Power distribution network voltage optimization method and optimization system under distributed power supply access

Abstract

The invention relates to the field of power distribution network operation control, and discloses a power distribution network voltage optimization method and optimization system under distributed power supply access, and the method comprises the steps: taking the minimization of node voltage deviation in a power distribution network and the reduction of the network loss of a power distribution network system as targets; constructing a multi-target optimization model on the premise of satisfying node voltage constraints, power flow balance constraints and distributed power supply active and reactive output constraints; on the basis of a standard particle swarm optimization algorithm, fusing a nonlinear decreasing inertia weight mechanism, a chaos initialization method based on Logistic mapping and a simulated annealing local search operator to obtain an improved particle swarm optimization algorithm; and solving a power distribution network voltage optimization problem based on an improved particle swarm optimization algorithm and a multi-objective optimization model to obtain an optimal operation strategy of the power distribution network. According to the optimization method and the optimization system, the node voltage compliance can be ensured, the system economy is considered, the collaborative optimization of the voltage quality and the energy loss is realized, the robustness is strong, and the engineering practical value is high.

Description

Technical Field

[0001] This invention relates to the field of distribution network operation control, and specifically to a distribution network voltage optimization method and system under distributed power source access. Background Technology

[0002] With the large-scale integration of high-proportion distributed energy sources into the distribution network, the unidirectional flow of electricity has been disrupted. Problems such as reverse flow, sudden power output fluctuations, and voltage rise under low load conditions have become prominent, leading to increased operational risks such as node voltage exceeding limits, increased network losses, protection malfunctions, and limited equipment rated capacity. This has become one of the main challenges for distribution network safety and power quality management.

[0003] Existing voltage control methods for distribution networks mainly rely on transformer tap changer adjustment, parallel capacitor switching, and reactive power control of distributed generation inverters. While these methods can maintain basic voltage stability in scenarios with low distributed generation penetration, they suffer from drawbacks such as slow response speed, limited control methods, and narrow optimization objectives when facing large-scale distributed photovoltaic and wind power integration. They struggle to simultaneously achieve voltage stability and reduce network losses, resulting in limited overall control effectiveness. With the increasing randomness and volatility of distributed generation output, traditional voltage regulation methods often lead to voltage exceedances at local nodes, and even cause voltage deviations on some feeders to exceed permissible ranges. Furthermore, frequent voltage adjustment actions can increase equipment wear and tear and maintenance costs.

[0004] On the other hand, while some existing studies have introduced optimization strategies based on power flow reconstruction or zone control, they generally suffer from slow convergence speed, susceptibility to local optima, and high algorithm complexity, making it difficult to meet the scheduling requirements of distribution networks under real-time or near-real-time operating conditions. In terms of multi-objective optimization, existing technologies often focus on a single indicator, such as minimizing voltage deviation or reducing network losses, lacking a comprehensive trade-off between the two, resulting in insufficient overall performance of the optimization results. Especially in scenarios with a high proportion of renewable energy grid integration, the contradiction between voltage constraint satisfaction rate and network loss control becomes more prominent, making it difficult to simultaneously achieve power quality and system economy.

[0005] Therefore, proposing a novel distribution network voltage optimization method that ensures node voltage compliance while taking into account system economy, and achieving coordinated optimization of voltage quality and energy loss, has become an urgent problem to be solved. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for optimizing distribution network voltage under distributed power source access, so as to solve the problems existing in the prior art.

[0007] This invention provides a method for optimizing distribution network voltage under distributed power source integration, comprising:

[0008] With the goals of minimizing node voltage deviation and reducing network losses in the distribution network, a multi-objective optimization model is constructed under the premise of satisfying node voltage constraints, power flow balance constraints, and active and reactive power output constraints of distributed generation.

[0009] Based on the standard particle swarm optimization algorithm, an improved particle swarm optimization algorithm is obtained by integrating a nonlinear decreasing inertia weight mechanism, a chaotic initialization method based on Logistic mapping, and a simulated annealing local search operator.

[0010] Based on the improved particle swarm optimization algorithm and the multi-objective optimization model, the voltage optimization problem of the distribution network is solved, and the optimal operation strategy of the distribution network is obtained.

[0011] Preferably, the objectives of the multi-objective optimization model are: to maintain the voltage amplitude of all nodes in the distribution network within the allowable deviation range of the rated value; and to reduce the total active power loss of the distribution network system relative to the initial state by more than the preset value.

[0012] Further optimization includes: node voltage constraint, which requires that the voltage amplitude of each node in the distribution network must fall within a preset safe range; power flow balance constraint, which requires that the power flow of the distribution network system be in a balanced state; and active and reactive power output constraint, which requires that the active and reactive power output of the connected distributed power sources be within their adjustable range.

[0013] Further preferably, the inertia weight coefficient in the nonlinear decreasing inertia weight mechanism The formula is as follows:

[0014] ;

[0015] In the formula, and Let $\mathbf$ and $\mathbf$ represent the initial maximum and minimum values ​​of the inertia weight, respectively, and $k$ be the current iteration number. γ represents the maximum number of iterations, and γ is a nonlinear adjustment factor.

[0016] Further preferably, the chaotic initialization method based on Logistic mapping uses Logistic mapping to generate a chaotic sequence, maps the chaotic sequence to the feasible region of the distribution network control variables, and generates the initial positions of the particle swarm, wherein the initial position of the i-th particle is... The formula is as follows:

[0017] ;

[0018] ;

[0019] In the formula, For the nth term of the chaotic sequence, For chaos control parameters, Let be the initial position of the i-th particle. To find the upper limit of the solution space, To solve for the lower bound of the space.

[0020] Further optimization yields the following mathematical expression for the simulated annealing local search operator:

[0021] ;

[0022] In the formula, This is the current globally optimal solution. These are candidate solutions after perturbation. The disturbance intensity coefficient is... This refers to the annealing temperature.

[0023] Further preferred, the solution to the distribution network voltage optimization problem based on the improved particle swarm optimization algorithm and the multi-objective optimization model includes:

[0024] The initial population was generated using the Logistic chaotic mapping technique;

[0025] In each iteration, the forward-backward substitution method is used to calculate the power flow of the distribution network for the combination of control variables corresponding to each particle in the population, to obtain the voltage amplitude of all nodes in the distribution network and the total active power loss of the distribution network system. Based on the power flow calculation results, the fitness function is used to evaluate the optimization effect.

[0026] The individual and global optimal solutions are dynamically updated, and the particle velocity and position are adjusted using a non-linear decreasing inertial weight.

[0027] During the convergence phase, the simulated annealing local search operator is used to perturb the global optimal particle and generate new candidate solutions.

[0028] When the number of iterations reaches the maximum number of iterations or the fitness value shows no significant improvement in consecutive iterations, the iteration terminates, and the optimal operation strategy of the distribution network corresponding to the globally optimal particle is obtained.

[0029] In a further preferred embodiment, the fitness function combines the optimization objective with a constraint penalty mechanism, transforming violations of constraints into penalty terms for the fitness value, thereby guiding the search direction.

[0030] This invention also provides a distribution network voltage optimization system under distributed power source access, including:

[0031] The multi-objective optimization model construction module is used to construct a multi-objective optimization model with the objectives of minimizing the voltage deviation of distribution network nodes and reducing the network loss of the distribution network system, while satisfying the constraints of node voltage, power flow balance and active and reactive power output of distributed sources.

[0032] The particle swarm optimization algorithm improvement module is used to improve the particle swarm optimization algorithm by integrating a nonlinear decreasing inertia weight mechanism, a chaotic initialization method based on Logistic mapping, and a simulated annealing local search operator on the basis of the standard particle swarm optimization algorithm.

[0033] The distribution network voltage optimization module is used to solve the distribution network voltage optimization problem based on the improved particle swarm optimization algorithm and the multi-objective optimization model, and obtain the optimal operation strategy of the distribution network.

[0034] Preferably, the distribution network voltage optimization module includes:

[0035] The initial population generation module is used to generate the initial population using the Logistic chaotic mapping technique;

[0036] The power flow calculation and fitness evaluation module is used in each iteration to perform power flow calculation of the distribution network using the forward and backward substitution method for the combination of control variables corresponding to each particle in the population. It obtains the voltage amplitude of all nodes in the distribution network and the total active power loss of the distribution network system. Based on the power flow calculation results, the fitness function is used to evaluate the optimization effect.

[0037] The particle update module is used to dynamically update individual and global optimal solutions, and adjusts particle velocity and position using nonlinear decreasing inertial weights.

[0038] The simulated annealing local perturbation module is used to perturb the globally optimal particle using the simulated annealing local search operator during the convergence phase, generating new candidate solutions.

[0039] The optimal operation strategy acquisition module for the distribution network is used to terminate the iteration when the number of iterations reaches the maximum number of iterations or the fitness value does not improve significantly in multiple consecutive iterations, thereby obtaining the optimal operation strategy of the distribution network corresponding to the globally optimal particle.

[0040] The voltage optimization method and system for distribution networks with distributed generation access provided by this invention combine nonlinear decreasing inertial weights, Logistic chaotic initialization, and simulated annealing local search. While maintaining the simplicity and ease of implementation of the traditional Particle Swarm Optimization (PSO) algorithm, it significantly enhances global search capabilities and convergence speed, avoids premature convergence, and effectively improves optimization accuracy and algorithm stability. Secondly, by establishing a multi-objective optimization model centered on minimizing voltage deviation and reducing network losses, this invention can ensure node voltage compliance while also considering system economy, achieving coordinated optimization of voltage quality and energy loss. Furthermore, the optimization method proposed in this invention exhibits strong robustness in the face of random fluctuations from distributed generation, maintaining a high voltage constraint satisfaction rate and stable network loss reduction effect, thereby ensuring the safe and efficient operation of the distribution network under uncertain conditions. Simulation results show that this method outperforms traditional schemes in key indicators such as voltage compliance rate, number of nodes exceeding limits, and overall network loss. In summary, this invention has significant technical advantages and broad application prospects in improving voltage stability, reducing distribution network energy loss, and enhancing operational robustness. Attached Figure Description

[0041] Figure 1 The flowchart illustrates the distribution network voltage optimization method under distributed power source access provided by this invention. Detailed Implementation

[0042] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0043] like Figure 1 As shown, this invention provides a method for optimizing distribution network voltage under distributed power source integration, comprising:

[0044] S1: Establish a distribution network operation optimization model: Focusing on scenarios with a high proportion of distributed power sources, with the goal of minimizing node voltage deviation and reducing system network losses, a multi-objective optimization model is constructed under the premise of satisfying node voltage constraints, power flow balance constraints and active and reactive power output constraints of distributed power sources.

[0045] This invention focuses on the operation optimization problem of distribution networks in scenarios with a high proportion of distributed generation. The core objective is to collaboratively suppress system voltage fluctuations and reduce network losses. The optimization problem is defined as improving two key performance indicators while meeting the constraints of safe distribution network operation. The primary objective is to ensure that the voltage of all nodes is strictly maintained within the allowable deviation range of the rated value. For example, for the voltage amplitude of each node… Must meet ,in, =0.95 and =1.05 These refer to the voltage based on the rated voltage. The lower and upper limits require that the optimized operating scheme results in an extremely low proportion of voltage over-limit nodes. The second core objective is to effectively reduce the total active power loss of the system, for example, by setting a reduction of more than 15% compared to the baseline operating state before optimization, mathematically expressed as... , It is the difference between the optimized network loss and the initial network loss. The initial state of the system has total active power loss. The optimized system has a total active power loss. These two objectives together constitute the optimization guideline of this invention, which aims to improve the power quality and economy of the distribution network.

[0046] To achieve the above objectives, a series of physical and operational constraints are followed during the optimization process. Node voltage constraints are a hard requirement; the voltage amplitude of each node must fall within a preset safe range, i.e. The constraints hold for all nodes. Simultaneously, the power flow balance constraints of the system must be satisfied, meaning that generation, load, losses, and network transmission characteristics must satisfy fundamental physical laws, specifically expressed as the active power balance equation. and reactive power balance equation , and These refer to the active and reactive power outputs of all power sources. and For the active and reactive power requirements of all loads, and These represent the total active and reactive power losses of the system. Furthermore, the active and reactive power output capabilities of the connected distributed generation (DG) sources have upper and lower limits; optimization schemes must not exceed their adjustable range. That is, for each DG source, its active power output constraint must be met. and reactive power constraint , and These refer to the active and reactive power outputs of the j-th distributed power source, respectively, with the subscripts min and max indicating the lower and upper limits of the power output capacity.

[0047] S2: Propose an improved particle swarm optimization algorithm: Based on the standard particle swarm optimization algorithm, an improved particle swarm optimization (PSO) algorithm is obtained by integrating a nonlinear decreasing inertia weight mechanism, a chaotic initialization method based on Logistic mapping, and a simulated annealing local search operator.

[0048] Standard particle swarm optimization (PSO) algorithms are prone to getting stuck in local optima, having insufficient convergence speed, and weak fine-grained search capabilities when solving complex distribution network voltage optimization problems. Therefore, this invention proposes an improved PSO algorithm that integrates three key improvement strategies.

[0049] The introduction of the nonlinear decreasing inertial weight mechanism can dynamically balance global exploration and local exploitation; the introduction of a chaotic initialization method based on Logistic mapping can enhance the coverage of the initial solution space; and the design of a simulated annealing local search operator can improve the accuracy of later optimization. These three strategies work synergistically to significantly improve the algorithm's convergence speed, global search capability, and ability to escape local optima.

[0050] The nonlinear decreasing inertia weight mechanism abandons the traditional linear descent mode and uses a more adaptive nonlinear function to dynamically adjust the inertia weight coefficients during the iteration process. :

[0051] ;

[0052] In the formula, and Let $\mathbf$ and $\mathbf$ represent the initial maximum and minimum values ​​of the inertia weight, respectively, and $k$ be the current iteration number. The maximum number of iterations is given by γ, which is a nonlinear adjustment factor.

[0053] This mechanism assigns a relatively large inertia weight value in the early stages of the algorithm search, which can enhance the global exploration capability of the particle swarm, enabling it to cover different regions of the solution space extensively and avoid focusing on local areas too early. As the iteration process progresses, the inertia weight decreases significantly according to a predetermined nonlinear trajectory, prompting the particle swarm to gradually enhance its local development capability in the later stages and conduct a more refined search around the potential optimal solution region, thereby effectively balancing the contradiction between global exploration and local development and improving the overall optimization efficiency.

[0054] This paper utilizes the extreme sensitivity and ergodicity of chaotic systems to initial conditions. A chaotic initialization method based on Logistic mapping generates a chaotic sequence, which is then mapped to the feasible region of distribution network control variables to generate the initial positions of the particle swarm. Each particle corresponds to a set of distribution network control variables (including active / reactive power output of distributed generation sources, transformer tap positions, etc.). This ensures that each particle is randomly and uniformly distributed within the allowed value range of the control variables, avoiding premature convergence caused by initial population aggregation. The initial position of the i-th particle is... The formula is as follows:

[0055] ;

[0056] ;

[0057] In the formula, It is the nth term of the chaotic sequence. These are chaos control parameters. It is the initial position of the i-th particle. , These are the upper and lower limits of the solution space;

[0058] This strategy can improve the diversity and coverage of the initial population distribution in the solution space, avoid premature convergence caused by the aggregation or uneven distribution of the initial population, and lay the foundation for subsequent global search.

[0059] The simulated annealing local search operator is introduced to enhance the algorithm's ability to perform fine-grained local searches in later stages. This operator is activated in the later stages of the algorithm's iterations when the particle swarm gradually converges or the search stagnates. Borrowing from the idea of ​​simulated annealing, it accepts temporarily inferior solutions with a certain probability, and explores the currently found global optimum through controlled random perturbation. The mathematical expression of the simulated annealing local search operator is:

[0060] ;

[0061] In the formula, This is the current globally optimal solution. These are candidate solutions after perturbation. This is the disturbance intensity coefficient, preferably set to 0.1. This is the annealing temperature term, and the perturbation intensity decreases with iteration.

[0062] This strategy gives the algorithm the ability to escape local optima when it is close to convergence and to perform finer-tuning searches near the optimal solution, thereby enhancing the algorithm's local exploitation depth and ability to escape local optima in the later stages of optimization.

[0063] The performance of the improved Particle Swarm Optimization (PSO) algorithm of this invention, as tested, is shown in Table 1:

[0064] Table 1 Algorithm Performance

[0065]

[0066] As shown in Table 1, the three strategies have a significant synergistic effect: the nonlinear weights dynamically balance global exploration and local development, the chaotic initialization effectively avoids premature convergence, and the simulated annealing perturbation enhances the fine search capability. This makes the improved PSO comprehensively surpass the single improved and standard algorithms in terms of convergence speed, optimization accuracy, and ability to escape local optima.

[0067] S3: Design the solution process for distribution network voltage optimization: Solve the distribution network voltage optimization problem based on the improved particle swarm optimization (PSO) algorithm and the multi-objective optimization model to obtain the optimal operation strategy of the distribution network;

[0068] The specific method is as follows:

[0069] Initial population generation: The initial population is generated using the Logistic chaotic mapping technique to ensure comprehensive coverage of the solution space;

[0070] The initial position of the entire particle swarm is generated using Logistic chaotic mapping technology. Each particle corresponds to a set of power distribution network control variables (including the active / reactive power output of distributed power sources, transformer tap positions, etc.), ensuring that each particle is randomly and uniformly distributed within the allowable range of control variables, thus creating favorable conditions for global search.

[0071] Power flow calculation and fitness evaluation: In each iteration, for each combination of control variables corresponding to each particle in the population (each particle represents a candidate solution for a specific combination of control variables), the forward-backward substitution method suitable for radial distribution networks is used to calculate the power flow of the distribution network (calculating the operating state of the entire system under the control variable settings represented by each particle, especially the voltage amplitude of all nodes in the distribution network and the total active power loss of the system). Based on the power flow calculation results, the optimization effect is evaluated using the fitness function.

[0072] As an improvement to the technical solution, the design of the fitness function is closely integrated with the optimization objective and constraints. The core is to minimize the total active power loss of the system and minimize the node voltage deviation. At the same time, by introducing a penalty function mechanism, the violation of voltage safety constraints or other hard constraints is transformed into a penalty term for the fitness value, thereby guiding the search toward a feasible and high-quality solution space region.

[0073] Particle Update: Dynamically update individual and global optimal solutions, using non-linear decreasing inertial weights to adjust particle velocity and position, balancing global search and local exploitation;

[0074] The algorithm updates each particle's historical best position and the global best position discovered so far for the entire population. Based on an improved velocity update rule (incorporating a non-linearly decreasing inertia weight), the velocity vector of the particle at the next moment is calculated. The new velocity vector determines the particle's direction and step size in the solution space. Then, the updated velocity is used to adjust the particle's position, i.e., update the values ​​of the control variables, completing one movement of the particle in the solution space.

[0075] Simulated annealing local perturbation: During the convergence phase, the simulated annealing local search operator is triggered to perturb the globally optimal particle, generate new candidate solutions, and decide whether to accept them according to the simulated annealing criterion, thus breaking through the local optimum trap.

[0076] In the later stages of the algorithm iteration, the embedded simulated annealing local search operator is triggered. This operator selects the current global optimal particle or an individual with excellent performance, applies a controllable random perturbation in its neighborhood, generates new candidate solutions, and decides whether to update the global optimal solution according to the probability acceptance criterion of simulated annealing, aiming to break through possible local optimal states.

[0077] Iteration Termination: The entire process (evaluation, update, movement, selective local perturbation) is repeated. When the number of iterations reaches the maximum number of iterations or the fitness value does not improve significantly in multiple consecutive iterations, the iteration terminates and outputs the combination of control variables corresponding to the globally optimal particle, which is the optimal (or near-optimal) distribution network operation strategy that satisfies the optimization objective and constraints.

[0078] This invention also provides a distribution network voltage optimization system under distributed power source access, including:

[0079] The multi-objective optimization model construction module is used to construct a multi-objective optimization model with the objectives of minimizing the voltage deviation of distribution network nodes and reducing the network loss of the distribution network system, while satisfying the constraints of node voltage, power flow balance and active and reactive power output of distributed sources.

[0080] The particle swarm optimization algorithm improvement module is used to improve the particle swarm optimization algorithm by integrating a nonlinear decreasing inertia weight mechanism, a chaotic initialization method based on Logistic mapping, and a simulated annealing local search operator on the basis of the standard particle swarm optimization algorithm.

[0081] The distribution network voltage optimization module is used to solve the distribution network voltage optimization problem based on the improved particle swarm optimization algorithm and the multi-objective optimization model, and obtain the optimal operation strategy of the distribution network.

[0082] Preferably, the distribution network voltage optimization module includes:

[0083] The initial population generation module is used to generate the initial population using the Logistic chaotic mapping technique;

[0084] The power flow calculation and fitness evaluation module is used in each iteration to perform power flow calculation of the distribution network using the forward and backward substitution method for the combination of control variables corresponding to each particle in the population. It obtains the voltage amplitude of all nodes in the distribution network and the total active power loss of the distribution network system. Based on the power flow calculation results, the fitness function is used to evaluate the optimization effect.

[0085] The particle update module is used to dynamically update individual and global optimal solutions, and adjusts particle velocity and position using nonlinear decreasing inertial weights.

[0086] The simulated annealing local perturbation module is used to perturb the globally optimal particle using the simulated annealing local search operator during the convergence phase, generating new candidate solutions.

[0087] The optimal operation strategy acquisition module for the distribution network is used to terminate the iteration when the number of iterations reaches the maximum number of iterations or the fitness value does not improve significantly in multiple consecutive iterations, thereby obtaining the optimal operation strategy of the distribution network corresponding to the globally optimal particle.

[0088] This invention provides a distribution network voltage optimization method and system under distributed generation access. By constructing a multi-objective optimization model aimed at reducing voltage deviation and network losses, it achieves both power quality and operational economy. Based on the traditional PSO algorithm, this method introduces improved strategies such as nonlinear decreasing inertia weights, Logistic chaotic initialization, and simulated annealing local search, significantly enhancing the balance between global search and local exploitation, effectively avoiding premature convergence, and improving optimization accuracy and algorithm stability. Furthermore, by combining power flow calculation and fitness function evaluation mechanisms, this invention can perform real-time assessment of node voltage and system losses, and continuously improve the optimal solution through dynamic updates and perturbation optimization.

[0089] The distribution network voltage optimization method and optimization system provided by this invention have significant technical advantages and broad application prospects in improving the voltage control performance of the distribution network, reducing energy loss, and ensuring stable operation of the system under complex operating conditions.

[0090] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0091] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0092] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0093] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0094] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0095] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for optimizing distribution network voltage under distributed power source integration, characterized in that, include: With the goals of minimizing node voltage deviation and reducing network losses in the distribution network, a multi-objective optimization model is constructed under the premise of satisfying node voltage constraints, power flow balance constraints, and active and reactive power output constraints of distributed generation. Based on the standard particle swarm optimization algorithm, an improved particle swarm optimization algorithm is obtained by integrating a nonlinear decreasing inertia weight mechanism, a chaotic initialization method based on Logistic mapping, and a simulated annealing local search operator. Based on the improved particle swarm optimization algorithm and the multi-objective optimization model, the voltage optimization problem of the distribution network is solved, and the optimal operation strategy of the distribution network is obtained.

2. The distribution network voltage optimization method under distributed power source access according to claim 1, characterized in that, The objectives of the multi-objective optimization model are: to maintain the voltage amplitude of all nodes in the distribution network within the allowable deviation range of the rated value; and to reduce the total active power loss of the distribution network system relative to the initial state by more than the preset value.

3. The distribution network voltage optimization method under distributed power source access according to claim 1, characterized in that, The node voltage constraint requires that the voltage amplitude of each node in the distribution network must fall within a preset safe range; the power flow balance constraint requires that the power flow of the distribution network system be in a balanced state. The active and reactive power output constraints of distributed power sources are defined as the active and reactive power outputs of the connected distributed power sources being within their adjustable ranges.

4. The distribution network voltage optimization method under distributed power source access according to claim 1, characterized in that, The inertia weight coefficient in the nonlinear decreasing inertia weight mechanism The formula is as follows: ； In the formula, and Let $\mathbf$ and $\mathbf$ represent the initial maximum and minimum values ​​of the inertia weight, respectively, and $k$ be the current iteration number. γ represents the maximum number of iterations, and γ is a nonlinear adjustment factor.

5. The distribution network voltage optimization method under distributed power source access according to claim 1, characterized in that, The chaotic initialization method based on Logistic mapping uses Logistic mapping to generate a chaotic sequence, maps the chaotic sequence to the feasible region of the distribution network control variables, and generates the initial position of the particle swarm.

6. The distribution network voltage optimization method under distributed power source access according to claim 1, characterized in that, The mathematical expression for the simulated annealing local search operator is: ； In the formula, This is the current globally optimal solution. These are candidate solutions after perturbation. The disturbance intensity coefficient is... This refers to the annealing temperature.

7. The distribution network voltage optimization method under distributed power source access according to claim 1, characterized in that, Solving the distribution network voltage optimization problem based on the improved particle swarm optimization algorithm and the multi-objective optimization model includes: The initial population was generated using the Logistic chaotic mapping technique; In each iteration, the forward-backward substitution method is used to calculate the power flow of the distribution network for the combination of control variables corresponding to each particle in the population, to obtain the voltage amplitude of all nodes in the distribution network and the total active power loss of the distribution network system. Based on the power flow calculation results, the fitness function is used to evaluate the optimization effect. The individual and global optimal solutions are dynamically updated, and the particle velocity and position are adjusted using a non-linear decreasing inertial weight. During the convergence phase, the simulated annealing local search operator is used to perturb the global optimal particle and generate new candidate solutions. When the number of iterations reaches the maximum number of iterations or the fitness value shows no significant improvement in consecutive iterations, the iteration terminates, and the optimal operation strategy of the distribution network corresponding to the globally optimal particle is obtained.

8. The distribution network voltage optimization method under distributed power source access according to claim 7, characterized in that, The fitness function combines the optimization objective with a constraint penalty mechanism, transforming violations of constraints into penalty terms for fitness values, thus guiding the search direction.

9. A distribution network voltage optimization system with distributed power source integration, characterized in that, include: The multi-objective optimization model construction module is used to construct a multi-objective optimization model with the objectives of minimizing the voltage deviation of distribution network nodes and reducing the network loss of the distribution network system, while satisfying the constraints of node voltage, power flow balance and active and reactive power output of distributed sources. The particle swarm optimization algorithm improvement module is used to improve the particle swarm optimization algorithm by integrating a nonlinear decreasing inertia weight mechanism, a chaotic initialization method based on Logistic mapping, and a simulated annealing local search operator on the basis of the standard particle swarm optimization algorithm. The distribution network voltage optimization module is used to solve the distribution network voltage optimization problem based on the improved particle swarm optimization algorithm and the multi-objective optimization model, and obtain the optimal operation strategy of the distribution network.

10. The distribution network voltage optimization system under distributed power source access according to claim 9, characterized in that, The power distribution network voltage optimization module includes: The initial population generation module is used to generate the initial population using the Logistic chaotic mapping technique; The power flow calculation and fitness evaluation module is used in each iteration to perform power flow calculation of the distribution network using the forward and backward substitution method for the combination of control variables corresponding to each particle in the population. It obtains the voltage amplitude of all nodes in the distribution network and the total active power loss of the distribution network system. Based on the power flow calculation results, the fitness function is used to evaluate the optimization effect. The particle update module is used to dynamically update individual and global optimal solutions, and adjusts particle velocity and position using nonlinear decreasing inertial weights. The simulated annealing local perturbation module is used to perturb the globally optimal particle using the simulated annealing local search operator during the convergence phase, generating new candidate solutions. The optimal operation strategy acquisition module for the distribution network is used to terminate the iteration when the number of iterations reaches the maximum number of iterations or the fitness value does not improve significantly in multiple consecutive iterations, thereby obtaining the optimal operation strategy of the distribution network corresponding to the globally optimal particle.

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