Adjustment force setting device, adjustment force setting method, and adjustment force setting program

Abstract

To set necessary adjustment force for absorbing predictive error in a planning period.SOLUTION: An adjustment force setting device 10 has a control unit 11 acquiring a standard deviation of a power demand prediction value by time zone as a first standard deviation with respect to a first prediction model used for a prediction of a power demand in a plurality of time zones, acquiring a standard deviation of a power generation amount prediction value by time zone as a second standard deviation with respect to a second prediction model used for a prediction of a power generation amount in the plurality of time zones, setting adjustment force by using the first standard deviation and the second standard deviation with respect to the time zone when power is generated among the plurality of time zones, setting adjustment force by using the first standard deviation with respect to the time zone when no power is generated among the plurality of time zones, and outputting a set value of the adjustment force by time zone.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to an adjustment force setting device, an adjustment force setting method, and an adjustment force setting program.

Background Art

[0002] Patent Document 1 discloses a supply-demand control device that controls the supply and demand of electric power in a system to which an internal combustion power generation device, a renewable energy power generation device, a power storage device, and a load are connected.

[0003] Patent Document 2 discloses a supply-demand balance control device that formulates a supply plan for a predetermined period.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0005] Retail electric power business operators or registered specific power transmission and distribution business operators must comply with the planned value simultaneous same quantity system so as not to disrupt the regional power supply and demand. In the planned value simultaneous same quantity system, submission of a facility operation plan within the jurisdiction area to OCCTO or, when joining a balancing group, to the representative contractor is required. "OCCTO" is an abbreviation of Organization for Cross-regional Coordination of Transmission Operators (organization for promoting wide-area operation of electric power). The facility operation plan is created through a procedure of (i) predicting electric power demand and solar power generation amount, and (ii) creating a facility operation plan that can cover the shortage of electric power amount in (i) and has the minimum cost.

[0006] The equipment operation plan requires ensuring adjustment capacity to absorb the prediction errors that occurred in (i).

[0007] The purpose of this disclosure is to determine the necessary adjustment capacity to absorb forecast errors during the planning period. [Means for solving the problem]

[0008] The adjustment force setting device relating to this disclosure is The system includes a control unit that, for a first forecasting model used to forecast electricity demand over multiple time periods, obtains the standard deviation of the forecasted electricity demand for each time period as the first standard deviation; for a second forecasting model used to forecast power generation over multiple time periods, obtains the standard deviation of the forecasted power generation for each time period as the second standard deviation; sets the adjustment capacity using the first and second standard deviations for time periods during which power generation occurs; sets the adjustment capacity using the first standard deviation for time periods during which power generation does not occur; and outputs the set value of the adjustment capacity for each time period.

[0009] The method for setting the adjustment power related to this disclosure is as follows: For the first forecasting model used to predict electricity demand across multiple time zones, the control unit acquires the standard deviation of the predicted electricity demand values ​​for each time zone as the first standard deviation. Regarding the second prediction model used to predict the amount of power generation in the aforementioned multiple time periods, the control unit acquires the standard deviation of the predicted power generation values ​​for each time period as the second standard deviation. Of the aforementioned multiple time periods, for the time periods during which power generation occurs, the control unit sets the adjustment force using the first standard deviation and the second standard deviation. Of the aforementioned multiple time periods, for the time periods when no power is generated, the control unit sets the adjustment force using the first standard deviation. The control unit outputs the set value of the adjustment force for each time period. Includes.

[0010] The adjustment force setting program related to this disclosure is For the first forecasting model used to predict electricity demand across multiple time zones, the standard deviation of the predicted electricity demand values ​​for each time zone is obtained as the first standard deviation, Regarding the second prediction model used to predict power generation amounts for the aforementioned multiple time periods, the standard deviation of the predicted power generation amounts for each time period is obtained as the second standard deviation, Among the aforementioned multiple time periods, the adjustment capacity is set using the first standard deviation and the second standard deviation for the time periods during which power generation occurs. Among the aforementioned multiple time periods, the adjustment capacity is set using the first standard deviation for the time periods when no power is generated, Output the set values ​​for adjustment capacity by time period. To have the computer perform an action that includes [this]. [Effects of the Invention]

[0011] According to this disclosure, it is possible to set the necessary adjustment force to absorb prediction errors during the planning period. [Brief explanation of the drawing]

[0012] [Figure 1] This is a block diagram showing the configuration of an adjustment force setting device according to an embodiment of the present disclosure. [Figure 2] This graph shows an example of the adjustment capacity required to absorb prediction errors during the planning period. [Figure 3] This is a flowchart showing the operation of the adjustment force setting device according to the embodiment of this disclosure. [Modes for carrying out the invention]

[0013] Hereinafter, one embodiment of this disclosure will be described with reference to the figures.

[0014] In each figure, identical or corresponding parts are denoted by the same reference numerals. In the description of this embodiment, the description of identical or corresponding parts will be omitted or simplified as appropriate.

[0015] Referring to FIGS. 1 and 2, the outline of this embodiment will be described.

[0016] The adjustment force setting device 10 is a general-purpose computer such as a PC, a server computer such as a cloud server, or a dedicated computer used or operated by an electric utility such as a retail electric utility or a registered specific transmission and distribution electric utility. "PC" is an abbreviation for personal computer.

[0017] For the first prediction model used for predicting the power demand in a plurality of time periods P, the adjustment force setting device 10 acquires the standard deviation of the power demand prediction values for each time period as the first standard deviation. For the second prediction model used for predicting the power generation amount in a plurality of time periods P, the adjustment force setting device 10 acquires the standard deviation of the power generation amount prediction values for each time period as the second standard deviation. The plurality of time periods P correspond to the planning period. The length of the planning period is, for example, 24 hours or 72 hours. Each time period Ti included in the plurality of time periods P corresponds to each segment of the planning period. The length of one segment is, for example, 30 minutes.

[0018] For the time period Tx with power generation among the plurality of time periods P, the adjustment force setting device 10 sets the adjustment force Ax using the first standard deviation and the second standard deviation. "Adjustment force" refers to the capacity of power generation facilities (including pumped-storage power generation facilities), power storage devices, DR, and other systems that control power supply and demand (excluding distribution facilities) required for frequency control, supply-demand balance adjustment, and other grid stabilization operations in the supply area. "DR" is an abbreviation for demand response. For the time period Ty without power generation among the plurality of time periods P, the adjustment force setting device 10 sets the adjustment force Ay using the first standard deviation. The adjustment force setting device 10 outputs the set value of the adjustment force Ai for each time period. The set value of the adjustment force Ai for each time period includes the set value of the adjustment force Ax for the time period Tx with power generation among the plurality of time periods P and the set value of the adjustment force Ay for the time period Ty without power generation among the plurality of time periods P.

[0019] According to this embodiment, the necessary adjustment force 20 for absorbing forecast errors during the planning period can be set as shown in Figure 2. The necessary adjustment force 20 for absorbing forecast errors during the planning period includes the necessary adjustment force 21 for absorbing demand forecast errors and the necessary adjustment force 22 for absorbing power generation forecast errors. The unit is kW.

[0020] In this embodiment, the second prediction model is a statistical model used to predict the amount of electricity generated by solar panels. That is, the second prediction model is used to predict the amount of solar power generated in multiple time periods P. Therefore, the sunshine period corresponds to the time period Tx when electricity is generated, and the non-sunshine period corresponds to the time period Ty when no electricity is generated. In other words, the adjustment force setting device 10 sets the adjustment force Ax for the sunshine period among the multiple time periods P using the first standard deviation and the second standard deviation, and sets the adjustment force Ay for the non-sunshine period among the multiple time periods P using the first standard deviation.

[0021] In the example shown in Figure 2, the planning period is 24 hours, from 0:00 to 24:00. Therefore, each time frame included in the time range R1 from sunrise to sunset corresponds to the time period Tx when power is generated, and each time frame included in the time range R2 from 0:00 to sunrise and from sunset to 24:00 corresponds to the time period Ty when no power is generated. The adjustment force setting device 10 assumes that the power generation prediction error = 0 for the time range R2 when no power is generated, and calculates the adjustment force using the standard deviation calculated from the prediction by the statistical model.

[0022] As a variation of this embodiment, the second prediction model may be a statistical model used to predict the amount of power generated by other types of power generation equipment, whose power generation is time-dependent, similar to solar panels.

[0023] Referring to Figure 1, the configuration of the adjustment force setting device 10 according to this embodiment will be described.

[0024] The adjustment force setting device 10 comprises a control unit 11, a storage unit 12, a communication unit 13, an input unit 14, and an output unit 15.

[0025] The control unit 11 includes at least one processor, at least one programmable circuit, at least one dedicated circuit, or any combination thereof. The processor is a general-purpose processor such as a CPU or GPU, or a dedicated processor specialized for a specific process. "CPU" is an abbreviation for central processing unit. "GPU" is an abbreviation for graphics processing unit. The programmable circuit is, for example, an FPGA. "FPGA" is an abbreviation for field-programmable gate array. The dedicated circuit is, for example, an ASIC. "ASIC" is an abbreviation for application specific integrated circuit. The control unit 11 controls each part of the adjustment force setting device 10 and executes processes related to the operation of the adjustment force setting device 10.

[0026] The storage unit 12 includes at least one semiconductor memory, at least one magnetic memory, at least one optical memory, or any combination thereof. The semiconductor memory is, for example, RAM, ROM, or flash memory. "RAM" is an abbreviation for random access memory. "ROM" is an abbreviation for read-only memory. RAM is, for example, SRAM or DRAM. "SRAM" is an abbreviation for static random access memory. "DRAM" is an abbreviation for dynamic random access memory. ROM is, for example, EEPROM. "EEPROM" is an abbreviation for electrically erasable programmable read-only memory. Flash memory is, for example, SSD. "SSD" is an abbreviation for solid-state drive. Magnetic memory is, for example, HDD. "HDD" is an abbreviation for hard disk drive. The storage unit 12 functions, for example, as main memory, auxiliary memory, or cache memory. The memory unit 12 stores information used for the operation of the adjustment force setting device 10 and information obtained through the operation of the adjustment force setting device 10.

[0027] The communication unit 13 includes at least one communication module. The communication module is, for example, a module compatible with a wired LAN communication standard such as Ethernet® or a wireless LAN communication standard such as IEEE 802.11. "LAN" is an abbreviation for local area network. "IEEE" is an abbreviation for Institute of Electrical and Electronics Engineers. The communication unit 13 receives information used for the operation of the adjustment force setting device 10 and transmits information obtained by the operation of the adjustment force setting device 10.

[0028] The input unit 14 is, for example, a physical key, a capacitive key, a pointing device, a touchscreen integrated with a display, a camera, or a microphone. The input unit 14 accepts operations to input information used for the operation of the adjustment force setting device 10. Instead of being provided in the adjustment force setting device 10, the input unit 14 may be connected to the adjustment force setting device 10 as an external input device. As a connection interface, an interface compatible with standards such as USB, HDMI®, or Bluetooth® can be used. "USB" is an abbreviation for Universal Serial Bus. "HDMI®" is an abbreviation for High-Definition Multimedia Interface.

[0029] The output unit 15 is, for example, a display, a speaker, or a printer. The display is, for example, an LCD or an organic EL display. "LCD" is an abbreviation for liquid crystal display. "EL" is an abbreviation for electroluminescent. The output unit 15 outputs information obtained by the operation of the adjustment force setting device 10. Instead of being provided in the adjustment force setting device 10, the output unit 15 may be connected to the adjustment force setting device 10 as an external output device. As a connection interface, an interface compatible with standards such as USB, HDMI (registered trademark), or Bluetooth (registered trademark) can be used.

[0030] The function of the adjustment force setting device 10 is realized by executing the adjustment force setting program according to this embodiment on the processor acting as the control unit 11. In other words, the function of the adjustment force setting device 10 is realized by software. The adjustment force setting program causes the computer to perform the operations of the adjustment force setting device 10, thereby causing the computer to function as the adjustment force setting device 10. That is, the computer functions as the adjustment force setting device 10 by performing the operations of the adjustment force setting device 10 according to the adjustment force setting program.

[0031] The program can be stored on a non-temporary computer-readable medium. Examples of non-temporary computer-readable media include flash memory, magnetic recording devices, optical discs, magneto-optical recording media, or ROM. The program can be distributed, for example, by selling, transferring, or lending portable media such as SD cards, DVDs, or CD-ROMs containing the program. "SD" is an abbreviation for Secure Digital. "DVD" is an abbreviation for digital versatile disc. "CD-ROM" is an abbreviation for compact disc read only memory. The program may also be distributed by storing it in server storage and transferring it from the server to other computers. The program may also be provided as a program product.

[0032] A computer, for example, stores a program stored on a portable medium or a program transferred from a server in its main memory. Then, the computer reads the program stored in the main memory with its processor and executes the processing according to the read program. The computer may also read the program directly from the portable medium and execute the processing according to the program. The computer may also execute the processing according to the received program sequentially each time a program is transferred to the computer from a server. Processing may also be performed by a so-called ASP-type service, which does not transfer programs from the server to the computer, but realizes its function only through execution instructions and result retrieval. "ASP" is an abbreviation for application service provider. A program includes information used for processing by an electronic computer that is equivalent to a program. For example, data that is not a direct instruction to the computer but has the nature of defining the computer's processing falls under "equivalent to a program".

[0033] Some or all of the functions of the adjustment force setting device 10 may be implemented by a programmable circuit or a dedicated circuit as the control unit 11. In other words, some or all of the functions of the adjustment force setting device 10 may be implemented by hardware.

[0034] Referring to Figure 3, the operation of the adjustment force setting device 10 according to this embodiment will be explained. The operation shown in Figure 3 corresponds to the adjustment force setting method according to this embodiment.

[0035] The processes from step S1 to step S5 are performed for each time slot in the planning period, i.e., for each time slot Ti included in the multiple time slots P.

[0036] In step S1, the control unit 11 obtains the standard deviation of the predicted power demand for time period Ti as the first standard deviation for the first prediction model. For example, the control unit 11 receives information regarding the first standard deviation from the administrator's terminal device or an external server device via the communication unit 13. Alternatively, the control unit 11 may directly receive information regarding the first standard deviation from the administrator via the input unit 14.

[0037] The first prediction model is a machine learning model that takes operator input values, calendar data, weather forecast data, measured data, pedestrian flow data, or any combination thereof as input and outputs a predicted value of electricity demand. Calendar data includes days of the week, working day flags to consider holidays, or any combination thereof. Weather forecast data includes temperature forecasts to predict air conditioning demand, humidity forecasts to consider whether it is sunny or rainy, solar radiation forecasts to predict air conditioning demand, or any combination thereof. Measured data includes actual demand, actual temperature to predict air conditioning demand, actual humidity to consider whether it is sunny or rainy, actual solar radiation to predict air conditioning demand, or any combination thereof. Pedestrian flow data includes predictions of urban population distribution, predictions of individual behavior, or any combination thereof. Wind speed, wind direction, or any combination thereof may be further input to consider perceived temperature or predict air conditioning demand. Dates may be further input to consider pedestrian flow or logistics events. For example, the first forecasting model may be constructed to predict low demand due to increased tourism during long holidays, high demand due to increased logistics during Christmas, or changes in demand due to changes in factory operating modes according to the season.

[0038] In step S2, the control unit 11 determines whether there is power generation during time period Ti. In the example shown in Figure 2, the control unit 11 determines whether the time frame corresponding to time period Ti is included in time range R1 or time range R2.

[0039] If the time frame corresponding to time zone Ti is included in time range R1, that is, if there is power generation during time zone Ti, the process in step S3 is executed. If the time frame corresponding to time zone Ti is included in time range R2, that is, if there is no power generation during time zone Ti, the process in step S5 is executed.

[0040] In step S3, the control unit 11 obtains the standard deviation of the predicted power generation value for time period Ti as the second standard deviation for the second prediction model. For example, the control unit 11 receives information regarding the second standard deviation from the administrator's terminal device or an external server device via the communication unit 13. Alternatively, the control unit 11 may directly receive input regarding the second standard deviation from the administrator via the input unit 14. After step S3, the processing in step S4 is executed.

[0041] The second prediction model is a machine learning model that takes operator input values, weather forecast data, measured data, or any combination thereof as input and outputs a predicted value of PV power generation. "PV" is an abbreviation for photovoltaic. Weather forecast data includes predictions of solar radiation to consider the reduction rate due to clouds or atmosphere, predictions of aerosol amount to consider the transmittance of sunlight, predictions of cloud cover to consider the shielding rate of sunlight, or any combination thereof. Measured data includes actual cloud cover to improve the accuracy of cloud cover prediction, actual PV power generation to improve the accuracy of aerosol or cloud cover prediction, panel conversion efficiency, number of days since installation to consider the efficiency reduction rate due to aging, panel surface temperature to consider the efficiency reduction rate due to surface temperature, power conditioner conversion efficiency, or any combination thereof. Geometric solar radiation to consider celestial positional relationships or PV panel installation angles, reduction rate due to building shadows, reduction rate due to panel installation orientation and angle, or any combination thereof may be further input.

[0042] In step S4, the control unit 11 sets the adjustment force Ai for time period Ti using the first standard deviation obtained in step S1 and the second standard deviation obtained in step S3. For example, when σdemand is the first standard deviation, σPV is the second standard deviation, and α and β are coefficients, the control unit 11 calculates the required adjustment force as α × σdemand + β × σPV. The units of σdemand and σPV are kW. α and β are adjusted as appropriate.

[0043] In step S5, the control unit 11 sets the adjustment force Ai for time zone Ti using only the first standard deviation obtained in step S1. For example, the control unit 11 calculates the required adjustment force as α × σdemand. σdemand, α, and β are the same as in step S4. It is also possible to consider σPV = 0.

[0044] When the processes from step S1 to step S5 have been executed for multiple time zones P, in step S6, the control unit 11 outputs the set value of the adjustment force Ai for each time zone. For example, the control unit 11 transmits the set value of the adjustment force Ai for each time zone to the administrator's terminal device or an external server device via the communication unit 13. Alternatively, the control unit 11 may output the set value of the adjustment force Ai for each time zone directly to the administrator via the output unit 15. After step S6, the operation shown in Figure 3 is completed.

[0045] In this embodiment, since the predicted PV power generation value is 0 during periods when no PV power generation occurs, the prediction error for PV power generation is set to 0. Furthermore, since predictions are made using a statistical model, the standard deviation can be calculated. By taking care to reduce the adjustment force in sections where there is confidence, the necessary adjustment force 20 for absorbing prediction errors during the planning period can be calculated as precisely as possible.

[0046] This disclosure is not limited to the embodiments described above. For example, two or more blocks described in the block diagram may be combined, or one block may be divided. Instead of executing two or more steps described in the flowchart in chronological order as described, they may be executed in parallel or in a different order, depending on the processing capacity of the device performing each step, or as necessary. Other modifications are possible without departing from the spirit of this disclosure. [Explanation of symbols]

[0047] 10 Adjustment force setting device 11 Control Unit 12 Storage section 13 Communications Department 14 Input section 15 Output section 20,21,22 Necessary adjustment force

Claims

1. The standard deviation of a first prediction model, which is a machine learning model that takes data for predicting air conditioning demand as input and outputs predicted values ​​of electricity demand for multiple time periods, is obtained as the first standard deviation σdemand, which shows the variability of the prediction error of electricity demand for each time period. The second prediction model, which is a machine learning model that takes data for predicting at least solar radiation, aerosol amount, or cloud cover as input and outputs predicted values ​​of solar power generation for the aforementioned multiple time periods, has its standard deviation representing the variability of the prediction error of solar power generation for each time period obtained as the second standard deviation σPV. With α and β as non-zero coefficients, for each time period included in the time range from sunrise to sunset among the multiple time periods, α × σdemand + β × σPV is calculated as the set value for the adjustment force. For each of the aforementioned time periods, specifically those included in the time ranges from 0:00 to sunrise and from sunset to 24:00, α × σdemand is calculated as the set value for the adjustment force. Output the set values ​​for adjustment capacity by time period. An adjustment force setting device equipped with a control unit.

2. The control unit acquires the standard deviation of the first prediction model, which is a machine learning model that takes data for predicting air conditioning demand as input and outputs predicted values ​​of electricity demand for multiple time periods, as the first standard deviation σdemand, which shows the variability of the prediction error of electricity demand for each time period, The control unit acquires the second standard deviation σPV, which represents the variability of the prediction error of solar power generation for each time period, from the second prediction model, which is a machine learning model that takes data for predicting at least solar radiation, aerosol amount, or cloud cover as input and outputs predicted values ​​of solar power generation for the multiple time periods, With α and β as non-zero coefficients, the control unit calculates α × σdemand + β × σPV as the set value for the adjustment force for each time period included in the time range from sunrise to sunset among the multiple time periods. For each of the aforementioned multiple time periods, within the time ranges from 0:00 to sunrise and from sunset to 24:00, the control unit calculates α × σdemand as the set value for the adjustment force. The control unit outputs the set value of the adjustment force for each time period. A method for setting the adjustment force, including the adjustment force.

3. Obtaining the first standard deviation σdemand, which represents the variability of the prediction error of electricity demand for each time period, from a first prediction model, which is a machine learning model that takes data for predicting air conditioning demand as input and outputs predicted values ​​of electricity demand for multiple time periods, The second prediction model is a machine learning model that takes data for predicting at least solar radiation, aerosol amount, or cloud cover as input and outputs predicted values ​​of solar power generation for the aforementioned multiple time periods. The standard deviation of the second prediction model, which shows the variability of the prediction error of solar power generation for each time period, is obtained as the second standard deviation σPV. With α and β as non-zero coefficients, for each time period included in the time range from sunrise to sunset among the multiple time periods, α × σdemand + β × σPV is calculated as the set value for the adjustment force, For each of the aforementioned time periods, specifically those included in the time ranges from 0:00 to sunrise and from sunset to 24:00, α × σdemand is calculated as the set value for the adjustment force. Output the set values ​​for adjustment capacity by time period. A setting program that causes a computer to perform actions including the following.

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