An optimized configuration method and system for availability backup

Abstract

An optimized configuration method and system for availability backup includes: S1: initializing transmission line capacity limits; S2: solving a pre-built backup optimization configuration model to obtain a backup configuration scheme and unit output under the transmission line capacity limits; S3: generating extreme scenarios based on the backup configuration scheme, unit output, wind power output variation range, and load power variation range; S4: verifying the backup configuration scheme using the extreme scenarios. If the verification fails, the transmission line capacity limits are adjusted, and step S2 is executed. If the verification passes, the backup configuration is set based on the transmission line capacity limits. This avoids situations where backup cannot be effectively invoked, allows for backup feasibility verification, and since each iteration only needs to return a coordination constraint to the main problem once, and the constraint form is efficient and concise without introducing new variables, it basically does not affect the solution efficiency of the backup optimization configuration model of the main problem.

Description

Technical Field

[0001] This invention relates to the field of the power industry, and specifically to an optimized configuration method and system for availability backup. Background Technology

[0002] Because power systems need to maintain real-time power balance during operation, a certain amount of spinning reserve (also known as load reserve) is required to track load changes and uncontrollable variations in the output of renewable energy sources such as wind and solar power. From the perspective of ensuring power system reliability and good power quality, the more spinning reserve, the better. This is because the time from when a generating unit is not in operation to when it is put into the system can be as short as a few minutes (e.g., hydropower plants) or as long as more than ten hours (e.g., thermal power plants). However, reserving spinning reserve will reduce the economic efficiency of power generation; therefore, from the perspective of ensuring system operational economy, spinning reserve should not be excessive. Therefore, it is necessary to optimize the configuration of spinning reserve in the power system to better balance reliability and economy. Traditional spinning reserve is usually reserved at 3%-5% of the normal peak load and is reserved as a whole according to the control balance zone. However, it does not consider the actual situation such as grid congestion when the reserve is called up, which has certain shortcomings. Summary of the Invention

[0003] To address the aforementioned shortcomings in the existing technology, the present invention provides an optimized configuration method and system for availability backup.

[0004] The technical solution provided by this invention is:

[0005] An optimized configuration method for availability standby, the method comprising:

[0006] S1: Initialize transmission line capacity limits;

[0007] S2: Solve based on the pre-built standby optimization configuration model to obtain the standby configuration scheme and unit output under the transmission line capacity limit;

[0008] S3: Generate extreme scenarios based on the aforementioned backup configuration scheme, unit output, wind power output variation range, and load power variation range;

[0009] S4: Verify the backup configuration scheme using the extreme scenario. If the verification fails, adjust the transmission line capacity limit and execute step S2. If the verification passes, configure the backup setting based on the transmission line capacity limit.

[0010] Preferably, the construction of the backup optimized configuration model includes:

[0011] A target function is constructed with the goal of balancing the power generation cost, start-up and shutdown cost, and reserve capacity cost of the power grid system.

[0012] Construct constraints;

[0013] The constraints include: system power balance constraints, upper and lower limits of thermal power unit output constraints, thermal power unit ramping constraints, minimum start-up and shutdown time constraints of thermal power units, standby constraints, unit output constraints in standby dispatch scenarios, and line safety constraints.

[0014] Preferably, the objective function is calculated as follows:

[0015]

[0016] In the formula, Let $t$ be the power generation cost of unit $i$ during time period $t$. Let i be the start-up cost of unit i during time period t; Let $t$ be the downtime cost of unit $i$ during time period $t$. Let be the standby capacity cost of unit i during time period t; T be the dispatch cycle; and NG be the number of thermal power units.

[0017] Preferably, the step of generating extreme scenarios based on the backup configuration scheme, the unit output, and the wind power output variation range and load power variation range includes:

[0018] The range of unit output adjustment is determined based on the aforementioned unit output and standby configuration scheme;

[0019] The maximum and minimum power flow values ​​for each line and each time period are calculated based on the unit output adjustment range and the wind power output and load power change range.

[0020] If the maximum or minimum value exceeds the limit of the transmission capacity of each corresponding line, the wind power output and load power of each node are obtained, and a limit scenario is generated based on the unit output adjustment range, the wind power output and load power of each node.

[0021] Preferably, the step of verifying the backup configuration scheme using the extreme scenario includes:

[0022] Based on a pre-built extreme scenario model, with the goal of minimizing the total relaxation amount during backup calls, the transmission capacity limits of each line in the configuration scheme are relaxed.

[0023] If all of the relaxation amounts are less than the set threshold, the transmission line capacity limit is configured as a spare.

[0024] Otherwise, adjust the relaxation amount of the line corresponding to the set threshold to be no less than the set threshold, redetermine the transmission line capacity limit, and continue to solve the constraints of the optimized configuration model until the relaxation amount is less than the set threshold.

[0025] Preferably, the construction of the extreme scenario model includes:

[0026] The unit output range is determined based on the aforementioned backup configuration scheme and unit output.

[0027] Based on the unit output range and the obtained wind power output and load power variation range, calculate the maximum and minimum power flow values ​​for each line at each time period;

[0028] The output range of thermal power units, wind power output, and load power are determined based on the maximum and minimum values ​​and the transmission capacity limits of each line.

[0029] A limit scenario model is constructed based on the output range of the thermal power unit, the wind power output and the load power, as well as the constraints.

[0030] The constraints include: power balance constraints, unit output adjustment orientation constraints, and network security constraints.

[0031] Preferably, adjusting the transmission line capacity limit and performing step S2 includes:

[0032] The adjustment factor is determined based on the slack of each line during the same period.

[0033] The transmission line capacity limit is determined based on the adjustment factor and the relaxation amount being not less than the maximum value among the set thresholds.

[0034] Based on the determined transmission line capacity limit, step S2 is executed again.

[0035] Preferably, the formula for calculating the transmission line capacity limit is as follows:

[0036]

[0037] In the formula, λ represents the line transmission capacity limit of the main problem in the nth iteration; (n) This is the adjustment factor for the nth iteration; for The maximum value among all subproblems in this iteration; NK is the number of scenarios; NT is the number of iterations; NL is the number of lines.

[0038] An optimized configuration system for availability backup, the system comprising:

[0039] Initialization module: Used to initialize transmission line capacity limits;

[0040] Solving module: used to solve based on a pre-built standby optimal configuration model to obtain the standby configuration scheme and unit output under the transmission line capacity limit;

[0041] Generation module: used to generate extreme scenarios based on the backup configuration scheme, unit output, wind power output variation range, and load power variation range;

[0042] Configuration module: Verify the backup configuration scheme using the extreme scenario. If the verification fails, adjust the transmission line capacity limit and execute the solution module. If the verification passes, configure the backup setting based on the transmission line capacity limit.

[0043] Preferably, the solution module further includes: a construction module;

[0044] The construction module is used to construct an objective function with the goal of balancing the power generation cost, start-up and shutdown cost, and reserve capacity cost of the power grid system.

[0045] Construct constraints;

[0046] The constraints include: system power balance constraints, upper and lower limits of thermal power unit output constraints, thermal power unit ramping constraints, minimum start-up and shutdown time constraints of thermal power units, standby constraints, unit output constraints in standby dispatch scenarios, and line safety constraints.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0048] 1. The technical solution of the present invention includes: S1: initializing the transmission line capacity limit; S2: solving based on a pre-constructed standby optimization configuration model to obtain the standby configuration scheme and unit output under the transmission line capacity limit; S3: generating extreme scenarios based on the standby configuration scheme, unit output, wind power output variation range, and load power variation range; S4: verifying the standby configuration scheme using the extreme scenarios. If the verification fails, the transmission line capacity limit is adjusted, and step S2 is executed. If the verification passes, the standby configuration is set based on the transmission line capacity limit. This avoids the situation where the standby cannot be effectively called, allows for standby feasibility verification, and since each iteration only needs to return a coordination constraint to the main problem once, and the constraint form is efficient and concise, without introducing new variables, it basically does not affect the solution efficiency of the standby optimization configuration model of the main problem.

[0049] 2. The technical solution of this invention, by analyzing the physical background of the model and obtaining prior knowledge, allows for the deletion and addition of model constraints, significantly improving model solution performance in terms of memory requirements and computational speed. It decouples the traditional time-coupled security constraint economic scheduling problem, enabling the effective use of parallel computing techniques and greatly accelerating the solution speed. Attached Figure Description

[0050] Figure 1A schematic diagram of the optimized configuration method steps for availability protection of the present invention;

[0051] Figure 2 This is a flowchart of the extreme power flow calculation of the present invention;

[0052] Figure 3 This is a flowchart of the algorithm of the present invention. Detailed Implementation

[0053] To better understand this invention, the following description, in conjunction with the accompanying drawings and examples, will further illustrate the invention.

[0054] Example 1

[0055] In traditional scheduling models, the requirement for reserve capacity configuration is typically that the total system reserve capacity is not less than the total reserve demand, while also meeting unit output constraints under the predicted scenario (such as ramp-up constraints, upper and lower output limits, etc.). However, little consideration is given to the unit output adjustment process during reserve call-up and its impact on system power flow. After large-scale wind power grid connection, the volatility of system imbalance power increases significantly. To ensure system reliability, the system's demand for reserve capacity is greater, and reserve calls are more frequent. Although the overall reserve reserve may be sufficient, during system operation, reserve may become unavailable due to failure to meet network security constraints during reserve call-up, resulting in insufficient actual reserve capacity. To address the aforementioned issues, this invention first establishes an optimized scheduling model that considers the feasibility of backup configuration schemes. Then, based on the range of unit output and the variation range of wind power output and load power during the backup call process, several extreme scenarios are generated. The model is then decomposed into a main problem of backup economic configuration under the predicted scenarios and several sub-problems of backup feasibility verification under the extreme scenarios. Backup optimization configuration is performed through the main problem, and backup availability is verified through the sub-problems. If the backup is unavailable, the main problem model and parameters are modified. The backup optimization configuration result that accurately considers the availability of rotating backup is obtained through an alternating iterative approach of main and sub-problems.

[0056] The present invention provides a technical solution, the improvement of which is as follows: Figure 1 As shown, the calculation method includes the following steps:

[0057] S1: Initialize transmission line capacity limits;

[0058] S2: Solve based on the pre-built standby optimization configuration model to obtain the standby configuration scheme and unit output under the transmission line capacity limit;

[0059] S3: Generate extreme scenarios based on the aforementioned backup configuration scheme, unit output, wind power output variation range, and load power variation range;

[0060] S4: Verify the backup configuration scheme using the extreme scenario. If the verification fails, adjust the transmission line capacity limit and execute step S2. If the verification passes, configure the backup setting based on the transmission line capacity limit.

[0061] Wherein, S1: Initializes the transmission line capacity limit, including:

[0062] Set initial values ​​for each transmission line.

[0063] S2: Solve based on the pre-built standby optimization configuration model to obtain the standby configuration scheme and unit output under the transmission line capacity limit, including:

[0064] Main problem model construction: Establishing an alternative configuration optimization model

[0065] 1) Construction of the objective function

[0066] The model aims to minimize the sum of system generation costs, start-up and shutdown costs, and reserve capacity costs.

[0067]

[0068] In the formula: The generation cost, start-up cost, and shutdown cost of unit i in time period t are respectively, all using a piecewise linear model; Let be the reserve capacity cost of unit i in time period t, including the upper reserve capacity cost and the lower reserve capacity cost, which are proportional to the upper reserve capacity and the lower reserve capacity, respectively; T is the dispatching cycle; NG is the number of thermal power units.

[0069] 1) Constraint Construction

[0070] a) System power balance constraints

[0071]

[0072] In the formula: NN represents the number of system nodes; k = 0 represents the prediction scenario, and k ≠ 0 represents each backup call scenario; These represent the output of thermal power units, wind power output, and load power of node n in scenario k during time period t.

[0073] b) Upper and lower limits of thermal power unit output constraints

[0074]

[0075] In the formula: P i，min P i，max These are the minimum and maximum technical outputs of thermal power unit i, respectively; u i，tTo predict the on / off status of unit i in time period t in the scenario, we take 1 to represent on and 0 to represent off.

[0076] c) Climbing constraints for thermal power units

[0077]

[0078] In the formula: Δ i and These are the downward and upward ramp rate limits for unit i, respectively.

[0079] d) Minimum start-up and shutdown time constraints for thermal power units

[0080]

[0081] In the formula: T i on T i off These represent the minimum start-up time and minimum downtime of unit i, respectively.

[0082] e) Alternative constraints

[0083]

[0084]

[0085] In the formula: These represent the upper and lower reserves provided by unit i during time period t; τ is the reserve response time. These represent the upper and lower backup requirements of the system during time period t.

[0086] Since determining the system reserve capacity requirement is not the focus of this patent, to simplify the analysis, the system reserve capacity requirement is determined based on the maximum prediction error of wind power output and load power in each time period. It is assumed that the variation range of wind power prediction error at node n in time period t is... The range of variation in load power prediction error is as follows The system's backup capacity requirement is

[0087]

[0088] f) Unit output constraints in standby call scenarios

[0089]

[0090] g) Line safety constraints

[0091]

[0092] In the formula: Let A be the transmission power of line l in scenario k during time period t; l，n The sensitivity of power injection at node n to power flow at line l can be calculated using a DC power flow model; P l，max NL represents the transmission capacity limit for line l; NL represents the number of lines.

[0093] S3: Based on the aforementioned backup configuration scheme and the unit output, as well as the wind power output variation range and load power variation range, generate extreme scenarios, including:

[0094] The approach to generating extreme scenarios in this invention is as follows: Based on the unit output and standby configuration scheme under the predicted scenario, the adjustment range of unit output in the standby call scenario is determined. Then, combined with the variation range of wind power output and load power, the maximum and minimum values ​​of power flow for each line at each time period under the standby call scenario are calculated. If these values ​​exceed their transmission capacity limits, then the output range of thermal power units, wind power output, and load power at this time constitute an extreme scenario. Taking the calculation of the maximum forward power flow value of a line as an example, the generation principle of the extreme scenarios in this patent is explained (the reverse power flow is analogous).

[0095] The maximum value of the forward power flow can first be calculated using the following model:

[0096]

[0097] In the formula: This is used to describe the range of power output variation of thermal power units at node n, and can be calculated based on the planned power output, upper and lower reserves of each thermal power unit, and the node it is located at.

[0098] This is clearly a simple linear programming problem. Based on the idea of ​​a continuous greedy algorithm, we can obtain the solution through simple calculations without directly solving the problem. First, we make the following variable substitutions to obtain the auxiliary variables and their ranges of variation:

[0099]

[0100]

[0101] The model then becomes

[0102]

[0103]

[0104] Furthermore, construct auxiliary variables.

[0105]

[0106] The model can then be transformed into a standard form.

[0107]

[0108] The maximum power flow value of line l can then be calculated using the following procedure.

[0109] like Figure 2 As shown, if the maximum power flow of line l exceeds the transmission capacity limit, the wind power output of each node can be obtained by substituting the variables in equations (12) and (15). and load power The extreme scenario is formed by combining the output range of thermal power units.

[0110] S4: Verify the backup configuration scheme using the extreme scenario. If the verification fails, adjust the transmission line capacity limit and execute step S2. If the verification passes, configure the backup setting based on the transmission line capacity limit, including:

[0111] The main problem is to make economical backup configurations, and to call up the lowest cost units as much as possible to meet the system load and backup requirements, without considering the backup call scenario. Therefore, its optimization objective is to minimize the system operating cost, as shown in equation (1). The constraints include equations (2) to (8) and equation (10), where k in each equation is 0, that is, only the prediction scenario is considered.

[0112] 1) Sub-problem of backup feasibility verification

[0113] The subproblem performs a feasibility check on the backup capacity configuration scheme of the main problem. Specifically, it considers power balance constraints, generator output adjustment range constraints, and network security constraints under extreme scenarios, determining whether the feasible region is an empty set. To avoid the subproblem's feasible region being empty and preventing iteration, the transmission capacity limits of each line are relaxed, with the optimization objective being to minimize the total relaxation amount during backup calls. If the relaxation amount of each line's transmission capacity limit in the subproblem's optimization result is less than a given threshold, the backup configuration scheme is considered feasible; otherwise, the infeasibility constraint is returned to the main problem, and the solution continues.

[0114]

[0115]

[0116] In the formula, This represents the transmission capacity relaxation of line l during time period t under extreme scenario k. It is also noted that there is no coupling between the subproblems under each extreme scenario, so they can be solved separately, further reducing the computational burden of the model.

[0117] 2) Harmony and convergence conditions of the principal problem

[0118] If the alternative configuration scheme in the predicted scenario fails the feasibility check of all subproblems, the following constraint is returned to the main problem:

[0119]

[0120] In the formula, λ represents the line transmission capacity limit of the main problem in the nth iteration, which is substituted into the line safety constraints in the prediction scenario during iterative solution; (n) This represents the adjustment factor for the nth iteration; express The maximum value among all subproblems in this iteration.

[0121] The above equation shows that the subproblem can not only perform backup feasibility checks, but also evaluate the additional transmission capacity required for each line when the configuration scheme is not feasible. By appropriately reducing the line transmission capacity limit in the main problem, line transmission capacity space can be reserved for the backup call process. At the same time, since each iteration only needs to return the coordination constraint to the main problem once, and the constraint form is efficient and concise without introducing new variables, it will not affect the solution efficiency of the main problem.

[0122] The adjustment factor ranges from 0 to 1. While a smaller value improves the accuracy of the solution, it increases the number of iterations. Conversely, a larger value can lead to over-adjustment, affecting the economic efficiency of the result and potentially causing the main problem to become unsolvable. Note that... The adjustment factor tends to decrease as iterations progress. To balance solution efficiency and accuracy, the adjustment factor in this paper is set to a smaller value in the initial iterations and gradually increases as iterations proceed.

[0123]

[0124] In the formula, λ max and λ min These are the maximum and minimum values ​​of the adjustment factor, respectively.

[0125] Convergence condition: When the relaxation amount of the line transmission capacity limit for all subproblems is less than a given threshold, it indicates that the backup configuration scheme can adapt to changes in wind power output and load power, and the iteration terminates.

[0126]

[0127] In the formula: ε is a given convergence threshold, which is a small positive number.

[0128] The specific algorithm flow is as follows: Figure 3 As shown.

[0129] The overall solution to the problem is as follows: First, establish an optimized scheduling model that considers the feasibility of backup configuration schemes. Then, based on the range of unit output and the variation range of wind power output and load power during the backup call process, generate several extreme scenarios. The model is then decomposed into a main problem of backup economic configuration under the predicted scenarios and several sub-problems of backup feasibility verification under the extreme scenarios. Backup optimization configuration is performed through the main problem, and backup availability is verified through the sub-problems. If the backup is unavailable, the main problem model and parameters are modified. The backup optimization configuration result that can accurately consider the availability of rotating backup is obtained by alternating between the main and sub-problems.

[0130] The method for generating extreme scenarios. Based on the unit output and standby configuration scheme under the predicted scenario, the range of unit output adjustment in the standby call scenario is determined. Then, combined with the variation range of wind power output and load power, the maximum and minimum values ​​of power flow of each line at each time period under the standby call scenario are calculated. If the transmission capacity limit is exceeded, the output range of thermal power units, wind power output and load power at this time constitute an extreme scenario.

[0131] Adjustments and parameter modifications to the backup configuration model are made based on extreme scenarios, as shown in equations (19) and (20).

[0132] Example 2

[0133] This application uses the New England 10-unit 39-bus system as an example to conduct algorithm testing and analysis. The system load forecast data is distributed across 19 different nodes; the two wind farms are connected to nodes 2 and 21, respectively; the variation range of wind power and negative power is taken as 15% and 5% of the power in the corresponding time period, respectively; the system contains a total of 46 lines, of which the transmission capacity of lines 9, 37, and 44 is limited to 500MW, and the transmission capacity of the remaining lines is 180MW; the unit uptime and downtime standby costs are both taken as $20.

[0134] To illustrate the issue of reserve availability in power system optimal scheduling, this invention uses the 6th time period as an example. The predicted wind power and load power, along with the extreme scenario power, are shown in Table 2 for this time period.

[0135] Table 2 shows the wind power output and load power during the 6th time period.

[0136]

[0137] In the extreme scenario shown in Table 2, the system requires a total of 53.37MW of reserve capacity to cope with the power imbalance caused by wind power and load forecast deviations. Table 2 shows that the system has a total of 81.83MW of reserve capacity reserved at this time. Traditional methods assume that the system can achieve power balance by adjusting the output of each unit. However, test results show that this extreme scenario fails the reserve feasibility check. Solving the corresponding sub-problems yields transmission capacity limit relaxations of 3 and 4 of 37.24MW and 10.69MW, respectively, indicating that the reserve capacity cannot be successfully utilized due to the transmission capacity limitations of lines 3 and 4. This test result shows that it is indeed possible for the system to have sufficient total reserve capacity, but the reserve configuration scheme may be infeasible due to line transmission capacity limitations. The improved method and system proposed in this invention can completely avoid the situation where the reserve cannot be effectively utilized.

[0138] Example 3

[0139] Based on the same inventive concept, this application also provides an optimized configuration system for availability backup, the system comprising:

[0140] Initialization module: Used to initialize transmission line capacity limits;

[0141] Solving module: used to solve based on a pre-built standby optimal configuration model to obtain the standby configuration scheme and unit output under the transmission line capacity limit;

[0142] Generation module: used to generate extreme scenarios based on the backup configuration scheme, unit output, wind power output variation range, and load power variation range;

[0143] Configuration module: Verify the backup configuration scheme using the extreme scenario. If the verification fails, adjust the transmission line capacity limit and execute the solution module. If the verification passes, configure the backup setting based on the transmission line capacity limit.

[0144] The solution module further includes: a construction module;

[0145] The construction module is used to construct an objective function with the goal of balancing the power generation cost, start-up and shutdown cost, and reserve capacity cost of the power grid system.

[0146] Construct constraints;

[0147] The constraints include: system power balance constraints, upper and lower limits of thermal power unit output constraints, thermal power unit ramping constraints, minimum start-up and shutdown time constraints of thermal power units, standby constraints, unit output constraints in standby dispatch scenarios, and line safety constraints.

[0148] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0149] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0150] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0151] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0152] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. An optimized configuration method for availability standby, characterized in that, The method includes: S1: Initialize transmission line capacity limits; S2: Solve based on the pre-built standby optimization configuration model to obtain the standby configuration scheme and unit output under the transmission line capacity limit; S3: Generate extreme scenarios based on the aforementioned backup configuration scheme, unit output, wind power output variation range, and load power variation range; S4: Verify the backup configuration scheme using the extreme scenario. If the verification fails, adjust the transmission line capacity limit and execute step S2. If the verification passes, configure the backup setting based on the transmission line capacity limit. The verification of the backup configuration scheme using the extreme scenario includes: Based on a pre-built extreme scenario model, with the goal of minimizing the total relaxation amount during backup calls, the transmission capacity limits of each line in the configuration scheme are relaxed. If all of the relaxation amounts are less than the set threshold, the transmission line capacity limit is configured as a spare. Otherwise, adjust the relaxation amount of the line corresponding to the set threshold to redetermine the transmission line capacity limit, and continue to solve the constraints of the optimization configuration model until the relaxation amount is less than the set threshold. The step S2, which involves adjusting the transmission line capacity limit, includes: The adjustment factor is determined based on the slack of each line during the same period. The transmission line capacity limit is determined based on the adjustment factor and the relaxation amount being not less than the maximum value among the set thresholds. Based on the determined transmission line capacity limit, step S2 is executed again; The formula for calculating the transmission line capacity limit is as follows: In the formula, λ represents the line transmission capacity limit of the main problem in the nth iteration; (n) This is the adjustment factor for the nth iteration; for The maximum value among all subproblems in this iteration; NK is the number of scenarios; NT is the number of iterations; NL is the number of lines; The generation of extreme scenarios based on the backup configuration scheme, unit output, wind power output variation range, and load power variation range includes: The range of unit output adjustment is determined based on the aforementioned unit output and standby configuration scheme; The maximum and minimum power flow values ​​for each line and each time period are calculated based on the unit output adjustment range and the wind power output and load power change range. If the maximum or minimum value exceeds the limit of the transmission capacity of each corresponding line, the wind power output and load power of each node are obtained, and a limit scenario is generated based on the unit output adjustment range, the wind power output and load power of each node.

2. The method as described in claim 1, characterized in that, The construction of the backup optimized configuration model includes: A target function is constructed with the goal of balancing the power generation cost, start-up and shutdown cost, and reserve capacity cost of the power grid system. Construct constraints; The constraints include: system power balance constraints, upper and lower limits of thermal power unit output constraints, thermal power unit ramping constraints, minimum start-up and shutdown time constraints of thermal power units, standby constraints, unit output constraints in standby dispatch scenarios, and line safety constraints.

3. The method as described in claim 2, characterized in that, The objective function is calculated as follows: In the formula, Let $t$ be the power generation cost of unit $i$ during time period $t$. Let i be the start-up cost of unit i during time period t; Let $t$ be the downtime cost of unit $i$ during time period $t$. Let be the standby capacity cost of unit i during time period t; T be the dispatch cycle; and NG be the number of thermal power units.

4. The method as described in claim 1, characterized in that, The construction of the extreme scenario model includes: The unit output range is determined based on the aforementioned backup configuration scheme and unit output. Based on the unit output range and the obtained wind power output and load power variation range, calculate the maximum and minimum power flow values ​​for each line at each time period; The output range of thermal power units, wind power output, and load power are determined based on the maximum and minimum values ​​and the transmission capacity limits of each line. A limit scenario model is constructed based on the output range of the thermal power unit, the wind power output and the load power, as well as the constraints. The constraints include: power balance constraints, unit output adjustment orientation constraints, and network security constraints.

5. An optimized configuration system for availability backup, characterized in that, The system includes: Initialization module: Used to initialize transmission line capacity limits; Solving module: used to solve based on a pre-built standby optimal configuration model to obtain the standby configuration scheme and unit output under the transmission line capacity limit; Generation module: used to generate extreme scenarios based on the backup configuration scheme, unit output, wind power output variation range, and load power variation range; Configuration module: The backup configuration scheme is verified using the extreme scenario. If the verification fails, the transmission line capacity limit is adjusted, and the solution module is executed. If the verification passes, the backup configuration is set based on the transmission line capacity limit. The verification of the backup configuration scheme using the extreme scenario includes: Based on a pre-built extreme scenario model, with the goal of minimizing the total relaxation amount during backup calls, the transmission capacity limits of each line in the configuration scheme are relaxed. If all of the relaxation amounts are less than the set threshold, the transmission line capacity limit is configured as a spare. Otherwise, adjust the relaxation amount of the line corresponding to the set threshold to redetermine the transmission line capacity limit, and continue to solve the constraints of the optimization configuration model until the relaxation amount is less than the set threshold. The step S2, which involves adjusting the transmission line capacity limit, includes: The adjustment factor is determined based on the slack of each line during the same period. The transmission line capacity limit is determined based on the adjustment factor and the relaxation amount being not less than the maximum value among the set thresholds. Based on the determined transmission line capacity limit, step S2 is executed again; The formula for calculating the transmission line capacity limit is as follows: In the formula, λ represents the line transmission capacity limit of the main problem in the nth iteration; (n) This is the adjustment factor for the nth iteration; for The maximum value among all subproblems in this iteration; NK is the number of scenarios; NT is the number of iterations; NL is the number of lines; The generation of extreme scenarios based on the backup configuration scheme, unit output, wind power output variation range, and load power variation range includes: The range of unit output adjustment is determined based on the aforementioned unit output and standby configuration scheme; The maximum and minimum power flow values ​​for each line and each time period are calculated based on the unit output adjustment range and the wind power output and load power change range. If the maximum or minimum value exceeds the limit of the transmission capacity of each corresponding line, the wind power output and load power of each node are obtained, and a limit scenario is generated based on the unit output adjustment range, the wind power output and load power of each node.

6. The system as described in claim 5, characterized in that, The solution module further includes: a construction module; The construction module is used to construct an objective function with the goal of balancing the power generation cost, start-up and shutdown cost, and reserve capacity cost of the power grid system. Construct constraints; The constraints include: system power balance constraints, upper and lower limits of thermal power unit output constraints, thermal power unit ramping constraints, minimum start-up and shutdown time constraints of thermal power units, standby constraints, unit output constraints in standby dispatch scenarios, and line safety constraints.

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