Dynamic reactive power optimization method and system based on discrete control variable master-slave classification space-time decoupling

Abstract

The invention provides a discrete variable master-slave classification space-time decoupling-based dynamic reactive power optimization method, which comprises the following steps of S1, performing static reactive power optimization on 24 time periods by utilizing load prediction data before a scheduling day, and giving an action table of discrete control variables; s2, dividing the control variables into a master-slave relation according to the electrical distance between the control variables and the master node; and S3, obtaining an action table of all control variables meeting action constraint conditions one by one through an ordered sample clustering method. According to the method, a dynamic optimization problem is converted into a static optimization problem, the solving model is simplified, the solving difficulty is reduced, and coordinated optimization among a plurality of discrete control variables such as the on-load tap changing transformer and the capacitor bank can be realized.

Description

Technical Field

[0001] The invention belongs to the technical field of reactive power planning of power systems, and relates to a dynamic reactive power optimization method based on discrete variable master-slave classification time-space decoupling, which is particularly suitable for processing multi-period dynamic reactive power optimization problems containing multiple discrete control variables. Background Art

[0002] In recent years, a large number of renewable energy sources have been connected to the grid for power generation, which has caused or is causing major changes in the energy structure of the power system. These changes have brought many new features to the power system, as well as many new problems. However, no matter how the power system changes, reactive voltage optimization control is always the main measure for the power grid to achieve safe, stable and economical operation. In the process of dynamic reactive voltage optimization, if the constraints on the number of daily actions of various discrete devices are considered, the actions of each control device have a strong correlation, that is, the action of a discrete control device at a certain moment will directly or indirectly affect whether other discrete control devices act and the size of the adjustment value, which greatly increases the complexity of solving the problem.

[0003] With the introduction and application of a large number of intelligent optimization algorithms, more ideas and methods are provided for solving various optimization problems in power systems. However, it is still difficult to directly solve the dynamic reactive power optimization problem that needs to consider the number of discrete device actions, and the solution process is still relatively complicated. At present, reducing the spatiotemporal coupling of discrete control variables by various means is still the key to solving dynamic reactive power optimization problems.

[0004] Therefore, how to provide a dynamic reactive power optimization method is a problem that needs to be solved urgently. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and seek to design a dynamic reactive power optimization method based on discrete variable master-slave classification time-space decoupling, so as to accurately and conveniently provide a day-ahead planning table of discrete control variables for reactive power voltage control problems for power system operators. Scheduling the gear position of the on-load tap changer transformer and the switching of capacitor banks according to the planning table within a day is a favorable measure to ensure the safe, stable and economical operation of the power system.

[0006] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0007] In order to achieve the above object, the present invention proposes a dynamic reactive power optimization method based on discrete variable master-slave classification time-space decoupling.

[0008] The method comprises: step S1, using the load forecast data before the dispatch day, performing static reactive power optimization for 24 time periods, and providing an action table of discrete control variables;

[0009] Step S2, dividing the control variables into master-slave relationships according to the electrical distance between the control variables and the master node;

[0010] Step S3, obtaining the action table of all control variables satisfying the action constraint condition one by one through the ordered sample clustering method.

[0011] Optionally, the step S1 specifically includes: under certain constraints, with the goal of minimizing network loss, using an improved particle swarm algorithm to perform static reactive power optimization for each time period, optimizing the values ​​of discrete control variables, and obtaining an action table of the discrete control variables.

[0012] Optionally, step S2 specifically includes: in a radial distribution network, starting from a main node connected to an upper-level power grid, the control variable that is closer to the main node in electrical distance is more important to reactive voltage control and has a wider impact range, and the master-slave relationship is distinguished accordingly.

[0013] Optionally, the step S3 specifically includes:

[0014] Step S31, according to the clustering method of ordered samples, in the order of master first and slave later, the most important control variables are first grouped according to the maximum number of actions allowed, and the distance between two adjacent samples in time sequence is calculated by the following formula:

[0015]

[0016] n d H is the number of individuals in the new sample formed by merging two adjacent samples; d is the value of the dth sample in the new sample, is the cluster center of the new sample, and the expression is:

[0017]

[0018] Select the two samples with the smallest distance to merge, and repeat the process of formula (1) and formula (2) until the number of samples meets the constraint of the maximum number of actions; if there are two groups of two adjacent samples with the same distance in the above process, and only one group of two adjacent samples can be merged, the two adjacent samples with the smallest difference between the maximum and minimum values ​​of the predicted net load in the two groups are merged into one group;

[0019] Step S32, rounding the mean of each merged sample to obtain the value of the discrete device.

[0020] Step S33, re-perform static optimization on the time periods when the value of the current main control variable changes in turn, and optimize the values ​​of all its slave control variables.

[0021] Step S34, repeating steps S2-S3 for the secondary control variables in sequence until the gear changes of all control variables meet the constraint conditions.

[0022] The present invention also proposes a dynamic reactive power optimization system based on discrete variable master-slave classification time-space decoupling, comprising:

[0023] The discrete control variable action table generation module uses the load forecast data before the dispatch day to perform static reactive power optimization for 24 time periods and provide the discrete control variable action table;

[0024] The master-slave relationship module divides the control variables into master-slave relationships according to the electrical distance between the control variables and the master node;

[0025] The action table output module obtains the action table of all control variables that meet the action constraints one by one through the ordered sample clustering method.

[0026] Optionally, the discrete control variable action table generation module specifically includes: under certain constraints, with the goal of minimizing network loss, using an improved particle swarm algorithm to perform static reactive power optimization for each time period, optimizing the values ​​of discrete control variables, and obtaining the action table of the discrete control variables.

[0027] Optionally, the master-slave relationship module specifically includes: in a radial distribution network, starting from a main node connected to the upper-level power grid, the control variable that is closer to the main node in electrical distance is more important to reactive voltage control and has a wider impact range, and its master-slave relationship is distinguished accordingly.

[0028] Optionally, the action table output module specifically includes:

[0029] Step S31, according to the clustering method of ordered samples, in the order of master first and slave later, the most important control variables are first grouped according to the maximum number of actions allowed, and the distance between two adjacent samples in time sequence is calculated by the following formula:

[0030]

[0031] n d H is the number of individuals in the new sample formed by merging two adjacent samples; d is the value of the dth sample in the new sample, is the cluster center of the new sample, and the expression is:

[0032]

[0033] Select the two samples with the smallest distance to merge, and repeat the process of formula (1) and formula (2) until the number of samples meets the constraint of the maximum number of actions; if there are two groups of two adjacent samples with the same distance in the above process, and only one group of two adjacent samples can be merged, the two adjacent samples with the smallest difference between the maximum and minimum values ​​of the predicted net load in the two groups are merged into one group;

[0034] Step S32, rounding the mean of each merged sample to obtain the value of the discrete device.

[0035] Step S33, re-perform static optimization on the time periods in which the value of the current main control variable changes in turn, and optimize the values ​​of all its slave control variables.

[0036] Step S34, repeating steps S2-S3 for the secondary controlled variables in sequence until the gear changes of all controlled variables meet the constraint conditions.

[0037] The present invention also proposes a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, a dynamic reactive power optimization method based on discrete variable master-slave classification time-space decoupling is implemented, and the method includes the following steps:

[0038] Step S1, using the load forecast data before the dispatch day, static reactive power optimization is performed for 24 time periods, and an action table of discrete control variables is given;

[0039] Step S2, dividing the control variables into master-slave relationships according to the electrical distance between the control variables and the master node;

[0040] Step S3, obtaining the action table of all control variables satisfying the action constraint condition one by one through the ordered sample clustering method.

[0041] Optionally, the step S3 specifically includes:

[0042] Step S31, according to the clustering method of ordered samples, in the order of master first and slave later, the most important control variables are first grouped according to the maximum number of actions allowed, and the distance between two adjacent samples in time sequence is calculated by the following formula:

[0043]

[0044] n d H is the number of individuals in the new sample formed by merging two adjacent samples; d is the value of the dth sample in the new sample, is the cluster center of the new sample, and the expression is:

[0045]

[0046] Select the two samples with the smallest distance to merge, and repeat the process of formula (1) and formula (2) until the number of samples meets the constraint of the maximum number of actions; if there are two groups of two adjacent samples with the same distance in the above process, and only one group of two adjacent samples can be merged, the two adjacent samples with the smallest difference between the maximum and minimum values ​​of the predicted net load in the two groups are merged into one group;

[0047] Step S32, rounding the mean of each merged sample to obtain the value of the discrete device.

[0048] Step S33, re-perform static optimization on the time periods in which the value of the current main control variable changes in turn, and optimize the values ​​of all its slave control variables.

[0049] Step S34, repeating steps S2-S3 for the secondary controlled variables in sequence until the gear changes of all controlled variables meet the constraint conditions.

[0050] The day-ahead planning of discrete control variables involved in the present invention can be obtained on the basis of static planning by only increasing the amount of calculation slightly.

[0051] Compared with the prior art, the dynamic programming operation principle of the present invention is simple and reliable, easy to implement, and fully meets engineering needs.

[0052] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 It is a flow chart of a simplified dynamic reactive power optimization method based on master-slave classification time-space decoupling of the present invention;

[0055] Figure 2 To improve the IEEE33 system structure diagram;

[0056] Figure 3 This is the forecast curve of net load in 24 periods of a typical day in spring;

[0057] Figure 4 This is a schematic diagram comparing the network loss in 24 time periods between static optimization and dynamic optimization;

[0058] Figure 5A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0059] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0060] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0061] As used herein, unless otherwise specified, the term "plurality" means two or more than two.

[0062] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0063] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0064] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0065] The present invention proposes a simplified dynamic reactive power optimization method based on master-slave classification time-space decoupling, such as Figure 1 As shown, the method includes:

[0066] Step S1, using the load forecast data before the dispatch day, static reactive power optimization is performed for 24 time periods, and an action table of discrete control variables is given;

[0067] Step S2, dividing the control variables into master-slave relationships according to the electrical distance between the control variables and the master node;

[0068] Step S3, obtaining the action table of all control variables satisfying the action constraint condition one by one through the ordered sample clustering method.

[0069] Optionally, the step S1 specifically includes: under certain constraints, with the goal of minimizing network loss, using an improved particle swarm algorithm to perform static reactive power optimization for each time period, optimizing the values ​​of discrete control variables, and obtaining an action table of the discrete control variables.

[0070] Optionally, step S2 specifically includes: in a radial distribution network, starting from a main node connected to an upper-level power grid, the control variable that is closer to the main node in electrical distance is more important to reactive voltage control and has a wider impact range, and the master-slave relationship is distinguished accordingly.

[0071] Optionally, the step S3 specifically includes:

[0072] Step S31, according to the clustering method of ordered samples, in the order of master first and slave later, the most important control variables are first grouped according to the maximum number of actions allowed, and the distance between two adjacent samples in time sequence is calculated by the following formula:

[0073]

[0074] n d H is the number of individuals in the new sample formed by merging two adjacent samples; d is the value of the dth sample in the new sample, is the cluster center of the new sample, and the expression is:

[0075]

[0076] Select the two samples with the smallest distance to merge, and repeat the process of formula (1) and formula (2) until the number of samples meets the constraint of the maximum number of actions; if there are two groups of two adjacent samples with the same distance in the above process, and only one group of two adjacent samples can be merged, the two adjacent samples with the smallest difference between the maximum and minimum values ​​of the predicted net load in the two groups are merged into one group;

[0077] Step S32, rounding the mean of each merged sample to obtain the value of the discrete device.

[0078] Step S33, re-perform static optimization on the time periods in which the value of the current main control variable changes in turn, and optimize the values ​​of all its slave control variables.

[0079] Step S34, repeating steps S2-S3 for the secondary controlled variables in sequence until the gear changes of all controlled variables meet the constraint conditions.

[0080] The day-ahead planning of discrete control variables involved in the present invention can be obtained on the basis of static planning by only increasing the amount of calculation slightly.

[0081] The method of the present invention is described in detail below with reference to specific embodiments.

[0082] This embodiment verifies the simplified dynamic reactive power optimization algorithm based on master-slave classification and spatiotemporal decoupling. Figure 2 Taking the improved IEEE33 system shown in the figure as an example, according to the net load forecast data of a typical day in spring, the day-ahead dynamic reactive power optimization is performed on the example, and the action table of the on-load tap-changing distribution transformer and capacitor bank is obtained. The result is compared with the network loss value of the static optimization result, which proves the effectiveness and feasibility of this method.

[0083] In the actual calculation, a reactive power optimization model with equality constraints and inequality constraints was established. The objective function of this model is to minimize the active power loss of the distribution network a day ago. The control variables include the number of capacitor banks (CB) switching groups and the tap position of the on-load tap changer (OLTC). The objective function is as follows:

[0084]

[0085] The equality constraint is the power flow equation:

[0086]

[0087] Inequality constraints are constraints on control variables and state variables:

[0088] T kmin ≤T k ≤T kmax

[0089] Qcmin ≤Q c ≤Q cmax

[0090]

[0091] V i,min ≤V i ≤V i,max

[0092] Where: X is a random variable, including the size of the load and the output of renewable energy generation. D is a control variable, including the number of capacitor bank switching groups and the OLTC tap position. u is a state variable, including the node voltage amplitude and phase angle. V i 、V j Represents the voltage amplitude of nodes i and j, V i,max and V i,min Represents the upper and lower limits of the voltage amplitude at node i. ij is the phase difference between the voltages at nodes i and j, G ij is the conductance of the tie line between nodes i and j, B ij is the susceptance of the tie line between nodes i and j, and N is the number of nodes. is , Q is Represents the active power and reactive power injected into node i. k is the transformation ratio of the kth transformer, T kmax , T kmin It is the upper and lower limits of the transformation ratio. c is the compensation capacity of the Cth reactive power compensation device, Q cmax , Q cmin It is the upper and lower limits of the compensation capacity. kmax 、N cmax Indicates the maximum number of operations of the transformer and compensation device in one day.

[0093] Figure 3 The prediction curve of net load in 24 periods of a typical day in spring is given. Usually the load is relatively large during working hours, but due to the high proportion of photovoltaic grid-connected power generation, the net load during this period is the lowest.

[0094] according to Figure 2 The electrical distances between the on-load tap-changing transformer OLTC and the capacitor banks CB1 and VB2 and the main node 0 are determined in the order of master first and slave later. Matlab is used as the simulation tool to determine that OLTC switches gears at 5, 9, 16, 22, and 24, CB1 is switched at 5, 9, 15, 22, and 24, and CB2 is switched at 4, 10, 14, 21, and 24, all of which meet the constraint requirement of the maximum number of actions being 5. The planning results are shown in Table 1.

[0095] Table 1

[0096]

[0097] Comparison of network loss in 24 time periods between static optimization and dynamic optimization Figure 4 As shown, the ordinate W represents the active network loss, and the abscissa t represents the time period. As can be seen from the figure, the total network loss of dynamic planning is 2636.5Kwh, and the total network loss of static planning is 2610.9Kwh. The total network loss for the whole day is only 25.8Kwh different. Among them, the dynamic network loss and the static network loss values ​​are the same in time periods 2, 3, 10, 12 and 22, because the gear positions of the static planning and dynamic planning control devices in these time periods are consistent. Although the network loss is different in some time periods, the difference is also small. This is because the number of actions of the adjustable equipment in dynamic optimization is limited, while the adjustable equipment in static optimization can be adjusted at will, which causes the active network loss of dynamic optimization to increase slightly.

[0098] The above analysis results show that this method converts the dynamic optimization problem into a static optimization problem, simplifies the solution model, reduces the difficulty of solution, and can achieve coordinated optimization among multiple discrete control variables such as on-load tap-changing transformers and capacitor banks. The method proposed in this invention can fully meet engineering needs.

[0099] In one embodiment, the present invention also proposes a dynamic reactive power optimization system based on discrete variable master-slave classification and spatiotemporal decoupling, including: a discrete control variable action table generation module, which uses the load forecast data before the dispatch day to perform static reactive power optimization for 24 time periods and provide an action table for discrete control variables; a master-slave relationship module, which divides the control variables into master-slave relationships according to the electrical distance between the control variables and the master node; and an action table output module, which obtains the action tables of all control variables that meet the action constraints one by one through an ordered sample clustering method.

[0100] Optionally, the discrete control variable action table generation module specifically includes: under certain constraints, with the goal of minimizing network loss, using an improved particle swarm algorithm to perform static reactive power optimization for each time period, optimizing the values ​​of discrete control variables, and obtaining the action table of the discrete control variables.

[0101] Optionally, the master-slave relationship module specifically includes: in a radial distribution network, starting from a main node connected to an upper-level power grid, the control variable that is closer to the main node in electrical distance is more important to reactive voltage control and has a wider impact range, and the master-slave relationship is distinguished accordingly.

[0102] Optionally, the action table output module specifically includes:

[0103] Step S31, according to the clustering method of ordered samples, in the order of master first and slave later, the most important control variables are first grouped according to the maximum number of actions allowed, and the distance between two adjacent samples in time sequence is calculated by the following formula:

[0104]

[0105] n d H is the number of individuals in the new sample formed by merging two adjacent samples; d is the value of the dth sample in the new sample, is the cluster center of the new sample, and the expression is:

[0106]

[0107] Select the two samples with the smallest distance to merge, and repeat the process of formula (1) and formula (2) until the number of samples meets the constraint of the maximum number of actions; if there are two groups of two adjacent samples with the same distance in the above process, and only one group of two adjacent samples can be merged, the two adjacent samples with the smallest difference between the maximum and minimum values ​​of the predicted net load in the two groups are merged into one group;

[0108] Step S32, rounding the mean of each merged sample to obtain the value of the discrete device.

[0109] Step S33, re-perform static optimization on the time periods in which the value of the current main control variable changes in turn, and optimize the values ​​of all its slave control variables.

[0110] Step S34, repeating steps S2-S3 for the secondary controlled variables in sequence until the gear changes of all controlled variables meet the constraint conditions.

[0111] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0112] Those skilled in the art will understand that Figure 5The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0113] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0114] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0115] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0116] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A dynamic reactive power optimization method based on discrete variable master-slave classification time-space decoupling, characterized in that: The following steps are involved: Step S1, using the load forecast data before the dispatch day, static reactive power optimization is performed for 24 time periods, and an action table of discrete control variables is given; Step S2, dividing the control variables into master-slave relationships according to the electrical distance between the control variables and the master node; Step S3, obtaining the action table of all control variables satisfying the action constraint condition one by one through the ordered sample clustering method.

2. A dynamic reactive power optimization method based on discrete variable master-slave classification time-space decoupling as claimed in claim 1, characterized in that: The step S1 specifically includes: under certain constraints, with the goal of minimizing network loss, using an improved particle swarm algorithm to perform static reactive power optimization for each time period, optimizing the values ​​of discrete control variables, and obtaining an action table of the discrete control variables.

3. A dynamic reactive power optimization method based on discrete variable master-slave classification time-space decoupling as claimed in claim 1, characterized in that: The step S2 specifically includes: in a radial distribution network, starting from a main node connected to an upper-level power grid, the control variable that is closer to the main node in electrical distance is more important to reactive voltage control and has a wider impact range, and the master-slave relationship is distinguished accordingly.

4. A dynamic reactive power optimization method based on discrete variable master-slave classification time-space decoupling as claimed in claim 1, characterized in that: The step S3 specifically includes: Step S31, according to the clustering method of ordered samples, in the order of master first and slave later, the most important control variables are first grouped according to the maximum number of actions allowed, and the distance between two adjacent samples in time sequence is calculated by the following formula: n d H is the number of individuals in the new sample formed by merging two adjacent samples; d is the value of the dth sample in the new sample, is the cluster center of the new sample, and the expression is: Select the two samples with the smallest distance to merge, and repeat the process of formula (1) and formula (2) until the number of samples meets the constraint of the maximum number of actions; if there are two groups of two adjacent samples with the same distance in the above process, and only one group of two adjacent samples can be merged, the two adjacent samples with the smallest difference between the maximum and minimum values ​​of the predicted net load in the two groups are merged into one group; Step S32, rounding the mean of each merged sample to obtain the value of the discrete device. Step S33, re-perform static optimization on the time periods when the value of the current main control variable changes in turn, and optimize the values ​​of all its slave control variables. Step S34, repeating steps S2-S3 for the secondary control variables in sequence until the gear changes of all control variables meet the constraint conditions.

5. A dynamic reactive power optimization system based on discrete variable master-slave classification time-space decoupling, characterized in that: include: The discrete control variable action table generation module uses the load forecast data before the dispatch day to perform static reactive power optimization for 24 time periods and provide the discrete control variable action table; The master-slave relationship module divides the control variables into master-slave relationships according to the electrical distance between the control variables and the master node; The action table output module obtains the action table of all control variables that meet the action constraints one by one through the ordered sample clustering method.

6. A dynamic reactive power optimization system based on discrete variable master-slave classification time-space decoupling as claimed in claim 5, characterized in that: The discrete control variable action table generation module specifically includes: under certain constraints, with the goal of minimizing network loss, using an improved particle swarm algorithm to perform static reactive power optimization for each time period, optimizing the values ​​of discrete control variables, and obtaining the action table of the discrete control variables.

7. A dynamic reactive power optimization system based on discrete variable master-slave classification time-space decoupling as claimed in claim 5, characterized in that: The master-slave relationship module specifically includes: in a radial distribution network, starting from a main node connected to the upper-level power grid, the control variable that is closer to the main node in electrical distance is more important to reactive voltage control and has a wider impact range, and its master-slave relationship is distinguished accordingly.

8. A dynamic reactive power optimization system based on discrete variable master-slave classification time-space decoupling as claimed in claim 5, characterized in that: The action table output module specifically includes: Step S31, according to the clustering method of ordered samples, in the order of master first and slave later, the most important control variables are first grouped according to the maximum number of actions allowed, and the distance between two adjacent samples in time sequence is calculated by the following formula: n d H is the number of individuals in the new sample formed by merging two adjacent samples; d is the value of the dth sample in the new sample, is the cluster center of the new sample, and the expression is: Select the two samples with the smallest distance to merge, and repeat the process of formula (1) and formula (2) until the number of samples meets the constraint of the maximum number of actions; if there are two groups of two adjacent samples with the same distance in the above process, and only one group of two adjacent samples can be merged, the two adjacent samples with the smallest difference between the maximum and minimum values ​​of the predicted net load in the two groups are merged into one group; Step S32, rounding the mean of each merged sample to obtain the value of the discrete device. Step S33, re-perform static optimization on the time periods when the value of the current main control variable changes in turn, and optimize the values ​​of all its slave control variables. Step S34, repeating steps S2-S3 for the secondary control variables in sequence until the gear changes of all control variables meet the constraint conditions.

9. A computer device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, a dynamic reactive power optimization method based on discrete variable master-slave classification time-space decoupling is implemented, characterized in that: The method comprises the following steps: Step S1, using the load forecast data before the dispatch day, static reactive power optimization is performed for 24 time periods, and an action table of discrete control variables is given; Step S2, dividing the control variables into master-slave relationships according to the electrical distance between the control variables and the master node; Step S3, obtaining the action table of all control variables satisfying the action constraint condition one by one through the ordered sample clustering method.

10. A computer device as claimed in claim 9, characterized in that: The step S3 specifically includes: Step S31, according to the clustering method of ordered samples, in the order of master first and slave later, the most important control variables are first grouped according to the maximum number of actions allowed, and the distance between two adjacent samples in time sequence is calculated by the following formula: n d H is the number of individuals in the new sample formed by merging two adjacent samples; d is the value of the dth sample in the new sample, is the cluster center of the new sample, and the expression is: Select the two samples with the smallest distance to merge, and repeat the process of formula (1) and formula (2) until the number of samples meets the constraint of the maximum number of actions; if there are two groups of two adjacent samples with the same distance in the above process, and only one group of two adjacent samples can be merged, the two adjacent samples with the smallest difference between the maximum and minimum values ​​of the predicted net load in the two groups are merged into one group; Step S32, rounding the mean of each merged sample to obtain the value of the discrete device. Step S33, re-perform static optimization on the time periods when the value of the current main control variable changes in turn, and optimize the values ​​of all its slave control variables. Step S34, repeating steps S2-S3 for the secondary control variables in sequence until the gear changes of all control variables meet the constraint conditions.

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