Extra-high voltage power grid state optimization method, device, equipment, medium and product
By constructing an optimized mathematical model of multiple power flow equations, the problem of inability to accurately control the power line closure in the ultra-high voltage power grid is solved, and the safety optimization and risk reduction of the power grid are achieved.
Patent Information
- Application Number
- CN202510312932.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-17
- Publication Date
- 2025-07-04
AI Technical Summary
The failure to accurately control the closure of ultra-high voltage power lines leads to high line risks.
Build an optimized mathematical model containing a variety of power flow equations, balance power transmission and risks through weight factors, solve the optimal power closure problem, and formulate power line switching decisions to ensure the safe operation of the UHV power grid.
Accurate control and optimization of the power network is achieved, line risks are reduced, customer power load reduction is reduced, and the power grid is maintained safely.
Smart Images
Figure CN120262373A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of UHV project evaluation, and particularly to a method, device, equipment, medium and product for optimizing the state of a UHV power grid. Background Art
[0002] As the best solution for realizing long-distance and large-capacity power transmission, UHV technology has played an increasingly important role in the global energy field in recent years. It not only optimizes the allocation of energy resources, promotes the large-scale development and efficient utilization of clean energy, improves the safety and reliability of power grid operation, effectively solves the periodic "power shortage" problem, promotes regional coordinated development, drives innovation and breakthroughs in high-end equipment manufacturing, provides key technical support for building a global energy Internet, realizes long-distance power transmission, saves land resources, and provides strong impetus for the sustainable development of the economy and society.
[0003] Developing UHV technology has far-reaching strategic significance and extensive social and economic impacts. It helps to optimize the allocation of energy resources, transmit clean energy from the western and northern regions to the load centers in the eastern and southern regions, reduce dependence on fossil energy, and promote the green transformation of the energy structure.
[0004] UHV power transmission technology is relatively new, and some key technologies are not yet fully mature. For example, further research and breakthroughs are still needed in aspects such as the insulation of UHV transmission lines and the materials of conductors. The inability to precisely control the shutdown of power lines leads to high line risks. Summary of the Invention
[0005] The purpose of the present application is to provide a method, device, equipment, medium and product for optimizing the state of a UHV power grid to solve the problem of high line risks caused by the inability to precisely control the shutdown of power lines.
[0006] To achieve the above purpose, the present application provides the following solutions:
[0007] In a first aspect, the present application provides a method for optimizing the state of a UHV power grid, including:
[0008] Construct an optimization mathematical model including multiple power flow equations according to the initial operation parameters of the UHV power grid; the multiple power flow equations include an AC power flow formula, a second-order cone power flow formula, a DC power flow formula, and a mesh power flow formula;
[0009] Determine the trade-off between power transmission and risk reduction through a weight factor, and determine the objective function and constraint equation of each power flow equation in the optimization mathematical model;
[0010] Based on the objective function and constraint equation, solve the optimal power shutdown problem of each power flow equation to determine the power line switching decision;
[0011] Integrate the power line switching decisions corresponding to each power flow equation to formulate the final cut-off decision for the UHV grid to maintain the safe operation of the UHV grid.
[0012] In a second aspect, the present application provides a UHV grid state optimization device, including:
[0013] An optimization mathematical model construction module for constructing an optimization mathematical model containing multiple power flow equations according to the initial operation parameters of the UHV grid; the multiple power flow equations include an AC power flow formula, a second-order cone power flow formula, a DC power flow formula, and a mesh power flow formula;
[0014] An objective function determination module for determining the trade-off between power transmission and risk reduction through a weight factor, and determining the objective function and constraint equations of each power flow equation in the optimization mathematical model;
[0015] A power line switching decision determination module for solving the optimal power shutdown problem of each power flow equation based on the objective function and constraint equations, and determining the power line switching decision;
[0016] A final cut-off decision making module for integrating the power line switching decisions corresponding to each power flow equation to formulate the final cut-off decision for the UHV grid to maintain the safe operation of the UHV grid.
[0017] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the UHV grid state optimization method described in any one of the above.
[0018] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the UHV grid state optimization method described in any one of the above.
[0019] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the UHV grid state optimization method described in any one of the above.
[0020] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application:
[0021] This application constructs an optimized mathematical model that combines DC linear power flow equations, AC power flow equations, second-order cone power flow equations, and network flow power flow equations to achieve precise control and optimization of the power grid. By weighing power supply and line risks, a power shutdown strategy with high computational efficiency and excellent solution quality is provided, namely the final cutoff decision for the UHV grid. Based on this final cutoff decision, the line risk is reduced by precisely controlling the power line switching decision, while minimizing the reduction of customer power load to maintain the safe operation of the UHV grid. Brief Description of the Drawings
[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.
[0023] Figure 1 Schematic flow chart of a method for optimizing the state of an UHV grid provided by this application;
[0024] Figure 2 Schematic flow chart of another method for optimizing the state of an UHV grid provided by this application;
[0025] Figure 3 Schematic diagram of a computer device provided by this application;
[0026] Figure 4 Schematic diagram of another computer device provided by this application. Detailed Description of the Embodiments
[0027] The following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the drawings in the embodiments of this application. Obviously, the described embodiments are only some embodiments of this application, rather than all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.
[0028] To make the above objects, features, and advantages of this application more obvious and understandable, the following will further describe this application in detail with reference to the drawings and specific embodiments.
[0029] The embodiments of this application provide a method for optimizing the state of an UHV grid. This method is executed by a computer device, and specifically can be executed alone by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of this application, as Figure 1 shown, this method includes the following steps.
[0030] S1: Construct an optimized mathematical model containing multiple power flow equations based on the initial operating parameters of the UHV power grid; the multiple power flow equations include the AC power flow formula, the second-order cone power flow formula, the DC power flow formula, and the mesh power flow formula, and each formula consists of an objective function and multiple constraint equations.
[0031] S2: Determine the trade-off between power transmission and risk reduction through weight factors, and determine the objective function and constraint equations of each power flow equation in the optimized mathematical model.
[0032] S3: Based on the objective function and constraint equations, solve the optimal power shutdown problem of each power flow equation and determine the power line switching decision.
[0033] S4: Integrate the power line switching decisions corresponding to each power flow equation to formulate the final cutoff decision of the UHV power grid to maintain the safe operation of the UHV power grid.
[0034] In an exemplary embodiment, the initial operating parameters include the number of nodes in the UHV power grid, the power lines connecting the nodes and their parameters, the generators at each node, the maximum and minimum powers of the generators, the power demand at each node, and the susceptance elements; among them, the parameters of the connecting nodes include resistance, reactance, and thermal power limit; the susceptance elements include susceptance conductance and susceptance reactance.
[0035] According to the above initial operating parameters of the UHV power grid, construct an optimized mathematical model containing multiple power flow equations, and the initial operating parameters of each component are represented in the following way:
[0036] i ∈ B
[0037] g ∈ G
[0038] ij ∈ L
[0039] d ∈ D
[0040] s ∈ S
[0041] Among them, i represents the node number; B represents the set of nodes; g represents the generator number; G represents the set of generators; ij represents the line number; represents the set of lines; d represents the power load number; represents the set of power loads; s represents the susceptance element number; represents the set of susceptance elements.
[0042] The energized state of the component is represented by the binary variable z ∈ {0, 1}; the subset of the component (such as the generator) at the node i is represented as B G i .
[0043] In an exemplary embodiment, a trade-off factor α∈[0,1] is set in the optimization mathematical model to balance power supply and line risk.
[0044] Use a solver to solve the optimization model to obtain the optimal decision on power line disconnection, and use the binary variable z to determine the component disconnection status.
[0045] In an exemplary embodiment, based on the AC power flow formula, the trade-off between power transmission and risk reduction is determined through a preset weight factor and the objective function is given. Solve the AC optimal power shutdown problem according to the energization constraint, generation constraint, and power flow constraint.
[0046] For the pre-specified weight factor, maximize the transmitted power and determine the trade-off between line load and risk avoidance. Set the objective function as:
[0047]
[0048] In the formula, the first term represents the total power provided in the system, P D d is the power demand, x d is the continuous variable of the power load, w d is the weight factor; the second term represents the line risk, z ij is the binary energization state; R ij is the risk parameter, R tot is the total risk.
[0049] Set the energization constraint:
[0050]
[0051]
[0052] In the formula, x s is the reduction variable of the susceptance element.
[0053] Set the generation constraint:
[0054]
[0055] In the formula, P G g is the active power of the generator, is the lower limit of the active power of the generator; is the upper limit of the active power of the generator; is the reactive power of the generator; is the upper limit of the reactive power of the generator; The lower limit of the reactive power of the generator. The power representation method of other components is similar to this.
[0056] Set the AC power flow constraint:
[0057]
[0058] where g s is the susceptance conductance, and b s is the susceptance reactance, and V i is the voltage amplitude.
[0059]
[0060] where T ij is the complex power, and the square of it represents the thermal power limit of the line.
[0061]
[0062] where V i is the upper limit of the voltage amplitude, V i is the lower limit of the voltage amplitude.
[0063]
[0064] where t ij is the voltage phase transformation, and t R ij is the real part transformation, and t I ij is the imaginary part transformation, and θ i is the voltage phase.
[0065]
[0066] where is the maximum voltage angle difference limit of the line, and θ Δ max is the predicted maximum voltage angle difference.
[0067] Generally, since the AC optimal power shutdown problem is a mixed integer non - linear (non - convex) programming problem, this problem is usually unsolvable in practice. To determine a practically feasible disconnection strategy, it is necessary to determine the minimum AC feasible disconnection strategy determined by these formulas. By using the AC power flow rescheduling method, replacing some of the constraints in the AC power flow problem, finding a feasible AC power flow, the AC optimal power shutdown problem is transformed into a non - linear programming problem, and solving this problem can restore the AC feasible solution, that is, replacing the objective function and constraint conditions.
[0068] The objective function after replacing the AC power flow formula is:
[0069]
[0070] Among them, d is the number of the power load; D is the set of power loads; x d is the continuous variable of the power load; w d is the weight factor; P d is the power of the power load.
[0071] The energization constraint of the AC power flow formula is:
[0072]
[0073] Among them, z ij is the binary energization state of the line; is the fixed value of the power cut-off decision of the line; ij is the number of the line; is the set of lines; z i is the binary energization state of the node; is the fixed value of the power cut-off decision of the node; is the set of nodes; i is the number of the node; z g is the binary energization state of the generator; is the fixed value of the power cut-off decision of the generator; g is the number of the generator; is the set of generators; x s is the reduction variable of the susceptance element; s is the number of the susceptance element; S is the set of susceptance elements.
[0074] The generation constraint of the AC power flow formula is:
[0075]
[0076] Among them, is the active power of the generator; is the lower limit of the active power of the generator; is the upper limit of the active power of the generator; is the reactive power of the generator; is the upper limit of the reactive power of the generator; The lower limit of the reactive power of the generator;
[0077] The AC power flow constraint of the AC power flow formula is:
[0078]
[0079] Among them, is the subset where the generator is located at node i; is the active power of the line from node i to node j; is the subset where the line is located at node i; is the subset where the power load is located at node i; is a subset where the susceptance element is located at node i; is the power of the power demand load; V i is the voltage magnitude of node i; is the reactive power of the line from node i to node j; is the reactive power of the power load; b s is the reactance of the susceptance; g s is the conductance of the susceptance; is the active power of the line from node j to node i; is the reactive power of the line from node j to node i; T ij is the complex power; V i is the lower limit of the voltage magnitude of node i; is the upper limit of the voltage magnitude of node i; g ij is the conductance of the line; g i is the conductance of the node; t ij is the voltage phase transformation; b ij is the reactance of the line; is the real part transformation; is the imaginary part transformation; V j is the voltage magnitude of node j; θ i is the voltage phase of node i; θ j is the voltage phase of node j; g j is the conductance of node j; b i is the reactance of node i; b j is the reactance of node j; is the maximum voltage angle difference limit of the line; is the predicted maximum voltage angle difference.
[0080] In an exemplary embodiment, the objective function, energization constraint, and generation constraint of the second-order cone power flow formula are the same as the corresponding objective function, energization constraint, and generation constraint before the transformation of the optimal power shutdown problem of the AC power flow, to solve the second-order cone optimal power shutdown problem.
[0081] The objective function of the second-order cone power flow formula is:
[0082]
[0083] where α is the trade-off factor; R ij is the risk parameter; R tot is the total risk; is the total power load.
[0084] The energization constraint of the second-order cone power flow formula is:
[0085]
[0086] Among them, z ig is the binary energization state of the generator at node i.
[0087] The power generation constraint of the second-order cone power flow formula is:
[0088]
[0089] The second-order cone power flow constraint of the second-order cone power flow formula is:
[0090]
[0091] Among them, V i is the lower limit of the voltage amplitude of node i; The upper limit of the voltage amplitude of node i; V j The lower limit of the voltage amplitude of node j; is the upper limit of the voltage amplitude of node j; W ii is the square voltage variable of node i; is the square voltage variable of the line source end; is the square voltage variable of the line terminal.
[0092]
[0093]
[0094] Among them, is the square voltage variable of the line source end; is the square voltage variable of the line terminal; W jj is the square voltage variable of node j; is the lower limit of the real part of the square cross-line voltage variable; is the real part of the square cross-line voltage variable; is the upper limit of the real part of the square cross-line voltage variable; is the upper limit of the imaginary part of the square cross-line voltage variable; is the imaginary part of the square cross-line voltage variable; is the lower limit of the imaginary part of the square cross-line voltage variable; θ ij is the minimum voltage angle difference limit of the line; is the maximum voltage angle difference limit of the line; is the upper limit of the square voltage variable of node j; is the upper limit of the square voltage variable of node i; is the square voltage variable of the susceptance.
[0095] In an exemplary embodiment, the objective function and energization constraint of the DC power flow formula are the same as those of the second-order cone power flow formula to solve the DC optimal power shutdown problem.
[0096] The power generation constraint of the DC power flow formula is as follows:
[0097]
[0098] The DC power flow constraint of the DC power flow formula is as follows:
[0099]
[0100] In an exemplary embodiment, the objective function, energization constraint, and power generation constraint of the grid power flow formula are the same as those of the DC power flow formula to solve the optimal power shutdown problem of the network flow.
[0101] The network power flow constraint of the DC power flow formula is as follows:
[0102]
[0103] In an exemplary embodiment, by comprehensively considering the power line disconnection decisions obtained from each power flow equation and making manual screening considering the solution time, the final disconnection decision of the UHV grid can be obtained, and this decision is executed to ensure the safe operation of the UHV grid.
[0104] As Figure 2 shown, this application determines the initial operating parameters of the UHV grid, i.e., the actual parameters; constructs an optimization model containing multiple power flow equations; sets a trade-off factor in the model to balance power supply and line risks; solves the AC optimal power shutdown problem, second-order cone optimal power shutdown problem, DC optimal power shutdown problem, and network flow optimal power shutdown problem; due to the particularity of the AC optimal power shutdown problem, the AC optimal power shutdown problem is transformed into a nonlinear programming problem, and solving this problem can restore the AC feasible solution; finally, by comprehensively considering the binary variable i obtained from each power flow equation, the final disconnection decision of the UHV grid can be obtained, and this decision is executed to ensure the safe operation of the UHV grid.
[0105] This application can achieve the following effects:
[0106] 1. By real-time monitoring the grid status and environmental factors and combining with an advanced prediction model, this application accurately predicts and identifies high-risk areas of power lines. Through an optimization algorithm, the system can automatically make decisions to shut down or reduce the load of power lines before the risk escalates, thus effectively reducing the probability of accidents in the UHV grid.
[0107] 2. When implementing the power line shutdown decision, this application comprehensively considers the operating status of the power grid and the power demand of customers. Through intelligent scheduling and load management, the system can minimize the impact on customer power supply while ensuring the safety of the power grid. For example, before predicting a high-risk weather event, the system can adjust the power grid operation mode in advance, transfer the load to other safe power lines, or use energy storage devices and backup power sources to make up for possible power gaps, thus ensuring the continuity and stability of power supply.
[0108] 3. The optimization algorithm and multi-objective optimization strategy adopted in this application are specially designed to process large-scale power grid data and complex power grid models. The algorithm utilizes the latest mathematical programming techniques and computational methods, such as parallel computing and distributed processing, to improve the solution speed and the ability to handle large-scale problems. In addition, the system can also adaptively adjust the algorithm parameters and solution strategies to adapt to different power grid conditions and changes, ensuring that the optimal solution can be quickly found in various situations.
[0109] 4. This application takes into account the diversity and complexity of UHV power grids in different regions and of different scales. It has the characteristics of modularity and configurability and can be customized and adjusted according to the requirements and characteristics of different UHV power grids. Whether it is a small local power grid or a large-scale provincial-crossing power grid, it can provide effective support for power line shutdown decisions.
[0110] Based on the same inventive concept, the embodiments of this application also provide a UHV power grid state optimization device for implementing the above-mentioned UHV power grid state optimization method. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the UHV power grid state optimization device provided below can refer to the limitations on the UHV power grid state optimization method in the above text and will not be elaborated here.
[0111] This application provides a UHV power grid state optimization device, including:
[0112] An optimization mathematical model construction module, configured to construct an optimization mathematical model containing multiple power flow equations according to the initial operation parameters of the UHV power grid; the multiple power flow equations include an AC power flow formula, a second-order cone power flow formula, a DC power flow formula, and a mesh power flow formula.
[0113] A target function determination module, configured to determine the trade-off between power transmission and risk reduction through a weight factor, and determine the target function and constraint equation of each power flow equation in the optimization mathematical model.
[0114] A power line switching decision determination module, configured to solve the optimal power shutdown problem of each power flow equation based on the target function and constraint equation, and determine the power line switching decision.
[0115] The final disconnection decision-making module is used to synthesize the power line switching decisions corresponding to each power flow equation and make the final disconnection decision for the UHV grid to maintain the safe operation of the UHV grid.
[0116] In an exemplary embodiment, a computer device is provided, such as Figure 3 shown. The computer device can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. 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, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the UHV grid state optimization data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for optimizing the state of the UHV grid.
[0117] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the above method is implemented.
[0118] The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 4As shown. The computer device includes a processor, a main memory, an input device, an output device, and an auxiliary memory. Among them, the processor, the input device, the output device, and the auxiliary memory are connected through the main memory. Among them, the processor (Central Processing Unit, CPU) is composed of an arithmetic unit and a controller, and the processor of this computer device is used to provide computing and control capabilities. Among them, the controller controls the arithmetic unit, the main memory, the auxiliary memory, the input device, and the output device, and exchanges addresses or instructions with the main memory. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of this computer device is used to exchange information between the processor and external devices. The communication interface of this computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a method for optimizing the state of a UHV power grid.
[0119] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above method when executed by a processor.
[0120] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above method when executed by a processor.
[0121] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random-access memories (ReRAM), magnetoresistive random-access memories (MRAM), ferroelectric random-access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.
[0122] In this application, all actions of obtaining signals, information, or data are carried out on the premise of complying with the corresponding data protection regulations and policies of the country where it is located and obtaining authorization from the owner of the corresponding device.
[0123] In the embodiments provided in the present application, the databases involved can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.
[0124] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.
[0125] In this article, specific examples are used to illustrate the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.
Claims
1. A method for optimizing the state of an ultra-high voltage power grid, characterized in that, The above-mentioned UHV grid state optimization method includes: Construct an optimization mathematical model containing multiple power flow equations according to the initial operation parameters of the UHV grid; the multiple power flow equations include AC power flow formula, second-order cone power flow formula, DC power flow formula, and mesh power flow formula; Determine the trade-off between power transmission and risk reduction through weight factors, and determine the objective function and constraint equations of each power flow equation in the optimization mathematical model; Based on the objective function and constraint equations, solve the optimal power shutdown problem of each power flow equation to determine the power line switching decision; Integrate the power line switching decisions corresponding to each power flow equation to formulate the final cut-off decision of the UHV grid to maintain the safe operation of the UHV grid.
2. The UHV grid state optimization method according to claim 1, wherein For the optimal power shutdown problem of AC power flow, determine the feasible AC power flow through the AC power flow rescheduling method, and transform the optimal power shutdown problem of AC power flow into a nonlinear programming problem.
3. The UHV power grid state optimization method according to claim 2, characterized in that The objective function of the AC power flow formula is: Among them, d is the number of the power load; D is the set of power loads; x d is the continuous variable of the power load; w d is the weight factor; P d is the power of the power load; The energization constraint of the AC power flow formula is: where z ij is the binary energized state of the line; is the power cut-off decision fixed value of the line; ij is the line number; is the set of lines; z i is the binary energized state of the node; is the power cut-off decision fixed value of the node; is the set of nodes; i is the node number; z g is the binary energized state of the generator; is the power cut-off decision fixed value of the generator; g is the generator number; is the set of generators; x s is the reduction variable of the susceptance element; s is the susceptance element number; S is the set of susceptance elements; The generation constraint of the AC power flow formula is: Among them, is the active power of the generator; is the lower limit of the active power of the generator; is the upper limit of the active power of the generator; is the reactive power of the generator; is the upper limit of the reactive power of the generator; the lower limit of the reactive power of the generator The AC power flow constraint of the AC power flow formula is: wherein, is a subset where the generator is located at node i; is the active power of the line from node i to node j; is a subset where the line is located at node i; is a subset where the power load is located at node i; is a subset where the susceptance element is located at node i; is the power of the power load; V i is the voltage amplitude of node i; is the reactive power of the line from node i to node j; is the reactive power of the power load; b s is the reactance of the susceptance; g s is the conductance of the susceptance; is the active power of the line from node j to node i; is the reactive power of the line from node j to node i; T ij is the complex power; V i is the lower limit of the voltage amplitude of node i; is the upper limit of the voltage amplitude of node i; g ij is the conductance of the line; g i is the conductance of node i; t ij is the voltage phase transformation; b ij is the reactance of the line; is the real part transformation; is the imaginary part transformation; V j is the voltage amplitude of node j; θ i is the voltage phase of node i; θ j is the voltage phase of node j; g j is the conductance of node j; b i is the reactance of node i; b j is the reactance of node j; is the maximum voltage angle difference limit of the line; is the predicted maximum voltage angle difference.
4. The UHV power grid state optimization method according to claim 3, characterized in that The objective function, energization constraint, and generation constraint of the second-order cone power flow formula are the same as the corresponding objective function, energization constraint, and generation constraint before the transformation of the optimal power shutdown problem of AC power flow; The objective function of the second-order cone power flow formula is: where α is a trade-off factor; R ij is a risk parameter; R tot is the total risk; is the total power load; The energization constraint of the second-order cone power flow formula is: where z ig is the binary energization state of the generator at node i; The generation constraint of the second-order cone power flow formula is: The second-order cone power flow constraint of the second-order cone power flow formula is: Among them, V i is the lower limit of the voltage amplitude of node i; is the upper limit of the voltage amplitude of node i; V j is the lower limit of the voltage amplitude of node j; is the upper limit of the voltage amplitude of node j; W ii is the square variable of the voltage of node i; is the square variable of the source - end voltage of the line; is the square variable of the terminal - end voltage of the line; Among them, is the squared variable of the line source - end voltage; is the squared variable of the line terminal voltage; W jj is the squared variable of the voltage at node j; is the lower limit of the real part of the squared cross - line voltage variable; is the real part of the squared cross - line voltage variable; is the upper limit of the real part of the squared cross - line voltage variable; is the upper limit of the imaginary part of the squared cross - line voltage variable; is the imaginary part of the squared cross - line voltage variable; is the lower limit of the imaginary part of the squared cross - line voltage variable; θ ij is the minimum voltage angle difference limit of the line; is the maximum voltage angle difference limit of the line; is the upper limit of the squared voltage variable at node j; is the upper limit of the squared voltage variable at node i; is the voltage - squared variable of the susceptance.
5. The method for optimizing the state of an UHV power grid according to claim 4, wherein The objective function and energization constraint of the DC power flow formula are the same as the objective function and energization constraint of the second-order cone power flow formula; The generation constraint of the DC power flow formula is: The DC power flow constraint of the DC power flow formula is:
6. The UHV grid state optimization method according to claim 5, wherein, The objective function, energization constraint, and generation constraint of the mesh power flow formula are the same as the objective function, energization constraint, and generation constraint of the DC power flow formula; The network power flow constraint of the DC power flow formula is:
7. A UHV power grid state optimization device, characterized in that, The above-mentioned UHV grid state optimization device includes: An optimization mathematical model construction module, which is used to construct an optimization mathematical model containing multiple power flow equations according to the initial operation parameters of the UHV grid; the multiple power flow equations include AC power flow formula, second-order cone power flow formula, DC power flow formula, and mesh power flow formula; An objective function determination module, which is used to determine the trade-off between power transmission and risk reduction through weight factors, and determine the objective function and constraint equations of each power flow equation in the optimization mathematical model; A power line switching decision determination module, which is used to solve the optimal power shutdown problem of each power flow equation based on the objective function and constraint equations to determine the power line switching decision; A final cut-off decision formulation module, which is used to integrate the power line switching decisions corresponding to each power flow equation to formulate the final cut-off decision of the UHV grid to maintain the safe operation of the UHV grid.
8. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the UHV grid state optimization method according to any one of claims 1-6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the UHV grid state optimization method according to any one of claims 1-6.
10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the UHV grid state optimization method according to any one of claims 1-6.