CN118199174A - New energy access generator output determining method and device and computer equipment - Google Patents
New energy access generator output determining method and device and computer equipment Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0499—Feedforward networks
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- G06N3/092—Reinforcement learning
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
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- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/40—Synchronising a generator for connection to a network or to another generator
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
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Abstract
The application relates to a method and a device for determining the output of a generator accessed by new energy and computer equipment. The method comprises the following steps: acquiring power grid state data of a target power grid, a power grid regulation intelligent model and a new energy uncertainty correction model; inputting the power grid state data into a power grid regulation intelligent model to obtain initial generator output data corresponding to a target power grid; inputting the initial generator output data to a new energy uncertainty correction model to obtain adjusted generator output data corresponding to a target power grid; under the condition that the disturbance of the output data of the adjusting generator is larger than the threshold value, the output data of the adjusting generator is fused to the state data of the power grid and fed back to the intelligent power grid regulation model until the disturbance of the output data of the adjusting generator is smaller than the threshold value, and the output data of the adjusting generator is taken as target output data of the adjusting generator. By adopting the method, the operation efficiency and reliability of the power grid can be improved, and the large-scale access and utilization of new energy are promoted.
Description
Technical Field
The application relates to the technical field of smart grids, in particular to a method and a device for determining the output of a generator for new energy access, computer equipment, a storage medium and a computer program product.
Background
With the development of computer technology, smart grid technology appears, and the calculation of the output of a generator aiming at high-proportion new energy access mainly depends on a traditional method based on a statistical model or a prediction algorithm. However, these methods often cannot effectively process the volatility and intermittence of the new energy, which results in unstable power grid dispatching, and it is difficult to accurately predict the new energy output, thereby affecting the efficiency and reliability of the power grid operation.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, a computer readable storage medium, and a computer program product for determining a generator output of a new energy access that can improve the efficiency and reliability of grid operation.
In a first aspect, the present application provides a method for determining the output of a generator for new energy access, including:
Acquiring power grid state data of a target power grid, a power grid regulation intelligent model and a new energy uncertainty correction model;
Inputting the power grid state data into the power grid regulation intelligent model to obtain initial generator output data corresponding to the target power grid;
inputting the initial generator output data to the new energy uncertainty correction model to obtain adjusted generator output data corresponding to the target power grid;
And under the condition that the disturbance of the output data of the adjusting generator is larger than a threshold value, fusing the output data of the adjusting generator to the power grid state data, feeding back to the power grid regulation intelligent model until the disturbance of the output data of the adjusting generator is smaller than the threshold value, and taking the output data of the adjusting generator as target output data of the generator.
In a second aspect, the present application further provides a generator output determining device for new energy access, including:
the power grid data acquisition module is used for acquiring power grid state data of a target power grid, a power grid regulation intelligent model and a new energy uncertainty correction model;
The initial data obtaining module is used for inputting the power grid state data into the power grid regulation intelligent model to obtain initial generator output data corresponding to the target power grid;
The adjustment data obtaining module is used for inputting the initial generator output data to the new energy uncertainty correction model to obtain adjustment generator output data corresponding to the target power grid;
And the target data obtaining module is used for fusing the output data of the adjusting generator to the power grid state data and feeding back to the power grid regulation intelligent model under the condition that the disturbance of the output data of the adjusting generator is larger than a threshold value until the disturbance of the output data of the adjusting generator is smaller than the threshold value, and taking the output data of the adjusting generator as the output data of the target generator.
In a third aspect, the present application also provides a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
Acquiring power grid state data of a target power grid, a power grid regulation intelligent model and a new energy uncertainty correction model;
Inputting the power grid state data into the power grid regulation intelligent model to obtain initial generator output data corresponding to the target power grid;
inputting the initial generator output data to the new energy uncertainty correction model to obtain adjusted generator output data corresponding to the target power grid;
And under the condition that the disturbance of the output data of the adjusting generator is larger than a threshold value, fusing the output data of the adjusting generator to the power grid state data, feeding back to the power grid regulation intelligent model until the disturbance of the output data of the adjusting generator is smaller than the threshold value, and taking the output data of the adjusting generator as target output data of the generator.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
Acquiring power grid state data of a target power grid, a power grid regulation intelligent model and a new energy uncertainty correction model;
Inputting the power grid state data into the power grid regulation intelligent model to obtain initial generator output data corresponding to the target power grid;
inputting the initial generator output data to the new energy uncertainty correction model to obtain adjusted generator output data corresponding to the target power grid;
And under the condition that the disturbance of the output data of the adjusting generator is larger than a threshold value, fusing the output data of the adjusting generator to the power grid state data, feeding back to the power grid regulation intelligent model until the disturbance of the output data of the adjusting generator is smaller than the threshold value, and taking the output data of the adjusting generator as target output data of the generator.
In a fifth aspect, the application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of:
Acquiring power grid state data of a target power grid, a power grid regulation intelligent model and a new energy uncertainty correction model;
Inputting the power grid state data into the power grid regulation intelligent model to obtain initial generator output data corresponding to the target power grid;
inputting the initial generator output data to the new energy uncertainty correction model to obtain adjusted generator output data corresponding to the target power grid;
And under the condition that the disturbance of the output data of the adjusting generator is larger than a threshold value, fusing the output data of the adjusting generator to the power grid state data, feeding back to the power grid regulation intelligent model until the disturbance of the output data of the adjusting generator is smaller than the threshold value, and taking the output data of the adjusting generator as target output data of the generator.
The method, the device, the computer equipment, the storage medium and the computer program product for determining the output of the generator accessed by the new energy are realized by acquiring the power grid state data of the target power grid, the intelligent power grid regulation model and the new energy uncertainty correction model; inputting the power grid state data into a power grid regulation intelligent model to obtain initial generator output data corresponding to a target power grid; inputting the initial generator output data to a new energy uncertainty correction model to obtain adjusted generator output data corresponding to a target power grid; under the condition that the disturbance of the output data of the adjusting generator is larger than the threshold value, the output data of the adjusting generator is fused to the state data of the power grid and fed back to the intelligent power grid regulation model until the disturbance of the output data of the adjusting generator is smaller than the threshold value, and the output data of the adjusting generator is taken as target output data of the adjusting generator.
The initial generator output data of the target power grid is obtained by inputting the power grid state data into the power grid regulation intelligent model, so that the system can make corresponding regulation according to the real-time power grid state, and the dynamic response capability of the power grid is improved. And then, the initial output data are input into a new energy uncertainty correction model to obtain adjusted output data of the generator, and the model can accurately estimate the output uncertainty of the new energy, so that the output of the generator is adjusted in a targeted manner, the instability caused by new energy fluctuation in the operation of the power grid is reduced, and the stability and reliability of the power grid are improved. And finally, fusing the adjusted output data of the generator into power grid state data, feeding back the power grid state data to the power grid regulation intelligent model, and enabling the power grid to quickly adapt to various changes in actual operation through continuous iterative optimization so as to keep stable operation. The comprehensive regulation strategy not only can improve the operation efficiency and reliability of the power grid, but also promotes the large-scale access and utilization of new energy, and promotes the development and application of clean energy.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.
FIG. 1 is an application environment diagram of a new energy accessed generator output determination method in one embodiment;
FIG. 2 is a flow chart of a method for determining generator output for new energy access in one embodiment;
FIG. 3 is a flow chart of a method for adjusting generator output data in one embodiment;
FIG. 4 is a flow chart of a method for obtaining correction amounts of processing data according to one embodiment;
FIG. 5 is a flow chart of a method of verifying correction amounts of processing data according to one embodiment;
FIG. 6 is a flow chart of a method of obtaining initial generator output data in one embodiment;
FIG. 7 is a flow chart of a method of obtaining initial generator output data in another embodiment;
FIG. 8 is a block diagram of a new energy accessed generator output determining device in one embodiment;
fig. 9 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The method for determining the output of the generator for new energy access, provided by the embodiment of the application, can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The server 104 acquires power grid state data of a target power grid, a power grid regulation intelligent model and a new energy uncertainty correction model from the terminal 102; inputting the power grid state data into a power grid regulation intelligent model to obtain initial generator output data corresponding to a target power grid; inputting the initial generator output data to a new energy uncertainty correction model to obtain adjusted generator output data corresponding to a target power grid; under the condition that the disturbance of the output data of the adjusting generator is larger than the threshold value, the output data of the adjusting generator is fused to the state data of the power grid and fed back to the intelligent power grid regulation model until the disturbance of the output data of the adjusting generator is smaller than the threshold value, and the output data of the adjusting generator is taken as target output data of the adjusting generator. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In an exemplary embodiment, as shown in fig. 2, a method for determining the output of a generator for new energy access is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps 202 to 208. Wherein:
Step 202, acquiring power grid state data of a target power grid, a power grid regulation intelligent model and a new energy uncertainty correction model.
The target power grid can be a power grid which needs to be known by considering the output condition of the generator with new energy access.
The power grid state data can be various information for describing the operation state of the power system, and also comprise the operation data of the new energy unit, and the information can be real-time data or historical data for monitoring, analyzing and controlling the operation of the power grid.
The power grid regulation intelligent model can be an artificial intelligent model for regulating and controlling parameters of a target power grid.
The new energy uncertainty correction model can be data of an intelligent power grid regulation model, and the new energy uncertainty correction model is a correction model under the condition of considering new energy.
Specifically, a sensor, a monitoring device or a power system control center is used to obtain power grid state data of a target power grid, including power load data, generator state data, power grid topology data, voltage data, frequency data, fault data and the like. The power grid state data can be obtained through real-time monitoring equipment, and can also be extracted and analyzed through historical records. Based on the obtained power grid state data, a power grid regulation intelligent model and a new energy uncertainty correction model are synchronously obtained, wherein the power grid regulation intelligent model can be built by adopting reinforcement learning, an optimization algorithm or a rule-based method and the like, and the power grid regulation intelligent model can select a proper power generator output regulation strategy according to the power grid state and the predicted load demand so as to maintain the stable operation of the power grid. The new energy uncertainty correction model can be constructed by using a statistical method, a machine learning method and the like, and can correct uncertainty of new energy prediction output based on factors such as historical new energy output data, weather forecast data, characteristic parameters of new energy power generation equipment and the like.
And 204, inputting the power grid state data into a power grid regulation intelligent model to obtain initial generator output data corresponding to a target power grid.
The initial generator output data may be output data of a generator of the target power grid, but is not corrected and verified.
Specifically, the power grid state data is input into a power grid regulation intelligent model, wherein the power grid state data may include information such as load conditions of a power grid, output conditions of a generator, a power grid topological structure, voltage frequency and the like. And the power grid regulation intelligent model calculates initial generator output data corresponding to the target power grid according to the input power grid state data. This process involves algorithms, strategies or rules in the model to dynamically determine the generator output based on the grid conditions to maintain stable operation of the grid. And taking the calculated initial generator output data as the output of the model for the subsequent regulation and control process.
And 206, inputting the initial generator output data into a new energy uncertainty correction model to obtain the adjusted generator output data corresponding to the target power grid.
The adjusted generator output data may be corrected generator output data.
Specifically, initial generator output data is input into the new energy uncertainty correction model, wherein the initial generator output data comprises initial output values of all generators and other information related to the state of the power grid. And the new energy uncertainty correction model calculates the adjusted generator output data corresponding to the target power grid according to the input initial generator output data and other related information. The process involves the steps of algorithm, feature extraction, parameter adjustment and the like in the model to correct the uncertainty of the new energy output, so that the predicted output is more accurate. And taking the output data of the regulated generator obtained by the model calculation as the output of the model, and using the output data of the regulated generator in the subsequent power grid regulation and control process.
And step 208, under the condition that the disturbance of the output data of the adjusting generator is larger than the threshold value, the output data of the adjusting generator is fused to the state data of the power grid and fed back to the intelligent power grid regulation model until the disturbance of the output data of the adjusting generator is smaller than the threshold value, and the output data of the adjusting generator is taken as the output data of the target generator.
The target generator output data may be output data of the resulting generator for the target grid.
Specifically, a difference, i.e., a disturbance, between the corrected generator output data and the historical generator output data is calculated. If the disturbance is less than the preset threshold, the target generator output data is considered to have 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 the fused grid state data is fed back to the grid regulation intelligent model. This means that the fused data is provided as input to the model and the adjusted generator output data is recalculated. And repeating the process until the disturbance of the generator output data is smaller than a preset threshold value, and taking the adjusted generator output data as target generator output data.
In the method for determining the output of the generator accessed by the new energy, the power grid state data of the target power grid, the power grid regulation intelligent model and the new energy uncertainty correction model are obtained; inputting the power grid state data into a power grid regulation intelligent model to obtain initial generator output data corresponding to a target power grid; inputting the initial generator output data to a new energy uncertainty correction model to obtain adjusted generator output data corresponding to a target power grid; under the condition that the disturbance of the output data of the adjusting generator is larger than the threshold value, the output data of the adjusting generator is fused to the state data of the power grid and fed back to the intelligent power grid regulation model until the disturbance of the output data of the adjusting generator is smaller than the threshold value, and the output data of the adjusting generator is taken as target output data of the adjusting generator.
The initial generator output data of the target power grid is obtained by inputting the power grid state data into the power grid regulation intelligent model, so that the system can make corresponding regulation according to the real-time power grid state, and the dynamic response capability of the power grid is improved. And then, the initial output data are input into a new energy uncertainty correction model to obtain adjusted output data of the generator, and the model can accurately estimate the output uncertainty of the new energy, so that the output of the generator is adjusted in a targeted manner, the instability caused by new energy fluctuation in the operation of the power grid is reduced, and the stability and reliability of the power grid are improved. And finally, fusing the adjusted output data of the generator into power grid state data, feeding back the power grid state data to the power grid regulation intelligent model, and enabling the power grid to quickly adapt to various changes in actual operation through continuous iterative optimization so as to keep stable operation. The comprehensive regulation strategy not only can improve the operation efficiency and reliability of the power grid, but also promotes the large-scale access and utilization of new energy, and promotes the development and application of clean energy.
In an exemplary embodiment, as shown in fig. 3, the initial generator output data is input to the new energy uncertainty correction model to obtain the adjusted generator output data corresponding to the target grid, including steps 302 to 306. Wherein:
Step 302, selecting at least one target correction network from the new energy uncertainty correction model according to the initial generator output data.
Wherein the target correction network may be a model for correcting the initial generator output data.
Specifically, since the new energy uncertainty correction model may include multiple target correction networks, each target correction network is configured to process different types of new energy uncertainty calculations or application scenarios, and at least one target correction network is selected from the new energy uncertainty correction model according to specific application requirements and problem backgrounds in the initial generator output data. The basis for selecting the target correction network can be indexes such as performance, accuracy and the like of the network in the process of specific type uncertainty or specific scene. In a specific embodiment, initial generator output data is input into a selected target correction network, ensuring that the format and structure of the input data matches the requirements of the target correction network. The target correction network calculates corrected generator output data according to the input initial generator output data, and the correction process may involve the steps of parameter adjustment, feature extraction, model training and the like in the network to correct uncertainty of new energy output, so that predicted output is more accurate. And outputting the corrected generator output data calculated by the target correction network.
Step 304, the initial generator output data are respectively input to each target correction network to obtain each processing data correction amount.
The process data modifier may be an amount that needs to be modified for the initial generator output data.
Specifically, the output data of the generator are respectively input to each target correction network for processing, and corresponding processing data correction amounts are obtained through calculation. The process data modifier may represent an adjusted value or a modified value of the initial generator output data that differs from the original value by model parameters and algorithms in the target modification network. And taking the processed data correction quantity calculated by each target correction network as output, wherein each processed data correction quantity corresponds to the processed result of the input generator output data under different correction networks, and reflects the uncertainty correction effect corresponding to each network.
And 306, correcting the initial generator output data according to the correction amount of each processing data to obtain the adjusted generator output data.
Specifically, the initial generator output data for each generator is added to or subtracted from the corresponding processed data modifier, which may be positive (increasing) or negative (decreasing), respectively. And obtaining corrected generator output data, namely adjusting the generator output data. The adjustment of the generator output data considers the correction influence of each target correction network on the initial data, so that the output condition of the actual generator is reflected more accurately. And taking the obtained output data of the adjusted generator as a final output.
In this embodiment, the process effectively solves the problem caused by uncertainty of new energy sources by selecting the target correction network according to the initial generator output data, inputting the data into the target network and obtaining the correction amount, and adjusting the initial data according to the correction amount. By selecting a proper correction network and acquiring correction, the output data of the generator can be adjusted according to real-time conditions so as to cope with the fluctuation and intermittence of new energy, thereby improving the stability and reliability of the power grid. The method provides more accurate data support for power grid dispatching, is beneficial to optimizing power grid operation, and provides a feasible solution for large-scale access of new energy.
In one exemplary embodiment, as shown in FIG. 4, initial generator output data is input to each target correction network separately, resulting in each process data correction, including steps 402-406. Wherein:
step 402, classifying each processing data information and each target correction network in the initial generator output data to obtain processing data classification information and correction network classification information.
The processed data classification information may be a classification result obtained after classifying each processed data information in the initial generator output data.
The correction network classification information may be a classification result obtained after each target correction network is classified.
Specifically, classifying the processed data correction amount of each generator in the initial generator output data according to a classification rule to obtain processed data classification information; and establishing attribute classification information for each target correction network, wherein the attribute classification information comprises processed generator data and corresponding correction amount information, and obtaining correction network classification information.
Step 404, based on the processed data classification information and the corrected network classification information, constructing a data network association relationship between the processed data information and the target corrected network according to the data network association rule.
The data network association rule may be an association rule that associates the processing data classification information with the correction network classification information.
The data network association relationship may be a relationship obtained by processing data classification information and correcting association of the network classification information.
In particular, data network association rules are defined, i.e. it is determined which types of processed data information are associated with which target correction networks, which may be formulated based on domain knowledge, data analysis or expert experience. Traversing the processed data classification information, searching for a corresponding target correction network for each processed data information, traversing the correction network classification information, and searching for corresponding processed data information for each target correction network. And according to the established data network association rule, associating each piece of processed data information with the target correction network, if the processed data information accords with the data network association rule with the corresponding target correction network, establishing a data network association relationship, wherein the process can be realized through logic judgment or condition screening, and judging whether the characteristics or attributes of the processed data meet the conditions associated with the target correction network. Recording the established data network association relationship to form a data network association relationship between the processed data information and the target correction network, wherein the data network association relationship can be represented in a graph form, the processed data is taken as a node, and the target correction network is taken as an edge to form a directed graph or undirected graph.
Step 406, inputting the processed data information into the target correction network for any data network association relationship to obtain the processed data correction amount.
Specifically, a data network association relationship is arbitrarily selected from the established data network association relationships, namely, the association between one piece of processing data information and a target correction network is determined, and relevant processing data information is acquired from the selected data network association relationship, wherein the information comprises generator output data to be corrected, characteristic parameters of a generator and the like. And determining a corresponding target correction network according to the selected data network association relation. And inputting the acquired processing data information into a determined target correction network, ensuring that the format and structure of the input data are matched with the requirements of the target correction network, and calculating the corresponding processing data correction amount by the target correction network according to the input processing data information.
In this embodiment, according to the classification information of the initial generator output data and the target correction network, and the constructed association relationship of the data network, an effective connection between the processing data and the target correction network can be established. This enables a more accurate mapping of the different types of generator output data into the corresponding correction networks, thereby obtaining a targeted correction. Through the process, the correction requirements of different types of data can be more effectively met, the accuracy and the applicability of a correction model are improved, and the accuracy and the stability of power grid dispatching are further improved.
In one exemplary embodiment, as shown in FIG. 5, after the steps of inputting the initial generator output data into each target correction network, respectively, resulting in each process data correction, the method further includes steps 502 through 508. Wherein:
Step 502, historical generator output data of a target power grid and a data correction simulation model are obtained.
Wherein the historical generator output data may be output data of the target grid in the historical record about the generator.
The data correction simulation model may be a model for simulating whether the calculated processed data correction amount meets the operation standard of the target power grid.
Specifically, server 104 retrieves historical generator output data for the target grid from memory along with a data correction simulation model.
And step 504, inputting the correction amount of each processing data and the initial generator output data into a data correction simulation model to obtain predicted generator output data.
The predicted generator output data may be predicted output data obtained by inputting corrected initial generator output data into the simulated power grid.
Specifically, each process data modifier and the initial generator output data are traversed to ensure consistency and integrity of the data format, and the time stamp or time period information of the process data modifier and the initial generator output data are matched. And (3) inputting the preprocessed correction amounts of the processing data and the initial generator output data into the established data correction simulation model, and ensuring that the format and structure of the input data are matched with the input requirements of the model so as to ensure that the model can accurately process and analyze the data. And the data correction simulation model predicts according to the input data and generates predicted generator output data. The model may adjust and correct the initial output data according to the mode and trend of the historical data and the correction amount of each processing data, so as to obtain a more accurate prediction result.
Step 506, checking for safety and accuracy differences between the historical generator output data and the predicted generator output data.
The safety and accuracy differences may be differences between safe and accurate values between the historical and predicted generator output data.
Specifically, the historical generator output data and the predicted generator output data are compared, and a safety difference between the historical generator output data and the predicted generator output data is checked, wherein the checking of the safety difference can comprise the aspects of extreme value conditions of the generator output, whether the fluctuation amplitude is in a reasonable range and the like. Analyzing the difference in accuracy between the historical generator output data and the predicted generator output data may use various evaluation metrics, such as Root Mean Square Error (RMSE), mean Absolute Error (MAE), correlation coefficients, etc., to quantify the difference in accuracy between the two. Abnormal conditions between the historical data and the predicted data, such as mutation, abnormal fluctuation, and the like, are detected. The anomaly detection can be realized by statistical methods, time series analysis and other technologies, and further analysis and processing are needed after the anomaly is found.
And step 508, returning 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 under the condition that the safety difference or the accuracy difference is larger than the difference threshold of the target power grid until the safety difference and the accuracy difference are smaller than the difference threshold of the target power grid.
Specifically, a difference threshold of the safety difference and the accuracy difference is set as a criterion for judging whether to continue to perform correction. These thresholds may be set according to the characteristics of the grid, the scheduling requirements and the expected effects. And judging whether the safety difference and the accuracy difference are smaller than a set difference threshold value of the target power grid. Under the condition that the safety difference or the accuracy difference is smaller than the difference threshold value of the target power grid, the correction effect is required, the correction process can be ended, and final adjustment generator output data can be output; and under the condition that the safety difference or the accuracy difference is larger than the difference threshold value of the target power grid, continuously selecting the target correction network from the new energy uncertainty correction model, performing further correction operation until the safety difference and the accuracy difference are smaller than the difference threshold value of the target power grid, ending the correction process, and outputting final adjustment generator output data.
In this embodiment, by acquiring the historical generator output data of the target power grid and establishing the data correction simulation model, the future generator output data can be predicted and compared with the historical data. By checking the safety difference and the accuracy difference between the historical data and the forecast data, the accuracy and the stability of the model can be found and evaluated in time. If the safety difference or the accuracy difference is found to exceed the difference threshold of the target power grid, the step of correcting the initial generator output data can be returned to be executed, so that the prediction result is further optimized. The process can ensure that the predicted output data of the generator is closer to the actual situation, and improves the accuracy of scheduling decisions and the safety of power grid operation.
In an exemplary embodiment, as shown in fig. 6, the power grid status data is input to the power grid regulation intelligent model to obtain initial power generator output data corresponding to the target power grid, including steps 602 to 604. Wherein:
Step 602, inputting power grid state data into a power grid regulation intelligent model, and reading at least one target environmental strategy data from an environmental strategy database associated with the power grid regulation intelligent model.
The environmental policy database may be a database storing a relationship between the regulation policy and the environmental factors.
The target environmental policy data may be a relationship between the selected regulatory policy and environmental factors.
Specifically, the power grid state data is input into the power grid regulation intelligent model, the matching of the format and structure of the power grid state data with the input requirements of the model is ensured, at least one target environment strategy data is read from an environment strategy database associated with the power grid regulation intelligent model, the database stores various environment strategy data, and the data may comprise information relations such as power market price relations, new energy forecast data relations, weather forecast and the like from the environment strategy database.
Step 604, inputting the target environmental strategy data and the power grid state data into a power grid regulation intelligent model to obtain initial generator output data.
Specifically, target environment strategy data and power grid state data are input into a power grid regulation intelligent model, and the format and structure of the input data are matched with the input requirements of the model so as to ensure that the model can process and analyze the data correctly. The power grid regulation intelligent model performs calculation and analysis according to the input target environment strategy data and power grid state data, wherein the model can adopt methods of machine learning, an optimization algorithm, a rule engine and the like, and comprehensively considers various factors to generate initial generator output data.
In this embodiment, the power grid state data is input into the power grid regulation intelligent model, and the target environmental strategy data in the environmental strategy database is combined, so that the initial generator output data can be effectively generated. The intelligent model can accurately adjust the output of the generator by comprehensively considering the current state of the power grid and various environmental factors so as to meet the operation requirement of the power grid. The process can improve the accuracy and response speed of power grid dispatching, promote the stable operation of the power grid, and provide more reliable support for new energy access.
In one exemplary embodiment, as shown in FIG. 7, the target environmental strategy data and the grid state data are input to the grid regulation intelligent model to obtain initial generator output data, including steps 702-706. Wherein:
step 702, selecting current environmental policy data from each target environmental policy data, and regulating and controlling the state data of the power grid to obtain current processing data.
The current processing data can be a regulation result obtained by regulating and controlling the power grid state data.
Specifically, the environmental strategy data at the current moment is selected from the target environmental strategy data, so that the selected data corresponds to the power grid state data at the current moment, and the most accurate environmental information can be provided. Integrating the selected current environmental strategy data with the power grid state data to form a complete input data set, and performing power grid regulation and control calculation on the integrated current environmental strategy data and the integrated power grid state data, wherein in the calculation, the current environmental factors and the current power grid state are considered, and the power generator output is adjusted so as to realize stable operation and economic dispatching of the power grid. In the power grid regulation and control calculation, current processing data are generated according to the influences of current environment strategy data and power grid state data.
Step 704, selecting next environmental policy data from the target environmental policy data according to the current processing data.
Specifically, based on the current process data, selecting next environmental policy data from the target environmental policy data, wherein the selection of the next environmental policy data may depend on feedback and status of the current process data to determine a next regulatory policy and direction.
And step 706, taking the next environmental strategy data as the current environmental strategy data, and returning to execute the step of regulating and controlling the power grid state data to obtain the current processing data until each target environmental strategy data is selected at least once.
Specifically, selecting the next environmental policy data from the target environmental policy data as the current environmental policy data can be achieved by iterating the target environmental policy data list, and sequentially selecting the next environmental policy data. And inputting the newly selected current environmental strategy data and the power grid state data into the power grid regulation model again, and carrying out regulation operation of the next round. This process is repeated until all the target environmental policy data is selected at least once, and when all the target environmental policy data is selected at least once, the process is stopped and ended.
In this embodiment, the current environmental policy data is selected from the target environmental policy data, so as to regulate and control the power grid state data, so that the current processing data can be obtained, and the power grid operation is in a state suitable for the current environment. And then, selecting the next environmental strategy data according to the current processing data, regulating and controlling the power grid state again, and circulating until all the target environmental strategy data are selected at least once. The process realizes dynamic regulation and control of the state data of the power grid, so that the power grid operation can timely respond to the changes of different environmental conditions, the flexibility and the adaptability of the power grid are improved, and the stable operation and the reliability of the power grid are ensured.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a new energy accessed generator output determining device for realizing the new energy accessed generator output determining method. The implementation scheme of the device for solving the problem is similar to that described in the above method, so the specific limitation in the embodiments of the device for determining the output of the generator for accessing one or more new energy sources provided below may be referred to the limitation of the method for determining the output of the generator for accessing one new energy source hereinabove, and will not be repeated herein.
In an exemplary embodiment, as shown in fig. 8, there is provided a new energy accessed generator output determining apparatus, including: a grid data acquisition module 802, an initial data obtaining module 804, an adjustment data obtaining module 806, and a target data obtaining module 808, wherein:
the power grid data acquisition module 802 is configured to acquire power grid state data of a target power grid, a power grid regulation intelligent model and a new energy uncertainty correction model;
The initial data obtaining module 804 is configured to input power grid state data to a power grid regulation intelligent model, and obtain initial generator output data corresponding to a target power grid;
The adjustment data obtaining module 806 is configured to input the initial generator output data to a new energy uncertainty correction model, so as to obtain adjustment generator output data corresponding to the target power grid;
and the target data obtaining module 808 is configured to fuse the adjusted generator output data to the power grid state data and feed back the power grid state data to the power grid regulation intelligent model when the disturbance of the adjusted generator output data is greater than the threshold value, until the disturbance of the adjusted generator output data is less than the threshold value, and take the adjusted generator output data as the target generator output data.
In one embodiment, the adjustment data obtaining module 806 is further configured to select at least one target correction network from the new energy uncertainty correction model according to the initial generator output data; respectively inputting the initial generator output data to each target correction network to obtain each processing data correction quantity; and correcting the initial generator output data according to the correction amount of each processing data to obtain the regulated generator output data.
In one embodiment, the adjustment data obtaining module 806 is further configured to classify each piece of processing data information and each piece of target correction network in the initial generator output data to obtain processing data classification information and correction network classification information; on the basis of processing data classification information and correcting network classification information, constructing a data network association relation between the processed data information and a target correction network according to a data network association rule; and inputting the processed data information into a target correction network aiming at any data network association relation to obtain a processed data correction quantity.
In one embodiment, the adjustment data obtaining module 806 is further configured to obtain historical generator output data of the target power grid and a data correction simulation model; inputting the correction amount of each processing data and the initial generator output data into a data correction simulation model to obtain predicted generator output data; checking safety difference and accuracy difference between the historical generator output data and the predicted generator output data; and under the condition that the safety difference or the accuracy difference is larger than the difference threshold value of the target power grid, returning 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 smaller than the difference threshold value of the target power grid.
In one embodiment, the initial data obtaining module 804 is further configured to input power grid state data into the power grid regulation intelligent model, and read at least one target environmental policy data from an environmental policy database associated with the power grid regulation intelligent model; and inputting the target environment strategy data and the power grid state data into a power grid regulation intelligent model to obtain initial generator output data.
In one embodiment, the initial data obtaining module 804 is further configured to select current environmental policy data from each target environmental policy data, and regulate and control the power grid state data to obtain current processing data; selecting next environmental strategy data from the target environmental strategy data according to the current processing data; and taking the next environmental strategy data as current environmental strategy data, and returning to execute the step of regulating and controlling the power grid state data to obtain current processing data until each target environmental strategy data is selected at least once.
All or part of each module in the generator output determining device for new energy access can be realized by software, hardware and combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one exemplary embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 9. The computer device includes a processor, a memory, an Input/Output interface (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. Wherein the processor of the computer device is configured 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, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing server data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by the processor is used for realizing a generator output determining method for new energy access.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, storing a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
In one embodiment, a computer program product or computer program is provided that includes computer instructions stored in a computer readable storage medium. The processor of the 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-described method embodiments.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are both information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (10)
1. The method for determining the power output of the generator for new energy access is characterized by comprising the following steps of:
Acquiring power grid state data of a target power grid, a power grid regulation intelligent model and a new energy uncertainty correction model;
Inputting the power grid state data into the power grid regulation intelligent model to obtain initial generator output data corresponding to the target power grid;
inputting the initial generator output data to the new energy uncertainty correction model to obtain adjusted generator output data corresponding to the target power grid;
And under the condition that the disturbance of the output data of the adjusting generator is larger than a threshold value, fusing the output data of the adjusting generator to the power grid state data, feeding back to the power grid regulation intelligent model until the disturbance of the output data of the adjusting generator is smaller than the threshold value, and taking the output data of the adjusting generator as target output data of the generator.
2. The method of claim 1, wherein said inputting the initial generator output data to the new energy uncertainty correction model to obtain adjusted generator output data corresponding to the target grid comprises:
Selecting at least one target correction network from the new energy uncertainty correction model according to the initial generator output data;
respectively inputting the initial generator output data to each target correction network to obtain each processing data correction;
And correcting the initial generator output data according to the correction amount of each processing data to obtain the adjusted generator output data.
3. The method of claim 2, wherein said inputting said initial generator output data into each of said target correction networks to obtain each processed data correction comprises:
Classifying each piece of processing data information and each piece of target correction network in the initial generator output data to obtain processing data classification information and correction network classification information;
On the basis of the processed data classification information and the corrected network classification information, constructing a data network association relationship between the processed data information and the target corrected network according to a data network association rule;
and inputting the processing data information into the target correction network according to any data network association relation to obtain the processing data correction quantity.
4. The method of claim 2, wherein after the step of inputting the initial generator output data to each of the target correction networks to obtain each of the processed data corrections, the method further comprises:
acquiring historical generator output data and a data correction simulation model of the target power grid;
inputting each processed data correction amount and the initial generator output data into a data correction simulation model to obtain predicted generator output data;
Checking a safety difference and an accuracy difference between the historical generator output data and the predicted generator output data;
and returning 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 under the condition that the safety difference or the accuracy difference is larger than a difference threshold value reaching the target power grid until the safety difference and the accuracy difference are smaller than the difference threshold value of the target power grid.
5. The method of claim 1, wherein the inputting the grid state data into the grid regulation intelligent model to obtain initial generator output data corresponding to the target grid comprises:
Inputting the power grid state data into the power grid regulation intelligent model, and reading at least one target environment strategy data from an environment strategy database associated with the power grid regulation intelligent model;
And inputting the target environment strategy data and the power grid state data into the power grid regulation intelligent model to obtain the initial generator output data.
6. The method of claim 5, wherein said inputting each of said target environmental strategy data and said grid state data into said grid conditioning smart model to obtain said initial generator output data comprises:
selecting current environment strategy data from each target environment strategy data, and regulating and controlling the power grid state data to obtain current processing data;
selecting next environmental strategy data from the target environmental strategy data according to the current processing data;
and taking the next environmental strategy data as the current environmental strategy data, and returning to execute the step of regulating and controlling the power grid state data to obtain the current processing data until each target environmental strategy data is selected at least once.
7. A new energy accessed generator output determining device, the device comprising:
the power grid data acquisition module is used for acquiring power grid state data of a target power grid, a power grid regulation intelligent model and a new energy uncertainty correction model;
The initial data obtaining module is used for inputting the power grid state data into the power grid regulation intelligent model to obtain initial generator output data corresponding to the target power grid;
The adjustment data obtaining module is used for inputting the initial generator output data to the new energy uncertainty correction model to obtain adjustment generator output data corresponding to the target power grid;
And the target data obtaining module is used for fusing the output data of the adjusting generator to the power grid state data and feeding back to the power grid regulation intelligent model under the condition that the disturbance of the output data of the adjusting generator is larger than a threshold value until the disturbance of the output data of the adjusting generator is smaller than the threshold value, and taking the output data of the adjusting generator as the output data of the target generator.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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