Modeling simulation management method and system for offshore wind power energy storage system based on waste batteries

Abstract

The present invention relates to a modeling and simulation management method and system for an offshore wind power energy storage system based on waste batteries. The method comprises: S1. establishing a mathematical model of a battery energy storage system, S2. estimating the life of waste power batteries in the battery energy storage system, S3. calculating the power of the battery energy storage system, and S4. energy management of the battery energy storage system based on an adaptive inversion sliding mode, so as to monitor and control the state of charge of the battery energy storage system, and control the energy fluctuation and load change of the battery energy storage system; the method realizes the organic combination of waste battery utilization and offshore wind power energy storage system, and monitors and manages the offshore wind power waste battery energy storage system, which is highly practical, feasible and scalable, solves the problem that the existing waste power battery management is difficult and difficult to directly apply to the offshore wind power energy storage system, and alleviates the problem of blank energy storage and increasing energy shortage in offshore wind power.

Description

Technical Field

[0001] The present invention belongs to the technical field of offshore wind power, and in particular relates to a modeling simulation management method for an offshore wind power energy storage system based on waste batteries. Background Art

[0002] The recycling and reuse of used batteries is of great significance. At present, this technology is still in the early stages of development, mainly targeting small-scale household electricity and commercial energy storage. The following problems still exist:

[0003] 1. As the battery is used continuously, its life span decreases, and its performance, stability and safety are not as good as those of new batteries. During use, problems such as short circuit and fire may occur.

[0004] 2. Imperfection of the battery management system; because various parameters are constantly changing during battery use, solving battery management adjustments has always been a big problem.

[0005] 3. Cost issues for battery recycling, testing, and use; the aging state of power batteries causes consistency and grouping issues. Because the batteries need to be tested and grouped after recycling, the cost of the recycling process will increase, and the cost-effectiveness may not be as good as directly using new batteries. Summary of the invention

[0006] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a method and system for modeling and simulating offshore wind power energy storage systems based on waste batteries, which can realize the organic combination of waste battery utilization and offshore wind power energy storage systems, and monitor and manage offshore wind power waste battery energy storage systems, and has high practicality, feasibility and scalability. It solves the problem that existing waste power batteries are difficult to manage and difficult to directly apply to offshore wind power energy storage systems, alleviates the problem of blank energy storage and increasing energy shortage in offshore wind power, and at the same time solves the problem of low control accuracy of existing battery energy storage system management and control technology for system energy fluctuations and load changes.

[0007] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0008] The present invention provides a modeling and simulation management method for an offshore wind power energy storage system based on waste batteries. The equivalent cycle life method is used to estimate the life loss of waste power storage batteries. The rain flow counting method is used to calculate the discharge depth and life budget of the waste power batteries. Then, an upper and lower two-level control method is used to manage the energy of the battery energy storage system composed of waste power batteries in the offshore wind power generation system, monitor and control the charge state of the battery energy storage system, and control the energy fluctuation and load change of the battery energy storage system.

[0009] Furthermore, the modeling simulation management method of the offshore wind power energy storage system based on waste batteries includes:

[0010] S1. Establish a mathematical model of the battery energy storage system: Based on the Shepherd model, describe the battery component model in the offshore wind power generation system, and combine the battery component model to reasonably reflect the current and voltage changes of the battery energy storage system; at the same time, introduce the Runge-Kutta algorithm and use the wind power generation system as one of the input signals for modeling and simulation. Based on the wind turbine simulation model of the battery energy storage system, use a variable step size to calculate and solve the battery component model, so that the mathematical model of the battery energy storage system can reflect the nonlinearity and time-varying characteristics of the battery, which is convenient for analyzing and controlling the energy storage system;

[0011] S2. Establish an equivalent loss circuit model for the battery energy storage system: In view of the problem of power allocation of battery energy storage unit groups, study the relationship between the charging and discharging power of battery energy storage units and the battery charging and discharging efficiency and PCS (energy conversion efficiency), establish an equivalent loss circuit model for the battery energy storage system, and optimize the number and power of battery energy storage units participating in the secondary frequency modulation response by implementing a power allocation strategy for battery energy storage unit groups that maximizes the overall efficiency of the battery energy storage system, so as to reduce power loss and improve operating efficiency. S3. Estimate the life of waste power batteries in the battery energy storage system: Establish a mathematical model for the optimal configuration of wind, solar and storage power stations, and use the equivalent cycle life method to estimate the life loss of waste power, or introduce a rain flow counting method under pulse load conditions to calculate the discharge depth of waste power batteries and predict the life of waste power batteries;

[0012] S4. Energy management strategy based on adaptive inversion sliding mode: A two-level energy management control strategy is designed. The first level is to accurately monitor and control the state of charge of the battery energy storage system through the extended Kalman particle filter, and the other level is to apply adaptive inversion sliding mode control to track the reference signal to achieve precise control of the energy fluctuations and load changes of the battery energy storage system.

[0013] Furthermore, the “wind turbine simulation model of the battery energy storage system” in S1 specifically includes: using Matlab / Simulink to construct a wind turbine simulation model based on the battery energy storage system.

[0014] Furthermore, in the wind turbine simulation model based on the battery energy storage system of S1, the Runge-Kutta algorithm and variable step size are used to solve the model, and the simulation parameters of the process are set as follows:

[0015] The rated power of the wind turbine is 1.5MW, the output line voltage is rated at 690V, and the frequency is 50Hz; the stator resistance of the permanent magnet synchronous generator is 0.017Ω, the stator inductance is 0.003L, the number of pole pairs is 32, and the filter inductance is 0.0035L; the rated voltage of the battery pack is

[0016] 610V, capacity is 500kVA / 5MJ; the DC side voltage rating of the grid-side converter is 1100V, the power device is IGBT, the switching frequency is 2.5kHz, and the capacitance is 6×10 -5 F, the output equivalent inductance is 0.0012L.

[0017] Further, step S2 is specifically as follows: establishing a maximum efficiency model for the battery energy storage unit group participating in the secondary frequency modulation power distribution of the power system, with the goal of maximizing the efficiency of the battery energy storage system, that is, minimizing the power loss, and establishing an objective function as shown in the following formula;

[0018]

[0019] Where: N is the number of battery energy storage units involved in frequency regulation;

[0020] α i is the switching factor of the i-th battery energy storage unit,

[0021] α i =0 means that the i-th battery energy storage unit is not working,

[0022] α i =1 means the i-th battery energy storage unit is working;

[0023] P disch i，loss and P cha i，loss are the discharge power loss and charging power loss of the i-th battery energy storage unit respectively;

[0024] The solution is:

[0025]

[0026] Where:

[0027] Z MINLP Corresponding to minimum power loss;

[0028] g(·) represents an equality constraint, which is a power balance constraint;

[0029] The optimization variable x is the charging and discharging power of the ith battery energy storage unit, which is a real number with upper and lower limits of x and i，max and x i，min ;

[0030] The optimization variable y is the switching factor of the ith battery energy storage unit, which is an integer.

[0031] Furthermore, the “establishment of an optimal configuration mathematical model for wind, solar and energy storage power stations” in S3 specifically includes:

[0032] This model is based on the equivalent loss circuit model of the battery energy storage system described in S2, and takes the number of battery energy storage charge and discharge times and charge and discharge depth as conditions to establish a mathematical model for the optimal configuration of the wind, solar and storage power station.

[0033] Furthermore, the method of the present invention also includes, after establishing the optimal configuration model of the wind, solar and storage power station, using an improved particle swarm algorithm and an 8760h production simulation method to optimize and solve the model; through the production simulation and model solution of a certain wind, solar and storage demonstration power station, it is shown that the proposed method can effectively consider the uncertainty of new energy power generation and the impact of battery energy storage life on the optimal configuration plan, and allocate suitable storage energy for batteries with different lifespans, with the aim of being able to use waste batteries for energy storage more reasonably and safely, thereby obtaining a reasonable battery energy storage system model.

[0034] Furthermore, the specific content of “using the equivalent cycle life method to estimate the life loss of waste power” in S3 is:

[0035] The equivalent cycle life method is used to convert the maximum number of cycles N corresponding to different DODs to the equivalent number of cycles N' under full charge and discharge conditions and add them up. Then, the actual working life of the energy storage system is deduced based on the maximum number of cycles N when DOD=I.

[0036] Furthermore, S3 "introducing a rain flow counting method under pulse load conditions to calculate the discharge depth of used power batteries and predict the life of used power batteries" specifically includes:

[0037] Use MATLAB programming to realize rain flow processing of SOC change curve;

[0038] The nonlinear change curve of the SOC-t of the energy storage battery under pulse load is processed by removing equivalent numbers and extracting the peak and valley values ​​of the curve through the rain flow counting method, and the cycle period is extracted based on the obtained data compression, that is, the discharge depth of 4 full cycles and 5 half cycles is calculated through the battery SOC change curve;

[0039] The N-order function method is used to fit the battery cycle number and charge and discharge depth data to obtain the fourth-order function of the following formula:

[0040] Ret=-3.278*DOD4-5*DOD3+12.823*DOD2-14.122*DOD+5.112

[0041] Among them, Ret is the number of cycles of the energy storage battery. It is known that the operation of the energy storage battery in one day can be divided into n charge and discharge cycles by the rain flow counting method, where DOD1, DOD2, …, DOD n is the depth of discharge, where the maximum number of cycles R can be obtained for the i-th discharge depth et (DOD i ), assuming that the battery can be cycled N times during the operating cycle, the battery life during the operating cycle is defined as:

[0042] T=1 / 3655·Re t

[0043]

[0044] Where T represents the life of the energy storage battery, expressed as 1 / R et (DOD i ) represents the loss of the energy storage battery in the i-th complete cycle, and L is the loss during the operation cycle of the energy storage battery; the current battery service life can be calculated through the battery life T and life loss L, or it can be used to predict the battery life.

[0045] The present invention also provides an offshore wind power energy storage system modeling simulation management system based on the utilization of waste batteries, including:

[0046] The mathematical model building unit is used to describe the battery component model in the offshore wind power generation system based on the Shepherd model, and reasonably reflect the current and voltage changes of the battery energy storage system in combination with the battery component model; at the same time, the Runge-Kutta algorithm is introduced and the wind power generation system is used as one of the input signals for modeling and simulation. Based on the wind turbine simulation model of the battery energy storage system, the battery component model is calculated and solved in a variable step size manner, so that the mathematical model of the battery energy storage system can reflect the nonlinearity and time-varying characteristics of the battery, which is convenient for analyzing and controlling the energy storage system;

[0047] The life estimation unit of used power batteries is used to establish an optimal configuration mathematical model with the maximum annual net income of wind, solar and storage power stations as the objective function. The life loss of used power is estimated by using the equivalent cycle life method, or the rain flow counting method under pulse load conditions is introduced to calculate the discharge depth of used power batteries and predict the life of used power batteries.

[0048] The power calculation unit of the battery energy storage system is used to establish an equivalent loss circuit model based on the loss-taking power allocation optimization model and the battery loss model, based on the working principle of the battery and the comprehensive solution of the battery energy storage system. The equivalent loss circuit model reflects the nonlinearity and time-varying characteristics of the battery, and realizes the analysis and precise control of the energy storage system.

[0049] The energy management unit is used to combine the comprehensive solution of the battery management system to manage the energy of the battery energy storage system composed of waste power batteries in the offshore wind power generation system using a two-level control method. The first level uses the extended Kalman particle filter to accurately monitor and control the charge state of the battery energy storage system, and the other level uses adaptive inversion sliding mode control to track the reference signal to achieve precise control of the energy fluctuations and load changes of the battery energy storage system.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] By adopting the above technical solution, the present invention can realize the organic combination of waste battery utilization and offshore wind power energy storage system. In view of the more unstable output characteristics of offshore wind power compared with household electricity and commercial energy storage, energy storage management is performed according to its volatility, so as to alleviate the problem of blank energy storage and increasing energy shortage of offshore wind power. An energy storage system configuration scheme suitable for offshore wind power scenarios is designed to realize the monitoring and management of offshore wind power waste battery energy storage system, and solve the following problems at the same time:

[0052] 1. It solves the problem that existing waste power batteries are difficult to manage and difficult to directly apply to offshore wind power energy storage systems;

[0053] 2. Solved the problem of low control accuracy of existing battery energy storage system management and control technology for system energy fluctuations and load changes;

[0054] 3. It solves the problem of the current lack of battery energy storage system management and control models suitable for offshore wind power scenarios.

[0055] The present invention has extremely high practicability, feasibility and scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flow chart of the modeling simulation management method of the offshore wind power energy storage system based on waste batteries according to the present invention;

[0057] Figure 2 is a schematic structural diagram of a battery energy storage system according to an embodiment of the present invention;

[0058] Figure 3 is a model diagram of a battery element in an embodiment of the present invention;

[0059] Figure 4 is a schematic structural block diagram of the wind power battery power supply system described in an embodiment of the present invention;

[0060] Figure 5 is a topological structure diagram of the permanent magnet direct-drive wind power generation system described in an embodiment of the present invention;

[0061] Figure 6 is a simulation model diagram of the permanent magnet direct-drive wind power system described in an embodiment of the present invention;

[0062] Figure 7 It is a simplified diagram of the wind speed model described in the embodiment of the present invention, wherein 7a is a simplified diagram of the wind speed model of gusts, 7b is a simplified diagram of the wind speed model of gradual wind, and 7c is a simplified diagram of the wind speed model of random wind; the above wind speed model diagram can effectively simulate the natural wind under different natural environments, thereby participating in the simulation of the parameters of the permanent magnet direct-drive wind turbine generator.

[0063] Figure 8 8a is a simulation waveform diagram of the grid-connected power and the wind turbine output power and the output of the energy storage system under the gust wind speed model of the embodiment of the present invention, wherein 8a is a simulation waveform diagram of the grid-connected power and the wind turbine output power under the gust model, and 8b is a simulation waveform diagram of the output of the energy storage system under the gust model;

[0064] Fig. 9 9a is a simulation waveform diagram of the grid-connected power and the wind turbine output power and the output of the energy storage system under the wind speed model of the gradual wind according to the embodiment of the present invention, wherein 9a is a simulation waveform diagram of the grid-connected power and the wind turbine output power under the gradual wind model, and 9b is a simulation waveform diagram of the output of the energy storage system under the gradual wind model;

[0065] Fig.10 10a is a simulation waveform diagram of the grid-connected power and the wind turbine output power and the output of the energy storage system under the random wind speed model in the embodiment of the present invention, wherein 10a is a simulation waveform diagram of the grid-connected power and the wind turbine output power under the random wind model, and 10b is a simulation waveform diagram of the output of the energy storage system under the random wind model;

[0066] Figure 8-10 The simulation waveforms of the grid-connected power P, wind turbine output power Pz and energy storage system output power P0 under different wind speed models of gust, gradual wind and random wind are respectively displayed. The working principle of the combination of permanent magnet direct-drive wind turbine and battery energy storage system is mainly explained. It can be seen from the figure that when the wind speed does not reach the optimal wind speed for grid connection, the battery energy storage system starts to discharge, and its output power compensates for the output power of the wind turbine; when the wind speed exceeds the optimal wind speed for grid connection, the battery energy storage system starts to charge and absorb power.

[0067] Fig.11 11a is a flowchart of calculating the battery discharge depth and battery life using the rain flow counting method in an embodiment of the present invention, wherein 11a is a data compression flowchart, and 11b is a cycle number extraction flowchart;

[0068] Fig.12 and 13This is a result diagram of removing equivalent values ​​and extracting peak and valley values ​​of the nonlinear change curve of the SOC-t of the energy storage battery under pulse load by using the rain flow counting method in an embodiment of the present invention;

[0069] Fig.14 is an equivalent circuit diagram of the battery energy storage unit described in an embodiment of the present invention;

[0070] Fig.15 is a schematic diagram of a power allocation strategy based on battery energy storage unit efficiency in an embodiment of the present invention;

[0071] Fig.16 It is a working flow chart of the battery energy storage system described in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0073] The present invention provides a modeling and simulation management method for an offshore wind power energy storage system based on waste batteries. The equivalent cycle life method is used to estimate the life loss of waste power storage batteries. The rain flow counting method is used to calculate the discharge depth and life budget of waste power batteries. Then, an upper and lower two-level control method is used to manage the energy of the battery energy storage system composed of waste power batteries in the offshore wind power generation system, monitor and control the charge state of the battery energy storage system, and control the energy fluctuation and load change of the battery energy storage system. The specific steps are as follows: Figure 1 As shown, including:

[0074] Step S100. Establishing a mathematical model of a battery energy storage system: describing a battery component model in an offshore wind power generation system based on the Shepherd model, and combining the battery component model to reasonably reflect the current and voltage changes of the battery energy storage system; at the same time, introducing the Runge-Kutta algorithm and taking the wind power generation system as one of the input signals for modeling and simulation, and taking the wind turbine simulation model of the battery energy storage system as the basis, calculating and solving the battery component model in a variable step size manner, so that the mathematical model of the battery energy storage system can reflect the nonlinearity and time-varying characteristics of the battery, and facilitate the analysis and control of the energy storage system;

[0075] Step S200. Establishing an equivalent loss circuit model of a battery energy storage system: In view of the problem of power allocation of a battery energy storage unit group, the relationship between the charging and discharging power of the battery energy storage unit and the battery charging and discharging efficiency and PCS is studied, and an equivalent loss circuit model of a battery energy storage system (i.e., a battery energy storage unit charging and discharging power-efficiency model) is established. By executing a power allocation strategy of a battery energy storage unit group that maximizes the overall efficiency of the battery energy storage system, the number and power of battery energy storage units participating in the secondary frequency modulation response are optimized, so that power loss is reduced and operating efficiency is improved;

[0076] Step S300. Estimate the life of the waste power batteries in the battery energy storage system: establish a mathematical model for the optimal configuration of the wind, solar and storage power station, calculate the life loss of the waste power by using the equivalent cycle life method, or introduce a rain flow counting method under pulse load conditions to calculate the discharge depth of the waste power batteries and predict the life of the waste power batteries;

[0077] Step S400. Energy management strategy based on adaptive inversion sliding mode: design a two-level energy management control strategy, the first level is to accurately monitor and control the state of charge of the battery energy storage system through the extended Kalman particle filter, and the other level is to apply adaptive inversion sliding mode control to track the reference signal to achieve accurate control of the energy fluctuation and load change of the battery energy storage system.

[0078] The present invention predicts the life of used batteries based on the rain flow counting method, and uses the equivalent life cycle method to model and calculate the life of the entire battery energy storage system. It is committed to applying the energy recovered from used batteries to the power supply of offshore wind power systems, which not only saves energy and protects the environment, but also provides a solution to the long-standing problem of stability of offshore wind power systems.

[0079] The modeling, simulation and management method and system of the offshore wind power energy storage system based on waste batteries described in the present invention are further described below through embodiments.

[0080] The modeling simulation management method of the offshore wind power energy storage system based on waste batteries of this embodiment includes:

[0081] 1. Establishment of mathematical model of battery energy storage system

[0082] Mathematical model of battery energy storage system Common energy storage system structure can be summarized into three parts: energy storage element, converter (DC / DC, DC / AC) and control system; Figure 2 As shown, the battery energy storage system is mainly composed of battery elements, inverters, control circuits and transformers.

[0083] The battery component model in the wind power generation system is described by using the Shepherd model proposed by Shepherd (the Shepherd model is the most common model in the world for describing the behavior of batteries in photovoltaic systems or wind power generation systems). Figure 3 As shown in Figure 2, the model can reasonably reflect the current and voltage changes of the battery energy storage system; Figure 4 As shown, the wind power battery power supply system includes a wind turbine, a wind power control system, a transformer unit, a load distribution unit, an independent power supply control system, a rectifier / inverter unit, an energy storage battery, a load and an emergency power supply system.

[0084] Considering the topology of the permanent magnet direct-drive wind power generation system based on the battery energy storage system, the wind turbine generator side converter is composed of a three-phase uncontrolled rectifier bridge and a filter capacitor (such as Figure 5 As shown), a mathematical model of a sinusoidal permanent magnet synchronous generator is established; specifically including:

[0085] First, the following assumptions need to be made:

[0086] 1) Ignore the saturation of the generator core;

[0087] 2) Ignore the eddy current and hysteresis losses of the generator;

[0088] 3) The motor current is a symmetrical three-phase sinusoidal current.

[0089] Then, the mathematical model of the aforementioned sinusoidal permanent magnet synchronous generator in the dq rotating coordinate system can be obtained as follows:

[0090]

[0091] In the formula: i d and i q are the d-axis current and q-axis current of the generator respectively; L d and L q are the d-axis inductance and q-axis inductance of the generator respectively; R a is the stator resistance of the permanent magnet synchronous generator; ω is the angular frequency; u d and u q are the d-axis voltage and q-axis voltage of the generator respectively;

[0092] The permanent magnet direct-drive wind power system uses a back-to-back dual PWM conversion method. The three-phase PWM voltage source converter (VSC) on the machine side and the grid side uses a controllable device (such as IGBT) and a freewheeling diode in anti-parallel connection. The three arms of the controllable full bridge have a total of 6 switch units, among which the machine side rectifier and the grid side inverter have similar working principles. After the three-phase stationary coordinate is converted to the two-phase stationary coordinate, and then from the two-phase stationary coordinate to the two-phase rotating coordinate, the mathematical model of the three-phase PWM voltage source converter in the dq two-phase rotating coordinate system is:

[0093]

[0094] According to the above formula, the overall mathematical model of the topological structure of the permanent magnet direct-drive wind power generation system in the dq coordinate system is as follows; the frequency in the dq rotating coordinate system ω is the AC system frequency (ie, angular frequency), the d-axis coincides with the system voltage vector, and the q-axis leads the d-axis by 90°.

[0095]

[0096] In this formula: i b is the battery discharge current; i d and i q are the dq axis current components on the AC side respectively; U dc is the DC side voltage; m represents the switching status of V1 and V2; m d and m q are the dq axis components of the converter switching function respectively; L B is the low-voltage side inductance of the chopper circuit; R1 is the equivalent resistance of the AC system; L1 is the output equivalent inductance; C1 is the DC side capacitance of the converter; U s , d is the d-axis component of the AC system voltage vector.

[0097] The overall mathematical model of the permanent magnet direct-drive wind power generation system in the dq coordinate system is a fourth-order linear system. b 、i d 、i q and U dc is the state quantity, m, m d and m q is the control quantity, L B 、ω、R1、L1、C1、U s ,d and U ab is a known quantity; when the control quantity is given, the state quantity can be calculated as follows:

[0098]

[0099] In this formula:

[0100] P is the grid-connected power;

[0101] P Z is the active power output by the wind turbine;

[0102] P0 is the output power of the battery energy storage system.

[0103] i b is the battery discharge current;

[0104] i d and i q They are the dq axis current components on the AC side respectively;

[0105] m represents the switching status of V1 and V2;

[0106] m d and m q are the dq axis components of the converter switching function respectively;

[0107] L B is the inductance on the low voltage side of the chopper circuit;

[0108] C1 is the DC side capacitor of the converter;

[0109] Soc is the state of charge of the battery:

[0110] CB is the standard capacity;

[0111] Ust is the voltage when the battery starts to discharge;

[0112] Ae, B, C, Kip, and Rip are undetermined parameters that can be obtained based on test data;

[0113] R ip I is the ohmic voltage loss;

[0114] K ip I×soc is the voltage drop caused by the electrode plate channel;

[0115] The simulation model of permanent magnet direct-drive wind power system based on battery energy storage system was constructed using Matlab / Simulink. Figure 6 shown.

[0116] In the simulation of wind turbine generator based on battery energy storage system, the Runge-Kutta algorithm and variable step size are used to solve the model. The simulation parameters are set as follows: the rated power of the wind turbine is 1.5MW, the output line voltage is rated at 690V (effective value), and the frequency is 50Hz; the stator resistance of the permanent magnet synchronous generator is 0.017, the stator inductance is 0.003L, the number of pole pairs is 32, and the filter inductance is 0.0035L; the rated voltage of the battery pack is 610V, and the capacity is 500kVA / 5MJ; the DC side voltage rating of the grid-side converter is 1100V, the power device is IGBT, the switching frequency is 2.5kHz, and the capacitor is C1=6×10 -5 F, the output equivalent inductance is L1=0.0012L.

[0117] All simulation results are expressed in per unit, and their corresponding rated values ​​are used as reference values. The corresponding wind speed is given by the step signal. For the power grid, the required grid-connected power P is constant and is 0.4MW. Due to the change in wind speed, there will be a difference between the output power Pz of the permanent magnet synchronous motor and the grid-connected power P. At this time, the battery energy storage system can play a good buffering role and can reuse or compensate for the difference power. For wind turbines, wind speed directly determines the dynamic characteristics of the unit. Common wind speed models include gusts, gradual wind and random wind, such as Figure 7 shown.

[0118] Under different wind speed models, the simulation waveforms of grid-connected power P, wind turbine output power Pz and energy storage system output power P0 are as follows: Figures 8 to 10 shown.

[0119] Depend on Figures 10-11 It can be seen that: under different wind speed models, in order to maintain the stability of wind power grid-connected power, the output power Pz of the wind turbine and the output power P0 of the battery energy storage system have undergone significant changes. When the wind speed does not reach the optimal wind speed for grid connection, the output power of the wind turbine is too small. At this time, the battery energy storage system begins to discharge, and its output power compensates for the output power of the wind turbine; when the wind speed exceeds the optimal wind speed for grid connection, the output power of the wind turbine is too large and exceeds the grid power P. At this time, the battery energy storage system begins to charge and absorb power (the absorbed power is expressed as negative in the figure). In general, the grid power P is approximately equal to the sum of the output power Pz of the wind turbine and the output power P0 of the battery energy storage system. However, compared with the actual situation, there are certain errors in the simulation results. The main reasons include: various losses in the actual grid connection; the leakage inductance of the transformer will affect the control circuit; the capacitor filter is uncontrollable, etc.

[0120] 2. Life estimation of energy storage battery pack

[0121] Solution 1: Optimal configuration method for wind-solar-storage power station energy storage taking into account battery energy storage life loss

[0122] The optimal configuration method of wind-solar-storage power station energy storage taking into account the battery energy storage life loss is a comprehensive model that combines battery energy storage. At the same time, considering the characteristics that the number of battery energy storage charge and discharge times and the charge and discharge depth have a great impact on the battery energy storage life, an optimal configuration model of wind-solar-storage power station is established; the improved particle swarm optimization algorithm (Improved Particle Swarm Optimization, IPSO) and 8760h production simulation method are used to optimize and solve the model; through the production simulation and model solution of a wind-solar-storage demonstration power station, it is shown that the proposed method can effectively consider the uncertainty of new energy power generation and the impact of battery energy storage life on the optimal configuration plan, and obtain a reasonable energy storage configuration plan.

[0123] Factors that affect the battery's service life include ambient temperature, peak charging current, depth of discharge (DOD), number of charge and discharge cycles, etc. Literature research shows that the battery's service life is closely related to its working mode, among which DOD is the most critical factor affecting the maximum number of battery cycles. The larger the DOD, the fewer battery cycles and the shorter the service life.

[0124] Algorithm: Use the equivalent cycle life method to scientifically calculate the life loss of energy storage batteries.

[0125] The equivalent cycle life method converts the maximum number of cycles N corresponding to different DODs to the equivalent number of cycles N' under full charge and discharge conditions (i.e., when DOD = 1) and adds them up. Then, the actual working life of the energy storage system is deduced based on the maximum number of cycles N when DOD = I. The charge and discharge cycle is usually characterized by the change curve of the SOC of the energy storage battery. A complete charge and discharge cycle is a half-cycle of a charge cycle plus a half-cycle of a discharge cycle, i.e., SOC a →SOC b →SOC a , a charging half cycle is SOC a →SOC b , a discharge half cycle is SOC b →SOC c , where 0≤SOC a ≠SOC b ≤1. Then the corresponding DOD is:

[0126] DOD=|SOC b -SOC c |

[0127] Define the equivalent cycle coefficient δDOD of energy storage battery k :

[0128]

[0129] In this formula: DOD k is the discharge depth at the kth cycle; N(1) is the maximum number of cycles corresponding to the discharge depth DOD = 1; N(D i ) is the maximum number of cycles corresponding to the depth of discharge; when a half cycle occurs in the actual working condition, it should be calculated in half. According to the actual SOC change curve of the battery during the research period, the rain flow counting method is used in engineering to count and extract each actual cycle. The total number of cycles is n, corresponding to DOD1, DOD2, ..., DOD n , then the equivalent number of cycles N' within this time is:

[0130]

[0131] Define the battery life loss factor β:

[0132]

[0133] When β=1, it can be considered that the battery life loss has reached its limit and a new battery needs to be replaced. Solution 2: Energy storage battery life prediction based on rain flow counting method under pulse load conditions

[0134] Algorithm: Rainflow counting rules

[0135] The rainflow counting method, also known as the "tower top method", is widely used in fatigue life calculations in engineering. The rainflow counting method counts based on the nonlinear relationship between stress and strain of the object under study, that is, the rainflow counting method is used to determine a set of non-periodic cycles of the data sample.

[0136] (1) The rain flow begins to flow downward at the beginning of the test record or at the inner edge of each peak.

[0137] (2) The rain flows to the maximum value and drips vertically, falling between the next two peak values ​​or to an extreme value that is larger (smaller) than the maximum (minimum) value at the beginning.

[0138] (3) The rain flow continues to flow until it reaches an extreme point with an absolute value larger than the initial extreme value, and when the rain flow encounters rain flowing down from the roof above, it stops flowing to form a cycle.

[0139] (4) According to the starting and ending points of the rain flow, draw each cycle, remove all the cycles one by one, and record their peak and valley values.

[0140] Estimating battery life based on the rain flow counting method The depth of discharge of a battery refers to the ratio of the amount of electricity discharged by the battery to its rated capacity in a complete charge-discharge cycle. A complete cycle includes a half-cycle of charging and a half-cycle of discharging. If the battery can be charged to a charge of SOC1 and discharged to SOC2, then a full cycle is SOC1-SOC2-SOC1, and the half-cycle of charging and discharging is SOC1-SOC2, and the depth of discharge DoD = SOC1-SOC2. However, in actual working conditions, energy storage batteries are used for power compensation of independent microgrids with pulse loads. Their output power is non-constant power, and the SOC change curve is extremely irregular, making it impossible to clearly divide the battery charge and discharge cycle. Therefore, it is first necessary to use the rain flow counting method to determine a series of cycles in the SOC change curve and the depth of discharge of each cycle (the difference in the horizontal peak-to-valley distance of each sub-cycle).

[0141] The rain flow counting method is used to calculate the battery discharge depth and battery life, specifically:

[0142] The program design of the rainflow counting method includes two steps: data compression and cycle number extraction. Data compression consists of two steps: peak and valley value extraction and invalid amplitude removal. Through data compression, the original data can be processed into an array that is convenient for cycle number extraction; cycle number extraction includes primary rainflow counting, docking and secondary rainflow counting to achieve waveform simplification and feature value extraction. Since the SOC curve of lithium iron phosphate batteries fluctuates greatly and has many peaks, this paper uses MATLAB programming to implement rainflow processing of the SOC change curve. Suppose the original data is W(t), the processed array is R(t), and the two array elements are numbered i and j respectively. The program flow chart is shown below. Fig.11 The data compression process shown ( Fig.11 a) and cycle number extraction process ( Fig.11 b).

[0143] The nonlinear change curve of SOC-t of the energy storage battery under pulse load is processed by removing equivalent numbers and extracting the peak and valley values ​​of the curve through the rain flow counting method. The processing results are as follows: Fig.12 and 13 As shown, the cycle period is extracted based on data compression, and the discharge depth of each cycle is calculated from the battery SOC-t nonlinear change curve. The calculation results are shown in Table 1 below;

[0144] Table 1: Discharge depth of battery in each cycle

[0145]

[0146] The N-order function method is used to fit the battery cycle number and charge and discharge depth data to obtain the fourth-order function of the following formula:

[0147] Ret=-3.278*DOD4-5*DOD3+12.823*DOD2-14.122*DOD+5.112

[0148] Among them, Re t is the number of cycles of the energy storage battery. It is known that the operation of the energy storage battery in one day can be divided into n charge and discharge cycles by the rain flow counting method, where DOD1, DOD2, …, DOD n is the depth of discharge, where the maximum number of cycles R can be obtained for the i-th discharge depth et (DOD i ), assuming that the battery can be cycled N times during the operating cycle, the battery life during the operating cycle is defined as:

[0149] T=1 / 3655·Re t

[0150]

[0151] Where T represents the life of the energy storage battery, expressed as 1 / R et (DOD i ) represents the loss of the energy storage battery in the i-th complete cycle, and L is the loss during the operation cycle of the energy storage battery; the current battery service life can be calculated through the battery life T and life loss L, or it can be used to predict the battery life.

[0152] 3. Power calculation of battery energy storage

[0153] (1) Objective function

[0154] Establish a maximum efficiency model for battery energy storage unit group participating in power system secondary frequency regulation power allocation. The equivalent circuit of the battery energy storage unit is as follows: Fig.14 In the figure: ii and Pi are the battery pack charging and discharging current and port power of the ith battery energy storage unit respectively.

[0155] Will Fig.14 The series-parallel battery pack in the figure is regarded as a whole. According to the battery equivalent circuit model, the battery pack port power P of the i-th battery energy storage unit i for:

[0156] P i =(U i，OCV -i i R i )i i

[0157] Where: U i，OCV and R i are the open circuit voltage and internal resistance of the i-th battery energy storage unit respectively.

[0158] PCS efficiency η PCSThe relationship with output power (per unit value) can be simplified by piecewise linearization as shown below.

[0159]

[0160] Where a and b are the piecewise linearization coefficients.

[0161] from Fig.14 It can be seen that the power on the DC side of the PCS and the power on the AC side have the following relationship:

[0162]

[0163] Where:

[0164] η inv i，PCS and η conv i，PCS are the inverter efficiency and rectification efficiency of the i-th PCS respectively.

[0165] Taking into account the power loss of the battery pack and the PCS, when the battery energy storage unit is discharged, the active power loss is the difference between the internal power of the battery pack and the power injected into the grid; when the battery energy storage unit is charged, the active power loss is the difference between the power from the grid and the power stored in the battery pack. Taking into account the power loss of the battery pack and the PCS, when the battery energy storage unit is discharged, the active power loss is the difference between the internal power of the battery pack and the power injected into the grid. When the battery energy storage unit is charged, the active power loss is the difference between the power from the grid and the power stored in the battery pack, as shown in the following formula.

[0166]

[0167] Where:

[0168] P disch i，loss and P cha i，loss are the discharge power loss and charging power loss of the i-th battery energy storage unit respectively;

[0169] η disch i，bat and η cha i，bat are the discharge efficiency and charge efficiency of the i-th battery pack respectively.

[0170] With the goal of maximizing the efficiency of the battery energy storage system, that is, minimizing power loss, the objective function is established as shown in the following formula.

[0171]

[0172] Where: N is the number of battery energy storage units involved in frequency regulation;

[0173] α i is the switching factor of the i-th battery energy storage unit,

[0174] α i =0 means that the i-th battery energy storage unit is not working,

[0175] α i =1 indicates that the i-th battery energy storage unit is working.

[0176] (2) Constraints

[0177] The battery pack and PCS in the battery energy storage unit must meet multiple constraints such as charging and discharging power and SOC.

[0178] 1) Power balance constraints

[0179] Each battery energy storage unit participating in frequency regulation must comply with the following power balance constraints:

[0180]

[0181] 2) Battery SOC constraint

[0182] When the battery operates at higher and lower SOC, its life will be greatly affected, so the battery SOC is limited to a certain range, as shown in the following formula.

[0183] S i，min ≤S i (t)≤S i，max

[0184] Where:

[0185] S i (t) is the real-time SOC of the i-th battery energy storage unit;

[0186] S i，min and S i，max are the lower and upper limits of SOC respectively.

[0187] 3) Battery charging and discharging power constraints

[0188] P i，min ≤P i ≤P i，max

[0189] Where: P i，min and P i，max are the minimum and maximum charge and discharge power of the battery respectively.

[0190] There is a nonlinear relationship between the maximum charge and discharge power of the battery and the SOC. When the battery pack operates in the boundary SOC range (the limit of whether the maximum charge and discharge power is constrained by the SOC), the maximum charge and discharge power is constrained by the SOC; when operating in the non-boundary SOC range, the maximum charge and discharge power is determined by the maximum charge and discharge current and the terminal voltage. When the battery is charged at a constant current, the terminal voltage gradually increases as the SOC increases, and the charging power gradually increases; when transitioning from constant current to constant voltage, the current decreases, causing the power to gradually decay. When the battery is discharged at a constant current, the terminal voltage gradually decreases as the SOC decreases, and the discharge power gradually decreases. After the terminal voltage reaches the discharge cut-off voltage, if you want to continue discharging, you need to reduce the discharge current, resulting in a decay in the discharge power.

[0191] 4) PCS transmission power constraints

[0192] P i，PCS ≤P i，PCS，N

[0193] Where: P i，PCS is the PCS output power; P i，PCS，N is the PCS rated power.

[0194] (3) Solution method

[0195] The mathematical problem corresponding to the maximum efficiency model of power allocation is a mixed integer nonlinear programming (MINLP) problem. The standard form of the MINLP model is shown in the following formula. The IPOPT algorithm package is called in the MATLAB platform to solve the problem. The IPOPT algorithm package uses the branch and bound method to solve the problem.

[0196]

[0197] Where:

[0198] Z MINLP Corresponding to minimum power loss;

[0199] g(·) represents an equality constraint, which is a power balance constraint. The optimization variable x is the charge and discharge power of the i-th battery energy storage unit, which is a real number, and its upper and lower limits are x i，max and x i，min ; The optimization variable y is the switching factor of the ith battery energy storage unit, which is an integer. The power allocation strategy based on the efficiency of the battery energy storage unit is as follows: Fig.15 Shown

[0200] 4. Energy management of battery energy storage system based on adaptive backstepping sliding mode

[0201] The adaptive inversion sliding mode control algorithm can control the output power of battery energy storage by controlling the converter. Traditional feedback control relies on parameters in the control process, but these parameters will be affected by factors such as temperature and voltage fluctuations and produce huge errors. Adaptive control can track the change of errors in a timely manner, read input and output data, identify and update model parameters online, overcome the interference of external factors, and improve the accuracy and stability of the system. Inversion control effectively solves the nonlinear problem between input and output and avoids the waste of a large amount of useful information. The discontinuity of sliding mode control and its high-frequency and rapid response characteristics can be consistent with the working state of the devices in the converter, achieving good control effects.

[0202] At present, the main electrochemical storage methods are mechanical energy storage, electromagnetic energy storage, electrochemical energy storage and phase change energy storage.

[0203] The first stage applies extended Kalman particle filtering to accurately monitor the state of charge of the battery energy storage system, and the other stage applies adaptive inversion sliding mode control to track the reference signal, which can accurately track energy fluctuations and load changes.

[0204] The wind power generation system includes wind turbines, battery energy storage systems, electrolyzers, generator converters and corresponding control strategies. Its structure diagram is shown in the figure below. Fig.15 As shown; the main working mode of the wind power generation system is:

[0205] (1) Method 1: When the wind speed is high, the power generation of the wind power generation system can meet the load demand, and the excess power is stored in the battery energy storage system.

[0206] (2) Method 2: When the wind speed is low and cannot meet the load demand, the electricity stored in the battery energy storage system is released to meet the load demand together with the wind power generation system.

[0207] (3) Method three: If the wind speed is extremely low and the battery energy storage system cannot meet the load demand, the power grid will supply power. In the above three methods, the battery energy storage system is connected to the electrolyzer to convert electrical energy into hydrogen for reuse, reducing the saturation frequency of the battery energy storage system so that it can remain in working condition for a long time. This makes full use of wind energy, improves wind energy utilization, and reduces wind abandonment. Throughout the process, precise control of the working mode of the battery energy storage system and stable tracking of the output power can improve the quality of power in the battery energy storage system, extend the battery life, provide a basis for battery selection, and avoid waste of resources. The working process of the battery energy storage system is as follows: Fig.16 shown.

[0208] In the two - level control process of the battery energy storage system, the advantages of centralized control and decentralized control are that the control instructions become more and more detailed from top to bottom, the feedback information becomes more and more refined from bottom to top, and the monitoring agencies at each level have a subordinate relationship. First, estimating the state of charge of the battery energy storage is the basis for whether the battery energy storage system works and switches between working modes. Improving the estimation accuracy can improve the accuracy and sensitivity of control, protect it from overcharging and over - discharging, and at the same time enable fast and accurate switching. Secondly, judging the difference between the output power of the wind power generation system and the load power, together with the estimation result of the state of charge of the battery, determines the working mode of the battery energy storage system. Stable tracking of the difference signal between powers can not only accurately compensate but also reduce output fluctuations and select an appropriate battery energy storage capacity. Several specific working modes are as follows:

[0209] (1) Mode 1: 0.3 < SOC < 0, the battery energy storage system works normally, can store excess wind energy, and enables normal hydrogen production at the positive electrode of the electrolyzer and the Electrical Measurement & Instrumentation.

[0210] (2) Mode 2: 0.3 < SOC < 0, the battery energy storage system is completely in the discharge state, supplying power to both the load and the electrolyzer simultaneously.

[0211] (3) Mode 3: SOC > 0.9 and P_wind - P_load > 0, the battery energy storage system reaches the charging limit, only supplies power to the electrolyzer, and the excess wind energy is wasted.

[0212] (4) Mode 4: SOC > 0.9 and P_wind - P_load < 0, the battery energy storage system discharges to the load and the electrolyzer to compensate for the power difference between the wind power generation system and the load.

[0213] (5) Mode 5: SOC < 0, the battery energy storage system only charges, stores the excess wind energy, and stops supplying power to the electrolyzer.

[0214] (6) Mode 6: SOC < 0, the battery energy storage system does not meet the working conditions and completely stops working.

[0215] As can be seen from the above, the present invention can be applied to the offshore wind power energy storage scenario. Utilizing the environmental benefits and economic value brought by the recycling and reuse of waste batteries, applying the management model of the waste power battery energy storage system to the offshore wind power energy storage system enables the full utilization of rich wind energy resources, realizes the modeling and simulation of the new offshore wind power energy storage system, reduces the impact of wind curtailment on the economic benefits of wind power enterprises, realizes the improvement of the power quality of wind power, promotes the development of offshore wind power energy storage technology and the utilization of energy, and facilitates the completion of the energy transformation path.

[0216] The above-mentioned embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and substitutions made by technicians in this field on the basis of the present invention shall fall within the scope of protection required by the present invention.

Claims

1. A modeling and simulation management method for an offshore wind power energy storage system based on waste batteries, characterized in that: include: S1. Establish a mathematical model of the battery energy storage system: Based on the Shepherd model, describe the battery component model in the offshore wind power generation system, and combine the battery component model to reasonably reflect the current and voltage changes of the battery energy storage system; at the same time, introduce the Runge-Kutta algorithm and use the wind power generation system as one of the input signals for modeling and simulation. Based on the wind turbine simulation model of the battery energy storage system, use a variable step size to calculate and solve the battery component model, so that the mathematical model of the battery energy storage system can reflect the nonlinearity and time-varying characteristics of the battery, which is convenient for analyzing and controlling the energy storage system; S2. Establish an equivalent loss circuit model for the battery energy storage system: To solve the problem of power allocation of battery energy storage unit groups, study the relationship between the charging and discharging power of battery energy storage units and battery charging and discharging efficiency and PCS, establish an equivalent loss circuit model for the battery energy storage system, and optimize the number and power of battery energy storage units participating in the secondary frequency modulation response by implementing a power allocation strategy for the battery energy storage unit group that maximizes the overall efficiency of the battery energy storage system, thereby reducing power loss and improving operating efficiency. S3. Estimation of the life of waste power batteries in battery energy storage systems: Establish a mathematical model for the optimal configuration of wind, solar and storage power stations, and use the equivalent cycle life method to estimate the life loss of waste power batteries. Estimate or introduce the rain flow counting method under pulse load conditions to calculate the discharge depth of used power batteries and predict the life of used power batteries; S4. Energy management strategy based on adaptive back-sliding mode: Design a two-level energy management control strategy. The first level is to accurately monitor and control the state of charge of the battery energy storage system through the extended Kalman particle filter, and the other level is to apply adaptive back-sliding mode control to track the reference signal to achieve accurate control of the energy fluctuation and load change of the battery energy storage system. Step S2 specifically includes: establishing a maximum efficiency model for the battery energy storage unit group to participate in the secondary frequency modulation power distribution of the power system, with the goal of maximizing the efficiency of the battery energy storage system, that is, minimizing the power loss, and establishing an objective function as shown in the following formula; Where: N is the number of battery energy storage units participating in frequency regulation; α i For the i The switching factor of a battery storage unit, α i =0 means i The battery storage unit is not working. α i =1 means the i Battery energy storage units work; and Respectively i The discharge power loss and charging power loss of each battery energy storage unit; The solution is: Where: Z MINLP corresponds to minimum power loss; g (·) represents an equality constraint, which is a power balance constraint; Optimization variables x For the i The charging and discharging power of a battery energy storage unit is a real number with upper and lower limits. xi , max and xi , min; Optimization variables y For the i The switching factor of a battery energy storage unit is an integer.

2. The method according to claim 1, characterized in that The "wind turbine simulation model of battery energy storage system" in S1 is specifically: using Matlab / Simulink to build a wind turbine simulation model based on battery energy storage system.

3. The method according to claim 2, characterized in that In the wind turbine simulation model based on the battery energy storage system of S1, the Runge-Kutta algorithm and variable step size are used to solve the model. The simulation parameters of the process are set as follows: The rated power of the wind turbine is 1.5MW, the output line voltage is rated at 690V, and the frequency is 50Hz; the stator resistance of the permanent magnet synchronous generator is 0.017Ω, and the stator inductance is 0.003L. The number of pole pairs is 32, and the filter inductance is 0.0035L; the rated voltage of the battery pack is 610V, and the capacity is 500kVA / 5MJ; the DC side voltage rating of the grid-side converter is 1100V, the power device is IGBT, the switching frequency is 2.5kHz, the capacitance is 6×10-5F, and the output equivalent inductance is 0.0012L.

4. The modeling and simulation management method for offshore wind power energy storage system based on waste batteries according to claim 2 or 3 is characterized in that: The "Establishment of an Optimal Configuration Mathematical Model for Wind, Solar and Storage Power Stations" in S3 specifically includes: This model is based on the equivalent loss circuit model of the battery energy storage system described in S2, and takes the number of battery energy storage charge and discharge times and charge and discharge depth as conditions to establish a mathematical model for the optimal configuration of the wind, solar and storage power station.

5. The method according to claim 4, characterized in that The improved particle swarm algorithm and 8760h production simulation method are used to solve the optimal configuration mathematical model, and the optimal solution for the configuration of the waste battery energy storage system is obtained for different application scenarios.

6. The method according to claim 4, characterized in that In S3, "the equivalent cycle life method is used to estimate the life loss of waste power" is specifically: The equivalent cycle life method is used to convert the maximum number of cycles N corresponding to different DODs to the equivalent number of cycles N' under full charge and discharge conditions and add them up. Then, the actual working life of the energy storage system is deduced based on the maximum number of cycles N when DOD=I.

7. The method according to claim 4, characterized in that S3 "Introducing the rain flow counting method under pulse load conditions to calculate the discharge depth of used power batteries and predict the life of used power batteries" specifically includes: Use MATLAB programming to realize rain flow processing of SOC change curve; The nonlinear change curve of the SOC-t of the energy storage battery under pulse load is processed by removing equivalent numbers and extracting the peak and valley values ​​of the curve through the rain flow counting method, and the cycle period is extracted based on the obtained data compression, that is, the discharge depth of 4 full cycles and 5 half cycles is calculated through the battery SOC change curve; The N-order function method is used to fit the battery cycle number and charge and discharge depth data to obtain the fourth-order function of the following formula: Where Ret is the number of cycles of the energy storage battery. It is known that the operation of the energy storage battery in one day can be divided into n charge and discharge cycles by the rain flow counting method, where DOD1, DOD2, ..., DODn are the depths of discharge, and the maximum number of cycles Ret (DODi) can be obtained by the i-th discharge depth. Assuming that the battery can be cycled N times in the operation cycle, the battery life in the operation cycle is defined as: Where T represents the life of the energy storage battery, 1 / Ret(DODi) represents the loss of the energy storage battery in the i-th complete cycle, and L is the loss during the operation cycle of the energy storage battery. The battery life T and life loss L can be used to calculate the current battery service life or predict the battery life.

8. A system for modeling and simulating management of offshore wind power energy storage system based on the utilization of waste batteries according to any one of claims 1 to 7, characterized in that: include: The mathematical model building unit is used to describe the battery component model in the offshore wind power generation system based on the Shepherd model, and reasonably reflect the current and voltage changes of the battery energy storage system in combination with the battery component model; at the same time, the Runge-Kutta algorithm is introduced and the wind power generation system is used as one of the input signals for modeling and simulation. Based on the wind turbine simulation model of the battery energy storage system, the battery component model is calculated and solved in a variable step size manner, so that the mathematical model of the battery energy storage system can reflect the nonlinearity and time-varying characteristics of the battery, which is convenient for analyzing and controlling the energy storage system; The life estimation unit of the used power battery is used to establish the mathematical model of the optimal configuration of the wind, solar and storage power station. The life loss of the used power is estimated by using the equivalent cycle life method, or the rain flow counting method under pulse load conditions is introduced to calculate the discharge depth of the used power battery and predict the life of the used power battery. The power calculation unit of the battery energy storage system is used to establish an equivalent loss circuit model based on the loss-taking power allocation optimization model and the battery loss model, based on the working principle of the battery and the comprehensive solution of the battery energy storage system. The equivalent loss circuit model reflects the nonlinearity and time-varying characteristics of the battery, and realizes the analysis and precise control of the energy storage system. The energy management unit is used to combine the comprehensive solution of the battery management system to manage the energy of the battery energy storage system composed of waste power batteries in the offshore wind power generation system using a two-level control method. The first level uses the extended Kalman particle filter to accurately monitor and control the charge state of the battery energy storage system, and the other level uses adaptive inversion sliding mode control to track the reference signal to achieve precise control of the energy fluctuations and load changes of the battery energy storage system.

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