JP2012044739A - Load estimation method of power distribution section and system for controlling power distribution system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a technology capable of estimating a load (an actual load) with high accuracy in a power distribution section based on the temperature of a time when an estimation is required to be made, even when the number of load data samples for a target power distribution section is insufficient.SOLUTION: A load estimation method of a power distribution section typically comprises: an information acquisition step 144 for accumulating load data of the power distribution section; a first information extraction step 148 for extracting the load data of a target time to create a first data group; a second information extraction step 152 for extracting the load data near to the target time to create a second information group; a scale conversion step 154 for converting the scale of a load value of the second data group; a data interpolation step 156 for interpolating the first data group with the scale-converted load data of the second data group; a relation expression deriving step 158 for a derivation of a relation expression between temperature and a load; and a load estimation step 160 for estimating the load (an actual load) based on the relation expression.

Description

  The present invention relates to a load prediction method for a distribution section and a distribution system control system to which the prediction method is applied.

  Introduction of photovoltaic generators that generate power by converting inexhaustible solar energy into electrical energy is being promoted. Due to a decrease in the price of introducing solar power generators, an increase in environmental conservation awareness, and demand for conversion to alternative energy due to fluctuations in oil prices, solar power generators are now becoming popular in ordinary homes.

  In general, solar power generators installed in general households are connected to the transmission lines (distribution system) of electric power companies, and the surplus of the power generation amount of the solar power generator is folded back to the connected distribution system. (Reverse current) and sold to a predetermined electric utility. The amount of electricity sold is recorded in a power sale meter installed in each household.

  In the section where the photovoltaic generators are connected, if a power failure occurs due to an accident, the power conditioner also stops the power generation by the photovoltaic generator. This is to prevent an electric shock from a worker who is recovering from a power outage due to the electric power returned from the solar power generator to the distribution system. The stopped solar generator cannot resume power generation immediately after recovering from a power failure, and is connected to the distribution system again after a delay of several minutes.

  From the above, when initially recovering from a power outage, the electric power (substation) sends the entire area load (actual load) that is usually covered by the power generated by the solar power generator to the distribution system. Must be covered by However, from the electric power company, only the transmitted power sent to the distribution system can be grasped, and the power provided by the power generation of the solar power generator cannot be grasped. As a result, there was a concern that it would be overloaded at the moment of recovery from a power outage and linked to a secondary power outage accident.

Therefore, conventionally, when recovering from a power outage as described above, an electric power company uses the total rated power generation capacity (module surface temperature) of a plurality of solar power generators connected to the power distribution system as the output power sent from the power distribution system. Operation of distribution system (switching distribution lines, etc.) by adding the total amount of power generation (JIS standard JIS C 8914)) at 25 degrees, spectral distribution AM (air mass) 1.5, and irradiance 1000 W / m ² I was going. In addition, assuming power to be sent to the power distribution system at this time, a power distribution facility sufficient to flow the power has been constructed.

  On the other hand, the solar power generator can actually generate power at about 70% to 80% of the rated power generation capacity. Moreover, since the amount of power generated by the solar power generator depends on the amount of solar radiation, the amount of power provided by the solar power generator increases or decreases depending on the time of day, the weather, and the like. Therefore, simply adding the total rated power generation capacity as described above has secured excessive power to prevent overload.

  In the future, the number of photovoltaic generators is expected to increase further. Therefore, the distribution system accurately grasps the total power generation and the actual load of the multiple photovoltaic generators connected to each other. It is required to operate. This actual load is said to correlate with temperature.

  In Patent Document 1, past power demand results and meteorological data (weather, temperature, humidity, etc.) are accumulated to obtain a relational expression between temperature and power demand for each weather. Based on this relational expression, the power demand on the forecast target date is obtained. Then, the calculated power demand on the forecasted target date is corrected by the power demand record on the past reference date with weather data similar to the forecasted weather change pattern in the three days of the forecasted date, the previous day, and the previous day. The corrected value is used as the power demand prediction value on the prediction target day.

Japanese Patent Laid-Open No. 5-38051

  However, the predicted power demand calculated in Patent Document 1 does not take into account the amount of power generated by the solar power generator (distributed power source). As described above, the electric power company can grasp the transmitted power, but cannot know how much the solar power generator connected to the distribution system is generating power. For this reason, if the distribution system is operated based on the predicted power demand obtained by this method, there is a risk of overloading and causing a power outage accident.

  In Patent Document 1, past power demand results and weather data (weather, temperature, humidity, etc.) are accumulated to obtain a relational expression between temperature and power demand for each weather. Is not sufficient (when the number of samples is small), there is a risk that an error from the actual condition of the derived relational expression becomes large (a large difference from the actual condition).

  The present invention has been made in view of such problems, and even when the number of load data samples including the temperature and load of the target distribution section is not sufficient, the temperature is estimated from the temperature at the time of prediction. It is an object of the present invention to provide a load prediction method for a distribution section capable of predicting a load (actual load) at a current distribution section with high accuracy, and a distribution system control system to which this prediction method is applied.

  In order to solve the above problems, the present inventors have intensively studied, and the actual load of the distribution section tends to be high at low and high temperatures regardless of the time zone, and relatively low at medium temperatures. I focused on that. And, by repeating research, if the number of load data samples at the time when the actual load is to be predicted is not sufficient, it is possible to interpolate by converting the scale of other time zones (scale adjustment) based on the above trend The present invention has been completed.

  That is, a typical configuration of the load prediction method for a distribution section according to the present invention is information acquisition for acquiring load data including a temperature and a load at that temperature every predetermined time and storing it in a data table in any distribution section. A first information extraction step of generating load data at a target time that is a time at which a load is to be predicted from the data table to generate a first data group, and load data at a time close to the target time from the data table A second information extraction step for extracting and generating a second data group, and determining a ratio of the load of the first data group and the second data group to the air temperature and converting the load value of the second data group using the ratio Scale conversion step to be performed, and data for interpolating the temperature range in which the load data of the first data group is insufficient with the load data of the second data group after the scale conversion. An interpolation step, a relational expression deriving step for deriving a relational expression between the temperature and load at the target time based on the first data group after the interpolation, and this distribution based on the relational expression from the temperature at the time when the load of the distribution section is to be predicted And a load prediction step for predicting the load in the section.

  In the above configuration, when the number of load data samples including the temperature and the load at the target time is not sufficient, the load data at the close time is scaled and interpolated. Thereby, the reliability of the relational expression between the temperature and the load at the target time can be improved, and the load in the power distribution section at that time can be predicted with high accuracy from the temperature at the time of the prediction.

  The load included in the above load data is an actual load obtained by adding the total power generation amount of a plurality of photovoltaic generators linked to this distribution section to the apparent load actually measured by the sensor built-in automatic switch in the distribution section. There should be. Thereby, it becomes possible to predict the actual load of the power distribution section at that time with high accuracy from the temperature at the time of the prediction.

  The total power generation amount of the plurality of solar power generators is calculated in advance by the following formula: “actual measurement value of solar power generator / rated power generation capacity of solar power generator = first coefficient × temperature + second coefficient × sunlight amount” Based on the “+ 3rd coefficient”, a correction formula expressed by the first coefficient × temperature + second coefficient × insolation amount + third coefficient is derived by multiple regression analysis, and the total rated power generation of a plurality of photovoltaic generators It may be calculated by multiplying the capacity by this correction formula. Thereby, the total power generation amount of a plurality of solar power generators can be accurately calculated, and the reliability of the actual load can be improved.

  In the first information extraction step and the second information extraction step, load data during clear weather may be extracted from the data table. This makes it difficult to grasp the exact amount of solar radiation (the calculation of the theoretical amount of solar radiation (the amount of solar radiation during fine weather) cannot be used to determine the actual amount of solar radiation). can do.

  A typical configuration of a power distribution system control system according to the present invention includes an information acquisition unit that acquires load data including an air temperature and a load at the air temperature every predetermined time in an arbitrary power distribution section, and data in which the load data is stored Extracted by a storage unit having a table, a first information extraction unit that extracts load data at a target time that is a time at which a load is to be predicted from the data table, and generates a first data group, and a first information extraction unit Sample number determination unit that determines whether or not the load data is more than a certain value for each predetermined temperature range, and when the sample number determination unit determines NO, load data at a time close to the target time is extracted from the data table Then, the second information extraction unit for generating the second data group, the ratio of the load of the first data group and the second data group to the air temperature is obtained, and the value of the load of the second data group is calculated using this ratio. Scale conversion unit that performs scale conversion, data interpolation unit that interpolates a temperature range in which load data of the first data group is insufficient using load data of the second data group after scale conversion, and first data after interpolation A relational expression deriving unit for deriving a relational expression between the temperature and load at the target time based on the group, and a load predicting unit for predicting the load in the distribution section based on the relational expression from the temperature at the time when the load in the distribution section is to be predicted And a switching control unit that operates the distribution system based on the load predicted by the load prediction unit.

  According to the above configuration, it is possible to accurately grasp (predict) the total power generation amount of a plurality of solar power generators linked to the power distribution section, and to operate the power distribution system without waste. In addition, the component corresponding to the technical idea in the load prediction method of the distribution section mentioned above, and its description are applicable also to the said distribution system control system.

  According to the present invention, even if the number of load data samples including the temperature and load of the target distribution section is not sufficient, the load (actual load) of the distribution section at that time is calculated from the temperature at the time of prediction. It is possible to provide a load prediction method for a distribution section that can be predicted with high accuracy, and a distribution system control system to which this prediction method is applied.

It is a figure which shows the power distribution system with which the power distribution system control system concerning embodiment of this invention is applied. It is a block diagram which shows schematic structure of the power distribution system control system shown in FIG. It is a flowchart explaining operation | movement of the power distribution system control system shown in FIG. It is a figure which shows the correlation of temperature and an actual load. It is a figure which shows the load data at 12:00. It is a figure which shows 9 o'clock load data. It is the figure which interpolated the load data at 12:00 with the load data at 9:00.

  Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. The dimensions, materials, and other specific numerical values shown in the embodiments are merely examples for facilitating understanding of the invention, and do not limit the present invention unless otherwise specified. In the present specification and drawings, elements having substantially the same function and configuration are denoted by the same reference numerals, and redundant description is omitted, and elements not directly related to the present invention are not illustrated. To do.

[Distribution system]
FIG. 1 is a diagram showing a power distribution system 132 to which the power distribution system control system 100 according to the embodiment of the present invention is applied. As shown in FIG. 1, the electric power sent from the substations 136 a and 136 b is supplied to a plurality of general households 140 a to 140 c by the power distribution system 132. The general households 140 b and 140 c are provided with solar power generators 142 a and 142 b and are connected to the power distribution system 132.

  The power distribution system 132 includes a plurality of sensor built-in automatic switches 138 a to 138 h controlled by the power distribution system control system 100. The sensor built-in automatic switches 138a to 138h are section switches that open and close (ON / OFF) distribution lines, and have a sensor function of measuring section power (current). In FIG. 1, the sensor built-in automatic switches 138a to 138f illustrated by triangles are section automatic switches, and the sensor built-in automatic switches 138g and 138h illustrated by squares are interconnected automatic switches.

  For example, when an accident occurs in the power distribution section 134, the sensor built-in automatic switch 138e and the sensor built-in automatic switch 138f in the vicinity thereof are closed and the power supply is stopped. If power is supplied from the substation 136a before the accident occurs, the power supply on the downstream side of the sensor built-in automatic switch 138f is also stopped. The power supply to the downstream side of the sensor built-in automatic switch 138f can be recovered early by switching the sensor built-in automatic switch 138h and performing reverse power transmission from the other substation 136b.

  The load (apparent load) in the distribution section 134 is transmitted by subtracting the measured value of the section power flow of the downstream sensor built-in automatic switch 138f from the measured value of the section power flow of the sensor built-in automatic switch 138e on the upstream side. Can be obtained by multiplying the voltage. However, since a plurality of solar power generators 142a and 142b are connected to the power distribution section 134, a true load (actual load) cannot be obtained by such a simple calculation.

  Therefore, the distribution system control system 100 stores a correction formula for predicting the total power generation amount of the plurality of solar power generators 142a and 142b connected to the power distribution section 134. The power distribution system control system 100 predicts the actual load from the temperature at the time when the actual load is desired to be predicted by accumulating load data including the temperature of the power distribution section 134 every predetermined time and the actual load at that temperature.

[Distribution system control system]
FIG. 2 is a block diagram illustrating a schematic configuration of the power distribution system control system 100. FIG. 3 is a flowchart for explaining the operation of the power distribution system control system 100. As shown in FIG. 2, the power distribution system control system 100 is a computer system that includes a system control unit 102 and a storage unit 104.

  The system control unit 102 is a semiconductor integrated circuit including a central processing unit (CPU) and manages and controls the entire power distribution system control system 100. The storage unit 104 includes a ROM, RAM, EEPROM, nonvolatile RAM, flash memory, HDD, and the like, and stores programs and various data used in the system. The storage unit 104 includes a rated power generation capacity storage unit 104a, a correction formula storage unit 104b, and a data table 104c.

  In the rated power generation capacity storage unit 104a, individual information of each of the plurality of solar power generators 142a and 142b linked to the power distribution system 132 is stored. For example, the rated power generation capacity that is required to be applied to the electric power company when first (newly) connected to the distribution system 132 is stored. The correction formula storage unit 104b is determined by performing multiple regression analysis of the following formula 1 using the actual power generation amount of the solar power generators 142a and 142b actually measured at the central interconnection solar power generator demonstration research site and the like. The first coefficient, the second coefficient, and the third coefficient are stored (the first coefficient, the second coefficient, and the third coefficient are constants, and the air temperature and the amount of solar radiation are variables). Hereinafter, in the present embodiment, “first coefficient × air temperature + second coefficient × insolation amount + third coefficient” is referred to as a correction formula. It should be noted that the “measured value of the power generation amount of the solar power generator” and the “rated power generation capacity of the solar power generator” on the left side of the formula 1 are the measured value of the total power generation amount of the plurality of solar power generators 142a and 142b and the plurality of power generation values. As the total rated power generation capacity of the solar power generators 142a and 142b, it is preferable to perform multiple regression analysis (the accuracy is improved because they are averaged compared to a single case).

  In the data table 104c, load data of the power distribution section 134 that is acquired every predetermined time (for example, every minute) by the information acquisition unit 110 described later is accumulated. The load data includes the temperature of the power distribution section 134 and the actual load at that temperature together with the time at that time.

  The power distribution system control system 100 includes an input unit 106 and an output unit 108. The input unit 106 takes in predetermined information from the outside to the system by a keyboard, a mouse, a touch panel, a file input / output device, data communication through a network, or the like. The output unit 108 includes a display, a printer, and the like, and displays information to the user and performs printing. It is also possible to save the output content as data in a recording medium, or to perform data communication or web display over a network.

  Hereinafter, the information acquisition unit 110, the solar radiation amount calculation unit 112, the total power generation amount calculation unit 114, the first information extraction unit 116, the sample number determination unit 118, the second information extraction unit 120, and the scale conversion unit 122 of the power distribution system control system 100. The data interpolation unit 124, the relational expression derivation unit 126, the load prediction unit 128, and the switching control unit 130 will be described with reference to the flowchart of FIG. As shown in FIG. 3, the distribution system control system 100 operates the distribution system 132 (distribution system operation step 162), information acquisition step 144, prediction time point input step 146, first information extraction step 148, sample number determination. The actual load in the power distribution section 134 is predicted by the step 150 and the relational expression derivation step 158 (load prediction step 160). As a feature of the present embodiment, when the number of samples of the load data extracted in the first information extraction step 148 is not sufficient, the load data is obtained by the second information extraction step 152, the scale conversion step 154, and the data interpolation step 156. Interpolated.

  In the information acquisition step 144, the load data of the power distribution section 134 is accumulated in the data table 104c by the first step 144a to the fifth step 144e. In the first step 144a, the information acquisition unit 110 acquires the weather and temperature of the power distribution section 134 and the apparent load. Specifically, the weather and temperature can be acquired from the regional meteorological observation system of the Japan Meteorological Agency, that is, AMeDAS (Automated Meteorological Data Acquisition System) (in AMeDAS, the temperature is updated every 10 seconds). Of course, the temperature and the like may be acquired by other methods such as placing a thermometer in the power distribution section 134. The apparent load is acquired based on the section tide measured by the sensor built-in automatic switches 138a to 138h.

  In the second step 144b, it is determined whether or not the weather acquired in the first step 144a is clear (sunny, clear). When the weather is not clear (No in the second step 144b), it is considered that the power generation of the connected solar power generators 142a and 142b is not performed, and the apparent load acquired in the first step 144a is the actual load. The information acquisition unit 110 stores the load data including the temperature, the actual load (apparent load), and the time at that time in the data table 104c (fifth step 144e).

  On the other hand, when the weather is sunny (Yes in the second step 144b), the actual load is the total power generation amount of the connected solar power generators 142a and 142b added to the apparent load. Therefore, as the third step 144c, the solar radiation amount calculation unit 112 calculates the theoretical solar radiation amount (sunny solar radiation amount) at the date and time (time) of the power distribution section 134 based on Equation 2. The theoretical solar radiation amount is calculated from the date and longitude and longitude. Of course, a solar radiation meter may be arranged in the power distribution section 134 and the amount of solar radiation may be acquired by actual measurement. However, by calculating from the date and time and longitude and latitude, labor is not required and the configuration can be realized more easily.

  When the solar radiation amount calculating unit 112 calculates the theoretical solar radiation amount, as the fourth step 144d, the total power generation amount calculating unit 114 is rated from the rated power generation capacity storage unit 104a to the solar power generators 142a and 142b connected to the power distribution section 134. The power generation capacity is read, and all are added together to calculate the total rated power generation capacity of the solar power generators 142a and 142b for the entire power distribution section 134. Then, as shown in Formula 3, the total power generation amount calculation unit 114 multiplies the total rated power generation capacity by the correction formula stored in the correction formula storage unit 104b to calculate the total power of the connected solar power generators 142a and 142b. Calculate power generation. In the correction formula, the calculated temperature and the theoretical amount of solar radiation are substituted.

  It is the amount of solar radiation that most depends on the total amount of power generated by the solar power generators 142a and 142b (there is a correlation), and then the temperature that depends on it. In the present embodiment, the total power generation amount of the solar power generators 142a and 142b is calculated based on a correction formula in which the amount of solar radiation and the temperature are set as parameters. Therefore, it is possible to predict the total power generation amount of the solar power generators 142a and 142b with high accuracy. Since the first coefficient, the second coefficient, and the third coefficient of the correction formula are determined by multiple regression analysis, for example, the rated ratio of the solar power generators 142a and 142b including the solar radiation amount instead of the solar radiation amount is determined. The theoretical power generation amount may be used. When the total power generation amount is calculated by the total power generation amount calculation unit 114, the information acquisition unit 110 stores the load data including the temperature, the actual load added to the apparent load and the time at that time, and the data table. 104c is stored (fifth step 144e).

  In the prediction time point input step 146, the time point that the administrator wants to predict is input from the input unit 106. In the first information extraction step 148, the first information extraction unit 116 extracts the load data at the target time, which is the time at which the load is to be predicted, from the data table 104c and generates a first data group.

  FIG. 4 is a diagram showing the correlation between the temperature and the actual load. As shown in FIG. 4, the actual load of the power distribution section 134 can be regarded as constant based on the temperature, and becomes high at low and high temperatures regardless of the time zone (time), and relatively at medium temperatures. Tend to be lower. In this embodiment, based on this tendency, the load data (load data of the first data group) extracted by the sample number determination unit 118 and the first information extraction unit 116 at the sample number determination step 150 is a predetermined temperature. It is determined whether there is a certain level or more for each area. If the sample number of the load data is not sufficient (No in the sample number determination step 150), the other time zone (close time) is scaled (scale adjustment) and interpolated.

  Hereinafter, with reference to FIGS. 5 to 7, a description will be given of a case where the load data at 12:00, which is load data at the target time, is interpolated with the load data at 9:00, which is load data at a time close to the target time. FIG. 5 is a diagram showing load data at 12:00, and FIG. 6 is a diagram showing load data at 9 o'clock. FIG. 7 is a diagram in which 12:00 load data is interpolated with 9 o'clock load data. In the present embodiment, the sample number determination unit 118 determines whether there are five or more load data in the range of 5 ° C. in the range from 0 ° C. to 35 ° C.

  As shown in FIG. 5, when the target time is 12:00, five or more load data do not exist in the range of 0 ° C. to 5 ° C., 5 ° C. to 10 ° C. (No in sample number determination step 150). Therefore, in the second information extraction step 152, the second information extraction unit 120 extracts load data at a time close to the target time from the data table 104c to generate a second data group.

  Specifically, the second information extraction unit 120 sequentially loads data at times close to the target time (for example, the order of load data at 11:00 and 1 o'clock close to 12:00, load data at 10:00 and 2 o'clock). Are read from the data table 104c. And when load data exists in the range of 0 degreeC-5 degreeC, 5 degreeC-10 degreeC, the load data are extracted as a 2nd data group. Here, as shown in FIG. 6, in the 9 o'clock load data, the load data exists in the range of 0 ° C. to 5 ° C., 5 ° C. to 10 ° C., so the 9 o'clock load data is the second data group. Extracted as

  As described above, the actual load tends to increase at low and high temperatures regardless of the time zone (time), and tends to be relatively low at moderate temperatures. The fluctuation varies (as shown in FIG. 4, the waveform of the actual load with respect to the air temperature moves up and down). Therefore, in the scale conversion step 154, the scale conversion unit 122 performs scale conversion on the load data extracted as the second data group.

  The scale converter 122 obtains the ratio of the actual load to the air temperature of the first data group (12 o'clock load data) and the second data group (9 o'clock load data), and calculates the ratio of the actual load of the second data group. Multiply by value. The ratio of the actual load to the temperature of the first data group (12 o'clock load data) and the second data group (9 o'clock load data) is, for example, every predetermined temperature (5 ° C, 10 ° C, 15 ° C, 20 ° C, 25 ° C). Calculated as the average value of the ratio of the actual load to the air temperature (° C., 30 ° C.).

  From the above, in the data interpolation step 156, the data interpolation unit 124 performs the scale conversion after the scale conversion to the temperature range (0 ° C. to 5 ° C., 5 ° C. to 10 ° C.) where the load data of the first data group is insufficient. Interpolate the load data of two data groups (see FIG. 7). If there is still a range in which 5 or more load data do not exist in increments of 5 ° C. (No in sample number determination step 150), the interpolation by the second information extraction step 152, the scale conversion step 154 and the data interpolation step 156 is repeated. .

  On the other hand, if there are five or more load data in 5 ° C increments in all temperature ranges (Yes in the sample number determination step 150), no further interpolation is required. Therefore, in the relational expression deriving step 158, the relational expression deriving unit 126 derives a relational expression between the temperature at the target time and the actual load. That is, an approximate expression is derived based on the load data (including the interpolated load data) of the first data group. This approximate expression expresses the relationship between the temperature and the actual load and is versatile regardless of the weather or season.

  Then, in the load prediction step 160, the load prediction unit 128 predicts the actual load at that time in the power distribution section 134 from the temperature at the time when the actual load is desired to be predicted based on this relational expression (approximate expression). The temperature at which the actual load is to be predicted may be acquired from the AMeDAS, or may be input from the input unit 106 by the administrator. When the actual load is predicted, in the distribution system operation step 162, the switching control unit 130 that controls the sensor built-in automatic switches 138a to 138h operates the distribution system 132 without waste.

  Table 1 shows the prediction accuracy when the actual load is predicted according to the flowchart of FIG. As shown on the left side of Table 1, according to the present embodiment, the actual load can be predicted with very high accuracy. This is because the total power generation amount can be predicted with very high accuracy by the correction formula in which the solar radiation amount most dependent on the total power generation amount of the connected solar power generators 142a and 142b, and the dependent temperature is set as a parameter. But there is. In fact, as shown on the right side of Table 1, when the correction formula “first coefficient × temperature + third coefficient” excluding the solar radiation amount is used (separately from the correction formula including the solar radiation amount, the first coefficient and the third The coefficient is determined by multiple regression analysis), and the accuracy is considerably reduced.

  The load data may include weather, and the first information extraction step 148 and the second information extraction step 152 may be configured to extract sunny load data from the data table 104c. In the above, when the weather is not sunny, it is considered that the power generation of the connected solar power generators 142a and 142b is not. However, even in rainy weather (cloudy weather), power is generated based on the amount of solar radiation. Therefore, it is possible to derive an accurate relational expression (approximate expression) by excluding load data during rainy weather (cloudy weather). Therefore, a more accurate actual load can be predicted.

  It should be noted that the power generation by the solar power generators 142a and 142b is hardly considered (the sun is not shining), and the load during rainy weather (cloudy weather) is extracted, and a relational expression (approximate expression) may be derived as an actual load. . The interpolation of load data, which is a feature of the present invention, can be applied regardless of whether or not the photovoltaic generators 142a and 142b generate power. That is, even when the temperature and the apparent load are accumulated as in the conventional case, if the total power generation amount of the solar power generators 142a and 142b does not become extremely large, data in other time zones (close time) The accuracy of the relational expression (approximation expression) (completion degree of the correlation function) can be improved by performing the interpolation with (1).

  According to the distribution system control system 100 described above, even if the number of load data samples including the temperature and actual load in the target distribution section 134 is not sufficient, the distribution section at that time is determined from the temperature at the time of prediction. The actual load 134 can be predicted with high accuracy. This technology is necessary in the target distribution section 134, taking into account changes in the distribution section, new construction or removal of houses, introduction of solar power generators 142a, 142b, etc. Is difficult to accumulate. That is, if only reliable and sufficiently new load data is used, the number of load data samples tends to be insufficient.

  As mentioned above, although preferred embodiment of this invention was described referring an accompanying drawing, it cannot be overemphasized that this invention is not limited to this example. It will be apparent to those skilled in the art that various changes and modifications can be made within the scope of the claims, and these are naturally within the technical scope of the present invention. Understood.

  INDUSTRIAL APPLICABILITY The present invention can be used as a solar power generation amount prediction method for predicting the total power generation amount of a plurality of solar power generators linked to an arbitrary power distribution section, and a distribution system control system using the method.

DESCRIPTION OF SYMBOLS 100 ... Distribution system control system, 102 ... System control part, 104 ... Memory | storage part (104a ... Rated power generation capacity storage part, 104b ... Correction type | mold storage part, 104c ... Data table), 106 ... Input part, 108 ... Output part, 110 Information acquisition unit 112 Solar radiation amount calculation unit 114 Total power generation calculation unit 116 First information extraction unit 118 Sample number determination unit 120 Second information extraction unit 122 Scale conversion unit 124 DESCRIPTION OF SYMBOLS ... Data interpolation part, 126 ... Relational expression deriving part, 128 ... Load prediction part, 130 ... Switching control part, 132 ... Distribution system, 134 ... Distribution section, 136a, 136b ... Substation, 138a-138h ... Automatic switch with built-in sensor 140a to 140c ... general home, 142a, 142b ... solar power generator, 144 ... information acquisition step (144a ... first step, 144b ... second , 144c ... third step, 144d ... fourth step, 144e ... fifth step), 146 ... predicted point input step, 148 ... first information extraction step, 150 ... sample number determination step, 152 ... second information extraction step 154 ... Scale conversion step, 156 ... Data interpolation step, 158 ... Relational expression derivation step, 160 ... Load prediction step, 162 ... Distribution system operation step

Claims (5)

In any power distribution section, an information acquisition step of acquiring load data including the temperature and a load at that temperature every predetermined time and storing it in a data table;
A first information extraction step of extracting load data at a target time, which is a time at which a load is to be predicted, from the data table to generate a first data group;
A second information extracting step of extracting load data at a time close to the target time from the data table to generate a second data group;
A scale conversion step of obtaining a ratio of the load of the first data group and the second data group to the temperature, and using this ratio to scale the load value of the second data group;
A data interpolation step of interpolating the temperature range in which the load data of the first data group is insufficient with the load data of the second data group after the scale conversion;
A relational expression deriving step for deriving a relational expression between the temperature and the load at the target time based on the first data group after interpolation;
A load prediction step of predicting the load of the power distribution section based on the relational expression from the temperature at the time of predicting the load of the power distribution section;
The load prediction method of the distribution area characterized by including this.   The load included in the load data is an actual load obtained by adding the total power generation amount of a plurality of photovoltaic generators connected to the distribution section to the apparent load actually measured by the sensor built-in automatic switch in the distribution section. The load prediction method for a power distribution section according to claim 1, wherein: The total power generation amount of the plurality of solar power generators is calculated in advance by the following formula “actual measurement value of power generation amount of solar power generator / rated power generation capacity of solar power generator = first coefficient × temperature + second coefficient × sunlight amount. Based on “+ 3rd coefficient”, a correction equation represented by 1st coefficient × temperature + second coefficient × insolation amount + third coefficient is derived by multiple regression analysis,
The load prediction method for the distribution section according to claim 2, wherein the calculation is performed by multiplying the total rated power generation capacity of the plurality of solar power generators by the correction formula.   4. The load prediction method for a distribution section according to claim 2, wherein, in the first information extraction step and the second information extraction step, load data during clear weather is extracted from the data table. 5. In any power distribution section, an information acquisition unit that acquires load data including temperature and load at that temperature every predetermined time;
A storage unit having a data table in which the load data is stored;
A first information extraction unit that extracts load data at a target time, which is a time at which the load is to be predicted, from the data table and generates a first data group;
A sample number determination unit for determining whether or not the load data extracted by the first information extraction unit is greater than or equal to a predetermined temperature range;
A second information extraction unit that extracts load data at a time close to the target time from the data table to generate a second data group when the sample number determination unit determines NO;
Obtaining a ratio of the load of the first data group and the second data group to the air temperature, and using this ratio, the scale conversion unit for converting the value of the load of the second data group;
A data interpolation unit that interpolates a temperature range in which the load data of the first data group is insufficient, using the load data of the second data group after the scale conversion;
A relational expression deriving unit for deriving a relational expression between the temperature and the load at the target time based on the first data group after interpolation;
From the temperature at the time when it is desired to predict the load of the distribution section, a load prediction unit that predicts the load of the distribution section based on the relational expression;
Based on the load predicted by the load prediction unit, a switching control unit that operates the distribution system,
A distribution system control system comprising:

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