A typical daily load curve solving method and related device

Abstract

The application discloses a kind of typical daily load curve solving method and related device, according to the load output data of electric power system, the output array of each minute of target load curve is established, after the output array is divided according to season, the mean and median of the load value under the same minute of target array are solved, determine the load typical value under the same minute in the target array, the load typical value under the same minute in the target array is fitted as curve, the typical daily load curve corresponding to target array is obtained, typical daily load curve is fitted from each load typical value, not dependent on artificial judgment and avoids randomness, the typical daily load curve obtained by solving is objective and accurate, solve the technical problem that the existing determination electric power system typical daily load curve mode is strong and poor in reliability.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method and related apparatus for solving a typical daily load curve. Background Technology

[0002] In power system data analysis, load curves reflect the pattern of load changes over time within a specific period. To promptly understand and analyze the load and operational status of the power system, certain days of the year with full load and normal operation are selected as typical load days. Typical load day curves are crucial indicators for optimizing the power system grid structure, substation placement, power source integration (such as renewable energy plants, thermal power, and hydropower), and the integration of new energy storage power stations. They can even directly determine whether power sources and new types of power sources can be integrated and the capacity level to be integrated. Objective and accurate typical load day curves are a strong guarantee for optimizing power system planning.

[0003] In existing technologies, the typical daily load curve is determined by manually observing and comparing the load curve levels of each day to identify a typical day, or by directly selecting the day with the highest load level as the typical day. However, manual observation is highly subjective and limited by the experience level of personnel, failing to objectively and accurately reflect the typical day results. Directly selecting the day with the highest load level as the typical day is highly random and often not a truly typical day, resulting in poor reliability. Therefore, how to objectively and accurately determine the load curve of a typical day in a power system is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] This invention provides a method and apparatus for solving typical daily load curves, which addresses the technical problems of high subjectivity and poor reliability in existing methods for determining typical daily load curves of power systems.

[0005] In view of this, the first aspect of the present invention provides a method for solving a typical daily load curve, comprising:

[0006] Acquire the load output data of the power system and establish a minute-by-minute output array for the target load curve;

[0007] The output array is divided into a first array, a second array, a third array, and a fourth array, based on the season.

[0008] Select a target array, wherein the target array is any one of the first array, the second array, the third array, and the fourth array;

[0009] Calculate the mean and median of the load values ​​in the target array for the same minute to determine the typical load value in the target array for the same minute;

[0010] By fitting the typical load values ​​for the same minute in the target array to a curve, the typical daily load curve corresponding to the target array is obtained.

[0011] Optionally, the mean and median of the load values ​​in the target array for the same minute are calculated to determine the typical load value in the target array for the same minute, including:

[0012] S1. Let t=1, where t is a time variable in minutes;

[0013] S2. Sort all load values ​​of the target array at time t in ascending order;

[0014] S3. Remove the load values ​​of the first and last preset percentages in the target array after ascending sorting to obtain the confidence load value sequence.

[0015] S4. Calculate the mean and median of the confidence load numerical sequence;

[0016] S5. Based on the mean and median of the confidence load numerical sequence, determine the typical load value of the confidence load numerical sequence using the Pearson empirical method;

[0017] S6. Determine if t is greater than 1441. If not, let t = t + 1 and return to step S2. If yes, store the typical load values ​​of the confidence load value sequence at all times t.

[0018] Optionally, in step S5, the formula for calculating the typical load value of the confidence load numerical sequence using the Pearson empirical method is as follows:

[0019]

[0020] in, Let be the typical load value of the confidence load numerical sequence at time t. Let be the mean of the confidence load numerical sequence at time t. This is the median of the confidence load numerical sequence.

[0021] Optionally, the preset percentage is 5%.

[0022] Optionally, acquire the load output data of the power system and establish a minute-by-minute output array for the target load curve, including:

[0023] Using natural days as the unit, export the annual load output data from the dispatch automation system to establish a minute-by-minute output array for the target load curve.

[0024] A second aspect of the present invention provides a typical daily load curve solving apparatus, comprising:

[0025] The load data acquisition module is used to acquire the load output data of the power system and establish the output array per minute of the target load curve;

[0026] The grouping module is used to divide the output array into a first array, a second array, a third array, and a fourth array based on seasons;

[0027] The selection module is used to select a target array, wherein the target array is any one of the first array, the second array, the third array, and the fourth array;

[0028] The typical value calculation module is used to calculate the mean and median of the load values ​​in the target array for the same minute, and to determine the typical load value in the target array for the same minute;

[0029] The fitting module is used to fit the typical load values ​​of the same minute in the target array into a curve to obtain the typical daily load curve corresponding to the target array.

[0030] Optionally, the typical value calculation module is specifically used to perform the following operations:

[0031] S1. Let t=1, where t is a time variable in minutes;

[0032] S2. Sort all load values ​​of the target array at time t in ascending order;

[0033] S3. Remove the load values ​​of the first and last preset percentages in the target array after ascending sorting to obtain the confidence load value sequence.

[0034] S4. Calculate the mean and median of the confidence load numerical sequence;

[0035] S5. Based on the mean and median of the confidence load numerical sequence, determine the typical load value of the confidence load numerical sequence using the Pearson empirical method;

[0036] S6. Determine if t is greater than 1441. If not, let t = t + 1 and return to step S2. If yes, store the typical load values ​​of the confidence load value sequence at all times t.

[0037] Optionally, in step S5, the formula for calculating the typical load value of the confidence load numerical sequence using the Pearson empirical method is as follows:

[0038]

[0039] in, Let be the typical load value of the confidence load numerical sequence at time t. Let be the mean of the confidence load numerical sequence at time t. This is the median of the confidence load numerical sequence.

[0040] Optionally, the preset percentage is 5%.

[0041] Optionally, acquire the load output data of the power system and establish a minute-by-minute output array for the target load curve, including:

[0042] Using natural days as the unit, export the annual load output data from the dispatch automation system to establish a minute-by-minute output array for the target load curve.

[0043] A third aspect of the present invention provides a typical daily load curve solving device, the device including a processor and a memory;

[0044] The memory is used to store program code and transmit the program code to the processor;

[0045] The processor is used to execute any one of the typical daily load curve solving methods described in the first aspect according to the instructions in the program code.

[0046] A fourth aspect of the present invention provides a computer-readable storage medium for storing program code for executing the typical daily load curve solution method described in any of the first aspects.

[0047] The fifth aspect of the present invention provides a computer program product including instructions that, when run on a computer, cause the computer to execute any of the typical daily load curve solving methods described in the first aspect.

[0048] As can be seen from the above technical solutions, the method for solving typical daily load curves provided by this invention has the following advantages:

[0049] The method for solving the typical daily load curve provided by this invention establishes a minute-by-minute output array of the target load curve based on the load output data of the power system. After dividing the output array by season, the mean and median of the load values ​​in the same minute of the target array are calculated to determine the typical load value in the same minute of the target array. The typical load value in the same minute of the target array is fitted into a curve to obtain the typical daily load curve corresponding to the target array. The typical daily load curve is fitted by each typical load value, which does not rely on manual judgment and avoids randomness. The typical daily load curve obtained is objective and accurate, solving the technical problems of strong subjectivity and poor reliability of existing methods for determining the typical daily load curve of the power system.

[0050] The related devices for solving the typical daily load curve provided in this invention are all used to execute the typical daily load curve solving method provided in this invention. Their principles and the technical effects achieved are the same as those of the typical daily load curve solving method provided in this invention, and will not be described again here. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart illustrating a typical daily load curve solution method provided in an embodiment of the present invention.

[0053] Figure 2 This is a logic block diagram of the typical daily load curve solving method provided in the embodiments of the present invention;

[0054] Figure 3 This is a schematic diagram of a typical daily load curve solving device provided in an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of a typical daily load curve solving device provided in an embodiment of the present invention. Detailed Implementation

[0056] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0058] The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of these specific details, or other methods, components, materials, devices, etc. In these cases, well-known structures, methods, devices, implementations, materials, or operations will not be shown or described in detail.

[0059] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0060] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0061] Example 1:

[0062] For easier understanding, please refer to Figure 1 This invention provides an embodiment of a typical daily load curve solution method, comprising:

[0063] Step 101: Obtain the load output data of the power system and establish the output array of the target load curve per minute.

[0064] Step 102: Divide the output array into four groups based on the season: the first group, the second group, the third group, and the fourth group.

[0065] Step 103: Select the target array. The target array can be any one of the first array, the second array, the third array, and the fourth array.

[0066] Step 104: Calculate the mean and median of the load values ​​in the target array for the same minute, and determine the typical load value in the target array for the same minute.

[0067] Step 105: Fit the typical load values ​​for the same minute in the target array to a curve to obtain the typical daily load curve corresponding to the target array.

[0068] The method for solving the typical daily load curve provided by this invention establishes a minute-by-minute output array of the target load curve based on the load output data of the power system. After dividing the output array by season, the mean and median of the load values ​​in the same minute of the target array are calculated to determine the typical load value in the same minute of the target array. The typical load value in the same minute of the target array is fitted into a curve to obtain the typical daily load curve corresponding to the target array. The typical daily load curve is fitted by each typical load value, which does not rely on manual judgment and avoids randomness. The typical daily load curve obtained is objective and accurate, solving the technical problems of strong subjectivity and poor reliability of existing methods for determining the typical daily load curve of the power system.

[0069] Example 2:

[0070] This invention provides another embodiment of a method for solving a typical daily load curve, including:

[0071] Step 101: Using natural days as the unit, export the annual load output data from the dispatch automation system and establish the output array for each minute of the target load curve.

[0072] Step 102: Divide the output array into four groups based on the season: the first group, the second group, the third group, and the fourth group.

[0073] Step 103: Select the target array. The target array can be any one of the first array, the second array, the third array, and the fourth array.

[0074] Step 104: Calculate the mean and median of the load values ​​in the target array for the same minute, and determine the typical load value in the target array for the same minute.

[0075] Step 105: Fit the typical load values ​​for the same minute in the target array to a curve to obtain the typical daily load curve corresponding to the target array.

[0076] The method for solving the typical daily load curve provided by this invention establishes a minute-by-minute output array of the target load curve based on the load output data of the power system. After dividing the output array by season, the mean and median of the load values ​​in the same minute of the target array are calculated to determine the typical load value in the same minute of the target array. The typical load value in the same minute of the target array is fitted into a curve to obtain the typical daily load curve corresponding to the target array. The typical daily load curve is fitted by each typical load value, which does not rely on manual judgment and avoids randomness. The typical daily load curve obtained is objective and accurate, solving the technical problems of strong subjectivity and poor reliability of existing methods for determining the typical daily load curve of the power system.

[0077] Example 3:

[0078] For easier understanding, please refer to Figure 1 and Figure 2 This invention provides an embodiment of a typical daily load curve solution method, comprising:

[0079] Step 101: Obtain the load output data of the power system and establish the output array of the target load curve per minute.

[0080] It should be noted that, in this embodiment of the invention, the annual load output data is exported from the dispatch automation system using natural days as the unit, and a minute-by-minute output array for the target load curve is established. , Where t is a time variable in minutes. The load output data is at time t, and there are 1441 minutes in a day.

[0081] Step 102: Divide the output array into four groups based on the season: the first group, the second group, the third group, and the fourth group.

[0082] It should be noted that the output array is divided into the first array based on the season. Second array The third array and the fourth array That is, the array corresponding to spring (March to May) is... Summer (June to August) corresponds to the array as follows: The array corresponding to autumn (September to November) is: The array corresponding to winter (December to February of the following year) is: .

[0083] Step 103: Select the target array. The target array can be any one of the first array, the second array, the third array, and the fourth array.

[0084] It should be noted that this problem involves finding typical days for the first, second, third, and fourth arrays. The array for which typical days are to be found is selected as the target array.

[0085] Step 104: Calculate the mean and median of the load values ​​in the target array for the same minute, and determine the typical load value in the target array for the same minute.

[0086] It should be noted that, for the target array, the typical load value is calculated for the same minute of each day in the target array. In one embodiment, the specific execution steps are as follows: Figure 2 As shown, it includes:

[0087] S1. Let t=1, where t is a time variable in minutes.

[0088] S2. All load values ​​of the target array at time t. Sort in ascending order. For example: sort the summer months (June to August). The values ​​are arranged in ascending order.

[0089] S3. Remove the load values ​​from the top and bottom preset percentages in the ascending-sorted target array to obtain a confidence load value sequence. In one embodiment, based on power system operation experience and considering a 95% confidence rate, remove the top 5% and bottom 5% load values ​​from the ascending-sorted target array to obtain a confidence load value sequence. , where n is the length of the confidence load numerical sequence.

[0090] S4. Calculate the mean and median of the confidence load numerical series. The formula for calculating the mean of the confidence load numerical series is:

[0091] ;

[0092] in, Let be the mean of the confidence load numerical sequence of the target array at time t.

[0093] If the length n of the confidence load numerical sequence is odd, then the median of the confidence load numerical sequence of the target array at time t is... for If the length n of the confidence load numerical sequence is even, then the median of the confidence load numerical sequence of the target array at time t is... for .

[0094] S5. Based on the mean and median of the confidence load numerical series, the typical load value of the confidence load numerical series is determined using the Pearson empirical method.

[0095] S6. Determine if t is greater than 1441. If not, let t = t + 1 and return to step S2. If yes, store the typical load values ​​of the confidence load value sequence at all times t, and obtain the typical load value sequence composed of the typical load values ​​at 1 to 1441 minutes.

[0096] Step 105: Fit the typical load values ​​for the same minute in the target array to a curve to obtain the typical daily load curve corresponding to the target array.

[0097] It should be noted that by fitting the typical load value corresponding to each minute (1~1441) to a curve, the typical daily load curve corresponding to the target array is obtained.

[0098] The method for solving the typical daily load curve provided by this invention establishes a minute-by-minute output array of the target load curve based on the load output data of the power system. After dividing the output array by season, the mean and median of the load values ​​in the same minute of the target array are calculated to determine the typical load value in the same minute of the target array. The typical load value in the same minute of the target array is fitted into a curve to obtain the typical daily load curve corresponding to the target array. The typical daily load curve is fitted by each typical load value, which does not rely on manual judgment and avoids randomness. The typical daily load curve obtained is objective and accurate, solving the technical problems of strong subjectivity and poor reliability of existing methods for determining the typical daily load curve of the power system.

[0099] Example 4:

[0100] For easier understanding, please refer to Figure 1 and Figure 2 This invention provides an embodiment of a typical daily load curve solution method, comprising:

[0101] Step 101: Obtain the load output data of the power system and establish the output array of the target load curve per minute.

[0102] It should be noted that, in this embodiment of the invention, the annual load output data is exported from the dispatch automation system using natural days as the unit, and a minute-by-minute output array for the target load curve is established. , Where t is a time variable in minutes. The load output data is at time t, and there are 1441 minutes in a day.

[0103] Step 102: Divide the output array into four groups based on the season: the first group, the second group, the third group, and the fourth group.

[0104] It should be noted that the output array is divided into the first array based on the season. Second array The third array and the fourth array That is, the array corresponding to spring (March to May) is... Summer (June to August) corresponds to the array as follows: The array corresponding to autumn (September to November) is: The array corresponding to winter (December to February of the following year) is: .

[0105] Step 103: Select the target array. The target array can be any one of the first array, the second array, the third array, and the fourth array.

[0106] It should be noted that this problem involves finding typical days for the first, second, third, and fourth arrays. The array for which typical days are to be found is selected as the target array.

[0107] Step 104: Calculate the mean and median of the load values ​​in the target array for the same minute, and determine the typical load value in the target array for the same minute.

[0108] It should be noted that, for the target array, the typical load value is calculated for the same minute of each day in the target array. In one embodiment, the specific execution steps are as follows: Figure 2 As shown, it includes:

[0109] S1. Let t=1, where t is a time variable in minutes.

[0110] S2. All load values ​​of the target array at time t. Sort in ascending order. For example: sort the summer months (June to August). The values ​​are arranged in ascending order.

[0111] S3. Remove the load values ​​from the top and bottom preset percentages in the ascending-sorted target array to obtain a confidence load value sequence. In one embodiment, based on power system operation experience and considering a 95% confidence rate, remove the top 5% and bottom 5% load values ​​from the ascending-sorted target array to obtain a confidence load value sequence. , where n is the length of the confidence load numerical sequence.

[0112] S4. Calculate the mean and median of the confidence load numerical series. The formula for calculating the mean of the confidence load numerical series is:

[0113] ;

[0114] in, Let be the mean of the confidence load numerical sequence of the target array at time t.

[0115] If the length n of the confidence load numerical sequence is odd, then the median of the confidence load numerical sequence of the target array at time t is... for If the length n of the confidence load numerical sequence is even, then the median of the confidence load numerical sequence of the target array at time t is... for .

[0116] S5. Based on the mean and median of the confidence load numerical series, determine the typical load value of the confidence load numerical series using the Pearson empirical method. The formula for calculating the typical load value of the confidence load numerical series using the Pearson empirical method is as follows:

[0117] ;

[0118] in, Let be the typical load value of the confidence load numerical sequence at time t. Let be the mean of the confidence load numerical sequence at time t. This is the median of the confidence load numerical sequence.

[0119] S6. Determine if t is greater than 1441. If not, let t = t + 1 and return to step S2. If yes, store the typical load values ​​of the confidence load value sequence at all times t, and obtain the typical load value sequence composed of the typical load values ​​at 1 to 1441 minutes.

[0120] Step 105: Fit the typical load values ​​for the same minute in the target array to a curve to obtain the typical daily load curve corresponding to the target array.

[0121] It should be noted that by fitting the typical load value corresponding to each minute (1~1441) to a curve, the typical daily load curve corresponding to the target array is obtained.

[0122] The method for solving the typical daily load curve provided by this invention establishes a minute-by-minute output array of the target load curve based on the load output data of the power system. After dividing the output array by season, the mean and median of the load values ​​in the same minute of the target array are calculated to determine the typical load value in the same minute of the target array. The typical load value in the same minute of the target array is fitted into a curve to obtain the typical daily load curve corresponding to the target array. The typical daily load curve is fitted by each typical load value, which does not rely on manual judgment and avoids randomness. The typical daily load curve obtained is objective and accurate, solving the technical problems of strong subjectivity and poor reliability of existing methods for determining the typical daily load curve of the power system.

[0123] Example 5:

[0124] For easier understanding, please refer to Figure 3 This invention provides an embodiment of a typical daily load curve solving device, comprising:

[0125] The load data acquisition module is used to acquire the load output data of the power system and establish a minute-by-minute output array for the target load curve.

[0126] It should be noted that, in this embodiment of the invention, the annual load output data is exported from the dispatch automation system using natural days as the unit, and a minute-by-minute output array for the target load curve is established. , Where t is a time variable in minutes. The load output data is at time t, and there are 1441 minutes in a day.

[0127] The grouping module is used to divide the output array into four groups: the first group, the second group, the third group, and the fourth group, based on the season.

[0128] It should be noted that the output array is divided into the first array based on the season. Second array The third array and the fourth array That is, the array corresponding to spring (March to May) is... Summer (June to August) corresponds to the array as follows: The array corresponding to autumn (September to November) is: The array corresponding to winter (December to February of the following year) is: .

[0129] The selection module is used to select a target array, which can be any one of the first, second, third, and fourth arrays.

[0130] It should be noted that this problem involves finding typical days for the first, second, third, and fourth arrays. The array for which typical days are to be found is selected as the target array.

[0131] The typical value calculation module is used to calculate the mean and median of the load values ​​in the target array for the same minute, and to determine the typical load value in the target array for the same minute.

[0132] It should be noted that, for the target array, the typical load value is calculated for the same minute of each day in the target array. In one embodiment, the specific execution steps are as follows: Figure 2 As shown, it includes:

[0133] S1. Let t=1, where t is a time variable in minutes.

[0134] S2. All load values ​​of the target array at time t. Sort in ascending order. For example: sort the summer months (June to August). The values ​​are arranged in ascending order.

[0135] S3. Remove the load values ​​from the top and bottom preset percentages in the ascending-sorted target array to obtain a confidence load value sequence. In one embodiment, based on power system operation experience and considering a 95% confidence rate, remove the top 5% and bottom 5% load values ​​from the ascending-sorted target array to obtain a confidence load value sequence. , where n is the length of the confidence load numerical sequence.

[0136] S4. Calculate the mean and median of the confidence load numerical series. The formula for calculating the mean of the confidence load numerical series is:

[0137] ;

[0138] in, Let be the mean of the confidence load numerical sequence of the target array at time t.

[0139] If the length n of the confidence load numerical sequence is odd, then the median of the confidence load numerical sequence of the target array at time t is... for If the length n of the confidence load numerical sequence is even, then the median of the confidence load numerical sequence of the target array at time t is... for .

[0140] S5. Based on the mean and median of the confidence load numerical series, determine the typical load value of the confidence load numerical series using the Pearson empirical method. The formula for calculating the typical load value of the confidence load numerical series using the Pearson empirical method is as follows:

[0141] ;

[0142] in, Let be the typical load value of the confidence load numerical sequence at time t. Let be the mean of the confidence load numerical sequence at time t. This is the median of the confidence load numerical sequence.

[0143] S6. Determine if t is greater than 1441. If not, let t = t + 1 and return to step S2. If yes, store the typical load values ​​of the confidence load value sequence at all times t, and obtain the typical load value sequence composed of the typical load values ​​at 1 to 1441 minutes.

[0144] The fitting module is used to fit the typical load values ​​of the same minute in the target array into a curve, thereby obtaining the typical daily load curve corresponding to the target array.

[0145] It should be noted that by fitting the typical load value corresponding to each minute (1~1441) to a curve, the typical daily load curve corresponding to the target array is obtained.

[0146] In one embodiment, the typical value retrieval module is specifically used to perform the following operations:

[0147] S1. Let t=1, where t is a time variable in minutes;

[0148] S2. Sort all load values ​​of the target array at time t in ascending order;

[0149] S3. Remove the load values ​​of the first and last preset percentages in the target array after ascending sorting to obtain the confidence load value sequence.

[0150] S4. Calculate the mean and median of the confidence load numerical series;

[0151] S5. Based on the mean and median of the confidence load numerical series, the typical load value of the confidence load numerical series is determined using the Pearson empirical method.

[0152] S6. Determine if t is greater than 1441. If not, let t = t + 1 and return to step S2. If yes, store the typical load values ​​of the confidence load value sequence at all times t.

[0153] In one embodiment, in step S5, the formula for calculating the typical load value of the confidence load numerical sequence using the Pearson empirical method is as follows:

[0154]

[0155] in, Let be the typical load value of the confidence load numerical sequence at time t. Let be the mean of the confidence load numerical sequence at time t. This is the median of the confidence load numerical sequence.

[0156] In one embodiment, the preset percentage is 5%.

[0157] In one embodiment, the load data acquisition module is specifically used for:

[0158] Using natural days as the unit, export the annual load output data from the dispatch automation system to establish a minute-by-minute output array for the target load curve.

[0159] The typical daily load curve solving device provided by this invention establishes a minute-by-minute output array of the target load curve based on the load output data of the power system. After dividing the output array by season, the mean and median of the load values ​​in the same minute of the target array are calculated to determine the typical load value in the same minute of the target array. The typical load value in the same minute of the target array is fitted into a curve to obtain the typical daily load curve corresponding to the target array. The typical daily load curve is fitted by each typical load value, which does not rely on manual judgment and avoids randomness. The typical daily load curve obtained is objective and accurate, solving the technical problems of strong subjectivity and poor reliability of existing methods for determining the typical daily load curve of the power system.

[0160] Example 6:

[0161] For easier understanding, please refer to Figure 4This invention provides an embodiment of a typical daily load curve solving device, which includes a processor and a memory:

[0162] The memory is used to store program code and transfer the program code to the processor;

[0163] The processor is used to execute any one of the typical daily load curve solving methods described in the foregoing embodiments according to the instructions in the program code.

[0164] In this embodiment of the invention, load output data for the entire year is exported from the dispatch automation system, using natural days as the unit, to establish a minute-by-minute output array for the target load curve. , Where t is a time variable in minutes. The load output data is at time t, and there are 1441 minutes in a day.

[0165] Divide the output array into the first array based on the season. Second array The third array and the fourth array That is, the array corresponding to spring (March to May) is... Summer (June to August) corresponds to the array as follows: The array corresponding to autumn (September to November) is: The array corresponding to winter (December to February of the following year) is: .

[0166] Find the typical days for the first, second, third, and fourth arrays. Select the array containing the typical days to be found as the target array.

[0167] For the target array, the typical load value is calculated for the same minute of each day in the target array. In one embodiment, the specific execution steps are as follows: Figure 2 As shown, it includes:

[0168] S1. Let t=1, where t is a time variable in minutes.

[0169] S2. All load values ​​of the target array at time t. Sort in ascending order. For example: sort the summer months (June to August). The values ​​are arranged in ascending order.

[0170] S3. Remove the load values ​​from the top and bottom preset percentages in the ascending-sorted target array to obtain a confidence load value sequence. In one embodiment, based on power system operation experience and considering a 95% confidence rate, remove the top 5% and bottom 5% load values ​​from the ascending-sorted target array to obtain a confidence load value sequence. , where n is the length of the confidence load numerical sequence.

[0171] S4. Calculate the mean and median of the confidence load numerical series. The formula for calculating the mean of the confidence load numerical series is:

[0172] ;

[0173] in, Let be the mean of the confidence load numerical sequence of the target array at time t.

[0174] If the length n of the confidence load numerical sequence is odd, then the median of the confidence load numerical sequence of the target array at time t is... for If the length n of the confidence load numerical sequence is even, then the median of the confidence load numerical sequence of the target array at time t is... for .

[0175] S5. Based on the mean and median of the confidence load numerical series, determine the typical load value of the confidence load numerical series using the Pearson empirical method. The formula for calculating the typical load value of the confidence load numerical series using the Pearson empirical method is as follows:

[0176] ;

[0177] in, Let be the typical load value of the confidence load numerical sequence at time t. Let be the mean of the confidence load numerical sequence at time t. This is the median of the confidence load numerical sequence.

[0178] S6. Determine if t is greater than 1441. If not, let t = t + 1 and return to step S2. If yes, store the typical load values ​​of the confidence load value sequence at all times t, and obtain the typical load value sequence composed of the typical load values ​​at 1 to 1441 minutes.

[0179] By fitting the typical load value corresponding to each minute (1~1441) to a curve, the typical daily load curve corresponding to the target array is obtained.

[0180] The typical daily load curve solving device provided by this invention establishes a minute-by-minute output array of the target load curve based on the load output data of the power system. After dividing the output array by season, the mean and median of the load values ​​in the same minute of the target array are calculated to determine the typical load value in the same minute of the target array. The typical load value in the same minute of the target array is fitted into a curve to obtain the typical daily load curve corresponding to the target array. The typical daily load curve is fitted by each typical load value, which does not rely on manual judgment and avoids randomness. The typical daily load curve obtained is objective and accurate, solving the technical problems of strong subjectivity and poor reliability of existing methods for determining the typical daily load curve of the power system.

[0181] Example 7:

[0182] The present invention also provides an embodiment of a computer-readable storage medium for storing program code for executing any one of the typical daily load curve solving methods described in the foregoing embodiments.

[0183] In this embodiment of the invention, load output data for the entire year is exported from the dispatch automation system, using natural days as the unit, to establish a minute-by-minute output array for the target load curve. , Where t is a time variable in minutes. The load output data is at time t, and there are 1441 minutes in a day.

[0184] Divide the output array into the first array based on the season. Second array The third array and the fourth array That is, the array corresponding to spring (March to May) is... Summer (June to August) corresponds to the array as follows: The array corresponding to autumn (September to November) is: The array corresponding to winter (December to February of the following year) is: .

[0185] Find the typical days for the first, second, third, and fourth arrays. Select the array containing the typical days to be found as the target array.

[0186] For the target array, the typical load value is calculated for the same minute of each day in the target array. In one embodiment, the specific execution steps are as follows: Figure 2 As shown, it includes:

[0187] S1. Let t=1, where t is a time variable in minutes.

[0188] S2. All load values ​​of the target array at time t. Sort in ascending order. For example: sort the summer months (June to August). The values ​​are arranged in ascending order.

[0189] S3. Remove the load values ​​from the top and bottom preset percentages in the ascending-sorted target array to obtain a confidence load value sequence. In one embodiment, based on power system operation experience and considering a 95% confidence rate, remove the top 5% and bottom 5% load values ​​from the ascending-sorted target array to obtain a confidence load value sequence. , where n is the length of the confidence load numerical sequence.

[0190] S4. Calculate the mean and median of the confidence load numerical series. The formula for calculating the mean of the confidence load numerical series is:

[0191] ;

[0192] in, Let be the mean of the confidence load numerical sequence of the target array at time t.

[0193] If the length n of the confidence load numerical sequence is odd, then the median of the confidence load numerical sequence of the target array at time t is... for If the length n of the confidence load numerical sequence is even, then the median of the confidence load numerical sequence of the target array at time t is... for .

[0194] S5. Based on the mean and median of the confidence load numerical series, determine the typical load value of the confidence load numerical series using the Pearson empirical method. The formula for calculating the typical load value of the confidence load numerical series using the Pearson empirical method is as follows:

[0195] ;

[0196] in, Let be the typical load value of the confidence load numerical sequence at time t. Let be the mean of the confidence load numerical sequence at time t. This is the median of the confidence load numerical sequence.

[0197] S6. Determine if t is greater than 1441. If not, let t = t + 1 and return to step S2. If yes, store the typical load values ​​of the confidence load value sequence at all times t, and obtain the typical load value sequence composed of the typical load values ​​at 1 to 1441 minutes.

[0198] By fitting the typical load value corresponding to each minute (1~1441) to a curve, the typical daily load curve corresponding to the target array is obtained.

[0199] The computer-readable storage medium provided by this invention establishes a minute-by-minute output array of the target load curve based on the load output data of the power system. After dividing the output array by season, the mean and median of the load values ​​in the same minute of the target array are calculated to determine the typical load value in the same minute of the target array. The typical load value in the same minute of the target array is fitted into a curve to obtain the typical daily load curve corresponding to the target array. The typical daily load curve is fitted by each typical load value, which does not rely on manual judgment and avoids randomness. The obtained typical daily load curve is objective and accurate, solving the technical problems of strong subjectivity and poor reliability of existing methods for determining the typical daily load curve of the power system.

[0200] Example 8:

[0201] The present invention also provides a computer program product including instructions that, when run on a computer, cause the computer to execute any of the typical daily load curve solving methods provided in the present invention.

[0202] In this embodiment of the invention, load output data for the entire year is exported from the dispatch automation system, using natural days as the unit, to establish a minute-by-minute output array for the target load curve. , Where t is a time variable in minutes. The load output data is at time t, and there are 1441 minutes in a day.

[0203] Divide the output array into the first array based on the season. Second array The third array and the fourth array That is, the array corresponding to spring (March to May) is... Summer (June to August) corresponds to the array as follows: The array corresponding to autumn (September to November) is: The array corresponding to winter (December to February of the following year) is: .

[0204] Find the typical days for the first, second, third, and fourth arrays. Select the array containing the typical days to be found as the target array.

[0205] For the target array, the typical load value is calculated for the same minute of each day in the target array. In one embodiment, the specific execution steps are as follows: Figure 2 As shown, it includes:

[0206] S1. Let t=1, where t is a time variable in minutes.

[0207] S2. All load values ​​of the target array at time t. Sort in ascending order. For example: sort the summer months (June to August). The values ​​are arranged in ascending order.

[0208] S3. Remove the load values ​​from the top and bottom preset percentages in the ascending-sorted target array to obtain a confidence load value sequence. In one embodiment, based on power system operation experience and considering a 95% confidence rate, remove the top 5% and bottom 5% load values ​​from the ascending-sorted target array to obtain a confidence load value sequence. , where n is the length of the confidence load numerical sequence.

[0209] S4. Calculate the mean and median of the confidence load numerical series. The formula for calculating the mean of the confidence load numerical series is:

[0210] ;

[0211] in, Let be the mean of the confidence load numerical sequence of the target array at time t.

[0212] If the length n of the confidence load numerical sequence is odd, then the median of the confidence load numerical sequence of the target array at time t is... for If the length n of the confidence load numerical sequence is even, then the median of the confidence load numerical sequence of the target array at time t is... for .

[0213] S5. Based on the mean and median of the confidence load numerical series, determine the typical load value of the confidence load numerical series using the Pearson empirical method. The formula for calculating the typical load value of the confidence load numerical series using the Pearson empirical method is as follows:

[0214] ;

[0215] in, Let be the typical load value of the confidence load numerical sequence at time t. Let be the mean of the confidence load numerical sequence at time t. This is the median of the confidence load numerical sequence.

[0216] S6. Determine if t is greater than 1441. If not, let t = t + 1 and return to step S2. If yes, store the typical load values ​​of the confidence load value sequence at all times t, and obtain the typical load value sequence composed of the typical load values ​​at 1 to 1441 minutes.

[0217] By fitting the typical load value corresponding to each minute (1~1441) to a curve, the typical daily load curve corresponding to the target array is obtained.

[0218] The computer program product provided by this invention includes instructions that establishes a minute-by-minute output array of the target load curve based on the load output data of the power system. After dividing the output array by season, the mean and median of the load values ​​in the same minute of the target array are calculated to determine the typical load value in the same minute of the target array. The typical load value in the same minute of the target array is then fitted into a curve to obtain the typical daily load curve corresponding to the target array. The typical daily load curve is fitted from the typical load values, which does not rely on manual judgment and avoids randomness. The obtained typical daily load curve is objective and accurate, solving the technical problems of strong subjectivity and poor reliability in existing methods of determining the typical daily load curve of the power system.

[0219] The terms “first,” “second,” “third,” etc., used in this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0220] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0221] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0222] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0223] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0224] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for solving a typical daily load curve, characterized in that, include: Acquire the load output data of the power system and establish a minute-by-minute output array for the target load curve; The output array is divided into a first array, a second array, a third array, and a fourth array, based on the season. Select a target array, wherein the target array is any one of the first array, the second array, the third array, and the fourth array; Calculate the mean and median of the load values ​​in the target array for the same minute to determine the typical load value in the target array for the same minute; By fitting the typical load values ​​for the same minute in the target array to a curve, the typical daily load curve corresponding to the target array is obtained.

2. The method for solving typical daily load curves according to claim 1, characterized in that, Calculate the mean and median of the load values ​​in the target array for the same minute, and determine the typical load value in the target array for the same minute, including: S1. Let t=1, where t is a time variable in minutes; S2. Sort all load values ​​of the target array at time t in ascending order; S3. Remove the load values ​​of the first and last preset percentages in the target array after ascending sorting to obtain the confidence load value sequence. S4. Calculate the mean and median of the confidence load numerical sequence; S5. Based on the mean and median of the confidence load numerical sequence, determine the typical load value of the confidence load numerical sequence using the Pearson empirical method; S6. Determine if t is greater than 1441. If not, let t = t + 1 and return to step S2. If yes, store the typical load values ​​of the confidence load value sequence at all times t.

3. The method for solving typical daily load curves according to claim 2, characterized in that, In step S5, the formula for calculating the typical load value of the confidence load numerical sequence using the Pearson empirical method is as follows: in, Let be the typical load value of the confidence load numerical sequence at time t. Let be the mean of the confidence load numerical sequence at time t. This is the median of the confidence load numerical sequence.

4. The method for solving typical daily load curves according to claim 2, characterized in that, The preset percentage is 5%.

5. The method for solving typical daily load curves according to claim 1, characterized in that, Acquire power system load output data and establish a minute-by-minute output array for the target load curve, including: Using natural days as the unit, export the annual load output data from the dispatch automation system to establish a minute-by-minute output array for the target load curve.

6. A typical daily load curve solving device, characterized in that, include: The load data acquisition module is used to acquire the load output data of the power system and establish the output array per minute of the target load curve; The grouping module is used to divide the output array into a first array, a second array, a third array, and a fourth array based on seasons; The selection module is used to select a target array, wherein the target array is any one of the first array, the second array, the third array, and the fourth array; The typical value calculation module is used to calculate the mean and median of the load values ​​in the target array for the same minute, and to determine the typical load value in the target array for the same minute; The fitting module is used to fit the typical load values ​​of the same minute in the target array into a curve to obtain the typical daily load curve corresponding to the target array.

7. The typical daily load curve solving device according to claim 6, characterized in that, The typical value calculation module is specifically used to perform the following operations: S1. Let t=1, where t is a time variable in minutes; S2. Sort all load values ​​of the target array at time t in ascending order; S3. Remove the load values ​​of the first and last preset percentages in the target array after ascending sorting to obtain the confidence load value sequence. S4. Calculate the mean and median of the confidence load numerical sequence; S5. Based on the mean and median of the confidence load numerical sequence, determine the typical load value of the confidence load numerical sequence using the Pearson empirical method; S6. Determine if t is greater than 1441. If not, let t = t + 1 and return to step S2. If yes, store the typical load values ​​of the confidence load value sequence at all times t.

8. The method for solving typical daily load curves according to claim 7, characterized in that, In step S5, the formula for calculating the typical load value of the confidence load numerical sequence using the Pearson empirical method is as follows: in, Let be the typical load value of the confidence load numerical sequence at time t. Let be the mean of the confidence load numerical sequence at time t. This is the median of the confidence load numerical sequence.

9. A typical daily load curve solving device, characterized in that, The device includes a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the typical daily load curve solution method according to any one of claims 1-5 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the typical daily load curve solution method according to any one of claims 1-5.

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