KR20220150260A - System and Method for estimating power demand over the long term, Computer readable storage medium - Google Patents

Abstract

The present invention relates to a system for estimating a long-term power demand which can calculate the simultaneous maximum load for distribution lines, estimate an amount of new supply for a large load and an increase of a future demand, and estimate a long-term power demand based on the estimation, thereby determining time of necessity for transformer equipment. The system for estimating a long-term power demand comprises: an input unit which receives basic data for demand estimation; a determining unit which determines, based on the basic data, whether there is an existing area which was reviewed previously or a new area; and a computation unit which, based on the determination result, determines the size of the supply load being provided, generates power demand estimation information by estimating power demand based on the determined size of the supply load and the size of the general load, and produces equipment expansion necessity timing information which indicates a period requiring power equipment expansion, using the power demand estimation information.

Description

Long-term power demand forecasting system and method, and computer readable storage medium storing the method {System and Method for estimating power demand over the long term, Computer readable storage medium}

The present invention relates to a power demand prediction technology, and more particularly, by calculating the simultaneous maximum load for each distribution line, predicting the actual supply amount and future demand increase, and predicting the long-term power demand based on this, and determining the required time of the substation facility It is about a long-term power demand forecasting system and method for judging.

In order to supply power at the right time and place, the substation facilities must have sufficient supply margin. To do this, it is necessary to predict the load demand and establish a plan for new/extension of transformers or substations in advance. Two factors are used in demand forecasting.

The first is to predict the load demand by considering the load increase rate calculated by dividing the country into 52 regions in the load load by substation. Second, for large-scale residential districts or industrial complex sites, after predicting the total load demand according to the area and number of population, the load demand is predicted by assuming that the load increases by year according to the ratio according to the standard. Demand forecasting is done by adding the demand from the first method and the second method.

However, in the first method, the load increase rate is different depending on regional characteristics such as downtown, industrial area, and rural areas, but this difference is simply divided into 52 regions across the country, and the load increase rate of substations belonging to the area is applied collectively. This creates a difference in demand forecasting.

In addition, the second method assumes that large-scale residential districts or industrial complexes are supplying loads according to a certain ratio at a certain time, but in reality, the demand for electricity does not increase by year as suggested above. In addition, the exact amount of load actually supplied to residential districts or industrial complexes is not properly grasped. Even if the actual demand load is smaller than the initially expected load, these factors are not reflected, and it is considered as an untransmitted load and calculated as a load that must be supplied sometime.

When establishing a supply plan for a new load, it is established using the remaining capacity by excluding the maximum load performance and the untransmitted load from the supply capacity of the transformer. If the supply margin is insufficient, new transformers or substations are built. The supply margin is calculated by subtracting the predicted demand value in consideration of the maximum load and future load increase rate and untransmitted load from the transformer's supply capacity.

However, due to such inaccurate demand forecasting, it is not possible to construct a transformer or a substation, which is a power supply facility, in accordance with the required time. In the case of late construction, over-investment occurs because it has to be supplied through an additional distribution line withdrawal from a substation located far away to supply a new load. ) and delaying the construction period, missing an opportunity to reduce financial costs.

There are two reasons why this problem occurs. The first is that it is not possible to grasp how much load is supplied relative to the total demand of large-scale residential land or industrial complexes.

1. Korea Patent Publication No. 10-2019-0051243

The present invention has been proposed to solve the problem according to the above background technology, by calculating the simultaneous maximum load for each distribution line, predicting the actual supply amount and future demand increase, and predicting the long-term power demand based on this, and the need for a substation facility An object of the present invention is to provide a long-term power demand forecasting system and method capable of determining the timing.

Another object of the present invention is to provide a long-term power demand forecasting system and method that can improve the accuracy of the distribution load demand prediction and increase the efficiency of the facility by calculating the required time for new/extension of the power supply facility.

The present invention predicts the actual supply amount and future demand increase by calculating the simultaneous maximum load for each distribution line in order to achieve the task presented above, and predicts long-term power demand based on this. It provides a long-term power demand forecasting system.

The long-term power demand forecasting system,

an input unit for receiving basic data for forecasting demand;

a determination unit for determining whether a previously reviewed area or a new area is based on the basic data; and

Power demand prediction for determining the size of the supply load being supplied (Load _00-k ) according to the result of the determination, and predicting the demand for power according to the size of the determined supply load (Load _00-k ) and the size of the general load and a calculating unit that generates information and generates facility expansion required time information indicating a time when power facility expansion is required by using the power demand prediction information.

At this time, the basic data is a large-scale load name indicating the name of the housing site or industrial complex to be stored in the database, the final predicted demand indicating the final electricity demand forecast amount of the new load set in advance, the supply substation name indicating the supply power plant supplying electricity to the consumers, A transformer number indicating a transformer to which a distribution line for supplying power to the consumer is connected, a distribution line name indicating a distribution line for supplying power to the consumer, a load ratio indicating the ratio of the power load that a specific assigned line supplies to a large-scale load, and a supply date indicating when the specific distribution line is supplied with a specific load ratio.

In addition, the determination unit compares the basic data with the stored data stored in the database, and according to the result of the comparison, if the basic data does not match the stored data, it is characterized in that it acquires measured external data.

In addition, the external data is characterized in that it includes a modified distribution line load obtained by the SOMAS (Substation Operation Result Management System) system, the number of transformers and the transformer capacity obtained by the system protection system.

로 정의되며, 상기 해당 날짜의 부하비율은 미리 설정되는 것을 특징으로 한다.In addition, the modified distribution line load is

is defined, and the load ratio of the corresponding day is characterized in that it is set in advance.

In addition, when there is a change in the transformer capacity, it is characterized in that it is obtained by searching for the capacity of each transformer of the transformer corresponding to the name of the supply substation.

In addition, if the basic data matches the stored data according to the result of the comparison, the determination unit outputs the name of the distribution line among the stored data, the date of extraction of the last data, and the final load ratio of the distribution line do it with

(여기서, 00은 대규모 부하명칭, k는 년도, A,B,C는 배전선로 부하 크기, a,b,c는 배전선로 부하중 에서 상기 00 부하에 공급하는 부하 부하비율(0〈a,b,c,≤100 사이의 실수), i는 k년의 연간시간(1 ~ 8760의 자연수) )로 정의되는 것을 특징으로 한다.In addition, the size of the supply load (Load _00-k ) is the equation

(Where 00 is the name of the large-scale load, k is the year, A, B, and C are the distribution line load sizes, and a, b, c are the load ratios of the distribution lines supplied to the 00 load (0 < a, b). , c, a real number between ≤ 100), and i is characterized as defined as an annual time of k years (a natural number from 1 to 8760) ).

In addition, the power demand forecast information is the size of the supply load (Load _00-k ) according to the comparison result of the preset final demand forecast value and the size of the supply load (Load _00-k ) exceeds the final demand forecast value It is characterized in that the size of the supply load (Load _00-k ) is calculated as a final demand forecast value.

In addition, the power demand forecast information is the size of the supply load (Load _00-k ) according to the comparison result of the preset final demand forecast value with the size of the supply load (Load 00-k ) If the size of the supply load (Load _00-k ) is less than or equal to the final demand forecast value, k After calculating the load supplied by the year (Load _00-k ), the demand forecast after year k+1 is calculated by applying the annual large-scale load supply ratio determined according to the lapse of the preset year, or It is characterized in that it is calculated by recalculating the expected load supply for each future year using the performance-based curve fitting method.

In addition, the large-scale load supply ratio by year is calculated as an average value of selected data within the upper 90% and lower 90% of the performance value for the load year-by-year supply performance of various large-scale loads stored in the database 140 as an average value, and the average value It is characterized in that it is the ratio of 100% from the supply period year to the supply completion period.

In addition, the preset large-scale load supply ratio for each year is a value obtained by recalculating the large-scale load supply ratio for each year, and is periodically updated and managed.

로 정의되며, 상기 일반 부하의 크기를 이용하여 일반 부하 증가율이 산출되는 것을 특징으로 한다.In addition, the magnitude of the general load is

is defined, and the general load increase rate is calculated using the size of the general load.

(여기서, a₀는 0년째의 일반 부하의 크기이고, a₃는 3년째의 일반 부하의 크기이고, a₀가 0이면

이며, len(a₃)는 a₃의 자리수이다)으로 정의되는 것을 특징으로 한다.In addition, the general load increase rate is

(Where a ₀ is the size of the general load in year 0, a ₃ is the size of the general load in year 3, and if a ₀ is 0,

, and len(a ₃ ) is the number of digits of a ₃ ).

In addition, the long-term power demand prediction system compares the power demand prediction information with the maximum capacity of the transformer of the substation, and outputs notification information indicating the time of new construction of the substation, the time of expansion of the transformer, and the time of capacity change. It is characterized in that it includes;

On the other hand, another embodiment of the present invention comprises the steps of: (a) receiving, by an input unit, basic data for demand forecasting; (b) determining whether a previous area or a new area previously reviewed by the determination unit based on the basic data; (c) the calculation unit determines the size of the supply load being supplied (Load _00-k ) according to the result of the determination, and determines the size of the supply load (Load _00-k ) and the demand for power according to the size of the general load generating power demand forecast information to be predicted; and (d) generating, by the calculation unit, information on when to require expansion of power facilities by using the power demand prediction information to generate facility expansion required time information; providing a long-term power demand prediction method comprising: do.

In addition, in the step (b), the determination unit compares the basic data with the stored data stored in the database, and according to the result of the comparison, if the basic data does not match the stored data, the measured external data is obtained. step; characterized in that it includes.

In addition, the external data is characterized in that it includes a modified distribution line load obtained by the SOMAS (Substation Operation Result Management System) system, the number of transformers and the transformer capacity obtained by the system protection system.

In addition, the long-term power acceptance prediction method compares the power demand prediction information with the maximum transformer capacity of the transformer, and outputs notification information indicating a new substation construction time, an extension time of the transformer, and a capacity change time to the output unit 160 ) to output; characterized in that it includes.

On the other hand, another embodiment of the present invention provides a computer-readable storage medium storing a program code for executing the long-term power demand forecasting method described above.

According to the present invention, it is possible to estimate the amount of untransmitted load by determining the pre-supply amount of a large-scale load (residential district, industrial complex) and predicting the amount of additional power required in the future, so that it is possible to accurately know the supply margin of the transformer.

In addition, as another effect of the present invention, it is possible to improve the accuracy of the load demand prediction in the future by calculating the load increase rate for each substation and calculating the load increase rate based on the performance.

In addition, as another effect of the present invention, a stable power supply is possible by accurately predicting the required time of the substation facility through this.

1 is a block diagram of a long-term power demand prediction system according to an embodiment of the present invention.
FIG. 2 is a conceptual diagram illustrating an operation unit shown in FIG. 1 using input data and external data.
FIG. 3 is a flowchart showing processes executed by the input unit and the determination unit shown in FIG. 1 .
FIG. 4 is a flowchart illustrating a process executed by the extraction unit for acquiring data from an external site based on information received from the determination unit shown in FIG. 1 .
FIG. 5 is a flowchart illustrating a process of predicting power demand and determining an optimal time to expand power facilities by using the information stored in the database in the calculating unit shown in FIG. 1 .
6 is a flowchart illustrating a process of estimating the large-scale load shown in FIG. 5 and distributing the untransmitted load by year.
7 is a flowchart illustrating a process of calculating a large-scale load supply ratio for each year in the calculation unit shown in FIG. 1 .
8 is a flowchart illustrating a process of calculating an increase rate of a general load shown in FIG. 5 .
9 is a flowchart illustrating a long-term demand forecasting process shown in FIG. 5 and a process of determining the construction timing of new and extended substation facilities.
10 is an example of an input screen according to an embodiment of the present invention.
11 is a result of extracting the supply performance of a large-scale load as an Excel file according to an embodiment of the present invention.

Since the present invention can have various changes and can have various embodiments, specific embodiments are illustrated in the drawings and will be described in detail in the detailed description. However, this is not intended to limit the present invention to a specific embodiment, it should be understood to include all modifications, equivalents and substitutes included in the spirit and scope of the present invention.

In describing each figure, like reference numerals are used for like elements. The terms first, second, etc. may be used to describe various elements, but the elements should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

For example, without departing from the scope of the present invention, a first component may be referred to as a second component, and similarly, a second component may also be referred to as a first component. The term “and/or” includes a combination of a plurality of related listed items or any of a plurality of related listed items.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

Terms such as those defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present application. shouldn't

Hereinafter, a system and method for predicting long-term power demand according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram of a long-term power demand prediction system 100 according to an embodiment of the present invention. Referring to FIG. 1 , the long-term power demand prediction system 100 includes an input unit 110 , a determination unit 120 , an extraction unit 130 , a database 140 , a calculation unit 150 , an output unit 160 , and the like. may be included.

The input unit 110 performs a function of receiving basic data for demand prediction. Accordingly, the input unit 110 may be a keyboard, a microphone, a Universal Serial Bus (USB), a CD-ROM drive, a DVD drive, or the like. Of course, the input unit 110 may be connected to a power grid (not shown) to receive data from a cloud server, a single server, or the like. To this end, the input unit 110 may include a communication modem, a processor, and the like.

The determination unit 120 functions to review whether the previously reviewed area or the new area is based on the input basic data.

When it is determined that new information is needed, the extraction unit 130 extracts and brings data from an external system.

The database 140 performs a function of storing the extracted new data, previously reviewed existing data, and the like. Of course, the database 140 may be configured in the system or may be configured as a separate database server.

The calculation unit 150 performs a function of generating power demand prediction information for predicting the demand for power according to the determined size of the load and/or information on when the expansion of the power facility is required indicating the time when the expansion of the power facility is required. In other words, it determines the size of the currently supplied load among large-scale loads, predicts future demand, and calculates the necessary time for new facility expansion.

The output unit 160 performs a function of notifying the review result as notification information. The notification information may be a combination of text, voice, and graphic. To this end, the output unit 160 may include a sound system, a display, and the like. The display may be a liquid crystal display (LCD), a light emitting diode (LED) display, a plasma display panel (PDP), an organic LED (OLED) display, a touch screen, a cathode ray tube (CRT), a flexible display, or the like. In the case of a touch screen, it may be used as an input means.

1 , the determination unit 120 , the extraction unit 130 , and the operation unit 150 refer to units that process at least one function or operation, which may be implemented in software and/or hardware. In hardware implementation, an application specific integrated circuit (ASIC), digital signal processing (DSP), programmable logic device (PLD), field programmable gate array (FPGA), processor, microprocessor, other It may be implemented as an electronic unit or a combination thereof.

In software implementation, software composition component (element), object-oriented software composition component, class composition component and task composition component, process, function, attribute, procedure, subroutine, segment of program code, driver, firmware, microcode, data , databases, data structures, tables, arrays, and variables. Software, data, etc. may be stored in a memory and executed by a processor. The memory or processor may employ various means well known to those skilled in the art.

In particular, the memory includes a flash memory type, a hard disk type, a multimedia card micro type, and a card type memory (eg, Secure Digital (SD) or eXtreme (XD) Digital) memory, etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read Only Memory), PROM (Programmable Read Only Memory), It may include at least one type of storage medium among a magnetic memory, a magnetic disk, and an optical disk.

The database 140 is also implemented in this memory.

FIG. 2 is a conceptual diagram illustrating an operation unit shown in FIG. 1 using input data (ie, an input value) and external data. Referring to FIG. 2 , the operation unit 150 checks the input data obtained through the input unit 110 , calculates power demand prediction information, and generates information on when the facility needs to be expanded based on the power demand prediction information. To this end, the input data may be a large-scale load name, a preset final predicted demand value, a supply substation name, a transformer number, a distribution line name, a load ratio, a supply date, and the like. A unique identification number is assigned to each large-scale load name, supply substation name, transformer number, and distribution line name for identification.

In general, the load ratio of housing complexes and industrial complexes is as follows. For reference, the unit is "%".

power demand 1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years 9 years 10 years >100 MW 15 20 30 40 50 60 70 80 90 100 100 MW or less 15 25 40 50 60 80 90 100 30 MW or less 20 55 80 100

power demand 1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years 9 years 10 years 11 years >100 MW 15 25 30 35 45 55 65 75 80 90 100 100 MW or less 15 20 25 30 40 50 60 80 100

The operation unit 150 acquires external data corresponding to newly added data among input data. In other words, the newly inputted data is compared with the existing data stored in the database 140 , and if not stored in the database 140 , the external data 220 is requested. The acquisition of external data is made through the SOMAS system and/or the power system protection system. The SOMAS (Substation Operation Result Management System) system is a substation operation performance management system. It is a system that collects and breaker status information from the Supervisory Control and Data System (SCADA) of the power system, and provides load information and operation information of lines related to substations scattered across the country.

In particular, the load (P) of the distribution line is calculated using the current value measured by the CT (ammeter) installed on the outgoing distribution line of the transformer substation and the voltage measured by the PT (voltmeter) installed on the bus bar from which the distribution line is drawn out. This can be expressed as a mathematical formula as follows.

Here, V is voltage and I is current.

Load information such as distribution line load, transformer load, and substation load is stored in the SOMAS system in units of time.

The power system protection system protects, monitors, and controls generators, transformers, motors, transmission lines, busbars and distribution lines, and capacitors. In particular, the present invention performs a function of acquiring information such as the number of transformers and the capacity of the transformer.

The load of the distribution line desired by the reviewer is called from the SOMAS system. The process of searching and extracting the load from SOMAS each time causes excessive traffic to the SOMAS server. Therefore, in one embodiment of the present invention, when necessary information is first inputted, SOMAS crawls, retrieves necessary load information, and stores it in the server of the prediction program. After that, the information stored in the server is used, and only when additional information is needed, SOMAS crawls and retrieves the information.

By using this method, the traffic of the external server is reduced and the data acquisition speed required for calculation is increased. In addition, as the frequency of use increases, the amount of valid data stored in the server increases, which has the advantage that the time required for data extraction gradually decreases.

FIG. 3 is a flowchart illustrating processes executed by the input unit 110 and the determination unit 120 shown in FIG. 1 . Referring to FIG. 3 , when the name of a residential land (industrial complex), which is a name of a large-scale load, is input among the input data input through the input unit 110 , the determination unit 120 stores the input large-scale load name as input to the database 140 . Check whether it is a name (steps S310, S320).

In step S320, as a result of checking, if the name is not stored in the database 140, external data is received and “DATA SCRAP1” is generated (steps S340 and S371). External data includes items of basic data. The external data looks like this:

- Housing site (industrial complex) name: name of a large-scale load to be stored as a new load in the database 140

- Final forecasted demand: The predicted final electricity demand for new loads in the electricity supply plan

- Supply period: the year in which electricity supply to consumers is decided

- Supply substation name: Substation name that supplies electricity to consumers

- Transformer number: The number of the transformer connected to the distribution line that supplies power to the consumer

- Distribution line name: Distribution line that supplies power to consumers

- Load ratio: The ratio of supply to residential districts (industrial complexes) because a specific distribution line can supply other nearby loads in addition to residential districts (industrial complexes)

- Supply date: When a specific distribution line was supplied with a specific load ratio

The dual supply date may include the year in which power supply to the consumer is to be commenced.

On the other hand, in step S320, if the name of the large-scale load is the name stored in the database 140 as a result of checking, the stored data of the database 140 is output to check whether data of the latest date is required (steps S330 and S350). In other words, if it has been reviewed before, the name of the distribution line stored in the database, the last data extraction date, and the final load ratio of the distribution line are output. As for the final load ratio of a distribution line, only the last changed information is provided because the ratio supplied by a specific distribution line may be changed in the middle. Also, the data extraction date is the last date stored in the database.

In step S350, as a result of the check, if the data of the latest date is not needed, using the stored data of the database 140 to generate power demand prediction information for predicting the demand for power, and using the power demand prediction information, power equipment A data operation is performed to generate information about the time when the facility needs to be expanded (step S351). In other words, if only the existing review data is available and can be used as it is, all the information of the housing district (industrial complex) to be reviewed stored in the DATABASE 140 is fetched and the data is transferred to the calculation unit 150 .

On the other hand, in step S350, as a result of the confirmation, if data of the latest date is required and other information is additionally required, the process proceeds to step S340 (step S360).

In step S360, if additional information is not required, it is checked whether there is a change or expansion of the transformer capacity of the supply substation (step S370). If only the date of the existing review data is to be updated until the latest date, information of the corresponding substation and distribution line and the load ratio are provided to the extraction unit 130 . For this purpose, "DATA SCRAP1" is created. When the supply substation transformer capacity is changed or transformer expansion occurs, substation information is also provided. For this purpose, "DATA SCRAP2" is created (step S371).

In the case of adding a new distribution line to the existing review data, the remaining information except for the name of the housing site (industrial complex), the final supply expected load, and the supply start time is input as a new load and the information is transferred to the extraction unit 130 . Accordingly, "DATA SCRAP1" is generated (step S371).

4 is a flowchart illustrating a process executed by the extraction unit 130 for acquiring data from an external site based on the information received from the determination unit 120 shown in FIG. 1 . Referring to FIG. 4 , when “DATA SCRAP1” is generated in FIG. 3 , it is checked whether the distribution line is a distribution line input by the reviewer (step S420).

As a result of the check, if it is a distribution line input by the reviewer, additional data about the load needs to be extracted. The substation and distribution line names input by the determination unit 120 are searched in the SOMAS system for each time period from the received date to the present. Scrap the load and receive it. The corrected wiring line load calculated in consideration of the received load and load ratio for each time period is stored in the DATABASE 140 (steps S431, S433, and S450). The correction part is defined by the following formula.

For example, in the case of distribution line A, if the load ratio is 20 [%] until May 1 and 2, and the load ratio is 50 [%] from May 3 to 4, the distribution line A is stored in the database 140 (D/L: Distribution Line) The load values are as follows.

A
D/L 5/1 5/2 5/3 5/4 date 20 30 20 … 30 20 40 … 30 40 40 … 60 60 40 … Hourly measurement load 4 6 4 … 6 4 8 … 15 20 20 30 30 20 … Modified load by time

On the other hand, in step S420, if it is not the distribution line input by the reviewer, the measured transformer load is stored in the database 140 (steps S432, S450). " is generated, the retrieved transformer capacity is acquired, and it is checked whether it is the same as the existing information (steps S440, S441, S443).

In step S443, as a result of checking, if the transformer capacity is not the same as the existing information, the retrieved transformer capacity is stored in the database 140 (step S450). In other words, when a new load is input or when the capacity of a transformer is changed or expanded among existing loads, the grid protection system searches for the capacity of each transformer of the transformer corresponding to the entered substation. In the case of a new load, it is immediately stored in the DATABASE 140, and in the case of existing data, only the changed information is stored.

Data scraped from an external system (not shown) and data directly input by the reviewer are stored in the database 140, and the stored information is as follows.

Data received from the input unit 110 and the extraction unit 130 Data provided by the calculation unit Large load designation and final forecast demand
Supply start time
Supply substation name
Transformer number
transformer capacity
Name of load supply distribution line and connected transformer number
Distribution Line Load Ratio and Period
Distribution line load by time period and modified distribution line load
Transformer load per hour
Last Extraction Date


Supply start time
Load increase rate by substation
Performance by year of large load
large untransmitted loads
Substation long-term expected load

FIG. 5 is a flowchart illustrating a process of predicting power demand and determining an optimal time to expand power facilities by using the information stored in the database 140 in the calculating unit 150 shown in FIG. 1 . Referring to FIG. 5 , a previously supplied large-scale load is estimated using information stored in the database 140 (step S520 ). In other words, in order to understand the annual supply of large-scale loads (residential districts, industrial complexes), the maximum load is obtained by adding the corrected distribution line load values by time of all distribution lines supplied to large-scale loads from March of the previous year to February of this year. . Since the winter period is considered as February, the load supplied for one year is considered as one year from March of the previous year to February of this year. If the ratios are a%, b% and c%, and the loads at time i are A, B, and C, then the size of the supply load (Load _00-k ) supplied to area oo until k year is calculated as follows: can be

Here, 00 is the name of the large-scale load, k is the year, A, B, and C are the distribution line load sizes, and a, b, c are the load ratios (0 < a, b, c, real number between ≤100), i is the annual time of k years (a natural number from 1 to 8760).

Based on the size of the supply load calculated above (Load _00-k ), the annual distribution of the untransmitted load is calculated, and the existing load increase rate is calculated (steps S530 and S540).

When the annual distribution of the untransmitted load and the load increase rate are calculated, a long-term demand forecast is calculated based on this and stored in the database 140 (steps S550 and S551).

As the long-term demand forecast is calculated, the time of the extension of the substation is reviewed using this (step S560).

6 is a flowchart showing the process of performing the pre-supplied large-scale load estimation (step S520) and year-by-year distribution of the untransmitted load (step S530) shown in FIG. 5 . Referring to FIG. 6 , as described above, when the previously supplied large-scale load performance is calculated, it is possible to obtain an expected load for each year of the large-scale load (step S610 ).

It is checked whether the magnitude of the supply load is greater than or equal to a preset reference value (step S620). The reference value is the final demand forecast X 5%.

As a result, in step S620, if the size of the supply load is smaller than the preset reference value, it is assumed that it is a temporary load and the supply has not started, and the supply period is delayed by one year from the current time, and the large-scale load supply ratio by year The long-term demand forecast value is determined by calculating the expected supply load by year by applying . (steps S640, S641, S643, S645).

On the other hand, as a result of the check, in step S620, if the size of the supply load is 5% or more of the final demand forecast, it is assumed that the load supply has started (step S630).

In step S650, if three years have not elapsed since the start of the supply, the expected supply load for each year is calculated by applying the entered large-scale load supply ratio for each year (step S651).

On the other hand, in step S650, if more than 3 years have passed since the start of the large-scale load supply, the expected load supply ratio for each future year is recalculated using the performance-based curve fitting method based on the results of the last 3 years (steps S652 and S653). The performance-based curve fitting method refers to calculating a mathematical straight line or curve expressing the data using actual data.

In other words, when multi-year supply and load performance is accumulated, performance curve fitting is applied using the same method as linear regression analysis based on the performance. Using this performance curve fitting, a polynomial function is created to estimate the load supply for future years. Years are incremented until the magnitude of the load supply equals the final forecast demand. The final demand value is the load supply at the point in time when the load supply is greater than the final demand.

The load demand in year n can be defined by the following equation.

7 is a flowchart illustrating a process of calculating a large-scale load supply ratio for each year in the calculating unit 150 shown in FIG. 1 . Referring to FIG. 7 , data on the annual supply performance ratio of large-scale loads such as large-scale residential districts and industrial complexes stored in DATABase 140 is acquired, and the same year has elapsed since the start of supplying large-scale loads. The data is classified into data and selected within the upper 90% of the performance ratio value for each year and the data of the lower 90% or more (step S730). Of course, it is also possible to further classify and manage the geographical conditions and load characteristics of a large-scale load in detail.

Thereafter, after calculating the data selected for each year as an average value, the “large-scale load supply ratio by year” is re-calculated at the rate of 100% from the supply period year to the supply completion period (steps S740 and S750).

In the early stage, when there is not much supply performance by year, the “large-scale load supply ratio by year” according to the 『Revision Results of Load Growth Trends for Housing and Industrial Complexes』 enacted in 2013 is used.

8 is a flowchart illustrating a process of calculating an increase rate of a general load shown in FIG. 5 (step S540). Referring to FIG. 8 , when the size of the previously supplied large-scale load is determined, the long-term demand for the general load may be predicted by calculating the increase rate of the general load. The maximum load performance for each transformer from March to February of the next year of the substation supplied to large-capacity customers is the maximum value of the substation's maximum load performance for that year. A value obtained by subtracting the previously supplied large-scale load performance from this maximum load performance becomes the normal load size (steps S801, S810, S820, and S830). The general load size is expressed as the following equation.

Thereafter, it is checked whether the performance three years ago is “0” (step S840). In step S840, as a result of checking, if the performance three years ago is “0”, a geometric average increase rate based on the performance of the current year is calculated, and the expected general load demand for each year is calculated using this increase rate (steps S842 and S850). On the other hand, if the performance for 3 years is not “0”, the geometric average increase rate is calculated using the performance 3 years ago, and the expected general load demand by year is calculated using this increase rate (steps S841 and S850).

Since the geometric average is applied when calculating the average of growth rates such as the inflation rate and population change rate, the average load increase rate of general loads is also calculated by applying the geometric average. In order to calculate the load increase rate of the substation, the performance of 4 years including the current year is used, and if there is no performance for 4 years due to the new substation construction, the maximum load performance of the current year is one digit, the performance of 3 years ago is 1, if there are two digits 10, three digits are calculated assuming 100. This is shown in a table as follows.

0 years 1 year 2 years 3 years geometric mean
Normal load size [MW]
a ₀
a ₁
a ₂
a ₃

Increase (%) r ₁ r ₂ r ₃

If a ₀ ≠ 0, normal load increase rate =

If a ₀ = 0, then

, (

) Normal load increase rate =

, (

)

Here, len(a ₃ ) is the number of digits of a ₃ .

If the normal load increase rate is less than 0, set it to 0.

Since the long-term demand forecast in the system plan considers up to 15 years, the demand forecast of the general load is calculated up to the next 15 years (steps S860 and S870).

On the other hand, when the increase rate is calculated, the general load is multiplied by the increase rate to estimate the general load for the next year (step S861). This can be expressed as a mathematical formula as follows.

9 is a flowchart illustrating a long-term demand forecasting process shown in FIG. 5 and determining the construction timing of new and extended substation facilities (steps S550 and S560). Referring to FIG. 9 , the total demand is predicted by adding the yearly untransmitted load value of the large-capacity load and the yearly existing load demand forecast value (steps S901, S902, S910). In general, long-term load demand is calculated for 15 years, so if the untransmitted load is supplied before then, the demand is predicted by adding the final value of the untransmitted load from the following year.

The demand values for each year predicted by the above process are stored in the DATABASE 140 by classifying them for each substation (step S911).

The reason for calculating by dividing the size of the large-capacity load and the size of the general load is that the natural increase rate is considered in the case of a general load, and the calculated natural increase rate is not considered as it is assumed that the natural increase rate is included in the final demand of the large-capacity load. Because.

DATABASE 140 takes the corresponding transformer capacity for each substation and calculates the effective capacity of the substation (steps S903 and S920). In the case of a substation, 4 transformers can be installed, but when considering the effective capacity, one is excluded from the calculation in case of a failure. Substation effective capacity is defined by the following equation.

For example, if the maximum capacity of a transformer is 60MVA, the effective capacity of the transformer is 55.2MW, and if the maximum capacity is 40MVA, the effective capacity of the transformer is 36.8MW.

Thereafter, the long-term power demand predicted value is compared with the effective capacity of the substation (step S930). As a result of comparison, if the predicted demand value is smaller than the effective capacity of the transformer in step S930, it is provided as notification information to the output unit that new/extension of the substation facility is not required (steps S931 and S960).

On the other hand, as a result of comparison, if the expected acceptance value is greater than the effective capacity of the transformer in step S930, it is checked whether there is a substation or not (step S940). In other words, the timing of new/extended substation facilities is reviewed by comparing the 15-year long-term electricity demand value with the effective capacity of the substation.

If the transformer of the substation has 4 banks (165.6MW), since there is no spare bank, the time for new substation is provided to the output unit 160, and when there are 2~3 banks of transformers in the substation, it outputs the time to expand the transformer. Information is provided to the unit 160, and when there is a transformer having a capacity of 40 MW in the substation, notification information is provided to the output unit 160 when the capacity is increased (steps S941, S950, S951, S952, S960).

To elaborate,

- In case the effective capacity of the substation is 165.6MW (there is no spare bank in the substation)

=> If the effective capacity of substation ≤ load demand in year i, new substation in year i

- In case of substation effective capacity of 55.2MW, 110.4MW (with substation spare bank)

=> If the effective capacity of the substation ≤ the load demand in year i, then the transformer is expanded in year i

- In case the effective capacity of the substation is other than that (there is a 40MW transformer)

=> If substation effective capacity ≤ load demand in year i, increase transformer capacity in year i

Accordingly, the output unit 160 provides the following data to the user.

- Annual supply performance of large-scale residential land (industrial complex)

- Demand forecast until the supply of large-scale residential land (industrial complex) is completed

- Load increase rate of general load by substation

- 15-year long-term demand forecast for each substation

- When it is necessary to build a new substation, expand a transformer, and increase the capacity of a transformer

10 is an example of an input screen according to an embodiment of the present invention. Referring to FIG. 10 , data can be added and deleted by clicking the add button 1010 and the remove button 1020 to input the substation name, distribution line name, and load ratio. Extraction is performed by clicking the Create Comma Separated Values (CSV) button 1030 .

11 is a result of extracting the supply performance of a large-scale load as an Excel file according to an embodiment of the present invention. Referring to FIG. 11 , a substation name, a distribution line name, a ratio, and the like are extracted from Excel.

In addition, the steps of the method or algorithm described in connection with the embodiments disclosed herein are implemented in the form of program instructions that can be executed through various computer means such as a microprocessor, a processor, a CPU (Central Processing Unit), etc. It can be recorded on any available medium. The computer-readable medium may include program (instructions) codes, data files, data structures, and the like alone or in combination.

The program (instructions) code recorded on the medium may be specially designed and configured for the present invention, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs, DVDs, Blu-rays, and the like, and ROM, RAM ( A semiconductor memory device specially configured to store and execute program (instruction) code such as RAM), flash memory, and the like may be included.

Here, examples of the program (instruction) code include not only machine language codes such as those generated by a compiler but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

*100: Long-term power demand forecasting system
110: input unit
120: judgment unit
130: extraction unit
140: database
150: arithmetic unit
160: output unit

Claims (1)

an input unit 110 for receiving basic data for forecasting demand;
a determination unit 120 for determining whether a previously reviewed area or a new area is based on the basic data; and
Power demand prediction for determining the size of the supply load being supplied (Load _00-k ) according to the result of the determination, and predicting the demand for power according to the size of the determined supply load (Load _00-k ) and the size of the general load and a calculator 150 for generating information and generating information on when to expand power facilities by using the power demand prediction information to indicate when power facilities need to be expanded.
The basic data is the name of a large-scale load indicating the name of the housing site or industrial complex to be stored in the database 140, the final predicted demand indicating the final electricity demand forecast amount of the new load set in advance, and the supply substation name indicating the supply power plant supplying electricity to the consumers , Transformer number indicating the transformer to which the distribution line for supplying power to the consumer is connected, the name of the distribution line indicating the distribution line for supplying power to the consumer, and the load ratio indicating the ratio of the power load that a specific allocation line supplies to a large-scale load , and a supply date indicating when the specific distribution line is supplied with a specific load ratio,
The determination unit 120 compares the basic data with the stored data stored in the database 140, and if the basic data does not match the stored data according to the result of the comparison, the external data to be measured is obtained and
The external data includes a modified distribution line load obtained with a Substation Operation Result Management System (SOMAS) system, a transformer quantity and a transformer capacity obtained with a grid protection system,
When the basic data matches the stored data according to the result of the comparison, the determination unit 120 outputs the name of the distribution line among the stored data, the last data extraction date, and the final load ratio of the distribution line. Long-term power demand forecasting system characterized.

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