A power system structure optimization method, system, device and medium

Abstract

This invention discloses a method, system, equipment, and medium for power system structure optimization, relating to the field of power system structure optimization technology. The key technical points are: optimizing the potential energy function of branches based on the potential energy tolerance of different lines; calculating the limiting potential energy of each branch based on the optimized branch potential energy function; calculating the potential energy impact rate of branches based on the limiting potential energy; calculating the power system vulnerability index based on the total number of lines and the potential energy impact rate of branches; constructing a power system structure optimization model, wherein the power system structure optimization model includes minimizing the power system vulnerability index as the objective function and the power system operating state as the constraint condition; and solving the power system structure optimization model using a tabu search algorithm to obtain the optimized power system structure. This invention can effectively reduce the power system vulnerability index, thereby improving the power system security.

Description

Technical Field

[0001] This invention relates to the field of power system structure optimization technology, and more specifically, to a power system structure optimization method, system, equipment, and medium. Background Technology

[0002] The power system is a crucial infrastructure for ensuring the normal operation of power transmission and distribution.

[0003] Frequent large-scale power outages, whether caused by natural or man-made events, have resulted in severe socio-economic losses and impacts. Related technical analysis and simulations indicate that large-scale power outages often evolve from the failure and shutdown of vulnerable components in the power grid due to various reasons, subsequently triggering the failure of other components. Among these various types of incidents, line outages account for the highest proportion, demonstrating that the vulnerability of the topology and system can affect the security of the power system. However, related technologies do not address optimizing the power system structure based on vulnerability indicators.

[0004] Therefore, it is crucial to accurately analyze the vulnerability indicators of power systems and optimize the power system topology based on these indicators to effectively reduce system vulnerability and thus improve power system security. Summary of the Invention

[0005] To address the lack of specific techniques in related technologies for optimizing power system structure based on vulnerability indices, this invention provides a method, system, equipment, and medium for power system structure optimization. This invention considers the changes and limits of active and reactive power transmission in the branch potential energy function, enabling a more comprehensive evaluation of the impact of load fluctuations on the vulnerability of power system components. This yields a more accurate vulnerability index for the power system. Minimizing this vulnerability index is used as the objective function of the power system structure optimization model, thus constructing a vulnerability-oriented structural optimization model. The optimized structure is then obtained using a tabu search algorithm. Compared to traditional structural optimization models, this model demonstrates better vulnerability optimization performance, effectively reducing the vulnerability index of the power system and thereby improving its security.

[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0007] A first aspect of this application provides a method for optimizing the structure of a power system, comprising:

[0008] The potential energy function of the branch is optimized based on the degree of potential energy tolerance of different lines. The limiting potential energy of each branch is calculated based on the optimized potential energy function of the branch, where the limiting potential energy includes the forward limiting potential energy and the reverse limiting potential energy.

[0009] The potential energy impact rate of the branch is calculated based on the ultimate potential energy, and the vulnerability index of the power system is calculated from the total number of lines and the potential energy impact rate of the branches.

[0010] A power system structure optimization model is constructed, which includes a power system vulnerability index as the objective function and the power system operating state as the constraint condition.

[0011] The tabu search algorithm is used to solve the power system structure optimization model, and the optimization results of the power system structure are obtained.

[0012] In some possible implementations, the potential energy function of the branch is optimized based on the different levels of potential energy tolerance of the branch. The mathematical expression for the optimized potential energy function of the branch is as follows:

[0013] ,in, , They represent the lines respectively. The forward limiting potential energy and the reverse limiting potential energy, , These are the upper and lower limits of the voltage phase angle difference constraint, respectively. , These are the upper and lower limits of the voltage amplitude difference, respectively. This represents the active power of branch ij before the change in the state of the power system. B represents the reactive power of branch ij before the change of power system state. ij It is the susceptance of branch ij, G ij It is the conductance of branch ij, U i with U j These are the node voltage amplitudes at both ends of branch ij, U ij and These represent the voltage amplitude difference and phase angle difference between the nodes at both ends of branch ij, respectively.

[0014] In some possible implementations, when the branch potential energy is the positive limiting potential energy, the potential energy impulse rate of the branch is equal to the branch potential energy divided by the positive limiting potential energy.

[0015] Alternatively, when the branch potential energy is the negative limiting potential energy, the potential energy impulse rate of the branch is equal to the branch potential energy divided by the negative limiting potential energy.

[0016] In some possible implementations, the mathematical expression for calculating the vulnerability index of the power system from the total number of lines and the potential energy impulse rate of branches is as follows: Where H represents the total number of power system lines, b ij Let be the potential energy impact rate of branch ij.

[0017] In some possible implementation schemes, the constraints on the operating state of the power system include active power constraints, reactive power constraints, line transmission power constraints, bus voltage constraints, line current constraints, and generator active power constraints; among which,

[0018] The mathematical expression for active power constraints is: ;

[0019] The mathematical expression for reactive power constraints is: ;

[0020] The mathematical expression for the line transmission power constraint is: ;

[0021] The mathematical expression for bus voltage constraint is: ;

[0022] The mathematical expression for line current constraint is: ;

[0023] The mathematical expression for the generator active power constraint is: Among them, P Gi P is the active power injected into bus i of the generator. i Q is the active power load demand of bus i. Gi The reactive power Q is injected into the generator at bus i. i Let P be the reactive load demand of bus i, m be the number of lines connected to bus i, and P be the reactive load demand of bus i. ij P represents the active power of line ij. ijmax U represents the upper limit of active power transmission on line ij. i U is the node voltage magnitude of bus i. imin and U imax These represent the lower and upper limits of the node voltage amplitude of bus i, respectively. ij Let I be the current amplitude of line ij. ijmin and I ijmax These represent the lower and upper limits of the allowable current amplitude for line ij, respectively. This represents the active power output value of the generator. This represents the upper limit of the generator's active power output.

[0024] In some possible implementations, the constraints also include:

[0025] The two buses connected by the added edge in the power system belong to the original power network and cannot be equal to the existing edge; the shortest path between the two buses connected by the added edge cannot be greater than the shortest path threshold.

[0026] A second aspect of this application provides a power system structure optimization system, comprising:

[0027] The branch potential energy optimization module is used to optimize the potential energy function of the branch based on the different line's tolerance for branch potential energy. It calculates the limiting potential energy of each branch based on the optimized branch potential energy function, where the limiting potential energy includes the forward limiting potential energy and the reverse limiting potential energy.

[0028] The vulnerability index calculation module is used to calculate the potential energy impact rate of the branch based on the ultimate potential energy, and to calculate the vulnerability index of the power system based on the total number of lines and the potential energy impact rate of the branches.

[0029] The structural optimization model construction module is used to construct a power system structural optimization model, which includes a power system structural optimization model with the objective function of minimizing the power system vulnerability index and the power system operating state as a constraint condition.

[0030] The result calculation module is used to solve the power system structure optimization model using the tabu search algorithm to obtain the optimization results of the power system structure.

[0031] In some possible implementations, the potential energy function of the branch is optimized based on the different levels of potential energy tolerance of the branch. The mathematical expression for the optimized potential energy function of the branch is as follows:

[0032] ,in, , They represent the lines respectively. The forward limiting potential energy and the reverse limiting potential energy, , These are the upper and lower limits of the voltage phase angle difference constraint, respectively. , These are the upper and lower limits of the voltage amplitude difference, respectively. This represents the active power of branch ij before the change in the state of the power system. B represents the reactive power of branch ij before the change of power system state. ij It is the susceptance of branch ij, G ij It is the conductance of branch ij, U i with U j These are the node voltage amplitudes at both ends of branch ij, U ij and These represent the voltage amplitude difference and phase angle difference between the nodes at both ends of branch ij, respectively.

[0033] A third aspect of this application provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein the computer program, when executed by the processor, implements the steps of a power system structure optimization method as described in any one of the first aspects of this application.

[0034] A fourth aspect of this application provides a computer-readable storage medium storing a computer program, wherein when executed by a processor, the computer program implements the steps of a power system structure optimization method as described in any one of the first aspects of this application.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] This invention considers the changes and limits of active and reactive power transmission in the branch potential energy function, enabling a more comprehensive evaluation of the impact of load fluctuations on the vulnerability of power system components. This results in a more accurate vulnerability index for the power system. Minimizing the vulnerability index is used as the objective function of the power system structural optimization model, thus constructing a structural optimization model oriented towards power system vulnerability. The tabu search algorithm is then used to obtain the optimized structure of this model. Compared with traditional structural optimization models, this model exhibits better vulnerability optimization performance, effectively reducing the vulnerability index of the power system and thereby improving the security of the power system. Attached Figure Description

[0037] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0038] Figure 1 A flowchart illustrating a power system structure optimization method provided for the implementation of this application;

[0039] Figure 2 Topology diagram of the IEEE 39-node system provided for implementation of this application;

[0040] Figure 3 Power system vulnerability variation diagrams for different numbers of edge stripes provided for the implementation of this application;

[0041] Figure 4 A comparison chart of network connectivity metrics and average load rate for different algorithms when adding an edge, provided for the implementation of this application;

[0042] Figure 5 A schematic diagram of a power system structure optimization system provided for the implementation of this application. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0044] Frequent large-scale power outages, whether caused by natural or man-made events, have resulted in severe socio-economic losses and impacts. Related technical analysis and simulations indicate that large-scale power outages often evolve from the failure and shutdown of vulnerable components in the power grid due to various reasons, subsequently triggering the failure of other components. Among these various types of incidents, line outages account for the highest proportion, demonstrating that the vulnerability of the topology and system can affect the security of the power system. However, related technologies do not address optimizing the power system structure based on vulnerability indicators.

[0045] Therefore, it is crucial to accurately analyze the vulnerability indicators of power systems and optimize the power system topology based on these indicators to effectively reduce system vulnerability and thus improve power system security.

[0046] To address the lack of specificity in related technologies regarding power system structure optimization based on vulnerability indices, this application provides a power system structure optimization method, system, equipment, and medium. The method considers the changes and limits of active and reactive power transmission in the branch potential energy function, enabling a more comprehensive evaluation of the impact of load fluctuations on the vulnerability of power system components. This yields a more accurate vulnerability index for the power system. Minimizing this vulnerability index is used as the objective function of the power system structure optimization model, thus constructing a vulnerability-oriented structural optimization model. A tabu search algorithm is then used to obtain the optimized structure of this model. Compared to traditional structural optimization models, this method demonstrates better vulnerability optimization performance, effectively reducing the power system's vulnerability index and thereby improving power system security.

[0047] In this embodiment, the power system structure optimization method is applicable to electronic devices such as terminal devices or servers. The operating system of the electronic device may include, but is not limited to, Android, iOS, Symbian, BlackBerry, Windows Phone 8, etc., and this embodiment does not impose any limitations. In this embodiment, the electronic device may be equipped with a user interface (UI), an interface module, and a central processing unit (CPU).

[0048] The optimization methods provided in the embodiments of this application will be explained and described below. Please refer to [link / reference]. Figure 1 , Figure 1 A flowchart illustrating a power system structure optimization method provided in this application embodiment is shown below. Figure 1 As shown, the method includes the following steps:

[0049] S110 optimizes the potential energy function of the branch based on the different line's potential energy capacity, and calculates the limiting potential energy of each branch based on the optimized branch potential energy function, where the limiting potential energy includes the forward limiting potential energy and the reverse limiting potential energy.

[0050] In this embodiment, the potential energy function of the branch is common knowledge in the art, that is, after the power system undergoes a change in operating state, all state variables of the components may change, and this change can be represented by the branch potential energy, as shown in the following formula:

[0051] In the formula, E ij Let represent the branch potential energy of branch ij. and These represent the active and reactive components of the potential energy in the branch, respectively. Branches representing the state changes of the power system active power, B represents the reactive power of branch ij before the change of power system state. ij It is the susceptance of branch ij, G ij It is the conductance of branch ij, U i with U j These are the node voltage amplitudes at both ends of branch ij, U ij and Branch roads The difference in node voltage amplitude and phase angle at both ends. Although the traditional branch potential energy function mentioned above considers the state information of the power system, it ignores the differences in the branch potential energy capacity of different lines. Therefore, in this embodiment, the branch potential energy function is optimized based on the branch potential energy capacity of different lines, so that the optimized branch potential energy function can be used to assess the vulnerability of the power system.

[0052] S120 calculates the potential energy impact rate of the branch based on the ultimate potential energy, and calculates the vulnerability index of the power system based on the total number of lines and the potential energy impact rate of the branches.

[0053] In this embodiment, as can be seen from step S120 above, the limiting potential energy includes either the positive limiting potential energy or the reverse limiting potential energy. Therefore, the potential energy impact rate of the branch should also include both cases. Thus, the final vulnerability index also includes the index under both the positive and reverse limiting potential energy conditions.

[0054] S130, Construct a power system structure optimization model, which includes a power system structure optimization model with the minimum vulnerability index of the power system as the objective function and the operating state of the power system as the constraint condition.

[0055] Here, it's important to understand that in a power network, denoted as network P=(V,E), where V is the set of all buses, i.e. E represents the set of all lines, i.e. Let A(P) be the adjacency matrix of network P, and its elements are defined as: ,in, Indicates connecting bus v i With v j The edges. Given the number of edges W to be added. add Under certain constraints, add W to network P. add The question concerns how to add edges to lower the vulnerability index of the power system. Therefore, this embodiment uses minimizing the power system vulnerability index as the objective function and the operating state of the power system as a constraint to construct a power system structure optimization model. Based on this model, minimizing the power system vulnerability index as the objective function is suitable for heuristic algorithm coding and movement methods in power systems. Specific steps include:

[0056] First, the power system is encoded. Assume that a given P-row, Q-column power system network structure adjacency matrix M can be represented as follows: In M, a value of 1 in row p and column q indicates a connection between bus p and bus q, while a value of 0 indicates no connection. Encoding is performed based on its upper triangular matrix. Positions with values ​​of 0 in the upper triangular matrix are designated as positions to be added edges. These positions are encoded in row-matrix order, resulting in the binary encoding form: , where n is the number of 0s in the upper triangular matrix. If a certain position is a side-adding position, it is 1 in binary encoding, otherwise it is 0.

[0057] Secondly, in terms of the heuristic optimization algorithm's movement method, the movement operation is to randomly reconnect one edge at a time. That is, randomly select a position that is 1 in the edge addition position encoding sequence and set it to 0; randomly select a position that is 0 in the edge addition sequence and set it to 1.

[0058] S140 uses the tabu search algorithm to solve the power system structure optimization model and obtains the optimization results of the power system structure.

[0059] In this embodiment, the steps of the tabu search algorithm to solve the power system structure optimization model are as follows: First, the network is encoded according to the encoding method to generate an initial solution and calculate the fitness value, which is used as the current solution and the optimal solution. Second, it is determined whether the stopping criterion is met. If it is met, the optimal solution is obtained and the optimized network is output. Otherwise, a new candidate solution set is generated using the moving method, the fitness values ​​of all candidate solution sets are calculated, and a relatively optimal solution is found. Third, it is determined whether the desire level is met. If it is, the optimal solution, the current solution, and the tabu list are directly updated. Otherwise, it is determined whether the relatively optimal solution is in the tabu list. If it is, the relatively optimal solution is searched according to the fitness value. If it is not, the current solution and the tabu list are updated. Finally, it is determined whether the stopping criterion is met. If it is met, the optimization result of the power system structure is output. If it is not met, the moving iteration continues. The above process is based on the conventional approach of the tabu search algorithm in heuristic algorithms. Since the power system structure optimization model constructed in this embodiment is a mixed integer nonlinear optimization (MINLP) problem, the tabu search algorithm cannot be directly applied. Therefore, it is necessary to make adaptive settings for its constituent elements, such as the encoding, solution initialization, and fitness values ​​in the above process.

[0060] In one embodiment, the potential energy function of a branch is optimized based on the degree of potential energy tolerance of different lines. The mathematical expression for the optimized potential energy function of the branch is as follows:

[0061] ,in, , They represent the lines respectively. The forward limiting potential energy and the reverse limiting potential energy, , These are the upper and lower limits of the voltage phase angle difference constraint, respectively. , These are the upper and lower limits of the voltage amplitude difference, respectively. Branches representing the state changes of the power system active power, Branches representing the state changes of the power system reactive power, B ij It is the susceptance of branch ij, G ij It is the conductance of branch ij, U i with U j These are the node voltage amplitudes at both ends of branch ij, U ij and These represent the voltage amplitude difference and phase angle difference between the nodes at both ends of branch ij, respectively.

[0062] In one embodiment, when the branch potential energy is the positive limiting potential energy, the potential energy impact rate of the branch is equal to the branch potential energy divided by the positive limiting potential energy.

[0063] Alternatively, when the branch potential energy is the negative limiting potential energy, the potential energy impulse rate of the branch is equal to the branch potential energy divided by the negative limiting potential energy.

[0064] Specifically, in this embodiment, the potential energy impact rate of the branch is calculated as follows:

[0065] In the formula, b ij E represents the potential energy impact rate of the branch. ij Let represent the branch potential energy of branch ij.

[0066] In one embodiment, the mathematical expression for calculating the vulnerability index of the power system from the total number of lines and the potential energy impact rate of branches is as follows: Where H represents the total number of power system lines, b ij Let be the potential energy impact rate of branch ij.

[0067] In this embodiment, since the power system structure optimization model described above needs to optimize the power system structure by minimizing the vulnerability index, the mathematical expression of the objective function for minimizing the vulnerability index is as follows:

[0068] .

[0069] In one embodiment, the constraints on the operating state of the power system include active power constraints, reactive power constraints, line transmission power constraints, bus voltage constraints, line current constraints, and generator active power constraints; wherein,

[0070] The mathematical expression for active power constraints is: ;

[0071] The mathematical expression for reactive power constraints is: ;

[0072] The mathematical expression for the line transmission power constraint is: ;

[0073] The mathematical expression for bus voltage constraint is: ;

[0074] The mathematical expression for line current constraint is: ;

[0075] The mathematical expression for the generator active power constraint is: Among them, P Gi P is the active power injected into bus i of the generator. i Q is the active power load demand of bus i. Gi The reactive power Q is injected into the generator at bus i. iLet P be the reactive load demand of bus i, m be the number of lines connected to bus i, and P be the reactive load demand of bus i. ij P represents the active power of line ij. ijmax U represents the upper limit of active power transmission on line ij. i U is the node voltage magnitude of bus i. imin and U imax These represent the lower and upper limits of the node voltage amplitude of bus i, respectively. ij Let I be the current amplitude of line ij. ijmin and I ijmax These represent the lower and upper limits of the allowable current amplitude for line ij, respectively. This represents the active power output value of the generator. This represents the upper limit of the generator's active power output.

[0076] Specifically, in this embodiment, the active power constraint, reactive power constraint, line transmission power constraint, bus voltage constraint, and line current constraint are all conventional constraints in existing power systems, and therefore will not be elaborated upon further. This embodiment mainly proposes the mathematical expression for the generator active power constraint as follows: This constraint means that the active power output of the generator cannot exceed its upper limit. The generator active power constraint defined in this embodiment can prevent a generator from generating more active power than its actual output limit when no generator power constraint is set, thus avoiding discrepancies with reality. Here, "gen" is the abbreviation for generator.

[0077] In some possible implementations, the constraints also include:

[0078] The two buses connected by the added edge in the power system belong to the original power network and cannot be equal to the existing edge; the shortest path between the two buses connected by the added edge cannot be greater than the shortest path threshold.

[0079] Specifically, the two buses to which the edge is added should belong to the original power network and cannot be equal to the existing edge. Considering the impracticality of adding an edge between two buses that are too far apart, the shortest path between the two buses is constrained to not be greater than the shortest path threshold. The shortest path threshold can be selected based on whether funds are sufficient, so as to avoid the unrealistic result of the optimization exceeding the economic affordability.

[0080] The optimization method in this application also provides a simulation process for power system structure optimization, as follows:

[0081] The IEEE 39-node system is selected as a case study, such as... Figure 2 As shown in the diagram, the power system comprises 10 generators, 39 nodes, 12 transformers, and 34 lines.

[0082] Figure 3 The figure shows the changes in system-level vulnerability indices when using a vulnerability analysis-based structural optimization method with different numbers of added edges. Figure 3 It is evident that when the number of edges is 1, the power system vulnerability index decreases by approximately 60%, and the overall vulnerability decreases as the number of edges increases. When the number of edges increases to 4, the index decreases by only about 17% more than when the number of edges is 1. Particularly when the number of edges is 2, although the vulnerability index decreases, it is almost identical to the system-level vulnerability when the number of edges is 1. This indicates that a small number of edges can significantly reduce vulnerability, while the reduction effect slows down as the number of edges continues to increase.

[0083] Figure 4 To analyze the effectiveness of the proposed algorithm (IBF), this paper uses height-number edge addition strategy (HD), low-degree edge addition strategy (LD), random edge addition strategy (RA), and initial network (IN) as comparative edge addition strategies. Network transmission performance metrics and average load rate are used to analyze the different performance improvements of different edge addition optimization methods on the initial power system. Network transmission performance metrics and average load rate are calculated for different edge addition strategies (where the number of added edges is set to one) in the IEEE 39-bus power system. It can be seen that the structural optimization method proposed in this application (i.e., Figure 4 In addition to reducing the vulnerability of power system lines, the IBF (internal load factor) can also effectively improve the network transmission performance of the power system and reduce the average load rate of the power system, which shows that the optimization method provided in this application can effectively improve the uniformity of transmission tasks undertaken by lines during power system operation.

[0084] In another embodiment of this application, such as Figure 5 As shown, this application also provides a power system structure optimization system. This system and the optimization method described in the above embodiments belong to the same inventive concept. Therefore, the principle of the optimization system in this embodiment for solving the problem is the same. Figure 1 The optimization method shown is similar to that described above; therefore, the implementation methods of these optimization systems can be found in [reference needed]. Figure 1 An embodiment of the method shown includes:

[0085] The branch potential energy optimization module is used to optimize the potential energy function of the branch based on the different line's tolerance for branch potential energy. It calculates the limiting potential energy of each branch based on the optimized branch potential energy function, where the limiting potential energy includes the forward limiting potential energy and the reverse limiting potential energy.

[0086] The vulnerability index calculation module is used to calculate the potential energy impact rate of the branch based on the ultimate potential energy, and to calculate the vulnerability index of the power system based on the total number of lines and the potential energy impact rate of the branches.

[0087] The structural optimization model construction module is used to construct a power system structural optimization model, which includes a power system structural optimization model with the objective function of minimizing the power system vulnerability index and the power system operating state as a constraint condition.

[0088] The result calculation module is used to solve the power system structure optimization model using the tabu search algorithm to obtain the optimization results of the power system structure.

[0089] As can be seen, the power system structure optimization system provided in the above embodiments considers the changes and limits of active and reactive power transmission in the branch potential energy function, enabling the branch potential energy function to more comprehensively evaluate the impact of load fluctuations on the vulnerability of power system components. This results in a more accurate vulnerability index for the power system. Minimizing the vulnerability index is used as the objective function of the power system structure optimization model, thus constructing a structural optimization model oriented towards power system vulnerability. The tabu search algorithm is then used to obtain the optimized structure of this structural optimization model. Compared with traditional structural optimization models, it has a better vulnerability optimization effect, effectively reducing the vulnerability index of the power system and thus improving the security of the power system.

[0090] In some possible implementations, the potential energy function of the branch is optimized based on the different levels of potential energy tolerance of the branch. The mathematical expression for the optimized potential energy function of the branch is as follows:

[0091] ,in, , They represent the lines respectively. The forward limiting potential energy and the reverse limiting potential energy, , These are the upper and lower limits of the voltage phase angle difference constraint, respectively. , These are the upper and lower limits of the voltage amplitude difference, respectively. This represents the active power of branch ij before the change in the state of the power system. B represents the reactive power of branch ij before the change of power system state. ij It is the susceptance of branch ij, G ij It is the conductance of branch ij, U i with U j These are the node voltage amplitudes at both ends of branch ij, U ij and These represent the voltage amplitude difference and phase angle difference between the nodes at both ends of branch ij, respectively.

[0092] In another embodiment of the present invention, an electronic device is provided, comprising one or more processors; a memory coupled to the processors for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the power system structure optimization method described in the above embodiments. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiments of the present invention can be used to execute the operation of a power system structure optimization method.

[0093] In another embodiment of the present invention, a readable storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the operating system of the terminal. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the power system structure optimization method in the above embodiments. Those skilled in the art should understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can 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.

[0094] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the structure of a power system, characterized in that, include: The potential energy function of a branch is optimized based on the different levels of potential energy tolerance of different lines. The limiting potential energy of each branch is then calculated based on the optimized branch potential energy function, which includes both forward and reverse limiting potential energy. The mathematical expression for the optimized branch potential energy function is as follows: ,in, , They represent the lines respectively. The forward limiting potential energy and the reverse limiting potential energy, , These are the upper and lower limits of the voltage phase angle difference constraint, respectively. , These are the upper and lower limits of the voltage amplitude difference, respectively. Branches representing the state changes of the power system active power, Branches representing the state changes of the power system reactive power, It is a side road susceptivity, It is a side road electrical conductivity, and They are branch roads The voltage amplitude at both ends of the node, and Branch roads The voltage magnitude difference and phase angle difference at the two nodes; The potential energy impact rate of the branch is calculated based on the ultimate potential energy, and the vulnerability index of the power system is calculated from the total number of lines and the potential energy impact rate of the branches. A power system structure optimization model is constructed, which includes a power system vulnerability index as the objective function and the power system operating state as the constraint condition. The tabu search algorithm is used to solve the power system structure optimization model, and the optimization results of the power system structure are obtained.

2. The power system structure optimization method according to claim 1, characterized in that, When the branch potential energy is the positive limiting potential energy, the potential energy impulse rate of the branch is equal to the branch potential energy divided by the positive limiting potential energy. Alternatively, when the branch potential energy is the negative limiting potential energy, the potential energy impulse rate of the branch is equal to the branch potential energy divided by the negative limiting potential energy.

3. The power system structure optimization method according to claim 2, characterized in that, The mathematical expression for calculating the vulnerability index of a power system based on the total number of lines and the potential energy impact rate of branches is as follows: Where H represents the total number of power system lines, Let be the potential energy impact rate of branch ij.

4. The power system structure optimization method according to claim 1, characterized in that, The constraints on the operating state of a power system include active power constraints, reactive power constraints, line transmission power constraints, bus voltage constraints, line current constraints, and generator active power constraints; among them, The mathematical expression for active power constraints is: ; The mathematical expression for reactive power constraints is: ; The mathematical expression for the line transmission power constraint is: ; The mathematical expression for bus voltage constraint is: ; The mathematical expression for line current constraint is: ; The mathematical expression for the generator active power constraint is: ;in, Injecting power into the generator bus active power, busbar The active power load demand, Injecting power into the generator bus reactive power, busbar reactive load demand, For the busbar Number of connected lines, For the line active power, For the line Upper limit of active power transmission busbar The node voltage amplitude, and busbars The lower and upper limits of the node voltage amplitude, For the line The current amplitude, and The lines are respectively The lower and upper limits of the allowable current amplitude. This represents the active power output value of the generator. This represents the upper limit of the generator's active power output.

5. The power system structure optimization method according to claim 4, characterized in that, The constraints also include: The two buses connected by the added edge in the power system belong to the original power network and cannot be equal to the existing edge; the shortest path between the two buses connected by the added edge cannot be greater than the shortest path threshold.

6. A power system structure optimization system, characterized in that, include: The branch potential energy optimization module optimizes the branch potential energy function based on the different levels of potential energy tolerance of different lines. It calculates the limiting potential energy of each branch based on the optimized branch potential energy function, where the limiting potential energy includes both forward and reverse limiting potential energy. The mathematical expression for the optimized branch potential energy function is as follows: ,in, , They represent the lines respectively. The forward limiting potential energy and the reverse limiting potential energy, , These are the upper and lower limits of the voltage phase angle difference constraint, respectively. , These are the upper and lower limits of the voltage amplitude difference, respectively. Branches representing the state changes of the power system active power, Branches representing the state changes of the power system reactive power, It is a side road susceptivity, It is a side road electrical conductivity, and They are branch roads The voltage amplitude at both ends of the node, and Branch roads The voltage magnitude difference and phase angle difference at the two nodes; The vulnerability index calculation module is used to calculate the potential energy impact rate of the branch based on the ultimate potential energy, and to calculate the vulnerability index of the power system based on the total number of lines and the potential energy impact rate of the branches. The structural optimization model construction module is used to construct a power system structural optimization model, which includes a power system structural optimization model with the objective function of minimizing the power system vulnerability index and the power system operating state as a constraint condition. The result calculation module is used to solve the power system structure optimization model using the tabu search algorithm to obtain the optimization results of the power system structure.

7. An electronic device, characterized in that, The electronic device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, it implements the steps of a power system structure optimization method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, it implements the steps of a power system structure optimization method as described in any one of claims 1 to 5.

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