Wind storage combined system frequency modulation method, system and equipment based on parameter setting and adaptive optimization and medium

Abstract

The invention relates to the technical field of wind storage combined system frequency modulation, in particular to a parameter setting and adaptive optimization-based wind storage combined system frequency modulation method, system and device and a medium, and the method comprises the steps of obtaining wind storage combined system data in real time; setting an initial value and an adjustment range of a frequency modulation parameter according to the wind storage combined system data; taking the adjustment range of the frequency modulation parameter as a constraint, inputting the wind turbine generator output distribution weight, the energy storage output distribution weight and the frequency modulation parameter as optimization variables into an improved particle swarm algorithm, and generating optimal values of the frequency modulation parameter, the wind turbine generator output distribution weight and the energy storage output distribution weight; generating a combined frequency modulation instruction, and controlling the wind turbine generator and the energy storage system to output frequency modulation power; and monitoring the parameters after frequency modulation in real time and updating the particle swarm algorithm output of the next period to form closed-loop control. According to the invention, the improved particle swarm optimization algorithm is used for dynamically setting the frequency modulation parameters and cooperatively optimizing the wind storage output weight, so that the targets of frequency deviation minimization and safe operation of equipment can be achieved.

Description

Background Art

[0002] With the increase in the penetration rate of renewable energy, the stability of grid frequency faces severe challenges. Wind power generation is difficult to provide reliable frequency regulation support due to its intermittent and low inertia characteristics, while energy storage systems, as flexible regulation resources, can complement wind power. Wind-storage combined frequency regulation technology has become an important direction for improving the frequency stability of power grids with a high proportion of new energy by integrating the dynamic response characteristics of the two.

[0003] Existing technologies usually use a combination of virtual inertia control and droop control to achieve frequency regulation of wind-storage combined systems. For example, wind turbines simulate the inertia response of synchronous machines through rotor kinetic energy release, and energy storage systems provide primary frequency regulation support through virtual droop coefficients; some schemes adjust the frequency regulation coefficient according to frequency deviations through preset fixed parameters or segmented regulation strategies. At the same time, some studies have introduced optimization algorithms to perform offline adjustment of frequency regulation parameters and coordinate wind-storage output based on power allocation weights.

[0004] However, the existing technology has the following shortcomings: fixed parameters or simple segmented adjustment are difficult to adapt to wind speed fluctuations, SOC Dynamic scenarios such as changes in frequency regulation capacity may lead to insufficient release of frequency regulation capacity or equipment overload risk; the lack of real-time optimization mechanism for wind and storage output weights may easily cause a single device to be overloaded or SOC Over-limit; frequency modulation parameter setting and power allocation are independent of each other, and a closed-loop optimization system has not been formed, making it difficult to balance frequency recovery speed and equipment operation safety. Summary of the invention

[0005] In order to address the technical problems that the existing frequency regulation method for a combined wind and storage system adopts fixed parameters and a static output allocation strategy, resulting in a mismatch between the frequency regulation response and the dynamic operating conditions, and lacks a closed-loop optimization mechanism, making it difficult to take into account both frequency stability and equipment safety, the present application provides a frequency regulation method, system, equipment and medium for a combined wind and storage system based on parameter setting and adaptive optimization. Through a closed-loop optimization framework driven by real-time data, the frequency regulation parameters are dynamically adjusted and the wind and storage output weights are collaboratively optimized, thereby achieving the dual goals of minimizing frequency deviation and ensuring safe operation of equipment.

[0006] In a first aspect, the present application provides a frequency regulation method for a wind-storage combined system based on parameter setting and adaptive optimization, comprising the following steps: S1. Real-time acquisition of wind and energy storage system data, including grid frequency deviation , Frequency deviation change rate , Energy storage system charge state , wind speed , rotor speed of wind turbine and pitch angle ; S2. Set the initial values and adjustment ranges of the frequency regulation parameters according to the data of the wind-storage combined system. The frequency regulation parameters include the virtual inertia coefficient of the wind turbine , the droop coefficient of the wind turbine , the virtual inertia coefficient of the energy storage and the droop coefficient of the energy storage ; S3. With the adjustment ranges of the frequency regulation parameters as constraints, input the power distribution weight of the wind turbine , the power distribution weight of the energy storage and the frequency regulation parameters as optimization variables into the improved particle swarm algorithm. Combine , , and the equipment safety threshold. With the goal of minimizing the frequency deviation, SOC fluctuation and frequency regulation power adjustment amount, generate the optimal values of the frequency regulation parameters, and ; S4. Generate a combined frequency regulation command according to the optimal values of the frequency regulation parameters, and to control the wind turbine and the energy storage system to output frequency regulation power. The combined frequency regulation command satisfies:

[0007] In the formula, is the frequency regulation power of the wind turbine; is the frequency regulation power of the energy storage system; S5. Real-time monitor the , and after frequency regulation, and feedback them to step S3 to update the optimal values of the frequency regulation parameters, and in the next cycle to form a closed-loop control.

[0008] Further, it should be noted that in step S2, the initial values and adjustment ranges of the virtual inertia coefficient of the wind turbine and the droop coefficient of the wind turbine are dynamically correlated with and through the hyperbolic tangent function. In the frequency deviation stage, it satisfies:

[0009] In the frequency recovery stage, it satisfies:

[0010] In the formula, , , , is a preset adjustment factor, all of which are positive numbers; is the virtual inertia control parameter initially set for the wind turbine system; is the virtual droop control parameter initially set for the wind turbine system; is the maximum allowable frequency deviation of the system.

[0011] It should be further noted that when the grid frequency change rate < 0, the wind turbine is in the frequency deviation stage; When the grid frequency change rate > 0, the wind turbine is in the frequency recovery stage.

[0012] It should be further noted that in step S2, the energy storage virtual inertia coefficient satisfies:

[0013] In the formula, is the currently available power of the energy storage system, which is determined by and the power limit of the energy storage system; is the anti-zero constant; is the upper limit of the energy storage virtual inertia coefficient; The energy storage droop coefficient is dynamically correlated with through an S-shaped function. In the charging stage, that is, when > 0, it satisfies:

[0014] In the discharging stage, that is, when < 0, it satisfies:

[0015] In the formula, , , , are all preset energy storage state of charge thresholds, satisfying ; is the preset maximum value; is the preset slope coefficient of the S-shaped function.

[0016] It should be further noted that in step S3, the equipment safety threshold includes the energy storage system safety threshold and the wind turbine safety threshold. The energy storage system safety threshold includes:

[0017] The safety thresholds of the wind turbine include:

[0018] In the formula, and are the upper and lower limits of the energy storage state of charge allowed by the system; is the rated power of the energy storage; is the minimum allowable up and down power change amount of the wind turbine; is the maximum allowable up and down power change amount of the wind turbine; and are the upper and lower limits of the wind turbine speed allowed by the system; and are the upper and lower limits of the pitch angle allowed by the system; is the maximum power of pitch angle control for frequency modulation allowed by the system.

[0019] Furthermore, it should be noted that in step S3, the objective function of the improved particle swarm optimization algorithm is:

[0020] Where:

[0021]

[0022]

[0023] In the formula, is the initial value; and and are weight coefficients, which are dynamically adjusted according to the frequency modulation stage. Among them, in the frequency deviation stage: , in the frequency recovery stage: .

[0024] Furthermore, it should be noted that in step S3, the optimization process of the improved particle swarm optimization algorithm includes: Updating the particle velocity with a dynamic inertia weight, and the dynamic inertia weight decreases nonlinearly according to the number of iterations; Dynamically adjusting the learning factor to balance global search and local exploitation; When the particle does not update its individual optimal solution within the preset number of iterations, a random perturbation is added to the particle position.

[0025] Furthermore, it should be noted that the dynamic inertia weight is updated according to the formula:

[0026] The formula for dynamically adjusting the learning factor is as follows:

[0027] In the formula, and are the upper and lower limits of the preset dynamic inertia weight; and are the initial value and the termination value of the individual learning factor respectively; is the maximum number of iterations; is the current iteration number.

[0028] Furthermore, it should be noted that the expression of the random perturbation is:

[0029] In the formula, is the current iteration number; is the mutation intensity; is a random number of the standard normal distribution.

[0030] Furthermore, it should be noted that in step S4, the frequency modulation power of the wind turbine satisfies: ; The frequency modulation power of the energy storage satisfies: .

[0031] Furthermore, it should be noted that the frequency modulation power of the wind turbine consists of the rotor kinetic energy release power and the pitch control power, and satisfies:

[0032] Among them,

[0033]

[0034] In the formula, is the moment of inertia of the wind turbine; is the load reduction rate; is the maximum power point tracking power.

[0035] In a second aspect, the present application provides a frequency modulation system for a wind-storage combined system based on parameter tuning and adaptive optimization, which is used to implement the above-mentioned frequency modulation method for the wind-storage combined system, and includes: A data acquisition module, which is used to acquire the data of the wind-storage combined system in real time; A parameter setting module, which is used to set the initial values and adjustment ranges of the frequency modulation parameters according to the data of the wind-storage combined system; An optimization calculation module, which takes the adjustment range of the frequency modulation parameters as a constraint, and inputs the power distribution weight of the wind turbine , the power distribution weight of the energy storage and the frequency modulation parameters as optimization variables into an improved particle swarm algorithm, and combines , , and the equipment safety threshold, and aims to minimize the frequency deviation, SOC fluctuation and the frequency modulation power adjustment amount, and generates the optimal values of the frequency modulation parameters, and ; An instruction generation and control module, which is used to generate a combined frequency modulation instruction according to the optimal values of the frequency modulation parameters, and , and controls the wind turbine and the energy storage system to output frequency modulation power; A monitoring and feedback module, which is used to monitor the , and after frequency modulation in real time, and feeds them back to step S3 to update the optimal values of the frequency modulation parameters, and in the next cycle, forming a closed-loop control.

[0036] In a third aspect, the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor is used to execute the computer program to implement the steps of the above-mentioned frequency modulation method for the wind-storage combined system based on parameter tuning and adaptive optimization.

[0037] In a fourth aspect, the present application provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned frequency modulation method for the wind-storage combined system based on parameter tuning and adaptive optimization are implemented.

[0038] From the above technical solutions, it can be seen that the present application has the following advantages: 1. The present application collects the grid frequency deviation, the rate of change of the frequency deviation, SOC, multi-dimensional dynamic parameters such as wind speed, rotor speed, and pitch angle, and combine the improved particle swarm optimization algorithm to jointly optimize the frequency modulation parameters and output distribution weights, forming a "perception - decision - execution - feedback" closed-loop control, breaking through the limitations of traditional open-loop control, being able to dynamically adjust the control strategy according to the real-time state of the system, and effectively improving the adaptive ability of the wind storage combined system to complex working conditions.

[0039] 2. This application incorporates the initial value setting and adjustment range constraint of the frequency modulation parameters into the optimization model, dynamically tunes the virtual inertia coefficient and droop coefficient to match the requirements of different frequency modulation stages, and at the same time uses the safety threshold as a hard constraint to ensure that the state of charge of the energy storage and the rotational speed of the wind turbine are always within the safe range during the frequency modulation process.

[0040] 3. This application takes the output distribution weight of the wind turbine and the energy storage weight as optimization variables, simultaneously optimizes with the frequency modulation parameters, establishes a multi-objective function of frequency deviation, SOC fluctuation, and frequency modulation power adjustment amount, and realizes the dynamic trade-off of the three through the improved particle swarm optimization algorithm, making the absolute value of the system frequency deviation as small as possible during the frequency modulation process, avoiding overcharging and over-discharging of the energy storage system, keeping SOC within a reasonable range, and reducing the adjustment amount of the wind power energy storage power, reducing the impact on the wind power generation efficiency and the life of the energy storage system. It not only avoids the rapid over-limit problem caused by solely relying on the energy storage, SOC but also prevents the rotational speed instability caused by overusing the rotor kinetic energy, and can stably improve the collaborative efficiency of the wind storage frequency modulation power.

[0041] 4. This application adopts a dynamic inertia weight and learning factor adjustment mechanism, maintaining high exploration ability in the initial stage of optimization and enhancing local development accuracy in the later stage; introducing a random perturbation strategy to avoid premature convergence. Compared with the traditional particle swarm optimization algorithm, it can stably improve the convergence speed and the probability of obtaining the global optimal solution in the wind storage frequency modulation scenario, ensuring the rapid generation of feasible solutions under complex non-linear constraints. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of this application, the drawings required for description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0043] Figure 1 is the flowchart of the frequency modulation method for the wind storage combined system based on parameter tuning and adaptive optimization in an embodiment of this application.

[0044] Figure 2 is the flowchart of the improved particle swarm optimization algorithm in an embodiment of this application.

[0045] Figure 3 It is a schematic block diagram of a frequency modulation system for a wind-storage integrated system based on parameter tuning and adaptive optimization in an embodiment of the present application.

[0046] Figure 4 It is a schematic diagram of the hardware structure of an electronic device in an embodiment of the present application. Specific embodiments

[0047] To make the application objectives, features, and advantages of the present application more obvious and understandable, the following will use specific embodiments and accompanying drawings to clearly and completely describe the technical solutions protected by the present application. Obviously, the embodiments described below are only a part of the embodiments of the present application, rather than all embodiments. Based on the embodiments in this patent, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this patent.

[0048] The frequency modulation method for a wind-storage integrated system based on parameter tuning and adaptive optimization involved in the present application mainly aims at the technical field of frequency modulation of wind-storage integrated systems. By real-time collecting multi-dimensional dynamic parameters such as grid frequency deviation, rate of change of frequency deviation, SOC , wind speed, rotor speed, and pitch angle, etc., combined with an improved particle swarm optimization algorithm to jointly optimize the frequency modulation parameters and output power distribution weights, forming a "perception - decision - execution - feedback" closed-loop control, breaking through the limitations of traditional open-loop control, and being able to dynamically adjust the control strategy according to the real-time state of the system, effectively improving the adaptive ability of the wind-storage integrated system to complex working conditions; integrating the initial value setting and adjustment range constraints of the frequency modulation parameters into the optimization model, dynamically tuning the virtual inertia coefficient and droop coefficient to match the requirements of different frequency modulation stages, and at the same time using the safety threshold as a hard constraint to ensure that the state of charge of the energy storage and the speed of the wind turbine are always within the safe range during the frequency modulation process; taking the output power distribution weight of the wind turbine and the energy storage weight as optimization variables, synchronously optimizing with the frequency modulation parameters, establishing a multi-objective function of frequency deviation, SOC fluctuation, and frequency modulation power adjustment amount, and realizing the dynamic trade-off of the three through the improved particle swarm optimization algorithm, which not only avoids the SOC rapid over-limit problem caused by solely relying on energy storage, but also prevents the speed instability caused by overusing the rotor kinetic energy, and can stably improve the collaborative efficiency of wind-storage frequency modulation power; adopting a dynamic inertia weight and learning factor adjustment mechanism to maintain high exploration ability in the initial stage of optimization and enhance local development accuracy in the later stage; introducing a random perturbation strategy to avoid premature convergence. Compared with the traditional particle swarm optimization algorithm, it can stably improve the convergence speed and the probability of obtaining the global optimal solution in the wind-storage frequency modulation scenario, and ensure the rapid generation of feasible solutions under complex non-linear constraints.

[0049] The frequency modulation method for a wind-storage integrated system based on parameter tuning and adaptive optimization involved in this application mainly aims at the technical problems of the existing frequency modulation methods for wind-storage integrated systems. Since fixed parameters and static output allocation strategies are adopted, the frequency modulation response mismatches with the dynamic operating conditions, and there is a lack of a closed-loop optimization mechanism, making it difficult to balance frequency stability and equipment safety.

[0050] The following will describe in detail the frequency modulation method for a wind-storage integrated system based on parameter tuning and adaptive optimization involved in this application. For the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to thoroughly understand the embodiments of this application. However, those skilled in the art should clearly understand that this application can also be implemented in other embodiments without these specific details.

[0051] In the frequency modulation method for a wind-storage integrated system based on parameter tuning and adaptive optimization involved in this application, the term "including" indicates the existence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the existence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations. The terms "including", "comprising", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0052] For the convenience of clearly describing the technical solutions of this application, terms such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and roles. Those skilled in the art can understand that the terms "first", "second", etc. do not limit the quantity and execution order, and the terms "first", "second", etc. do not necessarily mean different.

[0053] The statements such as "in one embodiment" or "in some embodiments" described in this application mean that the specific features, structures, or characteristics described in the embodiment are included in one or more embodiments of this application. Thus, the statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments" and the like that appear in different parts of this application do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways.

[0054] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the scope of protection of this application.

[0055] The frequency modulation method for a wind-storage integrated system based on parameter tuning and adaptive optimization provided by an embodiment of this application is executed by a computer device. Correspondingly, the frequency modulation system for a wind-storage integrated system based on parameter tuning and adaptive optimization runs in the computer device.

[0056] Figure 1 It is a flowchart of the frequency modulation method for a wind-storage integrated system based on parameter tuning and adaptive optimization in an embodiment of this application. Among them, Figure 1 The execution subject can be a frequency modulation system for a wind-storage integrated system based on parameter tuning and adaptive optimization. According to different requirements, the order of the steps in this flowchart can be changed, and some can be omitted.

[0057] As Figure 1 shown, the frequency modulation method for a wind-storage integrated system based on parameter tuning and adaptive optimization includes: Step S1, obtaining the data of the wind-storage integrated system in real time, including the grid frequency deviation , the rate of change of frequency deviation , the state of charge of the energy storage system , the wind speed , the rotor speed of the wind turbine and the pitch angle .

[0058] During the frequency modulation process of the wind-storage integrated frequency modulation system, the principle of its system equations is mainly based on the dynamic characteristics of the power system, the operating characteristics of the wind turbine, and the control characteristics of the energy storage system, and is usually described by the following equations:

[0059]

[0060] Among them, M is the equivalent inertia constant of the system, indicating the inertial response ability of the system to frequency changes; D is The damping coefficient of the system, indicating the damping effect of the system on frequency changes; Δ f is the frequency deviation, that is, the difference between the actual frequency and the rated frequency; Δ P m is the change in mechanical power, indicating the adjustment amount of the mechanical power of the generator set; Δ P e is the change in electrical power; Δ P g , Δ P bess , Δ P wind are the output power change amounts of the traditional generator set, the energy storage device, and the wind farm respectively; Δ P LThe disturbance power in the power system includes two aspects. On the one hand, the disturbance is caused by the difference in load. On the other hand, the disturbance is caused by the randomness of wind power generation. Performing Laplace transformation on the above formula, the system equations of the wind-storage combined frequency regulation system are obtained as follows:

[0061] Among them, H is the inertia time constant of the power grid.

[0062] Real-time acquisition of data from the wind-storage combined system comprehensively covers key information such as grid frequency deviation, rate of change of frequency deviation, state of charge of the energy storage system, wind speed, rotor speed of the wind turbine, and pitch angle, providing accurate, timely, and comprehensive data support for subsequent precise setting of frequency regulation parameters, optimization of frequency regulation strategies, and generation of reliable frequency regulation commands, which is the basis and prerequisite for achieving efficient frequency regulation.

[0063] Step S2, set the initial values and adjustment ranges of the frequency regulation parameters according to the data of the wind-storage combined system. The frequency regulation parameters include the virtual inertia coefficient of the wind turbine, the droop coefficient of the wind turbine, the virtual inertia coefficient of the energy storage, and the droop coefficient of the energy storage.

[0064] Setting the initial values and adjustment ranges of the frequency regulation parameters according to the acquired data limits a reasonable parameter space for the subsequent operation of the improved particle swarm algorithm, making the optimization process of the frequency regulation parameters have a clear direction and boundary, ensuring that the optimization results not only meet the actual operation requirements of the system but also have feasibility and effectiveness.

[0065] In some specific embodiments, the initial values and adjustment ranges of the virtual inertia coefficient and the droop coefficient of the wind turbine are dynamically correlated with and through the hyperbolic tangent function. During the frequency deviation stage, it satisfies:

[0066] During the frequency recovery stage, it satisfies:

[0067] In the formula, , , , are preset adjustment factors, all of which are positive numbers; is the virtual inertia control parameter initially set for the wind turbine system; The virtual droop control parameters initially set for the wind turbine system; The maximum frequency deviation allowed by the system; The minimum allowable up and down power change amount of the wind turbine; The maximum allowable up and down power change amount of the wind turbine.

[0068] Affected by the wind speed, the rotational speed of the wind turbine is constantly changing. If a large control coefficient is used, it is easy to cause the instability of the unit, while a small control coefficient cannot fully exert the frequency regulation ability of the unit. Therefore, a fixed frequency regulation coefficient cannot well adapt to the change of wind speed. Moreover, to further improve the system frequency stability, the proportional differential coefficient should also be different at different stages of frequency dynamic change. The rotational speed of the doubly-fed induction wind generator will change with the wind speed, and the wind speed is uncertain. Therefore, the frequency regulation energy that the wind turbine can provide during frequency fluctuations will be different. Therefore, at different wind speeds, the virtual inertia and droop coefficient should be adaptively changed considering the degree to which the wind turbine can participate in system frequency regulation to adapt to the change of wind speed. That is, a larger frequency regulation coefficient is set at high wind speeds to undertake the corresponding frequency regulation tasks, while at low wind speeds, the frequency regulation coefficient should not be set too large to ensure the safe and stable operation of the wind turbine itself.

[0069] Using the hyperbolic tangent function to dynamically associate the initial values and adjustment ranges of the virtual inertia coefficient and the droop coefficient of the wind turbine with the frequency deviation, satisfying specific formulas in the frequency deviation and recovery stages respectively, enabling the wind turbine to flexibly adjust its own characteristics according to the frequency change, more accurately respond to the frequency regulation demand, enhancing the adaptability and effectiveness of the wind turbine in participating in frequency regulation at different frequency stages, and thus improving the overall frequency regulation performance of the wind storage combined system.

[0070] In some specific embodiments, when the grid frequency change rate <0, the wind turbine is in the frequency deviation stage; When the grid frequency change rate >0, the wind turbine is in the frequency recovery stage.

[0071] Based on the positive and negative of the grid frequency change rate, clearly defining whether the wind turbine is in the frequency deviation stage or the frequency recovery stage, providing a clear and reliable judgment standard for the dynamic adjustment of the wind turbine frequency regulation parameters, enabling the wind turbine frequency regulation control to more accurately match different frequency conditions, and enhancing the pertinence and rationality of the frequency regulation control strategy.

[0072] In some specific embodiments, the energy storage virtual inertia coefficient Satisfies:

[0073] In the formula, is the current available power of the energy storage system, which is determined by and the power limit of the energy storage system; is the anti-zero constant; is the upper limit of the virtual inertia coefficient of the energy storage; The energy storage droop coefficient is dynamically correlated with through an S-shaped function. During the charging stage, i.e., when >0, it satisfies:

[0074] During the discharging stage, i.e., when <0, it satisfies:

[0075] In the formula, , , , are all preset state of charge thresholds of the energy storage, satisfying ; is the preset maximum value; is the preset slope coefficient of the S-shaped function.

[0076] The energy storage system directly adjusts the power balance of the power grid through fast charge and discharge to stabilize the frequency. When the power grid frequency drops (load increases), the energy storage system discharges and injects active power into the power grid to supplement the power deficit; when the power grid frequency rises (load decreases), the energy storage system charges and absorbs the excess power of the power grid.

[0077] In systems such as wind-storage coordinated frequency modulation, using the "S" function to adaptively adjust the energy storage output parameters according to the state of charge ( SOC ) can effectively improve the performance and frequency modulation effect of the energy storage system. The "S" function usually has a curve characteristic similar to an S shape, which is slow to change when the input value is small, changes rapidly in the middle region, and tends to be flat when the input value is large. In the scenario of adjusting the energy storage output parameters according to SOC , the "S" function can be used to smoothly adjust the charge and discharge power of the energy storage system, avoiding overcharge and over-discharge when SOC is at the extreme value and protecting the battery life.

[0078] Step S3, with the adjustment range of the frequency modulation parameters as the constraint, the output power distribution weight of the wind turbine and the output power distribution weight of the energy storage Input the improved particle swarm optimization algorithm with the frequency modulation parameter as the optimization variable, combined with , , and the equipment safety threshold, aiming to minimize the frequency deviation, SOC fluctuation and frequency modulation power adjustment amount, generate the optimal values of the frequency modulation parameter, and . The flow chart of the improved particle swarm optimization algorithm is as shown in Figure 2 .

[0079] Taking the adjustment range of the frequency modulation parameter as the constraint, input the power distribution weight of the wind turbine output, the power distribution weight of the energy storage output and the frequency modulation parameter as the optimization variable into the improved particle swarm optimization algorithm, and combine with the equipment safety threshold to optimize with a specific goal, which can comprehensively consider various constraint conditions and performance requirements of the system operation, accurately find the optimal value of the frequency modulation parameter, and achieve the comprehensive optimization of the frequency modulation performance of the wind-storage combined system.

[0080] In some specific embodiments, the equipment safety threshold includes the energy storage system safety threshold and the wind turbine safety threshold. The energy storage system safety threshold includes:

[0081] The wind turbine safety threshold includes:

[0082] In the formula, , are the upper and lower limits of the state of charge of the energy storage allowed by the system; is the rated power of the energy storage; is the minimum allowable up and down power change amount of the wind turbine; is the maximum allowable up and down power change amount of the wind turbine; , are the upper and lower limits of the rotational speed of the wind turbine allowed by the system; , are the upper and lower limits of the pitch angle allowed by the system; is the maximum power of pitch angle frequency modulation allowed by the system.

[0083] Set the energy storage system safety threshold and the wind turbine safety threshold, including the upper and lower limits of the state of charge of the energy storage, the rated power of the energy storage, the upper and lower limits of the rotational speed of the wind turbine, the upper and lower limits of the pitch angle, and the maximum power of pitch angle frequency modulation, etc., to limit the equipment from multiple key operating parameters, avoid damage to the equipment due to parameters exceeding the safety range during frequency modulation, and ensure the long-term, stable and reliable operation of the wind-storage combined system In some specific embodiments, the objective function of the improved particle swarm optimization algorithm is as follows:

[0084] Where:

[0085]

[0086]

[0087] In the formula, is the initial value of ; , , are weight coefficients, which are dynamically adjusted according to the frequency modulation stage. Among them, in the frequency deviation stage: , in the frequency recovery stage: .

[0088] Taking minimizing the frequency deviation, SOC fluctuation and the frequency modulation power adjustment amount as the goal, the objective function of the improved particle swarm optimization algorithm is constructed, and the weight coefficients are dynamically adjusted according to the frequency modulation stage. It can comprehensively balance various factors such as frequency stability, energy storage state stability, and power adjustment amount, comprehensively optimize the frequency modulation parameters, and thus significantly improve the overall performance and comprehensive benefits of the wind-storage combined system for frequency modulation.

[0089] In some specific embodiments, the optimization process of the improved particle swarm optimization algorithm includes: Updating the particle velocity using a dynamic inertia weight, and the dynamic inertia weight decreases nonlinearly according to the number of iterations; Dynamically adjusting the learning factor to balance global search and local exploitation; When the particle does not update its individual optimal solution within the preset number of iterations, a random perturbation is added to the particle position.

[0090] The improved particle swarm optimization algorithm updates the particle velocity using a dynamic inertia weight, dynamically adjusts the learning factor, and adds a random perturbation when the particle does not update its individual optimal solution. This optimization process effectively balances the global search ability and local exploitation ability of the algorithm, avoids the algorithm falling into a local optimal solution, improves the search efficiency of the algorithm, enables it to find the optimal value of the frequency modulation parameters faster and more accurately, and further improves the frequency modulation effect of the wind-storage combined system.

[0091] In some specific embodiments, the update formula of the dynamic inertia weight is:

[0092] The formula for dynamically adjusting the learning factor is:

[0093] In the formula, and are the upper and lower limits of the preset dynamic inertia weight; and are respectively the initial value and the termination value of the individual learning factor; is the maximum number of iterations; is the current number of iterations.

[0094] The update formulas of the dynamic inertia weight and the learning factor are quantitatively adjusted according to the preset upper and lower limits, the maximum number of iterations, and the current number of iterations, providing a scientific and accurate quantitative basis for the algorithm to adaptively adjust the search strategy during the iteration process, enabling the algorithm to better adapt to different search stages, and further enhancing the optimization ability of the algorithm in the process of finding the optimal value of the frequency modulation parameter.

[0095] In some specific embodiments, the expression of the random perturbation is:

[0096] In the formula, is the current number of iterations; is the mutation strength; is a random number of the standard normal distribution.

[0097] The random perturbation expression introduces a random number of the standard normal distribution according to the current number of iterations and the mutation strength, adding randomness to the particle position update, breaking the dilemma that the algorithm may fall into local optimum, helping the algorithm jump out of the local optimum region, exploring a wider solution space, so as to find a better frequency modulation parameter, and significantly improving the frequency modulation performance of the wind-storage combined system.

[0098] In some specific embodiments, the update formulas of the position and velocity of each particle are:

[0099] Wherein, is the velocity of the i-th particle; is the position of the i-th particle; is the inertia weight; and are both learning factors; and are random numbers between [0, 1]; is the individual optimal position of the i-th particle; is the global optimal position.

[0100] Step S4, according to the frequency modulation parameters, and generate a combined frequency modulation command based on the optimal values to control the wind turbine and the energy storage system to output frequency modulation power. The combined frequency modulation command satisfies:

[0101] where, is the frequency modulation power of the wind turbine; is the frequency modulation power of the energy storage system.

[0102] Generate a combined frequency modulation command based on the optimal value of the frequency modulation parameter, clearly control the wind turbine and the energy storage system to output frequency modulation power, so that the wind turbine and the energy storage system can work together according to the optimized strategy, accurately output the power that meets the frequency modulation requirements, and achieve rapid and effective adjustment of the power grid frequency deviation to ensure the stability of the power grid frequency.

[0103] In some specific embodiments, the frequency modulation power of the wind turbine satisfies: ; The frequency modulation power of the energy storage satisfies: .

[0104] Give the expressions of the frequency modulation power of the wind turbine and the energy storage, providing a specific and clear power control method for the wind turbine and the energy storage system to participate in frequency modulation, enabling the energy storage system to have a clear basis for power output during the combined wind-storage frequency modulation, ensuring that the energy storage system can accurately and efficiently play the role of frequency modulation, and enhancing the coordination and effectiveness of the combined wind-storage system for frequency modulation In some specific embodiments, the frequency modulation power of the wind turbine consists of the rotor kinetic energy release power and the pitch control power, and satisfies:

[0105] where,

[0106]

[0107] where, is the moment of inertia of the wind turbine; is the load reduction rate; is the maximum power point tracking power.

[0108] It is clear that the frequency modulation power of the wind turbine generator set consists of the rotor kinetic energy release power and the pitch adjustment power. This power composition method enables the wind turbine generator set to make full use of its own kinetic energy reserve and pitch adjustment ability during frequency modulation, realizing the synergistic effect of multiple frequency modulation methods, increasing the flexibility and diversity of frequency modulation means, and improving the effect and reliability of the wind turbine generator set for frequency modulation.

[0109] Step S5, continuously monitor the relevant parameters after frequency modulation 、 and , and feedback them to step S3 to update the optimal values of the frequency modulation parameters, and in the next cycle, forming a closed-loop control.

[0110] Continuously monitor the relevant parameters after frequency modulation and feedback them to the optimization link to update the optimal values of the frequency modulation parameters and and in the next cycle, forming a closed-loop control, enabling the system to adjust the control strategy in a timely manner according to the actual frequency modulation effect, continuously optimizing the frequency modulation performance, adapting to the dynamic changes of the grid operation conditions, and continuously ensuring the high efficiency and stability of the frequency modulation of the wind energy storage combined system.

[0111] In a specific embodiment, the frequency modulation method for the wind energy storage combined system based on parameter tuning and adaptive optimization includes: Step S1, continuously obtain the data of the wind energy storage combined system, including the grid frequency deviation , the rate of change of the frequency deviation , the state of charge of the energy storage system , the wind speed , the rotor speed of the wind turbine generator set, and the pitch angle ; Step S2, set the initial values and adjustment ranges of the frequency modulation parameters according to the data of the wind energy storage combined system. The frequency modulation parameters include the virtual inertia coefficient of the wind turbine generator set, the droop coefficient of the wind turbine generator set, the virtual inertia coefficient of the energy storage, and the droop coefficient of the energy storage; Among them, the initial values and adjustment ranges of the virtual inertia coefficient and the droop coefficient of the wind turbine generator set are dynamically correlated with and through the hyperbolic tangent function. It is stipulated that when the rate of change of the grid frequency <0, the wind turbine generator set is in the frequency deviation stage, and when the rate of change of the grid frequency >0, the wind turbine generator set is in the frequency recovery stage; In the frequency deviation stage, it satisfies:

[0112] During the frequency recovery stage, the following is satisfied:

[0113] where , , , are preset adjustment factors, = 1.5, = 2.0; is the virtual inertia control parameter initially set for the wind turbine system, = 8; is the virtual droop control parameter initially set for the wind turbine system, = 5; is the maximum frequency deviation allowed by the system; When the grid frequency change rate < 0, the wind turbine is in the frequency deviation stage; When the grid frequency change rate > 0, the wind turbine is in the frequency recovery stage; The energy storage virtual inertia coefficient satisfies:

[0114] where is the currently available power of the energy storage system, determined by and the power limit of the energy storage system; is the anti-zero constant, = 0.001 s; is the upper limit of the energy storage virtual inertia coefficient, = 10; The energy storage droop coefficient is dynamically correlated with through an S-shaped function. During the charging stage, i.e., > 0, it satisfies:

[0115] During the discharging stage, i.e., < 0, it satisfies:

[0116] where , , , are all preset energy storage state of charge thresholds, = 0.2, = 0.5, = 0.7, = 0.9; is the preset maximum value; is the preset S-shaped function slope coefficient, = 10; Step S3, with the adjustment range of the frequency modulation parameter as the constraint, allocate the output power weights of the wind turbine , the energy storage output power weight and the frequency modulation parameter as optimization variables and input them into the improved particle swarm algorithm. Combine , , and the equipment safety threshold, with the goal of minimizing the frequency deviation, SOC fluctuation and frequency modulation power regulation amount, generate the optimal values of the frequency modulation parameter, and ; Among them, the equipment safety threshold includes the energy storage system safety threshold and the wind turbine safety threshold. The energy storage system safety threshold includes:

[0117] The wind turbine safety threshold includes:

[0118] In the formula, , are the upper and lower limits of the energy storage state of charge allowed by the system; is the rated power of the energy storage, = 1 MW; is the minimum allowable up and down power change amount of the wind turbine; is the maximum allowable up and down power change amount of the wind turbine; , are the upper and lower limits of the wind turbine speed allowed by the system, = 0.7 * rated speed, = 1.2 * rated speed; , are the upper and lower limits of the pitch angle allowed by the system. For example = 0°, = 90°; It is the maximum power of pitch - controlled frequency modulation allowed by the system, = 0.2 MW; The objective function of the improved particle swarm optimization algorithm is:

[0119] Among them:

[0120]

[0121]

[0122] In the formula, is the initial value of; , , are weight coefficients, which are dynamically adjusted according to the frequency modulation stage. Among them, in the frequency deviation stage: = 0.6, = 0.3, = 0.1, and in the frequency recovery stage: = 0.3, = 0.6, = 0.1; The optimization process of the improved particle swarm optimization algorithm includes: Updating the particle velocity using a dynamic inertia weight. The dynamic inertia weight decreases non - linearly according to the number of iterations. The update formula for the dynamic inertia weight is: ; Dynamically adjusting the learning factors to balance global search and local exploitation. The formula for dynamically adjusting the learning factor is: ; When the particle does not update its individual optimal solution within the preset number of iterations, a random perturbation is added to the particle position. The expression for the random perturbation is:

[0123] In the formula, , are the upper and lower limits of the preset dynamic inertia weight, = 0.9, = 0.4; , are respectively the initial value and the termination value of the individual learning factor, = 2.0, = 1.2; is the maximum number of iterations, = 100; is the current number of iterations; is the mutation strength, = 0.1; is a random number from the standard normal distribution; Step S4, according to the frequency modulation parameters, and generate a combined frequency modulation command to control the frequency modulation power output of the wind turbine and the energy storage system. The combined frequency modulation command satisfies:

[0124] In the formula, is the frequency modulation power of the wind turbine; In the formula, is the frequency modulation power of the wind turbine, which satisfies: ; is the frequency modulation power of the energy storage system, which satisfies: ; At the same time, the frequency modulation power of the wind turbine consists of the rotor kinetic energy release power and the pitch control power, and satisfies:

[0125] Among them,

[0126]

[0127] In the formula, is the moment of inertia of the wind turbine; is the load reduction rate; is the maximum power point tracking power; Step S5, monitor the , and in real time after frequency modulation, and feedback them to Step S3 to update the optimal values of the frequency modulation parameters, and in the next cycle to form a closed-loop control.

[0128] The following are embodiments of a frequency modulation system for a wind-storage integrated system based on parameter tuning and adaptive optimization provided by embodiments of the present disclosure. The frequency modulation system for the wind-storage integrated system and the frequency modulation method for the wind-storage integrated system based on parameter tuning and adaptive optimization in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the frequency modulation system for the wind-storage integrated system based on parameter tuning and adaptive optimization, reference may be made to the embodiments of the frequency modulation method for the wind-storage integrated system based on parameter tuning and adaptive optimization.

[0129] Now, mobile terminals implementing various embodiments of the present application will be described with reference to the accompanying drawings. In the following description, suffixes such as "module", "component", or "unit" used to denote elements are only for facilitating the description of embodiments of the present application, and they have no specific meaning in themselves. Therefore, "module" and "component" can be used interchangeably.

[0130] As Figure 3 shown, the frequency modulation system for the wind-storage integrated system based on parameter tuning and adaptive optimization includes: A data acquisition module for real-time acquisition of wind-storage integrated system data; A parameter setting module for setting initial values and adjustment ranges of frequency modulation parameters according to the wind-storage integrated system data; An optimization calculation module for taking the adjustment range of the frequency modulation parameters as a constraint, inputting the power distribution weight of the wind turbine , the power distribution weight of the energy storage and the frequency modulation parameters as optimization variables into an improved particle swarm algorithm, and combining , , and the equipment safety threshold, aiming to minimize the frequency deviation, SOC fluctuation and the amount of frequency modulation power adjustment, to generate the optimal values of the frequency modulation parameters, and ; An instruction generation and control module for generating a combined frequency modulation instruction according to the optimal values of the frequency modulation parameters, and , and controlling the wind turbine and the energy storage system to output frequency modulation power; A monitoring and feedback module for real-time monitoring of the , and after frequency modulation, and feeding them back to step S3 to update the optimal values of the frequency modulation parameters, and in the next cycle, forming a closed-loop control.

[0131] The wind-storage integrated system in this embodiment is used to implement the frequency modulation method for the wind-storage integrated system based on parameter tuning and adaptive optimization, and the steps include: S1. Obtain the data of the wind-storage combined system in real time, including the power grid frequency deviation , the rate of change of frequency deviation , the state of charge of the energy storage system , the wind speed , the rotor speed of the wind turbine and the pitch angle ; S2. Set the initial values and adjustment ranges of the frequency modulation parameters according to the data of the wind-storage combined system. The frequency modulation parameters include the virtual inertia coefficient of the wind turbine , the droop coefficient of the wind turbine , the virtual inertia coefficient of the energy storage and the droop coefficient of the energy storage ; S3. With the adjustment range of the frequency modulation parameters as the constraint, input the power distribution weight of the wind turbine , the power distribution weight of the energy storage and the frequency modulation parameters as optimization variables into the improved particle swarm algorithm. Combine , , and the equipment safety threshold. With the goal of minimizing the frequency deviation, SOC fluctuation and the frequency modulation power adjustment amount, generate the optimal values of the frequency modulation parameters, and ; S4. Generate a combined frequency modulation command according to the optimal values of the frequency modulation parameters, and to control the wind turbine and the energy storage system to output frequency modulation power. The combined frequency modulation command satisfies:

[0132] In the formula, is the frequency modulation power of the wind turbine; is the frequency modulation power of the energy storage system; S5. Monitor the , and after frequency modulation in real time, and feedback them to step S3 to update the optimal values of the frequency modulation parameters, and in the next cycle to form a closed-loop control.

[0133] This application also provides an electronic device for implementing each embodiment of this application. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor.

[0134] Those skilled in the art can understand that the structure of the electronic device involved in the embodiments of the present application does not limit the electronic device. The electronic device may include more or fewer components than shown in the figures, or combine some components, or have different component arrangements.

[0135] Figure 4 Schematic diagram of the hardware structure of an electronic device for implementing each embodiment of the present application.

[0136] The electronic device includes, but is not limited to, components such as a processor and a memory. Those skilled in the art can understand that the structure of the electronic device involved in the embodiments of the present application does not limit the electronic device. The electronic device may include more or fewer components than shown in the figures, or combine some components, or have different component arrangements.

[0137] In the embodiments of the present application, the electronic device includes, but is not limited to, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described herein and / or claimed.

[0138] In the embodiments of the present application, the processor may be implemented by using at least one of an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to execute the functions described herein. In some cases, such an implementation may be implemented in the controller. For a software implementation, an implementation of a process or function may be implemented with a separate software module that allows execution of at least one function or operation. The software code may be implemented by a software application (or program) written in any suitable programming language. The software code may be stored in the memory and executed by the controller.

[0139] In addition, the electronic device includes some functional modules not shown herein and will not be elaborated further.

[0140] Those skilled in the art to which the present application pertains can understand that various aspects of the electronic device provided by the present application can be implemented as a system, a method, or a program product. Therefore, various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to herein as "circuit", "module", or "system".

[0141] The present application also provides a storage medium in which a program product capable of implementing a frequency modulation method for a wind-storage combined system based on parameter tuning and adaptive optimization is stored. In some possible implementation manners, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification.

[0142] The storage medium can adopt any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0143] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A frequency regulation method for a wind-storage combined system based on parameter setting and adaptive optimization, characterized in that: include: S1. Real-time acquisition of wind and energy storage system data, including grid frequency deviation , Frequency deviation change rate , Energy storage system charge state , wind speed , rotor speed of wind turbine and pitch angle ; S2. Set the initial value and adjustment range of the frequency modulation parameters according to the wind-storage combined system data. The frequency modulation parameters include the virtual inertia coefficient of the wind turbine. , Wind turbine droop coefficient , Energy storage virtual inertia coefficient and energy storage droop coefficient ; S3. Based on the adjustment range of frequency modulation parameters, the wind turbine output is assigned weights. , Energy storage output allocation weight Combined with the frequency modulation parameter as the optimization variable input to improve the particle swarm algorithm , , and device safety thresholds to minimize frequency deviations, SOC Fluctuation and frequency modulation power adjustment are used as targets to generate frequency modulation parameters, and The optimal value of S4. According to the frequency modulation parameters, and The optimal value of generates a joint frequency regulation command to control the wind turbine and energy storage system to output frequency regulation power. The joint frequency regulation command satisfies: In the formula, Frequency modulation power for wind turbines; Modulate power for energy storage systems; S5. Real-time monitoring of frequency modulation , and , and feed it back to step S3 to update the frequency modulation parameters of the next cycle. and The optimal value of forms a closed-loop control.

2. The frequency modulation method of the wind-storage combined system according to claim 1, characterized in that: In step S2, the virtual inertia coefficient of the wind turbine is and wind turbine droop coefficient The initial value and adjustment range of and Dynamic association, during the frequency deviation phase, satisfies: In the formula, , is the preset adjustment factor, which is a positive number; Virtual inertia control parameters initially set for the wind turbine system; Virtual droop control parameters initially set for the wind turbine system; is the maximum frequency deviation allowed by the system.

3. The frequency modulation method of the wind-storage combined system according to claim 1, characterized in that: and wind power During the frequency recovery phase: In the formula, , is the preset adjustment factor, which is a positive number; Virtual inertia control parameters initially set for the wind turbine system; Virtual droop control parameters initially set for the wind turbine system; is the maximum frequency deviation allowed by the system.

4. The frequency modulation method of the wind-storage combined system according to claim 1, characterized in that: In step S2, the energy storage virtual inertia coefficient satisfy: In the formula, is the current available power of the energy storage system, given by and energy storage system power limit determination; To prevent division by zero constant; is the upper limit of the virtual inertia coefficient of energy storage.

5. The frequency modulation method of the wind-storage combined system according to claim 1, characterized in that: In step S2, the energy storage droop coefficient Through the S-type function and Dynamic association, during the charging phase, i.e. >0, it satisfies: In the discharge phase, <0, satisfy: In the formula, , , , All are preset energy storage charge state thresholds, meeting ; For preset Maximum value; is the preset S-type function slope coefficient.

6. The frequency modulation method of the wind-storage combined system according to claim 1, characterized in that: In step S3, the equipment safety threshold includes the energy storage system safety threshold and the wind turbine group safety threshold. The energy storage system safety threshold includes: Wind turbine safety thresholds include: In the formula, , The upper and lower limits of the energy storage charge state allowed by the system; is the energy storage rated power; The minimum power variation allowed for wind turbines to be adjusted up or down; The maximum power change allowed for wind turbines; , The upper and lower limits of the wind turbine speed allowed by the system; , The upper and lower limits of the pitch angle allowed by the system; It is the maximum power of variable pitch frequency modulation allowed by the system.

7. The frequency modulation method of the wind-storage combined system according to claim 1, characterized in that: In step S3, the objective function of the improved particle swarm algorithm is: in: In the formula, for The initial value of , , is the weight coefficient, which is dynamically adjusted according to the frequency modulation stage, where the frequency deviation stage: , frequency recovery phase: .

8. The frequency modulation method of the wind-storage combined system according to claim 1, characterized in that: In step S3, the optimization process of the improved particle swarm algorithm includes: The particle velocity is updated using dynamic inertia weight, which decreases nonlinearly according to the number of iterations; Dynamically adjust learning factors to balance global search and local development; When a particle fails to update its individual optimal solution within a preset number of iterations, a random perturbation is added to the particle position.

9. The frequency modulation method of the wind-storage combined system according to claim 8, characterized in that: In step S4, the frequency modulation power of the wind turbine generator set satisfies: ； The energy storage frequency modulation power meets the following requirements: 。 10. A frequency regulation system for a wind-storage combined system based on parameter setting and adaptive optimization, characterized in that: A method for implementing a frequency modulation method of a wind-storage combined system as claimed in any one of claims 1 to 9, comprising: Data acquisition module, used to obtain wind and energy storage system data in real time; The parameter setting module is used to set the initial value and adjustment range of the frequency regulation parameters according to the wind-storage combined system data; The optimization calculation module is used to allocate weights to wind turbine outputs based on the adjustment range of frequency modulation parameters. , Energy storage output allocation weight Combined with the frequency modulation parameter as the optimization variable input to improve the particle swarm algorithm , , and equipment safety thresholds, with the goal of minimizing frequency deviation, SOC fluctuation, and frequency modulation power adjustment, to generate frequency modulation parameters, and The optimal value of Instruction generation and control module, used to generate and control and The optimal value of generates a joint frequency regulation command to control the wind turbine and energy storage system to output frequency regulation power; Monitoring feedback module, used to monitor the frequency modulation in real time , and , and feed it back to step S3 to update the frequency modulation parameters of the next cycle. and The optimal value of forms a closed-loop control.

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