Optimization control method and system for reactive compensation of power distribution network

Abstract

The invention discloses an optimal control method and system for reactive power compensation of a power distribution network, and the method comprises the steps: building a multi-target reactive power optimization model of the power distribution network based on the system parameters of the power distribution network, and carrying out the optimal control of the reactive power compensation of the power distribution network under the constraint condition of the multi-target reactive power optimization model of the power distribution network, solving the target function of the multi-target power distribution network reactive power optimization model by adopting a multi-target whale optimization algorithm to obtain an iterative optimization solution set output by the multi-target power distribution network reactive power optimization model, and processing the multi-target power distribution network reactive power optimization model based on the iterative optimization solution set and an improved multi-target grey wolf optimization algorithm to obtain a multi-target power distribution network reactive power optimization model. And taking a result output by the multi-target power distribution network reactive power optimization model as a node configuration optimal solution set of the power distribution network, and controlling the power distribution network by using the node configuration optimal solution set. According to the optimal control method and system for the reactive compensation of the power distribution network, the optimal control effect of the reactive compensation of the power distribution network is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of reactive power optimization of distribution networks, and in particular to an optimization control method and system for reactive power compensation of distribution networks. Background Art

[0002] With the continuous advancement of renewable energy power generation technology and the continuous expansion of its application scale, the utilization rate of renewable energy such as solar energy, wind energy and hydropower has been greatly improved. The large-scale access of renewable energy has reduced the proportion of traditional fossil energy in energy consumption and reduced the emission of carbon dioxide, sulfur dioxide and nitrogen oxides, thereby achieving the effect of alleviating the greenhouse effect, improving air quality and reducing carbon emission intensity.

[0003] Renewable energy is highly random, intermittent and volatile. Large-scale access to the distribution network will cause frequent fluctuations in the voltage of the distribution network nodes, and even seriously threaten the power quality and power supply reliability of the entire distribution network.

[0004] Reasonable reactive power configuration can not only improve the system's fault recovery and anti-interference capabilities, but also optimize the reactive power flow distribution, thereby effectively reducing the system's active network loss. The optimization algorithm can achieve reactive power optimization of the distribution network by adjusting the configuration of reactive power sources. However, since the load and operating status of the distribution network change in real time, the existing optimization algorithms often analyze static and specific operating conditions, resulting in poor reactive power compensation effects on the distribution network.

[0005] It can be seen that how to solve the problem that existing optimization algorithms are difficult to track changes in distribution networks in real time has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0006] The present invention provides an optimization control method and system for reactive power compensation of a distribution network, so as to solve the technical problem that the existing optimization algorithm is difficult to track the change of the distribution network in real time, so as to improve the optimization effect of reactive power compensation of the distribution network.

[0007] In order to solve the above technical problems, an embodiment of the present invention provides an optimization control method for reactive power compensation in a distribution network.

[0008] A multi-objective distribution network reactive power optimization model is constructed based on the system parameters of the distribution network; the reactive power compensation device of the multi-objective distribution network reactive power optimization model is a multi-time scale reactive power compensation device;

[0009] Under the constraints of the multi-objective distribution network reactive power optimization model, a multi-objective whale optimization algorithm is used to solve the objective function of the multi-objective distribution network reactive power optimization model to obtain an iterative optimization solution set output by the multi-objective distribution network reactive power optimization model;

[0010] Based on the iterative optimization solution set and the improved multi-objective grey wolf optimization algorithm, the multi-objective distribution network reactive power optimization model is processed, the result output by the multi-objective distribution network reactive power optimization model is used as the node configuration optimal solution set of the distribution network, and the distribution network is controlled by the node configuration optimal solution set; wherein, the iterative processing process of the multi-objective distribution network reactive power optimization model by the improved multi-objective grey wolf optimization algorithm is configured to repeatedly update the feedback search step until the iteration ends.

[0011] As one of the preferred solutions, the multi-objective distribution network reactive power optimization model is processed based on the iterative optimization solution set and the improved multi-objective grey wolf optimization algorithm, including:

[0012] The iterative optimization solution set is used as the input of the improved multi-objective grey wolf optimization algorithm. Before the multi-objective grey wolf optimization algorithm reaches a preset maximum number of iterations, the step size update process is iteratively executed to iteratively solve the objective function under the constraints. When the multi-objective grey wolf optimization algorithm reaches a preset maximum number of iterations, the result output by the multi-objective distribution network reactive power optimization model is used as the optimal solution set for the node configuration of the distribution network.

[0013] As one of the preferred solutions, the step size update process includes:

[0014] Calculating the individual fitness of each wolf based on the iterative optimization solution set, and determining each wolf and the ω wolf pack based on the ranking results of the individual fitness of all wolves;

[0015] Based on the linear relationship among the number of iterations, the individual fitness of all wolves in the current iteration, and the feedback search step length of the ω wolf group toward the direction of each alpha wolf in the previous iteration, the feedback search step length of the ω wolf group toward the direction of each alpha wolf in the current iteration is calculated;

[0016] All the feedback search steps are used as the search steps of the multi-objective grey wolf optimization algorithm, the iterative optimization solution set is optimized by the multi-objective grey wolf optimization algorithm, and the optimized iterative optimization solution set is output.

[0017] As one of the preferred solutions, the alpha wolves include alpha wolves, beta wolves and delta wolves, and the linear relationship between the number of iterations, the individual fitness of all wolves in the current iterative execution process, and the feedback search step length of the omega wolf group in the direction of each alpha wolf in the previous iterative execution process is calculated to obtain the feedback search step length of the omega wolf group in the direction of each alpha wolf in the current iterative execution process, including:

[0018] Based on the linear relationship between the individual fitness of all wolves in the current iterative execution process, the fitness proportional factor of each wolf in the current iterative execution process is calculated;

[0019] Based on the linear relationship between the number of iterations and the fitness proportional factors of all wolves in the current iterative execution process, a convergence factor of the current iterative execution process is calculated;

[0020] Based on the linear relationship between the individual fitness of all the alpha wolves in the current iterative execution process, the convergence factor of the current iterative execution process and the feedback search step of the ω wolf pack toward the direction of each alpha wolves in the previous iterative execution process, the feedback search step of the ω wolf pack moving toward the direction of each alpha wolves in the current iterative execution process is calculated.

[0021] As one of the preferred solutions, the fitness proportional factor of each wolf in the current iterative execution process is calculated based on the linear relationship between the individual fitness of all wolves in the current iterative execution process, including:

[0022] The maximum value of the individual fitness of all wolves during the current iteration is recorded as the best fitness, and the minimum value of the individual fitness of all wolves during the current iteration is recorded as the worst fitness;

[0023] The difference between the optimal fitness and the individual fitness of each wolf is recorded as the fitness difference index of each wolf in the current iterative execution process;

[0024] The difference between the best fitness and the worst fitness is recorded as the overall fitness difference index of the current iterative execution process;

[0025] The fitness proportional factor of each wolf in the current iterative execution process is calculated based on the normalized value of the difference between the fitness difference index of each wolf and the overall fitness difference index.

[0026] As one of the preferred solutions, the convergence factor of the current iterative execution process is calculated based on the linear relationship between the number of iterations and the fitness proportional factors of all wolves in the current iterative execution process, including the specific method of:

[0027] Based on the linear relationship of the fitness proportional factors of all wolves in the current iterative execution process, the fitness gain factor of the current iterative execution process is calculated;

[0028] The calculation formula of the convergence factor of each iterative execution process is:

[0029]

[0030] Among them, a nis the convergence factor of the nth iteration execution process, AF n is the fitness gain factor of the nth iteration execution process, cos() is the cosine function, π is the pi, n is the number of iterations, and N is the preset maximum number of iterations.

[0031] As one of the preferred solutions, the feedback search step length of the ω wolf group moving toward the direction of each alpha wolf in the current iterative execution process is calculated based on the linear relationship between the individual fitness of all the alpha wolves in the current iterative execution process, the convergence factor of the current iterative execution process, and the feedback search step length of the ω wolf group moving toward the direction of each alpha wolf in the previous iterative execution process, including:

[0032] Based on the linear relationship between the individual fitness of all the alpha wolves in the current iterative execution process and the convergence factor in the current iterative execution process, the search step gain coefficient of the current iterative execution process is calculated;

[0033] The product of twice the normalized value of the search step gain coefficient and the feedback search step of the ω wolf pack toward the direction of each alpha wolf in the previous iterative execution process is used as the feedback search step of the ω wolf pack moving toward the corresponding direction of the alpha wolf in the current iterative execution process.

[0034] As one of the preferred schemes, the iterative optimization solution set and the node configuration optimal solution set both include the output of each node in the distribution network; the constraints include distribution network flow constraints, node voltage constraints, long-time scale reactive compensation device operation constraints and short-time scale reactive compensation device operation constraints.

[0035] As one of the preferred solutions, the distribution network system parameters include the nominal voltage, actual voltage, active load, reactive load and impedance at each node;

[0036] The objective function is to minimize the voltage deviation and active network loss of the distribution network nodes, that is,

[0037]

[0038] Wherein, f is the objective function of the multi-objective distribution network reactive power optimization model, Vb i is the nominal voltage at node i, min{} is the minimum value function, Vr i is the actual voltage at node i, P i is the active load at node i, Q i is the reactive load at node i, R i is the impedance at node i, a is the first preset coefficient, b is the second preset coefficient, the sum of the first preset coefficient and the second preset coefficient is 1, and N is the number of nodes.

[0039] Another embodiment of the present invention provides an optimization control system for reactive power compensation in a distribution network. The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the optimization control method for reactive power compensation in a distribution network as described above.

[0040] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0041] (1) A multi-objective distribution network reactive power optimization model is constructed based on the system parameters of the distribution network. The reactive power compensation device of the multi-objective distribution network reactive power optimization model is a multi-time scale reactive power compensation device, which can meet the needs of flexible adjustment of the distribution network at multiple time scales and improve the dynamic response capability of the distribution network system;

[0042] (2) Under the constraints of the multi-objective distribution network reactive power optimization model, the multi-objective whale optimization algorithm is first used to solve the objective function of the multi-objective distribution network reactive power optimization model to obtain an iterative optimization solution set, and then the iterative optimization solution set is used as the input of the multi-objective gray wolf optimization algorithm. Before the preset maximum number of iterations is reached, the multi-objective gray wolf optimization algorithm is used to iteratively solve the objective function under the constraints to achieve iterative optimization of the iterative optimization solution set; when the preset maximum number of iterations is reached, the final iterative optimization solution set is used as the optimal solution set for node configuration. The multi-objective whale optimization algorithm is used in the early stage to achieve rapid convergence and obtain the iterative optimization solution set; in the later stage, the improved multi-objective gray wolf optimization algorithm is used to further optimize the reactive power compensation of the distribution network. The two algorithms are combined and complementary to dynamically adjust the global search and local search capabilities of the group, accelerate the optimization of the global group and avoid the group from falling into the local optimum, and improve the solution accuracy and reduce the amount of calculation, which solves the problem that the existing optimization algorithm is difficult to track the changes of the distribution network in real time, and improves the optimization control effect of the reactive power compensation of the distribution network;

[0043] (3) In each iterative execution process, the individual fitness of each wolf is calculated based on the iterative optimization solution set, and each wolf leader and ω wolf pack are determined based on the ranking results of the individual fitness of all wolves. Based on the linear relationship between the individual fitness of all wolves in the current iterative execution process, the fitness proportional factor of each wolf in the current iterative execution process is calculated. Based on the linear relationship between the number of iterations and the fitness proportional factors of all wolves in the current iterative execution process, the convergence factor of the current iterative execution process is calculated. The difference between the individual fitness of each wolf and the optimal fitness is analyzed, and the convergence factor of each iterative execution process is dynamically improved in combination with the cosine annealing strategy; based on the individual fitness of all wolves in the current iterative execution process, the convergence factor of the current iterative execution process and the convergence factor of the previous iterative execution process are calculated. The linear relationship between the feedback search steps of the ω wolf pack in the direction of each alpha wolf is calculated, and the feedback search steps of the ω wolf pack in the direction of each alpha wolf in the current iterative execution process are calculated. All feedback search steps are used as the search steps of the multi-objective gray wolf optimization algorithm, and the iterative optimization solution set is optimized by the multi-objective gray wolf optimization algorithm. The optimized iterative optimization solution set is output, and on the basis of the feedback search step of the ω wolf pack in the direction of each alpha wolf in the previous iterative execution process, the feedback search step is adaptively enlarged or reduced based on the improved convergence factor and the individual fitness of all alpha wolves, so that the improved multi-objective gray wolf optimization algorithm has a strong local optimal avoidance, a faster convergence speed, and a higher solution accuracy, which further improves the optimization control effect of reactive power compensation in the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of an optimization control method for reactive power compensation of a distribution network in one embodiment of the present invention;

[0045] Figure 2 A schematic diagram of obtaining an optimal solution set for node configuration in one embodiment of the present invention;

[0046] Figure 3 A schematic diagram of the number of nodes before and after optimization in one embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. 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.

[0048] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0049] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0050] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0051] An embodiment of the present invention provides an optimization control method for reactive power compensation of a distribution network. For details, see Figure 1 , Figure 1 The flowchart shows a method for optimizing reactive power compensation in a distribution network in one embodiment of the present invention.

[0052] Step S1: construct a multi-objective distribution network reactive power optimization model based on the system parameters of the distribution network.

[0053] Specifically, the objective function of the multi-objective distribution network reactive power optimization model is to minimize the voltage deviation and active network loss of the distribution network nodes, that is,

[0054]

[0055] Wherein, f is the objective function of the multi-objective distribution network reactive power optimization model, Vb iis the nominal voltage at node i, min{} is the minimum value function, Vr i is the actual voltage at node i, P i is the active load at node i, Q i is the reactive load at node i, R i is the impedance at node i, a is the first preset coefficient, b is the second preset coefficient, the sum of the first preset coefficient and the second preset coefficient is 1, and N is the number of nodes.

[0056] The constraints of the multi-objective distribution network reactive power optimization model include distribution network flow constraints, node voltage constraints, long-time scale reactive power compensation device operation constraints and short-time scale reactive power compensation device operation constraints.

[0057] It should be noted that the system parameters of the distribution network include the nominal voltage, actual voltage, active load, reactive load and impedance at each node. The reactive compensation device of the multi-objective distribution network reactive optimization model is a multi-time scale reactive compensation device, which includes a long time scale reactive compensation device and a short time scale reactive compensation device. The long time scale reactive compensation device includes an on-load tap-changing transformer and a capacitor bank; the short time scale reactive compensation device includes a static reactive generator, a distributed power supply and an intelligent soft switch.

[0058] Step S2: Under the constraints of the multi-objective distribution network reactive power optimization model, the multi-objective whale optimization algorithm is used to solve the objective function of the multi-objective distribution network reactive power optimization model to obtain the iterative optimization solution set output by the multi-objective distribution network reactive power optimization model.

[0059] It should be noted that the multi-objective whale optimization algorithm is a well-known technology and will not be described in detail in this embodiment.

[0060] Step S3: Based on the iterative optimization solution set and the improved multi-objective grey wolf optimization algorithm, the multi-objective distribution network reactive power optimization model is processed to obtain the optimal solution set of node configuration of the distribution network, and the distribution network is controlled by the optimal solution set of node configuration.

[0061] The iterative optimization solution set is used as the input of the improved multi-objective grey wolf optimization algorithm. Before the multi-objective grey wolf optimization algorithm reaches a preset maximum number of iterations, the step size update process is iteratively executed. The step size update process includes steps S3.1 to S3.4, so as to iteratively solve the objective function of the multi-objective distribution network reactive power optimization model under the constraints of the multi-objective distribution network reactive power optimization model; when the multi-objective grey wolf optimization algorithm reaches a preset maximum number of iterations, the result output by the multi-objective distribution network reactive power optimization model is used as the optimal solution set for the node configuration of the distribution network.

[0062] It should be noted that the preset maximum number of iterations is a manually preset value, and the iterative optimization solution set and the node configuration optimal solution set both include the output of each node in the distribution network.

[0063] For details, see Figure 2 , Figure 2 FIG. 1 is a schematic diagram showing how to obtain the optimal solution set for node configuration in one embodiment of the present invention. Figure 3 , Figure 3 FIG. 1 is a schematic diagram showing the number of nodes before and after optimization in one embodiment of the present invention. Figure 3 In the figure, the triangle represents the number of nodes before optimization, and the square represents the number of nodes after optimization.

[0064] Step S3.1: Calculate the individual fitness of each wolf based on the iterative optimization solution set, determine each wolf and the ω wolf pack based on the ranking results of the individual fitness of all wolves, and calculate the fitness proportional factor of each wolf in the current iterative execution process based on the linear relationship between the individual fitness of all wolves in the current iterative execution process.

[0065] Specifically, the maximum value of the individual fitness of all wolves during the current iterative execution process is recorded as the best fitness, and the minimum value of the individual fitness of all wolves during the current iterative execution process is recorded as the worst fitness; the difference between the best fitness and the individual fitness of each wolf is recorded as the fitness difference index of each wolf during the current iterative execution process.

[0066] Furthermore, the difference between the best fitness and the worst fitness is recorded as the overall fitness difference index of the current iterative execution process, and the fitness proportional factor of each wolf in the current iterative execution process is calculated based on the normalized value of the difference between the fitness difference index of each wolf and the overall fitness difference index.

[0067] As an embodiment of the present application, the ratio of the fitness difference index of each wolf to the overall fitness difference index is used as the fitness proportional factor of each wolf in the current iterative execution process.

[0068] It should be noted that the alpha wolf includes alpha wolf, beta wolf and delta wolf. When the difference between the fitness difference index of each wolf and the overall fitness difference index is greater, it means that the difference between the fitness of each wolf and the optimal fitness is relatively greater, and the fitness proportional factor value is greater.

[0069] Step S3.2: Based on the linear relationship between the number of iterations and the fitness proportional factors of all wolves in the current iterative execution process, the convergence factor of the current iterative execution process is calculated.

[0070] It should be noted that in the original gray wolf optimization algorithm, the convergence factor usually decreases linearly from a larger initial value to a smaller final value. This linear decrease method can balance the global search and local search capabilities of the algorithm to a certain extent. Therefore, by dynamically adjusting the convergence factor of each iterative execution process through individual fitness and the number of iterations, the global search and local search capabilities of the multi-objective gray wolf optimization algorithm can be taken into account.

[0071] Specifically, based on the linear relationship of the fitness proportional factors of all wolves in the current iterative execution process, the fitness gain factor of the current iterative execution process is calculated.

[0072] It should be noted that the larger the value of each fitness proportional factor in the current iterative execution process, the larger the difference between the individual fitness in the wolf pack and the optimal fitness is, and the larger the search step size of the wolf pack should be to increase the convergence speed of searching for the optimal solution. The larger the value of the fitness gain factor is.

[0073] As an embodiment of the present application, the average value of the fitness proportional factors of all wolves in the current iterative execution process is used as the fitness gain factor of the current iterative execution process.

[0074] Furthermore, the convergence factor of each iterative execution process is improved based on the cosine annealing strategy and the fitness gain factor:

[0075]

[0076] Among them, a n is the convergence factor of the nth iteration execution process, AF n is the fitness gain factor of the nth iteration execution process, cos() is the cosine function, π is the pi, n is the number of iterations, and N is the preset maximum number of iterations.

[0077] It should be noted that the preset maximum number of iterations is an artificially preset value. When the fitness gain factor is larger and the number of iterations is smaller, a smaller convergence factor should be set so that the multi-objective gray wolf optimization algorithm maintains a larger search range in the early stage of iteration to improve the global search capability; when the fitness gain factor is smaller and the number of iterations is more, it means that the wolf pack is more likely to be close to the optimal solution. The larger the convergence factor value, the smaller the search range should be to improve the local search accuracy.

[0078] Step S3.3: Based on the linear relationship between the individual fitness of all alpha wolves in the current iterative execution process, the convergence factor of the current iterative execution process and the feedback search step of the ω wolf pack toward the direction of each alpha wolf in the previous iterative execution process, the feedback search step of the ω wolf pack moving toward the direction of each alpha wolf in the current iterative execution process is calculated.

[0079] Specifically, based on the linear relationship between the individual fitness of all alpha wolves in the current iterative execution process and the convergence factor of the current iterative execution process, the search step gain coefficient of the current iterative execution process is calculated.

[0080] It should be noted that, when the convergence factor of the current iterative execution process is smaller and the individual fitness of each wolf is lower, it means that the optimization effect of the iterative optimization solution set is poor, and the search range should be increased to improve the global search capability, thereby improving the convergence speed of the gray wolf optimization algorithm, and the larger the search step gain coefficient value; when the convergence factor of the current iterative execution process is larger and the individual fitness of each wolf is higher, it means that the wolf pack is more likely to have gathered at the optimal solution, and the search range should be narrowed to improve the local search accuracy, thereby improving the optimization control effect of reactive compensation of the distribution network, and the smaller the search step gain coefficient value.

[0081] As an embodiment of the present application, the average value of the individual fitness of all alpha wolves in the current iterative execution process is multiplied by the convergence factor of the current iterative execution process as the search step attenuation coefficient of the current iterative execution process, and the inverse of the search step attenuation coefficient is used as the search step gain coefficient.

[0082] Furthermore, the product of twice the normalized value of the search step gain coefficient and the feedback search step of the ω wolf pack toward the direction of each alpha wolf in the previous iterative execution process is used as the feedback search step of the ω wolf pack moving toward the direction of the corresponding alpha wolf in the current iterative execution process.

[0083] It should be noted that the value range of twice the normalized value of the search step gain coefficient is 1 to 2. When the normalized value of the search step gain coefficient is less than 0.5, the feedback search step of the ω wolf pack toward the direction of each alpha wolf in the current iterative execution process is increased relative to that in the previous iterative execution process, and the increase factor is twice the normalized value of the search step gain coefficient; when the normalized value of the search step gain coefficient is greater than 0.5, the feedback search step of the ω wolf pack toward the direction of each alpha wolf in the current iterative execution process is equal to that in the previous iterative execution process; when the normalized value of the search step gain coefficient is greater than 0.5, the feedback search step of the ω wolf pack toward the direction of each alpha wolf in the current iterative execution process is reduced relative to that in the previous iterative execution process, and the reduction factor is twice the normalized value of the search step gain coefficient.

[0084] By multiplying twice the normalized value of the search step gain coefficient by the feedback search step of the ω wolf pack toward the direction of each leader in the previous iterative execution process, it is achieved that on the basis of the feedback search step of the ω wolf pack toward the direction of each leader in the previous iterative execution process, the feedback search step is adaptively enlarged or reduced based on the improved convergence factor and the individual fitness of all leader wolves, so that the improved multi-objective grey wolf optimization algorithm has strong local optimal avoidance, faster convergence speed and higher solution accuracy, which further improves the optimization effect of reactive power compensation in the distribution network.

[0085] Step S3.4: Use the feedback search step length of the ω wolf pack moving toward the direction of each wolf in the current iterative execution process as the search step length of the multi-objective gray wolf optimization algorithm, use the multi-objective gray wolf optimization algorithm to optimize the iterative optimization solution set, and output the optimized iterative optimization solution set.

[0086] Another embodiment of the present invention provides an optimization control system for reactive power compensation of a distribution network, the system comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the steps in the optimization control method for reactive power compensation of a distribution network as described in the above embodiment are implemented, for example Figure 1 Steps S1 to S3 described in .

[0087] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An optimization control method for reactive power compensation of a distribution network, characterized in that: include: Construct a multi-objective reactive power optimization model for distribution network based on the system parameters of distribution network; The reactive power compensation device of the multi-objective distribution network reactive power optimization model is a multi-time scale reactive power compensation device; Under the constraints of the multi-objective distribution network reactive power optimization model, a multi-objective whale optimization algorithm is used to solve the objective function of the multi-objective distribution network reactive power optimization model to obtain an iterative optimization solution set output by the multi-objective distribution network reactive power optimization model; Based on the iterative optimization solution set and the improved multi-objective grey wolf optimization algorithm, the multi-objective distribution network reactive power optimization model is processed, the result output by the multi-objective distribution network reactive power optimization model is used as the node configuration optimal solution set of the distribution network, and the distribution network is controlled by the node configuration optimal solution set; wherein, the iterative processing process of the multi-objective distribution network reactive power optimization model by the improved multi-objective grey wolf optimization algorithm is configured to repeatedly update the feedback search step until the iteration ends.

2. The optimization control method for reactive power compensation of a distribution network according to claim 1, characterized in that: The multi-objective distribution network reactive power optimization model is processed based on the iterative optimization solution set and the improved multi-objective grey wolf optimization algorithm, including: The iterative optimization solution set is used as the input of the improved multi-objective grey wolf optimization algorithm. Before the multi-objective grey wolf optimization algorithm reaches a preset maximum number of iterations, the step size update process is iteratively executed to iteratively solve the objective function under the constraints. When the multi-objective grey wolf optimization algorithm reaches a preset maximum number of iterations, the result output by the multi-objective distribution network reactive power optimization model is used as the optimal solution set for the node configuration of the distribution network.

3. The optimization control method for reactive power compensation of a distribution network according to claim 2, characterized in that: The step size update process includes: Calculating the individual fitness of each wolf based on the iterative optimization solution set, and determining each wolf and the ω wolf pack based on the ranking results of the individual fitness of all wolves; Based on the linear relationship among the number of iterations, the individual fitness of all wolves in the current iteration, and the feedback search step length of the ω wolf group toward the direction of each alpha wolf in the previous iteration, the feedback search step length of the ω wolf group toward the direction of each alpha wolf in the current iteration is calculated; All the feedback search steps are used as the search steps of the multi-objective grey wolf optimization algorithm, the iterative optimization solution set is optimized by the multi-objective grey wolf optimization algorithm, and the optimized iterative optimization solution set is output.

4. The optimization control method for reactive power compensation of a distribution network according to claim 3, characterized in that: The alpha wolves include alpha wolves, beta wolves, and delta wolves. The feedback search step lengths of the omega wolves in the direction of each alpha wolf in the current iterative execution process are calculated based on the linear relationship among the number of iterations, the individual fitness of all wolves in the current iterative execution process, and the feedback search step lengths of the omega wolves in the direction of each alpha wolf in the previous iterative execution process, including: Based on the linear relationship between the individual fitness of all wolves in the current iterative execution process, the fitness proportional factor of each wolf in the current iterative execution process is calculated; Based on the linear relationship between the number of iterations and the fitness proportional factors of all wolves in the current iterative execution process, a convergence factor of the current iterative execution process is calculated; Based on the linear relationship between the individual fitness of all the alpha wolves in the current iterative execution process, the convergence factor of the current iterative execution process and the feedback search step of the ω wolf pack toward the direction of each alpha wolves in the previous iterative execution process, the feedback search step of the ω wolf pack moving toward the direction of each alpha wolves in the current iterative execution process is calculated.

5. The optimization control method for reactive power compensation of a distribution network according to claim 4, characterized in that: The linear relationship between the individual fitness of all wolves in the current iterative execution process is calculated to obtain the fitness proportional factor of each wolf in the current iterative execution process, including: The maximum value of the individual fitness of all wolves during the current iteration is recorded as the best fitness, and the minimum value of the individual fitness of all wolves during the current iteration is recorded as the worst fitness; The difference between the optimal fitness and the individual fitness of each wolf is recorded as the fitness difference index of each wolf in the current iterative execution process; The difference between the best fitness and the worst fitness is recorded as the overall fitness difference index of the current iterative execution process; The fitness proportional factor of each wolf in the current iterative execution process is calculated based on the normalized value of the difference between the fitness difference index of each wolf and the overall fitness difference index.

6. The optimization control method for reactive power compensation of a distribution network according to claim 4, characterized in that: The method of calculating the convergence factor of the current iterative execution process based on the linear relationship between the number of iterations and the fitness proportional factors of all wolves in the current iterative execution process includes the following specific methods: Based on the linear relationship of the fitness proportional factors of all wolves in the current iterative execution process, the fitness gain factor of the current iterative execution process is calculated; The calculation formula of the convergence factor of each iterative execution process is: Among them, a n is the convergence factor of the nth iteration execution process, AF n is the fitness gain factor of the nth iteration execution process, cos() is the cosine function, π is the pi, n is the number of iterations, and N is the preset maximum number of iterations.

7. The optimization control method for reactive power compensation of a distribution network according to claim 4, characterized in that: The step of calculating the feedback search step of the ω wolf group moving toward the direction of each alpha wolf in the current iterative execution process based on the linear relationship between the individual fitness of all the alpha wolves in the current iterative execution process, the convergence factor in the current iterative execution process, and the feedback search step of the ω wolf group moving toward the direction of each alpha wolf in the previous iterative execution process comprises: Based on the linear relationship between the individual fitness of all the alpha wolves in the current iterative execution process and the convergence factor in the current iterative execution process, the search step gain coefficient of the current iterative execution process is calculated; The product of twice the normalized value of the search step gain coefficient and the feedback search step of the ω wolf pack toward the direction of each alpha wolf in the previous iterative execution process is used as the feedback search step of the ω wolf pack moving toward the corresponding direction of the alpha wolf in the current iterative execution process.

8. The optimization control method for reactive power compensation of a distribution network according to claim 1, characterized in that: The iterative optimization solution set and the node configuration optimal solution set both include the output of each node in the distribution network; the constraint conditions include distribution network flow constraints, node voltage constraints, long-time scale reactive compensation device operation constraints and short-time scale reactive compensation device operation constraints.

9. The optimization control method for reactive power compensation of a distribution network according to claim 1, characterized in that: The distribution network system parameters include the nominal voltage, actual voltage, active load, reactive load and impedance at each node; The objective function is to minimize the voltage deviation and active network loss of the distribution network nodes, that is, Wherein, f is the objective function of the multi-objective distribution network reactive power optimization model, Vb i is the nominal voltage at node i, min{} is the minimum value function, Vr i is the actual voltage at node i, P i is the active load at node i, Q i is the reactive load at node i, R i is the impedance at node i, a is the first preset coefficient, b is the second preset coefficient, the sum of the first preset coefficient and the second preset coefficient is 1, and N is the number of nodes.

10. An optimization control system for reactive power compensation in a distribution network, characterized in that: The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, an optimization control method for reactive power compensation of a distribution network as described in any one of claims 1 to 9 is implemented.

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