Active distribution network distributed voltage control method, device and terminal equipment

Abstract

The present application is applicable to the field of data processing technology, and provides a method, device and terminal equipment for distributed voltage control of an active distribution network, wherein the method comprises: obtaining distribution network related data; constructing an active distribution network voltage optimization control model based on distribution network related data and linearized voltage-reactive equation; constructing a linear power flow equation matrix based on distribution network related data and Koopman state space dimensionality increase transformation method; calculating node voltage and reactive power sensitivity coefficients based on the linear power flow equation matrix; solving the active distribution network voltage optimization control model based on node voltage and reactive power sensitivity coefficients and a distributed algorithm for matrix splitting to obtain a solution result; the solution result is used to control the distributed voltage of the active distribution network. The present application has a faster convergence speed and a better control convergence result, is not affected by the problem of imprecise parameters, and has stronger engineering applicability.

Description

Technical Field

[0001] The present application belongs to the field of distribution network optimization control, and in particular, relates to a distributed voltage control method, device and terminal equipment of an active distribution network based on state space linear transformation. Background Art

[0002] With the extensive development and utilization of distributed renewable energy, distributed generation represented by photovoltaics is connected to the active distribution network in a clustered form, which brings challenges to the safe and stable operation of the distribution network and the reliability of power supply. The uncertainty of distributed generation output has aggravated the fluctuation of the operating state of the distribution network, and the coordination of a large number of distributed generation has increased the complexity of voltage control in the distribution network. Specifically, the distributed generation connected to the end of the feeder has changed the traditional single flow direction of the distribution network, resulting in an increase in the voltage at the end of the feeder, which affects the life of the power equipment and causes the risk of distributed generation being disconnected from the grid. The grid-connected inverter of distributed generation has its own fast reactive power regulation capability. As its proportion gradually increases, its voltage control function has become an important means of regulation that cannot be ignored in the distribution network. Since there is no need to configure additional regulation devices, its regulation cost is lower and it has a faster response speed to random fluctuations in output, which is of key significance for ensuring the safe and efficient operation of the system.

[0003] The centralized voltage control method needs to maintain the global model uniformly based on the centralized communication network and collect a large amount of operating data in real time. Not only is the communication delay long, but the solution of the optimization model is more likely to encounter the problem of "dimensionality curse", and the convergence speed is slow or even difficult to converge. Distributed control is more suitable for active distribution network voltage control with a high proportion of distributed generation. Based on the application of multi-agent systems, distributed control methods generally use two standardized algorithms: one is an iterative method based on dual decomposition and subgradient, and the other is an iterative method based on consistency. However, both of these methods have only first-order convergence speed. When the number of distributed generation is large, the convergence speed of distribution network voltage control will be extremely slow and cannot meet the needs of online control.

[0004] In recent years, the distributed method based on second-order gradient has been widely studied and applied. Due to the use of second-order gradient, its iterative process has a superlinear convergence speed, but the inversion process of its matrix generally requires global information, which puts forward higher requirements for distributed iterative control. For the medium and low voltage distribution network where distributed generation is widely connected, the dispatching center often does not have its precise model parameters, which will have an adverse effect on the distributed voltage control effect and convergence speed. In order to solve the above problems, it is necessary to develop a distributed power supply voltage control method with faster convergence speed and better control convergence results, which is not affected by the problem of parameter inaccuracy and has stronger engineering applicability. Summary of the invention

[0005] In order to overcome the problems existing in the related technology, the embodiments of the present application provide a distributed voltage control method, device and terminal equipment for an active distribution network, which can have a faster convergence speed and better control convergence results, and is not affected by the problem of parameter inaccuracy, and has stronger engineering applicability.

[0006] This application is implemented through the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a distributed voltage control method for an active distribution network, comprising: acquiring distribution network related data; constructing an active distribution network voltage optimization control model based on the distribution network related data and a linearized voltage-reactive equation; constructing a linear power flow equation matrix based on the distribution network related data and a Koopman state space dimensionality increase transformation method; calculating node voltage and reactive power sensitivity coefficients based on the linear power flow equation matrix; solving the active distribution network voltage optimization control model based on the node voltage and reactive power sensitivity coefficients and a distributed algorithm of matrix splitting to obtain a solution result; the solution result is used to control the distributed voltage of the active distribution network.

[0008] In a possible implementation of the first aspect, the distribution network related data include: the access location and capacity of the distributed power source, the lower limit and upper limit of the reactive power regulation amount of the distributed power source, the initial values ​​of the reference voltage and reference power of the active distribution network, the safety voltage constraints of each node, the node voltage and reactive power historical measurement data, the number of training samples, the dimension of the dimension upgrade, the natural distribution vector of the node voltage, the initial value of the local second-order gradient matrix, the correction parameter, the iteration step size, and the convergence accuracy.

[0009] In a possible implementation manner of the first aspect, constructing an active distribution network voltage optimization control model based on the distribution network related data and the linearized voltage-reactive power equation includes:

[0010] pass

[0011]

[0012] Represents the linearized voltage-reactive power equation; where Δq is the reactive power injection adjustment of the node distributed generation, is the natural distribution vector of node voltage when the distributed generation reactive power is not regulated, and the reactive power sensitivity matrix Z;

[0013] Constructing active distribution network voltage optimization control model:

[0014]

[0015]

[0016] Where f(Δq) is the objective function, represents the sum of squares of matrix elements, v is the column vector of node rated voltages, is the deviation between the rated voltage and the natural voltage distribution, is the lower limit of the reactive power regulation of the node distributed generation, It is the upper limit of the reactive power regulation of the node distributed generation.

[0017] In a possible implementation of the first aspect, constructing a linear power flow equation matrix based on the distribution network related data and the Koopman state space dimensionality increase transformation method includes: taking the node active and reactive historical measurement data as input variables; performing dimensionality increase transformation on the input variables; estimating the value of the linear power flow equation matrix based on the input variables after dimensionality increase transformation and the least squares method; performing dimensionality increase transformation on the input variables includes:

[0018] pass

[0019]

[0020]

[0021] Perform dimensional transformation on the input variables; where x is the input variable, including the active power and reactive power injected by the node, ψ(x) represents the m-dimensional input variable expanded by dimensional transformation, x lift is the input variable after dimension increase; ψ t (x) is the tth element in ψ(x), f lift is a dimensionality-raising function, x v is the vth element in x, K is the dimension of x, c t is a randomly generated K-dimensional basis vector, c tv c t The vth element in ;

[0022] The estimating the value of the linear power flow equation matrix based on the least squares method includes:

[0023] pass

[0024]

[0025] Calculate the value of the linear power flow equation matrix L; where, is the input variable sample matrix, which contains the active power and reactive power vectors of nodes in S time sections, is the sample matrix of the input variable after dimensionality increase, which contains the input variables after dimensionality increase of S time sections, S is the number of training samples, is the state variable sample matrix, which contains the node phase angle and voltage amplitude vectors of S time sections, [·] +is the Moore-Penrose inverse of the matrix.

[0026] In a possible implementation manner of the first aspect, calculating the node voltage and reactive power sensitivity coefficient based on the linear power flow equation matrix includes:

[0027] pass

[0028]

[0029] Calculate node voltage and reactive power sensitivity coefficients; where Z ji is the element in the jth row and ith column of the node voltage and reactive power sensitivity matrix Z, L ji is the corresponding voltage V in matrix L j and reactive power adjustment Δq i The element L j,(K+t) is the corresponding voltage V in matrix L j The t-th dimension input variable ψ expanded by the dimensionality-raising transformation t (x), m is the dimension of the upgrade.

[0030] In a possible implementation of the first aspect, the distributed algorithm based on the node voltage and reactive sensitivity coefficient and matrix splitting solves the active distribution network voltage optimization control model to obtain a solution result, including: calculating the objective function of each node itself; calculating the gradient of the objective function of each node itself; calculating the value of the local second-order gradient matrix of each node; based on the gradient of the objective function and the value of the local second-order gradient matrix, calculating the distributed iteration direction component of the adjacent nodes of each node; based on the distributed iteration direction component, iteratively calculating the reactive injection adjustment amount of each node; based on the objective function and the reactive injection adjustment amount, establishing a convergence accuracy judgment standard; if the convergence accuracy of the voltage optimization control model meets the convergence accuracy judgment standard, outputting the solution result of the voltage optimization control model; the solution result includes: the objective function value, the voltage amplitude of each node and the reactive injection adjustment amount of the distributed power source.

[0031] In a possible implementation manner of the first aspect, calculating the objective function of each node itself includes:

[0032] pass

[0033]

[0034] Calculate the objective function of node i itself; where Δq(k) is the vector of reactive power injection adjustment of distributed generation at the kth iteration, N i is the set of nodes that are directly connected to node i through a branch and are located downstream of the reference node.j is the jth row in Z, for The jth element in ;

[0035] Calculate the gradient of the objective function of each node, including:

[0036] pass

[0037]

[0038] Calculate the gradient of the objective function of node i itself; where g i (k) is the i-th element in g(k), V j (k) is the voltage amplitude of node j at the kth iteration, v j is the rated voltage of node j;

[0039] The calculating the value of the local second-order gradient matrix of each node includes:

[0040] pass

[0041]

[0042]

[0043]

[0044] Calculate the local second-order gradient matrix H of node i i (k) where vectors or matrices are indicated by the subscript N i Represents the set N i The sub-vector or sub-matrix composed of the elements in , β is the correction parameter, is a diagonal matrix with diagonal elements of 1 / n i , n i For the set N i The number of elements in , I is the identity matrix;

[0045] The distributed iterative direction components of the neighboring nodes of each node are calculated, including

[0046] pass

[0047]

[0048] Calculate the distributed iterative direction component of the neighboring nodes of node i; where Γ is the correction parameter, The distributed iterative directional components of all neighboring nodes calculated for node i;

[0049] The iterative calculation of the reactive injection adjustment amount of each node includes:

[0050] pass

[0051]

[0052] Iteratively calculate the reactive injection adjustment amount of node i; where d i (k) is the final distributed iteration direction, Δq i (k) is the reactive power adjustment of the distributed generation of node i at the kth iteration, The distributed iteration direction component of node i calculated for node j, α is the iteration step size;

[0053] The convergence accuracy judgment criteria include:

[0054]

[0055] Where N is the total number of nodes and ω is the preset convergence accuracy.

[0056] In the second aspect, an embodiment of the present application provides a distributed voltage control device for an active distribution network, comprising: an input module for acquiring distribution network related data; a first operation module for constructing an active distribution network voltage optimization control model based on the distribution network historical operation data and the linearized voltage-reactive equation; a second operation module for constructing a linear power flow equation matrix based on the distribution network historical operation data and the Koopman state space dimensionality increase transformation method; a third operation module for calculating the node voltage and reactive power sensitivity coefficient based on the linear power flow equation matrix; an output module for solving the active distribution network voltage optimization control model based on the node voltage and reactive power sensitivity coefficient and a distributed algorithm of matrix splitting to obtain a solution result; the solution result is used to control the distributed voltage of the active distribution network.

[0057] In a third aspect, an embodiment of the present application provides a terminal device, comprising a memory and a processor, wherein the memory stores a computer program that can be executed on the processor, and wherein the processor implements the active distribution network distributed voltage control method as described in any one of the first aspects when executing the computer program.

[0058] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and wherein when the computer program is executed by a processor, the method for distributed voltage control in an active distribution network as described in any one of the first aspects is implemented.

[0059] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on a terminal device, enables the terminal device to execute any of the methods described in the first aspect above.

[0060] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0061] In the embodiment of the present application, based on the Koopman dimensionality increase method, the distribution network related data is used as a training sample to construct a high-dimensional linear accurate power flow model, thereby deriving the voltage-reactive global sensitivity, so as to correct the iteration direction in the distributed control. Compared with the distributed voltage control method that depends on the model, the method of the present invention has a faster convergence speed and better control convergence results, and is not affected by the problem of parameter inaccuracy, and has stronger engineering applicability.

[0062] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0063] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0065] Figure 1 It is a flow chart of a distributed voltage control method for an active power distribution network provided by an embodiment of the present application;

[0066] Figure 2 This is a graph showing changes in the objective function in a 33-node system along the iteration process provided by an embodiment of the present application;

[0067] Figure 3 This is a voltage distribution diagram in a 33-node system provided in an embodiment of the present application;

[0068] Figure 4 is a graph showing changes in the objective function in a 69-node system along the iteration process provided by an embodiment of the present application;

[0069] Figure 5 is a graph showing changes in the objective function in the 118-node system along the iteration process provided by an embodiment of the present application;

[0070] Figure 6 It is a structural schematic diagram of a distributed voltage control device for an active power distribution network provided by an embodiment of the present application;

[0071] Figure 7 It is a structural diagram of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0072] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0073] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0074] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0075] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0076] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0077] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0078] For the medium and low voltage distribution network where distributed generation is widely connected, the dispatch center often does not have the precise model parameters, which will have an adverse effect on the distributed voltage control effect and convergence speed. To solve the above problems, it is necessary to develop a distributed power supply voltage control method with faster convergence speed and better control convergence results, which is not affected by the problem of imprecise parameters and has stronger engineering applicability.

[0079] Based on the above problems, an active distribution network distributed voltage control method is provided in an embodiment of the present application, including: obtaining distribution network related data; constructing an active distribution network voltage optimization control model based on distribution network related data and linearized voltage-reactive equation; constructing a linear power flow equation matrix based on distribution network related data and Koopman state space dimensionality increase transformation method; calculating node voltage and reactive sensitivity coefficients based on the linear power flow equation matrix; solving the active distribution network voltage optimization control model based on node voltage and reactive sensitivity coefficients and a distributed algorithm of matrix splitting to obtain a solution result; the solution result is used to control the distributed voltage of the active distribution network. The method of the present invention realizes the distributed inversion of the matrix through the matrix splitting method, thereby increasing the convergence speed of distributed control to superlinear convergence, which is more suitable for online applications; based on the Koopman dimensionality increase method, using the distribution network historical operation data and other related data as training samples, constructing a high-dimensional linear precise power flow model, thereby deriving the voltage-reactive global sensitivity, thereby correcting the iteration direction in the distributed control. Compared with the distributed voltage control method that relies on the model, the method of the present invention has a faster convergence speed and a better control convergence result, is not affected by the problem of parameter inaccuracy, and has stronger engineering applicability.

[0080] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0081] Figure 1 is a schematic flow chart of a distributed voltage control method for an active distribution network provided by an embodiment of the present application, with reference to Figure 1 , the distributed voltage control method of the active distribution network is described in detail as follows:

[0082] In step 101, data related to the distribution network is obtained.

[0083] Specifically, the distribution network related data are obtained, including: according to the selected active distribution network, the access location and capacity of the input distributed power source, the lower and upper limits of the reactive power regulation of the distributed power source, the initial values ​​of the reference voltage and reference power of the active distribution network, the safety voltage constraints of each node, the node voltage and reactive power historical measurement data, the number of training samples, the dimension of dimension increase, the natural distribution vector of the node voltage, the initial value of the local second-order gradient matrix, the correction parameter, the iteration step size, and the convergence accuracy.

[0084] In step 102, an active distribution network voltage optimization control model is constructed based on distribution network related data and a linearized voltage-reactive power equation.

[0085] Exemplary: setting the minimum sum of active distribution network voltage deviations as the objective function, considering that the reactive power regulation capability of distributed generation is constrained by the inverter capacity.

[0086] Specifically, the active distribution network voltage optimization control model is constructed, including:

[0087] pass

[0088]

[0089] Represents the linearized voltage-reactive power equation; where Δq is the reactive power injection adjustment of the node distributed generation, is the natural distribution vector of node voltage when the distributed generation reactive power is not regulated, and the reactive power sensitivity matrix Z;

[0090] In addition, an active distribution network voltage optimization control model is constructed:

[0091]

[0092]

[0093] Where f(Δq) is the objective function, represents the sum of squares of matrix elements, v is the column vector of node rated voltages, is the deviation between the rated voltage and the natural voltage distribution, Δq is the lower limit of the reactive power regulation of the node distributed generation, It is the upper limit of the reactive power regulation of the node distributed generation.

[0094] In step 103, a linear power flow equation matrix is ​​constructed based on distribution network related data and Koopman state space dimensionality increase transformation method.

[0095] Specifically, based on the distribution network related data and the Koopman state space dimensionality increase transformation method, a linear power flow equation matrix is ​​constructed, including: taking the node active and reactive historical measurement data as input variables; performing dimensionality increase transformation on the input variables; and estimating the value of the linear power flow equation matrix based on the input variables after dimensionality increase transformation and the least squares method.

[0096] Performing a dimensionality-increasing transformation on the input variable includes:

[0097] pass

[0098]

[0099]

[0100] Perform dimensional transformation on the input variables; where x is the input variable, including the active power and reactive power injected by the node, ψ(x) represents the m-dimensional input variable expanded by dimensional transformation, x lift is the input variable after dimension increase; ψ t (x) is the tth element in ψ(x), f lift is a dimensionality-raising function, x v is the vth element in x, K is the dimension of x, c t is a randomly generated K-dimensional basis vector, c tv c t The vth element in ;

[0101] The value of the linear power flow equation matrix is ​​estimated based on the least square method, including:

[0102] pass

[0103]

[0104] Calculate the value of the linear power flow equation matrix L; where, is the input variable sample matrix, which contains the active power and reactive power vectors of nodes in S time sections, is the sample matrix of the input variable after dimensionality increase, which contains the input variables after dimensionality increase of S time sections, S is the number of training samples, is the state variable sample matrix, which contains the node phase angle and voltage amplitude vectors of S time sections, [·] + is the Moore-Penrose inverse of the matrix.

[0105] In step 104, node voltage and reactive power sensitivity coefficients are calculated based on the linear power flow equation matrix.

[0106] Specifically, the node voltage and reactive power sensitivity coefficients are calculated, including:

[0107] pass

[0108]

[0109] Calculate node voltage and reactive power sensitivity coefficients; where Z ji is the element in the jth row and ith column of the node voltage and reactive power sensitivity matrix Z, L ji is the corresponding voltage V in matrix L j and reactive power adjustment Δq i The element L j,(K+t) is the corresponding voltage V in matrix L j The t-th dimension input variable ψ expanded by the dimensionality-raising transformation t (x), m is the dimension of the upgrade.

[0110] In step 105, based on the node voltage and reactive sensitivity coefficient and the distributed algorithm of matrix splitting, the active distribution network voltage optimization control model is solved to obtain a solution result; the solution result is used to control the distributed voltage of the active distribution network.

[0111] Specifically, the active distribution network voltage optimization control model is solved to obtain a solution result, including: calculating the objective function of each node itself; calculating the gradient of the objective function of each node itself; calculating the value of the local second-order gradient matrix of each node; based on the gradient of the objective function and the value of the local second-order gradient matrix, calculating the distributed iteration direction component of the adjacent nodes of each node; based on the distributed iteration direction component, iteratively calculating the reactive injection adjustment amount of each node; based on the objective function and the reactive injection adjustment amount, establishing a convergence accuracy judgment standard; if the convergence accuracy of the voltage optimization control model meets the convergence accuracy judgment standard, outputting the solution result of the voltage optimization control model; the solution result includes: the objective function value, the voltage amplitude of each node and the reactive injection adjustment amount of the distributed power supply.

[0112] Specifically, the objective function of each node is calculated, including:

[0113] pass

[0114]

[0115] Calculate the objective function of node i itself; where Δq(k) is the vector of reactive power injection adjustment of distributed generation at the kth iteration, N i is the set of nodes that are directly connected to node i through a branch and are located downstream of the reference node. j is the jth row in Z, for The jth element in .

[0116] Calculate the gradient of the objective function of each node, including:

[0117] pass

[0118]

[0119] Calculate the gradient of the objective function of node i itself; where g i (k) is the i-th element in g(k), V j (k) is the voltage amplitude of node j at the kth iteration, v j is the rated voltage of node j.

[0120] Calculating the value of the local second-order gradient matrix of each node includes:

[0121] pass

[0122]

[0123]

[0124]

[0125] Calculate the local second-order gradient matrix H of node i i (k) where vectors or matrices are indicated by the subscript N i Represents the set N i The sub-vector or sub-matrix composed of the elements in , β is the correction parameter, is a diagonal matrix with diagonal elements of 1 / n i , n i For the set N i The number of elements in , and I is the identity matrix.

[0126] Calculating the distributed iteration direction components of the adjacent nodes of each node, including:

[0127] pass

[0128]

[0129] Calculate the distributed iterative direction component of the neighboring nodes of node i; where Γ is the correction parameter, The distributed iterative directional component of all neighboring nodes computed for node i.

[0130] Iteratively calculating the reactive injection adjustment amount of each node, including:

[0131] pass

[0132]

[0133] Iteratively calculate the reactive injection adjustment amount of node i; where d i(k) is the final distributed iteration direction, Δqi(k) is the reactive power adjustment of the distributed generation of node i at the kth iteration, The distributed iteration direction component of node i calculated for node j, α is the iteration step size.

[0134] Convergence accuracy judgment criteria include:

[0135]

[0136] Where N is the total number of nodes and ω is the preset convergence accuracy.

[0137] Exemplarily, the distributed voltage control method proposed in the present invention is verified in the IEEE-33, 69 and 118 node standard example systems. In order to simulate the situation of connecting distributed power generation in the active distribution network, three distributed photovoltaics with different penetration rates (calculated according to the ratio of photovoltaic capacity to load) are added in each example system: 15%, 30%, and 50%. In the voltage control process, the substation low-voltage bus and the reference node voltage are always assumed to be 1p.u., and the safety voltage constraint of each node is [0.95, 1.05]. The hardware platform for the simulation test is a quad-core PC with Intel Core (TM) i5-6300HQ CPU @ 2.30GHz and 8GB RAM; the software environment is a 64-bit Windows 10 operating system; the programming software is MATLAB.

[0138] The power flow distribution after distributed photovoltaic reactive power adjustment is calculated using MATPOWER, where accurate model parameters are used, and the result is used as the actual state feedback of the distribution network voltage. In each example system, the present invention uses MATPOWER to calculate 3000 cross-section power flow results as a sample set at each penetration rate, and upgrades the power flow model to 2000 dimensions through state space transformation, thereby calculating the voltage-reactive sensitivity results.

[0139] Exemplarily, in order to verify the effect of the method of the present invention, the solution of the present invention is compared with the distributed voltage control result using the linearized Distflow model, and the two cases of accurate and inaccurate model parameters are analyzed at the same time, so as to verify the advantages of the present invention under the condition of inaccurate model parameters. The inaccurate model parameters are simulated by adding an additional disturbance of ±3% to ±8% to the reactance value of each branch. At the same time, the centralized optimal power flow result based on MATPOWER is used as the theoretical optimal solution to compare the effect differences of different methods.

[0140] For example, the convergence and control effects of different methods are compared by adding 15% distributed photovoltaics to a 33-node test system. Figure 2The curves showing the change of the objective function value with the number of iterative control under the three different methods shown. It can be seen that under the control of the method of the present invention, the objective function value reaches convergence in about 8 iterations, and its speed is significantly faster than the other two cases. This is because the global sensitivity is calculated by using data-driven power flow results, thereby updating the distributed iteration direction. Compared with the method using the Distflow model, the method of the present invention does not ignore and approximate, so it converges faster. However, the model-based method requires 11 steps of iterative control convergence under the premise of accurate model parameters, and the optimality is a little different from the method of the present invention. If the model parameters are not accurate, its convergence not only requires 22 steps of iterative control, but also the optimality cannot be guaranteed, and the objective function value is very different from the theoretical optimal result. It can be seen that the distributed control method based on the model has a high dependence on the accuracy of the model parameters, and the present invention can solve this problem. In actual engineering operation, it is a common situation that the distribution network model parameters have deviations, and even the medium and low voltage distribution network may have a completely model-free situation. Therefore, the method proposed by the present invention has better engineering adaptability.

[0141] Under the same calculation conditions, the voltage distribution under different methods when convergence is achieved is as follows Figure 3 For comparison purposes, the natural voltage distribution without control is also shown in Figure 3 It can be seen that under the control method of the present invention, the voltage distribution in the distribution network is more balanced, and the over-limit problem under the natural voltage distribution is solved. In comparison, the distributed control result based on the Distflow model cannot completely eliminate the voltage over-limit, and as the parameter accuracy increases, the voltage over-limit problem becomes more prominent, which verifies the voltage control effect proposed by the present invention.

[0142] For example, Figure 4 The objective function of the 69-node example system after adding 15% distributed photovoltaics changes with the iteration process. It can be seen that the method of the present invention requires 23 steps to converge, while the model-based method requires 26 steps and 58 steps to converge, and the convergence results are not as good as the method of the present invention.

[0143] For example, Figure 5 The distributed voltage control convergence process after adding 15% distributed photovoltaics to the 118-node example system shown. It can be seen that the method of the present invention requires 28 steps to converge, while the model-based method requires 78 steps and 152 steps to converge, respectively. It can be seen that with the increase in the number of nodes, the convergence speed of the method of the present invention does not decrease significantly, while the convergence speed of the model-based method decreases significantly due to the accumulation of errors.

[0144] For example, the number of times required for convergence of the above three methods in different permeability scenarios in various example systems is shown in Tables 1 to 3. It can be seen that under all permeabilities of all example systems, the method proposed in the present invention converges to the optimal value at the fastest rate, and its convergence advantage becomes more obvious as the system scale increases. The above results verify the effect of the method of the present invention.

[0145] Table 1 Convergence times comparison under different penetration rates in 33-node system

[0146]

[0147] Table 269 Convergence times comparison under different penetration rates in the node system

[0148]

[0149] Table 3 Convergence times comparison under different penetration rates in 118-node system

[0150]

[0151] It can be seen that the distributed voltage control method for the active distribution network proposed in the present invention has a faster convergence speed and better control convergence results than the distributed voltage control method that relies on the model, is not affected by the problem of parameter inaccuracy, and has stronger engineering applicability.

[0152] It should be understood that the order of execution of the above steps does not necessarily mean the order in which they are executed. The order in which each process is executed should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0153] Corresponding to the distributed voltage control method for the active power distribution network described in the above embodiment, Figure 6 A structural block diagram of a distributed voltage control device for an active power distribution network provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0154] See also Figure 6 The active power distribution network distributed voltage control device in the embodiment of the present application may include an input module 201, a first operation module 202, a second operation module 203, a third operation module 204 and an output module 205.

[0155] Among them, the input module 201 is used to obtain distribution network related data; the first operation module 202 is used to construct an active distribution network voltage optimization control model based on the distribution network historical operation data and the linearized voltage-reactive equation; the second operation module 203 is used to construct a linear power flow equation matrix based on the distribution network historical operation data and the Koopman state space dimensionality increase transformation method; the third operation module 204 is used to calculate the node voltage and reactive power sensitivity coefficient based on the linear power flow equation matrix; the output module 205 is used to solve the active distribution network voltage optimization control model based on the node voltage and reactive power sensitivity coefficient and the distributed algorithm of matrix splitting to obtain the solution result; the solution result is used to control the distributed voltage of the active distribution network.

[0156] It should be noted that the information interaction, execution process and other contents between the above-mentioned devices are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0157] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0158] The present application also provides a terminal device, see Figure 7 The terminal device 300 may include: at least one processor 310 and a memory 320, wherein the memory 320 stores a computer program 321 that can be run on the at least one processor 310, and the processor 310 implements the steps in any of the above-mentioned method embodiments when executing the computer program, for example Figure 1 Steps 101 to 105 in the illustrated embodiment. Alternatively, when the processor 310 executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 6 Functions of modules 201 to 205 are shown.

[0159] Exemplarily, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory 320, and executed by the processor 310 to complete the present application. The one or more modules / units may be a series of computer program segments that can complete specific functions, and the program segments are used to describe the execution process of the computer program in the terminal device 300.

[0160] Those skilled in the art will understand that Figure 7 It is only an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, such as input and output devices, network access devices, buses, etc.

[0161] The processor 310 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0162] The memory 320 may be an internal storage unit of the terminal device, or an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. The memory 320 is used to store the computer program and other programs and data required by the terminal device. The memory 320 may also be used to temporarily store data that has been output or is to be output.

[0163] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0164] The distributed voltage control method for the active distribution network provided in the embodiment of the present application can be applied to terminal devices such as computers, tablet computers, laptops, netbooks, personal digital assistants (PDAs), etc. The embodiment of the present application does not impose any restrictions on the specific type of terminal devices.

[0165] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in each embodiment of the above-mentioned active power distribution network distributed voltage control method can be implemented.

[0166] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in each embodiment of the above-mentioned active power distribution network distributed voltage control method when executing the computer program product.

[0167] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0168] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0169] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0170] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0171] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0172] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A distributed voltage control method for an active power distribution network, characterized in that: include: Obtain distribution network related data; Based on the distribution network related data and the linearized voltage-reactive power equation, an active distribution network voltage optimization control model is constructed; The active distribution network voltage optimization control model is constructed based on the distribution network related data and the linearized voltage-reactive power equation, including: pass Represents the linearized voltage-reactive power equation; where Δq is the reactive power injection adjustment of the node distributed generation, is the natural distribution vector of node voltage when the distributed generation reactive power is not regulated, and the reactive power sensitivity matrix Z; Constructing active distribution network voltage optimization control model: Where f(Δq) is the objective function, represents the sum of squares of matrix elements, v is the column vector of node rated voltages, is the deviation between the rated voltage and the natural voltage distribution, Δq is the lower limit of the reactive power regulation of the node distributed generation, The upper limit of the reactive power regulation amount of the node distributed generation; Based on the distribution network related data and the Koopman state space dimensionality increase transformation method, a linear power flow equation matrix is ​​constructed; based on the distribution network related data and the Koopman state space dimensionality increase transformation method, a linear power flow equation matrix is ​​constructed, including: The node active and reactive historical measurement data are used as input variables; Performing a dimensionality-upgrading transformation on the input variable; Estimate the value of the linear power flow equation matrix based on the input variables after dimensionality increase transformation and the least square method; The step of performing a dimensionality-upgrading transformation on the input variable comprises: pass Perform dimensional transformation on the input variables; where x is the input variable, including the active power and reactive power injected by the node, ψ(x) represents the m-dimensional input variable expanded by dimensional transformation, x lift is the input variable after dimension increase; ψ t (x) is the tth element in ψ(x), f lift is a dimensionality-raising function, x v is the vth element in x, K is the dimension of x, c t is a randomly generated K-dimensional basis vector, c tv c t The vth element in ; The estimating the value of the linear power flow equation matrix based on the least squares method includes: pass Calculate the value of the linear power flow equation matrix L; where, is the input variable sample matrix, which contains the active power and reactive power vectors of nodes in S time sections, is the sample matrix of the input variable after dimensionality increase, which contains the input variables after dimensionality increase of S time sections, S is the number of training samples, is the state variable sample matrix, which contains the node phase angle and voltage amplitude vectors of S time sections, [·] + is the Moore-Penrose inverse of the matrix; Based on the linear power flow equation matrix, calculating node voltage and reactive power sensitivity coefficients; Based on the distributed algorithm of the node voltage and reactive sensitivity coefficient and matrix splitting, the active distribution network voltage optimization control model is solved to obtain a solution result, and the solution result is used to control the distributed voltage of the active distribution network; the distributed algorithm based on the node voltage and reactive sensitivity coefficient and matrix splitting solves the active distribution network voltage optimization control model to obtain a solution result, including: Calculate the objective function of each node; Calculating the gradient of the objective function of each node itself; Calculating the value of the local second-order gradient matrix of each node; Calculating the distributed iteration direction components of the neighboring nodes of each node based on the gradient of the objective function and the value of the local second-order gradient matrix; Iteratively calculating reactive injection adjustment amounts of each node based on the distributed iteration direction component; Based on the objective function and the reactive injection adjustment amount, establishing a convergence accuracy judgment standard; If the convergence accuracy of the voltage optimization control model meets the convergence accuracy judgment standard, the solution result of the voltage optimization control model is output; the solution result includes: the objective function value, the voltage amplitude of each node and the reactive injection adjustment amount of the distributed power supply.

2. The distributed voltage control method of active power distribution network according to claim 1, characterized in that: The distribution network related data include: the access location and capacity of distributed power sources, the lower and upper limits of the reactive power regulation of distributed power sources, the initial values ​​of the reference voltage and reference power of the active distribution network, the safety voltage constraints of each node, the node voltage and reactive power historical measurement data, the number of training samples, the dimension of dimension increase, the natural distribution vector of node voltage, the initial value of the local second-order gradient matrix, the correction parameter, the iteration step size, and the convergence accuracy.

3. The distributed voltage control method of the active power distribution network according to claim 1, characterized in that: The calculating node voltage and reactive power sensitivity coefficient based on the linear power flow equation matrix includes: pass Calculate node voltage and reactive power sensitivity coefficients; where Z ji is the element in the jth row and ith column of the node voltage and reactive power sensitivity matrix Z, L ji is the corresponding voltage V in matrix L j and reactive power adjustment Δq i The element L j,(K+t) is the corresponding voltage V in matrix L j The t-th dimension input variable ψ expanded by the dimensionality-raising transformation t (x), m is the dimension of the upgrade.

4. The distributed voltage control method of the active power distribution network according to claim 1, characterized in that: The calculation of the objective function of each node itself includes: pass Calculate the objective function of node i itself; where Δq(k) is the vector of reactive power injection adjustment of distributed generation at the kth iteration, N i is the set of nodes that are directly connected to node i through a branch and are located downstream of the reference node. j is the jth row in Z, for The jth element in ; The step of calculating the gradient of the objective function of each node includes: pass Calculate the gradient of the objective function of node i itself; where g i (k) is the i-th element in g(k), V j (k) is the voltage amplitude of node j at the kth iteration, v j is the rated voltage of node j; The calculating the value of the local second-order gradient matrix of each node includes: pass Calculate the local second-order gradient matrix H of node i i (k) where vectors or matrices are indicated by the subscript N i Represents the set N i The sub-vector or sub-matrix composed of the elements in , β is the correction parameter, is a diagonal matrix with diagonal elements of 1 / n i , n i For the set N i The number of elements in , I is the identity matrix; The distributed iterative direction components of the neighboring nodes of each node are calculated, including pass Calculate the distributed iterative direction component of the neighboring nodes of node i; where Γ is the correction parameter, The distributed iterative directional components of all neighboring nodes calculated for node i; The iterative calculation of the reactive injection adjustment amount of each node includes: pass Iteratively calculate the reactive injection adjustment amount of node i; where d i (k) is the final distributed iteration direction, Δq i (k) is the reactive power adjustment of the distributed generation of node i at the kth iteration, The distributed iteration direction component of node i calculated for node j, α is the iteration step size; The convergence accuracy judgment criteria include: Where N is the total number of nodes and ω is the preset convergence accuracy.

5. A distributed voltage control device for an active power distribution network, characterized in that: A method for implementing a distributed voltage control method for an active power distribution network as claimed in any one of claims 1 to 4, comprising: Input module, used to obtain distribution network related data; A first operation module is used to construct an active distribution network voltage optimization control model based on the distribution network historical operation data and the linearized voltage-reactive power equation; The second operation module is used to construct a linear power flow equation matrix based on the historical operation data of the distribution network and the Koopman state space dimensionality increase transformation method; the second operation module is specifically used to: pass Represents the linearized voltage-reactive power equation; where Δq is the reactive power injection adjustment of the node distributed generation, is the natural distribution vector of node voltage when the distributed generation reactive power is not regulated, and the reactive power sensitivity matrix Z; Constructing active distribution network voltage optimization control model: Where f(Δq) is the objective function, represents the sum of squares of matrix elements, v is the column vector of node rated voltages, is the deviation between the rated voltage and the natural voltage distribution, Δq is the lower limit of the reactive power regulation of the node distributed generation, The upper limit of the reactive power regulation amount of the node distributed generation; The third operation module is used to calculate the node voltage and reactive power sensitivity coefficient based on the linear power flow equation matrix; the third operation module is specifically used to: The node active and reactive historical measurement data are used as input variables; Performing a dimensionality-upgrading transformation on the input variable; Estimate the value of the linear power flow equation matrix based on the input variables after dimensionality increase transformation and the least square method; The step of performing a dimensionality-upgrading transformation on the input variable comprises: pass Perform dimensional transformation on the input variables; where x is the input variable, including the active power and reactive power injected by the node, ψ(x) represents the m-dimensional input variable expanded by dimensional transformation, x lift is the input variable after dimension increase; ψ t (x) is the tth element in ψ(x), f lift is a dimensionality-raising function, x v is the vth element in x, K is the dimension of x, c t is a randomly generated K-dimensional basis vector, c tv c t The vth element in ; The estimating the value of the linear power flow equation matrix based on the least squares method includes: pass Calculate the value of the linear power flow equation matrix L; where, is the input variable sample matrix, which contains the active power and reactive power vectors of nodes in S time sections, is the sample matrix of the input variable after dimensionality increase, which contains the input variables after dimensionality increase of S time sections, S is the number of training samples, is the state variable sample matrix, which contains the node phase angle and voltage amplitude vectors of S time sections, [·] + is the Moore-Penrose inverse of the matrix; The output module is used to solve the active distribution network voltage optimization control model based on the distributed algorithm of the node voltage, reactive sensitivity coefficient and matrix splitting to obtain a solution result; the solution result is used to control the distributed voltage of the active distribution network; the output module is specifically used to: Calculate the objective function of each node; Calculating the gradient of the objective function of each node itself; Calculating the value of the local second-order gradient matrix of each node; Calculating the distributed iteration direction components of the neighboring nodes of each node based on the gradient of the objective function and the value of the local second-order gradient matrix; Iteratively calculating reactive injection adjustment amounts of each node based on the distributed iteration direction component; Based on the objective function and the reactive injection adjustment amount, establishing a convergence accuracy judgment standard; If the convergence accuracy of the voltage optimization control model meets the convergence accuracy judgment standard, the solution result of the voltage optimization control model is output; the solution result includes: the objective function value, the voltage amplitude of each node and the reactive injection adjustment amount of the distributed power supply.

6. A terminal device, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the distributed voltage control method for the active power distribution network as described in any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the distributed voltage control method for an active power distribution network as claimed in any one of claims 1 to 4 is implemented.

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