Low-voltage power distribution network flexible topology construction method, system and device for new energy access and medium

Abstract

The invention discloses a low-voltage power distribution network flexible topology construction method, system and device for new energy access and a medium, and belongs to the technical field of power distribution network topology construction, and the method comprises the steps: collecting the operation data of a low-voltage power distribution network, carrying out the preprocessing of the operation data, and building a power distribution network topology model comprising a node set, an edge set and a weight matrix; constructing a multi-objective optimization model based on the power distribution network topology model; solving the multi-objective optimization model by adopting an improved genetic algorithm to obtain an optimal topological structure; and judging a topology change triggering condition according to an operation state monitoring result, and executing an optimal topology scheme in combination with an intelligent switch and a flexible interconnection device to realize real-time dynamic reconstruction of the power distribution network topology. According to the method, the intermittent, random and bidirectional characteristics of new energy are fully considered, the pertinence and effectiveness of topology design are improved, and the transformation of the power distribution network from a traditional static structure to a flexible dynamic structure is realized in combination with the intelligent switch and the flexible interconnection device.

Description

Technical Field

[0001] This invention relates to the field of power grid topology construction technology, specifically to a method, system, equipment, and medium for constructing a flexible topology for low-voltage distribution networks oriented towards new energy access. Background Technology

[0002] With the large-scale integration of distributed renewable energy sources into low-voltage distribution networks, the traditional distribution network topology faces severe challenges.

[0003] Regarding flexible topologies, existing technologies lack effective methods for dynamically adjusting network structures, making it difficult to achieve the goal of real-time topology optimization based on operational conditions. Existing protection configurations and control strategies are mostly designed for traditional radial networks, and their adaptability to flexible topologies needs improvement. In terms of fault recovery and self-healing capabilities, the response speed and recovery effectiveness of existing methods still require improvement. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention provides a method, system, equipment and medium for constructing a flexible topology for low-voltage distribution networks for new energy access.

[0005] Therefore, the technical problem solved by this invention is: how to solve the problems of poor topology adaptability, insufficient operational flexibility, and difficulty in multi-objective optimization caused by the large-scale integration of new energy into low-voltage distribution networks in existing flexible topology methods.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a method for constructing a flexible topology for low-voltage distribution networks oriented towards new energy access, comprising: collecting operational data of the low-voltage distribution network and preprocessing the operational data to establish a distribution network topology model including a set of nodes, a set of edges, and a weight matrix; constructing a multi-objective optimization model based on the distribution network topology model, wherein the multi-objective optimization model takes power supply reliability, network loss minimization, and voltage quality as objective functions; solving the multi-objective optimization model using an improved genetic algorithm, wherein the improved genetic algorithm introduces dynamic mutation control parameters and an elite preservation mechanism, and filters the non-dominated solution set based on Pareto dominance and hypervolume index to obtain the optimal topology structure; determining the topology change triggering conditions based on the operational status monitoring results, and executing the optimal topology scheme in conjunction with smart switches and flexible interconnection devices to achieve real-time dynamic reconstruction of the distribution network topology.

[0007] As a preferred embodiment of the flexible topology construction method for low-voltage distribution networks oriented towards new energy access described in this invention, the following steps are included: collecting operational data of the low-voltage distribution network and preprocessing the operational data to establish a distribution network topology model containing a set of nodes, a set of edges, and a weight matrix. This includes: cleaning and formatting the operational data; performing normalization and feature extraction on the cleaned data; determining the set of nodes and the set of edges based on the processed data; establishing the corresponding weight matrix and initializing the topology parameters.

[0008] As a preferred embodiment of the low-voltage distribution network flexible topology construction method for new energy access described in this invention, the step of constructing a multi-objective optimization model based on the distribution network topology model, wherein the multi-objective optimization model takes power supply reliability, network loss minimization, and voltage quality as objective functions, includes: constructing power supply reliability objective function, network loss objective function, and voltage quality objective function; setting power balance constraints and voltage upper and lower limit constraints; defining the objective function set and constraint conditions of the optimization problem; and determining the set of feasible topologies that satisfy the constraint conditions.

[0009] As a preferred embodiment of the flexible topology construction method for low-voltage distribution networks oriented towards new energy access described in this invention, the following steps are taken: An improved genetic algorithm is used to solve a multi-objective optimization model. The improved genetic algorithm introduces dynamic mutation control parameters and an elite preservation mechanism. Based on Pareto dominance relations and hypervolume indices, non-dominated solutions are screened to obtain the optimal topology. This includes: initializing the optimization population and calculating fitness; performing selection, crossover, and mutation operations, adjusting the mutation probability using dynamic control parameters during the mutation operation; implementing an elite preservation mechanism in each generation of evolution; and screening non-dominated solutions based on Pareto dominance relations and determining the optimal topology through hypervolume indices.

[0010] As a preferred embodiment of the flexible topology construction method for low-voltage distribution networks oriented towards new energy access described in this invention, the following steps are included: performing selection, crossover, and mutation operations; adjusting mutation probabilities using dynamic control parameters during mutation operations; dynamically adjusting mutation control parameters during each generation of population iteration to regulate mutation intensity and improve population diversity; retaining the current best individual in the population to prevent the best solution from being eliminated during evolution; applying Pareto dominance to determine the non-dominated solution set during population selection; calculating the corresponding hypervolume index for the non-dominated solution set; and selecting the target solution based on the hypervolume value.

[0011] This optimized scheme improves evolutionary stability and avoids the loss of optimal solutions by dynamically adjusting mutation parameters and preserving elite individuals. Combining Pareto dominance and hypervolume indices helps obtain reasonably distributed multi-objective optimization results.

[0012] As a preferred embodiment of the flexible topology construction method for low-voltage distribution networks oriented towards new energy access described in this invention, the step of determining the topology change triggering condition based on the operation status monitoring results includes: constructing the switch state matrix at the current moment; calculating the topology change amount between the current switch state matrix and the switch state matrix at the previous moment; measuring the topology change amount using the Frobenius norm; comparing the calculated Frobenius norm value with a preset threshold; and determining that the topology structure change is significant and triggering the topology reconstruction process when the Frobenius norm is greater than the threshold.

[0013] This preferred solution, based on a topology change matrix metric, can accurately identify structural changes. Reconstruction is triggered by comparing the result to a threshold, reducing unnecessary adjustments and improving system response efficiency.

[0014] As a preferred embodiment of the low-voltage distribution network flexible topology construction method for new energy access described in this invention, the step of combining smart switches and flexible interconnection devices to execute the optimal topology scheme and realize real-time dynamic reconstruction of the distribution network topology includes: generating corresponding switch state configurations based on the optimal topology structure; scheduling smart switches to perform switching operations according to the configuration; controlling the flexible interconnection devices to adjust power output and realize the switching of soft normally open points; monitoring the system operating status in real time after the operation is executed and obtaining the adjusted system topology parameters; and recording the state changes of each device and the final topology structure formed during the execution process.

[0015] This preferred solution utilizes switch control and flexible interconnection devices to collaboratively execute the topology scheme, enabling rapid topology reconfiguration. Process monitoring and status recording enhance the controllability and stability of system operation.

[0016] This invention provides a flexible topology construction system for low-voltage distribution networks oriented towards new energy access.

[0017] To address the aforementioned technical problems, this invention provides the following technical solution: a flexible topology construction system for low-voltage distribution networks oriented towards new energy access, comprising: a data processing module, an optimization modeling module, an optimization solution module, and an execution module; the data processing module is used to collect operational data of the low-voltage distribution network and preprocess the operational data to establish a distribution network topology model including a set of nodes, a set of edges, and a weight matrix; the optimization modeling module is used to construct a multi-objective optimization model based on the distribution network topology model, with power supply reliability, network loss minimization, and voltage quality as objective functions; the optimization solution module is used to solve the multi-objective optimization model using an improved genetic algorithm, which introduces dynamic mutation control parameters and an elite preservation mechanism, and filters the non-dominated solution set based on Pareto dominance and hypervolume indices to obtain the optimal topology structure; the execution module is used to determine the topology change triggering conditions based on the operational status monitoring results, and execute the optimal topology scheme in conjunction with smart switches and flexible interconnection devices to achieve real-time dynamic reconstruction of the distribution network topology.

[0018] The present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method for constructing a flexible topology of a low-voltage distribution network for new energy access.

[0019] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for constructing a flexible topology of a low-voltage distribution network for new energy access.

[0020] The beneficial effects of this invention are: This invention can accurately describe the impact of new energy access on the distribution network structure and performance, fully consider the intermittent, random and bidirectional characteristics of new energy, and improve the pertinence and effectiveness of topology design.

[0021] The multi-objective optimization topology evaluation index system achieves a comprehensive balance between multiple objectives such as power supply reliability, power quality, and economy, avoiding the limitations of traditional single-objective optimization. The Pareto optimal solution set provides decision-makers with diverse options.

[0022] The dynamic topology adjustment mechanism enables the distribution network to optimize its structure in real time according to operating conditions, significantly improving the system's operational flexibility and adaptability. Combined with smart switches and flexible interconnection devices, it realizes the transformation of the distribution network from a traditional static structure to a flexible dynamic structure. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a method for constructing a flexible topology for low-voltage distribution networks oriented towards new energy access, as provided in one embodiment of the present invention.

[0025] Figure 2 This is a flowchart illustrating a method for constructing a flexible topology for low-voltage distribution networks oriented towards new energy access, as provided in one embodiment of the present invention. Detailed Implementation

[0026] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0027] Example 1, referring to Figure 1 and Figure 2 This is one embodiment of the present invention, which provides a method for constructing a flexible topology for a low-voltage distribution network oriented towards new energy access, comprising: S1. Collect the operation data of the low-voltage distribution network, preprocess the operation data, and establish a distribution network topology model including the node set, edge set and weight matrix.

[0028] S2. Construct a multi-objective optimization model based on the distribution network topology model. The multi-objective optimization model takes power supply reliability, network loss minimization and voltage quality as objective functions.

[0029] S3. An improved genetic algorithm is used to solve the multi-objective optimization model. The improved genetic algorithm introduces dynamic mutation control parameters and an elite preservation mechanism. Based on the Pareto dominance relationship and the hypervolume index, the non-dominated solution set is screened to obtain the optimal topology.

[0030] S4. Based on the monitoring results of the operating status, determine the triggering conditions for topology changes, and combine the intelligent switches and flexible interconnection devices to execute the optimal topology scheme to realize the real-time dynamic reconfiguration of the distribution network topology.

[0031] It should be noted that, as Figure 1 and Figure 2As shown, this invention constructs a graph-based distribution network topology in steps S1-S4 and introduces a multi-objective optimization mechanism to address the instability of distribution network operation caused by new energy fluctuations. Through a mechanism combining real-time monitoring and dynamic optimization, it can effectively improve the operational stability and topology adaptability of the distribution network under conditions of high new energy penetration. At the same time, through the linkage control of the algorithm and the equipment layer, the real-time performance and controllability of the topology response are improved.

[0032] Example 2, an embodiment of the present invention, provides a method for constructing a flexible topology for low-voltage distribution networks oriented towards new energy access, based on the previous embodiment, including: In this application embodiment, the operating data in step S1 can be a combination of real-time operating monitoring data and historical operating data of the low-voltage distribution network. The real-time operating monitoring data includes information such as node voltage, current, active power, reactive power and phase angle, while the historical operating data includes new energy output curves, load curves and historical topology change records. The input dataset of the topology model is constructed after cleaning, normalizing and extracting features from the data.

[0033] In one alternative implementation, the operational data can also be time-series data derived from the power dispatching system, which is periodically uploaded by the distributed acquisition terminal to the edge computing gateway and used for topology modeling after preliminary filtering and time-series alignment at the edge.

[0034] In another alternative implementation, the operational data can also be predictive simulation data generated by a power grid simulation platform. This data is constructed based on a typical daily load model and a simulation of new energy output scenarios, and is used for optimization verification in scenarios where complete measured data is lacking.

[0035] This invention improves the accuracy of distribution network topology modeling by integrating real-time monitoring data with historical data to construct an operational data input set. The collected data includes the characteristics of new energy sources and the dynamic characteristics of loads, which enhances the ability of subsequent optimization models to characterize the operational status and provides highly reliable basic data support for dynamic reconfiguration decisions.

[0036] Furthermore, in step S1, operational data of the low-voltage distribution network is collected, and the operational data is preprocessed to establish a distribution network topology model containing a set of nodes, a set of edges, and a weight matrix, including the following steps A1-A4: A1. Clean and standardize the format of the running data.

[0037] A2. Perform normalization and feature extraction on the cleaned data.

[0038] A3. Determine the set of nodes and the set of edges based on the processed data.

[0039] A4. Establish the corresponding weight matrix and complete the topology parameter initialization.

[0040] In this embodiment of the application, the feature extraction in step A2 can be based on the measurement information of each node in the running data, such as voltage, current, active power, reactive power and phase angle, to construct a node state vector, which serves as the core feature data for the distribution network topology modeling and optimization input. The feature extraction process is performed after normalization processing, which preserves the relative change trends of different types of physical quantities and adapts to the needs of subsequent multi-objective optimization modeling.

[0041] In an alternative implementation, feature extraction can also be based on time-series data to extract statistical features of new energy output and load changes, such as mean, volatility, and peak periods, to characterize the operating characteristics of load centers and power access points.

[0042] In another alternative implementation, feature extraction can also be performed by compressing high-dimensional operating state data through dimensionality reduction techniques such as principal component analysis to extract the main feature components that can represent the operating state of the system, thereby reducing the computational complexity of the optimization model.

[0043] This invention improves the expressive power of topology modeling and optimization input data by constructing node state feature vectors that conform to the actual operating characteristics of the distribution network. The extracted features contain both static physical information and reflect temporal dynamic changes, thereby enhancing the accuracy and stability of subsequent optimization solutions.

[0044] Specifically, basic data on the low-voltage distribution network is collected, including network topology, line parameters, load data, and renewable energy output data. A graph theory model of the distribution network is established, defining the node set, edge set, and weight matrix. Algorithm parameters are set, including population size, crossover probability, and mutation probability for the genetic algorithm. Switch states and flexible interconnection device configurations are initialized.

[0045] Statistical analysis of historical operating data is conducted to determine the probability distribution characteristics of renewable energy output and load variation patterns. Predictive models for renewable energy output and load are established to provide data support for topology optimization. Weighting coefficients and constraints for multi-objective optimization are set.

[0046] The distribution network topology is represented as an undirected graph: (1) in, This is a power distribution network diagram. For a set of nodes, Let be the set of edges. This is the weight matrix.

[0047] Node state definition: (2) in, For the first The state vector of each node For node voltage, For node current, Active power Reactive power It is the phase angle.

[0048] Edge weight function: (3) in, For the edge The weight, For resistance, For reactance, It is in the on / off state.

[0049] New energy node model: (4) in, For the first Each new energy node at any time of efforts, Rated power, As capacity factor, For efficiency.

[0050] Adjacency matrix definition: (5) in, These are elements of the adjacency matrix, representing nodes. and nodes Are they adjacent?

[0051] Connectivity calculation: (6) in, For the image connectivity, The minimum cut vertex set that makes the graph disconnected. For set The number of elements.

[0052] Network diameter: (7) in, For network diameter, For nodes To the node The shortest path length.

[0053] Clustering coefficient: (8) in, For nodes Clustering coefficient, For nodes The number of edges between neighboring nodes. For nodes The degree.

[0054] In this embodiment of the application, the multi-objective optimization model in step S2 is an optimization model that simultaneously considers three types of objectives: power supply reliability, network loss minimization, and voltage quality. The power supply reliability objective is calculated by load point failure rate and repair time. The network loss objective is represented by the square of the branch current multiplied by the resistance. The voltage quality objective is measured based on the weighted sum of node voltage deviation and the set reference voltage. The model is constructed under the condition of satisfying power balance constraints and node voltage constraints, and the objective function and constraint relationship are represented by nonlinear mathematical expressions.

[0055] In an alternative implementation, the multi-objective optimization model can also be constructed using a hierarchical structure, where each objective function is defined as an independent sub-model, and then combined into a unified objective expression after weighted normalization, in order to adapt to the flexible configuration requirements of different operating preferences.

[0056] In another alternative implementation, the multi-objective optimization model can also be constructed using a constraint-first strategy, where priority is given to ensuring the feasibility of voltage and power flow safety constraints, and then the optimization objective is approximated through an evolutionary algorithm.

[0057] This invention constructs a multi-objective optimization model to achieve unified modeling of key operational indicators such as power supply reliability, power quality, and system loss in new energy access scenarios. This helps to find a more balanced and efficient topology reconfiguration scheme under complex operating conditions, and enhances the system's control capability and multi-objective coordination.

[0058] Furthermore, in step S2, a multi-objective optimization model is constructed based on the distribution network topology model. The multi-objective optimization model takes power supply reliability, network loss minimization, and voltage quality as objective functions, and includes the following steps B1-B4: B1. Construct the power supply reliability objective function, network loss objective function, and voltage quality objective function.

[0059] B2. Set power balance constraints and voltage upper and lower limit constraints.

[0060] B3. Define the objective function set and constraints of the optimization problem.

[0061] B4. Determine the set of feasible topologies that satisfy the constraints.

[0062] Specifically, the multi-objective function of the multi-objective optimization model: (9) in, Let the objective function vector be... For the first One objective function, For the decision variable vector, The number of objective functions, This is a transpose.

[0063] Power supply reliability target: (10) in, Let the power outage loss function be... For the first Failure rate at each load point For repair time, For load, This represents the number of load points.

[0064] Minimize network loss objective: (11) in, Let network loss function be used. branch road The current.

[0065] Voltage quality target: (12) in, It is a voltage deviation function. These are the weighting coefficients. This is the reference voltage.

[0066] Constraints: (13) (14) in, For the active power of the branch circuit, For the active power of the load, For branch reactive power, This represents the reactive power of the load.

[0067] Furthermore, in step S3, an improved genetic algorithm is used to solve the multi-objective optimization model. The improved genetic algorithm introduces dynamic mutation control parameters and an elite preservation mechanism, and filters the non-dominated solution set based on Pareto dominance and hypervolume index to obtain the optimal topology, including the following steps C1-C4: C1. Initialize and optimize the population and calculate fitness.

[0068] C2. Perform selection, crossover, and mutation operations, and use dynamic control parameters to adjust the mutation probability during the mutation operation.

[0069] C3. Implement an elite preservation mechanism in each generation of evolution.

[0070] C4. Screen non-dominated solutions based on Pareto dominance and determine the optimal topology using the hypervolume index.

[0071] In this embodiment of the application, the mutation probability in step C2 is a parameter that can be dynamically adjusted during the iteration of the genetic algorithm population based on the current generation, fitness distribution, or convergence trend. It is used to control the execution probability of individual mutation operations. A higher mutation probability is set in the early stage of the algorithm to maintain population diversity, and the mutation probability is gradually reduced in the convergence stage of the algorithm to stabilize the search results.

[0072] In an alternative implementation, the mutation probability can also be calculated based on the difference between individual fitness and the population average fitness. A higher mutation probability is assigned when an individual's fitness is below the average level, thereby enhancing the ability to escape from local inferior solutions.

[0073] In another alternative implementation, the mutation probability can also be set to adopt an exponential decay or piecewise linear descent strategy to control the evolution process of the algorithm from coarse search to fine search.

[0074] This invention introduces an adjustable mutation probability control mechanism, enabling the genetic algorithm to have a more reasonable search strategy at different evolution stages, avoiding premature convergence, increasing the probability of obtaining the optimal solution, and enhancing the global quality and distribution balance of the non-dominated solution set in the multi-objective optimization process.

[0075] Furthermore, in step C2, selection, crossover, and mutation operations are performed. During the mutation operation, dynamic control parameters are used to adjust the mutation probability, including the following steps C21-C24: C21. Dynamically adjust the mutation control parameters during each generation of population iteration to regulate the mutation intensity and improve population diversity.

[0076] C22. Retain the current best individual in the population to prevent the best solution from being eliminated during the evolution process.

[0077] C23. Pareto dominance relation is used to determine the non-dominated solution set during the population selection process.

[0078] C24. Calculate the hypervolume index corresponding to the non-dominated solution set, and select the target solution based on the hypervolume value.

[0079] Pareto dominance: (15) in, Representing the solution Dominant Solution .

[0080] Pareto optimal solution set: (16) in, This is the Pareto optimal solution set. This represents the feasible solution space.

[0081] Excess volume index: (17) in, This is an over-volume value. For non-dominated solution sets, For reference point number 3D coordinates.

[0082] Improved genetic algorithm: (18) in, for The generation Individual, This is the current optimal solution. , For randomly selected individuals, , These are control parameters.

[0083] Mutation operation: (19) in, For the first The first individual One gene, The mutation probability, This is a random number function.

[0084] Fitness function: (20) in, For the first The fitness value of each individual For the first The weight of each objective.

[0085] Furthermore, in step S4, the topology change triggering conditions are determined based on the operational status monitoring results. The optimal topology scheme is then executed in conjunction with the smart switch and flexible interconnection device to achieve real-time dynamic reconfiguration of the distribution network topology, including the following steps D1-D10: D1. Construct the switch state matrix at the current moment.

[0086] D2. Calculate the topological change between the current switch state matrix and the switch state matrix at the previous moment.

[0087] D3. Use the Frobenius norm to measure topological changes.

[0088] D4. Compare the calculated Frobenius norm value with the preset threshold.

[0089] D5. When the Frobenius norm is greater than the threshold, it is determined that the topology has changed significantly, and the topology reconstruction process is triggered.

[0090] D6. Generate the corresponding switch state configuration based on the optimal topology.

[0091] D7. The intelligent switch is scheduled to perform switching operations according to the configuration.

[0092] D8. Control the flexible interconnect device to adjust the power output and achieve the switching of the soft normally open point.

[0093] D9. Monitor the system's operating status in real time after the operation is performed, and obtain the adjusted system topology parameters.

[0094] D10. Record the status changes of each device during execution and the final topology structure.

[0095] Specifically, the switch state matrix: (twenty one) in, For a moment The switching state matrix, For switch At any moment The state.

[0096] Topology change detection: (twenty two) in, This represents the topological change. It is the Frobenius norm.

[0097] Refactoring trigger conditions: (twenty three) in, To reconstruct the trigger flag, For current performance metrics, The threshold value is used.

[0098] Fault detection function: (twenty four) in, For nodes Fault indicators, For the maximum allowable current, This is the minimum permissible voltage.

[0099] Fault isolation time: (25) in, Total quarantine time, For fault detection time, For decision-making time, For the action time.

[0100] Load restoration priority: (26) in, for Recovery priority As importance weight, Distance weights This is the load weight.

[0101] New energy access assessment, access capacity limitations: (27) in, For a collection of new energy nodes, For total load, This is the penetration rate limitation coefficient.

[0102] Voltage stability constraints: (28) in, , These are the lower and upper voltage limits, respectively.

[0103] Harmonic limitation: (29) in, For nodes Total harmonic distortion, For the first Subharmonic voltage The fundamental voltage, For limit values, It is the highest harmonic.

[0104] Calculation of device capacity for flexible interconnect devices: (30) in, For soft normally open contact devices in branch circuits capacity, Active power This refers to reactive power.

[0105] Control objective function: (31) in, For soft normally open point control target, For network loss, For the degree of imbalance, To optimize the time period.

[0106] Power control constraints: (32) (33) in, , The constraints are for active and reactive power, respectively.

[0107] Economic evaluation, investment cost function: (34) in, For investment costs, For the first Cost of a single switch For the first The cost of a software normally open point For the number of switches, This represents the number of soft normally open points.

[0108] Running cost function: (35) in, For operating costs, For network loss costs, For the cost of new energy, This refers to the cost of the load.

[0109] Net present value calculation: (36) Wherein, NPV stands for Net Present Value. for Annual income for Annual cost is the discount rate.

[0110] In the initialization and data preprocessing phase, basic data of the low-voltage distribution network is collected, including network topology, line parameters, load data, and renewable energy output data. A graph theory model of the distribution network is established, defining the node set, edge set, and weight matrix. Algorithm parameters are set, including population size, crossover probability, and mutation probability for the genetic algorithm. Switch states and flexible interconnection device configurations are initialized. Statistical analysis of historical operating data is performed to determine the probability distribution characteristics of renewable energy output and load variation patterns. Predictive models for renewable energy output and load are established to provide data support for topology optimization. Weight coefficients and constraints for multi-objective optimization are set.

[0111] In the topology performance evaluation phase, based on the current network topology, connectivity metrics are calculated, including connectivity degree, network diameter, and clustering coefficient. Power supply reliability is assessed by calculating the failure rate and average outage time at each load point. Power quality metrics are analyzed, including voltage deviation, harmonic content, and three-phase imbalance. Network losses and operating costs are calculated to evaluate the system's economics. The impact of renewable energy integration on system performance is analyzed, and potential technical problems are identified. A comprehensive evaluation index system is established to provide evaluation standards for topology optimization.

[0112] In the multi-objective optimization solution stage, an improved genetic algorithm is applied to solve the multi-objective optimization problem, generating an initial population and evaluating its fitness. Genetic operations such as selection, crossover, and mutation are performed to produce a new generation of individuals. Pareto dominance is used to screen non-dominated solutions, constructing a Pareto front. An elite preservation strategy is introduced to retain superior individuals for the next generation. A diversity preservation mechanism is employed to prevent premature convergence. A convergence criterion is set, and iteration stops when the convergence condition is met. The optimal topology is selected from the Pareto front.

[0113] During the dynamic topology adjustment phase, the system's operating status is monitored in real time, including load changes, changes in renewable energy output, and fault occurrences. A topology reconfiguration is determined based on preset trigger conditions. When the trigger conditions are met, the topology reconfiguration algorithm is initiated to calculate a new optimal topology. A sequence of switching operations is established to ensure system safety during topology changes. The switching operations are executed to achieve dynamic topology adjustment. System performance after topology adjustment is monitored to verify the reconfiguration effect. The history of topology changes is recorded to provide a reference for subsequent optimizations.

[0114] During the fault handling and recovery phase, a fault detection mechanism is established to monitor abnormal system states in real time. When a fault is detected, its location and type are quickly identified. Fault isolation operations are performed to prevent fault propagation. A load restoration strategy is developed, restoring power according to priority. The restoration path is optimized to maximize the restored load capacity. New energy output and energy storage systems are coordinated to support the system recovery process. The recovery effectiveness is evaluated, and outage losses and recovery time are statistically analyzed. The system operating status is updated to provide a basis for subsequent operation.

[0115] During the performance evaluation and optimization phase, the effectiveness of topology optimization is evaluated regularly, and the improvement in system performance is statistically analyzed. The impact of new energy access on system operation is analyzed, and optimization space is identified.

[0116] Adjust algorithm parameters and strategies based on operational experience. Establish a performance feedback mechanism to continuously improve the topology construction method.

[0117] Example 3 is an embodiment of the present invention, which provides a method for constructing a flexible topology of a low-voltage distribution network for new energy access. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0118] This invention is based on simulation verification of the IEEE 33-bus distribution system, which integrates 6 photovoltaic power generation devices and 2 wind power generation devices, with a total installed capacity of 2.5MW. A simulation model was built using the MATLAB / Simulink platform, considering various operating conditions such as load changes, fluctuations in renewable energy output, and equipment failures. The results are shown in Tables 1, 2, and 3.

[0119] Table 1: Performance Comparison of Different Topology Construction Methods

[0120] Table 2: Impact of renewable energy access capacity on system performance

[0121] Table 3: Analysis of Fault Recovery Effectiveness

[0122] Simulation results demonstrate that the flexible topology construction method proposed in this invention achieves significant results in reducing network losses, improving voltage quality, and enhancing reliability. Compared with traditional methods, network losses are reduced by 42.6%, voltage deviation is reduced by 66.1%, reliability is improved by 1.07%, and annual economic benefits increase by 241,000 yuan. The system operates well when the penetration rate of new energy sources is below 60%, but requires additional technical support when it exceeds 60%. Fault recovery capability is significantly improved, with the average recovery time shortened by more than 65%, verifying the effectiveness and practicality of the method.

[0123] Example 4 is an embodiment of the present invention. This embodiment provides a flexible topology construction system for low-voltage distribution networks oriented towards new energy access, including a data processing module, an optimization modeling module, an optimization solution module, and an execution module.

[0124] The data processing module is used to collect operational data of the low-voltage distribution network, preprocess the operational data, and establish a distribution network topology model that includes a set of nodes, a set of edges, and a weight matrix.

[0125] The optimization modeling module is used to build a multi-objective optimization model based on the distribution network topology model. The multi-objective optimization model takes power supply reliability, network loss minimization, and voltage quality as objective functions.

[0126] The optimization solution module is used to solve multi-objective optimization models using an improved genetic algorithm. The improved genetic algorithm introduces dynamic mutation control parameters and an elite preservation mechanism, and selects non-dominated solution sets based on Pareto dominance and hypervolume index to obtain the optimal topology.

[0127] The execution module is used to determine the triggering conditions for topology changes based on the results of operation status monitoring, and to execute the optimal topology scheme in combination with smart switches and flexible interconnection devices to realize real-time dynamic reconfiguration of the distribution network topology.

[0128] This embodiment also provides an electronic device applicable to a method for constructing a flexible topology of a low-voltage distribution network for new energy access, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the method for constructing a flexible topology of a low-voltage distribution network for new energy access as proposed in the above embodiment.

[0129] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for constructing a flexible topology for low-voltage distribution networks oriented towards new energy access, as proposed in the above embodiments.

[0130] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for constructing a flexible topology of a low-voltage distribution network for new energy access proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0131] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0132] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for constructing a flexible topology for low-voltage distribution networks oriented towards new energy access, characterized in that: include, Collect operational data of the low-voltage distribution network, preprocess the operational data, and establish a distribution network topology model that includes a set of nodes, a set of edges, and a weight matrix; A multi-objective optimization model is constructed based on the distribution network topology model. The multi-objective optimization model takes power supply reliability, network loss minimization and voltage quality as objective functions. An improved genetic algorithm is used to solve the multi-objective optimization model. The improved genetic algorithm introduces dynamic mutation control parameters and an elite preservation mechanism. Based on the Pareto dominance relationship and the hypervolume index, the non-dominated solution set is screened to obtain the optimal topology. Based on the results of operation status monitoring, the triggering conditions for topology changes are determined, and the optimal topology scheme is executed in combination with smart switches and flexible interconnection devices to achieve real-time dynamic reconfiguration of the distribution network topology.

2. The method for constructing a flexible topology for low-voltage distribution networks oriented towards new energy access as described in claim 1, characterized in that: The process involves collecting operational data from the low-voltage distribution network, preprocessing the data, and establishing a distribution network topology model that includes a set of nodes, a set of edges, and a weight matrix. Clean and format the runtime data; Normalization and feature extraction are performed on the cleaned data; The set of nodes and the set of edges are determined based on the processed data; Establish the corresponding weight matrix and complete the topology parameter initialization.

3. The method for constructing a flexible topology for low-voltage distribution networks oriented towards new energy access as described in claim 2, characterized in that: The multi-objective optimization model constructed based on the distribution network topology model has power supply reliability, network loss minimization, and voltage quality as objective functions, including: Construct the objective functions for power supply reliability, network loss, and voltage quality; Set power balance constraints and voltage upper and lower limit constraints; Define the objective function set and constraints of the optimization problem; Determine the set of feasible topologies that satisfy the constraints.

4. The method for constructing a flexible topology for low-voltage distribution networks oriented towards new energy access as described in claim 3, characterized in that: The improved genetic algorithm is used to solve the multi-objective optimization model. This improved algorithm introduces dynamic mutation control parameters and an elite preservation mechanism. Based on Pareto dominance and hypervolume indices, it filters the non-dominated solution set to obtain the optimal topology. Initialize and optimize the population and calculate fitness; Perform selection, crossover, and mutation operations, and use dynamic control parameters to adjust the mutation probability during the mutation operation; Implement an elite preservation mechanism in each generation of evolution; Non-dominated solutions are screened based on Pareto dominance and the optimal topology is determined by the hypervolume index.

5. The method for constructing a flexible topology for low-voltage distribution networks oriented towards new energy access as described in claim 4, characterized in that: The execution of selection, crossover, and mutation operations includes dynamically adjusting the mutation probability using control parameters during the mutation operation. During each generation of population iteration, the mutation control parameters are dynamically adjusted to regulate the mutation intensity and enhance population diversity. The best individual is retained in the population to prevent the best solution from being eliminated during the evolution process; Pareto dominance relation is used to determine the non-dominated solution set during the population selection process; Calculate the hypervolume index corresponding to the non-dominated solution set, and select the target solution based on the hypervolume value.

6. The method for constructing a flexible topology for low-voltage distribution networks oriented towards new energy access as described in claim 4, characterized in that: The topology change triggering conditions are determined based on the operational status monitoring results. include, Construct the switch state matrix at the current moment; Calculate the topological change between the current switch state matrix and the switch state matrix at the previous moment; The Frobenius norm is used to measure the amount of topological change; The calculated Frobenius norm value is compared with a preset threshold. When the Frobenius norm is greater than the threshold, it is determined that the topology has changed significantly, triggering the topology reconstruction process.

7. The method for constructing a flexible topology for low-voltage distribution networks oriented towards new energy access as described in claim 4, characterized in that: The method of combining intelligent switches and flexible interconnection devices to execute the optimal topology scheme and realize real-time dynamic reconfiguration of the distribution network topology includes: Generate the corresponding switch state configuration based on the optimal topology; The smart switch is scheduled to perform switching operations according to the configuration. Control the flexible interconnect device to adjust the power output and achieve the switching of soft normally open points; After the operation is performed, the system's operating status is monitored in real time, and the adjusted system topology parameters are obtained; Record the state changes of each device during execution and the final topology structure formed.

8. A flexible topology construction system for low-voltage distribution networks oriented towards new energy access, employing the flexible topology construction method for low-voltage distribution networks oriented towards new energy access as described in any one of claims 1 to 7, characterized in that, include: Data processing module, optimization modeling module, optimization solution module, and execution module; The data processing module is used to collect the operation data of the low-voltage distribution network, preprocess the operation data, and establish a distribution network topology model including a set of nodes, a set of edges, and a weight matrix. The optimization modeling module is used to construct a multi-objective optimization model based on the distribution network topology model. The multi-objective optimization model takes power supply reliability, network loss minimization and voltage quality as objective functions. The optimization solution module is used to solve the multi-objective optimization model using an improved genetic algorithm. The improved genetic algorithm introduces dynamic mutation control parameters and an elite preservation mechanism, and selects non-dominated solution sets based on Pareto dominance and hypervolume index to obtain the optimal topology. The execution module is used to determine the topology change triggering conditions based on the operation status monitoring results, and execute the optimal topology scheme in combination with smart switches and flexible interconnection devices to realize real-time dynamic reconfiguration of the distribution network topology.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of any one of the following claims: a method for constructing a flexible topology for a low-voltage distribution network oriented towards new energy access.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for constructing a flexible topology of a low-voltage distribution network for new energy access as described in any one of claims 1 to 7.

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