Wind power bearing limit analytical calculation method considering dynamic frequency stability and energy storage fast frequency support

Abstract

The invention discloses a wind power bearing limit analytical calculation method considering dynamic frequency stability and energy storage fast frequency support. The method comprises the following steps: firstly, constructing a system frequency response model containing energy storage fast frequency support, and deriving an accurate analytical expression of a maximum frequency change rate, a maximum frequency deviation and a quasi-steady-state frequency deviation; secondly, calculating corresponding wind power permeability by taking the three types of dynamic frequency safety indexes as constraint conditions; then, taking the minimum permeability under the three types of constraints as a system wind power bearing limit, and establishing an analytical calculation framework of a wind power bearing domain; and finally, outputting wind power bearing limit results under different frequency constraint boundaries and power disturbance. According to the method, the fast frequency supporting capacity of the energy storage system is fully fused, a complete frequency dynamic characteristic evaluation system is constructed, the maximum wind power access capacity is efficiently and accurately evaluated under the multi-frequency safety constraint, and the system frequency stability and the new energy consumption capacity are effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of power system safe and stable operation and new energy consumption technology, and in particular relates to an analytical calculation method for wind power carrying capacity limit considering dynamic frequency stability and energy storage fast frequency support. Background Technology

[0002] In recent years, with the continuous expansion of new energy power generation, the power system is undergoing a structural transformation from synchronous machine dominance to a high proportion of power electronic equipment integration. While promoting cleaner energy, this transformation has also brought about prominent problems such as a decrease in system inertia and deterioration of frequency stability. When power disturbances occur in the system, dynamic indicators such as the rate of change of frequency (RoCoF) and frequency deviation are very likely to exceed safety limits. In recent years, large-scale power outages in countries such as the UK, the US, and Brazil have been closely related to frequency stability problems caused by the increased penetration of new energy, seriously threatening the safe operation of the power grid.

[0003] To assess the wind power carrying capacity of a system, existing research has proposed various analytical methods. Some methods are based on detailed time-domain simulations, which, while highly accurate, are computationally inefficient and unsuitable for rapid analysis in the planning phase. Other studies employ analytical methods, but these often consider only a single frequency constraint or oversimplify the system's frequency response model, failing to comprehensively consider the interactive effects of multiple safety constraints such as the maximum rate of frequency change, maximum frequency deviation, and quasi-steady-state frequency deviation. Particularly noteworthy is that these methods generally fail to adequately consider the rapid frequency support characteristics of energy storage systems, neglecting their crucial role in providing virtual inertia in the early stages of disturbances and in providing rapid power support during frequency dips.

[0004] With the maturation of energy storage technology and the reduction in its cost, its application value in power system frequency support is becoming increasingly prominent. Energy storage systems have millisecond-level response speeds and can provide rapid virtual inertia and droop control by simulating synchronous machine characteristics. However, most existing wind power carrying capacity assessment methods have failed to establish an analytical model of the system frequency response that accurately considers the rapid frequency support capability of energy storage, and also lack quantitative analysis of the effect of energy storage in improving multiple frequency constraints. This deficiency makes it difficult to fully leverage the technical advantages of energy storage when assessing the wind power carrying capacity of a system, affecting the accurate assessment of the potential for renewable energy absorption and the optimal allocation of energy storage resources, and restricting the safe and stable operation of high-proportion renewable energy power systems. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the aforementioned background technology by providing an analytical calculation method for wind power carrying capacity limits that considers dynamic frequency stability and rapid frequency support from energy storage. This method is applicable to various power system planning and operation scenarios with high proportions of renewable energy integration. On one hand, this method constructs a system frequency response model incorporating rapid frequency support from energy storage, derives precise analytical expressions for the maximum frequency change rate, maximum frequency deviation, and quasi-steady-state frequency deviation, and establishes a complete frequency dynamic characteristic evaluation system. On the other hand, using multiple frequency security constraints as boundary conditions, it constructs an analytical calculation method for the wind power carrying capacity domain, achieving accurate assessment of the maximum wind power integration capacity of the system and overcoming the deficiencies of existing schemes in considering the interactive effects of multiple frequency constraints and the rapid support characteristics of energy storage.

[0006] To achieve the above-mentioned objectives, this method adopts the following technical solution:

[0007] An analytical calculation method for wind power carrying capacity considering dynamic frequency stability and rapid frequency support of energy storage includes the following steps:

[0008] Step S1: Construct a system frequency response model that considers the rapid frequency support of energy storage. Based on the system frequency response model, derive the precise expressions for the maximum frequency change rate, the maximum frequency deviation, and the quasi-steady-state frequency deviation.

[0009] Step S2: Construct dynamic frequency security constraints based on the maximum frequency change rate, maximum frequency deviation, and quasi-steady-state frequency deviation, and calculate wind power permeability;

[0010] Step S3: Take the minimum value among the above three wind power permeability as the wind power carrying capacity limit of the system, thereby establishing an analytical calculation framework for the wind power carrying capacity domain.

[0011] Step S4: Calculate the system wind power carrying capacity limit under different frequency constraint boundaries based on the wind power carrying capacity domain.

[0012] In step S1, the system frequency response model is a reduced-order model that considers the virtual inertia of energy storage and droop control, taking into account the rapid frequency support characteristics of the energy storage device. The expression is based on the transfer function of the reduced-order model. It can be derived through the inverse Laplace transform, the initial value theorem, and the final value theorem.

[0013] Power supported by fast frequency of energy storage By including active frequency regulation coefficient droop characteristic time constant Virtual inertia coefficient and virtual inertial time constant The control block diagram is derived; the improved system frequency response model is a simplified model after order reduction processing and neglecting the millisecond-level response time constant of the power electronic converter.

[0014] The expression for fast frequency support in energy storage is:

[0015]

[0016] in, To provide high-frequency power support for energy storage; This is the active frequency regulation coefficient; The time constant for the droop characteristic; This is the virtual inertia coefficient; This is the virtual inertial time constant; Wind power penetration rate; This refers to the allocation of energy storage installed capacity.

[0017] The grid frequency response transfer function considering fast frequency support for energy storage is:

[0018]

[0019] Where H is the equivalent inertia constant of the power system, and D is the system damping of the synchronous generator unit. This is the mechanical power gain coefficient. This is the unit reheat time constant. R represents the fraction of the total power of the high-pressure steam turbine, and R is the static droop coefficient of the synchronous unit.

[0020] Considering that the response time of power electronic converters is typically in the millisecond range, as mentioned above, the frequency response time in the grid can be neglected. and The time constant and the frequency domain expression of the power grid frequency response model are:

[0021]

[0022] in, and They are respectively:

[0023]

[0024] After the inverse Laplace transform, the time-domain expression for the frequency deviation is:

[0025]

[0026] in, This represents the change in power due to the disturbance experienced by the system.

[0027] Combining the initial value theorem and the final value theorem, the maximum rate of change of frequency is obtained. Maximum frequency deviation and quasi-steady-state frequency The parsing expression is:

[0028]

[0029]

[0030] in, The damped oscillation frequency of the system. , These are intermediate coefficients generated during the derivation process.

[0031] In step S2, referring to the key indicators for system frequency security, the following will be determined: , , As a key parameter for assessing the wind power carrying capacity limit.

[0032] The rate of frequency change is greatest at the instant of the disturbance, and after the disturbance... The following constraints shall be established to ensure that the anti-islanding protection threshold is not exceeded:

[0033]

[0034] in, As an anti-islanding protection threshold constraint, the limit for a strong inertia power grid mainly composed of traditional generators is no more than 0.5 Hz / s. Moreover, with the increase in wind power penetration, the power grid has the ability to cope with faster frequency changes through new technologies, and this value may be further relaxed to 1.5 Hz / s in the future.

[0035] The maximum deviation of the power grid frequency shall be constrained to be no less than the action threshold of the first round of low-frequency load shedding devices. The maximum frequency deviation constraint is established as follows:

[0036]

[0037] The system's ability to resist disturbances through primary frequency modulation is constrained to within ±0.2Hz.

[0038]

[0039] In step S2, “calculating wind power penetration rate” means substituting the analytical expressions of the maximum frequency change rate, the maximum frequency deviation, and the quasi-steady-state frequency deviation into their corresponding safety constraints, and then solving for the wind power penetration rate that satisfies each individual constraint.

[0040] In step S3, "establishing an analytical calculation framework for the wind power carrying capacity" means comparing the wind power penetration rates calculated in step S2 that satisfy three different frequency safety constraints, and using the minimum wind power penetration rate calculated under the three constraints as the actual wind power penetration rate limit of the power system, i.e., the wind power carrying capacity limit.

[0041] .

[0042] Step S4, "Calculating the system wind power carrying capacity under different frequency constraint boundaries," includes: analyzing the results under two scenarios with and without energy storage participating in frequency regulation, and under different power disturbance levels ΔP. L The variation of the wind power carrying limit is described below.

[0043] The method of the present invention is applicable to the rapid evaluation and optimization of high-proportion renewable energy access schemes during the power system planning stage. The fast frequency support characteristics of the energy storage include providing virtual inertia response in the early stage of disturbance and providing fast power support in the frequency drop stage.

[0044] The beneficial effects of this invention are:

[0045] (1) This invention establishes an analytical evaluation system for frequency dynamic characteristics by constructing a system frequency response model that considers the rapid frequency support of energy storage. Compared with traditional time-domain simulation methods, this method can quickly and accurately calculate key indicators such as the maximum frequency change rate, maximum frequency deviation, and quasi-steady-state frequency deviation of the system after power disturbance, significantly improving computational efficiency and providing convenience for engineering applications.

[0046] (2) The proposed analytical calculation method for wind power carrying capacity takes into account the interactive effects of multiple frequency safety constraints. By establishing a wind power carrying capacity domain with the maximum frequency change rate, the maximum frequency deviation and the quasi-steady-state frequency deviation as constraints, the method achieves an accurate assessment of the maximum wind power access capacity of the system, thus making up for the shortcomings of existing methods in considering multiple constraints.

[0047] (3) This invention fully considers the rapid frequency support capability of energy storage systems. By quantitatively analyzing the role of energy storage configuration in improving system frequency characteristics, it reveals the key technological value of energy storage in enhancing wind power carrying capacity. Compared with traditional methods, this method can more accurately assess the wind power carrying capacity limit of systems containing energy storage. Attached Figure Description

[0048] Figure 1 The flowchart shows the analytical calculation method for wind power carrying capacity considering dynamic frequency stability and rapid frequency support of energy storage in this invention.

[0049] Figure 2 Control block diagram for active frequency regulation control of energy storage;

[0050] Figure 3 A grid frequency response model diagram considering the fast frequency response of energy storage;

[0051] Figure 4 Wind power penetration rate under the constraint of maximum frequency change rate;

[0052] Figure 5 Wind power penetration rate under maximum frequency deviation constraint;

[0053] Figure 6 Wind power penetration rate under quasi-steady-state frequency constraints;

[0054] Figure 7 This is the wind power carrying capacity domain without energy storage participating in frequency regulation;

[0055] Figure 8 For wind power carrying capacity with energy storage participating in frequency regulation;

[0056] Figure 9 The diagram shows a simulation experiment of a system example.

[0057] Figure 10 The system frequency curve when energy storage does not participate in frequency regulation;

[0058] Figure 11 System frequency curve when energy storage participates in frequency regulation;

[0059] Figure 12 for Frequency dynamic characteristic curve of critical permeability at 0.075 pu;

[0060] Figure 13 for Frequency dynamic characteristic curve at critical permeability of 0.15 pu; Detailed Implementation

[0061] The technical solution of the present invention will be illustrated with an example simulation experiment below with reference to the accompanying drawings, but the implementation of the present invention is not limited thereto.

[0062] See attached document Figure 1 The present invention provides an analytical calculation method for wind power carrying capacity considering dynamic frequency stability and rapid frequency support of energy storage, comprising the following steps:

[0063] Step S1: Construct a system frequency response model that considers the rapid frequency support of energy storage. Based on the system frequency response model, derive the precise expressions for the maximum frequency change rate, the maximum frequency deviation, and the quasi-steady-state frequency deviation.

[0064] Step S2: Construct dynamic frequency security constraints based on the maximum frequency change rate, maximum frequency deviation, and quasi-steady-state frequency deviation, and calculate wind power permeability;

[0065] Step S3: Take the minimum value among the above three wind power permeability as the wind power carrying capacity limit of the system, thereby establishing an analytical calculation framework for the wind power carrying capacity domain.

[0066] Step S4: Calculate the system wind power carrying capacity limit under different frequency constraint boundaries based on the wind power carrying capacity domain.

[0067] In step S1, the system frequency response model is a reduced-order model that considers the virtual inertia of energy storage and droop control, taking into account the rapid frequency support characteristics of the energy storage device. The expression is based on the transfer function of the reduced-order model. It can be derived through the inverse Laplace transform, the initial value theorem, and the final value theorem.

[0068] Specifically, by simulating the frequency response mechanism of a synchronous generator, a system is constructed as follows: Figure 2 The diagram shown is a block diagram of active frequency regulation control for energy storage.

[0069] Power supported by fast frequency of energy storage By including active frequency regulation coefficient droop characteristic time constant Virtual inertia coefficient and virtual inertial time constant The control block diagram is derived; the improved system frequency response model is a simplified model after order reduction processing and neglecting the millisecond-level response time constant of the power electronic converter.

[0070] Based on the block diagram, the expression for rapid frequency support of energy storage is as follows:

[0071]

[0072] in, To provide high-frequency power support for energy storage; This is the active frequency regulation coefficient; The time constant for the droop characteristic; This is the virtual inertia coefficient; This is the virtual inertial time constant; Wind power penetration rate; This refers to the allocation of energy storage installed capacity.

[0073] After reducing the order of the high-order SFR model, the following is obtained: Figure 3 The improved system frequency response model is shown. Based on... Figure 3 Therefore, the grid frequency response transfer function considering fast frequency support for energy storage can be obtained as follows:

[0074]

[0075] Where H is the equivalent inertia constant of the power system, and D is the system damping of the synchronous generator unit. This is the mechanical power gain coefficient. This is the unit reheat time constant. R represents the fraction of the total power of the high-pressure steam turbine, and R is the static droop coefficient of the synchronous unit.

[0076] Considering that the response time of power electronic converters is typically in the millisecond range, as mentioned above, the frequency response time in the grid can be neglected. and The time constant and the frequency domain expression of the power grid frequency response model are:

[0077]

[0078] in, and They are respectively:

[0079]

[0080] After the inverse Laplace transform, the time-domain expression for the frequency deviation is:

[0081]

[0082] in, This represents the change in power due to the disturbance experienced by the system.

[0083] Combining the initial value theorem and the final value theorem, the maximum rate of change of frequency is obtained. Maximum frequency deviation and quasi-steady-state frequency The parsing expression is:

[0084]

[0085]

[0086] in, The damped oscillation frequency of the system. , These are intermediate coefficients generated during the derivation process.

[0087] In step S2, referring to the key indicators for system frequency security, the following will be determined: , , As a key parameter for assessing the wind power carrying capacity limit.

[0088] The rate of frequency change is greatest at the instant of the disturbance, and after the disturbance... The following constraints shall be established to ensure that the anti-islanding protection threshold is not exceeded:

[0089]

[0090] in, As an anti-islanding protection threshold constraint, the limit for a strong inertia power grid mainly composed of traditional generators is no more than 0.5 Hz / s. Moreover, with the increase in wind power penetration, the power grid has the ability to cope with faster frequency changes through new technologies, and this value may be further relaxed to 1.5 Hz / s in the future.

[0091] The maximum deviation of the power grid frequency shall be constrained to be no less than the action threshold of the first round of low-frequency load shedding devices. The maximum frequency deviation constraint is established as follows:

[0092]

[0093] The system's ability to resist disturbances through primary frequency modulation is constrained to within ±0.2Hz.

[0094]

[0095] In step S2, “calculating wind power penetration rate” means substituting the analytical expressions of the maximum frequency change rate, the maximum frequency deviation, and the quasi-steady-state frequency deviation into their corresponding safety constraints, and then solving for the wind power penetration rate that satisfies each individual constraint.

[0096] In step S3, "establishing an analytical calculation framework for the wind power carrying capacity" means comparing the wind power penetration rates calculated in step S2 that satisfy three different frequency safety constraints, and using the minimum wind power penetration rate calculated under the three constraints as the actual wind power penetration rate limit of the power system, i.e., the wind power carrying capacity limit.

[0097] .

[0098] Step S4, "Calculating the system wind power carrying capacity under different frequency constraint boundaries," includes: analyzing the results under two scenarios with and without energy storage participating in frequency regulation, and under different power disturbance levels ΔP. L The variation of the wind power carrying limit is described below.

[0099] Wind power carrying capacity without energy storage participating in frequency regulation, such as Figure 7 As shown.

[0100] Wind power carrying capacity with energy storage participating in frequency regulation, such as Figure 8 As shown.

[0101] Step S4 includes:

[0102] according to Figure 7 and Figure 8Given the system wind power carrying capacity domain, the calculation results of the system wind power carrying capacity limit are obtained under different power disturbances and different frequency constraint boundaries.

[0103] To verify the effectiveness of the wind power carrying capacity calculation method proposed in this paper, in Simulation platform construction, such as Figure 9 The system simulation model shown is as follows. The simulation model mainly includes one thermal power unit, one renewable energy power station, and one energy storage device. The renewable energy power station consists of several 1.5MW doubly-fed wind turbines. The capacity of the energy storage device is configured synchronously according to the ratio α and the wind power penetration rate. The specific number needs to be determined based on the system's wind power carrying capacity limit. The total installed capacity of the synchronous turbines is 2700MW, and the active power load of the system is 1800MW.

[0104] The value ranges of the main system parameters are shown in Table 1.

[0105]

[0106] Example 1:

[0107] The accuracy of the frequency dynamic index analytical model was verified by comparing the theoretical calculations with the actual frequency response results with and without energy storage.

[0108] When energy storage does not participate in frequency regulation, the theoretical calculated values ​​of wind power penetration are shown in Table 2, and the simulation results are as follows. Figure 10 As shown.

[0109] Table 2. Theoretical calculation values ​​of wind power penetration rate when energy storage does not participate in frequency regulation.

[0110]

[0111] When penetration rate When ω = 0.1: the theoretical maximum rate of change is -0.416 Hz / s, the actual value is -0.4143 Hz / s, and the relative error is 0.41%; the theoretical maximum frequency deviation is -0.408 Hz, the actual value is -0.4004 Hz, and the relative error is 0.52%; the theoretical steady-state frequency deviation is -0.196 Hz, the actual value is -0.1925 Hz, and the relative error is 1.79%.

[0112] The errors between theoretical and actual values ​​at other permeability levels also remained within a small range, verifying the high accuracy and applicability of the model in assessing the impact of different permeability levels on the power grid frequency response.

[0113] When energy storage participates in frequency regulation, the theoretically calculated values ​​of wind power penetration are shown in Table 3, and the simulation results are as follows. Figure 11 As shown.

[0114] Table 3. Theoretical calculation values ​​of wind power penetration rate when energy storage participates in frequency regulation;

[0115]

[0116] when When ω = 0.1, the calculated maximum frequency change rate is -0.411 Hz / s, with a relative error of 0.98% compared to the actual frequency response curve result of -0.407 Hz / s; the calculated maximum frequency deviation is -0.397 Hz, with a relative error of 1.59%; and the calculated steady-state frequency deviation is -0.191 Hz, with a relative error of 1.38%.

[0117] The error between the theoretical and actual values ​​of the calculated results for the renewable energy carrying capacity domain including energy storage remains within a small range. Using energy storage-assisted frequency regulation can improve this. , and This will enhance the system's immunity to disturbances and its ability to absorb new energy sources.

[0118] Example 2:

[0119] The accuracy of the analytical calculation was verified by comparing the theoretically calculated wind power carrying capacity limit of the energy storage system with the actual frequency response results under different power disturbances.

[0120] The calculated results of the wind power carrying capacity limit when the disturbance power is 0.075 pu are shown in Table 4, and the simulation results are as follows. Figure 12 As shown.

[0121] Table 4. Calculation results of wind power carrying capacity under different power disturbances.

[0122]

[0123] When the wind power carrying capacity reaches 16.7%, the initial frequency change rate is at its maximum of 0.4331 Hz / s, and the maximum frequency deviation is 0.3956 Hz. After the frequency dynamic process, the stable value reaches the safe limit of 49.8 Hz. The calculated wind power penetration limit is 16.2%, with a difference of 2.98%. If the quasi-steady-state frequency deviation constraint is relaxed to 0.5 Hz, and the penetration rate continues to increase to 30.4%, the initial frequency change rate reaches the upper limit of 0.5 Hz / s. The calculated penetration limit is 29.7%, with an error of 2.02%. If the maximum frequency change rate constraint is further relaxed and the penetration rate is increased to 52.5%, the maximum frequency deviation reaches the safe value of 0.5 Hz. The calculated penetration limit is 52.1%, with an error of 0.93%.

[0124] The calculated results of the wind power carrying capacity limit when the disturbance power is 0.15 pu are shown in Table 4, and the simulation results are as follows. Figure 13 As shown.

[0125] Constrained by the maximum frequency change rate, the wind power carrying capacity limit reaches 38.4%, which differs from the permeability limit of 40.9% calculated in Table 2 by 3.25%. If the maximum frequency change rate is relaxed, the permeability reaches the limit of 52.5% due to the influence of steady-state frequency deviation constraint, with an error of 2.07% compared with the calculated value.

[0126] The error between the theoretical calculation of the wind power carrying capacity limit of the energy storage system and the actual frequency response results remains within a small range, verifying the accuracy and adaptability of the proposed analytical method for wind power carrying capacity limit.

[0127] The above description of the embodiments is provided to enable those skilled in the art to understand and apply the present invention. It will be apparent to those skilled in the art that various modifications can be made to the above embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made to the present invention by those skilled in the art based on the disclosure thereof should be within the scope of protection of the present invention.

Claims

1. An analytical calculation method for wind power carrying capacity considering dynamic frequency stability and rapid frequency support of energy storage, characterized in that, Includes the following steps: Step S1: Construct a system frequency response model that considers the rapid frequency support of energy storage. Based on the system frequency response model, derive the precise expressions for the maximum frequency change rate, the maximum frequency deviation, and the quasi-steady-state frequency deviation. Step S2: Construct dynamic frequency security constraints based on the maximum frequency change rate, maximum frequency deviation, and quasi-steady-state frequency deviation, and calculate wind power permeability; Step S3: Take the minimum value among the above three wind power permeability as the wind power carrying capacity limit of the system, thereby establishing an analytical calculation framework for the wind power carrying capacity domain. Step S4: Calculate the system wind power carrying capacity limit under different frequency constraint boundaries based on the wind power carrying capacity domain.

2. The method as described in claim 1, characterized in that, In step S1, the system frequency response model is a reduced-order model that considers the energy storage virtual inertia and droop control; the expression is the transfer function based on the reduced-order model.

3. The method as described in claim 2, characterized in that, Power supported by fast frequency of energy storage By including active frequency regulation coefficient droop characteristic time constant Virtual inertia coefficient and virtual inertial time constant The control block diagram is derived from it; The improved system frequency response model is a simplified model that has been reduced in order and whose millisecond-level response time constant of the power electronic converter has been ignored.

4. The method as described in claim 3, characterized in that, The maximum frequency change rate The parsing expression is: ； The maximum frequency deviation The parsing expression is: ； The quasi-steady-state frequency deviation The parsing expression is: ； in, The system is subjected to disturbance power, H is the system inertia, α is the wind power penetration rate, and K is the system inertia. m R is the mechanical power gain coefficient, D is the synchronous generator static droop coefficient, and ω is the synchronous generator system damping. n ζ and ω represent the system's natural frequency and damping ratio, respectively. r Let be the damped oscillation frequency of the system, and β and φ be intermediate coefficients generated during the derivation process.

5. The method as described in claim 1, characterized in that, In step S2, the dynamic frequency security constraint specifically includes: The rate of frequency change is greatest at the instant of the perturbation, and is followed by RoCoF after the perturbation. MAX The following constraints shall be established to ensure that the anti-islanding protection threshold is not exceeded: Among them, RoCoF limit This is the threshold constraint value for anti-islanding protection; The maximum deviation of the power grid frequency shall not be less than the action threshold f of the first round of low-frequency load shedding device. UFLS The maximum frequency deviation constraint is established as follows: ； Δf ss The reaction system mitigates disturbances through primary frequency modulation, establishing a constraint within ±0.2Hz. 。 6. The method as described in claim 5, characterized in that, In step S2, "calculating wind power penetration rate" means substituting the analytical expressions for the maximum frequency change rate, maximum frequency deviation, and quasi-steady-state frequency deviation into their corresponding safety constraints to solve for the wind power penetration rate that satisfies each individual constraint.

7. The method as described in claim 1, characterized in that, In step S3, "establishing an analytical calculation framework for the wind power carrying capacity" means comparing the wind power penetration rates calculated in step S2 that satisfy three different frequency safety constraints, and taking the minimum value as the final wind power carrying capacity limit of the system, thereby defining the wind power carrying capacity based on this limit. 。 8. The method as described in claim 1, characterized in that, Step S4, "Calculating the system wind power carrying capacity under different frequency constraint boundaries," includes: analyzing the results under two scenarios—with and without energy storage participating in frequency regulation—and under different power disturbance levels. The variation of the wind power carrying limit is described below.

9. The method according to any one of claims 1-8, characterized in that, The method is applicable to the rapid evaluation and optimization of high-proportion renewable energy access schemes during the power system planning stage. The fast frequency support characteristics of the energy storage include providing virtual inertia response in the early stage of disturbance and providing fast power support in the frequency drop stage.

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