Intelligent power grid integrated breeze power generation and energy storage cooperation system

Abstract

The invention relates to the technical field of power grid energy storage systems, and discloses a smart power grid integrated breeze power generation and energy storage cooperation system, which comprises a first prediction module, a second prediction module, a data processing module, a strategy module and a monitoring module, wherein the first prediction module is used for performing wind speed prediction on an area where the breeze power generation network is located; the second prediction module is used for predicting the load capacity of the power grid; the data processing module calculates a generating capacity predicted value based on the wind speed predicted value; the strategy module formulates a charging and discharging strategy of the energy storage device based on the load capacity predicted value and the generating capacity predicted value; the monitoring module is used for monitoring power grid parameters in real time, including real-time generating capacity and load capacity, and adjusting charging and discharging strategies based on power grid parameter control. According to the invention, deep fusion of breeze power generation and an energy storage system is realized, and a new path is opened up for constructing a modern electric power system which is more intelligent, efficient and sustainable.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid energy storage systems, and in particular to a smart grid-integrated micro-wind power generation and energy storage collaborative system. Background Art

[0002] In recent years, micro-wind power generation technology has made significant progress, not only in terms of blade design and material selection, but also in terms of intelligent control systems. At the same time, through reasonable energy storage configuration and management, the fluctuation of output power of renewable energy such as wind power can be effectively smoothed, and the stability and reliability of power grid operation can be improved. Although micro-wind power generation technology and energy storage technology have each achieved certain achievements, they still face many challenges in practical applications.

[0003] Wind speed prediction, especially ultra-short-term prediction (such as within a few hours), is difficult, which directly affects the scheduling planning of micro-wind power generation networks. The currently used statistical methods or machine learning models often have difficulty in accurately capturing the complex patterns of wind speed changes, resulting in large deviations in prediction results. Although existing energy storage devices can alleviate the problem of unstable output of renewable energy to a certain extent, they themselves have problems such as low charging and discharging efficiency and high costs. In addition, the lack of effective strategies to guide the optimal charging and discharging time of energy storage equipment has resulted in energy storage resources not being fully utilized. The collaboration between micro-wind power generation and energy storage systems is not close enough. The two usually operate independently and lack a unified coordination mechanism. This means that when there is an excess of wind power generation, excess energy cannot be stored in time, and when there is insufficient power generation, the reserve energy cannot be quickly released to supplement the gap, affecting the flexibility and economy of the overall system. Faced with the ever-changing power grid environment, existing control methods mostly rely on preset rules or empirical judgments, lacking sufficient adaptability and rapid response capabilities. Especially under extreme weather conditions, how to ensure the safety and stability of power supply has become a major problem.

[0004] For example, a Chinese patent with the authorization announcement number CN116014866B discloses a power supply method and system based on a breeze wind power generation wall, which belongs to the technical field of clean energy power supply system, and cooperates with photovoltaic power generation technology and energy storage technology to achieve stable and convenient clean energy. The method includes obtaining the operating data of the main battery, the operating data includes multiple sampling moments and corresponding multiple sampling voltages; determining whether there is at least one sampling voltage lower than the set voltage among the multiple sampling voltages; if there is at least one sampling voltage, determining at least one sampling moment corresponding to the at least one sampling voltage; determining the maximum first time interval between at least one sampling moment, and determining the minimum second time interval between the current moment obtained and at least one sampling moment; generating and outputting a control instruction according to the first time interval and the second time interval; wherein the control instruction is used to control the backup battery to charge the main battery.

[0005] For example, a patent application with publication number CN101237162A discloses a micro-wind power generation and energy storage device, including: a fan; a generator connected to the fan and capable of converting into electrical energy; the generator is connected to an energy storage device; the energy storage device is connected to the outside through a diode and a switch; and a bracket for fixing the fan and the generator. The invention can convert kinetic energy into electrical energy and store the electrical energy.

[0006] The above patents all have the problem raised by this background technology: the cooperation between micro-wind power generation and energy storage system is not close enough, the two usually operate independently and lack a unified coordination mechanism.

[0007] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgement or any form of suggestion that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the invention

[0008] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a smart grid integrated micro-wind power generation and energy storage collaborative system to achieve deep integration of micro-wind power generation and energy storage systems, thus opening up a new path for building a more intelligent, efficient and sustainable modern power system.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0010] A smart grid integrated micro-wind power generation and energy storage collaborative system includes a first prediction module, a second prediction module, a data processing module, a strategy module, and a monitoring module; wherein:

[0011] The first prediction module is used to predict the wind speed in the area where the breeze power generation network is located and output the wind speed prediction value;

[0012] The second prediction module is used to predict the load of the power grid and output the load prediction value;

[0013] The data processing module calculates the power generation prediction value based on the wind speed prediction value;

[0014] The strategy module formulates a charging and discharging strategy for the energy storage device based on the load prediction value and the power generation prediction value;

[0015] The monitoring module is used to monitor the grid parameters in real time, including the real-time power generation and load, and adjust the charging and discharging strategy based on the grid parameter control.

[0016] As a preferred solution of the micro-wind power generation and energy storage collaborative system integrated with the smart grid of the present invention, wherein: the first prediction module includes a first statistical unit and a first prediction unit; wherein the first statistical unit is used to collect and count the historical wind speed data of the area where the micro-wind power generation network is located; the first prediction unit predicts the wind speed of the area where the micro-wind power generation network is located based on the historical wind speed data;

[0017] The first statistical unit cleans and normalizes the historical wind speed data and organizes them into a time series; the first prediction unit is configured with a time series prediction model, which is any one of an RNN model, an LSTM model, and a GRU model, and is used to process the time series of historical wind speed data and output continuous wind speed prediction values.

[0018] As a preferred solution of the micro-wind power generation and energy storage collaborative system integrated with the smart grid of the present invention, the second prediction module includes a second statistical unit and a second prediction unit; wherein the second statistical unit is used to collect and count the historical load data of the power grid; the second prediction unit predicts the load of the power grid based on the historical load data, and outputs the load prediction value of the future m time steps, where m is a positive integer;

[0019] The second statistical unit cleans and normalizes the historical load data and organizes it into a time series; each historical load data in the time series of the historical load data corresponds to a time step; the second prediction unit is configured with a time series prediction model, and the time series prediction model calculates the load prediction value of the next m time steps based on the time series of the historical load data.

[0020] As a preferred solution of the micro-wind power generation and energy storage collaborative system integrated with the smart grid described in the present invention, the data processing module includes a data sorting unit and a power generation prediction unit; wherein the data sorting unit is used to sort the wind speed prediction value, specifically as follows: the continuous wind speed prediction value is divided into m segments corresponding to time steps and load prediction values; the data sorting unit is configured with the rated wind speed of the micro-wind power generation network, and for the wind speed prediction value in any time step, the duration of the time period in which the wind speed prediction value is greater than the rated wind speed is counted, recorded as t1; the duration of the time period in which the wind speed prediction value is not greater than the rated wind speed is counted, recorded as t2, and the average wind speed in the time period in which the wind speed prediction value is not greater than the rated wind speed is calculated, recorded as v s .

[0022] As a preferred solution of the micro-wind power generation and energy storage synergy system integrated with the smart grid of the present invention, the power generation prediction unit is used to calculate the power generation prediction value of the next m time steps; the formula is as follows:

[0023]

[0024] Where E represents the predicted value of power generation at any time step in the future; C p represents the utilization rate of wind energy; ρ represents the air density; A represents the swept area of ​​the wind wheel; P0 represents the rated power of the breeze power generation network.

[0025] As a preferred solution of the micro-wind power generation and energy storage collaborative system integrated with the smart grid of the present invention, the strategy module includes an optimization algorithm unit and a simulation operation unit; wherein the optimization algorithm unit is configured with a genetic algorithm for formulating a charging and discharging strategy for the energy storage device within the next m time steps; the simulation operation unit is used to store the data generated by each round of iteration of the genetic algorithm; the simulation operation unit is also configured with a fitness function for calculating the fitness in each round of iteration of the genetic algorithm;

[0026] The method for the optimization algorithm unit to formulate the charging and discharging strategy of the energy storage device is as follows:

[0027] S1: Set the population size, crossover probability, mutation probability, maximum number of iterations, and generate N individuals to form the initial population; N is the population size;

[0028] S2: Calculate the fitness of each individual based on the simulation operation unit;

[0029] S3: Perform selection operation, crossover operation, and mutation operation in sequence to generate the next generation population;

[0030] S4: Repeat steps S2-S3 until the maximum number of iterations is reached;

[0031] S5: Obtain the individual with the highest fitness in all generations of populations as the optimal individual, and generate the charging and discharging strategy of the energy storage device in the next m time steps based on the chromosome of the optimal individual.

[0032] As a preferred solution of the micro-wind power generation and energy storage coordinated system integrated with the smart grid of the present invention, any of the individuals comprises chromosome 1 and chromosome 2; wherein chromosome 1 and chromosome 2 both comprise m genes; wherein the i-th gene of chromosome 1 represents the charge and discharge state of the energy storage device at the i-th time step in the future, including charging and discharging; the i-th gene of chromosome 2 represents the average power of charging or discharging of the energy storage device at the i-th time step in the future, and the value range of i is 1, 2, ..., m;

[0033] The crossover operation includes single-point crossover and ordered crossover; the object of the crossover operation is any pair of chromosomes with the same number in the two individuals participating in the crossover operation.

[0034] As a preferred solution of the smart grid integrated micro-wind power generation and energy storage collaborative system of the present invention, the fitness function of the simulation operation unit configuration is as follows:

[0035]

[0036] Among them, H represents the fitness of any individual; E j represents the predicted value of power generation at the jth time step in the future, and the value range of j is 1, 2, ..., m; L j represents the load forecast value at the jth time step in the future; C j represents the charge of the energy storage device at the jth time step in the future; D j represents the discharge amount of the energy storage device at the jth time step in the future; α j is the first adjustment factor, β j is the second adjustment factor, F j is the power change of the energy storage device at the jth time step in the future. The value selection rule is as follows: If the energy storage device is charged at the jth time step in the future, then α j The value of β is 1, j The value of F is 0, j Equal to C j ; If the energy storage device discharges at the jth time step in the future, then α j The value of β is 0, j The value of F is 1. j Equal to D j The opposite of F j-1 is the power change of the energy storage device in the future j-1th time step; w1, w2, w3 are all weight coefficients.

[0037] As a preferred solution of the micro-wind power generation and energy storage collaborative system integrated with the smart grid of the present invention, the monitoring module includes a power grid monitoring unit and an adjustment instruction unit; wherein the power grid monitoring unit is used to monitor the power generation of the micro-wind power generation network in real time at each time step, and compare it with the power generation prediction value of the corresponding time step; and is also used to monitor the load of the power grid in real time at each time step, and compare it with the load prediction value of the corresponding time step;

[0038] If the absolute value of the difference between n consecutive real-time power generation and the power generation forecast value of the corresponding time step is higher than the preset first power generation error threshold, or the absolute value of the difference between n consecutive real-time load and the load forecast value of the corresponding time step is higher than the preset first load error threshold, the adjustment instruction unit generates a first adjustment instruction; n is a positive integer, and its specific value is set by technical personnel in this field based on actual needs; the first prediction module responds to the first adjustment instruction, re-predicts the wind speed and updates the wind speed forecast value; the data processing module updates the power generation forecast value based on the updated wind speed forecast value; the second prediction module responds to the first adjustment instruction, re-predicts the load of the power grid and updates the load forecast value; the strategy module responds to the first adjustment instruction, and re-formulates the charging and discharging strategy of the energy storage device based on the updated load forecast value and the updated power generation forecast value.

[0039] As a preferred solution of the micro-wind power generation and energy storage collaborative system integrated with the smart grid described in the present invention, when a first adjustment instruction is generated, if the absolute value of the difference between at least one of the consecutive n real-time power generation and the power generation prediction value of the corresponding time step is higher than the preset second power generation error threshold, or if the absolute value of the difference between at least one of the consecutive n real-time loads and the load prediction value of the corresponding time step is higher than the preset second load error threshold, the adjustment instruction unit also generates a second adjustment instruction; the strategy module responds to the second adjustment instruction, and when re-formulating the charging and discharging strategy of the energy storage device, formulates the charging and discharging strategy of the energy storage device in the future k time steps, where k is a positive integer and k is less than m.

[0040] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0041] Advanced deep learning models are used to predict wind speed in the area where the micro-wind power generation network is located, and historical load data is combined to accurately estimate future power demand. This not only improves the accuracy of power generation forecasts, but also enhances the ability to forecast loads, providing a solid data foundation for subsequent power generation and energy storage collaborative planning.

[0042] Based on accurate wind speed and load forecast values, the system can dynamically adjust the charging and discharging strategies of the energy storage device to ensure timely storage of excess energy when power generation is in excess, and quickly release the reserve energy to make up for the gap when power generation is insufficient, thereby improving energy utilization efficiency and reducing waste. By reasonably controlling the amplitude and frequency of charge and discharge power changes, the life of energy storage devices such as batteries can be effectively extended, and maintenance costs can be reduced.

[0043] The system has strong adaptive capabilities by monitoring grid parameters in real time and quickly adjusting charging and discharging strategies according to actual conditions. When the forecast deviation is large or an emergency occurs, measures can be taken immediately to avoid system instability caused by forecast errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing 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 creative labor. Among them:

[0045] Figure 1 A schematic diagram of the structure of the wind power generation and energy storage collaborative system integrated with the smart grid provided by the present invention;

[0046] Figure 2 A flow chart of a method for formulating a charging and discharging strategy for an energy storage device provided by the present invention. DETAILED DESCRIPTION

[0047] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0048] This embodiment introduces a smart grid integrated micro-wind power generation and energy storage coordinated system, referring to Figure 1 The system includes a first prediction module, a second prediction module, a data processing module, a strategy module, and a monitoring module; wherein:

[0049] The first prediction module is used to predict the wind speed in the area where the breeze power generation network is located and output the wind speed prediction value;

[0050] The first prediction module includes a first statistical unit and a first prediction unit; wherein the first statistical unit is used to collect and count historical wind speed data of the area where the micro-wind power generation network is located; the first prediction unit predicts the wind speed of the area where the micro-wind power generation network is located based on the historical wind speed data;

[0051] The first statistical unit cleans and normalizes the historical wind speed data and organizes them into a time series; the first prediction unit is configured with a time series prediction model, which is any one of an RNN model, an LSTM model, and a GRU model, and is used to process the time series of historical wind speed data and output continuous wind speed prediction values.

[0052] Wind energy is an intermittent and highly random energy source, which is difficult to predict. However, accurate wind speed prediction is of great significance for improving power generation efficiency and optimizing resource allocation. With the powerful feature extraction and learning capabilities of deep learning models such as LSTM, high-precision, multi-point wind speed prediction can be achieved, providing a data basis for subsequent power generation and energy storage collaborative planning.

[0053] The second prediction module is used to predict the load of the power grid and output the load prediction value;

[0054] The second prediction module includes a second statistical unit and a second prediction unit; wherein the second statistical unit is used to collect and count historical load data of the power grid; the second prediction unit predicts the load of the power grid based on the historical load data, and outputs the load prediction value of the future m time steps, where m is a positive integer;

[0055] The second statistical unit cleans and normalizes the historical load data and organizes them into a time series; each historical load data in the time series of the historical load data corresponds to a time step; the second prediction unit is configured with a time series prediction model, such as an ARMA model or an ARIMA model, etc., and the time series prediction model calculates the load forecast value for the next m time steps based on the time series of the historical load data.

[0056] The data processing module calculates the power generation prediction value based on the wind speed prediction value;

[0057] The data processing module includes a data sorting unit and a power generation prediction unit; wherein the data sorting unit is used to sort the wind speed prediction value, specifically as follows: the continuous wind speed prediction value is divided into m segments corresponding to time steps and load prediction values; the data sorting unit is configured with the rated wind speed of the breeze power generation network, for the wind speed prediction value in any time step, the length of the time period when the wind speed prediction value is greater than the rated wind speed is counted, recorded as t1; the length of the time period when the wind speed prediction value is not greater than the rated wind speed is counted, recorded as t2, and the average wind speed in the time period when the wind speed prediction value is not greater than the rated wind speed is calculated, recorded as v s ;

[0058] The power generation prediction unit is used to calculate the power generation prediction value of the next m time steps; the formula is as follows:

[0059]

[0060] Where E represents the predicted value of power generation at any time step in the future; C p It represents the utilization rate of wind energy, which is based on experimental measurement; ρ represents the air density; A represents the swept area of ​​the wind rotor; P0 represents the rated power of the micro-wind power generation network.

[0061] The strategy module formulates a charging and discharging strategy for the energy storage device based on the load prediction value and the power generation prediction value;

[0062] The strategy module includes an optimization algorithm unit and a simulation operation unit; wherein the optimization algorithm unit is configured with a genetic algorithm for formulating a charging and discharging strategy for the energy storage device within the next m time steps; the simulation operation unit is used to store data generated by each iteration of the genetic algorithm; the simulation operation unit is also configured with a fitness function for calculating fitness in each iteration of the genetic algorithm;

[0063] like Figure 2 As shown, the method for the optimization algorithm unit to formulate the charging and discharging strategy of the energy storage device is as follows:

[0064] S1: Set the population size, crossover probability, mutation probability, maximum number of iterations, and generate N individuals to form the initial population; N is the population size;

[0065] Any individual contains chromosome 1 and chromosome 2; both chromosome 1 and chromosome 2 contain m genes; the i-th gene of chromosome 1 represents the charge and discharge state of the energy storage device at the i-th time step in the future, including charging and discharging; the i-th gene of chromosome 2 represents the average power of charging or discharging of the energy storage device at the i-th time step in the future, and the value range of i is 1, 2, ..., m;

[0066] S2: Calculate the fitness of each individual based on the simulation operation unit;

[0067] S3: Perform selection operation, crossover operation, and mutation operation in sequence to generate the next generation population;

[0068] The crossover operation includes single-point crossover and ordered crossover; the object of the crossover operation is any pair of chromosomes with the same number in the two individuals participating in the crossover operation;

[0069] S4: Repeat steps S2-S3 until the maximum number of iterations is reached;

[0070] S5: Obtain the individual with the highest fitness in all generations of populations as the optimal individual, and generate the charging and discharging strategy of the energy storage device in the next m time steps based on the chromosome of the optimal individual.

[0071] The fitness function of the simulation operation unit configuration is as follows:

[0072]

[0073] Among them, H represents the fitness of any individual; E j represents the predicted value of power generation at the jth time step in the future, and the value range of j is 1, 2, ..., m; L jrepresents the load forecast value at the jth time step in the future; C j represents the charge of the energy storage device at the jth time step in the future; D j represents the discharge amount of the energy storage device at the jth time step in the future; α j is the first adjustment factor, β j is the second adjustment factor, F j is the power change of the energy storage device at the jth time step in the future. The value selection rule is as follows: If the energy storage device is charged at the jth time step in the future, then α j The value of β is 1, j The value of F is 0, j Equal to C j ; If the energy storage device discharges at the jth time step in the future, then α j The value of β is 0, j The value of F is 1. j Equal to D j The opposite of F j-1 is the power change of the energy storage device in the future j-1th time step; w1, w2, and w3 are all weight coefficients, which are set by technicians in this field based on actual needs.

[0074] The fitness function mentioned above consists of three parts; the first part is used to calculate the utilization rate of the power generated by the breeze power generation, the second part is used to calculate the satisfaction rate of the load demand, and the third part is used to calculate the amplitude and frequency of the power change of the charging or discharging of the energy storage device; based on the selection of the fitness function mentioned above, the charging and discharging strategy formulated is encouraged to have the following characteristics: when the power generation is predicted to be higher than the grid demand, the energy storage system is charged first; when the power generation is predicted to be lower than the grid demand, the energy storage system is released to supplement the grid. By dynamically adjusting the charging and discharging rates, frequent adjustment of the charging and discharging power of the energy storage system is avoided to extend the service life of the energy storage equipment.

[0075] The monitoring module is used to monitor the grid parameters in real time, including the real-time power generation and load, and adjust the charging and discharging strategy based on the grid parameter control.

[0076] The monitoring module includes a power grid monitoring unit and an adjustment instruction unit; wherein the power grid monitoring unit is used to monitor the power generation of the micro-wind power generation network in real time at each time step, and compare it with the power generation prediction value of the corresponding time step; and is also used to monitor the load of the power grid in real time at each time step, and compare it with the load prediction value of the corresponding time step;

[0077] If the absolute value of the difference between the real-time power generation of n consecutive times and the predicted power generation value of the corresponding time step is higher than the preset first power generation error threshold, or the absolute value of the difference between the real-time load of n consecutive times and the predicted load value of the corresponding time step is higher than the preset first load error threshold, the adjustment instruction unit generates a first adjustment instruction; n is a positive integer, and its specific value is set by technicians in this field based on actual needs; the first prediction module responds to the first adjustment instruction, re-predicts the wind speed and updates the wind speed prediction value; the data processing module updates the power generation prediction value based on the updated wind speed prediction value; the second prediction module responds to the first adjustment instruction, re-predicts the load of the power grid and updates the load prediction value; the strategy module responds to the first adjustment instruction, and re-formulates the charging and discharging strategy of the energy storage device based on the updated load prediction value and the updated power generation prediction value. When the gap between the continuous n predicted values ​​and the measured values ​​is large, it means that the accuracy of the predicted value is poor, and the charging and discharging strategy formulated based on the predicted value may not be applicable to the actual power grid, so the charging and discharging strategy needs to be updated.

[0078] When generating the first adjustment instruction, if the absolute value of the difference between at least one of the consecutive n real-time power generation and the power generation forecast value of the corresponding time step is higher than the preset second power generation error threshold, or if the absolute value of the difference between at least one of the consecutive n real-time load and the load forecast value of the corresponding time step is higher than the preset second load error threshold, the adjustment instruction unit also generates a second adjustment instruction; the strategy module responds to the second adjustment instruction, and when re-formulating the charging and discharging strategy of the energy storage device, formulates the charging and discharging strategy of the energy storage device in the future k time steps, k is a positive integer and k is less than m. The second power generation error threshold is higher than the first power generation error threshold, and the second load error threshold is higher than the first load error threshold. When the prediction deviation is higher than the corresponding second error threshold, it means that there may be special factors that cause the prediction value to be abnormal. At this time, it is necessary to reduce the time coverage of the charging and discharging strategy planning to avoid frequent generation of the first adjustment instruction and increase the system burden.

[0079] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0080] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the purpose and scope of protection of the present invention, which are all within the protection of the present invention.

Claims

1. A smart grid integrated micro-wind power generation and energy storage coordinated system, characterized by: It includes a first prediction module, a second prediction module, a data processing module, a strategy module, and a monitoring module; wherein: The first prediction module is used to predict the wind speed in the area where the breeze power generation network is located and output the wind speed prediction value; The second prediction module is used to predict the load of the power grid and output the load prediction value; The data processing module calculates the power generation prediction value based on the wind speed prediction value; The strategy module formulates a charging and discharging strategy for the energy storage device based on the load prediction value and the power generation prediction value; The monitoring module is used to monitor the grid parameters in real time, including the real-time power generation and load, and adjust the charging and discharging strategy based on the grid parameter control.

2. The smart grid integrated micro-wind power generation and energy storage coordinated system according to claim 1, characterized in that: The first prediction module includes a first statistical unit and a first prediction unit; wherein the first statistical unit is used to collect and count historical wind speed data of the area where the micro-wind power generation network is located; the first prediction unit predicts the wind speed of the area where the micro-wind power generation network is located based on the historical wind speed data; The first statistical unit cleans and normalizes the historical wind speed data and organizes them into a time series; the first prediction unit is configured with a time series prediction model, which is any one of an RNN model, an LSTM model, and a GRU model, and is used to process the time series of historical wind speed data and output continuous wind speed prediction values.

3. The smart grid integrated micro-wind power generation and energy storage coordinated system as claimed in claim 2, characterized in that: The second prediction module includes a second statistical unit and a second prediction unit; wherein the second statistical unit is used to collect and count historical load data of the power grid; the second prediction unit predicts the load of the power grid based on the historical load data, and outputs the load prediction value of the future m time steps, where m is a positive integer; The second statistical unit cleans and normalizes the historical load data and organizes it into a time series; each historical load data in the time series of the historical load data corresponds to a time step; the second prediction unit is configured with a time series prediction model, and the time series prediction model calculates the load prediction value of the next m time steps based on the time series of the historical load data.

4. The smart grid integrated micro-wind power generation and energy storage coordinated system as claimed in claim 3, characterized in that: The data processing module includes a data sorting unit and a power generation prediction unit; wherein the data sorting unit is used to sort the wind speed prediction value, specifically as follows: the continuous wind speed prediction value is divided into m segments corresponding to time steps and load prediction values; the data sorting unit is configured with the rated wind speed of the breeze power generation network, for the wind speed prediction value in any time step, the length of the time period when the wind speed prediction value is greater than the rated wind speed is counted, recorded as t1; the length of the time period when the wind speed prediction value is not greater than the rated wind speed is counted, recorded as t2, and the average wind speed in the time period when the wind speed prediction value is not greater than the rated wind speed is calculated, recorded as v s .

5. The smart grid integrated micro-wind power generation and energy storage coordinated system as claimed in claim 4, characterized in that: The power generation prediction unit is used to calculate the power generation prediction value of the next m time steps; the formula is as follows: Where E represents the predicted value of power generation at any time step in the future; C p represents the utilization rate of wind energy; ρ represents the air density; A represents the swept area of ​​the wind wheel; P0 represents the rated power of the breeze power generation network.

6. The smart grid integrated micro-wind power generation and energy storage coordinated system as claimed in claim 5, characterized in that: The strategy module includes an optimization algorithm unit and a simulation operation unit; wherein the optimization algorithm unit is configured with a genetic algorithm for formulating a charging and discharging strategy for the energy storage device within the next m time steps; the simulation operation unit is used to store data generated by each iteration of the genetic algorithm; the simulation operation unit is also configured with a fitness function for calculating fitness in each iteration of the genetic algorithm; The method for the optimization algorithm unit to formulate the charging and discharging strategy of the energy storage device is as follows: S1: Set the population size, crossover probability, mutation probability, maximum number of iterations, and generate N individuals to form the initial population; N is the population size; S2: Calculate the fitness of each individual based on the simulation operation unit; S3: Perform selection operation, crossover operation, and mutation operation in sequence to generate the next generation population; S4: Repeat steps S2-S3 until the maximum number of iterations is reached; S5: Obtain the individual with the highest fitness in all generations of populations as the optimal individual, and generate the charging and discharging strategy of the energy storage device in the next m time steps based on the chromosome of the optimal individual.

7. The smart grid integrated micro-wind power generation and energy storage coordinated system according to claim 6, characterized in that: Any of the individuals comprises chromosome 1 and chromosome 2; wherein chromosome 1 and chromosome 2 each comprise m genes; wherein the i-th gene of chromosome 1 represents the charge and discharge state of the energy storage device at the i-th time step in the future, including charging and discharging; the i-th gene of chromosome 2 represents the average power of charging or discharging of the energy storage device at the i-th time step in the future, and the value range of i is 1, 2, ..., m; The crossover operation includes single-point crossover and ordered crossover; the object of the crossover operation is any pair of chromosomes with the same number in the two individuals participating in the crossover operation.

8. The smart grid integrated micro-wind power generation and energy storage coordinated system according to claim 7, characterized in that: The fitness function of the simulation operation unit configuration is as follows: Among them, H represents the fitness of any individual; E j represents the predicted value of power generation at the jth time step in the future, and the value range of j is 1, 2, ..., m; L j represents the load forecast value at the jth time step in the future; C j represents the charge of the energy storage device at the jth time step in the future; D j represents the discharge amount of the energy storage device at the jth time step in the future; α j is the first adjustment factor, β j is the second adjustment factor, F j is the power change of the energy storage device at the jth time step in the future. The value selection rule is as follows: If the energy storage device is charged at the jth time step in the future, then α j The value of β is 1, j The value of F is 0, j Equal to C j ; If the energy storage device discharges at the jth time step in the future, then α j The value of β is 0, j The value of F is 1. j Equal to D j The opposite of F j-1 is the power change of the energy storage device in the future j-1th time step; w1, w2, w3 are all weight coefficients.

9. The smart grid integrated micro-wind power generation and energy storage coordinated system as claimed in claim 8, characterized in that: The monitoring module includes a power grid monitoring unit and an adjustment instruction unit; wherein the power grid monitoring unit is used to monitor the power generation of the micro-wind power generation network in real time at each time step, and compare it with the power generation prediction value of the corresponding time step; and is also used to monitor the load of the power grid in real time at each time step, and compare it with the load prediction value of the corresponding time step; If the absolute value of the difference between n consecutive real-time power generation and the power generation forecast value of the corresponding time step is higher than the preset first power generation error threshold, or the absolute value of the difference between n consecutive real-time load and the load forecast value of the corresponding time step is higher than the preset first load error threshold, the adjustment instruction unit generates a first adjustment instruction; n is a positive integer; the first prediction module responds to the first adjustment instruction, re-predicts the wind speed and updates the wind speed forecast value; the data processing module updates the power generation forecast value based on the updated wind speed forecast value; the second prediction module responds to the first adjustment instruction, re-predicts the load of the power grid and updates the load forecast value; the strategy module responds to the first adjustment instruction, and re-formulates the charging and discharging strategy of the energy storage device based on the updated load forecast value and the updated power generation forecast value.

10. The smart grid integrated micro-wind power generation and energy storage coordinated system according to claim 9, characterized in that: When generating the first adjustment instruction, if the absolute value of the difference between at least one of the consecutive n real-time power generation and the power generation prediction value of the corresponding time step is higher than the preset second power generation error threshold, or if the absolute value of the difference between at least one of the consecutive n real-time load amounts and the load prediction value of the corresponding time step is higher than the preset second load error threshold, the adjustment instruction unit also generates a second adjustment instruction; the strategy module responds to the second adjustment instruction, and when re-formulating the charging and discharging strategy of the energy storage device, formulates the charging and discharging strategy of the energy storage device in the future k time steps, where k is a positive integer and k is less than m.

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