Collaborative Control Method and System for Distribution Network Reliability under Distributed Power Access

Abstract

The present invention provides a cooperative control method and system for the reliability of a distribution network under the access of distributed power sources, which relates to the field of power technology. A reliability index system of the distribution network considering distributed power sources is designed based on a load demand uncertainty model. The Monte Carlo simulation method is adopted to evaluate the reliability of the distribution network in combination with the reliability index system of the distribution network. According to the evaluation results, a cooperative control strategy including distributed power output control, load demand response management, and network topology reconfiguration is formulated. A multi-objective cooperative control optimization model with the objectives of reliability improvement, economic optimization, and maximum environmental benefits is established. An improved particle swarm algorithm is used to solve the multi-objective cooperative control optimization model to generate an optimal cooperative control strategy. Based on the optimal cooperative control strategy, control instructions are issued to and executed by each control object.

Description

Technical Field

[0001] The present invention relates to power technology, and in particular to a cooperative control method and system for the reliability of a distribution network under the access of distributed power sources. Background Art

[0002] Traditional reliability control methods for distribution networks mainly optimize for a single objective, such as minimizing the power outage time or maximizing the power supply reliability index. However, in the new situation of widespread access of distributed power sources, these methods often struggle to meet various requirements. For example, solely considering the reliability index may neglect economic and environmental benefits; simply pursuing economic benefits may reduce the reliability of the system. Therefore, there is an urgent need to develop a control method that can comprehensively consider multiple objectives and achieve the coordinated optimization of the reliability, economy, and environmental benefits of the distribution network.

[0003] In the prior art, although some studies have started to focus on the multi-objective optimization problem of distribution networks, most methods have the following deficiencies:

[0004] Lack of full consideration of the characteristics of distributed power sources, making it difficult to effectively utilize their flexibility and adjustability;

[0005] The efficiency of fault location and network reconfiguration algorithms is low, making it difficult to meet the requirements of real-time control;

[0006] The optimization objective is single, making it difficult to achieve the coordinated optimization of reliability, economy, and environmental benefits;

[0007] The control strategy lacks adaptability and is difficult to cope with the dynamic changes in the operating environment of the distribution network. Summary of the Invention

[0008] The present invention provides a cooperative control method and system for the reliability of a distribution network under the access of distributed power sources, which can solve the problems in the prior art.

[0009] In the first aspect of the present invention,

[0010] A cooperative control method for the reliability of a distribution network under the access of distributed power sources is provided, including:

[0011] Based on the established distribution network topology structure model, analyze the output characteristics of distributed power sources and construct a load demand uncertainty model; design a distribution network reliability index system considering distributed power sources based on the load demand uncertainty model, including power supply reliability rate, average power outage time, and load loss index;

[0012] The Monte Carlo simulation method is adopted to evaluate the reliability of the distribution network in combination with the reliability index system of the distribution network, including the distribution network state evaluation and reliability analysis. Among them, the distribution network state evaluation adopts the probabilistic power flow calculation method, considering the equipment failure rate, repair time and load importance, and the reliability analysis is based on the conditional value-at-risk assessment index; according to the evaluation results, a coordinated control strategy including distributed power generation output control, load demand response management and network topology reconstruction is formulated; a multi-objective coordinated control optimization model aiming at reliability improvement, economic optimization and environmental benefit maximization is established, taking distributed power generation output control, load demand response and network topology reconstruction as decision variables, considering voltage constraints, power constraints and equipment capacity constraints, and an improved particle swarm algorithm is used to solve the multi-objective coordinated control optimization model to generate the optimal coordinated control strategy;

[0013] Based on the optimal coordinated control strategy, control instructions are sent to each control object and executed; a fuzzy logic controller is used to realize the real-time output adjustment of distributed power generation, and a graph theory algorithm is used for fault location and network reconstruction; the operation state of the system is monitored in real time, and the control parameters are dynamically adjusted; the control effect is continuously optimized through a closed-loop feedback mechanism.

[0014] Preferably,

[0015] Based on the established distribution network topology structure model, analyzing the output characteristics of distributed power generation and constructing the load demand uncertainty model includes the following steps:

[0016] Establish a distribution network topology structure model, which is represented by a weighted undirected graph, where nodes represent buses in the distribution network, edges represent lines connecting nodes, and each edge is assigned a complex weight to represent the line impedance;

[0017] Based on the distribution network topology structure model, a nodal admittance matrix is constructed, and an initial power flow equation is established by using the nodal admittance matrix;

[0018] Identify the distributed power generation access nodes in the distribution network topology structure model, and analyze the probability distribution of the output characteristics of the distributed power generation access nodes, including:

[0019] For the photovoltaic power generation access node, a β probability distribution model is used to describe the randomness of the solar radiation intensity at this node, and the probability distribution of the output power of the photovoltaic cell at this node is calculated;

[0020] For the wind power generation access node, a Weibull distribution is used to describe the randomness of the wind speed at this node, and the probability distribution of the output power of the wind turbine at this node is calculated;

[0021] Integrate the probability distribution of the output characteristics of the analyzed distributed power generation access nodes into the initial power flow equation to obtain a modified power flow equation considering the uncertainty of distributed power generation output;

[0022] Identify load nodes in the distribution network topology structure model, and extract the initial load data of each load node based on the modified power flow equation;

[0023] Perform clustering analysis on the extracted initial load data to determine the typical load pattern of each load node;

[0024] Based on the typical load pattern of each load node, construct a load demand uncertainty model for that node; integrate the constructed load demand uncertainty models of each load node to form a load demand uncertainty model for the entire distribution network.

[0025] Preferably,

[0026] The steps of constructing a load demand uncertainty model for a node based on the load data of each load node include:

[0027] Construct a Gaussian mixture model based on the load data of each load node, and the Gaussian mixture model is expressed as:

[0028] ;

[0029] where x is the load data of the load node, θ is the set of model parameters, π k is the weight of the Gaussian component, N is the probability density function of the Gaussian component, μ k is the mean parameter of the Gaussian component, is the variance, k is the index variable, and K is the total number of Gaussian components;

[0030] Use the K-means clustering algorithm to preprocess the load data to obtain the initial value of the Gaussian mixture model parameter θ;

[0031] Execute the expectation-maximization algorithm to iteratively optimize the Gaussian mixture model, including the expectation step and the maximization step; in the expectation step, calculate the posterior probability that each load data point belongs to each Gaussian component; in the maximization step, update π of each Gaussian component based on the calculated posterior probability k 、μ k and ; calculate the log-likelihood function of the updated Gaussian mixture model; if the value of the log-likelihood function is less than the preset log-likelihood threshold, terminate the iteration and generate the optimal Gaussian mixture model for the node;

[0032] Calculate the Bayesian information criterion for different K values, select the best model complexity; based on the optimal Gaussian mixture model and the best model complexity, generate the load demand uncertainty model for the node.

[0033] Preferably,

[0034] The Monte Carlo simulation method is adopted to evaluate the reliability of the distribution network in combination with the reliability index system of the distribution network, including the distribution network state evaluation and reliability analysis. Among them, the distribution network state evaluation adopts the probabilistic power flow calculation method, considering the equipment failure rate, repair time and load importance. The steps of the reliability analysis based on the conditional value-at-risk assessment index include:

[0035] The distribution network state evaluation adopts probabilistic power flow calculation. The probabilistic power flow calculation adopts an improved Newton-Raphson method, including: establishing a nodal power balance equation considering the power injection characteristics of distributed power sources; initializing the nodal voltage and phase angle; calculating the power imbalance and Jacobian matrix; solving the linear equations to obtain the correction amounts of voltage and phase angle; updating the nodal voltage and phase angle; if the convergence condition is not met, return to the step of calculating the power imbalance, and if the convergence condition is met, output the nodal voltage and power injection results. The convergence condition is that the maximum absolute value of the power imbalance is less than the power imbalance threshold;

[0036] Based on the probabilistic power flow calculation results, calculate the evaluation indexes of nodal voltage deviation, line power flow and system loss state;

[0037] Use a two-state Markov model to describe the equipment operation state and calculate the equipment availability; assign importance weights to load nodes; combine the equipment availability and load importance weights to calculate the probabilistic power supply reliability rate, average power outage time and load loss index;

[0038] Introduce the conditional value-at-risk index and calculate the conditional value-at-risk of the load loss index. The conditional value-at-risk is obtained by sorting the load loss index samples, determining the value-at-risk and calculating the average value of the samples exceeding the value-at-risk;

[0039] Through the Monte Carlo simulation method, repeatedly execute the probabilistic power flow calculation and the reliability analysis based on the conditional value-at-risk of the calculated load loss index, generate samples and conduct statistical analysis; based on the statistical analysis of the Monte Carlo simulation method, output the evaluation results of the distribution network reliability, including the power supply reliability rate, average power outage time and load loss index.

[0040] Preferably,

[0041] Establish a multi-objective collaborative control optimization model aiming at reliability improvement, economic optimization and environmental benefit maximization, take the distributed power output control, load demand response and network topology reconstruction as decision variables, consider voltage constraints, power constraints and equipment capacity constraints, and adopt an improved particle swarm algorithm to solve the multi-objective collaborative control optimization model. The steps of generating the optimal collaborative control strategy include:

[0042] Construct a reliability improvement objective function, which is expressed as the weighted sum of the system average interruption frequency index and the system average interruption duration index, and determine the weight coefficients through the analytic hierarchy process;

[0043] Construct an economic optimization objective function, which takes into account the system operation cost and the economic benefits brought by reliability improvement. The system operation cost includes the power generation cost, the network loss cost and the load shedding cost, and the economic benefits brought by reliability improvement are quantified by the reduction of the outage cost;

[0044] Construct an environmental benefit maximization objective function, which is expressed by the system carbon emissions and is calculated through the carbon emission coefficients and outputs of various power generation units;

[0045] Set constraint conditions, including voltage constraints, power constraints and equipment capacity constraints. The power constraints consider the active and reactive power outputs of generators, the active and reactive power demands of loads, the node voltage magnitudes and phases, and the network admittance matrix;

[0046] Solve the multi-objective collaborative control optimization model by using an improved particle swarm optimization algorithm, and the improved particle swarm optimization algorithm includes:

[0047] Introduce a non-dominated sorting strategy and use the fast non-dominated sorting method to stratify the population;

[0048] Introduce the calculation of crowding distance to maintain the diversity of the population;

[0049] Design an adaptive inertia weight strategy to dynamically adjust the inertia weight according to the number of iterations and the particle fitness, and balance the global search and local search capabilities;

[0050] Introduce Gaussian mutation operation to enhance the global search ability of the algorithm;

[0051] Obtain a set of Pareto optimal solutions through the improved particle swarm optimization algorithm and generate an optimal collaborative control strategy. The optimal collaborative control strategy includes a distributed power generation output control scheme, a load demand response scheme and a network topology reconfiguration scheme.

[0052] Preferably,

[0053] The method further includes:

[0054] Use the Chebyshev decomposition method to transform the multi-objective problem into a series of single-objective sub-problems, assign weight vectors to each sub-problem, make the weight vectors evenly distributed in the objective space, and maintain the optimal solutions for each sub-problem to form an external elite archive;

[0055] Design an adaptive scaling factor and crossover probability, perform differential evolution operations on each particle to generate a trial vector, evaluate the fitness of the trial vector according to the Chebyshev method, and update the current particle position; assign a neighborhood to each particle, with the neighborhood size dynamically adjusted as the iteration progresses, and consider the global optimal solution, individual optimal solution, and neighborhood optimal solution when updating the particle position to achieve collaborative learning; design an adaptive mutation probability and perform Gaussian mutation on each dimension, where the mutation amplitude decreases as the iteration progresses;

[0056] Maintain an external elite archive, add non-dominated solutions to the external elite archive after each iteration, trim it based on the crowding distance when the size of the external elite archive exceeds the archive size threshold, and dynamically adjust the size of the external elite archive;

[0057] Perform local search on the solutions in the elite archive, use the pattern search method to explore in the neighborhood of the current solution, and update the elite archive if a better solution is found;

[0058] When the maximum number of iterations is reached or the preset convergence condition is satisfied, the algorithm terminates, and the non-dominated solution set in the elite archive is output as the Pareto optimal solution to generate the optimal collaborative control strategy.

[0059] Preferably,

[0060] The steps of using a fuzzy logic controller to achieve real-time output regulation of distributed power sources and using a graph theory algorithm for fault location and network reconstruction include:

[0061] Abstract the distribution network as an undirected graph, where nodes represent substations, switches, and load points, and edges represent lines;

[0062] Use the depth-first search algorithm to traverse the undirected graph, set voltage deviation thresholds and current deviation thresholds, when an abnormal node is detected, add the node to the set of potential fault points, backtrack to the previous normal node, and mark the line between the two as a potential fault line; based on the obtained set of potential fault points and potential fault lines, determine the final fault location;

[0063] Use an improved Kruskal algorithm for network reconstruction, including introducing a load imbalance index, introducing a radial topology constraint, and using a dynamic weight adjustment mechanism; sort all edges according to the dynamic weight and select the edges that meet the constraint conditions to add to the minimum spanning tree one by one; during the process of selecting edges, check voltage constraints, current constraints, and load balance constraints at the same time; according to the finally generated minimum spanning tree, determine the reconstructed network topology.

[0064] In the second aspect of the present invention,

[0065] Provide a collaborative control system for the reliability of a distribution network with distributed power source access, including:

[0066] The first unit is used to analyze the output characteristics of distributed power sources based on the established distribution network topology structure model, and construct a load demand uncertainty model; design a distribution network reliability index system considering distributed power sources based on the load demand uncertainty model, including power supply reliability rate, average power outage time, and load loss index;

[0067] The second unit is used to evaluate the reliability of the distribution network by using the Monte Carlo simulation method in combination with the distribution network reliability index system, including distribution network state evaluation and reliability analysis. Among them, the distribution network state evaluation adopts the probabilistic power flow calculation method, considering equipment failure rate, repair time, and load importance, and the reliability analysis is based on the conditional value-at-risk assessment index; according to the evaluation results, formulate a coordinated control strategy including distributed power source output control, load demand response management, and network topology reconstruction; establish a multi-objective coordinated control optimization model with the goals of reliability improvement, economic optimization, and environmental benefit maximization, take distributed power source output control, load demand response, and network topology reconstruction as decision variables, consider voltage constraints, power constraints, and equipment capacity constraints, and use an improved particle swarm optimization algorithm to solve the multi-objective coordinated control optimization model to generate an optimal coordinated control strategy;

[0068] The third unit is used to issue and execute control instructions to each control object based on the optimal coordinated control strategy; use a fuzzy logic controller to realize the real-time output regulation of distributed power sources, and use a graph theory algorithm for fault location and network reconstruction; monitor the system operation state in real time, dynamically adjust control parameters; continuously optimize the control effect through a closed-loop feedback mechanism.

[0069] In the third aspect of the present invention,

[0070] Provide an electronic device, including:

[0071] A processor;

[0072] A memory for storing instructions executable by the processor;

[0073] Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0074] In the fourth aspect of the present invention,

[0075] Provide a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0076] The method based on multi-objective collaborative control optimization adopted in this paper has various advantages. Firstly, it can simultaneously consider the objectives of reliability, economy, and environmental benefits, achieving the comprehensive optimization of the distribution network. Secondly, by taking the distributed power output control, load demand response, and network topology reconfiguration as decision variables, it fully utilizes the flexible resources of the distribution network and improves the operation efficiency of the system. Moreover, the improved particle swarm algorithm can effectively handle such complex multi-objective optimization problems and obtain a set of Pareto optimal solutions, providing multiple optional solutions for decision-makers. Finally, the application of this method helps to improve the reliability of the distribution network, reduce the operation cost, and reduce carbon emissions, realizing the coordinated unity of economic, social, and environmental benefits. By implementing the optimal collaborative control strategy, distribution network operators can better cope with challenges such as load fluctuations and renewable energy access, improve the flexibility and resilience of the system, and provide more reliable, economical, and clean power services for users.

[0077] The method combining fuzzy logic control and graph theory algorithm adopted in this paper has various advantages. Firstly, the fuzzy logic controller can flexibly handle the uncertainties and complexities in the system, realizing the intelligent regulation of distributed power sources and improving the stability and economy of the system. Secondly, the fault location algorithm based on graph theory can quickly and accurately identify the fault location, reducing the power outage scope and duration. Finally, when the improved Kruskal algorithm is used for network reconfiguration, it simultaneously considers network losses, load balance, and various constraints, and can generate a more optimized network topology. This comprehensive method significantly improves the reliability, flexibility, and economy of the distribution network, providing strong support for the operation and management of the intelligent distribution network. By adjusting the distributed power output in real time, quickly locating and isolating faults, and intelligently reconfiguring the network, the power outage time can be greatly reduced, the power supply quality can be improved, the operation cost can be reduced, and more reliable and high-quality power services can be provided for users. Brief Description of the Drawings

[0078] Figure 1 It is a schematic flowchart of the reliability collaborative control method for the distribution network under the access of distributed power sources in the embodiment of the present invention;

[0079] Figure 2 It is a schematic structural diagram of the reliability collaborative control system for the distribution network under the access of distributed power sources in the embodiment of the present invention. Detailed Embodiment

[0080] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0081] The technical solutions of the present invention will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0082] Figure 1 It is a schematic flowchart of a cooperative control method for the reliability of a distribution network under the access of distributed power sources in the embodiments of the present invention. As Figure 1 shown, the method includes:

[0083] S1. Based on the established distribution network topology structure model, analyze the output characteristics of distributed power sources, and construct a load demand uncertainty model; design a distribution network reliability index system considering distributed power sources based on the load demand uncertainty model, including power supply reliability rate, average power outage time, and load loss index;

[0084] S2. Adopt the Monte Carlo simulation method to evaluate the reliability of the distribution network in combination with the distribution network reliability index system, including distribution network state evaluation and reliability analysis. Among them, the distribution network state evaluation adopts the probability flow calculation method, considering equipment failure rate, repair time, and load importance. The reliability analysis is based on the conditional value-at-risk assessment index; according to the evaluation results, formulate a cooperative control strategy including distributed power source output control, load demand response management, and network topology reconstruction; establish a multi-objective cooperative control optimization model with the goals of reliability improvement, economic optimization, and maximum environmental benefits, take distributed power source output control, load demand response, and network topology reconstruction as decision variables, consider voltage constraints, power constraints, and equipment capacity constraints, and use an improved particle swarm algorithm to solve the multi-objective cooperative control optimization model to generate the optimal cooperative control strategy;

[0085] S3. Based on the optimal cooperative control strategy, issue and execute control instructions to each control object; use a fuzzy logic controller to realize the real-time output adjustment of distributed power sources, and use graph theory algorithms for fault location and network reconstruction; real-time monitor the operation state of the system, dynamically adjust control parameters; continuously optimize the control effect through a closed-loop feedback mechanism.

[0086] In an alternative embodiment,

[0087] Based on the established distribution network topology structure model, the steps of analyzing the output characteristics of distributed power sources and constructing a load demand uncertainty model include:

[0088] Establish a distribution network topology structure model, which is represented by a weighted undirected graph, where nodes represent buses in the distribution network, edges represent lines connecting nodes, and each edge is assigned a complex weight to represent line impedance;

[0089] Construct a nodal admittance matrix based on the distribution network topology structure model, and establish an initial power flow equation using the nodal admittance matrix;

[0090] Identify the distributed power source access nodes in the distribution network topology structure model, and analyze the probability distribution of the output characteristics of the distributed power source access nodes, including:

[0091] For a photovoltaic power generation access node, use the β probability distribution model to describe the randomness of the solar radiation intensity at this node, and calculate the probability distribution of the output power of the photovoltaic cells at this node;

[0092] For a wind power generation access node, use the Weibull distribution to describe the randomness of the wind speed at this node, and calculate the probability distribution of the output power of the wind turbine at this node;

[0093] Integrate the probability distribution of the output characteristics of the distributed power source access nodes into the initial power flow equation to obtain a modified power flow equation considering the uncertainty of distributed power source output;

[0094] Identify the load nodes in the distribution network topology structure model, and based on the modified power flow equation, extract the initial load data of each load node;

[0095] Conduct cluster analysis on the extracted initial load data to determine the typical load pattern of each load node;

[0096] Based on the typical load pattern of each load node, construct a load demand uncertainty model for this node; integrate the constructed load demand uncertainty models of each load node to form a load demand uncertainty model for the entire distribution network.

[0097] Exemplarily, the establishment of the distribution network topology structure model is the basis for analyzing the output characteristics of distributed power sources and constructing a load demand uncertainty model. This model is represented by a weighted undirected graph, where nodes represent buses in the distribution network and edges represent lines connecting nodes. Each edge is assigned a complex weight to represent line impedance. For example, in a distribution network model with 10 nodes, the line between node 1 and node 2 may be assigned a complex weight of (0.1 + 0.2j) ohms, indicating that the resistance of this line is 0.1 ohm and the reactance is 0.2 ohm.

[0098] Based on the established distribution network topology structure model, a nodal admittance matrix is constructed. The nodal admittance matrix is a complex matrix, where the diagonal elements represent the sum of the admittances of all lines connected to the node, and the off-diagonal elements represent the negative admittance of the line connecting two nodes. Through the nodal admittance matrix, an initial power flow equation can be established, which describes the relationship between power flow and nodal voltage in the distribution network.

[0099] In the distribution network topology structure model, it is necessary to identify the distributed power source access nodes and analyze the probability distribution of the output characteristics of these nodes. For the photovoltaic power generation access node, the β probability distribution model is used to describe the randomness of the solar radiation intensity at this node. The parameters of the β distribution can be estimated from historical data. For example, the β distribution parameters of a certain node may be α = 2.5 and β = 5.0. Based on the β distribution model, the probability distribution of the output power of the photovoltaic cells at this node can be calculated.

[0100] For the wind power generation access node, the Weibull distribution is used to describe the randomness of the wind speed at this node. The scale parameter and shape parameter of the Weibull distribution can also be estimated from historical data. For example, the Weibull distribution parameters of a certain node may be scale parameter c = 8.0 and shape parameter k = 2.0. Based on the Weibull distribution model, the probability distribution of the output power of the wind turbine at this node can be calculated.

[0101] Integrate the probability distribution of the output characteristics of the distributed power source access nodes obtained from the analysis into the initial power flow equation to obtain a modified power flow equation considering the uncertainty of distributed power source output. This modification process involves replacing the original deterministic power generation term with a probability distribution expression.

[0102] Identify the load nodes in the distribution network topology structure model, and based on the modified power flow equation, extract the initial load data of each load node. These initial load data are the basis for constructing the load demand uncertainty model.

[0103] Perform cluster analysis on the extracted initial load data to determine the typical load patterns of each load node. Cluster analysis can use the K-means algorithm or hierarchical clustering algorithm to group similar load curves into one category. For example, a certain load node may have three typical load patterns: weekdays, weekends, and holidays.

[0104] Based on the typical load patterns of each load node, construct the load demand uncertainty model for this node. Here, the Gaussian mixture model can be used to describe the uncertainty of the load demand. The Gaussian mixture model consists of multiple Gaussian components, and each component corresponds to a typical load pattern. The model parameters include the weights, means, and variances of each Gaussian component, and these parameters can be estimated using the expectation maximization (EM) algorithm.

[0105] For example, for a node with three typical load patterns, its Gaussian mixture model may contain three Gaussian components, with weights of 0.6, 0.3, and 0.1 respectively, corresponding means of 500 kW, 300 kW, and 200 kW, and variances of 10,000, 5,000, and 2,000.

[0106] Finally, integrate the constructed demand uncertainty models of each load node to form the load demand uncertainty model of the entire distribution network. This integration process needs to consider the correlation between nodes, which can be achieved by introducing a correlation coefficient matrix.

[0107] This method based on graph theory and probability model can effectively capture the uncertainties of distributed power generation output and load demand in the distribution network. By constructing detailed topological structure models and probability distribution models, the operating state of the distribution network can be described more accurately, providing a reliable basis for subsequent optimal scheduling and risk assessment.

[0108] In an alternative embodiment,

[0109] Based on the load data of each load node, the steps to construct the load demand uncertainty model of this node include:

[0110] Construct a Gaussian mixture model based on the load data of each load node, and the Gaussian mixture model is expressed as:

[0111] ;

[0112] where x is the load data of the load node, θ is the set of model parameters, π k is the weight of the Gaussian component, N is the probability density function of the Gaussian component, μ k is the mean parameter of the Gaussian component, is the variance, k is the index variable, and K is the total number of Gaussian components;

[0113] Use the K-means clustering algorithm to preprocess the load data to obtain the initial value of the Gaussian mixture model parameter θ;

[0114] Execute the expectation-maximization algorithm to iteratively optimize the Gaussian mixture model, including the expectation step and the maximization step; in the expectation step, calculate the posterior probability that each load data point belongs to each Gaussian component; in the maximization step, update π k , μ k and of each Gaussian component based on the calculated posterior probability; calculate the log-likelihood function of the updated Gaussian mixture model; if the value of the log-likelihood function is less than the preset log-likelihood threshold, terminate the iteration and generate the optimal Gaussian mixture model of the node;

[0115] Calculate the Bayesian Information Criterion for different K values and select the optimal model complexity; based on the optimal Gaussian mixture model and the optimal model complexity, generate the load demand uncertainty model for the node.

[0116] Exemplarily, constructing a Gaussian mixture model based on the load data of each load node is the core step to realize the modeling of load demand uncertainty. The Gaussian mixture model is a powerful probability model that can effectively capture the multimodal characteristics and uncertainties of load data. In this model, the load data is regarded as a mixed distribution composed of multiple Gaussian components. Each Gaussian component represents a typical load pattern and is described by three parameters: weight, mean, and variance. For example, for a node with three typical load patterns, its Gaussian mixture model may contain three Gaussian components with weights of 0.5, 0.3, and 0.2 respectively, corresponding means of 500kW, 300kW, and 200kW, and variances of 10000, 5000, and 2000.

[0117] To improve the training efficiency and convergence of the Gaussian mixture model, first use the K-means clustering algorithm to preprocess the load data. The K-means algorithm divides the load data into K clusters. The center of each cluster is used as the initial mean of the Gaussian component, the average squared distance between the data points in the cluster and the center is used as the initial variance, and the proportion of the data points in the cluster is used as the initial weight. This preprocessing method can provide a good starting point for subsequent parameter optimization. For example, for a node with 1000 load data points, if K = 3 is selected, the K-means algorithm may divide the data into three clusters containing 500, 300, and 200 data points respectively, and the corresponding initial weights are 0.5, 0.3, and 0.2.

[0118] Next, execute the Expectation-Maximization (EM) algorithm to iteratively optimize the parameters of the Gaussian mixture model. The EM algorithm includes an Expectation step (E-step) and a Maximization step (M-step). In the Expectation step, calculate the posterior probability that each load data point belongs to each Gaussian component. This probability reflects the "affinity" between the data point and each Gaussian component. In the Maximization step, update the weights, means, and variances of each Gaussian component based on the calculated posterior probabilities. For example, if a certain data point has a high posterior probability for the first Gaussian component, then during the update, this data point will have a greater impact on the parameters of the first component.

[0119] After each iteration, calculate the value of the log-likelihood function of the updated Gaussian mixture model. The log-likelihood function reflects the fitting degree of the model to the observed data. If the growth rate of the log-likelihood function value is less than a preset threshold (such as 0.001), it is considered that the model has converged and the iteration is terminated. At this time, the optimal Gaussian mixture model for the node is obtained.

[0120] To determine the optimal model complexity, i.e., the number K of Gaussian components, it is necessary to calculate the Bayesian Information Criterion (BIC) for different values of K. BIC seeks a balance between model fitness and model complexity to avoid overfitting. Typically, a series of K values, such as from 1 to 10, are tried, and the BIC corresponding to each K value is calculated. The K value that minimizes the BIC is selected as the optimal model complexity. For example, if the BIC is minimized when K = 4, then the model with 4 Gaussian components is selected as the final load demand uncertainty model.

[0121] Based on the optimal Gaussian mixture model and the optimal model complexity, a load demand uncertainty model for the node is generated. This model contains the optimal number of Gaussian components, and each component has optimized weights, means, and variances. For example, the final model may contain 4 Gaussian components with weights of 0.4, 0.3, 0.2, and 0.1, means of 600 kW, 400 kW, 300 kW, and 200 kW, and variances of 15000, 10000, 5000, and 2000.

[0122] This method of load demand uncertainty modeling based on the Gaussian mixture model has multiple advantages. First, it can effectively capture the multimodal characteristics of load data and adapt to different load patterns at different times. Second, through the iterative optimization of the EM algorithm, the model can accurately fit the distribution characteristics of actual load data. Third, the introduction of the BIC criterion to select the optimal model complexity achieves a good balance between model accuracy and computational efficiency. Finally, this probabilistic model can not only describe the expected value of the load but also quantify the uncertainty of the load, providing a solid foundation for subsequent risk assessment and robust optimization.

[0123] In an alternative embodiment,

[0124] The Monte Carlo simulation method is used to evaluate the reliability of the distribution network in combination with the reliability index system of the distribution network, including the distribution network state assessment and reliability analysis. Among them, the distribution network state assessment uses the probabilistic power flow calculation method, considering equipment failure rates, repair times, and load importance. The steps of reliability analysis based on the conditional value-at-risk assessment index include:

[0125] The state assessment of the distribution network uses probabilistic power flow calculation. The probabilistic power flow calculation adopts an improved Newton-Raphson method, including: establishing a nodal power balance equation considering the power injection characteristics of distributed power sources; initializing the nodal voltage and phase angle; calculating the power imbalance and the Jacobian matrix; solving the linear equations to obtain the correction amounts of the voltage and phase angle; updating the nodal voltage and phase angle; if the convergence condition is not met, returning to the step of calculating the power imbalance, and if the convergence condition is met, outputting the nodal voltage and power injection results. The convergence condition is that the maximum absolute value of the power imbalance is less than the power imbalance threshold.

[0126] Based on the probabilistic power flow calculation results, calculate the state assessment indexes of nodal voltage deviation, line power flow and system loss.

[0127] Use a two-state Markov model to describe the operating state of the equipment and calculate the equipment availability; assign importance weights to the load nodes; combine the equipment availability and the load importance weights to calculate the probabilistic power supply reliability rate, the average power outage time and the load loss index.

[0128] Introduce the conditional value-at-risk index and calculate the conditional value-at-risk of the load loss index. The conditional value-at-risk is obtained by sorting the samples of the load loss index, determining the value-at-risk and calculating the average value of the samples exceeding the value-at-risk.

[0129] Through the Monte Carlo simulation method, repeatedly execute the probabilistic power flow calculation and the reliability analysis based on the conditional value-at-risk of the calculated load loss index, generate samples and conduct statistical analysis; based on the statistical analysis of the Monte Carlo simulation method, output the evaluation results of the distribution network reliability, including the power supply reliability rate, the average power outage time and the load loss index.

[0130] Exemplarily, the reliability assessment of the distribution network is a key link to ensure the safe and stable operation of the power system. By using the Monte Carlo simulation method and combining a complete distribution network reliability index system, the reliability status of the distribution network can be comprehensively evaluated. This assessment process mainly includes two parts: the distribution network state assessment and the reliability analysis. Among them, the distribution network state assessment uses the probabilistic power flow calculation method, and the reliability analysis is based on the conditional value-at-risk assessment index.

[0131] The state assessment of the distribution network adopts probabilistic power flow calculation, specifically using the improved Newton-Raphson method (Newton iteration method). First, establish a nodal power balance equation considering the power injection characteristics of distributed power sources. For example, for a node containing photovoltaic power generation, its power balance equation needs to consider the randomness and intermittency of photovoltaic power generation. Initialize the nodal voltage and phase angle. It can be assumed that the voltage amplitude of all nodes is 1.0 per unit value and the phase angle is 0 degrees. Then calculate the power imbalance and the Jacobian matrix. The power imbalance reflects the difference between the actual injected power and the calculated power at the node in the current state, while the Jacobian matrix describes the sensitivity of power to voltage and phase angle changes.

[0132] Next, solve the linear equations to obtain the correction amounts of voltage and phase angle. For example, for a distribution network with 100 nodes, this step may involve solving a 200×200 linear equation system. After updating the nodal voltage and phase angle, check whether the convergence condition is satisfied. Usually, the convergence condition is set that the maximum absolute value of the power imbalance is less than a preset power imbalance threshold, such as 0.0001 per unit value. If the convergence condition is not satisfied, return to the step of calculating the power imbalance; if the convergence condition is satisfied, output the final nodal voltage and power injection results.

[0133] Based on the probabilistic power flow calculation results, calculate state assessment indicators such as nodal voltage deviation, line power flow, and system losses. For example, for a distribution network with a rated voltage of 10 kV, if the calculated voltage of a certain node is 9.8 kV, then its voltage deviation is 2%. The line power flow can be used to check whether there is an overload situation, while the system losses reflect the operating efficiency of the network.

[0134] In reliability analysis, a two-state Markov model is used to describe the operating state of equipment. Each device has two states: normal operation and failure, and the state transition is described by the failure rate and repair rate. For example, the annual failure rate of a transformer may be 0.05 times / year, and the average repair time is 10 hours. Based on this, its availability can be calculated to be approximately 99.94%. At the same time, assign importance weights to load nodes to reflect the priorities of different loads. For example, the load node of a hospital may be given a weight of 1.5, while the weight of an ordinary residential area is 1.0.

[0135] Combining the equipment availability and the load importance weights, calculate the probabilistic power supply reliability rate, average power outage time, and load loss index. For example, if the average annual number of power outages at a certain load point is 2 times, and each power outage lasts for 1 hour, then its average power outage time is 2 hours / year. The load loss index can be estimated by the product of the power outage time and the load size.

[0136] To better evaluate the risks in extreme situations, the Conditional Value at Risk (CVaR) indicator is introduced to calculate the CVaR of the load loss indicator. The specific steps include sorting the samples of the load loss indicator, determining the Value at Risk (VaR), and calculating the average value of the samples exceeding the VaR. For example, if the risk level is set at 95%, then the CVaR is the average load loss in the worst 5% of cases.

[0137] Through the Monte Carlo simulation method, the above-mentioned probabilistic power flow calculation and reliability analysis processes are repeatedly executed to generate a large number of samples and conduct statistical analysis. For example, it may be necessary to perform 10,000 simulations to obtain stable statistical results. Based on these statistical analyses, the evaluation results of the distribution network reliability are finally output, including the power supply reliability rate, the average power outage time, and the load loss indicator. For example, the evaluation results may show that the annual average power supply reliability rate of a certain distribution network is 99.99%, the average power outage time is 52.56 minutes / year, and the annual average load loss is 10,000 kWh.

[0138] This distribution network reliability evaluation method based on Monte Carlo simulation and Conditional Value at Risk has many advantages. First of all, it can comprehensively consider various uncertainty factors in the distribution network, including load fluctuations, distributed power generation output, and equipment failures. Secondly, by introducing the Conditional Value at Risk indicator, this method can better evaluate the system risks in extreme situations and provide more comprehensive risk information for decision-makers. Moreover, the use of the Monte Carlo simulation method makes the evaluation results have high statistical reliability. Finally, this method can not only evaluate the reliability status of the existing distribution network, but also be used to compare the reliability performances of different planning schemes, providing an important basis for the planning and transformation of the distribution network. Generally speaking, the application of this evaluation method will help improve the reliability level of the distribution network, reduce system risks, improve the power supply quality, and ultimately provide more reliable and high-quality power services for users.

[0139] In an alternative embodiment,

[0140] To establish a multi-objective collaborative control optimization model aiming at reliability improvement, economic optimization, and maximum environmental benefits, with the control of distributed power generation output, load demand response, and network topology reconstruction as decision variables, considering voltage constraints, power constraints, and equipment capacity constraints, and using an improved particle swarm optimization algorithm to solve the multi-objective collaborative control optimization model, the steps to generate the optimal collaborative control strategy include:

[0141] Construct a reliability improvement objective function, which is expressed as the weighted sum of the system average outage frequency indicator and the system average outage duration indicator, and determine the weight coefficients through the analytic hierarchy process;

[0142] Build an economic optimization objective function, which takes into account the system operation cost and the economic benefits brought by reliability improvement. Among them, the system operation cost includes power generation cost, network loss cost, and load shedding cost, and the economic benefits brought by reliability improvement are quantified by the reduction of power outage cost;

[0143] Build an objective function for maximizing environmental benefits, which is represented by the system carbon emission and is calculated through the carbon emission coefficients and outputs of various power generation units;

[0144] Set constraint conditions, including voltage constraints, power constraints, and equipment capacity constraints. Among them, the power constraints consider the active and reactive power outputs of generators, the active and reactive power demands of loads, the node voltage magnitudes and phases, and the network admittance matrix;

[0145] Use an improved particle swarm optimization algorithm to solve the multi-objective collaborative control optimization model. The improved particle swarm optimization algorithm includes:

[0146] Introduce a non-dominated sorting strategy and use the fast non-dominated sorting method to stratify the population;

[0147] Introduce the calculation of crowding distance to maintain the diversity of the population;

[0148] Design an adaptive inertia weight strategy to dynamically adjust the inertia weight according to the number of iterations and particle fitness, and balance the global search and local search capabilities;

[0149] Introduce Gaussian mutation operation to enhance the global search ability of the algorithm;

[0150] Obtain a set of Pareto optimal solutions through the improved particle swarm optimization algorithm and generate an optimal collaborative control strategy. The optimal collaborative control strategy includes a distributed power generation output control scheme, a load demand response scheme, and a network topology reconfiguration scheme.

[0151] Exemplarily, in order to achieve the efficient, reliable, and environmentally friendly operation of the distribution network, establishing a multi-objective collaborative control optimization model with the goals of reliability improvement, economic optimization, and environmental benefit maximization is an effective method. This model takes the distributed power generation output control, load demand response, and network topology reconfiguration as decision variables, and at the same time considers voltage constraints, power constraints, and equipment capacity constraints. It is solved by an improved particle swarm optimization algorithm and finally generates an optimal collaborative control strategy.

[0152] First, construct the reliability improvement objective function. This function is represented by the weighted sum of the System Average Interruption Frequency Index (SAIFI) and the System Average Interruption Duration Index (SAIDI). For example, assume that the SAIFI of a certain distribution network is 2 times / year and the SAIDI is 4 hours / year. By using the Analytic Hierarchy Process to determine that the weight of SAIFI is 0.6 and the weight of SAIDI is 0.4, the reliability improvement objective function can be expressed as the weighted sum of these two indices. The Analytic Hierarchy Process determines the weight coefficients by constructing a judgment matrix, calculating the eigenvector, and conducting a consistency test.

[0153] Secondly, construct the economic optimization objective function. This function takes into account the system operation cost and the economic benefits brought by reliability improvement. The system operation cost includes the power generation cost, the network loss cost, and the load shedding cost. For example, assume that the daily power generation cost of a certain distribution network is 10,000 yuan, the network loss cost is 1,000 yuan, and the load shedding cost is 500 yuan. Then the total operation cost is 11,500 yuan per day. The economic benefits brought by reliability improvement are quantified by the reduction of the outage cost. If the reliability improvement reduces the annual outage loss from 1 million yuan to 800,000 yuan, then it can bring 200,000 yuan of economic benefits per year.

[0154] Then, construct the objective function for maximizing environmental benefits. This function is represented by the system carbon emissions, which are calculated through the carbon emission coefficients and outputs of various power generation units. For example, assume that the distribution network includes gas-fired power generation units and a photovoltaic power generation system. The carbon emission coefficient of the gas-fired power generation unit is 0.5 kg / kWh, and the daily power generation is 10,000 kWh. The carbon emission coefficient of the photovoltaic power generation system is 0 kg / kWh, and the daily power generation is 5,000 kWh. Then the daily carbon emissions of the system are 5,000 kg.

[0155] Set the constraint conditions in the model, including voltage constraints, power constraints, and equipment capacity constraints. The voltage constraint requires that the node voltage be maintained within the allowable range, such as 0.95 - 1.05 per unit value. The power constraint takes into account the active and reactive power outputs of generators, the active and reactive power demands of loads, the voltage magnitudes and phase angles of nodes, and the network admittance matrix to ensure system power balance. The equipment capacity constraint requires that the operating status of each equipment does not exceed its rated capacity, such as the transformer load rate not exceeding 95%.

[0156] To solve this complex multi-objective optimization problem, an improved particle swarm algorithm is adopted. This algorithm first introduces the non-dominated sorting strategy and uses the fast non-dominated sorting method to stratify the population. For example, for a population containing 100 particles, it may be divided into 5 non-dominated layers. Then, the calculation of the crowding distance is introduced to maintain the diversity of the population. The crowding distance reflects the distribution density of particles in the solution space, and particles with a larger crowding distance are more likely to be retained.

[0157] During the algorithm iteration process, an adaptive inertia weight strategy is designed to dynamically adjust the inertia weight according to the iteration times and particle fitness, balancing the global search and local search capabilities. For example, a larger inertia weight (such as 0.9) can be used at the initial stage of iteration to enhance the global search ability, while a smaller inertia weight (such as 0.4) can be used at the later stage of iteration to enhance the local search ability. At the same time, Gaussian mutation operation is introduced to enhance the global search ability of the algorithm. Gaussian mutation can be performed on some dimensions of some particles, and the mutation probability can be set to 0.01.

[0158] Through the improved particle swarm algorithm, a set of Pareto optimal solutions is finally obtained to generate the optimal coordinated control strategy. This strategy includes distributed power output control scheme, load demand response scheme and network topology reconfiguration scheme. For example, the distributed power output control scheme may include increasing the output of the photovoltaic power generation system during peak load periods; the load demand response scheme may include reducing 20% of the controllable load during peak electricity price periods; the network topology reconfiguration scheme may include opening a tie line in some cases to reduce network losses.

[0159] This method based on multi-objective coordinated control optimization has many advantages. First of all, it can simultaneously consider the three aspects of objectives: reliability, economy and environmental benefits, and achieve the comprehensive optimization of the distribution network. Secondly, by taking distributed power output control, load demand response and network topology reconfiguration as decision variables, the flexible resources of the distribution network are fully utilized, and the operation efficiency of the system is improved. Moreover, the improved particle swarm algorithm can effectively handle such complex multi-objective optimization problems and obtain a set of Pareto optimal solutions, providing multiple optional solutions for decision-makers. Finally, the application of this method helps to improve the reliability of the distribution network, reduce the operation cost, reduce carbon emissions, and achieve the coordinated unity of economic, social and environmental benefits. By implementing the optimal coordinated control strategy, distribution network operators can better cope with challenges such as load fluctuations and renewable energy access, improve the flexibility and resilience of the system, and provide more reliable, economic and clean power services for users.

[0160] In an alternative embodiment,

[0161] The method further includes:

[0162] The Chebyshev decomposition method is used to transform the multi-objective problem into a series of single-objective sub-problems, a weight vector is assigned to each sub-problem, the weight vectors are evenly distributed in the objective space, and the optimal solutions of each sub-problem are maintained to form an external elite archive;

[0163] Design an adaptive scaling factor and crossover probability, perform differential evolution operations on each particle to generate a trial vector, evaluate the fitness of the trial vector according to the Chebyshev method, and update the current particle position; assign a neighborhood to each particle, and the neighborhood size is dynamically adjusted as the iteration progresses. When updating the particle position, consider the global optimal solution, the individual optimal solution, and the neighborhood optimal solution to achieve collaborative learning; design an adaptive mutation probability and perform Gaussian mutation on each dimension, where the mutation amplitude decreases as the iteration progresses.

[0164] Maintain an external elite archive, add non-dominated solutions to the external elite archive after each iteration. When the size of the external elite archive exceeds the archive size threshold, prune it based on the crowding distance, and dynamically adjust the size of the external elite archive.

[0165] Perform local search on the solutions in the elite archive, use the pattern search method to explore in the neighborhood of the current solution, and update the elite archive if a better solution is found.

[0166] When the maximum number of iterations is reached or the preset convergence condition is satisfied, the algorithm terminates, and the non-dominated solution set in the elite archive is output as the Pareto optimal solution to generate the optimal collaborative control strategy.

[0167] Exemplarily, to further improve the solution efficiency and solution quality of the multi-objective collaborative control optimization model, this method introduces a series of advanced algorithm strategies. These strategies include Chebyshev decomposition, differential evolution, adaptive parameter adjustment, collaborative learning, external elite archive maintenance, and local search and other technologies, forming a comprehensive optimization framework.

[0168] First, use the Chebyshev decomposition method to transform the multi-objective problem into a series of single-objective sub-problems. This decomposition method is based on the Chebyshev norm and can effectively handle the non-linear relationship between objective functions. For example, for a three-objective optimization problem, it can be decomposed into 100 single-objective sub-problems. Assign a weight vector to each sub-problem so that these weight vectors are evenly distributed in the objective space. Assume that the weight vector of the first sub-problem is (0.8, 0.1, 0.1), the weight vector of the second sub-problem is (0.7, 0.2, 0.1), and so on. Maintain the optimal solution for each sub-problem, and these optimal solutions form the external elite archive.

[0169] Next, design an adaptive scaling factor and crossover probability, and perform differential evolution operations on each particle. Differential evolution is an effective evolutionary algorithm that generates new trial vectors through vector differences. For example, the initial scaling factor can be set to 0.5, the initial crossover probability can be set to 0.9, and then these parameters are dynamically adjusted according to the evolutionary effect of the algorithm. After generating the trial vector, use the Chebyshev method to evaluate the fitness of the trial vector, and update the current particle position according to the evaluation result.

[0170] To enhance the search ability of the algorithm, a neighborhood is assigned to each particle, and the neighborhood size is dynamically adjusted as the iteration progresses. For example, the neighborhood size can be initially set to 20% of the population size and gradually reduced to 5% as the iteration progresses. When updating the particle position, the global optimal solution, the individual optimal solution, and the neighborhood optimal solution are considered simultaneously to achieve collaborative learning. This strategy can balance global search and local search and improve the convergence speed and solution quality of the algorithm.

[0171] To maintain population diversity, an adaptive mutation probability is designed, and Gaussian mutation is performed on each dimension. The mutation probability can be initially set to 0.1 and gradually reduced as the iteration progresses. The mutation amplitude also decreases as the iteration progresses. For example, it can gradually decrease from the initial standard deviation of 0.1 to 0.01. This strategy can increase exploration in the initial stage of the search and exploitation in the later stage.

[0172] During the operation of the algorithm, it is crucial to maintain an external elite archive. After each iteration, the newly generated non-dominated solutions are added to the external elite archive. When the size of the external elite archive exceeds the preset archive size threshold, pruning is performed based on the crowding distance. For example, the initial archive size threshold can be set to 100. When the number of solutions in the archive exceeds this number, the 100 solutions with the largest crowding distance are retained. At the same time, the size of the external elite archive is dynamically adjusted, and the archive size can be gradually increased according to the convergence of the algorithm, such as from 100 to 200.

[0173] To further improve the solution quality, local search is performed on the solutions in the elite archive. The pattern search method is used to explore in the neighborhood of the current solution. For example, perturbations of plus or minus 0.1% can be made on each dimension of the current solution, and the fitness of the perturbed solution is evaluated. If a better solution is found, the elite archive is updated. This local search strategy can help the algorithm jump out of local optima and find better solutions.

[0174] The algorithm terminates when it reaches the maximum number of iterations or meets the preset convergence condition. For example, the maximum number of iterations can be set to 1000, or the algorithm is considered to converge when the solutions in the elite archive have not improved significantly for 50 consecutive iterations. Finally, the non-dominated solution set in the elite archive is output as the Pareto optimal solution to generate the optimal collaborative control strategy.

[0175] This improved multi-objective optimization method has multiple advantages. First, the Chebyshev decomposition method can effectively handle the complex relationships between objective functions, transforming the multi-objective problem into a single-objective sub-problem that is easy to solve. Second, the differential evolution operation and the adaptive parameter adjustment strategy improve the search efficiency and adaptability of the algorithm. The collaborative learning mechanism balances the global search and local search capabilities of the algorithm by considering global, individual, and neighborhood information. The maintenance and dynamic adjustment of the external elite archive ensure the preservation of high-quality solutions and the maintenance of population diversity. The local search strategy further improves the quality of the solutions. These improvements enable the algorithm to better handle complex multi-objective optimization problems and obtain a more diverse and evenly distributed Pareto optimal solution set. In the multi-objective collaborative control optimization of the distribution network, this method can provide more high-quality optional solutions for decision-makers, better balance reliability, economy, and environmental benefits, and thus achieve the intelligent, efficient, and green operation of the distribution network.

[0176] In an alternative embodiment,

[0177] The steps of using a fuzzy logic controller to achieve real-time output regulation of distributed power sources and using graph theory algorithms for fault location and network reconstruction include:

[0178] Abstract the distribution network as an undirected graph, where nodes represent substations, switches, and load points, and edges represent lines;

[0179] Use the depth-first search algorithm to traverse the undirected graph, set voltage deviation thresholds and current deviation thresholds. When an abnormal node is detected, add the node to the set of potential fault points, and backtrack to the previous normal node, marking the line between the two as a potential fault line; Based on the obtained set of potential fault points and potential fault lines, determine the final fault location;

[0180] Use an improved Kruskal algorithm for network reconstruction, including introducing a load imbalance index, introducing a radial topology constraint, and using a dynamic weight adjustment mechanism; sort all edges according to the dynamic weight and select edges that meet the constraint conditions one by one to add to the minimum spanning tree; during the process of selecting edges, check voltage constraints, current constraints, and load balance constraints at the same time; According to the finally generated minimum spanning tree, determine the reconstructed network topology.

[0181] Exemplarily, in the multi-objective collaborative control optimization of the distribution network, it is crucial to achieve real-time output regulation of distributed power sources and network fault handling. Using a fuzzy logic controller and graph theory algorithms can effectively implement these functions and improve the reliability and flexibility of the distribution network.

[0182] First, a fuzzy logic controller is used to achieve real-time output regulation of distributed power sources. The fuzzy logic controller is based on human expert knowledge and experience and can handle uncertainties and non-linearities in the system. In this application, the inputs of the fuzzy logic controller can include load demand changes, system frequency deviations, voltage deviations, etc., and the output is the output adjustment amount of the distributed power source. For example, a fuzzy controller with 5 input linguistic variables (load change rate, system frequency deviation, voltage deviation, current output level, and available capacity) and 1 output linguistic variable (output adjustment amount) can be designed. Each linguistic variable can be divided into several fuzzy sets, such as "negative large", "negative small", "zero", "positive small", "positive large", etc. By designing an appropriate fuzzy rule base, such as "if the load change rate is positive large and the system frequency deviation is negative small, then the output adjustment amount is medium positive", intelligent output regulation is achieved. The fuzzy inference process uses the Mamdani method, and the defuzzification uses the centroid method. This method can flexibly adjust the output of the distributed power source according to the real-time state of the system, improving the stability and economy of the system.

[0183] Next, the distribution network is abstracted as an undirected graph for fault location and network reconfiguration. In this undirected graph, nodes represent substations, switches, and load points, and edges represent lines. For example, an undirected graph with 100 nodes and 120 edges can represent a medium-sized distribution network. Each node contains attribute information such as voltage and current, and each edge contains attribute information such as impedance and capacity. This abstraction enables the use of graph theory algorithms for network analysis and optimization.

[0184] The depth-first search (DFS) algorithm is used to traverse the undirected graph to achieve fault location. Voltage deviation thresholds (such as ±5% of the rated voltage) and current deviation thresholds (such as ±10% of the rated current) are set. Starting from the source node, when an abnormal node is detected, the node is added to the set of potential fault points, and the search backtracks to the previous normal node, marking the line between the two as a potential fault line. For example, if during the traversal, it is found that the voltage of node 25 is 0.93 per unit, lower than the threshold of 0.95, then node 25 is added to the set of potential fault points, and the line connecting node 25 and its upstream normal node is marked as a potential fault line. Based on the obtained set of potential fault points and potential fault lines, combined with other information (such as fault indicator status, user repair information, etc.), the final fault location is determined. This method can quickly and accurately locate faults, providing a basis for subsequent isolation and restoration operations.

[0185] After fault location, network reconstruction is required to restore power supply. The improved Kruskal algorithm is used for network reconstruction, and this algorithm introduces multiple improvements on the basis of the traditional Kruskal algorithm. First, the load imbalance index is introduced to calculate the load distribution among feeders, which serves as an optimization goal for reconstruction. Second, the radial topology constraint is introduced to ensure that the reconstructed network still maintains a radial structure and avoid forming loops. Third, a dynamic weight adjustment mechanism is adopted to dynamically adjust the weights of each edge according to the current network state. For example, the weight of an edge can be set as the weighted sum of the loss, load rate, and importance of its corresponding line, and the weight coefficients are dynamically adjusted according to the current operating conditions.

[0186] When executing the improved Kruskal algorithm, first, all edges are sorted according to the dynamic weights. Then, the edges that meet the constraint conditions are selected one by one and added to the minimum spanning tree. During the process of selecting edges, the voltage constraint (such as the node voltage must be within the range of 0.95 - 1.05 per unit value), current constraint (such as the line load rate does not exceed 90%), and load balance constraint (such as the load difference between feeders does not exceed 20%) are checked simultaneously. If the addition of a certain edge will violate these constraints, then this edge is skipped and the next edge is considered. The finally generated minimum spanning tree is the reconstructed network topology structure. For example, in a network with 100 nodes, the finally generated minimum spanning tree may contain 99 edges, forming a new radial network structure.

[0187] This method combining fuzzy logic control and graph theory algorithms has many advantages. First, the fuzzy logic controller can flexibly handle the uncertainties and complexities in the system, realize the intelligent regulation of distributed power sources, and improve the stability and economy of the system. Second, the fault location algorithm based on graph theory can quickly and accurately identify the fault location, reducing the power outage scope and duration. Finally, when the improved Kruskal algorithm is used for network reconstruction, it takes into account network losses, load balance, and various constraint conditions, and can generate a more optimized network topology structure. This comprehensive method significantly improves the reliability, flexibility, and economy of the distribution network, providing strong support for the operation and management of the intelligent distribution network. By adjusting the output of distributed power sources in real time, quickly locating and isolating faults, and intelligently reconstructing the network, the power outage time can be greatly reduced, the power supply quality can be improved, the operation cost can be reduced, and a more reliable and high-quality power service can be provided for users.

[0188] Figure 2 It is a schematic structural diagram of the cooperative control system for the reliability of the distribution network under the access of distributed power sources in the embodiment of the present invention. As Figure 2 shown, the system includes:

[0189] The first unit is used to analyze the output characteristics of distributed power sources based on the established distribution network topology structure model, and construct a load demand uncertainty model; design a distribution network reliability index system considering distributed power sources based on the load demand uncertainty model, including power supply reliability rate, average power outage time, and load loss index;

[0190] The second unit is used to evaluate the reliability of the distribution network by using the Monte Carlo simulation method in combination with the distribution network reliability index system, including distribution network state evaluation and reliability analysis. Among them, the distribution network state evaluation uses the probability flow calculation method, considering equipment failure rate, repair time, and load importance. The reliability analysis is based on the conditional value-at-risk evaluation index; according to the evaluation results, formulate a coordinated control strategy including distributed power source output control, load demand response management, and network topology reconstruction; establish a multi-objective coordinated control optimization model with the goals of reliability improvement, economic optimization, and environmental benefit maximization, taking distributed power source output control, load demand response, and network topology reconstruction as decision variables, considering voltage constraints, power constraints, and equipment capacity constraints, and use an improved particle swarm optimization algorithm to solve the multi-objective coordinated control optimization model to generate an optimal coordinated control strategy;

[0191] The third unit is used to issue and execute control instructions to each control object based on the optimal coordinated control strategy; use a fuzzy logic controller to realize the real-time output regulation of distributed power sources, and use graph theory algorithms for fault location and network reconstruction; monitor the system operation status in real time and dynamically adjust control parameters; continuously optimize the control effect through a closed-loop feedback mechanism.

[0192] In the third aspect of the embodiments of the present invention,

[0193] A kind of electronic device is provided, including:

[0194] A processor;

[0195] A memory for storing instructions executable by the processor;

[0196] Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0197] In the fourth aspect of the embodiments of the present invention,

[0198] A computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0199] The present invention can be a method, device, system, and / or computer program product. The computer program product can include a computer-readable storage medium, on which computer-readable program instructions for executing various aspects of the present invention are uploaded.

[0200] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A distribution network reliability collaborative control method under distributed power generation access, characterized in that: include: Based on the established distribution network topology model, the output characteristics of distributed power sources are analyzed and a load demand uncertainty model is constructed; Design a distribution network reliability index system considering distributed power sources based on the load demand uncertainty model, including power supply reliability rate, average power outage time and load loss index; The Monte Carlo simulation method is used in combination with the distribution network reliability index system to evaluate the reliability of the distribution network, including distribution network status evaluation and reliability analysis. The distribution network status evaluation adopts a probabilistic power flow calculation method, taking into account equipment failure rate, repair time and load importance, and the reliability analysis is based on the conditional value risk assessment index. According to the evaluation results, a collaborative control strategy including distributed power output control, load demand response management and network topology reconstruction is formulated. A multi-objective collaborative control optimization model with the goals of improving reliability, optimizing economy and maximizing environmental benefits is established. Distributed power output control, load demand response and network topology reconstruction are used as decision variables. Voltage constraints, power constraints and equipment capacity constraints are considered. An improved particle swarm algorithm is used to solve the multi-objective collaborative control optimization model and generate the optimal collaborative control strategy. Based on the optimal collaborative control strategy, control instructions are issued to each control object and executed; fuzzy logic controllers are used to achieve real-time output regulation of distributed power sources, and graph theory algorithms are used for fault location and network reconstruction; real-time monitoring of system operation status and dynamic adjustment of control parameters are performed; and closed-loop feedback mechanisms are used to continuously optimize control effects; Based on the established distribution network topology model, the steps of analyzing the output characteristics of distributed power sources and constructing the load demand uncertainty model include: Establishing a distribution network topology model, wherein the distribution network topology model is represented by a weighted undirected graph, wherein nodes represent buses in the distribution network, edges represent lines connecting nodes, and each edge is assigned a complex weight to represent line impedance; Constructing a node admittance matrix based on the distribution network topology model, and establishing an initial power flow equation using the node admittance matrix; Identifying distributed power access nodes in the distribution network topology model and analyzing the probability distribution of output characteristics of the distributed power access nodes, including: For photovoltaic power generation access nodes, the β probability distribution model is used to describe the randomness of the solar radiation intensity of the node, and the probability distribution of the photovoltaic cell output power of the node is calculated; For wind power generation access nodes, Weibull distribution is used to describe the randomness of the wind speed at the node, and the probability distribution of the wind turbine output power at the node is calculated; Integrate the probability distribution of the output characteristics of the distributed power access node into the initial power flow equation to obtain a modified power flow equation that takes into account the uncertainty of the distributed power output; Identifying load nodes in the distribution network topology model, and extracting initial load data of each load node based on the modified power flow equation; Perform cluster analysis on the extracted initial load data to determine the typical load pattern of each load node; Based on the typical load pattern of each load node, a load demand uncertainty model of the node is constructed; the constructed load demand uncertainty models of each load node are integrated to form a load demand uncertainty model of the entire distribution network.

2. The method according to claim 1, characterized in that Based on the load data of each load node, the steps of constructing the load demand uncertainty model of the node include: A Gaussian mixture model is constructed based on the load data of each load node, and the Gaussian mixture model is expressed as: ; Where x is the load data of the load node, θ is the model parameter set, π k is the weight of the Gaussian component, the probability density function of the N Gaussian components, μ k is the Gaussian component mean parameter, is the variance, k is the index variable, and K is the total number of Gaussian components; The load data is preprocessed using the K-means clustering algorithm to obtain the initial value of the Gaussian mixture model parameter θ; Execute the expectation maximization algorithm to iteratively optimize the Gaussian mixture model, including the expectation step and the maximization step; in the expectation step, calculate the posterior probability that each load data point belongs to each Gaussian component; in the maximization step, update the π of each Gaussian component based on the calculated posterior probability k , μ k and ; Calculate the log-likelihood function of the updated Gaussian mixture model; if the log-likelihood function value is less than the preset log-likelihood threshold, terminate the iteration and generate the optimal Gaussian mixture model of the node; The Bayesian information criterion is calculated for different K values ​​to select the optimal model complexity; based on the optimal Gaussian mixture model and the optimal model complexity, the load demand uncertainty model of the node is generated.

3. The method according to claim 1, characterized in that The Monte Carlo simulation method is used in combination with the distribution network reliability index system to evaluate the reliability of the distribution network, including distribution network status evaluation and reliability analysis. The distribution network status evaluation adopts a probabilistic power flow calculation method, taking into account equipment failure rate, repair time and load importance. The reliability analysis is based on the conditional value risk assessment index and includes the following steps: The distribution network status assessment adopts probabilistic power flow calculation, and the probabilistic power flow calculation adopts an improved Newton-Raphson method, including: establishing a node power balance equation considering the power injection characteristics of distributed power sources; initializing node voltage and phase angle; calculating power imbalance and Jacobian matrix; solving a linear equation system to obtain voltage and phase angle corrections; updating node voltage and phase angle; if the convergence condition is not met, returning to the step of calculating power imbalance, and if the convergence condition is met, outputting node voltage and power injection results, and the convergence condition is that the maximum absolute value of the power imbalance is less than the power imbalance threshold; Based on the probabilistic power flow calculation results, calculate the node voltage deviation, line power flow and system loss status evaluation indicators; The two-state Markov model is used to describe the equipment operation status and calculate the equipment availability; importance weights are assigned to load nodes; and the probabilistic power supply reliability rate, average power outage time and load loss index are calculated by combining equipment availability and load importance weights; A conditional value risk index is introduced to calculate the conditional value risk of the load loss index, wherein the conditional value risk is obtained by sorting the load loss index samples, determining the risk value, and calculating the average value of the samples exceeding the risk value; Through the Monte Carlo simulation method, the probability flow calculation and the reliability analysis based on the conditional value risk of the calculated load loss index are repeatedly performed to generate samples and perform statistical analysis. Based on the statistical analysis of the Monte Carlo simulation method, the evaluation results of the distribution network reliability are output, including the power supply reliability rate, the average power outage time and the load loss index.

4. The method according to claim 1, characterized in that: A multi-objective collaborative control optimization model with the goals of improving reliability, optimizing economy and maximizing environmental benefits is established. Distributed power output control, load demand response and network topology reconstruction are used as decision variables. Voltage constraints, power constraints and equipment capacity constraints are considered. An improved particle swarm algorithm is used to solve the multi-objective collaborative control optimization model. The steps to generate the optimal collaborative control strategy include: Constructing a reliability improvement objective function, wherein the reliability improvement objective function is represented by a weighted sum of a system average power outage frequency index and a system average power outage duration index, and a weight coefficient is determined by a hierarchical analysis method; Constructing an economic optimization objective function, wherein the economic optimization objective function takes into account the system operation cost and the economic benefits brought about by the reliability improvement, wherein the system operation cost includes the power generation cost, the network loss cost and the load removal cost, and the economic benefits brought about by the reliability improvement are quantified by the reduction of the power outage cost; Constructing an environmental benefit maximization objective function, wherein the environmental benefit maximization objective function is expressed by system carbon emissions and is calculated by carbon emission coefficients and outputs of various power generation units; Set constraints, including voltage constraints, power constraints, and equipment capacity constraints. The power constraints take into account the active and reactive output of the generator, the active and reactive demand of the load, the node voltage amplitude and phase angle, and the network admittance matrix. An improved particle swarm algorithm is used to solve the multi-objective collaborative control optimization model. The improved particle swarm algorithm includes: The non-dominated sorting strategy is introduced, and the population is stratified using the fast non-dominated sorting method; Introduce crowding distance calculation to maintain population diversity; Design an adaptive inertia weight strategy to dynamically adjust the inertia weight according to the number of iterations and particle fitness to balance global search and local search capabilities; Introducing Gaussian mutation operation to enhance the global search capability of the algorithm; A set of Pareto optimal solutions is obtained through the improved particle swarm algorithm to generate an optimal collaborative control strategy, which includes a distributed power output control scheme, a load demand response scheme and a network topology reconstruction scheme.

5. The method according to claim 4, characterized in that The method further comprises: The Chebyshev decomposition method is used to transform the multi-objective problem into a series of single-objective sub-problems, and a weight vector is assigned to each sub-problem so that the weight vector is evenly distributed in the objective space. The optimal solution for each sub-problem is maintained to form an external elite file. Design adaptive scaling factors and crossover probabilities, perform differential evolution operations on each particle, generate test vectors, evaluate the fitness of the test vectors according to the Chebyshev method, and update the current particle position; assign a neighborhood to each particle, and dynamically adjust the neighborhood size as the iteration proceeds. When updating the particle position, consider the global optimal solution, individual optimal solution, and neighborhood optimal solution to achieve collaborative learning; design adaptive mutation probability, perform Gaussian mutation on each dimension, and the mutation amplitude decreases as the iteration proceeds; Maintain an external elite archive, add non-dominated solutions to the external elite archive after each iteration, prune the external elite archive based on the crowding distance when the size exceeds the archive size threshold, and dynamically adjust the size of the external elite archive; Perform local search on the solutions in the elite archive, use pattern search method to explore in the neighborhood of the current solution, and update the elite archive if a better solution is found; The algorithm terminates when the maximum number of iterations is reached or the preset convergence condition is met, and the non-dominated solution set in the elite archive is output as the Pareto optimal solution to generate the optimal collaborative control strategy.

6. The method according to claim 1, characterized in that The steps of using fuzzy logic controller to realize real-time output regulation of distributed power sources and adopting graph theory algorithm for fault location and network reconstruction include: The distribution network is abstracted as an undirected graph, where nodes represent substations, switches and load points, and edges represent lines; The undirected graph is traversed by using a depth-first search algorithm, a voltage deviation threshold and a current deviation threshold are set, and when an abnormal node is detected, the node is added to a potential fault point set, and the previous normal node is traced back to mark the line between the two as a potential fault line; based on the obtained potential fault point set and potential fault line, the final fault location is determined; The improved Kruskal algorithm is used for network reconstruction, including the introduction of load imbalance index, radial topology constraints, and dynamic weight adjustment mechanism; all edges are sorted according to dynamic weights, and edges that meet the constraints are selected one by one to be added to the minimum spanning tree; in the process of selecting edges, voltage constraints, current constraints and load balance constraints are checked at the same time; according to the final generated minimum spanning tree, the reconstructed network topology is determined.

7. A distribution network reliability collaborative control system with distributed power generation access, used to implement the method described in any one of claims 1 to 6, characterized in that: include: The first unit is used to analyze the output characteristics of distributed power sources and build a load demand uncertainty model based on the established distribution network topology model; Design a distribution network reliability index system considering distributed power sources based on the load demand uncertainty model, including power supply reliability rate, average power outage time and load loss index; The second unit is used to evaluate the reliability of the distribution network by using the Monte Carlo simulation method in combination with the distribution network reliability index system, including distribution network status evaluation and reliability analysis, wherein the distribution network status evaluation adopts the probabilistic power flow calculation method, taking into account the equipment failure rate, repair time and load importance, and the reliability analysis is based on the conditional value risk assessment index; according to the evaluation results, a coordinated control strategy including distributed power output control, load demand response management and network topology reconstruction is formulated; A multi-objective collaborative control optimization model with the goals of improving reliability, optimizing economy and maximizing environmental benefits is established. Distributed power output control, load demand response and network topology reconstruction are used as decision variables. Voltage constraints, power constraints and equipment capacity constraints are considered. An improved particle swarm algorithm is used to solve the multi-objective collaborative control optimization model and generate the optimal collaborative control strategy. The third unit is used to issue and execute control instructions to each control object based on the optimal collaborative control strategy; use fuzzy logic controller to realize real-time output regulation of distributed power sources, and adopt graph theory algorithm to locate faults and reconstruct the network; monitor the system operation status in real time and dynamically adjust the control parameters; and continuously optimize the control effect through a closed-loop feedback mechanism.

8. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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