Bearing capacity evaluation method, system and device of power distribution network and computer equipment

Abstract

The invention relates to a bearing capacity evaluation method, system and device of a power distribution network and computer equipment, and the method comprises the steps: obtaining power distribution node parameters, line parameters and flexible interconnection device parameters of the power distribution network in a preset time period, and then enabling the power distribution node parameters, the line parameters and the flexible interconnection device parameters to be calculated according to the parameters; the method comprises the following steps: inputting the distributed energy into a preset prediction model for prediction, obtaining the distributed energy installed capacity which can be borne by the power distribution network under the condition that the distributed energy is injected into the power distribution network and the distributed energy is completely absorbed, and finally evaluating the bearing capacity of the power distribution network according to the distributed energy installed capacity. According to the method, the bearing capacity is evaluated by combining the multi-dimensional information of the power distribution node parameters, the line parameters and the flexible interconnection device parameters in the power distribution network, and the evaluation deviation caused by information loss in a traditional method can be avoided by comprehensively considering the multi-dimensional information, so that the evaluation accuracy is remarkably improved.

Description

Technical Field

[0001] The present application relates to the technical field of power system optimization, and in particular to a method, system, device and computer equipment for load-bearing capacity assessment based on a distribution network. Background Art

[0002] With the increasing penetration of renewable energy and rising user demands for power quality, traditional AC distribution networks face challenges such as wind and solar curtailment and reliability deficiencies. Flexible interconnection devices interconnect distribution networks of varying voltage levels and substations, enabling load transfer and power flow regulation during faults, thereby improving distribution network reliability and its ability to accommodate distributed energy resources. Distributed energy carrying capacity, which reflects the distribution network's maximum capacity and potential risks in accommodating distributed energy resources, is a crucial component in the promotion and application of flexible distribution networks. Therefore, effective assessment of the distribution network's carrying capacity is crucial when integrating distributed energy resources into the network.

[0003] Currently, the distribution network carrying capacity assessment methods recorded in related technologies have the problem of inaccuracy. Summary of the Invention

[0004] Based on this, it is necessary to provide a distribution network carrying capacity assessment method, system, device and computer equipment that can improve the accuracy of distribution network carrying capacity assessment in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for evaluating the carrying capacity of a distribution network, the method comprising:

[0006] Obtaining distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network within a preset time period;

[0007] The distribution node parameters, line parameters, and flexible interconnection device parameters are input into the preset prediction model to predict the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected and the distributed energy is fully absorbed;

[0008] The carrying capacity of the distribution network is evaluated based on the installed capacity of distributed energy.

[0009] In some embodiments, the distribution node parameters, line parameters, and flexible interconnection device parameters are input into a preset prediction model to perform a prediction to obtain the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected into the distribution network and the distributed energy is fully absorbed, including:

[0010] Extract the objective function and constraints from the preset prediction model;

[0011] Based on the constraints, the distribution node parameters, line parameters and flexible interconnection device parameters are input into the objective function for calculation to obtain the installed capacity of distributed energy.

[0012] In some embodiments, the constraints include active and reactive power flow constraints of the distribution network, safe operation constraints of the distribution network, interactive power constraints of the distribution network, and operation constraints of the flexible interconnection device.

[0013] In some embodiments, the method further comprises:

[0014] Obtain voltage conversion variables and current conversion variables;

[0015] The safe operation constraint conditions are converted according to the voltage conversion variables and the current conversion variables, and the active and reactive power flow constraint conditions of the distribution network are processed based on the converted safe operation constraint conditions to obtain the constraint conditions in the standard second-order cone form;

[0016] Based on the constraints, the distribution node parameters, line parameters, and flexible interconnection device parameters are input into the objective function for calculation to obtain the distributed energy installed capacity, including:

[0017] According to the converted safe operation constraints, standard second-order cone form constraints, interactive power constraints and flexible interconnection device operation constraints, the distribution node parameters, line parameters and flexible interconnection device parameters are input into the objective function for calculation to obtain the distributed energy installed capacity.

[0018] In some embodiments, the carrying capacity of the distribution network is evaluated based on the installed capacity of distributed energy resources, including:

[0019] Obtain the installed capacity of distributed power sources in the distribution network when distributed energy is fully absorbed;

[0020] Input the installed capacity of distributed power generation and distributed energy into a preset proportional model for calculation to obtain ratio information;

[0021] The carrying capacity of the distribution network is evaluated according to the ratio information to obtain an evaluation result.

[0022] In a second aspect, the present application also provides a distribution network carrying capacity assessment system, which includes a medium and low voltage flexible interconnected distribution network and a processor; the low voltage flexible interconnected distribution network includes multiple distribution nodes, a flexible interconnection device of at least one medium voltage substation, an interconnection device of at least one low voltage substation and a converter; the flexible interconnection device of at least one medium voltage substation and the interconnection device of at least one low voltage substation are connected through a converter, multiple distribution nodes are deployed on the lines of the distribution network, and distributed energy is injected into the distribution network through the distribution nodes; the processor is connected to the medium and low voltage flexible interconnected distribution network;

[0023] A low-voltage flexible interconnected distribution network is used to output distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network within a preset time period based on the input distributed energy;

[0024] A processor is used to execute the steps of any one of the methods of the first aspect above according to distribution node parameters, line parameters and flexible interconnection device parameters.

[0025] In a third aspect, the present application further provides a device for evaluating the carrying capacity of a distribution network, the device comprising:

[0026] An acquisition module is used to obtain distribution node parameters, line parameters and flexible interconnection device parameters in the distribution network within a preset time period;

[0027] The prediction module is used to input the distribution node parameters, line parameters and flexible interconnection device parameters into the preset prediction model to predict the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected into the distribution network and the distributed energy is fully absorbed;

[0028] The evaluation module is used to evaluate the carrying capacity of the distribution network based on the installed capacity of distributed energy.

[0029] In a fourth aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0030] Obtaining distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network within a preset time period;

[0031] The distribution node parameters, line parameters, and flexible interconnection device parameters are input into the preset prediction model to predict the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected and the distributed energy is fully absorbed;

[0032] The carrying capacity of the distribution network is evaluated based on the installed capacity of distributed energy.

[0033] In a fifth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0034] Obtaining distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network within a preset time period;

[0035] The distribution node parameters, line parameters, and flexible interconnection device parameters are input into the preset prediction model to predict the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected and the distributed energy is fully absorbed;

[0036] The carrying capacity of the distribution network is evaluated based on the installed capacity of distributed energy.

[0037] In a sixth aspect, the present application further provides a computer program product, the computer program product comprising a computer program, which, when executed by a processor, implements the following steps:

[0038] Obtaining distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network within a preset time period;

[0039] The distribution node parameters, line parameters, and flexible interconnection device parameters are input into the preset prediction model to predict the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected and the distributed energy is fully absorbed;

[0040] The carrying capacity of the distribution network is evaluated based on the installed capacity of distributed energy.

[0041] The above-mentioned distribution network carrying capacity assessment method, system, device and computer equipment obtains the distribution node parameters, line parameters and flexible interconnection device parameters within the distribution network within a preset time period, and then inputs the distribution node parameters, line parameters and flexible interconnection device parameters into a preset prediction model for prediction, thereby obtaining the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected and the distributed energy is fully absorbed. Finally, the carrying capacity of the distribution network is evaluated based on the distributed energy installed capacity. The above-mentioned method evaluates the carrying capacity by combining multi-dimensional information of the distribution node parameters, line parameters and flexible interconnection device parameters within the distribution network. On the one hand, the comprehensive consideration of multi-dimensional information can avoid the evaluation bias caused by information loss in traditional methods, making the evaluation process more rigorous. Moreover, it can deeply analyze the interaction and influence of various links within the distribution network, more accurately grasp the actual operating conditions of the distribution network, and thus significantly improve the accuracy of the evaluation. On the other hand, traditional evaluation methods are mostly limited to the same voltage level and are difficult to cope with complex actual scenarios. The above method fully considers the medium- and low-voltage flexible interconnection scenario where medium- and low-voltage substations coexist. The medium- and low-voltage flexible interconnection scenario can better meet the electricity needs of different users and improve the efficiency of electricity distribution. The above method evaluates this complex scenario and can flexibly adjust the evaluation strategy according to the characteristics and needs of different substations, thereby greatly improving the flexibility of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of a distribution network capacity assessment system in some embodiments;

[0043] Figure 2 This is one of the flow charts of a method for evaluating the carrying capacity of a distribution network in some embodiments;

[0044] Figure 3 This is a second flow chart of a method for evaluating the carrying capacity of a distribution network in some embodiments;

[0045] Figure 4 This is a third flow chart of a method for evaluating the carrying capacity of a distribution network in some embodiments;

[0046] Figure 5 This is a fourth flow chart of a method for evaluating the carrying capacity of a distribution network in some embodiments;

[0047] Figure 6 is a structural block diagram of a device for evaluating the carrying capacity of a distribution network in some embodiments;

[0048] Figure 7 1 is a diagram of the internal structure of a computer device in some embodiments. DETAILED DESCRIPTION

[0049] In the embodiments of this application, the term "and / or" describes the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0050] In the embodiments of the present application, the term "plurality" refers to two or more than two, and other quantifiers are similar.

[0051] In the embodiments of the present application, the term "at least one" means one or more. For example, at least one of A, B and C can mean the following six situations: A exists alone, B exists alone, C exists alone, A and B exist at the same time, A and C exist at the same time, B and C exist at the same time, and A, B and C exist at the same time.

[0052] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0053] As the penetration rate of renewable energy continues to increase and users' demand for power quality continues to rise, traditional AC distribution networks are facing challenges such as wind and solar power curtailment and insufficient reliability. Flexible interconnection devices interconnect distribution networks of different voltage levels and substations to achieve load transfer and power flow adjustment in the event of a fault, thereby improving the reliability of the distribution network and its ability to accommodate distributed energy. The distributed energy carrying capacity can reflect the maximum capacity and potential risks of the distribution network in accommodating distributed energy, and is an important part of the application and promotion of flexible distribution networks. Therefore, when distributed energy is injected into the distribution network, it is crucial to effectively evaluate the carrying capacity of the distribution network. At present, the distribution network carrying capacity assessment methods recorded in related technologies are inaccurate.

[0054] In view of this, the embodiments of the present application propose a method, system, device and computer equipment for evaluating the carrying capacity of a distribution network, which can perform carrying capacity evaluation by combining multi-dimensional information of distribution node parameters, line parameters and flexible interconnection device parameters in the distribution network, thereby improving the evaluation accuracy.

[0055] It should be noted that the beneficial effects or technical problems solved by the embodiments of the present application are not limited to this one, but may also include other implicit or related problems. For details, please refer to the description of the following embodiments.

[0056] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0057] In some embodiments, the distribution network carrying capacity evaluation method provided in the embodiments of the present application can be applied to Figure 1 In the distribution network carrying capacity assessment system shown, the distribution network carrying capacity assessment system includes a medium and low voltage flexible interconnected distribution network 101 (hereinafter referred to as the distribution network) and a processor 102; the low voltage flexible interconnected distribution network 101 includes multiple distribution nodes 1011, a flexible interconnection device 1012 of at least one medium voltage substation, an interconnection device 1013 of at least one low voltage substation and a converter 1014, the flexible interconnection device 1012 of at least one medium voltage substation and the interconnection device 1013 of at least one low voltage substation are connected through the converter 1014, multiple distribution nodes 1011 are deployed on the lines of the distribution network, distributed energy 1015 is injected into the distribution network 101 through the distribution nodes, and the processor 102 is connected to the medium and low voltage flexible interconnected distribution network 101. The low-voltage flexible interconnected distribution network 101 is configured to output distribution node parameters, line parameters, and flexible interconnected device parameters within the distribution network within a preset time period based on the input distributed energy resources 1015. The processor 102 is configured to execute the steps of the method described in any of the following embodiments based on the distribution node parameters, line parameters, and flexible interconnected device parameters. The processor 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. Optionally, the processor 102 can be implemented as a standalone server or a server cluster consisting of multiple servers.

[0058] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the distribution network carrying capacity assessment system to which the solution of the present application is applied. The specific distribution network carrying capacity assessment system may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0059] In some embodiments, as Figure 2 As shown in the figure, a method for evaluating the carrying capacity of a distribution network is provided. Figure 1 The following steps are taken as an example to illustrate the processor in the example:

[0060] S201, obtaining distribution node parameters, line parameters and flexible interconnection device parameters in the distribution network within a preset time period.

[0061] The preset time period can be determined based on assessment requirements and grid operation requirements. For example, the preset time period can be one day, one week, or one month. Distribution node parameters include load parameters (active power, reactive power), the rated output of distributed energy resources, the power parameters of the interaction between distributed energy resources and the grid, and the node rated voltage. Distribution network line parameters include line impedance parameters and line capacity. Flexible interconnection device parameters include the loss rate of each port and the rated power of each port.

[0062] In an embodiment of the present application, the processor can receive, through a user interface provided thereon, relevant status information of the distribution network within a preset time period when the distributed energy is injected, uploaded by the user, and the relevant status information includes distribution node parameters, line parameters, and flexible interconnection device parameters. Optionally, the user can upload the relevant status information of the distribution network within a preset time period when the distributed energy is injected to a preset path, and the processor can obtain the relevant status information from the preset path in real time, that is, obtain the distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network within the preset time period. Optionally, the processor can be connected to a signal acquisition device, and obtain the distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network in real time through the signal acquisition device, and then store the distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network obtained in real time. When carrying capacity assessment is required, the distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network within the preset time period can be intercepted from the previously stored information.

[0063] S202: Input the distribution node parameters, line parameters and flexible interconnection device parameters into a preset prediction model for prediction, and obtain the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected into the distribution network and the distributed energy is fully absorbed.

[0064] The preset prediction model may be a neural network model, specifically a convolutional neural network or a fully connected neural network, a machine learning model, or a mathematical relationship model. The installed capacity of distributed energy is the installed capacity of distributed energy.

[0065] In an embodiment of the present application, after the processor obtains the distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network within a preset time period based on the above steps, the distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network within the preset time period can be input into a preset prediction model, and the distributed energy installed capacity that the distribution network can carry is predicted by the preset prediction model to obtain the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected and the distributed energy is fully absorbed. It should be noted that the above-mentioned preset prediction model can be obtained by training the initial prediction model, and the specific training method is as follows: the first step is data collection and data preprocessing process: the distribution node sample parameters, line sample parameters, and flexible interconnection device sample parameters of the distribution network in different time periods can be first collected, and at the same time, the distributed energy installed capacity sample data actually injected into the distribution network in the corresponding time period can be collected, such as the installed capacity of photovoltaic power stations, the installed capacity of wind farms, etc. Next, the collected data is checked for missing values. Interpolation methods (such as linear interpolation and polynomial interpolation) can be used to fill in a small number of missing values. Data records with a large number of missing values can be directly deleted. For outlier processing, statistical methods (such as standard deviation-based methods and boxplots) can be used to identify outliers in the data and correct or delete them based on the actual situation. The distribution node sample parameters, line sample parameters, flexible interconnection device sample parameters, and distributed energy installed capacity sample data are normalized using minimum-maximum normalization and Z-score normalization methods, mapping the data to a specific range (such as [0, 1]) to eliminate the dimensionality effects between different parameters and improve the training efficiency and stability of the model. Finally, the preprocessed data is divided into training, validation, and test sets according to specific proportions (such as 70%, 15%, and 15%). The second step is determining the loss function and optimization algorithm. First, you can select an appropriate initial prediction model, such as a machine learning model (e.g., linear regression, decision tree, random forest, support vector machine, etc.); or a deep learning model (e.g., multi-layer perceptron (MLP), long short-term memory network (LSTM), convolutional neural network (CNN), etc.). Next, you can choose an appropriate loss function, such as mean squared error (MSE), root mean squared error (RMSE), or mean absolute error (MAE). Then, you can select an appropriate optimization algorithm to update the model parameters to minimize the loss function, such as stochastic gradient descent (SGD) or adaptive moment estimation (Adam).The third step is model training: The initial prediction model is trained using the training set data. In each training iteration, input data (sample parameters of distribution nodes, lines, and flexible interconnected devices) is passed into the model to obtain the model's predicted output. The loss between the predicted output and the actual installed capacity of distributed energy resources is then calculated. Based on this loss, an optimization algorithm is used to update the model parameters. Training continues iteratively until the loss function converges or the preset number of training rounds is reached. The trained model is then evaluated using the validation set data, and performance metrics are calculated on the validation set. Model hyperparameters can be adjusted based on the validation set performance using methods such as grid search, random search, and Bayesian optimization. The tuned model is then tested on the test set data to assess its generalization ability. If the model's performance on the test set meets the requirements, it is used as the default prediction model. Otherwise, steps 2 and 3 are repeated until satisfactory model performance is achieved. Finally, the trained default prediction model is deployed on the processor to predict the amount of distributed energy resources that the distribution network can support if distributed energy resources are injected.

[0066] S203: Evaluate the carrying capacity of the distribution network based on the installed capacity of distributed energy resources.

[0067] Among them, the carrying capacity of the distribution network refers to the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected into the distribution network and the distributed energy is fully absorbed.

[0068] In an embodiment of the present application, after the processor obtains the installed capacity of distributed energy based on the above steps, it can evaluate the carrying capacity of the distribution network according to the amount of distributed energy installed capacity to obtain an evaluation result, wherein the evaluation result may include an evaluation value, and / or the level of the evaluated carrying capacity. Specifically, the larger the amount of distributed energy installed capacity, the stronger the carrying capacity of the distribution network, and the smaller the amount of distributed energy installed capacity, the weaker the carrying capacity of the distribution network. Optionally, the processor can also input the installed capacity of distributed energy into a corresponding carrying capacity evaluation model for evaluation to obtain an evaluation result; wherein the evaluation model can be a pre-trained neural network or machine learning model, or a mathematical relationship equation.

[0069] The method for evaluating the carrying capacity of a distribution network provided in an embodiment of the present application obtains the distribution node parameters, line parameters and flexible interconnection device parameters in the distribution network within a preset time period, and then inputs the distribution node parameters, line parameters and flexible interconnection device parameters into a preset prediction model for prediction, thereby obtaining the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected and the distributed energy is fully absorbed, and finally evaluating the carrying capacity of the distribution network based on the distributed energy installed capacity. The above method evaluates the carrying capacity by combining multi-dimensional information of distribution node parameters, line parameters and flexible interconnection device parameters in the distribution network. On the one hand, the comprehensive consideration of multi-dimensional information can avoid the evaluation bias caused by information loss in traditional methods, making the evaluation process more rigorous. Moreover, it can deeply analyze the interaction and influence of various links within the distribution network, more accurately grasp the actual operating conditions of the distribution network, and thus significantly improve the accuracy of the evaluation. On the other hand, traditional evaluation methods are mostly limited to the same voltage level and are difficult to cope with complex actual scenarios. The above method fully considers the medium- and low-voltage flexible interconnection scenario where medium- and low-voltage substations coexist. The medium- and low-voltage flexible interconnection scenario can better meet the electricity needs of different users and improve the efficiency of electricity distribution. The above method evaluates this complex scenario and can flexibly adjust the evaluation strategy according to the characteristics and needs of different substations, thereby greatly improving the flexibility of the evaluation.

[0070] In some embodiments, a specific implementation method for obtaining the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected into the distribution network and the distributed energy is fully absorbed is also provided, such as Figure 3 As shown, the above S202 "inputting the distribution node parameters, line parameters and flexible interconnection device parameters into the preset prediction model for prediction to obtain the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected into the distribution network and the distributed energy is fully absorbed" includes:

[0071] S301, extracting the objective function and constraints from the preset prediction model.

[0072] Among them, the preset prediction model includes objective functions and constraints. The objective function represents the goal of maximizing the distributed energy absorption capacity of the distribution network within a preset time period. The constraints include the active and reactive power flow constraints of the distribution network, the safe operation constraints of the distribution network, the interactive power constraints of the distribution network, and the operation constraints of the flexible interconnection device.

[0073] In the embodiment of the present application, after the processor obtains the preset prediction model, it can extract the objective function and constraints from it. Specifically, the objective function can be expressed by the following relation (1):

[0074] (1)

[0075] Where T represents the total duration; Represents a collection of distributed energy nodes; It represents the active power generated by the distributed energy at node n during period t.

[0076] The constraints include the active and reactive power flow constraints of the distribution network, the safe operation constraints of the distribution network, the interactive power constraints of the distribution network, and the operation constraints of the flexible interconnection device. Specifically:

[0077] (1) The active and reactive power flow constraints can be expressed by the following equations (2)-(5):

[0078] (2)

[0079] (3)

[0080] (4)

[0081] (5)

[0082] in, Represents a node The set of branch headend nodes that are end nodes; represents the active power flowing from node n to node m at time t; represents the resistance of the branch nm; represents the branch current flowing from node n to node m at time t; Represents a node The set of branch end nodes of the head end node; represents the active power flowing from node j to node m at time t; represents the net active injected power of node m at time t; represents the reactive power flowing from node n to node m at time t; represents the reactance of branch nm; represents the reactive power flowing from node j to node m at time t; represents the net reactive injection power of node m at time t; represents the voltage amplitude of node m at time t; represents the voltage amplitude of node n at time t.

[0083] above and It can be expressed as:

[0084]

[0085] in, represents the active power injected by the substation at node m at time t; represents the active injected power of distributed energy at node m at time t; represents the load active power at node m at time t; represents the reactive power injected by the substation at node m at time t; Represents the load reactive power at node m at time t.

[0086] (2) The safe operation constraints can be expressed by the following equations (6)-(8):

[0087] (6)

[0088] (7)

[0089] (8)

[0090] in, represents the lower limit of the voltage at node i; represents the upper limit of the voltage at node i; Indicates the lower limit of the square of the current in line nm; Indicates the upper limit of the square of the current in the circuit nm; represents the two-norm; represents the active power flowing from node i to node j at time t; represents the reactive power flowing from node i to node j at time t; Represents the upper limit of power transmission of line ij.

[0091] (3) The interactive power constraint can be expressed by the following relations (9)-(10):

[0092] (9)

[0093] (10)

[0094] in, represents the active output power of substation i; represents the reactive output power of substation i; represents the upper limit of the capacity transmission of substation i; represents the active injected power of distributed energy at node i at time t; Represents the predicted output of distributed energy i.

[0095] (4) The operating constraints of the flexible interconnection device can be expressed by the following equations (11)-(13):

[0096] (11)

[0097] (12)

[0098] (13)

[0099] in, represents the active power of port j of flexible interconnection device i at time t; represents the loss at port j of flexible interconnect device i; represents the DC side output power of port j of flexible interconnection device i at time t; represents the active power of the flexible interconnection device i at time t; represents the reactive power of the flexible interconnection device i at time t; represents the rated capacity of the flexible interconnection device i; represents the port set of the flexible interconnection device i; It represents the DC tie line transmission power between flexible interconnection device i and flexible interconnection device m.

[0100] S302: Based on the constraints, the distribution node parameters, line parameters and flexible interconnection device parameters are input into the objective function for calculation to obtain the installed capacity of distributed energy.

[0101] In an embodiment of the present application, after the processor obtains the objective function and constraints based on the above steps, it can input the distribution node parameters, line parameters and flexible interconnection device parameters into the objective function based on the constraints to perform calculations to obtain the installed capacity of distributed energy.

[0102] In some embodiments, as Figure 4 As shown, the above-mentioned distribution network carrying capacity assessment method further includes:

[0103] S401: Obtain voltage conversion variables and current conversion variables.

[0104] The voltage conversion variable refers to a variable that converts voltage, and the current conversion variable refers to a variable that converts current.

[0105] In the embodiment of the present application, since the preset prediction model for the evaluation of the distributed energy carrying capacity of the distribution network containing the flexible interconnection device contains quadratic terms, it is a nonlinear programming problem. Therefore, the nonlinear model is converted into a standard second-order cone programming problem that can be solved efficiently by using second-order cone relaxation to improve the computational efficiency. Specifically, voltage conversion variables and current conversion variables can be introduced. The voltage conversion variable It can be expressed by the relationship (14), the current conversion variable It can be expressed by the relationship (15):

[0106] (14)

[0107] (15)

[0108] S402 , converting the safe operation constraint conditions according to the voltage conversion variables and the current conversion variables, and processing the active and reactive power flow constraint conditions of the distribution network based on the converted safe operation constraint conditions to obtain the constraint conditions in the standard second-order cone form.

[0109] In the embodiment of the present application, the processor can convert the safe operation constraint condition based on the voltage conversion variable and the current conversion variable. Specifically, the safe operation constraint condition is converted according to formulas (14) and (15) to obtain the converted safe operation constraint condition. The converted safe operation constraint condition is then used to process formula (5) in the active and reactive power flow constraint condition to obtain the constraint condition in the standard second-order cone form. The constraint condition in the standard second-order cone form can be expressed by the following relationship:

[0110]

[0111] in, represents the voltage conversion variable, Indicates the current conversion variable.

[0112] Correspondingly, when the processor executes the above S302 "based on the constraints, the distribution node parameters, line parameters and flexible interconnection device parameters are input into the objective function for calculation to obtain the installed capacity of distributed energy", it specifically executes the step of S403: according to the converted safe operation constraints, standard second-order cone form constraints, interactive power constraints and flexible interconnection device operation constraints, the distribution node parameters, line parameters and flexible interconnection device parameters are input into the objective function for calculation to obtain the installed capacity of distributed energy.

[0113] In the embodiment of the present application, the processor obtains the converted safe operation constraints and the standard second-order cone form constraints based on the above steps. Then, based on the converted safe operation constraints, the standard second-order cone form constraints, the interactive power constraints, and the flexible interconnection device operation constraints, the processor inputs the distribution node parameters, the line parameters, and the flexible interconnection device parameters into the objective function for calculation to obtain the installed capacity of distributed energy resources. Specifically, various optimization solution toolboxes can be used to solve the problem and obtain the installed capacity of distributed energy resources.

[0114] In some embodiments, a specific implementation method for evaluating the carrying capacity of the distribution network is also provided, such as Figure 5 As shown, the above-mentioned “evaluating the carrying capacity of the distribution network based on the installed capacity of distributed energy resources” in S203 includes:

[0115] S501, obtain the installed capacity of distributed power sources in the distribution network when distributed energy is fully absorbed.

[0116] Among them, the installed capacity of distributed power generation can be set according to the working requirements of the distribution network.

[0117] In an embodiment of the present application, the processor can receive, via a user interface provided thereon, information uploaded by a user regarding the installed capacity of distributed power sources in the distribution network when distributed energy resources are fully absorbed. Optionally, the user can upload the installed capacity of distributed power sources in the distribution network to a preset path, and the processor can obtain the installed capacity of distributed power sources in the distribution network from the preset path in real time. Optionally, the processor can be connected to a signal acquisition device to obtain, via the signal acquisition device, relevant status information of the distribution network in real time, including the installed capacity of distributed power sources in the distribution network.

[0118] S502: Input the installed capacity of distributed power generation and the installed capacity of distributed energy into a preset ratio model for calculation to obtain ratio information.

[0119] The distribution network's carrying capacity is the ratio of distributed energy capacity to installed power generation capacity that the network can support when no solar or wind power is curtailed, meaning distributed energy is fully absorbed. The ratio is the ratio of distributed power generation capacity to distributed energy capacity.

[0120] In an embodiment of the present application, after the processor obtains the installed capacity of distributed power sources in the distribution network when the distributed energy is fully absorbed based on the above steps, the installed capacity of distributed power sources and the installed capacity of distributed energy can be input into a preset proportional model for calculation to obtain ratio information.

[0121] S503: Evaluate the carrying capacity of the distribution network according to the ratio information to obtain an evaluation result.

[0122] The evaluation results indicate the carrying capacity of the distribution network.

[0123] In an embodiment of the present application, after the processor obtains the ratio information based on the above steps, it can evaluate the carrying capacity of the distribution network according to the ratio information and the preset ratio range to obtain an evaluation result. The preset ratio range includes a first ratio range, a second ratio range, and a third ratio range. When the ratio in the ratio information is in the first ratio range, it indicates that the carrying capacity of the distribution network is low. When the ratio in the ratio information is in the second ratio range, it indicates that the carrying capacity of the distribution network is medium. When the ratio in the ratio information is in the third ratio range, it indicates that the carrying capacity of the distribution network is high. It should be noted that in the embodiment of the present application, three ratio ranges are used for exemplary explanation. In actual applications, the ratio ranges can be refined.

[0124] In summary of all the above embodiments, a method for evaluating the carrying capacity of a distribution network is further provided, the method comprising:

[0125] S601, obtaining distribution node parameters, line parameters, and flexible interconnection device parameters in the distribution network within a preset time period.

[0126] S602: Extracting objective functions and constraints from a preset prediction model. The constraints include active and reactive power flow constraints of the distribution network, safe operation constraints of the distribution network, interactive power constraints of the distribution network, and operation constraints of the flexible interconnection device.

[0127] S603: Obtain voltage conversion variables and current conversion variables.

[0128] S604 , converting the safe operation constraint conditions according to the voltage conversion variables and the current conversion variables, and processing the active and reactive power flow constraint conditions of the distribution network based on the converted safe operation constraint conditions to obtain the constraint conditions in a standard second-order cone form.

[0129] S605: Based on the converted safe operation constraints, standard second-order cone form constraints, interactive power constraints, and flexible interconnection device operation constraints, the distribution node parameters, line parameters, and flexible interconnection device parameters are input into the objective function for calculation to obtain the distributed energy installed capacity.

[0130] S606, obtain the installed capacity of distributed power sources in the distribution network when distributed energy is fully absorbed.

[0131] S607: Input the installed capacity of distributed power generation and the installed capacity of distributed energy into a preset ratio model for calculation to obtain ratio information.

[0132] S608: Evaluate the carrying capacity of the distribution network according to the ratio information to obtain an evaluation result.

[0133] The methods described in the above steps are all described in the above embodiments. Please refer to the above description for details and will not be repeated here.

[0134] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0135] Based on the same inventive concept, an embodiment of the present application further provides a distribution network carrying capacity assessment device for implementing the distribution network carrying capacity assessment method involved above. The implementation solution provided by this device is similar to the implementation solution described in the above method. Therefore, the specific limitations in the embodiments of one or more distribution network carrying capacity assessment devices provided below can be found in the limitations of the distribution network carrying capacity assessment method above, and will not be repeated here.

[0136] In some embodiments, as Figure 6 As shown, a device for evaluating the carrying capacity of a distribution network is provided, comprising:

[0137] The acquisition module 10 is used to obtain distribution node parameters, line parameters and flexible interconnection device parameters in the distribution network within a preset time period.

[0138] The prediction module 11 is used to input the distribution node parameters, line parameters and flexible interconnection device parameters into the preset prediction model for prediction, so as to obtain the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected into the distribution network and the distributed energy is fully absorbed.

[0139] The evaluation module 12 is used to evaluate the carrying capacity of the distribution network according to the installed capacity of distributed energy resources.

[0140] In some embodiments, the prediction module includes:

[0141] The extraction unit is used to extract the objective function and constraints from the preset prediction model. The constraints include the active and reactive power flow constraints of the distribution network, the safe operation constraints of the distribution network, the interactive power constraints of the distribution network, and the operation constraints of the flexible interconnection device.

[0142] The calculation unit is used to input the distribution node parameters, line parameters and flexible interconnection device parameters into the objective function based on the constraint conditions to calculate and obtain the installed capacity of distributed energy.

[0143] In some embodiments, the prediction module further includes:

[0144] The first acquiring unit is configured to acquire a voltage conversion variable and a current conversion variable.

[0145] The conversion unit is used to convert the safe operation constraint conditions according to the voltage conversion variables and the current conversion variables, and process the active and reactive power flow constraint conditions of the distribution network based on the converted safe operation constraint conditions to obtain the constraint conditions in the standard second-order cone form.

[0146] Correspondingly, the above-mentioned calculation unit is specifically used to input the distribution node parameters, line parameters and flexible interconnection device parameters into the objective function for calculation based on the converted safe operation constraints, standard second-order cone form constraints, interactive power constraints and flexible interconnection device operation constraints to obtain the distributed energy installed capacity.

[0147] In some embodiments, the evaluation module includes:

[0148] The second acquisition unit is used to obtain the installed capacity of distributed power sources in the distribution network when the distributed energy is fully absorbed.

[0149] The calculation unit is used to input the installed capacity of distributed power generation and the installed capacity of distributed energy into a preset proportional model for calculation to obtain ratio information.

[0150] The evaluation unit is used to evaluate the carrying capacity of the distribution network according to the ratio information to obtain an evaluation result.

[0151] Each module in the aforementioned distribution network capacity assessment device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0152] In some embodiments, a computer device is provided. The computer device can be a terminal or a server. The internal structure diagram thereof can be as follows: Figure 7As shown, the computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, while the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication, which can be achieved via Wi-Fi, mobile cellular networks, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for assessing the carrying capacity of a distribution network. The display unit of the computer device is used to produce visual images and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0153] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0154] In some embodiments, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0155] Obtaining distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network within a preset time period;

[0156] The distribution node parameters, line parameters, and flexible interconnection device parameters are input into the preset prediction model to predict the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected and the distributed energy is fully absorbed;

[0157] The carrying capacity of the distribution network is evaluated based on the installed capacity of distributed energy.

[0158] In some embodiments, when the processor executes the computer program, it further implements the following steps:

[0159] Extract the objective function and constraints from the preset prediction model; the constraints include the active and reactive power flow constraints of the distribution network, the safe operation constraints of the distribution network, the interactive power constraints of the distribution network, and the operation constraints of the flexible interconnection device;

[0160] Based on the constraints, the distribution node parameters, line parameters and flexible interconnection device parameters are input into the objective function for calculation to obtain the installed capacity of distributed energy.

[0161] In some embodiments, when the processor executes the computer program, it further implements the following steps:

[0162] Obtain voltage conversion variables and current conversion variables;

[0163] The safe operation constraint conditions are converted according to the voltage conversion variables and the current conversion variables, and the active and reactive power flow constraint conditions of the distribution network are processed based on the converted safe operation constraint conditions to obtain the constraint conditions in the standard second-order cone form;

[0164] Based on the constraints, the distribution node parameters, line parameters, and flexible interconnection device parameters are input into the objective function for calculation to obtain the distributed energy installed capacity, including:

[0165] According to the converted safe operation constraints, standard second-order cone form constraints, interactive power constraints and flexible interconnection device operation constraints, the distribution node parameters, line parameters and flexible interconnection device parameters are input into the objective function for calculation to obtain the distributed energy installed capacity.

[0166] In some embodiments, when the processor executes the computer program, it further implements the following steps:

[0167] Obtain the installed capacity of distributed power sources in the distribution network when distributed energy is fully absorbed;

[0168] Input the installed capacity of distributed power generation and distributed energy into a preset proportional model for calculation to obtain ratio information;

[0169] The carrying capacity of the distribution network is evaluated according to the ratio information to obtain an evaluation result.

[0170] The computer device provided in the above embodiment has an implementation principle and technical effects similar to those of the above method embodiment, and will not be described in detail here.

[0171] In some embodiments, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0172] Obtaining distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network within a preset time period;

[0173] The distribution node parameters, line parameters, and flexible interconnection device parameters are input into the preset prediction model to predict the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected and the distributed energy is fully absorbed;

[0174] The carrying capacity of the distribution network is evaluated based on the installed capacity of distributed energy.

[0175] In some embodiments, when the computer program is executed by a processor, the following steps are further implemented:

[0176] The objective function and constraints are extracted from the preset prediction model; the constraints include the active and reactive power flow constraints of the distribution network, the safe operation constraints of the distribution network, the interactive power constraints of the distribution network, and the operation constraints of the flexible interconnection device.

[0177] Based on the constraints, the distribution node parameters, line parameters and flexible interconnection device parameters are input into the objective function for calculation to obtain the installed capacity of distributed energy.

[0178] In some embodiments, when the computer program is executed by a processor, the following steps are further implemented:

[0179] Obtain voltage conversion variables and current conversion variables;

[0180] The safe operation constraint conditions are converted according to the voltage conversion variables and the current conversion variables, and the active and reactive power flow constraint conditions of the distribution network are processed based on the converted safe operation constraint conditions to obtain the constraint conditions in the standard second-order cone form;

[0181] Based on the constraints, the distribution node parameters, line parameters, and flexible interconnection device parameters are input into the objective function for calculation to obtain the distributed energy installed capacity, including:

[0182] According to the converted safe operation constraints, standard second-order cone form constraints, interactive power constraints and flexible interconnection device operation constraints, the distribution node parameters, line parameters and flexible interconnection device parameters are input into the objective function for calculation to obtain the distributed energy installed capacity.

[0183] In some embodiments, when the computer program is executed by a processor, the following steps are further implemented:

[0184] Obtain the installed capacity of distributed power sources in the distribution network when distributed energy is fully absorbed;

[0185] Input the installed capacity of distributed power generation and distributed energy into a preset proportional model for calculation to obtain ratio information;

[0186] The carrying capacity of the distribution network is evaluated according to the ratio information to obtain an evaluation result.

[0187] The above embodiment provides a computer-readable storage medium, whose implementation principle and technical effects are similar to those of the above method embodiment, and will not be repeated here.

[0188] In some embodiments, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0189] Obtaining distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network within a preset time period;

[0190] The distribution node parameters, line parameters, and flexible interconnection device parameters are input into the preset prediction model to predict the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected and the distributed energy is fully absorbed;

[0191] The carrying capacity of the distribution network is evaluated based on the installed capacity of distributed energy.

[0192] In some embodiments, when the computer program is executed by a processor, the following steps are further implemented:

[0193] The objective function and constraints are extracted from the preset prediction model; the constraints include the active and reactive power flow constraints of the distribution network, the safe operation constraints of the distribution network, the interactive power constraints of the distribution network, and the operation constraints of the flexible interconnection device.

[0194] Based on the constraints, the distribution node parameters, line parameters and flexible interconnection device parameters are input into the objective function for calculation to obtain the installed capacity of distributed energy.

[0195] In some embodiments, when the computer program is executed by a processor, the following steps are further implemented:

[0196] Obtain voltage conversion variables and current conversion variables;

[0197] The safe operation constraint conditions are converted according to the voltage conversion variables and the current conversion variables, and the active and reactive power flow constraint conditions of the distribution network are processed based on the converted safe operation constraint conditions to obtain the constraint conditions in the standard second-order cone form;

[0198] Based on the constraints, the distribution node parameters, line parameters, and flexible interconnection device parameters are input into the objective function for calculation to obtain the distributed energy installed capacity, including:

[0199] According to the converted safe operation constraints, standard second-order cone form constraints, interactive power constraints and flexible interconnection device operation constraints, the distribution node parameters, line parameters and flexible interconnection device parameters are input into the objective function for calculation to obtain the distributed energy installed capacity.

[0200] In some embodiments, when the computer program is executed by a processor, the following steps are further implemented:

[0201] Obtain the installed capacity of distributed power sources in the distribution network when distributed energy is fully absorbed;

[0202] Input the installed capacity of distributed power generation and distributed energy into a preset proportional model for calculation to obtain ratio information;

[0203] The carrying capacity of the distribution network is evaluated according to the ratio information to obtain an evaluation result.

[0204] The above embodiment provides a computer program product, whose implementation principle and technical effects are similar to those of the above method embodiment, and will not be repeated here.

[0205] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0206] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0207] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for evaluating the carrying capacity of a distribution network, characterized in that: The method comprises: Obtaining distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network within a preset time period; Inputting the distribution node parameters, the line parameters, and the flexible interconnection device parameters into a preset prediction model for prediction, thereby obtaining the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected into the distribution network and the distributed energy is fully absorbed; The carrying capacity of the distribution network is evaluated based on the installed capacity of the distributed energy.

2. The method according to claim 1, characterized in that The power distribution node parameters, the line parameters, and the flexible interconnection device parameters are input into a preset prediction model for prediction to obtain the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected into the distribution network and the distributed energy is fully absorbed, including: Extracting the objective function and constraints from the preset prediction model; Based on the constraints, the distribution node parameters, the line parameters and the flexible interconnection device parameters are input into the objective function for calculation to obtain the distributed energy installed capacity.

3. The method according to claim 2, characterized in that The constraints include active and reactive power flow constraints of the distribution network, safe operation constraints of the distribution network, interactive power constraints of the distribution network, and operation constraints of the flexible interconnection device.

4. The method according to claim 3, characterized in that The method further comprises: Obtain voltage conversion variables and current conversion variables; The safe operation constraint condition is converted according to the voltage conversion variable and the current conversion variable, and the active and reactive power flow constraint condition of the distribution network is processed based on the converted safe operation constraint condition to obtain a constraint condition in a standard second-order cone form; The step of inputting the distribution node parameters, the line parameters, and the flexible interconnection device parameters into the objective function for calculation based on the constraint conditions to obtain the distributed energy installed capacity includes: According to the converted safe operation constraints, the standard second-order cone form constraints, the interactive power constraints and the operation constraints of the flexible interconnection device, the distribution node parameters, the line parameters and the flexible interconnection device parameters are input into the objective function for calculation to obtain the distributed energy installed capacity.

5. The method according to any one of claims 1 to 4, characterized in that The evaluating the carrying capacity of the distribution network according to the installed capacity of the distributed energy resources includes: Obtaining the installed capacity of distributed power sources of the distribution network when the distributed energy is fully absorbed; Inputting the installed capacity of the distributed power supply and the installed capacity of the distributed energy into a preset proportional model for calculation to obtain ratio information; The carrying capacity of the distribution network is evaluated according to the ratio information to obtain an evaluation result.

6. A distribution network carrying capacity assessment system, characterized in that: The distribution network carrying capacity assessment system includes a medium- and low-voltage flexible interconnected distribution network and a processor; the low-voltage flexible interconnected distribution network includes multiple distribution nodes, a flexible interconnection device of at least one medium-voltage substation, an interconnection device of at least one low-voltage substation, and a converter; the flexible interconnection device of at least one medium-voltage substation and the interconnection device of at least one low-voltage substation are connected via the converter, the multiple distribution nodes are deployed on the lines of the distribution network, and distributed energy is injected into the distribution network via the distribution nodes; the processor is connected to the medium- and low-voltage flexible interconnected distribution network; The low-voltage flexible interconnected distribution network is used to output distribution node parameters, line parameters and flexible interconnection device parameters within the distribution network within a preset time period based on the input distributed energy; The processor is configured to execute the steps of the method according to any one of claims 1 to 5 based on the power distribution node parameters, the line parameters, and the flexible interconnection device parameters.

7. A device for evaluating the carrying capacity of a distribution network, characterized in that: The device comprises: An acquisition module, configured to acquire distribution node parameters, line parameters, and flexible interconnection device parameters within the distribution network within a preset time period; a prediction module, configured to input the distribution node parameters, the line parameters, and the flexible interconnection device parameters into a preset prediction model to perform prediction, thereby obtaining the distributed energy installed capacity that the distribution network can carry when the distributed energy is injected into the distribution network and the distributed energy is fully absorbed; An evaluation module is used to evaluate the carrying capacity of the distribution network based on the installed capacity of the distributed energy.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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