Method and system for optimal virtual inertia allocation of offshore wind farm based on load reduction operation

Abstract

The application discloses a kind of offshore wind farm virtual inertia optimal distribution method and system based on load reduction operation, according to offshore wind farm historical data and real-time operation data, the output of offshore wind farm in different positions and capacities is predicted, the position and quantity of offshore wind farm participating in power system inertia support are determined;With offshore wind farm load reduction and system traditional unit operating variable as optimization variable, considering power system transient frequency index constraint, a mixed integer nonlinear optimization model is constructed;Solve the mixed integer nonlinear optimization model, obtain the real-time operation plan and optimal inertia distribution scheme of offshore wind farm.The advantages of the present application are that the inertia support can be provided by the offshore wind farm with frequency modulation capability, thus making up for the inertia loss caused by the access of new energy.The inertia distribution method avoids the situation of starting new traditional units to improve system reserve capacity or rotational inertia, and improves the economy of operation.

Description

Technical Field

[0001] This invention relates to a method and system for optimal allocation of virtual inertia in offshore wind farms based on load reduction operation, belonging to the field of stable operation technology of new energy power systems. Background Technology

[0002] Because renewable energy sources typically operate in maximum power capture mode, they cannot provide inertial support to the system. When the proportion of renewable energy in the power system is high, the system's rotational inertia, frequency regulation reserve capacity, and start-up methods continuously decrease, leading to a decline in the grid's frequency support capability. Traditional unit output optimization schemes may result in insufficient system inertia, easily causing system frequency fluctuations and even triggering serious cascading failures. Therefore, when scheduling the output of traditional units, the impact of a high proportion of renewable energy integration on system inertia and frequency changes must be considered. An optimal output scheme should be developed while ensuring frequency security, improving operational economy while increasing the absorption capacity of renewable energy.

[0003] However, with the increasing proportion of renewable energy output, the dual uncertainties on both the source and load sides have increased the risk of grid supply-demand mismatch and the risk of system frequency stability, exacerbating the contradiction between the inertia requirements of traditional units and the increasing proportion of renewable energy. To adapt to the development trend of renewable energy shifting from a substitute energy source to a dominant power source and to resolve the contradiction between traditional thermal power units and renewable energy development, my country has placed higher demands on the grid synchronization support capability of renewable energy. Currently, research has applied virtual synchronous generation technology to large power grids to simulate the inertia response and frequency damping effect of traditional units, improving the grid integration adaptability of renewable energy.

[0004] However, existing technologies do not regulate the diverse frequency regulation resources within the network from a system-wide perspective, and fail to fully tap the active support capabilities of new energy sources. When the combination of traditional units under conventional constraints does not meet the frequency limit requirements, new energy units with frequency regulation capabilities can reduce their load to provide inertia support, thereby compensating for the inertia loss caused by the access of new energy sources. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a method and system for optimal allocation of virtual inertia of offshore wind farm based on unloaded operation. By optimizing the virtual inertia allocation of offshore wind farm and evaluating the transient frequency index of the system, the power output of offshore wind farm is optimized, thereby enhancing the frequency stability of the system and improving the absorption capacity of new energy.

[0006] To address the aforementioned technical problems, this invention provides a method for optimal virtual inertia allocation in offshore wind farms based on unloaded operation, comprising:

[0007] Based on historical and real-time operational data of offshore wind farms, the power output of offshore wind farms at different locations and with different capacities is predicted to determine the location and number of offshore wind farms that will participate in the power system inertia support.

[0008] Using the load reduction of offshore wind farms and the operating variables of traditional units in the system as optimization variables, and considering the constraints of power system transient frequency index, a mixed integer nonlinear optimization model is constructed.

[0009] Solve the mixed-integer nonlinear optimization model to obtain the real-time operation plan and optimal inertia allocation scheme for the offshore wind farm.

[0010] Furthermore, solving the mixed-integer nonlinear optimization model to obtain the real-time operation plan and optimal inertia allocation scheme for the offshore wind farm includes:

[0011] The mixed-integer nonlinear optimization model is transformed into a master-slave bilevel optimization problem by using the supplementary optimization cut method. The master problem is a mixed-integer linear programming problem, and the slave problem is the evaluation of power system transient frequency index.

[0012] Through iterative calculations and evaluations of the master-slave problem, the mixed-integer nonlinear optimization model eventually converges to the optimal solution, yielding the real-time operation plan and optimal inertia allocation scheme for offshore wind farms.

[0013] Furthermore, the evaluation of the power system transient frequency index includes:

[0014] The start-up and shutdown flags of traditional wind turbines and the output reduction ratio of offshore wind farms under the current iteration are obtained by calculating the main problem.

[0015] The power disturbance is obtained, and the power disturbance, the start-stop flag of the traditional unit under the current iteration, and the output reduction ratio of the offshore wind farm are substituted into the multi-type power supply joint primary frequency regulation mathematical model that is pre-constructed based on the combination of the offshore wind farm group and the traditional unit frequency regulation model to calculate the system transient frequency deviation.

[0016] The system transient frequency deviation is compared with a pre-set deviation threshold. If it is not greater than the threshold, the power system transient frequency index is met, and the real-time output of the wind farm calculated in the current iteration is used as the real-time operation plan of the offshore wind farm. The load reduction calculated in the current iteration is used as the optimal inertia allocation scheme for the offshore wind farm. If it is greater than the threshold, the total equivalent inertia and the total equivalent frequency regulation coefficient calculated based on the traditional unit start-stop flags and the output reduction ratio of the offshore wind farm in the current iteration are used as the system inertia and frequency capability constraints for the next iteration, and the next round of iteration is carried out.

[0017] Furthermore, the mixed-integer nonlinear optimization model is as follows:

[0018]

[0019] In the formula, Let C be the operating cost of the i-th traditional generator unit within the system during time period t. i U C i D The start-up and shutdown costs incurred by the unit, u i,t δ represents the start / stop flag of the i-th traditional unit within time t. i,t Let P be the output load reduction ratio of the i-th offshore wind farm in time period t, μ be the unit output load reduction cost coefficient of the offshore wind farm, k be the unit output maintenance cost coefficient of the offshore wind farm, and P be the load reduction ratio of the i-th offshore wind farm in time period t. Ri,t Indicates the predicted power output of an offshore wind farm, a i b i c i U represents the coal consumption coefficient of traditional power units. i,t-1 H represents the start / stop flag of the i-th traditional unit within time t-1. i J represents the single startup cost of unit i. i Let N represent the cost of a single outage of unit i, N represent the number of traditional units in the system, M represent the number of wind farms in the system, and T represent the number of scheduling cycles.

[0020] The constraints of the mixed-integer nonlinear optimization model include traditional linear power constraints and system inertia and frequency capability constraints.

[0021] The system inertia and frequency capability constraints are as follows:

[0022]

[0023] In the formula, H eq For the total equivalent inertia, R T The total equivalent frequency modulation coefficient is . This represents the total equivalent inertia calculated in the previous iteration. This is the total equivalent frequency modulation coefficient obtained from the previous iteration.

[0024] Furthermore, the mathematical model for the joint primary frequency modulation of the multiple power sources is as follows:

[0025]

[0026] In the formula, Δω represents the system transient frequency deviation u i,t For traditional generator unit start / stop flags, the value is a 0-1 variable, where 0 indicates stop and 1 indicates start; R i For traditional generator units, the static droop coefficient is F. hi The fraction of the total power generated by the unit; T Riis the time constant of the traditional generator speed governor; s is the indicator of the Laplace transform; R T1 R represents the frequency regulation capability of all traditional units in the system. T2 ΔP represents the frequency regulation capability of all wind farms in the system. L R represents the power disturbance. vi H is the virtual static droop coefficient for offshore wind farms; Gi H represents the actual inertia of a traditional generator unit. vi The virtual inertia allocated to offshore wind farms is determined by the capacity retained after the farm is unloaded, and is expressed as:

[0027]

[0028] In the formula, S B For the capacity of offshore wind farms, RoCoF max Δω is the maximum allowable rate of change of frequency for the system. max The maximum allowable frequency deviation of the system, ω n Indicates the rated frequency.

[0029] A virtual inertia optimal allocation system for offshore wind farms based on unloaded operation includes:

[0030] The determination module is used to predict the power output of offshore wind farms at different locations and with different capacities based on historical and real-time operational data, and to determine the location and number of offshore wind farms that will participate in the power system inertia support.

[0031] The module is used to construct a mixed integer nonlinear optimization model with the load reduction of offshore wind farms and the operating variables of traditional units in the system as optimization variables, taking into account the transient frequency index constraints of the power system.

[0032] The solution module is used to solve the mixed integer nonlinear optimization model to obtain the real-time operation plan and optimal inertia allocation scheme of the offshore wind farm.

[0033] A computer-readable storage medium storing one or more programs, said one or more programs including instructions that, when executed by a computing device, cause the computing device to perform any of the methods described.

[0034] A computing device, comprising,

[0035] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described.

[0036] The beneficial effects achieved by this invention are as follows:

[0037] This invention provides inertia support by reducing the load on offshore wind farms with frequency regulation capabilities, thereby compensating for the inertia loss caused by the integration of new energy sources. This inertia allocation method avoids the need to start new conventional turbines to increase system reserve capacity or rotational inertia, thus improving operational economy. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating the present invention. Detailed Implementation

[0039] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0040] This invention discloses a method for optimal virtual inertia allocation in offshore wind farms based on unloaded operation, applicable to the safe and stable operation of high-proportion renewable energy power systems. The flowchart of the calculation process for optimal virtual inertia allocation in offshore wind farms proposed in this invention is shown below. Figure 1 As shown, this method mainly consists of two parts: optimal allocation of virtual inertia of wind farm and evaluation of system transient frequency index.

[0041] The optimal allocation of virtual inertia for offshore wind farms is based on the predicted results of offshore wind farm and load output. It optimizes the calculation of the actual output of each unit and wind farm to form an optimal allocation scheme for virtual inertia of offshore wind farms, providing inertia support for the power system. The evaluation of system transient frequency index is carried out by constructing a primary frequency regulation model of the system involving offshore wind farms. It evaluates the maximum rate of frequency change and the lowest frequency point after the system is disturbed, providing frequency safety constraints for the inertia allocation model of offshore wind farms.

[0042] The optimal allocation model of virtual inertia of wind farms considering the transient frequency index constraint of the power system is a mixed integer nonlinear optimization model, which is relatively complex to solve. Therefore, the supplementary optimization cut method is used to transform the nonlinear model into a master-slave two-level optimization problem. According to Figure 1 The solution process is shown below, and the specific calculation steps are as follows:

[0043] Step 1: Based on historical and real-time operational data of offshore wind farms, predict the power output of offshore wind farms at different locations and with different capacities, and determine the location and number of offshore wind farms that will participate in the power system inertia support.

[0044] Step 2: Using the load reduction of the offshore wind farm (distributed inertia) and the operating variables of the traditional units in the system (start-up status and output) as optimization variables, and considering the transient frequency index constraints of the power system, a mixed integer nonlinear optimization model is constructed.

[0045] Step 3: The nonlinear model established in Step 2 is transformed into a master-slave bilevel optimization problem using the supplementary optimization cut method. The master problem is a mixed-integer linear programming problem, and the slave problem is the evaluation of transient frequency indices.

[0046] Step 4: Through iterative calculation and evaluation of the master-slave problem, the model eventually converges to the optimal solution, obtaining the real-time operation plan and optimal inertia allocation scheme for the offshore wind farm.

[0047] The system transient frequency index assessment is conducted by constructing a primary frequency regulation model of the system involving offshore wind farms. The assessment evaluates the maximum rate of frequency change and the lowest frequency point after the system is disturbed. The specific assessment process is as follows:

[0048] Step 1: Based on the primary frequency regulation strategy and inertia allocation scheme of the offshore wind farm, calculate the equivalent inertia, equivalent frequency regulation coefficient and other parameters of the offshore wind farm group, and clarify the meaning of each regulation parameter;

[0049] Step 2: Combine the offshore wind farm cluster with the traditional turbine frequency regulation model to construct a mathematical model for joint primary frequency regulation of multiple power sources, and calculate the total equivalent inertia H within the system. eq and the total equivalent frequency modulation coefficient R T ;

[0050] Step 3: Using the power disturbance as input, calculate the minimum point of the system frequency and the maximum rate of change of the system frequency under the current inertia allocation scheme, and evaluate whether the transient frequency index constraint in the optimal inertia allocation model meets the conditions.

[0051] Utilizing the unloaded operation characteristics of offshore wind farms, an optimal inertia allocation model for wind farm participation in primary frequency regulation is established:

[0052]

[0053] In the formula, Let C be the operating cost of the i-th traditional generator unit within the system during time period t. i U C i D The start-up and shutdown costs incurred by the unit, u i,t δ represents the start / stop flag of the i-th traditional unit within time t. i,t Let μ be the output load reduction ratio of the i-th offshore wind farm in time period t, μ be the unit output load reduction cost coefficient of the offshore wind farm, and k be the unit output maintenance cost coefficient of the offshore wind farm. Constraints include:

[0054] 1) Equality constraints

[0055]

[0056] In the formula, N LP represents the total number of load nodes in the system. d,t This indicates the load on each node;

[0057] 2) Inequality constraints

[0058] a) Hot standby

[0059]

[0060] In the formula, P i,max ρ represents the upper limit of the output of the i-th unit, and ρ represents the thermal reserve coefficient.

[0061] b) Unit output constraints

[0062] u i,t P i,min ≤P i ≤u i,t P i,max (4)

[0063] In the formula, P i,min P represents the lower limit of the output of the i-th generator unit. i This represents the real-time output of the i-th generating unit;

[0064] c) Unit ramp-up constraints

[0065] P i,t -P i,t-1 ≤u i,t-1 (R u -S i,u )+S i,u (5)

[0066] P i,t-1 -P i,t ≤u i,t (R d -S i,d )+S i,d (6)

[0067] In the formula, P i,t-1 R represents the output of the i-th generator unit at time t-1. u R d S represents the ramp rate of a conventional generator unit. i,u S i,d This represents the maximum rate of acceleration during unit startup and the maximum rate of deceleration during shutdown, taken as:

[0068]

[0069] d) Unit start-up and shutdown time constraints

[0070]

[0071]

[0072] In the formula, TS and TO represent the minimum shutdown / startup time of the unit, and μ i,k This indicates the start / stop flag of the i-th traditional unit within time k.

[0073] e) Start-stop fee constraints

[0074]

[0075]

[0076] f) Current flow safety constraints

[0077] P l,min ≤P l,t ≤P l,max (11)

[0078] In the formula, P l,t P represents the power of the tie lines within the system. l,min P l,max Indicates the lower / upper limit of the tie line power.

[0079] Calculate the power flow transfer distribution factor matrix G, and rewrite the above equation as:

[0080]

[0081] Among them, G l-i To describe the impact of the injected power at node i on line l, G l-k P represents the transfer distribution factor of line lk. Rk,t This represents the predicted power output of the offshore wind farm at time k.

[0082] In addition to the traditional linear power constraints mentioned above, system inertia and frequency capability constraints are also included:

[0083]

[0084] In the formula, H eq For the total equivalent inertia, R T The total equivalent frequency modulation coefficient characterizes the primary frequency modulation capability of the system.

[0085] This invention utilizes a constructed primary frequency regulation model of the system involving offshore wind farms to evaluate whether the system inertia and frequency constraints in the above-mentioned optimal inertia allocation model meet the conditions. The mathematical model constructed for evaluating the system transient frequency index is as follows:

[0086]

[0087] In the formula, u iFor traditional unit start / stop flags, the value is a 0-1 variable; R i For traditional generator units, the static droop coefficient is F. hi The fraction of the total power generated by the unit; T Ri R is the time constant of a traditional generator speed governor; vi H is the virtual static droop coefficient for offshore wind farms; G H represents the actual inertia of a traditional generator unit. v The virtual inertia allocated to offshore wind farms is determined by the capacity retained after the farm is unloaded.

[0088]

[0089] In the formula, S B For the capacity of offshore wind farms, RoCoF max Δω is the maximum allowable rate of change of frequency for the system. max The maximum allowable frequency deviation of the system.

[0090] Example 2: Accordingly, the present invention also provides a virtual inertia optimal allocation system for offshore wind farms based on unloaded operation, comprising:

[0091] The determination module is used to predict the power output of offshore wind farms at different locations and with different capacities based on historical and real-time operational data, and to determine the location and number of offshore wind farms that will participate in the power system inertia support.

[0092] The module is used to construct a mixed integer nonlinear optimization model with the load reduction of offshore wind farms and the operating variables of traditional units in the system as optimization variables, taking into account the transient frequency index constraints of the power system.

[0093] The solution module is used to solve the mixed integer nonlinear optimization model to obtain the real-time operation plan and optimal inertia allocation scheme of the offshore wind farm.

[0094] Example 3: Accordingly, the present invention also provides a computer-readable storage medium for storing one or more programs, said one or more programs including instructions that, when executed by a computing device, cause the computing device to perform any of the methods according to claims 1 to 5.

[0095] Example 4: Accordingly, the present invention also provides a computing device, comprising,

[0096] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods according to claims 1 to 5.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimal virtual inertia allocation in offshore wind farms based on unloaded operation, characterized in that, include: Based on historical and real-time operational data of offshore wind farms, the power output of offshore wind farms at different locations and with different capacities is predicted to determine the location and number of offshore wind farms that will participate in the power system inertia support. Using the load reduction of offshore wind farms and the operating variables of traditional units in the system as optimization variables, and considering the constraints of power system transient frequency index, a mixed integer nonlinear optimization model is constructed. Solve the mixed integer nonlinear optimization model to obtain the real-time operation plan and optimal inertia allocation scheme of the offshore wind farm; The mixed-integer nonlinear optimization model is as follows: （1）； In the formula, C i f (P i,t Let C be the operating cost of the i-th traditional generator unit within the system during time period t. i U C i D The start-up and shutdown costs incurred by the unit, u i,t δ represents the start / stop flag of the i-th traditional unit within time t. i,t Let μ be the output load reduction ratio of the i-th offshore wind farm in time period t, μ be the unit output load reduction cost coefficient of the offshore wind farm, and k be the unit output maintenance cost coefficient of the offshore wind farm. This indicates the predicted power output of an offshore wind farm. , , This represents the coal consumption coefficient of a traditional unit. This represents the start / stop flag for the i-th traditional unit within time t-1. This represents the single startup cost of unit i. Let N represent the cost of a single outage of unit i, N represent the number of traditional units in the system, M represent the number of wind farms in the system, and T represent the number of scheduling cycles. P i,t Indicates the first i Taiwanese unit t Efforts made at all times; The constraints of the mixed-integer nonlinear optimization model include traditional linear power constraints and system inertia and frequency capability constraints. The system inertia and frequency capability constraints are as follows: （2）； In the formula, H eq For the total equivalent inertia, R T The total equivalent frequency modulation coefficient is . This represents the total equivalent inertia calculated in the previous iteration. This is the total equivalent frequency modulation coefficient obtained from the previous iteration.

2. The method for optimal virtual inertia allocation of offshore wind farms based on unloaded operation according to claim 1, characterized in that, Solving the mixed-integer nonlinear optimization model to obtain the real-time operation plan and optimal inertia allocation scheme for the offshore wind farm includes: The mixed-integer nonlinear optimization model is transformed into a master-slave bilevel optimization problem by using the supplementary optimization cut method. The master problem is a mixed-integer linear programming problem, and the slave problem is the evaluation of power system transient frequency index. Through iterative calculations and evaluations of the master-slave problem, the mixed-integer nonlinear optimization model eventually converges to the optimal solution, yielding the real-time operation plan and optimal inertia allocation scheme for offshore wind farms.

3. The method for optimal virtual inertia allocation of offshore wind farms based on unloaded operation according to claim 2, characterized in that, The evaluation of the power system transient frequency index includes: The start-up and shutdown flags of traditional wind turbines and the output reduction ratio of offshore wind farms under the current iteration are obtained by calculating the main problem. The power disturbance is obtained, and the power disturbance, the start-stop flag of the traditional unit under the current iteration, and the output reduction ratio of the offshore wind farm are substituted into the multi-type power supply joint primary frequency regulation mathematical model that is pre-constructed based on the combination of the offshore wind farm group and the traditional unit frequency regulation model to calculate the system transient frequency deviation. The system transient frequency deviation is compared with a pre-set deviation threshold. If it is not greater than the threshold, the power system transient frequency index is met, and the real-time output of the wind farm calculated in the current iteration is used as the real-time operation plan of the offshore wind farm. The load reduction calculated in the current iteration is used as the optimal inertia allocation scheme for the offshore wind farm. If it is greater than the threshold, the total equivalent inertia and the total equivalent frequency regulation coefficient calculated based on the traditional unit start-stop flags and the output reduction ratio of the offshore wind farm in the current iteration are used as the system inertia and frequency capability constraints for the next iteration, and the next round of iteration is carried out.

4. The method for optimal virtual inertia allocation of offshore wind farms based on unloaded operation according to claim 3, characterized in that, The mathematical model for the joint primary frequency modulation of multiple power sources is as follows: （3）； In the formula, Indicates the system transient frequency deviation u i,t For traditional generator unit start / stop flags, the value is a 0-1 variable, where 0 indicates stop and 1 indicates start; R i For traditional generator units, the static droop coefficient is F. hi The fraction of the total power generated by the unit; T Ri The time constant of a traditional generator set speed governor; s is a marker for the Laplace transform; This indicates the frequency regulation capability of all traditional units in the system; This indicates the frequency regulation capability of all wind farms in the system; R represents the power disturbance. vi H is the virtual static droop coefficient for offshore wind farms; Gi H represents the actual inertia of a traditional generator unit. vi The virtual inertia allocated to offshore wind farms is determined by the capacity retained after the farm is unloaded, and is expressed as: （4）； In the formula, S B For the capacity of offshore wind farms, RoCoF max ∆ω is the maximum allowable rate of change of frequency for the system. max The maximum allowable frequency deviation of the system, ω n Indicates the rated frequency.

5. A virtual inertia optimal allocation system for offshore wind farms based on unloaded operation, characterized in that, include: The determination module is used to predict the power output of offshore wind farms at different locations and with different capacities based on historical and real-time operational data, and to determine the location and number of offshore wind farms that will participate in the power system inertia support. The module is used to construct a mixed integer nonlinear optimization model with the load reduction of offshore wind farms and the operating variables of traditional units in the system as optimization variables, taking into account the transient frequency index constraints of the power system. The solution module is used to solve the mixed integer nonlinear optimization model to obtain the real-time operation plan and optimal inertia allocation scheme of the offshore wind farm; The mixed-integer nonlinear optimization model is as follows: （1）； In the formula, C i f (P i,t Let C be the operating cost of the i-th traditional generator unit within the system during time period t. i U C i D The start-up and shutdown costs incurred by the unit, u i,t δ represents the start / stop flag of the i-th traditional unit within time t. i,t Let μ be the output load reduction ratio of the i-th offshore wind farm in time period t, μ be the unit output load reduction cost coefficient of the offshore wind farm, and k be the unit output maintenance cost coefficient of the offshore wind farm. This indicates the predicted power output of an offshore wind farm. , , This represents the coal consumption coefficient of a traditional unit. This represents the start / stop flag for the i-th traditional unit within time t-1. This represents the single startup cost of unit i. Let N represent the cost of a single outage of unit i, N represent the number of traditional units in the system, M represent the number of wind farms in the system, and T represent the number of scheduling cycles. P i,t Indicates the first i Taiwanese unit t Efforts made at all times; The constraints of the mixed-integer nonlinear optimization model include traditional linear power constraints and system inertia and frequency capability constraints. The system inertia and frequency capability constraints are as follows: （2）； In the formula, H eq For the total equivalent inertia, R T The total equivalent frequency modulation coefficient is . This represents the total equivalent inertia calculated in the previous iteration. This is the total equivalent frequency modulation coefficient obtained from the previous iteration.

6. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods according to claims 1 to 4.

7. A computing device, characterized in that, include, One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods according to claims 1 to 4.

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