Network load cooperative reactive power control method based on multiple uncertainty robust optimization

Abstract

The invention provides a network load cooperative reactive power control method based on multiple uncertainty robust optimization, and relates to the field of power distribution network reactive power control optimization, and the method comprises the steps: obtaining the historical data of a power distribution network, constructing a multiple uncertainty set, selecting an influence factor, and carrying out the optimization of the multiple uncertainty set; the volume of a minimized ellipsoid containing historical data of a power distribution network is calculated through a projection algorithm to obtain an ellipsoid diagram of a multiple uncertainty set, then a robust optimization model is constructed, a reactive power output range is obtained through calculation in combination with a convex optimization technology, a determined region and an uncertain region of reactive power are divided, and the reactive power is obtained. Node voltage, a reactive power sensitivity index and a dominant node are obtained through load flow calculation to construct a reactive power optimization configuration model, and a reactive power optimization control result is obtained through calculation; according to the method, the network loss of the power distribution network can be effectively reduced, the reactive power configuration of the power distribution network can be optimized, and robust reactive power optimization can be realized, so that the reactive power compensation equipment of the power distribution network can be subjected to collaborative optimization configuration, and the economic benefit of a user is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of reactive power control optimization of distribution networks, and more particularly, 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. In order to meet user needs, ensure the safe and stable operation of the distribution network, and reduce economic costs, static reactive power compensation equipment is required to achieve the above objectives. However, with the continuous development of renewable energy technology, traditional reactive power control optimization technology results in high network losses in the distribution network and 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: The present application provides a network-load collaborative reactive power control method based on multi-uncertainty robust optimization, which includes the following steps: S1. Obtain the historical data of the distribution network and construct a multi-uncertainty set using mathematical methods; 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 an elliptical diagram of the multi-uncertainty set; 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 technology to calculate the reactive power output range; S4. Divide the determined area and the uncertain area of reactive power according to the reactive power output range; S5. Based on the divided determined area and uncertain area of 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.

[0005] Further, in step S1, the historical data of the distribution network includes the minute-level output curve of a photovoltaic power station in one year, load power time-series data, meteorological monitoring data, and voltage and current time-series values recorded by the grid SCADA.

[0006] Further, in step S2, the above influencing factors include photovoltaic output, temporal fluctuations of industrial and residential loads, power factor changes, topological structure changes, and line impedance parameter errors.

[0007] 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 through the projection algorithm includes: Initialize parameters: , , For each data point x i Construct the ellipsoid inclusion constraint: , Establish the objective function for minimizing the volume of the ellipsoid: , Update the ellipsoid parameters: , Obtain the final ellipsoid equation: , 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, k represents the kth iteration, μ (k) , μ (k+1) are the centers of the ellipsoids for the kth and (k + 1)th iterations respectively, Σ (k) , Σ (k+1) are the covariance matrices for the kth 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 sum of the slack variables, x is a two-dimensional vector, is the two-dimensional real number space,ℇ is the sum threshold of slack variables, μ * and Σ * are both optimal ellipsoid parameters.

[0008] Furthermore, in step S3, the process of constructing a robust optimization model based on the multi - uncertainty set of the ellipse diagram and establishing the deterministic constraint conditions related to the reactive power output range by combining 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 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.

[0009] 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: 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; Arrange the nodes in descending order of sensitivity, and select the node with the highest sensitivity in each partition as the dominant node.

[0010] Furthermore, in step S5, the calculation process of constructing the reactive power optimization configuration model includes: The sum of minimized voltage deviations is used as the auxiliary decision 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: , , 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 For t the voltage at the time node i , 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, which 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 standardized configuration capacity of the SCB at node i , S SVG,iCSVG is the standardized configuration capacity of the SVG at node i , Q SVGmax,i and Q SCBmax,i are the minimum capacity of a single unit of SVG and the capacity of each group of SCB at node i respectively, n INSSVG,i is the number of SVG single units installed at node i , n INSSCB,i is the number of each group of SCB installed at node i , R SCBfi , R SVGfiThe equivalent annual value coefficients of the SCB and SVG respectively, 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.

[0011] Compared with the prior art, the present invention has at least the following advantages or beneficial effects: (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, achieving robust reactive power optimization. (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 collaboratively optimize the configuration of reactive power compensation equipment in the distribution network and ensure the economic benefits of users. Description of the Drawings

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0013] Figure 1 is the flowchart of a network-load collaborative reactive power control method based on multi-uncertainty robust optimization of the present invention. Detailed Embodiments

[0014] 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 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 shown in the drawings here can be arranged and designed in various different configurations.

[0015] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application claimed, but merely represents the selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0016] It should be noted that like reference numerals and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.

[0017] It should be noted that in this text, the term "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitation, an element defined by the statement "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the said element.

[0018] The following will, with reference to the accompanying drawings, elaborate on some embodiments of the present application. Without conflict, the following various embodiments and the various features in the embodiments may be combined with each other.

[0019] Embodiment 1 Please refer to Figure 1 , Figure 1 which shows a 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.

[0020] The present invention provides a network-load collaborative reactive power control method based on multi-uncertainty robust optimization, which includes the following steps: S1. Obtain the historical data of the distribution network and construct a multi-uncertainty set using mathematical methods; 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 an elliptical diagram of the multi-uncertainty set; 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; S4. Divide the determined region and the uncertain region of the reactive power according to the reactive power output range; S5. Based on the divided determined region and uncertain region 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 voltage deviation and total investment cost as the objective function.

[0021] It should be noted that in step S1, the multiple uncertainty set is a set of joint probability distributions of multi-dimensional uncertainty parameters such as photovoltaic power output, load fluctuation, and environmental variables (irradiance, temperature); in step S2, the elliptical diagram of the multiple uncertainty set includes showing the joint distribution of photovoltaic power output and load power with a two-dimensional elliptical projection, marking the center of the ellipsoid (steady-state operating point), the major / minor axis (fluctuation range), and the confidence interval in the figure; in step S3, the reactive power feasible region is divided into a determined region directly used for inverter control; and an uncertain region that needs to be combined with dynamic adjustment strategies (such as energy storage charging and discharging, rapid response of SVG).

[0022] As a preferred implementation manner, in step S1, the historical data of the distribution network includes the minute-level power 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.

[0023] As a preferred implementation manner, in step S2, the influencing factors include the photovoltaic power output, the time-series fluctuations of industrial and residential loads, the change of power factor, the change of topological structure, and the error of line impedance parameters.

[0024] As a preferred implementation manner, 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 through the projection algorithm includes: Initializing parameters: , , For each data point x i Constructing the ellipsoid inclusion constraint: , Establishing the objective function for minimizing the volume of the ellipsoid: , Updating the ellipsoid parameters: , Obtaining the final ellipsoid equation: , 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) is a slack variable, 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 at the k-th and (k + 1)-th iterations respectively, Σ (k) 、Σ (k+1) are the covariance matrices at the k-th and (k + 1)-th iterations, Δ μ (k) and ΔΣ (k) are both in the gradient direction, δ is the step size factor, 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.

[0025] As a preferred implementation, in step S3, the process of constructing a robust optimization model based on the multi-uncertainty set's ellipse diagram and establishing the deterministic constraint conditions related to the reactive power output range by combining 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 thei The reactive power of a 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.

[0026] As a preferred implementation manner, in step S5, the process of obtaining the node voltage, reactive power sensitivity index, and dominant node through power flow calculation includes: Obtaining the node voltage and reactive power sensitivity matrix through power flow calculation, and quantifying the improvement effect of reactive power compensation at each node on the voltage according to the node voltage and reactive power sensitivity matrix; Arranging the nodes in descending order of sensitivity, and selecting the node with the highest sensitivity in each partition as the dominant node.

[0027] As a preferred implementation manner, in step S5, the calculation process of constructing the reactive power optimal configuration model includes: Taking the sum of minimized voltage deviations as the auxiliary decision 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: , , 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 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 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, SSCB,iCSCB is the standardized configuration capacity of the SCB at the node i . S SVG,iCSVG is the standardized configuration capacity of the SVG at the node i . Q SVGmax,i and Q SCBmax,i are respectively the minimum capacity of a single unit of SVG and the capacity of each group of SCBs at the node i . n INSSVG,i is the number of single SVG units installed at the node i . n INSSCB,i is the number of each group of SCBs installed at the node i . R SCBfi , R SVGfi are respectively the equal 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.

[0028] 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 configuration different from that shown in the figure. Each component shown in the figure can be implemented by hardware, software, or a combination thereof.

[0029] In the embodiments provided in this 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 this 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.

[0030] In addition, each functional module in the various embodiments of this application can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0031] If the above 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 this 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 can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The foregoing 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 codes.

[0032] The above are only the preferred embodiments of this application and are not used to limit this application. For those skilled in the art, this application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included in the protection scope of this application.

[0033] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

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 through a projection algorithm based on the influencing factors 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 determined area and the uncertain area of the reactive power according to the reactive power output range; S5. Based on the divided determined area and uncertain area 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 method for coordinated reactive power control of network and load based on multi-uncertainty robust optimization according to claim 1, wherein, 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 photovoltaic output, time series fluctuations of industrial and residential loads, power factor changes, topological structure changes, and line impedance parameter errors.

4. The method for coordinated reactive power control of network and load based on multi-uncertainty robust optimization according to claim 3, characterized in that, 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 an objective function for minimizing the ellipsoid volume: ， Update the ellipsoid parameters: ， Obtain the final ellipsoid equation: ， 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) are the 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 ellipsoid for the k-th and (k + 1)-th iterations respectively, Σ (k) , Σ (k+1) are the covariance matrices for the k-th and (k + 1)-th iterations, Δ μ (k) and ΔΣ (k) are both the gradient directions, δ is the step size factor, 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.

5. The reactive power control method for network-load coordination 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: ， ， ， ， ， ， ， ， ， Wherein, 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 rd sample node, M is the number of constraint conditions, N is the number of photovoltaic inverters, 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.

6. The method for coordinated reactive power control of network and load 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, wherein 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 an 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 an objective function for the total investment cost of reactive power equipment: ， Calculate the fixed investment cost to construct a 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 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 configured capacities of the SCB, SVG, and total PV group control system at node i respectively, S SCB,iCSCB is the standardized configured capacity of the SCB at node i , S SVG,iCSVG is the standardized configured capacity of the SVG at node i , Q SVGmax,i and Q SCBmax,i are the minimum capacity of a single unit of the SVG and the capacity of each group of SCB at node i respectively, n INSSVG,i is the number of single SVG 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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