A virtual power plant power load control method and system

Abstract

The present invention relates to a method and system for controlling power load of a virtual power plant. The method comprises obtaining power load data and corresponding power supply capacity data of the virtual power plant, generating a control strategy training sample set based on the power load data and the corresponding power supply capacity data; analyzing each power supply capacity data in the training sample set based on the stability of the power system to obtain multiple sub-supply capacity data of each power supply capacity; constructing a power load prediction and control model, training the power load prediction and control model based on the power load data, power supply capacity data, and multiple sub-supply capacity data of each power supply capacity in the training sample set to obtain a power load control model; and inputting the power load data of the virtual power plant to be controlled into the power load control model to obtain a power load control strategy for the virtual power plant to be controlled. This improves the reliability and flexibility of power supply.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of power system management, and more particularly to a method and system for controlling power load in a virtual power plant. Background Art

[0002] In existing power systems, virtual power plants (VPPs), a novel approach to power resource management, optimize the allocation and dispatch of power resources by integrating distributed generation, energy storage, and controllable loads. These VPPs can effectively improve the efficiency and reliability of power systems, reduce operating costs, and promote the utilization of renewable energy. However, with the continuous development of the power market and the increasing complexity of power demand, traditional load control methods are no longer able to meet the flexibility and responsiveness requirements of modern power systems.

[0003] Existing power load control methods typically rely on static load forecasting models and simple control strategies. These methods often fail to accurately predict dynamic changes in power demand and struggle to adapt to fluctuations in power supply capacity. Furthermore, traditional control strategies lack in-depth analysis of power system stability, making it difficult to optimize economic efficiency and environmental impact while ensuring power supply reliability. When it comes to power supply capacity analysis, existing technologies often overlook the segmentation and dynamic adjustment of power supply capacity, resulting in an inability to fully utilize the potential of power resources and difficulty in responding to rapid changes in the power market.

[0004] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the existing technology: the existing methods lack effective data preprocessing and feature extraction means when processing power load and supply capacity data, resulting in low model training efficiency and limited prediction accuracy; at the same time, the existing technology fails to fully consider the stability of the power system and multi-objective optimization requirements in power load control, resulting in insufficient adaptability and comprehensive performance of the control strategy. Summary of the Invention

[0005] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0006] The purpose of the embodiments of the present invention is to provide a virtual power plant power load control method and system, which, through a series of innovative training methods and technical strategies, achieves efficient, stable and economical operation of the power system, while reducing environmental impact and improving the reliability and flexibility of power supply.

[0007] In a first aspect, an embodiment of the present invention relates to a method for controlling power load in a virtual power plant, the method comprising:

[0008] Acquire power load data and corresponding power supply capacity data of the virtual power plant, and generate a control strategy training sample set based on the power load data and the corresponding power supply capacity data;

[0009] Analyze each power supply capacity data in the training sample set based on the stability of the power system to obtain multiple sub-supply capacity data for each power supply capacity;

[0010] Constructing a power load prediction and control model, and training the power load prediction and control model based on the power load data, power supply capacity data, and multiple sub-supply capacity data of each power supply capacity in the training sample set to obtain a power load control model;

[0011] The power load data of the virtual power plant to be controlled is input into the power load control model to obtain the power load control strategy of the virtual power plant to be controlled.

[0012] Furthermore, each power supply capability data in the training sample set is analyzed based on the stability of the power system to obtain multiple sub-supply capability data of each power supply capability, including:

[0013] The initial state of each power supply capacity is taken as the first type, and the stability of the state is calculated; each state of the power supply capacity is traversed in turn, and the stability of the current state is calculated. If the stability of the current state is the same as the stability of the previous state, then the current state and the previous state are of the same type; otherwise, the current state and the previous state are of different types;

[0014] A plurality of sub-supply capability data of the power supply capability is obtained according to the type of each state in the power supply capability.

[0015] Furthermore, based on the sub-supply capacity data of the power supply capacity, a composite loss function with load forecast error penalty is used to calculate the training loss of the power load forecasting and control model.

[0016] Furthermore, the load forecast error penalty matrix corresponding to the power supply matrix is calculated by the following steps:

[0017] Based on the power demand matrix The sub-mask matrix is calculated The intra-mask Euclidean distance matrix of the sub-mask matrices ; Based on the Euclidean distance matrix within the mask Calculate the The mask penalty term of the sub-mask matrix ;

[0018] Based on the power supply matrix The sub-mask matrix is calculated The mask-less Euclidean distance matrix of the sub-mask matrices ; Based on the Euclidean distance matrix outside the mask Calculate the The mask-outside penalty term of the sub-mask matrix ;

[0019] The penalty term within the mask and the out-of-mask penalty Normalize and add to get The distance penalty matrix corresponding to the sub-mask matrix ;

[0020] The distance penalty matrix corresponding to each sub-mask matrix The load forecast error penalty matrix corresponding to the power supply matrix is obtained by splicing .

[0021] Furthermore, the The intra-mask Euclidean distance matrix of the sub-mask matrices The first part of the power demand matrix The dimensions of the sub-mask matrices are the same;

[0022] Calculate the The intra-mask penalty term of the sub-mask matrix.

[0023] Calculate the The out-of-mask penalty term of the sub-mask matrix.

[0024] Furthermore, the The mask-less Euclidean distance matrix of the sub-mask matrices The power supply matrix The dimensions of the sub-mask matrices are the same;

[0025] In a second aspect, an embodiment of the present invention relates to a virtual power plant power load control system, comprising:

[0026] A feature extraction module is used to extract features of power load data and power supply capacity data using a deep learning network;

[0027] A prediction module is used to predict future power loads using a time series prediction algorithm based on the extracted features;

[0028] An optimization module for generating a power load control strategy using an optimization algorithm based on the prediction results and power supply capacity data;

[0029] Evaluation module for evaluating the performance of power load control strategies, including economic efficiency, reliability, and environmental impact.

[0030] Furthermore, generating a control strategy training sample set based on the power load data and the corresponding power supply capacity data includes:

[0031] Gaussian process regression is used to interpolate power load data and corresponding power supply capacity data to handle missing data;

[0032] Perform wavelet transform on power load data and power supply capacity data to extract time-frequency features;

[0033] For each sample data, principal component analysis is performed on the power load data and power supply capacity data to reduce the data dimension and improve the model training efficiency;

[0034] Based on the data after dimensionality reduction, a clustering algorithm is used to classify the power load types and power supply capacity types to generate training samples.

[0035] The above-mentioned embodiments of the present disclosure have the following beneficial effects: the virtual power plant power load control method of the present invention can improve the operating efficiency and stability of the power system. By extracting features from a deep learning network and combining it with a time series prediction algorithm, it is possible to more accurately predict future power loads, thereby making the power load control strategy more precise and timely. In addition, the power load control strategy generated by the optimization algorithm can optimize the economy, reliability, and environmental impact while ensuring the reliability of power supply, thereby achieving efficient utilization of power resources.

[0036] Furthermore, by introducing a composite loss function with a load forecast error penalty, the present invention can effectively reduce the model's prediction error during training and improve the accuracy of power load forecasting. At the same time, by calculating the load forecast error penalty matrix corresponding to the power supply matrix, the uncertainty and variability in power supply capacity data can be better handled, making the power load control strategy more flexible and adaptable. The application of these technical means can not only improve the response speed and regulation capability of the power system, but also reduce operating costs, and enhance the economic benefits and environmental friendliness of the power system.

[0037] In addition, the above summary of the invention does not list all the features required for the embodiments of the present invention, and other combinations of these feature groups can also become embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily apparent by reading the following detailed description with reference to the accompanying drawings, in which several embodiments of the present invention are shown by way of example and not limitation, in which:

[0039] Figure 1 A flow chart of a method for controlling power load in a virtual power plant according to one embodiment of the present invention;

[0040] Figure 2 A schematic diagram of the structure of a virtual power plant power load control system provided by one embodiment of the present invention;

[0041] Figure 3 The following schematically shows a structural diagram of a computing device according to an embodiment of the present invention;

[0042] In the drawings, the same or corresponding reference numerals denote the same or corresponding parts. DETAILED DESCRIPTION

[0043] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided solely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0044] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present disclosure may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software.

[0045] According to an embodiment of the present invention, a method and system for controlling power load of a virtual power plant are proposed.

[0046] It should be noted that any number of elements in the drawings is for illustration only and not for limitation, and any naming is only for distinction and does not have any limiting meaning.

[0047] The principles and spirit of the present invention are explained in detail below with reference to several representative embodiments of the present invention.

[0048] Reference below Figure 1 , Figure 1 This is a flow chart of a method for controlling power load in a virtual power plant according to an embodiment of the present invention. It should be noted that the embodiment of the present invention can be applied to any applicable scenario.

[0049] Figure 1The virtual power plant power load control method provided by one embodiment of the present invention is shown as follows. The method 100 includes:

[0050] Step 101: obtaining power load data and corresponding power supply capacity data of a virtual power plant, and generating a control strategy training sample set based on the power load data and the corresponding power supply capacity data;

[0051] Step 102: Analyze each power supply capability data in the training sample set based on the stability of the power system to obtain multiple sub-supply capability data of each power supply capability;

[0052] Step 103: constructing a power load forecasting and control model, and training the power load forecasting and control model based on the power load data, power supply capacity data, and multiple sub-supply capacity data of each power supply capacity in the training sample set to obtain a power load control model;

[0053] Step 104 : inputting the power load data of the virtual power plant to be controlled into the power load control model to obtain a power load control strategy for the virtual power plant to be controlled.

[0054] It should be noted that this embodiment relates to a virtual power plant power load control method. This method obtains the power load data of the virtual power plant and the corresponding power supply capacity data, and generates a control strategy training sample set based on this data. This allows the system to accurately match power supply and demand, optimizing the allocation and use of power resources.

[0055] Specifically, acquiring power load and power supply capacity data involves collecting real-time data from various nodes in the virtual power plant, including but not limited to power generation, power consumption, and energy storage status. This data is collected through smart meters, sensors, and data acquisition systems. Generating control strategy training samples relies on data preprocessing, feature extraction, and label definition to ensure that the model can learn effective control strategies from historical data.

[0056] To improve data quality and usability, data cleaning and missing value processing techniques, such as interpolation and time series forecasting, can be used to ensure the completeness and accuracy of the training sample set. Furthermore, machine learning algorithms, such as random forests, support vector machines, or neural networks, can be used to construct power load forecasting and control models. Cross-validation and other methods can be used to optimize model parameters and improve the model's generalization capabilities.

[0057] In some embodiments, each power supply capability data in the training sample set is analyzed based on the stability of the power system to obtain multiple sub-supply capability data for each power supply capability, including:

[0058] The initial state of each power supply capacity is taken as the first type, and the stability of the state is calculated; each state of the power supply capacity is traversed in turn, and the stability of the current state is calculated. If the stability of the current state is the same as the stability of the previous state, then the current state and the previous state are of the same type; otherwise, the current state and the previous state are of different types;

[0059] A plurality of sub-supply capability data of the power supply capability is obtained according to the type of each state in the power supply capability.

[0060] Specifically, power supply capacity stability analysis involves evaluating the initial state of each power supply capacity and calculating its stability index. Stability can be measured in a variety of ways, for example, based on power supply volatility, frequency response characteristics, or the supply-demand balance. In practical applications, a series of parameters can be set to quantify stability, such as supply fluctuation thresholds and frequency deviation tolerance. These parameters can be used to determine the stability of each state and, accordingly, to divide the power supply capacity into different sub-capacities.

[0061] To more accurately analyze the stability of power supply capacity, advanced data analysis techniques, such as machine learning algorithms, can be used to automatically identify and classify the stability status of power supply capacity. Furthermore, adaptive algorithms can be introduced to dynamically adjust stability analysis parameters based on real-time data to adapt to changes in power system operating conditions. When classifying power supply capacity, in addition to stability, other factors such as cost-effectiveness and environmental impact can also be considered to achieve more comprehensive power supply capacity management.

[0062] In some embodiments, based on the sub-supply capacity data of the power supply capacity, a composite loss function with load forecast error penalty is used to calculate the training loss of the power load forecasting and control model.

[0063] The calculation formula of the composite loss function is:

[0064]

[0065] in, represents the total loss value, Indicates the total amount of power supply capacity data, Indicates the The weight of the power supply capacity, Indicates the A power demand matrix, Indicates the A power supply matrix, Indicates the The load forecast error penalty matrix corresponding to the power supply matrix is: represents the regularization parameter, Represents the matrix norm.

[0066] Specifically, the calculation of the composite loss function involves multiple parameters and concepts. The total loss is the objective function to be minimized during model training. It consists of multiple components, including the weights of power supply capacity data, the power demand matrix, the power supply matrix, and the load forecast error penalty matrix. These parameters can be set using historical data and expert knowledge. For example, weights can be assigned based on the importance of power supply capacity, while the forecast error penalty matrix can be adjusted based on the magnitude of the forecast error.

[0067] To further improve model training effectiveness, various methods can be used to refine the calculation of the composite loss function. For example, regularization parameters can be introduced to prevent model overfitting, or different distance metrics can be used to calculate prediction errors. Furthermore, different optimization algorithms can be considered to minimize the loss function, such as gradient descent, stochastic gradient descent, or the Adam optimizer. These methods can be selected based on the complexity of the model and the characteristics of the training data to achieve the best training results.

[0068] In some embodiments, the load forecast error penalty matrix corresponding to the power supply matrix is calculated by the following steps:

[0069] Based on the power demand matrix The sub-mask matrix is calculated The intra-mask Euclidean distance matrix of the sub-mask matrices ; Based on the Euclidean distance matrix within the mask Calculate the The mask penalty term of the sub-mask matrix ;

[0070] Based on the power supply matrix The sub-mask matrix is calculated The mask-less Euclidean distance matrix of the sub-mask matrices ; Based on the Euclidean distance matrix outside the mask Calculate the The mask-outside penalty term of the sub-mask matrix ;

[0071] The penalty term within the mask and the out-of-mask penalty Normalize and add to get The distance penalty matrix corresponding to the sub-mask matrix ;

[0072] The distance penalty matrix corresponding to each sub-mask matrix The load forecast error penalty matrix corresponding to the power supply matrix is obtained by splicing .

[0073] It should be noted that this embodiment describes in detail how to calculate the load forecast error penalty matrix corresponding to the power supply matrix. This step is achieved by analyzing the sub-mask matrix of the power demand matrix, calculating the Euclidean distance matrix inside and outside the mask, and generating penalty terms based on this. Ultimately, these penalty terms are integrated into a matrix for use in the model training process.

[0074] Specifically, the sub-mask matrix of the power supply matrix refers to dividing the power supply matrix into multiple regions or subsets, each subset corresponding to a sub-mask matrix. The intra-mask Euclidean distance matrix refers to calculating the shortest distance from each point inside the sub-mask matrix to the mask boundary. These distances can be determined by computational geometry methods, such as using the Dijkstra algorithm or the Floyd-Warshall algorithm. The intra-mask penalty term is calculated based on these distances and can be defined using different mathematical functions, such as exponential functions or linear functions. The calculation of the extra-mask Euclidean distance matrix and the extra-mask penalty term is similar to that of the intra-mask, but focuses on the area outside the sub-mask matrix.

[0075] To more accurately calculate the penalty terms inside and outside the mask, more advanced numerical analysis methods, such as Gaussian blur or machine learning algorithms, can be used to optimize distance calculation and penalty term generation. Furthermore, adaptive mechanisms can be introduced to dynamically adjust the weight of the penalty term based on the model's performance during training to improve the model's generalization and prediction accuracy. In practical applications, real-time data and historical trends of the power system can also be taken into account to adjust the penalty terms inside and outside the mask in real time to adapt to changes in the power system's operating status. These methods can further improve the accuracy and robustness of the model's power load forecasting.

[0076] In some embodiments, the The intra-mask Euclidean distance matrix of the sub-mask matrices The first part of the power demand matrix The dimensions of the sub-mask matrices are the same; The intra-mask Euclidean distance matrix of the sub-mask matrices of The element value at position is:

[0077]

[0078] in, Indicates a point The shortest Euclidean distance to the mask boundary of this submask matrix, Indicates the The mask area of the sub-mask matrix.

[0079] Specifically, the intra-mask Euclidean distance matrix of the sub-mask matrix has the same dimensions as the sub-mask matrix of the power demand matrix. The value of each element represents the shortest Euclidean distance from the corresponding point to the boundary of the sub-mask region. This distance can be determined using computational geometry methods, such as the Bresenham algorithm or the Dijkstra algorithm to calculate the distance from a point to the nearest boundary. In practical applications, the calculation of this distance needs to take into account the topology of the power network and the characteristics of power flow.

[0080] To improve the efficiency and accuracy of calculating the within-mask Euclidean distance matrix, parallel computing techniques can be used to process large-scale power demand matrices. Furthermore, adaptive algorithms can be introduced to optimize the distance calculation process, for example, automatically adjusting calculation parameters based on dynamic changes in power demand. In some cases, machine learning methods can also be used to predict and estimate within-mask distances, especially when data is sparse or computing resources are limited.

[0081] In some embodiments, according to the formula

[0082]

[0083] Calculate the The mask penalty term of the sub-mask matrix, where Indicates the The intra-mask Euclidean distance matrix of the sub-mask matrices, Indicates the sub-mask matrix, Indicates that all elements of the matrix are replaced by the maximum element value in the matrix. Represents the multiplication of corresponding matrix elements.

[0084] It should be noted that this embodiment describes in detail how to calculate the intra-mask penalty terms of the sub-mask matrix. This step combines the intra-mask Euclidean distance matrix with the sub-mask matrix and uses specific mathematical operations to generate penalty terms. These penalty terms will be used to construct the subsequent load forecast error penalty matrix.

[0085] Specifically, the calculation of the mask penalty involves the mask-inside Euclidean distance matrix and the sub-mask matrix. The mask-inside Euclidean distance matrix is a matrix containing the distance of each point to the nearest mask boundary, while the sub-mask matrix is a binary matrix used to identify points inside and outside the mask region.

[0086] More specifically, when calculating the in-mask penalty, we first need to determine an appropriate mathematical function to process the two matrices. For example, we can use the maximum replacement function to replace all elements in the in-mask Euclidean distance matrix with their maximum values, and then perform element-wise multiplication with the sub-mask matrix. This operation can highlight points within the masked area, providing more accurate error penalty information for load forecasting.

[0087] Preferably, in order to improve the flexibility and adaptability of the calculation of the penalty term within the mask, a parameterized method can be introduced to adjust the calculation of the penalty term. For example, a penalty coefficient can be set to adjust the penalty intensity of the points within the mask.

[0088] Furthermore, various mathematical operations, such as weighted averaging or exponential functions, can be employed to enhance the model's sensitivity to different types of load forecast errors. In practical applications, these parameters can be optimized through methods such as cross-validation based on the specific needs of the power system and historical data to achieve optimal forecasting performance. These improvements can enable the model to more accurately reflect the dynamic changes in power load, thereby improving the operational efficiency and stability of the power system.

[0089] In some embodiments, according to the formula

[0090]

[0091] Calculate the The mask-outside penalty term of the sub-mask matrix, where Indicates the The mask-less Euclidean distance matrix of the sub-mask matrices, Indicates the sub-mask matrix, Indicates that all elements of the matrix are replaced by the maximum element value in the matrix. Represents the multiplication of corresponding matrix elements.

[0092] Specifically, the calculation of the off-mask penalty involves the off-mask Euclidean distance matrix and the sub-mask matrix. The off-mask Euclidean distance matrix records the distance from each point to the nearest mask boundary, but only for points outside the mask region. The sub-mask matrix is an identification matrix used to distinguish points inside and outside the mask region.

[0093] Furthermore, when calculating the penalty term outside the mask, a specific mathematical function can be used. For example, a maximum replacement function can be used to replace all elements in the Euclidean distance matrix outside the mask with the maximum value, and then multiply the elements with the complement of the sub-mask matrix, that is, 1 minus the sub-mask matrix. This operation can effectively identify and process points outside the mask area, providing the necessary error penalty information for the model.

[0094] Preferably, in order to improve the computational efficiency and accuracy of the out-of-mask penalty term, various strategies can be used to optimize this process. For example, a dynamically adjusted penalty coefficient can be introduced, which can be adjusted according to the real-time operating status and historical data of the power system.

[0095] In addition, advanced numerical analysis methods, such as machine learning algorithms, can be considered to predict and estimate distances outside the mask, especially when data is sparse or computing resources are limited. These methods can further improve the model's response speed and prediction accuracy to power load changes, thereby optimizing the operating efficiency and stability of the power system.

[0096] In some embodiments, the The mask-less Euclidean distance matrix of the sub-mask matrices The power supply matrix The dimensions of the sub-mask matrices are the same; the masked Euclidean distance matrix of The element value at position is:

[0097]

[0098] in, Indicates a point The shortest Euclidean distance to the mask boundary of this submask matrix, Indicates the The mask area of the sub-mask matrix.

[0099] It should be noted that this embodiment details how to calculate the off-mask Euclidean distance matrix of the sub-mask matrix. This matrix is a key component of the power supply matrix, which is used to determine the shortest distance from each point to the boundary of the sub-mask region, especially for those points located outside the mask region.

[0100] Specifically, the calculation of the mask-outside Euclidean distance matrix involves analyzing each point in the power supply matrix to determine its distance to the nearest mask boundary. This usually involves complex geometric calculations and can be implemented using various algorithms, such as Dijkstra's algorithm or algorithm.

[0101] Furthermore, in practical applications, the mask area can be defined by the topology of the power network, the path of power flow, or other relevant factors. The value of each element in the Euclidean distance matrix outside the mask is determined by the shortest distance from the point to the mask boundary. If the point is not within the mask area, the value is 0.

[0102] To improve the efficiency and accuracy of calculating the off-mask Euclidean distance matrix, parallel computing techniques can be used to process large-scale power supply matrices. Furthermore, adaptive algorithms can be introduced to optimize the distance calculation process, for example, automatically adjusting calculation parameters based on dynamic changes in power demand. In some cases, machine learning methods can also be used to predict and estimate off-mask distances, especially when data is sparse or computing resources are limited.

[0103] The above-described embodiments of the present disclosure have the following beneficial effects: The virtual power plant power load control method of the present invention can construct a power load prediction and control model by acquiring power load data and power supply capacity data, thereby achieving precise control and optimization of power load. This method can effectively improve the operating efficiency and stability of the power system while reducing the risks associated with load fluctuations. By segmenting and dynamically adjusting power supply capacity, it is possible to more flexibly respond to rapid changes in the power market and achieve efficient utilization of power resources.

[0104] Furthermore, the control method utilizes a composite loss function with a penalty for load forecast errors, improving prediction accuracy during model training. This approach reduces power load control errors caused by forecast errors, thereby improving power system reliability. Furthermore, through in-depth analysis of the sub-supply capacity data of power supply capacity, more detailed power load adjustments can be made, achieving more refined control of the power system and further improving the power system's economic efficiency and environmental friendliness.

[0105] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a virtual power plant power load control system. These device embodiments are similar to Figure 2 Corresponding to the method embodiment shown, the real-time image recognition model training system can be applied to various electronic devices. Figure 2 As shown, a virtual power plant power load control system 200 of some embodiments includes:

[0106] A feature extraction module 201 is used to extract features of power load data and power supply capacity data using a deep learning network;

[0107] A prediction module 202 is configured to predict future power loads using a time series prediction algorithm based on the extracted features;

[0108] An optimization module 203 is configured to generate a power load control strategy using an optimization algorithm based on the prediction results and the power supply capacity data;

[0109] The evaluation module 204 is used to evaluate the performance of the power load control strategy, including economic efficiency, reliability and environmental impact.

[0110] It is understandable that the modules recorded in the virtual power plant power load control system 200 are similar to those in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the virtual power plant power load control method are also applicable to the virtual power plant power load control system 200 and the modules contained therein, and will not be repeated here.

[0111] In some embodiments, generating a control strategy training sample set based on the power load data and the corresponding power supply capacity data includes:

[0112] Gaussian process regression is used to interpolate power load data and corresponding power supply capacity data to handle missing data;

[0113] Perform wavelet transform on power load data and power supply capacity data to extract time-frequency features;

[0114] For each sample data, principal component analysis is performed on the power load data and power supply capacity data to reduce the data dimension and improve the model training efficiency;

[0115] Based on the data after dimensionality reduction, a clustering algorithm is used to classify the power load types and power supply capacity types to generate training samples.

[0116] Specifically, the virtual power plant power load control system consists of a feature extraction module, a prediction module, an optimization module, and an evaluation module. The feature extraction module uses a deep learning network to analyze and extract key features from power load data and power supply capacity data. The prediction module uses a time series prediction algorithm based on the extracted features to predict future power loads. The optimization module applies an optimization algorithm based on the prediction results and power supply capacity data to generate a power load control strategy. The evaluation module is responsible for evaluating the performance of the control strategy, including its economic efficiency, reliability, and environmental impact.

[0117] Preferably, in order to improve the accuracy and efficiency of the system, advanced deep learning technologies such as convolutional neural networks (CNN) or recurrent neural networks (RNN) can be used in the feature extraction module to better capture the spatiotemporal characteristics of the data. In the prediction module, algorithms such as long short-term memory networks (LSTM) or gated recurrent units (GRU) can be used to improve the accuracy of time series predictions. The optimization module can use genetic algorithms, particle swarm optimization (PSO), or simulated annealing algorithms to find the optimal power load strategy. The evaluation module can introduce methods such as multi-objective optimization and life cycle assessment (LCA) to comprehensively evaluate the comprehensive performance of the control strategy. In addition, the system can integrate real-time data monitoring and adaptive learning mechanisms to dynamically adjust the control strategy to adapt to the constant changes in the power system.

[0118] Reference below Figure 3 , which shows a schematic structural diagram of an electronic device 300 suitable for implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0119] like Figure 3 As shown, electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage device 308 into a random access memory (RAM) 303. RAM 303 also stores various programs and data required for the operation of electronic device 300. Processing device 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0120] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0121] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from a network via the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are performed.

[0122] The storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a terminal device such as a computer, a server, a mobile phone, or a tablet.

[0123] It should be noted that in some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. Furthermore, in some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wire, optical cable, RF (radio frequency), or any suitable combination thereof.

[0124] In some embodiments, the client and server can communicate using any currently known or later developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or later developed network.

[0125] In some embodiments, the client and server can communicate using any currently known or later developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or later developed network.

[0126] The computer readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device

[0127] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0128] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0129] The units described in some embodiments of the present disclosure may be implemented in software or hardware. The units described may also be provided in a processor, and the functions described above may be at least partially performed by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0130] The above descriptions are merely some preferred embodiments of the present disclosure and illustrate the underlying technical principles. Those skilled in the art should understand that the scope of the invention encompassed by the embodiments of the present disclosure is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for controlling power load of a virtual power plant, characterized in that: The following steps are involved: Acquire power load data and corresponding power supply capacity data of the virtual power plant, and generate a control strategy training sample set based on the power load data and the corresponding power supply capacity data; Analyzing each corresponding power supply capability data in the control strategy training sample set based on the stability of the power system to obtain a plurality of sub-supply capability data of each corresponding power supply capability data; Constructing a power load prediction and control model, and training the power load prediction and control model based on the power load data, power supply capacity data, and multiple sub-supply capacity data of each corresponding power supply capacity data in the training sample set to obtain a power load control model; Inputting the power load data of the virtual power plant to be controlled into the power load control model to obtain a power load control strategy for the virtual power plant to be controlled; According to multiple sub-supply capacity data of the power supply capacity, a load forecast error penalty matrix with load forecast error penalty is used to calculate the training loss of the power load forecasting and control model; The load forecast error penalty matrix corresponding to the power supply matrix is calculated by the following steps: Calculate the intra-mask Euclidean distance matrix EDM of the mth sub-mask matrix based on the mth sub-mask matrix of the power demand matrix m ; Based on the Euclidean distance matrix EDM within the mask m Calculate the intra-mask penalty term penal of the mth sub-mask matrix int,m ; Calculate the masked Euclidean distance matrix EDM of the mth sub-mask matrix based on the mth sub-mask matrix of the power supply matrix ext,m ; Based on the masked Euclidean distance matrix EDM ext,m Calculate the mask penalty term penal of the mth sub-mask matrix ext,m ; The penalty term penal in the mask int,m And the mask penalty penal ext,m Normalize and add to get the distance penalty matrix penal corresponding to the mth sub-mask matrix m ; The distance penalty matrix penal corresponding to each sub-mask matrix m The load forecast error penalty matrix penal corresponding to the power supply matrix is obtained by splicing.

2. The method for controlling power load of a virtual power plant according to claim 1, characterized in that: Based on the stability of the power system, each power supply capacity data in the training sample set is analyzed to obtain multiple sub-supply capacity data for each power supply capacity, including: The initial state of each power supply capability is taken as the first type, and the stability of the initial state is calculated; each state of the power supply capability is traversed in turn, and the stability of the current state is calculated. If the stability of the current state is the same as the stability of the previous state, then the current state and the previous state are of the same type; otherwise, the current state and the previous state are of different types; A plurality of sub-supply capability data of the power supply capability is obtained according to the type of each state in the power supply capability.

3. The method for controlling power load of a virtual power plant according to claim 1, wherein: The intra-mask Euclidean distance matrix EDM of the m-th sub-mask matrix m The dimension is the same as the mth sub-mask matrix of the power demand matrix.

4. The method for controlling power load of a virtual power plant according to claim 1, wherein: Calculate the intra-mask penalty term of the mth sub-mask matrix.

5. The method for controlling power load of a virtual power plant according to claim 1, wherein: Calculate the out-of-mask penalty term for the mth sub-mask matrix.

6. The method for controlling power load of a virtual power plant according to claim 5, characterized in that: The masked Euclidean distance matrix EDM of the mth sub-mask matrix ext,m The same dimension as the mth sub-mask matrix of the power supply matrix.

7. A virtual power plant power load control system, the system implementing the method according to claim 1, characterized in that: The system comprises: A feature extraction module is used to extract features of power load data and power supply capacity data using a deep learning network; A prediction module is used to predict future power loads using a time series prediction algorithm based on the extracted features; An optimization module for generating a power load control strategy using an optimization algorithm based on the prediction results and power supply capacity data; Evaluation module for evaluating the performance of power load control strategies, including economic efficiency, reliability, and environmental impact.

8. The virtual power plant power load control system according to claim 7, characterized in that: Generating a control strategy training sample set based on the power load data and the corresponding power supply capacity data includes: Gaussian process regression is used to interpolate power load data and corresponding power supply capacity data to handle missing data; Perform wavelet transform on power load data and power supply capacity data to extract time-frequency features; For each sample data, principal component analysis is performed on the power load data and power supply capacity data to reduce the data dimension and improve the model training efficiency; Based on the data after dimensionality reduction, a clustering algorithm is used to classify the power load types and power supply capacity types to generate training samples.

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