Power estimation system and method

Abstract

To accurately estimate each component in measured power demand.SOLUTION: A system uses power system data and factor data for a location in a power system to extract sample data that includes a value for a date and time where a value of a predetermined factor related to a second component is less than or equal to a predetermined threshold, and generates a first component estimation model that estimates a first component from the sample data. The system calculates a first estimation value of the second component from the power system data, the factor data, and the first component estimation model, and generates a second component model that estimates the second component from predetermined factor data related to the second component and the first estimation value of the second component. The system uses the power system data, the predetermined factor data related to the second component, and the second component estimation model to calculate a second estimation value for the first component and a second estimation value for the second component.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates generally to techniques for estimating power. [Background technology]

[0002] In the field of electric power, there are attempts to reduce transmission losses by controlling equipment in a power transmission system and / or a power distribution system (hereinafter, the power transmission system and / or the power distribution system are referred to as the "power system") based on values ​​estimated from active power and / or reactive power (hereinafter, the active power and / or the reactive power are referred to as the "power demand") for every tens of minutes to every few hours for tens to thousands of substations. Therefore, in order to appropriately control the equipment, it is important to estimate the power demand with high accuracy.

[0003] Power companies and other entities that operate power systems measure and understand the demand for electricity supplied from the power system at certain points in the power system, such as the demand end of a substation. On the other hand, when photovoltaic power generation equipment (PV) or wind power generation equipment is connected to the power system, electricity generated by PV and electricity generated by wind power generation equipment flow to the power system. Therefore, electricity demand contains two or more components, such as a PV output component and a load component. Among the multiple components, some components are strongly correlated with a certain factor, but other components are not strongly correlated with the factor in question.

[0004] Since each component, such as the PV output component and the load component, has different fluctuation tendencies, it is important to estimate each component separately in order to estimate electricity demand with high accuracy.

[0005] In addition, the multiple components constituting the power demand can be considered to be composed of a second component that shows a relatively strong correlation with a single factor (for example, the strongest correlation with the single factor) and a first component other than the second component. The "first component" is, for example, a load component or a component that is a mixture of the load component and the second component. The "load component" is the active power or reactive power consumed by the operation of a load such as an electric device, and shows a correlation with factors such as temperature and the type of calendar day (such as weekday and holiday). The "second component" is, for example, a PV output component that shows a strong correlation with solar radiation (an example of a single factor) or a wind power output component that shows a strong correlation with wind speed (an example of a single factor). Since the second component is strongly correlated with a single factor, it tends to be possible to estimate the second component with higher accuracy than the first component by using information on a single factor that shows a strong correlation. The "PV output component" is the active power or reactive power of the power generated by the PV, and the "wind power output component" is the active power or reactive power of the power generated by the wind power generation facility. Examples of the first component and the second component include (x) to (z) below. (x) Active power of load component: Active power of PV output component (y) Active power of load component: Active power of wind power component (z) Mixture of active power of load component and active power of wind power generation component: Active power of PV output component

[0006] Although power companies can measure electricity demand that is the composite of each component, it is difficult to directly grasp each component, such as the PV output component and the load component, so it is necessary to estimate each component from the measured electricity demand.

[0007] Known technologies for estimating components such as PV output and load included in the measured power demand are disclosed in JP 2014-200167 A (Patent Document 1) and JP 2016-197948 A (Patent Document 2).

[0008] Patent Document 1 discloses a technique for generating a conversion formula (reactive power conversion formula) for deriving active power from reactive power based on measured active power, reactive power, and solar radiation, and estimating the active power output by PV and the active power consumed by a load using the generated reactive power conversion formula. Patent Document 2 discloses a technique for estimating a conversion formula (solar radiation conversion formula) for deriving PV output from solar radiation based on measured active power and solar radiation, and estimating the active power output by PV and the active power consumed by a load using the generated solar radiation conversion formula. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] JP 2014-200167 A [Patent Document 2] JP 2016-197948 A Summary of the Invention [Problem to be solved by the invention]

[0010] In Patent Document 1, when there is no PV output, the PV output and actual load are estimated based on the assumption that there is a linear relationship between the measured active power and reactive power. However, in reality, the measured reactive power value fluctuates depending on the settings of equipment that adjusts reactive power, such as a reactive power compensator, so this assumption does not hold and the estimation accuracy deteriorates.

[0011] In addition, in Patent Document 2, learning data for a regression equation is generated from the measured active power and solar radiation, parameters of a solar radiation conversion equation are calculated by the regression equation, and a model for estimating the active power of PV output is estimated. However, since the measured active power includes a load component, an error occurs in the estimated parameters, deteriorating the estimation accuracy.

[0012] The present invention has been made in consideration of the above points, and has an object to accurately estimate each component contained in a measured power demand. [Means for solving the problem]

[0013] The system includes power system data and factor data for a point in a power system. The power system data includes a time series of measured values ​​of power demand at the point. The factor data includes a time series of factor values ​​for a predetermined factor, of a first component and a second component included in the power demand at the point, in which the correlation with the second component is stronger than the correlation with the first component. The system extracts sample data, which is data including values ​​for dates and times when the value of the predetermined factor related to the second component is equal to or less than a predetermined threshold, from the power system data and factor data for the point, and generates a first component estimation model that estimates the first component from the sample data. The system calculates a first estimate of the second component from the power system data, the factor data, and the first component estimation model, and generates a second component model that estimates the second component from the data of the predetermined factor related to the second component and the first estimate of the second component. The system calculates a second estimate of the first component and a second estimate of the second component from the power system data, the data of the predetermined factor related to the second component, and the second component estimation model. Effect of the Invention

[0014] According to the present invention, each component contained in the measured power demand can be estimated with high accuracy.

[0015] Problems, configurations and effects other than those described above will become apparent from the following description of the embodiments. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 is an overall configuration diagram of a power grid management system. [Diagram 2] FIG. 2 is a conceptual diagram showing a data flow of a power grid management system. [Diagram 3] FIG. 2 is a functional configuration diagram of each device constituting the power estimation system. [Figure 4] FIG. 11 is a functional block diagram illustrating an embodiment of a first component model identification process. [Diagram 5] FIG. 2 is a conceptual diagram illustrating an embodiment of power system data. [Figure 6]FIG. 2 is a conceptual diagram illustrating an embodiment of factor data. [Figure 7] FIG. 2 is a conceptual diagram illustrating an embodiment of power estimation data. [Figure 8] 1 is a flowchart showing a processing procedure of the power estimation system. [Figure 9] 4 is a conceptual diagram showing the effects of Comparative Example 1, Comparative Example 2, and this embodiment. FIG. [Figure 10] FIG. 11 is a functional block diagram illustrating an embodiment of a first component model identification process. [Figure 11] FIG. 1 is a functional block diagram illustrating one embodiment of a power estimation system. [Figure 12] FIG. 2 is a schematic diagram of a processing procedure of the power estimation system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] In the following description, an "interface unit" may be one or more interface devices. The one or more interface devices may be at least one of the following: One or more I / O (Input / Output) interface devices. The I / O (Input / Output) interface devices are interface devices to at least one of the I / O devices and a remote display computer. The I / O interface device to the display computer may be a communications interface device. The at least one I / O device may be a user interface device, for example, either an input device such as a keyboard and a pointing device, or an output device such as a display device. One or more communication interface devices. The one or more communication interface devices may be one or more homogeneous communication interface devices (e.g., one or more NICs (Network Interface Cards)) or two or more heterogeneous communication interface devices (e.g., a NIC and an HBA (Host Bus Adapter)).

[0018] In the following description, a "memory" refers to one or more memory devices, typically a primary storage device. At least one memory device in the memory may be a volatile memory device or a non-volatile memory device.

[0019] In the following description, a "persistent storage device" refers to one or more persistent storage devices. A persistent storage device is typically a non-volatile storage device (e.g., an auxiliary storage device), and specifically, for example, a hard disk drive (HDD) or a solid state drive (SSD).

[0020] Also, in the following description, "storage device" may be at least memory, including memory and persistent storage device.

[0021] In the following description, a "processor" refers to one or more processor devices. The at least one processor device is typically a microprocessor device such as a CPU (Central Processing Unit), but may be other types of processor devices such as a GPU (Graphics Processing Unit). The at least one processor device may be a single-core or multi-core. The at least one processor device may be a processor core. The at least one processor device may be a processor device in a broader sense, such as a hardware circuit (e.g., an FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit)) that performs part or all of the processing.

[0022] In the following description, functions may be described using the expression "yyy unit", but the functions may be realized by one or more computer programs being executed by a processor, or by one or more hardware circuits (e.g., FPGA or ASIC), or by a combination thereof. When a function is realized by a program being executed by a processor, the function may be at least a part of the processor, since the specified processing is performed using a storage device and / or an interface device, etc., as appropriate. Processing described with a function as the subject may be processing performed by a processor or a device having the processor. A program may be installed from a program source. The program source may be, for example, a program distribution computer or a computer-readable recording medium (e.g., a non-transitory recording medium). The description of each function is an example, and multiple functions may be combined into one function, or one function may be divided into multiple functions.

[0023] An embodiment of the present invention will be described in detail below with reference to the drawings. In the following description, a "date and time" may be a point in time or a period of time having a certain length of time.

[0024] In this embodiment, for the sake of concrete explanation, the first component to be estimated is the active power of the load (hereinafter, load active power), and the second component is the active power of the PV output (hereinafter, PV output active power). However, in the present invention, the first component does not have to be limited to the active power of the load, and the second component does not have to be limited to the active power of the PV output. (1) Overall structure

[0025] FIG. 1 shows the overall configuration of a power grid management system 1 according to the present embodiment.

[0026] The power grid management system 1 is a system for calculating an estimated value of power demand at a predetermined future date and time (hereinafter referred to as a predicted value of power demand) based on factor data including observed data and predicted data of factors and power grid data including observed values ​​of power demand values ​​at each point of estimation target, and for controlling power grid equipment 41 based on the predicted value of power demand. The power grid management system 1 is composed of a power operator 2, a power grid 4, a factor information provider 5, and various devices owned by each of them. Furthermore, communication paths 60, 61, and 62 are communication paths (typically communication networks) that connect various devices such as a WAN (Wide Area Network) and a LAN (Local Area Network) so that they can communicate with each other.

[0027] In the power operator 2, the power estimation system 3 calculates a predicted value of power demand for several hours ahead at each point, for example, every hour, based on the power system data and factor data, determines the control content of the power system equipment 41 based on the predicted value, and controls the power system equipment 41 through the power system equipment management device 22. The power operator 2 includes a power estimation data management device 30 that manages data for calculating the predicted value of power demand, such as power system data and factor data, a power estimation device 31 that calculates the predicted value of power demand, a system control calculation device 21 that calculates the control content of the power system equipment 41, a system equipment management device 22 that manages and controls the power system equipment 41, and an information input / output terminal 20 for exchanging data with these devices. The power system data includes system observation data and system attribute data. The system observation data includes measurement data (voltage value, current value, power value, frequency, etc.) of each point of the power system 4 measured by the measurement device 40 of the power system 4. Note that the data of the power demand at each point to be estimated is included in the system observation data. The system attribute data includes, for example, information indicating functional specifications of the system equipment 41 of the power system 4, system configuration information indicating a connection relationship of the system equipment 41, geographical location information of the system equipment 41, planning information on future setting states and control values ​​of the system equipment 41, and planning information on future system configuration. Note that the control of the system equipment 41 is an operation of adjusting the output voltage by changing the winding ratio of the transformer, for example, by changing the tap position of the transformer.

[0028] The power system 4 is a facility that transmits power generated from a power generation facility to a load facility via a power distribution facility. The power system 4 is composed of a system facility 41 that supplies, distributes, and consumes power, and a measuring device 40 that measures the state of each point of the power system 4. The system facility 41 includes at least a power generation facility, a power distribution facility, and a load facility. The power generation facility is a facility that supplies power, and includes, for example, a power generation device that generates power by thermal power generation, hydroelectric power generation, nuclear power generation, solar power generation, wind power generation, etc., a power generation control device that controls the power generation device, and a storage battery that stores energy and supplies power as needed. The power distribution facility includes various facilities related to power transmission and distribution, and includes, for example, overhead transmission lines, underground transmission lines, transformers, circuit breakers, disconnecting switches, and phase modifying equipment. The load facility is a facility that consumes power, and includes, for example, household electrical appliances, industrial electrical appliances, and storage batteries. The measuring device 40 is a device that measures measurement data at each point in the power system 4, and includes, for example, a voltage measuring device, a current measuring device, a power measuring device, a frequency measuring device, and the like.

[0029] The factor information provider 5 is an enterprise that provides factor data including past observation data of factors and future prediction data. The factor information provider 5 includes a factor data distribution device 50 for distributing the factor data. The factor data may be time-series data including observed values ​​at each time point in the past and / or estimated values ​​(predicted values) at each time point in the future. For example, the factor data includes the following data (1) and / or (2). The unit of time may be any unit such as weeks, days, hours, or minutes. (1) Data that has a certain periodicity similar to the periodicity of electricity demand, such as meteorological data such as temperature, humidity, cloud cover, and solar radiation, and calendar data such as days of the week and holidays. (2) Data that has a small periodicity but has a correlation with electricity demand, such as data on events such as sporting events, mobile data such as GPS on cars and mobile devices, data on consumers such as factory operation status, and data on social activities such as industrial trends.

[0030] Next, the power grid management system 1 according to the present embodiment will be described with reference to FIG.

[0031] The measuring device 40 measures measurement data at each point in the power system 4 and transmits the data to the system equipment management device 22 .

[0032] The power system equipment management device 22 transmits power system data 3006 including system observation data including measurement data of each point of the power system 4 received from the measurement device 40 and system attribute data held by the power system equipment management device 22 to the power estimation system 3. The power system data 3006 may include system observation data of each substation.

[0033] The factor data distribution device 50 transmits factor data 3007 including observation data and estimation data of the factors to the power estimation system 3.

[0034] The power estimation system 3 calculates a predicted value of the power demand at each point based on the power system data 3006 received from the power system equipment management device 22 and the factor data 3007 received from the factor data distribution device 50, and transmits data including the calculated predicted values ​​to the system control calculation device 21.

[0035] The system control calculation device 21 generates control data for the system equipment 41 based on the data received from the power estimation device 31 (data including the predicted value of the power demand at each point), and transmits the control data to the system equipment management device 22.

[0036] The system equipment management device 22 controls the system equipment 41 based on the control data of the system equipment 41 received from the system control arithmetic device 21 .

[0037] The power estimation system 3 is a system that calculates a predicted value of power demand at each point based on the power system data 3006 and the factor data 3007, and outputs the predicted value of power demand at each point to the system control calculation device 21. As described above, the power estimation system 3 is composed of the power estimation data management device 30 and the power estimation device 31. The power estimation system 3 may be a physical computer system (one or more computers), or may be a logical computer system based on a physical computer system (for example, a cloud computing system on a cloud platform).

[0038] The power estimation data management device 30 stores power system data 3006 received from the power system equipment management device 22 and factor data 3007 received from the factor data distribution device 50, and transmits these data 3006 and 3007 to the power estimation device 31. The power system data 3006 includes time series data of measured load active power, and the factor data 3007 includes time series data of factor values ​​of factors that have a strong correlation with the PV output active power.

[0039] The power estimation device 31 calculates a predicted value of the power demand at each point based on the power system data 3006 and the factor data 3007 received from the power estimation data management device 30, and transmits data including the predicted value to the system control calculation device 21. The power estimation device 31 may be integrated with the power estimation data management device 30.

[0040] The power estimation device 31 has functions such as a first component model estimating unit 3106 , a first estimated value calculating unit 3107 , a second component model estimating unit 3108 , a second estimated value calculating unit 3109 , and a prediction processing unit 3110 .

[0041] The first component model estimation unit 3106 identifies (generates) each load model that estimates the load active power at each point at a predetermined date and time from the power system data 3006, the factor data 3007, and the threshold data 3111 in which a predetermined threshold is set in advance. Each load model (load model for each point in the power system (e.g., a substation)) identified by the first component model estimation unit 3106 is input to the first estimate value calculation unit 3107. The load model is an example of a first component estimation model.

[0042] The first estimate value calculation unit 3107 calculates an estimate of the PV output active power (a first estimate value of the PV output active power) included in the observed value of the power demand at a predetermined estimation target date and time at each point included in the power system data 3006, from each load model identified in the first component model estimation unit 3106 and the power system data 3006. The first estimate value of the PV output active power calculated by the first estimate value calculation unit 3107 is input to the second component model estimation unit 3108.

[0043] The second component model estimation unit 3108 identifies (generates) each PV output model that estimates the PV output active power at a predetermined date and time at each point, based on the first estimated value of the PV output active power calculated by the first estimated value calculation unit 3107 and time series data of the amount of solar radiation (an example of a factor value of a factor strongly correlated with the PV output active power) included in the factor data 3007. Each PV output model (PV output model for each point in the power system (e.g., a substation)) identified by the second component model estimation unit 3108 is input to a second estimated value calculation unit 3109. The PV output model is an example of a second component estimation model.

[0044] The second estimate value calculation unit 3109 calculates an estimate of the load active power (second estimate of the load active power) and an estimate of the PV output active power (second estimate of the PV output active power) included in the observed value of the power demand at a predetermined estimation target date and time at each point included in the power system data 3006, from each PV output model identified by the second component model estimation unit 3108, the power system data 3006, and the factor data 3007. Furthermore, the second estimate value calculation unit 3109 generates power estimation data 3112 including the second estimate of the load active power and the second estimate of the PV output active power. The power estimation data 3112 generated by the second estimate value calculation unit 3109 is input to the prediction processing unit 3110.

[0045] The prediction processing unit 3110 identifies (generates) a model for predicting the load active power and the PV output active power at a predetermined prediction target date and time from the power estimation data 3112 generated by the second estimate value calculation unit 3109 and the factor data 3007, and calculates the predicted value of the load active power and the predicted value of the PV output active power at the predetermined prediction target date and time. Furthermore, the prediction processing unit 3110 calculates the predicted value of the power demand at the predetermined prediction target date and time from the calculated predicted value of the load active power and the predicted value of the PV output active power, and transmits the prediction result data, which is data including the predicted value of the power demand, to the system control calculation device 21. The system control calculation device 21 is a device that generates control data for the system equipment 41 based on the predicted value of the power demand at each point in the power system, and transmits the generated control data to the system equipment management device 22 that controls the system equipment 41 based on the control data. The system control calculation device 21 generates the control data based on the prediction result data from the prediction processing unit 3110. In this way, the system equipment 41 is controlled based on the prediction result data output by the power estimation system 3. As a result, voltage optimization of the power grid 4 is realized, which has the effect of suppressing transmission losses, and it is expected that this will be incorporated into practical applications such as suppressing transmission losses for power transmission and distribution companies. (2) Internal structure

[0046] FIG. 3 shows the functional configuration of each device constituting the power estimation system 3.

[0047] The power estimation data management device 30 is composed of an information processing device such as a personal computer or a server computer, and includes a CPU (Central Processing Unit) 3001 that controls the overall operation of the power estimation data management device 30, an input device 3002, an output device 3003, a communication device 3004, and a storage device 3005. The CPU 3001 is connected to the devices 3002 to 3005 so as to be able to communicate with each other.

[0048] The input device 3002 is, for example, a keyboard or a mouse, and the output device 3003 is, for example, a display or a printer. The communication device 3004 is equipped with a NIC (Network Interface Card) for connecting to a wireless LAN or a wired LAN. The storage device 3005 includes memories such as a RAM (Random Access Memory) and a ROM (Read Only Memory).

[0049] The storage device 3005 stores power system data 3006 and factor data 3007. The power system data 3006 is data (e.g., a database) including system observation data including data such as observed values ​​of power demand at each point, and system attribute data including attribute data held by the power operator 2 such as system configuration information indicating the connection relationship of the system equipment 41.

[0050] The factor data 3007 is data (for example, a database) in which data showing a correlation with the power demand, such as weather data and calendar date data, is stored.

[0051] The power estimation device 31 is composed of an information processing device such as a personal computer or a server computer, and includes a CPU 3101 that controls the overall operation of the power estimation device 31, an input device 3102, an output device 3103, a communication device 3104, and a storage device 3105. The CPU 3101 is communicatively connected to the devices 3102 to 3105. The communication device 3104 is an example of an interface device. The input device 3102 and the output device 3103 may be an information input / output terminal 20 (for example, a remote client computer). The CPU 3101 is an example of a processor.

[0052] The input device 3102 is, for example, a keyboard or a mouse, and the output device 3103 is, for example, a display or a printer. The communication device 3104 is equipped with a NIC for connecting to a wireless LAN or a wired LAN. The storage device 3105 includes memories such as a RAM and a ROM.

[0053] The storage device 3105 stores various computer programs for implementing processing units (functions) such as a first component model estimation unit 3106, a first estimated value calculation unit 3107, a second component model estimation unit 3108, a second estimated value calculation unit 3109, and a prediction processing unit 3110. The various computer programs are read into the CPU 3101 and executed, thereby implementing the processing units 3106 to 3110.

[0054] The storage device 3105 also stores threshold data 3111 and power estimation data 3112 .

[0055] The threshold data 3111 is data (e.g., a database) including thresholds related to predetermined values ​​for extracting data used in estimating each load model in the first component model estimating unit 3106. For example, when extracting data on a day with low solar radiation, a predetermined solar radiation value serving as a reference for extraction is stored as a threshold.

[0056] The power estimation data 3112 is data (eg, a database) including the estimated value of the PV output active power and the estimated value of the load active power at a predetermined estimation target date and time at each point, which are the estimation results of the second estimate value calculation unit 3109. (3) Details of each processing section

[0057] Hereinafter, details of an embodiment of each processing unit of the power estimation device 31 will be described with reference to the drawings. Note that the power estimation device 31 calculates a predicted value of power demand at one or more points, but for simplicity, a case where a predicted value of power demand at one point is calculated will be described here. (3-1) First component model estimation unit 3106

[0058] FIG. 4 shows a detailed embodiment of the first component model estimator 3106 in the power estimation system 3. As shown in FIG.

[0059] The first component model estimation unit 3106 in this embodiment is made up of a sample extraction unit 31061 and a first component model identification processing unit 31062 .

[0060] The sample extraction unit 31061 extracts data of date and time that can be regarded as load active power from each data of the power system data 3006 and the factor data 3007. Specifically, for example, the sample extraction unit 31061 compares the value of the amount of solar radiation correlated with the power demand of the target point included in the factor data 3007 with a predetermined threshold of the threshold data 3111, and identifies a time range in which the value of the amount of solar radiation is equal to or less than the predetermined threshold (hereinafter referred to as an extraction time range). Furthermore, the sample extraction unit 31061 extracts data of the extraction time range of the observed value of the power demand of the target point in the power system data 3006 and data of the extraction time range of the factor data 3007, and outputs the data of the extraction result to the first component model identification processing unit 31062.

[0061] The processing of the sample extraction unit 31061 will be described in more detail with reference to FIG. 5 and FIG.

[0062] FIG. 5 shows a conceptual diagram of power system data 3006. Specifically, power system data 3006 is composed of an identifier column 30061 for each location, an observation date and time column 30062 for the estimation target, and an observation value column 30063 for power demand in each predetermined time range. Therefore, in the case of FIG. 5, each row is a single piece of data (sample) in which the observed value of power demand is divided at a time granularity of one hour. According to the first row, at the location ID "S0001", the observed value of power demand is "0.9" at 0:00 on December 10, 2020.

[0063] FIG. 6 shows a conceptual diagram of factor data 3007. The factor data 3007 is composed of an identifier column 30071 for each location, a date and time column 30072 for factor data, and a column 30073 showing the value of each factor. Therefore, in the case of FIG. 6, each row is one piece of data (sample) in which the value of each factor is divided at a time granularity of one hour. According to the first row, at the location ID "S0001", the solar radiation value is "100" and the temperature value is "20" at 0:00 on December 10, 2020. Note that, for the observation data among the factor data 3007, the observation time is stored in the date and time column 30072 of the factor data, and the observation value is stored in the column 30073 showing the value of each factor. In addition, for the prediction data among the factor data, the prediction target time is stored in the date and time column 30072 of the factor data, and the prediction value is stored in the column 30073 showing the value of each factor. In addition, only a part of the solar radiation and temperature data among the factor data is shown here.

[0064] Here, the predetermined threshold of the threshold data 3111 is assumed to be "150". The sample extraction unit 31061 extracts samples whose solar radiation values ​​in the column 30073 of FIG. 6 are less than or equal to "150" (for example, the samples in the first row of FIG. 6) from the factor data 3007. Furthermore, the sample extraction unit 31061 sets the value in the date and time column 30072 of the extracted samples as the extraction time range. Next, the sample extraction unit 31061 extracts samples in the extraction time range from the power system data 3006 in the date and time column 30062 of FIG. 5. The sample extraction unit 31061 outputs the data of the extraction result to the first component model identification processing unit 31062.

[0065] Next, the first component model identification processing unit 31062 identifies a load model that estimates the load active power at a predetermined date and time from the data extracted by the sample extraction unit 31061, and outputs the identified load model to the first estimate value calculation unit 3107. The load model is, for example, a regression model in which the objective variable is the observed value of the power demand value included in the extraction result data and the explanatory variable is the observed value of the temperature included in the extraction result data, or an average curve obtained by averaging the observed value of the power demand value included in the extraction result data for each time. The sample extraction unit 31061 may use data that has been processed by standardizing or normalizing the data extracted by the sample extraction unit 31061 for identifying the load model. (3-2) First estimate value calculation unit 3107

[0066] The first estimate value calculation unit 3107 receives the load model identified by the first component model estimation unit 3106, the power system data 3006, and the factor data 3007, inputs predetermined factor data (e.g., factor data of solar radiation) required by the load model from the factor data 3007 to the load model, and calculates an estimate of the load active power (first estimate of the load active power) at a predetermined estimation target date and time set in advance from the output of the load model. Note that the first estimate value calculation unit 3107 may correct the estimate as the output of the load model based on a value within a predetermined time range of the observed value of the power demand in the power system data 3006, and use the result as the first estimate of the load active power. The "correction" refers to a process of adding a statistical value (e.g., average value) of the difference between the observed value of the power demand and the output (estimated value) of the load model for the predetermined time range (e.g., a time period when solar radiation is low, such as a time period before sunrise) to the output of the load model. This improves the accuracy of the portion of the time series represented by the time series data as the output of the load model (the time series of the load active power as the first estimated value) corresponding to the predetermined time range.

[0067] Next, the first estimate value calculation unit 3107 calculates the difference between the first estimate value of the load active power at a predetermined estimation target date and time and the observed value of the power demand in the power system data 3006, and sets the calculation result as the estimate value of the PV output active power at the predetermined estimation target date and time (first estimate value of the PV output active power). To explain this in a specific example, when the first estimate value of the load active power at a predetermined estimation target date and time is "1.0" and the observed value of the power demand in the power system data 3006 is "0.9", the first estimate value of the PV output active power at the predetermined estimation target date and time is calculated as "1.0-0.9=0.1". The first estimate value calculation unit 3107 outputs the first estimate value of the PV output active power to the second component model estimation unit 3108 as data to be used for model identification of the PV output. (3-3) Second component model estimation unit 3108

[0068] The second component model estimating unit 3108 receives the first estimated value of the PV output active power calculated by the first estimated value calculating unit 3107 and the factor data 3007, and identifies a regression model (one example of a PV output model) in which the explanatory variable is the amount of solar radiation at a predetermined estimation target time included in the factor data 3007, and the objective variable is the first estimated value of the PV output active power. The second component model estimating unit 3108 may detect an incompatible value included in the first estimated value of the PV output active power in the estimation target period, perform a countermeasure such as deleting the detected incompatible value from the first estimated value of the PV output active power in the estimation target period (in addition to the deletion, for example, a process of lowering the weight of the incompatible value), and re-identify a regression model in which the first estimated value of the PV output active power subjected to the countermeasure is the objective variable and the solar radiation at a predetermined estimation target time included in the factor data 3007 is the explanatory variable. This is expected to improve the accuracy of the regression model. The "non-conforming value" is a value (a value of the first estimated value of the PV output active power) corresponding to a date and time when the error (modeling error) between the estimated value (estimated value of the PV output active power) obtained by inputting solar radiation into the regression model identified from the original first estimated value of the PV output active power and the first estimated value of the PV output active power is equal to or greater than a predetermined reference value. The "predetermined reference value" is a threshold value of the modeling error, such as a value obtained by multiplying the standard deviation of the modeling error by a predetermined multiple set in advance.

[0069] The second component model estimating unit 3108 outputs the identified regression model to the second estimated value calculating unit 3109 as a PV output model. (3-4) Second estimate value calculation unit 3109

[0070] The second estimate value calculation unit 3109 receives the PV output model identified by the second component model estimation unit 3108, the power system data 3006, and the factor data 3007. The second estimate value calculation unit 3109 inputs the amount of solar radiation at a predetermined estimation target date and time from the factor data 3007 to the PV output model, and calculates an estimate of the PV output active power (second estimate of the PV output active power) from the output of the PV output model. Next, the second estimate value calculation unit 3109 calculates the sum of the observed value of the power demand in the power system data 3006 at the predetermined estimation target date and time and the second estimate of the PV output active power, and sets the calculation result as an estimate of the load active power at the predetermined estimation target date and time (second estimate of the load active power). To explain this as a specific example, when the observed value of power demand in the power system data 3006 at a certain time is "0.9" and the second estimated value of the PV output active power is "0.1," the second estimated value of the load active power at that time is calculated as "0.9+0.1=1.0." Furthermore, the second estimate value calculation unit 3109 outputs data including the second estimated value of the load active power and the second estimated value of the PV output active power to the prediction processing unit 3110 as power estimation data 3112.

[0071] 7 shows a conceptual diagram of the power estimation data 3112. The power estimation data 3112 is composed of a column 31121 for an identifier of each point, a column 31122 for a date and time, a column 31123 for indicating an estimated value of the PV output active power, and a column 31124 for indicating an estimated value of the load active power. The second estimated values ​​of the PV output active power at a predetermined estimation target date and time estimated using the PV output model are written in each row of 31123. In addition, the second estimated values ​​of the load active power at a predetermined estimation target date and time are written in each row of 31124. (3-5) Prediction processing unit 3110

[0072] The prediction processing unit 3110 receives the power estimation data 3112 and the factor data 3007 output by the second estimate value calculation unit 3109. The prediction processing unit 3110 uses the power estimation data 3112 and the factor data 3007 to identify a load prediction model that predicts the load active power at a predetermined date and time and a PV output prediction model that predicts the PV output active power at a predetermined date and time. The "load prediction model" is, for example, a regression model in which the response variable is an estimated value of the load active power of the power estimation data 3112 and the explanatory variable is one or more factor data of the factor data 3007. The "PV output prediction model" is, for example, a regression model in which the response variable is an estimated value of the PV output active power of the power estimation data 3112 and the explanatory variable is one or more factor data of the factor data 3007.

[0073] Next, the prediction processing unit 3110 calculates a predicted value of the load active power at the predetermined prediction target date and time by inputting the factor data at the predetermined prediction target date and time of the factor data 3007 into the identified load prediction model. Also, the prediction processing unit 3110 calculates a predicted value of the PV output active power at the predetermined prediction target date and time by inputting the factor data at the predetermined prediction target date and time of the factor data 3007 into the identified PV output prediction model.

[0074] Next, the prediction processing unit 3110 calculates a predicted value of the power demand at a predetermined prediction target date and time by combining the predicted value of the load active power and the predicted value of the PV output active power at a predetermined prediction target date and time. Note that the predicted value may be a value obtained by estimating a value at a certain time with a single value, or an interval estimated value obtained by estimating a range of possible values ​​at a certain time with an interval.

[0075] With the above, the power demand estimation process of the power estimation device 31 is completed.

[0076] Furthermore, the power estimation device 31 transmits the predicted value of the power demand at each point to the system control calculation device 21.

[0077] Next, based on the received forecasted values ​​of power demand at each point, the system control calculation device 21 generates optimal voltage setting values ​​at each point in the power system at a predetermined future time and control instructions for each system equipment 41 to realize the optimal voltage setting values, and transmits control data including the control instructions for each system equipment 41 to the system equipment management device 22. Note that the "optimum voltage setting value" is, for example, a voltage value that maximizes the amount of reduction possible in transmission loss throughout the system, or a voltage value that suppresses failures of each device in the system equipment 41. The "control instructions for each system equipment 41" is, for example, tap changing of a transformer or increasing or suppressing reactive power output by a phase modifying equipment.

[0078] Next, the system equipment management device 22 operates the system equipment 41, for example, by changing the transformer tap from No. 1 to No. 2, based on the received control data of the system equipment 41. The system equipment 41 changes its operation due to the operation, and a physical change occurs, for example, the output voltage of the transformer becomes larger than before the change. (4) Flowchart

[0079] Fig. 8 shows the processing procedure of the power estimation system. Fig. 12 is a schematic diagram of the processing procedure of the power estimation system. This processing is started when the power estimation device 31 receives an input operation from the power manager 2, and the power estimation device 31 executes the processing from step S101 to step S105.

[0080] First, the first component model estimation unit 3106 extracts sample data (composed of observed values ​​of power demand and factor data) of a day when the amount of solar radiation at a target point is equal to or less than a predetermined threshold from the power system data 3006 and the factor data 3007, and generates a load model that estimates the load active power at a predetermined date and time using the extracted sample data (S101). Specifically, the first component model estimation unit 3106 identifies one or more dates and times when the factor value of a predetermined factor (e.g., solar radiation) related to the PV output active power is equal to or less than a predetermined threshold from the factor data 3007. The first component model estimation unit 3106 extracts measurement sample data including measurement values ​​(observation values) at the one or more dates and times from the power system data 3006, and extracts factor sample data including factor values ​​for one or more factors (e.g., all or some factors other than the predetermined factor (e.g., temperature)) at the one or more dates and times from the factor data 3007. The first component model estimating unit 3106 generates a load model (an example of a first component estimation model) that is a model for estimating load active power, based on the measurement sample data and the factor sample data.

[0081] Next, the first estimate value calculation unit 3107 calculates an estimate value of the PV output active power at a predetermined date and time in the past based on the load model identified by the first component model estimation unit 3106, the power system data 3006, and the factor data 3007 (S102). Specifically, the first estimate value calculation unit 3107 estimates a first estimate value of the load active power in the estimation target period by inputting factor values ​​of the one or more factors in the factor data 3007 in the estimation target period (e.g., one or more estimation target dates and times) to the load model, and calculates a first estimate value of the PV output active power in the estimation target period from the first estimate value of the load active power in the estimation target period and the power demand (so-called P value) in the power system data 3006 in the estimation target period (for example, by calculating the difference therebetween).

[0082] Next, the PV output estimation unit 3108 generates a PV output model that estimates the PV output active power at a predetermined date and time based on the first estimated value of the PV output calculated by the first estimate value calculation unit 3107 and the factor data 3007 (S103). Specifically, the PV output estimation unit 3108 generates a PV output model that is a model that estimates the PV output active power from factor value data for the estimation target period for the above-mentioned predetermined factor (e.g., solar radiation) among the factor data 3007 and the first estimated value of the PV output active power for the estimation target period.

[0083] Next, the second estimate value calculation unit 3109 inputs data of the factor data 3007 at a predetermined date and time into the PV output model based on the PV output model generated by the PV output estimation unit 3108, the power system data 3006, and the factor data 3007, calculates an estimate of the PV output active power at the predetermined date and time, calculates an estimate of the load active power at the predetermined date and time from the calculated estimate of the PV output active power and the observed value of the power demand in the power system data 3006, and generates power estimation data 3112 storing the estimate of the PV output active power and the estimate of the load active power (S104). Specifically, the second estimate value calculation unit 3109 calculates a second estimate of the PV output active power for the calculation target period by inputting factor value data for a predetermined factor (e.g., solar radiation) of the factor data 3007 for the calculation target period into the PV output model. Furthermore, the second estimate value calculation unit 3109 calculates a second estimate value for the calculation target period of the load active power by subtracting the second estimate value for the calculation target period of the PV output active power (the calculated second estimate value) from the power demand for the calculation target period in the power system data 3006. Note that the "calculation target period" may be a period that coincides with or overlaps with the estimation target period, or may be a different period that neither coincides with nor overlaps with the estimation target period.

[0084] By performing the above steps S101 to S104, each component included in the measured power demand can be estimated with high accuracy.

[0085] Next, the prediction processing unit 3110 generates a PV output prediction model that predicts PV output and a load prediction model that predicts an actual load, based on the power estimation data 3112 and the factor data 3007, calculates a predicted value of the PV output active power and a predicted value of the load active power for a specified date and time using these prediction models, and calculates a predicted value of power demand for the specified date and time based on the predicted value of the PV output active power and the predicted value of the load active power (S105).

[0086] With the above processing, the power demand estimation processing in this embodiment is completed. (4) Effects of this embodiment

[0087] FIG. 9 is a conceptual diagram illustrating the effect of this embodiment.

[0088] Figure 9 shows the observational data for a certain period (top of Figure 9), a conceptual diagram of the model estimated by each method (middle of Figure 9), and the estimated results of the PV output active power included in past electricity demand using each method (bottom of Figure 9).

[0089] In the upper part of Fig. 9, graph 900a is a graph showing observed values ​​of active power of power demand in a predetermined period, graph 900b is a graph showing observed values ​​of reactive power of power demand in the same period, and graph 900c is a graph showing observed values ​​of solar radiation in the same period. Below, the PV output active power included in the active power of power demand (graph 900a1) for the estimation target period "4 / 4" shown in the upper part of Fig. 9 is used as the estimation target, and a comparison result of the estimation accuracy is shown for the case where the method of Comparative Example 1 is used, the case where the method of Comparative Example 2 is used, and the case where the method of this embodiment is used.

[0090] The method of Comparative Example 1 extracts the active power and reactive power of power demand on days when it is considered that there is no PV output (such as days when the amount of solar radiation is below a predetermined threshold) from the active power and reactive power of power demand and the amount of solar radiation, generates a conversion formula (reactive power conversion formula) for deriving load active power from the reactive power of power demand, estimates the load active power by inputting the reactive power of power demand on the target estimation date and time into the generated reactive power conversion formula, and estimates the PV output active power from the difference between the estimated load active power and the active power of power demand.

[0091] The method of Comparative Example 2 generates a regression equation from the active power of power demand and the amount of insolation, with the amount of insolation as the explanatory variable and the active power of power demand as the objective variable, derives a PV output model that estimates the PV output effective power from the amount of insolation from the slope of the regression equation, and inputs the amount of insolation at the date and time to be estimated into the PV output model to estimate the PV output effective power.

[0092] The common input data used for estimation in each method are the observed value of active power of power demand, the amount of solar radiation, and calendar information (day of the week, holidays). Note that only the method of Comparative Example 1 uses the observed value of reactive power of power demand as input data.

[0093] Graph 901a shown in the middle of FIG. 9 is a conceptual diagram of a reactive power conversion formula estimated by the method of Comparative Example 1. Graph 901a shows an actual correlation 901a1 between reactive power of power demand and load active power, and a reactive power conversion formula 901a2 estimated from the reactive power and active power of the measured power demand. The reactive power of the measured power demand includes reactive power originating from a reactive power compensator, etc., and varies depending on the settings of the reactive power compensator. Therefore, the reactive power originating from the reactive power compensator becomes noise, and a deviation occurs in the estimation result of the reactive power conversion formula with respect to the actual correlation between the reactive power of the power demand to be estimated and the load active power.

[0094] Graph 902a shown in the lower part of Fig. 9 shows the estimation result for the estimation target period using the method of Comparative Example 1. Graph 902a1 shows the actual PV output active power, and graph 902a2 shows the estimated value of the PV output active power using the method of Comparative Example 1. Because the estimation accuracy of the reactive power conversion formula is low, an error occurs in the estimated value of the load active power on the estimation target day, which in turn causes an error in the estimated value of the PV output active power.

[0095] Graph 901b shown in the middle of FIG. 9 is a conceptual diagram of a PV output model estimated by the method of Comparative Example 2. Graph 901b shows an actual correlation 901b1 between the PV output active power and the amount of solar radiation, and a PV output model 901b2 estimated from the correlation between the measured active power of the power demand and the amount of solar radiation. Since the measured active power of the power demand also includes a load component that shows a correlation with the temperature, the value of the measured active power of the power demand varies depending on the change in the load temperature even for the same amount of solar radiation. Therefore, the correlation with the temperature included in the active power of the power demand becomes noise in the correlation between the amount of solar radiation to be estimated and the PV output active power, and the estimation accuracy of the PV output model decreases.

[0096] 9 shows the estimation result by the method of Comparative Example 2. Graph 902a2 shows the estimated value of the PV output active power by the method of Comparative Example 2, and since the estimation accuracy of the PV output model is low, an error occurs in the estimated value of the PV output active power.

[0097] 9 is a conceptual diagram of a PV output model estimated by the second component model estimating unit 3108 in the method of this embodiment. As shown in graph 901c, by calculating an estimated value of PV output by removing a load component that shows a correlation with temperature from the active power of the power demand in the first estimated value calculating unit 3107 and estimating a PV output model 901c1 from the correlation between the estimated value of PV output and the amount of solar radiation, noise due to the correlation with temperature is reduced, and it is possible to estimate the PV output model with high accuracy.

[0098] 9 shows the estimation result output by the second estimate value calculation unit 3109 in the method of this embodiment. Graph 902c1 shows the estimated value of the PV output active power by the method of this embodiment, and since no reactive power is used and a highly accurate PV output model is found based on the correlation between the estimated PV output and the amount of solar radiation, it can be confirmed that the error in the PV output active power during the estimation period is smaller than in the methods of Comparative Example 1 and Comparative Example 2, and that the estimation can be performed with high accuracy.

[0099] Furthermore, since past PV output and load can be estimated with high accuracy, future PV output and load can be predicted with high accuracy.

[0100] In addition, by accurately predicting the PV output and load, the predicted value of the power demand at each point can be accurately calculated, and the wide-ranging system equipment 41 of the power system 4 can be accurately controlled based on the predicted values ​​of the power demand at multiple points, enabling efficient operation of the system equipment 41, such as reducing transmission losses in the power system 4. (5) Other embodiments (5-1) Example of adding a threshold calculation unit

[0101] In the above-described embodiment, the first component model estimation unit 3106 performs data extraction processing in the sample extraction unit 31061 based on the threshold represented by the threshold data 3011 that has been determined in advance by input from the information input / output terminal 20, etc. However, without being limited to this, a threshold calculation unit 31603 shown in FIG. 10 may be added to the first component model estimation unit 3106, and the threshold calculation unit 31603 may calculate the threshold data 3011.

[0102] The threshold calculation unit 31603 determines an appropriate threshold to be compared with a factor value of a predetermined factor indicated by the factor data 3007 in order to specify a date and time at which the power demand indicated by the power system data 3006 can be substantially regarded as load active power, based on a parameter calculation method based on a predetermined performance index value, and outputs threshold data 3011 indicating the determined threshold. Measurement sample data including measurement values ​​at one or more dates and times at which the factor value of the predetermined factor is equal to or less than the determined threshold is extracted from the power system data 3006, factor sample data including factor values ​​for the one or more factors at the one or more dates and times is extracted from the factor data 3007, and a load model is identified based on the measurement sample data and the factor sample data.

[0103] The "predetermined factor" is a factor that shows a strong correlation with the PV output, such as the amount of solar radiation (at least a factor that has a stronger correlation with the PV output than with the load effective power). When there are multiple predetermined factors, the threshold indicated by the threshold data 3111 may exist for each predetermined factor.

[0104] In addition, the "parameter calculation method" may be a known method such as a grid search method in which the performance index value results when each of several previously prepared thresholds is adopted and the threshold value that gives the best performance index value is calculated from the calculated threshold values, or Bayesian optimization in which the threshold value that is likely to give the best performance index value is sequentially and probabilistically searched for and determined.

[0105] Moreover, the "performance index value" is a value indicating the estimation accuracy of the load model identified using the measurement sample data of the date and time that can be regarded as the extracted load active power. For example, for each threshold, the performance index value may be a value such as the average value of the absolute value of the estimation error. For each threshold, the "estimation error" is the difference between the estimated value of the load active power calculated by the load model identified using the measurement sample data extracted at the threshold and the observed value of the power demand represented by the data of the date and time that can be regarded as the extracted load active power. Specifically, an example of calculating the performance index value when the predetermined factor is "solar radiation" and the threshold is "50" will be described. First, it is assumed that three data, data A "solar radiation: 20, power demand: 1.5", data B "solar radiation: 40, power demand: 1.4", and data C "solar radiation: 10, power demand 1.6", are extracted as data of the date and time that can be regarded as the load active power with the solar radiation of "50" or less. To simplify the explanation, a load model is generated that outputs the average value of the power demand for each data, "1.5", as the load. The estimated value of the load model is "1.5" for each data, and the absolute value of the estimation error for each data is "Data A: 0.1, Data B: 0.0, Data C: 0.1". In addition, the average value of the absolute value of the estimation error, which is the performance index value, is "0.066".

[0106] This allows the load model identification process to be performed with high accuracy, thereby making it possible to improve the estimation accuracy. (5-2) Modification of the first component model estimation unit 3106

[0107] In the first component model estimating unit 3106 in the above embodiment, the sample extracting unit 31061 extracts the measurement sample data from the power system data 3006 and extracts the factor sample data from the factor data 3007, but both the measurement sample data and the factor sample data may be data configured with data having the same attribute value that affects the similarity of the fluctuation pattern of the load active power. The "attribute value that affects the similarity of the fluctuation pattern of the load active power" is a value that is predefined as a value that affects the similarity of the fluctuation pattern of the load active power, such as a value of a holiday and a weekday generated from calendar day data included in the factor data 3007. For example, in the power system data 3006 illustrated in FIG. 5, such an attribute value may be associated with each power demand, and in the factor data 3007 illustrated in FIG. 6, such an attribute value may be associated with each factor value for each factor. The measurement sample data includes one or more power demands (measurement values) associated with the same attribute value, and the factor sample data includes one or more factor values ​​associated with the same attribute value for each of one or more factors. Specifically, an example of extracting only weekday data from each data will be described. When there are three pieces of data, data A "attribute value: weekday, power demand: 1.0", data B "attribute value: weekday, power demand: 1.2", and data C "attribute value: holiday, power demand: 0.9", that are date and time data that can be regarded as load active power, only data A and data B are extracted as measurement sample data.

[0108] By extracting only data of specific attribute values, such as weekdays and holidays, and identifying the load model for each, it is possible to improve the estimation accuracy of the load model. (5-3) Modification of the second component model estimation unit 3108

[0109] The second component model estimating unit 3108 in the above-described embodiment identifies a regression model (an example of a PV output model) from the first estimated value of the PV output active power in the estimation target period calculated by the first estimated value calculating unit 3107 and the amount of solar radiation in the estimation target period included in the factor data 3007. Without being limited to this, the second component model estimating unit 3108 may identify a regression model from the first estimated value of the PV output active power in the estimation target period and the amount of solar radiation after correction (amount of solar radiation to which a predetermined correction process has been applied) at the estimation target time included in the factor data 3007. Note that the "predetermined correction process" is, for example, a process of subtracting a predetermined correction value from the amount of solar radiation at the predetermined estimation target time included in the factor data 3007. The "predetermined correction value" may be the threshold value itself indicated by the threshold data 3011 or a value based on the threshold value (for example, a statistical value (for example, an average value) of the amount of solar radiation in a time range in which the value of the amount of solar radiation stored in the factor data 3007 is equal to or less than the threshold value). At least a part of this "time range" may be included in the estimation target period. Specifically, an example will be described in which the correction process is performed when the amount of solar radiation at a certain time included in the factor data 3007 is "100." Since the threshold value indicated by the threshold data 3011 is "40," if it is assumed that the predetermined correction value is set to "40," the amount of solar radiation after correction at the certain time is calculated as "100-40=60."

[0110] This makes it possible to correct the effects of the PV output component derived from small amounts of solar radiation, which is included in the date and time data that can be considered as the load active power extracted by the first component model, and improves the estimation accuracy. (5-4) Modification of the second estimate value calculation unit 3109

[0111] The second estimate value calculation unit 3109 in the above-described embodiment stores the output of the PV output model in the power estimation data 3112 as the second estimate value of the PV output active power. However, without being limited to this, the second estimate value of the PV output active power may be a result of applying a predetermined correction process using the frequency characteristics of the power demand to the output of the PV output model (in other words, a value obtained by applying a predetermined correction process to the estimate value of the PV output active power calculated using the PV output model).

[0112] The "predetermined correction process using the frequency characteristics of the power demand" is a correction process based on frequency characteristics such as the fact that the PV output fluctuates greatly in a short period compared to the load. Specifically, for example, the "predetermined correction process using the frequency characteristics of the power demand" is a process of generating a frequency feature by performing a Fourier transform on the observed value of the power demand in the power system data 3006, generating a high-frequency component of the power demand by performing an inverse Fourier transform on the components of the frequency feature that are equal to or higher than a predetermined frequency, and performing an adjustment process on the output of the PV output model so that the high-frequency component and the output of the PV output model are similar. The "adjustment process" is, for example, a process of adding the difference between the output of the PV output model and the high-frequency component of the power demand to the output of the PV output model.

[0113] As a result, even if the solar radiation data included in the factor data 3007 does not have sufficient time granularity, minute changes in the PV output can be estimated, making it possible to improve the estimation accuracy. (5-5) Modification of the prediction processing unit 3110

[0114] The prediction processing unit 3110 in the above embodiment outputs a predicted value of power demand calculated from a predicted value of the load active power and a predicted value of the PV output active power at a predetermined prediction target date and time, but is not limited to this, and may output a predicted value of the load active power and a predicted value of the PV output active power at a predetermined prediction target date and time. The predicted value may be a value estimated as a single value at a certain time, or an interval estimated value estimated as a range of values ​​that can be taken at a certain time in an interval. Furthermore, the system control calculation device 21 may generate an optimal voltage setting value at each point in the power system at a predetermined future time based on the predicted value of the PV output active power, and a control instruction for each system equipment 41 for realizing the optimal voltage setting value, and transmit control data including the control instruction for each system equipment 41 to the system equipment management device 22. Below, a specific example of processing will be described assuming control in a situation where the fluctuation range of the PV output active power due to solar radiation (the range of the upper and lower limits of the interval estimated value) is large, such as on a cloudy day, but is larger than the fluctuation range of the load due to the temperature. The system control calculation device 21 refers to the maximum value of the range of the interval estimated value of the PV output active power, and generates a voltage setting value and control instructions so that the voltage reference range is not exceeded even if the PV output active power reaches its maximum value. A "control instruction" is, for example, an instruction to lower the output voltage of a transformer by changing the transformer tap from number 1 to number 2. The fluctuation range of the predicted value of power demand is a mixture of PV output fluctuation due to solar radiation and load fluctuation due to temperature, and is therefore larger than the fluctuation range of the predicted value of PV output.

[0115] As a result, by performing control based on the fluctuation range of the predicted value of PV output, compared to when control is performed based on the fluctuation range of the predicted value of power demand, control can be performed with reduced unnecessary control margin, enabling efficient operation of the power grid equipment 41, such as reducing transmission losses. (5-6) Example of adding an estimation processing management unit

[0116] In the above-described embodiment, the power estimation data 3112 output from the second estimate value calculation unit 3109 is input to the prediction processing unit 3110, but this is not limited thereto. As shown in FIG. 11 , an estimation processing management unit 3113 may be added after the second estimate value calculation unit 3109, and the estimation processing management unit 3113 may receive the power estimation data 3112.

[0117] The estimation process management unit 3113 may receive the power system data 3006, the factor data 3007, the power estimation data 3112, and the PV output model. The estimation process management unit 3113 may determine whether or not the power estimation data 3112 satisfies a predetermined condition. If the power estimation data 3112 satisfies the predetermined condition (if the estimation accuracy of the estimated value in the power estimation data 3112 is high), the estimation process management unit 3113 may output the power estimation data 3112 to the prediction processing unit 3110. If the power estimation data 3112 does not satisfy the predetermined condition (if the estimation accuracy of the estimated value in the power estimation data 3112 is low), the estimation process management unit 3113 may output the power estimation data 3112 to the first component model estimation unit 3106 and execute again a series of processes from the first component model estimation unit 3106 to the second estimated value calculation unit 3109 (the processes of S101 to S104 in the processing flow shown in FIG. 8 ). Specifically, in this series of processes, in addition to the extracted measurement sample data and factor sample data, at least a part of the power estimation data 3112 output to the first component model estimation unit 3106 is used to identify the load model.

[0118] The "predetermined condition" for the power estimation data 3112 is a condition based on the definition that the estimation accuracy of the value in the power estimation data 3112 is high, and for example, the average value of the error between the second estimated value of the load active power of the power estimation data 3112 and the time when there is no solar radiation in the power system data 3006 is equal to or less than a predetermined threshold, or the modeling error of the PV output model identified by the second component model estimation unit 3108 is equal to or less than a predetermined threshold. Note that the "modeling error" is a value obtained by performing statistical processing such as summation or averaging on the error between the input data used for identifying the model and the estimation result by the model. To explain using specific values ​​as an example, when the solar radiation at a certain time is "100", the input data of the PV output active power at that time is "1.0", and the estimated value by the model is "1.1", the error between the input data at that time and the estimation result by the model is "0.1".

[0119] When the first component model estimation unit 3106 receives power estimation data 3112 from the estimation processing management unit 3113, in the processing of the sample extraction unit 31061 of the first component model estimation unit 3106, it extracts data on the second estimated value of the load effective power from the power estimation data 3112 and data within a specified time range of the factor data 3007, and outputs the extracted data to the first component model identification processing unit 31062.

[0120] This makes it possible to estimate the load model and the PV output model repeatedly until a predetermined estimation accuracy is reached, thereby making it possible to estimate accurate models and improve the estimation accuracy. (5-7) Deformation of the estimated object

[0121] In the power estimation system 3 in the above embodiment, the estimation process is performed with the power demand at each point in the power system 4 as the estimation target, but the estimation process may be performed with a power demand value obtained by integrating the power demands at several points as the estimation target, and the estimation result of the integrated power demand may be used to calculate the estimated value at each point before integration using a predetermined distribution method. For example, the prediction processing unit 3110 may generate first integrated data that is data obtained by integrating second estimated values ​​of the load active power at multiple points in the power system 4, and generate second integrated data that is data obtained by integrating second estimated values ​​of the PV output active power from the multiple viewpoints. The prediction processing unit 3110 may generate integrated forecast data that is data obtained by integrating the forecast values ​​of the power demand at the multiple points from the first integrated data and the second integrated data. That is, the second estimated value of the load active power may be the "first integrated data", the second estimated value of the PV output active power may be the "second integrated data", and the forecast value of the load active power and the forecast value of the PV output active power in the forecast target period may be the "integrated forecast data". The prediction processing unit 3110 may calculate a predicted value of the power demand at each point before the integrated prediction data was generated (that is, before integration) based on the integrated prediction data, using a predetermined distribution method.

[0122] Incidentally, "integration" refers to converting multiple data series into one data series by statistical processing such as summation or averaging. One method of selecting multiple points to be integrated is to select points with similar trends in power demand. One method of selecting points with similar trends in power demand is to group points with high similarity by clustering points using features such as the average daily demand transition curve or daily maximum power value of each point, and to select points classified into the same group as targets for integration. A publicly known method may be used for the clustering method. For example, hierarchical clustering such as Ward's method or non-hierarchical clustering such as k-means is one example.

[0123] Also, the "predetermined distribution method" may be a method of multiplying the estimated value of the integrated power demand value of each point by the distribution ratio of each point to obtain an estimated value of each point (i.e., a method of distributing a value according to the distribution ratio of each point). As a specific example, in a case where the estimated value of the integrated power demand value of points A and B is "100", the distribution ratio of point A is "0.7", and the distribution ratio of point B is "0.3", the estimated value of point A is "70" and the estimated value of point B is "30". As a method of determining the distribution ratio, there is a method of calculating the distribution ratio based on the average value of the average daily demand of each point before integration, and setting the distribution ratio in advance for each value indicating the state of the power system generated based on system configuration information indicating the connection relationship of the system equipment 41 of the power system.

[0124] This reduces the processing load by reducing the number of estimation targets, and reduces the deterioration of estimation accuracy due to the law of large numbers. (5-8) Modification of the second component model estimation unit 3108

[0125] In the above embodiment, assuming a case where a predicted value of power demand at one point is calculated, the second component model estimation unit 3108 performs processing based on an estimated value of the load and an estimated value of the PV output at the one point. However, this is not limited to this, and when a predicted value of power demand at multiple points is calculated, data at other points may be used in the identification process of the PV output model at a certain point. As a specific example, an example will be described in which the second component model estimation unit 3108 uses the insolation at point B in the process of estimating the PV output model from the first estimated value of the insolation at point A and the PV output active power. Note that the insolation at point B is a value measured at a finer time interval than the insolation at point A. The second component model estimation unit 3108 generates a new insolation (corrected insolation) based on a predetermined data processing of the insolation at point A and the insolation at point B using the insolation at point A and the insolation at point B, and estimates the PV output model from the corrected insolation and the first estimated value of the PV output active power at point A. The "predetermined data processing" refers to a weighted average based on a predetermined weighting value, a data integration process that complements the amount of solar radiation at a time not recorded in the amount of solar radiation at point A with the amount of solar radiation at point B, etc. The "predetermined weighting value" may be calculated based on, for example, the distance between point A and point B, or the similarity between the amount of solar radiation at point A and the amount of solar radiation at point B.

[0126] In this way, the second component model estimation unit 3108 may apply predetermined data processing to the factor value of the amount of solar radiation for a second point in the power system 4 based on data including the factor value of the amount of solar radiation (an example of a predetermined factor) for a first point in the power system 4, thereby calculating new data including the factor value for the amount of solar radiation subjected to the predetermined data processing, and generating a PV output model for the second point from the new data and the first estimated value of the PV output active power. This makes it possible to accurately estimate the PV output model by using accurate data from other points, thereby improving the estimation accuracy. (5-9) Other variations

[0127] In the power estimation system 3 in the above embodiment, a display unit is omitted for simplicity of explanation, but the calculation results of each processing unit and intermediate results of each processing unit may be calculated as appropriate through an output device such as a display or a printer.

[0128] Although several embodiments of the present invention have been described above, these are merely examples for explaining the present invention, and the scope of the present invention is not limited to these embodiments. The present invention can be implemented in various other forms. For example, a form in which two or more of the above-described embodiments are used in combination may be adopted. [Explanation of symbols]

[0129] 1. Power grid management system 3. Power Estimation System 4 Power system 20 Information input / output terminal 21 System control calculation device 22 System equipment management device 30 Power estimation data management device 31 Power estimation device 40 Measuring Equipment 41 System equipment 50 factor data distribution device

Claims

1. an interface device for receiving the power system data and the factor data; A storage device that stores the power system data and the factor data; a processor connected to the interface device and the storage device; Equipped with For each of one or more points in the electric power system, The power system data includes a time series of measured values ​​of power demand at the location, the factor data includes a time series of factor values ​​for a predetermined factor having a stronger correlation with the second component out of a first component and a second component included in the power demand at the location than with the first component; The processor, (A) identifying from the factor data one or more dates and times at which a factor value of the predetermined factor related to the second component is equal to or less than a predetermined threshold, extracting from the power system data measurement sample data including measurement values ​​at the one or more dates and times, extracting from the factor data factor sample data including factor values ​​for one or more factors at the one or more dates and times, and generating a first component estimation model that is a model for estimating the first component based on the measurement sample data and the factor sample data; (B) estimating a first estimate value of the first component in the estimation period by inputting factor values ​​of the one or more factors among the factor data into the first component estimation model, and calculating a first estimate value of the second component in the estimation period from the first estimate value of the first component in the estimation period and an electric power demand among the electric power system data in the estimation period; (C) generating a second component estimation model that is a model for estimating the second component from factor value data for the predetermined factor among the factor data during the estimation period and a first estimate of the second component during the estimation period; (D) calculating a second estimate of the second component for the calculation period by inputting factor value data for the specified factor among the factor data into the second component estimation model, and calculating a second estimate of the first component for the calculation period by subtracting the second estimate of the second component for the calculation period from the power demand for the calculation period among the power system data; A power estimation system comprising:

2. the processor calculates an estimate of the first component for the estimation period using the first component estimation model, performs a correction process to add to the calculated estimate of the first component a statistical value of a difference between an estimate of the first component for a predetermined time period and the power demand of the power system data for the predetermined time period, and calculates the estimate after the correction process as a first estimate of the first component for the estimation period. The power estimation system of claim 1 .

3. The processor sets a threshold value when a performance index value, which is the estimation accuracy of the first component estimation model, is optimized as a threshold value to be compared with the factor value of the predetermined factor. The power estimation system of claim 1 .

4. Both the measurement sample data and the factor sample data are data composed of the same attribute value that affects the similarity of the fluctuation pattern of the first component. The power estimation system of claim 1 .

5. the processor performs a correction process of subtracting a correction value from a factor value of the predetermined factor in the estimation period, and generates the second component estimation model from data of factor values ​​of the predetermined factor in the estimation period, the data including the factor values ​​subjected to the correction process, and a first estimated value of the second component in the estimation period. The power estimation system of claim 1 .

6. the processor detects an incompatible value included in the first estimated value for the estimation period of the second component, performs a process to deal with the incompatible value, and generates the second component estimation model. The power estimation system of claim 1 .

7. the processor performs a predetermined correction process on the estimated value of the second component calculated using the second component estimation model, based on a frequency characteristic of the power demand, and calculates a result of the predetermined correction process as a second estimated value of the second component. The power estimation system of claim 1 .

8. For each of the one or more locations, the processor calculates a predicted value of the first component at the location during a prediction target period and a predicted value of the second component at the location during the prediction target period from the second estimated value of the first component at the location and the second estimated value of the second component at the location. The power estimation system of claim 1 .

9. The processor, determining whether or not power estimation data including the second estimate value of the first component and the second estimate value of the second component satisfies a predetermined condition based on a definition of high estimation accuracy; if the predetermined condition is not satisfied, generate a first component estimation model using at least a part of the power estimation data in addition to the measurement sample data and the factor sample data, and then execute (B) to (D). The power estimation system of claim 1 .

10. The processor, generating first integrated data that is data obtained by integrating second estimates of the first component at a plurality of points in the power system; generating second integrated data that is data obtained by integrating second estimates of the second component from the multiple viewpoints; generating integrated forecast data that is data obtained by integrating forecast values ​​of the power demands at the multiple locations from the first integrated data and the second integrated data; Calculating a predicted value of the power demand at each point before the generation of the integrated forecast data, based on the integrated forecast data, using a predetermined distribution method. The power estimation system of claim 1 .

11. The processor performs a predetermined data processing on the factor value of the predetermined factor for a second point in the power system based on the data including the factor value of the predetermined factor for a first point in the power system, thereby calculating new data including the factor value subjected to the predetermined data processing for the predetermined factor, and generating the second component estimation model for the second point from the new data and the first estimated value of the second component. The power estimation system of claim 1 .

12. The interface device is communicatively connected to a system control arithmetic device, The processor, calculating a predicted value of the first component during a prediction period and a predicted value of the second component during a prediction period from the second estimated value of the first component and the second estimated value of the second component for the one or more locations; transmitting prediction result data, which is data based on predicted values ​​calculated for the first component and the second component, to the system control arithmetic device; the system control arithmetic device is a device configured to generate control data for a system equipment based on a predicted value of power demand at the one or more points, and to transmit the generated control data to a system equipment management device configured to control the system equipment based on the control data; the system control arithmetic device generates the control data based on the prediction result data from the processor. The power estimation system of claim 1 .

13. receiving power system data and factor data, for each of one or more points in the power system, the power system data including a time series of measured values ​​of power demand at the point, and the factor data including a time series of factor values ​​for a predetermined factor, the predetermined factor having a stronger correlation with the second component than with the first component, of a first component and a second component included in the power demand at the point; one or more dates and times at which a factor value of the predetermined factor related to the second component is equal to or less than a predetermined threshold are identified from the factor data, measurement sample data including measurement values ​​at the one or more dates and times are extracted from the power system data, factor sample data including factor values ​​for one or more factors at the one or more dates and times are extracted from the factor data, and a first component estimation model that is a model for estimating the first component is generated based on the measurement sample data and the factor sample data; estimating a first estimate value of the first component in the estimation period by inputting factor values ​​of the one or more factors among the factor data into the first component estimation model, and calculating a first estimate value of the second component in the estimation period from the first estimate value of the first component in the estimation period and an electric power demand among the electric power system data in the estimation period; generating a second component estimation model that is a model for estimating the second component from factor value data for the predetermined factor among the factor data during the estimation period and a first estimate of the second component during the estimation period; calculating a second estimated value of the second component for the calculation target period by inputting factor value data for the specified factor among the factor data into the second component estimation model, and calculating a second estimated value of the first component for the calculation target period by subtracting the second estimated value of the second component for the calculation target period from the power demand for the calculation target period among the power system data; The power estimation method includes the steps of:

14. A power estimation system; A system control and arithmetic device connected to the power estimation system; A system equipment management device that controls the system equipment; Equipped with For each of one or more points in the electric power system, The power system data includes a time series of measurements of power demand at the location, the factor data includes a time series of factor values ​​for a predetermined factor having a stronger correlation with the second component out of a first component and a second component included in the power demand at the location than with the first component; The power estimation system comprises: receiving the power system data and the factor data; one or more dates and times at which a factor value of the predetermined factor related to the second component is equal to or less than a predetermined threshold are identified from the factor data, measurement sample data including measurement values ​​at the one or more dates and times are extracted from the power system data, factor sample data including factor values ​​for one or more factors at the one or more dates and times are extracted from the factor data, and a first component estimation model that is a model for estimating the first component is generated based on the measurement sample data and the factor sample data; estimating a first estimate value of the first component in the estimation period by inputting factor values ​​of the one or more factors among the factor data into the first component estimation model, and calculating a first estimate value of the second component in the estimation period from the first estimate value of the first component in the estimation period and an electric power demand among the electric power system data in the estimation period; generating a second component estimation model that is a model for estimating the second component from factor value data for the predetermined factor among the factor data during the estimation period and a first estimate of the second component during the estimation period; calculating a second estimate of the second component for the calculation period by inputting factor value data for the specified factor among the factor data into the second component estimation model, and calculating a second estimate of the first component for the calculation period by subtracting the second estimate of the second component for the calculation period from the power demand for the calculation period among the power system data; calculating a predicted value of the first component during a prediction period and a predicted value of the second component during a prediction period from the second estimated value of the first component and the second estimated value of the second component for the one or more locations; transmitting prediction result data, which is data based on predicted values ​​calculated for the first component and the second component, to the system control arithmetic device; the system control arithmetic device generates control data for the system equipment based on the predicted value of the power demand at the one or more points, and transmits the generated control data to the system equipment management device; The power system equipment management device controls the power system equipment based on the control data from the power system control arithmetic device. Grid management system.

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