A shared energy storage virtual inertia compensation control method for wind power cluster aggregation

Abstract

The present application relates to a shared energy storage virtual inertia compensation control method for wind power cluster collection, belonging to the technical field of power system control. In view of the problem of insufficient coordinated control of wind power system and energy storage system in the prior art, the present application establishes a dynamic model by obtaining operating parameters, including a wind turbine active power control model and an energy storage system charging and discharging model; a virtual inertia control strategy is designed, a virtual inertia control is introduced to simulate the inertia response characteristics of a synchronous generator; shared energy storage coordinated control is performed, and a strategy is designed according to the inertia demand and the SOC state by using the MPC method; a frequency dynamic response model is established and the inertia response time is calculated as the start-stop condition of virtual inertia control; and the intelligent algorithm is used to optimize the controller parameters and design the locking logic. The method can improve the system performance and efficiency, enhance the frequency stability, improve the energy utilization efficiency of the energy storage system, optimize the system performance, and improve the system stability and reliability.

Description

Technical Field

[0001] This invention relates to a virtual inertia compensation control method for shared energy storage in wind power clusters, belonging to the field of power system control technology, and particularly to related technologies for the coordinated control of wind power and energy storage systems. Background Technology

[0002] Existing technologies for the coordinated control of wind power clusters and energy storage systems have several shortcomings. First, due to inherent characteristics such as wind speed fluctuations, wind power systems cannot provide stable inertial support like traditional synchronous generators during grid frequency disturbances, easily leading to grid frequency instability. Second, energy storage system control strategies do not fully consider factors such as the inertia requirements of wind power clusters and their own state of charge (SOC), resulting in low energy utilization efficiency and difficulty in effectively coordinating with wind power systems to stabilize grid frequency. Existing solutions, such as some technologies, attempt to control wind power systems or energy storage systems separately using simple control algorithms, but fail to comprehensively consider the coordination between the two and the impact of various operating conditions.

[0003] Existing solutions have shortcomings. Individual control cannot achieve effective coordination between wind power and energy storage systems. They cannot rationally allocate charging and discharging tasks based on the inertia requirements of the wind power cluster and the SOC state of the energy storage system. They also cannot simulate the inertial response characteristics of synchronous generators to enhance system stability under frequency disturbances. At the same time, they do not fully consider the differences and changes in the operating conditions of wind turbines and energy storage units, resulting in insufficient precision and efficiency in control.

[0004] To address this, we propose a shared energy storage virtual inertia compensation control method for wind power cluster aggregation. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to improve the performance and efficiency of the ventilation system by adopting advanced data acquisition technology, intelligent algorithms and optimized control strategies.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for virtual inertia compensation control of shared energy storage in a wind power cluster includes the following steps:

[0008] S1. System parameter acquisition and modeling: acquire the operating parameters of the wind power cluster and energy storage system, and use the acquired operating parameters to establish dynamic models of the wind power cluster and energy storage system. The dynamic models include the active power control model of the wind turbine and the charging and discharging model of the energy storage system.

[0009] S2. Virtual Inertia Control Strategy Design: Utilizing the operating parameters obtained in S1 and the established dynamic model, virtual inertia control is introduced into the active power control system of the wind turbine to simulate the inertial response characteristics of the synchronous generator. A virtual inertia control strategy is designed using a phase-locked loop (PLL) and a proportional-integral (PI) controller, where the PLL is used to track the grid frequency and the PI controller is used to regulate the active power output.

[0010] S3. Coordinated control of shared energy storage: Based on S2, according to the inertia requirements of the wind power cluster and the SOC state of the energy storage system, the model predictive control (MPC) method is used to design a coordinated control strategy for shared energy storage, taking into account the differences and changes in the operating conditions of the wind turbine and the energy storage unit.

[0011] S4. Frequency dynamic response model establishment: Based on the virtual inertia control strategy designed in S2 and the shared energy storage coordinated control strategy designed in S3, a frequency dynamic response model of wind and energy storage joint with virtual inertia is established, the inertial response time of the frequency dynamic response model is calculated, and the inertial response time is used as the start and stop condition of virtual inertial control.

[0012] S5. Controller parameter optimization and interlocking: Intelligent algorithms are used to optimize the controller parameters of the frequency dynamic response model. The optimized controller parameters are used to design the controller interlocking logic. When the frequency of the frequency dynamic response model returns to the normal range, the virtual inertia control is turned off in time to avoid frequency overshoot and system instability.

[0013] Furthermore, the operating parameters include wind turbine speed, generator speed, wind speed, and state of charge (SOC) of the energy storage system.

[0014] Furthermore, in S1, the method for establishing the active power control model of the wind turbine is as follows:

[0015] S11. The simplified power system frequency model is given, which is expressed as follows:

[0016]

[0017] Where, ΔP m ΔE is the change in mechanical power, ΔE is the change in energy storage, and α, T, L, M, and D are system parameters;

[0018] S12. Considering the aerodynamic characteristics of wind turbines, an aerodynamic torque model T of the wind turbine is introduced. a T a It is related to factors such as wind speed v, rotor radius R, air density ρ, rotor tip speed ratio λ, and blade pitch angle β, and can be expressed as:

[0019]

[0020] Among them, C p (λ,β) is the wind energy utilization coefficient, which is a function of the tip speed ratio λ and the blade pitch angle β;

[0021] S13. Considering the torsional model of the drive shaft, the torque balance equation of the drive shaft is:

[0022]

[0023] Among them, J t It is the moment of inertia of the drive shaft, ω t It is the rotational speed of the drive shaft, T g It is the electromagnetic torque of the generator, D t It is the damping coefficient of the drive shaft;

[0024] S14. Examine the relationship between power and rotational speed:

[0025] According to the electromagnetic torque T of the generator g With generator output power P g and the generator speed ω g Relationship P g =T g ω g Under ideal conditions (ignoring some losses), the output power P of the wind turbine is out Equal to generator output power P g ;

[0026] From the torque balance equation of the drive shaft Solve T g Substituting into the equation, we get P. g =T g ω g get Right now

[0027] Considering the wind turbine speed ω and drive shaft speed ω during stable operation t and generator speed ω g There is a proportional relationship between them, i.e., ω t =k1ω,ω g =k2ω, where k1 and k2 are both proportionality coefficients, then we have:

[0028]

[0029] Will Substitution The active power control model of the wind turbine is obtained as follows:

[0030]

[0031] Furthermore, in S1, the charging and discharging model of the energy storage system includes an equivalent circuit model and a charging and discharging power relationship;

[0032] The equivalent circuit model considers the electrochemical characteristics of the energy storage system and establishes an equivalent circuit model based on the principle of battery electrochemistry. The equivalent circuit model is expressed as follows:

[0033]

[0034] U b E0 is the battery terminal voltage, R0 is the battery open-circuit voltage, and C is the battery internal resistance in ohms. b It is the battery's equivalent capacitance, I b This is the battery charging and discharging current; the battery's open-circuit voltage E0 is related to the battery's SOC state, and E0 is expressed as:

[0035] E0=k1-k2SOC-k3ln(SOC)+k4ln(1-SOC);

[0036] Where k1, k2, k3, and k4 are constants related to battery characteristics, and SOC is the state of charge of the battery; the -k2SOC term indicates that as SOC decreases (i.e., the battery capacity decreases), the open-circuit voltage will decrease accordingly, and the magnitude of the decrease is related to the values ​​of k2 and SOC; the -k3 ln(SOC) and k4 ln(1-SOC) terms further consider the influence of the logarithmic relationship on the open-circuit voltage. This logarithmic relationship is derived based on the electrochemical characteristics of the battery and can more accurately describe the nonlinear relationship of the open-circuit voltage with changes.

[0037] The relationship between charge and discharge power is expressed as follows:

[0038] P b =ηU b I b ;

[0039] P b η is the actual charge / discharge power; η is the charge / discharge efficiency; U b It is the battery terminal voltage; I b It is the battery charging and discharging current.

[0040] Furthermore, S2 includes:

[0041] S21. Determine the virtual inertia coefficient based on the equivalent inertia time constant H of the wind turbine. ω Calculate the virtual inertia coefficient K I The formula is:

[0042]

[0043] Among them, P rIt is the rated power of the wind turbine, f r It is the system's rated frequency;

[0044] S22. Design droop control, which couples the output power of the wind turbine with the grid frequency to achieve frequency regulation; the formula for droop control is:

[0045] P out =P opt +K D ·Δf;

[0046] Where P out It is the output power of the wind turbine; P opt It is the optimal power output under maximum power point tracking (MPPT); K D It is the droop coefficient, which determines the degree of response of droop control to grid frequency deviation, that is, the proportional relationship of droop control adjusting output power according to frequency deviation; Δf is the grid frequency deviation;

[0047] S23. Implement a phase-locked loop (PLL). The PLL is a synchronous coordinate system-based phase-locked loop (SRF-PLL) structure. The principle of the synchronous coordinate system-based PLL is to transform the grid voltage from a three-phase stationary coordinate system to a two-phase rotating coordinate system (dq coordinate system). Phase tracking is achieved by controlling the q-axis voltage component to be zero. In the dq coordinate system, the expression for the grid voltage is:

[0048]

[0049] Where T is the coordinate transformation matrix; u a u b u c It is the three-phase voltage of the power grid; u d For the d-axis voltage component; u q This refers to the q-axis voltage component.

[0050] The governing equations for the PLL are:

[0051]

[0052] In the formula, u q (s) represents the q-axis voltage component; u′ q (s) is the reference value of the q-axis voltage, u′ q (s)=0;K p K represents the proportional gain of the PLL. i is the integral gain of the PLL; s is the complex variable in the Laplace transform;

[0053] S24. Design a proportional-integral (PI) controller. The PI controller is used to regulate the active power output of the wind turbine in response to changes in the grid frequency. The transfer function of the PI controller is:

[0054]

[0055] K′ p It is the proportional gain of the (PI) controller; K′ i It is the integral gain of the (PI) controller;

[0056] S25. Integrate control strategies, integrating virtual inertia control, droop control, and PI controller into the active power control system of the wind turbine; wherein, virtual inertia control provides the initial frequency response, droop control adjusts the output power to maintain the grid frequency, and PI controller refines power regulation to reduce steady-state error.

[0057] The power adjustment calculated by virtual inertia control needs to be transmitted to droop control and PI controller as a reference for subsequent calculations. Based on the frequency deviation and the output of virtual inertia control, droop control calculates its own power adjustment command and transmits it to the PI controller and the wind turbine's actuator. The PI controller then comprehensively considers all input information to calculate the final power control signal, controlling the active power output of the wind turbine. The final power control signal is:

[0058]

[0059] ΔP f This is the final power control signal, used to control the active power output of the wind turbine; ΔP D The power adjustment command calculated for droop control; ΔP VI It is the power adjustment amount obtained from virtual inertia control calculations. ΔP PI-D Δf is the power adjustment of the PI controller output for droop control; K is the grid frequency deviation, i.e., the difference between the actual grid frequency and the rated frequency; D It is the droop coefficient; K′ p It is the proportional gain of the (PI) controller; K i ′ is the integral gain of the (PI) controller.

[0060] Further, S3 includes:

[0061] S31. Analyze the inertia demand of wind power clusters and the SOC state of energy storage systems, based on the allowable grid frequency deviation Δf′ and the coefficient k related to grid characteristics. f Through formula I d =k f Δf′ estimates the inertia requirement I d ;

[0062] State of Charge (SOC) monitoring acquires real-time SOC information through monitoring devices built into the energy storage system. The formula for calculating SOC is... Q c Q is the current charge. t It is the total charge;

[0063] S32.MPC model is established, which includes the output power P of the wind turbine. w The charging and discharging power of the energy storage system and the grid frequency f g And inertia-related variables such as J; the dynamic equations of the model can be expressed as:

[0064]

[0065] In the formula, a1, a2, and a3 are coefficients determined based on the characteristics of the wind turbine; b1, b2, and b3 are coefficients determined based on the characteristics of the energy storage system.

[0066] S33. Objective function setting, considering the energy utilization rate E of the energy storage system. u Related target term λ E ·E u , where λ E Energy utilization rate E of energy storage system u The weighting coefficients are determined by considering the number of charge-discharge cycles N of the energy storage system. c The influence term λ on the objective function N ·N c , where λ N The number of charge-discharge cycles N for the energy storage system c The weighting coefficients; the objective function is:

[0067] F = λ ω ·∑(Δω r,i -Δω r,ave ) 2 +λ B ·∑|f L (k) 2 +λ E ·E u +λ N ·N c

[0068] λ ω It is a weighting coefficient related to the rotor speed balance of wind turbine units; Δω r,i Δω represents the difference between the rotor speed of the i-th wind turbine and a certain reference speed; r,ave It is the average rotor speed of all wind turbine units; λ B These are the weighting coefficients for targets related to power grid frequency; f L(k) is a function related to the grid frequency and the operating parameters of the wind turbine and energy storage system;

[0069] In solving the objective function, it is necessary to consider the differences and changes in the operating conditions of the wind turbine and the energy storage unit. The output power of the wind turbine will change with the wind speed, and the charging and discharging power of the energy storage system will be affected by the SOC state and the charging and discharging efficiency. By comprehensively considering these factors, the optimal control sequence that minimizes the objective function is found, including the power adjustment command of the wind turbine and the charging and discharging command of the energy storage system.

[0070] Further, S4 includes:

[0071] S41. Model Establishment: Based on the virtual inertia control strategy designed in S2 and the shared energy storage coordinated control strategy designed in S3, a frequency dynamic response model of the wind-storage combined system with virtual inertia is established; Frequency dynamic response model of the wind-storage combined system with virtual inertia:

[0072]

[0073] Among them, J v For virtual inertia; P ω P represents the output power of the wind turbine generator set. s The charging and discharging power of the energy storage system; f g For grid frequency; I d For inertia requirements; c1-c6 are coefficients determined based on system characteristics; Q c Q is the current charge of the energy storage system; t It is the total charge; Q o It is the actual amount of electricity released during discharge;

[0074] S42. Inertial response time calculation: Based on the system's dynamic equations and initial conditions, calculate the time required for the system frequency to change from its initial value to its steady-state value, i.e., the inertial response time.

[0075] The change in system frequency conforms to a first-order linear differential equation: f s ′(t)+αf(t) s =β,f s ′ is the rate of change of system frequency, f s Let be the system frequency, and α and β be constants related to the system parameters; solving this first-order equation yields the following solution:

[0076]

[0077] When the system reaches a steady state, that is, t→∞, The inertial response time is defined as the time required for the system frequency to change from its initial value to near its steady-state value. The inertial response time is obtained by solving the above equation:

[0078]

[0079] Among them, f s (0) represents the system frequency starting from the initial value; T w This is the frequency stability value; T r This refers to the inertial response time.

[0080] S43. Virtual inertial control start / stop condition setting, setting the inertial response time T r As a start / stop condition for virtual inertial control, when the system frequency changes and the inertial response time T... r When the frequency is below the set threshold, virtual inertial control is activated; when the system frequency returns to the normal range and the inertial response time T... r When the value exceeds the set threshold, virtual inertial control is turned off;

[0081] The impact of wind turbine startup time and energy storage system response time on inertial response time is considered. When the system frequency changes and the wind turbine startup time is less than the set threshold, virtual inertial control is activated. When the system frequency returns to the normal range and the wind turbine startup time is greater than the set threshold, virtual inertial control is deactivated.

[0082] Further, S5 includes:

[0083] S51. Parameter optimization: The particle swarm optimization algorithm is used with the objective function in S3 as the fitness function. The fitness value is calculated based on the position vector of the particle. By iteratively updating the position and velocity of the particle, the particle position that minimizes the fitness function is found, which is the optimal controller parameter.

[0084] S52. Lockout Logic Design: Design the controller lockout logic using the optimized controller parameters; the lockout logic is as follows: when the system frequency recovers to the normal range, that is, when the system frequency range belongs to [f... min ,f max When the system frequency range falls within [f], virtual inertia control is disabled to avoid frequency overshoot and system instability; when the system frequency range falls within [f], virtual inertia control is disabled to avoid frequency overshoot and system instability. min ,f max When [f], disable virtual inertia control to avoid frequency overshoot and system instability. min ,f max ].

[0085] In summary, due to the adoption of the above technical solution, the beneficial technical effects of the invention are as follows:

[0086] The system's behavioral characteristics are described more accurately by establishing dynamic models using operating parameters of wind power clusters and energy storage systems. These models include active power control models for wind turbines and charging / discharging models for energy storage systems. These models accurately describe the behavioral characteristics of wind turbines and energy storage systems, providing a theoretical basis for subsequent control strategies and improving the accuracy and effectiveness of control.

[0087] To enhance system frequency stability, a virtual inertia control strategy simulates the inertial response characteristics of a synchronous generator. During grid frequency disturbances, the energy storage system can provide virtual inertia according to this strategy, enhancing system stability under frequency disturbances and contributing to maintaining grid frequency stability.

[0088] To improve the energy utilization efficiency of energy storage systems, a shared energy storage coordinated control strategy is employed. Based on the inertia requirements of the wind power cluster and the SOC state of the energy storage system, model predictive control methods are used to rationally allocate charging and discharging tasks. This avoids unreasonable charging and discharging of the energy storage system, improves its energy utilization efficiency, and ensures efficient system operation.

[0089] Optimizing system performance involved establishing a frequency dynamic response model, calculating inertial response time, and determining the start and stop conditions for virtual inertial control. By rationally controlling the start and stop of virtual inertial control, frequency overshoot and system instability were avoided.

[0090] To improve system stability and reliability, intelligent algorithms are used in the controller parameter optimization and interlocking mechanisms to find the optimal controller parameters and design reasonable interlocking logic. This further enhances the system's stability and reliability, ensuring stable operation under various working conditions. Attached Figure Description

[0091] Figure 1 A logic block diagram of a shared energy storage virtual inertia compensation control method for wind power clusters;

[0092] Figure 2 A logic block diagram illustrating the method for establishing an active power control model for wind turbine generators;

[0093] Figure 3 A logical block diagram of the design method for virtual inertia control strategy;

[0094] Figure 4 A logical block diagram of a coordinated control strategy for shared energy storage;

[0095] Figure 5 A logical block diagram of the method for establishing a frequency dynamic response model;

[0096] Figure 6 A logic block diagram for controller parameter optimization and locking methods. Detailed Implementation

[0097] To make the objectives, technical solutions, and advantages of the invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0098] Overall approach:

[0099] A virtual inertia compensation control method for shared energy storage in wind power clusters first focuses on acquiring the operating parameters of the wind power cluster and the energy storage system, which form the basis for subsequent modeling and control strategy design. By establishing an accurate dynamic model, the behavioral characteristics of the wind turbines and the energy storage system can be described, providing a theoretical basis for virtual inertia control and coordinated control of shared energy storage. The virtual inertia control strategy aims to utilize the energy storage system to simulate inertia response, enhancing system stability under frequency disturbances. The coordinated control strategy for shared energy storage rationally allocates charging and discharging tasks based on inertia demand and the SOC state of the energy storage system, ensuring efficient system operation. After establishing the frequency dynamic response model, the dynamic changes in system frequency can be analyzed, and the start and stop conditions for virtual inertia control can be determined by calculating the inertia response time, further optimizing system performance. Finally, the controller parameter optimization and interlocking loop uses intelligent algorithms to find the optimal controller parameters and designs reasonable interlocking logic to avoid frequency overshoot and system instability.

[0100] Specific implementation steps:

[0101] To make the objectives, technical solutions, and advantages of the invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0102] like Figure 1 As shown, a method for virtual inertia compensation control of shared energy storage in a wind power cluster includes the following steps:

[0103] S1. System parameter acquisition and modeling: acquire the operating parameters of the wind power cluster and energy storage system, and use the acquired operating parameters to establish dynamic models of the wind power cluster and energy storage system. The dynamic models include the active power control model of the wind turbine and the charging and discharging model of the energy storage system.

[0104] S2. Virtual Inertia Control Strategy Design: Utilizing the operating parameters obtained in S1 and the established dynamic model, virtual inertia control is introduced into the active power control system of the wind turbine to simulate the inertial response characteristics of the synchronous generator. A virtual inertia control strategy is designed using a phase-locked loop (PLL) and a proportional-integral (PI) controller, where the PLL is used to track the grid frequency and the PI controller is used to regulate the active power output.

[0105] S3. Coordinated control of shared energy storage: Based on S2, according to the inertia requirements of the wind power cluster and the SOC state of the energy storage system, the model predictive control (MPC) method is used to design a coordinated control strategy for shared energy storage, taking into account the differences and changes in the operating conditions of the wind turbine and the energy storage unit.

[0106] S4. Frequency dynamic response model establishment: Based on the virtual inertia control strategy designed in S2 and the shared energy storage coordinated control strategy designed in S3, a frequency dynamic response model of wind and energy storage joint with virtual inertia is established, the inertial response time of the frequency dynamic response model is calculated, and the inertial response time is used as the start and stop condition of virtual inertial control.

[0107] S5. Controller parameter optimization and interlocking: Intelligent algorithms are used to optimize the controller parameters of the frequency dynamic response model. The optimized controller parameters are used to design the controller interlocking logic. When the frequency of the frequency dynamic response model returns to the normal range, the virtual inertia control is turned off in time to avoid frequency overshoot and system instability.

[0108] The operating parameters include wind turbine speed, generator speed, wind speed, and state of charge (SOC) of the energy storage system.

[0109] like Figure 2 As shown, the method for establishing the active power control model of the wind turbine in S1 is as follows:

[0110] S11. The simplified power system frequency model is given, which is expressed as follows:

[0111]

[0112] Where, ΔP m ΔE is the change in mechanical power, ΔE is the change in energy storage, and α, T, L, M, and D are system parameters;

[0113] S12. Considering the aerodynamic characteristics of wind turbines, an aerodynamic torque model T of the wind turbine is introduced. a T a It is related to factors such as wind speed v, rotor radius R, air density ρ, rotor tip speed ratio λ, and blade pitch angle β, and can be expressed as:

[0114]

[0115] Among them, C p (λ,β) is the wind energy utilization coefficient, which is a function of the tip speed ratio λ and the blade pitch angle β;

[0116] S13. Considering the torsional model of the drive shaft, the torque balance equation of the drive shaft is:

[0117]

[0118] Among them, J t It is the moment of inertia of the drive shaft, ω t It is the rotational speed of the drive shaft, T g It is the electromagnetic torque of the generator, D t It is the damping coefficient of the drive shaft;

[0119] S14. Examine the relationship between power and rotational speed:

[0120] According to the electromagnetic torque T of the generator g With generator output power P g and the generator speed ω g Relationship P g =T g ω g Under ideal conditions (ignoring some losses), the output power P of the wind turbine is out Equal to generator output power P g ;

[0121] From the torque balance equation of the drive shaft Solve T g Substituting into the equation, we get P. g =T g ω g get Right now

[0122] Considering the wind turbine speed ω and drive shaft speed ω during stable operation t and generator speed ω g There is a proportional relationship between them, i.e., ω t =k1ω,ω g =k2ω, where k1 and k2 are both proportionality coefficients, then we have:

[0123]

[0124] Will Substitution The active power control model of the wind turbine is obtained as follows:

[0125]

[0126] In S1, the charging and discharging model of the energy storage system includes an equivalent circuit model and a charging and discharging power relationship;

[0127] The equivalent circuit model considers the electrochemical characteristics of the energy storage system and establishes an equivalent circuit model based on the principle of battery electrochemistry. The equivalent circuit model is expressed as follows:

[0128]

[0129] U b E0 is the battery terminal voltage, R0 is the battery open-circuit voltage, and C is the battery internal resistance in ohms. b It is the battery's equivalent capacitance, I b This is the battery charging and discharging current; the battery's open-circuit voltage E0 is related to the battery's SOC state, and E0 is expressed as:

[0130] E0=k1-k2 / SOC-k3ln(SOC)+k4ln(1-SOC);

[0131] Where k1, k2, k3, and k4 are constants related to battery characteristics, and SOC is the state of charge of the battery; the -k2 / SOC term indicates that as SOC decreases (i.e., the battery capacity decreases), the open-circuit voltage will decrease accordingly, and the magnitude of the decrease is related to the values ​​of k2 and SOC; the -k3ln(SOC) and k4ln(1-SOC) terms further consider the influence of the logarithmic relationship on the open-circuit voltage. This logarithmic relationship is derived based on the electrochemical characteristics of the battery and can more accurately describe the nonlinear relationship of the open-circuit voltage with changes.

[0132] The relationship between charge and discharge power is expressed as follows:

[0133] P b =ηU b I b ;

[0134] P b η is the actual charge / discharge power; η is the charge / discharge efficiency; U b It is the battery terminal voltage; I b It is the battery charging and discharging current.

[0135] like Figure 3 As shown, S2 includes:

[0136] S21. Determine the virtual inertia coefficient based on the equivalent inertia time constant H of the wind turbine. ω Calculate the virtual inertia coefficient K I The formula is:

[0137]

[0138] Among them, P r It is the rated power of the wind turbine, f r It is the system's rated frequency;

[0139] S22. Design droop control, which couples the output power of the wind turbine with the grid frequency to achieve frequency regulation; the formula for droop control is:

[0140] P out =Popt +K D ·Δf;

[0141] Where P out It is the output power of the wind turbine; P opt It is the optimal power output under maximum power point tracking (MPPT); K D It is the droop coefficient, which determines the degree of response of droop control to grid frequency deviation, that is, the proportional relationship of droop control adjusting output power according to frequency deviation; Δf is the grid frequency deviation;

[0142] S23. Implement a phase-locked loop (PLL). The PLL is a synchronous coordinate system-based phase-locked loop (SRF-PLL) structure. The principle of the synchronous coordinate system-based PLL is to transform the grid voltage from a three-phase stationary coordinate system to a two-phase rotating coordinate system (dq coordinate system). Phase tracking is achieved by controlling the q-axis voltage component to be zero. In the dq coordinate system, the expression for the grid voltage is:

[0143]

[0144] Where T is the coordinate transformation matrix; u a u b u c It is the three-phase voltage of the power grid; u d For the d-axis voltage component; u q This refers to the q-axis voltage component.

[0145] The governing equations for the PLL are:

[0146]

[0147] In the formula, u q (s) represents the q-axis voltage component; u′ q (s) is the reference value of the q-axis voltage, u′ q (s)=0;K p K represents the proportional gain of the PLL. i is the integral gain of the PLL; s is the complex variable in the Laplace transform;

[0148] K p The control equations of the PLL determine the response speed and intensity of the q-axis voltage deviation. When the q-axis voltage component u... q (s) Deviation from reference value u′(s) q At that time, the proportional gain K p This will cause the control loop to adjust the output proportionally according to the magnitude of the deviation, thereby prompting u q (s) approaches the reference value more quickly; larger K pA higher value makes the PLL more sensitive to deviations and can correct them more quickly, but it can also lead to decreased system stability and oscillations.

[0149] K i Integral gain is used to eliminate steady-state error. In the control process of PLL, the integral gain k i The q-axis voltage deviation will be integrated; over time, even if the proportional control cannot completely eliminate the deviation, the integral action will continuously accumulate deviation information, ultimately enabling more accurate control of the q-axis voltage at the reference value u′. q (s), thereby achieving accurate tracking of the power grid frequency; however, if the value is too large, it will cause the system response speed to slow down and may lead to problems such as integral saturation, affecting the normal operation of the system;

[0150] S24. Design a proportional-integral (PI) controller. The PI controller is used to regulate the active power output of the wind turbine in response to changes in the grid frequency. The transfer function of the PI controller is:

[0151]

[0152] K′ p It is the proportional gain of the (PI) controller, K′ p The initial response speed and amplitude of the PI controller to the grid frequency deviation are determined by a larger K′. p This will make the controller more sensitive to frequency deviations, but will lead to increased system overshoot; a smaller K′ p This will slow down the system's response, but make it relatively more stable; K i ′ is the integral gain of the (PI) controller, K i A larger K affects the strength of the integral element of the PI controller. i A smaller K can eliminate steady-state errors more quickly, but it will worsen the dynamic process of the system and even cause instability; i This will slow down the elimination of steady-state error, but improve stability;

[0153] S25. Integrate control strategies, integrating virtual inertia control, droop control, and PI controller into the active power control system of the wind turbine; wherein, virtual inertia control provides the initial frequency response, droop control adjusts the output power to maintain the grid frequency, and PI controller refines power regulation to reduce steady-state error.

[0154] The power adjustment calculated by virtual inertia control needs to be transmitted to the droop control and PI controller as a reference for their subsequent calculations. The droop control calculates its own power adjustment command based on the frequency deviation and the output of the virtual inertia control, and transmits it to the PI controller and the wind turbine's actuators. The PI controller then comprehensively considers all input information to calculate the final power control signal, controlling the active power output of the wind turbine. The final power control signal is:

[0155]

[0156] ΔP f This is the final power control signal, used to control the active power output of the wind turbine; ΔP D The power adjustment command calculated for droop control; ΔP VI It is the power adjustment amount obtained from virtual inertia control calculations. ΔP PI-D Δf is the power adjustment of the PI controller output for droop control; K is the grid frequency deviation, i.e., the difference between the actual grid frequency and the rated frequency; D It is the droop coefficient; K′ p It is the proportional gain of the (PI) controller; K i ′ is the integral gain of the (PI) controller.

[0157] like Figure 4 As shown, S3 includes:

[0158] S31. Analyze the inertia requirements of the wind power cluster and the SOC status of the energy storage system. Continuously collect operational data of the wind power cluster, including the output power, speed, and grid frequency of each wind turbine. Combine the dynamic model of the wind turbine (such as the active power control model established in step S1) and the grid frequency stability requirements to analyze the inertia requirements of the wind power cluster under different operating conditions. For example, based on the allowable grid frequency deviation Δf′ and the coefficient k related to grid characteristics... f Through formula I d =k f Δf′ estimates the inertia requirement I d ;

[0159] State of Charge (SOC) monitoring acquires real-time SOC information through monitoring devices built into the energy storage system. The formula for calculating SOC is... Q c Q is the current charge. t It is the total charge;

[0160] S32.MPC model is established, which includes the output power P of the wind turbine. w The charging and discharging power of the energy storage system and the grid frequency f gAnd inertia-related variables such as J; the dynamic equations of the model can be expressed as:

[0161]

[0162] In the formula, a1, a2, and a3 are coefficients determined based on the characteristics of the wind turbine; b1, b2, and b3 are coefficients determined based on the characteristics of the energy storage system.

[0163] S33. Objective function setting, considering the energy utilization rate E of the energy storage system. u Related target term λ E ·E u , where λ E Energy utilization rate E of energy storage system u The weighting coefficients are determined by considering the number of charge-discharge cycles N of the energy storage system. c The influence term λ on the objective function N ·N c , where λ N The number of charge-discharge cycles N for the energy storage system c The weighting coefficients; the objective function is:

[0164] F = λ ω ·∑(Δω r,i -Δω r,ave ) 2 +λ B ·∑|f L (k) 2 +λ E ·E u +λ N ·N c

[0165] λ ω It is a weighting coefficient related to the rotor speed balance of wind turbine units; Δω r,i Δω represents the difference between the rotor speed of the i-th wind turbine and a certain reference speed; r,ave It is the average rotor speed of all wind turbine units; λ B These are the weighting coefficients for targets related to power grid frequency; f L (k) is a function related to the grid frequency and the operating parameters of the wind turbine and energy storage system;

[0166] The solution process needs to consider the differences and variations in the operating conditions of wind turbines and energy storage units. For example, the output power of wind turbines varies with wind speed, and the charging and discharging power of the energy storage system is affected by the state of charge (SOC) and charging and discharging efficiency. By comprehensively considering these factors, the optimal control sequence that minimizes the objective function is found, including power adjustment commands for wind turbines and charging and discharging commands for the energy storage system. By implementing such a coordinated control strategy, the energy of the energy storage system can be rationally utilized while meeting the inertia requirements of the wind power cluster, thereby improving the overall system's operating efficiency and stability.

[0167] like Figure 5 As shown, S4 includes:

[0168] S41. Model Establishment: Based on the virtual inertia control strategy designed in S2 and the shared energy storage coordination control strategy designed in S3, a frequency dynamic response model of the wind-storage joint system containing virtual inertia is established.

[0169] Based on the virtual inertia control strategy designed by S2 and the shared energy storage coordination control strategy designed by S3, a frequency dynamic response model of a wind-storage joint system with virtual inertia is established.

[0170] Consider the impact of the virtual inertia control strategy (S2) on the model.

[0171] In S2, the virtual inertia control strategy simulates the inertial response characteristics of a synchronous generator by introducing virtual inertia control into the active power control system of the wind turbine. This means that in the frequency dynamic response model, virtual inertia affects changes in system frequency. Specifically, when the system frequency changes, the virtual inertia adjusts the output power of the wind turbine according to its set control strategy, thereby affecting the dynamic changes in system frequency. Virtual inertia causes the wind turbine to increase its output power when the frequency drops, providing additional inertial support and slowing down the rate of frequency decline.

[0172] Meanwhile, the phase-locked loop (PLL) and proportional-integral (PI) controller in S2 also affect the model. The PLL is used to track the grid frequency, and its control equations and parameters affect the system's response speed and accuracy to changes in grid frequency. The PI controller is used to regulate the active power output of the wind turbines, and its transfer function and parameters adjust the wind turbine output power according to the grid frequency deviation, thus affecting the dynamic changes in system frequency.

[0173] Considering the impact of the shared energy storage coordinated control strategy (S3) on the model

[0174] In S3, the shared energy storage coordinated control strategy adjusts the charging and discharging power of the energy storage system based on the inertia requirements of the wind power cluster and the SOC state of the energy storage system. In the frequency dynamic response model, the charging and discharging power of the energy storage system directly affects the dynamic changes of the system frequency. When the system frequency decreases and the SOC state of the energy storage system allows it, the energy storage system will discharge, increasing the total power of the system and thus raising the system frequency.

[0175] Furthermore, the MPC model and objective function settings in S3 take into account the differences and variations in the operating conditions of wind turbines and energy storage units. The output power of wind turbines varies with factors such as wind speed and pitch angle, while the charging and discharging power of energy storage systems is affected by factors such as state of charge (SOC), charging and discharging efficiency, and temperature. The interrelationships between these factors and their combined impact on system frequency need to be accurately described in the model.

[0176] Frequency dynamic response model of wind-storage combined system with virtual inertia:

[0177]

[0178] Among them, J v For virtual inertia; P ω P represents the output power of the wind turbine generator set. s The charging and discharging power of the energy storage system; f g For grid frequency; I d For inertia requirements; c1-c6 are coefficients determined based on system characteristics; Q c Q is the current charge of the energy storage system; t It is the total charge; Q o It is the actual amount of electricity released during discharge;

[0179] S42. Inertial response time calculation: Based on the system's dynamic equations and initial conditions, calculate the time required for the system frequency to change from its initial value to its steady-state value, i.e., the inertial response time.

[0180] Assume the change in system frequency conforms to a first-order linear differential equation: f s ′(t)+αf(t) s =β,f s ′ is the rate of change of system frequency, f s Let be the system frequency, and α and β be constants related to the system parameters; for this first-order equation, the solution is:

[0181]

[0182] When the system reaches a steady state, that is, t→∞, The inertial response time is defined as the time required for the system frequency to change from its initial value to near its steady-state value (e.g., reaching 95% of its steady-state value). The inertial response time is obtained by solving the above equation:

[0183]

[0184] Among them, f s (0) represents the system frequency starting from the initial value; T w This is the frequency stability value; T r This refers to the inertial response time.

[0185] S43. Virtual inertial control start / stop condition setting, setting the inertial response time T r As a start / stop condition for virtual inertial control, when the system frequency changes and the inertial response time T... r When the frequency is below the set threshold, virtual inertial control is activated; when the system frequency returns to the normal range and the inertial response time T... r When the value exceeds the set threshold, virtual inertial control is turned off;

[0186] Consider the impact of wind turbine startup time and energy storage system response time on inertial response time. Assume the total inertial response time of the system is [value missing]. When the system frequency changes and the wind turbine startup time is less than a set threshold, virtual inertial control is activated; when the system frequency returns to the normal range and the wind turbine startup time is greater than the set threshold, virtual inertial control is deactivated.

[0187] like Figure 6 As shown, S5 includes:

[0188] S51. Parameter optimization: The particle swarm optimization algorithm is used with the objective function in S3 as the fitness function. The fitness value is calculated based on the position vector of the particle. By iteratively updating the position and velocity of the particle, the particle position that minimizes the fitness function is found, which is the optimal controller parameter.

[0189] S52. Lockout Logic Design: Design the controller lockout logic using the optimized controller parameters; the lockout logic is as follows: when the system frequency recovers to the normal range, that is, when the system frequency range belongs to [f... min ,f max When the system frequency range falls within [f], virtual inertia control is disabled to avoid frequency overshoot and system instability; when the system frequency range falls within [f], virtual inertia control is disabled to avoid frequency overshoot and system instability. min ,f max When [f], disable virtual inertia control to avoid frequency overshoot and system instability. min ,f max ].

[0190] The above description is a preferred embodiment of the invention and is not intended to limit the scope of the invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the invention should be included within the scope of protection of the invention.

Claims

1. A method for virtual inertia compensation control of shared energy storage in wind power clusters, characterized in that, Includes the following steps: S1. System parameter acquisition and modeling: acquire the operating parameters of the wind power cluster and energy storage system, and use the acquired operating parameters to establish dynamic models of the wind power cluster and energy storage system. The dynamic models include the active power control model of the wind turbine and the charging and discharging model of the energy storage system. S2. Virtual Inertia Control Strategy Design: Utilizing the operating parameters obtained in S1 and the established dynamic model, virtual inertia control is introduced into the active power control system of the wind turbine to simulate the inertial response characteristics of the synchronous generator. A virtual inertia control strategy is designed using a phase-locked loop (PLL) and a proportional-integral (PI) controller, where the PLL is used to track the grid frequency and the PI controller is used to regulate the active power output. S3. Coordinated control of shared energy storage: Based on S2, according to the inertia requirements of the wind power cluster and the SOC state of the energy storage system, a coordinated control strategy for shared energy storage is designed using model predictive control methods, taking into account the differences and changes in the operating conditions of the wind turbine and the energy storage unit. S4. Frequency dynamic response model establishment: Based on the virtual inertia control strategy designed in S2 and the shared energy storage coordinated control strategy designed in S3, a frequency dynamic response model of wind and energy storage joint with virtual inertia is established, the inertial response time of the frequency dynamic response model is calculated, and the inertial response time is used as the start and stop condition of virtual inertial control. S5. Controller parameter optimization and interlocking: Intelligent algorithms are used to optimize the controller parameters of the frequency dynamic response model. The optimized controller parameters are used to design the controller interlocking logic. When the frequency of the frequency dynamic response model returns to the normal range, the virtual inertia control is turned off in time to avoid frequency overshoot and system instability.

2. The shared energy storage virtual inertia compensation control method for wind power cluster aggregation according to claim 1, characterized in that, The operating parameters include wind turbine speed, generator speed, wind speed, and state of charge (SOC) of the energy storage system.

3. The shared energy storage virtual inertia compensation control method for wind power cluster aggregation according to claim 1, characterized in that, The method for establishing the active power control model of the wind turbine in S1 is as follows: S11. The simplified power system frequency model is given, which is expressed as follows: Wherein, ΔP m ΔE is the change in mechanical power, ΔE is the change in energy storage, and α, T, L, M, and D are system parameters; S12. Considering the aerodynamic characteristics of wind turbines, an aerodynamic torque model T of the wind turbine is introduced. a T a It is related to wind speed v, wind turbine radius R, air density ρ, and the tip speed ratio λ and pitch angle β of the wind turbine, and is expressed as: Among them, C p (λ,β) is the wind energy utilization coefficient, which is a function of the tip speed ratio λ and the blade pitch angle β; S13. Considering the torsional model of the drive shaft, the torque balance equation of the drive shaft is: Among them, J t It is the moment of inertia of the drive shaft, ω t It is the rotational speed of the drive shaft, T g It is the electromagnetic torque of the generator, D t It is the damping coefficient of the drive shaft; S14. Test your ability to establish the relationship between power and speed; According to the electromagnetic torque T of the generator g With generator output power P g and the generator speed ω g Relationship P g =T g ω g Under ideal conditions, the wind turbine output power P out Equal to generator output power P g ; From the torque balance equation of the drive shaft Solve Substituting Tg into the equation yields P. g =T g ω g get Right now Considering the wind turbine speed ω and drive shaft speed ω during stable operation t and generator speed ω g There is a proportional relationship between them, i.e., ω t =k1ω,ω g =k2ω, where k1 and k2 are both proportionality coefficients, then we have: Will Substitution The active power control model of the wind turbine is obtained as follows:

4. The shared energy storage virtual inertia compensation control method for wind power cluster aggregation according to claim 1, characterized in that, In S1, the charging and discharging model of the energy storage system includes an equivalent circuit model and a charging and discharging power relationship; The equivalent circuit model considers the electrochemical characteristics of the energy storage system and establishes an equivalent circuit model based on the principle of battery electrochemistry. The equivalent circuit model is expressed as follows: U b E0 is the battery terminal voltage, R0 is the battery open-circuit voltage, and C is the battery internal resistance in ohms. b It is the battery's equivalent capacitance, I b This is the battery charging and discharging current; the battery's open-circuit voltage E0 is related to the battery's SOC state, and E0 is expressed as: E0=k1-k2SOC-k3ln(SOC)+k4ln(1-SOC); Where k1, k2, k3, and k4 are constants related to battery characteristics, and SOC is the state of charge of the battery; the -k2SOC term indicates that as SOC decreases, the open-circuit voltage will decrease accordingly, and the magnitude of the decrease is related to the values ​​of k2 and SOC; the -k3ln(SOC) and k4ln(1-SOC) terms further consider the influence of logarithmic relationship on open-circuit voltage; The relationship between charge and discharge power is expressed as follows: P b =ηU b AND b ; P b η is the actual charge / discharge power; η is the charge / discharge efficiency; U b It is the battery terminal voltage; I b It is the battery charging and discharging current.

5. The shared energy storage virtual inertia compensation control method for wind power cluster aggregation according to claim 1, characterized in that, S2 includes: S21. Determine the virtual inertia coefficient based on the equivalent inertia time constant H of the wind turbine. ω Calculate the virtual inertia coefficient K I The formula is: Among them, P r It is the rated power of the wind turbine, f r It is the system's rated frequency; S22. Design droop control, which couples the output power of the wind turbine with the grid frequency to achieve frequency regulation; the formula for droop control is: P out =P opt +K D ·Δf; Where P out It is the output power of the wind turbine; P opt It is the optimal power output under maximum power point tracking; K D It is the droop coefficient, which determines the degree of response of droop control to grid frequency deviation, that is, the proportional relationship of droop control adjusting output power according to frequency deviation; Δf is the grid frequency deviation; S23. Implement a phase-locked loop (PLL). The PLL is based on a synchronous coordinate system. The principle of a synchronous coordinate system PLL is to transform the grid voltage from a three-phase stationary coordinate system to a two-phase rotating coordinate system. Phase tracking is achieved by controlling the q-axis voltage component to be zero. In the dq coordinate system, the expression for the grid voltage is: Where T is the coordinate transformation matrix; u a u b u c It is the three-phase voltage of the power grid; u d For the d-axis voltage component; u q This refers to the q-axis voltage component. The governing equations for the PLL are: In the formula, u q (s) represents the q-axis voltage component; u′ q (s) is the reference value of the q-axis voltage, u′ q (s)=0;K p K represents the proportional gain of the PLL. i is the integral gain of the PLL; s is the complex variable in the Laplace transform; S24. Design a proportional-integral (PI) controller. The PI controller is used to regulate the active power output of the wind turbine in response to changes in the grid frequency. The transfer function of the PI controller is: K′ p It is the proportional gain of the PI controller; K′ i It is the integral gain of the PI controller; S25. Integrate control strategies, integrating virtual inertia control, droop control, and PI controller into the active power control system of the wind turbine; wherein, virtual inertia control provides the initial frequency response, droop control adjusts the output power to maintain the grid frequency, and PI controller refines power regulation to reduce steady-state error. The power adjustment calculated by virtual inertia control needs to be transmitted to droop control and PI controller as a reference for subsequent calculations. Based on the frequency deviation and the output of virtual inertia control, droop control calculates its own power adjustment command and transmits it to the PI controller and the wind turbine's actuator. The PI controller then comprehensively considers all input information to calculate the final power control signal, controlling the active power output of the wind turbine. The final power control signal is: ΔP f This is the final power control signal, used to control the active power output of the wind turbine; ΔP D The power adjustment command calculated for droop control; ΔP VI It is the power adjustment amount obtained from virtual inertia control calculations. ΔP PI-D Δf is the power adjustment of the PI controller output for droop control; K is the grid frequency deviation, i.e., the difference between the actual grid frequency and the rated frequency; D It is the droop coefficient; K′ p It is the proportional gain of the (PI) controller; K′ i It is the integral gain of the PI controller.

6. The shared energy storage virtual inertia compensation control method for wind power cluster aggregation according to claim 1, characterized in that, S3 includes: S31. Analyze the inertia demand of wind power clusters and the SOC state of energy storage systems, based on the allowable grid frequency deviation Δf′ and the coefficient k related to grid characteristics. f Through formula I d =k f Δf′ estimates the inertia requirement I d ; State of Charge (SOC) monitoring acquires real-time SOC information through monitoring devices built into the energy storage system. The formula for calculating SOC is... Q c Q is the current charge. t It is the total charge; S32.MPC model establishment, the model includes the output power P of the wind turbine. w The charging and discharging power of the energy storage system and the grid frequency f g And the inertia-related variable J; the dynamic equation of the model is expressed as: In the formula, a1, a2, and a3 are coefficients determined based on the characteristics of the wind turbine; b1, b2, and b3 are coefficients determined based on the characteristics of the energy storage system. S33. Objective function setting, considering the energy utilization rate E of the energy storage system. u Related target term λ E ·E u , where λ E Energy utilization rate E of energy storage system u The weighting coefficients are determined by considering the number of charge-discharge cycles N of the energy storage system. c The influence term λ on the objective function N ·N c , where λ N The number of charge-discharge cycles N for the energy storage system c The weighting coefficients; the objective function is: F=λ ω ·∑(See r,i -See r,ave ) 2 +λ B ·∑|f L (k)| 2 +λ E ·E u +λ N ·N c ; λ ω It is a weighting coefficient related to the rotor speed balance of wind turbine units; Δω r,i Δω represents the difference between the rotor speed of the i-th wind turbine and a certain reference speed; r,ave It is the average rotor speed of all wind turbine units; λ B These are the weighting coefficients for targets related to power grid frequency; f L (k) is a function related to the grid frequency and the operating parameters of the wind turbine and energy storage system; In solving the objective function, it is necessary to consider the differences and changes in the operating conditions of the wind turbine and the energy storage unit. The output power of the wind turbine will change with the wind speed, and the charging and discharging power of the energy storage system will be affected by the SOC state and the charging and discharging efficiency. By comprehensively considering these factors, the optimal control sequence that minimizes the objective function is found, including the power adjustment command of the wind turbine and the charging and discharging command of the energy storage system.

7. The shared energy storage virtual inertia compensation control method for wind power cluster aggregation according to claim 1, characterized in that, S4 includes: S41. Model Establishment: Based on the virtual inertia control strategy designed in S2 and the shared energy storage coordinated control strategy designed in S3, a frequency dynamic response model of the wind-storage combined system with virtual inertia is established; Frequency dynamic response model of the wind-storage combined system with virtual inertia: Among them, J v For virtual inertia; P ω P represents the output power of the wind turbine generator set. s The charging and discharging power of the energy storage system; f g For grid frequency; I d For inertia requirements; c1-c6 are coefficients determined based on system characteristics; Q c Q is the current charge of the energy storage system. t It is the total charge; Q o It is the actual amount of electricity released during discharge; S42. Inertial response time calculation: Based on the system's dynamic equations and initial conditions, calculate the time required for the system frequency to change from its initial value to its steady-state value, i.e., the inertial response time. The change in system frequency conforms to a first-order linear differential equation: f s ′(t)+αf(t) s =β,f s ′ is the rate of change of system frequency, f s Let be the system frequency, and α and β be constants related to the system parameters; solving this first-order equation yields the following solution: When the system reaches a steady state, that is, t→∞, The inertial response time is defined as the time required for the system frequency to change from its initial value to near its steady-state value. The inertial response time is obtained by solving the above equation: Among them, f s (0) represents the system frequency starting from the initial value; T w This is the frequency stability value; T r This refers to the inertial response time. S43. Virtual inertial control start / stop condition setting, setting the inertial response time T r As a start / stop condition for virtual inertial control, when the system frequency changes and the inertial response time T... r When the frequency is below the set threshold, virtual inertial control is activated; when the system frequency returns to the normal range and the inertial response time T... r When the value exceeds the set threshold, virtual inertial control is turned off; The impact of wind turbine startup time and energy storage system response time on inertial response time is considered. When the system frequency changes and the wind turbine startup time is less than the set threshold, virtual inertial control is activated. When the system frequency returns to the normal range and the wind turbine startup time is greater than the set threshold, virtual inertial control is deactivated.

8. The shared energy storage virtual inertia compensation control method for wind power cluster aggregation according to claim 1, characterized in that, S5 includes: S51. Parameter optimization: The particle swarm optimization algorithm is used with the objective function in S3 as the fitness function. The fitness value is calculated based on the position vector of the particle. By iteratively updating the position and velocity of the particle, the particle position that minimizes the fitness function is found, which is the optimal controller parameter. S52. Lockout Logic Design: Design the controller lockout logic using the optimized controller parameters; the lockout logic is as follows: when the system frequency recovers to the normal range, that is, when the system frequency range belongs to [f... min ,f max When the system frequency range falls within [f], virtual inertia control is disabled to avoid frequency overshoot and system instability; when the system frequency range falls within [f], virtual inertia control is disabled to avoid frequency overshoot and system instability. min ,f max When [f], disable virtual inertia control to avoid frequency overshoot and system instability. min ,f max ].

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