JP2005237187A - Method and device for assuming distribution system load - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To improve the accuracy of load assumption in units of a customer and a section, in a distribution system. <P>SOLUTION: Prior to computation, daily load measurement data of measurable consumers and monthly power consumption data of all the consumers are loaded to a database 58. A load assumption calculation control section 52 calculates standard deviations from the yearly fluctuations of monthly power consumption for all the consumers, and obtains a shape correlation and an absolute value correlation among the consumers as objects to be assumed and the consumers with daily load measurement. The consumers, whose both correlations are high and at least a threshold level, are extracted as similar consumers, and thus a daily load as the objects-to-be-assumed of the consumer as the objects-to-be-assumed is determined, on the basis of past daily load measurement data of the similar consumers in the same month, the same week and the same day of the week. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a distribution system load assumption method, and more particularly to a load assumption method and apparatus for estimating a consumer load and a section load in a distribution system.

  In the distribution system, it is assumed that the future power consumption (hereinafter referred to as load) is assumed along with the distribution facility plan. Here, the distribution system is a substation, a power plant or a transmission line and a power demand facility, or a power line (hereinafter referred to as a line or section) and a SW device (hereinafter referred to as a switch) between demand facilities. It is a power facility composed of

  In the power distribution system, when a future load becomes necessary in accordance with the above-described plan, a future estimated load of the assumed target section is obtained.

  In other words, out of the future loads that will be required in connection with the distribution system equipment plan, it is the distribution load assumption problem that the future daily load for each section, feeder, and bank is determined based on the past actual data. is there.

  For this distribution load assumption problem, a method of “distributing the feeder load based on the equipment capacity ratio of each section” is used. This method will be referred to as a “prorated load assumption method” in this specification for the sake of convenience.

  Various solutions based on the above apportionment scheme have been presented. For example, in Patent Document 1, the accuracy of load estimation is improved by considering regional characteristics in the distribution section load estimation method. Moreover, in patent document 2, while calculating the load assumption amount after the predetermined period of each load based on the distribution rate, the distribution condition is displayed visually. Patent Document 3 supports the calculation of a predicted power demand in the future, the creation of a predicted accident case in the future, and the minimization of countermeasure costs.

JP 7-79530 A (paragraphs 0015-0016) JP-A-11-206016 (paragraphs 0010-0016) JP 2001-112171 (paragraphs 0024-0027)

  In the section load assumption by the prorated load assumption method, since the load shape of the assumed target is similar to the feeder load, the characteristics of each section are not reflected and a problem remains in terms of accuracy. The power demand in recent years has a wide variety of load shapes such as “midnight power” and “seasonal power”. Conventionally, it has been limited only to calculating the peak load several years ahead, but more detailed assumed load in time increments is required. From such a current situation, the apportioned load assumption method has a problem in the accuracy of the assumption and is difficult to apply. Furthermore, in order to increase the accuracy, the amount of data must be increased by installing a measuring instrument or the like, resulting in an increase in cost.

  An object of the present invention is to provide a distribution system load estimation method and apparatus that can overcome the above-described problems of the prior art and that can perform load estimation with low cost and high accuracy.

  In order to solve the above problems, the present invention can measure from a consumer in a distribution load assumption method for predicting the future load for a distribution system in which a consumer with daily load measurement data and a consumer without it are mixed. Using the annual fluctuation of monthly power consumption, obtain the correlation of the shape between the assumed target consumer and the consumer with the daily load measurement data, and the similar consumer whose correlation coefficient is greater than or equal to the threshold or the highest value The daily load data of the assumed target consumer is determined based on the daily load measurement data. The consumers who seek the correlation may be performed between groups having the same consumer type.

  Alternatively, the shape load and the absolute value correlation between the assumed target customer and the customer having the daily load measurement data are respectively obtained, and the daily load of similar customers whose both correlation coefficients are both high and equal to or greater than the threshold value. The daily load data of the assumed target consumer is determined based on the measurement data.

  Further, the daily load data of the assumed target consumer is corrected according to the monthly power consumption ratio of the target month of the similar consumer and the assumed target consumer. Further, the daily load data of the assumed target consumer is corrected according to the temperature data of the target day.

  Further, for each section of the distribution system, the daily load data of all the assumed target consumers and the daily load data of all the consumers having the daily load measurement data are aggregated to assume the section load data. Also good.

  The distribution load assumption device of the present invention predicts the future load for a distribution system in which consumers with and without daily load measurement data are mixed. The correlation between the database that stores the load measurement data for each consumer and the shape of the monthly variation in monthly power consumption between the assumed target consumer and the consumer with the daily load measurement data is calculated, and the correlation coefficient is a threshold value. It is provided with the load assumption calculation control part which determines the daily load data of the said assumption object consumer based on the daily load measurement data of the similar consumer which becomes the above or the highest value.

  Alternatively, the correlation between the shape and the absolute quantity is calculated, respectively, and based on the daily load measurement data of similar customers whose shape correlation and absolute quantity correlation are both high and the correlation coefficient is equal to or higher than the threshold value. And a load assumption calculation control unit for determining daily load data of the assumption target consumer.

  Further, the daily load data determined by the load assumption calculation control unit is displayed in a graph for each customer or section.

  According to the present invention, load assumption for each customer and section, which has been difficult in the past, can be solved by using the correlation of the shape of monthly power consumption annual fluctuation, so the cost for load assumption is low and the accuracy is low. Get higher.

  Furthermore, since the problem can be solved by utilizing the correlation between the shapes and the correlation between the absolute amounts, the accuracy for load assumption is further increased.

  Embodiments according to the present invention will be described with reference to FIGS. FIG. 2 shows an example of a distribution system to which the present invention makes a load assumption. Reference numeral 1 denotes a CB (distributor breaker) in a substation managed by an electric power company, 2 denotes a switch, 3 denotes a transformer, and 4 denotes each customer. A distribution network that supplies electric power from the CB to each consumer is called a feeder. Locations such as 1-2-1, 2-1 and 2-2 indicate sections.

  In the power distribution system shown in the figure, the daily load is measured in time increments in the CB1, whereas the actual load data is not measured in the switch 2 or the transformer 3. For the customer 4, the power company measures power consumption (monthly power consumption) data every month.

  FIG. 7 shows an example of the annual fluctuation of the monthly power consumption in the consumer. The vertical axis represents the load, and the horizontal axis represents the moon. Monthly power consumption is accumulated data of monthly power consumption, and each power company conducts monthly meter reading to collect electricity charges for each consumer.

  3-5 show examples of customer loads. The vertical axis of each figure represents a value obtained by normalizing the load with the average value of the day, and the horizontal axis represents time. FIG. 3 shows a typical daily load shape of a general house. FIG. 4 shows a load shape of a consumer who consumes power mainly at midnight. FIG. 5 shows a daily load shape of a commercial facility.

  As shown in the figure, the load shape varies widely depending on the customer. For this reason, it turns out that the exact load assumption of the day by a consumer unit and a section unit becomes difficult depending on the prorated load assumption method.

  FIG. 6 shows an example of load assumption calculation by the “prorated load assumption method”. The assumed target sections are the sections 1-2-1, 2-1-2-2, and 2-2-2-3 in FIG. 1. The current current of the section is calculated by apportioning from the current current of the feeder. The apportionment can be obtained by calculating the ratio of the installed capacity of the section to the installed capacity of the entire feeder and multiplying it by the current current of the feeder. Since the load curve calculated as described above has a shape similar to the feeder load, there is a problem in the accuracy of the section load assumption.

  Therefore, in the present invention, based on the load measurement data of some consumers and the monthly power consumption data of all consumers, the monthly power between consumers with and without daily load measurement data for each consumer. The correlation of annual fluctuations in consumption is obtained to obtain a more accurate forecast load.

  Next, the load assumption method according to the present embodiment will be described. In FIG. 2, it is assumed that the customer 4-1 has a daily load data for the past year, and the customer 4-2 has no daily load measurement data. Further, both the customers 4-1 and 4-2 have the monthly power consumption (FIG. 7) for the past year. In addition, the monthly power consumption of the consumer 4-1 may be calculated from the daily load measurement data.

  8 to 10 are explanatory diagrams of the load assumption method. When the customer 4-1 possessing the daily load data assumes a future load, as shown in FIG. 8, the daily load data of the same month, the same week, the same day of the previous year of the assumption target date, The daily load data of the same week and the same day is adopted as the daily load of the assumed day. Since the adopted daily load is a load value at the temperature of the previous year, the load is corrected by the temperature of the assumed date. The load correction based on the temperature will be described later.

  On the other hand, when the future load of the customer 4-2 is assumed, since the daily load data is not possessed, the daily load of the customer similar to the customer 4-2 is used. Here, it is based on the hypothesis that, when the annual fluctuation and absolute amount of monthly power consumption of two consumers are similar, the daily load is also similar.

  FIG. 9 shows the relationship of this hypothesis. From the group of customers with the same contract type as the customer 4-2 and with daily load data, the correlation coefficient of the shape of the annual fluctuation of the monthly power consumption for the customer 4-2 and the correlation coefficient of the absolute amount are calculated. The customer who exceeds the threshold of the correlation coefficient determined in advance is the similar customer 4-2 ′. When a similar customer is not detected in a customer of the same contract type as that of the customer 4-2, the correlation coefficient calculation range is expanded and a threshold value is set again to perform recalculation. If a similar customer is not detected even if the calculation range is expanded, the customer having the highest correlation coefficient so far may be adopted as the similar customer.

  FIG. 10 shows load assumptions when there are similar customers. Daily load data of the same month, the same week, the same day of the previous year of similar customer 4-2 'of the customer 4-2 or daily load data of the same month, the same week, the same day of the past as the assumed target daily load data To do. At this time, since the daily load measurement data is the value of the consumer 4-2 ', the absolute amount is corrected by the ratio of the monthly power consumption of the consumer 4-2 and the consumer 4-2'. The load correction based on the monthly power consumption will be described later. Further, since the areas of the customer 4-2 and the customer 4-2 'are not necessarily the same, load correction based on the temperature is also performed.

  FIG. 11 shows an example of load correction by temperature. In the figure, the vertical axis represents the daily average load, and the horizontal axis represents the daily average temperature. A scatter diagram as shown in the figure is drawn from the daily load average for one year of customers with daily load data and the daily average temperature for one year. Apply a regression curve to the drawn scatter plot and formulate the relationship between load and temperature. By substituting the average temperature of the assumed target day into the mathematical curve, the daily average load of the assumed target day is derived. By multiplying the assumed target daily load by using the daily average load (normalized value) of the assumed target day, the assumed target daily load for one day can be corrected by the temperature.

  FIG. 12 shows an example of load correction based on monthly power consumption. The ratio of the monthly power consumption of the same month of the previous year of the assumption target date of the consumer 4-2 and the monthly power consumption of the same month of the previous year of the assumption date of the similar customer 4-2 'is calculated. The absolute amount is corrected by multiplying the assumed daily load by this ratio.

  According to this embodiment, the daily load measurement data of a measurable consumer is used, and the correlation coefficient of the annual fluctuation of monthly power consumption is calculated between the consumer without the measurement data and the consumer with the measurement data. It is determined that the daily load data is similar between highly correlated customers, and the load is estimated based on the daily load data of similar customers. Therefore, it is possible to estimate the load with high accuracy for each consumer and even for each section without increasing the number of measuring devices.

  The section load is the sum of the daily load data assumed for the customers in the section and the daily load assumed as shown in FIG. 8 from the daily load measurement data.

  Next, Embodiment 1 of the present invention will be described with reference to FIGS. 1 and 13 to 15.

  FIG. 1 is a block diagram of a distribution load assumption device showing an embodiment of the present invention. The power distribution system control device as a power distribution load assumption device includes a computer 50, an input device 60, and a display device 70, and the computer 50 is connected to the power distribution system 90 via a remote monitoring control device 80. Here, the power distribution system 90 indicates an actual power transmission / distribution facility constituted by distribution lines, switches, and the like.

  The remote monitoring control device 80 is configured to monitor the state of the distribution system 90 such as the switching state of the switch and the power flow using communication means such as a communication line, and to control the switching operation of the switch. ing.

  The input device 60 includes a keyboard and a mouse for inputting data to the computer 50, and the display device 70 includes a CRT that displays an image related to data output from the computer 50.

  The computer 50 is configured to calculate a future load based on data input or taken from the input device 60 or the remote monitoring control device 80 and to display or output the result on the display device 70 or the remote monitoring control device 80. Has been.

  Such a computer 50 includes an overall control unit 51, a load assumption calculation control unit 52, a customer type determination unit 53, a correlation processing unit 54, a similar customer extraction unit 55, a load correction unit 56, and an assumption result output unit 57. It consists of functional blocks and a database 58. Each functional block is processed by a program for realizing the function, and each block may be combined as appropriate.

  The overall control unit 51 controls data exchange from the input device 60, the display device 70, and the remote monitoring control device 80. At the same time, when a load assumption instruction is received from the input device 60 or the remote monitoring control device 80, a load assumption calculation control is performed to search for an assumed load value for the data fetched from the input device 60 or the remote monitoring control device 80. The unit 52 is instructed, and the result is received and output to the display device 70 and the remote monitoring control device 80. Data collected from the input device 60 and the remote monitoring control device 80 is stored in the database 58 and referred to as necessary.

  When the load assumption calculation control unit 52 receives an instruction regarding the load assumption calculation from the overall control unit 51 based on the data stored in the database 58, the customer type determination unit 53, the correlation processing unit 54, and similar customer extraction In cooperation with the unit 55 and the load correction unit 56, it is configured as an assumed load value calculation means for calculating an optimum estimated load value and outputting the result to the display device 70 and the remote monitoring control device 80.

  The consumer type determination unit 53 is configured as a means for classifying consumers based on the type of consumer. Specifically, it classifies for each consumer having the same code from the consumer type information of each consumer.

  The correlation processing unit 54 is configured as a calculation value calculation unit that calculates a correlation coefficient of a shape of an annual variation in monthly power consumption and a correlation coefficient of an absolute amount between an arbitrary consumer and other consumers. Yes.

  The similar customer extraction unit 55 is configured as a similar customer extraction unit that determines whether the correlation coefficient calculated by the correlation processing unit 54 satisfies the correlation coefficient threshold value instructed by the load assumption calculation control unit 52. Has been.

  The load correction unit 56 is configured as a load correction unit that reads the load of the similar customer extracted by the similar customer extraction unit 55 from the database 58 and corrects it with the temperature and the monthly power consumption.

  The assumed load output unit 57 is configured to display the load assumption calculation result calculated by the load assumption calculation control unit 52 on the display device 70 in the form of a table or a graph.

  The database 58 is configured to manage data input from the input device 60 and the remote monitoring control device 80 and system configuration data held by the load assumption calculation control unit 52.

  Next, specific contents of the load assumption processing by the load assumption calculation control unit 52, the customer type determination unit 53, the correlation processing unit 54, the similar customer extraction unit 55, the load correction unit 56, and the assumption result output unit 57 will be described. To do.

  FIG. 13 is a flowchart showing the procedure of load assumption processing. First, the daily load measurement data and the monthly power consumption data for each consumer necessary for starting the load assumption calculation are read into the database unit 58 (step S1).

  Next, based on the read daily load measurement data and monthly power consumption data, annual monthly power consumption is calculated for all consumers (step S2). Specifically, for a customer who possesses daily load measurement data, the daily load measurement data is accumulated on a monthly basis to create an annual monthly power consumption. For consumers who do not have daily load measurement data, the read monthly power consumption data for one year is used as it is.

  The standard deviation s of the monthly fluctuation of the monthly power consumption is calculated for each consumer (step S3). In the calculation, the standard deviation is calculated according to Equation 1.

  A customer counter is initialized (step S4, step S5). The customer i is a consumer who does not own the daily load measurement data (total number N), and the customer j is a consumer who owns the daily load measurement data (total number M). Next, the correlation coefficient r of the shape is calculated between the monthly fluctuations in the monthly power consumption of each of the customer i and the customer j (S6). At this time, the customers are classified into consumer types, and a correlation coefficient is calculated for each classification. The calculation of the shape correlation coefficient r follows Formula 2.

  Next, a correlation coefficient r ′ of an absolute amount is calculated between the monthly fluctuations in monthly power consumption of each of the customer i and the customer j. The absolute amount correlation coefficient r 'is calculated based on the error rate of the monthly fluctuation of the monthly power consumption of each of the customer i and the customer j. The error rate E is calculated according to Equation 3.

  Based on the error rate calculated by Equation 3, an absolute correlation coefficient r ′ is calculated according to Equation 4.

  If all (M) correlation coefficients of the customer counter j have not been calculated, the process returns to S6 (S8). Next, when the correlation coefficients of all the consumers are calculated (S7), the consumers whose ranks are higher in both the correlation of the shape of the annual fluctuation of the monthly power consumption and the correlation of the absolute amount are extracted. For example, if there is a consumer whose shape correlation is 1st and whose absolute quantity correlation is 2nd and a consumer whose shape correlation is 3rd and whose absolute quantity correlation is 1st, the sum of the former ranks Is 3, and the total of the latter rank is 4. Therefore, the former is extracted as a higher rank. As described above, the correlation coefficients of the shape and the absolute quantity are ranked in descending order, and the customer j having the smallest rank total value is extracted as a similar customer (S9). This is repeated until the customer counter i reaches the total customer count (N) (S10). Finally, the daily load measurement data of similar customers extracted in S9 for each customer i is output as an assumed load (S12).

  However, with S12 as it is, the load is equivalent to that of a similar consumer, and therefore it is necessary to correct using the monthly power consumption of the assumption target consumer and the temperature data of the assumption target area.

  FIG. 14 is a flowchart showing the temperature correction process. First, temperature data for one year is read (S20). Regarding temperature data, time step data for each area is available from the Japan Meteorological Agency. Next, the daily load data for one year is read (S21), and the day counter d is initialized (S22). The average temperature on day d is calculated (S23), the daily average load on day d is calculated (S24), and this is repeated for one year (S25). The daily load data is data of similar customers or data of a group having the same customer type as that of the assumed target customer.

  Next, a regression curve is derived from the daily average temperature and daily average load for one year (S27). The daily average load is calculated by inputting the daily average temperature of the prediction target day with respect to the derived regression curve (S28). Finally, the daily load data of the assumed target customer extracted in S12 is multiplied by the daily average load (normalization) of the assumed target date calculated in S28 to obtain an assumed load (S29).

  Further, the load correction based on the monthly power consumption is extracted in S12 from the ratio between the monthly power consumption including the assumed target date of the assumed target consumer and the monthly power consumption including the assumed target date of the similar consumer. Multiply the daily load data of the assumed target consumer to obtain the assumed load.

  The assumed load calculated as described above is output as a form by the assumed result output unit 57. The assumed result output content is a graph of an assumed target date, an assumed target daily temperature, an assumed target customer information, similar customer information, a correlation coefficient, an assumed load value, and an assumed load.

  FIG. 15 shows a specific form example of the assumed load. (A) is a list of calculation conditions and results. As the load prediction calculation condition, the prediction target month and day, the predicted daily average temperature, and the correlation coefficient threshold value of monthly power consumption annual fluctuation are displayed. As forecast customer information, the contract number for each customer, the customer type name, the belonging power pole number, the presence of daily load data, the similar customer contract number, the similar customer type name, and the monthly power consumption correlation coefficient are displayed. ing. In the predicted section load, an assumed section load value in increments of 30 minutes is displayed. In the section affiliated customer load, the assumed load for each consumer belonging to the section in 30 minute increments is displayed. Furthermore, by selecting the assumed section load and the contract number from the contract number list, it is possible to display a graph of the corresponding assumed daily load as shown in (b).

  In the embodiment described above, the correlation between the shape and the absolute amount are obtained between the assumed target consumer and the consumer having the daily load measurement data, and both correlation coefficients are both high and similar to the threshold value or more. The daily load data of the assumed target consumer is determined based on the daily load measurement data of the consumer. However, only the shape correlation may be obtained, and the daily load data of the assumed target consumer may be determined based on the daily load measurement data of a similar customer whose correlation coefficient is equal to or higher than the threshold value. Even with this, although the accuracy is lower than the former, it is possible to provide a distribution system load assumption device at a low cost for load assumption.

The block diagram which shows the structure of the distribution system load assumption apparatus by one Example of this invention. The system diagram which shows the structural example of a power distribution system. The graph which shows the shape of the daily load data of a consumer classification (general house). The graph which shows the shape of the daily load data of a customer classification (midnight power). The graph which shows the shape of the daily load data of a consumer classification (commercial facility). The graph which shows the section load shape in the same feeder by the proportional load assumption system. The graph which shows the annual fluctuation of monthly power consumption. Explanatory drawing which shows the load assumption method of the consumer with daily load measurement data. Explanatory drawing for demonstrating the hypothesis of the load assumption system of this invention. Explanatory drawing which shows the load assumption method of the consumer without daily load measurement data. The graph which shows the regression curve for carrying out load correction by temperature. The comparison figure of the monthly power consumption for carrying out load correction by the monthly power consumption. The flowchart which shows the load assumption method by Example 1 of this invention. The flowchart which shows the load correction | amendment by temperature. The display figure which shows a load assumption result.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... CB (breaker for distribution lines), 2,2-1,2-2,2-3 ... switch, 3 ... transformer, 4,4-1,4-2,4-3 ... customer, DESCRIPTION OF SYMBOLS 50 ... Computer, 51 ... Overall control part, 52 ... Load assumption calculation control part, 53 ... Consumer type determination part, 54 ... Correlation processing part, 55 ... Similar consumer extraction part, 56 ... Load correction part, 57 ... Assumption result Output unit 58 ... database 60 ... input device 70 ... display device 80 ... remote monitoring control device 90 90 power distribution system.

Claims (12)

In the distribution load assumption method for predicting the future load for a distribution system in which consumers with daily load measurement data and consumers without it are mixed,
Using the annual fluctuation of monthly power consumption that can be measured by all consumers, the correlation of the shape is calculated between the assumed target consumer and the consumer having the daily load measurement data, and the correlation coefficient is greater than or equal to the threshold value or The distribution load assumption method characterized by determining the daily load data of the said assumption object consumer based on the daily load measurement data of the similar consumer used as the highest value. In the distribution load assumption method for predicting the future load for a distribution system in which consumers with daily load measurement data and consumers without it are mixed,
Using the annual fluctuations in monthly power consumption that can be measured by all consumers, the correlation between the shape and the absolute amount are calculated between the assumed target consumer and the consumer with the daily load measurement data. A distribution load assumption method, wherein the daily load data of the assumed target consumer is determined based on the daily load measurement data of a similar consumer having both high correlation coefficients and a threshold value or more.   In Claim 2, when both said correlation coefficients are high, it ranks in descending order about each calculation result of the correlation of the said shape, and the correlation of absolute quantity, and the similarity with the smallest total value of the rank calculated | required for every consumer Distribution load assumption method that is a correlation coefficient of consumers.   The distribution load assumption method according to claim 1 or 2, wherein the consumers for which the correlation is obtained are performed in a group having the same consumer type.   The distribution load according to claim 1 or 2, wherein the daily load data of the assumed target consumer is corrected according to a ratio of monthly power consumption of a target month of the similar consumer and the assumed target consumer. Assumed method.   In Claim 1 or 2, the consumer who asks for the correlation should be performed in the same group of consumers, and the daily load data of the assumed target consumer is the target month of the similar consumer and the assumed target consumer. A method for assuming a distribution load, which is corrected according to a ratio of monthly power consumption.   The distribution load assumption method according to claim 1, wherein the daily load data of the assumed target consumer is corrected according to the temperature data of the target day.   In any one of Claims 1-7, the daily load data of all the consumers who have the daily load data of all the said assumption object consumers, and the said daily load measurement data are totaled for every area of a distribution system, and an area A distribution load assumption method characterized by load data. In a distribution load assumption device that predicts the future load for a distribution system in which consumers with daily load measurement data and consumers without it are mixed,
A database that stores the monthly power consumption of the customer and the daily load measurement data for each customer;
Find the correlation of the shape of the annual fluctuation of the monthly power consumption between the assumed target consumer and the consumer with the daily load measurement data, and the day of the similar consumer whose correlation coefficient is equal to or higher than the threshold value or the highest value A load assumption calculation control unit that determines daily load data of the assumption target consumer based on load measurement data; In a distribution load assumption device that predicts the future load for a distribution system in which consumers with daily load measurement data and consumers without it are mixed,
A database that stores the monthly power consumption of the customer and the daily load measurement data for each customer;
A correlation between the shape of the annual fluctuation of the monthly power consumption and the correlation of the absolute amount is calculated between the assumed target consumer and the consumer having the daily load measurement data, and both correlation coefficients are both high and a threshold value. A load assumption calculation control unit that determines the daily load data of the assumption target consumer based on the daily load measurement data of the similar consumer as described above.   In Claim 9 or 10, The load amendment part which amends daily load data of the assumption object consumer based on temperature data of the area and / or monthly electric power consumption ratio of the assumption object consumer and the similar consumer. The distribution load assumption apparatus characterized by providing. The distribution load assumption device according to claim 9 or 10, wherein the daily load data determined by the load assumption calculation control unit is displayed as a graph for each customer or section.

Images