Method and device for obtaining reactive power support capability index of photovoltaic power station

Abstract

The application provides a reactive power support capability index acquisition method and device of a photovoltaic power station, and relates to the technical field of new energy sources.The method comprises the following steps: obtaining a multi-source coordinated reactive power voltage optimization model; determining preset initial values of Lagrange multipliers, collected node voltages and reactive power source outputs as initial variables of an optimality condition decomposition algorithm, and solving the reactive power voltage optimization model by using the optimality condition decomposition algorithm to obtain a reactive power support capability index.The device executes the above method.The reactive power support capability index acquisition method and device provided in the application can improve the reactive power support capability index calculation efficiency of the photovoltaic power station, thereby providing technical support for the reactive power support capability evaluation of the photovoltaic power station.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, specifically to a method and apparatus for obtaining reactive power support capacity indicators of photovoltaic power plants. Background Technology

[0002] The weak synchronous support of the DC sending-end grid is characterized by insufficient local synchronous power supply and weak disturbance immunity. It is prone to problems such as overvoltage caused by continuous commutation failures due to large-scale renewable energy aggregation, and power oscillations caused by impedance mismatch between renewable energy units and the flexible DC grid, which increases operational risks and control difficulties. In addition, existing monitoring methods can only obtain the operating status information of renewable energy power plants, and cannot obtain in-depth information such as their grid connection adaptability and support capabilities. In particular, the comprehensive analysis of the reactive power / voltage support capabilities of renewable energy power plants with multiple reactive power regulation resources under different operating conditions becomes more difficult.

[0003] Timely understanding of the reactive power / voltage support capacity, operational risks, and safety boundaries of each renewable energy power plant to the power grid is crucial for developing effective control measures and ensuring the operational stability of renewable energy power plants and even renewable energy bases. To meet the safe and stable operation requirements of voltage at each node in a photovoltaic power plant, and considering the presence of various reactive power regulation resources within the plant such as photovoltaic inverters and SVG, a multi-source coordinated reactive power and voltage optimization model is established, fully exploring its own reactive power regulation potential. This model assesses the reactive power regulation capability of the photovoltaic power plant in real time based on the current operating status.

[0004] Existing methods for calculating the reactive power support capacity index of photovoltaic power plants are time-consuming due to the complexity of the models, and there is an urgent need to improve the calculation efficiency. Summary of the Invention

[0005] To address the problems in the prior art, embodiments of the present invention provide a method and apparatus for obtaining reactive power support capacity indicators of photovoltaic power plants, which can at least partially solve the problems existing in the prior art.

[0006] On the one hand, this invention proposes a method for obtaining the reactive power support capability index of a photovoltaic power station, including:

[0007] Obtain a multi-source coordinated reactive power and voltage optimization model; the objective function of the reactive power and voltage optimization model is to minimize the active power loss of the power grid in the new energy base area.

[0008] The constraints of the reactive voltage optimization model include inequality constraints and equality constraints.

[0009] The inequality constraints include the fluctuation range of the reactive power output within its reactive power regulation range, and the fluctuation range of the node voltage within its corresponding voltage level.

[0010] The equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area should satisfy.

[0011] The initial values ​​of the Lagrange multipliers, the collected node voltages, and the output of the reactive power source are determined as the initial variables of the optimality condition decomposition algorithm. The optimality condition decomposition algorithm is then used to solve the reactive power voltage optimization model to obtain the reactive power support capacity index.

[0012] The step of solving the reactive voltage optimization model using the optimality condition decomposition algorithm includes:

[0013] The active power loss obtained from the decomposition and corresponding to each region is determined as the first solution term of the objective function, and the product of the equality constraints of each region in the objective function and their corresponding Lagrange multipliers is determined as the second solution term of the objective function.

[0014] The equality constraints of each region obtained from the decomposition are determined as the first constraint condition for solving the objective function, and the inequality constraints of each region obtained from the decomposition are determined as the second constraint condition for solving the objective function.

[0015] Obtain the sub-problem search direction; the sub-problem search direction is a first sub-problem search direction that includes node voltage and reactive power output, and a second sub-problem search direction that includes Lagrange multipliers;

[0016] Update the node voltage and reactive power output according to the search direction of the first subproblem, and update the Lagrange multipliers according to the search direction of the second subproblem;

[0017] Calculate the updated data corresponding to each region. If the optimality condition decomposition algorithm satisfies the preset convergence condition based on the summary calculation results, then terminate the calculation.

[0018] The process of obtaining the search direction for the sub-problem includes:

[0019] The nonlinear interior point method is used to handle the inequality constraints;

[0020] The search direction for the subproblem is obtained by solving the results using a modified Newton's method.

[0021] The process of obtaining the search direction for the sub-problem includes:

[0022] The search direction for the sub-problem is calculated using distributed computing.

[0023] The method for obtaining the reactive power support capacity index of the photovoltaic power station also includes:

[0024] The updated data corresponding to each region is calculated using distributed computing. The calculation results of each distributed computing method are received and aggregated to obtain the aggregated calculation result.

[0025] The method for obtaining the reactive power support capacity index of the photovoltaic power station also includes:

[0026] If the optimality condition decomposition algorithm does not meet the preset convergence condition based on the summary calculation results, then the process of obtaining the subproblem search direction and subsequent steps will continue.

[0027] On one hand, this invention proposes a device for obtaining the reactive power support capacity index of a photovoltaic power station, comprising:

[0028] The acquisition unit is used to acquire a multi-source coordinated reactive power and voltage optimization model; the objective function of the reactive power and voltage optimization model is to minimize the active power loss of the power grid in the new energy base area.

[0029] The constraints of the reactive voltage optimization model include inequality constraints and equality constraints.

[0030] The inequality constraints include the fluctuation range of the reactive power output within its reactive power regulation range, and the fluctuation range of the node voltage within its corresponding voltage level.

[0031] The equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area should satisfy.

[0032] The solution unit is used to determine the preset initial value of the Lagrange multiplier, the collected node voltage, and the output of the reactive power source as the initial variables of the optimality condition decomposition algorithm, and to use the optimality condition decomposition algorithm to solve the reactive power voltage optimization model to obtain the reactive power support capacity index.

[0033] Specifically, the solution unit is used for:

[0034] The active power loss obtained from the decomposition and corresponding to each region is determined as the first solution term of the objective function, and the product of the equality constraints of each region in the objective function and their corresponding Lagrange multipliers is determined as the second solution term of the objective function.

[0035] The equality constraints of each region obtained from the decomposition are determined as the first constraint condition for solving the objective function, and the inequality constraints of each region obtained from the decomposition are determined as the second constraint condition for solving the objective function.

[0036] Obtain the sub-problem search direction; the sub-problem search direction is a first sub-problem search direction that includes node voltage and reactive power output, and a second sub-problem search direction that includes Lagrange multipliers;

[0037] Update the node voltage and reactive power output according to the search direction of the first subproblem, and update the Lagrange multipliers according to the search direction of the second subproblem;

[0038] Calculate the updated data corresponding to each region. If the optimality condition decomposition algorithm satisfies the preset convergence condition based on the summary calculation results, then terminate the calculation.

[0039] Specifically, the solution unit is also used for:

[0040] The nonlinear interior point method is used to handle the inequality constraints;

[0041] The search direction for the subproblem is obtained by solving the results using a modified Newton's method.

[0042] Specifically, the solution unit is also used for:

[0043] The search direction for the sub-problem is calculated using distributed computing.

[0044] The reactive power support capacity index acquisition device for the photovoltaic power station is also used for:

[0045] The updated data corresponding to each region is calculated using distributed computing. The calculation results of each distributed computing method are received and aggregated to obtain the aggregated calculation result.

[0046] The reactive power support capacity index acquisition device for the photovoltaic power station is also used for:

[0047] If the optimality condition decomposition algorithm does not meet the preset convergence condition based on the summary calculation results, then the process of obtaining the subproblem search direction and subsequent steps will continue.

[0048] In another aspect, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the following method:

[0049] Obtain a multi-source coordinated reactive power and voltage optimization model; the objective function of the reactive power and voltage optimization model is to minimize the active power loss of the power grid in the new energy base area.

[0050] The constraints of the reactive voltage optimization model include inequality constraints and equality constraints.

[0051] The inequality constraints include the fluctuation range of the reactive power output within its reactive power regulation range, and the fluctuation range of the node voltage within its corresponding voltage level.

[0052] The equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area should satisfy.

[0053] The initial values ​​of the Lagrange multipliers, the collected node voltages, and the output of the reactive power source are determined as the initial variables of the optimality condition decomposition algorithm. The optimality condition decomposition algorithm is then used to solve the reactive power voltage optimization model to obtain the reactive power support capacity index.

[0054] This invention provides a computer-readable storage medium, comprising:

[0055] The computer-readable storage medium stores a computer program that, when executed by a processor, implements the following method:

[0056] Obtain a multi-source coordinated reactive power and voltage optimization model; the objective function of the reactive power and voltage optimization model is to minimize the active power loss of the power grid in the new energy base area.

[0057] The constraints of the reactive voltage optimization model include inequality constraints and equality constraints.

[0058] The inequality constraints include the fluctuation range of the reactive power output within its reactive power regulation range, and the fluctuation range of the node voltage within its corresponding voltage level.

[0059] The equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area should satisfy.

[0060] The initial values ​​of the Lagrange multipliers, the collected node voltages, and the output of the reactive power source are determined as the initial variables of the optimality condition decomposition algorithm. The optimality condition decomposition algorithm is then used to solve the reactive power voltage optimization model to obtain the reactive power support capacity index.

[0061] The present invention provides a method and apparatus for obtaining reactive power support capability indicators of photovoltaic power plants, which obtains a multi-source coordinated reactive power and voltage optimization model. The objective function of the reactive power and voltage optimization model is to minimize the active power loss of the power grid in the new energy base area. The constraints of the reactive power and voltage optimization model include inequality constraints and equality constraints. The inequality constraints include the fluctuation range of the output of the reactive power source within its reactive power regulation range, and the fluctuation range of the node voltage within its corresponding voltage level within a specified range. The equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area should satisfy. The preset initial value of the Lagrange multiplier, the collected node voltage, and the output of the reactive power source are determined as the initial variables of the optimality condition decomposition algorithm, and the optimality condition decomposition algorithm is used to solve the reactive power and voltage optimization model to obtain the reactive power support capability indicators. This can improve the calculation efficiency of the reactive power support capability indicators of photovoltaic power plants, and thus provide technical support for the assessment of the reactive power support capability of photovoltaic power plants. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0063] Figure 1 This is a flowchart illustrating a method for obtaining reactive power support capacity indicators of a photovoltaic power station according to an embodiment of the present invention.

[0064] Figure 2 This is a schematic diagram illustrating the network topology of the power grid in the new energy base area provided in an embodiment of the present invention.

[0065] Figure 3 This is a schematic diagram of the structure of a device for obtaining reactive power support capacity index of a photovoltaic power station according to an embodiment of the present invention.

[0066] Figure 4 This is a schematic diagram of the physical structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments and descriptions of the present invention are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0068] Figure 1 This is a flowchart illustrating a method for obtaining reactive power support capacity indicators of a photovoltaic power station according to an embodiment of the present invention, as shown below. Figure 1 As shown in the embodiment of the present invention, the method for obtaining the reactive power support capability index of a photovoltaic power station includes:

[0069] Step S1: Obtain a multi-source coordinated reactive power and voltage optimization model; the objective function of the reactive power and voltage optimization model is to minimize the active power loss of the power grid in the new energy base area.

[0070] The constraints of the reactive voltage optimization model include inequality constraints and equality constraints.

[0071] The inequality constraints include the fluctuation range of the reactive power output within its reactive power regulation range, and the fluctuation range of the node voltage within its corresponding voltage level.

[0072] The equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area should satisfy.

[0073] Step S2: Determine the initial values ​​of the Lagrange multipliers, the collected node voltages, and the output of the reactive power source as the initial variables of the optimality condition decomposition algorithm, and use the optimality condition decomposition algorithm to solve the reactive power voltage optimization model to obtain the reactive power support capacity index.

[0074] In step S1 above, the device obtains a multi-source coordinated reactive voltage optimization model; the objective function of the reactive voltage optimization model is to minimize the active power loss of the power grid in the new energy base area.

[0075] The constraints of the reactive voltage optimization model include inequality constraints and equality constraints.

[0076] The inequality constraints include the fluctuation range of the reactive power output within its reactive power regulation range, and the fluctuation range of the node voltage within its corresponding voltage level.

[0077] The equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area must satisfy. The device can be a computer or similar equipment, such as a server, that executes the method. The acquisition, storage, use, and processing of data in this application's technical solution all comply with relevant national laws and regulations.

[0078] Photovoltaic power plants typically equip their low-voltage side transformers with reactive power compensation devices. However, photovoltaic inverters themselves also possess strong reactive power regulation capabilities. Photovoltaic power plants should fully utilize their own reactive power regulation capabilities to achieve inverter-based reactive power voltage control. Therefore, the assessment of a photovoltaic power plant's reactive power support capability to the power grid is conducted within the context of multi-reactive power source coordination. The magnitude of reactive power injected into the grid by a renewable energy power plant directly determines the strength of its reactive power support capability to the grid connection point and even the entire grid. Therefore, the output of reactive power sources in a renewable energy power plant is an important indicator for measuring the plant's reactive power support capability. Furthermore, while renewable energy power plants provide reactive power support to the grid, the active power losses caused by the reactive power allocation scheme and the economic operation requirements of the entire system should be fully considered. Therefore, the active power losses of the renewable energy base are selected as another important indicator for evaluating the reactive power support capability of renewable energy power plants.

[0079] like Figure 2 As shown, a regional power grid with weak synchronous support for the DC transmission end of the new energy base is selected. Among them... This indicates the grid connection voltage of the photovoltaic power station in the power grid of the new energy base area. This indicates the low-voltage side voltage of the main transformer in a photovoltaic power station.

[0080] The objective function of the reactive voltage optimization model is explained as follows:

[0081] Active power loss f in the power grid of the new energy base area loss It is mainly divided into active power loss f on the grid side. loss1and the active power loss f inside the photovoltaic power station loss2 The objective function is as follows:

[0082] min f loss In equation (1-1), f loss This indicates the active power loss of the power grid in the new energy base area.

[0083]

[0084] In the formula: f loss1 G represents the active power loss on the grid side of the power grid in the new energy base area, where N represents the number of nodes on the grid side of the regional power grid, and G represents the active power loss on the grid side. gij U represents the conductance between node i and node j on the grid side of the regional power grid. gi and U gj Let θ represent the voltage magnitudes at nodes i and j, respectively. ij This represents the phase angle difference between node i and node j.

[0085]

[0086] In the formula: m represents the number of collector lines in a large-scale photovoltaic power plant, n represents the number of photovoltaic power generation units on each collector line, and G kij U represents the electrical conductance between photovoltaic power generation unit node i and node j on the k-th collector line. ki_2 and U kj_2 Let θ represent the voltages on the low-voltage side of the box-type transformers in the i-th and j-th photovoltaic power generation units on the k-th collector line, respectively. ki2_kj2 G represents the phase angle difference between the low-voltage side voltages of the box-type transformers in the i-th and j-th photovoltaic power generation units on the k-th collector line; Tki Let G represent the conductance of the box-type transformer of the i-th photovoltaic power generation unit on the k-th collector line. Assuming that the box-type transformers used in all photovoltaic power generation units have the same parameters, then G... Tki For the same constant value, U ki_1 and U ki_2 Let θ represent the voltages on the high-voltage and low-voltage sides of the box-type transformer in the i-th photovoltaic power generation unit on the k-th collector line, respectively. ki1_ki2 This represents the phase angle difference between the high-voltage and low-voltage sides of the box-type transformer in the i-th photovoltaic power generation unit on the k-th collector line.

[0087] The inequality constraints for the reactive power voltage optimization model are explained below:

[0088] To ensure the safe and stable operation of the system, the output of each reactive power source should be within its allowable range, and the node voltage should fluctuate within the specified range of its corresponding voltage level.

[0089] Q imin ≤Q i≤Q imax (1-4)

[0090] In the formula: Q i Q represents the reactive power output of the i-th reactive power source in the power grid of the new energy base area. imax and Q imin These represent the upper limit and lower limit of the reactive power adjustment range for the i-th reactive power source, respectively.

[0091] U imin ≤U i ≤U imax (1-5)

[0092] In the formula: U i U represents the voltage of node i in the power grid of the new energy base area (including the grid side and the interior of large photovoltaic power plants). imax and U imin These represent the upper and lower voltage limits corresponding to the voltage at that node, respectively.

[0093] The equality constraints of the reactive power voltage optimization model are explained as follows:

[0094] Each node of the power grid in the new energy base area should satisfy the power flow equation constraints.

[0095]

[0096]

[0097] In the formula: M represents the number of nodes in the power grid of the new energy base area (including the grid side and the interior of large photovoltaic power plants), G ij and B ij P represents the conductance and susceptance between node i and node j in the power grid of the new energy base area (including the grid side and the interior of large photovoltaic power plants), respectively. is and Q is U represents the active power injection and reactive power injection at node i, respectively. i and U j Let θ represent the voltage amplitudes at nodes i and j in the power grid of the new energy base area, respectively. ij This represents the phase angle difference between node i and node j.

[0098] In step S2 above, the device determines the preset initial value of the Lagrange multiplier, the collected node voltage, and the output of the reactive power source as the initial variables of the optimality condition decomposition algorithm, and uses the optimality condition decomposition algorithm to solve the reactive power voltage optimization model to obtain the reactive power support capacity index.

[0099] Based on the above, a multi-source coordinated reactive power and voltage optimization model was established to evaluate the reactive power and voltage support capacity of photovoltaic power plants. However, it can be seen that due to the large number of nodes within the photovoltaic power plant and the multivariate coupling characteristics of the equality constraints, the evaluation problem becomes a large-scale nonlinear nonconvex problem with complex constraints. If a conventional centralized algorithm is used, it will lead to a large computational scale and slow calculation speed of the reactive power support capacity index, which cannot meet the requirements of real-time solution. Therefore, the optimal condition decomposition algorithm is proposed to solve the aforementioned problem. The optimal condition decomposition algorithm aims to decompose the original large-scale optimization problem into several regional sub-problems, which are calculated by the reactive power and voltage control centers in their respective regions. This can realize hierarchical distributed solution between the regional power grid dispatch center and the plant-level reactive power and voltage control centers, as well as parallel solution between the plant-level reactive power and voltage control centers. Only a few variables need to be interacted in each iteration, which can improve computational efficiency and evaluate the reactive power support capacity of photovoltaic power plants in real time according to the current operating status of the power grid.

[0100] The optimality condition decomposition algorithm is explained as follows:

[0101] Optimality condition decomposition can be considered a special form of Lagrange relaxation, inspired by the natural decomposition of the optimality conditions of the original problem. General mathematical programming problems typically have the following structure:

[0102]

[0103] The constraints are:

[0104] a(x)=0 (2-2)

[0105] b(x)≤0 (2-3)

[0106] c(x)=0 (2-4)

[0107] d(x)≤0 (2-5)

[0108] In formulas (2-1) to (2-5), f(x): IR n →IR, a(x): b(x): c(x): d(x): n a n b n c and n d is a scalar; equations (2-4) and (2-5) are complex constraints. For ease of analysis, equations (2-1) to (2-5) can be written in the following form:

[0109]

[0110] The constraints are:

[0111] h(x1,x2...x A )≤0; a=1,2...A (2-7)

[0112] g a (x a )≤0; a=1,2...A (2-8)

[0113] In equations (2-6) to (2-8), x a These are all the variables in region a decomposed from the original problem; the sets of equations (2-7) and (2-8) both contain equality and inequality constraints, where equation (2-7) represents complex constraints, and the equality constraints contain variables from different regions, thus hindering the independent solution of each subproblem. If these complex equality constraints can be removed from the original problem, the resulting new problem can be simply decomposed into a subproblem in each region. Equation (2-8) represents simple constraints, and the variables it contains only come from this region. Considering that the optimal values ​​of the Lagrange multipliers in the problems described by equations (2-6) to (2-8) are known, the problem can be expressed in the following equivalent form:

[0114]

[0115] The constraints are:

[0116] h a (x1,x2...x A )≤0; a=1,2...A (2-10)

[0117] g a (x a )≤0; a=1,2...A(2-11)

[0118] As can be seen from equation (2-10), the complex constraint (2-7) has been assigned to different regions. It should be noted that the way these constraints are assigned does not affect the solution of the original problem; that is, they are assigned based on an engineering perspective.

[0119] Assuming that all variables outside region a and the values ​​of the Lagrange multipliers are given, equations (2-9) to (2-11) can be simplified to the following form:

[0120]

[0121] The constraints are:

[0122]

[0123] g a (x a )≤0; a=1,2...A(2-14)

[0124] In equation (2-12) It is a constant. The dual variable vector corresponding to equation (2-13) is defined as λ. a This simplified problem (2-12) to (2-14) can be obtained from each partition of the original problem.

[0125] The decomposition method used in this invention is actually based on the solution scheme of these simplified region-dependent problems, and is a decomposition of the optimality conditions of the global problems (2-6) to (2-8). According to standard optimization theory, the first-order KKT optimality conditions for problems (2-6) to (2-8) can be expressed as:

[0126]

[0127] a = 1, 2, ..., A(2-15)

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134] These conditions are based on the assumption that the optimal value is known. and To build, and These are the optimal Lagrange multiplier values ​​associated with equations (2-7) and (2-8), respectively.

[0135] For simplicity, regarding the optimal value and The simplified subproblems (2-12) to (2-14) can be restated in the following form:

[0136]

[0137] The constraints are:

[0138] h a (x a )≤0 (2-23)

[0139] g a (x a )≤0 (2-24)

[0140] In the above formula

[0141] If we combine the first-order KKT conditions of each subproblem (2-22) to (2-24), we can see that it is equivalent to the first-order KKT conditions (2-15) to (2-21) of the global problem (2-6) to (2-8). It is important to note that this is a relevant result used in the algorithm below. Similar to the previous description, subproblems (2-22) to (2-24) in this region are obtained by relaxing all the complex constraints of other regions, that is, adding the complex constraints of other regions to the original global problem while retaining its own complex constraints. Using the Lagrange multiplier associated with (2-23) to coordinate the global problem ensures that the complex constraints are satisfied. Then, given the experimental (initial) values ​​of the optimization variables, this decomposition can be achieved. The main difference between the Lagrange relaxation algorithm and the decomposition algorithm used here is that the former requires adding all complex constraints to the objective function and requires an auxiliary program to update the Lagrange multipliers. In contrast, this decomposition algorithm does not require any special procedure to update the multipliers. The update of the multipliers is the result of each subproblem maintaining its own complex constraints (2-23), and can be done automatically. The advantage of the optimality condition decomposition algorithm is that it does not require obtaining the optimal solution of the subproblem in each iteration of the algorithm; it is sufficient to update the variable values ​​after performing one iteration for each subproblem. Therefore, compared with other methods that require calculating the optimal values ​​of the subproblems to achieve convergence, the computation time can be significantly reduced.

[0142] The convergence analysis of the algorithm is explained below:

[0143] The convergence analysis of the optimality condition decomposition is as follows. For simplicity without loss of generality, the simple constraint condition with independent separability (2-8) can be ignored, as it can be introduced into the objective function using the interior-point method. Furthermore, the entire main problem is divided into only two regions, m and n, each corresponding to a subproblem. In the centralized algorithm, the search direction of the subproblem... It is calculated by solving a system of linear equations in the form of equation (2-25) in each iteration.

[0144]

[0145] In the above formula, the superscript N' represents the Newtonian direction.

[0146]

[0147]

[0148] L is the Lagrangian function of equations (2-6) to (2-8), expressed by equation (2-26):

[0149]

[0150] Based on the above analysis, the search direction for the subproblem in step two of the proposed decomposition algorithm can also be obtained by solving the decomposable approximate linear equation system (2-27).

[0151]

[0152] Based on the above definition and the parallel computation of step two of the decomposition algorithm, equation (2-28) gives the sufficient condition for the convergence of the decomposition algorithm:

[0153]

[0154] in Defined as a matrix Let I be the spectral radius and I be the identity matrix. Assuming problems (2-6) to (2-8) are continuously quadratically differentiable and their optimal solutions satisfy equation (2-28), the decomposition algorithm applied to them will locally converge to the optimal solutions of each subproblem at a linear rate. Equation (2-28) can be seen as a measure of the coupling between the global problem and the regional problem; this measure is relatively small for problems with a few complex constraints. This convergence property is satisfied for most practical cases. In particular, some literature indicates that condition (2-28) has been verified to apply to all multi-region optimal power flow scenarios that can be used to test the program.

[0155] It should be noted that the proposed optimality condition decomposition algorithm can also be used to solve subproblems until the optimal solution is reached, rather than just one iteration. This may result in a loss of efficiency, but it is simpler to implement.

[0156] Based on the above analysis, the optimality condition decomposition algorithm is used to solve the model. Lagrange multipliers are introduced to relax complex equality constraints into the objective function. Given all variables outside region a and the initial values ​​of the Lagrange multipliers, the subproblems in region a can be decomposed from the original problem. In this invention, since the inequality constraints are simple, the interior-point method can be used for processing. The subproblems obtained from the decomposition in different regions are calculated in parallel at the reactive power and voltage control centers corresponding to their respective regions. Then, the key information requiring interaction (i.e., variables coupled to other regions and values ​​related to convergence conditions calculated in this region) is uploaded to the regional power grid dispatch center for convergence verification. If convergence is achieved, the calculation stops; otherwise, the variable values ​​of each region are updated based on the previous calculation results, and the next calculation is performed until convergence. The specific steps are as follows:

[0157] The first step is to initialize the variables for each region using the corresponding data collected by the data acquisition and monitoring system at a certain moment (the corresponding node voltage and reactive power output) as initial values, namely the node voltage and reactive power output of the reactive power source. This will be illustrated using region a as an example, i.e., initialization... initial values ​​of Lagrange multipliers Values ​​can be assigned independently based on the actual situation.

[0158] The second step is to iterate each region separately on its corresponding subproblem equations (2-29) to (2-31).

[0159]

[0160] In the formula: f a (x a (Corresponding to the first solution term) Corresponding to the second solution term, f a (x a ) represents the active power loss in region a, h k (x a The expression ) indicates the introduction of complex equality constraints in the k-th region of the original objective function, i.e., power flow equation constraints. For Lagrange multipliers corresponding to complex equality constraints. x a The variables to be determined in region a are the node voltages and the output of the reactive power sources. This represents a given variable value from another region.

[0161] The constraints are:

[0162] h a (x a )≤0 (2-30)

[0163] g a (x a)≤0 (2-31)

[0164] Where: h a (x a ) represents the complex equality constraints of region a itself, namely the power flow equation constraints (corresponding to the first constraint condition), g a (x a The 'a' represents the simple constraints of region a itself, namely voltage constraints and reactive power output constraints (corresponding to the second constraint). For calculating the search direction of the subproblems, the inequality constraints can first be processed using the nonlinear interior-point method, transforming the subproblems into unconstrained optimization problems, which can then be solved using a modified Newton's method. It is worth noting that the search directions of these subproblems can be obtained in parallel and independently in a distributed environment. Through iterative steps in this process, the search direction Δx of the subproblems can be obtained. a and Δλ a (These correspond to the search directions of the first subproblem and the second subproblem, respectively).

[0165] The third step is to update the corresponding variables for each zone. Then, the key information requiring interaction (the calculation results corresponding to the updated data) is uploaded. This step requires coordination from the regional power grid dispatch center (the recipient of the calculation results). Specifically, the regional power grid dispatch center receives the key information from the sub-problems, integrates it, judges it, and distributes it. In reality, very little information is exchanged between the sub-problems and the regional power grid dispatch center. In the Lagrange relaxation and augmented Lagrange algorithms, the regional power grid dispatch center needs to perform internal calculations and update the information before allocating it to different sub-problems. However, in the optimality condition decomposition algorithm, the regional power grid dispatch center does not need to update any information; the information update is implemented in each iteration of the sub-problems. It only needs to receive, integrate, judge, and distribute the updated information to the corresponding sub-problems.

[0166] The fourth step is to determine convergence at the regional power grid dispatch center. Define matrix H:

[0167]

[0168] Substitute the variable values ​​obtained from this update into the calculation. If ||H||≤ε (corresponding to the satisfaction of the preset convergence condition), it means that the variable to be optimized in the original problem has not changed significantly in two consecutive iterations, and the calculation is terminated. Otherwise (corresponding to the failure to satisfy the preset convergence condition), the reactive voltage control center of each region (corresponding to the distributed computing party) returns to the first step to continue the calculation with the variable values ​​updated in this calculation as the initial values. Here, ε is the preset error threshold, which can be set independently according to the actual situation.

[0169] The purpose of this invention is to address the problem that existing monitoring methods cannot obtain in-depth information on the reactive power and voltage support capabilities of new energy power plants. Considering the existence of various reactive power resources within the plant, and under the premise of fully utilizing the reactive power regulation capabilities of its own inverters, a multi-source coordinated reactive power and voltage optimization model is established. A real-time calculation method is proposed to quickly calculate the reactive power output of each reactive power source within the photovoltaic power plant based on the current operating status of the new energy base, thereby realizing online assessment of the reactive power and voltage support capabilities of the photovoltaic power plant.

[0170] This invention belongs to the field of grid-connected adaptability assessment of new energy power plants, and particularly relates to the assessment of reactive power support capability and real-time calculation of reactive power support indicators for photovoltaic power plants in new energy base area power grids. Based on the network topology of the new energy base area power grid, and considering the adjustable range of various reactive power resources within the photovoltaic power plant, a multi-source coordinated reactive power and voltage optimization model is constructed. On this basis, to fully realize the key information exchange between the photovoltaic power plant's AVC master station and the external network dispatch center, and the online assessment of the photovoltaic power plant's reactive power support capability, a real-time calculation method for reactive power support capability indicators based on the optimality condition decomposition algorithm is proposed.

[0171] The content of this invention can be summarized in the following two aspects: 1. Based on the power grid network topology of the new energy base area, and considering the adjustable range of various reactive resources inside the photovoltaic power station, a multi-source coordinated reactive voltage optimization model is established; 2. According to the typical characteristics of the model, an appropriate solution algorithm is selected to realize the key information interaction between the photovoltaic power station AVC master station and the external network dispatch center, and on this basis, the real-time calculation of the reactive voltage support capability evaluation index of the photovoltaic power station is completed.

[0172] The present invention provides a method for obtaining reactive power support capability indicators for photovoltaic power plants, which obtains a multi-source coordinated reactive power and voltage optimization model. The objective function of the reactive power and voltage optimization model is to minimize the active power loss of the power grid in the new energy base area. The constraints of the reactive power and voltage optimization model include inequality constraints and equality constraints. The inequality constraints include the fluctuation range of the output of the reactive power source within its reactive power regulation range, and the fluctuation range of the node voltage within its corresponding voltage level within a specified range. The equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area should satisfy. The preset initial value of the Lagrange multiplier, the collected node voltage, and the output of the reactive power source are determined as the initial variables of the optimality condition decomposition algorithm, and the optimality condition decomposition algorithm is used to solve the reactive power and voltage optimization model to obtain the reactive power support capability indicators. This method can improve the calculation efficiency of the reactive power support capability indicators of photovoltaic power plants, thereby providing technical support for the assessment of the reactive power support capability of photovoltaic power plants.

[0173] Furthermore, the step of solving the reactive voltage optimization model using the optimality condition decomposition algorithm includes:

[0174] The active power loss obtained from the decomposition and corresponding to each region is determined as the first solution term of the objective function, and the product of the equality constraints of each region in the objective function and their corresponding Lagrange multipliers is determined as the second solution term of the objective function; the above explanation is provided and will not be repeated here.

[0175] The equality constraints of each region obtained from the decomposition are determined as the first constraint condition for solving the objective function, and the inequality constraints of each region obtained from the decomposition are determined as the second constraint condition for solving the objective function; the above explanation is provided and will not be repeated here.

[0176] Obtain the search direction for the sub-problem; the search direction for the sub-problem includes the first sub-problem search direction containing node voltage and reactive power output, and the second sub-problem search direction containing Lagrange multipliers; refer to the above description, and will not be repeated here.

[0177] The node voltage and reactive power output are updated according to the search direction of the first subproblem, and the Lagrange multipliers are updated according to the search direction of the second subproblem; please refer to the above description, which will not be repeated here.

[0178] Calculate the updated data corresponding to each region. If the optimality condition decomposition algorithm satisfies the preset convergence condition based on the summarized calculation results, the calculation is terminated. Refer to the above explanation; further details are omitted.

[0179] Furthermore, obtaining the search direction for the sub-problem includes:

[0180] The nonlinear interior point method is used to handle the inequality constraints; please refer to the above explanation, which will not be repeated here.

[0181] The search direction for the subproblem is obtained by modifying the Newton's method to solve the problem. This can be referred to the above explanation and will not be repeated here.

[0182] Furthermore, obtaining the search direction for the sub-problem includes:

[0183] The search direction for the sub-problem is calculated using distributed computing. This can be referred to the above explanation and will not be repeated here.

[0184] Furthermore, the method for obtaining the reactive power support capacity index of the photovoltaic power station also includes:

[0185] The updated data corresponding to each region is calculated using distributed computing. The calculation results from each distributed computing method are received and aggregated to obtain the aggregated calculation result. This can be referred to the above description and will not be repeated here.

[0186] Furthermore, the method for obtaining the reactive power support capability index of the photovoltaic power station also includes:

[0187] If the summarizing calculation results determine that the optimality condition decomposition algorithm does not meet the preset convergence condition, then the process of obtaining the sub-problem search direction and subsequent steps continues. Refer to the above explanation; further details are omitted.

[0188] Figure 3 This is a schematic diagram of the structure of a photovoltaic power station reactive power support capacity index acquisition device according to an embodiment of the present invention, as shown below. Figure 3 As shown, the reactive power support capacity index acquisition device for photovoltaic power plants provided in this embodiment of the invention includes an acquisition unit 301 and a solution unit 302, wherein:

[0189] The acquisition unit 301 is used to acquire a multi-source coordinated reactive power and voltage optimization model; the objective function of the reactive power and voltage optimization model is to minimize the active power loss of the power grid in the new energy base area; the constraints of the reactive power and voltage optimization model include inequality constraints and equality constraints; wherein, the inequality constraints include the fluctuation range of the output of the reactive power source within its reactive power regulation range, and the fluctuation range of the node voltage within its corresponding voltage level within a specified range; the equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area should satisfy; the solution unit 302 is used to determine the preset initial value of the Lagrange multiplier, the collected node voltage, and the output of the reactive power source as the initial variables of the optimality condition decomposition algorithm, and use the optimality condition decomposition algorithm to solve the reactive power and voltage optimization model to obtain the reactive power support capacity index.

[0190] Specifically, the acquisition unit 301 in the device is used to acquire a multi-source coordinated reactive power and voltage optimization model; the objective function of the reactive power and voltage optimization model is to minimize the active power loss of the power grid in the new energy base area; the constraints of the reactive power and voltage optimization model include inequality constraints and equality constraints; wherein, the inequality constraints include the fluctuation range of the output of the reactive power source within its reactive power regulation range, and the fluctuation range of the node voltage within the specified range of its corresponding voltage level; the equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area should satisfy; the solution unit 302 is used to determine the preset initial value of the Lagrange multiplier, the collected node voltage, and the output of the reactive power source as the initial variables of the optimality condition decomposition algorithm, and use the optimality condition decomposition algorithm to solve the reactive power and voltage optimization model to obtain the reactive power support capacity index.

[0191] The reactive power support capability index acquisition device for photovoltaic power plants provided in this embodiment of the invention acquires a multi-source coordinated reactive power and voltage optimization model. The objective function of the reactive power and voltage optimization model is to minimize the active power loss of the power grid in the new energy base area. The constraints of the reactive power and voltage optimization model include inequality constraints and equality constraints. The inequality constraints include the fluctuation range of the output of the reactive power source within its reactive power regulation range, and the fluctuation range of the node voltage within its corresponding voltage level within a specified range. The equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area should satisfy. The preset initial value of the Lagrange multiplier, the collected node voltage, and the output of the reactive power source are determined as the initial variables of the optimality condition decomposition algorithm, and the reactive power and voltage optimization model is solved using the optimality condition decomposition algorithm to obtain the reactive power support capability index. This can improve the calculation efficiency of the reactive power support capability index of photovoltaic power plants, thereby providing technical support for the assessment of the reactive power support capability of photovoltaic power plants.

[0192] Furthermore, the solving unit 302 is specifically used for:

[0193] The active power loss obtained from the decomposition and corresponding to each region is determined as the first solution term of the objective function, and the product of the equality constraints of each region in the objective function and their corresponding Lagrange multipliers is determined as the second solution term of the objective function.

[0194] The equality constraints of each region obtained from the decomposition are determined as the first constraint condition for solving the objective function, and the inequality constraints of each region obtained from the decomposition are determined as the second constraint condition for solving the objective function.

[0195] Obtain the sub-problem search direction; the sub-problem search direction is a first sub-problem search direction that includes node voltage and reactive power output, and a second sub-problem search direction that includes Lagrange multipliers;

[0196] Update the node voltage and reactive power output according to the search direction of the first subproblem, and update the Lagrange multipliers according to the search direction of the second subproblem;

[0197] Calculate the updated data corresponding to each region. If the optimality condition decomposition algorithm satisfies the preset convergence condition based on the summary calculation results, then terminate the calculation.

[0198] Furthermore, the solving unit 302 is specifically used for:

[0199] The nonlinear interior point method is used to handle the inequality constraints;

[0200] The search direction for the subproblem is obtained by solving the results using a modified Newton's method.

[0201] Furthermore, the solving unit 302 is specifically used for:

[0202] The search direction for the sub-problem is calculated using distributed computing.

[0203] Furthermore, the device for obtaining the reactive power support capacity index of the photovoltaic power station is also used for:

[0204] The updated data corresponding to each region is calculated using distributed computing. The calculation results of each distributed computing method are received and aggregated to obtain the aggregated calculation result.

[0205] Furthermore, the device for obtaining the reactive power support capacity index of the photovoltaic power station is also used for:

[0206] If the optimality condition decomposition algorithm does not meet the preset convergence condition based on the summary calculation results, then the process of obtaining the subproblem search direction and subsequent steps will continue.

[0207] The embodiments of the present invention provide a device for obtaining reactive power support capacity indicators of photovoltaic power plants, which can be used to execute the processing flow of the above-described method embodiments. Its functions will not be repeated here, but can be referred to the detailed description of the above-described method embodiments.

[0208] Figure 4 This is a schematic diagram of the physical structure of a computer device provided in an embodiment of the present invention, such as... Figure 4 As shown, the computer device includes: a memory 401, a processor 402, and a computer program stored in the memory 401 and executable on the processor 402. When the processor 402 executes the computer program, it implements the following method:

[0209] Obtain a multi-source coordinated reactive power and voltage optimization model; the objective function of the reactive power and voltage optimization model is to minimize the active power loss of the power grid in the new energy base area.

[0210] The constraints of the reactive voltage optimization model include inequality constraints and equality constraints.

[0211] The inequality constraints include the fluctuation range of the reactive power output within its reactive power regulation range, and the fluctuation range of the node voltage within its corresponding voltage level.

[0212] The equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area should satisfy.

[0213] The initial values ​​of the Lagrange multipliers, the collected node voltages, and the output of the reactive power source are determined as the initial variables of the optimality condition decomposition algorithm. The optimality condition decomposition algorithm is then used to solve the reactive power voltage optimization model to obtain the reactive power support capacity index.

[0214] This embodiment discloses a computer program product, which includes a computer program that, when executed by a processor, implements the following method:

[0215] Obtain a multi-source coordinated reactive power and voltage optimization model; the objective function of the reactive power and voltage optimization model is to minimize the active power loss of the power grid in the new energy base area.

[0216] The constraints of the reactive voltage optimization model include inequality constraints and equality constraints.

[0217] The inequality constraints include the fluctuation range of the reactive power output within its reactive power regulation range, and the fluctuation range of the node voltage within its corresponding voltage level.

[0218] The equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area should satisfy.

[0219] The initial values ​​of the Lagrange multipliers, the collected node voltages, and the output of the reactive power source are determined as the initial variables of the optimality condition decomposition algorithm. The optimality condition decomposition algorithm is then used to solve the reactive power voltage optimization model to obtain the reactive power support capacity index.

[0220] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the following method:

[0221] Obtain a multi-source coordinated reactive power and voltage optimization model; the objective function of the reactive power and voltage optimization model is to minimize the active power loss of the power grid in the new energy base area.

[0222] The constraints of the reactive voltage optimization model include inequality constraints and equality constraints.

[0223] The inequality constraints include the fluctuation range of the reactive power output within its reactive power regulation range, and the fluctuation range of the node voltage within its corresponding voltage level.

[0224] The equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area should satisfy.

[0225] The initial values ​​of the Lagrange multipliers, the collected node voltages, and the output of the reactive power source are determined as the initial variables of the optimality condition decomposition algorithm. The optimality condition decomposition algorithm is then used to solve the reactive power voltage optimization model to obtain the reactive power support capacity index.

[0226] Compared with existing technologies, this invention provides a multi-source coordinated reactive power and voltage optimization model. The objective function of this model is to minimize the active power loss of the power grid in the new energy base area. The model's constraints include inequality constraints and equality constraints. The inequality constraints include the fluctuation range of reactive power source output within its reactive power regulation range and the fluctuation range of node voltage within its corresponding voltage level. The equality constraints include the power flow equation constraints that each node in the new energy base area power grid must satisfy. The initial values ​​of the Lagrange multipliers, the collected node voltages, and the reactive power source output are determined as the initial variables for the optimality condition decomposition algorithm. This algorithm is then used to solve the reactive power and voltage optimization model, obtaining a reactive power support capability index. This improves the calculation efficiency of the reactive power support capability index for photovoltaic power plants, thereby providing technical support for the assessment of the reactive power support capability of photovoltaic power plants.

[0227] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0228] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0229] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0230] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0231] In the description of this specification, the references to terms such as "an embodiment," "a specific embodiment," "some embodiments," "for example," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0232] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for obtaining reactive power support capacity indicators of a photovoltaic power station, characterized in that, include: Obtain a multi-source coordinated reactive power and voltage optimization model; the objective function of the reactive power and voltage optimization model is to minimize the active power loss of the power grid in the new energy base area. The active power loss of the power grid in the new energy base area This can be expressed by the following formula: ； ； in, This indicates the active power loss on the grid side of the power grid in the new energy base area. This indicates the number of nodes on the grid side of the regional power grid. Represents the grid-side nodes in a regional power grid With nodes The electrical conductance between them and Representing nodes respectively and nodes voltage amplitude, Represents a node With nodes The phase angle difference between them; ； in, This indicates the active power loss within a photovoltaic power station. This indicates the number of collector lines in a large photovoltaic power plant. This indicates the number of photovoltaic power generation units on each collector line. Indicates the first Photovoltaic power generation unit nodes on the power line With nodes The electrical conductance between them and They represent the first The first of the cable lines The and the first The voltage on the low-voltage side of the box-type transformer in each photovoltaic power generation unit. Indicates the first The first of the cable lines The and the first The phase angle difference of the low-voltage side voltage of the box-type transformer in each photovoltaic power generation unit; Indicates the first The first of the cable lines The conductance of the box-type transformer in each photovoltaic power generation unit, assuming that the parameters of the box-type transformer used in each photovoltaic power generation unit are the same. For the same constant value, and They represent the first The first of the cable lines The voltages on the high-voltage and low-voltage sides of the box-type transformer in each photovoltaic power generation unit. Indicates the first The first of the cable lines The phase angle difference between the high-voltage and low-voltage sides of the box-type transformer in a photovoltaic power generation unit; The constraints of the reactive voltage optimization model include inequality constraints and equality constraints. The inequality constraints include the fluctuation range of the reactive power output within its reactive power regulation range, and the fluctuation range of the node voltage within its corresponding voltage level. The inequality constraints include: ； in, Indicating the first in the regional power grid of the new energy base The unproductive output of a power source and They represent the first The upper limit and lower limit of the reactive power adjustment range of each reactive power source; ； in, Indicates nodes in the power grid of the new energy base area voltage, and These represent the upper and lower voltage limits corresponding to the voltage at that node, respectively. The equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area should satisfy. The initial values ​​of the Lagrange multipliers, the collected node voltages, and the output of the reactive power source are determined as the initial variables of the optimality condition decomposition algorithm. The optimality condition decomposition algorithm is then used to solve the reactive power voltage optimization model to obtain the reactive power support capacity index.

2. The method for obtaining the reactive power support capacity index of a photovoltaic power station according to claim 1, characterized in that, Solving the reactive voltage optimization model using the optimality condition decomposition algorithm includes: The active power loss obtained from the decomposition and corresponding to each region is determined as the first solution term of the objective function, and the product of the equality constraints of each region in the objective function and their corresponding Lagrange multipliers is determined as the second solution term of the objective function. The equality constraints of each region obtained from the decomposition are determined as the first constraint condition for solving the objective function, and the inequality constraints of each region obtained from the decomposition are determined as the second constraint condition for solving the objective function. Obtain the sub-problem search direction; the sub-problem search direction is a first sub-problem search direction that includes node voltage and reactive power output, and a second sub-problem search direction that includes Lagrange multipliers; Update the node voltage and reactive power output according to the search direction of the first subproblem, and update the Lagrange multipliers according to the search direction of the second subproblem; Calculate the updated data corresponding to each region. If the optimality condition decomposition algorithm satisfies the preset convergence condition based on the summary calculation results, then terminate the calculation.

3. The method for obtaining the reactive power support capacity index of a photovoltaic power station according to claim 2, characterized in that, The process of obtaining the search direction for the sub-problem includes: The nonlinear interior point method is used to handle the inequality constraints; The search direction for the subproblem is obtained by solving the results using a modified Newton's method.

4. The method for obtaining the reactive power support capacity index of a photovoltaic power station according to claim 2, characterized in that, The process of obtaining the search direction for the sub-problem includes: The search direction for the sub-problem is calculated using distributed computing.

5. The method for obtaining the reactive power support capacity index of a photovoltaic power station according to claim 2, characterized in that, The method for obtaining the reactive power support capacity index of the photovoltaic power station also includes: The updated data corresponding to each region is calculated using distributed computing. The calculation results of each distributed computing method are received and aggregated to obtain the aggregated calculation result.

6. The method for obtaining the reactive power support capacity index of a photovoltaic power station according to claim 2, characterized in that, The method for obtaining the reactive power support capacity index of the photovoltaic power station also includes: If the optimality condition decomposition algorithm does not meet the preset convergence condition based on the summary calculation results, then the process of obtaining the subproblem search direction and subsequent steps will continue.

7. A device for obtaining reactive power support capacity indicators of a photovoltaic power station, characterized in that, include: The acquisition unit is used to acquire a multi-source coordinated reactive power and voltage optimization model; the objective function of the reactive power and voltage optimization model is to minimize the active power loss of the power grid in the new energy base area. The active power loss of the power grid in the new energy base area This can be expressed by the following formula: ； ； in, This indicates the active power loss on the grid side of the power grid in the new energy base area. This indicates the number of nodes on the grid side of the regional power grid. Represents the grid-side nodes in a regional power grid With nodes The electrical conductance between them and Representing nodes respectively and nodes voltage amplitude, Represents a node With nodes The phase angle difference between them; ； in, This indicates the active power loss within a photovoltaic power station. This indicates the number of collector lines in a large photovoltaic power plant. This indicates the number of photovoltaic power generation units on each collector line. Indicates the first Photovoltaic power generation unit nodes on the power line With nodes The electrical conductance between them and They represent the first The first of the cable lines The and the first The voltage on the low-voltage side of the box-type transformer in each photovoltaic power generation unit. Indicates the first The first of the cable lines The and the first The phase angle difference of the low-voltage side voltage of the box-type transformer in each photovoltaic power generation unit; Indicates the first The first of the cable lines The conductance of the box-type transformer in each photovoltaic power generation unit, assuming that the parameters of the box-type transformer used in each photovoltaic power generation unit are the same. For the same constant value, and They represent the first The first of the cable lines The voltages on the high-voltage and low-voltage sides of the box-type transformer in each photovoltaic power generation unit. Indicates the first The first of the cable lines The phase angle difference between the high-voltage and low-voltage sides of the box-type transformer in a photovoltaic power generation unit; The constraints of the reactive voltage optimization model include inequality constraints and equality constraints. The inequality constraints include the fluctuation range of the reactive power output within its reactive power regulation range, and the fluctuation range of the node voltage within its corresponding voltage level. The inequality constraints include: ； in, Indicating the first in the regional power grid of the new energy base The unproductive output of a power source and They represent the first The upper limit and lower limit of the reactive power adjustment range of each reactive power source; ； in, Indicates nodes in the power grid of the new energy base area voltage, and These represent the upper and lower voltage limits corresponding to the voltage at that node, respectively. The equality constraints include the power flow equation constraints that each node of the power grid in the new energy base area should satisfy. The solution unit is used to determine the preset initial value of the Lagrange multiplier, the collected node voltage, and the output of the reactive power source as the initial variables of the optimality condition decomposition algorithm, and to use the optimality condition decomposition algorithm to solve the reactive power voltage optimization model to obtain the reactive power support capacity index.

8. The device for obtaining reactive power support capacity index of a photovoltaic power station according to claim 7, characterized in that, The solution unit is specifically used for: The active power loss obtained from the decomposition and corresponding to each region is determined as the first solution term of the objective function, and the product of the equality constraints of each region in the objective function and their corresponding Lagrange multipliers is determined as the second solution term of the objective function. The equality constraints of each region obtained from the decomposition are determined as the first constraint condition for solving the objective function, and the inequality constraints of each region obtained from the decomposition are determined as the second constraint condition for solving the objective function. Obtain the sub-problem search direction; the sub-problem search direction is a first sub-problem search direction that includes node voltage and reactive power output, and a second sub-problem search direction that includes Lagrange multipliers; Update the node voltage and reactive power output according to the search direction of the first subproblem, and update the Lagrange multipliers according to the search direction of the second subproblem; Calculate the updated data corresponding to each region. If the optimality condition decomposition algorithm satisfies the preset convergence condition based on the summary calculation results, then terminate the calculation.

9. The device for obtaining reactive power support capacity index of a photovoltaic power station according to claim 8, characterized in that, The solution unit is also specifically used for: The nonlinear interior point method is used to handle the inequality constraints; The search direction for the subproblem is obtained by solving the results using a modified Newton's method.

10. The device for obtaining reactive power support capacity index of a photovoltaic power station according to claim 8, characterized in that, The solution unit is also specifically used for: The search direction for the sub-problem is calculated using distributed computing.

11. The device for obtaining reactive power support capacity index of a photovoltaic power station according to claim 8, characterized in that, The device for obtaining the reactive power support capacity index of the photovoltaic power station is also used for: The updated data corresponding to each region is calculated using distributed computing. The calculation results of each distributed computing method are received and aggregated to obtain the aggregated calculation result.

12. The device for obtaining reactive power support capacity index of a photovoltaic power station according to claim 8, characterized in that, The device for obtaining the reactive power support capacity index of the photovoltaic power station is also used for: If the optimality condition decomposition algorithm does not meet the preset convergence condition based on the summary calculation results, then the process of obtaining the subproblem search direction and subsequent steps will continue.

13. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

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