Power grid adaptive scheduling optimization system and method, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a power grid adaptive scheduling optimization system and method, electronic equipment and a storage medium, and belongs to the technical field of electric power. The system comprises a power grid operation state sensing module used for monitoring power grid operation data, meteorological data and user demand data in real time; the data fusion module is used for performing fusion processing on the power grid operation data, the meteorological data and the user demand data to obtain multi-source fusion data; and the power grid dispatching module is used for generating a power grid dispatching optimization scheme based on a pre-constructed adaptive dispatching model and the multi-source fusion data, and the construction of the adaptive dispatching model is a model obtained after multiple times of fitting training based on the historical operation data of the power grid. Through the multi-source heterogeneous data in the operation of the power grid and by means of the constructed adaptive scheduling model, the system has intelligent and adaptive capabilities to adapt to various changes, the problem of uneven distribution of power resources is avoided, and the operation efficiency and the energy utilization efficiency of the power grid are further improved.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a power grid adaptive dispatch optimization system, method, electronic equipment and storage medium. Background Art

[0002] Under the background of global energy structure transformation, renewable energy such as solar energy and wind energy have gradually taken the leading position, while the proportion of traditional fossil energy has gradually decreased. This transformation has not only brought about the diversity of energy supply, but also put forward higher requirements for the dispatching and operation of power grids. At the same time, with the rapid economic growth and the improvement of people's living standards, the demand for electricity has shown a trend of continuous growth, especially during peak hours, the pressure on power supply has doubled.

[0003] At present, power grid dispatching mainly relies on preset dispatching rules and empirical judgments, lacking sufficient intelligence and adaptive capabilities. In addition, traditional power grid dispatching systems often ignore the fusion and processing of multi-source heterogeneous data in power grid operation. These problems make it difficult for power grid dispatching to adapt to these changes, resulting in uneven distribution of power resources, which limits the operation efficiency and energy utilization efficiency of the power grid. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a power grid adaptive scheduling optimization system, method, electronic device and storage medium, which are used to fully or at least partially solve the technical problems existing in the above-mentioned prior art.

[0005] In order to achieve the above object, an embodiment of the present invention provides a power grid adaptive dispatch optimization system, including:

[0006] The power grid operation status perception module is used to monitor the power grid operation data, meteorological data and user demand data in real time;

[0007] Data fusion module, used to fuse power grid operation data, meteorological data, and user demand data to obtain multi-source fusion data;

[0008] The power grid dispatching module is used to generate a power grid dispatching optimization plan based on a pre-built adaptive dispatching model and multi-source fusion data. The power grid dispatching optimization plan at least includes optimizing the parameters of power generation, transmission and distribution. The adaptive dispatching model is constructed based on the historical operation data of the power grid after multiple fitting training.

[0009] Optionally, the grid operation status perception module adopts a deep learning method to learn the relationship between the grid operation status and the grid operation data, and identify normal, abnormal or fault modes in the grid operation.

[0010] Optionally, the data fusion module is specifically used to clean the meteorological data and user demand data, and correspondingly integrate the normal, abnormal or fault mode of the power grid identified based on the relationship between the power grid operation status and the power grid operation data during the power grid operation to obtain multi-source fusion data:

[0011] Selecting features closely related to the fusion target from the power grid operation data, meteorological data and user demand data, wherein the features closely related to the fusion target at least include voltage, current and power parameters in the power grid operation data, temperature, humidity and wind speed parameters in the meteorological data, and power consumption and power consumption time parameters in the user demand data;

[0012] Extract feature data closely related to the fusion target, map the extracted feature data to the same feature space, and concatenate the mapped feature data to generate a fused feature vector;

[0013] The fused feature vector is output as the fusion result to obtain multi-source fusion data.

[0014] Optionally, the power grid adaptive dispatch optimization system further includes: an early warning module for issuing an early warning when an abnormality or failure occurs during power grid operation.

[0015] Optional, power grid dispatch module, used to generate power grid dispatch optimization solutions based on pre-built adaptive dispatch models and multi-source fusion data, including:

[0016] Inputting the multi-source fusion data into a pre-built adaptive scheduling model, so that the adaptive scheduling model analyzes the operation status of the power grid based on the power grid operation data in the multi-source fusion data, wherein the operation status of the power grid at least includes load level, power generation capacity and transmission capacity;

[0017] The adaptive scheduling model predicts the power generation of renewable energy and the impact of meteorological changes on the operation status of the power grid based on the meteorological data in the multi-source fusion data;

[0018] The adaptive scheduling model predicts the power demand and power price trend in future periods based on the user demand data in the multi-source fusion data;

[0019] Based on the operating status of the power grid, the predicted power generation of renewable energy, the impact of meteorological changes on the operating status of the power grid, and the predicted power demand and electricity price trends in future periods, the output allocation on the power generation side, the line scheduling on the transmission and distribution side, and the demand response on the load side are adjusted.

[0020] Optionally, the adaptive scheduling model is:

[0021]

[0022] In the formula, s(h) represents the grid operation state predicted based on grid operation data, α represents the grid operation data influencing factor, n represents a positive integer, and X h Represents the matrix corresponding to the input power grid operation data, W h represents the weight corresponding to the grid operation data, h represents the number of grid operation data, b represents the bias, s(i) represents the power generation of renewable energy predicted based on meteorological data and the impact of meteorological changes on the grid operation status, β represents the meteorological data impact factor, m represents a positive integer, and X i Represents the matrix corresponding to the input meteorological data, W i represents the weight corresponding to the meteorological data, i represents the number of meteorological data, s(j) represents the power demand and electricity price trend in the future period predicted based on user demand data, γ represents the influencing factor of user demand data, k represents a positive integer, and X j Represents the matrix corresponding to the input user demand data, W j represents the weight corresponding to the user demand data, j represents the number of user demand data, dt l The expected power generation time to meet the power demand in the future period, st l To meet the actual power generation time in the future period, |dt l -st l |→0.

[0023] Optionally, the power grid adaptive dispatch optimization system further includes: a power equipment improvement module, which is used to remotely monitor and diagnose faults of each power equipment in the power grid and then automatically adjust the parameters of each power equipment.

[0024] Optionally, the power grid adaptive dispatch optimization system also includes: a flexible dispatch strategy construction module, which is used to construct flexible dispatch strategies including demand side management, distributed energy access, and energy storage technology application, so as to achieve refined management and optimized dispatch of the power grid.

[0025] On the other hand, the present invention also provides a power grid adaptive dispatch optimization method applied to a power grid adaptive dispatch optimization system, comprising:

[0026] Real-time monitoring of power grid operation data, meteorological data, and user demand data;

[0027] Fuse power grid operation data, meteorological data, and user demand data to obtain multi-source fusion data;

[0028] A power grid dispatch optimization plan is generated based on a pre-built adaptive dispatch model and multi-source fusion data, wherein the power grid dispatch optimization plan at least includes optimizing the parameters of power generation, transmission and distribution links. The adaptive dispatch model is constructed based on the historical operation data of the power grid after multiple fitting training.

[0029] On the other hand, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned power grid adaptive scheduling optimization method when executing the program.

[0030] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned power grid adaptive scheduling optimization method are implemented.

[0031] Through the above technical solutions, multi-source heterogeneous data in the operation of the power grid, and with the help of the constructed adaptive scheduling model, the system has intelligent and adaptive capabilities to adapt to various changes, avoid the problem of uneven distribution of power resources, and thus improve the operation efficiency and energy utilization efficiency of the power grid.

[0032] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0034] Figure 1 It is a structural diagram of a power grid adaptive dispatch optimization system provided by an embodiment of the present invention;

[0035] Figure 2 It is an implementation flow chart of a power grid adaptive dispatch optimization method applied to a power grid adaptive dispatch optimization system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0037] See also Figure 1 As shown, it is a structural diagram of a power grid adaptive dispatch optimization system provided by an embodiment of the present invention, including:

[0038] The power grid operation status perception module 10 is used to monitor the power grid operation data, meteorological data and user demand data in real time.

[0039] Preferably, the grid operation data includes at least real-time voltage, current, power, frequency, equipment status, fault information and other data; the meteorological data includes meteorological information such as wind speed, wind direction, temperature, humidity, rainfall, etc.; the user demand data includes power demand forecast and electricity price information.

[0040] In some embodiments, the grid operation status perception module uses a deep learning method to learn the relationship between the grid operation status and the grid operation data, and identify normal, abnormal or fault modes in the grid operation.

[0041] The data fusion module 11 is used to fuse the power grid operation data, meteorological data, and user demand data to obtain multi-source fusion data.

[0042] Preferably, the data fusion module is specifically used to clean the meteorological data and user demand data, and correspondingly integrate the normal, abnormal or fault mode of the power grid identified based on the relationship between the power grid operation status and the power grid operation data during the power grid operation to obtain multi-source fusion data:

[0043] Selecting features closely related to the fusion target from the power grid operation data, meteorological data and user demand data, wherein the features closely related to the fusion target at least include voltage, current and power parameters in the power grid operation data, temperature, humidity and wind speed parameters in the meteorological data, and power consumption and power consumption time parameters in the user demand data;

[0044] Extract feature data closely related to the fusion target, map the extracted feature data to the same feature space, and concatenate the mapped feature data to generate a fused feature vector;

[0045] The fused feature vector is output as the fusion result to obtain multi-source fusion data.

[0046] For example, cleaning meteorological data and integrating it with grid operation specifically involves converting meteorological information such as wind speed, wind direction, temperature, humidity, and rainfall into meteorological characteristics required for grid dispatch, such as predicted wind power and photovoltaic output.

[0047] In some embodiments, the power grid adaptive dispatch optimization system further includes an early warning module for issuing an early warning when an abnormality or failure occurs during power grid operation.

[0048] The power grid dispatching module 12 is used to generate a power grid dispatching optimization plan based on a pre-built adaptive dispatching model and multi-source fusion data, wherein the power grid dispatching optimization plan at least includes optimizing the parameters of power generation, transmission and distribution links, and the adaptive dispatching model is constructed based on the historical operation data of the power grid after multiple fitting training.

[0049] It should be understood that the adaptive scheduling model obtained through multiple fitting training based on the historical operation data of the power grid specifically includes initialization parameters: setting initial values ​​for various parameters of the model. These parameters may include learning rate, number of iterations, weights, etc. Define the loss function: define a suitable loss function according to the characteristics of the problem. The loss function is used to measure the gap between the predicted results of the model and the actual results. The model is trained using the preprocessed data set. In each training iteration, the input data is passed to the model and the value of the loss function is calculated. The parameters of the model are adjusted according to the value of the loss function to optimize the performance of the model. Repeat the above process until the preset stop condition is met (such as reaching the maximum number of iterations, the loss function value is lower than a certain threshold, etc.).

[0050] In some embodiments, the power grid dispatch module is used to generate a power grid dispatch optimization solution based on a pre-built adaptive dispatch model and multi-source fusion data, including:

[0051] Inputting the multi-source fusion data into a pre-built adaptive scheduling model, so that the adaptive scheduling model analyzes the operation status of the power grid based on the power grid operation data in the multi-source fusion data, wherein the operation status of the power grid at least includes load level, power generation capacity and transmission capacity;

[0052] The adaptive scheduling model predicts the power generation of renewable energy and the impact of meteorological changes on the operation status of the power grid based on the meteorological data in the multi-source fusion data;

[0053] The adaptive scheduling model predicts the power demand and power price trend in future periods based on the user demand data in the multi-source fusion data;

[0054] Based on the operating status of the power grid, the predicted power generation of renewable energy, the impact of meteorological changes on the operating status of the power grid, and the predicted power demand and electricity price trends in future periods, the output allocation on the power generation side, the line scheduling on the transmission and distribution side, and the demand response on the load side are adjusted.

[0055] In some embodiments, the power grid adaptive dispatch optimization system further includes: a power equipment improvement module, which is used to remotely monitor and diagnose faults of each power equipment in the power grid and automatically adjust the parameters of each power equipment.

[0056] Specifically, the adaptive scheduling model is:

[0057]

[0058] In the formula, s(h) represents the grid operation state predicted based on grid operation data, α represents the grid operation data influencing factor, n represents a positive integer, and X h Represents the matrix corresponding to the input power grid operation data, W h represents the weight corresponding to the grid operation data, h represents the number of grid operation data, b represents the bias, s(i) represents the power generation of renewable energy predicted based on meteorological data and the impact of meteorological changes on the grid operation status, β represents the meteorological data impact factor, m represents a positive integer, and X i Represents the matrix corresponding to the input meteorological data, W i represents the weight corresponding to the meteorological data, i represents the number of meteorological data, s(j) represents the power demand and electricity price trend in the future period predicted based on user demand data, γ represents the influencing factor of user demand data, k represents a positive integer, and X j Represents the matrix corresponding to the input user demand data, W j represents the weight corresponding to the user demand data, j represents the number of user demand data, dt l The expected power generation time to meet the power demand in the future period, st l To meet the actual power generation time in the future period, |dt l -st l |→0.

[0059] In some embodiments, the power grid adaptive dispatch optimization system also includes: a flexible dispatch strategy construction module, which is used to construct a flexible dispatch strategy including demand side management, distributed energy access, and energy storage technology application to achieve refined management and optimized dispatch of the power grid.

[0060] Through the above technical solutions, multi-source heterogeneous data in the operation of the power grid, and with the help of the constructed adaptive scheduling model, the system has intelligent and adaptive capabilities to adapt to various changes, avoid the problem of uneven distribution of power resources, and thus improve the operation efficiency and energy utilization efficiency of the power grid.

[0061] See also Figure 2 As shown, it is an implementation flow chart of a power grid adaptive dispatch optimization method applied to a power grid adaptive dispatch optimization system provided by an embodiment of the present invention, including the following execution steps:

[0062] Step 200: Real-time monitoring of power grid operation data, meteorological data, and user demand data.

[0063] Step 201: Fusing power grid operation data, meteorological data, and user demand data to obtain multi-source fusion data.

[0064] Step 202: Generate a power grid dispatch optimization plan based on a pre-built adaptive dispatch model and multi-source fusion data, wherein the power grid dispatch optimization plan at least includes optimizing parameters of power generation, transmission and distribution links, and the adaptive dispatch model is constructed based on the historical operation data of the power grid after multiple fitting training.

[0065] On the other hand, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned power grid adaptive scheduling optimization method when executing the program.

[0066] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned power grid adaptive scheduling optimization method are implemented.

[0067] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0068] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0069] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0071] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0072] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0073] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0074] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0075] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A power grid adaptive dispatch optimization system, characterized in that: include: The power grid operation status perception module is used to monitor the power grid operation data, meteorological data and user demand data in real time; Data fusion module, used to fuse power grid operation data, meteorological data, and user demand data to obtain multi-source fusion data; The power grid dispatching module is used to generate a power grid dispatching optimization plan based on a pre-built adaptive dispatching model and multi-source fusion data. The power grid dispatching optimization plan at least includes optimizing the parameters of power generation, transmission and distribution. The adaptive dispatching model is constructed based on the historical operation data of the power grid after multiple fitting training.

2. The power grid adaptive dispatch optimization system according to claim 1, characterized in that: The grid operation status perception module adopts a deep learning method to learn the relationship between the grid operation status and the grid operation data, and identify normal, abnormal or fault modes in the grid operation.

3. The power grid adaptive dispatch optimization system according to claim 2, characterized in that: The data fusion module is specifically used to clean meteorological data and user demand data, and integrate the normal, abnormal or fault mode of the power grid identified based on the relationship between the power grid operation status and the power grid operation data during power grid operation to obtain multi-source fusion data: Selecting features closely related to the fusion target from the power grid operation data, meteorological data and user demand data, wherein the features closely related to the fusion target at least include voltage, current and power parameters in the power grid operation data, temperature, humidity and wind speed parameters in the meteorological data, and power consumption and power consumption time parameters in the user demand data; Extract feature data closely related to the fusion target, map the extracted feature data to the same feature space, and concatenate the mapped feature data to generate a fused feature vector; The fused feature vector is output as the fusion result to obtain multi-source fusion data.

4. The power grid adaptive dispatch optimization system according to claim 2, characterized in that: The power grid adaptive dispatch optimization system also includes: an early warning module, which is used to issue an early warning when an abnormality or failure occurs during the operation of the power grid.

5. The power grid adaptive dispatch optimization system according to claim 1, characterized in that: The power grid dispatch module is used to generate power grid dispatch optimization solutions based on the pre-built adaptive dispatch model and multi-source fusion data, including: Inputting the multi-source fusion data into a pre-built adaptive scheduling model, so that the adaptive scheduling model analyzes the operation status of the power grid based on the power grid operation data in the multi-source fusion data, wherein the operation status of the power grid at least includes load level, power generation capacity and transmission capacity; The adaptive scheduling model predicts the power generation of renewable energy and the impact of meteorological changes on the operation status of the power grid based on the meteorological data in the multi-source fusion data; The adaptive scheduling model predicts the power demand and power price trend in future periods based on the user demand data in the multi-source fusion data; Based on the operating status of the power grid, the predicted power generation of renewable energy, the impact of meteorological changes on the operating status of the power grid, and the predicted power demand and electricity price trends in future periods, the output allocation on the power generation side, the line scheduling on the transmission and distribution side, and the demand response on the load side are adjusted.

6. The power grid adaptive dispatch optimization system according to claim 1, characterized in that: The adaptive scheduling model is: In the formula, s(h) represents the grid operation state predicted based on grid operation data, α represents the grid operation data influencing factor, n represents a positive integer, and X h Represents the matrix corresponding to the input power grid operation data, W h represents the weight corresponding to the grid operation data, h represents the number of grid operation data, b represents the bias, s(i) represents the power generation of renewable energy predicted based on meteorological data and the impact of meteorological changes on the grid operation status, β represents the meteorological data impact factor, m represents a positive integer, and X i Represents the matrix corresponding to the input meteorological data, W i represents the weight corresponding to the meteorological data, i represents the number of meteorological data, s(j) represents the power demand and electricity price trend in the future period predicted based on user demand data, γ represents the influencing factor of user demand data, k represents a positive integer, and x j Represents the matrix corresponding to the input user demand data, W j represents the weight corresponding to the user demand data, j represents the number of user demand data, dt l The expected power generation time to meet the power demand in the future period, st l To meet the actual power generation time in the future period, |dt l -st l |→0.

7. The power grid adaptive dispatch optimization system according to claim 1, characterized in that: The power grid adaptive dispatch optimization system also includes: a power equipment improvement module, which is used to remotely monitor and diagnose faults of various power equipment in the power grid and then automatically adjust the parameters of various power equipment.

8. A power grid adaptive dispatch optimization method applied to the power grid adaptive dispatch optimization system according to any one of claims 1 to 7, characterized in that: include: Real-time monitoring of power grid operation data, meteorological data, and user demand data; Fuse power grid operation data, meteorological data, and user demand data to obtain multi-source fusion data; A power grid dispatch optimization plan is generated based on a pre-built adaptive dispatch model and multi-source fusion data, wherein the power grid dispatch optimization plan at least includes optimizing the parameters of power generation, transmission and distribution links. The adaptive dispatch model is constructed based on the historical operation data of the power grid after multiple fitting training.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the power grid adaptive scheduling optimization method as described in claim 8 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power grid adaptive scheduling optimization method as described in claim 8 are implemented.

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