A Network-Load Cooperative Reactive Power Control Method Based on Multidisciplinary Uncertainty Robust Optimization

Abstract

The present invention proposes a network-load collaborative reactive power control method based on multi-uncertainty robust optimization, which relates to the field of reactive power control optimization of distribution networks. The method includes: obtaining historical data of the distribution network, constructing a multi-uncertainty set, selecting influencing factors, calculating the minimum ellipsoid volume containing the historical data of the distribution network through a projection algorithm to obtain the elliptical diagram of the multi-uncertainty set, then constructing a robust optimization model, and calculating the reactive power output range in combination with convex optimization technology, dividing the determined area and the uncertain area of the reactive power, obtaining the node voltage and the reactive power sensitivity index and the dominant node through power flow calculation to construct a reactive power optimization configuration model, and calculating the reactive power optimization control result; the present invention can effectively reduce the network loss of the distribution network, optimize the reactive power configuration of the distribution network, realize robust reactive power optimization, and thus can perform collaborative optimization configuration on the reactive power compensation equipment of the distribution network, ensuring the economic benefits of users.

Description

Technical Field

[0001] The present invention relates to the technical field of reactive power control optimization of distribution networks, and in particular, to a network-load collaborative reactive power control method based on multi-uncertainty robust optimization. Background Art

[0002] In a distribution network, the uncertainties of reactive power sources and loads on the demand side will affect the operation of the distribution network. And to meet user requirements, ensure the safe and stable operation of the distribution network, and reduce economic costs, static reactive power compensation equipment is needed to achieve the purpose of reducing the above goals. However, with the continuous development of renewable energy technologies, traditional reactive power control optimization technologies result in relatively high network losses in the distribution network and it is difficult to adapt to the new situation under a high proportion of distributed renewable energy. Summary of the Invention

[0003] The purpose of the present invention is to provide a network-load collaborative reactive power control method based on multi-uncertainty robust optimization, which can effectively reduce the network losses of the distribution network, optimize the reactive power configuration of the distribution network, and achieve robust reactive power optimization.

[0004] The technical solution of the present invention is as follows:

[0005] The present application provides a network-load collaborative reactive power control method based on multi-uncertainty robust optimization, which includes the following steps:

[0006] S1. Obtain the historical data of the distribution network and construct a multi-uncertainty set by using mathematical methods;

[0007] S2. Select influencing factors from the multi-uncertainty set, and calculate the minimum ellipsoid volume containing the historical data of the distribution network based on the influencing factors through a projection algorithm to obtain an elliptical diagram of the multi-uncertainty set;

[0008] S3. Construct a robust optimization model based on the elliptical diagram of the multi-uncertainty set, and establish deterministic constraint conditions related to the reactive power output range in combination with convex optimization techniques to calculate the reactive power output range;

[0009] S4. Divide the determined area and the uncertain area of the reactive power according to the reactive power output range;

[0010] S5. Based on the divided determined area and uncertain area of the reactive power, obtain the node voltage and the reactive power sensitivity index and the dominant node through power flow calculation to construct a reactive power optimization configuration model, and calculate the reactive power optimization control result with the minimization of the voltage deviation and the total investment cost as the objective function.

[0011] Further, in step S1, the above-mentioned historical data of the distribution network includes the minute-level output curve of a photovoltaic power station within one year, load power time-series data, meteorological monitoring data, and voltage and current time-series values recorded by the grid SCADA.

[0012] Further, in step S2, the above-mentioned influencing factors include photovoltaic output, time-series fluctuations of industrial and residential loads, power factor changes, topological structure changes, and line impedance parameter errors.

[0013] Further, in step S2, the calculation process of obtaining the elliptical diagram of the multiple uncertainty set by calculating the minimum volume ellipsoid containing the historical data of the distribution network based on the influencing factors includes:

[0014] Initialize parameters:

[0015] ,

[0016] ,

[0017] For each data point x i Construct the ellipsoid inclusion constraint:

[0018] ,

[0019] Establish the objective function for minimizing the volume of the ellipsoid:

[0020] ,

[0021] Update the ellipsoid parameters:

[0022] ,

[0023] Obtain the final ellipsoid equation:

[0024] ,

[0025] In the formula, μ (0) is the center of the ellipsoid, N is the number of samples, x i represents the two relevant uncertainty variables of the i th sample, i is the i th sample node, Σ (0) is the covariance matrix, T represents the transpose of the matrix, ϵ i (k) are slack variables, log det(Σ (k) ) is the logarithm of the determinant of the covariance matrix, λ is the penalty coefficient, and k represents the kth iteration.μ (k) and μ (k+1) are the center points of the ellipsoids for the k-th and (k + 1)-th iterations respectively, and Σ (k) and Σ (k+1) are the covariance matrices for the k-th and (k + 1)-th iterations respectively, and Δ μ (k) and ΔΣ (k) are both in the gradient direction, δ is the step size factor, and Σ ϵ i (k) is the sum of the slack variables, x is a two-dimensional vector, is the two-dimensional real space, ℇ is the sum threshold of the slack variables, μ * and Σ * are both the optimal ellipsoid parameters.

[0026] Furthermore, in step S3, the process of constructing a robust optimization model based on the multi-uncertainty set and establishing the deterministic constraint conditions related to the reactive power output range by combining convex optimization techniques includes:

[0027] ,

[0028] ,

[0029] ,

[0030] ,

[0031] ,

[0032] ,

[0033] ,

[0034] ,

[0035] ,

[0036] wherein, f () is a convex optimization function, x () is the reactive power of the -th photovoltaic inverter, Q G () is the active power of the photovoltaic inverter, k represents the -th photovoltaic inverter, k and P G is the active power of the photovoltaic inverter, k represents k the -th photovoltaic inverter, QG ( i ) is the reactive power of the i th photovoltaic inverter, P G ( i ) is the active power of the i th photovoltaic inverter, S G ( i ) is the apparent power of the i th photovoltaic inverter, i is the i th sample node, M is the number of constraint conditions, N is the number of photovoltaic inverters, and N G is the total number of photovoltaic inverters, is the minimum active power of the i th photovoltaic inverter, P G , i is the total active power of all photovoltaic inverters at a certain node, is the maximum active power of the i th photovoltaic inverter, φ is the power factor angle, Q G , k is the reactive power of the k th photovoltaic inverter, Q max G , k is the maximum reactive power of the k th photovoltaic inverter, S G is the apparent power of the photovoltaic inverter, P G ( k ) is the active power of the k th photovoltaic inverter, P G max is the maximum active power of the photovoltaic inverter.

[0037] Furthermore, in step S5, the process of obtaining the node voltage and reactive power sensitivity index and the dominant node through power flow calculation includes:

[0038] Obtain the node voltage and reactive power sensitivity matrix through power flow calculation, and quantify the improvement effect of reactive power compensation at each node on the voltage according to the node voltage and reactive power sensitivity matrix;

[0039] Arrange the nodes in descending order of sensitivity, and select the node with the highest sensitivity in each partition as the dominant node.

[0040] Further, in step S5, the calculation process of constructing the reactive power optimization configuration model includes:

[0041] Taking the sum of minimized voltage deviations as the auxiliary decision objective function:

[0042] ,

[0043] Maximizing the configuration network control function of the photovoltaic inverter to establish the operation objective function of the DPV cluster control system:

[0044] ,

[0045] Establishing the objective function of the total investment cost of reactive power equipment:

[0046] ,

[0047] Calculating the fixed investment cost to construct the reactive power optimization configuration model:

[0048] ,

[0049] ,

[0050] Wherein, minF2 is the sum of minimized voltage deviations, T m is the total number of moments in a day, n is the number of system nodes, i is the i th sample node, U t,i is t the voltage of node i at moment U 0 is the per-unit value of the node voltage, maxF3 represents maximizing the configuration network control function of the photovoltaic inverter, f t,i ( P PVinv ) is the output power of the DPV cluster control system at node i during time t, n fr，t,i is the time t during which the number of free-state inverters in the DPV cluster control system at node i is minC, the minimum total investment cost of reactive power equipment, C fi is the annual investment cost, C om is the annual operation and maintenance cost of reactive power compensation equipment, C SCBfi , C SVGfi andC PV,fi They are the annual investment costs of the control systems of the SCB, SVG, and PV groups respectively, S SCB,i 、 S SVG,i and S PV,i They are respectively the SCB configuration capacity, SVG configuration capacity, and total PV group control system capacity at node i ; S SCB,iCSCB is the standardized SCB configuration capacity at node i ; S SVG,iCSVG is the standardized SVG configuration capacity at node i ; Q SVGmax,i and Q SCBmax,i They are respectively the minimum capacity of a single SVG unit and the capacity of each group of SCBs at node i ; n INSSVG,i is the number of single SVG units installed at node i ; n INSSCB,i is the number of each group of SCBs installed at node i ; R SCBfi 、 R SVGfi They are respectively the equivalent annual value coefficients of the SCB and SVG, and R is the economic coefficient, r is the depreciation rate of the installed equipment, L f is the service life of the installed equipment, L f-1 is the reciprocal of the service life of the equipment.

[0051] Compared with the prior art, the present invention has at least the following advantages or beneficial effects:

[0052] (1) A network-load collaborative reactive power control method based on multi-uncertainty robust optimization of the present invention constructs a robust optimization model through a projection algorithm and combines convex optimization technology, effectively reducing the network loss of the distribution network and optimizing the reactive power configuration of the distribution network, realizing robust reactive power optimization;

[0053] (2) The present invention obtains the node voltage and reactive power sensitivity index and the dominant node through power flow calculation to construct a reactive power optimization configuration model, and calculates the reactive power optimization control result with the minimization of voltage deviation and total investment cost as the objective function, which can perform collaborative optimization configuration on the reactive power compensation equipment of the distribution network and guarantee the economic benefits of users. Brief Description of the Drawings

[0054] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for the embodiments will be briefly introduced below. It should be understood that the following accompanying drawings only show some embodiments of the present invention, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related accompanying drawings can also be obtained based on these drawings.

[0055] Figure 1 It is a flowchart of a method for coordinated reactive power control of network load based on multi - uncertainty robust optimization of the present invention. Detailed implementation manners

[0056] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Usually, the components of the embodiments of the present application described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0057] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application claimed, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0058] It should be noted that similar reference numerals and letters denote similar items in the following accompanying drawings. Therefore, once an item is defined in one accompanying drawing, it does not need to be further defined and explained in subsequent accompanying drawings.

[0059] It should be noted that in this article, the term "comprising" or any other variant thereof is intended to cover a non - exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, the elements defined by the statement "comprising..." do not exclude the presence of additional identical elements in the process, method, article or device comprising the said elements.

[0060] The following will describe in detail some embodiments of the present application with reference to the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0061] Embodiment 1

[0062] Please refer to Figure 1 ,Figure 1 The figure shows the step diagram of a network-load collaborative reactive power control method based on multi-uncertainty robust optimization provided by an embodiment of the present application.

[0063] The present invention provides a network-load collaborative reactive power control method based on multi-uncertainty robust optimization, which includes the following steps:

[0064] S1. Obtain the historical data of the distribution network and construct a multi-uncertainty set by using mathematical methods;

[0065] S2. Select influencing factors from the multi-uncertainty set, and calculate the minimum ellipsoid volume containing the historical data of the distribution network through a projection algorithm based on the influencing factors to obtain the elliptical diagram of the multi-uncertainty set;

[0066] S3. Construct a robust optimization model based on the elliptical diagram of the multi-uncertainty set, and establish deterministic constraint conditions related to the reactive power output range in combination with convex optimization techniques to calculate the reactive power output range;

[0067] S4. Divide the reactive power into a determined area and an uncertain area according to the reactive power output range;

[0068] S5. Based on the divided determined area and uncertain area of the reactive power, obtain the node voltage, reactive power sensitivity index and dominant node through power flow calculation to construct a reactive power optimization configuration model, and calculate the reactive power optimization control result with the minimum voltage deviation and total investment cost as the objective function.

[0069] It should be noted that in step S1, the multi-uncertainty set is the joint probability distribution set of multi-dimensional uncertainty parameters such as photovoltaic output, load fluctuation, environmental variables (irradiance, temperature), etc.; in step S2, the elliptical diagram of the multi-uncertainty set includes the joint distribution of photovoltaic output and load power shown by two-dimensional elliptical projection, the center of the ellipsoid (steady-state operating point), the major / minor axis (fluctuation range), and the confidence interval marked in the figure; in step S3, the reactive power feasible region is divided into a determined area directly used for inverter control and an uncertain area that needs to be combined with dynamic adjustment strategies (such as energy storage charging and discharging, SVG fast response).

[0070] As a preferred implementation manner, in step S1, the historical data of the distribution network includes the minute-level output curve of the photovoltaic power station in one year, the time-series data of the load power, the meteorological monitoring data, and the time-series values of the voltage and current recorded by the grid SCADA.

[0071] As a preferred implementation manner, in step S2, the influencing factors include photovoltaic output, time-series fluctuations of industrial and residential loads, power factor changes, topological structure changes, and line impedance parameter errors.

[0072] As a preferred embodiment, in step S2, the calculation process of calculating the minimum volume ellipsoid containing the historical data of the distribution network by the projection algorithm based on the influencing factors to obtain the elliptical diagram of the multiple uncertainty sets includes:

[0073] Initialize parameters:

[0074] ,

[0075] ,

[0076] For each data point x i Construct the ellipsoid inclusion constraint:

[0077] ,

[0078] Establish the objective function for minimizing the volume of the ellipsoid:

[0079] ,

[0080] Update the ellipsoid parameters:

[0081] ,

[0082] Obtain the final ellipsoid equation:

[0083] ,

[0084] In the formula, μ (0) is the center of the ellipsoid, N is the number of samples, x i represents the two related uncertainty variables of the i th sample, i is the i th sample node, Σ (0) is the covariance matrix, T represents the transpose of the matrix, ϵ i (k) is the slack variable, log det(Σ (k) ) is the logarithm of the determinant of the covariance matrix, λ is the penalty coefficient, k represents the kth iteration, μ (k) , μ (k+1) are the centers of the ellipsoid at the kth and (k + 1)th iterations respectively, Σ (k) , Σ (k+1) are the covariance matrices at the kth and (k + 1)th iterations, Δ μ (k) and ΔΣ (k) are both gradient directions, δ is the step size factor, Σϵ i (k) For the sum of slack variables, x is a two-dimensional vector, is the two-dimensional real space, ℇ is the sum threshold of the slack variables, μ * and Σ * are both optimal ellipsoid parameters.

[0085] As a preferred implementation, in step S3, the process of constructing a robust optimization model based on the elliptical graph of the multiple uncertainty sets and establishing the deterministic constraint conditions related to the reactive power output range by combining convex optimization techniques includes:

[0086] ,

[0087] ,

[0088] ,

[0089] ,

[0090] ,

[0091] ,

[0092] ,

[0093] ,

[0094] ,

[0095] In the formula, f ( x ) is a convex optimization function, Q G ( k ) is the reactive power of the k th photovoltaic inverter, P G is the active power of the photovoltaic inverter, k represents the k th photovoltaic inverter, Q G ( i ) is the reactive power of the i th photovoltaic inverter, P G ( i ) is the reactive power of the i th photovoltaic inverter, S G ( i ) is thei The apparent power of a photovoltaic inverter, i is the i th sample node, M is the number of constraint conditions, N is the number of photovoltaic inverters, and N G is the total number of photovoltaic inverters, is the i th minimum active power of a photovoltaic inverter, P G , i is the total active power of all photovoltaic inverters at a certain node, is the i th maximum active power of a photovoltaic inverter, φ is the power factor angle, Q G , k is the k th reactive power of a photovoltaic inverter, Q max G , k is the k th maximum reactive power of a photovoltaic inverter, S G is the apparent power of a photovoltaic inverter, P G ( k ) is the k th active power of a photovoltaic inverter, P G max is the maximum active power of a photovoltaic inverter.

[0096] As a preferred implementation manner, in step S5, the process of obtaining the node voltage and the reactive power sensitivity index and the dominant node through power flow calculation includes:

[0097] Obtain the node voltage and reactive power sensitivity matrix through power flow calculation, and quantify the improvement effect of reactive power compensation at each node on the voltage according to the node voltage and reactive power sensitivity matrix;

[0098] Arrange the nodes in descending order of sensitivity, and select the node with the highest sensitivity in each partition as the dominant node. ]>

[0099] As a preferred implementation manner, in step S5, the calculation process of constructing the reactive power optimization configuration model includes:

[0100] Minimize the sum of voltage deviations as the auxiliary decision objective function:

[0101] ,

[0102] Maximize the configuration network control function of the photovoltaic inverter to establish the operation objective function of the DPV cluster control system:

[0103] ,

[0104] Establish the objective function of the total investment cost of reactive power equipment:

[0105] ,

[0106] Calculate the fixed investment cost to construct the reactive power optimization configuration model:

[0107] ,

[0108] ,

[0109] In the formula, minF2 is the sum of minimized voltage deviations, T m is the total number of moments in a day, n is the number of system nodes, i is the i th sample node, U t,i is t the voltage of node i at moment U 0 is the per-unit value of the node voltage, maxF3 represents maximizing the configuration network control function of the photovoltaic inverter, f t,i ( P PVinv ) is the output power of the DPV cluster control system at node i at time t, n fr，t,i is the time t during which the number of free-state inverters in the DPV cluster control system at node i is minC, the minimum total investment cost of reactive power equipment, C fi is the annual investment cost, C om is the annual operation and maintenance cost of the reactive power compensation equipment, C SCBfi , C SVGfi and C PV,fi are the annual investment costs of the SCB, SVG, and PV group control systems respectively, S SCB,i , S SVG,i and S PV,i are the nodes iThe SCB configuration capacity, SVG configuration capacity, and total photovoltaic group control system capacity at S SCB,iCSCB are for node i The SCB standardized configuration capacity at S SVG,iCSVG is for node i The SVG standardized configuration capacity at Q SVGmax,i and Q SCBmax,i are respectively the minimum capacity of a single SVG unit and the capacity of each group of SCBs at node i The capacity of each group of SCBs at n INSSVG,i is the number of SVG single units installed at node i The number of SVG single units installed at n INSSCB,i is the number of each group of SCBs installed at node i The number of each group of SCBs installed at R SCBfi and R SVGfi are respectively the equivalent annual value coefficients of SCB and SVG, and R is the economic coefficient. r is the depreciation rate of the installed equipment. L f is the service life of the installed equipment. L f-1 is the reciprocal of the service life of the equipment.

[0110] It can be understood that the structure shown in the figure is only schematic. A network-load collaborative reactive power control method based on multi-uncertainty robust optimization may also include more or fewer components than those shown in the figure, or have a different configuration from that shown in the figure. Each component shown in the figure can be implemented by hardware, software, or a combination thereof.

[0111] In the embodiments provided in the present application, it should be understood that the disclosed method can also be implemented in other ways. The embodiments described above are merely illustrative. For example, the flowcharts or block diagrams in the accompanying drawings show the possible architectures, functions, and operations of the methods and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0112] In addition, each functional module in various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0113] If the described functions are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs that can store program code.

[0114] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0115] For those skilled in the art, it is obvious that the present application is not limited to the details of the above-mentioned exemplary embodiments, and without departing from the spirit or basic characteristics of the present application, the present application can be implemented in other specific forms. Therefore, in any regard, the embodiments should be regarded as exemplary and non-limiting. The scope of the present application is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed within the present application. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A network-load collaborative reactive power control method based on multi-uncertainty robust optimization, characterized in that It includes the following steps: S1. Obtain the historical data of the distribution network and construct multiple uncertainty sets by using mathematical methods; S2. Select influencing factors from the multiple uncertainty sets, and calculate the minimum ellipsoid volume containing the historical data of the distribution network based on the influencing factors through a projection algorithm to obtain the elliptical diagram of the multiple uncertainty sets; S3. Construct a robust optimization model based on the elliptical diagram of the multiple uncertainty sets, and establish deterministic constraint conditions related to the reactive power output range in combination with convex optimization techniques to calculate the reactive power output range; S4. Divide the deterministic region and the uncertain region of the reactive power according to the reactive power output range; S5. Based on the divided deterministic region and uncertain region of the reactive power, obtain the node voltage, reactive power sensitivity index and dominant nodes through power flow calculation to construct a reactive power optimization configuration model, and calculate the reactive power optimization control result with the minimum voltage deviation and total investment cost as the objective function.

2. The reactive power control method for network-load coordination based on multi-uncertainty robust optimization according to claim 1, characterized in that In step S1, the historical data of the distribution network includes the minute-level output curve of the photovoltaic power station in one year, the load power time-series data, the meteorological monitoring data, and the voltage and current time-series values recorded by the grid SCADA.

3. A network-load collaborative reactive power control method based on multi-uncertainty robust optimization according to claim 1, characterized in that In step S2, the influencing factors include the photovoltaic output, the time-series fluctuations of industrial and residential loads, the power factor changes, the topology structure changes, and the line impedance parameter errors.

4. The method for network-load collaborative reactive power control based on multi-uncertainty robust optimization according to claim 3, wherein, In step S2, the calculation process of calculating the minimum volume ellipsoid containing the historical data of the distribution network through a projection algorithm based on the influencing factors to obtain the elliptical diagram of the multiple uncertainty sets includes: Initialize parameters: ， ， For each data point x i Construct ellipsoidal inclusion constraints: ， Establish the objective function for minimizing the ellipsoid volume: ， Update the ellipsoid parameters: ， Obtain the final ellipsoid equation: ， wherein, μ (0) is the center of the ellipsoid, N is the number of samples, x i represents two related uncertainty variables of the i th sample, i is the i th sample node, Σ (0) is the covariance matrix, T represents the transpose of the matrix, ϵ i (k) are slack variables, log det(Σ (k) ) is the logarithm of the determinant of the covariance matrix, λ is the penalty coefficient, k represents the k-th iteration, μ (k) , μ (k+1) are the centers of the ellipsoids of the k-th and (k + 1)-th iterations respectively, Σ (k) , Σ (k+1) are the covariance matrices of the k-th and (k + 1)-th iterations, Δ μ (k) and ΔΣ (k) are both gradient directions, δ is the step size factor, Σ ϵ i (k) is the total sum of the slack variables, x is a two-dimensional vector, is the two-dimensional real number space, ℇ is the total sum threshold of the slack variables, μ * and Σ * are both the optimal ellipsoid parameters.

5. A network-load collaborative reactive power control method based on multi-uncertainty robust optimization according to claim 1, characterized in that In step S3, the calculation process of constructing a robust optimization model based on the elliptical diagram of the multiple uncertainty sets and establishing deterministic constraint conditions related to the reactive power output range in combination with convex optimization techniques includes: ， ， ， ， ， ， ， ， ， In the formula, f ( x ) is a convex optimization function, Q G ( k ) is the reactive power of the k th photovoltaic inverter, P G is the active power of the photovoltaic inverter, k represents the k th photovoltaic inverter, Q G ( i ) is the reactive power of the i th photovoltaic inverter, P G ( i ) is the active power of the i th photovoltaic inverter, S G ( i ) is the apparent power of the i th photovoltaic inverter, i is the i th sample node, M is the number of constraint conditions, N is the number of photovoltaic inverters, and N G is the total number of photovoltaic inverters, is the minimum active power of the i th photovoltaic inverter, P G , i is the total active power of all photovoltaic inverters at a certain node, is the i th photovoltaic inverter's maximum active power, φ is the power factor angle, Q G , k is the k th photovoltaic inverter's reactive power, Q max G , k is the k th photovoltaic inverter's maximum reactive power, S G is the apparent power of the photovoltaic inverter, P G ( k ) is the k th photovoltaic inverter's active power, P G max is the maximum active power of the photovoltaic inverter.

6. The reactive power control method for network-load coordination based on multi-uncertainty robust optimization according to claim 1, characterized in that In step S5, the process of obtaining the node voltage, reactive power sensitivity index and dominant nodes through power flow calculation includes: Obtain the node voltage and reactive power sensitivity matrix through power flow calculation, and quantify the improvement effect of reactive power compensation of each node on the voltage according to the node voltage and reactive power sensitivity matrix; Arrange the nodes in descending order of sensitivity, and select the node with the highest sensitivity in each partition as the dominant node.

7. The reactive power control method for network-load coordination based on multi-uncertainty robust optimization according to claim 1, characterized in that In step S5, the calculation process of constructing a reactive power optimization configuration model includes: The sum of the minimized voltage deviations is used as the auxiliary decision-making objective function: ， Maximize the configuration network control function of the photovoltaic inverter to establish the operation objective function of the DPV cluster control system: ， Establish the objective function of the total investment cost of reactive power equipment: ， Calculate the fixed investment cost to construct the reactive power optimization configuration model: ， ， where minF2 is the sum of minimized voltage deviations, T m is the total number of moments in a day, n is the number of system nodes, i is the i th sample node, U t,i is t the voltage of node i at moment U 0 is the per-unit value of the node voltage, maxF3 represents maximizing the configuration network control function of the PV inverter, f t,i ( P PVinv ) is the output power of the DPV cluster control system at node i at time t, n fr，t,i is the time t during which the number of free-state inverters in the DPV cluster control system at node i , minC is the minimum total investment cost of reactive power equipment, C fi is the annual investment cost, C om is the annual operation and maintenance cost of the reactive power compensation equipment, C SCBfi , C SVGfi and C PV,fi are the annual investment costs of the SCB, SVG, and PV group control systems respectively, S SCB,i , S SVG,i and S PV,i are the SCB configuration capacity, SVG configuration capacity, and total PV group control system capacity at node i respectively, S SCB,iCSCB is the SCB standardized configuration capacity at node i , S SVG,iCSVG is the SVG standardized configuration capacity at node i , Q SVGmax,i and Q SCBmax,i are the minimum capacity of a single SVG unit and the capacity of each group of SCBs at node i respectively, n INSSVG,i is the number of SVG single units installed at node i , n INSSCB,i For the number of each group of SCBs installed on the node i , R SCBfi , R SVGfi are the equivalent annual value coefficients of SCB and SVG respectively, R is the economic coefficient, r is the depreciation rate of the installed equipment, L f is the service life of the installed equipment, L f-1 is the reciprocal of the service life of the equipment.

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