Grid self-balancing planning method and system based on load frequency domain matching

Abstract

The embodiment of the invention provides a grid self-balancing planning method and system based on load frequency domain matching, and belongs to the technical field of control scheduling of a power grid. The method comprises the following steps: acquiring load data of each power supply grid unit of a power supply grid unit set; performing time domain conversion operation on the load data; performing frequency domain matching operation on the load data after the time domain conversion operation; performing phase matching operation on the load data after the frequency domain matching operation to obtain power supply grid unit combinations with similar frequency domains; superposing the load data of the power supply grid unit combination to obtain a new power supply grid unit set; judging whether the power supply grid unit set meets a preset requirement or not; returning to the step of performing the frequency domain matching operation on the load data after the time domain conversion operation under the condition of judging that the power supply grid unit set does not meet the preset requirement; and when it is judged that the power supply grid unit set meets the preset requirement, load configuration is adjusted according to the power supply grid units.

Description

Technical Field

[0001] The present invention relates to the technical field of control and dispatching of power grids, and in particular to a grid self-balancing planning method and system based on load frequency domain matching. Background Art

[0002] As my country's power market enters a new stage of development, power loads are becoming increasingly diversified and complex, and the peak and valley characteristics of power consumption in different regions and for different types of users vary significantly, posing new challenges to distribution network planning. To address the above problems, existing methods include, on the one hand, actively increasing capacity based on peak power consumption. Although this can solve the problem of power demand, due to the fluctuation of power load, the effective utilization rate of infrastructure increment is low and the investment efficiency is poor; on the other hand, it guides users to use power through time-of-use electricity prices, which is relatively passive in shaving peaks and filling valleys, but the application scenarios and regulation capabilities of this method are very limited. Summary of the invention

[0003] The purpose of the embodiments of the present invention is to provide a grid self-balancing planning method and system based on load frequency domain matching, which can improve the overall load scheduling efficiency.

[0004] In order to achieve the above object, an embodiment of the present invention provides a grid self-balancing planning method based on load frequency domain matching, comprising:

[0005] Obtaining load data of each power supply grid unit in the power supply grid unit set;

[0006] Performing a time domain conversion operation on the load data;

[0007] Performing a frequency domain matching operation on the load data after the time domain conversion operation;

[0008] Performing a phase matching operation on the load data after the frequency domain matching operation to obtain a power supply grid unit combination with a similar frequency domain;

[0009] Superimposing the load data of the power supply grid unit combination to obtain a new power supply grid unit set;

[0010] Determining whether the power supply grid unit set meets preset requirements;

[0011] If it is determined that the power supply grid unit set does not meet the preset requirements, returning to the step of performing a frequency domain matching operation on the load data after the time domain conversion operation;

[0012] When it is determined that the power supply grid unit set meets the preset requirement, the load configuration is adjusted according to the power supply grid unit.

[0013] Optionally, obtaining load data of each power supply grid unit in the power supply grid unit set includes:

[0014] The load data is normalized.

[0015] Optionally, normalizing the load data includes:

[0016] The normalization process is performed according to formula (1):

[0017]

[0018] in, is the normalized load data, {q i} j is the load data before normalization, q min,j ,q max,j They are load data {q i} j The minimum and maximum load values.

[0019] Optionally, performing a time domain conversion operation on the load data includes:

[0020] A fast Fourier transform operation is performed on the load data to obtain frequency spectrum information of each load data.

[0021] Optionally, performing a time domain conversion operation on the load data includes:

[0022] Using a K-means method to perform a clustering operation based on the frequency spectrum information to obtain a plurality of load data classes;

[0023] Performing amplitude filtering processing on the load data of each load data class;

[0024] Performing frequency domain filtering on the load data after the amplitude filtering;

[0025] The lower limit, the upper limit and the characteristic value of the frequency of the load data after the frequency domain filtering process in each load data class are respectively selected as the characteristic frequency of the load data class.

[0026] Optionally, performing a phase matching operation on the load data after the frequency domain matching operation to obtain a power supply grid unit combination with a similar frequency domain includes:

[0027] For each load data class, construct a corresponding relationship table between the characteristic frequency and phase of each load data;

[0028] In the correspondence table, each load data is sorted in order from left to right in an ascending order;

[0029] Traversing each of the corresponding relationship tables, and selecting one of the load data in order from low frequency to high frequency;

[0030] Calculating a phase complementary coefficient between the selected load data and the remaining load data;

[0031] Filtering the load data whose maximum value of the phase complementary coefficient is greater than a first preset value as a set to be matched;

[0032] Select the load data corresponding to the maximum value of the phase complementary coefficient in the to-be-matched set and associate it with the currently selected load data for matching;

[0033] Determining whether there is any unselected load data;

[0034] In the case where it is determined that the unselected load data does not currently exist, the power supply grid unit combination is generated.

[0035] Optionally, calculating the phase complementary coefficient between the selected load data and the remaining load data includes:

[0036] The phase complementarity coefficient is calculated according to formula (2):

[0037]

[0038] Wherein, θ is the phase complementarity coefficient, are the frequencies of the two load data being calculated, respectively.

[0039] Optionally, determining whether the power supply grid unit set meets preset requirements includes:

[0040] Determining whether there are any combinable power supply grid units in the power supply grid unit set;

[0041] In the case where it is determined that there is no combinable power supply grid unit in the power supply grid unit set, it is determined that the power supply grid unit meets the preset requirement.

[0042] On the other hand, the present invention also provides a grid self-balancing planning system based on load frequency domain matching, the system comprising a processor, and the processor is configured to execute any of the grid self-balancing planning methods based on load frequency domain matching as described above.

[0043] Through the above technical solution, the embodiment of the present invention provides a grid self-balancing planning method and system based on load frequency domain matching. The method and system obtain the load data of each power supply grid unit and perform frequency matching so that two power supply grid units with opposite phases can match each other, thereby reducing the workload of power scheduling based on load data. Compared with the conventional capacity increase scheduling method in the prior art, the method and system provided by the present invention improve the scheduling efficiency.

[0044] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0046] Figure 1 is a flow chart of a grid self-balancing planning method based on load frequency domain matching according to an embodiment of the present invention;

[0047] Figure 2 is a flow chart of a method for time domain conversion operation according to one embodiment of the present invention;

[0048] Figure 3 is an example diagram of spectrum information according to an embodiment of the present invention;

[0049] Figure 4 is a flow chart of a method for obtaining a power supply grid unit combination according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0051] like Figure 1 FIG. 1 is a flow chart of a grid self-balancing planning method based on load frequency domain matching according to an embodiment of the present invention. Figure 1 In the method, the method may include the following steps:

[0052] In step S10, load data of each power supply grid unit in the power supply grid unit set is obtained;

[0053] In step S11, a time domain conversion operation is performed on the load data;

[0054] In step S12, a frequency domain matching operation is performed on the load data after the time domain conversion operation;

[0055] In step S13, a phase matching operation is performed on the load data after the frequency domain matching operation to obtain a power supply grid unit combination with a similar frequency domain;

[0056] In step S14, the load data of the power supply grid unit combination are superimposed to obtain a new power supply grid unit set;

[0057] In step S15, it is determined whether the power supply grid unit set meets the preset requirements;

[0058] If it is determined that the power supply grid unit set does not meet the preset requirements, return to the step of performing a frequency domain matching operation on the load data after the time domain conversion operation, that is, return to step S13;

[0059] In step S16, when it is determined that the power supply grid unit set meets the preset requirements, the load configuration is adjusted according to the power supply grid unit.

[0060] In this Figure 1 In the method shown, step S10 may be used to obtain load data of a set of power grid units. In this embodiment, the load data may be obtained directly by accessing the power system. Specifically, the load data may be represented as {q i} j (i=1,2,…,I;j=1,2,…,J). Among them, i=1,2,…,I represents each time point, and j=1,2,…,J represents each power supply grid unit. The time point may be a time point known to those skilled in the art, including but not limited to 1s, 1min, 1h, etc. In one example of the present invention, the time point may be 15min. The overall time span I may be, for example, 1 year. Taking into account that the values ​​of the load data are relatively discrete, in order to facilitate subsequent processing, in this embodiment, the load data may be normalized. The specific method of the normalization process may be a variety of forms known to those skilled in the art. In one example of the present invention, the normalization process may also be expressed by the following formula (1):

[0061]

[0062] in, is the normalized load data, {q i} j is the load data before normalization, q min,j ,q max,j The load data {q i} j The minimum and maximum load values.

[0063] Step S11 may be used to perform a time domain conversion operation on the load data. In this embodiment, the time domain conversion operation may be

[0064] In step S20, the load data is subjected to a fast Fourier transform (FFT) operation to obtain the spectrum information of each load data (including the amplitude at different frequencies {A k} j and phase angle k = 1, 2, ..., [I / 2] (integer);

[0065] In step S21, a K-means method is used to perform clustering operations based on the spectrum information to obtain multiple load data classes. In an example of the present invention, the spectrum information can be, for example, Figure 3 As shown. Figure 3 In the figure, the amplitude spectra corresponding to the load data classified into the same load data class have similar peak characteristics. Figure 3 The first and second rows belong to two categories. The first row has two obvious peaks near 0 and 30, while the second row has a peak only near 0.

[0066] In step S22, the load data of each load data class is subjected to amplitude filtering. Specifically, the step S22 may first filter out the frequencies of low dB values ​​through amplitude filtering, retain the frequencies of high dB values, and eliminate the influence of random noise. It is worth noting that the dB threshold here is determined based on the actual frequency-dB distribution of the sequence. Then, the step S22 may be to retain the period range of interest through frequency domain filtering. Generally speaking, the smaller the period, the higher the frequency, the stronger the randomness of the load, and the more difficult it is to achieve matching between power grids and units. Therefore, the lower limit of the retained period should not be too low, and the recommended value is above the daily period. The frequencies finally retained should not be too many, and may be, for example, 3 or less.

[0067] In step S23, frequency domain filtering is performed on the load data after the amplitude filtering process;

[0068] In step S25, the lower limit, upper limit and characteristic value of the frequency of the load data after the frequency domain filtering process in each load data class are respectively selected as the characteristic frequency of the load data class. Among them, the lower limit can be the minimum value representing the frequency of the load data, the upper limit can be the maximum value representing the frequency of the load data, and the characteristic value can be including but not limited to the mode, average value, median, etc. of the frequency. In this example, the characteristic value can be the average value after removing the maximum value and the minimum value.

[0069] Step S13 may be used to perform a phase matching operation on the load data after the frequency domain matching operation to obtain a power grid unit combination with a similar frequency domain. Specifically, in one example of the present invention, step S13 may further include: Figure 4 In the steps shown in Figure 4 In the method for obtaining a power supply grid unit combination, the method may include the following steps:

[0070] In step S30, for each load data class, a corresponding relationship table of characteristic frequency and phase of each load data is constructed. In this example, the corresponding relationship table can be, for example, as shown in Table 1 below:

[0071]

[0072] In step S31, in the correspondence table, each load data is sorted in order from left to right from small to large;

[0073] In step S32, each corresponding relationship table is traversed, and a load data is selected in sequence from low frequency to high frequency;

[0074] In step S33, a phase complementarity coefficient between the selected load data and the remaining load data is calculated.

[0075] The phase complementarity coefficient can be calculated using the following formula (2):

[0076]

[0077] Where θ is the phase complementarity coefficient, are the frequencies of the two load data being calculated.

[0078] In step S34, load data whose maximum value of phase complementary coefficient is greater than a first preset value is selected as a set to be matched;

[0079] In step S35, the load data corresponding to the maximum value of the phase complementary coefficient in the to-be-matched set is selected to be associated with the currently selected load data for matching;

[0080] In step S36, it is determined whether there is any unselected load data;

[0081] In step S37, if it is determined that there is no unselected load data, a power supply grid unit combination is generated. On the contrary, if it is determined that there is unselected load data, the process may return to step S32.

[0082] Step S15 can be used to determine whether the power supply grid unit set meets the preset requirements. The preset requirements can be in various forms known to those skilled in the art. In one example of the present invention, the preset requirements can be to first determine whether there are any combinable power supply grid units in the power supply grid unit set, and if it is determined that there are no combinable power supply grid units in the power supply grid unit set, determine that the power supply grid unit meets the preset requirements. On the contrary, if it is determined that there are combinable power supply grid units in the power supply grid unit set, the method can be re-executed at this time, that is, return to execute step S13.

[0083] On the other hand, the present invention also provides a grid self-balancing planning system based on load frequency domain matching, the system comprising a processor, the processor being configured to execute any of the above-mentioned grid self-balancing planning methods based on load frequency domain matching. Specifically, the method may include the following steps:

[0084] In step S10, load data of each power supply grid unit in the power supply grid unit set is obtained;

[0085] In step S11, a time domain conversion operation is performed on the load data;

[0086] In step S12, a frequency domain matching operation is performed on the load data after the time domain conversion operation;

[0087] In step S13, a phase matching operation is performed on the load data after the frequency domain matching operation to obtain a power supply grid unit combination with a similar frequency domain;

[0088] In step S14, the load data of the power supply grid unit combination are superimposed to obtain a new power supply grid unit set;

[0089] In step S15, it is determined whether the power supply grid unit set meets the preset requirements;

[0090] If it is determined that the power supply grid unit set does not meet the preset requirements, return to the step of performing a frequency domain matching operation on the load data after the time domain conversion operation, that is, return to step S13;

[0091] In step S16, when it is determined that the power supply grid unit set meets the preset requirements, the load configuration is adjusted according to the power supply grid unit.

[0092] In this Figure 1 In the method shown, step S10 may be used to obtain load data of a set of power grid units. In this embodiment, the load data may be obtained directly by accessing the power system. Specifically, the load data may be represented as {q i} j(i=1,2,…,I;j=1,2,…,J). Among them, i=1,2,…,I represents each time point, and j=1,2,…,J represents each power supply grid unit. The time point may be a time point known to those skilled in the art, including but not limited to 1s, 1min, 1h, etc. In one example of the present invention, the time point may be 15min. The overall time span I may be, for example, 1 year. Taking into account that the values ​​of the load data are relatively discrete, in order to facilitate subsequent processing, in this embodiment, the load data may be normalized. The specific method of the normalization process may be a variety of forms known to those skilled in the art. In one example of the present invention, the normalization process may also be expressed by the following formula (1):

[0093]

[0094] in, is the normalized load data, {q i} j is the load data before normalization, q min,j ,q max,j They are load data {q i} j The minimum and maximum load values.

[0095] Step S11 may be used to perform a time domain conversion operation on the load data. In this embodiment, the time domain conversion operation may be

[0096] In step S20, the load data is subjected to a fast Fourier transform (FFT) operation to obtain the spectrum information of each load data (including the amplitude at different frequencies {A k} j and phase angle k = 1, 2, ..., [I / 2] (integer);

[0097] In step S21, a K-means method is used to perform clustering operations based on the spectrum information to obtain multiple load data classes. In an example of the present invention, the spectrum information can be, for example, Figure 3 As shown. Figure 3 In the figure, the amplitude spectra corresponding to the load data classified into the same load data class have similar peak characteristics. Figure 3 The first and second rows belong to two categories. The first row has two obvious peaks near 0 and 30, while the second row has a peak only near 0.

[0098] In step S22, the load data of each load data class is subjected to amplitude filtering. Specifically, the step S22 may first filter out the frequencies of low dB values ​​through amplitude filtering, retain the frequencies of high dB values, and eliminate the influence of random noise. It is worth noting that the dB threshold here is determined based on the actual frequency-dB distribution of the sequence. Then, the step S22 may be to retain the period range of interest through frequency domain filtering. Generally speaking, the smaller the period, the higher the frequency, the stronger the randomness of the load, and the more difficult it is to achieve matching between power grids and units. Therefore, the lower limit of the retained period should not be too low, and the recommended value is above the daily period. The frequencies finally retained should not be too many, and may be, for example, 3 or less.

[0099] In step S23, frequency domain filtering is performed on the load data after the amplitude filtering process;

[0100] In step S25, the lower limit, upper limit and characteristic value of the frequency of the load data after the frequency domain filtering process in each load data class are respectively selected as the characteristic frequency of the load data class. Among them, the lower limit can be the minimum value representing the frequency of the load data, the upper limit can be the maximum value representing the frequency of the load data, and the characteristic value can be including but not limited to the mode, average value, median, etc. of the frequency. In this example, the characteristic value can be the average value after removing the maximum value and the minimum value.

[0101] Step S13 may be used to perform a phase matching operation on the load data after the frequency domain matching operation to obtain a power grid unit combination with a similar frequency domain. Specifically, in one example of the present invention, step S13 may further include: Figure 4 In the steps shown in Figure 4 In the method for obtaining a power supply grid unit combination, the method may include the following steps:

[0102] In step S30, for each load data class, a corresponding relationship table of characteristic frequency and phase of each load data is constructed. In this example, the corresponding relationship table can be, for example, as shown in Table 1 below:

[0103]

[0104]

[0105] In step S31, in the correspondence table, each load data is sorted in order from left to right from small to large;

[0106] In step S32, each corresponding relationship table is traversed, and a load data is selected in sequence from low frequency to high frequency;

[0107] In step S33, a phase complementarity coefficient between the selected load data and the remaining load data is calculated.

[0108] The phase complementarity coefficient can be calculated using the following formula (2):

[0109]

[0110] Where θ is the phase complementarity coefficient, are the frequencies of the two load data being calculated.

[0111] In step S34, load data whose maximum value of phase complementary coefficient is greater than a first preset value is selected as a set to be matched;

[0112] In step S35, the load data corresponding to the maximum value of the phase complementary coefficient in the to-be-matched set is selected to be associated with the currently selected load data for matching;

[0113] In step S36, it is determined whether there is any unselected load data;

[0114] In step S37, if it is determined that there is no unselected load data, a power supply grid unit combination is generated. On the contrary, if it is determined that there is unselected load data, the process may return to step S32.

[0115] Step S15 can be used to determine whether the power supply grid unit set meets the preset requirements. The preset requirements can be in various forms known to those skilled in the art. In one example of the present invention, the preset requirements can be to first determine whether there are any combinable power supply grid units in the power supply grid unit set, and if it is determined that there are no combinable power supply grid units in the power supply grid unit set, determine that the power supply grid unit meets the preset requirements. On the contrary, if it is determined that there are combinable power supply grid units in the power supply grid unit set, the method can be re-executed at this time, that is, return to execute step S13.

[0116] Through the above technical solution, the embodiment of the present invention provides a grid self-balancing planning method and system based on load frequency domain matching. The method and system obtain the load data of each power supply grid unit and perform frequency matching so that two power supply grid units with opposite phases can match each other, thereby reducing the workload of power scheduling based on load data. Compared with the conventional capacity increase scheduling method in the prior art, the method and system provided by the present invention improve the scheduling efficiency.

[0117] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0118] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0121] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0122] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0123] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0124] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0125] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A grid self-balancing planning method based on load frequency domain matching, characterized in that: The method comprises: Obtaining load data of each power supply grid unit in the power supply grid unit set; Performing a time domain conversion operation on the load data; Performing a frequency domain matching operation on the load data after the time domain conversion operation; Performing a phase matching operation on the load data after the frequency domain matching operation to obtain a power supply grid unit combination with a similar frequency domain; Superimposing the load data of the power supply grid unit combination to obtain a new power supply grid unit set; Determining whether the power supply grid unit set meets preset requirements; If it is determined that the power supply grid unit set does not meet the preset requirements, returning to the step of performing a frequency domain matching operation on the load data after the time domain conversion operation; When it is determined that the power supply grid unit set meets the preset requirement, the load configuration is adjusted according to the power supply grid unit.

2. The method according to claim 1, characterized in that Obtain the load data of each power supply grid unit in the power supply grid unit set, including: The load data is normalized.

3. The method according to claim 2, characterized in that The load data is normalized, including: The normalization process is performed according to formula (1): in, is the normalized load data, {q i } j is the load data before normalization, q min,j ,q max,j They are load data {q i } j The minimum and maximum load values.

4. The method according to claim 1, characterized in that: Performing a time domain conversion operation on the load data includes: A fast Fourier transform operation is performed on the load data to obtain frequency spectrum information of each load data.

5. The method according to claim 4, characterized in that Performing a time domain conversion operation on the load data includes: Using a K-means method to perform a clustering operation based on the frequency spectrum information to obtain a plurality of load data classes; Performing amplitude filtering processing on the load data of each load data class; Performing frequency domain filtering on the load data after the amplitude filtering; The lower limit, the upper limit and the characteristic value of the frequency of the load data after the frequency domain filtering process in each load data class are respectively selected as the characteristic frequency of the load data class.

6. The method according to claim 5, characterized in that Performing a phase matching operation on the load data after the frequency domain matching operation to obtain a power supply grid unit combination with a similar frequency domain includes: For each load data class, construct a corresponding relationship table between the characteristic frequency and phase of each load data; In the correspondence table, each load data is sorted in order from left to right in an ascending order; Traversing each of the corresponding relationship tables, and selecting one of the load data in order from low frequency to high frequency; Calculating a phase complementary coefficient between the selected load data and the remaining load data; Filtering the load data whose maximum value of the phase complementary coefficient is greater than a first preset value as a set to be matched; Select the load data corresponding to the maximum value of the phase complementary coefficient in the to-be-matched set and associate it with the currently selected load data for matching; Determining whether there is any unselected load data; In the case where it is determined that the unselected load data does not currently exist, the power supply grid unit combination is generated.

7. The method according to claim 6, characterized in that Calculating the phase complementary coefficient between the selected load data and the remaining load data, comprising: The phase complementarity coefficient is calculated according to formula (2): Wherein, θ is the phase complementarity coefficient, are the frequencies of the two load data being calculated, respectively.

8. The method according to claim 1, characterized in that: Determining whether the power supply grid unit set meets preset requirements includes: Determining whether there are any combinable power supply grid units in the power supply grid unit set; In the case where it is determined that there is no combinable power supply grid unit in the power supply grid unit set, it is determined that the power supply grid unit meets the preset requirement.

9. A grid self-balancing planning system based on load frequency domain matching, characterized in that: The system includes a processor configured to execute the grid self-balancing planning method based on load frequency domain matching according to any one of claims 1 to 8.

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