Active regulation power quantitative evaluation method and device for adaptive frequency modulation of wind storage system

Abstract

The invention relates to the technical field of participation of a wind storage system in power grid frequency modulation, and particularly provides an active regulation power quantitative evaluation method and device for self-adaptive frequency modulation of a wind storage system, and the method comprises the steps: constructing an energy storage system frequency response model, and calculating the active power of energy storage participating in frequency modulation; constructing a wind turbine generator wind speed fluctuation model, simulating the wind speed fluctuation condition in the same wind power plant according to the wind speed probability distribution, and generating a section of random wind speed disturbance; constructing a frequency response model of the wind turbine generator, and calculating active power participating in frequency modulation; a wind storage system adaptive distribution model is constructed based on a depth deterministic strategy gradient algorithm, and optimal distribution of a virtual droop control coefficient and a virtual inertia control coefficient of the wind storage system is realized; and calculating the active regulation power of the wind storage system after self-adaptive frequency modulation. And calculating the adjustable active power of the wind turbine generator and the energy storage equipment participating in the frequency modulation strategy when the optimal frequency modulation effect is achieved and the service life of the equipment is balanced.

Description

Technical Field

[0001] The present invention relates to the technical field of wind-storage system participating in power grid frequency regulation, and in particular to a method and device for quantitatively evaluating active regulation power of wind-storage system adaptive frequency regulation. Background Art

[0002] Due to its natural characteristics, such as the instability of wind power, wind power generation is discontinuous and unpredictable. When a large amount of wind power is connected to the grid, the inertia of the grid will be reduced, thereby reducing the stability of the grid in the face of frequency fluctuations. In order to enhance the stability of the grid, wind farms must be able to provide inertial response and primary frequency regulation functions to help maintain the frequency stability of the grid.

[0003] After the wind farm is integrated with the energy storage system, the energy storage needs to cooperate with wind power to participate in frequency regulation to improve the frequency regulation effect. While sending or absorbing active power for peak regulation, a certain amount of active power should also be reserved to meet the frequency regulation requirements. In order to make full use of the wind storage system support power as much as possible and avoid resource waste, it is necessary to quantitatively evaluate the frequency regulation capability of the wind storage system. However, under wind speed fluctuations, the strategies and states of wind turbines participating in frequency regulation are not the same, and there is no unified quantitative standard for the power used for frequency regulation. When the energy storage state of charge (SOC) is close to the upper and lower limits, the problem of insufficient energy storage power will occur. If the energy storage is charged and discharged with maximum control, and the active power is insufficient, it may cause a frequency regulation failure, and eventually cause the system frequency deviation to increase. Or the energy storage frequently exceeds the maximum power due to frequency regulation, causing damage to the energy storage equipment. In addition, since the energy storage itself has capacity limitations, it is necessary to reserve a certain amount of active power to meet the frequency regulation requirements when discharging at a low charge state and charging at a high charge state to prevent overcharging or over-discharging.

[0004] In the process of integrating energy storage systems, wind farms lack consideration of the wind-storage system as part of the overall power system; the frequency regulation strategy is relatively simple and fails to fully utilize the adaptive frequency regulation control potential of the wind-storage system, thus failing to accurately quantify the adjustable active power of the wind-storage system. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for quantitatively evaluating the active regulation power of a wind-storage system with adaptive frequency modulation to solve the above-mentioned technical problems.

[0006] In a first aspect, the present invention provides a method for quantitatively evaluating active regulation power of a wind-storage system with adaptive frequency modulation, comprising the following steps: Acquire the charge and discharge state and charge state of the energy storage system, and adjust the first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage participating in frequency modulation according to the charge and discharge state and charge state; According to the power output characteristics of the wind turbine generator set, a second virtual droop control coefficient and a second virtual inertia control coefficient of the wind turbine generator set participating in frequency regulation are obtained; Constructing an adaptive allocation model for a wind-storage system, and performing optimal weight configuration for a first virtual droop control coefficient and a first virtual inertia control coefficient based on the adaptive allocation model for the wind-storage system, and calculating corresponding first virtual droop control frequency modulation power and first virtual inertia control frequency modulation power; Based on the weighted first virtual droop control frequency modulation power, the second virtual droop control coefficient is updated and the second virtual droop control frequency modulation power is calculated; based on the weighted first virtual inertia control frequency modulation power, the second virtual inertia control coefficient is updated and the second virtual inertia control frequency modulation power is calculated; Based on the first virtual droop control frequency modulation power, the first virtual inertia control frequency modulation power, the second virtual droop control frequency modulation power and the second virtual inertia control frequency modulation power, the active regulation power of the wind-storage system after adaptive frequency modulation is calculated.

[0007] In an optional embodiment, the wind storage system adaptive allocation model is constructed based on a deep deterministic policy gradient algorithm, a wind turbine wind speed fluctuation model is constructed based on Weibull distribution, and an intelligent agent is trained under wind turbine wind speed fluctuation based on a deep deterministic policy gradient algorithm to achieve optimal allocation of a first virtual droop control coefficient and a first virtual inertia control coefficient, specifically including: Set initial parameters for the policy network, target policy network, value network, and target value network; Generate simulated wind speed according to Weibull distribution of wind speed and generate continuous disturbance data through wind turbines; The intelligent agent determines the action based on the current system frequency difference and the frequency difference change rate, and the energy storage system executes the corresponding weight configuration of the first virtual droop control coefficient and the virtual inertia control coefficient; After the agent performs an action, it feeds back the current reward and the system state at the next moment, stores the experience data of the current system state, the action taken, the reward obtained, and the next system state in the playback buffer, and extracts samples from it to update the network parameters; Repeat the above steps until the maximum number of steps in a single round is reached; The round iteration is repeated continuously until the preset maximum number of training rounds is reached, and the optimal weight configuration of the first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage system is obtained.

[0008] In an optional embodiment, the reward obtained includes a reward and rewards :

[0009] in, and is the ratio coefficient of the two parts of the reward, is the frequency error of the system, The power frequency regulation control change of the wind storage system, reward Measure the effect of the intelligent agent's actions on the energy storage's participation in frequency regulation, and reward Measure the impact of the long-term power participation of the wind storage system in frequency regulation control on its own service life.

[0010] In an optional implementation, after obtaining the optimal weight configuration, calculating the corresponding first virtual inertia control frequency modulation power and the first virtual droop control frequency modulation power includes:

[0011]

[0012]

[0013] In the formula, is the first virtual inertia control frequency modulation power after optimal weight configuration, is the first virtual droop control frequency modulation power after optimal weight configuration, The active power of energy storage participating in frequency regulation after optimal weight configuration, , is the optimal first virtual inertia weight coefficient, is the optimal first virtual droop weight coefficient, represents the first virtual inertia control coefficient of the energy storage system, represents the first virtual droop control coefficient of the energy storage system, is the frequency change rate, is the frequency deviation.

[0014] In an optional implementation manner, the updated second virtual droop control coefficient and the updated second virtual inertia control coefficient specifically include:

[0015]

[0016] in, and is the adjustable power constraint, is the output power at the MPPT point, is the initial load shedding output power, Release power for the rotor's kinetic inertial response, is the updated second virtual droop control coefficient, is the updated second virtual inertia control coefficient.

[0017] In an optional implementation manner, the second virtual droop control frequency modulation power and the second virtual inertia control frequency modulation power are specifically calculated as: Calculate the active power change of wind turbines through virtual inertia control :

[0018]

[0019]

[0020] in, is the rated power frequency of the power grid, is the rated active power of the wind turbine, is the second virtual inertia control coefficient of the wind turbine, is the active power change value of the wind turbine, is the lower limit of the inertial response power change limit, is the upper limit of the inertial response power change limit, The reference value of the wind turbine active power during the inertial response period is given. It is the active power value when the inertial response frequency modulation frequency starts to change; Calculate the active power change of wind turbines through virtual droop control :

[0021]

[0022]

[0023] in, is the second virtual droop control coefficient of the primary frequency modulation during the frequency drop disturbance or the frequency increase disturbance, is the lower limit of the primary frequency modulation power change limit, is the upper limit of the primary frequency modulation power change limit, is the wind turbine operating frequency change, The deviation value set for the primary FM frequency; is the reference value of the wind turbine active power given value during the primary frequency regulation period, It is the active power value at the moment when the primary frequency modulation frequency starts to change; Calculate the active power of wind turbines participating in frequency regulation:

[0024] in, It is the active power regulation value of wind turbine participating in frequency regulation.

[0025] In an optional implementation, calculating the active regulated power of the wind-storage system after adaptive frequency regulation specifically includes: Calculate the active power regulation of wind turbines after adaptive frequency regulation :

[0026]

[0027] in, Indicates the number of wind turbines in the wind storage system; For the Active power regulation when virtual inertia and virtual droop control of wind turbines participate in frequency regulation; Calculate the active power regulation of the energy storage system after adaptive frequency regulation :

[0028]

[0029] in, Indicates the number of energy storage devices in the wind storage system; For the The virtual inertia and virtual droop control of each battery energy storage device participates in the active power regulation when the frequency is modulated; Active power regulation after adaptive frequency modulation of wind power storage system Calculated as: .

[0030] In an optional implementation, the first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage system with the optimal weight configuration are comprehensively considered to calculate the frequency response model transfer function of the energy storage system:

[0031] in, is the response time constant of the energy storage system, is the transformation parameter in the Laplace transform.

[0032] In an optional implementation, the virtual inertia control of the wind turbine is the rotor kinetic energy control, the virtual droop control of the wind turbine is mainly the pitch angle control, and the wind turbine frequency response model transfer function is calculated by comprehensively updating the second virtual droop control coefficient and the updated second virtual inertia control coefficient:

[0033] in, is the rotor inertia response time constant, is the pitch response time constant.

[0034] In a second aspect, a device for quantitatively evaluating active regulation power of a wind-storage system with adaptive frequency modulation is provided, comprising: An energy storage coefficient acquisition module is used to acquire the charge and discharge state and the state of charge of the energy storage system, and adjust the first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage participating in the frequency modulation according to the charge and discharge state and the state of charge; A wind turbine coefficient acquisition module, which acquires a second virtual droop control coefficient and a second virtual inertia control coefficient of the wind turbine participating in frequency modulation according to the power output characteristics of the wind turbine; The energy storage coefficient weight configuration module constructs an adaptive allocation model of the wind-storage system, and performs optimal weight configuration for the first virtual droop control coefficient and the first virtual inertia control coefficient based on the adaptive allocation model of the wind-storage system, and calculates the corresponding first virtual droop control frequency modulation power and first virtual inertia control frequency modulation power; A fan coefficient update module updates a second virtual droop control coefficient based on the first virtual droop control frequency modulation power after the weight is configured and calculates the second virtual droop control frequency modulation power, and updates a second virtual inertia control coefficient based on the first virtual inertia control frequency modulation power after the weight is configured and calculates the second virtual inertia control frequency modulation power; The active regulating power calculation module calculates the active regulating power of the wind-storage system after adaptive frequency regulation based on the first virtual droop control frequency modulation power, the first virtual inertia control frequency modulation power, the second virtual droop control frequency modulation power and the second virtual inertia control frequency modulation power.

[0035] In an optional embodiment, the wind storage system adaptive allocation model is constructed based on a deep deterministic policy gradient algorithm, a wind turbine wind speed fluctuation model is constructed based on Weibull distribution, and an intelligent agent is trained under wind turbine wind speed fluctuation based on a deep deterministic policy gradient algorithm to achieve optimal allocation of a first virtual droop control coefficient and a first virtual inertia control coefficient, specifically including: Set initial parameters for the policy network, target policy network, value network, and target value network; Generate simulated wind speed according to Weibull distribution of wind speed and generate continuous disturbance data through wind turbines; The intelligent agent determines the action based on the current system frequency difference and the frequency difference change rate, and the energy storage system executes the corresponding weight configuration of the first virtual droop control coefficient and the virtual inertia control coefficient; After the agent performs an action, it feeds back the current reward and the system state at the next moment, stores the experience data of the current system state, the action taken, the reward obtained, and the next system state in the playback buffer, and extracts samples from it to update the network parameters; Repeat the above steps until the maximum number of steps in a single round is reached; The round iteration is repeated continuously until the preset maximum number of training rounds is reached, and the optimal weight configuration of the first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage system is obtained.

[0036] In an optional embodiment, the reward obtained includes a reward and rewards :

[0037] in, and is the ratio coefficient of the two parts of the reward, is the frequency error of the system, The power frequency regulation control change of the wind storage system, reward Measure the effect of the intelligent agent's actions on the energy storage's participation in frequency regulation, and reward Measure the impact of the long-term power participation of the wind storage system in frequency regulation control on its own service life.

[0038] In an optional implementation, after obtaining the optimal weight configuration, calculating the corresponding first virtual inertia control frequency modulation power and the first virtual droop control frequency modulation power includes:

[0039]

[0040]

[0041] In the formula, is the first virtual inertia control frequency modulation power after optimal weight configuration, is the first virtual droop control frequency modulation power after optimal weight configuration, The active power of energy storage participating in frequency regulation after optimal weight configuration, , is the optimal first virtual inertia weight coefficient, is the optimal first virtual droop weight coefficient, represents the first virtual inertia control coefficient of the energy storage system, represents the first virtual droop control coefficient of the energy storage system, is the frequency change rate, is the frequency deviation.

[0042] In an optional implementation manner, the updated second virtual droop control coefficient and the updated second virtual inertia control coefficient specifically include:

[0043]

[0044] in, and is the adjustable power constraint, is the output power at the MPPT point, is the initial load shedding output power, Release power for the rotor's kinetic inertial response, is the updated second virtual droop control coefficient, is the updated second virtual inertia control coefficient.

[0045] In an optional implementation manner, the second virtual droop control frequency modulation power and the second virtual inertia control frequency modulation power are specifically calculated as: Calculate the active power change of wind turbines through virtual inertia control :

[0046]

[0047]

[0048] in, is the rated power frequency of the power grid, is the rated active power of the wind turbine, is the second virtual inertia control coefficient of the wind turbine, is the active power change value of the wind turbine, is the lower limit of the inertial response power change limit, is the upper limit of the inertial response power change limit, The reference value of the wind turbine active power during the inertial response period is given. It is the active power value when the inertial response frequency modulation frequency starts to change; Calculate the active power change of wind turbines through virtual droop control :

[0049]

[0050]

[0051] in, is the second virtual droop control coefficient of the primary frequency modulation during the frequency drop disturbance or the frequency increase disturbance, is the lower limit of the primary frequency modulation power change limit, is the upper limit of the primary frequency modulation power change limit, is the wind turbine operating frequency change, The deviation value set for the primary FM frequency; is the reference value of the wind turbine active power given value during the primary frequency regulation period, It is the active power value at the moment when the primary frequency modulation frequency starts to change; Calculate the active power of wind turbines participating in frequency regulation:

[0052] in, It is the active power regulation value of wind turbine participating in frequency regulation.

[0053] In an optional implementation, calculating the active regulated power of the wind-storage system after adaptive frequency regulation specifically includes: Calculate the active power regulation of wind turbines after adaptive frequency regulation :

[0054]

[0055] in, Indicates the number of wind turbines in the wind storage system; For the Active power regulation when virtual inertia and virtual droop control of wind turbines participate in frequency regulation; Calculate the active power regulation of the energy storage system after adaptive frequency regulation :

[0056]

[0057] in, Indicates the number of energy storage devices in the wind storage system; For the The virtual inertia and virtual droop control of each battery energy storage device participates in the active power regulation when the frequency is modulated; Active power regulation after adaptive frequency modulation of wind power storage system Calculated as: .

[0058] In an optional implementation, the first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage system with the optimal weight configuration are comprehensively considered to calculate the frequency response model transfer function of the energy storage system:

[0059] in, is the response time constant of the energy storage system, is the transformation parameter in the Laplace transform.

[0060] In an optional implementation, the virtual inertia control of the wind turbine is the rotor kinetic energy control, the virtual droop control of the wind turbine is mainly the pitch angle control, and the wind turbine frequency response model transfer function is calculated by comprehensively updating the second virtual droop control coefficient and the updated second virtual inertia control coefficient:

[0061] in, is the rotor inertia response time constant, is the pitch response time constant.

[0062] The beneficial effect of the present invention lies in that, in view of the natural fluctuations of wind speed in actual wind farms and the performance of wind-storage systems under different operating conditions, an adaptive frequency regulation control strategy is adopted to ensure that the wind-storage system has optimal frequency regulation under the optimal conditions of system life, quantitatively analyze the frequency regulation active power during frequency regulation of the wind power and energy storage combined system, calculate the adjustable active power of each wind turbine and energy storage device participating in the frequency regulation strategy, evaluate the performance of the system, and further optimize the frequency regulation strategy based on the evaluation results to achieve the best balance between frequency regulation effect and equipment life.

[0063] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0065] Figure 1 It is a schematic flow chart of a method for quantitatively evaluating active regulation power of adaptive frequency modulation of a wind-storage system according to an embodiment of the present invention.

[0066] Figure 2 It is a detailed flow chart of a method for quantitatively evaluating active regulation power of adaptive frequency modulation of a wind-storage system according to an embodiment of the present invention.

[0067] Figure 3 The curve adaptive coefficient provided by the embodiment of the present invention and droop control coefficient Changes in situation.

[0068] Figure 4 This is a maximum power tracking curve provided by an embodiment of the present invention.

[0069] Figure 5The load reduction operation principle provided by the embodiment of the present invention.

[0070] Figure 6 A general strategy diagram for wind turbine frequency regulation provided in an embodiment of the present invention.

[0071] Figure 7 An adaptive controller for energy storage participating in frequency modulation provided in an embodiment of the present invention.

[0072] Figure 8 The overall framework of the virtual inertia and virtual droop control adaptive allocation model based on the DDPG algorithm provided in the embodiment of the present invention.

[0073] Fig. 9 It is a schematic structural block diagram of a device for quantitatively evaluating active regulation power of a wind-storage system with adaptive frequency modulation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0074] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0075] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0076] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1 The execution subject may be a wind-storage system adaptive frequency modulation active power regulation power quantitative evaluation device. According to different requirements, the order of the steps in the flow chart can be changed, and some can be omitted.

[0077] like Figure 1 and Figure 2 As shown, the method includes: Step S1, obtaining the charge-discharge state and the state of charge of the energy storage system, and adjusting the first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage participating in frequency modulation according to the charge-discharge state and the state of charge; According to the power output characteristics of the wind turbine generator set, a second virtual droop control coefficient and a second virtual inertia control coefficient of the wind turbine generator set participating in frequency regulation are obtained; Step S2, constructing an adaptive allocation model of the wind-storage system, and performing optimal weight configuration for the first virtual droop control coefficient and the first virtual inertia control coefficient based on the adaptive allocation model of the wind-storage system, and calculating the corresponding first virtual droop control frequency modulation power and first virtual inertia control frequency modulation power; Step S3, based on the first virtual droop control frequency modulation power after the weight is configured, the second virtual droop control coefficient is updated and the second virtual droop control frequency modulation power is calculated, and based on the first virtual inertia control frequency modulation power after the weight is configured, the second virtual inertia control coefficient is updated and the second virtual inertia control frequency modulation power is calculated; Step S4, calculating the active regulated power of the wind-storage system after adaptive frequency regulation based on the first virtual droop control frequency modulation power, the first virtual inertia control frequency modulation power, the second virtual droop control frequency modulation power and the second virtual inertia control frequency modulation power.

[0078] Optionally, as an embodiment of the present invention, in step S1, two basic control strategies for battery energy storage to participate in frequency modulation are virtual inertia control and virtual droop control. The expression for obtaining the energy storage frequency modulation control instruction according to the system frequency change rate and the frequency difference and through the above two control strategies is as shown in the formula:

[0079]

[0080]

[0081] in, Indicates the active power of energy storage participating in frequency regulation, It represents the active frequency control obtained by energy storage through virtual inertia control. It represents the active frequency regulation control obtained by energy storage through virtual droop control, represents the first virtual inertia control coefficient of the energy storage system, represents the first virtual droop control coefficient of the energy storage system, is the frequency change rate, is the frequency deviation. Under the step disturbance of new energy power, since the virtual inertia control is proportional to the frequency difference change rate, usually in the early stage of the disturbance, the system frequency difference change rate is large, and the energy storage can be quickly controlled through virtual inertia control to hinder the change of the frequency difference change rate, but it has no effect on the steady-state frequency difference of the system, and when the direction of the frequency difference change rate is inconsistent with the frequency difference direction, it will inhibit the frequency recovery. Virtual droop control is proportional to the system frequency difference, and there is a certain delay in frequency regulation, which mainly regulates the steady-state deviation of the system frequency. Considering that the two control strategies of current battery energy storage frequency regulation play different roles in different periods of frequency regulation, if the two can be properly combined in the frequency regulation process to make their advantages complement each other, it can better suppress the frequency disturbance caused by the fluctuation of wind turbine output, further give play to the advantages of fast frequency regulation of energy storage, and reduce the frequency regulation pressure on the grid side.

[0082] The control of energy storage is closely related to its own state of charge. If the change of energy storage charge state during system operation is not considered, it may cause excessive charging and discharging of energy storage, which will not only affect the life of energy storage, but may even cause it to exit operation prematurely due to its own limitations, causing greater harm to the system. In order to effectively utilize the charging and discharging characteristics of the energy storage system, the droop control coefficient involved in frequency modulation is adjusted by adjusting the charge state of the energy storage to maintain the charge state of the energy storage in a healthy working range, prevent the energy storage system from being in a high or low charge state for a long time, and realize efficient frequency modulation of the energy storage system. The definition of the energy storage droop coefficient is as follows:

[0083] in, is the droop control coefficient during energy storage discharge; It is the droop control coefficient when the energy storage is charged. In order to make the energy storage have a good frequency modulation effect, when the SOC of the energy storage is too large, Set to the maximum value, It decreases with the increase of SOC; when the SOC of energy storage is small, Set to the maximum value, As SOC decreases, it decreases, so the S function (sigmoid function) is selected to simulate the change of energy storage droop coefficient. The specific relationship is as follows:

[0084]

[0085] In the formula, is the maximum value of the droop control coefficient; is the adaptive coefficient of the curve, which determines the changing trend of the curve; It is the maximum limit of the state of charge during charging; It is the minimum limit of the state of charge during discharge; is the state of charge value. When taking different values, the change curve of the S function is: The value of has an important impact on system performance, such as Figure 3 shown. The selection of the value needs to balance the energy storage frequency regulation effect and Adaptability to changes in energy storage SOC. When the value is large, The range of change is small and the adaptability is weak; When the value is small, it may affect the frequency modulation effect of energy storage. Therefore, choose an appropriate The value is the key. In practical applications, Taking 15 can ensure the energy storage frequency regulation effect while also taking into account Adaptability to changes in SOC. Optionally, as an embodiment of the present invention, in step S2, the wind storage system adaptive allocation model is constructed based on a deep deterministic policy gradient algorithm, and the agent is trained under the wind turbine output control disturbance to achieve the optimal allocation of the first virtual droop control coefficient and the first virtual inertia control coefficient. According to the constructed regional frequency response model, the reinforcement learning agent needs to continuously learn based on historical experience to update the agent parameters under the wind farm output disturbance, and the control of the wind farm is mainly obtained by the wind turbine model shown in the wind speed input. The distribution model currently commonly used to fit wind speed is mainly the Weibull distribution, and its probability distribution is:

[0086] In the formula, is the size parameter, is the shape parameter, is the wind speed at the location of the wind turbine. In order to apply disturbances with historical control characteristics to the agent for training, it is first necessary to collect a large amount of historical wind speed data from wind farms to calculate the wind speed probability statistical distribution parameters and obtain the statistical distribution law of the wind speed in the area. Then, a random sequence is generated based on the wind speed probability distribution, and finally, the control fluctuation data can be obtained based on the wind turbine model shown.

[0087] Optionally, as an embodiment of the present invention, in step S3, the mechanical power captured by the fan It can be expressed as:

[0088] In the formula, is the tip speed ratio. is the air density; is the wind energy utilization coefficient; is the blade pitch angle; is the fan rotor speed; is the radius of the wind wheel; is the wind speed at the location of the fan; To calculate the intermediate variable. The power captured by the wind turbine is mainly related to the wind speed, rotation speed and pitch angle. The operating point with the maximum power output of the wind turbine is called the maximum power point tracking (MPPT) point. As the wind speed changes, the rotation speed corresponding to the maximum power output point changes accordingly, and the two form the MPPT working curve. The power extreme value is obtained at the vertex of the quadratic curve :

[0089] in, , , are all linear expressions of the wind energy utilization coefficient with respect to the pitch angle, and are updated immediately after the pitch angle changes. is the MPPT curve fitting coefficient.

[0090] During the normal operation of the wind turbine, the maximum power tracking can be achieved through converter control and blade pitch angle control. The maximum power tracking curve is as follows: Figure 4 As shown, the wind turbine output power reference instruction is as follows: shown.

[0091]

[0092] In the formula, is the proportional coefficient of the maximum power tracking curve of the wind turbine; , , They are the minimum electrical angular velocity, the electrical angular velocity in the constant speed zone, and the maximum electrical angular velocity of the fan respectively; It is the maximum output power of the fan.

[0093] When the wind turbine is operating normally, it is divided into different operating areas according to the wind speed. When the wind speed is low, the wind turbine speed is also low, the wind turbine is in an idling state, and no active power is generated. As the wind speed gradually increases, the output power increases with the increase in the rotor speed, and the wind turbine operates in the maximum power tracking mode. In the constant speed area, the rotor speed reaches the maximum value. At this time, the wind speed change has little effect on the wind turbine rotor speed, but the power continues to increase. As the wind speed increases further, the wind turbine operates in the constant power area. At this time, it is necessary to introduce pitch angle control to limit the output power of the wind turbine. According to the operating characteristics of the wind turbine, when frequency oscillation occurs inside the power grid, the wind turbine still outputs power to the power grid at the current speed and cannot provide the necessary power support to the power grid. Therefore, it is necessary to provide the kinetic energy stored in the rotor to participate in the system frequency modulation.

[0094] Optionally, as an embodiment of the present invention, the most important frequency regulation strategies of wind turbines are currently divided into two categories: one is the rotor kinetic energy control that utilizes its own inertia to participate in system frequency regulation; the other is the active standby control that reserves active standby to participate in frequency regulation. The rotor kinetic energy control of wind turbines is mainly virtual inertia control, and the inertia response function is to respond to the rate of change of grid frequency. It can provide short-term power support by adjusting the rotor stored kinetic energy, and the response speed is fast; the active standby control is mainly pitch angle control, which responds to grid frequency deviations through virtual droop control, and adjusts the pitch angle for long-term power support, and the response speed is slow. As the two most common wind farm frequency support strategies, they have certain representative significance. Under the joint action of the two methods, the frequency regulation capability of the wind farm will be significantly improved.

[0095] Rotor kinetic energy control (inertia response control) adjusts the kinetic energy input / output stored in the impeller to change the fan output power and suppress the rate of change of the system frequency. Inertia reflects the characteristic of the unit using its own rotor kinetic energy to suppress the change of system frequency. Generally, the inertia time constant is used. Characterize this action characteristics:

[0096] In the formula, is the inertia time constant of the fan; is the rotor kinetic energy of the wind turbine; is the rated power of the wind turbine; is the moment of inertia of the wind turbine; is the rated speed of the fan.

[0097] For synchronous generator sets, the units run at rated speed in steady state. When the system power (torque) is unbalanced and the frequency drops, the unit will release the stored rotor kinetic energy to suppress the frequency fluctuation. In this process, the inertia response expression is as follows:

[0098] In the formula, and are mechanical torque and electromagnetic torque; and is the mechanical power and the electromagnetic power.

[0099] Analogous to synchronous generators, if a doubly-fed wind turbine is to have inertia support capability, its output electromagnetic power must be able to respond to system frequency changes. When the rotor kinetic energy of a doubly-fed wind turbine is output in the form of electromagnetic power in response to frequency fluctuations, the inertia support power can be obtained as shown in the following formula:

[0100]

[0101] In the formula, The initial speed The corresponding rotor kinetic energy; The rotor speed after the unit participates in frequency regulation The corresponding rotor kinetic energy.

[0102] After simplification, the inertia response power output by the doubly-fed wind turbine when providing inertia support can be obtained as follows:

[0103] Active reserve control (pitch angle control) is to control the blade pitch angle at a certain wind speed. When the load on the power grid suddenly increases, the wind turbine can reduce the pitch angle and increase the output power of the wind turbine. When the load on the power grid suddenly decreases, the power output of the wind turbine can be limited to participate in the frequency regulation by increasing the pitch angle, thereby improving the stability of the system. Pitch control can achieve load shedding and reserve power at all wind speeds. With the development of pitch control technology, the delay of pitch angle adjustment is further reduced, so the pitch angle is used to control the reserved power. The principle of pitch control for load shedding is as follows: Figure 5 As shown, , For different sizes of paddle pitch angles.

[0104] When the wind turbine adopts the active reserve strategy, the wind turbine pitch angle is increased to achieve load shedding control and reserve power, and its operating point will be fixed on the pre-set load shedding curve. And MPPT point output power The relationship is:

[0105] In the formula, It is the load reduction factor of the fan, which is generally set as a percentage.

[0106] The frequency regulation strategy combining rotor kinetic energy control and active reserve control mainly considers the frequency support process under the inertial scale and primary scale, and analyzes and evaluates the frequency regulation capability of the wind farm. The rotor kinetic energy power component mainly acts on the inertial time scale within a short period of time after the disturbance occurs, playing a short-term support role; while the reserve power component mainly acts on the primary time scale, playing a long-term support role.

[0107] Optionally, as an embodiment of the present invention, when an active power disturbance occurs in the power system, the wind farm will adjust its active power to provide frequency support. The active power of the wind power system response can be divided into two components according to the source of energy: a rotor kinetic energy power component stored in the rotor rotation kinetic energy and a reserve power component stored in the wind energy.

[0108] When the inertia response function is triggered, the fan system will obtain the active power change value of the unit. As shown below:

[0109]

[0110] in, is the rated power frequency of the power grid; is the rated active power of the wind turbine; is the second virtual inertia control coefficient of the wind turbine.

[0111] The increase in active power during the inertia response of the wind turbine mainly comes from the kinetic energy stored in the wind turbine blades. Considering the load limit of the wind turbine and the safe operation of the wind turbine, the change in active power of the inertia response is calculated. Limiting is performed to obtain The new expression of is as follows:

[0112] in, The lower limit of the power change for inertial response is generally -10%; The upper limit of the power change limit for inertia response is generally 10%. In summary, the active power setting value of the wind turbine during the inertia response period is as follows:

[0113] in, The reference value of the wind turbine active power during the inertial response period is given. It is the active power value when the inertial response frequency modulation frequency starts to change.

[0114] Primary frequency modulation active power given value calculation, when When , the primary frequency modulation active power change is as follows:

[0115] when When , the primary frequency modulation active power change is as follows:

[0116] in, The lower limit deviation value set for the primary frequency modulation frequency. The upper limit deviation value set for the primary frequency modulation frequency.

[0117] During a frequency modulation process, the variable pitch system needs to be coordinated to open and close the pitches, and the wind turbine gradually releases about 10%. The power is limited by 20% at most. Power, need to be the primary frequency modulation active change value After the limiting process is performed, the new expression is as follows:

[0118] in, , are the second virtual droop control coefficients of the primary frequency modulation during the frequency drop disturbance and the frequency increase disturbance respectively; The lower limit of the primary frequency modulation power change limit is generally 10%; The upper limit of the power change limit of the primary frequency modulation is generally 20%. The active power setting value of the wind turbine during the primary frequency modulation is as follows:

[0119] in, is the reference value of the wind turbine active power given value during the primary frequency regulation period, It is the active power value at the moment when the primary frequency modulation starts to change.

[0120]

[0121] in, It is the active power regulation value of the wind turbine inertial response and primary frequency regulation participating in frequency support.

[0122] The specific situation of wind turbines participating in frequency regulation is as follows Figure 6 shown.

[0123] Optionally, as an embodiment of the present invention, the overall control of energy storage when participating in frequency regulation is composed of virtual inertia control and virtual droop control, and the weights of the two in different frequency regulation periods are: and From adaptive adjustment, and The specific value of is obtained by the trained agent. and They are the scaling factors of frequency difference and frequency difference change rate, respectively. When battery energy storage participates in frequency regulation, the system frequency difference enters the energy storage controller after the dead zone link. At this time, the frequency difference and the frequency difference change rate are obtained through the intelligent agent after the proportional link to obtain the virtual inertia distribution coefficient, and the virtual droop distribution coefficient is calculated at the same time; then the virtual inertia and virtual droop control are calculated according to the distribution coefficient and the corresponding frequency difference and frequency difference change rate, and finally the control of the two control strategies is added to obtain the final frequency regulation control instruction during energy storage frequency regulation. The specific principle is as follows: Figure 7 As shown, For differential meaning.

[0124] Optionally, as an embodiment of the present invention, the deep deterministic policy gradient (DDPG) algorithm is a reinforcement learning algorithm suitable for solving continuous control problems, and its main framework is a policy-value network. The DDPG algorithm is used to solve the adaptive allocation model of virtual inertia and virtual droop control when the wind-storage system participates in frequency modulation. Based on the output fluctuation data of wind turbines, the intelligent agent is interactively learned in the frequency response model of the wind-storage system, and the intelligent agent is continuously trained based on the acquired experience, so that the energy storage can adaptively allocate the weights of the two control methods during frequency modulation to meet the frequency modulation effect and optimal control.

[0125] Among them, the state variable Defined as the system frequency difference and frequency difference change rate after the battery energy storage dead zone; the action of the intelligent agent Defined as the virtual inertia distribution coefficient when battery energy storage participates in frequency regulation, satisfying According to the action of the intelligent agent and the relationship between the virtual inertia distribution coefficient and the virtual droop distribution coefficient, the specific value of the virtual droop distribution coefficient can be calculated, and then the coefficient of the wind turbine participating in the frequency regulation control can be adaptively updated. Reward function Mainly include and Two parts, where and is the ratio coefficient of the two parts of the reward, is the frequency error of the system, The power frequency regulation control change of the wind storage system, reward It is used to measure the effect of the intelligent agent's actions on the energy storage's participation in frequency regulation. This avoids the situation where the wind storage system's long-term power participation in frequency regulation affects its own service life.

[0126]

[0127] in, and is the ratio coefficient of the two parts of the reward, is the frequency error of the system, The power frequency regulation control change of the wind storage system, reward Measure the effect of the intelligent agent's actions on the energy storage's participation in frequency regulation, and reward Measuring the impact of long-term power participation of wind storage system on its service life The overall framework of the adaptive allocation model of virtual inertia and virtual droop control based on the DDPG algorithm is as follows: Figure 7 As shown, and are the transfer functions of the frequency responses of wind turbines and battery energy storage systems, is the system inertia of the wind storage system.

[0128] Optionally, as an embodiment of the present invention, in step S4, the battery energy storage uses virtual inertial response and virtual droop control to handle frequency fluctuations. Combined with the analysis of the energy storage frequency regulation control strategy, the energy storage system frequency model transfer function is:

[0129] In the formula, is the response time constant of the energy storage system, is the transformation parameter in the Laplace transform.

[0130] In order to achieve the optimal allocation of virtual inertia control and virtual droop control when battery energy storage participates in frequency modulation, the principle of the proposed adaptive allocation method is shown in the formula:

[0131]

[0132]

[0133] In the formula, and and are the first virtual inertia control and the first virtual droop control frequency modulation powers after optimal weight configuration, respectively. is the active power of energy storage participating in frequency regulation after optimal weight configuration; among them, . and They are the virtual inertia distribution coefficient and virtual droop distribution coefficient obtained by the proposed optimal distribution method respectively.

[0134] In the process of joint frequency regulation of wind and energy storage system, wind turbines can be controlled by adjusting their power margin. According to the frequency regulation coefficient model of energy storage adaptive allocation, the second virtual inertia control coefficient and the second virtual droop control coefficient of wind turbines participating in frequency regulation can be changed to assist energy storage in joint frequency regulation of the system. The adjustment strategy principle is shown in the following formula:

[0135]

[0136] In the formula, is the second virtual inertia control coefficient of the wind turbine; is the second droop control coefficient of the wind turbine; , is an adjustable power constraint.

[0137] Optionally, as an embodiment of the present invention, in step S4, When the fan frequency is modulated, the fan speed can be adjusted, the fan blade angle can be adjusted, and the control of the rotor inertia and pitch angle can be increased. Therefore, the fan system mainly includes two frequency modulation methods: rotor inertia and variable pitch control. The characteristics of the rotor inertia control technology are suitable for simulating the inertial response of traditional generators. The transfer function of its frequency model is:

[0138] In the formula, is the inertia response coefficient of the wind turbine; is the rotor inertia response time constant; is the transformation parameter in the Laplace transform.

[0139] The technical characteristics of wind turbine variable pitch control are suitable for simulating the primary frequency modulation of traditional generators, and the transfer function of its frequency model is:

[0140] In the formula, The second virtual droop control coefficient for the primary frequency regulation of the wind turbine; is the pitch response time constant.

[0141] Combining the inertial control and pitch control of the doubly-fed wind turbine generator set enables the wind farm to have the inertial response and primary frequency regulation capability similar to that of traditional generator sets. The frequency model transfer function is: .

[0142] Optionally, as an embodiment of the present invention, the DDPG algorithm as a whole includes 4 neural networks: Figure 8 As shown in the figure, the strategy network and its target strategy network, the value network and the target value network. represents the evaluation function of the value network, represents the policy function of the target policy network; represents the network parameters of the target network; Network parameters representing the policy or value network; Update the parameters for the target network. The environment for the agent training is the wind-storage system frequency response model built in the first part. Before each training round, a random wind speed disturbance is first generated according to the regional wind speed probability distribution, and then a continuous power disturbance is generated through the wind turbine model to simulate the frequency modulation scenario during the simulation environment training. Assume that in the first round The state of the system at a moment , the action at this time can be obtained through the policy network That is, the virtual inertia distribution coefficient of energy storage is:

[0143] in, Output of the policy network; Output noise to the policy network, which can be used to increase the agent's exploration. Energy storage to perform actions in the simulation environment You can get the current moment reward and the state at the next moment The DDPG algorithm uses experience replay technology to replay the interaction between the agent and the environment during training. The sequences are stored in the experience replay pool, from which they are randomly drawn at each iteration by batch sampling. The policy network and value network parameters are updated based on historical data, and the target network parameters are updated through soft update in each round.

[0144] Optionally, as an embodiment of the present invention, in step S5, the operating characteristics of the wind turbine and the energy storage system, as well as the dynamic interaction between them are analyzed. Based on the historical wind speed data of the wind farm, a wind speed fluctuation model is constructed to simulate the wind speed change, and an adaptive frequency regulation strategy is implemented on the basis of dynamically adjusting the active power output of the system to achieve the optimization of both the frequency regulation effect and the life of the wind storage system, and obtain the second virtual inertia control coefficient and the second virtual droop control coefficient when the wind turbine and energy storage participate in the frequency regulation. Finally, the performance of the wind-storage combined system during frequency regulation is evaluated, and the active regulation power of each part of the wind-storage system participating in the frequency regulation is quantified.

[0145] When a sudden load disturbance occurs, the frequency fluctuates. After the wind turbine is distributed by the adaptive controller, the frequency-regulated active power is as follows:

[0146]

[0147] in, Indicates the number of wind turbines in the wind storage system; For the The virtual inertia and virtual droop control of each wind turbine generator participates in the active power regulation when the frequency is modulated.

[0148] After the battery energy storage device is distributed through the adaptive controller, the frequency modulation active power is shown in the following formula.

[0149]

[0150]

[0151] in, Indicates the number of energy storage devices in the wind storage system; For the The virtual inertia and virtual droop control of a battery energy storage device participates in the active power regulation during frequency modulation.

[0152] Therefore, the active power regulation of the wind-storage system adaptive frequency regulation is as follows: .

[0153] In some embodiments, the wind-storage system adaptive frequency modulation active power regulation quantitative evaluation device may include a plurality of functional modules composed of computer program segments. The computer program of each program segment in the wind-storage system adaptive frequency modulation active power regulation quantitative evaluation device may be stored in the memory of a computer device and executed by at least one processor to execute (see Figure 1 Description) The function of quantitative evaluation of active regulation power of adaptive frequency regulation of wind-storage system.

[0154] In this embodiment, the active power quantitative evaluation device for adaptive frequency modulation of the wind-storage system can be divided into multiple functional modules according to the functions it performs, such as Fig. 9 As shown. The functional modules of the device may include: an energy storage coefficient adjustment module, a fan coefficient acquisition module, a coefficient optimal allocation module and an active power adjustment power calculation module. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0155] The device includes: An energy storage coefficient acquisition module is used to acquire the charge and discharge state and the state of charge of the energy storage system, and adjust the first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage participating in the frequency modulation according to the charge and discharge state and the state of charge; A wind turbine coefficient acquisition module, which acquires a second virtual droop control coefficient and a second virtual inertia control coefficient of the wind turbine participating in frequency modulation according to the power output characteristics of the wind turbine; The energy storage coefficient weight configuration module constructs an adaptive allocation model of the wind-storage system, and performs optimal weight configuration for the first virtual droop control coefficient and the first virtual inertia control coefficient based on the adaptive allocation model of the wind-storage system, and calculates the corresponding first virtual droop control frequency modulation power and first virtual inertia control frequency modulation power; A fan coefficient update module updates a second virtual droop control coefficient based on the first virtual droop control frequency modulation power after the weight is configured and calculates the second virtual droop control frequency modulation power, and updates a second virtual inertia control coefficient based on the first virtual inertia control frequency modulation power after the weight is configured and calculates the second virtual inertia control frequency modulation power; The active regulating power calculation module calculates the active regulating power of the wind-storage system after adaptive frequency regulation based on the first virtual droop control frequency modulation power, the first virtual inertia control frequency modulation power, the second virtual droop control frequency modulation power and the second virtual inertia control frequency modulation power.

[0156] Through the coordinated operation of the modules, the corresponding coefficients can be acquired, adjusted and optimally allocated according to the relevant status of the energy storage system, the power output characteristics of the wind turbines, etc., thereby realizing accurate calculation of the active power regulation after adaptive frequency regulation of the wind-storage system, which helps to improve the accuracy and adaptability of the frequency regulation of the wind-storage system.

[0157] Optionally, as an embodiment of the present invention, the wind-storage system adaptive allocation model is constructed based on a deep deterministic policy gradient algorithm, and an intelligent agent is trained under a wind turbine output control disturbance to achieve optimal allocation of a first virtual droop control coefficient and a first virtual inertia control coefficient, specifically including: Set initial parameters for the policy network, target policy network, value network, and target value network; Generate simulated wind speed according to the probability distribution of wind speed, and generate continuous disturbance data through wind turbines; The intelligent agent determines the action based on the current system frequency difference and the frequency difference change rate, and the energy storage system executes the corresponding weight configuration of the first virtual droop control coefficient and the virtual inertia control coefficient; After the agent performs an action, it feeds back the current reward and the system state at the next moment, stores the experience data of the current system state, the action taken, the reward obtained, and the next system state in the playback buffer, and extracts samples from it to update the network parameters; Repeat the above steps until the maximum number of steps in a single round is reached; The round iteration is repeated continuously until the preset maximum number of training rounds is reached, and the optimal weight configuration of the first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage system is obtained.

[0158] Optionally, as an embodiment of the present invention, the reward obtained includes a reward and rewards :

[0159] in, and is the ratio coefficient of the two parts of the reward, is the frequency error of the system, The power frequency regulation control change of the wind storage system, reward Measure the effect of the intelligent agent's actions on the energy storage's participation in frequency regulation, and reward Measure the impact of the long-term power participation of the wind storage system in frequency regulation control on its own service life.

[0160] Optionally, as an embodiment of the present invention, after obtaining the optimal weight configuration, the corresponding first virtual inertia control frequency modulation power and the first virtual droop control frequency modulation power are calculated. The frequency modulation power includes:

[0161]

[0162]

[0163] In the formula, is the first virtual inertia control frequency modulation power after optimal weight configuration, is the first virtual droop control frequency modulation power after optimal weight configuration, The active power of energy storage participating in frequency regulation after optimal weight configuration, , is the optimal first virtual inertia weight coefficient, is the optimal first virtual droop weight coefficient, represents the first virtual inertia control coefficient of the energy storage system, represents the first virtual droop control coefficient of the energy storage system, is the frequency change rate, is the frequency deviation.

[0164] Optionally, as an embodiment of the present invention, the updated second virtual droop control coefficient and the updated second virtual inertia control coefficient specifically include:

[0165]

[0166] in, and is the adjustable power constraint, is the output power at the MPPT point, is the initial load shedding output power, Release power for the rotor's kinetic inertial response, is the updated second virtual droop control coefficient, is the updated second virtual inertia control coefficient.

[0167] Optionally, as an embodiment of the present invention, the second virtual droop control frequency modulation power and the second virtual inertia control frequency modulation power are specifically calculated as follows: Calculate the active power change of wind turbines through virtual inertia control :

[0168]

[0169]

[0170] in, is the rated power frequency of the power grid, is the rated active power of the wind turbine, is the second virtual inertia control coefficient of the wind turbine, is the active power change value of the wind turbine, is the lower limit of the inertial response power change limit, is the upper limit of the inertial response power change limit, The reference value of the wind turbine active power during the inertial response period is given. It is the active power value when the inertial response frequency modulation frequency starts to change; Calculate the active power change of wind turbines through virtual droop control :

[0171]

[0172]

[0173] in, is the second virtual droop control coefficient of the primary frequency modulation during the frequency drop disturbance or the frequency increase disturbance, is the lower limit of the primary frequency modulation power change limit, is the upper limit of the primary frequency modulation power change limit, is the wind turbine operating frequency change, The deviation value set for the primary FM frequency; is the reference value of the wind turbine active power given value during the primary frequency regulation period, It is the active power value at the moment when the primary frequency modulation frequency starts to change; Calculate the active power of wind turbines participating in frequency regulation:

[0174] in, It is the active power regulation value of wind turbine participating in frequency regulation.

[0175] Optionally, as an embodiment of the present invention, calculating the active regulation power of the wind-storage system after adaptive frequency regulation specifically includes: Calculate the active power regulation of wind turbines after adaptive frequency regulation :

[0176]

[0177] in, Indicates the number of wind turbines in the wind storage system; For the Active power regulation when virtual inertia and virtual droop control of wind turbines participate in frequency regulation; Calculate the active power regulation of the energy storage system after adaptive frequency regulation :

[0178]

[0179] in, Indicates the number of energy storage devices in the wind storage system; For the The virtual inertia and virtual droop control of each battery energy storage device participates in the active power regulation when the frequency is modulated; Active power regulation after adaptive frequency modulation of wind power storage system Calculated as: .

[0180] Optionally, as an embodiment of the present invention, the first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage system with the optimal weight configuration are comprehensively considered to calculate the transfer function of the frequency response model of the energy storage system:

[0181] in, is the response time constant of the energy storage system, is the transformation parameter in the Laplace transform.

[0182] Optionally, as an embodiment of the present invention, the virtual inertia control of the wind turbine generator set is rotor kinetic energy control, the virtual droop control of the wind turbine generator set is mainly pitch angle control, and the wind turbine generator set frequency response model transfer function is calculated by comprehensively updating the second virtual droop control coefficient and the updated second virtual inertia control coefficient:

[0183] in, is the rotor inertia response time constant, is the pitch response time constant Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.

[0184] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0185] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, which can be electrical, mechanical or other forms.

[0186] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0187] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0188] Although the present invention has been described in detail by referring to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person skilled in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, and they shall be within the scope of protection of the present invention.

Claims

1. A method for quantitatively evaluating active regulation power of wind-storage system adaptive frequency modulation, characterized in that: The following steps are involved: Acquire the charge and discharge state and charge state of the energy storage system, and adjust the first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage participating in frequency modulation according to the charge and discharge state and charge state; According to the power output characteristics of the wind turbine generator set, a second virtual droop control coefficient and a second virtual inertia control coefficient of the wind turbine generator set participating in frequency regulation are obtained; Constructing an adaptive allocation model for a wind-storage system, and performing optimal weight configuration for a first virtual droop control coefficient and a first virtual inertia control coefficient based on the adaptive allocation model for the wind-storage system, and calculating corresponding first virtual droop control frequency modulation power and first virtual inertia control frequency modulation power; Based on the weighted first virtual droop control frequency modulation power, the second virtual droop control coefficient is updated and the second virtual droop control frequency modulation power is calculated; based on the weighted first virtual inertia control frequency modulation power, the second virtual inertia control coefficient is updated and the second virtual inertia control frequency modulation power is calculated; Based on the first virtual droop control frequency modulation power, the first virtual inertia control frequency modulation power, the second virtual droop control frequency modulation power and the second virtual inertia control frequency modulation power, the active regulation power of the wind-storage system after adaptive frequency modulation is calculated.

2. The method for quantitatively evaluating active power regulation of wind-storage system adaptive frequency modulation according to claim 1 is characterized in that: The wind storage system adaptive allocation model is constructed based on a deep deterministic policy gradient algorithm, a wind turbine wind speed fluctuation model is constructed based on Weibull distribution, and an intelligent agent is trained under wind turbine wind speed fluctuation based on a deep deterministic policy gradient algorithm to achieve optimal allocation of a first virtual droop control coefficient and a first virtual inertia control coefficient, specifically including: Set initial parameters for the policy network, target policy network, value network, and target value network; Generate simulated wind speed according to Weibull distribution of wind speed and generate continuous disturbance data through wind turbines; The intelligent agent determines the action based on the current system frequency difference and the frequency difference change rate, and the energy storage system executes the corresponding weight configuration of the first virtual droop control coefficient and the virtual inertia control coefficient; After the agent performs an action, it feeds back the current reward and the system state at the next moment, stores the experience data of the current system state, the action taken, the reward obtained, and the next system state in the playback buffer, and extracts samples from it to update the network parameters; Repeat the above steps until the maximum number of steps in a single round is reached; The round iteration is repeated continuously until the preset maximum number of training rounds is reached, and the optimal weight configuration of the first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage system is obtained.

3. The method for quantitatively evaluating the active power regulation of the wind-storage system adaptive frequency modulation according to claim 2 is characterized in that: The rewards received include rewards and rewards : in, and is the ratio coefficient of the two parts of the reward, is the frequency error of the system, The power frequency regulation control change of the wind storage system, reward Measure the effect of the intelligent agent's actions on the energy storage's participation in frequency regulation, and reward Measure the impact of the long-term power participation of the wind storage system in frequency regulation control on its own service life.

4. The method for quantitatively evaluating the active power regulation of the wind-storage system adaptive frequency modulation according to claim 2 is characterized in that: After obtaining the optimal weight configuration, the corresponding first virtual inertia control frequency modulation power and the first virtual droop control frequency modulation power are calculated. The frequency modulation power includes: In the formula, is the first virtual inertia control frequency modulation power after optimal weight configuration, is the first virtual droop control frequency modulation power after optimal weight configuration, The active power of energy storage participating in frequency regulation after optimal weight configuration, , is the optimal first virtual inertia weight coefficient, is the optimal first virtual droop weight coefficient, represents the first virtual inertia control coefficient of the energy storage system, represents the first virtual droop control coefficient of the energy storage system, is the frequency change rate, is the frequency deviation.

5. The method for quantitatively evaluating the active power regulation of the wind-storage system adaptive frequency modulation according to claim 4 is characterized in that: The updated second virtual droop control coefficient and the updated second virtual inertia control coefficient specifically include: in, and is the adjustable power constraint, is the output power at the MPPT point, is the initial load shedding output power, Release power for the rotor's kinetic inertial response, is the updated second virtual droop control coefficient, is the updated second virtual inertia control coefficient.

6. The method for quantitatively evaluating active regulation power of wind-storage system adaptive frequency modulation according to claim 5 is characterized in that: The second virtual droop control frequency modulation power and the second virtual inertia control frequency modulation power are specifically calculated as: Calculate the active power change of wind turbines through virtual inertia control : in, is the rated power frequency of the power grid, is the rated active power of the wind turbine, is the second virtual inertia control coefficient of the wind turbine, is the active power change value of the wind turbine, is the lower limit of the inertial response power change limit, is the upper limit of the inertial response power change limit, The reference value of the wind turbine active power during the inertial response period is given. It is the active power value when the inertial response frequency modulation frequency starts to change; Calculate the active power change of wind turbines through virtual droop control : in, is the second virtual droop control coefficient of the primary frequency modulation during the frequency drop disturbance or the frequency increase disturbance, is the lower limit of the primary frequency modulation power change limit, is the upper limit of the primary frequency modulation power change limit, is the wind turbine operating frequency change, The deviation value set for the primary FM frequency; is the reference value of the wind turbine active power given value during the primary frequency regulation period, It is the active power value at the moment when the primary frequency modulation frequency starts to change; Calculate the active power of wind turbines participating in frequency regulation: in, It is the active power regulation value of wind turbine participating in frequency regulation.

7. The method for quantitatively evaluating the active power regulation of the wind-storage system adaptive frequency modulation according to claim 6 is characterized in that: Calculation of active regulation power after adaptive frequency regulation of wind-storage system specifically includes: Calculate the active power regulation of wind turbines after adaptive frequency regulation : in, Indicates the number of wind turbines in the wind storage system; For the Active power regulation when virtual inertia and virtual droop control of wind turbines participate in frequency regulation; Calculate the active power regulation of the energy storage system after adaptive frequency regulation : in, Indicates the number of energy storage devices in the wind storage system; For the The virtual inertia and virtual droop control of each battery energy storage device participates in the active power regulation when the frequency is modulated; Active power regulation after adaptive frequency modulation of wind power storage system Calculated as: 。 8. The method for quantitatively evaluating the active power regulation of the wind-storage system adaptive frequency modulation according to claim 6 is characterized in that: The first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage system with comprehensive optimal weight configuration are used to calculate the transfer function of the frequency response model of the energy storage system: in, is the response time constant of the energy storage system, is the transformation parameter in the Laplace transform.

9. The method for quantitatively evaluating active power regulation of wind-storage system adaptive frequency modulation according to claim 8 is characterized in that: The virtual inertia control of the wind turbine is the rotor kinetic energy control, and the virtual droop control of the wind turbine is mainly the pitch angle control. The wind turbine frequency response model transfer function is calculated by combining the updated second virtual droop control coefficient and the updated second virtual inertia control coefficient: in, is the rotor inertia response time constant, is the pitch response time constant.

10. A device for quantitatively evaluating active power regulation of wind-storage system adaptive frequency modulation, characterized in that: The following steps are involved: An energy storage coefficient acquisition module is used to acquire the charge and discharge state and the state of charge of the energy storage system, and adjust the first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage participating in the frequency modulation according to the charge and discharge state and the state of charge; A wind turbine coefficient acquisition module, which acquires a second virtual droop control coefficient and a second virtual inertia control coefficient of the wind turbine participating in frequency modulation according to the power output characteristics of the wind turbine; The energy storage coefficient weight configuration module constructs an adaptive allocation model of the wind-storage system, and performs optimal weight configuration for the first virtual droop control coefficient and the first virtual inertia control coefficient based on the adaptive allocation model of the wind-storage system, and calculates the corresponding first virtual droop control frequency modulation power and first virtual inertia control frequency modulation power; A fan coefficient update module updates a second virtual droop control coefficient based on the first virtual droop control frequency modulation power after the weight is configured and calculates the second virtual droop control frequency modulation power, and updates a second virtual inertia control coefficient based on the first virtual inertia control frequency modulation power after the weight is configured and calculates the second virtual inertia control frequency modulation power; The active regulating power calculation module calculates the active regulating power of the wind-storage system after adaptive frequency regulation based on the first virtual droop control frequency modulation power, the first virtual inertia control frequency modulation power, the second virtual droop control frequency modulation power and the second virtual inertia control frequency modulation power.

11. The active power quantitative evaluation device for adaptive frequency modulation of wind-storage system according to claim 10 is characterized in that: The wind storage system adaptive allocation model is constructed based on a deep deterministic policy gradient algorithm, a wind turbine wind speed fluctuation model is constructed based on Weibull distribution, and an intelligent agent is trained under wind turbine wind speed fluctuation based on a deep deterministic policy gradient algorithm to achieve optimal allocation of a first virtual droop control coefficient and a first virtual inertia control coefficient, specifically including: Set initial parameters for the policy network, target policy network, value network, and target value network; Generate simulated wind speed according to Weibull distribution of wind speed and generate continuous disturbance data through wind turbines; The intelligent agent determines the action based on the current system frequency difference and the frequency difference change rate, and the energy storage system executes the corresponding weight configuration of the first virtual droop control coefficient and the virtual inertia control coefficient; After the agent performs an action, it feeds back the current reward and the system state at the next moment, stores the experience data of the current system state, the action taken, the reward obtained, and the next system state in the playback buffer, and extracts samples from it to update the network parameters; Repeat the above steps until the maximum number of steps in a single round is reached; The round iteration is repeated continuously until the preset maximum number of training rounds is reached, and the optimal weight configuration of the first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage system is obtained.

12. The active power quantitative evaluation device for adaptive frequency modulation of wind-storage system according to claim 11 is characterized in that: The rewards received include rewards and rewards : in, and is the ratio coefficient of the two parts of the reward, is the frequency error of the system, The power frequency regulation control change of the wind storage system, reward Measure the effect of the intelligent agent's actions on the energy storage's participation in frequency regulation, and reward Measure the impact of the long-term power participation of the wind storage system in frequency regulation control on its own service life.

13. The active power quantitative evaluation device for adaptive frequency modulation of wind-storage system according to claim 11, characterized in that: After obtaining the optimal weight configuration, the corresponding first virtual inertia control frequency modulation power and the first virtual droop control frequency modulation power are calculated. The frequency modulation power includes: In the formula, is the first virtual inertia control frequency modulation power after optimal weight configuration, is the first virtual droop control frequency modulation power after optimal weight configuration, The active power of energy storage participating in frequency regulation after optimal weight configuration, , is the optimal first virtual inertia weight coefficient, is the optimal first virtual droop weight coefficient, represents the first virtual inertia control coefficient of the energy storage system, represents the first virtual droop control coefficient of the energy storage system, is the frequency change rate, is the frequency deviation.

14. The active power quantitative evaluation device for adaptive frequency modulation of wind-storage system according to claim 13 is characterized in that: The updated second virtual droop control coefficient and the updated second virtual inertia control coefficient specifically include: in, and is the adjustable power constraint, is the output power at the MPPT point, is the initial load shedding output power, Release power for the rotor's kinetic inertial response, is the updated second virtual droop control coefficient, is the updated second virtual inertia control coefficient.

15. The active power quantitative evaluation device for adaptive frequency modulation of wind-storage system according to claim 14 is characterized in that: The second virtual droop control frequency modulation power and the second virtual inertia control frequency modulation power are specifically calculated as: Calculate the active power change of wind turbines through virtual inertia control : in, is the rated power frequency of the power grid, is the rated active power of the wind turbine, is the second virtual inertia control coefficient of the wind turbine, is the active power change value of the wind turbine, is the lower limit of the inertial response power change limit, is the upper limit of the inertial response power change limit, The reference value of the wind turbine active power during the inertial response period is given. It is the active power value when the inertial response frequency modulation frequency starts to change; Calculate the active power change of wind turbines through virtual droop control : in, is the second virtual droop control coefficient of the primary frequency modulation during the frequency drop disturbance or the frequency increase disturbance, is the lower limit of the primary frequency modulation power change limit, is the upper limit of the primary frequency modulation power change limit, is the wind turbine operating frequency change, The deviation value set for the primary FM frequency; is the reference value of the wind turbine active power given value during the primary frequency regulation period, It is the active power value at the moment when the primary frequency modulation frequency starts to change; Calculate the active power of wind turbines participating in frequency regulation: in, It is the active power regulation value of wind turbine participating in frequency regulation.

16. The active power quantitative evaluation device for adaptive frequency modulation of wind-storage system according to claim 15, characterized in that: Calculation of active regulation power after adaptive frequency regulation of wind-storage system specifically includes: Calculate the active power regulation of wind turbines after adaptive frequency regulation : in, Indicates the number of wind turbines in the wind storage system; For the Active power regulation when virtual inertia and virtual droop control of wind turbines participate in frequency regulation; Calculate the active power regulation of the energy storage system after adaptive frequency regulation : in, Indicates the number of energy storage devices in the wind storage system; For the The virtual inertia and virtual droop control of each battery energy storage device participates in the active power regulation when the frequency is modulated; Active power regulation after adaptive frequency modulation of wind power storage system Calculated as: 。 17. The active power quantitative evaluation device for adaptive frequency modulation of wind-storage system according to claim 16 is characterized in that: The first virtual droop control coefficient and the first virtual inertia control coefficient of the energy storage system with comprehensive optimal weight configuration are used to calculate the transfer function of the frequency response model of the energy storage system: in, is the response time constant of the energy storage system, is the transformation parameter in the Laplace transform.

18. The active power quantitative evaluation device for adaptive frequency modulation of wind-storage system according to claim 17, characterized in that: The virtual inertia control of the wind turbine is the rotor kinetic energy control, and the virtual droop control of the wind turbine is mainly the pitch angle control. The wind turbine frequency response model transfer function is calculated by combining the updated second virtual droop control coefficient and the updated second virtual inertia control coefficient: in, is the rotor inertia response time constant, is the pitch response time constant.

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