A method and system for flexible load frequency regulation

Abstract

The application discloses a flexible load frequency modulation method and system, which deploys an internet of things device in each load area, collects electrical parameters when a frequency change event occurs, and establishes a frequency modulation feature matrix of a distributed load monitoring point; based on the upstream and downstream logical relationship of power flow and frequency disturbance, the conduction difference is evaluated and the matrix is updated to generate a frequency change event conduction matrix; by calculating the frequency modulation sensitive correlation degree between the distributed monitoring points, the object sequence to be modulated is determined and sent to the power grid operation and maintenance port. The application can identify high sensitive load areas and devices to realize dynamic monitoring and accurate evaluation of the flexible load frequency modulation characteristics.

Description

Technical Field

[0001] The present invention relates to the technical field of load frequency regulation, in particular to a method and system for flexible load frequency regulation. Background Art

[0002] As an important means to maintain the stability of the power grid frequency, the core of flexible load frequency regulation technology lies in quickly and accurately identifying the load frequency regulation characteristics and implementing effective control; however, there are still significant deficiencies in the existing load frequency regulation technology in aspects such as dynamic monitoring, conduction path analysis, and frequency regulation object screening.

[0003] Traditional load frequency regulation methods mostly adopt a centralized monitoring mode, relying on limited key node data, which is difficult to cover the wide-area distribution characteristics of distributed flexible loads. Such methods are prone to spatio-temporal information deviation due to clock asynchronization during data collection, thus affecting the accuracy of frequency regulation characteristic analysis; in addition, the existing technology analyzes the conduction mechanism of frequency disturbances rather roughly, usually only focusing on the self-response of a single region and ignoring the mutual influence between regions, resulting in incomplete identification of the conduction path. For example, when a frequency disturbance occurs, traditional methods cannot effectively distinguish the self-conduction characteristics within the load region from the mutual-conduction characteristics between adjacent regions, making it difficult to locate the key disturbance sources and highly sensitive regions.

[0004] In terms of frequency regulation control strategies, the existing technology mostly relies on static models or historical data and lacks the ability to adapt to real-time dynamic changes. Especially when facing large-scale distributed flexible loads, traditional methods are difficult to generate frequency regulation commands in a timely manner due to low data processing efficiency, resulting in insufficient real-time performance of power grid frequency control; in addition, the existing frequency regulation object screening algorithms often rely on manual experience or simple threshold judgment, lacking quantitative evaluation means, which is likely to cause too wide frequency regulation range or omission of key equipment, reducing the pertinence and efficiency of frequency regulation control. Summary of the Invention

[0005] Object of the Invention: The object of the present invention is to provide a real-time and effective method and system for flexible load frequency regulation based on the evaluation of the logical differences between upstream and downstream.

[0006] Technical Solution: The method for flexible load frequency regulation according to the present invention includes the following steps:

[0007] Divide load regions according to the signal coverage range of distributed load monitoring points in the power grid, and deploy Internet of Things devices within each load region;

[0008] When a frequency change event occurs, obtain the electrical parameters collected by all Internet of Things devices within the load region corresponding to the frequency change event, and establish a frequency regulation characteristic matrix for each distributed load monitoring point under this frequency change event;

[0009] For each element in the frequency modulation characteristic matrix, the conduction difference is analyzed according to the upstream and downstream logical relationship of the power flow direction and the frequency disturbance propagation order, including the conduction difference of the load area itself and the conduction difference of the impact on the downstream load area;

[0010] updating the value of each element in the frequency modulation characteristic matrix according to the conduction difference to obtain a frequency change event conduction matrix;

[0011] Calculating the frequency modulation sensitivity correlation between the two distributed load monitoring points according to the frequency change event conduction matrix of the two distributed load monitoring points;

[0012] According to the relationship between the frequency modulation sensitivity correlation degree and its corresponding threshold value, the two distributed load monitoring points are added to the sequence of objects to be frequency regulated;

[0013] Send the sequence of objects to be frequency regulated to the grid operation and maintenance port for frequency regulation.

[0014] Furthermore, when a frequency change event occurs, obtaining electrical parameters collected by all IoT devices in the load area corresponding to the frequency change event, and establishing a frequency modulation feature matrix for each distributed load monitoring point under the frequency change event includes:

[0015] The IoT devices in each load area form a regional device group. When a frequency change event occurs within the area, the corresponding regional equipment group is triggered to run and generate a frequency characteristic flow , Indicates the load response timestamp of the qth synchronization trigger.

[0016] Furthermore, when a frequency change event occurs, obtaining electrical parameters collected by all IoT devices in the load area corresponding to the frequency change event, and establishing a frequency modulation feature matrix for each distributed load monitoring point under the frequency change event includes:

[0017] Distributed flexible load monitoring points In frequency change events The frequency modulation characteristic matrix under In the example, the number of synchronous triggers is the horizontal dimension, the load area is the vertical dimension, and the element corresponding to the qth row and the eth column is the grid state feature label. ;

[0018] ,in, Represents frequency feature flow Underload area monitoring period, Indicates monitoring period Underload area The power variation within indicates a monitoring period lower load area frequency deviation value in the inner.

[0019] Further, the analysis of the conduction difference according to the upstream and downstream logical relationship of the power flow direction and the frequency disturbance propagation order for each element in the frequency modulation characteristic matrix includes the self-conduction difference of the load area and the impact conduction difference on the downstream load area, including:

[0020] The self-conduction difference of the load area is:

[0021] ;

[0022] The impact conduction difference on the downstream load area is:

[0023] .

[0024] Further, the updating of the value of each element in the frequency modulation characteristic matrix according to the conduction difference to obtain the frequency change event conduction matrix includes:

[0025] If the self-conduction difference of the load area is greater than the impact conduction difference on the downstream load area, then , otherwise ;

[0026] remove the last row and last column of the frequency modulation characteristic matrix and update the value to 0 or 1 to obtain the frequency change event conduction matrix .

[0027] Further, the calculation of the frequency modulation sensitive correlation degree between the two distributed load monitoring points according to the frequency change event conduction matrix of the two distributed load monitoring points includes:

[0028] The frequency change event modulation sensitive correlation degree between the distributed flexible load monitoring points and ;

[0029] wherein, the number of matrix elements with a value of 1 included in the Boolean intersection operation between the frequency change event conduction matrix and , the number of matrix elements with a value of 1 included in the Boolean intersection operation between the frequency change event conduction matrix and . ​

[0030] Further, the adding the two distributed load monitoring points into the sequence of objects to be frequency-adjusted according to the relationship between the frequency-adjustment-sensitive correlation degree and the corresponding threshold value comprises:

[0031] If the frequency-adjustment-sensitive correlation degree is not less than the corresponding threshold value, the two distributed load monitoring points are recorded in the frequency-adjustment control range, the distributed flexible load monitoring points in the frequency-adjustment control range are de-duplicated, and the largest continuous distributed flexible load monitoring points after de-duplication are selected to form the sequence of objects to be frequency-adjusted.

[0032] Further, the load area division according to the signal coverage range of the distributed load monitoring points in the power grid comprises:

[0033] Based on the upstream and downstream logical relationship of the power flow direction and the frequency disturbance propagation sequence, the load response time stamp of the Internet of Things device is calibrated.

[0034] The flexible load frequency adjustment system provided by the application comprises:

[0035] The Internet of Things device deployment unit is configured to divide the load area according to the signal coverage range of the distributed load monitoring points in the power grid, and deploy the Internet of Things device in each load area.

[0036] The frequency adjustment characteristic matrix establishing unit is configured to, when a frequency change event occurs, acquire the electrical parameters collected by all Internet of Things devices in the load area corresponding to the frequency change event, and establish a frequency adjustment characteristic matrix of each distributed load monitoring point under the frequency change event.

[0037] The conduction difference analysis unit is configured to, for each element in the frequency adjustment characteristic matrix, analyze the conduction difference according to the upstream and downstream logical relationship of the power flow direction and the frequency disturbance propagation sequence, including the self-conduction difference of the load area and the influence conduction difference on the downstream load area; and update the values of the elements in the frequency adjustment characteristic matrix according to the conduction difference to obtain a frequency change event conduction matrix.

[0038] The frequency adjustment sensitive analysis unit is configured to calculate the frequency adjustment sensitive correlation degree between the two distributed load monitoring points according to the frequency change event conduction matrix of the two distributed load monitoring points.

[0039] The frequency adjustment unit is configured to add the two distributed load monitoring points into the sequence of objects to be frequency-adjusted according to the relationship between the frequency adjustment sensitive correlation degree and the corresponding threshold value; and send the sequence of objects to be frequency-adjusted to the power grid operation and maintenance port for frequency adjustment.

[0040] The electronic device comprises a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the computer program implements the flexible load frequency modulation method when loaded into the processor.

[0041] The computer readable storage medium stores a computer program, and the computer program implements the flexible load frequency modulation method when executed by the processor.

[0042] Advantages: Compared with the prior art, the present application has the advantages that: based on the conduction difference evaluation of upstream and downstream logic, the self-conduction in the load area and the mutual conduction characteristics between areas of frequency disturbance are determined, and the high-sensitive load area and equipment are automatically identified to realize dynamic monitoring and accurate evaluation of the flexible load frequency modulation characteristics, thereby improving the pertinence and efficiency of frequency modulation control, and the real-time and effectiveness of power grid frequency control. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 The flexible load frequency modulation method of the present application is shown in the flowchart. DETAILED DESCRIPTION

[0044] The technical solutions of the present application are further described below in conjunction with the drawings.

[0045] As shown in the figure, the flexible load frequency modulation method comprises the following steps. Figure 1

[0046] Step S1: setting up distributed flexible load monitoring points and dividing load areas, deploying Internet of Things devices in the load areas, and making each Internet of Things device have running synchronicity.

[0047] The signal coverage range of the distributed flexible load monitoring points is divided into load areas, and Internet of Things devices for collecting electrical parameters are deployed in each load area, the electrical parameters including load response time nodes, power change amounts, and frequency deviation values. Based on the upstream and downstream logical relationship of the power flow direction and the frequency disturbance propagation sequence, the Internet of Things devices are calibrated for load response time stamp synchronicity, and the operation of each Internet of Things device is instructed at the load response time node with synchronicity attribute.

[0048] Step S2: based on the distributed flexible load monitoring points, configuring an identification information library, and generating a regional equipment group.

[0049] An identification information library is configured for each distributed flexible load monitoring point, and the identification information library records the indexes of the distributed flexible load monitoring points, frequency change events, load areas, and Internet of Things devices, including: the mth distributed flexible load monitoring point , the nth frequency change event , and the e load area​ and the rth Internet of Things device .

[0050] All Internet of Things devices in the load area are coordinated and a regional device group is generated , R represents the total number of Internet of Things devices, if a frequency change event occurs in the load area , the operation of each Internet of Things device in the regional device group is triggered, and a frequency characteristic stream with synchronous load response time stamp is generated , , q is the number of synchronous triggering times, T is the initialized load response time stamp, and , represents the gth load response time node, and G represents the total number of load response time nodes;

[0051] In this embodiment, the following parameter settings are taken as examples for illustration.

[0052] Distributed monitoring points: 3 ;

[0053] Load area: 3 (corresponding to the signal coverage range of );

[0054] Internet of Things devices: 2 in each area - , - , - );

[0055] Frequency change event: (frequency deviation initial value 0.5 Hz);

[0056] Monitoring period: synchronous triggering times , each period contains 2 load response event nodes (G=2, i.e. , ).

[0057] Step S3: Capture the electrical parameters collected by all Internet of Things devices under the same frequency change event to generate power grid state feature labels and form a frequency modulation characteristic matrix.

[0058] Based on the frequency characteristic stream and the load area, the electrical parameters collected by all Internet of Things devices in the corresponding load area under the frequency change event are captured to generate power grid state feature labels, denoted as , wherein represents the frequency characteristic stream lower load area monitoring period, representing the monitoring period lower load area power variation amount within the monitoring period, representing the monitoring period lower load area frequency deviation value within the monitoring period the difference between the output power of the load area at the start load response time node and the end load response time node within the monitoring period the difference between the frequency value of the load area at the start load response time node and the end load response time node within the monitoring period.

[0059] A frequency modulation characteristic matrix is established with the number of times of synchronous triggering as the horizontal dimension and the load area as the vertical dimension. The frequency modulation characteristic matrix generated when the frequency variation event is recorded as The matrix element corresponding to the qth row and the e th column in the frequency modulation characteristic matrix is the grid state characteristic label .

[0060] In this embodiment, the load area is taken as an example to collect electrical parameters under frequency variation events, including the following contents.

[0061] Period ( ): power variation amount ; frequency deviation value ;

[0062] Grid state characteristic label: ;

[0063] Similarly, the labels of other areas are generated to construct the frequency modulation characteristic matrix (simplified as 2 rows and 3 columns, , ): .

[0064] Step S4: Select the difference analysis object in the frequency modulation characteristic matrix, and evaluate the conduction difference of the difference analysis object based on the upstream and downstream logical relationship of the power flow direction and the frequency disturbance propagation sequence.

[0065] Based on the frequency modulation characteristic matrix, the matrix element ​For difference analysis objects, the first conduction difference and the second conduction difference in the upstream and downstream logical relationship are evaluated, the first conduction difference refers to the self-conduction difference of the load area when flowing through the monitoring period, and the second conduction difference refers to the influence conduction difference of the load area on the adjacent downstream load area when flowing through the monitoring period:

[0066] ;

[0067] In the formula, the first conduction difference is represented by, the second conduction difference is represented by.

[0068] The maximum value is selected from the first conduction difference and the second conduction difference , if:

[0069] ,

[0070] then let , if:

[0071] ,

[0072] then let .

[0073] In this embodiment, it is taken as an example.

[0074] , ; , that is, it indicates that the conduction influence on the downstream is dominant.

[0075] Step S5: Based on the conduction difference, the frequency modulation characteristic matrix is updated to generate a frequency change event conduction matrix, and the frequency modulation sensitive correlation degree between distributed flexible load monitoring points is evaluated.

[0076] The last row and the last column of the frequency modulation characteristic matrix are removed and updated to 0 or 1 to obtain the frequency change event conduction matrix, denoted as ;

[0077] Based on the frequency change event conduction matrix , the frequency modulation sensitive correlation degree between distributed flexible load monitoring points is evaluated:

[0078] ;

[0079] In the formula, the frequency modulation sensitive correlation degree between the distributed flexible load monitoring points and in the same frequency change event is represented by,​ the number of matrix elements with value 1 contained in the Boolean intersection operation between and , the number of matrix elements with value 1 contained in the Boolean intersection operation between and ;

[0080] Let m = m + 1, and perform iterative evaluation of the frequency change sensitive correlation degree.

[0081] This embodiment takes updating the frequency change event (remove the last row and the last column) as an example for illustration.

[0082] , ;

[0083] Calculate the frequency change sensitive correlation degree of and , .

[0084] Step S6: Based on the frequency change sensitive correlation degree, analyze and generate the sequence of objects to be frequency changed, and send to the power grid operation port.

[0085] A preset frequency change correlation threshold is set, and if the frequency change sensitive correlation degree is greater than or equal to the frequency change correlation threshold, the distributed flexible load monitoring points and are recorded in the frequency change control range , otherwise not recorded in the frequency change control range ;

[0086] The distributed flexible load monitoring points in the frequency change control range are de-duplicated, and after de-duplication, the largest continuous distributed flexible load monitoring points are selected to constitute the sequence of objects to be frequency changed, and sent to the power grid operation port.

[0087] In this embodiment, the preset frequency change correlation threshold is 0.4, and since , the distributed flexible load monitoring points and are recorded in the frequency change control range ; After de-duplication, the largest continuous sequence is selected and sent to the power grid operation port.

[0088] The flexible load frequency change system described in the present application comprises:

[0089] The Internet of Things device deployment unit is used to divide the load area according to the signal coverage range of the distributed load monitoring points in the power grid, and deploy Internet of Things devices inside each load area.

[0090] The frequency modulation characteristic matrix establishing unit is configured to, when a frequency change event occurs, acquire electrical parameters collected by all Internet of Things devices in a load area corresponding to the frequency change event, and establish a frequency modulation characteristic matrix of each distributed load monitoring point under the frequency change event.

[0091] The conduction difference analysis unit is configured to, for each element in the frequency modulation characteristic matrix, analyze conduction differences according to an upstream and downstream logical relationship of a power flow direction and a frequency disturbance propagation sequence, including self-conduction differences of the load area and influence conduction differences on downstream load areas; and update values of elements in the frequency modulation characteristic matrix according to the conduction differences to obtain a frequency change event conduction matrix.

[0092] The frequency modulation sensitivity analysis unit is configured to calculate a frequency modulation sensitivity correlation between two distributed load monitoring points according to the frequency change event conduction matrix of the two distributed load monitoring points.

[0093] The frequency modulation unit is configured to, according to a relationship between the frequency modulation sensitivity correlation and a corresponding threshold value, add the two distributed load monitoring points to a to-be-frequency-modulated object sequence; and send the to-be-frequency-modulated object sequence to a power grid operation and maintenance terminal for frequency modulation.

[0094] The electronic device described in the present application includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program realizes the flexible load frequency modulation method when loaded into the processor.

[0095] The computer readable storage medium described in the present application stores a computer program, and the computer program realizes the flexible load frequency modulation method when executed by the processor.

[0096] The computer readable storage medium can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, flash memory, or any other medium that can be used to store program codes in the form of instructions or data structures and can be accessed by a computer.

[0097] The processor is configured to execute the computer program stored in the memory to realize each step in the method involved in the above-mentioned embodiments.

Claims

1. A method of flexible load frequency regulation, characterized by, The method comprises the following steps: According to the signal coverage range of the distributed load monitoring point in the power grid, the load area is divided, and the Internet of Things device is deployed in each load area; When a frequency change event occurs, the electrical parameters collected by all Internet of Things devices in the load area corresponding to the frequency change event are obtained, and a frequency adjustment characteristic matrix of each distributed load monitoring point under the frequency change event is established; For each element in the frequency adjustment characteristic matrix, the conduction difference is analyzed according to the upstream and downstream logical relationship of the power flow direction and the frequency disturbance propagation sequence, including the self-conduction difference of the load area and the influence conduction difference on the downstream load area; According to the conduction difference, the value of each element in the frequency adjustment characteristic matrix is updated to obtain a frequency change event conduction matrix; According to the frequency change event conduction matrix of the two distributed load monitoring points, the frequency adjustment sensitive correlation between the two distributed load monitoring points is calculated; According to the relationship between the frequency adjustment sensitive correlation and its corresponding threshold value, the two distributed load monitoring points are added to the frequency adjustment object sequence; The frequency adjustment object sequence is sent to the power grid operation and maintenance port for frequency adjustment; The conduction difference includes: The conduction difference K1[P(T q |Q e )→P(T q+1 |Q e )] of the load region itself is: The influence conduction difference K2[P(T q |Q e )→P(T q+1 |Q e+1 )] is: The frequency adjustment sensitive correlation between the two distributed load monitoring points is calculated according to the frequency change event conduction matrix of the two distributed load monitoring points. Frequency change event F n Distributed flexible load monitoring point L m And L m+1 Frequency modulation sensitive correlation degree between wherein NUM[Y(L m |F n )∩Y(L m+1 |F n )] represents the number of matrix elements having a value of 1 included in the Boolean intersection operation between the frequency variation event transmission matrix Y(L m |F n ) and Y(L m+1 |F n ), and NUM[Y(L m |F n )∪Y(L m+1 |F n )] represents the number of matrix elements having a value of 1 included in the Boolean union operation between the frequency variation event transmission matrix Y(L m |F n ) and Y(L m+1 |F n ).

2. The method of flexible load frequency regulation of claim 1, wherein, The frequency change event corresponding to the load area is obtained, and the electrical parameters collected by all Internet of Things devices in the load area are obtained. The Internet of Things devices in each load area form a regional device group, and when a frequency change event occurs in the load area Q e , the corresponding regional device group is triggered to run and generate a frequency feature stream T q |Q e , T q represents the load response timestamp of the qth synchronization trigger.

3. The method of flexible load frequency regulation of claim 2, wherein, The frequency change event corresponding to the load area is obtained, and the electrical parameters collected by all Internet of Things devices in the load area are obtained. Distributed flexible load monitoring point L m In the frequency characteristic matrix X(L n |F m ) under the frequency variation event F n , the number of times of synchronous triggering is numbered as the horizontal dimension, the load area is the vertical dimension, and the corresponding element of the qth row and the e th column is the grid state feature label F n (T q |Q e ). F n (T q |Q e )=[U[P(T q |Q e )], Δf[P(T q |Q e )]], where P(T q |Q e ) represents the frequency characteristic flow T q |Q e Lower load area Q e During the monitoring period, U[P(T q |Q e )] represents the monitoring period P(T q |Q e ) Lower load area Q e The power variation within Δf[P(T q |Q e )] represents the monitoring period P(T q |Q e ) Lower load area Q e The frequency deviation value within.

4. The method of flexible load frequency regulation of claim 1, wherein, The value of each element in the frequency adjustment characteristic matrix is updated according to the conduction difference to obtain a frequency change event conduction matrix. If the self-conduction difference of the load region is greater than the influence-conduction difference on the downstream load region, then let F n (T q |Q e ) = 0, otherwise let F n (T q |Q e ) = 1; The last column of the last row of the frequency modulation characteristic matrix X(L m |F n ) is removed and updated with a value of 0 or 1 to obtain a frequency change event transmission matrix Y(L m |F n ).

5. The method of flexible load frequency regulation of claim 1, wherein, The two distributed load monitoring points are added to the frequency adjustment object sequence according to the relationship between the frequency adjustment sensitive correlation and its corresponding threshold value. If the frequency adjustment sensitive correlation is not less than its corresponding threshold value, the two distributed load monitoring points are recorded in the frequency adjustment control range, the distributed flexible load monitoring points in the frequency adjustment control range are de-duplicated, and the largest continuous distributed flexible load monitoring points are selected to form the frequency adjustment object sequence.

6. The method of flexible load frequency regulation of claim 1, wherein, According to the signal coverage range of the distributed load monitoring point in the power grid, the load area is divided, and the Internet of Things device is deployed in each load area. Based on the upstream and downstream logical relationship of the power flow direction and the frequency disturbance propagation sequence, the Internet of Things device is calibrated for load response timestamp synchronization.

7. A flexible load frequency system, characterized by, It comprises: The Internet of Things device deployment unit is used to divide the load area according to the signal coverage range of the distributed load monitoring point in the power grid, and deploy the Internet of Things device in each load area. The frequency modulation characteristic matrix establishing unit is configured to, when a frequency change event occurs, acquire electrical parameters collected by all Internet of Things devices in a load area corresponding to the frequency change event, and establish a frequency modulation characteristic matrix of each distributed load monitoring point under the frequency change event. The conduction difference analysis unit is configured to, for each element in the frequency modulation characteristic matrix, analyze conduction differences according to an upstream and downstream logical relationship of a power flow direction and a frequency disturbance propagation sequence, including a self conduction difference of the load area and an impact conduction difference on a downstream load area; and update values of elements in the frequency modulation characteristic matrix according to the conduction differences to obtain a frequency change event conduction matrix. The frequency modulation sensitivity analysis unit is configured to calculate a frequency modulation sensitivity correlation between two distributed load monitoring points according to the frequency change event conduction matrix of the two distributed load monitoring points. The frequency modulation unit is configured to, according to a relationship between the frequency modulation sensitivity correlation and a corresponding threshold value, add the two distributed load monitoring points to a to-be-modulated object sequence; and send the to-be-modulated object sequence to a power grid operation and maintenance terminal for frequency modulation. In the conduction difference analysis unit, the conduction difference K1[P(T q |Q e )→P(T q+1 |Q e )] of the load region is: The influence conduction difference K2[P(T q |Q e )→P(T q+1 |Q e+1 )] is: In the frequency modulation sensitive analysis unit, the frequency change event F n The distributed flexible load monitoring point L m And L m+1 The frequency modulation sensitive correlation degree between where NUM[Y(L m |F n )∩Y(L m+1 |F n )] represents the number of matrix elements with a value of 1 included in the Boolean intersection operation between the frequency variation event transmission matrix Y(L m |F n ) and Y(L m+1 |F n ), and NUM[Y(L m |F n )∪Y(L m+1 |F n )] represents the number of matrix elements with a value of 1 included in the Boolean union operation between the frequency variation event transmission matrix Y(L m |F n ) and Y(L m+1 |F n ).

8. The flexible load frequency system of claim 7, wherein, In the frequency characteristic matrix establishing unit, the Internet of Things devices in each load area form a regional device group. When a frequency change event occurs in the load area Q e , the corresponding regional device group is triggered to run and generate a frequency characteristic stream T q |Q e , T q represents the load response timestamp of the qth synchronization trigger.

9. The flexible load frequency system of claim 8, wherein, In the frequency characteristic matrix establishing unit, the distributed flexible load monitoring point L m In the frequency characteristic matrix X(L n |F m ) under the frequency change event F n , the number of synchronous triggering is numbered as the horizontal dimension, the load area is the vertical dimension, and the corresponding element of the qth row and the e th column is the grid state characteristic label F n (T q |Q e ). F n (T q |Q e )=[U[P(T q |Q e )], Δf[P(T q |Q e )]], where P(T q |Q e ) represents the frequency characteristic flow T q |Q e Lower load area Q e During the monitoring period, U[P(T q |Q e )] represents the monitoring period P(T q |Q e ) Lower load area Q e The power variation within Δf[P(T q |Q e )] represents the monitoring period P(T q |Q e ) Lower load area Q e The frequency deviation value within.

10. The flexible load frequency system of claim 7, wherein, In the conduction difference analysis unit, if the self conduction difference of the load region is greater than the influence conduction difference on the downstream load region, then F n (T q |Q e ) = 0, otherwise F n (T q |Q e ) = 1; The last column of the last row of the frequency modulation characteristic matrix X(L m |F n ) is removed and updated with a value of 0 or 1 to obtain the frequency change event transmission matrix Y(L m |F n ).

11. The flexible load frequency system of claim 7, wherein, In the frequency modulation unit, if the frequency modulation sensitivity correlation is not less than the corresponding threshold value, the two distributed load monitoring points are recorded in a frequency modulation control range, distributed flexible load monitoring points in the frequency modulation control range are deduplicated, and after deduplication, the largest continuous distributed flexible load monitoring points are selected to form the to-be-modulated object sequence.

12. The flexible load frequency system of claim 7, wherein, In the Internet of Things device deployment unit, based on an upstream and downstream logical relationship of a power flow direction and a frequency disturbance propagation sequence, the Internet of Things devices are subjected to synchronization calibration of load response time stamps.

13. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The computer program, when loaded into the processor, implements the method for flexible load frequency modulation according to any one of claims 1-6.

14. A computer-readable storage medium, the computer-readable storage medium storing a computer program, characterized in that, The computer program, when executed by the processor, implements the method for flexible load frequency modulation according to any one of claims 1-6.

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