.
  • the demand response planning system 1 in the second embodiment complements the deficiency of power data by calculating the average value of data for the past several days in the same time zone.
  • FIG. 8 is a diagram illustrating power data complementation.
  • there is a deficiency in past data that is the basis for calculating the predicted baseline for the November 17 demand response.
  • some of the past data for several days in the baseline calculation target time zone (9: 00-11: 00: 00) is missing in a part of the time zone (10: 30-11: 00: 00) on November 14 ing.
  • an average value of past data for n days (n is an arbitrary number) from the date of deficiency is used.
  • the first missing power data complementing unit 212 calculates the average value of the data in the same time zone in which the first deficit occurs for n days (n is a natural number) before the date on which the first deficiency occurs. And the first deficit is complemented using this average value.
  • the demand response planning system 1 can calculate the prediction baseline by a complementing method using the date as the time axis when there is a deficiency in the power data used for calculating the prediction baseline value.
  • the demand response planning system 1 supplements the date as a time axis when there is a deficiency in the power data used to calculate the predicted baseline value.
  • the predicted value of the missing data is calculated from the tendency of the data on the day when the missing date is complemented.
  • the first missing power data complementing unit 212 compensates for missing past power data by the following processing.
  • FIG. 9 is a diagram illustrating power data complementation. As shown in FIG. 9, a part of time zone (C) (8: 45-8: 50) of the predicted baseline calculation time zone (A) of November 17 (8: 20-8: 50) In addition, power data loss occurs.
  • the data of the deficient portion is based on the tendency of the power data of n units (n is an arbitrary natural number) immediately before the data in the time zone (C) (8: 45-8: 50).
  • the predicted value of the data is calculated as 525 based on Equation 1. As shown in FIG. 9, the predicted value 525 is used as supplementary data.
  • an average value of 6 units (1 unit is, for example, 5 minutes) of the predicted baseline calculation target time zone (A) is represented as the predicted baseline value.
  • the predicted baseline b is determined by arranging the predicted baseline value 512.5 (kWh) for each time zone of the demand response execution time (B) (9: 00-10: 00).
  • This supplement method may be applied when there is a deficiency in the data of the first unit time zone (8: 20-8: 25) of the forecast baseline calculation target time zone (A).
  • the day with the same trend is not limited to one day.
  • n days n is a natural number
  • the average value of the power data in the same time zone as the time zone with the deficit is calculated, and the deficit is compensated. You may go.
  • the first missing power data complementing unit 212 calculates the predicted value based on the tendency of the data stored in the same time period in the past when the first missing is generated in the first power data. Then, the first deficit is complemented using the predicted value. And according to the demand response planning system 1, even if the missing data used for the calculation of the prediction baseline is at the beginning or end of the hourly arrangement in the calculation time zone, the predicted value of the missing data is calculated and the prediction baseline is calculated. Can be calculated.
  • the demand response planning system 1 complements according to the trend of the power data on the demand response implementation date when there is a deficiency in the power data used for calculation of the prediction baseline.
  • the demand response planning system 1 in the fourth embodiment selects the data of the day with the most similar tendency from the past data, and calculates the predicted value of the missing data from the selected data. And complement it.
  • the first missing power data complementing unit 212 compensates for missing past power data by the following processing.
  • FIG. 10 is a diagram illustrating power data complementation.
  • a partial time zone (C) (8: 45-8: 50) of the 11/17 predicted baseline calculation target time zone (A) (8: 20-8: 50) The power data is missing.
  • n is a natural number
  • the missing data in the time zone (C) is calculated to have a predicted value of 525 based on 11/10 power data.
  • the predicted baseline b is determined by arranging the predicted baseline value 512.5 (kWh) for each time zone of the demand response execution time (B) (9: 00-10: 00).
  • the first missing power data complementing unit 212 has the tendency of the data on the date on which the first loss occurs from the data for n days (n is a natural number) before the date on which the first loss occurs.
  • the predicted value based on the date data having the closest tendency is calculated, and the first deficit is complemented using the predicted value.
  • a prediction baseline can be determined using the calculated predicted value.
  • each of these methods may be an arbitrary combination to obtain an optimal solution. Good.
  • provisions determined in advance by a contract with the customer may be provided.
  • the power in which the customer's past power usage state is recorded
  • the power data deficiency determination unit 211 that acquires data and determines whether or not deficiency occurs in the power data, and the deficiency determined to have occurred by the power data deficiency determination unit 211 based on a predetermined complementing method.
  • a forecast baseline calculation for calculating a forecast baseline to be notified to the customer when a demand response is issued Part 213 can reduce the error in the baseline value calculated when issuing a demand response. That.
  • the demand response planning system 1 for reducing the amount of electric power has been described as an example.
  • the demand response planning system 1 is not limited to this case. You may apply to the energy resource supply system which supplies resources.
  • the demand response planning system 1 may be applied to a system that targets CO2 credits related to CO2 emission transactions and emissions of energy resources such as other greenhouse gases.
  • the demand response planning system 1 suppresses the supply and demand of supply and demand objects such as gas, heat, energy resources, exhaust gas, and water that are subject to market transactions through the supply facilities such as plants, in addition to the purpose of suppressing power supply and demand. May be used for

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Abstract

L'invention concerne un système de planification de réponse à une demande, un procédé de planification de réponse à une demande, et un programme, permettant de réduire une erreur dans une valeur de ligne de base calculée lors de l'émission d'une réponse à une demande. Selon un mode de réalisation, un système de planification de réponse à une demande comprend une unité de détermination d'insuffisance de données d'énergie, une unité de compensation de données d'énergie insuffisantes et une unité de calcul de ligne de base prédite. Sur la base d'une requête externe destinée à notifier à un client l'exécution d'une réponse à une demande dans le but de favoriser une suppression de l'alimentation et de la demande en énergie, l'unité de détermination d'insuffisance de données d'énergie obtient des données d'énergie enregistrant l'état antérieur d'utilisation en énergie de clients, et détermine si une insuffisance est apparue dans les données d'énergie. L'unité de compensation de données d'énergie insuffisantes compense, sur la base d'un procédé de compensation prescrit, une insuffisance déterminée par l'unité de détermination d'insuffisance de données d'énergie. Sur la base de données d'énergie compensées dont l'insuffisance a été compensée par l'unité de compensation de données d'énergie insuffisantes, l'unité de calcul de ligne de base prédite calcule une ligne de base prédite qui est rapportée au client lors de l'émission de la réponse à la demande.
PCT/JP2017/030456 2016-10-03 2017-08-25 Système de planification de réponse à une demande, procédé de planification de réponse à une demande et programme de planification de réponse à une demande WO2018066260A1 (fr)

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JP2016195863A JP6818491B2 (ja) 2016-10-03 2016-10-03 デマンドレスポンス計画システム、デマンドレスポンス計画方法、及びデマンドレスポンス計画プログラム
JP2016-195863 2016-10-03

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JP2019007882A (ja) * 2017-06-27 2019-01-17 京セラ株式会社 管理方法及び管理装置

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KR102648458B1 (ko) * 2021-12-10 2024-03-19 한국전력공사 전력사용량 추정 장치 및 방법
CN116881234A (zh) * 2023-06-20 2023-10-13 北京自动化控制设备研究所 磁探系统遥测数据实时预测补数方法及系统

Citations (3)

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WO2015075794A1 (fr) * 2013-11-20 2015-05-28 株式会社 東芝 Système de prévision de demande d'énergie, procédé de prévision de demande d'énergie, système de profilage de client, et procédé de profilage de client
WO2016017647A1 (fr) * 2014-07-31 2016-02-04 ダイキン工業株式会社 Dispositif de gestion d'appareil
WO2016017424A1 (fr) * 2014-07-31 2016-02-04 日本電気株式会社 Dispositif de commande, dispositif de commande d'appareil, procédé de rapport, et support d'enregistrement

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015075794A1 (fr) * 2013-11-20 2015-05-28 株式会社 東芝 Système de prévision de demande d'énergie, procédé de prévision de demande d'énergie, système de profilage de client, et procédé de profilage de client
WO2016017647A1 (fr) * 2014-07-31 2016-02-04 ダイキン工業株式会社 Dispositif de gestion d'appareil
WO2016017424A1 (fr) * 2014-07-31 2016-02-04 日本電気株式会社 Dispositif de commande, dispositif de commande d'appareil, procédé de rapport, et support d'enregistrement

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2019007882A (ja) * 2017-06-27 2019-01-17 京セラ株式会社 管理方法及び管理装置

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