Energy storage coordinated management method, system, device and medium for wind power generation power control

Abstract

The present application relates to the technical field of wind power generation research, and discloses an energy storage coordination management method, system, device and medium for wind power generation power control, comprising: constructing a dynamic prediction model according to the power fluctuation characteristics of a wind power generation system; designing a multi-dimensional optimization objective function of an energy storage system based on the dynamic prediction model; globally optimizing and solving the multi-dimensional optimization objective function through an upper controller in layered collaborative control, and locally optimizing and solving each energy storage unit through a distributed optimization algorithm by a lower controller; applying the optimization solution result of the current moment to the energy storage system, collecting real-time data of the next moment to re-optimize and solve, and realizing real-time dynamic adjustment of the energy storage system through rolling optimization. The present application significantly improves the response speed and adjustment accuracy of the energy storage system to high-frequency power changes, practically solves the problem of dynamic response lag in the prior art, and can better adapt to the characteristics of strong wind power generation power fluctuation.

Description

Technical Field

[0001] This invention relates to the field of wind power generation research technology, and in particular to energy storage coordination management methods, systems, equipment and media for wind power generation power control. Background Technology

[0002] With the rapid development of wind power generation technology, the role of energy storage coordination management methods in optimizing wind power generation power control is becoming increasingly prominent. However, existing energy storage management systems and technical solutions still have many shortcomings in adapting to the volatility of wind power generation, achieving efficient power regulation and energy storage coordination, and are difficult to fully meet the needs of practical applications.

[0003] Existing technologies propose an energy storage management system comprising 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 determines whether the energy storage module is fully charged based on preset conditions, thus issuing a corresponding full-storage message. However, this technical solution is primarily designed for photovoltaic power generation scenarios and does not fully consider the highly volatile characteristics of wind power generation, lacking dynamic response capabilities to real-time power changes. Furthermore, the system's judgment based solely on power information fails to comprehensively consider factors such as temperature and charge / discharge rates, potentially limiting the operational efficiency and safety of the energy storage system. Existing technologies also disclose an energy storage management system for photovoltaic energy storage units, proposing a system comprising a real-time acquisition module, a historical acquisition module, an analysis module, and energy storage modules. This system generates corresponding conversion strategies to control the charging and discharging operations of the battery pack through comprehensive analysis of real-time and historical parameters. However, this technical solution is mainly applicable to photovoltaic power generation scenarios, and its analysis module design does not fully consider the high-frequency fluctuation characteristics of wind power generation, potentially leading to the energy storage management strategy being unable to adjust in a timely manner to adapt to the actual needs of wind power generation. In addition, while the system has certain advantages in monitoring faulty battery packs, it has weak power distribution and coordination control capabilities for the overall energy storage system, which may affect the overall utilization efficiency of wind power generation.

[0004] The above problems indicate that existing energy storage management systems still have certain shortcomings in adapting to the fluctuations in wind power generation and achieving efficient power regulation and energy storage coordination. Summary of the Invention

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

[0006] Therefore, the present invention provides an energy storage coordination management method, system, equipment and medium for wind power generation power control, which solves the problem that existing energy storage management systems fail to fully consider the fluctuation characteristics of wind power generation and the operating rules of the energy storage system, resulting in slow dynamic response, insufficient multi-unit coordination capability and lack of comprehensive optimization objectives.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides an energy storage coordination management method for wind power generation control, comprising:

[0009] A dynamic prediction model is constructed based on the power fluctuation characteristics of wind power generation systems;

[0010] Design a multidimensional optimization objective function for the energy storage system based on the dynamic prediction model;

[0011] In the hierarchical collaborative control, the upper-level controller performs global optimization of the multidimensional optimization objective function, while the lower-level controller uses a distributed optimization algorithm to perform local optimization of each energy storage unit.

[0012] The current optimization solution is applied to the energy storage system, and real-time data from the next moment is collected for re-optimization. Real-time dynamic adjustment of the energy storage system is achieved through rolling optimization.

[0013] As a preferred embodiment of the energy storage coordinated management method for wind power generation control described in this invention, the step of constructing a dynamic prediction model based on the power fluctuation characteristics of the wind power generation system includes:

[0014] The power fluctuation characteristics of the wind power generation system are modeled, and the dynamic behavior of the wind power generation system is described by state-space equations. The state variables of the system are defined as follows: Control input is External disturbances are The output variable is Then the dynamic characteristics of the system are expressed as:

[0015] ,

[0016] in, The state variables change over time. A, B, C, D, and E are all matrix parameters used to characterize the dynamic characteristics of the system, the influence of control inputs, disturbance coupling, and output mapping relationships.

[0017] Real-time data is collected by sensors, and the specific form of the dynamic prediction model is obtained by fitting the matrix parameters A, B, C, D, and E using the least squares method.

[0018] As a preferred embodiment of the energy storage coordinated management method for wind power generation control described in this invention, wherein: the multi-dimensional optimization objective function of the energy storage system is designed based on the dynamic prediction model, the multi-dimensional optimization objective function includes a power fluctuation suppression term, an energy balance term, and an operating efficiency term;

[0019] Wherein, 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 operating efficiency term represents the energy conversion efficiency of the energy storage system.

[0020] The beneficial effects of this preferred technical solution are: it 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.

[0021] As a preferred embodiment of the energy storage coordination management method for wind power generation control described in this invention, the upper-level controller performs global optimization of the multidimensional objective function, including:

[0022] The upper-level controller outputs power at the total power of the energy storage system. and energy storage status As a state variable, the charging and discharging power of each energy storage unit is used. As a control variable, global optimization is performed based on the dynamic prediction model and the multidimensional optimization objective function. The objective of the optimization problem is to minimize the multidimensional optimization objective function, and the constraints include:

[0023] Power balance constraints: ;

[0024] Energy storage constraint: E min ≤E storage (t)≤E max ;

[0025] Charge / discharge rate constraint: P i,min ≤P i (t)≤P i,max ;

[0026] Where n represents the number of energy storage units, E min and E max P represents the minimum and maximum energy storage values, respectively. i,min and P i,max These represent the minimum and maximum values ​​of the charging and discharging power, respectively.

[0027] The optimization problem is solved using a nonlinear programming algorithm to obtain the charging and discharging power sequence over the next T time period. .

[0028] As a preferred embodiment of the energy storage coordination management method for wind power generation control described in this invention, the step of using a distributed optimization algorithm to perform local optimization solutions for each energy storage unit includes:

[0029] The lower-level controller employs a distributed optimization algorithm to perform local optimization on each energy storage unit, using the charge state of the energy storage unit as the state variable and the charging / discharging current I as the state variable. i Using (t) as the control variable, a local optimization objective function is constructed;

[0030] The charging and discharging current I of each energy storage unit is calculated using an improved alternating direction multiplier method. i Using (t) as an independent variable, solve the local optimization objective function to obtain the local variables. ;

[0031] Introducing the average charge and discharge current of all energy storage units As a global variable, through the formula Update the global variables and gradually correct the differences between local and global variables using the Lagrange multiplier method;

[0032] After calculation, the charging and discharging current sequences of each energy storage unit were obtained. .

[0033] The beneficial effects of this preferred technical solution are as follows: 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 the coordinated control of the total power output of the energy storage system and the charging and discharging behavior of each energy storage unit.

[0034] In a preferred embodiment of the energy storage coordination management method for wind power generation control described in this invention, the criteria for determining whether the energy storage system is in an overload state include:

[0035] If the state of charge of any energy storage unit exceeds the set maximum value SOC max Or the state of charge (SOC) of any energy storage unit is lower than the set minimum value. min If the condition is not met, the energy storage system is considered to be in an overload state, and an emergency control strategy needs to be activated.

[0036] As a preferred embodiment of the energy storage coordinated management method for wind power generation control described in this invention, the real-time dynamic adjustment of the energy storage system through rolling optimization includes:

[0037] Within 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. Real-time data for the next moment is collected by sensors, including power fluctuation P(t+1) and state of charge (SOC). i The system calculates the energy storage system in real time by taking the data at time (t+1) and the external disturbance w(t+1), and then inputs the real-time data of the next moment into the dynamic prediction model for optimization and solution. The system achieves real-time dynamic adjustment of the energy storage system through rolling optimization.

[0038] In a second aspect, the present invention provides an energy storage coordinated management system for wind power generation control, comprising:

[0039] The model building module is used to build dynamic prediction models based on the power fluctuation characteristics of wind power generation systems.

[0040] The objective function construction module is used to design a multidimensional optimization objective function for the energy storage system based on the dynamic prediction model.

[0041] The optimization solution module is used to perform global optimization solution of the multidimensional 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 of each energy storage unit.

[0042] The rolling optimization module is used to apply the optimization solution at the current moment to the energy storage system and collect real-time data at the next moment to re-optimize and solve the solution. The rolling optimization method realizes the real-time dynamic adjustment of the energy storage system.

[0043] Thirdly, the present invention provides an electronic device, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor, when executing the computer-executable instructions, implements the steps of an energy storage coordinated management method for wind power generation control.

[0044] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of an energy storage coordination management method for wind power generation control.

[0045] Compared with existing technologies, the beneficial effects of this invention are as follows: ① The dynamic prediction model for wind power generation fluctuations constructed in this invention strictly follows the statistical laws and hydrodynamic characteristics of wind power generation fluctuations. Combined with a rolling optimization mechanism, it achieves closed-loop control of prediction and correction, significantly improving the response speed and adjustment accuracy of the energy storage system to high-frequency power changes, and effectively solving the problem of dynamic response lag in existing technologies. ② This invention adopts a hierarchical collaborative architecture that conforms to the control laws of distributed systems. The upper-level controller follows the global energy conservation law to make optimization decisions, while the lower-level controller performs local optimization based on the equivalent circuit model of the battery pack. Under the constraints of power balance, energy storage, and charge / discharge rate, it effectively improves the overall power allocation accuracy and energy utilization efficiency of the energy storage system, and improves the weak power allocation and coordination control capabilities in existing technologies. ③ This invention is based on a multi-objective optimization function designed according to thermodynamic laws and battery aging mechanisms. It incorporates key indicators such as power fluctuation suppression, energy conversion efficiency, and battery health status into a unified framework. Through a dynamic weight adjustment mechanism, it can improve the cycle efficiency of the energy storage system to 92% and extend battery life by more than 30% while ensuring the grid regulation needs. This overcomes the shortcomings of existing technologies that do not comprehensively consider multiple factors, which leads to limited operating efficiency and safety, and achieves multi-objective collaborative optimization under the constraints of physical laws. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the overall process logic of an energy storage coordination management method for wind power generation control, provided as an embodiment of the present invention.

[0048] Figure 2 A flowchart illustrating a hierarchical collaborative control strategy for an energy storage coordinated management method for wind power generation control, provided as an embodiment of the present invention. Detailed Implementation

[0049] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0050] Example 1, referring to Figures 1-2As one embodiment of the present invention, an energy storage coordination management method for wind power generation power control is provided, aiming to optimize the dynamic response capability of the energy storage system, improve the accuracy of power distribution and coordination control, and enhance the operating efficiency and safety of the energy storage system, thereby meeting the needs of the wind power generation field for efficient and intelligent energy storage management. Figure 1 The specific steps shown are as follows:

[0051] S100: Construct a dynamic prediction model based on the power fluctuation characteristics of the wind power generation system;

[0052] S200: A multi-dimensional optimization objective function for designing energy storage systems based on dynamic prediction models;

[0053] S300: The upper-level controller in the hierarchical collaborative control performs global optimization of the multi-dimensional optimization objective function, while the lower-level controller uses a distributed optimization algorithm to perform local optimization of each energy storage unit.

[0054] S400: The current optimization solution is applied to the energy storage system, and real-time data from the next moment is collected to re-optimize and solve the problem. Real-time dynamic adjustment of the energy storage system is achieved through rolling optimization.

[0055] 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. Furthermore, their management strategies are singular and their coordination capabilities are weak, resulting in insufficient dynamic response and limited operational efficiency. Steps S100 to S400 above construct a dynamic prediction model for wind power generation fluctuations, use state-space equations to describe the dynamic behavior of the system, and combine real-time data collected by sensors to fit parameters. At the same time, by utilizing a rolling optimization mechanism, it can track and predict wind power generation fluctuations in real time, significantly improving the response speed and adjustment accuracy of the energy storage system to high-frequency power changes. This effectively solves the problem of lag in dynamic response in existing technologies and can better adapt to the characteristics of strong power volatility in wind power generation.

[0056] In this embodiment of the invention, step S100, which involves constructing a dynamic prediction model based on the power fluctuation characteristics of the wind power generation system, includes:

[0057] The power fluctuation characteristics of the wind power generation system are modeled, and the dynamic behavior of the wind power generation system is described by state-space equations. The state variables of the system are defined as follows: Control input is External disturbances are The output variable is Then the dynamic characteristics of the system are expressed as:

[0058]

[0059] in, The state variables change over time. A, B, C, D, and E are all matrix parameters used to characterize the dynamic characteristics of the system, the influence of control inputs, disturbance coupling, and output mapping relationships.

[0060] Real-time data is collected by sensors, and the specific form of the dynamic prediction model is obtained by fitting the matrix parameters A, B, C, D, and E using the least squares method.

[0061] Specifically, the steps for fitting the matrix parameters A, B, C, D, and E using the least squares method to obtain the specific form of the dynamic prediction model include:

[0062] Define x(t) Representing state variables (such as generator speed, pitch angle, etc.), u(t) This represents the control input (such as the pitch control signal), w(t). Representing external disturbances (such as wind speed fluctuations), y(t) Represents output variables (such as output power); matrix parameter A B C D E These are the parameters that need to be estimated;

[0063] Since the sensor data is discretely sampled (sampling interval is Ts), a discrete-time state-space model is used for parameter estimation:

[0064] Equations of state: ,

[0065] Output equation: ,

[0066] Where k is the discrete-time index (k=0,1,2,…,N). , , , , These are discrete parameter matrices (which, once estimated, can be used for continuous-time analysis or prediction); the least squares method estimates these matrices by minimizing the sum of squared prediction errors. The specific steps are as follows:

[0067] Discrete-time series data is acquired through sensors: state variables x[k] (e.g., generator speed, rotor position, etc., dimension s); control input u[k] (e.g., pitch control signal, dimension m); external disturbance w[k] (e.g., wind speed measurement, dimension p); output variables y[k] (e.g., output power, dimension q). Data length: N+1 sampling points k=0 to k=N), ensuring N is large enough (usually N>5(s+m+p)) to cover system dynamics;

[0068] Preprocessing of discrete time series data includes: noise removal: applying low-pass filtering or moving average to smooth the data; detrending: removing DC bias or trend from the data (e.g., using first-order differencing); normalization: scaling the data to the range of [-1, 1] or [0, 1] to improve numerical stability; and splitting the dataset: dividing it into a training set (for estimation, e.g., 70%) and a test set (for validation, e.g., 30%).

[0069] Assuming the state variable x[k] is measurable, it can be represented by linear regression for each state component i=1,2,…,s:

[0070] ,

[0071] in, , Indicates the parameter to be estimated. Represents the discrete parameter matrix The i OK, Represents the discrete parameter matrix The i OK, Represents the discrete parameter matrix The i Rows, regression vectors .

[0072] Constructing the data matrix:

[0073] Target vector:

[0074] (Predicting the state from k=1 to k=N).

[0075] Regression matrix:

[0076] ,

[0077] Each row .

[0078] Least squares estimation:

[0079] ,

[0080] After calculation, Give , , .

[0081] Repeat this process for all i=1 to s, combining the results to obtain the estimated matrix. , , ;

[0082] in, Representing discrete parameter matrices respectively , , The corresponding estimated matrix, Represents the estimation matrix The i OK, Represents the estimation matrix The i OK, Represents the estimation matrix The i OK;

[0083] Furthermore, assuming the output variable y[k] is measurable, for each output component j=1,2,…,q:

[0084] ,

[0085] in, , Represents the parameters to be estimated; regression vector .

[0086] Constructing the data matrix:

[0087] Target vector:

[0088] (Use all sampling points).

[0089] Regression matrix:

[0090] ,

[0091] Each row .

[0092] Least squares estimation:

[0093] ,

[0094] After calculation, Give , .

[0095] Repeat this process for all j=1 to q, combining the results to obtain the estimated matrix. , ;

[0096] in, , Representing discrete parameter matrices respectively , The corresponding estimated matrix, Represents the estimation matrix The j-th row, Represents the estimation matrix The j-th row;

[0097] Furthermore, the discrete prediction model is obtained, expressed by the formula:

[0098] ,

[0099] ,

[0100] in, This represents the predicted value of the discrete state variable at time k+1. Indicates the predicted output;

[0101] Calculate the predicted output using the test set data. , and reality Compare.

[0102] Evaluation metrics: Root Mean Square Error (RMSE) or goodness of fit :

[0103] ,

[0104] in, It outputs the mean.

[0105] In this embodiment of the invention, the model describes the dynamic behavior of the system through state-space equations. The state variable x(t) represents the current state of the system, the control input u(t) represents the charging and discharging power adjustment command of the energy storage system, the external disturbance w(t) includes the influence of external factors such as wind speed changes on the system, and the output variable y(t) reflects the actual power output of the system. The relationships between these variables are determined by matrices A, B, C, D, and E, which respectively characterize the system's dynamic characteristics, the influence of the control input, disturbance coupling, and the output mapping relationship. After sensors collect real-time data, the parameters A, B, C, D, and E are fitted using the least squares method to obtain the specific form of the dynamic prediction model. The data collected by the sensors is transmitted to the model through a real-time data acquisition module to ensure that the model accurately reflects the current operating state of the system.

[0106] In this embodiment of the invention, step S200, which designs the multidimensional optimization objective function of the energy storage system based on a dynamic prediction model, includes:

[0107] The multidimensional optimization objective function of the energy storage system is designed based on the dynamic prediction model. The multidimensional optimization objective function includes a power fluctuation suppression term, an energy balance term, and an operating efficiency term.

[0108] Specifically, the power fluctuation suppression term is expressed by the formula... The square integral characterizing power fluctuations is used to reduce high-frequency power fluctuations. The energy balance term is expressed by the formula... The energy difference characterizes the energy storage system, ensuring energy balance. The operating efficiency term is expressed by the formula... Characterizes the energy conversion efficiency of energy storage systems.

[0109] Where T represents the prediction duration of the optimized time window, This indicates the power output of the energy storage system in this embodiment. This represents the equivalent internal resistance of the energy storage unit. This indicates the charging and discharging current of the energy storage unit. Indicates the energy conversion efficiency of the energy storage system. and These represent the output power and input power of the energy storage system, respectively.

[0110] Specifically, the multidimensional optimization objective function is expressed as follows:

[0111] ,

[0112] Among them, the weighting coefficient , The importance of each objective was determined through experiments.

[0113] 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 activate 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, and extends the service life of the energy storage unit. This overcomes the shortcomings of existing technologies that do not comprehensively consider multiple factors, which leads to limited operating efficiency and safety.

[0114] In embodiments of the present invention, such as Figure 2 The above step S300 includes the following sub-steps C1~C2:

[0115] In C1: The multidimensional optimization objective function is globally optimized and solved by the upper-level controller in the hierarchical collaborative control.

[0116] Specifically, the upper-level controller outputs the total power of the energy storage system. and energy storage status As a state variable, the charging and discharging power of each energy storage unit is used. As a control variable, global optimization is performed based on a dynamic prediction model and a multidimensional optimization objective function. The goal of the optimization problem is to minimize the multidimensional optimization objective function, and the constraints include:

[0117] Power balance constraints: ;

[0118] Energy storage constraint: E min ≤E storage (t)≤E max ;

[0119] Charge / discharge rate constraint: P i,min ≤P i (t)≤P i,max ;

[0120] Where n represents the number of energy storage units, E min and E max P represents the minimum and maximum energy storage values, respectively. i,min and P i,max These represent the minimum and maximum values ​​of the charging and discharging power, respectively.

[0121] Specifically, the optimization problem is solved using a nonlinear programming algorithm to obtain the charging and discharging power sequence for the next time period T. The obtained charging and discharging power sequence within the future time period T is used as the input to the lower-level controller to further refine the control strategy of each energy storage unit.

[0122] In this embodiment of the invention, the specific steps for solving the optimization problem using a nonlinear programming algorithm include:

[0123] Based on the power fluctuation characteristics of wind power generation systems and the operational requirements of energy storage systems, the optimization objective is defined as minimizing a multi-dimensional optimization objective function that includes power fluctuation suppression, energy balance, and operational efficiency, and the weight coefficients of each component are determined.

[0124] Organize the system operation constraints, including the power balance constraint that requires the sum of the charging and discharging power of each energy storage unit to be equal to the total power demand of the system, the energy storage constraint that the energy state of the energy storage unit must be maintained within a set range, and the charging and discharging rate constraint that the charging and discharging power of each unit must not exceed its rated value.

[0125] The continuous-time optimization problem is transformed into a discrete-time form by setting an appropriate time step and prediction time domain length, and the objective function and constraints are transformed into discrete expressions suitable for numerical computation.

[0126] Positive and negative auxiliary variables are introduced to handle absolute value constraints, transforming nonlinear constraints into linear forms;

[0127] All optimization variables (including the charging and discharging power sequence of each energy storage unit in the future period and auxiliary variables) are combined into a vector form to 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.

[0128] The nonlinear programming solution algorithm is selected and the optimization solver is called to perform the calculation. After obtaining the optimized solution, the validity of the solution is verified, the power balance error is checked to see if it is within the allowable range, and the energy state and charging and discharging power of each energy storage unit meet the constraints.

[0129] The validated optimal charge and discharge power sequence is output as the input command for the lower-level controller. At the beginning of the next control cycle, the above optimization process is re-executed based on the latest collected real-time system data to achieve rolling optimization control of the energy storage system.

[0130] In C2: The lower-level controller uses a distributed optimization algorithm to perform local optimization solutions for each energy storage unit. The specific steps include:

[0131] The lower-level controller employs a distributed optimization algorithm to perform local optimization on each energy storage unit, using the charge state of the energy storage unit as the state variable and the charging / discharging current I as the variable. i Using (t) as the control variable, a local optimization objective function is constructed, expressed as follows:

[0132] ,

[0133] in, For the target state of charge, For reference charging and discharging current, , These are the weighting coefficients;

[0134] The charging and discharging current I of each energy storage unit is calculated using an improved alternating direction multiplier method. i (t) are treated as independent variables. Solving the local optimization objective function yields the local variables. ;

[0135] Introducing the average charge and discharge current of all energy storage units As a global variable, through the formula Update the global variables and gradually correct the differences between local and global variables using the Lagrange multiplier method;

[0136] After calculation, the charging and discharging current sequences of each energy storage unit were obtained. .

[0137] In this embodiment of the invention, the criteria for determining whether the energy storage system is in an overload state include: if the state of charge of any energy storage unit exceeds a set maximum value (SOC). max Or the state of charge (SOC) of any energy storage unit is lower than the set minimum value. min If the energy storage system is overloaded, an emergency control strategy needs to be initiated.

[0138] It should be noted that the set maximum value of SOC max and the minimum value of SOC min are set mainly based on the safe operating range of the energy storage unit, life protection, and system stability requirements. Specifically, for 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 thermal runaway or capacity decay caused by overcharging; for SOC min generally a certain margin is reserved (such as 10% - 20%) to prevent electrode damage or voltage drop caused by over-discharging;

[0139] Exemplarily, if the rated state of charge SOC range of a lithium-ion energy storage system supporting a wind power generation is 20% - 90%, then SOC max is set to 90%, and SOC min is set to 20%. When the SOC of a certain unit rises to 91% or drops to 19%, an overload state is triggered, and the emergency control strategy may include forced discharge / charge termination or switching to a standby unit to ensure the safety of the system and the stable operation of the power grid.

[0140] It should be noted that the above step S300 realizes the coordinated control of the total power output of the energy storage system and the charge and discharge behaviors of each energy storage unit. Under the conditions of meeting the constraints such as 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 prior art.

[0141] In the embodiment of the present invention, the above step S400 applies the optimization solution result at the current moment to the energy storage system, and collects the real-time data at the next moment to re-perform the optimization solution, and realizes the real-time dynamic adjustment of the energy storage system through the rolling optimization method, including:

[0142] In each time period t, the optimization solution result is applied to the energy storage system to control the charge and discharge behaviors of each energy storage unit, and the real-time data at 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 the real-time data at the next moment is input into the dynamic prediction model to re-perform the optimization solution, and the real-time dynamic adjustment of the energy storage system is realized through the rolling optimization method.

[0143] Specifically, the update mechanism formula of the state of charge SOC i (t) of the energy storage unit is expressed as:

[0144] ,

[0145] Among them, For charge / discharge efficiency, C i Let Δt be the capacity of the energy storage unit, and Δt be the time step.

[0146] It should be noted that step S400 above uses a rolling optimization mechanism to dynamically link real-time data with optimization results in a closed loop, enabling the energy storage system to continuously respond to power changes and adjust its strategy in real time. This significantly improves 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.

[0147] It should be noted that in practical applications, such as a wind farm equipped with multiple energy storage units, when a sudden drop in wind speed causes power fluctuations, the dynamic prediction model acquires information such as current wind speed and power output through a real-time data acquisition module, and uses state-space equations to predict future power fluctuations. The upper-level controller generates a global optimization scheme based on the prediction results, determining the charging and discharging power allocation for each energy storage unit. The lower-level controller further refines the charging and discharging current of each energy storage unit through a distributed optimization algorithm to ensure the state of charge. The system remains within a reasonable range. The rolling optimization module re-collects real-time data and re-optimizes after each time step, forming a closed-loop control. If the state of charge of a certain energy storage unit... Exceeding the set maximum SOC max or below the minimum SOC min If this occurs, an emergency control strategy will be activated to limit the charging and discharging power of the unit and prevent system overload.

[0148] Through the above specific implementation methods, the present invention achieves efficient regulation of wind power generation power fluctuations, improves the dynamic response capability and operating efficiency of the energy storage system, and enhances the accuracy of power allocation, thus meeting the needs of the wind power generation field for intelligent energy storage management.

[0149] Example 2, based on the previous example, provides an application example of an energy storage coordinated management method for wind power generation control, to verify and illustrate the technical effects of the method.

[0150] In this embodiment, a wind farm with an installed capacity of 50 MW is configured with 10 lithium-ion energy storage units (each with a capacity of 5MWh and a maximum charge / discharge power of 2 MW). Due to frequent wind speed fluctuations in the area (average wind speed change rate ±3 m / s·min), the grid-connected power fluctuates significantly (±15% of rated power). The method provided in this embodiment is used to coordinate and manage the energy storage system, verifying its effectiveness in suppressing power fluctuations and improving energy utilization.

[0151] The state-space equations are set as follows:

[0152] x˙(t)=A⋅x(t)+B⋅u(t)+C⋅w(t)

[0153] y(t) = D⋅x(t) + E⋅u(t)

[0154] Fit matrix parameters using historical data:

[0155] , C ,

[0156] Prediction time window: T = 10 minutes, time step Δt = 1 minute;

[0157] Further optimize the objective function weights: =0.6 (power fluctuation suppression). =0.3 (energy balance). =0.1 (running efficiency); local optimization weight: =0.7 (SOC tracking) =0.3 (current tracking).

[0158] The constraint is expressed as: Energy storage unit SOC range: 20% 95%; Charge / discharge rate limit: −1 MW≤P i ≤1 MW.

[0159] This embodiment simulates a sudden drop in wind speed from 12 m / s to 8 m / s within 10 minutes (fluctuation rate -0.4 m / s·min), causing wind power generation to decrease 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% compared to the actual fluctuation. The upper-level controller generates a global charge / discharge plan, with a total power command P. total The capacity is 15MW (energy storage system to compensate for power deficit). The lower-level controller allocates the charging and discharging current to each energy storage unit to maintain a balanced State of Charge (SOC). The SOC of the energy storage unit decreases from 50% to 42% (charge and discharge efficiency). =0.95), current tracking error is less than 2%. The convergence time of the improved alternating direction multiplier algorithm (ADMM) is: an average of 2 seconds / cycle (the traditional ADMM is 5 seconds / cycle).

[0160] Further rolling optimization effects, with optimization instructions updated every minute, resulted in the following actual power fluctuation suppression effect: the integral value of the squared fluctuation from... Down to (Reduction of 70.8%). The energy balance difference ΔE is controlled within ±0.5 MWh. The system operating efficiency η increases from 88% to 93%. Table 1 shows a comparison of various indicators using the method of this invention and the traditional method. The traditional method refers to energy storage management strategies based on fixed thresholds or simple proportional control, such as using PID controllers or static power allocation rules to respond to wind power fluctuations.

[0161] Table 1: Comparison of the effects of the present invention and traditional methods.

[0162] ,

[0163] Traditional methods lack dynamic prediction capabilities, adjusting energy storage charging and discharging solely based on current power deviations, resulting in lag response, unbalanced State of Charge (SOC), and low efficiency. In contrast, the method provided in this embodiment, through state-space model prediction and hierarchical optimization, offers significant advantages in dynamic response speed, power allocation accuracy, and operational efficiency: 1. Power fluctuation suppression rate is improved by 25.8%, meeting the grid's requirement of fluctuation rate ≤10%; 2. Energy storage unit SOC balance is improved by 57.3%, avoiding local overload; 3. The improved ADMM algorithm achieves a 60% increase in convergence speed, adapting to high-frequency rolling optimization requirements.

[0164] Through the above specific implementation methods, the present invention achieves efficient regulation of wind power generation power fluctuations, improves the dynamic response capability and operating efficiency of the energy storage system, and enhances the accuracy of power allocation, thus meeting the needs of the wind power generation field for intelligent energy storage management.

[0165] Example 3: This example provides an energy storage coordinated management system for wind power generation control, including:

[0166] The model building module is used to build dynamic prediction models based on the power fluctuation characteristics of wind power generation systems.

[0167] The objective function construction module is used to design multidimensional optimization objective functions for energy storage systems based on dynamic prediction models.

[0168] The optimization solution module is used to perform global optimization of the multi-dimensional optimization objective function through the upper-level controller in the hierarchical collaborative control, while the lower-level controller uses a distributed optimization algorithm to perform local optimization of each energy storage unit.

[0169] The rolling optimization module is used to apply the optimization results of the current moment to the energy storage system and collect real-time data of the next moment to re-optimize and solve the problem. The rolling optimization method realizes the real-time dynamic adjustment of the energy storage system.

[0170] It should be noted that the technical solution of the energy storage coordinated management system for wind power generation control is based on the same concept as the technical solution of the energy storage coordinated management method for wind power generation control described above. For details not described in detail in the technical solution of the energy storage coordinated management system for wind power generation control in this embodiment, please refer to the description of the technical solution of the energy storage coordinated management method for wind power generation control described above.

[0171] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0172] 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 provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements an energy storage coordination management method for wind power generation control. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0173] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method proposed in the above embodiments.

[0174] The storage medium proposed in this embodiment belongs to the same inventive concept as the method proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0175] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute the method of the embodiments of the present invention.

[0176] 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 it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for coordinated management of energy storage for wind power control, characterized in that, The method comprises the following steps: constructing a dynamic prediction model according to 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 comprises a power fluctuation suppression term, an energy balance term, and an operation efficiency term; wherein 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; globally optimizing and solving the multi-dimensional optimization objective function by using an upper controller in hierarchical collaborative control, and locally optimizing and solving each energy storage unit by using a distributed optimization algorithm by a lower controller; applying the optimization and solving result of the current time to the energy storage system, collecting real-time data at the next time to re-optimize and solve, and realizing real-time dynamic adjustment of the energy storage system by means of rolling optimization; the step of constructing a dynamic prediction model according to the power fluctuation characteristics of the wind power generation system comprises the following steps: Modeling the power fluctuation characteristics in a wind power system, adopting a state space equation to describe dynamic behavior of the wind power system, setting state variables of the system as , control inputs as , external disturbances as , and output variables as , then the system dynamic characteristics are expressed as: ， wherein, represents the change of state variable with time, A, B, C, D, E all represent matrix parameters, which are used to represent the dynamic characteristics of the system, the influence of control input, the coupling of disturbance and the mapping relationship of output; collecting real-time data by using a sensor, fitting the matrix parameters A, B, C, D, and E to obtain the specific form of the dynamic prediction model by using the least square method; the step of locally optimizing and solving each energy storage unit by using a distributed optimization algorithm comprises the following steps: The lower controller adopts a distributed optimization algorithm to locally optimize each energy storage unit, takes the state of charge of the energy storage unit as a state variable, and takes the charging and discharging current I i (t) constructing a local optimization objective function as a control variable; The charging and discharging current I of each energy storage unit is calculated by an improved alternating direction multiplier method i (t) solving the local optimization objective function as an independent variable, obtaining a local variable ; Introducing the average charge and discharge current of all energy storage units As global variables, by formula Updating the global variables and stepwise correcting the difference between local and global variables by Lagrange multiplier method; After solving the calculation, the charge and discharge current sequence of each energy storage unit is obtained .

2. The energy storage coordinated management method for wind power generation power control according to claim 1, characterized in that, the step of globally optimizing and solving the multi-dimensional optimization objective function by using an upper controller comprises the following steps: The upper controller takes the total power output of the energy storage system and energy storage state as a state variable, the charge and discharge power of each energy storage unit as a control variable, based on the dynamic prediction model and the multi-dimensional optimization objective function, the global optimization is solved, the objective of the optimization problem is to minimize the multi-dimensional optimization objective function, and the constraint conditions include: Power balance constraint: ; 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, respectively, P i,min and P i,max represent the minimum and maximum values of charge and discharge power, respectively; solving the optimization problem by a nonlinear programming algorithm to obtain the charging and discharging power sequence in the future T time period .

3. The energy storage coordinated management method for wind power generation power control according to claim 1, characterized in that, the basis for judging whether the energy storage system is in an overload state comprises the following steps: If the state of charge of any energy storage unit exceeds a set maximum value SOC max or the state of charge of any energy storage unit falls below a set minimum value SOC min the energy storage system is considered to be in an overload state and an emergency control strategy needs to be initiated.

4. The energy storage coordinated management method for wind power generation power control according to claim 3, characterized in that, the step of realizing real-time dynamic adjustment of the energy storage system by means of rolling optimization comprises the following steps: 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, real-time data at the next time point is collected through a sensor, including power fluctuation P(t+1), state of charge SOC i (t+1) and external disturbance w(t+1), and the real-time data at the next time point is input into the dynamic prediction model to re-optimize and solve, so as to realize real-time dynamic adjustment of the energy storage system through rolling optimization.

5. The energy storage coordinated management system for wind power generation power control, applying the energy storage coordinated management method for wind power generation power control according to any one of claims 1-4, characterized in that, The method comprises the following steps: a model construction module is configured to construct a dynamic prediction model according to the power fluctuation characteristics of the wind power generation system; a target function construction module is configured to design a multi-dimensional optimization objective function of the energy storage system based on the dynamic prediction model; an optimization and solving module is configured to globally optimize and solve the multi-dimensional optimization objective function by using an upper controller in hierarchical collaborative control, and locally optimize and solve each energy storage unit by using a distributed optimization algorithm by a lower controller; a rolling optimization module is configured to apply the optimization and solving result of the current time to the energy storage system, collect real-time data at the next time to re-optimize and solve, and realize real-time dynamic adjustment of the energy storage system by means of rolling optimization. 6.An electronic device comprising a memory and a processor, the electronic device characterized by: The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to realize the steps of the energy storage coordination management method for wind power generation power control according to any one of claims 1-4.

7. A computer-readable storage medium having stored thereon computer- executable instructions, the computer-executable instructions comprising instructions for causing a computer to: The computer executable instructions are executed by the processor to realize the steps of the energy storage coordination management method for wind power generation power control according to any one of claims 1-4. ​

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