Method, device and computer equipment for determining output of generator connected to new energy

Abstract

The present application relates to a method, device and computer equipment for determining the output of a generator connected to a new energy source. The method comprises: obtaining the grid state data of the target power grid, the grid control intelligent model and the new energy uncertainty correction model; inputting the grid state data into the grid control intelligent model to obtain the initial generator output data corresponding to the target power grid; inputting the initial generator output data into the new energy uncertainty correction model to obtain the adjusted generator output data corresponding to the target power grid; when the disturbance of the adjusted generator output data is greater than a threshold, the adjusted generator output data is integrated into the grid state data and fed back to the grid control intelligent model until the disturbance of the adjusted generator output data is less than the threshold, and the adjusted generator output data is used as the target generator output data. The use of this method can improve the operating efficiency and reliability of the power grid and promote the large-scale access and utilization of new energy sources.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a method, device, computer equipment, storage medium and computer program product for determining the output of a generator connected to a new energy source. Background Art

[0002] With the development of computer technology, smart grid technology has emerged. The calculation of generator output for high-proportion renewable energy access mainly relies on traditional methods based on statistical models or prediction algorithms. However, these methods often cannot effectively deal with the volatility and intermittency of renewable energy, resulting in unstable grid dispatching and difficulty in accurately predicting renewable energy output, which in turn affects the efficiency and reliability of grid operation. Summary of the invention

[0003] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for determining the generator output of new energy access that can improve the efficiency and reliability of power grid operation in response to the above technical problems.

[0004] In a first aspect, the present application provides a method for determining the output of a generator connected to a new energy source, comprising:

[0005] Obtain the grid status data of the target grid, the grid control intelligent model, and the new energy uncertainty correction model;

[0006] Inputting the grid state data into the grid control intelligent model to obtain initial generator output data corresponding to the target grid;

[0007] Inputting the initial generator output data into the new energy uncertainty correction model to obtain the adjusted generator output data corresponding to the target power grid;

[0008] When the disturbance of the adjusted generator output data is greater than a threshold value, the adjusted generator output data is integrated into the grid state data and fed back to the grid control intelligent model until the disturbance of the adjusted generator output data is less than a threshold value, and the adjusted generator output data is used as the target generator output data.

[0009] In a second aspect, the present application also provides a device for determining the output of a generator connected to a new energy source, comprising:

[0010] The power grid data acquisition module is used to obtain the power grid status data of the target power grid, the power grid control intelligent model and the new energy uncertainty correction model;

[0011] An initial data acquisition module is used to input the power grid state data into the power grid control intelligent model to obtain initial generator output data corresponding to the target power grid;

[0012] An adjustment data obtaining module, used for inputting the initial generator output data into the new energy uncertainty correction model to obtain the adjusted generator output data corresponding to the target power grid;

[0013] The target data obtaining module is used to fuse the adjusted generator output data into the grid state data when the disturbance of the adjusted generator output data is greater than a threshold value, and feed back the adjusted generator output data to the grid control intelligent model until the disturbance of the adjusted generator output data is less than a threshold value, and the adjusted generator output data is used as the target generator output data.

[0014] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0015] Obtain the grid status data of the target grid, the grid control intelligent model, and the new energy uncertainty correction model;

[0016] Inputting the grid state data into the grid control intelligent model to obtain initial generator output data corresponding to the target grid;

[0017] Inputting the initial generator output data into the new energy uncertainty correction model to obtain the adjusted generator output data corresponding to the target power grid;

[0018] When the disturbance of the adjusted generator output data is greater than a threshold value, the adjusted generator output data is integrated into the grid state data and fed back to the grid control intelligent model until the disturbance of the adjusted generator output data is less than a threshold value, and the adjusted generator output data is used as the target generator output data.

[0019] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0020] Obtain the grid status data of the target grid, the grid control intelligent model, and the new energy uncertainty correction model;

[0021] Inputting the grid state data into the grid control intelligent model to obtain initial generator output data corresponding to the target grid;

[0022] Inputting the initial generator output data into the new energy uncertainty correction model to obtain the adjusted generator output data corresponding to the target power grid;

[0023] When the disturbance of the adjusted generator output data is greater than a threshold value, the adjusted generator output data is integrated into the grid state data and fed back to the grid control intelligent model until the disturbance of the adjusted generator output data is less than a threshold value, and the adjusted generator output data is used as the target generator output data.

[0024] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0025] Obtain the grid status data of the target grid, the grid control intelligent model, and the new energy uncertainty correction model;

[0026] Inputting the grid state data into the grid control intelligent model to obtain initial generator output data corresponding to the target grid;

[0027] Inputting the initial generator output data into the new energy uncertainty correction model to obtain the adjusted generator output data corresponding to the target power grid;

[0028] When the disturbance of the adjusted generator output data is greater than a threshold value, the adjusted generator output data is integrated into the grid state data and fed back to the grid control intelligent model until the disturbance of the adjusted generator output data is less than a threshold value, and the adjusted generator output data is used as the target generator output data.

[0029] The above-mentioned method, device, computer equipment, storage medium and computer program product for determining the generator output of a new energy access obtain the grid state data, grid control intelligent model and new energy uncertainty correction model of the target grid; input the grid state data into the grid control intelligent model to obtain the initial generator output data corresponding to the target grid; input the initial generator output data into the new energy uncertainty correction model to obtain the adjusted generator output data corresponding to the target grid; when the disturbance of the adjusted generator output data is greater than a threshold, the adjusted generator output data is integrated into the grid state data and fed back to the grid control intelligent model until the disturbance of the adjusted generator output data is less than the threshold, and the adjusted generator output data is used as the target generator output data.

[0030] By inputting the grid state data into the grid control intelligent model, the initial generator output data of the target grid is obtained, which enables the system to make corresponding adjustments according to the real-time grid state and improves the dynamic response capability of the grid. Then, these initial output data are input into the new energy uncertainty correction model to obtain the adjusted generator output data. The model can accurately estimate the output uncertainty of new energy, thereby adjusting the output of the generator in a targeted manner, reducing the instability caused by the fluctuation of new energy in the grid operation, and improving the stability and reliability of the grid. Finally, the adjusted generator output data is integrated into the grid state data and fed back to the grid control intelligent model. Through continuous iterative optimization, the grid can quickly adapt to various changes in actual operation and maintain stable operation. This comprehensive control strategy can not only improve the operation efficiency and reliability of the grid, but also promote the large-scale access and utilization of new energy and promote the development and application of clean energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 An application environment diagram of a method for determining the output of a generator connected to a new energy source in one embodiment;

[0033] Figure 2 A schematic flow chart of a method for determining the output of a generator connected to a new energy source in one embodiment;

[0034] Figure 3 A schematic flow chart of a method for adjusting generator output data in an embodiment;

[0035] Figure 4 A schematic flow chart of a method for obtaining correction amounts of each processed data in one embodiment;

[0036] Figure 5 Schematic diagram of the flow of a method for checking correction amounts of each processed data in one embodiment;

[0037] Figure 6 It is a flowchart of a method for obtaining initial generator output data in one embodiment;

[0038] Figure 7 It is a flowchart of a method for obtaining initial generator output data in another embodiment;

[0039] Figure 8 A structural block diagram of a device for determining the output of a generator connected to a new energy source in one embodiment;

[0040] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0042] The method for determining the output of a generator connected to a new energy source provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 obtains the grid state data, the grid control intelligent model and the new energy uncertainty correction model of the target grid from the terminal 102; the grid state data is input into the grid control intelligent model to obtain the initial generator output data corresponding to the target grid; the initial generator output data is input into the new energy uncertainty correction model to obtain the adjusted generator output data corresponding to the target grid; when the disturbance of the adjusted generator output data is greater than the threshold, the adjusted generator output data is integrated into the grid state data and fed back to the grid control intelligent model until the disturbance of the adjusted generator output data is less than the threshold, and the adjusted generator output data is used as the target generator output data. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices, and the Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. The portable wearable device may be a smart watch, a smart bracelet, a head-mounted device, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0043] In an exemplary embodiment, Figure 2 As shown, a method for determining the output of a generator connected to a new energy source is provided, and the method is applied to Figure 1 The server in the example is used to illustrate, including the following steps 202 to 208. Among them:

[0044] Step 202, obtaining the grid status data of the target grid, the grid control intelligent model and the new energy uncertainty correction model.

[0045] The target power grid may be a power grid that needs to understand the output of generators with new energy access.

[0046] Among them, the grid status data can be various information used to describe the operating status of the power system, including the operating data of new energy units. This information can be real-time data or historical data, which is used to monitor, analyze and control the operation of the power grid.

[0047] Among them, the power grid control intelligent model can be an artificial intelligence model used to control the parameters of the target power grid.

[0048] Among them, the new energy uncertainty correction model can be a correction model for the data of the power grid control intelligent model taking new energy into consideration.

[0049] Specifically, sensors, monitoring equipment or power system control centers are used to obtain the grid state data of the target power grid, including power load data, generator state data, grid topology data, voltage data, frequency data and fault data. The grid state data can be obtained through real-time monitoring equipment, or extracted and analyzed through historical records. Based on the acquired grid state data, the grid control intelligent model and the new energy uncertainty correction model are synchronously obtained, wherein the establishment of the grid control intelligent model can be constructed by reinforcement learning, optimization algorithm or rule-based method, etc. The grid control intelligent model can select the appropriate generator output adjustment strategy according to the grid state and predicted load demand to maintain the stable operation of the grid. The new energy uncertainty correction model can be constructed using statistical methods, machine learning methods, etc. The new energy uncertainty correction model can be based on historical new energy output data, weather forecast data and characteristic parameters of new energy power generation equipment. Factors such as factors can be used to correct the uncertainty of the predicted output of new energy.

[0050] Step 204 , inputting the grid state data into the grid control intelligent model to obtain the initial generator output data corresponding to the target grid.

[0051] The initial generator output data may be output data of the generator of the target power grid, but has not been corrected and verified.

[0052] Specifically, the grid state data is input into the grid control intelligent model. The grid state data may include information such as grid load, generator output, grid topology, voltage frequency, etc. The grid control intelligent model calculates the initial generator output data corresponding to the target grid based on the input grid state data. This process involves algorithms, strategies or rules in the model, which dynamically determine the generator output according to the grid state to maintain the stable operation of the grid. The calculated initial generator output data is used as the output of the model for subsequent control process.

[0053] Step 206, inputting the initial generator output data into the new energy uncertainty correction model to obtain the adjusted generator output data corresponding to the target power grid.

[0054] The adjusted generator output data may be the output data of the generator that has been corrected.

[0055] Specifically, the initial generator output data is input into the new energy uncertainty correction model. The initial generator output data includes the initial output value of each generator and other information related to the power grid status. The new energy uncertainty correction model calculates the adjusted generator output data corresponding to the target power grid based on the input initial generator output data and other related information. This process involves steps such as algorithms, feature extraction, and parameter adjustment in the model to correct the uncertainty of the new energy output and make the predicted output more accurate. The adjusted generator output data calculated by the model is used as the output of the model for the subsequent power grid control process.

[0056] Step 208, when the disturbance of the adjusted generator output data is greater than the threshold, the adjusted generator output data is integrated into the grid state data and fed back to the grid control intelligent model until the disturbance of the adjusted generator output data is less than the threshold, and the adjusted generator output data is used as the target generator output data.

[0057] The target generator output data may be the output data of the generator finally obtained for the target power grid.

[0058] Specifically, the difference between the corrected generator output data and the historical generator output data, i.e., the disturbance, is calculated. If the disturbance is less than a preset threshold, it is considered that the target generator output data has converged, the iterative process can be stopped, and the adjusted generator output data is used as the target generator output data. If the disturbance is still greater than the threshold, the corrected generator output data is fused with the original grid state data, which involves updating the generator output field in the grid state data, or adding a new field to store the corrected output data, and feeding the fused grid state data back to the grid control intelligent model. This means that the fused data is provided as input to the model, and the adjusted generator output data is recalculated. This process is repeated until the disturbance of the generator output data is less than the preset threshold, and the adjusted generator output data is used as the target generator output data.

[0059] In the above-mentioned method for determining the generator output of a new energy access, the grid state data of the target grid, the grid control intelligent model and the new energy uncertainty correction model are obtained; the grid state data is input into the grid control intelligent model to obtain the initial generator output data corresponding to the target grid; the initial generator output data is input into the new energy uncertainty correction model to obtain the adjusted generator output data corresponding to the target grid; when the disturbance of the adjusted generator output data is greater than a threshold, the adjusted generator output data is integrated into the grid state data and fed back to the grid control intelligent model until the disturbance of the adjusted generator output data is less than the threshold, and the adjusted generator output data is used as the target generator output data.

[0060] By inputting the grid state data into the grid control intelligent model, the initial generator output data of the target grid is obtained, which enables the system to make corresponding adjustments according to the real-time grid state and improves the dynamic response capability of the grid. Then, these initial output data are input into the new energy uncertainty correction model to obtain the adjusted generator output data. The model can accurately estimate the output uncertainty of new energy, thereby adjusting the output of the generator in a targeted manner, reducing the instability caused by the fluctuation of new energy in the grid operation, and improving the stability and reliability of the grid. Finally, the adjusted generator output data is integrated into the grid state data and fed back to the grid control intelligent model. Through continuous iterative optimization, the grid can quickly adapt to various changes in actual operation and maintain stable operation. This comprehensive control strategy can not only improve the operation efficiency and reliability of the grid, but also promote the large-scale access and utilization of new energy and promote the development and application of clean energy.

[0061] In an exemplary embodiment, Figure 3As shown, the initial generator output data is input into the new energy uncertainty correction model to obtain the adjusted generator output data corresponding to the target power grid, including steps 302 to 306. Among them:

[0062] Step 302: Select at least one target correction network from the new energy uncertainty correction model according to the initial generator output data.

[0063] The target correction network may be a model for correcting initial generator output data.

[0064] Specifically, since the new energy uncertainty correction model may have multiple target correction networks, each target correction network is used to process different types of new energy uncertainty calculations or application scenarios. According to the specific application requirements and problem background in the initial generator output data, at least one target correction network is selected from the new energy uncertainty correction model. The basis for selecting the target correction network can be the performance, accuracy and other indicators of the network in processing specific types of uncertainty or specific scenarios. In a specific embodiment, the initial generator output data is input into the selected target correction network to ensure that the format and structure of the input data match the requirements of the target correction network. The target correction network calculates the corrected generator output data based on the input initial generator output data. The correction process may involve parameter adjustment, feature extraction, model training and other steps in the network to correct the uncertainty of the new energy output, so that the predicted output is more accurate. The corrected generator output data calculated by the target correction network is used as output.

[0065] Step 304, inputting the initial generator output data into each target correction network respectively to obtain each processed data correction amount.

[0066] The processed data correction amount may be an amount that needs to be corrected for the initial generator output data.

[0067] Specifically, the generator output data is input into each target correction network for processing, and the corresponding processed data correction amount is calculated. The processed data correction amount can represent the difference between the adjusted value of the initial generator output data or the corrected value and the original value. This process is completed through the model parameters and algorithms in the target correction network. The processed data correction amount calculated by each target correction network is used as the output. Each processed data correction amount corresponds to the processing result of the input generator output data under different correction networks, reflecting the uncertainty correction effect corresponding to each network.

[0068] Step 306, correcting the initial generator output data according to the correction amount of each processed data to obtain adjusted generator output data.

[0069] Specifically, the corresponding processed data correction amount is added or subtracted from the initial generator output data of each generator, and the correction amount can be a positive value (increase) or a negative value (decrease). The corrected generator output data is obtained, that is, the adjusted generator output data. The adjusted generator output data takes into account the correction effect of each target correction network on the initial data, thereby more accurately reflecting the actual generator output. The obtained adjusted generator output data is used as the final output.

[0070] In this embodiment, the process of selecting a target correction network based on the initial generator output data, inputting the data into the target network and obtaining the correction amount, and adjusting the initial data based on the correction amount effectively handles the problem caused by the uncertainty of new energy. By selecting a suitable correction network and obtaining the correction amount, we can adjust the generator output data according to the real-time situation to cope with the volatility and intermittency of new energy, thereby improving the stability and reliability of the power grid. This method provides more accurate data support for power grid dispatching, helps optimize power grid operation, and provides a feasible solution for large-scale access to new energy.

[0071] In an exemplary embodiment, Figure 4 As shown, the initial generator output data is input into each target correction network to obtain each processed data correction amount, including steps 402 to 406. Wherein:

[0072] Step 402, classify each processed data information and each target correction network in the initial generator output data to obtain processed data classification information and correction network classification information.

[0073] The processed data classification information may be a classification result obtained after classifying each processed data information in the initial generator output data.

[0074] The correction network classification information may be the classification result obtained after classification of each target correction network.

[0075] Specifically, the processed data correction amount of each generator in the initial generator output data is classified according to the classification rules to obtain processed data classification information; an attribute classification information is established for each target correction network, including the generator data it processes and the corresponding correction amount information, to obtain correction network classification information.

[0076] Step 404, based on the processed data classification information and the modified network classification information, according to the data network association rule, a data network association relationship between the processed data information and the target modified network is established.

[0077] The data network association rule may be an association rule for associating the processed data classification information with the modified network classification information.

[0078] The data network association relationship may be a relationship obtained by associating the processed data classification information with the corrected network classification information.

[0079] Specifically, define data network association rules, that is, determine which types of processing data information are associated with which target correction networks. Data network association rules can be formulated based on domain knowledge, data analysis or expert experience. Traverse the processing data classification information, and for each processing data information, find its corresponding target correction network. At the same time, traverse the correction network classification information, and for each target correction network, find its corresponding processing data information. According to the established data network association rules, associate each processing data information with the target correction network. If a certain processing data information and the corresponding target correction network meet the data network association rules, a data network association relationship is established. This process can be achieved through logical judgment or conditional screening to determine whether the characteristics or attributes of the processing data meet the conditions associated with the target correction network. Record the established data network association relationship to form a data network association relationship between the processing data information and the target correction network. The data network association relationship can be represented in the form of a graph, in which the processing data is used as a node and the target correction network is used as an edge to form a directed graph or an undirected graph.

[0080] Step 406: for any data network association relationship, input the processed data information into the target correction network to obtain the processed data correction amount.

[0081] Specifically, a data network association relationship is arbitrarily selected from the established data network association relationships, that is, the association between a processing data information and a target correction network is determined, and relevant processing data information is obtained from the selected data network association relationship, which includes the generator output data to be corrected, the characteristic parameters of the generator, etc. According to the selected data network association relationship, the corresponding target correction network is determined. The obtained processing data information is input into the determined target correction network, ensuring that the format and structure of the input data match the requirements of the target correction network, and the target correction network calculates the corresponding processing data correction amount according to the input processing data information.

[0082] In this embodiment, according to the classification information of the initial generator output data and the target correction network, as well as the constructed data network association relationship, an effective connection between the processed data and the target correction network can be established. This makes it possible to more accurately map different types of generator output data to the corresponding correction network, thereby obtaining targeted correction amounts. Through this process, we can more effectively respond to the correction needs of different types of data, improve the accuracy and applicability of the correction model, and thus enhance the accuracy and stability of power grid dispatching.

[0083] In an exemplary embodiment, Figure 5 As shown, after the initial generator output data is input into each target correction network to obtain each processed data correction amount, the method further includes steps 502 to 508. Among them:

[0084] Step 502, obtaining historical generator output data of the target power grid and a data correction simulation model.

[0085] The historical generator output data may be output data of the generator in the historical records of the target power grid.

[0086] The data correction simulation model may be a model used to simulate whether the calculated processed data correction amount meets the operating standard of the target power grid.

[0087] Specifically, the server 104 retrieves historical generator output data of the target power grid and a data correction simulation model from the memory.

[0088] Step 504 , inputting each processed data correction amount and the initial generator output data into a data correction simulation model to obtain predicted generator output data.

[0089] The predicted generator output data may be predicted output data obtained by inputting the corrected initial generator output data into a simulated power grid.

[0090] Specifically, traverse each processed data correction amount and initial generator output data to ensure the consistency and integrity of the data format, and the matching of the timestamp or time period information of the processed data correction amount and the initial generator output data. Input the preprocessed processed data correction amount and the initial generator output data into the established data correction simulation model to ensure that the format and structure of the input data match the input requirements of the model to ensure that the model can correctly process and analyze the data. The data correction simulation model predicts and generates predicted generator output data based on the input data. The model may adjust and correct the initial output data based on the patterns and trends of historical data and the correction amounts of each processed data to obtain more accurate prediction results.

[0091] Step 506 , checking the safety difference and accuracy difference between the historical generator output data and the predicted generator output data.

[0092] The safety difference and the accuracy difference may be the difference between the safety value and the accuracy value between the historical generator output data and the predicted generator output data.

[0093] Specifically, compare the historical generator output data with the predicted generator output data to check the safety difference between the two. The safety difference check can include the extreme value of the generator output and whether the fluctuation amplitude is within a reasonable range. Analyze the accuracy difference between the historical generator output data and the predicted generator output data. Various evaluation indicators, such as root mean square error (RMSE), mean absolute error (MAE), correlation coefficient, etc., can be used to quantify the accuracy difference between the two. Detect abnormal situations between historical data and predicted data, such as mutations, abnormal fluctuations, etc. Anomaly detection can be achieved through statistical methods, time series analysis and other technologies. Further analysis and processing are required after abnormal situations are found.

[0094] Step 508, when the safety difference or the accuracy difference is greater than the difference threshold of the target power grid, return to the step of selecting at least one target correction network from the new energy uncertainty correction model based on the initial generator output data, until the safety difference and the accuracy difference are both less than the difference threshold of the target power grid.

[0095] Specifically, the difference thresholds of the safety difference and the accuracy difference are set as the criteria for judging whether to continue the correction. These thresholds can be set according to the characteristics of the power grid, the dispatching requirements and the expected effects. It is judged whether the safety difference and the accuracy difference are both less than the set difference threshold of the target power grid. When the safety difference or the accuracy difference is less than the difference threshold of the target power grid, it means that the correction effect has met the requirements, the correction process can be ended and the final adjusted generator output data can be output; when the safety difference or the accuracy difference is greater than the difference threshold of the target power grid, the target correction network is continued to be selected from the new energy uncertainty correction model, and further correction operations are performed until the safety difference and the accuracy difference are both less than the difference threshold of the target power grid, the correction process is ended, and the final adjusted generator output data is output.

[0096] In this embodiment, by obtaining the historical generator output data of the target power grid and establishing a data correction simulation model, the future generator output data can be predicted and compared with the historical data. By checking the safety difference and accuracy difference between the historical data and the predicted data, the accuracy and stability of the model can be discovered and evaluated in a timely manner. If it is found that the safety difference or the accuracy difference exceeds the difference threshold of the target power grid, the step of correcting the initial generator output data can be returned to further optimize the prediction result. This process can ensure that the predicted generator output data is closer to the actual situation, improve the accuracy of the dispatching decision and the safety of the power grid operation.

[0097] In an exemplary embodiment, Figure 6As shown, the grid state data is input into the grid control intelligent model to obtain the initial generator output data corresponding to the target grid, including steps 602 to 604. Among them:

[0098] Step 602: inputting grid state data into a grid control intelligent model, and reading at least one target environmental policy data from an environmental policy database associated with the grid control intelligent model.

[0099] The environmental policy database may be a database storing the relationship between control policies and environmental factors.

[0100] The target environmental strategy data may be the relationship between the selected control strategy and the environmental factors.

[0101] Specifically, the grid status data is input into the grid control intelligent model to ensure that the format and structure of the grid status data match the input requirements of the model, and at least one target environmental policy data is read from an environmental policy database associated with the grid control intelligent model. The database stores a variety of environmental policy data. These data from the environmental policy database may include information relationships such as electricity market price relationships, new energy forecast data relationships, and weather forecasts.

[0102] Step 604, input each target environment strategy data and grid status data into the grid control intelligent model to obtain initial generator output data.

[0103] Specifically, the target environment strategy data and the grid status data are input into the grid control intelligent model, and the format and structure of the input data match the input requirements of the model to ensure that the model can correctly process and analyze the data. The grid control intelligent model performs calculations and analyses based on the input target environment strategy data and grid status data. The model can use machine learning, optimization algorithms, rule engines and other methods to comprehensively consider various factors and generate initial generator output data.

[0104] In this embodiment, by inputting the grid state data into the grid control intelligent model and combining it with the target environmental strategy data in the environmental strategy database, the initial generator output data can be effectively generated. By comprehensively considering the current state of the grid and various environmental factors, the intelligent model can accurately adjust the generator output to meet the operation requirements of the grid. This process can improve the accuracy and response speed of grid dispatch, promote the stable operation of the grid, and provide more reliable support for the access of new energy.

[0105] In an exemplary embodiment, Figure 7 As shown, each target environment strategy data and grid state data are input into the grid control intelligent model to obtain initial generator output data, including steps 702 to 706. Among them:

[0106] Step 702 , selecting current environmental strategy data from each target environmental strategy data, regulating the power grid state data, and obtaining current processing data.

[0107] The currently processed data may be a control result obtained by controlling the power grid status data.

[0108] Specifically, the environmental strategy data at the current moment is selected from each target environmental strategy data to ensure that the selected data corresponds to the power grid status data at the current moment and can provide the most accurate environmental information. The selected current environmental strategy data is integrated with the power grid status data to form a complete input data set, and the integrated current environmental strategy data and power grid status data are used for power grid control calculation. In the calculation, the current environmental factors and power grid status are considered to adjust the generator output to achieve stable operation and economical dispatch of the power grid. In the power grid control calculation, the current processing data is generated according to the influence of the current environmental strategy data and power grid status data.

[0109] Step 704 , selecting the next environmental policy data from each target environmental policy data according to the currently processed data.

[0110] Specifically, based on the current processing data, the next environmental policy data is selected from each target environmental policy data, wherein the selection of the next environmental policy data may depend on the feedback and status of the current processing data to determine the next step of regulation strategy and direction.

[0111] Step 706, taking the next environmental strategy data as the current environmental strategy data, and returning to execute the step of regulating the power grid state data to obtain the current processing data, until each target environmental strategy data is selected at least once.

[0112] Specifically, the next environmental policy data is selected from the target environmental policy data as the current environmental policy data. This can be achieved by iterating the target environmental policy data list, and selecting the next environmental policy data in turn. The newly selected current environmental policy data and the power grid state data are input into the power grid control model again to perform the next round of control operations. This process will be repeated until all target environmental policy data are selected at least once. When all target environmental policy data are selected at least once, the execution is stopped and the process ends.

[0113] In this embodiment, by selecting the current environmental strategy data from each target environmental strategy data and regulating the power grid state data, the current processing data can be obtained, so that the power grid operation is in a state adapted to the current environment. Subsequently, according to the current processing data, the next environmental strategy data is selected, and the power grid state is regulated again, and this cycle is repeated until all target environmental strategy data are selected at least once. This process realizes the dynamic regulation of the power grid state data, so that the power grid operation can respond to changes in different environmental conditions in a timely manner, improves the flexibility and adaptability of the power grid, and thus ensures the stable operation and reliability of the power grid.

[0114] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0115] Based on the same inventive concept, the embodiment of the present application also provides a device for determining the output of a generator connected to a new energy source for implementing the above-mentioned method for determining the output of a generator connected to a new energy source. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more devices for determining the output of a generator connected to a new energy source provided below can be referred to the limitations of the method for determining the output of a generator connected to a new energy source above, and will not be repeated here.

[0116] In an exemplary embodiment, Figure 8 As shown, a device for determining the output of a generator connected to a new energy source is provided, comprising: a power grid data acquisition module 802, an initial data acquisition module 804, an adjustment data acquisition module 806 and a target data acquisition module 808, wherein:

[0117] A power grid data acquisition module 802 is used to acquire the power grid status data of the target power grid, the power grid control intelligent model and the new energy uncertainty correction model;

[0118] The initial data acquisition module 804 is used to input the power grid state data into the power grid control intelligent model to obtain the initial generator output data corresponding to the target power grid;

[0119] The adjustment data obtaining module 806 is used to input the initial generator output data into the new energy uncertainty correction model to obtain the adjusted generator output data corresponding to the target power grid;

[0120] The target data acquisition module 808 is used to adjust the generator output data. When the disturbance of the generator output data is greater than a threshold, the adjusted generator output data is integrated into the grid state data and fed back to the grid control intelligent model until the disturbance of the generator output data is less than a threshold, and the adjusted generator output data is used as the target generator output data.

[0121] In one embodiment, the adjustment data obtaining module 806 is also used to select at least one target correction network from the new energy uncertainty correction model based on the initial generator output data; input the initial generator output data into each target correction network respectively to obtain each processed data correction amount; according to each processed data correction amount, correct the initial generator output data to obtain adjusted generator output data.

[0122] In one embodiment, the adjustment data acquisition module 806 is also used to classify each processed data information and each target correction network in the initial generator output data to obtain processed data classification information and correction network classification information; on the basis of the processed data classification information and the correction network classification information, according to the data network association rules, a data network association relationship between the processed data information and the target correction network is constructed; for any data network association relationship, the processed data information is input into the target correction network to obtain the processed data correction amount.

[0123] In one embodiment, the adjustment data obtaining module 806 is also used to obtain the historical generator output data of the target power grid and the data correction simulation model; input each processed data correction amount and the initial generator output data into the data correction simulation model to obtain the predicted generator output data; check the safety difference and accuracy difference between the historical generator output data and the predicted generator output data; when the safety difference or the accuracy difference is greater than the difference threshold to the target power grid, return to execute the step of selecting at least one target correction network from the new energy uncertainty correction model according to the initial generator output data, until the safety difference and the accuracy difference are both less than the difference threshold of the target power grid.

[0124] In one embodiment, the initial data acquisition module 804 is also used to input the grid status data into the grid control intelligent model, read at least one target environmental strategy data from the environmental strategy database associated with the grid control intelligent model; input each target environmental strategy data and grid status data into the grid control intelligent model to obtain initial generator output data.

[0125] In one embodiment, the initial data acquisition module 804 is also used to select the current environmental policy data from each target environmental policy data, regulate the power grid status data, and obtain the current processing data; select the next environmental policy data from each target environmental policy data according to the current processing data; use the next environmental policy data as the current environmental policy data, return to execute the step of regulating the power grid status data to obtain the current processing data, until each target environmental policy data is selected at least once.

[0126] Each module in the above-mentioned device for determining the output of a generator connected to a new energy source can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0127] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store server data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining the output of a generator with new energy access is implemented.

[0128] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0129] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0130] In one embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0131] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-mentioned method embodiments.

[0132] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0133] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0134] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0135] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining the output of a generator connected to a new energy source, characterized in that: The method comprises: Obtain the grid status data of the target grid, the grid control intelligent model, and the new energy uncertainty correction model; Inputting the grid state data into the grid control intelligent model to obtain initial generator output data corresponding to the target grid; The initial generator output data is input into the new energy uncertainty correction model, and at least one target correction network is selected from the new energy uncertainty correction model according to the initial generator output data; the initial generator output data is respectively input into each of the target correction networks, and each processed data information in the initial generator output data and each of the target correction networks are classified to obtain processed data classification information and correction network classification information; on the basis of the processed data classification information and the correction network classification information, a data network association relationship between the processed data information and the target correction network is constructed according to a data network association rule; for any of the data network association relationships, the processed data information is input into the target correction network to obtain each processed data correction amount, and the target electricity The historical generator output data of the power grid and the data correction simulation model are inputted into the data correction simulation model; the safety difference and accuracy difference between the historical generator output data and the predicted generator output data are checked; when the safety difference or the accuracy difference is greater than the difference threshold of the target power grid, the step of selecting at least one target correction network from the new energy uncertainty correction model according to the initial generator output data is returned to be executed until both the safety difference and the accuracy difference are less than the difference threshold of the target power grid; the initial generator output data is corrected according to each of the processed data correction amounts to obtain the adjusted generator output data corresponding to the target power grid; When the disturbance of the adjusted generator output data is greater than a threshold value, the adjusted generator output data is integrated into the grid state data and fed back to the grid control intelligent model until the disturbance of the adjusted generator output data is less than a threshold value, and the adjusted generator output data is used as the target generator output data.

2. The method according to claim 1, characterized in that The step of inputting the grid state data into the grid control intelligent model to obtain initial generator output data corresponding to the target grid includes: Inputting the power grid state data into the power grid regulation intelligent model, and reading at least one target environmental policy data from an environmental policy database associated with the power grid regulation intelligent model; The target environment strategy data and the power grid status data are input into the power grid control intelligent model to obtain the initial generator output data.

3. The method according to claim 2, characterized in that The step of inputting the target environment strategy data and the grid state data into the grid control intelligent model to obtain the initial generator output data includes: Selecting current environmental strategy data from each of the target environmental strategy data, regulating the power grid state data, and obtaining current processing data; Selecting the next environmental policy data from each of the target environmental policy data according to the current processing data; The next environmental strategy data is used as the current environmental strategy data, and the step of regulating the power grid state data to obtain the current processing data is returned to be executed until each of the target environmental strategy data is selected at least once.

4. A device for determining the output of a generator connected to a new energy source, characterized in that: The device comprises: The power grid data acquisition module is used to obtain the power grid status data of the target power grid, the power grid control intelligent model and the new energy uncertainty correction model; An initial data acquisition module is used to input the power grid state data into the power grid control intelligent model to obtain initial generator output data corresponding to the target power grid; The adjustment data acquisition module is used to input the initial generator output data into the new energy uncertainty correction model, and select at least one target correction network from the new energy uncertainty correction model according to the initial generator output data; input the initial generator output data into each of the target correction networks respectively, classify each processed data information in the initial generator output data and each of the target correction networks to obtain processed data classification information and correction network classification information; on the basis of the processed data classification information and the correction network classification information, according to the data network association rules, construct a data network association relationship between the processed data information and the target correction network; for any of the data network association relationships, input the processed data information into the target correction network to obtain each processed data correction amount, and obtain Obtain the historical generator output data and the data correction simulation model of the target power grid; input each of the processed data correction amounts and the initial generator output data into the data correction simulation model to obtain predicted generator output data; check the safety difference and accuracy difference between the historical generator output data and the predicted generator output data; when the safety difference or the accuracy difference is greater than the difference threshold of the target power grid, return to execute the step of selecting at least one target correction network from the new energy uncertainty correction model according to the initial generator output data until the safety difference and the accuracy difference are both less than the difference threshold of the target power grid; correct the initial generator output data according to each of the processed data correction amounts to obtain the adjusted generator output data corresponding to the target power grid; The target data obtaining module is used to fuse the adjusted generator output data into the grid state data when the disturbance of the adjusted generator output data is greater than a threshold value, and feed back the adjusted generator output data to the grid control intelligent model until the disturbance of the adjusted generator output data is less than a threshold value, and the adjusted generator output data is used as the target generator output data.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

Images