Wind-storage cooperative control method for mountain distributed wind storage system

Abstract

The invention discloses a wind-storage cooperative control method for a mountain distributed wind storage system, relates to the technical field of cooperative control of distributed wind storage systems, and solves the problems that in the prior art, wind-storage resource allocation is low in efficiency, frequency and voltage regulation is separated, and cooperative support cannot be achieved. According to the method, fan operation parameters and an energy storage SoC state are collected in real time, a fan transient supporting capacity quantitative model based on power-kinetic energy double-boundary constraint and an energy storage supporting capacity evaluation index based on SoC segmentation constraint are constructed, and when frequency disturbance is detected, the supporting capacity coefficients of the fan transient supporting capacity quantitative model and the energy storage supporting capacity evaluation index are introduced into self-adaptive inertia control, so that the power-kinetic energy double-boundary constraint-based energy storage supporting capacity quantitative model is obtained. Dynamic distribution of frequency modulation power of the wind power plant and the energy storage power station according to needs is achieved, and meanwhile secondary optimization distribution is carried out in the wind power plant according to the supporting capacity difference of all draught fans. According to the method, the potential of the high-supporting-capacity unit is brought into full play, overload of the low-supporting-capacity unit is avoided, and the wind storage resource utilization efficiency and the frequency supporting response speed are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of distributed wind-storage system collaborative control technology, and particularly to a wind-storage collaborative control method for a mountainous distributed wind-storage system. Background Technology

[0002] As the global energy structure shifts towards cleaner and lower-carbon energy sources, distributed wind and energy storage systems in mountainous areas are gaining increasing penetration in distribution networks and microgrids due to their advantages such as flexible deployment and localized energy consumption. They are becoming a key support for mitigating the intermittency of centralized renewable energy generation and enhancing the stability of local power grids. However, due to the complex terrain, dispersed generator distribution, and variable operating environment in mountainous areas, these systems face significant technical challenges in supporting grid frequency. Traditional centralized or uniform control methods are insufficient to fully leverage the synergistic support potential of wind farms and energy storage power stations.

[0003] From the perspective of the wind farm, a distributed wind-storage system in a mountainous area typically consists of multiple wind turbines distributed at different altitudes and slopes. Due to the combined effects of terrain differences, airflow disturbances, and turbine wake effects, the actual wind speeds received by each turbine exhibit significant heterogeneity. This difference directly leads to a significant divergence in the operating states (such as rotor speed and output power) of different turbines. Some high-wind-speed turbines located on the windward slope or ridge possess strong kinetic energy reserves and power output capabilities, while turbines on the leeward slope or those shielded by wake effects experience a significant weakening of their power and kinetic energy characteristics due to reduced wind speeds. Traditional frequency support control often employs "average distribution" or relies solely on a single quantitative logic based on rotor kinetic energy, failing to fully consider the differentiated power-kinetic energy synergy characteristics of turbines under mountainous conditions. This can easily lead to overcapacity power in high-support-capacity turbines and overload operation in low-support-capacity turbines, wasting wind farm support resources and potentially posing safety risks to the turbines.

[0004] From the perspective of energy storage power stations, in distributed wind-storage systems in mountainous areas, energy storage power stations are often dispersed across different geographical regions based on load distribution and wind turbine location. Influenced by charging and discharging strategies, operating duration, and local load fluctuations, the state of charge (SoC) of each energy storage power station varies significantly. SoC, as a core indicator determining the upper limit of the energy storage power station's output power and its safe operating boundary, directly affects its frequency support capability—high SoC energy storage power stations can release more power to participate in frequency regulation, while low SoC energy storage power stations need to limit their discharge power to avoid excessive losses. Existing control methods often ignore the differentiated SoC characteristics of energy storage power stations in distributed mountainous structures, adopting a uniform power command allocation mode. This results in the high SoC energy storage power station's support potential not being fully explored, while low SoC energy storage power stations face the risk of over-discharge, severely restricting the overall support efficiency of the energy storage system.

[0005] From a system coordination perspective, when a distribution network or microgrid experiences a sudden load surge or power outage, a distributed wind-storage system in mountainous terrain needs to quickly provide frequency support to suppress frequency drops. However, traditional control strategies often separate the frequency support functions of wind farms and energy storage power stations, failing to establish a coordinated allocation mechanism based on their differentiated support capabilities in mountainous environments. This results in wind and storage resources being unable to match the power deficit in fault scenarios as needed—if the wind farm's support capacity is insufficient and the energy storage system fails to replenish energy in time, frequency recovery will be slow; if the energy storage system provides excessive support while the wind farm's potential remains idle, energy storage losses will be exacerbated. These issues highlight the urgency of constructing a coordinated control method based on the differentiated support capabilities of wind and storage systems to achieve optimal allocation of frequency support resources for distributed wind-storage systems in mountainous terrain, ensuring the safe and stable operation of the power grid.

[0006] In summary, existing distributed wind and energy storage systems in mountainous areas have significant limitations in frequency and voltage control during large-scale local power grid failures. They fail to consider the differentiated power and kinetic energy characteristics caused by the wake effect of wind turbines in complex terrain, nor do they account for the differentiated state of charge (SoC) of distributed energy storage stations. Instead, they quantify support capacity and allocate power using only a single indicator or a uniform model, resulting in inefficient wind-storage resource allocation. Furthermore, the disconnect between frequency and voltage regulation makes them prone to conflicts such as excessive frequency drops or insufficient voltage recovery under large disturbances, hindering coordinated support. Therefore, it is urgent to research active frequency and voltage support control methods for distributed wind and energy storage systems adapted to the geographical and operational characteristics of mountainous areas to address these key issues and ensure stable recovery after power grid failures.

[0007] Therefore, a wind-storage coordinated control method for mountain-based distributed wind-storage systems is needed. Summary of the Invention

[0008] To address the problems of existing technologies that quantify support capacity and allocate power using only a single indicator or uniform model, resulting in inefficient wind-storage resource allocation and disconnected frequency and voltage regulation, which easily leads to conflicts such as excessive frequency drops or insufficient voltage recovery under large disturbances and fail to achieve coordinated support, this invention provides a wind-storage coordinated control method for mountainous distributed wind-storage systems. This method enables high-support-capacity units to fully realize their potential while preventing low-support-capacity units from overloading, significantly improving the efficiency of wind-storage resource utilization and frequency support response speed, and ensuring the safe and stable operation of mountainous distributed wind-storage systems under large disturbances. The specific technical solution is as follows: A wind-storage coordinated control method for a distributed wind-storage system in mountainous areas includes the following steps: Real-time operating parameters of each wind turbine in the mountainous distributed wind and energy storage system and the state of charge (SoC) parameters of each energy storage power station are collected. For each wind turbine, its power characteristic support capability based on the maximum power point tracking curve and its kinetic energy characteristic support capability based on rotor kinetic energy storage are considered, and the minimum value of the two is taken as the transient support capability of a single wind turbine. Based on the SoC parameters of each energy storage power station, the maximum output power is dynamically limited by a piecewise function, and the equivalent inertia is calculated by combining the rotor motion equation to construct the transient support capability index of the energy storage power station. When a grid frequency disturbance is detected, the inertia coefficients of the wind farm and the energy storage station are adaptively adjusted based on the transient support capabilities of the wind farm and the energy storage station. Based on the adjusted inertia coefficient, the frequency regulation power demand of the system is proportionally and collaboratively allocated to the wind farm and energy storage power station; within the wind farm, secondary power allocation is carried out according to the differences in transient support capabilities of each wind turbine; during the frequency support process, parameters are periodically updated and power allocation is dynamically optimized to achieve collaborative frequency support for wind and energy storage resources in mountainous environments.

[0009] Preferably, the process for obtaining the transient support capability of the wind turbine is as follows: The transient support capacity of a single wind turbine must simultaneously meet power boundary constraints (to prevent power from exceeding the safe range) and kinetic energy reserve constraints (to prevent the speed from dropping below the minimum limit), defined as: in, To provide support capabilities based on kinetic energy characteristics, This refers to the support capability based on power characteristics.

[0010] Preferred support capability based on kinetic energy characteristics The supporting power, which reflects the maximum kinetic energy that the wind turbine can release, is calculated using the following formula: .

[0011] Preferred support capability based on power characteristics This reflects the maximum supporting power that the wind turbine can provide within the power safety boundary. The calculation formula is: .

[0012] Preferably, the transient support capability indicators for constructing an energy storage power station are as follows: In the formula, This refers to the inertia of the energy storage power station under rated operating conditions. This represents the minimum inertia required for the energy storage power station to reach its maximum rate of frequency change.

[0013] The preferred method for adjusting the adaptive inertia coefficient is as follows: in It is a constant of the inertia coefficient.

[0014] Preferably, the secondary power allocation is performed based on the differences in the transient support capabilities of each wind turbine, as follows: .

[0015] A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the wind-storage coordinated control method for a mountainous distributed wind-storage system as described above.

[0016] A processor for running a program, wherein the program executes the wind-storage coordinated control method for a mountainous distributed wind-storage system as described above.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention constructs a quantitative model of wind turbine transient support capability based on power-kinetic energy dual boundary constraints and an evaluation index of energy storage support capability based on SoC segmentation constraints by real-time acquisition of wind turbine operating parameters and energy storage SoC status. Upon detecting frequency disturbances, the support capability coefficients of both are introduced into adaptive inertial control, enabling dynamic on-demand allocation of frequency regulation power between the wind farm and the energy storage power station. Simultaneously, secondary optimization allocation is performed within the wind farm based on the differences in support capabilities of each wind turbine. This solution effectively solves the resource mismatch problem caused by neglecting the differentiated support capabilities of wind turbines and energy storage in mountainous environments in existing technologies. It allows high-support-capacity units to fully realize their potential while preventing low-support-capacity units from overloading, significantly improving wind and energy storage resource utilization efficiency and frequency support response speed, and ensuring the safe and stable operation of distributed wind and energy storage systems in mountainous areas under large disturbances. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0019] Figure 1 These are the abstract drawings of the present invention; Figure 2 This is a topology diagram of the mountain-based distributed wind storage system in this invention; Figure 3 This is a real-time wind turbine speed diagram using the no-control strategy and wind-storage coordinated control strategy mentioned in this invention; Figure 4 This is a real-time output power diagram of an energy storage power station employing the controlless strategy and the wind-storage coordinated control strategy mentioned in this invention. Figure 5This is a diagram of the transient support capacity of wind turbines using the no-control strategy and wind-storage coordinated control strategy mentioned in this invention; Figure 6 This is a diagram illustrating the transient support capability of an energy storage power station employing the controlless strategy and the wind-storage coordinated control strategy mentioned in this invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0022] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] like Figures 1-6 As shown, in one embodiment of the present invention, a wind-storage coordinated control method for a mountainous distributed wind-storage system is provided, as detailed below: 1) A method for quantifying the transient support capacity of wind turbines considering their power and kinetic energy characteristics: In large-scale wind farm grid-connected scenarios, the wake effect of wind turbines leads to significant differences in the wind speed received by turbines at different locations, resulting in inconsistent operating states (such as speed and output power) among the turbines and significant heterogeneity in their transient support capabilities. Traditional methods for quantifying transient support capabilities rely solely on the kinetic energy distribution of the turbine rotor, neglecting the turbine's power characteristics (such as the maximum tracking power curve and minimum output power limit). This can easily lead to an overcapacity of support capabilities for high-speed turbines and an undercapacity of support capabilities for low-speed turbines, failing to fully realize the overall support potential of the wind farm.

[0025] Wind turbine mechanical power: Based on aerodynamic principles, the mechanical power captured by the wind turbine is: (1) in, ρ air density (kg / m³). R The radius of the wind turbine is (m). Cp ( λ , β The wind energy utilization coefficient (ratio to blade tip speed) is the ratio of wind energy utilization coefficient to blade tip speed. λ Pitch angle β (related) v The actual wind speed received by the fan (affected by the wake effect, different fans) v different).

[0026] Maximum Power Tracking (MPPT) curve: When the wind turbine is operating in the maximum power region (pitch angle) β When =0), Cp Approximating a constant, the output power simplifies to a function of rotational speed: (3) in, k opt The slope of the MPPT curve. ω The rotor speed of the fan (rad / s) The kinetic energy of the wind turbine rotor reflects its instantaneous energy reserve that can be released, and the calculation formula is as follows: (4) in, J WT The moment of inertia of the fan rotor (kg·m²) is given. ω The speed is real-time (ω varies depending on the wake effect for different fans).

[0027] The transient support capacity of a single wind turbine must simultaneously meet power boundary constraints (to prevent power from exceeding the safe range) and kinetic energy reserve constraints (to prevent the speed from dropping below the minimum limit), defined as: (5) in, C WT,kin,i To provide support capabilities based on kinetic energy characteristics, C WT,pow,i This refers to the support capability based on power characteristics.

[0028] Support capability based on kinetic energy characteristics C WT,kin,i The supporting power, which reflects the maximum kinetic energy that the wind turbine can release, is calculated using the following formula: (6) Support capability based on power characteristicsC WT,pow,i This reflects the maximum supporting power that the wind turbine can provide within the power safety boundary. The calculation formula is: (7) 2) A method for quantifying the transient support capability of the State of Charge (SoC) of an energy storage power station: A high proportion of wind power generation systems can weaken the system's ability to operate safely and stably. Therefore, it is necessary to configure energy storage power stations (ESS) for wind farms to provide more reliable support services to the system and improve its capabilities. ESS can provide a rapid frequency response to the grid during power disturbances, thereby suppressing frequency fluctuations and improving power system performance. By quantifying the transient support capabilities of ESS, the frequency / voltage backup services provided by ESS can be utilized more rationally.

[0029] The SoC (System-on-Chips) of an energy storage power station represents the percentage of energy stored in the station relative to its total energy capacity, and can be expressed as: (8) In the formula, S SoC This indicates the charging status of the energy storage power station. S SoC0 This indicates the initial value of the SoC. η For battery efficiency. I t This indicates the output current.

[0030] The support capacity of an energy storage power station depends on its operating status, mainly reflected in its output power and system-on-a-chip (SoC). To ensure the safe operation and lifespan of the energy storage power station, its output power is not always kept at its maximum value, but is adjusted reasonably according to the SoC, while also taking into account frequency support capabilities and the safe operation of the energy storage power station.

[0031] Therefore, it is necessary to limit the output power of the energy storage power station according to its different System of Functions (SoC). The maximum output power of the energy storage power station under different SoCs can be expressed as: (9) In the formula, P ESS This indicates the super-power of the energy storage power station. P rate This indicates the maximum rated output power of the energy storage power station. S SoC,min and S SoC,max These are the minimum and maximum values ​​for the SoC, respectively. S SoC,low This indicates the safety SoC value of the energy storage power station during discharge. SSoC1 This represents the critical state of charge value at which the output power of an energy storage power station begins to decrease from its rated power during discharge.

[0032] When the system is operating at its rated state, the energy storage power station neither absorbs nor releases power, that is... P ESS =0. When system fluctuations occur, the energy storage power station reduces the system deficit through charging and discharging. To reasonably analyze the transient support capability of the energy storage power station, it is necessary to use the rotor motion equation to measure its anti-unbalance power, which is expressed as: (10) In the formula, H ESS,k This indicates the inertia provided by the energy storage power station. f 0 This indicates the system's rated frequency. P in,k and P out,k Representing nodes respectively k The input power and output power.

[0033] Combining the above formula, we can obtain the magnitude of the inertia of the energy storage power station under different SoCs: (11) In the formula, RoCoF represents the rate of change of frequency.

[0034] Based on the inertia calculations of the above-mentioned energy storage power station, the transient support capability indicators of the energy storage power station are constructed as follows: (12) In the formula, H ESS,k0 This refers to the inertia of the energy storage power station under rated operating conditions. H ESS,min This represents the minimum inertia required for the energy storage power station to reach its maximum rate of frequency change.

[0035] 3) A wind-storage coordinated control method for a mountain-based distributed wind-storage system based on transient support from wind farms and energy storage power stations: When frequency interference occurs in the system, wind farms and energy storage power stations need to jointly provide frequency support services for the system, which can be represented as: (13) In the formula K in,WT and K in,ESS These are the inertia coefficients of wind farms and energy storage power stations during the frequency support period, respectively.

[0036] During frequency support, changes in wind turbine rotor speed and a decrease in the system-on-chip (SoC) of the energy storage system occur. Therefore, traditional fixed-parameter inertial control and droop control strategies cannot fully utilize the frequency support capability of distributed, longer systems. Thus, it is necessary to introduce support capability coefficients for both wind farms and energy storage stations, and to construct an adaptive control strategy to coordinate the operation of both, as shown below: (14) (15) in k 0 It is a constant of the inertia coefficient.

[0037] As can be seen from the above formula, when the transient support capacity of wind farms and energy storage power stations changes during the frequency support process, its C WT and C ESS Corresponding changes will also occur, thus affecting K in,WT and K in,ESS This allows for the redistribution of frequency regulation power supplied to wind farms and energy storage power stations, ensuring effective frequency support.

[0038] Since a wind farm consists of multiple wind turbines, and the transient support capabilities of these turbines vary depending on operating conditions, they need to be allocated appropriately to fully utilize the frequency support capabilities of the wind farm, as shown in the figure below: (16) Figure 1 This is a diagram of the frequency-voltage system control strategy proposed in this invention. Figure 2 This demonstration showcases the topology of a distributed wind-storage system used for verification in mountainous terrain. The verification system comprises four synchronous generators and three distributed wind-storage combined units (WSCS). Specifically, the three WSCS units are equipped with 12, 15, and 3 wind turbine generators (WTs), respectively, and each WSCS is configured with a corresponding energy storage power station (ESS). The state of charge (SoC) of each energy storage power station is different. Due to the wake effect of the wind turbine generators, the effective wind speeds received by different wind turbine generators within the same WSCS unit vary, directly leading to significant differences in the operating conditions (such as output power and rotor speed) of each wind turbine generator. Furthermore, the verification system includes two nodes connected to constant impedance loads; the system's rated operating parameters are: rated voltage 220kV and rated frequency 50Hz.

[0039] To verify the effectiveness and reliability of the proposed wind-storage coordinated control method for mountain distributed wind-storage systems, different control methods were employed on the model, including: no wind-storage coordinated control method for the mountain distributed wind-storage system; and wind-storage coordinated control method for the mountain distributed wind-storage system. A test system was established using the MATLAB / Simulink simulation platform. Figures 3 to 6 In the diagram, the dashed lines indicate that the distributed wind-storage system in the mountainous area does not employ a wind-storage coordinated control method, while the solid lines indicate that the distributed wind-storage system in the mountainous area does employ a wind-storage coordinated control method. Under conditions of a sudden increase in load, such as... Figure 3 and Figure 4 As shown, the wind turbine releases its rotor kinetic energy to increase active power output and provide frequency support services. The energy storage station releases its stored electrical energy to increase active or reactive power output to provide frequency support services. Due to the different wind turbine speeds, the corresponding rate of change in support capacity also differs during the support process. The lower the wind turbine speed, the weaker its support capacity. The corresponding energy storage station will provide more power support to compensate for its losses and provide better support services. To coordinate wind farms and energy storage stations, the support capabilities of wind farms and energy storage stations are quantified separately, such as... Figure 5 and Figure 6 As shown, during the support process, the wind farm's support capacity gradually decreases, and its frequency regulation capability becomes insufficient. Energy storage power stations, with their more flexible and stronger support capabilities, will provide more support to compensate for the wind farm's insufficient support capacity. Therefore, the corresponding ESS support capacity will also decrease, manifesting as... C WF1 > C WF2 > C WF3 and C ESS1 > C ESS2 > C ESS3 The distributed wind-storage system proposed in this invention employs a wind-storage coordinated control method to coordinate the control of wind farms and energy storage power stations based on their respective support capabilities. When the support capacity of the wind farm is insufficient / excessive, and the support capacity of the energy storage power station is excessive / insufficient, the support capacity of the wind farm and the stored energy of the energy storage power station are fully utilized. The difference in support performance between the wind farm and the energy storage power station is reduced, resulting in better frequency support performance for both the wind farm and the energy storage power station within the distributed wind-storage system.

[0040] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0041] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0042] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0043] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for wind-storage collaborative control of a mountainous distributed wind-storage system, characterized in that, The method comprises the following steps: Collecting real-time operation parameters of each wind turbine and state of charge (SoC) parameters of each energy storage power station in a mountainous distributed wind storage system; For each wind turbine, taking the minimum value of its power characteristic support capacity based on a maximum power tracking curve and kinetic characteristic support capacity based on rotor kinetic energy reserve as the transient support capacity of the single wind turbine; Based on the SoC parameters of each energy storage power station, limiting the maximum output power of the energy storage power station by a piecewise function, and combining the rotor motion equation to calculate the equivalent inertia, the transient support capacity index of the energy storage power station is constructed; When detecting a power grid frequency disturbance, the inertia coefficient of the wind farm and the energy storage power station is adaptively adjusted according to the transient support capacity of the wind farm and the transient support capacity of the energy storage power station; Based on the adjusted inertia coefficient, the system frequency modulation power demand is proportionally and cooperatively allocated to the wind farm and the energy storage power station; within the wind farm, secondary power allocation is performed according to the transient support capacity difference of each wind turbine; during the frequency support process, the parameters are periodically updated and the power allocation is dynamically optimized, so as to realize the cooperative frequency support of the wind storage resources in the mountainous environment.

2. The wind-storage collaborative control method of a mountainous distributed wind-storage system according to claim 1, wherein, The transient support capacity of the wind turbine is obtained as follows: The transient support capacity of a single wind turbine needs to meet both the power boundary constraint and the kinetic energy reserve constraint, and is defined as: wherein, is the support capacity based on kinetic properties, is the support capacity based on power properties.

3. The wind-storage collaborative control method of a mountainous distributed wind-storage system according to claim 2, wherein, Supporting capacity based on kinetic energy characteristics , reflecting the maximum kinetic energy that the fan can release corresponding to the supporting power, the calculation formula is: 。 4. The wind-storage collaborative control method of a mountainous distributed wind-storage system according to claim 2, wherein, Support capability based on power characteristics The maximum support power that the wind farm can provide within the power safety margin is calculated as: 。 5. The wind-storage collaborative control method of a mountainous distributed wind-storage system according to claim 1, wherein, The transient support capacity index of the energy storage power station is constructed as follows: wherein J is the inertia of the energy storage plant at rated operating conditions, Jmin represents the minimum inertia required for the energy storage plant at which the system reaches the maximum rate of change of frequency.

6. The wind-storage collaborative control method of a mountainous distributed wind-storage system according to claim 1, wherein, The adaptive inertia coefficient adjustment method is as follows: wherein is a constant of the inertia coefficient.

7. The wind-storage collaborative control method of a mountainous distributed wind-storage system according to claim 1, wherein, The secondary power allocation according to the transient support capacity difference of each wind turbine is specifically as follows: 。 8. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored program, wherein when the program runs, the device where the computer readable storage medium is located is controlled to perform the wind-storage cooperative control method of the mountainous distributed wind storage system according to any one of claims 1 to 7.

9. A processor, comprising: The processor is used to run a program, wherein when the program runs, the wind-storage cooperative control method of the mountainous distributed wind storage system according to any one of claims 1 to 7 is executed.

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