Multi-time scale inertial frequency cooperative adaptive control method for high-proportion new energy power grid

Abstract

The invention discloses a multi-time scale inertial frequency cooperative adaptive control method for a high-proportion new energy power grid. The method comprises the following steps: S1, establishing a frequency and inertia cooperative control topological structure of a high-new energy penetration power system; s2, acquiring parameters of the power system, constructing a multi-time-scale power system inertia-frequency control interconnection model, and acquiring fast and slow variables of the power system; s3, carrying out fast and slow time scale separation on the multi-time scale power system inertia-frequency control interconnection model to obtain a slow subsystem model and a fast subsystem model; s4, setting a control evaluation index, and optimizing a control parameter based on the control evaluation index; and S5, according to the optimized control parameters, based on the slow subsystem and the fast subsystem, calculating to obtain a control quantity. According to the method, inertia control and load frequency control are effectively integrated, and the overall stability and reliability of a power system under the high and new energy permeation condition are improved.

Description

Technical Field

[0001] This invention relates to the field of power system stability control technology, and in particular to a multi-time-scale inertial frequency cooperative adaptive control method for a high-proportion renewable energy power grid. Background Technology

[0002] With the increasing global emphasis on renewable energy, especially the rapid development of wind and solar power, the power system is facing unprecedented challenges. While high-penetration renewable energy generation provides clean energy to the power system, it also presents problems such as reduced inertia and increased frequency fluctuations in the power generation system due to the low mechanical inertia of renewable energy generation and its significant susceptibility to climate and environmental influences.

[0003] In traditional power systems, conventional power generation methods such as thermal power provide frequency stability through their physical inertia. However, with the increasing proportion of new energy sources, the relative share of these conventional energy sources is gradually declining, and the problem of insufficient system inertia is becoming increasingly significant. Frequency fluctuations not only affect the safety and reliability of the power system but may also negatively impact the operation of the electricity market. Furthermore, there are significant differences in time scale between batteries and conventional power generation methods like thermal power. Batteries, as fast-response energy storage devices, can adjust power output within milliseconds, while conventional thermal power typically requires a longer time to adjust its output. This difference in time scale is often overlooked in existing control methods, leading to an inability to effectively coordinate the scheduling of various power generation resources, thereby reducing the system's responsiveness to frequency fluctuations.

[0004] Currently, inertia control and load frequency control are often treated as independent control processes, lacking sufficient consideration of their coupling nature. Such separate control methods may lead to inefficiencies in system frequency regulation and inertia support, failing to fully leverage the synergistic effects of various power generation resources. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a multi-timescale inertial-frequency cooperative adaptive control method for high-proportion renewable energy power grids, which effectively integrates inertial control and load frequency control, thereby improving the overall stability and reliability of the power system under conditions of high renewable energy penetration.

[0006] This invention provides a multi-time-scale inertial frequency cooperative adaptive control method for high-proportion renewable energy power grids, the specific technical solution of which is as follows: S1: Establish a frequency and inertia coordinated control topology for a high-energy-penetration power system; S2: Obtain the parameters of the power system, construct a multi-time-scale power system inertia-frequency control interconnection model, and obtain the fast and slow variables of the power system; S3: Separate the fast and slow time scales of the multi-time-scale power system inertia-frequency control interconnection model to obtain the slow subsystem model and the fast subsystem model; S4: Set control evaluation indicators and optimize control parameters based on the control evaluation indicators; S5: Based on the optimized control parameters, calculate and obtain the control quantity based on the slow subsystem and the fast subsystem.

[0007] Furthermore, in step S2, the parameters of the power system include the guide vane opening deviation of the speed governor. Xg i Speed ​​controller gain Kgov i Speed ​​controller time constant Tgov i Unit output power Pm i Unit gain Ktur i Unit time constant Ttur i Energy storage device output power P Vi Energy storage device gain K EESi Energy storage device time constant T EESi System inertia coefficient H i System damping coefficient D i System gain K PSi Frequency deviation change rate Δf i and ROCOF i Power deviation of connecting lines Ptie ij Load disturbances unrelated to rotating equipment PL i ’ New energy power output PR i ’ System output disturbance PL Sag coefficient R i Deviation coefficient β i Connection line power Ptie i Regional control deviation ACE i Control quantities of the secondary frequency modulation control loop and the inertia control loop u 1i and u2i .

[0008] Furthermore, the multi-timescale power system inertia-frequency control interconnection model is expressed as follows:

[0009]

[0010]

[0011] Where, X1=[ P m1 , X g1 ] T For the slow variable of the system state, X2=[Δ f 1, P v1 , ROCOF 1, P tie12 , P tie31 ] T For system state fast variables, u =[ u 1, u 2] is the system input. u 1, u 2 represents the slow control quantity for system load frequency control and the fast control quantity for system inertia, respectively. Y =[ ACE 1, ROCOF 1, ACE 2, ROCOF 2, ACE 3, ROCOF 3] T For system output, μ For system perturbation parameters, ω For system disturbance; A 11 , A 12 , A 21 , A 22 , B 11 , B 12 , C 11 , C 12 They are respectively represented as follows:

[0012]

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] .

[0019] Furthermore, in step S3, the slow subsystem model is obtained by separating the fast and slow time scales, as shown below:

[0020] in, A 0= A 11 - A 12 A 22 -1 A 21 ; B 0= B 1- A 12 A 22 -1 A 21 ; C 0= C 1- C 2 A 22 -1 A 21 ; D 0=- C 2 A 22 -1 B 2; The fast subsystem model is obtained as follows: .

[0021] in, F 2 indicates the following: .

[0022] Furthermore, in step S5, the control quantity is obtained, and the specific process is as follows: S501: Discretize the slow subsystem model and the fast subsystem model; S502: Collect the output frequencies of the fast subsystem model and the slow subsystem model, and calculate the frequency ratio n; S503: Based on the multi-timescale power system inertia-frequency control interconnection model, establish the augmented state-space equations for the slow and fast subsystems, and calculate the output predictions; S504: Based on the obtained output predictions, set the optimization objective functions for the slow subsystem model and the fast subsystem model respectively; S505: Set constraints, including setting control variable amplitude and its increment constraints, and system frequency fluctuation constraints; S506: Solve based on the constraints and the aforementioned objective function to obtain the minimized cost function. J The control increment at (k); S507: The control quantity is calculated based on the control quantity increment.

[0023] Furthermore, in step S504, the optimization objective function of the slow subsystem model is expressed as follows:

[0024] The optimization objective function of the fast subsystem model is expressed as follows:

[0025] in, Q f and R f This is the weight matrix.

[0026] Furthermore, in step S505, the specific constraints are as follows:

[0027]

[0028]

[0029]

[0030]

[0031] in, u 1max and u 1min These are the upper and lower limits of the load frequency control quantity, respectively. u 2max and u 2minThese are the upper and lower limits of the control quantity for the inertia control loop, Δ f max and Δ f min These are the upper and lower limits of system frequency fluctuation, respectively.

[0032] Furthermore, the control evaluation indicators include total frequency regulation error, inertia burden index, cost function index reflecting the construction phase of inertia control loop, total regulation mileage index reflecting energy storage equipment, and index reflecting the number of times energy storage equipment is charged and discharged.

[0033] Furthermore, the specific process for optimizing the control parameters is as follows: S401: Set parameters for the multi-target GSA algorithm, including the number of targets. M GSA Population size N GSA External archives scale N GSA Search space dimension D GSA Spatial upper and lower limits u GSA , l GSA and maximum number of calculations F GSA ; S402: Initialize the position of each individual in the population x i and speed v i Perform iterative calculations; S403: Calculate the fitness value of each individual in the population under the cost function, and update the external archive, as follows: If the number of non-dominated solutions in the external archive exceeds the capacity limit, calculate the potential energy of each particle, remove the non-dominated solution with the highest potential energy, and continue until the number of non-dominated solutions in the external archive is restored to the limit. N GSA ; If the number of non-dominated solutions in the external archive does not exceed the capacity limit, calculate the potential energy of each particle and select the particle with the lowest potential energy as the first column of guiding particles GF. S404: Select the second type of guiding particle GS.

[0034] S405: Based on the first type of guiding particle GF and the second type of guiding particle GS, update the velocity and position of each particle in the population until the maximum number of iterations is reached. F GSA Output external files and stored non-dominated solutions; if the number of iterations does not reach the maximum number of computations. F GSAThen return to step S403.

[0035] The beneficial effects of this invention are as follows: 1. This invention establishes a frequency and inertia coordinated control topology for a high-penetration renewable energy power system and constructs a multi-timescale power system inertia-frequency control system model. Based on this model, fast and slow timescales are separated to obtain corresponding fast and slow subsystems. Then, through a multi-timescale control strategy combined with load frequency control (LFC) and battery-based inertia control, the frequency stability of the power system is significantly improved. This invention, by specifically adjusting frequency fluctuations at different timescales, can quickly respond to instantaneous load changes, ensuring the system maintains frequency stability under various operating conditions. It effectively mitigates the adverse effects of high-penetration renewable energy on system frequency, improves the overall reliability and security of the power system, and lays the foundation for future integrated source-grid-load-storage control.

[0036] 2. This invention designs indices for inertia burden and battery operating burden, optimizes parameters, and effectively reduces the construction and operation costs of power systems. By quantifying the usage of inertia and batteries, resource allocation and utilization can be optimized, thereby reducing unnecessary investment and operating expenditures. This not only improves system economics but also provides a more competitive solution for the electricity market, promotes the widespread application of renewable energy, and helps the power industry develop towards a low-carbon and sustainable direction.

[0037] 3. The control parameters of this invention are based on feedback correction of model predictive control. Through rolling optimization adjustment, it can quickly respond to changes in the external environment and has good adaptability. It can maintain stable operating performance when wind and solar power output fluctuations cause the system to deviate from the optimal operating conditions, ensuring the stability and reliability of the system under various uncertain conditions. By dynamically adjusting the control parameters, the system can adapt to different loads and power generation conditions, improve the overall flexibility and anti-interference ability, and provide a strong guarantee for the safe operation of the power system. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0039] Figure 2 This is a schematic diagram of the multi-timescale frequency control model for a high-energy-penetration power system according to the present invention.

[0040] Figure 3 This is a schematic diagram of the sampling frequencies of the fast subsystem and the slow subsystem of the present invention.

[0041] Figure 4 This is a schematic diagram illustrating the principle of the model predictive control process of the present invention.

[0042] Figure 5This is a flowchart illustrating the multi-timescale multi-input multi-output model predictive control algorithm of the present invention.

[0043] Figure 6 This is a schematic diagram of the operational burden of the inertia control loop of the present invention.

[0044] Figure 7 This is a schematic diagram illustrating the dynamic behavior of the various state variables in this invention. Detailed Implementation

[0045] The technical solutions in the embodiments of the present invention are clearly and completely described in the following description. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0046] In the description of the embodiments of the present invention, it should be noted that the indicated orientation or positional relationship is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of the invention is conventionally placed during use, or the orientation or positional relationship in which those skilled in the art conventionally understand it during use. This is only for the convenience of describing the present invention and simplifying the description, and is not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of the present invention. Furthermore, the terms "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0047] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0048] Example 1 Embodiment 1 of the present invention discloses a multi-time-scale inertial frequency cooperative adaptive control method for a high-proportion renewable energy power grid, which is applied to a power system with high renewable energy penetration. The inertia-frequency coordination refers to inertia-frequency coordination. The power system includes traditional power generation energy such as thermal power, photovoltaic power generation, loads and batteries, among which traditional power generation energy provides base load power and rapid regulation capability, while photovoltaic power generation provides renewable energy with volatility and randomness.

[0049] like Figure 1 As shown, the specific method is as follows: S1: Establish a frequency and inertia coordinated control topology for a high-energy-penetration power system, such as... Figure 2 As shown, this ensures the stability and reliability of the system under frequency fluctuations and load changes; The topology implements inertia control, primary frequency regulation, and secondary frequency regulation strategies: inertia control relies on batteries to ensure that the system load and power generation are balanced within a millisecond-level response time; primary frequency regulation relies on the automatic adjustment of traditional power generation energy units such as thermal power plants; and secondary frequency regulation further finely adjusts the system frequency to ensure that the frequency is restored to the set value, all within a second-level time scale.

[0050] S2: Obtain the parameters of the power system, construct a multi-time-scale power system inertia-frequency control interconnection model based on singular perturbation theory, and obtain the fast and slow variables of the power system; In this embodiment, the parameters of the power system include the guide vane opening deviation of the speed governor. Xg i Speed ​​controller gain Kgov i Speed ​​controller time constant Tgov i Unit output power Pm i Unit gain Ktur i Unit time constant Ttur i Energy storage device output power P Vi Energy storage device gain K EESi Energy storage device time constant T EESi System inertia coefficient H i System damping coefficient D i System gain K PSi Frequency deviation change rate Δf i and ROCOF i Power deviation of connecting lines Ptie ij Load disturbances unrelated to rotating equipment PL i ’ New energy power output PR i ’ System output disturbance PL Sag coefficient R i Deviation coefficient β iConnection line power Ptie i Regional control deviation ACE i Control quantities of the secondary frequency modulation control loop and the inertia control loop u 1i and u 2i ; The multi-timescale power system inertia-frequency control interconnection model is represented as follows:

[0051]

[0052]

[0053] Where, X1=[ P m1 , X g1 ] T For the slow variable of the system state, X2=[Δ f 1, P v1 , ROCOF 1, P tie12 , P tie31 ] T For system state fast variables, u =[ u 1, u 2] is the system input. u 1, u 2 represents the slow control quantity for system load frequency control and the fast control quantity for system inertia, respectively. Y =[ ACE 1, ROCOF 1, ACE 2, ROCOF 2, ACE 3, ROCOF 3] T For system output, μ For system perturbation parameters, ω For system disturbance; A 11 , A 12 , A 21 , A 22 , B 11 , B 12 , C11 , C 12 As an intermediate variable, specifically as follows:

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] .

[0061] S3: Separate the fast and slow time scales of the multi-time-scale power system inertia-frequency control interconnection model to obtain the slow subsystem model and the fast subsystem model; The slow subsystem model is represented as follows:

[0062] in, A 0= A 11 - A 12 A 22 -1 A 21 ; B 0= B 1- A 12 A 22 -1 A 21 ; C 0= C 1- C 2 A 22 -1 A 21 ; D 0=- C 2 A 22 -1 B 2; The fast subsystem model is obtained as follows: .

[0063] in, F 2 represents the intermediate variable of the fast subsystem, as follows: .

[0064] S4: Set control evaluation indicators and optimize control parameters based on the control evaluation function.

[0065] In this embodiment, the control evaluation indicators include: Total frequency modulation error (TFE) comprehensively reflects the degree of deviation of the system frequency:

[0066] Inertia load index, which reflects the burden of system inertia control:

[0067] like Figure 6 As shown, the cost function index reflects the construction phase of the inertia control loop:

[0068] Indicators reflecting the total regulation mileage of energy storage devices:

[0069] Indicators reflecting the number of times an energy storage device is charged and discharged: .

[0070] In this embodiment, the specific process of optimizing the control parameters is as follows: S401: Set parameters for the multi-target GSA algorithm, including the number of targets. M GSA Population size N GSA External archives scale N GSA Search space dimension D GSA Spatial upper and lower limits u GSA , l GSA and maximum number of calculations F GSA ; S402: Initialize the position of each individual in the population x i and speed v i Perform iterative calculations; S403: Calculate the value of each individual in the population. J1、- J 2、- J 3、- J 4、- J 5. Fitness values ​​under the cost function, update external files as follows: If the number of non-dominated solutions in the external archive exceeds the capacity limit, i.e., the population size... N GSA Then calculate the potential energy of each particle, remove the non-dominated solution with the highest potential energy, until the number of non-dominated solutions in the external archive is restored to [value missing]. N GSA ; If the number of non-dominated solutions in the external archive does not exceed the capacity limit, i.e., the population size N GSA Then calculate the potential energy of each particle and select the particle with the smallest potential energy as the first column of guiding particles GF. The particle potential energy is calculated as follows:

[0071] in, R ij For the first i The and the first j The Euclidean distance of each non-dominated solution in the target space. ρ j For the first j The quality of a nondominated solution σ It is the impact factor.

[0072] S404: Select the second type of guide particle GS to enhance the diversity of guide particles.

[0073] S405: Based on the first type of guiding particle GF and the second type of guiding particle GS, update the velocity and position of each particle in the population as follows:

[0074]

[0075] Until the maximum number of iterations is reached. F GSA Output external files and stored non-dominated solutions; if the number of iterations does not reach the maximum number of computations. F GSA Then return to step S403.

[0076] S5: Based on the optimized parameters and the slow and fast subsystems, perform rolling optimization of the control quantity and apply it to the load frequency controller and inertia controller respectively. u s , u fIt also monitors the frequency and load changes of the power system in real time.

[0077] like Figure 7 As shown, the u s , u f The expression is as follows:

[0078] .

[0079] like Figure 4 and Figure 5 As shown, the calculation process for the control quantity is as follows: S501: Discretize the slow subsystem model and the fast subsystem model; The discrete slow subsystem model is as follows:

[0080] in, As , Bs , Cs The state matrix of the discrete slow subsystem is... k To calculate the number of steps, x s , y s These are the state variables and output variables of the slow subsystem model.

[0081] The discretized fast subsystem model is as follows:

[0082] in, A f , B f , C f The state matrix of the discretized fast subsystem is... x f , y f These are the state variables and output variables of the tachy subsystem model.

[0083] S502: Acquire the output frequencies of the fast subsystem model and the slow subsystem model, and calculate the frequency ratio. n = T f / T s ,in, T f Indicates the sampling frequency of the fast subsystem. T s The sampling frequency of the slow subsystem is represented as follows: The sampling frequencies of the fast and slow subsystems are as follows: Figure 3 As shown.

[0084] S503: Based on the multi-timescale power system inertia-frequency control interconnection model, establish the augmented state-space equations for the slow and fast subsystems, and calculate the output predictions; In this embodiment, the Laguerre function is used to construct the augmented state-space equations; The control variable under the Laguerre function is defined as follows:

[0085] Where, Δ u ( k + m | k ) indicates that in the first k Step to the first m Predicted values ​​of control quantities for each step η =[ c 1, c 2,..., c N Let ] be the decision coefficient to be optimized—the Laguerre coefficient; let , l n For the nth Γ n ( z , a The inverse z-transform form of ) .

[0086] The augmented state-space equations of the slow subsystem based on the Laguerre function are as follows:

[0087] in, , , They are respectively , , , o m =[0, 0..., 0].

[0088] The slow subsystem state prediction x ( k + m | k Specifically:

[0089] The predicted system output of the slow subsystem Specifically:

[0090] ,

[0091] in, N P To predict time-domain constants, N C To control the time-domain constant.

[0092] The augmented state-space equations of the fast subsystem based on the Laguerre function are as follows:

[0093] In the above formula, , , They are respectively , , , o m =[0, 0..., 0].

[0094] The fast subsystem state prediction quantity x ( k + m | k Specifically:

[0095] The predicted system output of the fast subsystem Specifically:

[0096] , .

[0097] S504: Based on the obtained output predictions, construct the predictive control cost functions for the slow subsystem model and the fast subsystem model respectively; The predictive control cost function of the slow subsystem model is expressed as follows:

[0098] The predictive control cost function of the fast subsystem model is expressed as follows:

[0099] in, Q f and R f This is the weight matrix.

[0100] S505: Based on the obtained predictive control cost function, let respectively... Calculate the optimal control input at the current moment for both the fast and slow subsystems.u s ( k ), u f ( k Set constraints, including setting control variable amplitude and its increment constraints, and system frequency fluctuation constraints; make u 1max and u 1min Let the upper and lower limits of the load frequency control quantity be respectively set. u 2max and u 2min These represent the upper and lower limits of the control quantity in the inertia control loop. By constraining the amplitude and increment of the control quantity, the control energy and its rate of change can be limited.

[0101]

[0102]

[0103]

[0104] Δ f max and Δ f min These are the upper and lower limits for system frequency fluctuations, and the specific mathematical expression of the system frequency fluctuation constraints is as follows: .

[0105] S506: Solve based on the constraints and the predicted cost control function to obtain the minimized cost function. J ( k ) Control increment Δ u s and Δ u f ; S507: The control quantity is calculated based on the control quantity increment.

[0106] This invention is not limited to the specific embodiments described above. The invention extends to any new feature or combination disclosed in this specification, as well as any new method or process step or combination disclosed herein.

Claims

1. A multi-time-scale inertial frequency cooperative adaptive control method for a high-proportion renewable energy power grid, characterized in that, include: S1: Establish a frequency and inertia coordinated control topology for a high-energy-penetration power system; S2: Obtain the parameters of the power system, construct a multi-time-scale power system inertia-frequency control interconnection model, and obtain the fast and slow variables of the power system; S3: Separate the fast and slow time scales of the multi-time-scale power system inertia-frequency control interconnection model to obtain the slow subsystem model and the fast subsystem model; S4: Set control evaluation indicators and optimize control parameters based on the control evaluation indicators; S5: Based on the optimized control parameters, calculate and obtain the control quantity based on the slow subsystem and the fast subsystem.

2. The multi-time-scale inertial-frequency cooperative adaptive control method for high-proportion renewable energy power grids according to claim 1, characterized in that, In step S2, the parameters of the power system include the guide vane opening deviation of the speed governor. Xg i Speed ​​controller gain Kgov i Speed ​​controller time constant Tgov i Unit output power Pm i Unit gain Ktur i Unit time constant Ttur i Energy storage device output power P Vi Energy storage device gain K EESi Energy storage device time constant T EESi System inertia coefficient H i System damping coefficient D i System gain K PSi Frequency deviation change rate Δf i and ROCOF i Power deviation of connecting lines Ptie ij Load disturbances unrelated to rotating equipment PL i ’ New energy power output PR i ’ System output disturbance PL Sag coefficient R i Deviation coefficient β i Connection line power Ptie i Regional control deviation ACE i Control quantities of the secondary frequency modulation control loop and the inertia control loop u 1i and u 2i .

3. The multi-time-scale inertial-frequency cooperative adaptive control method for high-proportion new energy power grids according to claim 2, characterized in that, The multi-timescale power system inertia-frequency control interconnection model is represented as follows: Where, X1=[ P m1 , X g1 ] T X2 is a slow variable representing the system state, where X2 = [Δ] f 1, P v1 , ROCOF 1, P tie12 , P tie31 ] T For system state fast variables, u =[ u 1, u 2] is the system input. u 1, u 2 represents the slow control quantity for system load frequency control and the fast control quantity for system inertia, respectively. Y =[ ACE 1, ROCOF 1, ACE 2, ROCOF 2, ACE 3, ROCOF 3] T For system output, μ For system perturbation parameters, ω For system disturbance; A 11 , A 12 , A 21 , A 22 , B 11 , B 12 , C 11 , C 12 They are respectively represented as follows: 。 4. The multi-time-scale inertial-frequency cooperative adaptive control method for high-proportion new energy power grids according to claim 3, characterized in that, In step S3, the slow subsystem model is obtained by separating the fast and slow time scales, as shown below: in, A 0= A 11 - A 12 A 22 -1 A 21 ; B 0= B 1- A 12 A 22 -1 A 21 ; C 0= C 1- C 2 A 22 -1 A 21 ; D 0=- C 2 A 22 -1 B 2; The fast subsystem model is obtained as follows: in, F 2 indicates the following: 。 5. The multi-time-scale inertial-frequency cooperative adaptive control method for high-proportion renewable energy power grids according to claim 4, characterized in that, In step S5, the control quantity is obtained, and the specific process is as follows: S501: Discretize the slow subsystem model and the fast subsystem model; S502: Collect the output frequencies of the fast subsystem model and the slow subsystem model, and calculate the frequency ratio n; S503: Based on the multi-timescale power system inertia-frequency control interconnection model, establish the augmented state-space equations for the slow and fast subsystems, and calculate the output predictions; S504: Based on the obtained output predictions, set the optimization objective functions for the slow subsystem model and the fast subsystem model respectively; S505: Set constraints, including setting control variable amplitude and its increment constraints, and system frequency fluctuation constraints; S506: Solve based on the constraints and the aforementioned objective function to obtain the minimized cost function. J The control increment at (k); S507: The control quantity is calculated based on the control quantity increment.

6. The multi-timescale inertial-frequency cooperative adaptive control method for high-proportion renewable energy power grids according to claim 5, characterized in that, In step S504, the optimization objective function of the slow subsystem model is expressed as follows: The optimization objective function of the fast subsystem model is expressed as follows: in, Q f and R f This is the weight matrix.

7. The multi-time-scale inertial-frequency cooperative adaptive control method for high-proportion renewable energy power grids according to claim 5, characterized in that, In step S505, the specific constraints are as follows: in, u 1max and u 1min These are the upper and lower limits of the load frequency control quantity, respectively. u 2max and u 2min These are the upper and lower limits of the control quantity for the inertia control loop, Δ f max and Δ f min These are the upper and lower limits of system frequency fluctuation, respectively.

8. The multi-time-scale inertial-frequency cooperative adaptive control method for high-proportion renewable energy power grids according to claim 1, characterized in that, The control evaluation indicators include total frequency regulation error, inertia burden index, cost function index reflecting the construction stage of inertia control loop, total regulation mileage index reflecting energy storage equipment, and index reflecting the number of times energy storage equipment is charged and discharged.

9. The multi-time-scale inertial-frequency cooperative adaptive control method for high-proportion renewable energy power grids according to claim 8, characterized in that, The specific process for optimizing the control parameters is as follows: S401: Set parameters for the multi-target GSA algorithm, including the number of targets. M GSA Population size N GSA External archives scale N GSA Search space dimension D GSA Spatial upper and lower limits u GSA , l GSA and maximum number of calculations F GSA ; S402: Initialize the position of each individual in the population x i and speed v i Perform iterative calculations; S403: Calculate the fitness value of each individual in the population under the cost function, and update the external archive, as follows: If the number of non-dominated solutions in the external archive exceeds the capacity limit, calculate the potential energy of each particle, remove the non-dominated solution with the highest potential energy, and continue until the number of non-dominated solutions in the external archive is restored to the limit. N GSA ; If the number of non-dominated solutions in the external archive does not exceed the capacity limit, calculate the potential energy of each particle and select the particle with the lowest potential energy as the first column of guiding particles GF. S404: Select the second type of guiding particle GS; S405: Based on the first type of guiding particle GF and the second type of guiding particle GS, update the velocity and position of each particle in the population until the maximum number of iterations is reached. F GSA Output external files and stored non-dominated solutions; if the number of iterations does not reach the maximum number of computations. F GSA Then return to step S403.

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