Method for determining power supply recovery mode

Abstract

The invention relates to a method for determining a power supply recovery mode, and the method comprises the steps: obtaining a plurality of sub-regions and sub-region parameters of a pre-divided active power distribution system, aiming at each sub-region, constructing a target power supply recovery model based on the minimum load value loss of each stage as a target, and the sub-region parameters, and performing initial value solving, integer variable solving and real number variable staged solving processing on the sub-regions to obtain a target recovery mode. Through staged processing of initial value solving, integer variable solving and real number variable solving, the complex power supply recovery problem is gradually refined and solved, the calculation complexity caused by processing all variables at a time is avoided, the calculation task of each stage is clearer and more concentrated, and therefore the calculation efficiency is greatly improved.

Description

Technical Field

[0001] The present application relates to the technical field of active power distribution systems, and in particular to a method for determining a power supply restoration mode. Background Art

[0002] In modern power systems, active distribution systems play an increasingly important role in the field of power supply because they can integrate distributed power sources (such as solar energy, wind energy, etc.) and have many advantages such as improving energy efficiency and enhancing power supply reliability. However, with the development of distribution systems, the challenges they face are becoming more and more prominent.

[0003] The vulnerability of the power distribution system has gradually become an issue that cannot be ignored. Natural disasters, human factors, and equipment failures may cause the power distribution system to fail, thereby affecting the normal supply of electricity. Building a highly resilient active distribution network has become a key measure to deal with these potential threats and ensure the stable operation of the power system.

[0004] However, traditional power supply restoration algorithms are inefficient in solving power supply restoration plans after a distribution system failure and cannot respond to the failure in a timely manner. Summary of the invention

[0005] Based on this, it is necessary to provide a method for determining a power supply restoration method that can solve the above-mentioned technical problems.

[0006] In a first aspect, the present application provides a method for determining a power supply restoration mode, the method comprising:

[0007] Acquire a plurality of sub-areas and sub-area parameters of a pre-divided active power distribution system;

[0008] For each sub-area, based on the pre-built target power supply restoration model and sub-area parameters, the sub-area is solved in stages to obtain the target restoration method; the target power supply restoration model is constructed with the goal of minimizing the load value loss in each stage; the staged solution includes initial value solution, integer variable solution and real number variable solution.

[0009] In one embodiment, the sub-region parameters include first-stage parameters, second-stage parameters, and third-stage parameters; the target power supply restoration model includes a first-stage model, a second-stage model, and a third-stage model; based on the pre-constructed target power supply restoration model and sub-region parameters, the sub-region is solved in stages to obtain a target restoration method, including:

[0010] The first-stage parameters are input into the first-stage model for initial value solution processing to obtain the target initial value; the first-stage model is constructed with the goal of minimizing the first-stage load value loss;

[0011] The second-stage parameters are input into the second-stage model for integer variable solving to obtain the target integer variable; the second-stage model is constructed with the goal of minimizing the loss of the second-stage load value;

[0012] The third-stage parameters are input into the third-stage model for integer variable solution processing to obtain the target real number variable; the third-stage model is constructed with the goal of minimizing the third-stage load value loss;

[0013] Determine the target recovery method based on the target initial value, target integer variable and target real variable.

[0014] In one embodiment, the method further comprises:

[0015] Based on the pre-acquired distribution system parameter information, an initial power supply restoration model is constructed;

[0016] Constructing a first-stage model according to the initial power supply restoration model and the first solution model;

[0017] According to the first-stage model and the second solution model, the second-stage model is constructed;

[0018] Based on the second-stage model and the third solution model, the third-stage model is constructed.

[0019] In one embodiment, the power distribution system parameter information includes at least one of multiple nodes in the sub-area, the total active and reactive loads of each node, and the load importance weight value; based on the pre-acquired power distribution system parameter information, an initial power supply restoration model is constructed, including:

[0020] According to the distribution system parameter information, determine the objective function and the first constraint condition; the first constraint condition includes safety operation constraint, power flow constraint and distributed power supply operation constraint; the objective function is based on the total active load, load importance weight value and load loss rate; the objective function is used to characterize the priority of different loads in power supply restoration;

[0021] According to the objective function and the first constraint, an initial power supply restoration model is constructed.

[0022] In one embodiment, the first-stage model is constructed according to the initial power supply restoration model and the first solution model, including:

[0023] Performing real number variable conversion processing on integer variables of the initial power supply restoration model to obtain a first restoration model;

[0024] The first stage model is composed of the first recovery model and the first solution model.

[0025] In one embodiment, the process of constructing the first solution model includes:

[0026] Decomposing the objective function to obtain a plurality of first-stage objective functions; the first-stage objective function is based on the first iteration variable and the first dual variable;

[0027] Based on the first convergence parameter obtained in advance, constructing a first convergence condition; the first convergence parameter includes the current boundary variable and the first K boundary variables of the first iteration variable;

[0028] A first solution model is determined according to each first-stage objective function, a first convergence condition and a first constraint condition.

[0029] In one embodiment, constructing the second stage model according to the first stage model and the second solution model includes:

[0030] Performing integer variable retention processing on the first recovery model to obtain a second recovery model;

[0031] The second stage model is composed of the second recovery model and the second solution model.

[0032] In one embodiment, the process of constructing the second solution model includes:

[0033] Solving the first-stage model to obtain the initial value of the first-stage model, and performing parameter conversion on the first constraint condition according to the initial value of the first-stage model to obtain the second constraint condition;

[0034] The objective function of the initial power supply restoration model is simplified by removing the part containing real number variables to obtain the second stage objective function; the second stage objective function is based on the second iteration variable and the second dual variable;

[0035] The projection function is composed of the obtained integer variable and the projection parameter;

[0036] Based on the pre-acquired second convergence parameters, constructing a second convergence condition; the second convergence parameters include the current boundary variable and the first K boundary variables of the second iteration variable;

[0037] A second solution model is constructed according to the second constraint condition, each second stage objective function, the projection function, and the second convergence condition.

[0038] In one embodiment, the third stage model is constructed based on the second stage model and the third solution model, including:

[0039] Assigning the solutions of the integer variables to the second recovery model to obtain a third recovery model;

[0040] The third stage model is composed of the third recovery model and the third solution model.

[0041] In one embodiment, constructing the third stage model according to the third recovery model and the third solution model includes:

[0042] Assigning a value to the objective function of the initial power supply restoration model, removing the part containing the integer variable, and obtaining the third stage objective function; the third stage objective function is based on the third iteration variable and the third dual variable;

[0043] Based on the third convergence parameter obtained in advance, a third convergence condition is constructed; the second convergence parameter includes the current boundary variable of the third iteration variable and the first K boundary variables of the third iteration variable;

[0044] The third solution model is composed of the pre-acquired third constraint conditions, the third stage objective functions, and the third convergence conditions.

[0045] In a second aspect, the present application also provides a device for determining a power supply restoration mode. The device includes:

[0046] A data acquisition module, used for acquiring a plurality of sub-areas and sub-area parameters of a pre-divided active power distribution system;

[0047] The method determination module is used to solve the sub-area in stages based on the pre-built target power supply restoration model and sub-area parameters to obtain the target restoration method for each sub-area; the target power supply restoration model is constructed based on the goal of minimizing the load value loss in each stage; the staged solution includes initial value solution, integer variable solution and real number variable solution.

[0048] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0049] Acquire a plurality of sub-areas and sub-area parameters of a pre-divided active power distribution system;

[0050] For each sub-area, based on the pre-built target power supply restoration model and sub-area parameters, the sub-area is solved in stages to obtain the target restoration method; the target power supply restoration model is constructed with the goal of minimizing the load value loss in each stage; the staged solution includes initial value solution, integer variable solution and real number variable solution.

[0051] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0052] Acquire a plurality of sub-areas and sub-area parameters of a pre-divided active power distribution system;

[0053] For each sub-area, based on the pre-built target power supply restoration model and sub-area parameters, the sub-area is solved in stages to obtain the target restoration method; the target power supply restoration model is constructed with the goal of minimizing the load value loss in each stage; the staged solution includes initial value solution, integer variable solution and real number variable solution.

[0054] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0055] Acquire a plurality of sub-areas and sub-area parameters of a pre-divided active power distribution system;

[0056] For each sub-area, based on the pre-built target power supply restoration model and sub-area parameters, the sub-area is solved in stages to obtain the target restoration method; the target power supply restoration model is constructed with the goal of minimizing the load value loss in each stage; the staged solution includes initial value solution, integer variable solution and real number variable solution.

[0057] The above-mentioned method for determining the power supply restoration mode obtains multiple sub-areas and sub-area parameters of the pre-divided active distribution system, and for each sub-area, a target power supply restoration model and sub-area parameters are constructed based on the goal of minimizing the load value loss at each stage, and the sub-area is processed by initial value solution, integer variable solution and real variable solution in stages to obtain the target restoration mode. This application gradually refines and solves the complex power supply restoration problem through the staged processing of initial value solution, integer variable solution and real variable solution, avoiding the computational complexity brought by processing all variables at one time, making the computational tasks of each stage clearer and more centralized, thereby greatly improving the computational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 An application environment diagram of a method for determining a power supply restoration mode in an embodiment;

[0059] Figure 2 A schematic flow chart of a method for determining a power supply restoration mode in one embodiment;

[0060] Figure 3 A schematic diagram of a process for obtaining a target recovery method in an embodiment;

[0061] Figure 4 A schematic flow chart of a method for determining a power supply restoration mode in another embodiment;

[0062] Figure 5 A schematic diagram of a process for constructing an initial power restoration model in an embodiment;

[0063] Figure 6 A schematic diagram of a process for constructing a first-stage model in an embodiment;

[0064] Figure 7 A schematic diagram of a process of constructing a first solution model in an embodiment;

[0065] Figure 8 A schematic diagram of a process for constructing a second-stage model in one embodiment;

[0066] Fig. 9 A schematic diagram of a process of constructing a second solution model in an embodiment;

[0067] Fig.10 A schematic diagram of a process for constructing a third-stage model in one embodiment;

[0068] Fig.11 A schematic diagram of a process of constructing a third solution model in one embodiment;

[0069] Fig.12 A schematic diagram of an improved IEEE123 node network in an embodiment;

[0070] Fig.13 IEEE123 node network partitioning in one embodiment;

[0071] Fig.14 The power supply recovery result of scheme I in one embodiment;

[0072] Fig.15 This is a power restoration result of Scheme II in an embodiment;

[0073] Fig.16 A structural block diagram of determining a power supply recovery mode in an embodiment;

[0074] Fig.17 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0076] The method for determining the power supply recovery mode provided in the embodiment of the present application can be applied to the following examples: Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud, other network servers or in the terminal 102. The server obtains multiple sub-areas and sub-area parameters of the pre-divided active distribution system, and for each sub-area, based on the target power supply recovery model and sub-area parameters constructed with the minimum load value loss in each stage as the goal, the sub-area is solved for initial value, integer variable solution and real number variable stage solution processing to obtain the target recovery method. Among them, the terminal 102 can be the core control node of the entire active distribution network, receiving data information from sensors in each sub-area, including voltage, current, power, distributed power supply status, switch position, etc. It can also be a distributed power supply controller, an intelligent substation terminal, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0077] In one embodiment, Figure 2 As shown, a method for determining a power supply restoration mode is provided, and the method is applied to Figure 1 The server in the example is used to illustrate the following steps:

[0078] S201, obtaining a plurality of sub-areas and sub-area parameters of a pre-divided active power distribution system.

[0079] Among them, the active distribution network is a distribution system containing distributed power sources (such as solar energy, wind energy, etc.), which can realize the bidirectional flow of power. It is more complex than the traditional distribution network, but also has higher flexibility and reliability.

[0080] The nodes of the active distribution network are divided into two categories: the nodes connected to the switch are called boundary nodes, and the nodes not connected to the switch are called internal nodes; the sub-areas are formed by non-switch branches connecting the boundary nodes and internal nodes. Under the premise that there are no switches inside the sub-areas, the division scheme with the least number of sub-areas is used as the sub-area division scheme of the active distribution system.

[0081] The sub-area parameters of the active distribution network include: line parameters of each sub-area, load level, distribution network topology connection relationship, node voltage upper and lower limits, line capacity limit, maximum square voltage difference allowed at both ends when the switch is disconnected, type, access location, capacity, active output limit, and reactive output limit of controllable distributed power sources.

[0082] The embodiments of the present application may divide the sub-areas based on other criteria such as electrical distance or load characteristics. For example, nodes may be divided according to the electrical distance between them, or nodes with similar load characteristics may be divided into a sub-area. For obtaining sub-area parameters, data may be collected in real time by sensors installed on various nodes and lines of the distribution network, or historical data and real-time monitoring data may be obtained from an existing distribution management system.

[0083] Another implementation method: sub-areas can be divided based on other criteria such as electrical distance or load characteristics. For example, nodes can be divided based on the electrical distance between them, or nodes with similar load characteristics can be divided into a sub-area. To obtain sub-area parameters, data can be collected in real time through sensors installed on various nodes and lines of the distribution network, or historical data and real-time monitoring data can be obtained from the existing distribution management system.

[0084] S202 , for each sub-area, based on a pre-built target power supply restoration model and sub-area parameters, performing a phased solution process on the sub-area to obtain a target restoration method.

[0085] Among them, the target power supply restoration model is a model built with the goal of minimizing the loss of load value at each stage, and is used to determine the power supply restoration method of the active distribution network after a fault. The model takes into account factors such as safe operation constraints, power flow constraints, and distributed power generation operation constraints, and is built with the goal of minimizing the loss of load value at each stage.

[0086] The staged solution in the embodiment of the present application includes initial value solution, integer variable solution and real number variable solution.

[0087] Initial value solution sets the integer variables in the target power restoration model to real variables and builds a new model. The purpose of this stage is to obtain the initial value of the power restoration problem and provide a good starting point for subsequent solutions.

[0088] Integer variable solution Based on the original power supply restoration model and the initial values ​​obtained in the first stage, a new active distribution network power supply restoration model is constructed, retaining only the part containing integer variables, with the objective function unchanged and the constraints changed.

[0089] The real number variables are solved based on the original power supply restoration model and the solutions of the integer variables obtained in the second stage. A new active distribution network power supply restoration model is constructed, and the integer variables in the original model are assigned values ​​according to the obtained solutions.

[0090] Another implementation method: In the initial value solution stage, other initialization methods can be used, such as random initialization or initialization based on empirical values. In the integer variable solution and real variable solution stages, different ADMM algorithm variants or other optimization algorithms can be tried to improve the solution efficiency and accuracy. For the judgment of convergence conditions, different convergence criteria can be used, such as judging convergence based on the change of the objective function value or the relative change of the variable.

[0091] The above-mentioned method for determining the power supply restoration mode obtains multiple sub-areas and sub-area parameters of the pre-divided active distribution system, and for each sub-area, a target power supply restoration model and sub-area parameters are constructed based on the goal of minimizing the load value loss at each stage, and the sub-area is processed by initial value solution, integer variable solution and real variable solution in stages to obtain the target restoration mode. This application gradually refines and solves the complex power supply restoration problem through the staged processing of initial value solution, integer variable solution and real variable solution, avoiding the computational complexity brought by processing all variables at one time, making the computational tasks of each stage clearer and more centralized, thereby greatly improving the computational efficiency.

[0092] In an exemplary embodiment, based on the above embodiment, see Figure 3 The sub-region parameters of the embodiment of the present application include first-stage parameters, second-stage parameters and third-stage parameters; the target power supply restoration model includes a first-stage model, a second-stage model and a third-stage model; based on the pre-constructed target power supply restoration model and sub-region parameters, the sub-region is solved in stages to obtain a target restoration method, including the following steps:

[0093] S301, input the first-stage parameters into the first-stage model for initial value solution processing to obtain the target initial value; the first-stage model is constructed with the goal of minimizing the loss of the first-stage load value; input the second-stage parameters into the second-stage model for integer variable solution processing to obtain the target integer variable; the second-stage model is constructed with the goal of minimizing the loss of the second-stage load value; input the third-stage parameters into the third-stage model for integer variable solution processing to obtain the target real variable; the third-stage model is constructed with the goal of minimizing the loss of the third-stage load value; input the first-stage parameters into the first-stage model for initial value solution processing to obtain the target initial value; input the second-stage parameters into the second-stage model for integer variable solution processing to obtain the target integer variable; input the third-stage parameters into the third-stage model for integer variable solution processing to obtain the target real variable.

[0094] Among them, the first-stage model refers to a model built with the goal of minimizing the loss of load value in the first stage, which is used to solve the initial value of the power supply restoration problem of the active distribution network. In this stage, the integer variables are temporarily regarded as real variables for solution. The second-stage model refers to a model built with the goal of minimizing the loss of load value in the second stage, which is used to solve the integer variables in the power supply restoration problem of the active distribution network. The third-stage model refers to a model built with the goal of minimizing the loss of load value in the third stage, which is used to solve the real variables in the power supply restoration problem of the active distribution network. The target initial value refers to the initial value obtained by solving the first-stage model, which provides a good starting point for subsequent solutions. The target integer variable refers to the integer variable solution obtained by solving the second-stage model, which determines the network topology of the active distribution network. The target real variable refers to the real variable solution obtained by solving the third-stage model, which obtains accurate information such as the load and distributed power output.

[0095] In the embodiment of the present application, for solving the initial value of the first stage, different initialization methods can be tried, such as random initialization or initialization based on empirical values. At the same time, the objective function and constraints of the first stage model can be adjusted to better adapt to the actual problem.

[0096] In the second stage of integer variable solution, different projection methods can be used to calculate the projection values ​​of integer variables. In addition, different ADMM algorithm variants or other optimization algorithms can be tried to improve the solution efficiency and accuracy.

[0097] For solving the real variables in the third stage, different numerical optimization algorithms can be used to solve subproblems 1 and 2. At the same time, the objective function and constraints of the third stage model can be adjusted according to actual conditions to better meet actual needs.

[0098] S302, determining a target recovery method according to a target initial value, a target integer variable and a target real number variable.

[0099] In the embodiment of the present application, the target initial value obtained in the first stage, the target integer variable obtained in the second stage, and the target real variable obtained in the third stage can be combined to obtain a complete solution to the power supply restoration problem of the active distribution network. According to this complete solution, the target restoration method is determined, that is, the power supply restoration plan of the active distribution network after a fault. The plan includes the adjustment of the switch state, the output control of the distributed power source, the recovery order of the load, and the like.

[0100] Another implementation method: The target initial value, target integer variable and target real number variable can be further optimized and adjusted to improve the performance of the power supply restoration scheme. For example, a heuristic algorithm or other optimization method can be used to search for a local solution to find a better solution. The target restoration method can be evaluated and verified according to actual conditions, such as by simulation experiments or actual applications to verify the effectiveness and feasibility of the scheme. If problems are found, the scheme can be adjusted and improved.

[0101] In the embodiment of the present application, a complete active distribution network power supply restoration scheme is obtained by combining the target initial value, the target integer variable and the target real number variable. This scheme takes into account the goal of minimizing the load value loss, and at the same time satisfies the conditions such as safe operation constraints, flow constraints and distributed power supply operation constraints, and can effectively restore the power supply of the active distribution network and improve the reliability and stability of the system. Optimizing and evaluating the target recovery method can further improve the performance and feasibility of the scheme, and ensure that the scheme can achieve good results in practical applications.

[0102] In an exemplary embodiment, based on the above embodiment, see Figure 4 , the method of the embodiment of the present application further includes the following steps:

[0103] S401, constructing an initial power supply restoration model based on pre-acquired power distribution system parameter information.

[0104] Among them, the initial power supply restoration model refers to the initial model constructed based on the distribution system parameter information, which provides a basis for the subsequent construction of models in each stage.

[0105] In the embodiments of the present application, more advanced data acquisition technologies and sensor equipment can be used to improve the accuracy and real-time performance of parameter information. For example, intelligent sensors and wireless communication technologies can be used to achieve real-time monitoring and acquisition of distribution network parameters. When constructing objective functions and constraints, more factors, such as environmental factors, economic factors, etc., can be considered to make the model more comprehensive and practical. For example, an environmental impact assessment of distributed power sources can be added, or factors such as the cost-effectiveness of power restoration can be considered.

[0106] S402: construct a first-stage model according to the initial power supply restoration model and the first solution model.

[0107] The first solution model is used to construct a specific model of the first stage model, which includes specific objective functions and constraints.

[0108] In the embodiment of the present application, the first solution model can be optimized and adjusted to better adapt to different active distribution network structures and fault conditions. For example, the weights of the objective function and the constraints can be adjusted according to the actual situation, or new constraints can be introduced to improve the accuracy and reliability of the model. When solving sub-problems, different optimization algorithms and solution methods can be tried to improve the calculation efficiency and solution accuracy. For example, heuristic algorithms, parallel computing and other technologies can be used to accelerate the solution process.

[0109] S403, constructing a second stage model according to the first stage model and the second solution model.

[0110] The second solution model is used to construct a specific model of the second-stage and third-stage models, including specific objective functions and constraints.

[0111] In the embodiment of the present application, the second solution model can be further optimized and adjusted according to the results of the first stage to improve the solution efficiency and accuracy of the second stage model. For example, the parameters of the objective function and the constraint conditions can be adjusted according to the initial value information obtained in the first stage, or a more efficient projection method can be used to calculate the projection value of the integer variable. When solving the sub-problem, it can be considered to initialize using the results of the first stage to speed up the convergence speed. For example, the solution obtained in the first stage can be used as the initial solution of the sub-problem in the second stage, and then iterative optimization can be performed.

[0112] S404: construct a third stage model according to the second stage model and the third solution model.

[0113] In the embodiment of the present application, the third solution model can be optimized and adjusted according to the results of the second stage to improve the solution efficiency and accuracy of the third stage model. For example, the parameters of the objective function and the constraint conditions can be adjusted according to the integer variable solution obtained in the second stage, or a more efficient numerical optimization algorithm can be used to solve the real number variables. When solving subproblems, it can be considered to initialize using the results of the second stage to speed up the convergence rate. For example, the solution obtained in the second stage can be used as the initial solution of the third stage subproblem, and then iterative optimization can be performed.

[0114] The embodiment of the present application can effectively solve the power supply restoration problem of the active distribution network after a fault by sequentially constructing the initial power supply restoration model, the first stage model, the second stage model and the third stage model. The method has the advantages of high computational efficiency, good solution accuracy, and applicability to large-scale systems, and provides an effective solution for improving the reliability and stability of the active distribution network.

[0115] In an exemplary embodiment, based on the above embodiment, see Figure 5The power distribution system parameter information of the embodiment of the present application includes at least one of the multiple nodes in the sub-area, the total active and reactive loads of each node, and the load importance weight value; based on the pre-acquired power distribution system parameter information, constructing an initial power supply restoration model includes the following steps:

[0116] S501, determining an objective function and a first constraint condition according to power distribution system parameter information;

[0117] S502: construct an initial power supply restoration model according to the objective function and the first constraint condition.

[0118] Among them, the objective function is based on the total active load, the load importance weight value, and the load loss rate; the objective function is used to characterize the priority of different loads in power supply restoration. The parameters of the ADMM algorithm include: the convergence threshold and penalty factor of the ADMM algorithm.

[0119] The objective function is to minimize the load value loss, and linearized safety operation constraints, power flow constraints, and distributed power generation operation constraints are adopted, where:

[0120] The objective function of minimizing the load value loss is expressed as formula (1):

[0121]

[0122] Among them, Ω z represents the set of nodes in sub-region z; represents the total amount of active load of node i; e i represents the load importance weight value of node i, l i is the load loss rate of node i, which means the proportion of the unrecovered load of node i in all loads.

[0123] The linearized safety operation constraints, power flow constraints, distributed generation operation constraints and topology constraints are expressed as formula (2):

[0124]

[0125] Among them, Ω i,p represents the set of parent nodes of node i; P hi represents the active power flowing from node h to node i; P i G represents the active power injected by the distributed generation of node i; Ω i,c represents the set of child nodes of node i; P ij represents the active power flowing from node i to node j; represents the active power of the load at node i; Q hi represents the reactive power flowing from node h to node i; represents the reactive power injected by the distributed generation at node i; Q ij represents the reactive power flowing from node i to node j; Represents the reactive power of the load at node i; U i represents the square of the voltage at node i; U j represents the square of the voltage at node j; r ij represents the impedance of line ij; x ij Represents the impedance of line ij; Ω in represents a node that is not connected to a switch; represents the square of the voltage of node j outside the sub-region connected to the switch; Ω sw represents the node connected to the switch; Ω z′ represents the set of nodes in the sub-regions adjacent to sub-region z; It indicates the maximum square difference of voltage allowed across the two ends when the switch is turned on; is an integer variable representing the state of the switch, Indicates that the switch is closed. Indicates the switch is on; represents the minimum voltage allowed at node i; represents the maximum voltage allowed at node i; M represents a very large number, which is 10000; S ij is an auxiliary variable; represents the capacity of the line between nodes i and j; Indicates the active power limit of distributed generation; Indicates the reactive power limit of distributed generation; is the variable representing the flow direction of the branch, Indicates that the power flow direction of the switch branch is from node i to node j; is the variable representing the flow direction of the branch, Indicates that the power flow direction of the switch branch is from node j to node i; represents a collection of sub-areas equipped with distributed generation; represents the set of all sub-regions;

[0126] In the active distribution network power restoration model, is an integer variable and the rest are real number variables.

[0127] The embodiment of the present application determines the objective function and the first constraint condition according to the distribution system parameter information, constructs an initial power supply restoration model, and provides a basis for the construction and solution of subsequent stage models. The method has the advantages of high computational efficiency, good solution accuracy, and applicability to large-scale systems.

[0128] In an exemplary embodiment, based on the above embodiment, see Figure 6The embodiment of the present application relates to constructing a first-stage model according to an initial power supply restoration model and a first solution model, including the following steps:

[0129] S601, converting integer variables of an initial power supply restoration model into real number variables to obtain a first restoration model.

[0130] In the embodiment of the present application, in the initial power supply restoration model, there may be some integer variables, such as variables representing switch states. In order to facilitate solving in the first stage, these integer variables are converted into real variables.

[0131] Another implementation method: When performing real variable conversion processing, different conversion methods and ranges can be used. For example, integer variables can be converted into continuous real variable intervals according to actual conditions, or other relaxation methods can be used. The converted real variables can be further constrained and adjusted to ensure that they are within a reasonable range and can better approximate the behavior of integer variables.

[0132] S602, a first stage model is formed by a first recovery model and a first solution model.

[0133] The first solution model contains specific objective functions and constraints to meet the solution requirements of the first stage.

[0134] The embodiment of the present application combines the first recovery model and the first solution model to form a first stage model. The objective function of the first stage model can be a combination of the objective function of the first recovery model and the objective function of the first solution model, and the constraint condition can be a combination of the constraint condition of the first recovery model and the constraint condition of the first solution model.

[0135] In the embodiment of the present application, the first stage model is composed of the first recovery model and the first solution model, which can make full use of the advantages of the two models and improve the accuracy and adaptability of the first stage model. The first recovery model provides an initial model framework based on real variable conversion, while the first solution model provides specific objective functions and constraints based on the characteristics of the first stage. The combination of the two can better solve the initial value of the active distribution network power supply restoration problem.

[0136] In an exemplary embodiment, based on the above embodiment, see Figure 7 The construction process of the first solution model in the embodiment of the present application includes the following steps:

[0137] S701, decompose the objective function to obtain multiple first-stage objective functions; the first-stage objective function is based on the first iterative variable and the first dual variable; based on the first convergence parameter obtained in advance, construct the first convergence condition; the first convergence parameter includes the current boundary variable and the first K boundary variables of the first iterative variable.

[0138] S702, determining a first solution model according to each first-stage objective function, a first convergence condition and a first constraint condition.

[0139] The active distribution network power restoration problem in phase 1 is decomposed into sub-problem 1 and sub-problem 2, where:

[0140] The objective function of sub-problem 1 is expressed as formula (3), and the constraints are the constraints of the active distribution network power restoration model of the first stage established in step 3:

[0141]

[0142] in, represents the active power flowing from node i to node j in the internal branch of the sub-area; represents the reactive power flowing from node i to node j in the internal branch of the sub-area; Represents the square of the voltage of the boundary node; ρk represents the penalty factor at the kth iteration; N represents the variable of subproblem 2 obtained in the k-1th iteration; sw represents the set of boundary nodes in sub-region z, N p Represents the set of parent nodes of node i, N c Represents the set of child nodes of node i; represents the dual variable obtained in the k-1th iteration;

[0143] The objective function of sub-problem 2 is expressed as formula (4), and the constraints are the constraints of the active distribution network power restoration model of the first stage established in step 3:

[0144]

[0145] in, Represents the variable obtained at the kth iteration The value of Represents the sub-problem 2 variable.

[0146] (2) According to formula (5), solve sub-problem 1:

[0147] X *k =argminf 1 (X, Y*k-1 , Λ *,k-1 ) (5)

[0148] Among them, variable X represents the variables of sub-problem 1, including U i , l i , X br Represents the sub-region internal variables in variable X, including U i , l i ; represents the active power flowing from node i to node j in the internal branch of the sub-area; represents the reactive power flowing from node i to node j in the internal branch of the sub-area; X sw Represents the boundary variables in variable X, including The variable Y represents the variables of sub-problem 2, including The variable Λ represents the dual variable, including μ ij , k ij , π i , χ ij , ij , σ i ,φ ij ,ω ij , X *,k represents the value of variable X solved at the kth iteration; Y *,k-1 represents the value of Y solved in the k-1th iteration, Λ *,k-1 represents the value of the dual variable Λ obtained in the k-1th iteration;

[0149] (3) According to formula (6), solve sub-problem 2:

[0150] Y *，k =argminf II (x *，k ,Y,Λ *，k-1 ) (6)

[0151] Among them, Y *,k Represents the value of variable Y solved at the kth iteration;

[0152] (4) Update the dual variables according to formula (7):

[0153]

[0154] Among them, Λ *,k represents the value of the dual variable Λ solved at the kth iteration; represents the value of the boundary variable in the variable X solved at the kth iteration;

[0155] (5) According to formula (8), judge the convergence:

[0156]

[0157] Among them, ε represents the convergence threshold of the ADMM algorithm, Represents the residual of distributed computing. When formula (8) is satisfied, the ADMM algorithm converges. Otherwise, it returns to (2) and continues iterative computing until convergence.

[0158] In the embodiment of the present application, the first stage model is composed of the first recovery model and the first solution model, which can make full use of the advantages of the two models and improve the accuracy and adaptability of the first stage model. The first recovery model provides an initial model framework based on real variable conversion, while the first solution model provides specific objective functions and constraints based on the characteristics of the first stage. The combination of the two can better solve the initial value of the active distribution network power supply restoration problem.

[0159] In an exemplary embodiment, based on the above embodiment, see Figure 8 The embodiment of the present application relates to constructing a second-stage model according to the first-stage model and the second solution model, including the following steps:

[0160] S801, performing integer variable retention processing on the first recovery model to obtain a second recovery model.

[0161] In the embodiment of the present application, in the first stage model, the first recovery model is obtained by converting the integer variables of the initial power supply recovery model into real variables. When constructing the second stage model, the first recovery model needs to be processed to retain integer variables to focus on solving the integer variables.

[0162] S802, a second stage model is formed by a second recovery model and a second solution model.

[0163] Among them, the second solution model contains specific objective functions and constraints, which are used to construct the second and third stage models.

[0164] In the embodiment of the present application, the second recovery model and the second solution model are combined to form a second stage model. The objective function of the second stage model can be a combination of the objective function of the second recovery model and the objective function of the second solution model, and the constraint condition can be a combination of the constraint condition of the second recovery model and the constraint condition of the second solution model.

[0165] The embodiment of the present application is composed of a second recovery model and a second solution model to form a second stage model, which can fully utilize the advantages of the two models to improve the accuracy and adaptability of the second stage model. The second recovery model provides a model framework based on integer variable retention processing, while the second solution model provides specific objective functions and constraints based on the characteristics of the second stage. The combination of the two can better solve the integer variables in the power supply restoration problem of the active distribution network.

[0166] In an exemplary embodiment, based on the above embodiment, see Fig. 9 The construction process of the second solution model in the embodiment of the present application includes the following steps:

[0167] S901, solve the first-stage model to obtain the initial value of the first-stage model, and perform parameter conversion on the first constraint condition according to the initial value of the first-stage model to obtain the second constraint condition; simplify the objective function of the initial power supply recovery model, remove the part containing real variables, and obtain the second-stage objective function; the second-stage objective function is based on the second iterative variable and the second dual variable; the projection function is composed of the obtained integer variables and the projection parameters; based on the pre-acquired second convergence parameters, construct the second convergence condition; the second convergence parameters include the current boundary variables and the previous K boundary variables of the second iterative variable.

[0168] S902, constructing a second solution model according to the second constraint condition, each second stage objective function, the projection function, and the second convergence condition.

[0169] The active distribution network power restoration problem in phase 1 is decomposed into sub-problem 1 and sub-problem 2, where:

[0170] The objective function of sub-problem 1 is expressed as formula (10), and the constraints are the constraints of the active distribution network power restoration model of the second stage established in step 4:

[0171]

[0172] The initial value of the active distribution network power restoration problem obtained in step 3 is As The value of

[0173] The objective function of sub-problem 2 is expressed as formula (11), and the constraints are the constraints of the active distribution network power restoration model of the second stage established in step 4:

[0174]

[0175] in, express The projection value of

[0176] According to formula (12), solve sub-problem 1:

[0177] X *，k =argminf I (, Y *-1 ,Λ *，k-1 ) (12)

[0178] According to formula (13), we get The projection value of ;

[0179]

[0180] Among them, proj Represents the projection function, which transforms the real variable Round variables to integers;

[0181] According to formula (14), solve sub-problem 2:

[0182] Y *，k =argminf II (X *，k ,Y,Λ *，k-1 ) (14)

[0183] Among them, Y *,k Represents the value of variable Y solved at the kth iteration;

[0184] According to formula (15), update the dual variables:

[0185]

[0186] According to formula (16), convergence is judged as:

[0187]

[0188] When formula (16) is satisfied, the ADMM algorithm converges, otherwise it returns to (2) and continues iterative calculation until convergence.

[0189] In an embodiment of the present application, the second solution model constructed by the second constraint condition, each second-stage objective function, the projection function and the second convergence condition can more accurately describe the integer variable solution process in the active distribution network power supply restoration problem, and improve the efficiency and accuracy of the solution.

[0190] In an exemplary embodiment, based on the above embodiment, see Fig.10 The embodiment of the present application relates to constructing a third-stage model based on the second-stage model and the third solution model, including the following steps:

[0191] S1001, assigning values ​​to integer variable solutions of the second recovery model to obtain a third recovery model.

[0192] In the embodiment of the present application, in the process of solving the second stage model, solutions of integer variables are obtained, which represent the values ​​of key decision variables such as the switch state in the active distribution network.

[0193] The second recovery model is subjected to assignment processing of the solutions of the integer variables, that is, the solutions of the integer variables obtained in the second stage are substituted into the second recovery model, and the corresponding integer variables are assigned.

[0194] After the assignment process, the third recovery model is obtained. The objective function and constraint conditions of the third recovery model are basically the same as those of the second recovery model, but the integer variables have been determined to be the values ​​obtained by solving the second stage.

[0195] S1002, a third stage model is formed by a third recovery model and a third solution model.

[0196] The third solution model of the embodiment of the present application includes a specific objective function and constraints for constructing a third stage model. The third recovery model and the third solution model are combined to form a third stage model. The objective function of the third stage model can be a combination of the objective function of the third recovery model and the objective function of the third solution model, and the constraints can be a combination of the constraints of the third recovery model and the constraints of the third solution model.

[0197] The embodiment of the present application is composed of a third recovery model and a third solution model to form a third stage model, which can make full use of the advantages of the two models and improve the accuracy and adaptability of the third stage model. The third recovery model provides a model framework based on integer variable solution assignment processing, while the third solution model provides specific objective functions and constraints based on the characteristics of the third stage. The combination of the two can better solve the real variables in the active distribution network power supply restoration problem.

[0198] In an exemplary embodiment, based on the above embodiment, see Fig.11 The embodiment of the present application relates to constructing a third stage model according to a third recovery model and a third solution model, and includes the following steps:

[0199] S1101, assigning a value to the objective function of the initial power supply recovery model, removing the part containing integer variables, and obtaining the third stage objective function; constructing a third convergence condition based on the third convergence parameter obtained in advance; the third stage objective function is based on the third iterative variable and the third dual variable; the second convergence parameter includes the current boundary variable of the third iterative variable and the first K boundary variables of the third iterative variable.

[0200] S1102, forming a third solution model by using the pre-acquired third constraint conditions, the third stage objective functions, and the third convergence conditions.

[0201] The active distribution network power supply restoration problem in the third stage is decomposed into sub-problem 1 and sub-problem 2, where:

[0202] The objective function of sub-problem 1 is expressed as formula (17), and the constraints are the constraints of the active distribution network power supply restoration model of the third stage established in step 5:

[0203]

[0204] The objective function of sub-problem 2 is expressed as formula (18), and the constraints are the constraints of the active distribution network power restoration model of the third stage established in step 5:

[0205]

[0206] According to formula (19), solve sub-problem 1:

[0207] X *，k =argminf I (X, Y *，k-1 ,Λ *，k-1 ) (19)

[0208] According to formula (20), solve sub-problem 2:

[0209] Y *，k =argminf II (X *，k ,Y,Λ *，k-1 ) (20)

[0210] According to formula (21), update the dual variables:

[0211]

[0212] According to formula (22), the convergence is judged as:

[0213]

[0214] When formula (7) is satisfied, the ADMM algorithm converges, otherwise it returns to (2) and continues iterative calculation until convergence.

[0215] The obtained solution of the integer variables of the active distribution network power supply restoration problem and the obtained solution of the real variables of the active distribution network power supply restoration problem are combined as the solution of the established active distribution network power supply restoration model, thereby completing the solution of the active distribution network power supply restoration problem.

[0216] A specific example is given below to verify the effect of this application.

[0217] It should be understood that, although the above-mentioned implementations first input the impedance value of the line element in the improved IEEE 123 node system, the active power reference value and power factor of the load element, and the network topology connection relationship, the example structure is as follows: Fig.12 As shown, detailed parameters are shown in Table 1, Table 2, Table 3 and Table 4; nodes 39, 48, 85, 104, 250, and 450 are respectively connected to controllable distributed power sources, and their active output limit is 300kW and reactive output limit is 200kva r ; Node 300 is connected to the external power grid, the active output limit is 1MW, and the reactive output limit is 800kva r The system's reference voltage is set to 4.16 kV and the reference power is set to 1 MVA. Each value in the system is normalized. Finally, the upper and lower limits of the voltage amplitude (normalized value) of each node are set to be 1.05 and 0.95 respectively. The initial value of the penalty parameter ρ is 0.1, and the iterative convergence standard ε is 0.001.

[0218] Table 1 Load access location and power of the improved IEEE 123 node example

[0219]

[0220] Table 2 Improved IEEE 123 node load importance weight values

[0221] Node Number Load weight Node Number Load weight 1 1.290 64 1.887 2 0.753 65 1.275 3 0.382 66 1.915 4 0.857 67 0.481 5 0.964 68 1.352 6 0.241 69 0.578 7 1.179 70 1.344 8 0.452 71 1.390 9 0.769 72 0.136 10 1.166 73 0.510 11 0.504 74 0.448 12 0.581 75 1.336 13 1.234 76 1.689 14 0.531 77 0.689 15 1.649 78 1.561 16 1.965 79 1.351 17 1.461 80 0.013 18 0.688 81 1.204 19 1.168 82 0.774 20 0.216 83 1.832 21 1.813 84 0.002 22 1.759 85 0.925 23 1.636 86 0.849 24 0.522 87 0.922 25 1.189 88 1.540 26 0.045 89 0.645 27 0.851 90 1.570 28 0.625 91 0.943 29 0.323 92 0.072 30 0.358 93 0.352 31 0.846 94 1.444 32 0.189 95 0.947 33 1.197 96 0.305 34 0.942 97 0.682 35 1.392 98 1.215 36 1.400 99 0.384 37 1.277 100 1.477

[0222] Table 3 Improved IEEE 123 node line parameters

[0223]

[0224] Table 4 Switch branch parameters of the improved IEEE 123 node example

[0225] Switch No. Head end End Resistance (Ω) Reactance(Ω) 1 13 18 0.0042 0.0095 2 18 135 0.0019 0.0044 3 13 152 0.0020 0.0047 4 60 160 0.0018 0.0041 5 72 76 0.0010 0.0023 6 97 197 0.0013 0.0029 7 39 66 0.0013 0.0029 8 54 94 0.0013 0.0029 9 151 300 0.0013 0.0029

[0226] Two schemes are used for comparative analysis:

[0227] Scheme I: using the method of this application for treatment;

[0228] Scheme II: Use P-ADMM algorithm for processing.

[0229] The computer hardware environment for performing optimization calculations is Intel(R)Cor e (TM)i7-10750H, main frequency is 2.59GHz, memory is 16GB; software environment is Windows 11 operating system.

[0230] The improved IEEE123 node network partitioning results using the method (Scheme I) of this application are as follows: Fig.13 As shown in the figure, the power supply restoration strategy is solved as Fig.14 Scheme II is used to verify the difference in the number of iterations of solving the power restoration strategy problem of Scheme I. The power restoration strategy solved by Scheme II is shown in Fig.15 shown.

[0231] By adopting the method of the present application, after a fault occurs, the distributed solution of the power supply restoration problem can be completed within a relatively small number of iterations to obtain a power supply restoration strategy. The method reduces the number of iterations with an acceptable optimization effect. Taking the IEEE123 node network as an example, the calculation time required for Scheme I and Scheme II is shown in Table 5. Compared with Scheme II, the present application can effectively reduce the number of iterations.

[0232] Table 5 Comparison of the computation time required for Scheme I and Scheme II

[0233] plan Iterations The time for each iteration Calculation time plan 51 0.12 6.12s Solution Method II 111 0.27 29.97s

[0234] The various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indications of the arrows, but these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed 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 part of the steps or stages in other steps.

[0235] Based on the same inventive concept, the embodiment of the present application also provides a device for determining a power supply restoration mode for implementing the method for determining a power supply restoration mode involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more power supply restoration mode determination devices provided below can refer to the limitations of the power supply restoration mode determination method above, and will not be repeated here.

[0236] In one embodiment, Fig.16 As shown, a device 1200 for determining a power supply restoration mode is provided, comprising:

[0237] The data acquisition module 1201 is used to acquire multiple sub-areas and sub-area parameters of a pre-divided active power distribution system;

[0238] The method determination module 1202 is used to perform phased solution processing on each sub-area based on a pre-constructed target power supply restoration model and sub-area parameters to obtain a target recovery method; the target power supply restoration model is constructed based on the goal of minimizing the load value loss in each stage; the phased solution includes initial value solution, integer variable solution and real number variable solution.

[0239] In one embodiment, the sub-region parameters include first-stage parameters, second-stage parameters, and third-stage parameters; the target power supply restoration model includes a first-stage model, a second-stage model, and a third-stage model; and the mode determination module includes:

[0240] The target initial value determination unit is used to input the first-stage parameters into the first-stage model for initial value solution processing to obtain the target initial value; the first-stage model is constructed with the goal of minimizing the first-stage load value loss;

[0241] The target integer variable determination unit is used to input the second-stage parameters into the second-stage model to perform integer variable solution processing to obtain the target integer variable; the second-stage model is constructed with the goal of minimizing the loss of the second-stage load value;

[0242] The real variable determination unit is used to input the third-stage parameters into the third-stage model to perform integer variable solution processing to obtain the target real number variables; the third-stage model is constructed with the goal of minimizing the third-stage load value loss;

[0243] The method determination unit is used to determine the target recovery method according to the target initial value, the target integer variable and the target real number variable.

[0244] In one embodiment, the above device further comprises:

[0245] An initial model determination subunit, used to construct an initial power supply restoration model based on pre-acquired power distribution system parameter information;

[0246] A first model determination subunit is used to construct a first-stage model according to the initial power supply restoration model and the first solution model;

[0247] The second model determination subunit is used to construct a second stage model according to the first stage model and the second solution model;

[0248] The third model determination subunit is used to construct a third stage model according to the second stage model and the third solution model.

[0249] In one embodiment, the power distribution system parameter information includes at least one of a plurality of nodes in the sub-area, the total active and reactive loads of each node, and a load importance weight value; the initial model determination subunit is specifically used for:

[0250] According to the distribution system parameter information, determine the objective function and the first constraint condition; the first constraint condition includes safety operation constraint, power flow constraint and distributed power supply operation constraint; the objective function is based on the total active load, load importance weight value and load loss rate; the objective function is used to characterize the priority of different loads in power supply restoration;

[0251] According to the objective function and the first constraint, an initial power supply restoration model is constructed.

[0252] In one embodiment, the first model determination subunit is specifically used for:

[0253] Performing real number variable conversion processing on integer variables of the initial power supply restoration model to obtain a first restoration model;

[0254] The first stage model is composed of the first recovery model and the first solution model.

[0255] In one embodiment, the first model determination subunit is further specifically configured to:

[0256] Decomposing the objective function to obtain a plurality of first-stage objective functions; the first-stage objective function is based on the first iteration variable and the first dual variable;

[0257] Based on the first convergence parameter obtained in advance, constructing a first convergence condition; the first convergence parameter includes the current boundary variable and the first K boundary variables of the first iteration variable;

[0258] A first solution model is determined according to each first-stage objective function, a first convergence condition and a first constraint condition.

[0259] In one embodiment, the second model determination subunit is specifically used for:

[0260] Performing integer variable retention processing on the first recovery model to obtain a second recovery model;

[0261] The second stage model is composed of the second recovery model and the second solution model.

[0262] In one embodiment, the second model determination subunit is further specifically configured to:

[0263] Solving the first-stage model to obtain the initial value of the first-stage model, and performing parameter conversion on the first constraint condition according to the initial value of the first-stage model to obtain the second constraint condition;

[0264] The objective function of the initial power supply restoration model is simplified by removing the part containing real number variables to obtain the second stage objective function; the second stage objective function is based on the second iteration variable and the second dual variable;

[0265] The projection function is composed of the obtained integer variable and the projection parameter;

[0266] Based on the pre-acquired second convergence parameters, constructing a second convergence condition; the second convergence parameters include the current boundary variable and the first K boundary variables of the second iteration variable;

[0267] A second solution model is constructed according to the second constraint condition, each second stage objective function, the projection function, and the second convergence condition.

[0268] In one embodiment, the third model determination subunit is further specifically configured to:

[0269] Assigning the solutions of the integer variables to the second recovery model to obtain a third recovery model;

[0270] The third stage model is composed of the third recovery model and the third solution model.

[0271] In one embodiment, the third model determination subunit is specifically used for:

[0272] Assigning a value to the objective function of the initial power supply restoration model, removing the part containing the integer variable, and obtaining the third stage objective function; the third stage objective function is based on the third iteration variable and the third dual variable;

[0273] Based on the third convergence parameter obtained in advance, a third convergence condition is constructed; the second convergence parameter includes the current boundary variable of the third iteration variable and the first K boundary variables of the third iteration variable;

[0274] The third solution model is composed of the pre-acquired third constraint conditions, the third stage objective functions, and the third convergence conditions.

[0275] Each module in the above power supply recovery mode determination device can be implemented in whole or in part by software, hardware and a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0276] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig.17As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining a power recovery mode is implemented.

[0277] Those skilled in the art will understand that Fig.17 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0278] In one embodiment, 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:

[0279] Acquire a plurality of sub-areas and sub-area parameters of a pre-divided active power distribution system;

[0280] For each sub-area, based on the pre-built target power supply restoration model and sub-area parameters, the sub-area is solved in stages to obtain the target restoration method; the target power supply restoration model is constructed with the goal of minimizing the load value loss in each stage; the staged solution includes initial value solution, integer variable solution and real number variable solution.

[0281] According to some embodiments of the present application, there is also provided a non-transitory computer-readable storage medium including instructions, such as a memory including instructions, and the instructions can be executed by a processor of an electronic device to complete the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0282] According to some embodiments of the present application, a computer program product is also provided, and when the computer program is executed by a processor, the above method can be implemented. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, part or all of the above method can be implemented in whole or in part according to the process or function described in the embodiment of the present application.

[0283] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0284] The technical features of the above embodiments may 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.

[0285] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining a power supply restoration mode, characterized in that: The method comprises: Acquire a plurality of sub-areas and sub-area parameters of a pre-divided active power distribution system; For each of the sub-areas, based on a pre-constructed target power supply restoration model and the sub-area parameters, the sub-area is solved in stages to obtain a target recovery method; the target power supply restoration model is constructed based on the goal of minimizing the load value loss in each stage; the staged solution includes initial value solution, integer variable solution and real number variable solution.

2. The method according to claim 1, characterized in that The sub-region parameters include first-stage parameters, second-stage parameters, and third-stage parameters; the target power supply restoration model includes a first-stage model, a second-stage model, and a third-stage model; based on the pre-built target power supply restoration model and the sub-region parameters, the sub-region is solved in stages to obtain a target restoration method, including: Inputting the first stage parameters into the first stage model for initial value solution processing to obtain target initial values; the first stage model is constructed with the goal of minimizing the first stage load value loss; The second stage parameters are input into the second stage model for integer variable solving to obtain a target integer variable; the second stage model is constructed with the goal of minimizing the loss of the second stage load value; The third-stage parameters are input into the third-stage model for integer variable solving to obtain target real number variables; the third-stage model is constructed with the goal of minimizing the third-stage load value loss; The target recovery method is determined according to the target initial value, the target integer variable and the target real number variable.

3. The method according to claim 2, characterized in that The method further comprises: Based on the pre-acquired distribution system parameter information, an initial power supply restoration model is constructed; Constructing the first stage model according to the initial power supply restoration model and the first solution model; Constructing the second-stage model according to the first-stage model and the second solution model; The third stage model is constructed according to the second stage model and the third solution model.

4. The method according to claim 3, characterized in that The power distribution system parameter information includes at least one of the multiple nodes in the sub-area, the total active and reactive loads of each node, and the load importance weight value; the initial power supply restoration model is constructed based on the pre-acquired power distribution system parameter information, including: According to the power distribution system parameter information, determine the objective function and the first constraint condition; the first constraint condition includes a safe operation constraint, a power flow constraint and a distributed power supply operation constraint; the objective function is based on the total active load, the load importance weight value, and the load loss rate; the objective function is used to characterize the priority of different loads in power supply restoration; The initial power supply restoration model is constructed according to the objective function and the first constraint condition.

5. The method according to claim 4, characterized in that The step of constructing the first-stage model according to the initial power supply restoration model and the first solution model includes: Performing real number variable conversion processing on integer variables of the initial power supply restoration model to obtain a first restoration model; The first stage model is composed of the first recovery model and the first solution model.

6. The method according to claim 5, characterized in that The construction process of the first solution model includes: Decomposing the objective function to obtain a plurality of first-stage objective functions; the first-stage objective function is based on a first iteration variable and a first dual variable; Based on the first convergence parameter obtained in advance, constructing a first convergence condition; the first convergence parameter includes the current boundary variable and the first K boundary variables of the first iteration variable; The first solution model is determined according to each of the first-stage objective functions, the first convergence condition and the first constraint condition.

7. The method according to claim 6, characterized in that The step of constructing the second-stage model according to the first-stage model and the second solution model comprises: Performing integer variable retention processing on the first recovery model to obtain a second recovery model; The second stage model is composed of the second restoration model and the second solution model.

8. The method according to claim 7, characterized in that The construction process of the second solution model includes: Solving the first-stage model to obtain initial values ​​of the first-stage model, and performing parameter conversion on the first constraint condition according to the initial values ​​of the first-stage model to obtain second constraint conditions; The objective function of the initial power supply restoration model is simplified by removing the part containing real number variables to obtain a second-stage objective function; the second-stage objective function is based on a second iterative variable and a second dual variable; The projection function is composed of the obtained integer variable and the projection parameter; Based on the pre-acquired second convergence parameter, constructing a second convergence condition; the second convergence parameter includes the current boundary variable and the first K boundary variables of the second iteration variable; The second solution model is constructed according to the second constraint condition, each second-stage objective function, the projection function, and the second convergence condition.

9. The method according to claim 8, characterized in that The step of constructing the third stage model according to the second stage model and the second solution model comprises: Assigning values ​​to the solutions of the integer variables of the second recovery model to obtain a third recovery model; The third stage model is composed of the third restoration model and the third solution model.

10. The method according to claim 9, characterized in that The construction process of the third solution model includes: Assigning a value to the objective function of the initial power supply restoration model, removing the part containing the integer variable, and obtaining the third stage objective function; the third stage objective function is based on the third iteration variable and the third dual variable; Based on the third convergence parameter obtained in advance, constructing a third convergence condition; the second convergence parameter includes the current boundary variable of the third iteration variable and the first K boundary variables of the third iteration variable; The third solution model is composed of the pre-acquired third constraint conditions, each of the third stage sub-functions, and the third convergence condition.

Images