Intelligent power grid distributed energy management system and optimal scheduling method thereof

Abstract

According to the distributed energy management system for the smart power grid and the optimal scheduling method thereof, efficient, intelligent and sustainable management of power resources is realized by integrating advanced data acquisition, processing, optimal scheduling, control execution and user interaction modules. The system adopts a multi-dimensional optimization scheduling strategy and a self-adaptive control strategy, and is combined with a digital twinning technology, so that the accuracy of real-time monitoring and prediction of the running state of the power grid is improved, and the response speed and the decision-making capability of the system are enhanced. The system can automatically adjust the operation parameters of the distributed power generation resources and the energy storage system, realizes accurate load control, improves the energy utilization efficiency, reduces the operation cost, and promotes the sustainable development of a power grid. Meanwhile, the transparency of the system and the participation degree of users are improved through the use of the user interaction module, and powerful technical support is provided for construction and operation of an intelligent power grid.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a distributed energy management system that integrates advanced data collection, processing, analysis, optimized scheduling and user interaction functions. Background Art

[0002] With the continuous growth of global energy demand and the increasingly severe environmental problems, smart grid has attracted wide attention as an important development direction of power system. Smart grid realizes efficient, reliable and flexible operation of power system by applying advanced information and communication technology, automation control technology and power electronics technology. It can not only better integrate renewable energy such as wind and solar energy, but also improve the adaptability of power grid to various uncertain factors, such as load fluctuation and supply change.

[0003] However, the existing smart grid management system still faces many challenges in dealing with the optimal dispatch of distributed energy. The randomness and uncertainty of distributed energy make the dispatch and management of the power grid more complicated. In addition, how to effectively integrate and utilize a large amount of distributed energy to improve energy efficiency and reduce operating costs is also a key issue that needs to be solved in the development of smart grids. Existing systems often lack sufficient flexibility and intelligence, and are difficult to adapt to the rapidly changing power grid environment and user needs.

[0004] In order to solve these problems, it is necessary to develop a new type of distributed energy management system for smart grids, which can monitor the state of the grid in real time, accurately predict changes in load and power generation, dynamically optimize the scheduling of distributed energy resources, and provide an intuitive user interface. Such a system will help improve the operating efficiency of the grid, reduce energy waste, enhance the reliability and security of the grid, and also provide users with a better service experience. Summary of the invention

[0005] The present invention aims to propose a distributed energy management system for smart grids, which realizes efficient and intelligent management of distributed energy in the power grid through a series of highly integrated modules. The design of the system takes into account the entire process from data collection to user interaction, ensuring the optimization and automation of power grid operation. The following is the details of the present invention:

[0006] The data acquisition module of the present invention is the basis of the system. It monitors and collects the key operating parameters of the power grid in real time by deploying a series of intelligent sensors and establishing an efficient communication network. These parameters include but are not limited to power data, involving voltage, current, power factor and frequency, etc., to ensure a comprehensive grasp of the electrical characteristics of the power grid. At the same time, the collection of environmental data covers temperature, humidity and light intensity, which are crucial for predicting the potential for renewable energy generation and adjusting the power grid operation strategy. In addition, the module is also responsible for collecting policy-related data, such as electricity price information and renewable energy policies, which is instructive for optimizing the allocation of power resources and responding to market changes. More importantly, the data acquisition module also closely monitors the user-side load data and the operating status of distributed energy, providing detailed first-hand information for subsequent processing and analysis of the system, so that the operating status of the entire power grid can be fully and carefully monitored.

[0007] The data preprocessing module performs a series of refined processing operations on the raw data collected from the data acquisition module to improve data quality and prepare for subsequent analysis. First, the module identifies and removes invalid data, such as readings outside the normal operating range or outliers that are significantly deviated from expectations, and duplicate data through well-defined standards and algorithms to ensure that the data set does not contain redundant information. Next, the denoising step removes high-frequency noise by applying filtering techniques such as low-pass filters, and reduces random fluctuations in the data through smoothing algorithms such as moving averages or exponential smoothing, thereby extracting the true trend of the signal. Finally, in the formatting step, data normalization scales all data to a uniform numerical range, such as the [0,1] interval, to eliminate the impact of different dimensions and numerical ranges on the analysis results, while the unit unification step ensures that all data use consistent measurement units, such as converting all electrical energy data into kilowatt-hours (kWh). Through these steps, the data preprocessing module provides accurate, consistent, and standardized data sets for data analysis and decision support, laying a solid foundation for the performance and reliability of the system.

[0008] The feature extraction module is a key component, which is responsible for extracting key features from the preprocessed data, which are crucial for subsequent data analysis and decision-making. This module adopts two main technical methods: statistical feature extraction and machine learning feature extraction. In terms of statistical feature extraction, this module describes the distribution and characteristics of the data by calculating basic statistics of the data, such as mean, median, standard deviation, range, skewness and kurtosis. These statistical features help us understand the central tendency, degree of dispersion and shape characteristics of the data, thereby providing basic information for data analysis and machine learning models. Machine learning feature extraction technology uses advanced algorithms such as principal component analysis (PCA) and linear discriminant analysis (LDA) to extract key information from high-dimensional data. PCA transforms potentially related variables into a set of linearly unrelated variables through orthogonal transformation, and is usually used for dimensionality reduction and feature abstraction. LDA is a supervised learning method that aims to find feature subspaces that maximize the separation between categories. This module may also include other machine learning techniques such as Gaussian mixture model (GMM) and local binary pattern (LBP) to capture the distribution characteristics and texture characteristics of the data. Histogram methods, such as color histogram and histogram of gradient orientation (HOG), are also used to count the color or gradient direction information in local areas of the image to extract features.

[0009] The pattern recognition module is a key technological breakthrough. It is specially designed to extract key features that can represent the operation status and load changes of the power grid from the preprocessed data. The module uses advanced time series analysis and cluster analysis techniques to identify and predict the behavior patterns and trends of the power grid. In terms of time series analysis, the module analyzes historical power grid data, such as hourly power consumption readings over the past month, to identify periodic patterns in power usage, such as daily peak and trough periods. By using the autoregressive moving average (ARMA) model, the module is able to predict load changes in future time periods, with a time step of one data point every 15 minutes, thereby providing precise guidance for the scheduling and planning of the power grid. Cluster analysis technology also plays an important role in this module. By applying the K-means clustering algorithm, the module is able to classify a large number of users into different categories based on their power consumption patterns. For example, by analyzing energy consumption data at different times of the day, users can be divided into two groups: those with more power consumption during peak hours and those with more power consumption during trough hours. Such classification helps power grid operators better understand and manage the power consumption behavior of different user groups, thereby optimizing the load distribution of the power grid. In addition, the module also uses advanced statistical methods such as Sample Entropy and Approximate Entropy to measure the complexity and regularity of time series data. These methods can provide more detailed quantitative indicators for the operation status of the power grid and help identify potential abnormal patterns, such as signs before equipment failure or sudden changes in demand.

[0010] The data analysis module is a core component that reveals the complex relationship between data and provides scientific support for decision-making by deeply analyzing the preprocessed data. The module combines two key technologies: association rule mining and causal relationship analysis. In terms of association rule mining, the module first determines the minimum support threshold, for example, set it to 5%, which means that only item sets with a frequency of at least 5% in the data set will be considered. Then, through algorithms such as Apriori or FP-Growth, frequent item sets are identified, which are sets of items that often appear together in the data set. For example, if an item set {milk, bread, eggs} appears more than 5% of the time in all shopping baskets, then the item set will be marked as frequent. Next, the module calculates the confidence of these item sets, which is an indicator of the dependency between item sets, which represents the conditional probability of the occurrence of the latter item under the condition that the former item appears. For example, if the confidence of the rule {milk, bread}→{egg} exceeds a preset threshold, such as 30%, then the rule will be considered meaningful for shopping basket analysis. In terms of causal relationship analysis, the module uses statistical methods and machine learning methods to infer the causal effects between variables. For example, through randomized experiments or observational studies, the module can analyze whether drinking alcohol leads to an increased risk of heart disease. In the analysis, the module will consider potential interfering factors such as age, gender, and lifestyle to ensure the accuracy of the causal relationship. In addition, the module will also use various statistical test methods, such as regression analysis and hypothesis testing, to further verify the existence of causal relationships.

[0011] The optimization scheduling module is the core of smart grid operation and management. It develops an efficient distributed energy scheduling plan by deeply analyzing the grid operation data. The module first uses advanced data analysis techniques, such as time series analysis and cluster analysis, to deeply mine the historical load and power generation data of the power grid, so as to identify the patterns and trends of the grid operation. On this basis, the module further applies load forecasting technology to predict future changes in electricity demand by taking into account multiple factors such as weather conditions, socio-economic activities and user behavior patterns. For example, by analyzing the electricity consumption data every half hour in the past five years, combined with factors such as dry bulb temperature, dew point temperature, humidity, electricity price, etc., the BP neural network model is used for training to achieve accurate prediction of power load. The module also integrates power generation prediction technology to predict the future power generation of renewable energy sources such as photovoltaics and wind power by simulating their power generation process. This process involves real-time monitoring and analysis of meteorological conditions such as solar irradiance and wind speed to ensure the accuracy of power generation prediction.

[0012] The predictive control module is a key component of the smart grid optimization dispatching system. It is specially designed to dynamically adjust the optimization dispatching scheme according to the real-time operation status and prediction data of the power grid. The module adopts adaptive control and robust control technology to adapt to the rapid changes of the power grid. Adaptive control technology can automatically adjust the control parameters according to the real-time data of the power grid, such as voltage, current, power factor and frequency. For example, when the power grid frequency deviates from the nominal value of 50Hz, adaptive control can quickly restore the power grid frequency to the normal range by adjusting the output of the power generation resources or the load demand. This control strategy can not only cope with the immediate changes of the power grid, but also adapt to the changes of the power grid on different time scales, such as the fluctuation of the load during the day. Robust control technology ensures that the system can still maintain stable operation in the face of uncertain factors, such as the volatility of renewable energy and power grid failure. By designing a robust controller based on μ comprehensive analysis, the module can maximize the robust stability and performance of the system, and ensure the safe operation of the power grid even in the case of changes in system parameters or external disturbances. The predictive control module also uses model predictive control (MPC) technology. By establishing a predictive model of the power grid, the module can predict the operating status of the power grid in the future and optimize the dispatch plan accordingly. For example, by predicting the future power generation of solar photovoltaic panels, MPC can adjust the power grid's power generation and load distribution in advance to maximize the use of renewable energy.

[0013] The control execution module is a key link in the smart grid optimization scheduling system. It is responsible for converting the scheduling scheme generated by the optimization scheduling module into specific executable control instructions and ensuring that these instructions are accurately sent to the corresponding distributed generation resources and energy storage systems. The core functions of this module include two main parts: instruction generation and execution feedback. In terms of instruction generation, the control execution module generates detailed control instructions based on the optimization scheduling scheme. For example, if the prediction control module predicts that the grid load will increase in a certain period of time in the future, the control execution module will generate instructions to increase power generation output, such as increasing the output power of solar photovoltaic panels from 5kW to 10kW, or adjusting the blade angle of wind turbines to capture more wind energy. These instructions will be converted into standardized control signals and sent to the target device through the communication network. In terms of execution feedback, the control execution module not only sends control instructions, but also continuously monitors the execution of instructions, and ensures the correct execution of control instructions through feedback mechanisms. For example, the module may receive feedback signals from the battery management system (BMS) of the energy storage system to confirm the charge and discharge status of the battery, or receive signals from the inverter of the photovoltaic power generation system to verify whether its output voltage and current meet the instruction requirements. If the execution result deviates from the expectation, the control execution module will adjust according to the feedback information and regenerate the control command to ensure that the grid operation status can be adjusted according to the optimized dispatching plan. The control execution module also has the ability to handle exceptions. When equipment failure or communication interruption is detected, it can quickly take emergency measures, such as switching to backup power or adjusting the output of other power generation resources to maintain the stable operation of the grid. Through this precise instruction generation and execution feedback mechanism, the control execution module ensures the efficiency and reliability of the smart grid optimization dispatching system.

[0014] The state monitoring module is a key link to ensure the stable operation of the power grid and the effective implementation of the dispatching scheme. The module deploys a series of high-performance intelligent sensors and data collectors to monitor the operation status of the power grid in real time, including key parameters such as voltage level, current intensity, power factor, frequency, and the output power and operating efficiency of distributed energy such as solar photovoltaic panels and wind turbines. These sensors collect data at a frequency of once per second, ensuring the real-time and accuracy of the data. The state monitoring module also integrates advanced anomaly detection algorithms, such as machine learning-based anomaly detection models, which can identify and warn of abnormal conditions in power grid operation, such as voltage sags, frequency deviations, or equipment failures. When an anomaly is detected, the system automatically triggers an alarm and provides detailed information on the abnormal data, such as the time of occurrence, duration, and scope of impact, as well as possible cause analysis, to help operation and maintenance personnel respond and handle quickly. In addition, the module also has data storage and analysis functions, which can store real-time monitoring data and abnormal event records in the database for subsequent data analysis and auditing. Through these functions, the state monitoring module provides comprehensive and real-time operation status monitoring for the smart grid, ensures the effective implementation of the dispatching scheme, and promptly discovers and handles potential risks and problems, thereby ensuring the stable operation of the power grid.

[0015] The security monitoring module is a key component in the smart grid optimization dispatching system. It is specially designed to monitor the security status of the power grid and promptly detect and respond to potential security threats. The module includes two main monitoring functions: intrusion detection and fault prediction to ensure the security of the power grid. In terms of intrusion detection, the security monitoring module uses anomaly detection models based on machine learning, such as the K-means clustering algorithm, to perform real-time analysis of network traffic data. The model is able to identify abnormal traffic patterns that are significantly different from normal behavior. For example, by analyzing parameters such as source ports, destination ports, packet sizes, and transmission protocols in network traffic, traffic that does not conform to normal communication patterns is detected, thereby identifying potential network intrusion behaviors. When the system detects abnormal traffic, it automatically triggers an alarm and provides detailed information on the abnormal data, such as the time of occurrence, duration, and scope of impact, as well as possible cause analysis, to help operation and maintenance personnel respond and handle quickly. In terms of fault prediction, the module collects and analyzes the status data of power grid equipment, such as temperature, vibration, current, and voltage, and uses advanced data analysis techniques such as trend analysis and pattern recognition to predict possible equipment failures. For example, by monitoring the dissolved gas analysis (DGA) data in transformer oil, it is possible to predict internal transformer faults and take measures in advance to avoid them. The safety monitoring module also has data storage and analysis functions, which can store real-time monitoring data and abnormal events.

[0016] The event records are stored in the database for subsequent data analysis and auditing. Through these functions, the security monitoring module provides comprehensive and real-time security monitoring for the smart grid, ensuring the effective implementation of the dispatch plan, timely discovering and handling potential risks and problems, and thus ensuring the stable operation of the power grid.

[0017] The user interaction module is designed to provide an intuitive and easy-to-use user interface that enables users to conveniently view the operation status and dispatch results of the power grid in real time, while performing personalized user settings and feedback. The design of this module takes into account the maximization of user experience, including personalized interface design and multi-language support to meet the needs of different users. In terms of personalized interface design, the user interaction module provides a configurable dashboard, and users can choose to display different data views and charts according to their needs and preferences. For example, users can choose to view the real-time load curve of the power grid, the output power of power generation resources, the charge and discharge status of the energy storage system, or key operating parameters such as the frequency and voltage level of the power grid. In addition, the dashboard also supports custom alarm threshold settings, allowing users to set the alarm limits of power grid operation parameters according to their risk preferences. In terms of multi-language support, the user interaction module provides multiple language interfaces such as English, Chinese, and Spanish to meet the needs of users in different countries and regions. Users can choose the corresponding language interface for operation according to their language habits. In addition, the user interaction module also provides a user feedback function, through which users can provide feedback to system administrators, such as reporting system errors, making improvement suggestions, or asking questions related to power grid operation. System administrators can view and respond to user feedback in real time to improve the system's service quality and user experience.

[0018] The communication management module is the core to ensure the smooth and secure information flow inside and outside the smart grid optimization dispatching system. The module supports industrial standard communication protocols such as Modbus, DNP3, OPC UA, etc., to achieve seamless data exchange between different devices and systems. Through dynamic protocol conversion, such as converting the Modbus data of smart sensors into DNP3 format in real time, the communication management module ensures the compatibility and interoperability of the power grid control system. At the same time, encryption algorithms such as AES-256 or RSA are used to encrypt the transmitted data, and combined with the SSL / TLS security protocol, end-to-end protection is provided for data transmission, preventing data interception and tampering, and ensuring the security of communication. The data storage module plays the role of a data warehouse in the smart grid optimization dispatching system, responsible for the storage of collected data, processing results and historical records. The module adopts a distributed storage architecture to store data in multiple physical locations, and protects data through database encryption technology to ensure data security and privacy. At the same time, it provides a regular backup function to copy key data to the cloud or external storage device, and uses RAID technology to distribute and copy data between multiple hard disks to enhance data reliability and fault tolerance, ensuring that data can be quickly restored when lost or damaged.

[0019] The report generation module provides users with powerful decision support and performance evaluation tools. The module integrates advanced data analysis and visualization technology, allowing users to select data views, charts and analysis indicators according to specific needs and create personalized report templates. It supports multiple output formats such as PDF, Excel or HTML to meet the viewing and sharing needs of different users. In addition, the regular reporting function automatically generates and distributes reports at preset time intervals, providing users with a regular overview of the operation status of the power grid, while different levels of report access and customization capabilities ensure the security and suitability of information.

[0020] The specific scheduling method of the system is as follows:

[0021] S1: Data collection and preprocessing. In the distributed energy management system for smart grids, the first step is data collection and preprocessing. The system collects key parameters of the power grid in real time through deployed smart sensors, including power data such as voltage, current, power factor, frequency, and environmental data such as temperature, humidity, and light intensity. At the same time, the system also collects policy data, such as electricity price information and renewable energy policies, user-side load data, and the operating status of distributed energy. The collected raw data will be cleaned, denoised, and formatted through the data preprocessing module to ensure data consistency and availability, and provide accurate data support for subsequent analysis and decision-making.

[0022] S2: Feature extraction and pattern recognition. Based on data preprocessing, the second step of the system is feature extraction and pattern recognition. In this step, statistical analysis and machine learning techniques are used to extract key features from the data, such as dimensionality reduction through principal component analysis (PCA) or using cluster analysis to identify natural groups in the data. Then, pattern recognition techniques such as time series analysis are used to identify the patterns and trends of power grid operation status and load changes, providing a scientific basis for short-term and long-term forecasts of the power grid.

[0023] S3: Data Analysis and Optimized Scheduling,The third step is data analysis and optimized scheduling, in which the system uses methods such as association rule mining and causal analysis to deeply analyze the extracted features and identified patterns to understand the complex relationships between data. Based on these analysis results and the operation strategy of the power grid, the system formulates an optimized scheduling plan for distributed energy, including accurate load forecasting and power generation forecasting, to ensure the optimal allocation of resources in the power grid to achieve the best balance between economy and reliability.

[0024] S4: Predictive control and command generation. After generating the optimized dispatching plan, the fourth step of the system is predictive control and command generation. In this step, the system dynamically adjusts the dispatching plan according to the real-time operation status and forecast data of the power grid, and adopts adaptive control and robust control technology to adapt to the rapid changes of the power grid. Then, the system converts the optimized dispatching plan into specific control instructions, which will be sent to the corresponding distributed generation resources and energy storage systems, and the correct execution of the control instructions will be ensured through the execution feedback mechanism.

[0025] S5: Condition Monitoring and Safety Monitoring,The fifth step is condition monitoring and safety monitoring, in which the system monitors the operating status of the power grid and the execution of distributed energy in real time to ensure the effective implementation of the dispatch plan. This includes real-time data acquisition and anomaly detection, such as monitoring the voltage and current of the power grid through smart sensors to see if they are within a safe range. At the same time, the system also monitors the security status of the power grid, promptly discovers and responds to potential security threats, such as identifying abnormal network behavior through intrusion detection systems, or warning of potential equipment failures through fault prediction models.

[0026] S6: User interaction and communication management,The sixth step is user interaction and communication management, in which the system provides a user interface, allowing users to view the grid operation status and dispatch results in real time, as well as to make user settings and feedback. The interface supports personalized design and multiple languages ​​to meet the needs of different users. At the same time, the system's communication management module is responsible for managing and maintaining communication connections inside and outside the system, ensuring real-time data transmission and accurate communication of instructions, including protocol conversion and data encryption, to ensure the reliability and security of communication.

[0027] S7: Data storage and report generation,The last step is data storage and report generation, in which the system stores the collected data, processing results and historical records in the database for subsequent analysis and auditing. The data storage module uses distributed storage and data backup to ensure data integrity and traceability. In addition, the system automatically generates various reports and analysis reports, including custom reports and regular reports, based on user needs and system operation, to provide decision support and performance evaluation, helping users better understand and participate in the operation and management of the power grid.

[0028] Through the collaborative work of the above modules, the present invention realizes the comprehensive management and optimized scheduling of distributed energy in the smart grid, improves the operating efficiency and reliability of the grid, reduces operating costs, and provides users with a better service experience.

[0029] By adopting the technical solution of the present invention, the following technical effects can be specifically achieved:

[0030] 1. Optimize the allocation of power resources: The system can accurately predict the load demand of the power grid and the power generation potential of distributed energy by collecting power data, environmental data and policy data of the power grid in real time, combined with advanced data analysis and pattern recognition technology. The optimization scheduling module automatically adjusts the output of distributed energy based on these prediction results to ensure the best match between power supply and demand. This intelligent scheduling mechanism significantly improves the utilization efficiency of power resources and reduces energy waste caused by mismatch between supply and demand.

[0031] 2. Enhance the adaptive ability and stability of the power grid: The system's status monitoring module can monitor the operating status of the power grid in real time, including key parameters such as voltage, current and frequency. Once an abnormality is found, the safety monitoring module will immediately start and take necessary control measures, such as adjusting the output of power generation resources or redistributing loads, to maintain the stable operation of the power grid. This rapid response mechanism greatly enhances the power grid's adaptive ability to emergencies and reduces the risk of power outages caused by power grid failures.

[0032] 3. Improve user engagement and satisfaction: The user interaction module provides an intuitive and easy-to-use user interface, where users can view the operating status of the power grid, their own energy consumption data and dispatch results in real time. Users can also personalize the interface according to their needs and preferences, such as adjusting load usage time or subscribing to regular energy consumption reports. This high degree of transparency and interactivity enables users to better understand and participate in power grid management, thereby improving user satisfaction and engagement. At the same time, the system further improves the user experience by providing customized reports and analysis to help users make more informed energy usage decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1This is a block diagram of the distributed energy management system of the present invention.

[0034] Figure 2 The distributed energy management system scheduling method of the present invention is shown in FIG. DETAILED DESCRIPTION

[0035] The following will be combined with the attached embodiment of the present invention Figure 1 and Figure 2 , the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] The present invention aims to propose a distributed energy management system for smart grids, which realizes efficient and intelligent management of distributed energy in the power grid through a series of highly integrated modules. The design of the system takes into account the entire process from data collection to user interaction, ensuring the optimization and automation of power grid operation. The following is the details of the present invention:

[0037] The data acquisition module uses a well-designed intelligent sensor array and a stable communication network to monitor and accurately collect multiple key parameters of the power grid in real time, including voltage (recording range from 100V to 380V), current (measurement accuracy of 0.1A), power factor (accuracy of 0.01), frequency (measurement range 48Hz to 52Hz) in power data; ambient temperature (measurement range -40℃ to 85℃), relative humidity (measurement accuracy of ±3%RH), light intensity (measurement range from 0 to 100,000lux) in environmental data ; electricity price information in policy data (recording range from 0.05 yuan / kWh to 1.5 yuan / kWh), renewable energy policy (including detailed information such as policy effective time and subsidy intensity); as well as user-side load data and the operating status of distributed energy, such as the output power of photovoltaic panels (measurement range from 0W to 5kW), the speed of wind turbines (measurement accuracy of ±0.01 rpm), etc. These parameter values ​​accurate to several decimal places provide the system with detailed real-time data, ensuring comprehensive monitoring of the grid operation status and the accuracy of subsequent processing and analysis.

[0038] The data preprocessing module performs a series of refined processing operations on the raw data collected from the data acquisition module to improve data quality and prepare for subsequent analysis. First, the module identifies and removes invalid data, such as readings outside the normal operating range or outliers that deviate significantly from expectations, as well as duplicate data, through well-defined standards and algorithms to ensure that the data set does not contain redundant information. Next, the denoising step removes high-frequency noise by applying filtering techniques such as low-pass filters, and reduces random fluctuations in the data through smoothing algorithms such as moving averages or exponential smoothing, thereby extracting the true trend of the signal. Finally, in the formatting step, data normalization scales all data to a uniform numerical range, such as the [0,1] interval, to eliminate the impact of different dimensions and numerical ranges on the analysis results, while the unit unification step ensures that all data use consistent measurement units, such as converting all electrical energy data into kilowatt-hours (kWh). Through these steps, the data preprocessing module provides accurate, consistent, and standardized data sets for data analysis and decision support, laying a solid foundation for the performance and reliability of the system.

[0039] The feature extraction module accurately extracts key information from the data output by the data preprocessing module by applying statistical and machine learning techniques. The module first calculates statistical features, including mean (for example, the mean of the power grid frequency is set to 50Hz), standard deviation (such as the standard deviation of voltage fluctuation is controlled within 0.5%), range (for example, the range of ambient temperature does not exceed the range of 40℃ to 85℃), etc., to quantify the central tendency, dispersion and distribution of the data. Then, machine learning algorithms such as principal component analysis (PCA) are used to reduce the data dimension from the original 100 dimensions to 10 dimensions while minimizing information loss, or linear discriminant analysis (LDA) is applied to project the data to the best distinguishing dimension to extract the features that best represent the differences in the data. In addition, the module may also use Gaussian mixture models (GMM) to identify the mixed distribution parameters in the data, for example, by fitting light intensity data to determine the mixing ratio of different light sources in the scene. Local Binary Pattern (LBP) is used to extract texture features of images, histogram methods, such as color histogram, analyze the frequency of color distribution in images, and histogram of directional gradients (HOG) counts the gradient direction information in local areas, providing powerful visual features for image recognition of power grid status. Through these feature extraction technologies, this module provides rich, accurate and highly discriminative data features for data analysis and decision support of smart grids, making power grid management more efficient and intelligent.

[0040] The pattern recognition module accurately identifies the patterns and trends of power grid operation from the preprocessed data by applying time series analysis and cluster analysis techniques. In terms of time series analysis, the module uses the autoregressive moving average (ARMA) model, such as the ARMA (2,1) model, where p = 2 represents the number of autoregressive terms and q = 1 represents the number of moving average terms, to analyze the time series data of power grid load. Through this model, we can predict the load changes every hour in the next 24 hours with a prediction time step of 1 hour, thereby providing accurate guidance for the scheduling and planning of the power grid. Cluster analysis technology also plays an important role in this module. By applying the K-means clustering algorithm, the module is able to classify a large number of users into different categories according to their power consumption patterns. For example, by analyzing the energy consumption data at different times of the day, users can be divided into two groups: those with more power consumption during peak hours and those with more power consumption during valley hours. The silhouette coefficient is used to evaluate the effect of clustering. When the silhouette coefficient is close to 1, it means that the sample is very similar to the samples in its own cluster and is not similar to the samples in other clusters, which indicates that the clustering effect is good. In addition, this module also uses advanced statistical methods such as sample entropy and approximate entropy to measure the complexity and regularity of time series data. In the calculation of sample entropy, the parameters m = 2 and r = 0.2*std are usually selected, where std represents the standard deviation of the original time series. These methods can provide more detailed quantitative indicators for the operation status of the power grid and help identify potential abnormal patterns, such as signs before equipment failure or sudden changes in demand. Through these feature extraction techniques, this module provides rich, accurate and highly discriminative data features for data analysis and decision support of smart grids, making power grid management more efficient and intelligent.

[0041] The data analysis module uses a series of advanced data analysis techniques to reveal the complex relationships between data and provide scientific support for decision-making by deeply analyzing the preprocessed data. This module mainly uses two key technologies: association rule mining and causal relationship analysis. In terms of association rule mining, the module first determines the minimum support threshold, for example, set it to 5%, which means that only item sets with a frequency of at least 5% in the data set will be considered. Then, through algorithms such as Apriori or FP-Growth, frequent item sets are identified, which are sets of items that often appear together in the data set. For example, if an item set {milk, bread, eggs} appears more than 5% of the time in all shopping baskets, then this item set will be marked as frequent. Next, the module calculates the confidence of these item sets, which is an indicator of the dependency between item sets, which represents the conditional probability of the occurrence of the latter item under the condition that the former item appears. For example, if the confidence of the rule {milk, bread}→{egg} exceeds the preset threshold, such as 30%, then this rule will be considered meaningful for shopping basket analysis. In terms of causal relationship analysis, the module uses statistical methods and machine learning methods to infer the causal effects between variables. For example, through randomized experiments or observational studies, the module can analyze whether drinking alcohol leads to an increased risk of heart disease. In the analysis, the module will consider potential interfering factors such as age, gender, and lifestyle to ensure the accuracy of the causal relationship. In addition, the module will also use various statistical test methods, such as regression analysis and hypothesis testing, to further verify the existence of causal relationships.

[0042] The optimization dispatch module carefully develops an efficient distributed energy optimization dispatch scheme by comprehensively applying data analysis results and accurate grid operation strategies. The module first uses time series analysis, such as the autoregressive integrated moving average (ARIMA) model, to predict the change in power demand in the next 24 to 48 hours based on historical load data from the past hour to a week, with a prediction accuracy of more than 95%. At the same time, weather forecast data such as temperature, humidity and wind speed are combined to further improve the accuracy of load forecasting. In terms of power generation forecasting, the module uses machine learning algorithms such as random forests to analyze the irradiance of solar photovoltaic panels and wind speed data of wind turbines to predict future renewable energy generation with an accuracy of more than 90%. These prediction results provide important input parameters for grid dispatching. Furthermore, the optimization dispatch module uses a mixed integer linear programming (MILP) algorithm to consider the operating constraints and economics of the grid, such as power generation costs, transmission losses and demand response, and develop a dispatch scheme that maximizes cost-effectiveness while meeting the balance of power supply and demand. The scheme includes real-time optimization and adjustment of distributed energy such as energy storage systems, demand-side management, and the output of traditional power stations. Ultimately, the optimization scheduling module can achieve the optimal allocation of power resources in the power grid, ensuring that while meeting the power needs of users, the utilization rate of renewable energy is improved, operating costs are reduced, and the reliability and resilience of the power grid are enhanced. Through the integrated application of these technologies, the present invention provides strong support for the efficient, economical and green operation of smart grids.

[0043] The predictive control module dynamically adjusts and optimizes the dispatching scheme to adapt to the rapid changes of the power grid by monitoring the key indicators of power grid operation in real time, such as voltage, current, power factor and frequency, and combining advanced adaptive control and robust control technologies. Specifically, the adaptive control part uses a proportional-integral-differential (PID) controller to automatically adjust the control parameters based on real-time data feedback. For example, when a frequency deviation of the power grid is detected, the PID controller can respond quickly and restore the frequency to the nominal value of 50Hz by adjusting the output of the generator set or changing the charge and discharge state of the energy storage system. At the same time, the robust control part adopts μ comprehensive analysis design to ensure that the system can maintain stable operation in the face of uncertain factors such as wind power generation fluctuations caused by wind speed changes or intermittent solar power generation. Through the preset control strategy, the impact of these disturbances on the stability of the power grid is reduced. In addition, the module also integrates model predictive control (MPC) technology, which optimizes the dispatching scheme and achieves the optimal allocation of resources based on the load and power generation forecasts every 15 minutes in the next 24 hours of the power grid. MPC technology predicts and optimizes control decisions by considering the dynamic characteristics and operating constraints of the power grid to maximize cost-effectiveness and balance supply and demand. Combining these technologies, the predictive control module can achieve real-time optimized scheduling of the smart grid, improve the operating efficiency and reliability of the grid, while reducing operating costs and enhancing the adaptability and resilience of the grid.

[0044] The control execution module is the key link in the actual application of the optimized scheduling scheme to the power grid. It ensures the efficient operation of the power grid by accurately converting these schemes into specific control instructions for distributed generation resources and energy storage systems. The module carefully designs the instruction generation process, which involves generating detailed control instructions based on the optimized scheduling scheme. For example, if the predictive control module predicts that the grid load will increase in a certain period of time in the future, the control execution module will generate instructions to increase the power generation output, such as increasing the output power of the solar photovoltaic panel from 5kW to 10kW, or adjusting the blade angle of the wind turbine to capture more wind energy. In terms of execution feedback, the control execution module not only sends control instructions, but also continuously monitors the execution of the instructions and ensures the correct execution of the control instructions through feedback mechanisms. For example, the module may receive feedback signals from the battery management system (BMS) of the energy storage system to confirm the charge and discharge status of the battery, or receive signals from the inverter of the photovoltaic power generation system to verify whether its output voltage and current meet the instruction requirements. If there is a deviation from the expected execution result, the control execution module will make adjustments based on the feedback information and regenerate control instructions to ensure that the operation status of the power grid can be adjusted according to the optimized scheduling scheme. In addition, the control execution module also has the ability to handle exceptions. When a device failure or communication interruption is detected, emergency measures can be taken quickly, such as switching to backup power or adjusting the output of other power generation resources to maintain grid stability. The module's precise instruction generation and execution feedback mechanism, as well as its effective exception handling capabilities, together ensure the efficient execution and high reliability of the smart grid optimization dispatching system, providing solid technical support for the intelligent management of the grid.

[0045] The condition monitoring module is a key component of the smart grid optimization dispatching system. It ensures the effective implementation of the dispatching plan by monitoring the operation status of the power grid and the execution of distributed energy in real time. The module includes two main functions: real-time data acquisition and anomaly detection to maintain the stable operation of the power grid. In terms of real-time data acquisition, the condition monitoring module accurately monitors the key parameters of the power grid, such as voltage level (100V to 380V), current intensity (0.1A to 100A), power factor, etc. by deploying intelligent sensors and data collectors.

[0046] (0.8 to 1.0), frequency (48Hz to 52Hz), etc. These sensors can collect data at a frequency of once per second, ensuring the real-time and accuracy of the data. In terms of anomaly detection, the module adopts an anomaly detection model based on machine learning, which can identify and warn of abnormal conditions in power grid operation, such as voltage sag, frequency deviation or equipment failure. When an anomaly is detected, the system automatically triggers an alarm and provides detailed information on the abnormal data, such as the time of occurrence, duration and scope of impact, as well as possible cause analysis, to help operation and maintenance personnel respond and handle it quickly. The condition monitoring module also has data storage and analysis functions, which can store real-time monitoring data and abnormal event records in the database for subsequent data analysis and auditing. Through these functions, the condition monitoring module provides comprehensive and real-time operation status monitoring for the smart grid, ensures the effective implementation of the dispatch plan, and promptly discovers and handles potential risks and problems, thereby ensuring the stable operation of the power grid.

[0047] The security monitoring module is a key component in the smart grid optimization dispatching system. It is specially designed to monitor the security status of the power grid and promptly detect and respond to potential security threats. The module includes two main monitoring functions: intrusion detection and fault prediction to ensure the security of the power grid. In terms of intrusion detection, the security monitoring module uses machine learning-based anomaly detection models, such as the K-means clustering algorithm, to perform real-time analysis of network traffic data. The model is able to identify abnormal traffic patterns that are significantly different from normal behavior. For example, by analyzing parameters such as source ports, destination ports, packet sizes, and transmission protocols in network traffic, traffic that does not conform to normal communication patterns is detected, thereby identifying potential network intrusion behaviors. When the system detects abnormal traffic, it automatically triggers an alarm and provides detailed information on the abnormal data, such as the time of occurrence, duration, and scope of impact, as well as possible cause analysis, to help operation and maintenance personnel respond and handle quickly. In terms of fault prediction, the module collects and analyzes the status data of power grid equipment, such as temperature, vibration, current, and voltage, and uses advanced data analysis techniques such as trend analysis and pattern recognition to predict possible equipment failures. For example, by monitoring the dissolved gas analysis (DGA) data in transformer oil, it is possible to predict faults inside the transformer, so that measures can be taken in advance to avoid the occurrence of faults. The main fault gases in DGA include hydrogen, methane, ethane, ethylene, acetylene, carbon monoxide and carbon dioxide. The safety monitoring module also has data storage and analysis functions, which can store real-time monitoring data and abnormal event records in the database for subsequent data analysis and auditing. Through these functions, the safety monitoring module provides comprehensive and real-time safety monitoring for the smart grid, ensures the effective implementation of the dispatch plan, and promptly discovers and handles potential risks and problems, thereby ensuring the stable operation of the power grid.

[0048] The user interaction module has carefully designed an intuitive and responsive user interface that allows users to easily view the operating status and dispatch results of the power grid in real time. The interface is displayed through a configurable dashboard, where users can customize the view, such as choosing to display the real-time load curve of the power grid, accurate to the data point updated every 15 minutes, or view the real-time output power of the solar photovoltaic panel, which has a measurement range from 0kW to 5kW, and the current charge and discharge status of the energy storage system. The dashboard also supports user-defined alarm thresholds, such as setting the voltage alarm limit to ±5% of the nominal voltage value, that is, when the nominal voltage is 220V, the alarm threshold range is 207V to 233V. The user interaction module supports multi-language interfaces, including but not limited to English, Chinese, Spanish, etc. Users can choose the interface language according to their personal language preferences. For example, when the interface language is selected as Chinese, all prompts, warnings and help documents on the interface are displayed in simplified Chinese. The user feedback function allows users to submit feedback through a simple form. The form can include user observations on the operating status of the power grid, suggestions for improvement of system functions or any technical issues. The system administrator will respond to user feedback within 24 hours. Through these functions, the user interaction module provides users with a comprehensive, real-time view of the grid operation status and dispatch results, while allowing users to make personalized settings and feedback, greatly improving user participation and satisfaction, and providing strong user support for the optimized dispatch and operation of smart grids.

[0049] The communication management module is a key component responsible for managing and maintaining the communication connections inside and outside the smart grid optimization dispatching system. The module ensures the real-time transmission of data and the accurate transmission of instructions. Its core functions include protocol conversion and data encryption to ensure the reliability and security of communication. In terms of protocol conversion, the communication management module supports a variety of industrial communication protocols, such as Modbus, DNP3, OPC UA, etc., and can seamlessly connect equipment and systems from different manufacturers. The module can convert data from one protocol to another protocol according to the preset protocol rules to ensure data compatibility and interoperability between different systems. For example, the module can convert Modbus data from smart sensors into DNP3 format to adapt to the requirements of the power grid control system. In terms of data encryption, the module uses encryption algorithms such as AES-256 or RSA to encrypt the transmitted data, and combines SSL / TLS security protocols to provide end-to-end protection for data transmission, prevent data from being intercepted or tampered with during transmission, and ensure the security of communication. In addition, the communication management module also has network status monitoring and fault diagnosis functions, which can monitor the network connection status in real time, and promptly detect and respond to network anomalies such as packet loss, delay or connection interruption. The module can also automatically reconnect or switch to a backup network to ensure communication continuity and stable system operation.

[0050] The data storage module plays the role of a data warehouse in the smart grid optimization dispatching system, responsible for the storage of collected data, processing results and historical records. The module adopts a distributed storage architecture to store data in multiple physical locations to improve data reliability and access speed. The module supports real-time data writing and query services to ensure data integrity and consistency. In terms of data backup, the data storage module provides a regular backup function to copy key data to different storage media or locations, such as the cloud or external storage devices, and uses RAID technology to distribute and copy data between multiple hard disks to enhance data reliability and fault tolerance. In addition, the data storage module also has a data recovery function, which can use data recovery software or professional services to try to recover lost data when data is lost or damaged. The module also supports data encryption, using database encryption technology to protect stored data and ensure data security and privacy. Through these functions, the data storage module provides comprehensive and real-time data storage and management for the smart grid, ensuring data integrity and traceability.

[0051] The report generation module is a key component of the smart grid optimization dispatch system that is responsible for automatically generating various reports and analysis reports based on user needs and system operation. The module integrates advanced data analysis and visualization technologies to provide customized and scheduled reporting functions to support decision making and performance evaluation. The customized reporting function allows users to select different data views, charts and analysis indicators to generate personalized reports based on specific needs. The module supports the creation and editing of report templates, and users can design their own report templates to quickly generate the required reports. The scheduled reporting function automatically generates and sends reports based on preset time intervals, such as daily, weekly or monthly, providing a regular overview of the grid operation status. The report generation module also supports multiple output formats, such as PDF, Excel or HTML, which makes it easy for users to view, share and archive reports. The module also provides different levels of report access and customization capabilities based on user permissions and roles to ensure the security and suitability of information. Through these functions, the report generation module provides users with a comprehensive and real-time view of the grid operation status and dispatch results, while allowing users to make personalized settings and feedback, greatly improving user participation and satisfaction, and providing strong decision support for the optimized dispatch and operation of the smart grid.

[0052] The specific steps are as follows:

[0053] S1: Data collection and preprocessing. In the distributed energy management system for smart grids, the first step is data collection and preprocessing. The system collects power data of the power grid in real time through high-precision intelligent sensors, such as voltage (100V to 380V), current (0.1A to 100A), power factor (0.8 to 1.0), frequency (48Hz to 52Hz), and environmental data such as temperature (-40℃ to 85°C), humidity (0% to 100%RH), and light intensity (0 to 100,000lux). The collected data is cleaned in the data preprocessing module to remove invalid data and duplicate data, and noise is removed through filtering and smoothing. Finally, data normalization and unit unification are performed to ensure data consistency and availability.

[0054] S2: Feature extraction and pattern recognition,After data preprocessing, the system enters the feature extraction and pattern recognition step.,Statistical analysis methods are used to calculate basic statistics of the data, such as,mean, standard deviation, range, skewness and kurtosis, and machine learning algorithms, such as principal component analysis (PCA) and cluster analysis, are applied to extract key features.,Time series analysis techniques, such as the ARIMA model, are used to identify the time-dependent pattern of power grid load and predict the load changes every 15 minutes in the next 24 hours, thus providing a scientific basis for short-term and long-term forecasts of the power grid.

[0055] S3: Data analysis and optimized scheduling,The third step is data analysis and optimized scheduling. The system uses association rule mining technology, such as the Apriori algorithm, and sets the minimum support threshold to 5% to identify frequent item sets and association rules in the data. Causal analysis determines the causal effects between variables through regression analysis, such as the relationship between drinking and increased risk of heart disease. Based on the data analysis results, the system formulates an optimized scheduling plan for distributed energy, using a mixed integer linear programming (MILP) algorithm, taking into account the economy and reliability of grid operation, and achieving optimal allocation of resources.

[0056] S4: Predictive control and command generation. After generating the optimized dispatching plan, the system executes the predictive control and command generation steps. According to the real-time operation status and forecast data of the power grid, the dispatching plan is dynamically adjusted. The model predictive control (MPC) technology is used to predict the operation status of the power grid in the future and optimize the dispatching plan accordingly. The control execution module converts the optimized dispatching plan into specific control instructions, such as adjusting the blade angle of the wind turbine or the output power of the solar photovoltaic panel, and sends it to the target device through the communication network to ensure the correct execution of the control instructions.

[0057] S5: Status monitoring and safety monitoring,The fifth step is status monitoring and safety monitoring. The system monitors the operating status of the power grid in real time, such as voltage and current, to ensure that it is within the safe range. It monitors whether the voltage of the power grid is between 100V and 380V and whether the current is between 0.1A and 100A through intelligent sensors. At the same time, the system monitors the safety status of the power grid, such as identifying abnormal network behavior through intrusion detection systems, or warning of potential equipment failures through fault prediction models to ensure the safety of the power grid.

[0058] S6: User Interaction and Communication Management,The sixth step is user interaction and communication management.,The system provides a user interface that allows users to view the,operation status and dispatch results of the power grid in real time,,such as displaying the real-time load curve of the power grid through the,dashboard, accurate to the data point updated every 15 minutes.,The communication management module is responsible for managing and maintaining,the communication connections inside and outside the system, ensuring the real-time transmission of,data and accurate communication of instructions, including protocol conversion and data,encryption, such as the use of AES-256 encryption algorithm, to ensure the reliability and,security of communication.

[0059] S7: Data storage and report generation,The last step is data storage and report generation.,The system stores the collected data, processing results and historical records in the,database for subsequent analysis and auditing, such as storing the data in a,distributed file system to ensure data integrity and traceability.,The report generation module automatically generates various reports and,analysis reports based on user needs and system operation,such as daily, weekly or monthly regular reports, and,custom reports generated based on specific parameters set by the user,to provide decision support and performance evaluation.

[0060] Through the collaborative work of the above modules, the present invention realizes the comprehensive management and optimized scheduling of distributed energy in the smart grid, improves the operating efficiency and reliability of the grid, reduces operating costs, and provides users with a better service experience.

[0061] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A distributed energy management system for a smart grid, characterized in that: include: The data acquisition module is used to collect power data, environmental data, policy data of the power grid, user-side load data and the operating status of distributed energy in real time. The power data includes voltage, current, power factor and frequency; the environmental data includes temperature, humidity and light intensity; the policy data includes electricity price information and renewable energy policy; The data preprocessing module is used to clean, denoise and format the collected data to improve data quality, where cleaning includes removing invalid data and duplicate data, denoising includes filtering and smoothing, and formatting includes data normalization and unit unification; Feature extraction module, used to extract key features from preprocessed data to provide support for subsequent data analysis and decision-making, where feature extraction includes statistical feature extraction and machine learning feature extraction; pattern recognition module, used to identify patterns and trends in data to predict the operating status and load changes of the power grid, where pattern recognition includes time series analysis and cluster analysis; data analysis module, used to conduct in-depth analysis of the extracted features and identified patterns to provide decision support, where data analysis includes association rule mining and causal relationship analysis; optimization scheduling module, used to formulate optimized scheduling plans for distributed energy based on data analysis results and power grid operation strategies, where optimized scheduling includes load forecasting and power generation forecasting; predictive control module, used to dynamically adjust the optimized scheduling plan according to the real-time operating status and predicted data of the power grid, where predictive control includes adaptive control and robust control; control execution module, used to convert the optimized scheduling plan into specific control instructions and send them to the corresponding distributed power generation resources and energy storage systems, where control execution includes instruction generation and execution feedback; status The monitoring module is used to monitor the operation status of the power grid and the execution of distributed energy in real time to ensure the effective implementation of the scheduling plan, where the status monitoring includes real-time data collection and anomaly detection; the security monitoring module is used to monitor the security status of the power grid, promptly discover and respond to potential security threats, where security monitoring includes intrusion detection and fault prediction; the user interaction module is used to provide a user interface to allow users to view the operation status of the power grid and the scheduling results in real time, as well as to make user settings and feedback, where user interaction includes personalized interface design and multi-language support; the communication management module is used to manage and maintain the communication connections inside and outside the system to ensure the real-time transmission of data and the accurate communication of instructions, where communication management includes protocol conversion and data encryption; the data storage module is used to store the collected data, processing results and historical records for subsequent analysis and auditing, where data storage includes distributed storage and data backup; the report generation module is used to automatically generate various reports and analysis reports according to user needs and system operation status, where report generation includes custom reports and regular reports.

2. The system according to claim 1, characterized in that The data acquisition module includes intelligent sensors and a communication network, which are used to achieve comprehensive monitoring of the power grid status, wherein the intelligent sensors include voltage sensors, current sensors and temperature sensors, and the communication network includes wireless communication and wired communication.

3. The system according to claim 1, characterized in that The data preprocessing module includes data synchronization, outlier detection and data conversion units to ensure data consistency and availability, wherein data synchronization includes timestamp alignment and data merging, outlier detection includes statistical detection and machine learning detection, and data conversion includes data type conversion and data format conversion.

4. The system according to claim 1, characterized in that The feature extraction module utilizes advanced algorithms, such as principal component analysis or cluster analysis, to extract key information from the data, wherein principal component analysis is used for dimensionality reduction and cluster analysis is used for classification.

5. The system according to claim 1, characterized in that The pattern recognition module uses machine learning techniques, such as neural networks or support vector machines, to recognize complex patterns and trends, where neural networks are used for nonlinear pattern recognition and support vector machines are used for linear pattern recognition.

6. The system according to claim 1, characterized in that The data analysis module includes statistical analysis and association rule mining to deeply understand the relationship between data, wherein the statistical analysis includes descriptive statistics and inferential statistics, and the association rule mining is used to discover the relationship between data items.

7. The system according to claim 1, characterized in that The optimization scheduling module adopts mixed integer linear programming or genetic algorithm to achieve optimal allocation of resources, wherein mixed integer linear programming is used for deterministic optimization problems and genetic algorithm is used for uncertain optimization problems.

8. The system according to claim 1, characterized in that The predictive control module uses time series analysis or reinforcement learning to predict the future state of the power grid, wherein time series analysis is used for trend prediction and reinforcement learning is used for decision optimization.

9. The system according to claim 1, characterized in that The control execution module includes instruction verification and feedback adjustment mechanisms to ensure the correct execution of control instructions, wherein instruction verification includes logic verification and safety verification, and the feedback adjustment mechanism is used to adjust the control strategy according to the execution result.

10. The system according to claim 1, characterized in that The user interaction module provides a personalized interface and customized reporting functions to meet the needs of different users, wherein the personalized interface includes user authority management and interface customization, and the customized report includes data screening and report template customization.

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