Load characteristics constant estimation method, load characteristics constant estimation program, and load characteristics constant estimation device

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By using actual measurement data and estimated solar radiation to calculate PV output and correct for it in load characteristic constant estimation, the method addresses inaccuracies in existing methods, achieving improved estimation accuracy.

JP2025001108APending Publication Date: 2025-01-08CENTRAL RESEARCH INSTITUTE OF ELECTRIC POWER INDUSTRY

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Patent Information

Application Number: JP2023100510
Authority / Receiving Office: JP · JP
Patent Type: Applications
Current Assignee / Owner
Filing Date: 2023-06-20
Publication Date: 2025-01-08

AI Technical Summary

Technical Problem

Existing methods for estimating load characteristic constants are inaccurate due to the inclusion of PV output in apparent load measurements, leading to decreased estimation accuracy, particularly when using sensor information on distribution lines and meteorological satellite data, which have low time resolution and are affected by weather conditions.

Method used

A method that utilizes actual measurement data of load capacity combined with estimated solar radiation values to calculate PV output, allowing for the estimation of net load by subtraction, and subsequently calculating load characteristic constants using corrected measurement data.

Benefits of technology

Improves the estimation accuracy of load characteristic constants by correcting for PV output, reducing errors and variations, and enabling more precise load characteristic constant estimation.

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Abstract

To provide a load characteristics constant estimation method, a load characteristics constant estimation program, and a load characteristics constant estimation device for improving estimation accuracy of a load characteristics constant.SOLUTION: A load characteristics constant estimation device acquires actual measurement data of a load capacity in a load line, determines whether or not the actual measurement data is an analysis target based on time-series data of a solar radiation amount estimation value included in a time window corresponding to measurement time of the actual measurement data, and when the actual measurement data is defined as the analysis target, a PV output in the load line is calculated based on the solar radiation amount estimation value at measurement time of the actual measurement data, the PV output is subtracted from analysis target actual measurement data to calculate an estimation value of a net load, and processing for calculating a load characteristics constant in the load line is executed based on the calculated estimation value of the net load.SELECTED DRAWING: Figure 4

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Description

[Technical field]

[0001] The present invention relates to a load characteristic constant estimation method, a load characteristic constant estimation program, and a load characteristic constant estimation device. [Background technology]

[0002] In recent years, with the expansion of renewable energy sources, the output ratio of photovoltaic (PV) power generation and other sources to the power consumption of load equipment in load supply systems is increasing. The power consumption of load equipment is called the "pure load" as a pure load. In addition, the self-controllability (load characteristics) of the pure load against voltage and frequency fluctuations during system disturbances has changed due to the inverterization of load equipment, and it has become more important to understand the actual impact of load characteristics on system stability.

[0003] Conventionally, the load characteristic constant has been estimated using the actual measurement data of the sending bus, mainly using the sending bus as the load line, for the load configuration in various time periods. However, the actual measurement data is generally an "apparent load" that is a mixture of pure load and current including PV output, which is the output of photovoltaic power generation. When estimating the load characteristic constant, there is a problem that the estimation accuracy decreases if the deviation between the apparent load and the pure load is large.

[0004] Therefore, in order to accurately estimate the load characteristic constant using actual measured data, it is desirable to estimate the PV output included in the load line as a pre-processing of the data and calculate the net load accurately by subtracting the PV output from the apparent load. To achieve this, methods of estimating PV output on a load line basis or with a finer resolution are being considered. One method that has been proposed is a technology that estimates PV output mainly by utilizing sensor information from distribution lines. Another method that has been proposed is a technology that estimates the amount of solar radiation using image data from meteorological satellites to estimate PV output.

[0005] In addition, a technology has been proposed in which a perpendicular line is dropped from a point representing active power and reactive power obtained from a power system on a plane representing active power and reactive power to a line passing through the origin on the plane, and the load characteristics are obtained from a histogram of the position of the foot of the perpendicular line. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2020-127292 A [Non-patent literature]

[0007] [Non-Patent Document 1] Sakaguchi, Kamimura, Matsuda, "Development of a method to estimate photovoltaic power output for each section using distribution line sensor information", Electric Power Research Institute Report, R14012 (2014) [Non-Patent Document 2] Satoshi Uemura, “DEVELOPMENT ON ESTIMATION METHOD OF OUTPUT OF PHOTOVOLTAIC POWER GENERATIONS FOR MICROGRIDS OPERATI OPERATION”, CIRED Workshop - Ljubljana, 7-8 June 2018 [Non-Patent Document 3] Hashimoto, Yumoto, "Development of a short-term solar radiation forecast method combining satellite image prediction and numerical weather model", Journal of Electrical Engineering B, Vol. 143, No. 2, p. 89-96 (2023) Summary of the Invention [Problem to be solved by the invention]

[0008] However, in the technology that estimates PV output using sensor information from distribution lines, the estimation period is the same as the measurement period of the sensor installed on the distribution line, and the time resolution is low, so it is difficult to use it for load characteristic estimation. In addition, in the technology that estimates PV output by estimating solar radiation using image data from meteorological satellites, PV output is not estimated using actual measured data, and weather conditions can make it difficult to estimate PV output accurately. In addition, in the technology that obtains load characteristics from the histogram of the foot positions when a perpendicular line is dropped to a straight line passing through the origin on a plane representing active power and reactive power, the actual solar radiation, etc. are not taken into account, and there is a risk that the accuracy of PV output estimation will decrease due to the influence of weather conditions.

[0009] The disclosed technology has been made in consideration of the above, and aims to provide a load characteristic constant estimation method, a load characteristic constant estimation program, and a load characteristic constant estimation device that improve the estimation accuracy of the load characteristic constant. [Means for solving the problem]

[0010] In one aspect of the load characteristic constant estimation method, load characteristic constant estimation program, and load characteristic constant estimation device disclosed in the present application, the load characteristic constant estimation device acquires actual measurement data of load capacity on a load line, and determines whether or not to analyze the actual measurement data based on time series data of an estimated solar radiation value included in a time window corresponding to the measurement time of the actual measurement data. If the actual measurement data is to be analyzed, the device calculates a PV output on the load line based on the estimated solar radiation value at the measurement time of the actual measurement data, subtracts the PV output from the actual measurement data to be analyzed to calculate an estimated value of a net load, and executes a process of calculating a load characteristic constant on the load line based on the calculated estimated value of a net load. Effect of the Invention

[0011] According to one aspect of the present invention, the estimation accuracy of a load characteristic constant can be improved. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a block diagram of a load characteristic constant estimating device according to an embodiment. [Diagram 2] FIG. 2 is a diagram showing an example of estimated solar radiation values for different weather conditions and signal strengths over a 30-minute period. [Diagram 3] FIG. 3 shows the signal strength and the number of data points for a 30-minute period on a clear day and on a rainy day. [Figure 4] FIG. 4 is a flowchart of a load characteristic constant estimation process performed by the load characteristic constant estimation device according to the embodiment. [Diagram 5] FIG. 5 is a diagram showing an outline of the test system. [Figure 6] FIG. 6 is a diagram showing the amount of solar radiation in the verification test. [Figure 7] FIG. 7 is a diagram showing the distribution of the estimation results of the load characteristic constant. [Figure 8] FIG. 8 is a diagram showing the median and variance of the estimation results of the load characteristic constants. [Figure 9] FIG. 9 is a hardware configuration diagram of the load characteristic constant estimation device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, the load characteristic constant estimation method, the load characteristic constant estimation program, and the load characteristic constant estimation device disclosed in the present application will be described in detail with reference to the drawings. Note that the load characteristic constant estimation method, the load characteristic constant estimation program, and the load characteristic constant estimation device disclosed in the present application are not limited to the following embodiments. EXAMPLES

[0014] 1 is a block diagram of a load characteristic constant estimation device according to an embodiment of the present invention. The load characteristic constant estimation device 1 is connected to a PQVF measurement device 2 and a solar radiation estimation device 3.

[0015] The PQVF measuring device 2 is a device that measures the active power (P), reactive power (Q), voltage (V), and frequency (F) of the load line from which the load characteristic constant is to be estimated. The active power, reactive power, voltage, and frequency are collectively referred to as "measured data." The PQVF measuring device 2 acquires the measured data at intervals of, for example, 50 μs to 0.05 s.

[0016] The solar radiation estimation device 3 estimates the amount of solar radiation using satellite images obtained from meteorological satellites. The solar radiation estimation device 3 can estimate the amount of solar radiation with a spatial resolution of 1 km and in units of 1 minute, for example.

[0017] The load characteristic constant estimation device 1 estimates the load characteristic constant of a target load line using actual measurement data obtained from the PQVF measuring device 2 and solar radiation estimated values obtained from the solar radiation estimation device 3. Here, for example, if the PQVF measuring device 2 has a time resolution of 50 μs to 0.05 s and the solar radiation estimation device 3 has a time resolution of 1 minute, the load characteristic constant estimation device 1 can estimate the load characteristic constant with a time resolution of 1 minute.

[0018] The load characteristic constant estimation device 1 may constantly estimate the load characteristic constant, or may estimate the load characteristic constant when a specific condition occurs, such as when a system disturbance occurs. The load characteristic constant estimation device 1 can contribute to the stability evaluation of the power system by estimating the load characteristic constant when a specific condition occurs.

[0019] The load characteristic constant estimation device 1 will be described in detail below. Here, the case where a load characteristic constant is estimated when a system disturbance occurs will be described. In the following, the load line for which the load characteristic constant estimation device 1 estimates the load characteristic constant will be simply called the load line. As shown in FIG. 1, the load characteristic constant estimation device 1 has a data management unit 11, a preprocessing necessity determination unit 12, a target range selection unit 13, a correction unit 14, and a load characteristic constant estimation unit 15.

[0020] The data management unit 11 constantly acquires and stores actual measurement data from the PQVF measuring device 2. A timestamp indicating the measurement time is added to each actual measurement data. The data management unit 11 also constantly acquires and stores solar radiation estimation values from the solar radiation estimation device 3.

[0021] The data management unit 11 then detects whether or not a system disturbance has occurred on the load line from the acquired actual measurement data. When the data management unit 11 detects the occurrence of a system disturbance on the load line, it extracts actual measurement data measured during a period according to a predetermined condition around the time of detection. The predetermined condition is set, for example, as 1 to 3 minutes before and after the timing when the instantaneous system disturbance occurs. Here, the actual measurement data is collected periodically, and therefore is time-series data. Hereinafter, the group of actual measurement data extracted by the data management unit 11 is referred to as "time-series actual measurement data."

[0022] For example, in the case of an instantaneous system disturbance, the data management unit 11 may extract time-series measured data included in 1 to 3 minutes before and after the data. In addition, in the case of a system disturbance that continues due to a system accident or the like, the data management unit 11 may extract the measured data included in the duration as time-series measured data. In addition, in the case of a configuration in which the load characteristic constant is constantly estimated, the preprocessing necessity determination unit 12 can use all measured data as time-series measured data.

[0023] Furthermore, the data management unit 11 extracts solar radiation estimated values corresponding to each piece of actual measurement data included in the time-series actual measurement data from the solar radiation estimated value data it holds. For example, if the collection period of the actual measurement data is shorter than the collection period of the solar radiation estimated values, the data management unit 11 sets a specific solar radiation estimated value as a solar radiation estimated value corresponding to each piece of actual measurement data measured within that collection period. The data management unit 11 then outputs the time-series actual measurement data and the solar radiation estimated values corresponding to each piece of actual measurement data included in the time-series actual measurement data to the preprocessing necessity determination unit 12.

[0024] The preprocessing necessity determination unit 12 receives input of the extracted time-series actual measurement data and the solar radiation estimated value corresponding to each actual measurement data included in the time-series actual measurement data from the data management unit 11. Next, the preprocessing necessity determination unit 12 determines the presence or absence of PV output at the measurement time of each actual measurement data from the acquired solar radiation estimated value. Then, when there is no PV output, that is, when there is no photovoltaic power generation output at that time, it is considered that the actual measurement data does not include PV output, and therefore the preprocessing necessity determination unit 12 decides not to perform preprocessing on the actual measurement data. In that case, the preprocessing necessity determination unit 12 outputs the acquired time-series data as it is to the load characteristic constant estimation unit 15. A case where there is no PV output is, for example, nighttime.

[0025] On the other hand, when there is PV output, the preprocessing necessity determination unit 12 decides to perform preprocessing on the actual measurement data. When it is decided to perform preprocessing, the preprocessing necessity determination unit 12 outputs the time-series actual measurement data to the target range selection unit 13. Here, for example, when the time-series actual measurement data includes actual measurement data for 1 to 3 minutes before and after the occurrence time of the grid disturbance, in most cases, PV output either exists or does not exist during that time. Therefore, the following will explain a case where the preprocessing necessity determination unit 12 determines that there is or does not exist PV output for all actual measurement data included in the time-series actual measurement data.

[0026] In this embodiment, the necessity of preprocessing is determined by determining the presence or absence of PV output from the estimated solar radiation value, but the method of determining the necessity of preprocessing is not limited to this. For example, most of the time when there is no PV output is at night. Therefore, the preprocessing necessity determination unit 12 may store in advance the time corresponding to the nighttime as an exclusion period, and determine to perform preprocessing when the determination target period does not fall within the exclusion period.

[0027] When there is PV output at the measurement time of the time series data, the target range selection unit 13 receives the input of the time series data from the preprocessing necessity determination unit 12. Then, the target range selection unit 13 determines whether or not it is possible to extract a net load using the estimated solar radiation value based on the vibration component of the estimated solar radiation value for each actual measurement data, and selects analysis target data to be analyzed from the time series data. Details of the selection process of data to be analyzed by the target range selection unit 13 are described below.

[0028] During the time periods when oscillations in the amount of solar radiation due to cumulus clouds are observed, the accuracy of the solar radiation estimation by the solar radiation estimation device 3 tends to decrease, and the estimation accuracy of the PV output also tends to decrease. Therefore, it is difficult to accurately estimate the net load from the measured data obtained during the time periods when oscillations in the amount of solar radiation due to cumulus clouds are observed, and therefore it is preferable to exclude them from the analysis.

[0029] It is known that the time scale of cumulus clouds, which are a factor that reduces the accuracy of solar radiation estimation, is about 30 minutes. FIG. 2 is a diagram showing an example of solar radiation estimation values by weather and signal strength in a 30-minute cycle. In each of graphs 101 to 103, the horizontal axis represents time, and the vertical axis represents signal strength and solar radiation in a 30-minute cycle. Graph 101 corresponds to a clear day, graph 102 corresponds to a clear or cloudy day, and graph 103 corresponds to a rainy day. In graphs 101 to 103, the curves shown by solid lines (the curves on the top of the page) represent the solar radiation, and the curves shown by dashed lines (the curves on the bottom of the page) represent signal strength in a 30-minute cycle.

[0030] On the clear days and rainy days shown in graphs 101 and 103, the signal strength in a 30-minute cycle is generally 40 or less, which is significantly smaller than that of the clear or cloudy days shown in graph 102. In this way, it can be considered that there is a possibility of cumulus clouds forming on clear or cloudy days, and it is possible to identify the time period when cumulus clouds are forming using the signal strength in a 30-minute cycle.

[0031] Therefore, in this embodiment, the target range selection unit 13 has a threshold value for determining whether or not cumulus clouds are formed using the signal strength in a 30-minute cycle. For example, the target range selection unit 13 has a 99th percentile value of the signal strength in a 30-minute cycle on a clear day or a rainy day when cumulus clouds are not formed as a threshold value. FIG. 3 is a diagram showing the signal strength and the number of data in a 30-minute cycle on a clear day and a rainy day. In FIG. 3, point 111 indicates the 99th percentile value of the signal strength in a 30-minute cycle on a clear day and a rainy day. In the case of FIG. 3, the target range selection unit 13 can use 60 as a threshold value, which is approximately the 99th percentile value of the signal strength in a 30-minute cycle.

[0032] The target range selection unit 13 sets the time window to 30 minutes before the measurement time of each actual measurement data included in the time series actual measurement data, and acquires the solar radiation estimate value for each time included in the time window from the solar radiation estimation device 3. Here, the solar radiation estimate values for the previous 30 minutes of each actual measurement data included in the time series actual measurement data overlap with each other between the respective actual measurement data, so in practice, the preprocessing necessity determination unit 12 only needs to acquire the solar radiation estimate value from 30 minutes before the measurement time of the first actual measurement data in the time series actual measurement data to the measurement time of the last actual measurement data.

[0033] Then, the target range selection unit 13 performs frequency analysis of the time series data of the solar radiation estimate values for each piece of time series measured data, for example using FFT (Fast Fourier Transform), for the solar radiation estimate values at times included in the time window of the previous 30 minutes, and selects the measured data whose vibration components are below a threshold value as the analysis target.

[0034] Thereafter, the target range selection unit 13 outputs the actual measurement data selected as the analysis target and the solar radiation estimate value at the measurement time of the actual measurement data to the correction unit 14. In this case, the target range selection unit 13 discards the performance data that was not selected as the analysis target. In other words, for the actual measurement data that is not selected as the analysis target, the target range selection unit 13 does not perform the load characteristic constant estimation process because it is difficult to estimate the load characteristic constant.

[0035] The correction unit 14 receives input of the actual measurement data selected as the analysis target and the solar radiation estimate value at the measurement time of the actual measurement data from the target range selection unit 13. Then, the correction unit 14 multiplies the solar radiation estimate value by a predetermined coefficient to calculate the PV output at the measurement time of the actual measurement data.

[0036] For example, the correction unit 14 has in advance information such as the PV facility capacity, the power generation efficiency of the PV panel, the conversion efficiency of the PCS (Power Conditioning System), and the power factor of the PCS, in units of load lines or 1 km resolution. Here, for example, the correction unit 14 may acquire this information from a general electricity distribution company. Alternatively, the correction unit 14 may acquire the PV facility capacity from publicly available information such as a website that publishes information on the feed-in tariff system, or may acquire the average value of major manufacturers' commercially available products as the power generation efficiency of the PV panel.

[0037] Then, the correction unit 14 multiplies the PV equipment capacity, the power generation efficiency of the PV panel, the conversion efficiency of the PCS, the power factor of the PCS, and the estimated solar radiation to calculate the PV output at the measurement time of each actual measurement data. That is, the correction unit 14 calculates the PV output using the formula "PV output = PV equipment capacity x power generation efficiency of the PV panel x conversion efficiency of the PCS x power factor of the PCS x estimated solar radiation". In this case, the PV equipment capacity x power generation efficiency of the PV panel x conversion efficiency of the PCS x power factor of the PCS corresponds to the predetermined coefficient. Here, the predetermined coefficient may be determined using information other than the power generation efficiency of the PV panel, the conversion efficiency of the PCS, and the power factor of the PCS.

[0038] Although the present embodiment does not take into account overloading of the PV panels, the correction unit 14 may calculate the PV output by taking into account information on overloading of the PV panels if available. This can improve the estimation accuracy of the PV output.

[0039] Then, the correction unit 14 calculates corrected actual measurement data corresponding to the net load by subtracting the PV output at the measurement time of each calculated actual measurement data from the actual measurement data. Then, the correction unit 14 outputs the corrected actual measurement data to the load characteristic constant estimation unit 15.

[0040] When there is no PV output and preprocessing is unnecessary, the load characteristic constant estimation unit 15 receives an input of uncorrected measured data from the preprocessing necessity determination unit 12. Then, the load characteristic constant estimation unit 15 estimates the load characteristic constant of the load line using the uncorrected measured data.

[0041] Furthermore, when there is a PV output and there is actual measurement data selected as an analysis target, the load characteristic constant estimation unit 15 receives an input of corrected actual measurement data from the correction unit 14. Then, the load characteristic constant estimation unit 15 estimates the load characteristic constant of the load line using the corrected actual measurement data.

[0042] For example, a load characteristic model in power system analysis is generally expressed by an exponential function model using the following equations (1) and (2).

[0043]

number

[0044] Here, α P represents the voltage characteristic index of the active power, and α Q represents the voltage characteristic index of reactive power, and β P represents the frequency characteristic coefficient of the active power, and β Q represents the frequency characteristic coefficient of reactive power. In addition, P0 represents the initial value of active power, Q0 represents the initial value of reactive power, V0 represents the initial value of voltage, and Δf represents the deviation from the reference frequency.

[0045] In this way, the voltage characteristic is expressed by an exponent, and the frequency characteristic is expressed by a coefficient. P and β P corresponds to the load characteristic constant. P and α Q When the value is 0, 1, or 2, the load characteristics are constant power characteristics, constant current characteristics, and low impedance characteristics.

[0046] Therefore, the load characteristic constant estimating unit 15 can estimate the load characteristic constant by using, for example, the following equations (3) and (4).

[0047]

number

[0048] However, the estimation using formulas (3) and (4) is based on the condition that voltage fluctuations and frequency fluctuations do not occur simultaneously. If voltage fluctuations and frequency fluctuations occur simultaneously, it is preferable that the load characteristic constant estimation unit 15 use a load characteristic constant estimation method corresponding to the voltage fluctuations and frequency fluctuations.

[0049] The results of the load characteristic constant estimation by the load characteristic constant estimating unit 15 are sent to, for example, a system stability evaluation device and used for evaluating the system stability including each load line.

[0050] 4 is a flowchart of the load characteristic constant estimation process by the load characteristic constant estimation device according to the embodiment. Next, the flow of the load characteristic constant estimation process by the load characteristic constant estimation device 1 according to the embodiment will be described with reference to FIG.

[0051] The data management unit 11 collects and accumulates actual measurement data of the load capacity on the load line obtained by periodic measurement using the PQVF measuring device 2. The data management unit 11 also collects and accumulates solar radiation estimation values periodically calculated by the solar radiation estimation device 3. Then, the data management unit 11 uses the actual measurement data to determine whether or not a grid disturbance has occurred (step S1). If no grid disturbance has occurred (step S1: No), the data management unit 11 waits until a grid disturbance occurs.

[0052] When a system disturbance occurs (step S1: Yes), the data management unit 11 acquires time-series actual measurement data, which is a collection of actual measurement data for a period according to a predetermined condition (step S2).

[0053] The pre-processing necessity determination unit 12 receives the input of the time-series actual measurement data and the corresponding solar radiation estimated value from the data management unit 11. Then, the pre-processing necessity determination unit 12 selects one unselected actual measurement data from among the actual measurement data included in the time-series actual measurement data (step S3).

[0054] The pre-processing necessity determining unit 12 determines the presence or absence of PV output from the estimated value of the amount of solar radiation at the measurement time of the selected actual measurement data, and determines whether or not to perform pre-processing (step S4).

[0055] When performing preprocessing (step S4: Yes), the target range selection unit 13 acquires time series data of the estimated solar radiation value within a time window of the previous 30 minutes of the selected actual measurement data. Then, the target range selection unit 13 performs frequency analysis on the acquired time series data of the estimated solar radiation value to acquire the vibration component of the estimated solar radiation value within the time window of the previous 30 minutes of the actual measurement data (step S5).

[0056] Then, the target range selection unit 13 judges whether the oscillation component of the estimated solar radiation amount for the time window of the previous 30 minutes of the actual measurement data is equal to or smaller than the threshold value (step S6). If the oscillation component of the estimated solar radiation amount for the time window of the previous 30 minutes of the actual measurement data is greater than the threshold value (step S6: No), the load characteristic constant estimation process proceeds to step S10.

[0057] On the other hand, if the oscillation component of the solar radiation estimated value for the time window of the previous 30 minutes of the actual measurement data is equal to or less than the threshold value (step S6: Yes), the target range selection unit 13 selects the time series data as an analysis target and outputs it together with the solar radiation estimated value to the correction unit 14. For each actual measurement data included in the selected time series data, the correction unit 14 multiplies the PV installed capacity, the power generation efficiency of the PV panel, the conversion efficiency of the PCS, the power factor of the PCS, and the solar radiation estimated value to calculate the corresponding PV output. Then, the correction unit 14 subtracts the calculated PV output from the actual measurement data to correct the actual measurement data (step S7).

[0058] Thereafter, the load characteristic constant estimating unit 15 estimates the load characteristic constant by using the actual measurement data corrected by the correcting unit 14 (step S8). Thereafter, the load characteristic constant estimation process proceeds to step S10.

[0059] On the other hand, if preprocessing is not performed (step S4: No), the target range selection unit 13 outputs the uncorrected actual measurement data to the load characteristic constant estimation unit 15. The load characteristic constant estimation unit 15 estimates the load characteristic constant using the uncorrected actual measurement data (step S9). After that, the load characteristic constant estimation process proceeds to step S10.

[0060] The pre-processing necessity determining unit 12 determines whether the estimation process of the load characteristic constants has been completed for all actual measurement data included in the time-series actual measurement data (step S10).

[0061] If actual measurement data remains for which the load characteristic constant estimation process has not been performed (step S10: No), the load characteristic constant estimation process returns to step S3. On the other hand, if the load characteristic constant estimation process has been completed for all actual measurement data included in the time-series actual measurement data (step S10: Yes), the load characteristic constant estimation device 1 ends the load characteristic constant estimation process.

[0062] Fig. 5 is a diagram showing an outline of a test system. Next, a verification test using a power system simulator having the test system 200 shown in Fig. 5 will be described.

[0063] The test system 200 includes a load line 201, a PV panel 202, a PCS 203, a dummy load 204, and an inverter power supply 205. The PV panel 202 is connected to the load line 201 via the PCS 203. In addition, the dummy load 204 and the inverter power supply 205 are connected to the load line 201.

[0064] The reference voltage of the test system 200 is 200V, and the reference frequency is 50Hz. The PV panel 202 is a photovoltaic power generation panel with a rating of 12kVA. The simulated load 204 corresponds to the pure load on the load line 201, and is composed of an inverter load with a rating of 20kVA, an induction motor load with a rating of 30kVA, and a resistive load with a rating of 10kW. The inverter power supply 205 is a large-capacity inverter power supply with a rating of 200kVA. For system disturbance, the inverter power supply 205 is used to simulate a 10% voltage constant or a 1Hz step frequency drop alternately every minute. The voltage characteristic coefficient of the pure load on the load line 201 is 1.13, and the frequency characteristic coefficient is 0.49.

[0065] Fig. 6 is a diagram showing the amount of solar radiation in the verification test. Graph 210 in Fig. 6 shows the amount of solar radiation over five days, with the horizontal axis showing time and the vertical axis showing the amount of solar radiation. The shaded area in graph 210 is the range where the test was not conducted. The target range selection unit 13 excluded the measured data of the parts shown by X from the analysis target, and selected the measured data of the other parts as the analysis target.

[0066] The load characteristic constant estimation unit 15 calculated the load characteristic constant using formulas (3) and (4) by using the average values (P0, V0, f0) of the active power, voltage, and frequency for the past 1 second based on the time of disturbance occurrence, and the average values (P, V, f) for 1 second from 5 to 6 seconds after the voltage drop and frequency step drop as the settling value after the disturbance occurrence. Note that in the verification test, since voltage fluctuations and frequency fluctuations do not occur simultaneously, it is possible to estimate the load characteristic constant by using formulas (3) and (4).

[0067] Fig. 7 is a diagram showing the distribution of the estimation results of the load characteristic constants, and Fig. 8 is a diagram showing the median and variance of the estimation results of the load characteristic constants.

[0068] Graph 220 in Fig. 7 shows the distribution of estimated values of the voltage characteristic index, and graph 230 shows the distribution of estimated values of the frequency characteristic coefficient. Graph 220 shows the voltage characteristic index on the horizontal axis and the number of data on the vertical axis. Graph 230 shows the frequency characteristic coefficient on the horizontal axis and the number of data on the vertical axis. Results 221 and 231 show the pure load values, results 222 and 232 show the values in the case of "with preprocessing" where correction is applied to the actually measured data, and results 223 and 233 show the values in the case of "without preprocessing" where no correction is applied to the actually measured data.

[0069] As shown in the results 221 of the graph 220, the voltage characteristic index of the pure load is in the range of 1.0 to 1.2. Also, as shown in the results 231 of the graph 230, the frequency characteristic coefficient of the pure load is in the range of 0.4 to 0.7% MW / % Hz. This is because the pure load is not always constant, but fluctuates by up to about 0.05 kW in a period of 1 second or more due to the response of the induction motor load.

[0070] Table 240 in Fig. 8 shows the median and variance obtained from the results 221-223 and 231-233 shown in graphs 220 and 230 in Fig. 7. As shown in graph 220 and table 240, in the case of no preprocessing, the error in the voltage characteristic index is about 15% based on the estimation result of the pure load. Also, as shown in graph 230 and table 240, in the case of no preprocessing, the error in the frequency characteristic coefficient is about 37% based on the estimation result of the pure load. In other words, the estimated value of the load characteristic constant in the case of no preprocessing is significantly different from the true value.

[0071] On the other hand, as shown in graph 220 and table 240, when preprocessing is performed, the error in the voltage characteristic index is about 1% based on the estimated result of the pure load. Also, as shown in graph 230 and table 240, when preprocessing is performed, the estimated value of the frequency characteristic coefficient almost matches the estimated value of the pure load. In this way, by performing preprocessing that applies a correction to the actual measured data by subtracting the PV output, the estimation accuracy of the load characteristic constant is greatly improved.

[0072] Also, as shown in Table 240, in the case of pure load, the variance is 0, and there is no variance in the estimation results. In contrast, in the case of preprocessing, the variance of the estimation results is significantly reduced compared to the case of no preprocessing. Specifically, with preprocessing, the variance of the voltage characteristic index is reduced by about 57%, and the variance of the frequency characteristic coefficient is reduced by about 89%, based on the variance value without preprocessing.

[0073] In this way, the load characteristic constant estimation device 1 can significantly reduce the estimation error and variation of the load characteristic constant compared to the case where the load characteristic constant is estimated using the apparent load without correcting the actual measurement data, and can perform estimation with an accuracy close to that of estimation using a pure load.

[0074] As described above, the load characteristic constant estimation device according to the present embodiment determines whether or not there is PV output, and if there is PV output, identifies a time period when there are no cumulus clouds using an estimated solar radiation value, calculates the PV output during a time period when there are cumulus clouds, corrects the measured data with the calculated PV output, and estimates the net load.The load characteristic constant estimation device then calculates the load characteristic constant using the measured data corrected to be estimated as the net load.

[0075] In a time period when there are no cumulus clouds and PV output can be estimated accurately, the accuracy of the net load estimation can be improved by estimating the net load by applying a correction to the actual measurement data. Furthermore, the accuracy of the load characteristic constant estimation can be improved by performing the load characteristic constant estimation process using an accurate estimated value of the net load. Furthermore, the load characteristic constant estimation device according to this embodiment can estimate the load characteristic constant by applying a correction to the actual measurement data even when the deviation between the apparent load and the net load becomes large, making it possible to use data that was previously difficult to use, and contributing to an increase in the amount of accumulated data.

[0076] (Hardware configuration) 9 is a hardware configuration diagram of the load characteristic constant estimation device 100. Next, an example of a hardware configuration for realizing each function of the load characteristic constant estimation device 1 will be described with reference to FIG.

[0077] 9, the load characteristic constant estimation device 1 includes, for example, a CPU (Central Processing Unit) 91, a memory 92, a hard disk 93, and a network interface 94. The CPU 91 is connected to the memory 92, the hard disk 93, and the network interface 94 via a bus.

[0078] The network interface 94 is an interface for communication between the load characteristic constant estimation device 1 and an external device. The network interface 94 relays communication between the PQVF measurement device 2 and the solar radiation estimation device 3 and the CPU 91, for example.

[0079] The hard disk 93 is an auxiliary storage device and stores various programs including programs for implementing the functions of the data management unit 11, the preprocessing necessity determination unit 12, the target range selection unit 13, the correction unit 14, and the load characteristic constant estimation unit 15 illustrated in FIG.

[0080] The memory 92 is a main storage device, and may be, for example, a dynamic random access memory (DRAM).

[0081] The CPU 91 reads out various programs from the hard disk 93, and deploys and executes them in the memory 92. In this way, the CPU 91 realizes the functions of the data management unit 11, the preprocessing necessity determination unit 12, the target range selection unit 13, the correction unit 14, and the load characteristic constant estimation unit 15 illustrated in FIG. [Explanation of symbols]

[0082] 1. Load characteristic constant estimator 2 PQVF measuring device 3 Solar radiation estimation device 11 Data Management Department 12 Pre-processing necessity determination section 13. Scope Selection Section 14 Correction section 15 Load characteristic constant estimation section

Claims

1. A load characteristic constant estimation device Obtain actual measurement data of the load capacity on the load line, determining whether or not to subject the actual measurement data to analysis based on time series data of the estimated solar radiation value included in a time window corresponding to the measurement time of the actual measurement data; When the actual measurement data is to be analyzed, a PV output on the load line is calculated based on an estimated value of solar radiation at a measurement time of the actual measurement data, and an estimated value of a net load is calculated by subtracting the PV output from the actual measurement data; Calculate the load characteristic constant on the load line based on the calculated estimated value of the pure load. A load characteristic constant estimation method comprising the steps of:

2. The load characteristic constant estimation device comprises: A frequency analysis is performed on the time series data of the estimated solar radiation value included in the time window to obtain an oscillation component, and when the magnitude of the oscillation component is equal to or smaller than a threshold, the actual measurement data is selected as the analysis target.