Intelligent power system carbon footprint real-time monitoring and optimizing tool based on Internet of Things

Abstract

The invention introduces an intelligent electric power system carbon footprint monitoring tool based on the Internet of Things technology, and the tool collects the operation data of an electric power system in real time through deploying sensors at key nodes of the electric power system. Advanced data processing algorithms including machine learning and big data analysis are utilized, and the tool can analyze and predict the carbon emission mode of the power system. The system also integrates equipment health management and optimization scheduling technologies so as to prevent faults and improve energy efficiency. The user interface is visual in design, supports customized services, and allows a user to adjust monitoring parameters according to requirements. The tool aims at providing a real-time and accurate carbon footprint monitoring and optimization solution for an electric power system so as to promote green transformation and sustainable development of the electric power industry.

Description

Technical Field

[0001] The present invention relates to the technical field of power system and environment monitoring, and in particular to an intelligent tool integrating Internet of Things technology. Background Art

[0002] With the acceleration of industrialization, global energy consumption has increased dramatically, especially in the power industry, whose carbon emissions account for a considerable part of the world's total emissions. The carbon footprint of the power system mainly comes from the combustion of fossil fuels such as coal, oil and natural gas, which produce a large amount of carbon dioxide and other greenhouse gases during the power generation process, exacerbating the problem of global climate change.

[0003] Existing methods for monitoring the carbon footprint of power systems usually rely on regular manual statistics and reports, which are not only inefficient but also lack real-time and accuracy. In addition, due to the lack of effective monitoring and optimization tools, power system operators often find it difficult to adjust power generation strategies in a timely manner to reduce carbon emissions. Therefore, it is particularly important to develop a tool that can monitor and optimize the carbon footprint of power systems in real time.

[0004] In recent years, the development of Internet of Things technology has provided new possibilities for the intelligentization of power systems. By deploying a large number of sensors in the power system, the Internet of Things technology can collect various data on the operation of the power system in real time and transmit them to the central processing system through wireless networks. However, despite the increasing application of Internet of Things technology in power systems, how to effectively use this data for carbon footprint monitoring and optimization remains a technical challenge that needs to be solved.

[0005] It is in this context that the present invention is proposed, and aims to utilize Internet of Things technology, combined with advanced data processing and analysis algorithms, to develop an intelligent tool to achieve real-time monitoring and optimal management of the carbon footprint of the power system, thereby providing technical support for the sustainable development of the power industry. Summary of the invention

[0006] The purpose of the present invention is to provide a real-time monitoring and optimization tool for carbon footprint of intelligent power system based on Internet of Things, which can accurately monitor the carbon emissions of power system and provide effective optimization strategies to reduce carbon emissions and improve the operating efficiency of power system.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] The IoT sensor module is a key component of the present invention. It consists of a series of high-precision sensors, which are specially designed to monitor the operating status of the power system in real time. These sensors include voltage sensors, current sensors, power sensors and frequency sensors, each of which accurately measures a specific parameter in the power system.

[0009] First, voltage sensors monitor the high-voltage side of the power system, with a measurement range of 0 to 1000 kilovolts (kV) and an accuracy of ±0.5%. These sensors sample at a frequency of 1000 times per second to ensure that any rapid changes in voltage are captured. Similarly, current sensors are installed on the low-voltage side of the main transformer, with a measurement range of 0 to 10,000 amperes (A), and are also sampled at a frequency of 1000 times per second to ensure real-time and accurate data. Power sensors are used to measure active and reactive power in the power system. The active power sensor has a measurement range of 0 to 1000 megawatts (MW), and the reactive power sensor has a measurement range of 0 to 1000 megavars (Mvar), both with an accuracy of ±0.5%, and a sampling frequency of 1000 times per second. These sensors are critical to understanding the energy efficiency of the power system. The frequency stability of the power grid is monitored by frequency sensors, which have a measurement range of 48 to 52 hertz (Hz), an accuracy of ±0.01Hz, and a sampling frequency of 2000 times per second. This ensures that we can track small changes in grid frequency in real time, which is critical to maintaining the stability of the power system. These sensors are strategically deployed at key nodes of the power system, including power stations, substations, and transmission lines. At power stations, sensors are installed at the output of generators to monitor power generation and power quality. At substations, sensors are installed at the input and output of transformers to monitor changes in voltage and current to ensure the stability of power transmission. At key nodes of transmission lines, sensors monitor power loss and efficiency during long-distance transmission. To ensure that data can be transmitted to the central processing unit in real time, each sensor is equipped with a wireless communication module that supports cellular networks (such as 4G / 5G) or Wi-Fi. The sensor module is powered by a built-in lithium battery with a design life of 5 years and supports solar charging to improve the sustainability and self-sufficiency of the system. Finally, the sensor module is designed to be easy to maintain, and all sensors support remote firmware upgrades and self-diagnosis functions to ensure long-term stable operation. This highly integrated and precise IoT sensor module provides the present invention with comprehensive monitoring of the operating status of the power system and provides accurate data support for subsequent data analysis and optimized scheduling.

[0010] The data acquisition and transmission module is a crucial component of the present invention. It is responsible for efficiently collecting data from the IoT sensor module and transmitting the data to the central processing unit through a wireless or wired network. The following is a specific implementation description of the module: First, the data acquisition module receives the analog signals from the IoT sensor module in real time through a high-precision analog-to-digital converter (ADC) and converts them into digital signals. These digital signals are accurate records of the real-time operating parameters of the power system, including voltage, current, power, and frequency. The sampling rate of the ADC matches the sampling frequency of the sensor to ensure the integrity and accuracy of the data.

[0011] Next, in order to ensure the quality of the transmitted data, the acquisition and transmission module will perform necessary preprocessing steps. This includes signal amplification, filtering and normalization to improve data quality, reduce noise and interference, and ensure that the transmitted data truly reflects the operating status of the power system. In terms of wireless communication, the module has built-in advanced wireless communication technology and supports a variety of wireless transmission protocols, such as cellular networks (4G / 5G), Wi-Fi, LoRa or NB-IoT. The most suitable communication protocol can be selected according to the site environment and data transmission distance. The wireless communication module is designed with a high-gain antenna to enhance the signal transmission capability and ensure the stability and reliability of data transmission. In order to ensure foolproof data transmission, the module also provides wired network transmission as a backup option. In areas where wireless signals are unstable or cannot be covered, data transmission can be carried out through wired networks such as Ethernet to ensure data link redundancy and high availability of the system. In terms of data security, the module uses end-to-end data encryption technology, such as TLS / SSL protocol, to ensure the security and privacy of data during transmission. In addition, data integrity check mechanisms, such as CRC or MD5 check, are implemented to detect and correct errors that may occur during transmission. Finally, in order to adapt to different network conditions and data volumes, the module also has data transmission optimization functions. It can dynamically adjust the data transmission rate according to network bandwidth and latency, use data compression technology to reduce the amount of transmitted data, and ensure efficient data transmission through queue management and flow control strategies.

[0012] The central processing unit is the brain of the present invention. It is composed of multiple submodules, each of which performs its own duties and jointly completes the data processing and analysis tasks. The following is a specific implementation description of the central processing unit and its submodules:

[0013] First, the data storage submodule is the foundation of the central processing unit. It uses a high-performance solid-state drive (SSD) to store real-time data received from the data acquisition and transmission module. The data is saved in a structured format for subsequent analysis. The storage system is carefully designed with fault tolerance to ensure data integrity and reliability, and the capacity is large enough to store at least one year of data to meet the needs of long-term trend analysis. Next, the data analysis submodule comes into play, which uses machine learning algorithms to perform intelligent analysis on the stored data. Through algorithms such as random forests, support vector machines (SVMs), and neural networks, this submodule is able to process complex data sets and identify power system operation patterns and carbon emission trends. These analysis results are crucial to understanding the carbon footprint of the power system. In addition, the data prediction submodule uses big data analysis technology to predict future carbon emissions. It is able to process large-scale data sets and apply time series analysis and forecasting models to predict carbon emission trends in the short term. These forecasts provide data-driven decision support for power system operators to help them formulate emission reduction strategies to cope with possible high-emission periods. Finally, the equipment condition monitoring submodule is responsible for real-time monitoring of key performance indicators (KPIs) of power system equipment, such as temperature, vibration, and pressure. By analyzing the changes in these indicators, the submodule can predict possible equipment failures and perform maintenance in advance. This preventive maintenance strategy helps reduce additional carbon emissions caused by equipment failures.

[0014] The user interface module is a key component of the present invention, which provides an intuitive and easy-to-operate platform for power system operators. The design of this module focuses on user experience, ensuring clear display of information and convenient operation. First, the interface displays monitoring results in real time through a graphical user interface (GUI), including key parameters of the power system and carbon emission data. It presents indicators such as voltage, current, power, frequency, as well as historical trends and real-time data of carbon emissions in the form of a dashboard. This information is intuitively displayed through charts and graphs, allowing operators to quickly understand the status of the system. Secondly, the user interface module provides the function of customized views, allowing users to choose to display data from specific sensors or filter data for a specific time range as needed. In addition, users can also set alarm thresholds. When carbon emissions exceed these values, the system will notify users through visual and auditory prompts, so that timely action can be taken. In addition, in order to meet users' needs for data reporting, the module includes a report generation tool. Users can choose different report templates and customize the time range and format of the report, such as PDF or Excel. Detailed data analysis, charts and graphs can be included in the report to support decision making. Users can also easily adjust monitoring parameters such as data collection frequency and parameters of data analysis algorithms. This flexibility enables the system to adapt to different monitoring needs and environmental conditions, improving the system's adaptability and user convenience. Finally, to help users better use the system, the interface module has built-in help documents and online support functions. Users can learn how to operate the interface through the help documents, or contact the technical support team through the online support function to get professional help when encountering problems.

[0015] The optimization scheduling algorithm module is a key part of the present invention for improving the operating efficiency of the power system and reducing carbon emissions. This module handles the multi-objective optimization problems of the power system through advanced optimization algorithms, such as linear programming, integer programming or multi-objective particle swarm optimization (MOPSO). These problems include improving energy utilization efficiency, reducing operating costs, reducing emissions, etc., aiming to find a balanced solution between different goals, that is, the Pareto optimal solution set. The resource allocation optimization submodule is responsible for automatically generating optimization schemes based on data analysis and prediction results. It considers the real-time demand of the power grid and the availability of power generation resources, including wind, solar, hydropower and thermal power, and calculates the optimal power generation plan for each energy source. In addition, the use of energy storage devices, such as battery energy storage or pumped storage, is also considered to balance supply and demand and improve the flexibility of the system. The parameters and objective functions of the algorithm include environmental protection costs, such as the calculation of carbon emissions, and economic costs, such as the price of purchasing and selling electricity. The optimization strategy calculates the amount of electricity that needs to be purchased from or sold to the main power grid within a specific time period to achieve the best balance between cost-effectiveness and environmental impact. After implementing these optimization schemes, the operating efficiency of the power system can be significantly improved, energy costs can be reduced, and the impact on the environment can be reduced. For example, by optimizing scheduling, the system can reduce its reliance on thermal power generation when renewable energy generation is high, thereby reducing carbon emissions. At the same time, by storing energy when electricity demand is low and using the stored energy during peak hours, reliance on expensive peak power can be reduced. The user interface module provides a platform that allows users to adjust optimization parameters and objective functions as needed. Users can set different optimization goals, such as prioritizing cost, renewable energy utilization, or carbon emissions, and adjust the scheduling strategy based on real-time data and forecast results. This flexibility allows the system to adapt to different operating conditions and user needs.

[0016] The data security module is a crucial component of the present invention, which is specially designed to ensure the security of data during transmission and storage. The module uses the Advanced Encryption Standard (AES) algorithm to encrypt the data, wherein the AES algorithm supports a 256-bit key length to ensure the confidentiality of the data. During data transmission, the Secure Sockets Layer (SSL) / Transport Layer Security (TLS) protocol is used to protect the integrity and confidentiality of the data to prevent the data from being tampered with or intercepted during transmission. In order to enhance the security of the data, the data security module implements role-based access control (RBAC) to ensure that only authorized users can access sensitive data. In addition, the module also includes a multi-factor authentication (MFA) system. When users log in to the system, in addition to entering their username and password, they also need to perform secondary verification through a mobile phone SMS verification code or an email link. Data storage security is also taken seriously. The data storage submodule uses encrypted storage solutions such as transparent data encryption (TDE) to ensure the security of static data. At the same time, the storage system also implements regular backup and disaster recovery plans to prevent data loss or damage. In addition, the data security module also includes security auditing and monitoring functions. The system records all user operations and conducts regular audits, which helps detect and prevent unauthorized data access or potential security threats. The system also implements an abnormal behavior detection mechanism that can monitor and alert suspicious activities in real time. Through these measures, the data security module ensures the security of power system data throughout its life cycle, from data collection, transmission, storage to final processing and use, each step is strictly protected. This not only meets the requirements of the national data security law, but also protects the sensitive data of the power system from unauthorized access and abuse.

[0017] The remote access submodule is an innovative feature of the present invention, which allows authorized users to securely access and monitor the carbon footprint data of the power system through the Internet, no matter where they are. The module uses VPN technology and SSL / TLS protocol to encrypt data transmission and ensure the security of user connection. Users are authenticated through multi-factor authentication (MFA), including username and password, and a one-time verification code sent via SMS or email to provide an additional layer of security. In addition, the remote access submodule supports 4G LTE network, which can provide up to 150Mbps downlink transmission rate and 50Mbps uplink transmission rate under ideal conditions, ensuring that users can get a smooth experience of real-time data access even in remote locations. The module is compatible with a variety of devices, including smartphones, tablets and laptops, through which users can access customized web interfaces or mobile applications. The user interface is intuitively designed and provides a real-time data monitoring dashboard, showing key parameters such as power consumption, voltage, current, etc. of the power grid, as well as historical trends and real-time data of carbon emissions. This information is presented through easy-to-understand charts and graphs, allowing operators to quickly grasp the status of the system and make timely decisions. To further enhance the user experience, the user interface module allows users to customize views according to specific needs, choose to display data from specific sensors or filter data from a specific time range. Users can also set alarm thresholds. When carbon emissions exceed the preset value, the system will automatically issue visual and auditory alarms to ensure that users can respond in time.

[0018] The method for using the tool of the present invention comprises the following specific steps:

[0019] Step S1: Deploy IoT sensor modules to key nodes of the power system.

[0020] IoT sensor modules are deployed at key nodes of the power system, such as power stations, substations, and transmission lines. These sensors include voltage, current, power, and frequency sensors, all of which have the characteristics of high precision and high sampling rate, such as the voltage sensor measurement range of 0 to 1000kV and the current sensor measurement range of 0 to 10000A.

[0021] Step S2: Collect data collected by the sensor through the data acquisition and transmission module.

[0022] The data acquisition and transmission module is responsible for collecting data from the IoT sensor module. The module supports multiple communication protocols, including 4G / 5G and Wi-Fi, to ensure that data can be transmitted in real time and accurately.

[0023] Step S3: Transmit the collected data to the central processing unit.

[0024] The collected data is transmitted to the central processing unit via wireless or wired network. During data transmission, the AES-256 encryption algorithm is used to ensure data security.

[0025] Step S4: storing data in the data storage submodule of the central processing unit.

[0026] The data storage submodule of the central processing unit is responsible for storing the received data. The data is stored on a high-capacity SSD, ensuring that detailed data records can be stored for at least one year.

[0027] Step S5: Analyze the stored data using the data analysis submodule.

[0028] The data analysis submodule uses machine learning algorithms, such as random forests and neural networks, to analyze the stored data to identify operating patterns and carbon emission trends of the power system.

[0029] Step S6: Use the data prediction submodule to predict future carbon emission trends.

[0030] The data forecasting submodule uses big data analysis techniques, such as time series analysis, to predict carbon emissions within the next 24 hours to a week, with a prediction accuracy of 95%.

[0031] Step S7: Monitor the operating status of the power system equipment through the equipment status monitoring submodule.

[0032] The equipment status monitoring submodule monitors key performance indicators of power system equipment, such as temperature and vibration, in real time to prevent possible failures.

[0033] Step S8: Based on the data analysis and prediction results, the optimization scheduling algorithm module generates an optimization plan.

[0034] The optimization scheduling algorithm module automatically generates optimization plans based on data analysis and prediction results to improve energy efficiency and reduce carbon emissions.

[0035] Step S9: Display monitoring results and optimization suggestions in the user interaction interface module.

[0036] The user interface module displays monitoring results and optimization suggestions in the form of a graphical dashboard, providing real-time data and historical trend analysis.

[0037] Step S10: Allow the user to adjust monitoring parameters and report format through the user interaction interface module.

[0038] Users can customize monitoring parameters and report formats, such as selecting data from specific sensors or setting alarm thresholds.

[0039] Step S11: Ensure data transmission and storage security through a data security module.

[0040] The data security module uses SSL / TLS protocol and AES-256 encryption algorithm to ensure the security of data during transmission and storage.

[0041] Step S12: Using the remote access submodule, the user is allowed to view the carbon footprint of the power system at different locations.

[0042] The remote access submodule supports users to access the tool through the Internet, using VPN and two-factor authentication mechanisms to ensure access security.

[0043] Step S13: Automatically generate a carbon emission report based on user needs and monitoring results.

[0044] Based on user needs and monitoring results, the system automatically generates a detailed carbon emission report, including carbon emissions, energy consumption efficiency and optimization scheduling recommendations.

[0045] Through these steps, the tool of the present invention provides a comprehensive solution for monitoring and managing the carbon footprint of the power system while ensuring the security of the data and the efficient operation of the system.

[0046] The above technical solution can bring the following technical effects:

[0047] 1. Improve the real-time and accuracy of carbon emission monitoring: By deploying IoT sensor modules at key nodes of the power system, the voltage, current, power, frequency and other parameters of the power system operation can be collected in real time. These data are transmitted to the central processing unit in real time through the data acquisition and transmission module, and analyzed instantly by the data analysis submodule. This real-time monitoring mechanism significantly improves the speed and accuracy of carbon emission data acquisition, allowing power system operators to understand the carbon emission status in a timely manner, thereby managing carbon emissions more effectively.

[0048] 2. Optimize the dispatch of power resources and reduce carbon emissions: The optimization dispatch algorithm module of the present invention can automatically generate optimization plans based on the real-time data and prediction results provided by the data analysis submodule and the data prediction submodule. These plans may include measures such as adjusting the distribution of power generation, optimizing grid operation parameters, and improving energy utilization efficiency. By implementing these optimization plans, it is possible to reduce dependence on high-carbon emission energy and increase the proportion of clean energy use, thereby reducing the carbon emissions of the entire power system.

[0049] 3. Enhance the reliability of power system equipment and reduce unplanned downtime: The equipment status monitoring submodule can monitor the operating status of power grid equipment in real time and predict potential failures and performance degradation by analyzing equipment data. In this way, power system operators can carry out maintenance and repairs in advance to avoid unplanned downtime and additional carbon emissions caused by equipment failure. In addition, through preventive maintenance, the service life of equipment can be extended and the waste and carbon emissions generated by equipment replacement can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a module block diagram of the carbon footprint real-time monitoring and optimization tool of the present invention.

[0051] Figure 2 A diagram showing the method of using the tool of the present invention DETAILED DESCRIPTION

[0052] 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.

[0053] The purpose of the present invention is to provide a real-time monitoring and optimization tool for carbon footprint of intelligent power system based on Internet of Things, which can accurately monitor the carbon emissions of power system and provide effective optimization strategies to reduce carbon emissions and improve the operating efficiency of power system.

[0054] In order to achieve the above object, the present invention adopts the following technical solutions:

[0055] The IoT sensor module is a key component of the present invention. It consists of multiple professional sensors and is responsible for real-time collection of operating parameters of the power system. The module includes voltage sensors, current sensors, power sensors and frequency sensors, which monitor the voltage, current, power and frequency of the power system respectively. The design parameters of these sensors ensure high-precision and high-frequency real-time data acquisition. The voltage sensor is responsible for monitoring the high-voltage side of the power system, with a measurement range of 0 to 1000 kilovolts (kV) and an accuracy of ±0.5%. They sample at a frequency of 1000 times per second to ensure that rapid changes in voltage can be captured. The current sensor is installed on the low-voltage side of the main transformer, with a measurement range of 0 to 10000 amperes (A), and is also sampled at a frequency of 1000 times per second to ensure the real-time and accuracy of current data. The power sensor is used to measure active power and reactive power in the power system. The measurement range of the active power sensor is 0 to 1000 megawatts (MW), and the measurement range of the reactive power sensor is 0 to 1000 megavars (Mvar). Both have an accuracy of ±0.5% and a sampling frequency of 1000 times per second. The frequency sensors monitor the frequency stability of the power grid, with a measurement range of 48 to 52 Hertz (Hz), an accuracy of ±0.01Hz, and a sampling frequency of 2000 times per second. These sensors are strategically deployed at key nodes of the power system, including the generator output of the power station, the transformer input and output of the substation, and key nodes of the transmission line. Each sensor is equipped with a wireless communication module that supports cellular networks (such as 4G / 5G) or Wi-Fi to ensure that data can be transmitted to the central processing unit in real time. In terms of power supply, the sensor module is powered by a built-in lithium battery with a design life of 5 years and supports solar charging, which improves the sustainability and self-sufficiency of the system. In order to ensure long-term stable operation, the sensor module is designed to be easy to maintain, and all sensors support remote firmware upgrades and self-diagnosis functions. This highly integrated and accurate IoT sensor module provides the present invention with comprehensive monitoring of the operating status of the power system, and provides accurate data support for subsequent data analysis and optimized scheduling. Through the real-time monitoring of these sensors, power system operators can understand the carbon emission status in a timely manner, so as to manage carbon emissions more effectively.

[0056] The data acquisition and transmission module is a crucial component of the present invention. It is responsible for efficiently collecting data from the IoT sensor module and transmitting the data to the central processing unit through a wireless or wired network. The following is a specific implementation description of the module: First, the data acquisition module receives the analog signal from the IoT sensor module in real time through a high-precision analog-to-digital converter (ADC) and converts it into a digital signal. These digital signals are accurate records of the real-time operating parameters of the power system, including voltage, current, power, and frequency. The sampling rate of the ADC matches the sampling frequency of the sensor to ensure the integrity and accuracy of the data. Then, in order to ensure the quality of the transmitted data, the acquisition and transmission module will perform necessary preprocessing steps. This includes signal amplification, filtering, and normalization to improve data quality, reduce noise and interference, and ensure that the transmitted data truly reflects the operating status of the power system. In terms of wireless communication, the module has built-in advanced wireless communication technology and supports a variety of wireless transmission protocols, such as cellular networks (4G / 5G), Wi-Fi, LoRa, or NB-IoT. Depending on the site environment and data transmission distance, the most suitable communication protocol can be selected. The wireless communication module is designed with a high-gain antenna to enhance the transmission capability of the signal and ensure the stability and reliability of data transmission. In order to ensure foolproof data transmission, the module also provides wired network transmission as a backup option. In areas where wireless signals are unstable or cannot be covered, data transmission can be carried out through wired networks such as Ethernet to ensure data link redundancy and high availability of the system. In terms of data security, the module uses end-to-end data encryption technology, such as TLS / SSL protocol, to ensure the security and privacy of data during transmission. In addition, data integrity check mechanisms, such as CRC or MD5 check, are implemented to detect and correct errors that may occur during transmission. Finally, in order to adapt to different network conditions and data volumes, the module also has data transmission optimization functions. It can dynamically adjust the data transmission rate according to network bandwidth and delay conditions, use data compression technology to reduce the amount of transmitted data, and ensure efficient data transmission through queue management and flow control strategies. Through these carefully designed measures, the data acquisition and transmission module not only ensures real-time data acquisition and accurate transmission, but also provides data security and transmission optimization, providing reliable data input for the central processing unit, thereby providing a solid data foundation for carbon footprint monitoring and optimization of the entire power system.

[0057] The central processing unit is the heart of the invention, which processes and analyzes the large amount of data collected from the IoT sensor modules through carefully designed submodules. The data storage submodule uses a high-performance solid-state drive (SSD) that can store data for at least one year. The data is saved in a structured format to ensure the need for long-term trend analysis. The storage system has fault tolerance to ensure the integrity and reliability of the data. The data analysis submodule is the intelligent core of the central processing unit, which uses machine learning algorithms to conduct in-depth analysis of the stored data. These algorithms, including random forests, support vector machines (SVMs) and neural networks, can process complex data sets and identify patterns in them. The data analysis submodule has very powerful computing power and can analyze large-scale data sets in real time, ensuring that high concurrent processing requirements are met. The data prediction submodule uses big data analysis technology to predict future carbon emissions. It is capable of processing large-scale data and applying time series analysis and prediction models to predict carbon emission trends in the short term. The prediction accuracy target is set at 95%, ensuring the accuracy of decision-making. This submodule provides power system operators with data-driven emission reduction strategies to help them cope with possible high-emission periods. The equipment status monitoring submodule is another key part of the central processing unit. It is responsible for real-time monitoring of the key performance indicators (KPIs) of power grid equipment. By analyzing changes in indicators such as temperature, vibration and pressure of the equipment, this submodule can predict possible equipment failures and perform maintenance in advance. The monitoring frequency is set to at least 2,000 times per second, ensuring that small changes in equipment status can be captured in real time.

[0058] In summary, the central processing unit provides a comprehensive carbon footprint view for the power system through the collaborative work of these sub-modules, and provides real-time decision support for operators. This not only improves the operating efficiency of the power system, but also helps to achieve more environmentally friendly energy management.

[0059] The user interface module is a key component of the present invention, which provides an intuitive and easy-to-operate platform for power system operators. The design of this module focuses on user experience, ensuring clear display of information and convenient operation. First, the interface displays monitoring results in real time through a graphical user interface (GUI), including key parameters of the power system and carbon emission data. It presents indicators such as voltage, current, power, frequency, as well as historical trends and real-time data of carbon emissions in the form of a dashboard. This information is intuitively displayed through charts and graphs, allowing operators to quickly understand the status of the system. Secondly, the user interface module provides the function of customized views, allowing users to choose to display data from specific sensors or filter data for a specific time range as needed. In addition, users can also set alarm thresholds. When carbon emissions exceed these values, the system will notify users through visual and auditory prompts, so that timely action can be taken. In addition, in order to meet users' needs for data reporting, the module includes a report generation tool. Users can choose different report templates and customize the time range and format of the report, such as PDF or Excel. Detailed data analysis, charts and graphs can be included in the report to support decision making. Users can also easily adjust monitoring parameters such as data collection frequency and parameters of data analysis algorithms. This flexibility enables the system to adapt to different monitoring needs and environmental conditions, improving the adaptability of the system and the user's convenience of operation. Finally, in order to help users better use the system, the interface module has built-in help documents and online support functions. Users can learn how to operate the interface through the help documents, or contact the technical support team through the online support function to get professional help when encountering problems. In general, the user interaction interface module is a crucial component of the present invention. It enables power system operators to easily monitor and manage the carbon footprint of the power system by providing an intuitive and customizable interface. This user-friendly design improves the system's usability and user satisfaction.

[0060] The optimization scheduling algorithm module is the core of the present invention. It improves the operating efficiency of the power system and reduces carbon emissions by integrating efficient optimization algorithms. This module adopts the multi-objective particle swarm optimization (MOPSO) algorithm, which is a heuristic algorithm that can find the optimal trade-off solution between multiple objectives. For example, the algorithm may consider minimizing operating costs and maximizing renewable energy utilization at the same time, where the operating cost target may involve parameters such as the purchase price of electricity of 0.48 yuan / kWh and the sales price of electricity of 0.25 yuan / kWh. The resource allocation optimization submodule automatically generates a scheduling plan based on the real-time demand of the power system and the availability of power generation resources. It may consider the maximum power output of wind power generation to be 100MW, the maximum power output of solar power generation to be 50MW, and the stable output of hydropower generation to be 200MW. Through these parameters, the submodule can calculate the optimal power generation plan for each energy source within a specific time period. The parameters and objective functions of the algorithm also include environmental protection costs, such as the calculation of carbon emissions. For example, the carbon emissions of thermal power generation may be set to 1 ton / MWh, while the carbon emissions of wind and solar energy are 0. The optimization strategy calculates the amount of electricity that needs to be purchased from or sold to the main grid during a specific time period to achieve the best balance between cost-effectiveness and environmental impact. After implementing these optimization schemes, the operating efficiency of the power system can be significantly improved. For example, by optimizing scheduling, the system may reduce its reliance on thermal power generation during periods of high wind and solar power generation, thereby reducing carbon emissions from 500 tons per day to 200 tons. At the same time, by storing energy when electricity demand is low and using the stored energy during peak hours, reliance on expensive peak power can be reduced, such as setting the charging time of the battery energy storage system to the period when electricity prices are lower at night.

[0061] The user interface module provides an intuitive platform that allows users to adjust optimization parameters and objective functions as needed. Users can set different optimization goals, such as prioritizing cost, renewable energy utilization or carbon emissions, and adjust the dispatch strategy based on real-time data and forecast results. This flexibility allows the system to adapt to different operating conditions and user needs. Through these carefully designed measures, the optimized dispatch algorithm module not only ensures the efficient operation of the power system, but also helps to achieve more environmentally friendly energy management. For example, through optimized dispatch, the system may reduce its reliance on thermal power generation during periods of high wind and solar power generation, thereby reducing carbon emissions from 500 tons per day to 200 tons. This optimized dispatch strategy provides power system operators with a powerful tool to support sustainable energy production and consumption. The data security module is a crucial component of the present invention, which is specially designed to ensure the security of data during transmission and storage. The module uses industry-standard encryption technology, such as the Advanced Encryption Standard (AES) algorithm, to encrypt data. The AES algorithm supports a 256-bit key length to ensure the confidentiality of data. During data transmission, the Secure Sockets Layer (SSL) / Transport Layer Security (TLS) protocol is used to protect the integrity and confidentiality of data and prevent data from being tampered with or intercepted during transmission. To enhance data security, the data security module implements role-based access control (RBAC) to ensure that only authorized users can access sensitive data. In addition, the module also includes a multi-factor authentication (MFA) system. When users log in to the system, in addition to entering their username and password, they also need to perform secondary verification through a mobile phone SMS verification code or an email link to ensure the legitimacy of the login request. Data storage security is also taken seriously. The data storage submodule uses encrypted storage solutions such as transparent data encryption (TDE), such as AES_256_CBC or SM4_CTR algorithms, to ensure the security of static data. At the same time, the storage system also implements regular backup and disaster recovery plans to prevent data loss or damage. Automation and regularization of the backup process, as well as regular testing of the recovery process of backup data, are important components of ensuring data backup and recovery strategies. In addition, the data security module also includes security auditing and monitoring functions. The system records all user operations and conducts regular audits, which helps detect and prevent unauthorized data access or potential security threats. The system also implements an abnormal behavior detection mechanism that can monitor and alert suspicious activities in real time, such as using the KNN non-parametric kernel density estimation algorithm combined with the C3D model to detect abnormal human behavior. Through these measures, the data security module ensures the security of power system data throughout its life cycle, from data collection, transmission, storage to final processing and use, each step is strictly protected. This not only meets the requirements of the National Data Security Law, but also protects the sensitive data of the power system from unauthorized access and abuse.

[0062] The remote access submodule is an innovative feature of the present invention, which allows authorized users to securely access and monitor the carbon footprint data of the power system through the Internet, no matter where they are. The module uses VPN technology and SSL / TLS protocol to encrypt data transmission and ensure the security of user connections. Users are authenticated through multi-factor authentication (MFA), including username and password, and a one-time verification code sent via SMS or email to provide an additional layer of security. In addition, the remote access submodule supports 4G LTE networks, which can provide up to 150Mbps downlink transmission rate and 50Mbps uplink transmission rate under ideal conditions, ensuring that users can get a smooth experience of real-time data access even in remote locations. The module is compatible with a variety of devices, including smartphones, tablets and laptops, through which users can access customized web interfaces or mobile applications. The user interface is intuitively designed and provides a real-time data monitoring dashboard that displays key parameters such as power consumption, voltage, current, etc. of the power system, as well as historical trends and real-time data of carbon emissions. This information is presented through easy-to-understand charts and graphs, allowing operators to quickly grasp the system status and make timely decisions. To further enhance the user experience, the user interface module allows users to customize views according to specific needs, choose to display data from specific sensors or filter data from a specific time range. Users can also set alarm thresholds. When carbon emissions exceed the preset value, the system will automatically issue visual and auditory alarms to ensure that users can respond in time.

[0063] The method for using the tool of the present invention comprises the following specific steps:

[0064] Step S1: Deploy IoT sensor modules to key nodes of the power system.

[0065] IoT sensor modules are deployed at key nodes of the power system, such as power stations, substations, and transmission lines. These sensors include voltage, current, power, and frequency sensors, all of which have the characteristics of high precision and high sampling rate, such as the voltage sensor measurement range of 0 to 1000kV and the current sensor measurement range of 0 to 10000A.

[0066] Step S2: Collect data collected by the sensor through the data acquisition and transmission module.

[0067] The data acquisition and transmission module is responsible for collecting data from the IoT sensor module. The module supports multiple communication protocols, including 4G / 5G and Wi-Fi, to ensure that data can be transmitted in real time and accurately.

[0068] Step S3: Transmit the collected data to the central processing unit.

[0069] The collected data is transmitted to the central processing unit via wireless or wired network. During data transmission, the AES-256 encryption algorithm is used to ensure data security.

[0070] Step S4: storing data in the data storage submodule of the central processing unit.

[0071] The data storage submodule of the central processing unit is responsible for storing the received data. The data is stored on a high-capacity SSD, ensuring that detailed data records can be stored for at least one year.

[0072] Step S5: Analyze the stored data using the data analysis submodule.

[0073] The data analysis submodule uses machine learning algorithms, such as random forests and neural networks, to analyze the stored data to identify operating patterns and carbon emission trends of the power system.

[0074] Step S6: Use the data prediction submodule to predict future carbon emission trends.

[0075] The data forecasting submodule uses big data analysis techniques, such as time series analysis, to predict carbon emissions within the next 24 hours to a week, with a prediction accuracy of 95%.

[0076] Step S7: Monitor the operating status of the power system equipment through the equipment status monitoring submodule.

[0077] The equipment status monitoring submodule monitors key performance indicators of power grid equipment, such as temperature and vibration, in real time to prevent possible failures.

[0078] Step S8: Based on the data analysis and prediction results, the optimization scheduling algorithm module generates an optimization plan.

[0079] The optimization scheduling algorithm module automatically generates optimization plans based on data analysis and prediction results to improve energy efficiency and reduce carbon emissions.

[0080] Step S9: Display monitoring results and optimization suggestions in the user interaction interface module.

[0081] The user interface module displays monitoring results and optimization suggestions in the form of a graphical dashboard, providing real-time data and historical trend analysis.

[0082] Step S10: Allow the user to adjust monitoring parameters and report format through the user interaction interface module.

[0083] Users can customize monitoring parameters and report formats, such as selecting data from specific sensors or setting alarm thresholds.

[0084] Step S11: Ensure data transmission and storage security through a data security module.

[0085] The data security module uses SSL / TLS protocol and AES-256 encryption algorithm to ensure the security of data during transmission and storage.

[0086] Step S12: Using the remote access submodule, the user is allowed to view the carbon footprint of the power system at different locations.

[0087] The remote access submodule supports users to access the tool through the Internet, using VPN and two-factor authentication mechanisms to ensure access security.

[0088] Step S13: Automatically generate a carbon emission report based on user needs and monitoring results.

[0089] Based on user needs and monitoring results, the system automatically generates a detailed carbon emission report, including carbon emissions, energy consumption efficiency and optimization scheduling recommendations.

[0090] Through these steps, the tool of the present invention provides a comprehensive solution for monitoring and managing the carbon footprint of the power system while ensuring the security of the data and the efficient operation of the system.

[0091] 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 real-time monitoring and optimization tool for carbon footprint of intelligent power system based on Internet of Things, characterized by: include: IoT sensor module, used to collect the operating parameters of the power system in real time; A data acquisition and transmission module is used to collect the data collected by the IoT sensor module and transmit it to the central processing unit; the central processing unit, a data storage submodule is used to store the received data; A data analysis submodule, used for analyzing the data; Data prediction submodule, used to predict future carbon emissions; Equipment status monitoring submodule, used to monitor the operating status of power system equipment; User interaction interface module, used to display monitoring results and optimization suggestions, and allow users to perform operations; The optimization scheduling algorithm module is used to automatically generate optimization plans based on monitoring data and user-set goals; Data security module is used to ensure the security of data transmission and storage process.

2. The tool according to claim 1, characterized in that The IoT sensor module includes but is not limited to a voltage sensor, a current sensor, a power sensor and a frequency sensor.

3. The tool according to claim 1, characterized in that The data analysis submodule uses a machine learning algorithm to perform intelligent analysis on the collected data.

4. The tool according to claim 1, characterized in that The data prediction submodule includes big data analysis technology for identifying carbon emission patterns and trends.

5. The tool according to claim 1, characterized in that The equipment status monitoring submodule can predict and prevent possible power system equipment failures.

6. The tool according to claim 1, characterized in that The optimization scheduling algorithm module includes a resource allocation optimization submodule for optimizing the scheduling of power resources.

7. The tool according to claim 1, characterized in that The user interaction interface module supports users to adjust monitoring parameters and report formats according to needs.

8. The tool according to claim 1, characterized in that The data security module includes data encryption and access control mechanisms.

9. The tool according to claim 1, characterized in that The tool also includes a remote access submodule that allows users to view the carbon footprint of the power system from anywhere.

10. The tool according to claim 1, characterized in that The method of using the tool comprises the following steps: s1: Deploy IoT sensor modules to key nodes of the power system; s2: collects data collected by sensors through the data acquisition and transmission module; s3: transmit the collected data to the central processing unit; s4: storing data in the data storage submodule of the central processing unit; s5: Analyze the stored data using the data analysis submodule; s6: Use the data prediction submodule to predict future carbon emission trends; s7: Monitor the operating status of power system equipment through the equipment status monitoring submodule; s8: Based on the data analysis and prediction results, the optimization scheduling algorithm module generates an optimization plan; s9: Display monitoring results and optimization suggestions in the user interaction interface module; s10: Allows users to adjust monitoring parameters and report formats through the user interface module; s11: Ensure data transmission and storage security through data security module; s12: Using the remote access submodule, users can view the carbon footprint of the power system at different locations; s13: Automatically generate carbon emission reports based on user needs and monitoring results.

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