MIQP problem optimization processing method and system based on improved random branch strategy

Abstract

The invention discloses an MIQP problem optimization processing method and system based on an improved random branch strategy, and the method comprises the steps: constructing a graph network model according to a multi-source historical data set, and traversing a path from a power node to each load node in a target power distribution network based on a graph search algorithm, generating a branch strategy candidate set of target power distribution network reconstruction according to the path; reconstructing a target power distribution network in the typical power distribution network operation scene, and calculating expected values of different branch strategies in the branch strategy candidate set in the reconstruction process; inputting the real-time load characteristic data, the real-time line characteristic data and the real-time topological characteristic data into a pre-trained decision tree model to obtain an initial weight parameter of each branch strategy in a target power distribution network reconstruction process; and generating a strategy weight parameter of the branch strategy based on the initial weight parameter and the expected value, and optimizing a solution process of target power distribution network reconstruction according to the strategy weight parameter. According to the method provided by the invention, the efficiency of the reconstruction process is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular, to an optimization processing method and system for MIQP problems based on an improved random branching strategy. Background Art

[0002] The distribution network is a key component of the power system, undertaking the important task of safely and efficiently distributing electric power from the transmission network to each user. With the development of social economy, the electricity consumption demand of users is constantly changing, and the load fluctuation is becoming increasingly frequent, which puts higher requirements on the adaptability and flexibility of the distribution network.

[0003] Traditional distribution network reconfiguration solvers lack effective variable selection and pruning strategies, and it is difficult to find a satisfactory solution within the specified time.

[0004] Therefore, how to optimize the existing solution process to improve the efficiency of distribution network reconfiguration has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] The present application provides an optimization processing method for MIQP problems based on an improved random branching strategy to solve the technical problem of how to optimize the existing optimization processing method for MIQP problems based on an improved random branching strategy, so as to achieve the technical effect of improving the efficiency of distribution network reconfiguration.

[0006] To solve the above technical problem, an embodiment of the present application provides an optimization processing method for MIQP problems based on an improved random branching strategy, which is characterized by including: Constructing a multi-source historical data set based on the obtained historical load characteristic data, historical line characteristic data, and historical topology characteristic data of the target distribution network; Constructing a graph network model according to the multi-source historical data set, traversing the paths from the power supply nodes to each load node in the target distribution network based on the graph search algorithm, and generating a candidate set of branching strategies for the reconfiguration of the target distribution network according to the paths; Generating multiple typical distribution network operation scenarios based on the multi-source historical data set, reconfiguring the target distribution network under the typical distribution network operation scenarios according to the branch and bound method, and calculating the expected values of different branching strategies in the candidate set of branching strategies during the reconfiguration process, where the expected values reflect the number of nodes and time information searched by each branching strategy during the reconfiguration process of the target distribution network; During the reconfiguration process of the target distribution network, inputting the obtained real-time load characteristic data, real-time line characteristic data, and real-time topology characteristic data into a pre-trained decision tree model to obtain the initial weight parameters of each branching strategy during the reconfiguration process of the target distribution network; Generate the policy weight parameters of the branch strategy based on the initial weight parameters and the expected values, and optimize the solution process of the target distribution network reconstruction according to the policy weight parameters.

[0007] As one of the preferred solutions, the method for constructing a graph network model based on the multi-source historical data set, traversing the paths from the power supply nodes to each load node in the target distribution network based on a graph search algorithm, and generating a candidate set of branch strategies for the target distribution network reconstruction according to the paths includes: Use the power supply nodes and load nodes in the target distribution network as nodes in the graph network, and the lines in the target distribution network as nodes in the graph network to construct the graph network model; Analyze all the paths obtained by traversing from the power supply nodes to the load nodes, and generate different branch strategies according to the analysis results; Screen the branch strategies, and obtain the candidate set of branch strategies for the target distribution network reconstruction according to the screening results.

[0008] As one of the preferred solutions, the method for generating multiple typical distribution network operation scenarios based on the multi-source historical data set includes: Determine the number of clusters of the multi-source historical data set according to the elbow method; Perform clustering processing on the multi-source historical data set based on the K-means clustering algorithm and the number of clusters to obtain multiple clusters; where the clustering center of each cluster represents a typical distribution network operation scenario; Generate multiple distribution network operation scenarios based on the clustering centers.

[0009] As one of the preferred solutions, the method for reconstructing the target distribution network under the typical distribution network operation scenario according to the branch and bound method, and calculating the expected values of different branch strategies in the candidate set of branch strategies during the reconstruction process includes: Construct a reconstruction optimization model of the target distribution network; Input the load characteristic data, line characteristic data, and topological characteristic data under the typical distribution network operation scenario into the reconstruction optimization model, and solve it according to the branch and bound method; Obtain the number of nodes searched and time information under each branch strategy during the solution process, and assign corresponding expected values to each branch strategy according to the number of nodes and the time information.

[0010] As one of the preferred solutions, the method for assigning corresponding expected values to each branch strategy according to the number of nodes and the time information further includes: Obtain the occurrence probability of each distribution network operation scenario based on the multi-source historical data set, and generate the weight of each distribution network operation scenario based on the occurrence probability; Weighted-sum the initial expected values of the branch strategies under each of the typical distribution network operation scenarios based on the weights to obtain the expected values of each of the branch strategies.

[0011] As one preferred solution, the reconstruction optimization model of the target distribution network includes: Wherein, represents the set of time periods, represents the set of generating units, represents the generating unit The power generation cost per unit time, represents the generating unit In the time period The power generation power, represents the set of lines, represents the line The loss cost coefficient, represents the line In the time period The power flow, represents the set of opening and closing operations, represents the line The opening operation cost, represents the line The closing operation cost, and respectively represent the closing and opening operation decision variables of the line In the time period

[0012] As one preferred solution, inputting the obtained real-time multi-source prediction data set and the branch strategy candidate set into the pre-trained decision tree model to obtain the initial weight parameters of the target distribution network reconstruction branch strategy includes: Construct an initial decision tree model based on Extra Trees; Train the initial decision tree model according to the multi-source historical data set, the branch strategy candidate set and the expected value data corresponding to each branch strategy to obtain a trained decision tree model; Input the obtained real-time multi-source prediction data set into the decision tree model to obtain the initial weight parameters of each branch strategy in the distribution network reconstruction process.

[0013] As one preferred solution, the initial decision tree model includes: Where Gini is the Gini impurity, used to evaluate the quality of the split point, and is defined as:​ Among them, is the probability of the category , and is the total number of categories.

[0014] As one of the preferred solutions, after generating the policy weight parameters of the branch strategy based on the initial weight parameters and the expected values, and optimizing the solution process of the target distribution network reconstruction according to the policy weight parameters, it further includes: Execute the target reconstruction strategy determined by the solution result, and monitor the operation state data of the distribution network in real time; Update the target reconstruction strategy in real time according to the operation state data to complete the adaptive adjustment of the target distribution network reconstruction.

[0015] Another embodiment of the present application provides an optimization processing system for MIQP problems based on an improved random branch strategy, including: An acquisition module, configured to construct a multi-source historical data set based on the acquired historical load characteristic data, historical line characteristic data, and historical topology characteristic data of the target distribution network; A construction module, configured to construct a graph network model according to the multi-source historical data set, traverse the paths from the power source nodes to each load node in the target distribution network based on the graph search algorithm, and generate a candidate set of branch strategies for the target distribution network reconstruction according to the paths; A solution module, configured to generate multiple typical distribution network operation scenarios based on the multi-source historical data set, reconstruct the target distribution network under the typical distribution network operation scenarios according to the branch and bound method, and calculate the expected values of different branch strategies in the candidate set of branch strategies during the reconstruction process, where the expected values reflect the number of nodes and time information searched by each branch strategy during the target distribution network reconstruction process; A prediction module, configured to input the acquired real-time load characteristic data, real-time line characteristic data, and real-time topology characteristic data into a pre-trained decision tree model during the reconstruction process of the target distribution network to obtain the initial weight parameters of each branch strategy during the reconstruction process of the target distribution network; An optimization module, configured to generate the policy weight parameters of the branch strategy based on the initial weight parameters and the expected values, and optimize the solution process of the target distribution network reconstruction according to the policy weight parameters.

[0016] Compared with the prior art, the beneficial effects of the embodiments of the present application are at least one of the following: (1) This application constructs a graph network model using multi-source historical datasets, and traverses the paths from power supply nodes to load nodes through graph search algorithms, systematically generating a candidate set of branch strategies for target distribution network reconstruction. This method can comprehensively consider various possible path combinations in the distribution network, avoid missing important reconstruction schemes, and provide a wider selection space for finding the optimal or sub-optimal reconstruction strategy.

[0017] (2) During the reconstruction process, this application calculates the expected values of different branch strategies in the candidate set of branch strategies. This expected value reflects the number of search nodes and time information. Through this quantitative evaluation method, the advantages and disadvantages of different branch strategies can be intuitively compared, providing an objective and reliable basis for strategy selection, and helping to improve the efficiency and accuracy of the reconstruction process.

[0018] (3) Through the optimized reconstruction strategy, this application can quickly and effectively perform load transfer and power supply restoration when some lines or equipment fail, reduce the power outage time and scope, ensure the normal power consumption of users, and improve the power supply reliability of the distribution network. Brief Description of the Drawings

[0019] Figure 1 It is a schematic flowchart of an optimization processing method for the MIQP problem based on an improved random branch strategy in one embodiment of this application; Figure 2 It is a schematic diagram of an optimization processing system for the MIQP problem based on an improved random branch strategy in one embodiment of this application. Detailed Embodiments

[0020] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. The purpose of providing these embodiments is to make the disclosure of this application more thorough and comprehensive. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0021] In the description of this application, the terms "first", "second", "third", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0022] In the description of the present application, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for the purpose of illustration, rather than indicating or implying that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0023] In the description of the present application, it should be noted that unless otherwise defined, all technical and scientific terms used in the present application have the same meanings as those commonly understood by those skilled in the technical field to which this application belongs. The terms used in the specification of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0024] An embodiment of the present application provides an optimization processing method for the MIQP problem based on an improved random branching strategy. Specifically, please refer to Figure 1 , Figure 1 which shows a schematic flowchart of the optimization processing method for the MIQP problem based on an improved random branching strategy in one of the embodiments of the present application, and it includes S1 - S6: S1: Construct a multi - source historical data set based on the obtained historical load characteristic data, historical line characteristic data, and historical topology characteristic data of the target distribution network; Among them, the mixed - integer quadratic programming (MIQP) problem is a mathematical optimization problem that combines a quadratic objective function and linear constraints. In the distribution network reconfiguration problem based on MIQP, it is necessary to collect various data related to the distribution network, and these data are the basis for subsequent analysis and processing. The specific data collected includes data such as substations, transformers, loads, switches, disconnectors, and topological relationships, and data cleaning and pre - processing are carried out.

[0025] Specifically, for the missing load data and branch parameters, the interpolation method is used to fill them, while for discrete data such as switch states, the mode is used for filling.

[0026] After dealing with the missing data, statistical principles are utilized, such as calculating statistical quantities like the mean and standard deviation of the data, and outliers are identified by setting thresholds. For example, for load data, if the deviation of a data point from the mean exceeds 3 times the standard deviation, then this data point may be an outlier. The outliers are corrected according to the actual physical meaning. For example, if the sudden increase in load data is due to recording errors, the load values in adjacent time periods can be referred to for reasonable correction.

[0027] Since different types of data have different magnitudes and units, it will have an adverse impact on the training of machine learning models. Therefore, min-max normalization processing is performed on the load data and branch parameters. Through normalization, the impact of magnitude differences on the model can be eliminated, enabling the model to treat different features more fairly.

[0028] Based on the characteristics of the distribution network reconfiguration problem, features that have a significant impact on the optimization solution are selected. These features can better reflect the operating state and reconfiguration requirements of the distribution network, specifically including: Load-related features: The load demands of each node directly reflect the power consumption situation of the distribution network, and the peak load reflects the maximum power consumption demand of the distribution network within a certain time period, which is very important for evaluating the power supply capacity and load distribution of the distribution network; Line-related features: Line impedance affects the power loss during power transmission, and active power reflects the actual power transmitted by the line. These features play a key role in analyzing the operating state of the line and optimizing the power distribution of the line; Topological features: The node degree represents the number of lines connected to the node, and the connection relationship describes the topological structure between nodes. They are crucial for understanding the network layout and connectivity of the distribution network and are important bases for distribution network reconfiguration.

[0029] In an embodiment of the present application, constructing new features can improve the learning ability and generalization performance of the machine learning model. Specifically, it can include: Comprehensive load index: Considering multiple load-related factors comprehensively, such as load changes in different time periods, load distribution, etc., a comprehensive index is constructed to more comprehensively describe the load characteristics; Branch utilization rate: By calculating the ratio of the actual power of the branch to the rated power, the branch utilization rate is obtained. This index can reflect the usage situation of the branch and helps to optimize the load distribution of the branch and the network topological structure.

[0030] S2: Construct a graph network model based on the multi-source historical data set, traverse the paths from the power source nodes to each load node in the target distribution network based on the graph search algorithm, and generate a candidate set of branch strategies for the target distribution network reconfiguration according to the paths; Preferably, in an embodiment of the present application, a graph network model is constructed based on a multi-source historical dataset, and a graph search algorithm is used to traverse the paths from power nodes to each load node in the target distribution network, and a candidate set of branch strategies for reconstructing the target distribution network is generated, including: Taking the power nodes and load nodes in the target distribution network as nodes in the graph network, and the lines in the target distribution network as nodes in the graph network to construct a graph network model; Analyze all the paths obtained from traversing from power nodes to load nodes, and generate different branch strategies according to the analysis results; Screen the branch strategies, and obtain a candidate set of branch strategies for reconstructing the target distribution network according to the screening results.

[0031] Identify the power nodes and load nodes in the target distribution network from the multi-source historical dataset and define them as nodes in the graph network. Power nodes are the sources of power supply, such as substation buses; load nodes are places where electrical energy is consumed, such as factories and residential areas. These nodes can carry various attribute information, such as the generation capacity and generation cost of power nodes, and the historical load curve and load prediction value of load nodes. These attributes help to more accurately analyze the operating status of the distribution network in the follow-up.

[0032] Regard the lines in the target distribution network as edges in the graph network. The attributes of the edges can be extracted from the multi-source historical dataset, such as the resistance, reactance, susceptance, current-carrying capacity, and line length of the lines. These attributes are very crucial for calculating the power loss during the transmission of electrical energy on the lines and judging the load-bearing capacity of the lines. By determining the relationship between nodes and edges, the topological structure of the graph network can be constructed.

[0033] Conduct a comprehensive analysis of all the paths obtained from traversing from power nodes to load nodes, mainly including: Path length: The longer the path, the greater the possible power loss on the line, which will affect the operating efficiency of the distribution network. The path length can be measured by calculating the sum of the weights of each edge on the path; Line load condition: Check the historical load data and current load prediction of each line on the path, and avoid selecting lines whose load is already close to or exceeds their capacity to prevent line overload and cause faults; Switch state: The realization of the path often requires adjusting the opening and closing states of switches in the distribution network. Factors such as the feasibility, operation cost, and operation time of switch operations need to be considered; Power supply reliability: Analyze the impact of the path on power supply reliability. For example, some paths may have a single-point failure risk. Once a certain line or equipment fails, it may cause power outages for some loads.

[0034] According to the results of path analysis, generate different branch strategies, specifically including: Switch operation strategy: By changing the opening and closing states of certain switches, the topological structure of the distribution network is changed, so as to realize different power supply paths. For example, closing a certain tie switch and opening some sectionalizing switches to transfer the load from one line to another line; Load transfer strategy: According to the line load conditions on the path, part of the load is transferred from the line with higher load to the line with lower load to achieve balanced distribution of the load, reduce line losses, and improve the operation efficiency of the distribution network; Power supply distribution strategy: Adjust the output power of the power supply nodes so that different power supply nodes supply power to different load nodes, optimize the utilization efficiency of the power supply, and improve the stability and reliability of power supply.

[0035] According to the set screening conditions, the generated branch strategies are evaluated and screened one by one, and those strategies that do not meet the conditions are removed. After screening, the remaining branch strategies constitute the candidate set of branch strategies for the target distribution network reconstruction. This candidate set provides multiple feasible options for the subsequent distribution network reconstruction decision-making. Through further evaluation and optimization, the optimal or sub-optimal reconstruction strategy can be selected from it.

[0036] S3: Generate multiple typical distribution network operation scenarios based on the multi-source historical data set, reconstruct the target distribution network under the typical distribution network operation scenarios according to the branch and bound method, and calculate the expected values of different branch strategies in the candidate set of branch strategies during the reconstruction process. The expected value reflects the number of nodes and time information searched by each branch strategy during the target distribution network reconstruction process; Preferably, in an embodiment of the present application, generating multiple typical distribution network operation scenarios based on the multi-source historical data set includes: Determine the number of clusters of the multi-source historical data set according to the elbow method; Perform clustering processing on the multi-source historical data set based on the K-means clustering algorithm and the number of clusters to obtain multiple clustering clusters; where the clustering center of each clustering cluster represents a typical distribution network operation scenario; Generate multiple distribution network operation scenarios based on the clustering centers.

[0037] Based on the multi-source historical data set, covering historical load characteristic data, historical line characteristic data, and historical topological characteristic data, use appropriate methods to generate multiple typical distribution network operation scenarios. This can be achieved by means of data mining techniques such as clustering analysis, classifying historical data according to factors such as load patterns and topological structures, and forming representative operation scenarios. For example, according to the load characteristics in different seasons and different time periods, typical scenarios such as summer peak and winter low valley are divided.

[0038] Preferably, in an embodiment of the present application, the target distribution network in the typical distribution network operation scenario is reconstructed according to the branch and bound method, and the expected values of different branch strategies in the branch strategy candidate set during the reconstruction process are calculated, including: Construct a reconstruction optimization model for the target distribution network; Input the load characteristic data, line characteristic data, and topological characteristic data in the typical distribution network operation scenario into the reconstruction optimization model, and solve it according to the branch and bound method; Obtain the number of nodes searched and time information under each branch strategy during the solution process, and assign corresponding expected values to each branch strategy according to the number of nodes and time information.

[0039] Input the load characteristic data, line characteristic data, and topological characteristic data in the typical distribution network operation scenario into the reconstruction optimization model, and solve it using the branch and bound method. The specific steps are as follows: Set the initial feasible solution and the optimal solution. The initial feasible solution can be the operating state of the current distribution network, and the optimal solution is initialized to a relatively large value.

[0040] Divide the solution space of the problem into multiple sub-problems, and each sub-problem corresponds to a possible switch state combination or power generation power distribution scheme. For example, select a switch with an undetermined state, and set it to the closed and open states respectively to form two sub-problems.

[0041] For each sub-problem, calculate the upper and lower bounds of its objective function. The upper bound can be obtained by relaxing the constraint conditions, etc., and the lower bound can be obtained by some heuristic algorithms or lower bound estimation methods. If the lower bound of a certain sub-problem is greater than the current optimal solution, then this sub-problem can be pruned and no further search is required.

[0042] Continuously select a promising sub-problem for branching and bounding until all possible sub-problems are searched or the termination condition is met (such as reaching the maximum search depth, finding the optimal solution, etc.).

[0043] During the solution process, record the number of nodes searched and time information under each branch strategy. The number of nodes reflects the complexity of the search process, and the time information reflects the search efficiency. Assign initial expected values to each branch strategy according to this information.

[0044] Specifically, statistically analyze the exploration costs of all recorded sample paths, calculate statistical quantities such as the average value, median, and standard deviation to understand the distribution of the exploration costs. Determine the prior probabilities of different topological adjustment strategies based on historical data or experience. Based on the prior probabilities and the sample paths of the exploration costs , calculate the total expected number of nodes under the given prior probabilities , this value reflects the number of nodes that need to be explored during the search process of the branch-and-bound algorithm under different topological adjustment strategies, and further evaluates the overall performance of this strategy combination in the distribution network reconstruction.

[0045] Define the objective of the distribution network reconstruction as the objective function, and the strategy weight parameters of different branch strategies as the input variables of the objective function. The goal is to find a set of optimal strategy weight parameters to minimize the objective function. Use the Gaussian process regression model to model the posterior distribution of the objective function. Among them, the Gaussian process is a probability model that can model unknown functions and give the probability distribution of function values. Learn the distribution characteristics of the objective function through the set of solutions in the historical dataset (that is, the previously recorded different combinations of strategy weight parameters and their corresponding objective function values).

[0046] In each iteration, the Bayesian optimizer uses the Gaussian process to predict the combinations of reconstruction schemes that have not been evaluated (that is, different combinations of strategy weight parameters), and obtains the probability distribution of the objective function values corresponding to each combination. Then, according to a certain acquisition function (such as expected improvement, upper confidence bound, etc.), select the combination with the highest potential improvement effect for evaluation. Conduct an actual evaluation of the selected combination to obtain its corresponding objective function value. Add this data to the historical dataset and update the Gaussian process regression model to more accurately reflect the distribution of the objective function.

[0047] Repeat the above processes of prediction, selection, and update for multiple rounds of iterative optimization. As the number of iterations increases, the Bayesian optimizer gradually narrows the search range and approaches the optimal solution. When certain termination conditions are met (such as reaching the maximum number of iterations, convergence of the objective function value, etc.), the iteration stops. At this time, the combination of strategy weight parameters obtained is the optimal combination of strategy weight parameters applicable to the current distribution network operating state, corresponding to the initial expected value of each branch strategy.

[0048] Preferably, in an embodiment of the present application, allocating corresponding expected values to each branch strategy according to the number of nodes and time information further includes: Obtain the occurrence probability of each distribution network operating scenario based on the multi-source historical dataset, and generate the weight of each distribution network operating scenario based on the occurrence probability; Perform a weighted sum of the initial expected values of the branch strategies under each typical distribution network operating scenario based on the weights to obtain the expected value of each branch strategy.

[0049] Based on multi-source historical data sets, the occurrence probability of each distribution network operation scenario is obtained through methods such as statistical analysis and machine learning. For example, according to historical data and meteorological forecasts and other information, the probability of the summer peak scenario occurring is predicted. The weight of each distribution network operation scenario is generated according to the scenario occurrence probability, and the weight is usually proportional to the occurrence probability. The initial expected values of the branch strategies under each typical distribution network operation scenario are weighted and summed based on these weights to obtain the final expected value of each branch strategy. The expected value obtained in this way comprehensively considers the influence of different scenarios and can better reflect the performance of the branch strategy in actual operation.

[0050] Preferably, in an embodiment of the present application, the reconstruction optimization model of the target distribution network includes: Wherein, represents the set of time periods, represents the set of generating units, represents the generating unit The power generation cost per unit time, represents the generating unit In the time period The generated power, represents the set of lines, represents the line The loss cost coefficient, represents the line In the time period The power flow, represents the set of opening and closing operations, represents the line The cost of the opening operation, represents the line The cost of the closing operation, and respectively represent the line In the time period The closing and opening operation decision variables.

[0051] The goal of distribution network reconstruction is to minimize the total operating cost of the system. However, in the optimization process, a series of constraints must be considered to ensure the safe, stable and feasible operation of the system. The following are the common constraints of distribution network reconstruction: Power balance constraint: The power demand of each node must be met, and the power balance relationship needs to be satisfied among all the generated power, load demand and line power flow in the power grid. The specific expression is: Wherein, represents the th node in the time period The power generation is the th node's load demand during the time period . is the set of lines connected to node . is the power flow from node to node during the time period .

[0052] Generator output constraint: The power output of a generator cannot exceed its capacity upper limit or be lower than its minimum output limit, which can generally be expressed as: where and are the minimum and maximum outputs of generator respectively, is the output of generator during the time period .

[0053] Line power flow constraint: To prevent overload, the power flow of each line must meet its maximum transmission capacity. It can be expressed as: where is the power flow of line during the time period , is the maximum power transmission capacity of line .

[0054] Voltage magnitude constraint: The voltage magnitude of each node needs to be maintained within a certain range to ensure the stable operation of the power grid. The voltage magnitude constraint can be expressed as: where represents the voltage magnitude of node during the time period , and are the minimum and maximum allowable values of the voltage respectively.

[0055] Switch operation constraint: In the distribution network reconfiguration, the switch operations of nodes or lines must follow certain constraints. For line switch operations, the following constraints need to be met: If a certain line is opened during the time period , the power flow of this line must be zero: If a certain line is during the time period If it is closed, the power flow of the line is non-zero, and the power flow constraint of the line needs to be satisfied: where, is the switch state of line in the time period . indicates that line is disconnected, 1 indicates that line is connected.

[0056] Load scheduling constraint: The load of the power grid must be scheduled according to the actual demand. The load scheduling constraint usually takes into account the volatility of the load and the prediction error, and the specific form is: Minimum opening and closing time constraint: In order to avoid frequent switching operations, the reconfiguration of the distribution network also needs to limit the operation frequency of the switching equipment. Generally speaking, the minimum opening and closing time intervals of the switching equipment are a specific value to prevent overly frequent operations. It can be expressed as: .

[0057] S4: During the reconfiguration of the target distribution network, the obtained real-time load characteristic data, real-time line characteristic data, and real-time topology characteristic data are input into the pre-trained decision tree model to obtain the initial weight parameters of each branch strategy during the reconfiguration of the target distribution network; Preferably, in an embodiment of the present application, the obtained real-time multi-source prediction data set and the branch strategy candidate set are input into the pre-trained decision tree model to obtain the initial weight parameters of the target distribution network reconfiguration branch strategy, including: Construct an initial decision tree model based on Extra Trees; Train the initial decision tree model according to the multi-source historical data set, the branch strategy candidate set, and the expected value data corresponding to each branch strategy to obtain a trained decision tree model; Input the obtained real-time multi-source prediction data set into the decision tree model to obtain the initial weight parameters of each branch strategy during the reconfiguration of the distribution network.

[0058] Among them, Extra Trees (Extremely Randomized Trees) is an ensemble learning method and an extension of decision trees. Different from traditional decision trees, Extra Trees introduces more randomness in the process of constructing decision trees. When dividing nodes, instead of finding the optimal division among all possible division points of all features, it randomly selects features and division points and then selects the optimal one from them. This additional randomness makes the model have stronger generalization ability and can effectively avoid overfitting problems.

[0059] Organize the multi-source historical data set, the candidate set of branch strategies, and the expected value data to make them meet the model input requirements. Preferably, in an embodiment of the present application, the initial decision tree model includes: Among them, Gini is the Gini impurity, which is used to evaluate the quality of the split point and is defined as: Among them, is the probability of class , and is the total number of classes.

[0060] Optimize the hyperparameters of the Extra Trees classifier, such as the number of trees , the maximum depth , the number of features randomly selected for each node , etc., to obtain the best performance on the validation set . Randomly sample parameter combinations within the predefined parameter space and select the parameter combination that minimizes the loss function .

[0061] Comprehensively use other appropriate evaluation metrics such as accuracy, recall, and precision to evaluate the prediction performance of the Extra Trees classifier on the validation set , and ensure that it can accurately predict the policy weight parameters. The calculation formulas for each evaluation metric are as follows: Accuracy: Among them: TP is True Positive, that is, the number of samples correctly predicted as the positive class.

[0062] TN is True Negative, that is, the number of samples correctly predicted as the negative class.

[0063] FP is False Positive, that is, the number of samples incorrectly predicted as the positive class.

[0064] FN stands for False Negative, which is the number of positive class samples that are incorrectly predicted as negative class samples.

[0065] Precision: This represents the proportion of actual positive class samples among the samples predicted as positive class.

[0066] Recall: This represents the proportion of samples correctly predicted as positive class among the actual positive class samples.

[0067] F1-score: F1-score is the harmonic mean of precision and recall, which is used to comprehensively measure the performance of the classification model on positive class samples. F1-score is more stable under the trade-off between precision and recall, especially in the case of class imbalance.

[0068] During the reconstruction process of the target distribution network, load characteristic data, line characteristic data, and topological characteristic data of the distribution network are collected in real time to form a real-time multi-source prediction data set. These data reflect the current actual operating state of the distribution network. The obtained real-time multi-source prediction data set is input into the trained decision tree model, and the model will predict the performance of each branch strategy according to the learned rules to obtain the expected value corresponding to each branch strategy. According to the predicted expected value, the initial weight parameters of each branch strategy during the reconstruction process of the target distribution network are determined. For example, the expected value can be normalized, and the normalized expected value is used as the initial weight parameter so that the sum of the weight parameters is 1 to reflect the relative importance of each branch strategy under the current operating state of the distribution network.

[0069] S5: Generate the strategy weight parameters of the branch strategy based on the initial weight parameters and the expected value, and optimize the solution process of the reconstruction of the target distribution network according to the strategy weight parameters.

[0070] Preferably, in an embodiment of the present application, generating the strategy weight parameters of the branch strategy based on the initial weight parameters and the expected value, and optimizing the solution process of the reconstruction of the target distribution network according to the strategy weight parameters further includes: Execute the target reconstruction strategy determined by the solution result, and monitor the operating state data of the distribution network in real time; Update the target reconstruction strategy in real time according to the operating state data to complete the adaptive adjustment of the reconstruction of the target distribution network.

[0071] To ensure the efficiency and adaptability of the policy weight parameters in practical applications, the system introduces a dynamic optimization mechanism, continuously updates the policy weight parameters in combination with the prediction results of the Extra Trees classifier, and solves them. The specific steps are as follows: When processing each new MIQP problem instance, first predict the preliminary policy weight parameters through the Extra Trees classifier , and then combine the stored optimal policy weights to obtain the final policy weight parameters through weighted fusion .

[0072] During the actual solution process, record the performance of the policy weight parameters such as exploration cost , solution time etc., and use this feedback information to continuously optimize the Extra Trees classifier and the Bayesian optimization model

[0073] Regularly update the Extra Trees classifier and the Bayesian optimization model using newly collected feedback data to ensure that the models can adapt to the dynamic changes of MIQP problem instances and newly emerging complex constraint conditions, and update the training data set: Retrain the models, using the updated Retrain the Extra Trees classifier and readjust the policy weight parameters through Bayesian optimization

[0074] Adaptive weight adjustment: According to the system performance metrics (such as exploration cost , solution time etc.), dynamically adjust the weight fusion factor and the prediction weights of the Extra Trees classifier to achieve the best balance of policy weight parameters. The adjustment formula is as follows: where is a function adaptively adjusted according to the system performance metrics. For example, the following linear adjustment strategy can be adopted: where is the baseline exploration cost, is the current exploration cost, is a small constant to prevent division by zero

[0075] Configure the updated policy weight parameters into the heuristic algorithm and pruning strategy of the solver to guide the solution process. Run the solver and execute the branch and bound algorithm. During the search process, adjust the heuristic search and pruning strategies according to the policy weight parameters to improve the solution efficiency and quality.

[0076] Through the above steps, the system can effectively integrate the Extra Trees classifier and the Bayesian optimization model into the storage and update process of the policy weight parameters, not only improving the prediction accuracy of the policy weights, but also ensuring that the branch and bound algorithm always maintains high efficiency and adaptability in the solution of the distribution network reconfiguration problem through a dynamic optimization mechanism. This method significantly improves the solution efficiency and quality of the distribution network reconfiguration problem, ensuring that the optimal or near-optimal solution can be found under various complex constraint conditions.

[0077] Preferably, another embodiment of the present application provides an MIQP problem optimization processing system based on an improved random branching strategy. Specifically, please refer to Figure 2 , Figure 2 which is shown as a schematic diagram of an MIQP problem optimization processing system based on an improved random branching strategy in one of the embodiments of the present application. It includes: An acquisition module 11, configured to construct a multi-source historical data set based on the acquired historical load characteristic data, historical line characteristic data, and historical topology characteristic data of the target distribution network; A construction module 12, configured to construct a graph network model according to the multi-source historical data set, traverse the paths from the power supply nodes to each load node in the target distribution network based on the graph search algorithm, and generate a candidate set of branching strategies for the target distribution network reconfiguration according to the paths; A solution module 13, configured to generate multiple typical distribution network operation scenarios based on the multi-source historical data set, reconstruct the target distribution network under the typical distribution network operation scenarios according to the branch and bound method, and calculate the expected values of different branching strategies in the candidate set of branching strategies during the reconstruction process. The expected value reflects the number of nodes and time information searched by each branching strategy during the target distribution network reconstruction process; A prediction module 14, configured to input the acquired real-time load characteristic data, real-time line characteristic data, and real-time topology characteristic data into a pre-trained decision tree model during the reconstruction process of the target distribution network to obtain the initial weight parameters of each branching strategy during the target distribution network reconstruction process; An optimization module 15, configured to perform fusion processing on the initial weight parameters and the expected values, and execute the target reconstruction strategy determined by the fusion processing result.

[0078] Compared with the prior art, the beneficial effects of the embodiments of the present application are at least one of the following: (1) This application constructs a graph network model using a multi-source historical data set, and traverses the paths from power supply nodes to load nodes through a graph search algorithm, systematically generating a candidate set of branch strategies for target distribution network reconstruction. This method can comprehensively consider various possible path combinations in the distribution network, avoid missing important reconstruction schemes, and provide a wider selection space for finding the optimal or sub-optimal reconstruction strategy.

[0079] (2) This application calculates the expected values of different branch strategies in the candidate set of branch strategies during the reconstruction process, and this expected value reflects the number of search nodes and time information. Through this quantitative evaluation method, the advantages and disadvantages of different branch strategies can be intuitively compared, providing an objective and reliable basis for the selection of strategies, and helping to improve the efficiency and accuracy of the reconstruction process.

[0080] (3) Through the optimized reconstruction strategy, this application can quickly and effectively transfer loads and restore power supply when some lines or equipment fail, reduce the power outage time and scope, ensure the normal power consumption of users, and improve the power supply reliability of the distribution network.

[0081] The above embodiments only represent several implementation manners of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the patent scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several deformations and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of the patent of this application shall be subject to the appended claims.

Claims

1. A MIQP problem optimization processing method based on an improved random branching strategy, characterized in that: include: Construct a multi-source historical data set based on the acquired historical load characteristic data, historical line characteristic data and historical topology characteristic data of the target distribution network; Constructing a graph network model according to the multi-source historical data set, traversing the paths from the power source node to each load node in the target distribution network based on a graph search algorithm, and generating a branch strategy candidate set for reconstructing the target distribution network according to the paths; Generate multiple typical distribution network operation scenarios based on the multi-source historical data set, reconstruct the target distribution network under the typical distribution network operation scenario according to the branch definition method, and calculate the expected values ​​of different branch strategies in the branch strategy candidate set during the reconstruction process, wherein the expected values ​​reflect the number of nodes and time information searched by each branch strategy during the reconstruction process of the target distribution network; During the reconstruction of the target distribution network, the acquired real-time load characteristic data, real-time line characteristic data and real-time topology characteristic data are input into a pre-trained decision tree model to obtain initial weight parameters of each branch strategy during the reconstruction of the target distribution network; A strategy weight parameter of the branch strategy is generated based on the initial weight parameter and the expected value, and the solution process of the target distribution network reconstruction is optimized according to the strategy weight parameter.

2. The MIQP problem optimization processing method based on the improved random branching strategy according to claim 1 is characterized in that: The step of constructing a graph network model according to the multi-source historical data set, traversing the paths from the power source node to each load node in the target distribution network based on a graph search algorithm, and generating a branch strategy candidate set for reconstructing the target distribution network according to the paths includes: The power supply nodes and load nodes in the target distribution network are taken as nodes in the graph network, and the lines in the target distribution network are taken as nodes in the graph network, so as to construct the graph network model; Analyze all the traversed paths from the power node to the load node, and generate different branch strategies based on the analysis results; The branch strategies are screened, and a candidate set of branch strategies for reconfiguring the target distribution network is obtained according to the screening results.

3. The MIQP problem optimization processing method based on the improved random branching strategy according to claim 1 is characterized in that: The generating of a plurality of typical distribution network operation scenarios based on the multi-source historical data set includes: Determining the number of clusters of the multi-source historical data set according to the elbow rule; Clustering the multi-source historical data set based on the K-means clustering algorithm and the cluster number to obtain a plurality of clusters; wherein the cluster center of each cluster represents a typical distribution network operation scenario; A plurality of distribution network operation scenarios are generated based on the cluster centers.

4. The MIQP problem optimization processing method based on the improved random branching strategy according to claim 1 is characterized in that: The step of reconstructing the target distribution network under the typical distribution network operation scenario according to the branch definition method and calculating expected values ​​of different branch strategies in the branch strategy candidate set during the reconstruction process includes: Constructing a reconstruction optimization model of the target distribution network; Inputting the load characteristic data, line characteristic data and topology characteristic data under the typical distribution network operation scenario into the reconstruction optimization model, and solving it according to the branch and bound method; The number of nodes searched and the time information under each branch strategy during the solution process are obtained, and a corresponding expected value is assigned to each branch strategy according to the number of nodes and the time information.

5. The MIQP problem optimization processing method based on the improved random branching strategy according to claim 4 is characterized in that: The assigning of a corresponding expected value to each branch strategy according to the number of nodes and the time information also includes: Acquire the occurrence probability of each of the distribution network operation scenarios based on the multi-source historical data set, and generate the weight of each of the distribution network operation scenarios based on the occurrence probability; Based on the weights, a weighted sum is performed on the initial expected values ​​of the branching strategies in each of the typical distribution network operation scenarios to obtain the expected value of each of the branching strategies.

6. The MIQP problem optimization processing method based on the improved random branching strategy according to claim 4 is characterized in that: The target distribution network reconstruction optimization model includes: in, Represents a set of time periods. represents a collection of generator sets, Indicates the generator set The cost of electricity generation per unit time, Indicates the generator set In time period The power generation capacity, Represents a collection of lines. Indicates line The loss cost coefficient, Indicates line In time period The power flow, Represents a set of opening and closing operations, Indicates line The cost of breaking operation, Indicates line The closing operation cost, and Respectively indicate lines In time period The decision variables for closing and opening operations.

7. The MIQP problem optimization processing method based on the improved random branching strategy according to claim 1 is characterized in that: The step of inputting the acquired real-time multi-source prediction data set and the branch strategy candidate set into a pre-trained decision tree model to obtain the initial weight parameters of the target distribution network reconstruction branch strategy includes: Build an initial decision tree model based on Extra Trees; Training the initial decision tree model according to the multi-source historical data set, the branch strategy candidate set, and the expected value data corresponding to each branch strategy to obtain a trained decision tree model; The acquired real-time multi-source prediction data set is input into the decision tree model to obtain the initial weight parameters of each branch strategy in the distribution network reconstruction process.

8. The MIQP problem optimization processing method based on the improved random branching strategy according to claim 7 is characterized in that: The initial decision tree model includes: Among them, Gini is the Gini impurity, which is used to evaluate the quality of the split point and is defined as: in, For Category The probability of is the total number of categories.

9. The MIQP problem optimization processing method based on the improved random branching strategy according to claim 1, characterized in that: After generating a strategy weight parameter of the branch strategy based on the initial weight parameter and the expected value, and optimizing the solution process of the target distribution network reconstruction according to the strategy weight parameter, the method further includes: Executing a target reconstruction strategy determined by a solution result, and monitoring the operating status data of the distribution network in real time; The target reconstruction strategy is updated in real time according to the operating status data to complete the adaptive adjustment of the target distribution network reconstruction.

10. A MIQP problem optimization processing system based on an improved random branching strategy, characterized in that: include: An acquisition module, used to construct a multi-source historical data set based on the acquired historical load characteristic data, historical line characteristic data and historical topology characteristic data of the target distribution network; A construction module, used to construct a graph network model according to the multi-source historical data set, traverse the path from the power node to each load node in the target distribution network based on a graph search algorithm, and generate a branch strategy candidate set for reconstruction of the target distribution network according to the path; A solution module, used to generate multiple typical distribution network operation scenarios based on the multi-source historical data set, reconstruct the target distribution network under the typical distribution network operation scenario according to the branch boundary method, and calculate the expected values ​​of different branch strategies in the branch strategy candidate set during the reconstruction process, wherein the expected values ​​reflect the number of nodes and time information searched by each branch strategy during the reconstruction process of the target distribution network; A prediction module, used for inputting the acquired real-time load characteristic data, real-time line characteristic data and real-time topology characteristic data into a pre-trained decision tree model during the reconstruction process of the target distribution network, so as to obtain the initial weight parameters of each branch strategy during the reconstruction process of the target distribution network; An optimization module is used to generate a strategy weight parameter of the branch strategy based on the initial weight parameter and the expected value, and optimize the solution process of the target distribution network reconstruction according to the strategy weight parameter.

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