A method, system, device and medium for ensuring power supply with electromagnetic full topology optimization

Abstract

The present invention relates to the technical field of power system optimization and control, and discloses a method, system, equipment and medium for ensuring power supply through electromagnetic full-topology optimization. The method comprises: dividing the power grid and acquiring parameters such as power grid parameters and dynamic topology variables in real time to adapt to electromagnetic changes and load fluctuations in the power grid; and reconstructing the topology based on the optimization results of the global state matrix, so that the reconstructed topology model can not only meet the power supply efficiency requirements, but also effectively avoid the risk of disconnection caused by electromagnetic interference, thereby effectively ensuring power supply.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and control, and in particular to a method, system, equipment and medium for ensuring power supply through electromagnetic full topology optimization. Background Art

[0002] Traditional power grid topology optimization methods primarily focus on the economic efficiency of static structures (such as minimizing line losses), but ignore the impact of dynamic electromagnetic field changes on topological stability. However, in actual power grid operation, electromagnetic fields are in a state of dynamic change. For example, electromagnetic transients can occur in fault scenarios, potentially leading to problems such as resonance and harmonic amplification. Similarly, when the power grid faces dynamically changing load conditions, such as sudden increases or decreases in load, static topologies cannot adapt in a timely manner.

[0003] Therefore, due to its own limitations, traditional topology optimization methods cannot adapt to the changes in electromagnetic fields and rapid fluctuations in loads under dynamic loads, resulting in problems such as unstable operation and decreased power supply quality in the power grid under dynamic loads. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method, system, device and medium for ensuring power supply with electromagnetic full topology optimization, which can solve the problems of unstable power grid operation and deteriorated power supply quality caused by changes in electromagnetic fields under dynamic loads.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for ensuring power supply through electromagnetic full topology optimization, comprising:

[0008] Acquire a first real-time grid parameter of the target grid, and establish a first topology model according to the first real-time grid parameter;

[0009] The target power grid includes new energy power supply units and other power supply units;

[0010] Dividing the target power grid into a plurality of subnets, obtaining electromagnetic field distributions of the plurality of subnets, and coupling the electromagnetic field distributions of the respective subnets to obtain a total electromagnetic field distribution;

[0011] Acquire a second real-time grid parameter of the target grid and a dynamic topology variable for representing the electrical connection state between the subgrids, and establish a first variable matrix and a second node admittance matrix;

[0012] Optimizing the global state matrix, wherein the optimization result includes an updated first variable matrix and a second node admittance matrix;

[0013] The global state matrix is constructed by the first variable matrix, the second node admittance matrix and the total electromagnetic field distribution;

[0014] The first topology model is reconstructed according to the updated first variable matrix and the second node admittance matrix.

[0015] As a preferred solution of the electromagnetic full topology optimization power supply guarantee method of the present invention, wherein: dividing the target power grid into a plurality of subnets and obtaining the electromagnetic field distribution of the plurality of subnets includes:

[0016] Preset the first improved heuristic algorithm;

[0017] inputting the first real-time power grid parameter and the first topology model into the first improved heuristic algorithm;

[0018] The first improved heuristic algorithm is used to output a network division result to divide the target power grid into a plurality of subnets.

[0019] As a preferred solution of the electromagnetic full topology optimization power supply guarantee method of the present invention, the first improved heuristic algorithm includes:

[0020] performing a first marking operation on the first topology model;

[0021] The first marking operation is used to mark all renewable energy power supply units in the target power grid;

[0022] Obtaining the impedance of the new energy power supply unit in the marked first topology model, and obtaining the electrical coupling degree according to the impedance;

[0023] The electrical coupling degree is used as an influencing factor and introduced into a first objective function of the heuristic algorithm to obtain a first improved heuristic algorithm.

[0024] As a preferred solution of the electromagnetic full topology optimization power supply guarantee method of the present invention, wherein: coupling the electromagnetic field distributions of the subnets to obtain the total electromagnetic field distribution includes:

[0025] Applying electromagnetic field equations to each of the subnets, and performing time-space discrete solutions to each of the electromagnetic field equations based on the finite element method to obtain the electromagnetic field distribution of each of the subnets;

[0026] The electromagnetic field distribution structures of the subnets are coupled through boundary conditions to obtain the total electromagnetic field distribution.

[0027] As an optimal solution of the electromagnetic full topology optimization power supply guarantee method described in the present invention, the total electromagnetic field distribution includes the vector of the real-time load of each node in the first topology model, the electromagnetic field coupling coefficient between subnets, and the electromagnetic field strength at the subnet boundary.

[0028] As a preferred solution of the electromagnetic full topology optimization power supply guarantee method of the present invention, the first marking operation includes:

[0029] Mark all nodes corresponding to the new energy power supply unit as key dynamic nodes;

[0030] The electrical coupling degree of a node pair formed by an i-th node and a j-th node is obtained, and at least one of the i-th node and the j-th node is the key dynamic node.

[0031] As a preferred solution of the electromagnetic full topology optimization power supply guarantee method of the present invention, the first marking operation further includes:

[0032] Set the electrical coupling weight threshold;

[0033] If the electrical coupling degree corresponding to any node pair in the first topology model exceeds the electrical coupling degree weight threshold, the corresponding node pair is forcibly merged.

[0034] In a second aspect, the present invention provides an electromagnetic full topology optimized power supply system, comprising:

[0035] a model building module, configured to obtain a first real-time grid parameter of a target grid and build a first topology model according to the first real-time grid parameter;

[0036] The target power grid includes new energy power supply units and other power supply units;

[0037] a division module, configured to divide the target power grid into a plurality of subnets, obtain electromagnetic field distributions of the plurality of subnets, and couple the electromagnetic field distributions of the respective subnets to obtain a total electromagnetic field distribution;

[0038] a matrix acquisition module, configured to acquire a second real-time grid parameter of the target grid and a dynamic topology variable representing the electrical connection state between the subgrids, and to establish a first variable matrix and a second node admittance matrix;

[0039] An optimization module, configured to optimize the global state matrix, wherein the optimization result includes an updated first variable matrix and a second node admittance matrix;

[0040] The global state matrix is constructed by the first variable matrix, the second node admittance matrix and the total electromagnetic field distribution;

[0041] A reconstruction module is used to reconstruct the first topology model according to the updated first variable matrix and the second node admittance matrix.

[0042] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-described method when executing the computer program.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described above when the computer program is executed by a processor.

[0044] Compared with the prior art, the present invention has the following beneficial effects: the present invention proposes a method, system, device, and medium for ensuring power supply through electromagnetic full topology optimization, which obtains first real-time grid parameters of a target power grid and establishes a first topology model based on the first real-time grid parameters. The target power grid is divided into several subnets, and the electromagnetic field distributions of the several subnets are obtained. The electromagnetic field distributions of the subnets are coupled to obtain a total electromagnetic field distribution. Second real-time grid parameters of the target power grid and dynamic topological variables used to represent the electrical connection status between subnets are obtained. A first variable matrix and a second node admittance matrix are established, and the global state matrix is optimized. The first topology model is reconstructed based on the updated first variable matrix and second node admittance matrix. By dividing the power grid and obtaining parameters such as grid parameters and dynamic topological variables in real time to adapt to electromagnetic changes and load fluctuations in the power grid, the topology is reconstructed based on the optimization results of the global state matrix, so that the reconstructed topology model can not only meet the power supply efficiency requirements but also effectively avoid the risk of disconnection caused by electromagnetic interference, thereby effectively ensuring power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0046] Figure 1 A flow chart of a method for ensuring power supply through electromagnetic full topology optimization is provided in accordance with an embodiment of the present invention.

[0047] Figure 2 An internal structural diagram of a computer device providing an electromagnetic full topology optimization power supply guarantee method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0049] Example 1, with reference to Figure 1-Figure 2 , which is the first embodiment of the present invention, provides a method for ensuring power supply through electromagnetic full topology optimization, comprising:

[0050] Existing technologies present several challenges. For example, traditional static topology optimization methods are unable to adapt promptly to dynamically changing load conditions, leading to unstable grid operation and reduced power supply quality. Traditional grid topology optimization methods primarily employ static models to describe the physical topological connections between grid devices. Once the system model is established, it remains relatively stable, with only the addition or modification of new equipment causing changes to the static topology model. However, this approach struggles to adapt to changes in electromagnetic fields and rapid load fluctuations under dynamic loads. In some cases, power generators may disconnect from the grid, impacting power supply capacity.

[0051] This application provides a method that can effectively solve the above-mentioned problems. Next, we will explain in detail how to implement the electromagnetic full topology optimization power supply method with multiple embodiments.

[0052] Figure 1 A flow chart of a method for ensuring power supply through electromagnetic full topology optimization is shown, including:

[0053] S101, obtaining first real-time grid parameters of a target grid, and establishing a first topology model according to the first real-time grid parameters;

[0054] In an embodiment of the present application, the target power grid includes new energy power supply units and other power supply units.

[0055] In an optional embodiment, the other power supply units may also include at least one of a traditional thermal power generation unit, a hydropower generation unit, a wind power generation unit, or a solar power generation unit to ensure stable power supply and diversified energy supply to the power grid. These different types of power supply units may have different operating and electrical characteristics. Therefore, when establishing the first topology model, these differences need to be fully considered to ensure the accuracy and practicality of the model.

[0056] In an optional embodiment, the first real-time power grid parameter may be collected by deploying information collection devices at key nodes of the power grid, including but not limited to substations, output terminals of power plants, and access points for large power-consuming devices. The information collection devices include but are not limited to sensors, such as current sensors, voltage sensors, and power sensors. Power grid parameters include but are not limited to current, voltage, and power.

[0057] In an optional embodiment, the information collection device transmits the collected power grid parameters to a data processing center in real time via wired or wireless communication; the data processing center cleans, filters and integrates the received power grid parameter data; removes noise data and abnormal data to ensure the accuracy and reliability of the data.

[0058] In the embodiments of the present application, any software capable of constructing a topology model can be used to establish a first topology model that accurately reflects the current operating state of the power grid based on processed power grid parameters. Commonly used software capable of constructing a topology model includes, but is not limited to, software tools such as PSS / E and MATPOWER. These tools can automatically generate a topology model of the power grid using a power system analysis algorithm based on input power grid parameters (such as node voltage, current, power, etc.) and network structure information (such as line impedance, transformer parameters, etc.). This model represents generators, substations, and other key equipment in the power grid as nodes, and depicts the electrical connection relationships between them based on the actual connection conditions.

[0059] It should be noted that obtaining the first real-time grid parameters of the target power grid and establishing a first topology model based on the first real-time grid parameters can ensure that the models used in subsequent steps are accurate and reliable and can reflect the current actual situation of the power grid. By obtaining grid parameters in real time, the dynamic changes of the power grid, including load fluctuations and changes in the electromagnetic field, can be captured, thereby providing basic data support for subsequent power grid optimization. In addition, establishing the first topology model can also provide the necessary model foundation for subsequent operations such as grid partitioning, electromagnetic field distribution coupling, global state matrix optimization, and topology reconstruction, ensuring the smooth progress of the entire electromagnetic full topology optimization process.

[0060] S102, dividing the target power grid into several subnets, obtaining electromagnetic field distributions of the several subnets, and coupling the electromagnetic field distributions of the subnets to obtain a total electromagnetic field distribution;

[0061] In an optional embodiment, there are various ways to divide subnets, such as by geographic location, type of power supply unit, or load characteristics. The subnets should be kept relatively independent as much as possible, while also considering electromagnetic coupling effects between subnets to ensure accurate acquisition and coupling of electromagnetic field distribution in subsequent steps.

[0062] In an optional embodiment, a preset partitioning algorithm may be used to partition the target power grid into a plurality of subnets. The partitioning algorithm may automatically determine the partitioning boundaries and the number of subnets based on the structural characteristics and operating parameters of the power grid.

[0063] In an optional embodiment, after the division is completed, an electromagnetic field analysis is performed on each subnet to obtain the electromagnetic field distribution of each subnet.

[0064] In an optional embodiment, the electromagnetic field analysis may adopt numerical methods such as finite element method, finite difference method or boundary element method to obtain high-precision electromagnetic field distribution results.

[0065] In the embodiment of the present application, the target power grid is divided into several subnets, and obtaining the electromagnetic field distribution of the several subnets includes:

[0066] Preset the first improved heuristic algorithm;

[0067] inputting the first real-time power grid parameter and the first topology model into the first improved heuristic algorithm;

[0068] The first improved heuristic algorithm is used to output a network division result to divide the target power grid into several subnetworks.

[0069] Specifically, a first topology model of the target power grid is obtained. The objective function of the heuristic algorithm is defined as the first objective function for ease of distinction. Based on the first objective function, subnets are divided to ensure a relatively balanced load distribution within each subnet, thereby reducing power transmission losses between subnets. The heuristic algorithm can be any of a variety of algorithms, such as a genetic algorithm, a greedy algorithm, or a community algorithm. Here, the genetic algorithm is used as an example for illustration.

[0070] Integer coding is used to represent the grid structure. Assuming that the grid contains N nodes, the chromosome is represented as:

[0071] ,

[0072] Among them, c i ∈{1,2,...,k} represents the subnet number to which the i-th node belongs, k is the preset number of subnets, and N represents the total number of nodes. Tests on the IEEE 33-node system (a standard power system topology model) show that this encoding method can reduce the probability of invalid solutions by 28.6% compared to binary encoding.

[0073] Furthermore, the first objective function of the genetic algorithm is:

[0074] ,

[0075] Among them, F blanceis the load balancing degree, ɑ and β represent weight coefficients, and their sum is 1. loss For loss assessment items:

[0076] ,

[0077] ,

[0078] Where, P m is the total load of subnet m, is the average load, σ(S) represents the standard deviation, and S is a display variable representing the standard deviation and has no specific meaning.

[0079] Furthermore, F loss For loss assessment items:

[0080] ,

[0081] Where m represents the mth subnet, Γ m Indicates the internal branch of subnet m, is the transmission power between subnets, η is the cross-network loss coefficient (usually ranging from 0.85 to 1.2), R mn is the resistance between subnets, I mn is the current between subnets.

[0082] Furthermore, M chromosomes that satisfy topological connectivity are generated, and the connectivity of each subnet is guaranteed by the Prim algorithm (minimum spanning tree algorithm); the operator adopts a tournament selection strategy, and the selection pressure coefficient is set to 0.75.

[0083] Furthermore, we set the crossover operator C':

[0084] ,

[0085] In the formula, 、 is a random cut point, is the subnet mapping function; C p is the parent individual, C q For offspring individuals.

[0086] Furthermore, a directed mutation strategy is implemented, and the mutation probability of the mutation operator is set to p m = 0.05, and iterative optimization is performed based on the above content to obtain the network division results. In addition, convergence judgment requirements are set, such as: the intergenerational improvement rate is less than 0.1%, the population diversity index is greater than 0.25, and the maximum number of iterations is 200, then the iteration is stopped, and the results are output and the network is divided based on the results.

[0087] In the embodiment of the present application, the electromagnetic field equation is applied to each subnet, and each electromagnetic field equation is solved in time and space based on the finite element method to obtain the electromagnetic field distribution of each subnet. Here, the corresponding result can be obtained by using the Max electromagnetic equation.

[0088] In the embodiments of the present application, the electromagnetic field distribution structures of each subnet are coupled through boundary conditions to obtain the electromagnetic field distribution of the power grid. Specifically, boundary conditions such as the electric field and magnetic field strength at the subnet boundaries are clearly defined, and then coupling equations are established accordingly. During this process, the boundary element method can be used to express the boundary electromagnetic field variables as integral equations, or any other coupling method can be used to achieve this, which will not be described in detail here. The obtained electromagnetic field distribution of the subnet is then used to solve the coupling equations, taking into account the mutual influence between the subnets and performing multiple iterations to finally obtain the total electromagnetic field distribution.

[0089] It should be noted that in traditional power systems, power generation primarily relies on traditional methods such as thermal power generation, resulting in relatively stable dynamic characteristics. Conventional simulation methods can be used to construct a topology diagram of the electromagnetic transient network and then perform simulations. However, with the integration of renewable energy sources (such as wind power and photovoltaics), the power output has become more volatile and uncertain, with unstable dynamic characteristics, further increasing the volatility of the power grid. To this end, based on the above-mentioned embodiment, an improved first heuristic algorithm is used to optimize the network partitioning process.

[0090] In the embodiment of the present application, the first improved heuristic algorithm includes:

[0091] performing a first marking operation on the first topology model;

[0092] The first marking operation is used to mark all renewable energy power supply units in the target power grid;

[0093] Obtaining the impedance of the new energy power supply unit in the marked first topology model, and obtaining the electrical coupling degree according to the impedance;

[0094] Taking the electrical coupling degree as an influencing factor, the first objective function of the heuristic algorithm is introduced to obtain the first improved heuristic algorithm.

[0095] The above-mentioned "electrical coupling" refers to an indicator that measures the strength of the electrical connection between renewable energy power supply units or other grid components. It can be obtained by calculating the impedance of renewable energy power supply units, which usually involves analyzing the impedance value between specific nodes. Specifically, it can be obtained by formula Z ij Electrical coupling is evaluated using the impedance between nodes i and j (which will be explained in detail later). Higher impedance indicates lower electrical coupling, and vice versa. This concept is primarily used to optimize the subnetting process, ensuring that nodes with high electrical coupling are assigned to the same subnet to maintain their physical association.

[0096] In this embodiment of the present application, the first marking operation includes:

[0097] Mark all nodes corresponding to new energy power supply units as key dynamic nodes;

[0098] An electrical coupling degree of a node pair formed by an i-th node and a j-th node is obtained, and at least one of the i-th node and the j-th node is a key dynamic node.

[0099] In this embodiment of the present application, the first marking operation further includes:

[0100] Set the electrical coupling weight threshold;

[0101] If the electrical coupling degree corresponding to any node pair in the first topology model exceeds the electrical coupling degree weight threshold, the corresponding node pair is forcibly merged.

[0102] In the embodiment of the present application, the electromagnetic field distributions of the subnets are coupled to obtain a total electromagnetic field distribution including:

[0103] Apply the electromagnetic field equation to each subnet and solve each electromagnetic field equation in time and space based on the finite element method to obtain the electromagnetic field distribution of each subnet;

[0104] The electromagnetic field distribution structures of each subnet are coupled through boundary conditions to obtain the total electromagnetic field distribution.

[0105] In the embodiment of the present application, the total electromagnetic field distribution includes the vector of the real-time load of each node in the first topology model, the electromagnetic field coupling coefficient between subnets, and the electromagnetic field strength at the subnet boundary.

[0106] Specifically, a first marking operation is performed on all renewable energy power supply units in the power grid to generate a first topology model containing the markings;

[0107] Furthermore, the electrical coupling degree of the renewable energy power supply unit is obtained based on the impedance of the renewable energy power supply unit, and the electrical coupling degree is introduced into the first objective function of the heuristic algorithm as an influencing factor to obtain an improved heuristic algorithm; based on the improved heuristic algorithm, the electromagnetic transient network is divided into N subnets.

[0108] It should be noted that, since the locations of renewable energy power generation units in the power grid are known, they can be marked in the topology diagram. This allows for the adjustment of subnets based on the marked renewable energy power generation units. During the subnetting process, an improved heuristic algorithm (using electrical coupling as a weight) and a segmentation method based on dynamic characteristic weights are introduced. This ensures that strongly coupled nodes are assigned to the same subnet to maintain physical connectivity, while also rationally dividing weakly coupled regions to decompose the electromagnetic transient network into N subnets capable of parallel computation, effectively reducing the scale complexity of a single simulation module. This ensures that the subnets are tightly coupled internally and weakly coupled between them, thereby reducing computational complexity while optimizing the subnetting results for better simulation results.

[0109] For example, unlike the genetic algorithm used in the previous embodiment, the heuristic algorithm in this example uses the Louvain algorithm (community discovery algorithm), which is improved to obtain an improved heuristic algorithm. Specifically, the first objective function is the block module in the community algorithm, and weights are introduced as influencing factors to simultaneously consider the topological connection density and the electrical coupling degree of the power grid, that is:

[0110] ;

[0111] in, is the total weight, i.e., the sum of the electrical coupling degrees of the grid between all pairs of nodes in the grid; is the preset parameter, It is an indicator function (or characteristic function) used to determine whether nodes i and j are divided into the same subnet. Represent the degrees of node i and node j respectively. The "degree" here is a concept in graph theory, which is used to describe how many other nodes a node is directly connected to.

[0112] Therefore, the strongly coupled nodes are divided into the same subnet and operated in parallel with several other subnets, so that the subnetting result meets the characteristics of the new power grid that has both new energy power supply units and traditional power generation units.

[0113] In some embodiments, during the network segmentation process, all nodes corresponding to the renewable energy power supply units are marked as critical dynamic nodes (CDN) based on a topological model containing tags, thereby clustering and segmenting the network based on the dynamic characteristics and coupling degree of each renewable energy power supply unit and combined with electrical distance.

[0114] Specifically, by calculating the electrical coupling degree of the key dynamic node (CDN) :

[0115] ;

[0116] Among them, i and j refer to the i-th node and the j-th node; Z ij is the impedance between nodes i and j.

[0117] In other words, the electrical coupling It is the electrical coupling degree of a node pair formed by the i-th node and the j-th node, and each pair of nodes contains at least one key dynamic node.

[0118] It is worth noting that electrical coupling, as a physical indicator for quantifying the intensity of dynamic interaction between nodes, can reflect the electrical correlation between the new energy power supply unit (CDN) and adjacent equipment; therefore, network segmentation can be carried out based on electrical coupling, that is, electrical coupling As edge weights, a heuristic algorithm is introduced to improve electrical coupling as a weight, thereby constructing a weighted power grid topology map, prioritizing the merging of highly coupled nodes. This prioritizes the merging of highly coupled nodes, ensuring the electrical connectivity between nodes within the subnet; this connectivity is crucial for maintaining power grid stability. For example, when the output power of renewable energy power generation units fluctuates significantly, it allows for a more accurate assessment of the mutual impact between different subnets.

[0119] Then, set the coupling weight threshold , if the electrical coupling degree corresponding to any pair of nodes is If the threshold is exceeded, the corresponding node pairs are forced to merge, otherwise the network is divided according to the normal method.

[0120] Exemplarily, the nodes include node i and node j.

[0121] like , forcefully merge nodes i and j (even if the topology is disconnected and a new virtual connection is required);

[0122] like , allowing for conventional network division.

[0123] For example, the coupling weight threshold for:

[0124] ;

[0125] in, is the parameter, is the electrical coupling between all node pairs The maximum value in ; is the electrical coupling between all node pairs In this way, by adjusting the parameters , can flexibly control the coupling weight threshold , thereby achieving precise control of the power grid subdivision process.

[0126] When the electrical coupling between nodes exceeds this threshold, they will be forced to merge into the same subnet to ensure the electrical correlation within the subnet.

[0127] Therefore, based on the threshold judgment, it is possible to dynamically adjust the network division strategy according to the actual operating status of the power grid; based on the threshold judgment structure, the network division results can be adaptively adjusted dynamically; when the new energy power supply units produce large fluctuations, the sub-networks can be automatically refined, and the sub-networks can be merged when they are stable.

[0128] It should be noted that dividing the target power grid into several subgrids, obtaining the electromagnetic field distributions of these subgrids, and coupling the electromagnetic field distributions of each subgrid to obtain the total electromagnetic field distribution can significantly improve the accuracy and efficiency of electromagnetic field simulation. By rationally dividing the target power grid into several subgrids, solving the electromagnetic field for each subgrid separately, and then coupling the electromagnetic field distributions of each subgrid through boundary conditions, the electromagnetic field distribution of the entire power grid can be obtained. This method not only takes into account the complexity of the power grid but also reduces the computational difficulty and time through parallel computing. In addition, introducing electrical coupling as an influencing factor in the heuristic algorithm further optimizes the grid partitioning process, making the grid partitioning results more consistent with the actual operating status of the power grid, thereby improving the accuracy of the simulation. This electromagnetic full-topology optimization power supply system and method is of great significance for improving the stability and reliability of power systems.

[0129] S103, obtaining a second real-time grid parameter of the target grid and a dynamic topology variable used to represent the electrical connection status between subgrids, and establishing a first variable matrix and a second node admittance matrix;

[0130] In an embodiment of the present application, grid parameters and a dynamic topology variable z representing the electrical connection status between subnets are acquired in real time, and the dynamic topology variable z constitutes a first variable matrix Z; a plurality of admittance values are obtained according to the grid parameters, and a second node admittance matrix Y(t) is constructed based on the admittance values;

[0131] Specifically, the grid parameters and the dynamic topology variable z used to represent the electrical connection status between subgrids are obtained in real time, where z=0 is disconnected and z=1 is closed; the second node admittance matrix Y(t) is obtained based on the grid parameters.

[0132] In this method, the dynamic topology variable z is used to represent the real-time electrical connection status between subnets in the power grid. Specifically, when z = 0, the corresponding inter-subnet connection is disconnected, meaning there is no direct electrical connection path. When z = 1, the corresponding inter-subnet connection is closed, meaning there is a direct electrical connection path. In other words, by monitoring the changes in z, we can promptly understand changes in the power grid topology model.

[0133] For example, if the power grid is divided into subnets, dynamic topology variables Indicates the electrical connection status between subnet a and subnet b, defined as follows:

[0134] ,

[0135] By all The matrix formed is called the first variable matrix, denoted as Z, and its form is:

[0136] ,

[0137] Among them, each element describes the connection relationship between subnet a and subnet b; the matrix size is ,in Represents the total number of subnets; the elements on the diagonal It can be set to 1, indicating that the subnet is connected; the matrix is symmetrical, that is, , because the connection between subnets is bidirectional; this matrix reflects the current electrical connection status between subnets in the entire power grid and is a dynamically updated variable matrix.

[0138] The second node admittance matrix Y(t) is used to describe the electrical connection between each node in the power grid. The specific expression is given later.

[0139] In the embodiment of the present application, Y(t) is a time-varying matrix whose elements represent the admittance values between nodes. Admittance is the ratio of current to voltage, which reflects the degree of electrical coupling between nodes in the power grid. The admittance value can be calculated based on the power grid parameters, and the specific calculation process is not detailed here. Each element of the node admittance matrix Y(t) corresponds to the admittance value between a pair of nodes in the power grid, and the admittance value can be a real number or a complex number, depending on the frequency and impedance characteristics of the power grid.

[0140] It should be noted that acquiring the target grid's second real-time grid parameters and dynamic topological variables representing the electrical connectivity between subgrids, and establishing the first variable matrix and the second node admittance matrix, can reflect the grid's operating status in real time, providing foundational data for subsequent grid analysis and control. Acquiring second real-time grid parameters, such as voltage, current, and power, allows for understanding the grid's real-time load and operating status. Furthermore, the introduction of the dynamic topological variable z allows for real-time representation of the electrical connectivity between subgrids within the grid, which is crucial for analyzing the grid's topology and stability. The establishment of the first variable matrix Z and the second node admittance matrix Y(t) further provides a mathematical model for grid simulation and analysis. This foundation enables power flow calculations, stability analysis, and fault prediction, providing decision support for optimal grid scheduling and safe operation. Furthermore, acquiring real-time data and establishing the matrix enable intelligent grid management, contributing to the automation and intelligent development of the grid.

[0141] S104, optimizing the global state matrix, where the optimization result includes the updated first variable matrix and the second node admittance matrix;

[0142] In the embodiment of the present application, the global state matrix is constructed by the first variable matrix, the second node admittance matrix and the total electromagnetic field distribution. The specific construction method will be given later.

[0143] It should be noted that the global state matrix can be optimized by iteratively adjusting the elements in the matrix to improve the accuracy and efficiency of the power grid simulation.

[0144] In an optional embodiment, during the optimization process, the first variable matrix Z and the second node admittance matrix Y(t) in the global state matrix are modified by comprehensively considering the real-time operating status of the power grid, the electrical connectivity between subnets, and the electromagnetic field distribution. By continuously adjusting the element values in the matrix, the global state matrix more accurately reflects the actual operating status of the power grid.

[0145] In an optional embodiment, the optimization process also considers the dynamic characteristics and stability requirements of the power grid, ensuring that power grid analysis and control based on the optimized matrix are more reliable and effective. The specific optimization algorithm and iterative process can be selected and adjusted based on the actual situation and needs of the power grid to achieve the best optimization effect.

[0146] S105 , reconstructing the first topology model according to the updated first variable matrix and the second node admittance matrix.

[0147] In an embodiment of the present application, an updated dynamic first variable matrix Z and a second node admittance matrix Y(t) are obtained to reconstruct the topological model of the power grid to meet the working requirements of the power grid, thereby ensuring that the reconstructed topology not only meets the efficiency requirements but also avoids the risk of disconnection caused by electromagnetic interference, thereby achieving the purpose of ensuring power supply.

[0148] Specifically, the updated second node admittance matrix Y(t) and the first variable matrix Z are used as input parameters of the topology model to reconstruct the topology model based on the updated first variable matrix Z and the second node admittance matrix Y(t), and then S101-S104 are repeated.

[0149] For example, when the coupling field strength Φ between subnets m and n is detected mn When the limit is exceeded, the dynamic optimization model will automatically adjust z according to the output optimization results. mn = 0 (disconnected), the circuit breaker between subnets m and n will be disconnected. Once the connection between subnets m and n is broken, the admittance values between the relevant nodes return to zero, cutting off the power transmission path and forcing current redistribution, thereby updating the corresponding second-node admittance matrix Y(t). This redistribution of power flow through Y(t) ultimately triggers topology reconstruction, preventing unit disconnection caused by electromagnetic resonance.

[0150] It's understandable that by deploying information collection devices to collect various grid parameters in real time and building a topology model based on these parameters, dynamic changes in the grid's operating status can be captured promptly. Furthermore, by dividing the grid into multiple subnets and obtaining the electromagnetic field distribution of each subnet, and then coupling the boundary conditions to obtain the total electromagnetic field distribution, this allows for a more accurate description of the grid's complex electromagnetic environment. This allows for the real-time acquisition of dynamic topological variables and node admittance matrices, enabling dynamic analysis based on the actual grid connection status and changes in electrical connections.

[0151] Then, a global state matrix is constructed and optimized based on the grid parameters, dynamic topological variables, the node admittance matrix, and the total electromagnetic field distribution. The resulting optimization results affect the changes in the dynamic topological variables. Changes in the dynamic topological variables, in turn, lead to changes in the admittance values between related nodes, which in turn updates the node admittance matrix. The updated node admittance matrix serves as an input parameter for the topological model and is used to reconstruct the topological model.

[0152] The model optimization results after optimizing the global state matrix are then used to adjust dynamic topological variables and alter the grid's electrical connectivity based on factors such as excessive inter-subgrid coupling field strength. These topological variable changes directly impact the admittance matrix, causing the admittance values of relevant nodes to change to reflect the new electrical connectivity. The updated admittance matrix and dynamic variable matrix then become input parameters for the topological model, reconstructing the topological model. The system then returns to the information collection phase, looping through steps S101-S104 to capture grid changes in real time, continuously optimizing to improve power supply efficiency, mitigate disconnection risks, and ensure stable and reliable grid operation. This completes the closed-loop process of "model optimization results → topological variables → admittance matrix → topological model."

[0153] It should be noted that according to the electromagnetic full-topology optimization power supply guarantee method of the embodiment of the present application, the power grid is divided into sub-grids, and parameters such as power grid parameters and dynamic topology variables are obtained in real time to adapt to the electromagnetic changes and load fluctuations of the power grid; and the real-time acquired power grid parameters and the first variable matrix Z, the second node admittance matrix Y(t), and the total electromagnetic field distribution are used to construct a global state matrix, so as to perform topology reconstruction based on the optimization results of the global state matrix, so that the reconstructed topology model can not only meet the power supply efficiency requirements, but also effectively avoid the risk of disconnection caused by electromagnetic interference, thereby effectively guaranteeing power supply and ensuring that the power grid can provide stable and reliable power services to users.

[0154] In summary, the present invention proposes a method for ensuring power supply through electromagnetic full topology optimization, which obtains the first real-time grid parameters of the target power grid, establishes a first topology model based on the first real-time grid parameters, divides the target power grid into several subnets, obtains the electromagnetic field distribution of the several subnets, couples the electromagnetic field distributions of the subnets to obtain the total electromagnetic field distribution, obtains the second real-time grid parameters of the target power grid and dynamic topology variables used to represent the electrical connection status between subnets, establishes a first variable matrix and a second node admittance matrix, optimizes the global state matrix, and reconstructs the first topology model based on the updated first variable matrix and the second node admittance matrix. By dividing the power grid and obtaining parameters such as power grid parameters and dynamic topology variables in real time to adapt to electromagnetic changes and load fluctuations in the power grid, the topology is reconstructed based on the optimization results of the global state matrix, so that the reconstructed topology model can not only meet the power supply efficiency requirements, but also effectively avoid the risk of disconnection caused by electromagnetic interference, thereby effectively ensuring power supply.

[0155] Example 2: In a preferred embodiment, the optimization of the global state matrix can be achieved through the following specific steps, wherein:

[0156] The global state matrix is constructed based on the grid parameters, the first variable matrix Z, the second node admittance matrix Y(t), and the total electromagnetic field distribution. .

[0157] In this embodiment, the global state matrix needs to integrate the real-time operation status of the power grid, the topological connection relationship and the electromagnetic field coupling characteristics. Its structure is designed in the form of a block matrix. Then the global state matrix It can be expressed as follows:

[0158] ,

[0159] Among them, the second node admittance matrix Each element in is the admittance value, the second node The admittance matrix is expressed as:

[0160] ,

[0161] Where R ik is the resistance of the branch between node k and node i, X ik is the reactance of the branch between node k and node i, and j is an imaginary unit. The j here is just an imaginary unit and has nothing to do with the j mentioned above.

[0162] It is the electromagnetic field-topology interaction block, which is used to characterize the constraints of the electromagnetic field on the topological connection, that is, the variable matrix Z and the electromagnetic field strength at the subnet boundary direct product of electromagnetic field strength The specific calculation of is not described here.

[0163] It is a load-coupling matrix block, which is used to express the vector of the real-time load of each node in the topology model (because it is one-dimensional, it can also be understood as a vector). The elements are the real-time loads of each node, such as the real-time load P of node i. i :

[0164] P i =V i *I i *cosθ i

[0165] Where V i is the voltage, I i is the current, θ i is the phase angle.

[0166] is the coupling weight matrix, which is used to represent the electromagnetic field coupling coefficient between subnets. It can be obtained by solving the coupling equation by the boundary element method. The elements are the electromagnetic field coupling coefficients between each subnet, such as Φ mn is the electromagnetic field coupling coefficient between subnet m and subnet n.

[0167] Furthermore, a dynamic optimization model is constructed based on a multi-objective optimization algorithm to transform the global state matrix As input variables, the optimization results are output; here, the objective function of the dynamic optimization model is defined as the second objective function:

[0168] ,

[0169] Where, Tr(Y T LY) represents electrical performance, Y represents matrix Y(t), Y T is the transpose of the matrix Y(t), L is the grid topology Laplace matrix, L = D − A (D is the degree matrix, A is the adjacency matrix); is the electromagnetic compatibility (square of the norm), To constrain the electromagnetic coupling coefficient between subnets to be close to a reference value, usually set by electromagnetic safety standards, to avoid excessive coupling leading to harmonic resonance;

[0170] ∑z mn ⋅C mn is the topology switching cost, C mn is the switching cost, Z mn is the dynamic topology variable between subnet m and subnet n; λ1, λ2, λ3 are all coefficients.

[0171] Set constraints:

[0172] , to ensure that the divided subnets are connected and have no islands. rank(L(Z)) represents the rank of the Laplace matrix L(Z) adjusted based on the dynamic topology variable matrix Z. By dynamically adjusting the topology variable matrix Z, the power grid is ensured to be divided into k connected subnets, and the connectivity within each subnet is maintained to avoid islanding.

[0173] , in order to limit the maximum coupling strength between subnets.

[0174] , where Re(λ(Y)) represents the real part of the eigenvalue of matrix Y, is a preset positive threshold (or tolerance), so that the real part of the eigenvalue of matrix Y must be greater than the threshold − , ensuring small disturbance stability.

[0175] Furthermore, the optimization results obtained by the second objective function include the variable matrix Z and the node admittance matrix Y(t). For better distinction, they are defined here as the updated variable matrix Z and the node admittance matrix Y(t).

[0176] Therefore, the electromagnetic field-topology interaction block can be used to reflect the constraints of electromagnetic field changes on topological connections (for example, strong magnetic field interference causes the switch to automatically disconnect); and, Y(t) is a sparse matrix, so that Φ only needs to calculate the coupling of adjacent subnets, thereby reducing the computational complexity. For example, when Z changes, only the E of the affected subnet needs to be locally updated. field and Φ, rather than recalculating the global matrix, reducing the amount of calculation.

[0177] Embodiment 3: This embodiment also provides an electromagnetic full topology optimized power supply system, including:

[0178] A model building module, configured to obtain a first real-time grid parameter of the target grid and build a first topology model according to the first real-time grid parameter;

[0179] The target power grid includes new energy power supply units and other power supply units;

[0180] A division module is used to divide the target power grid into several subnets, obtain the electromagnetic field distribution of the several subnets, and couple the electromagnetic field distribution of each subnet to obtain the total electromagnetic field distribution;

[0181] A matrix acquisition module is used to obtain a second real-time grid parameter of the target grid and a dynamic topology variable used to represent the electrical connection status between subgrids, and to establish a first variable matrix and a second node admittance matrix;

[0182] An optimization module is used to optimize the global state matrix, and the optimization results include the updated first variable matrix and the second node admittance matrix;

[0183] The global state matrix is constructed by the first variable matrix, the second node admittance matrix and the total electromagnetic field distribution;

[0184] The reconstruction module is used to reconstruct the first topology model according to the updated first variable matrix and the second node admittance matrix.

[0185] The above-mentioned unit modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0186] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 2As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. Among them, 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 and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, it realizes an electromagnetic full-topology optimized power supply method. The display screen of the computer device can be a liquid crystal display 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 provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0187] This embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the following steps are implemented:

[0188] Acquiring a first real-time grid parameter of the target grid, and establishing a first topology model according to the first real-time grid parameter;

[0189] The target power grid includes new energy power supply units and other power supply units;

[0190] Divide the target power grid into several subnets, obtain the electromagnetic field distribution of several subnets, and couple the electromagnetic field distribution of each subnet to obtain the total electromagnetic field distribution;

[0191] Acquire a second real-time grid parameter of the target grid and a dynamic topology variable for representing an electrical connection state between subgrids, and establish a first variable matrix and a second node admittance matrix;

[0192] Optimize the global state matrix, and the optimization results include the updated first variable matrix and the second node admittance matrix;

[0193] The global state matrix is constructed by the first variable matrix, the second node admittance matrix and the total electromagnetic field distribution;

[0194] The first topology model is reconstructed according to the updated first variable matrix and the second node admittance matrix.

[0195] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0196] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application may be implemented using various computer languages.

[0197] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0198] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0199] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0200] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0201] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for ensuring power supply by electromagnetic full topology optimization, characterized in that: include: Acquire a first real-time grid parameter of the target grid, and establish a first topology model according to the first real-time grid parameter; The target power grid includes new energy power supply units and other power supply units; Dividing the target power grid into a plurality of subnets, obtaining electromagnetic field distributions of the plurality of subnets, and coupling the electromagnetic field distributions of the respective subnets to obtain a total electromagnetic field distribution; Acquire a second real-time grid parameter of the target grid and a dynamic topology variable for representing the electrical connection state between the subgrids, and establish a first variable matrix and a second node admittance matrix; Optimizing the global state matrix, wherein the optimization result includes an updated first variable matrix and a second node admittance matrix; The global state matrix is constructed by the first variable matrix, the second node admittance matrix and the total electromagnetic field distribution; Among them, the global state matrix It is expressed as follows: ， Among them, the second node admittance matrix Each element in is the admittance value, is the electromagnetic field-topology interaction block, is the load-coupling matrix block, is the coupling weight matrix; Based on the multi-objective optimization algorithm, a dynamic optimization model is constructed to transform the global state matrix As input variables, the optimization results are output; the objective function of the dynamic optimization model is defined as the second objective function: ， Where, Tr(Y T LY) represents electrical performance, Y represents matrix Y(t), Y T is the transpose of the matrix Y(t), L is the grid topology Laplace matrix, L=DA, D is the degree matrix, and A is the adjacency matrix; For electromagnetic compatibility, is the reference value of electromagnetic coupling coefficient between constrained subnets; ∑z mn ⋅C mn is the topology switching cost, C mn is the switching cost, Z mn is the dynamic topology variable between subnet m and subnet n; λ1, λ2, λ3 are all coefficients; The first topology model is reconstructed according to the updated first variable matrix and the second node admittance matrix.

2. The method for ensuring power supply by electromagnetic full topology optimization according to claim 1, characterized in that: The step of dividing the target power grid into a plurality of subnets and obtaining electromagnetic field distributions of the plurality of subnets includes: Preset the first improved heuristic algorithm; inputting the first real-time power grid parameter and the first topology model into the first improved heuristic algorithm; The first improved heuristic algorithm is used to output a network division result to divide the target power grid into a plurality of subnets.

3. The method for ensuring power supply by electromagnetic full topology optimization according to claim 2, characterized in that: The first improved heuristic algorithm includes: performing a first marking operation on the first topology model; The first marking operation is used to mark all renewable energy power supply units in the target power grid; Obtaining the impedance of the new energy power supply unit in the marked first topology model, and obtaining the electrical coupling degree according to the impedance; The electrical coupling degree is used as an influencing factor and introduced into a first objective function of the heuristic algorithm to obtain a first improved heuristic algorithm.

4. The method for ensuring power supply by electromagnetic full topology optimization according to claim 3, characterized in that: The coupling of the electromagnetic field distributions of the subnets to obtain the total electromagnetic field distribution includes: Applying electromagnetic field equations to each of the subnets, and performing time-space discrete solutions to each of the electromagnetic field equations based on the finite element method to obtain the electromagnetic field distribution of each of the subnets; The electromagnetic field distribution structures of the subnets are coupled through boundary conditions to obtain the total electromagnetic field distribution.

5. The method for ensuring power supply by electromagnetic full topology optimization according to claim 4, characterized in that: The total electromagnetic field distribution includes the vector of the real-time load of each node in the first topology model, the electromagnetic field coupling coefficient between the subnets, and the electromagnetic field intensity at the subnet boundary.

6. The method for ensuring power supply by electromagnetic full topology optimization according to claim 5, characterized in that: The first marking operation includes: Mark all nodes corresponding to the new energy power supply unit as key dynamic nodes; The electrical coupling degree of a node pair formed by an i-th node and a j-th node is obtained, and at least one of the i-th node and the j-th node is the key dynamic node.

7. The method for ensuring power supply by electromagnetic full topology optimization according to claim 6, characterized in that: The first marking operation further includes: Set the electrical coupling weight threshold; If the electrical coupling degree corresponding to any node pair in the first topology model exceeds the electrical coupling degree weight threshold, the corresponding node pair is forcibly merged.

8. An electromagnetic full topology optimized power supply system, applying the method according to any one of claims 1 to 7, characterized in that: include: a model building module, configured to obtain a first real-time grid parameter of a target grid and build a first topology model according to the first real-time grid parameter; The target power grid includes new energy power supply units and other power supply units; a division module, configured to divide the target power grid into a plurality of subnets, obtain electromagnetic field distributions of the plurality of subnets, and couple the electromagnetic field distributions of the respective subnets to obtain a total electromagnetic field distribution; a matrix acquisition module, configured to acquire a second real-time grid parameter of the target grid and a dynamic topology variable representing the electrical connection state between the subgrids, and to establish a first variable matrix and a second node admittance matrix; An optimization module, configured to optimize the global state matrix, wherein the optimization result includes an updated first variable matrix and a second node admittance matrix; The global state matrix is constructed by the first variable matrix, the second node admittance matrix and the total electromagnetic field distribution; A reconstruction module is used to reconstruct the first topology model according to the updated first variable matrix and the second node admittance matrix.

9. 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 electromagnetic full topology optimization power supply guarantee method according to any one of claims 1 to 7 are implemented.

10. 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 electromagnetic full topology optimization power supply guarantee method according to any one of claims 1 to 7 are implemented.

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