Energy storage coordination management method, system and equipment for wind power generation power control and medium

Abstract

The invention relates to the technical field of wind power generation research, and discloses an energy storage coordination management method, system, equipment and medium for wind power generation power control, and the method comprises the steps: building a dynamic prediction model according to the power fluctuation characteristics of a wind power generation system; designing a multi-dimensional optimization objective function of the energy storage system based on the dynamic prediction model; performing global optimization solution on the multi-dimensional optimization objective function through an upper-layer controller in hierarchical cooperative control, and performing local optimization solution on each energy storage unit through a lower-layer controller by adopting a distributed optimization algorithm; and the optimization solution result at the current moment is applied to the energy storage system, real-time data at the next moment are collected for optimization solution again, and real-time dynamic adjustment of the energy storage system is achieved in a rolling optimization mode. According to the method, the response speed and the adjustment precision of the energy storage system to high-frequency power change are remarkably improved, the problem of dynamic response lag in the prior art is practically solved, and the method can better adapt to the characteristic of high fluctuation of wind power generation power.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation research, and in particular to an energy storage coordination management method, system, equipment and medium for wind power generation power control. Background Art

[0002] With the rapid development of wind power technology, energy storage coordination and management methods are becoming increasingly important in optimizing wind power control. However, existing energy storage management systems and technical solutions still have many shortcomings in adapting to wind power fluctuations and achieving efficient power regulation and energy storage coordination, making it difficult to fully meet practical application requirements.

[0003] Prior art proposes an energy storage management system that includes energy storage components, photovoltaic power generation windows, and a central server. This system receives power information from a power detector via the central server and, based on preset conditions, determines whether the energy storage module is fully charged, thereby issuing a corresponding full-storage information. However, this technical solution is primarily designed for photovoltaic power generation scenarios and fails to fully consider the high power fluctuations of wind power generation, lacking the ability to dynamically respond to real-time power changes. Furthermore, the system's judgment is based solely on power information, failing to comprehensively consider factors such as temperature and charge / discharge rates, potentially limiting the energy storage system's operating efficiency and safety. Prior art also discloses an energy storage management system for photovoltaic energy storage units, including a real-time acquisition module, a historical acquisition module, an analysis module, and an energy storage module. This system comprehensively analyzes real-time and historical parameters to generate a corresponding conversion strategy to control the charging and discharging operations of the battery pack. However, this technical solution is primarily applicable to photovoltaic power generation scenarios, and the design of its analysis module fails to fully consider the high-frequency fluctuations of wind power generation, potentially preventing the energy storage management strategy from being adjusted promptly to meet the actual needs of wind power generation. In addition, although the system has certain advantages in monitoring faulty battery packs, its overall power distribution and coordinated control capabilities for the energy storage system are relatively weak, which may affect the overall utilization efficiency of wind power generation.

[0004] The above problems indicate that the existing energy storage management system still has certain deficiencies in adapting to the fluctuation of wind power generation and achieving efficient power regulation and energy storage coordination. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method, system, device and medium for energy storage coordination management for wind power generation power control, which solves the problem that the existing energy storage management system fails to fully consider the fluctuation characteristics of wind power generation and the operating laws of the energy storage system, resulting in slow dynamic response, insufficient multi-unit coordination capabilities and lack of comprehensive optimization goals.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for coordinated energy storage management for wind power generation control, comprising: Construct a dynamic prediction model based on the power fluctuation characteristics of the wind power generation system; Designing a multi-dimensional optimization objective function of the energy storage system based on the dynamic prediction model; The upper controller in the hierarchical collaborative control performs global optimization on the multi-dimensional optimization objective function, and the lower controller uses a distributed optimization algorithm to perform local optimization on each energy storage unit; The optimization solution result at the current moment is applied to the energy storage system, and the real-time data at the next moment is collected to re-optimize and solve, so as to realize the real-time dynamic adjustment of the energy storage system through rolling optimization.

[0008] As a preferred solution of the energy storage coordinated management method for wind power generation power control according to the present invention, the construction of a dynamic prediction model based on the power fluctuation characteristics of the wind power generation system includes: The power fluctuation characteristics in the wind power generation system are modeled, and the state space equation is used to describe the dynamic behavior of the wind power generation system. The state variables of the system are set as , the control input is , the external disturbance is , the output variable is , then the system dynamic characteristics are expressed as: , in, Indicates the change of state variables over time. A, B, C, D, and E all represent matrix parameters, which are used to characterize the dynamic characteristics of the system, the influence of control input, disturbance coupling, and output mapping relationship; Real-time data is collected by sensors, and the matrix parameters A, B, C, D, and E are fitted using the least squares method to obtain the specific form of the dynamic prediction model.

[0009] As a preferred embodiment of the energy storage coordinated management method for wind power generation control according to the present invention, the multidimensional optimization objective function of the energy storage system is designed based on the dynamic prediction model, and the multidimensional optimization objective function includes a power fluctuation suppression term, an energy balance term, and an operating efficiency term; The power fluctuation suppression term represents the square integral of the power fluctuation, the energy balance term represents the energy difference of the energy storage system, and the operation efficiency term represents the energy conversion efficiency of the energy storage system.

[0010] The beneficial effects of this preferred technical solution are: it not only effectively suppresses the power fluctuations of wind power generation and ensures the energy balance of the energy storage system, but also significantly improves the operating efficiency and safety of the energy storage system by dynamically adjusting the charging and discharging strategy.

[0011] As a preferred solution of the energy storage coordinated management method for wind power generation power control according to the present invention, wherein: the upper-layer controller performs global optimization on the multi-dimensional optimization objective function, including: The upper controller outputs the total power of the energy storage system and energy storage status As the state quantity, the charging and discharging power of each energy storage unit As the control variable, a global optimization solution is performed based on the dynamic prediction model and the multi-dimensional optimization objective function. The goal of the optimization problem is to minimize the multi-dimensional optimization objective function. The constraints include: Power balance constraints: ; Energy storage constraint: E min ≤E storage (t)≤E max ; Charge and discharge rate constraint: P i,min ≤P i (t)≤P i,max ; Where n represents the number of energy storage units, E min and E max Represent the minimum and maximum values ​​of energy storage, P i,min and P i,max Respectively represent the minimum and maximum values ​​of charge and discharge power; The optimization problem is solved by nonlinear programming algorithm to obtain the charge and discharge power sequence in the future T time period .

[0012] As a preferred solution of the energy storage coordinated management method for wind power generation power control according to the present invention, the method of using a distributed optimization algorithm to perform local optimization on each energy storage unit includes: The lower controller uses a distributed optimization algorithm to perform local optimization on each energy storage unit, taking the charge state of the energy storage unit as the state quantity and the charge and discharge current I i (t) is used as the control variable to construct the local optimization objective function; The charge and discharge current I of each energy storage unit is converted to i (t) is used as an independent variable to solve the local optimization objective function and obtain the local variable ; Introducing the average charge and discharge current of all energy storage units As a global variable, through the formula Update global variables and gradually correct the difference between local variables and global variables through the Lagrange multiplier method;

[0013] After solving the calculation, the charge and discharge current sequence of each energy storage unit is obtained .

[0014] The beneficial effects of this preferred technical solution are: the upper-level controller performs global optimization based on the dynamic prediction model and objective function, and the lower-level controller uses a distributed optimization algorithm to achieve local optimization, thereby realizing coordinated regulation of the total power output of the energy storage system and the charging and discharging behavior of each energy storage unit.

[0015] As a preferred solution of the energy storage coordination management method for wind power generation power control described in the present invention, the basis for determining whether the energy storage system is in an overload state includes: If the state of charge of any energy storage unit exceeds the set maximum SOC max Or the state of charge of any energy storage unit is lower than the set minimum SOC min , it is considered that the energy storage system is in an overload state and an emergency control strategy needs to be activated.

[0016] As a preferred solution of the energy storage coordinated management method for wind power generation power control according to the present invention, the real-time dynamic adjustment of the energy storage system by rolling optimization includes: In each time period t, the optimization solution is applied to the energy storage system to control the charging and discharging behavior of each energy storage unit, and the real-time data of the next moment is collected through sensors, including power fluctuation P(t+1), state of charge SOC i (t+1) and external disturbance w(t+1), and input the real-time data of the next moment into the dynamic prediction model for re-optimization and solution, and realize the real-time dynamic adjustment of the energy storage system through rolling optimization.

[0017] In a second aspect, the present invention provides an energy storage coordination management system for wind power generation power control, comprising: A model building module is used to build a dynamic prediction model based on the power fluctuation characteristics of the wind power generation system; An objective function construction module, used for designing a multi-dimensional optimization objective function of an energy storage system based on the dynamic prediction model; An optimization solution module is used to perform global optimization solution on the multi-dimensional optimization objective function through the upper-level controller in the hierarchical collaborative control, and the lower-level controller uses a distributed optimization algorithm to perform local optimization solution on each energy storage unit; The rolling optimization module is used to apply the optimization solution results at the current moment to the energy storage system, collect real-time data at the next moment to re-optimize and solve, and realize real-time dynamic adjustment of the energy storage system through rolling optimization.

[0018] In a third aspect, the present invention provides an electronic device comprising a memory and a processor; the memory is used to store computer-executable instructions, and the processor implements the steps of a method for coordinated energy storage management for wind power generation control when executing the computer-executable instructions.

[0019] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of a method for coordinated energy storage management for wind power generation control.

[0020] Compared with the existing technology, the beneficial effects of the present invention are as follows: ① The dynamic prediction model of wind power fluctuation constructed by the present invention strictly follows the statistical laws and fluid mechanics characteristics of wind power fluctuation, and combines the rolling optimization mechanism to realize closed-loop control of prediction-correction, which significantly improves the response speed and adjustment accuracy of the energy storage system to high-frequency power changes, and effectively solves the problem of dynamic response lag in the existing technology. ② The present invention adopts a hierarchical collaborative architecture that conforms to the control law of distributed systems. The upper-level controller follows the global law of conservation of energy to make optimization decisions, and the lower-level controller implements local optimization based on the battery pack equivalent circuit model. Under the condition of meeting the constraints of power balance, energy storage and charge and discharge rate, it effectively improves the overall power distribution accuracy and energy utilization efficiency of the energy storage system, and improves the weak power distribution and coordinated control capabilities in the existing technology. ③ The present invention designs a multi-objective optimization function based on the laws of thermodynamics and the mechanism of battery aging, incorporating key indicators such as power fluctuation suppression, energy conversion efficiency, and battery health status into a unified framework. Through a dynamic weight adjustment mechanism, while ensuring the grid regulation needs, the energy storage system cycle efficiency is increased to 92%, and the battery life is extended by more than 30%. This overcomes the shortcomings of the existing technology that do not comprehensively consider multiple factors, resulting in limited operating efficiency and safety, and realizes multi-objective collaborative optimization under the constraints of physical laws. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1A schematic diagram of the overall process logic of an energy storage coordinated management method for wind power generation control provided by one embodiment of the present invention.

[0023] Figure 2 A flowchart of a hierarchical collaborative control strategy for a method of coordinated energy storage management for wind power generation control provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0024] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0025] Example 1, reference Figure 1-Figure 2 As one embodiment of the present invention, a method for coordinated energy storage management for wind power generation power control is provided, which aims to optimize the dynamic response capability of the energy storage system, improve the accuracy of power distribution and coordinated control, and enhance the operating efficiency and safety of the energy storage system, thereby meeting the demand for efficient and intelligent energy storage management in the field of wind power generation. Figure 1 The specific steps shown include: S100: Build a dynamic prediction model based on the power fluctuation characteristics of the wind power generation system; S200: Design of multi-dimensional optimization objective function for energy storage system based on dynamic prediction model; S300: The upper-level controller in the hierarchical collaborative control performs global optimization on the multi-dimensional optimization objective function, and the lower-level controller uses a distributed optimization algorithm to perform local optimization on each energy storage unit. S400: Apply the optimization solution result at the current moment to the energy storage system, collect real-time data at the next moment and re-optimize and solve, and realize real-time dynamic adjustment of the energy storage system through rolling optimization.

[0026] It should be noted that existing energy storage management systems are mainly designed for photovoltaic power generation and are difficult to effectively adapt to the high volatility characteristics of wind power generation. In addition, the management strategy is single and the coordination ability is weak, resulting in insufficient dynamic response and limited operating efficiency. The above steps S100~S400 construct a dynamic prediction model for wind power fluctuations, use state-space equations to describe the dynamic behavior of the system, and fit parameters based on real-time data collected by sensors. At the same time, a rolling optimization mechanism is used to track and predict wind power fluctuations in real time, significantly improving the response speed and regulation accuracy of the energy storage system to high-frequency power changes, effectively solving the problem of dynamic response lag in existing technologies, and can better adapt to the strong fluctuation characteristics of wind power.

[0027] In an embodiment of the present invention, the step S100 of constructing a dynamic prediction model according to the power fluctuation characteristics of the wind power generation system includes: The power fluctuation characteristics in the wind power generation system are modeled, and the state space equation is used to describe the dynamic behavior of the wind power generation system. The state variables of the system are set as , the control input is , the external disturbance is , the output variable is , then the system dynamic characteristics are expressed as:

[0028] in, Indicates the change of state variables over time. A, B, C, D, and E all represent matrix parameters, which are used to characterize the dynamic characteristics of the system, the influence of control input, disturbance coupling, and output mapping relationship; Real-time data is collected by sensors, and the matrix parameters A, B, C, D, and E are fitted using the least squares method to obtain the specific form of the dynamic prediction model.

[0029] Specifically, the steps of using the least squares method to fit the matrix parameters A, B, C, D, and E to obtain the specific form of the dynamic prediction model include: Define x(t) represents state variables (such as generator speed, pitch angle, etc.), u(t) represents the control input (such as pitch control signal), w(t) represents external disturbances (such as wind speed fluctuations), y(t) Represents the output variable (such as output power); the matrix parameter A , B , C , D , E is the parameter to be estimated; Since the sensor data is discretely sampled (sampling interval is Ts), a discrete-time state space model is used for parameter estimation: Equation of state: , Output equation: , where k is a discrete time index (k=0,1,2,…,N), , , , , are discrete parameter matrices (which can be used for continuous-time analysis or forecasting after estimation); the least squares method estimates these matrices by minimizing the sum of squared prediction errors. The specific steps are as follows: Sensors acquire discrete time series data: state variables x[k] (e.g., generator speed, rotor position, etc., dimension s); control inputs u[k] (e.g., pitch control signals, dimension m); external disturbances w[k] (e.g., wind speed measurements, dimension p); and output variables y[k] (e.g., output power, dimension q). Data length: N+1 sampling points from k=0 to k=N, ensuring that N is sufficiently large (typically N>5(s+m+p)) to cover system dynamics. Preprocessing of discrete time series data, including: noise removal: applying low-pass filtering or moving average to smooth the data; detrending: removing DC bias or trend in the data (e.g., using first-order differences); normalization: scaling the data to the range [-1, 1] or [0, 1] to improve numerical stability; splitting the data set: dividing it into a training set (for estimation, e.g., 70%) and a test set (for validation, e.g., 30%); Assuming that the state variable x[k] is measurable, it can be expressed in linear regression form. For each state component i=1,2,…,s: , in, , represents the parameters to be estimated, Represents the discrete parameter matrix No. i OK, Represents the discrete parameter matrix No. i OK, Represents the discrete parameter matrix No. i Row, regression vector .

[0030] Construct the data matrix: Target vector: (corresponding to state prediction from k=1 to k=N).

[0031] Regression matrix: , Each row .

[0032] Least squares estimation: , After calculation, Give , , .

[0033] Repeat for all i=1 to s and combine to get the estimated matrix , , ; in, Represents the discrete parameter matrix 、 、 The corresponding estimation matrix is, Represents the estimation matrix No. i OK, Represents the estimation matrix No. i OK, Represents the estimation matrix No. i OK; Furthermore, assuming that the output variable y[k] is measurable, for each output component j=1,2,…,q: , in, , Represents the parameters to be estimated; regression vector .

[0034] Construct the data matrix: Target vector: (Use all sampling points).

[0035] Regression matrix: ,

[0036] Each row .

[0037] Least squares estimation: , After calculation, Give , .

[0038] Repeat for all j=1 to q, and combine to get the estimated matrix , ; in, 、 Represents the discrete parameter matrix 、 The corresponding estimation matrix is, Represents the estimation matrix The jth row of Represents the estimation matrix The jth row of Furthermore, the discrete prediction model is obtained, and the formula is expressed as: , , in, represents the predicted value of the discrete state variable at time k+1, Represents the predicted output; Using the test set data, calculate the predicted output , and the actual Compare.

[0039] Evaluation metric: Root mean square error (RMSE) or goodness of fit : , in, is the output mean.

[0040] In this embodiment of the present invention, the model describes the system's dynamic behavior using state-space equations. The state variable x(t) represents the current system state, the control input u(t) represents the charge and discharge power regulation command for the energy storage system, the external disturbance w(t) includes the impact of external factors such as wind speed changes on the system, and the output variable y(t) reflects the system's actual output power. The relationship between these variables is defined by the matrices A, B, C, D, and E, which respectively characterize the system's dynamic characteristics, the influence of control inputs, disturbance coupling, and output mapping. After sensors collect real-time data, the least squares method is used to fit the parameters A, B, C, D, and E to obtain the specific form of the dynamic prediction model. The sensor data is transmitted to the model via a real-time data acquisition module to ensure that the model accurately reflects the current system operating status.

[0041] In an embodiment of the present invention, the multi-dimensional optimization objective function of designing the energy storage system based on the dynamic prediction model in step S200 includes: Design a multi-dimensional optimization objective function for the energy storage system based on a dynamic prediction model. The multi-dimensional optimization objective function includes power fluctuation suppression, energy balance, and operating efficiency. Specifically, the power fluctuation suppression term is expressed as Characterizes the square integral of power fluctuations and is used to reduce high-frequency power fluctuations. The energy balance term is expressed by the formula Characterize the energy difference of the energy storage system to ensure energy balance. The operating efficiency term is expressed by the formula Characterize the energy conversion efficiency of energy storage systems.

[0042] Among them, T represents the prediction duration of the optimization time window, represents the power output of the energy storage system in this embodiment, Represents the equivalent internal resistance of the energy storage unit, Indicates the charging and discharging current of the energy storage unit, represents the energy conversion efficiency of the energy storage system, and Represent the output power and input power of the energy storage system respectively;

[0043] Specifically, the multidimensional optimization objective function formula is expressed as: , Among them, the weight coefficient , They are determined through experiments to reflect the importance of each goal.

[0044] It should be noted that the above step S200 comprehensively considers power fluctuation suppression, energy balance and operating efficiency. At the same time, through the calculation and monitoring of the state of charge, it can determine whether the energy storage system is in an overload state and initiate the corresponding emergency control strategy. This not only effectively suppresses the power fluctuation of wind power generation and ensures the energy balance of the energy storage system, but also significantly improves the operating efficiency and safety of the energy storage system by dynamically adjusting the charging and discharging strategy, extends the service life of the energy storage unit, and overcomes the shortcomings of the existing technology that the operating efficiency and safety are limited due to the lack of comprehensive consideration of multiple factors.

[0045] In the embodiment of the present invention, Figure 2 The above step S300 includes the following sub-steps C1-C2: In C1: the multi-dimensional optimization objective function is globally optimized and solved through the upper-level controller in the hierarchical cooperative control; Specifically, the upper controller uses the total power output of the energy storage system and energy storage status As the state quantity, the charging and discharging power of each energy storage unit As the control variable, a global optimization solution is performed based on the dynamic prediction model and the multi-dimensional optimization objective function. The goal of the optimization problem is to minimize the multi-dimensional optimization objective function. The constraints include: Power balance constraints: ; Energy storage constraint: E min ≤E storage (t)≤E max ; Charge and discharge rate constraint: P i,min ≤P i (t)≤P i,max ; Where n represents the number of energy storage units, E min and E max Represent the minimum and maximum values ​​of energy storage, Pi,min and P i,max Represent the minimum and maximum values ​​of charge and discharge power respectively.

[0046] Specifically, the optimization problem is solved by nonlinear programming algorithm to obtain the charge and discharge power sequence in the future T time period , the acquired charge and discharge power sequence in the future T time period is used as the input of the lower-level controller to further refine the control strategy of each energy storage unit.

[0047] In an embodiment of the present invention, the specific steps of solving the optimization problem by using a nonlinear programming algorithm include: Based on the power fluctuation characteristics of the wind power generation system and the operating requirements of the energy storage system, the optimization goal is to minimize the multi-dimensional optimization objective function that includes power fluctuation suppression, energy balance and operating efficiency, and determine the weight coefficient of each sub-item; Organize system operating constraints, including a power balance constraint requiring the sum of the charge and discharge power of each energy storage unit to equal the total system power demand, an energy storage constraint requiring the energy state of the energy storage unit to remain within a set range, and a charge and discharge rate constraint requiring the charge and discharge power of each unit to not exceed its rated value. Convert the continuous-time optimization problem into a discrete-time form, set the appropriate time step and prediction time domain length, and convert the objective function and constraints into discrete expressions suitable for numerical calculation; Positive and negative auxiliary variables are introduced to handle absolute value constraints and transform nonlinear constraints into linear forms; Combine all optimization variables (including the future charge and discharge power sequences of each energy storage unit and auxiliary variables) into a vector form and construct a standard quadratic programming problem, including the quadratic term matrix and linear term vector that form the objective function, as well as the coefficient matrix and boundary vector of the constraint conditions; Select a nonlinear programming algorithm and call the optimization solver for calculation. After obtaining the optimized solution, verify the validity of the solution, check whether the power balance error is within the allowable range, and whether the energy state and charge and discharge power of each energy storage unit meet the constraints. The verified optimal charge and discharge power sequence is output as the input instruction of the lower-level controller, and the above optimization process is re-executed at the beginning of the next control cycle based on the latest collected real-time data of the system to realize rolling optimization control of the energy storage system.

[0048] In C2: The lower-level controller uses a distributed optimization algorithm to perform local optimization on each energy storage unit. The specific steps include: The lower controller uses a distributed optimization algorithm to perform local optimization on each energy storage unit, taking the charge state of the energy storage unit as the state quantity and the charge and discharge current I as the i(t) is used as the control variable to construct the local optimization objective function, which is expressed as follows: , in, is the target state of charge, is the reference charge and discharge current, , is the weight coefficient; The charge and discharge current I of each energy storage unit is converted to i (t) is used as an independent variable to solve the local optimization objective function and obtain the local variable ; Introducing the average charge and discharge current of all energy storage units As a global variable, through the formula Update global variables and gradually correct the difference between local variables and global variables through the Lagrange multiplier method;

[0049] After solving the calculation, the charge and discharge current sequence of each energy storage unit is obtained .

[0050] In the embodiment of the present invention, the basis for determining whether the energy storage system is in an overload state includes: if the state of charge of any energy storage unit exceeds the set maximum value SOC max Or the state of charge of any energy storage unit is lower than the set minimum SOC min , the energy storage system is considered to be in an overloaded state and an emergency control strategy needs to be activated.

[0051] The maximum SOC setting should be specified. max and minimum SOC min The setting basis is mainly based on the safe operation range, life protection and system stability requirements of the energy storage unit. Specifically, SOC max It is usually set slightly lower than the theoretical upper limit of the battery chemical material (such as 90%~95% for lithium-ion batteries) to avoid overcharging leading to thermal runaway or capacity decay; SOC min Generally, a certain margin (such as 10%~20%) is reserved to prevent electrode damage or voltage drop caused by over-discharge; For example, if the rated state of charge (SOC) range of a lithium-ion energy storage system supporting a certain wind power generation is 20% to 90%, then the SOC max =90%, SOC min = 20%. When a unit's SOC rises to 91% or drops to 19%, an overload state is triggered. Emergency control strategies may include forced discharge / charging termination or switching to a backup unit to ensure system safety and smooth grid operation.

[0052] It should be noted that step S300 achieves coordinated control of the energy storage system's total power output and the charge and discharge behavior of each energy storage unit. While satisfying constraints such as power balance, energy storage capacity, and charge and discharge rates, this effectively improves the overall power allocation accuracy and energy utilization efficiency of the energy storage system, addressing the weak power allocation and coordinated control capabilities of existing technologies.

[0053] In an embodiment of the present invention, step S400 applies the optimization solution result at the current moment to the energy storage system, collects real-time data at the next moment and performs the optimization solution again. The real-time dynamic adjustment of the energy storage system is achieved through rolling optimization, which includes: In each time period t, the optimization solution is applied to the energy storage system to control the charging and discharging behavior of each energy storage unit, and the real-time data of the next moment is collected through sensors, including power fluctuation P(t+1), state of charge SOC i (t+1) and external disturbance w(t+1), and input the real-time data of the next moment into the dynamic prediction model for re-optimization and solution, and realize the real-time dynamic adjustment of the energy storage system through rolling optimization.

[0054] Specifically, the state of charge SOC of the energy storage unit i The update mechanism formula of (t) is expressed as: , in, is the charge and discharge efficiency, C i is the capacity of the energy storage unit, and Δt is the time step.

[0055] It should be noted that step S400 above uses a rolling optimization mechanism to dynamically close-loop real-time data and optimization solution results, enabling the energy storage system to continuously respond to power changes and adjust strategies in real time, thereby significantly improving the system's adaptability to fluctuations, dynamic response speed, and regulation accuracy, effectively solving the regulation failure problem caused by prediction lag and static management in traditional technologies.

[0056] It should be noted that in actual applications, for example, a wind farm is equipped with multiple energy storage units. When the wind speed suddenly drops, causing power fluctuations, the dynamic prediction model obtains information such as the current wind speed and power output through the real-time data acquisition module, and uses the state space equation to predict future power fluctuations. The upper-level controller generates a global optimization plan based on the prediction results to determine the charge and discharge power distribution of each energy storage unit. The lower-level controller further refines the charge and discharge current of each energy storage unit through a distributed optimization algorithm to ensure the state of charge. Keep it within a reasonable range. The rolling optimization module re-collects real-time data and re-optimizes every time step to form a closed-loop control. Exceeds the set maximum SOC max Or below the minimum SOC min , the emergency control strategy is activated to limit the charging and discharging power of the unit to avoid system overload.

[0057] Through the above-mentioned specific implementation methods, the present invention realizes efficient regulation of wind power fluctuations, improves the dynamic response capability and operating efficiency of the energy storage system, and at the same time enhances the accuracy of power distribution, meeting the demand for intelligent energy storage management in the field of wind power generation.

[0058] Example 2: Based on the previous example, this example provides an application example of the energy storage coordinated management method for wind power generation power control, in order to verify and illustrate the technical effects adopted in this method.

[0059] In this example, a wind farm with an installed capacity of 50 MW is equipped with 10 lithium-ion energy storage units (5 MWh per unit, with a maximum charge / discharge power of 2 MW). Frequent wind speed fluctuations in the area (average wind speed variation rate of ±3 m / s·min) result in significant grid-connected power fluctuations (±15% of rated power). The method provided in this example is used to coordinate and manage the energy storage system, verifying its effectiveness in suppressing power fluctuations and improving energy utilization.

[0060] Set the state space equation to: x˙(t)=A⋅x(t)+B⋅u(t)+C⋅w(t) y(t)=D⋅x(t)+E⋅u(t) Fitting matrix parameters to historical data: , , C , Forecast time window: T = 10 minutes, time step Δt = 1 minute; Further optimize the objective function weight: =0.6 (power fluctuation suppression), =0.3 (energy balance), =0.1 (operation efficiency); local optimization weight: =0.7 (SOC tracking), =0.3 (current tracking).

[0061] The constraint condition is expressed as: Energy storage unit SOC range: 20% 95%; Charge and discharge rate limit: −1 MW≤P i ≤1 MW.

[0062] This example simulates a sudden drop in wind speed from 12 m / s to 8 m / s (fluctuation rate -0.4 m / s·min) within 10 minutes, causing the wind power generation to drop from 45 MW to 30 MW (fluctuation amplitude -33%). The prediction model outputs a power fluctuation curve for the next 10 minutes, with an error of less than 5% from the actual fluctuation. The upper-level controller generates a global charge and discharge plan, and the total power instruction P total The lower controller distributes the charge and discharge current of each energy storage unit to maintain SOC balance. The SOC of the energy storage unit is reduced from 50% to 42% (charge and discharge efficiency =0.95), with current tracking error less than 2%. The improved alternating direction multiplier algorithm (ADMM) converges in an average of 2 seconds per cycle (compared to 5 seconds per cycle for conventional ADMM).

[0063] Further rolling optimization effect, update the optimization instruction every 1 minute, the actual power fluctuation suppression effect is: the square integral value of the fluctuation changes from down to (A 70.8% decrease). The energy balance difference ΔE was controlled within ±0.5 MWh. The system operating efficiency η increased from 88% to 93%. Table 1 compares the performance of the proposed method with traditional methods. Traditional methods refer to energy storage management strategies based on fixed thresholds or simple proportional control, such as using a PID controller or static power allocation rules to respond to wind power fluctuations.

[0064] Table 1: Comparison of the effects of the present invention and the traditional method.

[0065] , Traditional methods lack dynamic prediction capabilities and adjust energy storage charging and discharging only based on current power deviations, resulting in delayed response, uneven SOC, and low efficiency. In contrast, the method provided in this embodiment, through state-space model prediction and hierarchical optimization, has significant advantages in dynamic response speed, power allocation accuracy, and operational efficiency: 1. The power fluctuation suppression rate is improved by 25.8%, meeting the grid's requirement for a fluctuation rate of ≤10%; 2. The SOC balance of energy storage units is improved by 57.3%, avoiding local overloads; 3. The convergence speed of the improved ADMM algorithm is increased by 60%, adapting to high-frequency rolling optimization needs.

[0066] Through the above-mentioned specific implementation methods, the present invention realizes efficient regulation of wind power fluctuations, improves the dynamic response capability and operating efficiency of the energy storage system, and at the same time enhances the accuracy of power distribution, meeting the demand for intelligent energy storage management in the field of wind power generation.

[0067] Example 3: This embodiment provides an energy storage coordination and management system for wind power generation power control, including: A model building module is used to build a dynamic prediction model based on the power fluctuation characteristics of the wind power generation system; Objective function construction module, used to design multi-dimensional optimization objective functions of energy storage systems based on dynamic prediction models; The optimization solution module is used to perform global optimization solutions for the multi-dimensional optimization objective function through the upper-level controller in the hierarchical collaborative control, and the lower-level controller uses a distributed optimization algorithm to perform local optimization solutions for each energy storage unit; The rolling optimization module is used to apply the optimization solution results at the current moment to the energy storage system, and collect real-time data at the next moment to re-optimize the solution, thereby realizing real-time dynamic adjustment of the energy storage system through rolling optimization.

[0068] It should be noted that the technical solution of the energy storage coordination management system for wind power generation power control and the technical solution of the energy storage coordination management method for wind power generation power control mentioned above belong to the same concept. For details not described in detail in the technical solution of the energy storage coordination management system for wind power generation power control in this embodiment, please refer to the description of the technical solution of the energy storage coordination management method for wind power generation power control mentioned above.

[0069] The above-mentioned unit modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0070] This embodiment also provides an electronic device, which includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, it implements a storage energy coordination management method for wind power generation power control. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the electronic device housing, or an external keyboard, touchpad or mouse.

[0071] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method proposed in the above embodiment is implemented.

[0072] The storage medium proposed in this embodiment and the method proposed in the above embodiment belong to the same inventive concept. For technical details not fully described in this embodiment, please refer to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0073] From the above description of the embodiments, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware. Of course, it can also be implemented using hardware, but in many cases the former is the preferred embodiment. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This software product can be stored on a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk, or optical disk, and includes instructions for enabling an electronic device (such as a personal computer, server, or network device) to execute the methods of the embodiments of the present invention.

[0074] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for coordinated energy storage management for wind power generation control, characterized in that: include: Construct a dynamic prediction model based on the power fluctuation characteristics of the wind power generation system; Designing a multi-dimensional optimization objective function of the energy storage system based on the dynamic prediction model; The multi-dimensional optimization objective function includes a power fluctuation suppression term, an energy balance term, and an operation efficiency term; The power fluctuation suppression term represents the square integral of the power fluctuation, the energy balance term represents the energy difference of the energy storage system, and the operation efficiency term represents the energy conversion efficiency of the energy storage system; The upper controller in the hierarchical collaborative control performs global optimization on the multi-dimensional optimization objective function, and the lower controller uses a distributed optimization algorithm to perform local optimization on each energy storage unit; The optimization solution result at the current moment is applied to the energy storage system, and the real-time data at the next moment is collected to re-optimize and solve, so as to realize the real-time dynamic adjustment of the energy storage system through rolling optimization.

2. The energy storage coordination management method for wind power generation power control according to claim 1, characterized in that: The construction of a dynamic prediction model based on the power fluctuation characteristics of the wind power generation system includes: The power fluctuation characteristics in the wind power generation system are modeled, and the state space equation is used to describe the dynamic behavior of the wind power generation system. The state variables of the system are set as , the control input is , the external disturbance is , the output variable is , then the system dynamic characteristics are expressed as: ， in, Indicates the change of state variables over time. A, B, C, D, and E all represent matrix parameters, which are used to characterize the dynamic characteristics of the system, the influence of control input, disturbance coupling, and output mapping relationship; Real-time data is collected by sensors, and the matrix parameters A, B, C, D, and E are fitted using the least squares method to obtain the specific form of the dynamic prediction model.

3. The energy storage coordination management method for wind power generation power control according to claim 2, characterized in that: The upper controller performs global optimization on the multi-dimensional optimization objective function, including: The upper controller outputs the total power of the energy storage system and energy storage status As the state quantity, the charging and discharging power of each energy storage unit As the control variable, a global optimization solution is performed based on the dynamic prediction model and the multi-dimensional optimization objective function. The goal of the optimization problem is to minimize the multi-dimensional optimization objective function. The constraints include: Power balance constraints: ; Energy storage constraint: E min ≤E storage (t)≤E max ; Charge and discharge rate constraint: P i,min ≤P i (t)≤P i,max ; Where n represents the number of energy storage units, E min and E max Represent the minimum and maximum values ​​of energy storage, P i,min and P i,max Respectively represent the minimum and maximum values ​​of charge and discharge power; The optimization problem is solved by nonlinear programming algorithm to obtain the charge and discharge power sequence in the future T time period .

4. The energy storage coordination management method for wind power generation power control according to claim 3, characterized in that: The use of a distributed optimization algorithm to perform local optimization on each energy storage unit includes: The lower controller uses a distributed optimization algorithm to perform local optimization on each energy storage unit, taking the charge state of the energy storage unit as the state quantity and the charge and discharge current I i (t) is used as the control variable to construct the local optimization objective function; The charge and discharge current I of each energy storage unit is converted to i (t) is used as an independent variable to solve the local optimization objective function and obtain the local variable ; Introducing the average charge and discharge current of all energy storage units As a global variable, through the formula Update global variables and gradually correct the difference between local variables and global variables through the Lagrange multiplier method; After solving the calculation, the charge and discharge current sequence of each energy storage unit is obtained .

5. The energy storage coordination management method for wind power generation power control according to claim 4, characterized in that: The basis for determining whether the energy storage system is in an overload state includes: If the state of charge of any energy storage unit exceeds the set maximum SOC max Or the state of charge of any energy storage unit is lower than the set minimum SOC min , it is considered that the energy storage system is in an overload state and an emergency control strategy needs to be activated.

6. The energy storage coordination management method for wind power generation power control according to claim 5, characterized in that: The real-time dynamic adjustment of the energy storage system by rolling optimization includes: In each time period t, the optimization solution is applied to the energy storage system to control the charging and discharging behavior of each energy storage unit, and the real-time data of the next moment is collected through sensors, including power fluctuation P(t+1), state of charge SOC i (t+1) and external disturbance w(t+1), and input the real-time data of the next moment into the dynamic prediction model for re-optimization and solution, and realize the real-time dynamic adjustment of the energy storage system through rolling optimization.

7. An energy storage coordination management system for wind power generation power control, applying the energy storage coordination management method for wind power generation power control according to any one of claims 1 to 6, characterized in that: include: A model building module is used to build a dynamic prediction model based on the power fluctuation characteristics of the wind power generation system; An objective function construction module, used for designing a multi-dimensional optimization objective function of an energy storage system based on the dynamic prediction model; An optimization solution module is used to perform global optimization solution on the multi-dimensional optimization objective function through the upper-level controller in the hierarchical collaborative control, and the lower-level controller uses a distributed optimization algorithm to perform local optimization solution on each energy storage unit; The rolling optimization module is used to apply the optimization solution results at the current moment to the energy storage system, collect real-time data at the next moment to re-optimize and solve, and realize real-time dynamic adjustment of the energy storage system through rolling optimization.

8. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and when the processor executes the computer-executable instructions, it implements the steps of the energy storage coordination management method for wind power generation control according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that: When the computer executable instructions are executed by a processor, the steps of the energy storage coordinated management method for wind power generation control according to any one of claims 1 to 6 are implemented.

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