Frequency regulation method, system, device and medium for wind storage combined system based on parameter setting and adaptive optimization

Abstract

The application relates to the technical field of frequency modulation of a wind storage combined system, in particular to a wind storage combined system frequency modulation method, system, device and medium based on parameter setting and adaptive optimization, which comprises the following steps: acquiring wind storage combined system data in real time; setting initial values and adjustment ranges of frequency modulation parameters according to the wind storage combined system data; taking the adjustment ranges of the frequency modulation parameters as constraints, inputting wind turbine output distribution weights, energy storage output distribution weights and the frequency modulation parameters into an improved particle swarm optimization algorithm as optimization variables to generate optimal values of the frequency modulation parameters, the wind turbine output distribution weights and the energy storage output distribution weights; generating a joint frequency modulation instruction to control the wind turbine 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 optimization algorithm output of the next cycle to form a closed-loop control. The improved particle swarm optimization algorithm is used to dynamically set the frequency modulation parameters and cooperatively optimize the wind storage output weights, so that the frequency deviation minimization and the safe operation of equipment can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of frequency regulation of a wind-storage combined system, and in particular to a frequency regulation method, system, equipment and medium for a wind-storage combined system based on parameter setting and adaptive optimization. Background Art

[0002] With the increasing penetration of renewable energy, grid frequency stability faces significant challenges. Wind power, due to its intermittent nature and low inertia, struggles to provide reliable frequency regulation. Energy storage systems, as a flexible regulation resource, can complement wind power. Combined wind and energy storage frequency regulation technology, by integrating the dynamic response characteristics of both, is becoming a key approach to improving frequency stability in grids with a high proportion of renewable energy.

[0003] Existing technologies typically implement frequency regulation in wind-storage systems using a combination of virtual inertia control and droop control. For example, wind turbines simulate the inertia response of synchronous machines through rotor kinetic energy release, while energy storage systems provide primary frequency regulation support through virtual droop coefficients. Some solutions use preset fixed parameters or segmented regulation strategies to adjust the frequency regulation coefficient based on frequency deviation. Furthermore, some research has introduced optimization algorithms for offline tuning of frequency regulation parameters and coordinated 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 response to the technical problems that the existing frequency regulation method of the wind-storage combined system adopts fixed parameters and static output distribution strategy, resulting in mismatch between frequency regulation response and dynamic operating conditions, and lacks a closed-loop optimization mechanism, making it difficult to balance frequency stability and equipment safety, the present application provides a frequency regulation method, system, equipment and medium for the wind-storage combined 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-storage output weights are collaboratively optimized, which can achieve the dual goals of minimizing frequency deviation and 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:

[0007] 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 ;

[0008] S2. Set the initial value and adjustment range of the frequency regulation parameters based on the wind-storage combined system data. The frequency regulation parameters include the virtual inertia coefficient of the wind turbine. , wind turbine droop coefficient , energy storage virtual inertia coefficient and energy storage droop coefficient ;

[0009] S3. Assign weights to wind turbine outputs based on the adjustment range of frequency modulation parameters. , Energy storage output allocation weight Improved particle swarm optimization algorithm with frequency modulation parameters as optimization variable input, combined with 、 、 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

[0010] 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:

[0011]

[0012] Where, Frequency modulation power for wind turbines;

[0013] Frequency modulation power for energy storage systems;

[0014] 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.

[0015] It should be further explained 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 the hyperbolic tangent function are and Dynamic association, during the frequency deviation phase, satisfies:

[0016]

[0017] During the frequency recovery phase, the following conditions are met:

[0018]

[0019] Where, , , 、 are preset adjustment factors, all of which are positive numbers;

[0020] Initial setting of virtual inertia control parameters for wind turbine system;

[0021] Initial setting of virtual droop control parameters for the wind turbine system;

[0022] is the maximum frequency deviation allowed by the system.

[0023] It should be further explained that when the grid frequency change rate When <0, the wind turbine is in the frequency deviation stage;

[0024] When the grid frequency change rate When >0, the wind turbine is in the frequency recovery stage.

[0025] It should be further explained that in step S2, the energy storage virtual inertia coefficient satisfy:

[0026]

[0027] Where, is the current available power of the energy storage system, and energy storage system power limit determination;

[0028] To prevent division by zero constant;

[0029] is the upper limit of the virtual inertia coefficient of energy storage;

[0030] Energy storage droop coefficient Through the S-type function and Dynamic association, in the charging phase, i.e. When >0, it satisfies:

[0031]

[0032] During the discharge phase, <0, satisfy:

[0033]

[0034] are preset energy storage state of charge thresholds, satisfying ; , , are preset maximum values.

[0035] is a preset S-shaped function slope coefficient.

[0036] Further need to explain is, in step S3, the device safety threshold includes the energy storage system safety threshold and the wind turbine safety threshold, the energy storage system safety threshold includes:

[0037]

[0038]

[0039] The wind turbine safety threshold includes:

[0040]

[0041] are system allowed upper and lower energy storage state of charge limits; ,

[0042] is the energy storage rated power;

[0043] is the minimum up and down adjustable power change amount allowed by the wind turbine;

[0044] is the maximum up and down adjustable power change amount allowed by the wind turbine;

[0045] , are system allowed upper and lower wind turbine speed limits;

[0046] , are system allowed upper and lower pitch angle limits;

[0047] is the maximum pitch frequency modulation power allowed by the system.

[0048] Further need to explain is, in step S3, the objective function of the improved particle swarm algorithm is:

[0049]

[0050] wherein:​​​

[0051]

[0052]

[0053]

[0054] Where, for The initial value of

[0055] 、 、 is the weight coefficient, which is dynamically adjusted according to the frequency modulation stage, where the frequency deviation stage: , frequency recovery phase: .

[0056] It should be further explained that, in step S3, the optimization process of the improved particle swarm algorithm includes:

[0057] The particle velocity is updated using dynamic inertia weight, which decreases nonlinearly according to the number of iterations.

[0058] Dynamically adjust learning factors to balance global search and local development;

[0059] When a particle fails to update its individual optimal solution within a preset number of iterations, random perturbations are added to the particle position.

[0060] It should be further explained that the dynamic inertia weight The update formula is:

[0061]

[0062] Dynamically adjust learning factors The formula is:

[0063]

[0064] Where, 、 The preset upper and lower limits of dynamic inertia weight;

[0065] 、 are the initial and final values ​​of the individual learning factor respectively;

[0066] is the maximum number of iterations;

[0067] is the current iteration number.

[0068] It should be further explained that the expression of random perturbation is:

[0069]

[0070] Where, is the current iteration number;

[0071] is the variation intensity;

[0072] is a standard normally distributed random number.

[0073] It should be further explained that, in step S4, the frequency modulation power of the wind turbine generator set satisfies:

[0074] ;

[0075] Energy storage frequency modulation power meets the following requirements:

[0076] .

[0077] It should be further explained that the frequency regulation power of a wind turbine is composed of the rotor kinetic energy release power and the pitch regulation power, which satisfies:

[0078]

[0079] in,

[0080]

[0081]

[0082] Where, is the moment of inertia of the wind turbine;

[0083] is the load shedding rate;

[0084] Tracking power for maximum power point.

[0085] In a second aspect, the present application provides a frequency regulation system for a wind-storage combined system based on parameter setting and adaptive optimization, which is used to implement the above-mentioned frequency regulation method for the wind-storage combined system, including:

[0086] Data acquisition module, used to obtain wind-storage combined system data in real time;

[0087] Parameter setting module, used to set the initial value and adjustment range of frequency regulation parameters according to the wind-storage combined system data;

[0088] 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 Improved particle swarm optimization algorithm with frequency modulation parameters as optimization variable input, combined with 、 、 and device safety thresholds to minimize frequency deviation, SOC Fluctuation and frequency modulation power adjustment are the targets to generate frequency modulation parameters, and The optimal value of

[0089] Instruction generation and control module, used to generate and control the 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;

[0090] Monitoring feedback module, used for 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.

[0091] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the above-mentioned frequency regulation method for a wind-storage combined system based on parameter setting and adaptive optimization when executing the computer program.

[0092] In a fourth aspect, the present application provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned wind-storage combined system frequency regulation method based on parameter setting and adaptive optimization are implemented.

[0093] It can be seen from the above technical solutions that this application has the following advantages:

[0094] 1. This application collects the grid frequency deviation, frequency deviation change rate, SOC , wind speed, rotor speed, pitch angle and other multi-dimensional dynamic parameters, combined with the improved particle swarm algorithm, jointly optimize the frequency modulation parameters and output distribution weights to form 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 status of the system, effectively improving the adaptability of the wind-storage combined system to complex working conditions.

[0095] 2. This application incorporates the initial value setting and adjustment range constraints of the frequency regulation parameters into the optimization model, dynamically adjusts the virtual inertia coefficient and droop coefficient to match the requirements of different frequency regulation stages, and uses the safety threshold as a hard constraint to ensure that the energy storage charge state and wind turbine speed are always within the safe range during the frequency regulation process.

[0096] 3. This application uses the wind turbine output distribution weight and energy storage weight as optimization variables, and optimizes them synchronously with the frequency regulation parameters to establish frequency deviation, SOC The multi-objective function of fluctuation and frequency modulation power regulation is realized by improving the particle swarm algorithm to achieve dynamic balance among the three, so that the absolute value of the system frequency deviation is as small as possible during the frequency modulation process, avoiding overcharging and discharging of the energy storage system, and making the energy storage system SOC Keep it within a reasonable range and reduce the adjustment amount of wind power storage power, reduce the impact on wind power generation efficiency and energy storage system life, and avoid the single reliance on energy storage caused by SOC It can solve the problem of rapid over-limit and prevent speed instability caused by excessive use of rotor kinetic energy, and can stably improve the power coordination efficiency of wind storage frequency regulation.

[0097] 4. This application adopts a dynamic inertia weight and learning factor adjustment mechanism to maintain high exploration capabilities in the early stages of optimization and enhance local development accuracy in the later stages. It introduces a random perturbation strategy to avoid premature convergence. Compared with the traditional particle swarm algorithm, it can steadily 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 nonlinear constraints. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for the description. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0099] Figure 1 This is a flow chart of a frequency regulation method for a wind-storage combined system based on parameter setting and adaptive optimization in one embodiment of the present application.

[0100] Figure 2 This is a flowchart of an improved particle swarm algorithm in one embodiment of the present application.

[0101] Figure 3 It is a schematic block diagram of a frequency regulation system of a wind-storage combined system based on parameter setting and adaptive optimization in one embodiment of the present application.

[0102] Figure 4 It is a schematic diagram of the hardware structure of an electronic device in one embodiment of the present application. DETAILED DESCRIPTION

[0103] In order to make the application objectives, features, and advantages of this application more obvious and easy to understand, the technical solutions protected by this application will be clearly and completely described below using specific embodiments and drawings. Obviously, the embodiments described below are only part of the embodiments of this application, not all of them. Based on the embodiments in this patent, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this patent.

[0104] The wind-storage combined system frequency regulation method based on parameter setting and adaptive optimization involved in this application is mainly aimed at the field of wind-storage combined system frequency regulation technology, through real-time collection of grid frequency deviation, frequency deviation change rate, SOC , wind speed, rotor speed, pitch angle and other multi-dimensional dynamic parameters, combined with the improved particle swarm 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, and being able to dynamically adjust the control strategy according to the real-time status of the system, effectively improving the adaptability of the wind-storage joint system to complex working conditions; integrating the initial value setting and adjustment range constraints of the frequency modulation parameters into the optimization model, and matching the requirements of different frequency modulation stages through dynamic adjustment of the virtual inertia coefficient and the droop coefficient, while taking the safety threshold as a hard constraint to ensure that the energy storage charge state and wind turbine speed are always in a safe range during the frequency modulation process; taking the wind turbine output distribution weight and energy storage weight as optimization variables, and optimizing them synchronously with the frequency modulation parameters, establishing the frequency deviation, SOC The multi-objective function of power regulation of frequency modulation and fluctuation is realized by improving the particle swarm algorithm to achieve dynamic balance among the three, which not only avoids the single reliance on energy storage. SOC The rapid over-limit problem can be solved, and the speed instability caused by excessive use of rotor kinetic energy can be prevented, which can stably improve the power synergy efficiency of wind-storage frequency regulation. The dynamic inertia weight and learning factor adjustment mechanism is adopted to maintain high exploration ability in the early stage of optimization and enhance local development accuracy in the later stage. The random perturbation strategy is introduced to avoid premature convergence. Compared with the traditional particle swarm algorithm, it can stably improve the convergence speed and the probability of obtaining the global optimal solution in the wind-storage frequency regulation scenario, ensuring the rapid generation of feasible solutions under complex nonlinear constraints.

[0105] The frequency regulation method of a wind-storage combined system based on parameter setting and adaptive optimization involved in this application is mainly aimed at the technical problems that the existing frequency regulation method of a wind-storage combined system adopts fixed parameters and static output distribution strategy, resulting in a mismatch between frequency regulation response and dynamic operating conditions, and lacks a closed-loop optimization mechanism, making it difficult to take into account both frequency stability and equipment safety.

[0106] The following describes in detail the frequency regulation method for a wind-storage integrated system based on parameter tuning and adaptive optimization involved in this application. Specific details, such as specific system structures and technologies, are provided for illustrative purposes, not for limitation, to facilitate a thorough understanding of the embodiments of this application. However, it should be apparent to those skilled in the art that this application may also be implemented in other embodiments without these specific details.

[0107] In the frequency regulation method for a wind-storage integrated system based on parameter tuning and adaptive optimization involved in this application, the term "comprising" is used to indicate the presence of the described features, entities, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, entities, steps, operations, elements, components, and / or their collections. The terms "including," "comprising," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0108] To facilitate the clear description of the technical solutions of this application, the words "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that the words "first" and "second" do not limit the quantity or order of execution, and the words "first" and "second" do not necessarily mean different.

[0109] The phrases "one embodiment" or "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 the application. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in other embodiments," etc. that appear in different places in this application do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized.

[0110] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0111] The frequency regulation method of the wind-storage combined system based on parameter setting and adaptive optimization provided in the embodiment of the present application is executed by a computer device. Accordingly, the frequency regulation system of the wind-storage combined system based on parameter setting and adaptive optimization runs in the computer device.

[0112] Figure 1 This is a flow chart of a frequency regulation method for a wind-storage combined system based on parameter setting and adaptive optimization according to an embodiment of the present application. Figure 1 The execution subject can be a wind storage combined system frequency regulation system based on parameter setting and adaptive optimization. According to different needs, the order of steps in the flowchart can be changed, and some can be omitted.

[0113] As shown in the figure, the wind storage combined system frequency regulation method based on parameter setting and adaptive optimization includes: Figure 1

[0114] Step S1, real-time acquisition of wind storage combined system data, including grid frequency deviation , frequency deviation rate , state of charge of energy storage system , wind speed , rotor speed of wind turbine and pitch angle .

[0115] In the process of wind storage combined frequency regulation system, the principle of system equation set is mainly based on the dynamic characteristics of power system, the operating characteristics of wind turbine and the control characteristics of energy storage system, which is usually described by the following equations:

[0116]

[0117]

[0118] Among them, M is the equivalent inertia constant of the system, which represents the inertia response ability of the system to frequency change; D is is the damping coefficient of the system, which represents the damping effect of the system to frequency change; Δ f is the frequency deviation, that is, the difference between actual frequency and rated frequency; Δ P m is the mechanical power change, which represents the mechanical power adjustment amount of the generator set; Δ P e is the electrical power change; Δ P g , Δ P bess , Δ P wind are the output power change amounts of traditional generator set, energy storage device and wind farm respectively; Δ P L is the disturbance power in the power system, including two aspects, one is caused by the difference of load, and the other is caused by the randomness of wind power generation. The wind storage combined frequency regulation system equation set is obtained by Laplace transformation of the above formula:

[0119]

[0120] Among them,​H is the inertia time constant of the power grid.

[0121] Real-time acquisition of wind-storage combined system data comprehensively covers key information such as grid frequency deviation, frequency deviation change rate, energy storage system charge state, wind speed, wind turbine rotor speed and pitch angle. This provides accurate, timely and comprehensive data support for the subsequent precise setting of frequency regulation parameters, optimization of frequency regulation strategies and generation of reliable frequency regulation instructions, and is the basis and prerequisite for achieving efficient frequency regulation.

[0122] Step 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 .

[0123] The initial value and adjustment range of the frequency modulation parameters are set according to the acquired data, which limits the reasonable parameter space for the subsequent improvement of the particle swarm algorithm operation, so that the optimization process of the frequency modulation parameters has a clear direction and boundary, ensuring that the optimization results not only meet the actual operation requirements of the system, but also are feasible and effective.

[0124] In some specific embodiments, the virtual inertia coefficient of the wind turbine is and wind turbine droop coefficient The initial value and adjustment range of the hyperbolic tangent function are and Dynamic association, during the frequency deviation phase, satisfies:

[0125]

[0126] During the frequency recovery phase, the following conditions are met:

[0127]

[0128] Where, , , 、 are preset adjustment factors, all of which are positive numbers;

[0129] Initial setting of virtual inertia control parameters for wind turbine system;

[0130] Initial setting of virtual droop control parameters for the wind turbine system;

[0131] is the maximum frequency deviation allowed by the system;

[0132] The minimum power variation allowed for wind turbines to be adjusted up or down;

[0133] The maximum power change allowed for a wind turbine generator system.

[0134] Affected by wind speed, wind turbine speed constantly fluctuates. Using a large control coefficient can easily lead to unit instability, while a small control coefficient underutilizes the unit's frequency regulation capabilities. Therefore, a fixed frequency regulation coefficient cannot effectively adapt to wind speed fluctuations. Furthermore, to further improve system frequency stability, the proportional differential coefficient should be set differently at different stages of frequency dynamics. The speed of a doubly-fed induction wind turbine varies continuously with wind speed, which is uncertain. Therefore, the frequency regulation energy provided by the wind turbine varies during frequency fluctuations. Therefore, the virtual inertia and droop coefficient should be adaptively adjusted at different wind speeds, considering the extent to which the wind turbine can participate in system frequency regulation. This allows for adaptation to varying wind speeds. Specifically, a larger frequency regulation coefficient should be set at high wind speeds to accommodate the corresponding frequency regulation tasks. At lower wind speeds, the frequency regulation coefficient should be kept relatively low to ensure safe and stable operation of the wind turbine itself.

[0135] The hyperbolic tangent function is used to dynamically associate the initial value and adjustment range of the wind turbine's virtual inertia coefficient and wind turbine droop coefficient with the frequency deviation, satisfying specific formulas in the frequency deviation and recovery stages respectively. This allows the wind turbine to flexibly adjust its own characteristics according to frequency changes and respond to frequency regulation requirements more accurately, thereby enhancing the adaptability and effectiveness of the wind turbine in participating in frequency regulation at different frequency stages, thereby improving the overall frequency regulation performance of the wind-storage combined system.

[0136] In some specific embodiments, when the grid frequency change rate When <0, the wind turbine is in the frequency deviation stage;

[0137] When the grid frequency change rate When >0, the wind turbine is in the frequency recovery stage.

[0138] Based on the positive or negative value of the grid frequency change rate, it is clearly defined whether the wind turbine is in the frequency deviation stage or the frequency recovery stage, which provides a clear and reliable judgment standard for the dynamic adjustment of the wind turbine frequency regulation parameters, so that the wind turbine frequency regulation control can more accurately match different frequency operating conditions, and enhance the pertinence and rationality of the frequency regulation control strategy.

[0139] In some embodiments, the energy storage virtual inertia coefficient satisfy:

[0140]

[0141] Where, is the current available power of the energy storage system, and energy storage system power limit determination;

[0142] To prevent division by zero constant;

[0143] is the upper limit of the virtual inertia coefficient of energy storage;

[0144] Energy storage droop coefficient Through the S-type function and Dynamic association, in the charging phase, i.e. When >0, it satisfies:

[0145]

[0146] During the discharge phase, <0, satisfy:

[0147]

[0148] Where, 、 、 、 All are preset energy storage charge state thresholds, meeting ;

[0149] For preset Maximum value;

[0150] is the preset S-shaped function slope coefficient.

[0151] The energy storage system stabilizes the frequency by directly adjusting the power balance of the grid through rapid charging and discharging. When the grid frequency drops (load increases), the energy storage system discharges, injecting active power into the grid to make up for the power shortage; when the grid frequency rises (load decreases), the energy storage system charges and absorbs excess power from the grid.

[0152] In wind-storage coordinated frequency regulation systems, the “S” function is used to calculate the state of charge ( SOC ) Adaptive adjustment of energy storage output parameters can effectively improve the performance and frequency regulation effect of energy storage systems. The "S" function usually has an S-shaped curve feature, which is characterized by slow changes when the input value is small, rapid changes in the middle area, and flattening when the input value is large. SOC In the scenario of adjusting the energy storage output parameters, the “S” function can be used to smoothly adjust the charge and discharge power of the energy storage system to avoid SOC Overcharge and discharge at extreme values ​​to protect battery life.

[0153] Step S3: using the adjustment range of the frequency modulation parameters as a constraint, assign weights to the wind turbine output. , Energy storage output allocation weight Improved particle swarm optimization algorithm with frequency modulation parameters as optimization variable input, combined with 、 、 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 the improved particle swarm algorithm is as follows Figure 2 shown.

[0154] Taking the adjustment range of frequency regulation parameters as a constraint, the wind turbine output distribution weight, energy storage output distribution weight and frequency regulation parameters are used as optimization variables to input into the improved particle swarm algorithm. Combined with the equipment safety threshold, optimization is performed with specific goals. This algorithm can comprehensively consider various constraints and performance requirements of system operation, accurately find the optimal value of frequency regulation parameters, and achieve comprehensive optimization of the frequency regulation performance of the wind-storage combined system.

[0155] In some specific embodiments, the equipment safety threshold includes an energy storage system safety threshold and a wind turbine safety threshold. The energy storage system safety threshold includes:

[0156]

[0157] Wind turbine safety thresholds include:

[0158]

[0159] Where, 、 The upper and lower limits of the energy storage charge state allowed by the system;

[0160] is the energy storage rated power;

[0161] The minimum power variation allowed for wind turbines to be adjusted up or down;

[0162] The maximum power change allowed for wind turbines;

[0163] 、 The upper and lower limits of wind turbine speed allowed by the system;

[0164] 、 The upper and lower limits of the pitch angle allowed by the system;

[0165] It is the maximum power of pitch frequency modulation allowed by the system.

[0166] Set safety thresholds for the energy storage system and wind turbines, including upper and lower limits for energy storage state of charge, energy storage rated power, upper and lower limits for wind turbine speed, upper and lower limits for pitch angle, and maximum power for variable pitch frequency modulation. Limit the equipment from multiple key operating parameters to avoid damage to the equipment during frequency modulation due to parameters exceeding the safety range, ensuring long-term, stable, and reliable operation of the wind-storage combined system.

[0167] In some specific embodiments, the objective function of the improved particle swarm optimization algorithm is:

[0168]

[0169] in:

[0170]

[0171]

[0172]

[0173] Where, for The initial value of

[0174] 、 、 is the weight coefficient, which is dynamically adjusted according to the frequency modulation stage, where the frequency deviation stage: , frequency recovery phase: .

[0175] To minimize frequency deviation, SOC The objective function of the improved particle swarm algorithm is constructed with fluctuation and frequency regulation power regulation as the target, and the weight coefficient is dynamically adjusted according to the frequency regulation stage. It can comprehensively weigh multiple factors such as frequency stability, energy storage state stability and power regulation, and comprehensively optimize the frequency regulation parameters, thereby significantly improving the overall performance and comprehensive benefits of the frequency regulation of the wind-storage combined system.

[0176] In some specific embodiments, the optimization process of improving the particle swarm optimization algorithm includes:

[0177] The particle velocity is updated using dynamic inertia weight, which decreases nonlinearly according to the number of iterations.

[0178] Dynamically adjust learning factors to balance global search and local development;

[0179] When a particle fails to update its individual optimal solution within a preset number of iterations, random perturbations are added to the particle position.

[0180] The improved particle swarm algorithm uses a dynamic inertia weight to update the particle velocity, dynamically adjusts the learning factor, and adds a random disturbance optimization process when the particle is not updated individual optimal solution, effectively balances the global search ability and local development ability of the algorithm, avoids the algorithm falling into a local optimal solution, improves the search efficiency of the algorithm, makes it faster and more accurate to find the optimal value of the frequency modulation parameter, and further improves the frequency modulation effect of the wind storage combined system.

[0181] In some embodiments, the dynamic inertia weight is updated by the following formula:

[0182]

[0183] The formula for dynamically adjusting the learning factor is as follows:

[0184]

[0185] In the formula, , are the upper and lower limits of the preset dynamic inertia weight;

[0186] , are the initial value and the terminal value of the individual learning factor, respectively;

[0187] is the maximum number of iterations;

[0188] is the current number of iterations.

[0189] The update formula of the dynamic inertia weight and the learning factor is quantitatively adjusted according to the preset upper and lower limits, the maximum number of iterations, and the current number of iterations, which provides a scientific and accurate quantitative basis for the algorithm to adaptively adjust the search strategy during the iteration process, so that the algorithm can better adapt to different search stages, and further enhance the optimization ability of the algorithm in finding the optimal value of the frequency modulation parameter.

[0190] In some embodiments, the expression of the random disturbance is as follows:

[0191]

[0192] In the formula, is the current number of iterations;

[0193] is the mutation strength;

[0194] is a standard normal distribution random number.

[0195] The random perturbation expression introduces standard normally distributed random numbers according to the current number of iterations and mutation intensity, which adds randomness to the particle position update, breaking the dilemma that the algorithm may fall into local optimality. It helps the algorithm to jump out of the local optimal area and explore a broader solution space, thereby finding better frequency regulation parameters and significantly improving the frequency regulation performance of the wind-storage combined system.

[0196] In some specific embodiments, the position and velocity update formula of each particle is:

[0197]

[0198] in, is the velocity of the i-th particle;

[0199] is the position of the i-th particle;

[0200] is the inertia weight;

[0201] and All are learning factors;

[0202] and is a random number between [0,1];

[0203] is the individual optimal position of the i-th particle;

[0204] is the global optimal position.

[0205] Step 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:

[0206]

[0207] Where, Frequency modulation power for wind turbines;

[0208] Frequency modulation power for energy storage system.

[0209] Generate joint frequency regulation instructions based on the optimal values ​​of the frequency regulation parameters, and clearly control the output frequency regulation power of the wind turbines and energy storage systems, so that the wind turbines and energy storage systems can work together according to the optimized strategy, accurately output power that meets the frequency regulation requirements, and achieve rapid and effective adjustment of grid frequency deviations to ensure grid frequency stability.

[0210] In some specific embodiments, the frequency modulation power of the wind turbine generator set satisfies:

[0211] ;

[0212] Energy storage frequency modulation power meets the following requirements:

[0213] .

[0214] The expressions for wind turbine frequency regulation power and energy storage frequency regulation power are given, providing a specific and clear power control method for wind turbines and energy storage systems to participate in frequency regulation. This provides a clear basis for power output of the energy storage system during the wind-storage combined frequency regulation process, ensuring that the energy storage system can accurately and efficiently play its frequency regulation role, and enhancing the coordination and effectiveness of the wind-storage combined system frequency regulation.

[0215] In some specific embodiments, the frequency regulation power of the wind turbine generator set is composed of the rotor kinetic energy release power and the pitch regulation power, which satisfies:

[0216]

[0217] in,

[0218]

[0219]

[0220] Where, is the moment of inertia of the wind turbine;

[0221] is the load shedding rate;

[0222] Tracking power for maximum power point.

[0223] It is clear that the frequency regulation power of wind turbines is composed of rotor kinetic energy release power and variable pitch regulation power. This power composition method enables wind turbines to fully utilize their own kinetic energy reserves and pitch regulation capabilities during frequency regulation, realize the synergistic effect of multiple frequency regulation methods, increase the flexibility and diversity of frequency regulation means, and improve the effect and reliability of wind turbine frequency regulation.

[0224] Step S5, real-time monitoring of the frequency modulated 、 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.

[0225] Real-time monitoring of relevant parameters after frequency modulation and feedback to the optimization link to update the optimal value of frequency modulation parameters for the next cycle and and the optimal value, forming a closed-loop control, so that the system can adjust the control strategy in time according to the actual frequency modulation effect, continuously optimize the frequency modulation performance, adapt to the dynamic changes of the operation condition of the power grid, and continuously guarantee the efficiency and stability of the frequency modulation of the wind storage combined system.

[0226] In one specific embodiment, the wind storage combined system frequency modulation method based on parameter setting and adaptive optimization comprises:

[0227] Step S1, real-time acquisition of wind storage combined system data, including power grid frequency deviation , frequency deviation change rate , state of charge of energy storage system , wind speed , rotor speed of wind turbine and pitch angle ;

[0228] Step S2, setting initial values and adjustment ranges of frequency modulation parameters according to wind storage combined system data, the frequency modulation parameters including wind turbine virtual inertia coefficient , wind turbine droop coefficient , energy storage virtual inertia coefficient and energy storage droop coefficient ;

[0229] Wherein, the initial values and adjustment ranges of wind turbine virtual inertia coefficient and wind turbine droop coefficient are dynamically associated with hyperbolic tangent function and and , which stipulate that when the power grid frequency change rate <0, the wind turbine is in the frequency deviation stage, and when the power grid frequency change rate >0, the wind turbine is in the frequency recovery stage.

[0230] In the frequency deviation stage, it is satisfied that:

[0231]

[0232] In the frequency recovery stage, it is satisfied that:

[0233]

[0234] In the formula, , , , is a preset adjustment factor, =1.5, =2.0;

[0235] is the initial virtual inertia control parameter of the wind turbine system set initially, =8;

[0236] The virtual droop control parameters initially set for the wind turbine system are: =5;

[0237] is the maximum frequency deviation allowed by the system;

[0238] When the grid frequency change rate When <0, the wind turbine is in the frequency deviation stage;

[0239] When the grid frequency change rate When >0, the wind turbine is in the frequency recovery stage;

[0240] Energy storage virtual inertia coefficient satisfy:

[0241]

[0242] Where, is the current available power of the energy storage system, and energy storage system power limit determination;

[0243] To prevent division by zero constant, =0.001s;

[0244] is the upper limit of the virtual inertia coefficient of energy storage, =10;

[0245] Energy storage droop coefficient Through the S-type function and Dynamic association, in the charging phase, i.e. When >0, it satisfies:

[0246]

[0247] During the discharge phase, <0, satisfy:

[0248]

[0249] Where, 、 、 、 These are all preset energy storage state of charge thresholds. =0.2, =0.5, =0.7, =0.9;

[0250] For preset Maximum value;

[0251] is the preset S-type function slope coefficient, =10;

[0252] Step S3: using the adjustment range of the frequency modulation parameters as a constraint, assign weights to the wind turbine output. , Energy storage output allocation weight Improved particle swarm optimization algorithm with frequency modulation parameters as optimization variable input, combined with 、 、 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

[0253] The equipment safety thresholds include the energy storage system safety thresholds and the wind turbine safety thresholds. The energy storage system safety thresholds include:

[0254]

[0255] Wind turbine safety thresholds include:

[0256]

[0257] Where, 、 The upper and lower limits of the energy storage charge state allowed by the system;

[0258] is the energy storage rated power, =1MW;

[0259] The minimum power variation allowed for wind turbines to be adjusted up or down;

[0260] The maximum power change allowed for wind turbines;

[0261] 、 The upper and lower limits of the wind turbine speed allowed by the system, =0.7*rated speed, =1.2*rated speed;

[0262] 、 The upper and lower limits of the pitch angle allowed by the system, for example =0°, =90°;

[0263] is the maximum power of variable pitch frequency modulation allowed by the system, =0.2MW;

[0264] The objective function of the improved particle swarm optimization algorithm is:

[0265]

[0266] in:

[0267]

[0268]

[0269]

[0270] Where, for The initial value of

[0271] 、 、 is the weight coefficient, which is dynamically adjusted according to the frequency modulation stage, where the frequency deviation stage: =0.6, =0.3, =0.1, frequency recovery stage: =0.3, =0.6, =0.1;

[0272] The optimization process of the improved particle swarm algorithm includes:

[0273] Use dynamic inertia weight to update particle velocity. Dynamic inertia weight decreases nonlinearly according to the number of iterations. The update formula is:

[0274] ;

[0275] Dynamically adjust the learning factor, balance global search and local development, and dynamically adjust the learning factor The formula is:

[0276] ;

[0277] When a particle fails to update its individual optimal solution within the preset number of iterations, a random perturbation is added to the particle position. The expression of the random perturbation is:

[0278]

[0279] Where, 、 is the preset upper and lower limits of dynamic inertia weight, =0.9, =0.4;

[0280] 、 are the initial and final values ​​of the individual learning factor, =2.0, =1.2;

[0281] is the maximum number of iterations, =100;

[0282] is the current iteration number;

[0283] is the variation intensity, =0.1;

[0284] is a standard normal distribution random number;

[0285] Step 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:

[0286]

[0287] Where, Frequency modulation power for wind turbines;

[0288] Where, The frequency modulation power of the wind turbine generator set must meet the following requirements:

[0289] ;

[0290] Frequency modulation power for energy storage system, satisfying:

[0291] ;

[0292] At the same time, the frequency regulation power of the wind turbine is composed of the rotor kinetic energy release power and the variable pitch regulation power, which meets the following requirements:

[0293]

[0294] in,

[0295]

[0296]

[0297] Where, for the wind turbine;

[0298] for the load shedding rate;

[0299] for the maximum power point tracking power;

[0300] Step S5, real-time monitoring of the frequency after the frequency modulation , and , and feeding back to step S3 to update the optimal values of the frequency modulation parameters and of the next cycle, forming a closed-loop control.

[0301] The following is an embodiment of the wind storage combined system frequency modulation system based on parameter setting and adaptive optimization provided by the embodiment of the present disclosure, which belongs to the same inventive concept as the wind storage combined system frequency modulation method based on parameter setting and adaptive optimization described above. Details not described in the embodiment of the wind storage combined system frequency modulation system based on parameter setting and adaptive optimization can be referred to the embodiment of the wind storage combined system frequency modulation method based on parameter setting and adaptive optimization described above.

[0302] A mobile terminal implementing various embodiments of the present application will now be described with reference to the accompanying drawings. In the following description, the suffixes "module" and "unit" used for components are merely intended for brevity of description and do not have specific meanings or roles distinct from each other.

[0303] As shown in Figure 3 , the wind storage combined system frequency modulation system based on parameter setting and adaptive optimization includes:

[0304] a data acquisition module for acquiring wind storage combined system data in real time;

[0305] a parameter setting module for setting initial values and adjustment ranges of frequency modulation parameters according to wind storage combined system data;

[0306] an optimization calculation module for inputting an improved particle swarm algorithm with wind turbine output distribution weight , energy storage output distribution weight and frequency modulation parameters as optimization variables, combining , , and equipment safety threshold values, and taking minimizing frequency deviation SOC , fluctuation and frequency modulation power adjustment amount as the target to generate frequency modulation parameters and The optimal value of

[0307] Instruction generation and control module, used to generate and control the 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;

[0308] Monitoring feedback module, used for 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.

[0309] The wind-storage combined system of this embodiment is used to implement a frequency regulation method for the wind-storage combined system based on parameter setting and adaptive optimization, and the steps include:

[0310] 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 ;

[0311] S2. Set the initial value and adjustment range of the frequency regulation parameters based on the wind-storage combined system data. The frequency regulation parameters include the virtual inertia coefficient of the wind turbine. , wind turbine droop coefficient , energy storage virtual inertia coefficient and energy storage droop coefficient ;

[0312] S3. Assign weights to wind turbine outputs based on the adjustment range of frequency modulation parameters. , Energy storage output allocation weight Improved particle swarm optimization algorithm with frequency modulation parameters as optimization variable input, combined with 、 、 and device safety thresholds to minimize frequency deviation, SOC Fluctuation and frequency modulation power adjustment are the targets to generate frequency modulation parameters, and The optimal value of

[0313] 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:

[0314]

[0315] Where, Frequency modulation power for wind turbines;

[0316] Frequency modulation power for energy storage systems;

[0317] 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.

[0318] The present application also provides an electronic device for implementing various embodiments of the present application. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0319] Those skilled in the art will understand that the electronic device structure involved in the embodiments of the present application does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0320] Figure 4 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present application.

[0321] The electronic device includes, but is not limited to, components such as a processor and a memory. Those skilled in the art will appreciate that the electronic device structures described in the embodiments of the present application do not limit the electronic device, and the electronic device may include more or fewer components than shown, or may combine certain components or arrange the components differently.

[0322] In the embodiments of the present application, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices 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 and / or claimed herein.

[0323] In the embodiment of the present application, the processor can 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 perform the functions described herein. In some cases, such an embodiment can be implemented in a controller. For software implementation, an embodiment such as a process or function can be implemented with a separate software module that allows the execution of at least one function or operation. The software code can be implemented by a software application (or program) written in any appropriate programming language, and the software code can be stored in a memory and executed by a controller.

[0324] In addition, the electronic device includes some functional modules not shown, which will not be described here.

[0325] Those skilled in the art will appreciate that various aspects of the electronic device provided herein may be implemented as a system, method, or program product. Therefore, various aspects of the present disclosure may be implemented in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits," "modules," or "systems."

[0326] The present application also provides a storage medium storing a program product capable of implementing a frequency regulation method for a wind-storage integrated system based on parameter tuning and adaptive optimization. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product comprising program code. When the program product is executed on a terminal device, the program code is configured to cause the terminal device to execute the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of the present disclosure.

[0327] The storage medium can be any combination of one or more readable media. The readable medium 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, device or component, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0328] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to 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 regulation parameters based on the wind-storage combined system data. The frequency regulation 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. Assign weights to wind turbine outputs based on the adjustment range of frequency modulation parameters. , Energy storage output allocation weight Improved particle swarm optimization algorithm with frequency modulation parameters as optimization variable input, combined with 、 、 and device safety thresholds to minimize frequency deviation, SOC Fluctuation and frequency modulation power adjustment are the 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: Where, Frequency modulation power for wind turbines; Frequency modulation 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 the hyperbolic tangent function are and Dynamic association, during the frequency deviation phase, satisfies: Where, , is the preset adjustment factor, which is a positive number; Initial setting of virtual inertia control parameters for wind turbine system; Initial setting of virtual droop control parameters 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: Where, , is the preset adjustment factor, which is a positive number; Initial setting of virtual inertia control parameters for wind turbine system; Initial setting of virtual droop control parameters 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: Where, is the current available power of the energy storage system, 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, in the charging phase, i.e. When >0, it satisfies: During the discharge phase, <0, satisfy: Where, 、 、 、 All are preset energy storage charge state thresholds, meeting ; For preset Maximum value; is the preset S-shaped 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: Where, 、 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 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 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 optimization algorithm is: in: Where, 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, random perturbations are 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: ； 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 regulation method for a wind-storage combined system according to any one of claims 1 to 9, comprising: Data acquisition module, used to obtain wind-storage combined system data in real time; Parameter setting module, used to set the initial value and adjustment range of 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 Improved particle swarm optimization algorithm with frequency modulation parameters as optimization variable input, combined with 、 、 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 the 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 for 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.

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