Equivalent load modeling method and system containing distributed photovoltaic

Abstract

The invention discloses a distributed photovoltaic equivalent load modeling method and system, and the method comprises the steps: calculating a modularity index and an active balance index of an electrical distance, and constructing a comprehensive division objective function through the calculated modularity index and active balance index of the electrical distance; using an improved AE optimization algorithm to carry out cluster division on the distributed photovoltaic; and finally, carrying out equivalent calculation on the clustered distributed photovoltaic power supply so as to realize distributed photovoltaic load modeling. The method can significantly improve the clustering and clustering precision of the distributed photovoltaic power supply, and better achieves the modeling and construction of the load containing the distributed photovoltaic power supply.

Description

Technical Field

[0001] The present invention belongs to the technical field of load modeling of distributed photovoltaic systems, and in particular relates to an equivalent load modeling method and system including distributed photovoltaics. Background Art

[0002] Distributed photovoltaics can be installed in a dispersed manner to form a multi-point power generation pattern, reducing dependence on a single large power station and improving the stability and risk resistance of the power grid. During peak periods of electricity demand, distributed photovoltaics can effectively alleviate the pressure on the power grid and balance the supply and demand relationship. However, due to the strong randomness and intermittent characteristics of distributed photovoltaic output and the access to the distribution network in a multi-point highly dispersed form, centralized control methods are difficult to meet the system's requirements for communication and computing capabilities. This has led to practical problems such as the intensification of power electronics on the load side and the complexity and diversification of load composition. Therefore, it is imperative to carry out the construction and application of accurate load modeling systems.

[0003] In this process, distributed photovoltaic equivalent modeling is particularly important. Its core purpose is to accurately capture the dynamic response characteristics of loads under large disturbance conditions, thereby improving the accuracy of large-scale power grid simulation analysis and providing effective reference suggestions for further improving the practical level of accurate load modeling systems. Therefore, an equivalent load modeling method and system containing distributed photovoltaics is urgently needed. Summary of the invention

[0004] The present invention provides an equivalent load modeling method and system including distributed photovoltaics, which are used to solve the technical problem of low load modeling accuracy.

[0005] In a first aspect, the present invention provides an equivalent load modeling method including distributed photovoltaics, comprising:

[0006] Calculate the modularity index and active power balance index of electrical distance;

[0007] Constructing a comprehensive partitioning objective function according to the modularity index and the active power balance index;

[0008] Solving the comprehensive partitioning objective function according to a preset improved AE optimization algorithm, and outputting a distributed photovoltaic optimal cluster, wherein the improved AE optimization algorithm includes an escape formula and a Tent-Logistic-Cosine chaotic mapping;

[0009] An equivalent calculation is performed on the distributed photovoltaic optimal cluster to obtain an equivalent load modeling result containing distributed photovoltaics.

[0010] In a second aspect, the present invention provides an equivalent load modeling system including distributed photovoltaics, comprising:

[0011] A first calculation module is configured to calculate a modularity index and an active power balance index of an electrical distance;

[0012] A construction module configured to construct a comprehensive partitioning objective function according to the modularity index and the active power balance index;

[0013] A solution module is configured to solve the comprehensive partition objective function according to a preset improved AE optimization algorithm, and output an optimal distributed photovoltaic cluster, wherein the improved AE optimization algorithm includes an escape formula and a Tent-Logistic-Cosine chaotic mapping;

[0014] The second calculation module is configured to perform equivalent calculation on the distributed photovoltaic optimal cluster to obtain an equivalent load modeling result containing distributed photovoltaics.

[0015] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the method for equivalent load modeling containing distributed photovoltaics according to any embodiment of the present invention.

[0016] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of the method for equivalent load modeling containing distributed photovoltaics according to any embodiment of the present invention.

[0017] The equivalent load modeling method and system of the present application including distributed photovoltaic, BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A flow chart of a method for modeling equivalent loads including distributed photovoltaics provided by an embodiment of the present invention;

[0020] Figure 2 A structural block diagram of an equivalent load modeling system including distributed photovoltaics provided by an embodiment of the present invention;

[0021] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 , which shows a flow chart of an equivalent load modeling method containing distributed photovoltaics in the present application.

[0024] like Figure 1 As shown in FIG. 1 , the equivalent load modeling method including distributed photovoltaics specifically includes the following steps:

[0025] Step S101, calculating the modularity index and active power balance index of the electrical distance.

[0026] In this step, in terms of structure, the community structure in a complex network is generally composed of nodes with similar functions or properties. The essence is the regional coupling of physical, chemical or social interaction relationships between nodes. The distributed photovoltaic cluster in the distribution network has similar properties to the community structure, and the partitioning index of the complex network can be used. Modularity is used to measure whether the result of cluster partitioning is more reasonable. The larger the value, the more reasonable the partitioned cluster; when the entire network is divided into one cluster, the modularity is 0; when each node is divided into different clusters, the modularity is negative, and there is no community structure at this time. The expression for calculating the modularity index is:

[0027]

[0028]

[0029] In the formula, is the modularity index, m is the sum of the edge weights of the entire network, and k i is the sum of the edge weights of all edges connected to node i, k j is the sum of the edge weights of all edges connected to node j, δ is a 0-1 matrix. If nodes i and j are in the same cluster, then δ(i, j) = 1; if they are not in the same cluster, then δ(i, j) = 0. ij is the edge weight connecting node i and node j. When node i and node j are directly connected, A ij Take 1, when not connected A ij Take 0, e ij is the electrical distance between node i and node j, e is the electrical distance matrix composed of the electrical distances between any two nodes in the network, d jnis the location of node jn, d in is the position of node in;

[0030] ΔV=S vQ ΔQ,

[0031]

[0032] Where ΔV is the voltage amplitude change, S vQ is the voltage reactive power sensitivity matrix, ΔQ is the reactive power change, d ij is the ratio of the voltage amplitude change between node j and node i, SV Q，jj is the voltage-reactive sensitivity matrix of the system at node j, S VQ，ij is the voltage reactive power sensitivity matrix of the system between node j and node i.

[0033] The expression for calculating the active power balance index is:

[0034]

[0035] Where N c is the number of clusters, M is the set of all clusters, is the active power balance index of cluster c, P c，t is the net power of cluster c at time t, T is the duration of the specified scenario, It is an indicator of active power balance.

[0036] Step S102: constructing a comprehensive partitioning objective function according to the modularity index and the active power balance index.

[0037] In this step, the expression of the comprehensive partitioning objective function is:

[0038]

[0039] Where ρ is the comprehensive partition objective function, w 1 is the weight of the modularity index, is the modularity index, w 2 is the weight of active power balance index, It is an indicator of active power balance.

[0040] Step S103, solving the comprehensive partitioning objective function according to a preset improved AE optimization algorithm, and outputting an optimal distributed photovoltaic cluster, wherein the improved AE optimization algorithm includes an escape formula and a Tent-Logistic-Cosine chaotic mapping.

[0041] In this step, the Tent-Logistic-Cosine chaotic map is introduced to initialize the population, and the expression is:

[0042]

[0043] x i =(ub-lb)×rand+lb,

[0044]

[0045] In the formula, X is the matrix of the initial population, is a matrix element in the matrix, L and D are the row and column positions of each matrix element in the matrix, ub and 1b are the upper and lower bounds of the problem, rand is a random number between 0 and 1, r is a random number between 0 and 1, x(i+1) is the position of the individual after Tent-Logistic-Cosine chaotic mapping, x i is the position of the individual before the Tent-Logistic-Cosine chaotic mapping;

[0046] In order to solve the problem that the search range of the AE algorithm is too small in the early stage of iteration, an adaptive elite selection formula is introduced in the operator part of the AE optimization algorithm to expand its search range. The expression is:

[0047]

[0048] In the formula, X is the candidate matrix, E is the evolution matrix, → indicates the sampling operation with replacement, and N is a positive integer set;

[0049] In order to solve the problem that the search range of the AE algorithm is too small in the early stage of iteration, an adaptive elite selection formula is introduced in the operator part of the AE optimization algorithm to expand its search range. The expression is:

[0050]

[0051] In the formula, is the evolutionary solution of the t+1th iteration in the matrix, P is the basis vector, α is the attenuation factor, Δr i is the step size, θ is the control parameter, W i , L i are all solutions extracted from the matrix of the initial population, is the evolutionary solution of the tth iteration in the matrix, η is a random number between 0 and 1, E best is the optimal value of the current iteration, e is the natural base, and T is the maximum number of iterations;

[0052] In order to solve the problem that the AE algorithm converges too slowly in the middle of the iteration, the original formula is improved, and the improved expression is:

[0053]

[0054] Where diagonal(A) is the diagonal of A, A is a D-th order square matrix, B is a matrix with K rows and D columns, ω is the solution, is the cardinality vector of a at the tth iteration, is the basis vector of b at the tth iteration, is the cardinality vector of a at the t+1th iteration, is the basis vector of b at the t+1th iteration, is the cardinality vector of a at the t-1th iteration, is the basis vector of b at the t-1th iteration, c a 、c b are learning rates;

[0055] In order to solve the problem that the AE algorithm is prone to fall into the local optimal value in the later stage of iteration, an escape formula is introduced in the later stage of the algorithm to make it jump out when it falls into the local optimal value. Its expression is:

[0056]

[0057] ECP=Gaussian×(X avg -x(t))+lb+r 1 ×(ub-lb) / 2

[0058]

[0059] θ 2 =e k·cos(π·(1-(t / T))) ,

[0060] In the formula, is the position of the individual at the t+1 iteration, is the position of the individual at the tth iteration, represents the position of the worst individual, rand represents a random number between 0 and 1, σ represents a constant that changes with the number of iterations, ECP is a discrete variable that simulates the distribution of complex changes in the ellipse, Gaussian is a Gaussian walk operator, and X avg is the average position of the population, r i and r 2 is a random value from [0, 1], θ 1 and θ 2 is a constant value that changes with the number of iterations, T is the maximum number of iterations, and RB is an array representing random data generated from a standard normal distribution.

[0061] Set boundary constraints and select strategies. The boundary constraint expression is:

[0062]

[0063] In the formula, E i，j Responsible for generating target solutions;

[0064] The expression of the selection strategy is:

[0065]

[0066] In the formula, is the individual at the t+1th iteration, is the individual at the tth iteration, is the evolutionary solution of the t+1th iteration in the matrix, is the fitness value of the evolutionary solution at the t+1th iteration in the matrix.

[0067] In order to solve the problem that the AE algorithm is prone to fall into the local optimal value in the later stage of iteration, an escape formula is introduced in the later stage of the algorithm to make it jump out when it falls into the local optimal value. Its expression is:

[0068]

[0069] ECP=Gaussian×(X avg -X(t))+lb+r 1 ×(ub-lb) / 2

[0070]

[0071] θ 2 =e k·cos(π·(1-(t / T)))

[0072] in, is the position of the individual at the t+1 iteration, is the position of the aurora individual at the tth iteration, represents the position of the worst individual, rand represents a random number between 0 and 1, σ represents a constant that changes with the number of iterations, ECP is a discrete variable that simulates the distribution of complex changes in the ellipse, Gaussian is a Gaussian walk operator, and X avg is the average position of the population, r 1 and r 2 is a random value from [0, 1], θ 1 and θ 2 is a constant value that changes with the number of iterations, T is the maximum number of iterations, and RB is an array randomly generated from a standard normal distribution (with a mean of 0 and a standard deviation of 1).

[0073] It should be noted that since the cluster division result is a collection of lines between nodes, the adjacency matrix between distribution network nodes is used for encoding. First, a 0-1 adjacency matrix is ​​obtained based on the connection status of the distribution network nodes. The row and column numbers of the matrix are the node numbers of the network. The matrix element 0 indicates that there is no line connection between the two nodes, and 1 indicates that there is a line connection. During encoding, the improved and optimized AE algorithm forms a new adjacency matrix by searching and modifying element 1 in the matrix. Modification to 0 indicates that the line between the two nodes is disconnected, and modification to 1 indicates that the connection remains. The modified matrix represents a division result of the network cluster.

[0074] Step S104, performing equivalent calculation on the distributed photovoltaic optimal cluster to obtain an equivalent load modeling result including distributed photovoltaics.

[0075] In this step, the linear independence parameter is calculated, and the expression is:

[0076]

[0077] In the formula, S eq is the rated capacity of distributed photovoltaic after equalization, S i is the rated capacity of the i-th distributed photovoltaic, n is the number of distributed photovoltaics, K P2，eq is the equivalent value of the distributed photovoltaic low voltage ride-through control parameter, I P0，i is the active current of the i-th distributed photovoltaic before entering low voltage ride-through, K P2，i is the low voltage ride-through control parameter of the i-th distributed photovoltaic system, I Pset I is the set value of the distributed photovoltaic grid-connected output active current after equalization. Pset，i is the set value of active current output of the i-th distributed photovoltaic grid-connected device;

[0078] Calculate the linear coupling parameters, the expression is:

[0079]

[0080] In the formula, I QLV，eq is the reactive current during the low voltage ride-through period after the distributed photovoltaic equalization, I QLV，i is the reactive current during the low voltage ride-through period of the i-th distributed photovoltaic;

[0081] V PCC -V t，i =-I QLN，i X i ,

[0082] V PCC -V t，eq =-I QLV，eq X eq ,

[0083] Where V PCC is the PCC point voltage, V t，i is the terminal voltage of the ith distributed photovoltaic at time t, X i is the total reactance between the i-th distributed photovoltaic terminal and the PCC point, X eq is the equivalent value of the total reactance between the distributed photovoltaic terminal and the PCC point, V t，eq is the terminal voltage after distributed photovoltaic equalization;

[0084]

[0085] In the formula, K Q1，eq is the linear coupling parameter after equalization, K Q1，i is the linear coupling parameter of the i-th distributed photovoltaic;

[0086] Calculate the nonlinear coupling parameters, the expression is:

[0087] P LV =I PLV ×V t ,

[0088]

[0089] Where P LV is the active power of distributed photovoltaic during ride-through, I PLV is the active current during the distributed photovoltaic ride-through period, V t is the terminal voltage at time t, K PLV is the nonlinear coupling parameter, P 0 is the active power of distributed photovoltaic before crossing, P setLV For safety power.

[0090] In summary, the method of the present application calculates the modularity index and active power balance index of the electrical distance, and constructs a comprehensive partitioning objective function through the calculated modularity index and active power balance index of the electrical distance. In view of the shortcomings of the AE optimization algorithm itself, the escape formula and Tent-Logistic-Cosine chaotic mapping are introduced to improve the AE optimization algorithm, and the improved AE optimization algorithm is used to cluster distributed photovoltaics; finally, the clustered distributed photovoltaic power sources are calculated with equivalent values, thereby realizing load modeling containing distributed photovoltaics. It can significantly improve the accuracy of clustering of distributed photovoltaic power sources and better realize the construction of load modeling containing distributed photovoltaics.

[0091] See also Figure 2 , which shows a structural block diagram of an equivalent load modeling system containing distributed photovoltaics in the present application.

[0092] like Figure 2As shown, the equivalent load modeling system 200 includes a first calculation module 210 , a construction module 220 , a solution module 230 and a second calculation module 240 .

[0093] Among them, the first calculation module 210 is configured to calculate the modularity index and active power balance index of the electrical distance; the construction module 220 is configured to construct a comprehensive partitioning objective function based on the modularity index and the active power balance index; the solution module 230 is configured to solve the comprehensive partitioning objective function according to a preset improved AE optimization algorithm, and output the distributed photovoltaic optimal cluster, wherein the improved AE optimization algorithm includes an escape formula and a Tent-Logistic-Cosine chaotic mapping; the second calculation module 240 is configured to perform equivalent calculation on the distributed photovoltaic optimal cluster to obtain an equivalent load modeling result containing distributed photovoltaics.

[0094] It should be understood that Figure 2 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 2 The modules in it will not be described in detail here.

[0095] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the equivalent load modeling method containing distributed photovoltaics in any of the above method embodiments;

[0096] As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows:

[0097] Calculate the modularity index and active power balance index of electrical distance;

[0098] Constructing a comprehensive partitioning objective function according to the modularity index and the active power balance index;

[0099] Solving the comprehensive partitioning objective function according to a preset improved AE optimization algorithm, and outputting a distributed photovoltaic optimal cluster, wherein the improved AE optimization algorithm includes an escape formula and a Tent-Logistic-Cosine chaotic mapping;

[0100] An equivalent calculation is performed on the distributed photovoltaic optimal cluster to obtain an equivalent load modeling result containing distributed photovoltaics.

[0101] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the equivalent load modeling system containing distributed photovoltaics, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the equivalent load modeling system containing distributed photovoltaics via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0102] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3 In the example, the bus connection is used. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the equivalent load modeling method of distributed photovoltaics in the above-mentioned method embodiment is realized. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the equivalent load modeling system containing distributed photovoltaics. The output device 340 may include display devices such as display screens.

[0103] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0104] As an implementation mode, the electronic device is applied to an equivalent load modeling system including distributed photovoltaics, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can:

[0105] Calculate the modularity index and active power balance index of electrical distance;

[0106] Constructing a comprehensive partitioning objective function according to the modularity index and the active power balance index;

[0107] Solving the comprehensive partitioning objective function according to a preset improved AE optimization algorithm, and outputting a distributed photovoltaic optimal cluster, wherein the improved AE optimization algorithm includes an escape formula and a Tent-Logistic-Cosine chaotic mapping;

[0108] An equivalent calculation is performed on the distributed photovoltaic optimal cluster to obtain an equivalent load modeling result containing distributed photovoltaics.

[0109] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these 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 invention.

Claims

1. A method for modeling equivalent loads including distributed photovoltaics, characterized in that: include: Calculate the modularity index and active power balance index of electrical distance; Constructing a comprehensive partitioning objective function according to the modularity index and the active power balance index; The comprehensive partitioning objective function is solved according to a preset improved AE optimization algorithm, and an optimal distributed photovoltaic cluster is output, wherein the improved AE optimization algorithm includes an escape formula and a Tent-Logistic-Cosine chaotic mapping; An equivalent calculation is performed on the distributed photovoltaic optimal cluster to obtain an equivalent load modeling result containing distributed photovoltaics.

2. The equivalent load modeling method containing distributed photovoltaics according to claim 1 is characterized in that: The expression for calculating the modularity index is: In the formula, is the modularity index, m is the sum of the edge weights of the entire network, and k i is the sum of the edge weights of all edges connected to node i, k j is the sum of the edge weights of all edges connected to node j, δ is a 0-1 matrix. If nodes i and j are in the same cluster, then δ(i, j) = 1; if they are not in the same cluster, then δ(i, j) = 0. ij is the edge weight connecting node i and node j. When node i and node j are directly connected, A ij Take 1, when not connected A ij Take 0, e ij is the electrical distance between node i and node j, e is the electrical distance matrix composed of the electrical distances between any two nodes in the network, d jn is the location of node jn, d in is the position of node in; The expression for calculating the active power balance index is: Where N c is the number of clusters, M is the set of all clusters, is the active power balance index of cluster c, P c，t is the net power of cluster c at time t, T is the duration of the specified scenario, It is an indicator of active power balance.

3. The equivalent load modeling method containing distributed photovoltaics according to claim 1 is characterized in that: The expression of the comprehensive partitioning objective function is: In the formula, ρ is the comprehensive partitioning objective function, w1 is the weight of the modularity index, is the modularity index, w2 is the weight of the active power balance index, It is an indicator of active power balance.

4. The equivalent load modeling method containing distributed photovoltaics according to claim 1 is characterized in that: The comprehensive partitioning objective function is solved according to the preset improved AE optimization algorithm, and the output of the distributed photovoltaic optimal cluster includes: In order to solve the problem that the search range of the AE algorithm is too small in the early stage of iteration, an adaptive elite selection formula is introduced in the operator part of the AE optimization algorithm to expand its search range. The expression is: In the formula, is the evolutionary solution of the t+1th iteration in the matrix, P is the basis vector, α is the attenuation factor, Δr i is the step size, θ is the control parameter, W i , L i are all solutions extracted from the matrix of the initial population, is the evolutionary solution of the tth iteration in the matrix, η is a random number between 0 and 1, E best is the optimal value of the current iteration, e is the natural base, and T is the maximum number of iterations; In order to solve the problem that the AE algorithm converges too slowly in the middle of the iteration, the original formula is improved, and the improved expression is: Where diagonal(A) is the diagonal of A, A is a D-th order square matrix, B is a matrix with K rows and D columns, ω is the solution, is the cardinality vector of a at the tth iteration, is the basis vector of b at the tth iteration, is the cardinality vector of a at the t+1th iteration, is the basis vector of b at the t+1th iteration, is the cardinality vector of a at the t-1th iteration, is the basis vector of b at the t-1th iteration, c a 、c b are learning rates; In order to solve the problem that the AE algorithm is prone to fall into the local optimal value in the later stage of iteration, an escape formula is introduced in the later stage of the algorithm to make it jump out when it falls into the local optimal value. Its expression is: ECP=Gaussian×(X avg -X(t))+lb+r1×(ub-lb) / 2 θ2=e k·cos(π-(1-(t / T))) , In the formula, is the position of the individual at the t+1 iteration, is the position of the individual at the tth iteration, represents the position of the worst individual, rand represents a random number between 0 and 1, σ represents a constant that changes with the number of iterations, ECP is a discrete variable that simulates the distribution of complex changes in the ellipse, Gaussian is a Gaussian walk operator, and X avg is the average position of the population, r1 and r2 are random values ​​in [0,1], θ1 and θ2 are constant values ​​that change with the number of iterations, T is the maximum number of iterations, and RB is an array randomly generated from a standard normal distribution.

5. The equivalent load modeling method containing distributed photovoltaics according to claim 1 is characterized in that: The equivalent calculation of the distributed photovoltaic optimal cluster to obtain the equivalent load modeling result containing distributed photovoltaics includes: Calculate the linear independence parameter, the expression is: In the formula, S eq is the rated capacity of distributed photovoltaic after equalization, S i is the rated capacity of the i-th distributed photovoltaic, n is the number of distributed photovoltaics, K P2，eq is the equivalent value of the distributed photovoltaic low voltage ride-through control parameter, I P0，i is the active current of the i-th distributed photovoltaic before entering low voltage ride-through, K P2，i is the low voltage ride-through control parameter of the i-th distributed photovoltaic system, I Pset I is the set value of the distributed photovoltaic grid-connected output active current after equalization. Pset，i is the set value of active current output of the i-th distributed photovoltaic grid-connected device; Calculate the linear coupling parameters, the expression is: In the formula, I QLV，eq is the reactive current during the low voltage ride-through period after the distributed photovoltaic equalization, I QLV，i is the reactive current during the low voltage ride-through period of the i-th distributed photovoltaic; V PCC -V t，i =-I QLV，i X i , V PCC -V t，eq =-I QLV，eq X eq , Where V PCC is the PCC point voltage, V t，i is the terminal voltage of the ith distributed photovoltaic at time t, X i is the total reactance between the i-th distributed photovoltaic terminal and the PCC point, X eq is the equivalent value of the total reactance between the distributed photovoltaic terminal and the PCC point, V t，eq is the terminal voltage after distributed photovoltaic equalization; In the formula, K Ql，eq is the linear coupling parameter after equalization, K Ql，i is the linear coupling parameter of the i-th distributed photovoltaic; Calculate the nonlinear coupling parameters, the expression is: P LV =I PLV ×V t , Where P LV is the active power of distributed photovoltaic during ride-through, I PLV is the active current during the distributed photovoltaic ride-through period, V t is the terminal voltage at time t, K PLV is the nonlinear coupling parameter, P0 is the active power before the distributed photovoltaic crossover, P setLV For safe power.

6. An equivalent load modeling system including distributed photovoltaics, characterized in that: include: A first calculation module is configured to calculate a modularity index and an active power balance index of an electrical distance; A construction module configured to construct a comprehensive partitioning objective function according to the modularity index and the active power balance index; A solution module is configured to solve the comprehensive partition objective function according to a preset improved AE optimization algorithm, and output an optimal distributed photovoltaic cluster, wherein the improved AE optimization algorithm includes an escape formula and a Tent-Logistic-Cosine chaotic mapping; The second calculation module is configured to perform equivalent calculation on the distributed photovoltaic optimal cluster to obtain an equivalent load modeling result containing distributed photovoltaics.

7. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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