Wind energy-stored energy regulation and control method and system based on physical constraint

Abstract

The invention provides a wind energy-stored energy regulation and control method and system based on physical constraints, and relates to the field of wind storage, and the method comprises the steps: obtaining active support transient and steady state control mechanisms of wind storage multi-type and multi-distributed active and reactive regulation and control resources; establishing a dynamic coupling regulation and control mathematical model, and predicting active and reactive regulation and control resources by using the dynamic coupling regulation and control mathematical model; obtaining multi-dimensional space measurement data of a fan position and an anemometer tower, and analyzing a multi-temporal-spatial-scale random fluctuation characteristic and a temporal-spatial distribution characteristic of operation reliability of the multi-temporal-spatial-scale random fluctuation characteristic; according to an analysis result, establishing an active support control boundary quantitative evaluation model of the wind storage multi-type distributed adjustable and controllable resources by using a high-precision wind speed and wind power prediction technology; and according to the prediction result and the evaluation model, constructing a wind storage combined transient state and steady state active and reactive power multi-time scale regulation capability dynamic evaluation system. The method is used for overcoming the defect of efficiency loss of a physical energy storage technology in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of wind energy storage technology, and in particular to a wind energy-energy storage control method and system based on physical constraints. Background Art

[0002] As a renewable and clean energy, the development and utilization of wind energy is of great significance to promoting energy transformation and reducing carbon emissions. However, wind energy is intermittent and unstable, which brings challenges to the stable operation of the power system. In order to overcome this challenge, energy storage technology is widely used in wind energy systems to smooth wind power output, improve system stability and power supply quality. In wind energy-energy storage regulation methods, physical energy storage technology occupies an important position. Physical energy storage technology mainly includes pumped storage, compressed air energy storage and flywheel energy storage. These technologies convert electrical energy into mechanical energy, potential energy or kinetic energy for storage, and release it when needed to provide power support. These technologies have played an important role in large-scale wind power grid connection, peak-filling and valley-filling, and improving system stability.

[0003] Physical energy storage technology usually requires the construction of special energy storage facilities, such as pumped storage power stations, compressed air energy storage caves, etc. The construction cost of these facilities is relatively high, which may limit their widespread application; physical energy storage technology, such as pumped storage, has high requirements on geographical conditions and requires certain terrain and water resources conditions, which limits the flexibility of its site selection; compared with chemical energy storage technology, the energy density of physical energy storage technology is usually lower, and the amount of electrical energy that can be stored at the same volume or weight is relatively less; in the process of energy conversion and storage, physical energy storage technology may have certain efficiency losses, such as the efficiency of pumps and turbines in pumped storage power stations, and thermal energy losses in the compressed air energy storage process. Summary of the invention

[0004] The present invention provides a wind energy-energy storage control method and system based on physical constraints, which are used to solve the defect of efficiency loss in physical energy storage technology in the prior art.

[0005] In one aspect, the present invention provides a wind energy-energy storage control method based on physical constraints, comprising:

[0006] Obtain active support transient and steady-state control mechanisms for multiple types of distributed active and reactive regulation resources of wind and storage, including transient and steady-state regulation response characteristics at multiple time scales, operating domain constraints, dynamic distribution laws, and interactive coupling mechanisms;

[0007] According to the control mechanism, a dynamic coupling control mathematical model of multiple types of distributed active and reactive control resources of wind and storage joint stations is established, and the dynamic coupling control mathematical model is used to predict the active and reactive control resources to obtain the prediction results;

[0008] Obtain multi-dimensional spatial measurement data of wind turbine positions and wind towers, analyze the temporal and spatial distribution characteristics of random fluctuations at multiple temporal and spatial scales and their operational reliability to obtain analysis results;

[0009] Based on the analysis results, a quantitative assessment model for the active support control boundary of multi-type distributed controllable resources of wind storage is established using high-precision wind speed and wind power prediction technology;

[0010] Based on the prediction results and evaluation model, a dynamic evaluation system for the transient and steady-state active and reactive power multi-time scale control capabilities of wind and storage is constructed.

[0011] Furthermore, the active support transient and steady-state control mechanism of multiple types of distributed active and reactive power regulation resources of wind and storage is obtained. The control mechanism includes multi-time scale transient and steady-state regulation response characteristics, operation domain constraints, dynamic distribution laws and interactive coupling mechanisms, including:

[0012] Obtain performance parameters of various types of distributed active and reactive control resources of wind and storage;

[0013] According to the performance parameters, the transient and steady states of the active support are analyzed to obtain the analysis results;

[0014] Based on the analysis results, the performance parameters are monitored in real time to obtain the control mechanism.

[0015] Furthermore, according to the control mechanism, a dynamic coupling control mathematical model of multiple types of distributed active and reactive control resources of wind and storage joint stations is established, and the dynamic coupling control mathematical model is used to predict the active and reactive control resources to obtain prediction results, including:

[0016] According to the control mechanism, use fx k+1 =Ax k +Bu k +w k ,fy k =Cx k +Du k +v k A dynamic coupling control mathematical model for multiple types of distributed active and reactive control resources of wind and storage joint stations is established, where x k is the value of internal state variables such as wind power, state of energy storage device, grid voltage and current at time k, u k is the external control input to the system at time k, y k is the active power and reactive power output of the wind farm and energy storage system at time k, A is the transition law of the system state from time k to time k+1, and B is the input vector u k Changes that affect the system state, C is the system state x kMapped to the output vector y k , D is the input vector u k Directly affects the output vector y k , w k is the process noise vector, v k is the measurement noise vector;

[0017] Obtain historical operation data of the wind-storage combined station and perform preprocessing to obtain preprocessed data;

[0018] The pre-processed data is input into the dynamic coupling control mathematical model, and the dynamic coupling control mathematical model is used to predict the active and reactive control resources of the wind-storage combined station to obtain the prediction results;

[0019] Obtain the actual operating data of the wind and energy storage combined station, and compare the predicted results with the actual operating data to obtain comparative results;

[0020] According to the comparison results, the dynamic coupling control mathematical model is iteratively upgraded to obtain an optimized dynamic coupling control mathematical model.

[0021] Furthermore, the multi-dimensional spatial measurement data of wind turbine positions and wind towers are obtained, and the temporal and spatial distribution characteristics of random fluctuations at multiple temporal and spatial scales and their operational reliability are analyzed to obtain analysis results, including:

[0022] Obtain the multi-dimensional spatial measurement data of the wind turbine position and the wind tower, and preprocess them to obtain the preprocessed measurement data:

[0023] Extract key features based on the preprocessed measurement data, wherein the key features include the average value, standard deviation, extreme value, spectrum characteristics of wind speed, distribution characteristics of wind direction, and temperature variation range;

[0024] Based on the key features, use X t =φ1X t-1 +φ2X t-2 +…+φ p X t-p +∈ t +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q Multi-scale temporal and spatial analysis is performed to obtain the multi-scale random fluctuation characteristics and temporal and spatial distribution characteristics, where X t is the observed value at time t, φ1, φ2, …, φ p is the autoregressive coefficient, ∈ t is the white noise error term at time t, θ1, θ2, …, θ qis the sliding average coefficient, p is the order of the autoregressive part, and q is the order of the sliding average part;

[0025] According to the random fluctuation characteristics and spatiotemporal distribution characteristics of multiple spatiotemporal scales, the influence of wind turbine location and wind tower on wind turbine operation reliability is analyzed to obtain the analysis results.

[0026] Furthermore, based on the key features, time series analysis is used to perform multi-time and space scale analysis to obtain multi-time and space scale random fluctuation characteristics and time and space distribution characteristics analysis, including:

[0027] According to the key characteristics, the random fluctuation characteristics at different time scales are analyzed to obtain the random fluctuation characteristic analysis results;

[0028] According to the key features, the spatial distribution and correlation of wind characteristics at different spatial scales are analyzed to obtain the wind characteristics analysis results;

[0029] According to the results of random fluctuation characteristics analysis and wind condition characteristics analysis, the reliability of wind turbine operation under different wind conditions is evaluated to obtain reliability evaluation results;

[0030] According to the reliability assessment results, the temporal and spatial distribution characteristics of the wind turbine operation reliability are analyzed to obtain the temporal and spatial distribution characteristics.

[0031] Furthermore, based on the analysis results, a quantitative assessment model of the active support control boundary of multi-type distributed controllable resources of wind storage is established using high-precision wind speed and wind power prediction technology, including:

[0032] Obtain data from wind farm centralized control and booster stations;

[0033] Based on the analysis results and the data from the wind farm centralized control and booster station, a recurrent neural network is used to model the time series data to obtain a prediction model.

[0034] The wind speed and wind power are predicted using a prediction model to obtain wind speed and power prediction values;

[0035] According to the wind speed and power forecast values, a multi-type distributed controllable resource model of wind storage is established to obtain the control capacity prediction results;

[0036] According to the prediction results of wind power and regulation capacity, a quantitative evaluation model of active support control boundary of multi-type distributed controllable resources of wind and storage is established.

[0037] Furthermore, based on the prediction results and evaluation model, a dynamic evaluation system for the transient and steady-state active and reactive power multi-time scale control capability of wind and storage combined is constructed, including:

[0038] Based on the prediction results, the transient and steady-state models of the wind-storage combined system are established, and the active and reactive power regulation capabilities of the electric field and energy storage system are determined using the transient and steady-state models;

[0039] The time scale is divided into multiple levels. At each time scale, the evaluation model is used to dynamically evaluate the control capability of the wind-storage combined system to obtain the evaluation results.

[0040] Based on the assessment results, a dynamic assessment system for multi-time scale regulation capabilities will be developed.

[0041] On the other hand, the present invention also provides a wind energy-energy storage control system based on physical constraints, including an acquisition module and a processing module.

[0042] On the other hand, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the wind energy-energy storage control method based on physical constraints as described in any one of the above is implemented.

[0043] On the other hand, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the wind energy-energy storage control methods based on physical constraints as described above.

[0044] On the other hand, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the wind energy-energy storage control methods based on physical constraints as described above.

[0045] The wind energy-energy storage control method and system based on physical constraints provided by the present invention can more accurately understand and predict the output power of the wind farm and the response characteristics of the energy storage system by obtaining the active support transient and steady-state control mechanism of multiple types of distributed active and reactive control resources of wind storage, which helps to reduce the impact of wind power grid connection on the grid and improve the stability and reliability of the grid.

[0046] The established dynamic coupling control mathematical model can comprehensively consider multiple factors such as wind power, energy storage device status, grid voltage and current, and realize the optimal prediction and dispatch of active and reactive control resources of wind and energy storage combined stations, which helps to improve resource utilization efficiency, reduce wind and power abandonment, and reduce operating costs;

[0047] By real-time monitoring of the performance parameters of the wind-storage combined station and combining the analysis results of the random fluctuation characteristics and spatiotemporal distribution characteristics at multiple spatiotemporal scales, the control strategy can be dynamically adjusted to enable the wind farm to better adapt to different wind conditions and grid requirements, thus enhancing the wind farm's adaptive ability and flexibility.

[0048] Through in-depth analysis of wind turbine locations and multi-dimensional spatial measurement data from wind towers, key factors affecting wind turbine operation reliability can be identified, and corresponding measures can be taken for prevention and maintenance, which helps to extend the service life of wind turbines, reduce downtime, and improve the overall power generation efficiency of wind farms.

[0049] The constructed dynamic evaluation system for the transient and steady-state active and reactive power control capabilities of the wind-storage joint system at multiple time scales provides strong support for the refined management and decision-making of wind farms. It can intuitively understand the control capabilities and operating status of wind farms at different time scales, and provide a scientific basis for formulating reasonable power generation plans and scheduling strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 It is a flow chart of a wind energy-energy storage control method based on physical constraints provided by an embodiment of the present invention;

[0052] Figure 2 is a schematic diagram of a wind energy-energy storage control system based on physical constraints provided by an embodiment of the present invention;

[0053] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Figure 1 It is one of the flow charts of the wind energy-energy storage control method based on physical constraints provided in an embodiment of the present invention.

[0056] like Figure 1 As shown, the wind energy-energy storage control method based on physical constraints provided by an embodiment of the present invention mainly includes the following steps:

[0057] 11. Obtain active support transient and steady-state control mechanisms for multiple types of distributed active and reactive power regulation resources of wind and storage, including transient and steady-state regulation response characteristics at multiple time scales, operating domain constraints, dynamic distribution laws, and interactive coupling mechanisms;

[0058] 12. According to the control mechanism, a dynamic coupling control mathematical model of multiple types of distributed active and reactive control resources of wind and storage joint stations is established, and the dynamic coupling control mathematical model is used to predict the active and reactive control resources to obtain the prediction results;

[0059] 13. Obtain multi-dimensional spatial measurement data of wind turbine positions and wind towers, analyze the temporal and spatial distribution characteristics of random fluctuations at multiple temporal and spatial scales and their operational reliability to obtain analysis results;

[0060] 14. Based on the analysis results, a quantitative assessment model of the active support control boundary of multi-type distributed controllable resources of wind storage is established using high-precision wind speed and wind power prediction technology;

[0061] 15. Based on the prediction results and evaluation model, a dynamic evaluation system for the transient and steady-state active and reactive power multi-time scale control capabilities of wind and storage is constructed.

[0062] In an embodiment of the present invention, a wind energy-energy storage control method based on physical constraints improves the active support capability of the wind-storage joint station to the power grid, optimizes the configuration of active and reactive control resources, enhances the stability and reliability of wind power grid connection, and provides a scientific basis for the refined management and decision-making of wind farms; collects operating data of various types of distributed active and reactive control resources of wind storage, analyzes these data to reveal multi-time scale transient and steady-state control response characteristics, operating domain constraints, dynamic distribution laws and interactive coupling mechanisms; based on the analysis results, constructs a mathematical model or logical framework to describe these control mechanisms, which can deeply understand the active and reactive power of the wind-storage joint station. The dynamic behavior and control characteristics of the control resources provide a theoretical basis for subsequent mathematical modeling and prediction; according to the control mechanism, mathematical methods are used to establish a dynamic coupling control mathematical model for multiple types of distributed active and reactive control resources in the wind-storage joint station, and the parameters in the model are determined by actual operation data or experimental data. The model is applied to the prediction of actual operation data to obtain the future state or output of the active and reactive control resources. It can accurately predict the dynamic behavior of the active and reactive control resources of the wind-storage joint station, and provide decision support for the optimal allocation and scheduling of resources; collect multi-dimensional spatial measurement data of wind turbine positions and wind towers, including wind speed, wind direction, temperature, etc. By processing and analyzing these data, extracting the random fluctuation characteristics of multiple spatiotemporal scales, analyzing the distribution characteristics of these characteristics in time and space, and their impact on the reliability of wind turbine operation, we can gain an in-depth understanding of the spatiotemporal distribution characteristics of wind conditions, provide important information for the operation and maintenance of wind farms, and improve the accuracy of wind turbine operation reliability assessments. We integrate the analysis results of wind turbine locations and wind towers and other relevant data, use high-precision wind speed and wind power prediction technology, and combine control mechanisms and constraints to establish a quantitative assessment model for the active support control boundary of multiple types of distributed and adjustable resources of wind and storage, and calculate the control boundaries of wind and storage joint stations under different conditions through the model. , quantitatively evaluate the active support capability of the wind-storage combined station under different conditions, and provide a scientific basis for the optimal allocation of resources and the stable operation of the power grid; according to the prediction results and evaluation model, design a dynamic evaluation system for the transient and steady-state active and reactive multi-time scale control capability of the wind-storage combined station, and determine the key indicators of the evaluation system, such as response time, control accuracy, stability, etc. In actual operation, according to the real-time data and model prediction results, the control capability of the wind-storage combined station is dynamically evaluated, and the real-time and dynamic evaluation of the control capability of the wind-storage combined station is realized, which provides strong support for the refined management and decision-making of wind farms, and at the same time improves the stability and reliability of wind power grid connection.

[0063] like Figure 1 As shown, 11. Obtain active support transient and steady-state control mechanisms for multiple types of distributed active and reactive control resources of wind and storage, the control mechanisms include multi-time scale transient and steady-state control response characteristics, operation domain constraints, dynamic distribution laws and interactive coupling mechanisms, including:

[0064] 111. Obtain the performance parameters of various types of distributed active and reactive control resources of wind and storage;

[0065] 112. According to the performance parameters, the transient and steady states of the active support are analyzed to obtain the analysis results;

[0066] 113. Based on the analysis results, the performance parameters are monitored in real time to obtain the control mechanism.

[0067] In the embodiments of the present invention, by obtaining the performance parameters of various types of distributed active and reactive control resources of wind and energy storage, the performance characteristics and operating status of these resources can be fully and accurately understood; by analyzing the transient and steady states of active support, the weak links and potential risks in the system can be identified, and then measures can be taken to improve the stability of the entire system; based on the real-time monitored performance parameters and control mechanisms, more scientific and reasonable control strategies can be formulated to improve resource utilization efficiency and reduce operating costs; the analysis results and control mechanisms provided provide powerful decision-making support for the operation and management of wind farms and energy storage systems, which helps to achieve refined management and intelligent management. Energy-based operation, in step 111, obtain the performance parameters of various types of distributed active and reactive control resources of wind and storage, first clarify the type of performance parameters to be obtained, including but not limited to power output, response time, efficiency, capacity, etc., select a suitable measurement method according to the parameter type, such as direct measurement, indirect calculation or sensor, etc., use the selected measurement method to collect data for various types of distributed active and reactive control resources of wind and storage, organize the collected data to ensure the accuracy and completeness of the data, and provide basic data support for subsequent analysis and monitoring; in step 112, according to the performance parameters, the transient and stable active support The transient and steady-state characteristics of the system, as well as potential stability problems and risk points, are identified by analyzing the transient and steady-state characteristics of the system, and the potential problems of the wind-storage joint station in actively supporting the power grid can be revealed, providing a basis for the subsequent control strategy formulation; in step 113, the performance parameters are monitored in real time according to the analysis results to obtain the control mechanism. First, according to the analysis results, the performance parameters are monitored in real time to obtain the control mechanism. Based on the monitoring data and analysis results, the control mechanism of various types of distributed active and reactive control resources of wind and storage is extracted, including response characteristics, control rules, etc.; it can realize real-time monitoring and dynamic evaluation of the performance parameters of wind and storage joint stations, provide real-time data support for the formulation and optimization of control strategies, and reveal the control mechanism of resources, laying the foundation for subsequent refined management and intelligent operation.

[0068] like Figure 1 As shown in FIG12, according to the control mechanism, a dynamic coupling control mathematical model of multiple types of distributed active and reactive control resources of wind and storage joint stations is established, and the dynamic coupling control mathematical model is used to predict the active and reactive control resources to obtain the prediction results, including:

[0069] 121. According to the control mechanism, use fx k+1 =Ax k +Bu k +w k ,fy k =cx k +Du k +v k A dynamic coupling control mathematical model for multiple types of distributed active and reactive control resources of wind and storage joint stations is established, where x k is the value of internal state variables such as wind power, state of energy storage device, grid voltage and current at time k, u k is the external control input to the system at time k, y k is the active power and reactive power output of the wind farm and energy storage system at time k, A is the transition law of the system state from time k to time k+1, and B is the input vector u k Changes that affect the system state, c is the system state x k Mapped to the output vector y k , D is the input vector u k Directly affects the output vector y k , w k is the process noise vector, v k is the measurement noise vector;

[0070] 122. Obtain historical operation data of the wind-storage combined station and perform preprocessing to obtain preprocessed data;

[0071] 123. Input the preprocessed data into the dynamic coupling control mathematical model, and use the dynamic coupling control mathematical model to predict the active and reactive control resources of the wind-storage combined station to obtain the prediction results;

[0072] 124. Obtain the actual operation data of the wind and energy storage combined station, and compare the predicted results with the actual operation data to obtain a comparative result;

[0073] 125. According to the comparison results, the dynamic coupling control mathematical model is iteratively upgraded to obtain an optimized dynamic coupling control mathematical model.

[0074] In the embodiment of the present invention, the effect

[0075] By implementing the following steps, a dynamic coupling control mathematical model for multiple types of distributed active and reactive control resources in wind-storage combined stations is established, which helps to deeply understand the operation mechanism and control characteristics of the wind-storage combined system; using the dynamic coupling control mathematical model to predict the active and reactive control resources of the wind-storage combined station can improve the accuracy of the prediction, thereby achieving more refined control and optimizing the allocation and utilization of resources; by comparing the prediction results with the actual operation data and continuously iterating and upgrading the model, the accuracy and adaptability of the model can be improved, making it better adapt to changes in actual operation scenarios; the optimized dynamic coupling control mathematical model provides strong decision-making support for the operation and management of wind farms and energy storage systems, and helps to achieve more efficient and more Stable system operation; in step 121, a dynamic coupling control mathematical model is established, firstly clarifying the internal state variables, external control inputs, system state transfer laws, the impact of input vectors on system states, the mapping of system states to output vectors, the direct impact of input vectors on output vectors, process noise vectors and measurement noise vectors, etc. in the model; according to the control mechanism, a dynamic coupling control mathematical model of multiple types of distributed active and reactive control resources of wind-storage joint stations is constructed, which can accurately reflect the relationship between the internal state variables of the system and the external control inputs, as well as the change law of the system state over time; through theoretical analysis and simulation verification, the correctness and effectiveness of the constructed model are ensured; it provides a theoretical basis and model support for subsequent prediction and control In step 122, relevant data is collected from the historical operation data of the wind-storage combined station, including wind power, energy storage device status, grid voltage and current, etc. The collected data is cleaned to remove abnormal values ​​and missing values ​​to ensure the accuracy and completeness of the data; the data is normalized to eliminate the influence between different dimensions and improve the convergence speed and prediction accuracy of the model; high-quality data input is provided for subsequent model training and prediction; in step 123, the pre-processed data is input into the dynamic coupling control mathematical model; the model is trained with the input data so that the model can learn the dynamic characteristics and change laws of the active and reactive control resources of the wind-storage combined station; according to the trained model, the future wind power The power, state of the energy storage device, etc. are predicted to obtain the prediction results; the model is used to predict the active and reactive control resources of the wind-storage combined station to provide a basis for subsequent control decisions; in step 124, the actual operation data of the wind-storage combined station is obtained, the prediction results are compared with the actual operation data, and the prediction error and accuracy are analyzed to evaluate the prediction performance of the model and find the problems and deficiencies of the model; in step 125, according to the comparison results, the source and cause of the prediction error are analyzed; according to the source and cause of the error, the dynamic coupling control mathematical model is optimized and adjusted, such as modifying the model parameters, improving the model structure, etc.; the model is iteratively trained using the optimized model and new data to improve the accuracy and adaptability of the model;Verify the optimized model again to ensure the stability and reliability of the model; improve the accuracy and adaptability of the model through continuous iteration and upgrading, so that it can better adapt to changes in actual operation scenarios and provide more powerful decision-making support for the operation and management of wind farms and energy storage systems.

[0076] like Figure 1 As shown in 13, obtain the multi-dimensional spatial measurement data of the wind turbine position and the wind tower, analyze the temporal and spatial distribution characteristics of the random fluctuation characteristics of multiple temporal and spatial scales and their operational reliability, and obtain the analysis results, including:

[0077] 131. Obtain multi-dimensional spatial measurement data of wind turbine positions and wind towers, and perform preprocessing to obtain preprocessed measurement data:

[0078] 132. Extract key features based on the preprocessed measurement data, where the key features include the average value, standard deviation, extreme value, and spectrum characteristics of wind speed, as well as the distribution characteristics of wind direction and the range of temperature variation;

[0079] 133. Based on key features, use X t =φ1X t-1 +φ2X t-2 +…+φ p v t-p +∈ t +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q Multi-scale temporal and spatial analysis is performed to obtain the multi-scale random fluctuation characteristics and temporal and spatial distribution characteristics, where X t is the observed value at time t, φ1, φ2, …, φ p is the autoregressive coefficient, ∈ t is the white noise error term at time t, θ1, θ2, …, θ q is the sliding average coefficient, p is the order of the autoregressive part, and q is the order of the sliding average part;

[0080] 134. According to the characteristics of random fluctuations and spatiotemporal distribution at multiple spatiotemporal scales, the influence of wind turbine position and wind tower on wind turbine operation reliability is analyzed to obtain the analysis results.

[0081] In the embodiment of the present invention, the multi-dimensional spatial measurement data of the wind turbine position and the wind tower are preprocessed to extract key features, providing an accurate and effective data basis for subsequent analysis; the multi-temporal and spatial scale analysis method is used to reveal the random fluctuation characteristics and spatiotemporal distribution characteristics of key meteorological factors such as wind speed and wind direction at different time and space scales, providing a scientific basis for in-depth understanding of the operating environment of the wind turbine; based on the multi-temporal and spatial scale random fluctuation characteristics and spatiotemporal distribution characteristics, the influence of the wind turbine position and the wind tower on the reliability of wind turbine operation is analyzed, providing an important reference for the layout optimization of wind farms, wind turbine selection, and operation and maintenance strategy formulation; in step 131, ... the multi-temporal and spatial scale random fluctuation characteristics and spatiotemporal distribution characteristics are used to analyze the influence of the wind turbine position and the wind tower on the reliability of wind turbine operation, providing an important reference for the layout optimization of dimensional space measurement data, including wind speed, wind direction, temperature, etc.; clean the collected data to remove outliers, missing values, etc. to ensure the accuracy and completeness of the data; standardize the data to eliminate the influence of different dimensions and improve the accuracy of subsequent analysis; provide high-quality data input for subsequent feature extraction and multi-space-time scale analysis; in step 132, calculate the average value of wind speed, temperature and other data to reflect their overall level; calculate the standard deviation of wind speed, temperature and other data to reflect their degree of fluctuation; extract the extreme values ​​of wind speed, temperature and other data to understand their performance under extreme conditions; perform spectral analysis on wind speed data to obtain its spectral characteristics and understand wind The energy distribution of wind speed at different frequencies is analyzed; the distribution characteristics of wind direction data are analyzed to understand the main direction and distribution range of wind direction; the temperature variation range is analyzed to understand the possible impact of temperature on the operation of the wind turbine; key features are extracted to provide important input parameters for subsequent multi-time and space scale analysis; in step 133, a time series model is constructed based on the preprocessed measurement data, and the model parameters are estimated using the observation data; the constructed time series model is used to perform multi-time and space scale analysis to reveal the random fluctuation characteristics and spatiotemporal distribution characteristics of key meteorological factors such as wind speed and wind direction at different time and space scales. Through multi-time and space scale analysis, the operation environment of the wind turbine is deeply understood. environment, providing a scientific basis for the subsequent operational reliability assessment; in step 134, based on the multi-space-time scale random fluctuation characteristics and space-time distribution characteristics, the impact of meteorological factors such as wind speed, wind direction, and temperature on the operational reliability of the wind turbine is analyzed; according to the impact analysis results, the impact of the wind turbine position and the wind tower on the operational reliability of the wind turbine is evaluated to obtain the analysis results; the analysis results are interpreted to identify the key factors that may affect the operational reliability of the wind turbine, and corresponding improvement suggestions or measures are put forward; through the operational reliability assessment, it provides important references for the layout optimization of the wind farm, the selection of wind turbines, the formulation of operation and maintenance strategies, etc., which is helpful to improve the overall operation level and economic benefits of the wind farm.

[0082] like Figure 1 As shown in 133, based on the key characteristics, time series analysis is used to perform multi-temporal and spatial scale analysis to obtain multi-temporal and spatial scale random fluctuation characteristics and spatiotemporal distribution characteristics analysis, including:

[0083] 1331. According to the key characteristics, the random fluctuation characteristics at different time scales are analyzed to obtain the random fluctuation characteristic analysis results;

[0084] 1332. According to the key features, the spatial distribution and correlation of wind characteristics at different spatial scales are analyzed to obtain the wind characteristics analysis results;

[0085] 1333. According to the random fluctuation characteristic analysis results and the wind condition characteristic analysis results, the reliability of the operation of the wind turbine under different wind conditions is evaluated to obtain the reliability evaluation results;

[0086] 1334. Based on the reliability assessment results, the spatiotemporal distribution characteristics of the wind turbine operation reliability are analyzed to obtain the spatiotemporal distribution characteristics.

[0087] In the embodiments of the present invention, by analyzing the random fluctuation characteristics of key characteristics such as wind speed and wind direction at different time scales, it is possible to more accurately understand the temporal evolution of wind conditions, and provide a key basis for optimizing the operation strategy of wind turbines; in-depth analysis of the spatial distribution of wind condition characteristics at different spatial scales and their correlation is helpful to reveal the heterogeneity of wind conditions in wind farms, as well as the mutual influence of wind conditions between various machine positions, and provide scientific guidance for the layout planning of wind farms and the selection of wind turbines; combining the random fluctuation characteristics analysis results and the wind condition characteristics analysis results, a comprehensive evaluation of the operation reliability of wind turbines under different wind conditions is carried out, which can identify potential operation risk points and provide decision support for the operation and maintenance management of wind farms; by analyzing the spatiotemporal and temporal distribution of wind turbine operation reliability Analyzing the distribution characteristics can more intuitively understand the operating stability of each position in the wind farm in different time periods, and provide strong support for the long-term planning and short-term scheduling of the wind farm; in step 1331, the pre-processed measurement data is segmented according to different time scales; for each time period, the statistical indicators and spectrum characteristics of key characteristics such as wind speed and wind direction are extracted; the random fluctuation characteristics of each characteristic at different time scales are analyzed by time series analysis, spectrum analysis and other methods; the analysis results are sorted out to form a random fluctuation characteristic analysis report; it is helpful to deeply understand the time evolution law of wind conditions and provide basic data support for the optimization of wind turbine operation strategies; in step 1332, the pre-processed measurement data is segmented according to spatial position Sorting out; using spatial statistical methods, analyzing the distribution characteristics of key features such as wind speed and wind direction at different spatial scales; calculating the correlation coefficient of wind condition characteristics between each machine position to reveal the spatial correlation of wind conditions; sorting out the analysis results to form a spatial distribution and correlation analysis report of wind condition characteristics; helping to reveal the heterogeneity of wind conditions within the wind farm and the mutual influence of wind conditions between each machine position, and providing a scientific basis for the layout planning of the wind farm and the selection of wind turbines; in step 1333, combining the random fluctuation characteristic analysis results and the wind condition characteristic analysis results, construct a wind turbine operation reliability evaluation model; according to the operating characteristics and actual needs of the wind turbine, determine the evaluation indicators; use the evaluation model and evaluation indicators to evaluate the operating reliability of the wind turbine under different wind conditions ; Organize the evaluation results to form a wind turbine operation reliability evaluation report; be able to identify potential operation risk points of wind turbines under different wind conditions, and provide decision support for the operation and maintenance management of wind farms; in step 1334, combine the wind turbine operation reliability evaluation results with time and space information to form a spatiotemporal data set; use spatiotemporal statistical methods to analyze the spatiotemporal distribution characteristics of wind turbine operation reliability; use visualization technology to intuitively display the analysis results; organize the analysis results to form a wind turbine operation reliability spatiotemporal distribution characteristics analysis report; be able to intuitively display the operation stability of each unit in the wind farm in different time periods, provide strong support for the long-term planning and short-term scheduling of the wind farm, and also help to promptly discover and deal with potential operation problems.

[0088] like Figure 1 As shown in Figure 14, based on the analysis results, a quantitative evaluation model of active support control boundaries of multi-type distributed controllable resources of wind storage is established using high-precision wind speed and wind power prediction technology, including:

[0089] 141. Obtain data from wind farm centralized control and booster stations;

[0090] 142. Based on the analysis results and the data from the wind farm centralized control and booster station, a recurrent neural network is used to model the time series data to obtain a prediction model;

[0091] 143. Use the prediction model to predict wind speed and wind power to obtain wind speed and power prediction values;

[0092] 144. According to the wind speed and power forecast values, a multi-type distributed controllable resource model of wind storage is established to obtain the control capacity prediction results;

[0093] 145. Based on the prediction results of wind power and regulation capacity, a quantitative evaluation model for the active support control boundary of multi-type distributed controllable resources of wind power storage is established.

[0094] In the embodiment of the present invention, by acquiring the data of the wind farm centralized control and booster station, a rich data foundation is provided for the subsequent time series modeling. These data contain the key information of the wind farm operation and are the premise for establishing an accurate prediction model. The time series data are modeled by using a recurrent neural network, which can capture the time dependence and long-term trend in the data, thereby realizing the accurate prediction of wind speed and wind power, and providing a scientific decision-making basis for the operation scheduling of the wind farm and the formulation of the charging and discharging strategy of the energy storage system. Based on the wind speed and power prediction values, a multi-type distributed adjustable resource model of wind and energy storage is established, which can quantitatively evaluate the control capabilities of wind farms and energy storage systems, and provide strong support for the optimal allocation and efficient utilization of resources. By establishing a multi-type distributed adjustable resource model of wind and energy storage The quantitative assessment model of the active support control boundary of distributed controllable resources can clarify the active support capabilities of wind farms and energy storage systems in the power grid, and provide technical support for the stable operation of the power grid and the efficient consumption of wind power. In step 141, the data types and time ranges of the wind farm centralized control and booster stations to be obtained are clarified. Relevant data, including but not limited to wind speed, wind power, voltage, current, power factor, etc., are obtained from the data acquisition and monitoring control system of the wind farm centralized control system and the booster station. The collected data are cleaned, denoised, and missing values ​​are processed to ensure the quality and integrity of the data. High-quality data input is provided for subsequent time series modeling. In step 142, a suitable recurrent neural network is selected according to the characteristics of the data and the prediction requirements. The method comprises the following steps: first, using the preprocessed time series data to train the selected recurrent neural network model, and iteratively optimizing the model parameters so that the model can accurately capture the time dependency and long-term trend in the data; second, using the validation data set to validate the trained model and evaluate the prediction performance of the model, such as accuracy and stability; third, establishing a time series prediction model based on a recurrent neural network, which provides a core tool for subsequent wind speed and wind power prediction; fourth, inputting the preprocessed time series data into the trained recurrent neural network model; fourth, using the model to predict the wind speed and wind power in the future to obtain the predicted values ​​of wind speed and wind power; fifth, analyzing the prediction results, evaluating the accuracy and reliability of the prediction, and performing error correction if necessary. Correction; accurate prediction of wind speed and wind power is achieved, providing a scientific basis for the operation and scheduling of wind farms and the formulation of charging and discharging strategies for energy storage systems; in step 144, a controllable resource model is established; first, the controllable resources in wind farms and energy storage systems are identified, including wind turbines and energy storage batteries; based on the predicted values ​​of wind speed and power, combined with the characteristics and constraints of controllable resources, a wind-storage multi-type distributed controllable resource model is established; the controllable resource control capability of wind farms and energy storage systems is evaluated using the model, and the controllable resource control capability of wind farms and energy storage systems is quantified, providing strong support for the optimal allocation and efficient utilization of resources; in step 145, an active support control boundary quantitative evaluation model is established, firstly clarifying the active support control boundary of wind farms and energy storage systems in the power grid;Based on the prediction results of wind power and regulation capacity, combined with the operating characteristics and constraints of the power grid, a quantitative evaluation model for the active support control boundary of wind and energy storage multi-type distributed adjustable resources is established; the model is used to evaluate and analyze the active support capacity of wind farms and energy storage systems, and clarify their roles and positioning in the power grid; the active support capacity of wind farms and energy storage systems in the power grid is clarified, providing technical guarantees for the stable operation of the power grid and the efficient consumption of wind power, and also providing a scientific basis for the optimal design and operation strategy formulation of wind farms and energy storage systems. ;

[0095] like Figure 1 As shown in Figure 15, based on the prediction results and evaluation model, a dynamic evaluation system for wind-storage combined transient and steady-state active and reactive power multi-time scale control capability is constructed, including:

[0096] 151. Based on the prediction results, establish the transient and steady-state models of the wind-storage combined system, and use the transient and steady-state models to determine the active and reactive power regulation capabilities of the electric field and energy storage system;

[0097] 152. Divide the time scale into multiple levels, and use the evaluation model to dynamically evaluate the control capability of the wind-storage combined system at each time scale to obtain the evaluation results;

[0098] 153. Based on the assessment results, a dynamic assessment system for multi-time scale regulatory capabilities will be developed.

[0099] In the embodiment of the present invention, a transient and steady-state model of the wind-storage combined system is established, which can comprehensively describe the behavioral characteristics of the system under different operating conditions, and provide a basis for accurately evaluating the active and reactive control capabilities of the electric field and energy storage system; the time scale is divided into multiple levels, and the control capability of the wind-storage combined system is dynamically evaluated at each time scale, which can reveal the law of changes in the control capability of the system at different time scales, and provide a basis for formulating flexible control strategies; based on the evaluation results, a multi-time scale control capability dynamic evaluation system is constructed, which can realize continuous monitoring and evaluation of the control capability of the wind-storage combined system, and provide decision support for the optimal operation and scheduling of the system; in step 151, a wind-storage combined system is established. Quantification of model and control capabilities; first, based on the transient analysis theory of power system, a transient model of the wind-storage combined system is established, taking into account factors such as the dynamic response of wind turbines, the charging and discharging characteristics of the energy storage system, and the topological structure of the power grid; based on the steady-state analysis theory of power system, a steady-state model of the wind-storage combined system is established, focusing on the system's active and reactive balance, voltage and frequency stability and other issues; through model analysis, the active output or absorption capacity of the wind farm and the energy storage system under different operating conditions is determined; also through model analysis, the reactive output or absorption capacity of the wind farm and the energy storage system under different operating conditions is determined, including reactive compensation capacity and voltage regulation capacity; this provides a theoretical basis for accurately evaluating the control capabilities of the wind-storage combined system In step 152, the time scale is divided into multiple levels, such as seconds, minutes, hours, days, etc., to reflect the operating characteristics and regulation requirements of the system at different time scales; at each time scale, the established wind-storage combined system model and regulation capacity quantification method are used to dynamically evaluate the regulation capacity of the system; the regulation capacity is updated and predicted in real time by considering the system's operating state changes at different time scales, the influence of external conditions, and the charging and discharging state of the energy storage system; the evaluation results are analyzed and compared to reveal the law of changes in the system's regulation capacity at different time scales and identify potential regulation risks and opportunities; it can reveal the wind-storage combined system at different time scales. The dynamic changes of the control capacity under the time scale are used to provide data support for the formulation of flexible control strategies; in step 153, based on the multi-time scale dynamic evaluation results, a complete set of dynamic evaluation index system for control capacity is constructed, including active control capacity index, reactive control capacity index and comprehensive control capacity index under different time scales; the evaluation process and method are clarified, including data collection, model update, evaluation calculation, result analysis and other links, to ensure the accuracy and timeliness of the evaluation; the evaluation results are applied to the optimized operation and dispatching decision of the wind-storage joint system to guide the daily operation management and future development planning of the system; according to the actual application effect, the evaluation system is continuously improved and optimized to improve the accuracy and practicality of the evaluation;A dynamic evaluation system for multi-time scale regulation capability has been constructed, which provides a scientific basis and decision-making support for the optimized operation and dispatch of the wind-storage combined system, and helps to improve the overall operation efficiency and reliability of the system. ;

[0100] Based on the same general inventive concept, the present invention also protects a wind energy-energy storage control system 20 based on physical constraints. The wind energy-energy storage control system based on physical constraints provided by the present invention is described below. The wind energy-energy storage control system based on physical constraints described below and the wind energy-energy storage control method based on physical constraints described above can refer to each other, including an acquisition module 21 and a processing module 22.

[0101] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0102] like Figure 3 As shown, the electronic device may include: a processor (processor) 610, a communication interface (Communications Interface) 620, a memory (memory) 630 and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute the wind energy-energy storage control method based on physical constraints.

[0103] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0104] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the wind energy-energy storage control method based on physical constraints provided by the above methods.

[0105] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the wind energy-energy storage control method based on physical constraints provided by the above methods.

[0106] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0107] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wind energy-storage control method based on physical constraints, characterized in that: include: Obtain active support transient and steady-state control mechanisms for multiple types of distributed active and reactive regulation resources of wind and storage, including transient and steady-state regulation response characteristics at multiple time scales, operating domain constraints, dynamic distribution laws, and interactive coupling mechanisms; According to the control mechanism, a dynamic coupling control mathematical model of multiple types of distributed active and reactive control resources of wind and storage joint stations is established, and the dynamic coupling control mathematical model is used to predict the active and reactive control resources to obtain the prediction results; Obtain multi-dimensional spatial measurement data of wind turbine positions and wind towers, analyze the temporal and spatial distribution characteristics of random fluctuations at multiple temporal and spatial scales and their operational reliability to obtain analysis results; Based on the analysis results, a quantitative assessment model for the active support control boundary of multi-type distributed controllable resources of wind storage is established using high-precision wind speed and wind power prediction technology; Based on the prediction results and evaluation model, a dynamic evaluation system for the transient and steady-state active and reactive power multi-time scale control capabilities of wind and storage is constructed.

2. The wind energy-storage control method based on physical constraints according to claim 1 is characterized in that: Obtain active support transient and steady-state control mechanisms for multiple types of distributed active and reactive power regulation resources of wind and storage. The control mechanisms include multi-time scale transient and steady-state regulation response characteristics, operating domain constraints, dynamic distribution laws, and interactive coupling mechanisms, including: Obtain performance parameters of various types of distributed active and reactive control resources of wind and storage; According to the performance parameters, the transient and steady states of the active support are analyzed to obtain the analysis results; Based on the analysis results, the performance parameters are monitored in real time to obtain the control mechanism.

3. The wind energy-storage control method based on physical constraints according to claim 2 is characterized in that: According to the control mechanism, a dynamic coupling control mathematical model of multiple types of distributed active and reactive control resources of wind and storage joint stations is established, and the dynamic coupling control mathematical model is used to predict the active and reactive control resources to obtain the prediction results, including: According to the control mechanism, use fx k+1 =Ax k +Bu k +w k ,fy k =Cx k +Du k +v k A dynamic coupling control mathematical model for multiple types of distributed active and reactive control resources of wind and storage joint stations is established, where x k is the value of internal state variables such as wind power, state of energy storage device, grid voltage and current at time k, u k is the external control input to the system at time k, y k is the active power and reactive power output of the wind farm and energy storage system at time k, A is the transition law of the system state from time k to time k+1, and B is the input vector u k Changes that affect the system state, C is the system state x k Mapped to the output vector y k , D is the input vector u k Directly affects the output vector y k , w k is the process noise vector, v k is the measurement noise vector; Obtain historical operation data of the wind-storage combined station and perform preprocessing to obtain preprocessed data; The preprocessed data is input into the dynamic coupling control mathematical model, and the dynamic coupling control mathematical model is used to predict the active and reactive control resources of the wind-storage combined station to obtain the prediction results; Obtain the actual operating data of the wind and energy storage combined station, and compare the predicted results with the actual operating data to obtain comparative results; According to the comparison results, the dynamic coupling control mathematical model is iteratively upgraded to obtain an optimized dynamic coupling control mathematical model.

4. The wind energy-storage control method based on physical constraints according to claim 3 is characterized in that: Obtain multi-dimensional spatial measurement data of wind turbine positions and wind towers, analyze the temporal and spatial distribution characteristics of random fluctuations at multiple temporal and spatial scales and their operational reliability, and obtain analysis results, including: Obtain the multi-dimensional spatial measurement data of the wind turbine position and the wind tower, and preprocess them to obtain the preprocessed measurement data: Extract key features based on the preprocessed measurement data, wherein the key features include the average value, standard deviation, extreme value, spectrum characteristics of wind speed, distribution characteristics of wind direction, and temperature variation range; Based on the key features, use X t =φ1X t-1 +φ2X t-2 +…+φ p X t-p +∈ t +θ1∈ t-1 +θ2∈ t-2 +…+θ q ∈ t-q Multi-scale temporal and spatial analysis is performed to obtain the multi-scale random fluctuation characteristics and temporal and spatial distribution characteristics, where X t is the observed value at time t, φ1, φ2, …, φ p is the autoregressive coefficient, ∈ t is the white noise error term at time t, θ1,θ2,…,θ q is the sliding average coefficient, p is the order of the autoregressive part, and q is the order of the sliding average part; According to the random fluctuation characteristics and spatiotemporal distribution characteristics of multiple spatiotemporal scales, the influence of wind turbine location and wind tower on wind turbine operation reliability is analyzed to obtain the analysis results.

5. The wind energy-storage control method based on physical constraints according to claim 4 is characterized in that: Based on the key characteristics, time series analysis is used to perform multi-temporal and spatial scale analysis to obtain multi-temporal and spatial scale random fluctuation characteristics and spatiotemporal distribution characteristics analysis, including: According to the key characteristics, the random fluctuation characteristics at different time scales are analyzed to obtain the random fluctuation characteristic analysis results; According to the key features, the spatial distribution and correlation of wind characteristics at different spatial scales are analyzed to obtain the wind characteristics analysis results; According to the results of random fluctuation characteristics analysis and wind condition characteristics analysis, the reliability of wind turbine operation under different wind conditions is evaluated to obtain reliability evaluation results; According to the reliability assessment results, the temporal and spatial distribution characteristics of the wind turbine operation reliability are analyzed to obtain the temporal and spatial distribution characteristics.

6. The wind energy-storage control method based on physical constraints according to claim 5 is characterized in that: According to the analysis results, a quantitative assessment model of active support control boundary of multi-type distributed controllable resources of wind storage is established using high-precision wind speed and wind power prediction technology, including: Obtain data from wind farm centralized control and booster stations; Based on the analysis results and the data from the wind farm centralized control and booster station, a recurrent neural network is used to model the time series data to obtain a prediction model. The wind speed and wind power are predicted using a prediction model to obtain wind speed and power prediction values; According to the wind speed and power forecast values, a multi-type distributed controllable resource model of wind storage is established to obtain the control capacity prediction results; According to the prediction results of wind power and regulation capacity, a quantitative evaluation model of active support control boundary of multi-type distributed controllable resources of wind and storage is established.

7. The wind energy-storage control method based on physical constraints according to claim 6 is characterized in that: Based on the prediction results and evaluation model, a dynamic evaluation system for the transient and steady-state active and reactive power multi-time scale control capability of wind and storage is constructed, including: Based on the prediction results, the transient and steady-state models of the wind-storage combined system are established, and the active and reactive power regulation capabilities of the electric field and energy storage system are determined using the transient and steady-state models; The time scale is divided into multiple levels. At each time scale, the evaluation model is used to dynamically evaluate the control capability of the wind-storage combined system to obtain the evaluation results. Based on the assessment results, a dynamic assessment system for multi-time scale regulation capabilities will be developed.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the wind energy-energy storage control method based on physical constraints as described in any one of claims 1 to 7 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the wind energy-energy storage control method based on physical constraints as described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the wind energy-energy storage control method based on physical constraints as described in any one of claims 1 to 7 is implemented.

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