A power distribution network D-SVC adaptive optimization configuration method and device and a storage medium

Abstract

This application provides a D-SVC adaptive optimization configuration method, device, and storage medium for distribution networks, belonging to the field of power system automation technology. It solves the problems of insufficient optimization algorithm performance, lack of consideration for dynamic load changes, and missing multi-objective optimization in existing D-SVC configuration methods. This method addresses the problems of imbalance between exploration and utilization, and easy getting trapped in local optima, in traditional optimization methods by constructing an adaptive optimization algorithm that includes a cooperative search operator (CSO) and a time function (TF). It establishes a multi-objective optimization model considering 24-hour dynamic load changes, achieving coordinated optimization that minimizes energy loss, balances operating benefits and investment costs, and reduces apparent power demand. This application is applicable to reactive power management and voltage stability control of distribution networks of different scales.

Description

Technical Field

[0001] This application relates to the field of power system automation technology, and in particular to a method, device and storage medium for adaptive optimization configuration of distribution network D-SVC. Background Technology

[0002] As a crucial link connecting power sources and users in the power system, the distribution network's operating efficiency and power quality directly impact the overall performance of the power system. Reactive power imbalance can lead to voltage fluctuations, increased power losses, and even voltage collapse in the distribution network, severely affecting power supply reliability. Distributed static var compensators (D-SVCs), characterized by fast response and wide adjustment range, can absorb or inject reactive power in real time, making them a core device for improving the operating performance of the distribution network.

[0003] However, existing D-SVC configuration methods have several shortcomings: First, the optimization algorithms mostly employ traditional genetic algorithms and gray wolf optimization algorithms, which suffer from an imbalance between exploration and utilization, easily getting trapped in local optima and resulting in insufficient accuracy of the configuration scheme. Second, most methods only consider a single load level (such as peak load), ignoring the dynamic changes in load over 24 hours, resulting in poor adaptability of the configuration scheme and inability to meet the real-time operation requirements of the distribution network. Third, the optimization objectives are singular, focusing primarily on reducing energy loss, without fully considering key indicators such as D-SVC investment costs and apparent power demand reduction, leading to a technical and economic imbalance. Fourth, some methods do not consider the dynamic adjustment characteristics of D-SVC, failing to adjust reactive power output in real time according to load changes, thus affecting the compensation effect.

[0004] Therefore, developing a D-SVC configuration method that can dynamically adapt to load changes, take into account multiple objectives, and optimize performance is of great engineering significance for improving the operating efficiency of distribution networks, reducing operating costs, and ensuring voltage stability. Summary of the Invention

[0005] To address the technical problems of insufficient optimization algorithm performance, lack of consideration for dynamic load changes, and missing multi-objective optimization in existing D-SVC configuration methods, this application proposes an adaptive optimization configuration method, device, and storage medium for distribution network D-SVC. Through innovative optimization algorithm structure and multi-objective model design, the optimal configuration and dynamic operation of D-SVC are achieved, realizing the synergistic goals of minimizing energy loss, balancing cost and benefit, and reducing apparent power demand.

[0006] The technical solution adopted in this application is: a D-SVC adaptive optimization configuration method for distribution networks, including the following steps:

[0007] Step 1: Establish a power distribution network parameter model;

[0008] Step 2: Construct an adaptive optimization algorithm: By introducing a cooperative search operator and a time function, the balance between exploration and utilization in the optimization process is dynamically adjusted. The cooperative search operator optimizes the search direction by associating the current search individual with the iterative optimal solution, and the time function is used to linearly adjust the exploration and utilization weights.

[0009] Step 3: Establish an adaptive multi-objective optimization model for D-SVC. This multi-objective optimization model includes three optimization objectives: minimizing annual energy consumption loss, balancing operational benefits with the total life cycle cost of D-SVC, and reducing the apparent power demand of the distribution network.

[0010] Step 4: Set constraints;

[0011] Step 5: Solve the multi-objective optimization model based on the adaptive optimization algorithm constructed in Step 2, and output the optimal installation node, rated capacity, and reactive power injection / absorption strategy for each time period within 24 hours for D-SVC;

[0012] Step 6: Apply the optimization results to the distribution network so that the D-SVC can adaptively adjust its operating status according to real-time load changes.

[0013] Furthermore, the distribution network parameter model in step 1 is constructed by acquiring the distribution network topology, node load data, line parameters, and D-SVC equipment parameters. The node load data includes the load level and corresponding duration for different time periods within 24 hours.

[0014] Furthermore, the mathematical model for the cooperative search operator is as follows:

[0015] ;

[0016] ;

[0017] In the formula: For the first The search individual in the first The updated position in the next iteration For the first The search individual in the first The current position in the next iteration. For the randomly selected number The search individual in the first The position of the next iteration. This is the globally optimal solution up to the current iteration. For the first The direction adjustment term of the cooperative search operator in the next iteration. A standard function to control the magnitude of individual position updates during the search. A random number in the interval [0,1]. For the floor function, For random selection function, A random number in the interval [0,1]. This represents the current iteration number. The maximum number of iterations, It is a natural constant.

[0018] Furthermore, the mathematical model for the time function is as follows:

[0019] ;

[0020] In the formula: For the first The time function value at the next iteration is used to determine whether to accept the updated individual position.

[0021] Furthermore, the formula for minimizing annual energy loss is:

[0022] ;

[0023] In the formula: As a measure of annual energy savings, For energy costs, The number of days in a year Number the load level. This represents the total number of load levels. Active power loss of the system before configuring D-SVC. The active power loss of the system after configuring D-SVC For the first The duration of each load level;

[0024] Balancing operational benefits with the total lifecycle cost of D-SVC, the formula is:

[0025] ;

[0026] In the formula: The balance between operating revenue and costs. The cost recovery factor for D-SVC The fixed installation cost for a single D-SVC unit, The annual operating cost per unit capacity of D-SVC, This represents the total number of D-SVCs installed. For the first Rated capacity of one D-SVC unit;

[0027] Among them, cost recovery factor The calculation formula is:

[0028] ;

[0029] In the formula: The annual interest rate is The design life of D-SVC;

[0030] The apparent power demand of the distribution network is given by the formula:

[0031] ;

[0032] In the formula: To achieve the comprehensive optimization goal of including apparent power savings, Apparent power rate, Apparent power requirements of the system before configuring D-SVC Apparent power requirements of the system after configuring D-SVC.

[0033] Furthermore, the constraints include node voltage constraints, line current constraints, D-SVC capacity constraints, and reactive power balance constraints.

[0034] Furthermore, step 5 specifically includes:

[0035] Step 5.1: Initialize parameters: Set population size ,in The total number of individuals searched, and the maximum number of iterations. Lower bound of control variables Upper bound of control variables Randomly generate initial population locations ( ),in It is a random vector in the interval [0,1].

[0036] Step 5.2: Fitness Calculation: Based on the multi-objective optimization model in Step 3, a fitness function is constructed in combination with the constraints to calculate the fitness value of each individual in the population and to impose penalties on individuals that violate the constraints.

[0037] Step 5.3: Iterative Update:

[0038] Calculate the energy factor ,in For the first Energy factor at the next iteration A random number in the interval [0,1]. It is the natural logarithm function;

[0039] like The individual positions are updated through a collaborative search operator, generating random numbers. ,like If the new position is accepted, the original position will be retained.

[0040] like Cave locations are generated using a cave generation formula, and individual locations are updated using a random hiding strategy.

[0041] The cave generation formula is as follows:

[0042] ;

[0043] In the formula: For the first The search individual in the first The location of the cave in Vi. A random integer that takes the value 0 or 1. Choose a function for the dimension when Set to 1 if it is the target dimension for the current individual, otherwise set to 0;

[0044] During the iterative update process, if the fitness value of an individual's updated position is better than its original position, then the original position is replaced with the new position; otherwise, the original position is retained. The formula is as follows:

[0045] ;

[0046] In the formula: For the fitness function, For the first The search individual in the first The final position of the next iteration;

[0047] Step 5.4: Record the optimal solution: After each iteration, save the current optimal fitness value and the corresponding individual until the maximum number of iterations is reached, and output the global optimal solution.

[0048] Furthermore, the average convergence rate of the adaptive optimization algorithm is calculated using the following formula, which characterizes the speed at which the algorithm approaches the optimal solution:

[0049] ;

[0050] In the formula: The average convergence rate, The optimal fitness value achievable by all optimization algorithms. The average fitness value at the maximum number of iterations. This is the fitness value at the initial iteration.

[0051] This application also proposes a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0052] This application also proposes a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the method.

[0053] The advantages of this application compared to the prior art are as follows: The Cooperative Search Operator (CSO) proposed in this application abandons the random search mechanism of traditional algorithms. By associating the current search individual with the optimal solution found in the iteration process, it guides the search direction toward the optimal solution region, enhances the utilization capability of the algorithm, maintains search diversity, and avoids local optima. The time function (TF) proposed in this application changes linearly with the number of iterations. In the early stage of iteration, the TF value is small, and the algorithm focuses on exploration (expanding the search range). In the later stage of iteration, the TF value increases, and the algorithm focuses on utilization (finely searching the optimal solution region). This dynamically balances the relationship between exploration and utilization, and solves the problems of slow convergence speed and premature convergence of traditional algorithms.

[0054] The multi-objective optimization model proposed in this application has the following advantages:

[0055] Dynamic load adaptation: Based on 24-hour load change data of the distribution network, the day is divided into multiple load levels, each load level corresponding to a fixed duration, so that the configuration scheme can adapt to the load characteristics of different time periods;

[0056] Multi-objective coordination: It simultaneously considers three major objectives: reducing energy consumption loss, balancing cost and benefit, and reducing apparent power demand. This ensures the efficiency of distribution network operation while also taking into account economic rationality, thereby improving the overall benefits of the configuration scheme. Attached Figure Description

[0057] The following description, in conjunction with the accompanying drawings, further illustrates this application:

[0058] Figure 1 A flowchart illustrating the method provided in this application embodiment;

[0059] Figure 2 This is a schematic diagram of the computer device structure provided in an embodiment of this application. Detailed Implementation

[0060] like Figure 1 and Figure 2 As shown, this application provides an adaptive optimization configuration method for D-SVC in distribution networks. By constructing an adaptive optimization algorithm that includes a cooperative search operator (CSO) and a time function (TF), it solves the problems of imbalance in exploration and utilization and easy getting trapped in local optima in traditional optimization methods. By establishing a multi-objective optimization model that considers 24-hour dynamic load changes, it achieves coordinated optimization that minimizes energy loss, balances operating benefits and investment costs, and reduces apparent power demand. This application has been verified in IEEE 33-bus and 85-bus distribution networks. Compared with existing technologies, power loss is reduced by up to 19.69%, voltage stability is significantly improved, convergence speed is faster, and robustness is stronger. It is suitable for D-SVC optimization configuration and dynamic operation of distribution networks of different sizes.

[0061] The adaptive optimization configuration method for distribution network D-SVC proposed in this application specifically includes the following steps:

[0062] Step 1: Establish the distribution network parameter model: Obtain the distribution network topology, node load data, line parameters and D-SVC equipment parameters. The node load data includes the load level and corresponding duration for different time periods within 24 hours.

[0063] The load levels at different times within 24 hours are obtained by statistically analyzing historical operating data of the distribution network. They are expressed as a percentage of the rated load, with each load level corresponding to a fixed duration, and the sum of the durations of all load levels is 24 hours.

[0064] Step 2: Construct an adaptive optimization algorithm: This algorithm dynamically adjusts the balance between exploration and utilization during the optimization process by introducing a cooperative search operator (CSO) and a time function (TF);

[0065] The Cooperative Search Operator (CSO) optimizes the search direction by associating the current search entity with the iterative optimal solution. The mathematical model is as follows:

[0066] ;

[0067] ;

[0068] In the formula: For the first The search individual in the first The updated position in the next iteration For the first The search individual in the first The current position in the next iteration. For the randomly selected number The search individual in the first The position of the next iteration. This is the globally optimal solution up to the current iteration. For the first The direction adjustment term of the cooperative search operator in the next iteration. A standard function to control the magnitude of individual position updates during the search. A random number in the interval [0,1]. For the floor function, For random selection function (when (Takes 1 if the dimension is randomly selected, otherwise takes 0). A random number in the interval [0,1]. This represents the current iteration number. The maximum number of iterations, It is a natural constant.

[0069] The time function (TF) is used to linearly adjust the exploration and exploitation weights, and the mathematical model is as follows:

[0070] ;

[0071] In the formula: For the first The time function value at the next iteration is used to determine whether to accept the updated individual position.

[0072] Step 3: Establish a D-SVC adaptive multi-objective optimization model and set three core optimization objectives:

[0073] Objective 1: Minimize annual energy loss, as shown in the formula:

[0074] ;

[0075] In the formula: As a measure of annual energy savings, For energy costs, The number of days in a year (default 365 days). The load level is numbered (representing the load status at different times within 24 hours). This represents the total number of load levels. Active power loss of the system before configuring D-SVC (unit: kilowatt, kW). Active power loss of the system after configuring D-SVC (unit: kW). For the first The duration of each load level (in hours, h).

[0076] Objective 2: Balance operational revenue with the total lifecycle cost of D-SVC, using the following formula:

[0077] ;

[0078] In the formula: The balance between operating revenue and costs. The cost recovery factor for D-SVC The fixed installation cost for a single D-SVC unit, The annual operating cost per unit capacity of D-SVC, This represents the total number of D-SVCs installed. For the first Rated capacity of D-SVC unit.

[0079] Among them, cost recovery factor The calculation formula is:

[0080] ;

[0081] In the formula: The annual interest rate is The design life of D-SVC (unit: years).

[0082] Objective 3: Reduce the apparent power demand of the distribution network, as shown in the formula:

[0083] ;

[0084] In the formula: To achieve the comprehensive optimization goal of including apparent power savings, Apparent power rate, Apparent power requirements of the system before configuring D-SVC Apparent power requirements of the system after configuring D-SVC.

[0085] Step 4: Set constraints, including node voltage constraints, line current constraints, D-SVC capacity constraints, and reactive power balance constraints. Node voltage constraints ensure that the voltage of each node fluctuates within the allowable range, guaranteeing power supply quality; line current constraints prevent overcurrent operation of lines and prevent equipment damage; D-SVC capacity constraints limit the output capacity of D-SVCs within the rated range, extending equipment lifespan; reactive power balance constraints ensure the balance of reactive power supply and demand in the distribution network at different times, maintaining voltage stability.

[0086] The constraints are expressed as follows:

[0087] Node voltage constraints: ,in For the first Under the load level, the first The voltage of each node, , The first The allowable upper and lower limits of voltage for each node;

[0088] Line current constraints: ,in For the first The operating current of a certain line under a certain load level. This is the maximum allowable current for this circuit;

[0089] D-SVC capacity constraint: ,in This represents the maximum allowable capacity of a single D-SVC unit. For the first Installed at the first load level The actual output capacity of each node's D-SVC;

[0090] Reactive power balance constraints: ,in For the first Under the load level, the first Reactive power demand of each node (unit: kVAr). For the first Reactive power loss of the system at each load level (unit: kVAr). For the first Reactive power provided by the power grid at each load level (unit: kVAr). This represents the total number of nodes in the distribution network (unitless).

[0091] Step 5: Solve the multi-objective optimization model based on the adaptive optimization algorithm constructed in Step 2, and output the optimal installation node, rated capacity, and reactive power injection / absorption strategies for each time period within 24 hours for the D-SVC. The specific solution process includes:

[0092] Step 5.1: Initialize parameters: Set population size ( (Total number of individuals searched), maximum number of iterations. upper and lower bounds of control variables (Lower Boundary) (Upper bound) Randomly generate the initial population position ( ),in It is a random vector in the interval [0,1].

[0093] The control variables include the D-SVC installation node number (with a value range of [2, ...). Excluding the first node where the substation is located), the rated capacity of D-SVC (the value range is [0, ]) and the output capacity of D-SVC at various load levels (the value range is [ ]);

[0094] Step 5.2: Fitness Calculation: Based on the multi-objective optimization model in Step 3, a fitness function is constructed in combination with the constraints to calculate the fitness value of each individual in the population and to impose penalties on individuals that violate the constraints.

[0095] Step 5.3: Iterative Update:

[0096] Calculate the energy factor ,in For the first The energy factor at the next iteration (used to switch search modes) A random number in the interval [0,1]. It is the natural logarithm function;

[0097] like The individual positions are updated through the Cooperative Search Operator (CSO), generating random numbers. (in the interval [0,1]), if If the new position is accepted, the original position will be retained.

[0098] like Cave locations are generated using a cave generation formula, and individual locations are updated using a random hiding strategy.

[0099] The cave generation formula is as follows:

[0100] ;

[0101] In the formula: For the first The search individual in the first The location of the cave in Vi. A random integer that takes the value 0 or 1. Choose a function for the dimension when Set to 1 if it is the target dimension for the current individual, otherwise set to 0;

[0102] During the iterative update process, if the fitness value of an individual's updated position is better than its original position, then the original position is replaced with the new position; otherwise, the original position is retained. The formula is as follows:

[0103] ;

[0104] In the formula: For the fitness function, For the first The search individual in the first The final position of the next iteration;

[0105] Step 5.4: Record the optimal solution: After each iteration, save the current optimal fitness value and the corresponding individual until the maximum number of iterations is reached, and output the global optimal solution.

[0106] The average convergence rate of this adaptive optimization algorithm is calculated using the following formula, which characterizes the speed at which the algorithm approaches the optimal solution:

[0107] ;

[0108] In the formula: The average convergence rate (unitless). The optimal fitness value achievable by all optimization algorithms. This represents the average fitness value at the maximum number of iterations (based on the results of multiple independent runs). This is the fitness value at the initial iteration.

[0109] Step 6: Apply the optimization results to the distribution network so that the D-SVC can adaptively adjust its operating status according to real-time load changes, thereby achieving stable and efficient operation of the distribution network voltage.

[0110] During the iterative update process, the energy factor controls the activation of the Cooperative Search Operator (CSO), and the fitness value determines the position update, forming a hierarchical and progressive iterative update logic: the energy factor is a switch for switching search modes, determining whether to generate new candidate positions through the Cooperative Search Operator (CSO); the fitness value is the core criterion for position selection, determining whether to replace the original position with the generated new candidate position. Together, they constitute the complete process of algorithm iterative update. The Cooperative Search Operator (CSO) is the method for generating new positions, and the fitness value is the standard for verifying the quality of the new positions.

[0111] Figure 2 A structural block diagram of a computer device according to a specific embodiment of this application is shown. Figure 2 As shown, the computer device includes a memory and a processor, the memory storing instructions executable on the processor. When the processor executes the instructions, it implements the methods described in the above embodiments. The number of memories and processors can be one or more. This computer device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The computer device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0112] The computer device may also include a communication interface for communicating with external devices and exchanging data. The devices are interconnected using different buses and can be mounted on a common motherboard or otherwise as needed. The processor can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as a display device coupled to the interface). In other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). The bus can be divided into address buses, data buses, control buses, etc. For ease of illustration, Figure 2 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0113] Optionally, in a specific implementation, if the memory, processor, and communication interface are integrated on a single chip, then the memory, processor, and communication interface can communicate with each other through an internal interface.

[0114] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting advanced RISC machines (ARM) architecture.

[0115] This application provides a computer-readable storage medium (such as the memory described above) storing computer instructions that, when executed by a processor, implement the method provided in this application.

[0116] Optionally, the memory may include a stored program area and a stored data area, wherein the stored program area may store the operating system and application programs required for at least one function; the stored data area may store data created based on the use of the computer device for mapping. Furthermore, the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the computer device for mapping via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A D-SVC adaptive optimization configuration method for distribution networks, characterized in that: Includes the following steps: Step 1: Establish a power distribution network parameter model; Step 2: Construct an adaptive optimization algorithm: By introducing a cooperative search operator and a time function, the balance between exploration and utilization in the optimization process is dynamically adjusted. The cooperative search operator optimizes the search direction by associating the current search individual with the iterative optimal solution, and the time function is used to linearly adjust the exploration and utilization weights. Step 3: Establish an adaptive multi-objective optimization model for D-SVC. This multi-objective optimization model includes three optimization objectives: minimizing annual energy consumption loss, balancing operational benefits with the total life cycle cost of D-SVC, and reducing the apparent power demand of the distribution network. Step 4: Set constraints; Step 5: Solve the multi-objective optimization model based on the adaptive optimization algorithm constructed in Step 2, and output the optimal installation node, rated capacity, and reactive power injection / absorption strategy for each time period within 24 hours for D-SVC; Step 6: Apply the optimization results to the distribution network so that the D-SVC can adaptively adjust its operating status according to real-time load changes.

2. The adaptive optimization configuration method for D-SVC in a distribution network according to claim 1, characterized in that: The distribution network parameter model in step 1 is constructed by acquiring the distribution network topology, node load data, line parameters and D-SVC equipment parameters. The node load data includes the load level and corresponding duration at different times within 24 hours.

3. The adaptive optimization configuration method for D-SVC in a distribution network according to claim 2, characterized in that: The mathematical model for the cooperative search operator is as follows: ； ； In the formula: For the first The search individual in the first The updated position in the next iteration For the first The search individual in the first The current position in the next iteration. For the randomly selected number The search individual in the first The position of the next iteration. This is the globally optimal solution up to the current iteration. For the first The direction adjustment term of the cooperative search operator in the next iteration. A standard function to control the magnitude of individual position updates during the search. A random number in the interval [0,1]. For the floor function, For random selection function, A random number in the interval [0,1]. This represents the current iteration number. The maximum number of iterations, It is a natural constant.

4. The D-SVC adaptive optimization configuration method for a distribution network according to claim 3, characterized in that: The mathematical model for the time function is as follows: ； In the formula: For the first The time function value at the next iteration is used to determine whether to accept the updated individual position.

5. The D-SVC adaptive optimization configuration method for a distribution network according to claim 4, characterized in that: The formula for minimizing annual energy loss is: ； In the formula: As a measure of annual energy savings, For energy costs, The number of days in a year Number the load level. This represents the total number of load levels. Active power loss of the system before configuring D-SVC. The active power loss of the system after configuring D-SVC For the first The duration of each load level; Balancing operational benefits with the total lifecycle cost of D-SVC, the formula is: ； In the formula: The balance between operating revenue and costs. The cost recovery factor for D-SVC The fixed installation cost for a single D-SVC unit, The annual operating cost per unit capacity of D-SVC, This represents the total number of D-SVCs installed. For the first Rated capacity of one D-SVC unit; Among them, cost recovery factor The calculation formula is: ； In the formula: The annual interest rate is The design life of D-SVC; The apparent power demand of the distribution network is given by the formula: ； In the formula: To achieve the comprehensive optimization goal of including apparent power savings, Apparent power rate, Apparent power requirements of the system before configuring D-SVC Apparent power requirements of the system after configuring D-SVC.

6. The adaptive optimization configuration method for D-SVC in a distribution network according to claim 5, characterized in that: The constraints include node voltage constraints, line current constraints, D-SVC capacity constraints, and reactive power balance constraints.

7. The D-SVC adaptive optimization configuration method for a distribution network according to claim 6, characterized in that: Step 5 specifically includes: Step 5.1: Initialize parameters: Set population size ,in The total number of individuals searched, and the maximum number of iterations. Lower bound of control variables Upper bound of control variables Randomly generate initial population locations ( ),in It is a random vector in the interval [0,1]. Step 5.2: Fitness Calculation: Based on the multi-objective optimization model in Step 3, a fitness function is constructed in combination with the constraints to calculate the fitness value of each individual in the population and to impose penalties on individuals that violate the constraints. Step 5.3: Iterative Update: Calculate the energy factor ,in For the first Energy factor at the next iteration A random number in the interval [0,1]. It is the natural logarithm function; like The individual positions are updated through a collaborative search operator, generating random numbers. ,like If the new position is accepted, the original position will be retained. like Cave locations are generated using a cave generation formula, and individual locations are updated using a random hiding strategy. The cave generation formula is as follows: ； In the formula: For the first The search individual in the first The location of the cave in Vi. A random integer that takes the value 0 or 1. Choose a function for the dimension when Set to 1 if it is the target dimension for the current individual, otherwise set to 0; During the iterative update process, if the fitness value of an individual's updated position is better than its original position, then the original position is replaced with the new position; otherwise, the original position is retained. The formula is as follows: ； In the formula: For the fitness function, For the first The search individual in the first The final position of the next iteration; Step 5.4: Record the optimal solution: After each iteration, save the current optimal fitness value and the corresponding individual until the maximum number of iterations is reached, and output the global optimal solution.

8. The D-SVC adaptive optimization configuration method for a distribution network according to claim 7, characterized in that: The average convergence rate of the adaptive optimization algorithm is calculated using the following formula, which characterizes the speed at which the algorithm approaches the optimal solution: ； In the formula: The average convergence rate, The optimal fitness value achievable by all optimization algorithms. The average fitness value at the maximum number of iterations. This is the fitness value at the initial iteration.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that: When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-8.

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