Electric energy meter cloud cooperative control method and system

Abstract

The invention discloses an electric energy meter cloud cooperative control method and system, and relates to the technical field of electric energy meters, the system collects and analyzes power utilization data and power generation data in real time, calculates power utilization energy Euse and power generation energy Egen of a factory in a specific time period, carries out difference processing to obtain power purchase energy Egrid, effectively identifies power purchase demands and optimizes space, and improves the power purchase efficiency. And the data are transmitted to a cloud server by using a wireless communication technology, and are stored, preprocessed and analyzed through a time sequence database and a machine learning algorithm, and long-term trends and short-term fluctuations of power utilization and power generation modes are identified. And comparing the electricity purchasing energy Egrid with a preset threshold value G to generate an alarm and an optimization suggestion. And calculating by combining the predicted electricity price Cgrid (t) to obtain the total electricity purchase cost Cost (t), carrying out secondary comparison evaluation on the total electricity purchase cost Cost (t) through a regulation and control threshold T, analyzing the operation condition of the system, and formulating optimization measures.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy meters, and in particular to a cloud-based collaborative control method and system for electric energy meters. Background Art

[0002] The cloud-based collaborative control system for electric energy meters belongs to the smart grid technology in the field of energy management. Smart grid is an electric power system based on modern information technology and communication technology, which realizes the intelligence of each link of power generation, transmission, transformation, distribution and consumption. Specifically, the cloud-based collaborative control system for electric energy meters is a subsystem in the smart grid. It collects electric energy data, distributed energy data and electricity price data in real time, processes and analyzes data, thereby optimizing power dispatch and electric energy utilization efficiency.

[0003] At present, traditional power management systems have problems such as untimely data collection, insufficient data processing capabilities, and lack of optimization algorithms. These deficiencies have led to waste of power resources and inefficient use of distributed energy. In addition, the storage and analysis methods of power data and power generation data are relatively simple, and it is not easy to effectively identify and utilize the patterns and laws in the data, resulting in a lack of scientific basis for the dispatch and management of distributed energy, increased waste of power resources, rising power costs, and difficulty for users to obtain timely energy-saving suggestions and optimization measures. At the same time, the lack of data analysis and optimization algorithms makes it difficult to realize the intelligent dispatch of the power system, further limiting the efficient use of distributed energy. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a cloud-based collaborative control method and system for an electric energy meter, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: including an electric energy data acquisition module, a data processing and analysis module, an optimization algorithm module, an electricity purchase cost evaluation module and a user feedback interaction module;

[0006] The electric energy data collection module is used to collect the electric energy meter's electric energy consumption data and the distributed energy generation data in real time, and upload the collected electric energy consumption data and generation data to the cloud server via a wireless network;

[0007] The data processing and analysis module is used to establish a database on the cloud server to store the historical data of the electric energy meter and distributed energy sources with the collected power consumption data and power generation data;

[0008] The optimization algorithm module is used to construct an algorithm formula, calculate and obtain the electricity consumption energy Euse and the power generation energy Egen according to the electricity consumption data and the power generation data, and perform difference calculation to obtain the power purchase energy value Egrid, and then perform comparative evaluation and analysis of the power purchase demand by using the preset power purchase evaluation threshold G;

[0009] The electricity purchase cost evaluation module is used to calculate the total electricity purchase cost Cost(t) at time t, and to perform a secondary comparison between the preset control threshold T and the obtained total electricity purchase cost Cost(t) to evaluate and analyze the electricity purchase situation of the distributed system operation and generate optimization measures;

[0010] The user feedback interaction module is used to build a user interaction platform, display real-time electricity consumption and power generation, and provide energy-saving suggestions and distributed energy use suggestions.

[0011] Preferably, the data acquisition module includes an electric equipment acquisition unit, a power generation data acquisition unit, an electricity price data acquisition unit and a data transmission unit;

[0012] The power consumption equipment collection unit is used to install an integrated power consumption sensor group inside the power meter to collect power consumption data of the power meter every 30 minutes at a fixed period. The integrated sensor group includes a current sensor and a voltage sensor. The power consumption data includes current I and voltage V.

[0013] The power generation data acquisition unit is used to install a power generation integrated sensor on the distributed power generation equipment, and collect the power generation data of the distributed power generation equipment every 30 minutes at a fixed period. The power generation integrated sensor includes an inverter, and the power generation data includes a DC current Idc, a DC voltage Vdc and an inverter efficiency N inv ;

[0014] The electricity price data acquisition unit obtains the current electricity price Cgrid in real time through the API application program interface provided by the power company, and uses the moving time series average method based on the historical electricity price Cgrid records to identify the long-term trend in the electricity price data, and combines the seasonal STL decomposition to decompose the time series into trend, seasonal and residual parts, identify the periodic electricity price change pattern, and predict the electricity price Cgrid(t) at time t;

[0015] The data transmission unit is used to package the power consumption data and power generation data collected 6 times, and transmit the real-time collected power consumption data and power generation data to the cloud server by using Zigbee wireless communication technology and Modbus technology.

[0016] Preferably, the data processing and analysis module includes a data storage unit, a data preprocessing unit and a data analysis unit;

[0017] The data storage unit constructs a time series database through InfluxDB and TimescaleDB, classifies and stores the real-time collected power consumption data and power generation data, as well as the historical power consumption data and power generation data, and sets an API application query interface to extract relevant power consumption data and power generation data in different time periods in real time;

[0018] The data preprocessing unit is used to detect abnormal values ​​of the power consumption data and power generation data in the time series database using statistical methods and machine learning algorithms, and make corrections according to actual conditions, while using linear interpolation and polynomial interpolation to fill in missing data points;

[0019] The data analysis unit is used to use a cluster analysis algorithm to analyze the patterns in the electricity consumption data and the power generation data and the correlation between the data, and at the same time use regression analysis and neural networks as a basic framework to build prediction models and classification models, perform trend analysis and periodicity analysis on historical data, and predict future factory electricity consumption, power generation and electricity price trends.

[0020] Preferably, the optimization algorithm module includes an electricity consumption energy calculation unit, a power generation energy calculation unit and an electricity purchase analysis unit;

[0021] The power consumption energy calculation unit is used to construct a power consumption energy calculation formula, and substitute the real-time collected power consumption data set into the formula to calculate and analyze the current factory power consumption Euse, and analyze the current power consumption of the factory;

[0022] The electric energy Euse is calculated by the following electric energy calculation formula:

[0023]

[0024] In the formula, t0 represents the initial time, t represents the end time, Euse(t) represents the electrical energy Euse at time t, V(t) represents the voltage V at time t, I(t) represents the current I at time t, V(t)×I(t) represents the electrical power, and dt represents the integral of the electrical power consumption time. The total electrical energy of the project within a period of time is calculated by integrating the electrical power consumption with respect to time.

[0025] Preferably, the power generation energy calculation unit is used to construct a power generation energy calculation formula, and substitute the power generation data collected in real time into the formula to calculate and obtain the power generation energy Egen of the distributed power generation equipment;

[0026] The power generation energy Egen is obtained by the following power generation energy calculation formula:

[0027]

[0028] Where, Egen(t) represents the generated energy Egen at time t, Vdc(t) represents the DC voltage Vdc at time t, Idc(t) represents the DC current Idc at time t, and dt represents the integral of the generating time. The total energy of the project electricity consumed within a period of time is calculated by integrating the generated power with respect to time.

[0029] Preferably, the electricity purchase analysis unit includes an electricity purchase calculation unit and an electricity purchase evaluation unit;

[0030] The electricity purchase calculation unit is used to perform difference calculation based on the electricity consumption energy Euse(t) and the generated energy Egen(t) obtained at the same time t to obtain the electricity purchase energy value Egrid;

[0031] The purchased electricity energy value Egrid is obtained by the following calculation formula:

[0032] Egrid(t)=Euse(t)-Egen(t);

[0033] In the formula, Egrid(t) represents the purchased electricity energy value Egrid at time t;

[0034] The power purchase evaluation unit is used to preset a power purchase evaluation threshold G based on the power consumption baseline of the factory, and then compare and evaluate the power purchase energy value Egrid(t) obtained at time t to analyze the power purchase energy required by the current factory;

[0035] The specific evaluation plan is as follows:

[0036] When the purchased electricity energy value Egrid(t) at time t ≥ the purchased electricity evaluation threshold G, it means that the current purchased electricity demand is zero;

[0037] When the electricity purchase energy value Egrid(t) at time t is less than the electricity purchase evaluation threshold G, it means that electricity purchase is currently required.

[0038] Preferably, the electricity purchase cost evaluation module includes an electricity purchase cost calculation unit and a benefit evaluation optimization unit;

[0039] The electricity purchase cost calculation unit is used to perform associated calculations based on the predicted electricity price Cgrid(t) at time t and the electricity purchase energy value Egrid(t) at time t to obtain the total electricity purchase cost Cost(t) at time t;

[0040] The total electricity purchase cost Cost(t) at time t is calculated by the following algorithm formula:

[0041]

[0042] Where dt represents the time integral, and the total power consumption of the project within a period of time is calculated by integrating the purchased power energy value Egrid with time.

[0043] Preferably, the benefit evaluation and optimization unit is used to preset a control threshold T based on the average benefit of factory electricity consumption, and then compare and evaluate it with the total electricity purchase cost Cost(t) obtained at time t, to analyze the control status of the factory meter and the distributed energy management system within time t;

[0044] The specific evaluation plan is as follows:

[0045] When the total electricity purchase cost Cost(t) at time t> the control threshold T, it means that the total amount of electricity purchased by the factory system exceeds the average benefit during the time period t. At this time, an alarm is generated through the power meter to remind relevant staff that there is an abnormality in distributed energy;

[0046] During this time period, the system purchased a large amount of electricity to meet user needs. This may be due to insufficient generation of distributed energy or failure to effectively utilize it, resulting in the system relying on the grid to purchase more electricity. This may mean that the system has a higher economic cost and needs to further optimize the scheduling strategy of distributed energy to reduce dependence on grid power and thus reduce costs;

[0047] When the total electricity purchase cost Cost(t) at time t> the control threshold T, it means that the factory system successfully utilizes distributed energy in the time period t, and there is no need to generate an alarm;

[0048] It means that the system successfully utilizes distributed energy and reduces the purchase of grid electricity, so the cost of purchasing electricity is lower. This shows that the system is more economically efficient and can effectively utilize renewable energy and self-generated energy to achieve the goal of energy conservation and emission reduction.

[0049] Preferably, the user feedback interaction module includes a front-end interaction unit and a suggestion optimization unit;

[0050] The front-end interactive unit is used to construct a monitoring interactive interface, display the power consumption and distributed energy generation of the factory in the form of charts and real-time data updates, specifically including real-time curves of power consumption and distributed energy generation, and integrate with the time series database to build a front-end search window, set a user query historical data function, and specifically view historical power consumption and generation by date range and time granularity;

[0051] The suggestion optimization unit is used to integrate the data analysis unit to analyze historical data, identify the factory's power consumption pattern and distributed energy generation pattern, generate energy-saving suggestions and energy optimization suggestions based on the analysis results, and display reminders through the monitoring interactive interface, such as peak-hour power consumption reminders and photovoltaic system maintenance suggestions.

[0052] A cloud-based collaborative control method for an electric energy meter comprises the following steps:

[0053] S1. First, collect the electricity consumption data of the energy meter and the power generation data of the distributed energy in real time, and upload the collected electricity consumption data and power generation data to the cloud server through the wireless network;

[0054] S2. The collected electricity consumption data and power generation data are then used to establish a database on the cloud server to store the historical data of electricity meters and distributed energy;

[0055] S3. By constructing an algorithm formula, based on the electricity consumption data and power generation data, the electricity consumption energy Euse and the power generation energy Egen are calculated, and the difference calculation is performed to obtain the purchased electricity energy value Egrid, and then the preset purchase electricity evaluation threshold G is used to compare and evaluate the purchase electricity demand;

[0056] S4, recalculate the total electricity purchase cost Cost(t) at time t, and perform a secondary comparison between the preset control threshold T and the obtained total electricity purchase cost Cost(t) to evaluate and analyze the electricity purchase situation of the distributed system operation and generate optimization measures;

[0057] S5. Finally, a user interaction platform is built to display real-time electricity consumption and power generation, and provide energy-saving suggestions and distributed energy use suggestions.

[0058] The present invention provides a cloud-based collaborative control method and system for electric energy meters, which has the following beneficial effects:

[0059] (1) The system collects and analyzes electricity consumption and power generation data in real time, accurately calculates the power consumption Euse and power generation Egen of the factory in a specific time period, and effectively identifies the power purchase demand and cost optimization space by evaluating the power purchase energy Egrid and the total power purchase cost Cost in real time. The power data collection module uses sensors and wireless communication technology to ensure the accuracy and real-time nature of the data. The data processing and analysis module uses a time series database and machine learning algorithms to efficiently store, preprocess and analyze data, and identify long-term trends and short-term fluctuations in power consumption and power generation patterns. The optimization algorithm module constructs complex algorithm formulas, comprehensively calculates real-time power consumption and power generation data, obtains power consumption Euse and power generation Egen, and calculates the power purchase energy Egrid. By comparing the power purchase energy Egrid with the preset threshold G, the system identifies the urgency of the power purchase demand and generates alarms and optimization suggestions. The power purchase cost evaluation module combines the predicted electricity price Cgrid(t) with the control threshold T to conduct a secondary comparative evaluation of the total power purchase cost Cost(t), analyze the system operation status and formulate optimization measures. The benefit evaluation and optimization unit evaluates the electricity purchase cost within a specific time period and generates energy-saving optimization suggestions and system control strategies.

[0060] (2) By using statistical methods, machine learning algorithms and cluster analysis, the system can detect outliers in the data, fill in missing data, and analyze patterns and correlations in electricity consumption and power generation data. Based on these analyses, the system can build a prediction model to accurately predict future electricity consumption, power generation and electricity price trends, thereby achieving intelligent management and optimized scheduling. With the continuous development of distributed energy, the system can not only improve the energy management level of the factory, but also promote the efficient use of renewable energy and promote the development of energy systems towards intelligence and sustainability.

[0061] (3) The system provides an intuitive monitoring interface through the user feedback interaction module, displays real-time power consumption curves and distributed energy generation power curves, and supports users to query historical data. Energy-saving and optimization suggestions generated by the data analysis unit are displayed in real time through the front-end interactive interface, helping users understand the power consumption and power generation patterns of the factory and take timely measures to optimize energy use. This not only improves users' transparency into energy use, but also enhances user participation and energy-saving awareness. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a schematic diagram of a process flow of a cloud-based collaborative control system for an electric energy meter according to the present invention;

[0063] Figure 2 A schematic diagram of the steps of a cloud-based collaborative control method for an electric energy meter according to the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0065] Example 1

[0066] See also Figure 1 , the present invention provides a cloud-based collaborative control system for electric energy meters. To achieve the above purpose, the present invention is implemented through the following technical solutions: including an electric energy data acquisition module, a data processing and analysis module, an optimization algorithm module, an electricity purchase cost evaluation module and a user feedback interaction module;

[0067] The electric energy data collection module is used to collect the power consumption data of the electric energy meter and the power generation data of the distributed energy in real time, and upload the collected power consumption data and power generation data to the cloud server through the wireless network;

[0068] The data processing and analysis module is used to build a database on the cloud server to store the historical data of electric energy meters and distributed energy sources for the collected power consumption data and power generation data;

[0069] The optimization algorithm module is used to construct an algorithm formula, calculate and obtain the electricity consumption energy Euse and the power generation energy Egen according to the electricity consumption data and the power generation data, and perform difference calculation to obtain the power purchase energy value Egrid, and then perform comparative evaluation and analysis of the power purchase demand by using the preset power purchase evaluation threshold G;

[0070] The power purchase cost evaluation module is used to calculate the total power purchase cost Cost(t) at time t, and to perform a secondary comparison between the preset control threshold T and the obtained total power purchase cost Cost(t) to evaluate and analyze the power purchase situation of the distributed system operation and generate optimization measures;

[0071] The user feedback interaction module is used to build a user interaction platform, display real-time electricity consumption and power generation, and provide energy-saving suggestions and distributed energy use suggestions.

[0072] In this embodiment, the system comprehensively improves the intelligence and efficiency of power management through the power data acquisition module, data processing and analysis module, optimization algorithm module, power purchase cost assessment module and user feedback interaction module. The power data acquisition module collects data from power meters and distributed energy in real time, and uploads it to the cloud server through a wireless network to ensure the accuracy and real-time nature of the data. The data processing and analysis module establishes a database in the cloud to store and process historical data, and uses machine learning algorithms and statistical methods to perform data analysis to identify long-term trends and short-term fluctuations in power consumption and power generation patterns.

[0073] The optimization algorithm module constructs a complex algorithm formula to comprehensively calculate the electricity consumption energy Euse and the power generation energy Egen, and accurately evaluates the electricity purchase demand. The electricity purchase cost evaluation module combines the real-time electricity price and the electricity purchase energy to accurately calculate the total electricity purchase cost Cost(t), and conducts a secondary comparative evaluation to generate optimization measures. Through the electricity purchase evaluation threshold G and the control threshold T, the system can timely identify the urgency of the electricity purchase demand, generate alarms and optimization suggestions, and help the factory make the best decision during peak electricity consumption and photovoltaic system maintenance. The user feedback interaction module builds an intuitive user interaction platform to display real-time electricity consumption and power generation, and provide energy-saving and distributed energy use suggestions. Compared with traditional power management methods, this system not only improves the efficiency of data processing and analysis, but also effectively reduces the electricity purchase cost and improves energy utilization efficiency through intelligent algorithm optimization and cost evaluation. Overall, the system achieves the dual goals of energy conservation and emission reduction and cost control, and significantly improves the intelligence level and economic benefits of energy management.

[0074] Example 2

[0075] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the data acquisition module includes an electricity equipment acquisition unit, a ,generation data acquisition unit, an electricity price data acquisition unit and a data transmission unit;

[0076] The power consumption equipment collection unit is used to install an integrated power consumption sensor group inside the power meter to collect power consumption data of the power meter every 30 minutes at a fixed period. The integrated sensor group includes a current sensor and a voltage sensor. The power consumption data includes current I and voltage V.

[0077] The power generation data acquisition unit is used to install power generation integrated sensors on distributed power generation equipment to collect power generation data of distributed power generation equipment every 30 minutes. The power generation integrated sensors include inverters, and the power generation data include DC current Idc, DC voltage Vdc and inverter efficiency N. inv ;

[0078] The electricity price data acquisition unit obtains the current electricity price Cgrid in real time through the API application program interface provided by the power company, and uses the moving time series average method based on the historical electricity price Cgrid records to identify the long-term trend in the electricity price data, and combines the seasonal STL decomposition to decompose the time series into trend, seasonal and residual parts, identify the periodic electricity price change pattern, and predict the electricity price Cgrid(t) at time t;

[0079] The data transmission unit is used to package the electricity consumption data and power generation data collected 6 times, and transmit the real-time collected electricity consumption data and power generation data to the cloud server by using Zigbee wireless communication technology and Modbus technology.

[0080] In this embodiment, the system collects data from the energy meter and distributed power generation equipment every 30 minutes through the power equipment collection unit and the power generation data collection unit by installing an integrated sensor group to ensure the accuracy and timeliness of the data. The electricity price data collection unit obtains electricity price information in real time through the API interface provided by the power company, and uses the moving time series average method and seasonal STL decomposition to analyze the electricity price trend and predict future electricity prices. The data transmission unit uses Zigbee and Modbus technology to package and transmit the collected electricity consumption and power generation data to the cloud server, realizing efficient data transmission and real-time update. Through the collaborative work of these modules, the system can collect, analyze and transmit electricity consumption data, power generation data and electricity price information in real time, and accurately identify the long-term trend and short-term fluctuation of electricity consumption and power generation patterns. Compared with traditional power management methods, this system has made significant improvements in the accuracy of data collection, the real-time transmission and the depth of data analysis. More efficient energy management and cost control are achieved, and the goals of energy conservation and emission reduction and optimization of distributed energy use are finally achieved, which greatly improves the intelligence level and economic benefits of power management.

[0081] Example 3

[0082] This embodiment is explained in Example 2. Please refer to Figure 1 ,Specifically: the data processing and analysis module includes a data storage unit, a data preprocessing unit and a data analysis unit;

[0083] The data storage unit uses InfluxDB and TimescaleDB to build a time series database, classify and store the real-time collected power consumption data and power generation data, as well as historical power consumption data and power generation data, and set up an API application query interface to extract relevant power consumption data and power generation data in different time periods in real time;

[0084] The data preprocessing unit is used to detect abnormal values ​​of the power consumption data and power generation data in the time series database using statistical methods and machine learning algorithms, and make corrections based on actual conditions, while using linear interpolation and polynomial interpolation to fill in missing data points;

[0085] The data analysis unit is used to use cluster analysis algorithms to analyze the patterns in electricity consumption data and power generation data and the correlation between data. At the same time, it uses regression analysis and neural networks as the basic framework to build prediction models and classification models, conduct trend analysis and periodic analysis on historical data, and predict future factory electricity consumption, power generation and electricity price trends.

[0086] In this embodiment, the data processing and analysis module of the system includes a data storage unit, a data preprocessing unit and a data analysis unit, which effectively improves the intelligent level of power data management and analysis. The data storage unit uses InfluxDB and TimescaleDB to build a time series database, classifies and stores the power consumption data and power generation data collected in real time and recorded in history, and realizes real-time data extraction through the API interface. The data preprocessing unit uses statistical methods and machine learning algorithms to detect and correct outliers in the data, and uses linear interpolation and polynomial interpolation to fill missing data points to ensure the integrity and accuracy of the data. The data analysis unit uses a clustering analysis algorithm to identify patterns and correlations in power consumption and power generation data, and builds a prediction model and a classification model based on regression analysis and neural networks to perform trend and periodicity analysis on historical data to predict future power consumption, power generation and electricity price trends. Through the collaborative work of these modules, the system can efficiently store, preprocess and analyze a large amount of power data, and provide accurate trend prediction and pattern recognition. Compared with traditional power management methods, this system has significantly improved the efficiency of data management, the accuracy of analysis and the reliability of prediction. It achieves more precise energy management and cost control, ultimately achieving the goals of optimizing energy use, reducing operating costs and improving system reliability, and significantly improving the intelligence and economic benefits of power management.

[0087] Example 4

[0088] This embodiment is explained in Example 3, please refer to Figure 1 ,Specifically: the optimization algorithm module includes an electricity energy calculation unit, a ,generation energy calculation unit and an electricity purchase analysis unit;

[0089] The power consumption calculation unit is used to construct a power consumption calculation formula, and substitute the real-time collected power consumption data set into the formula to calculate and analyze the current power consumption of the factory Euse, and analyze the current power consumption of the factory;

[0090] The electric energy Euse is calculated by the following electric energy calculation formula:

[0091]

[0092] In the formula, t0 represents the initial time, t represents the end time, Euse(t) represents the electrical energy Euse at time t, V(t) represents the voltage V at time t, I(t) represents the current I at time t, V(t)×I(t) represents the electrical power, and dt represents the integral of the electrical power consumption time. The total electrical energy of the project within a period of time is calculated by integrating the electrical power consumption with respect to time.

[0093] The power generation energy calculation unit is used to construct a power generation energy calculation formula, and substitute the power generation data collected in real time into the formula to calculate and obtain the power generation energy Egen of the distributed power generation equipment;

[0094] The power generation energy Egen is obtained by the following power generation energy calculation formula:

[0095]

[0096] Where, Egen(t) represents the generated energy Egen at time t, Vdc(t) represents the DC voltage Vdc at time t, Idc(t) represents the DC current Idc at time t, and dt represents the integral of the generating time. The total energy of the project electricity consumed within a period of time is calculated by integrating the generated power with respect to time.

[0097] The power purchase analysis unit includes a power purchase calculation unit and a power purchase evaluation unit;

[0098] The power purchase calculation unit is used to perform difference calculation based on the power consumption energy Euse(t) and the power generation energy Egen(t) obtained at the same time t to obtain the power purchase energy value Egrid;

[0099] The purchased electricity energy value Egrid is obtained by the following calculation formula;

[0100] Egrid(t)=Euse(t)-Egen(t);

[0101] In the formula, Egrid(t) represents the purchased electricity energy value Egrid at time t;

[0102] The power purchase evaluation unit is used to preset the power purchase evaluation threshold G based on the power consumption baseline of the factory, and then compare and evaluate the power purchase energy value Egrid(t) obtained at time t to analyze the power purchase energy required by the current factory;

[0103] The specific evaluation plan is as follows:

[0104] When the purchased electricity energy value Egrid(t) at time t ≥ the purchased electricity evaluation threshold G, it means that the current purchased electricity demand is zero;

[0105] When the electricity purchase energy value Egrid(t) at time t is less than the electricity purchase evaluation threshold G, it means that electricity purchase is currently required.

[0106] In this embodiment, the system is composed of an electricity consumption energy calculation unit, a power generation energy calculation unit and a power purchase analysis unit through an optimization algorithm module. By accurately calculating the power consumption and power generation energy of the factory, the power purchase demand is comprehensively evaluated. The electricity consumption energy calculation unit uses the voltage V and current I data collected in real time, and uses an integral formula to calculate the power consumption Euse in a specific time period, thereby accurately analyzing the power consumption of the factory. The power generation energy calculation unit uses the DC current Idc, DC voltage Vdc and inverter efficiency N inv , the integral method is applied to calculate the power generation energy Egen of distributed power generation equipment. The power purchase analysis unit determines the power purchase energy value Egrid by calculating the difference between power consumption energy and power generation energy, and evaluates the power purchase demand of the factory according to the preset power purchase evaluation threshold G. The introduction of this optimization algorithm module significantly improves the accuracy and efficiency of power management. Compared with traditional methods, the system can calculate and analyze power consumption and power generation energy in real time and accurately, identify power purchase demand in time, avoid unnecessary power purchase, and optimize energy use. Through precise calculation and evaluation, the system can help the factory reasonably arrange power resources in different time periods, reduce operating costs, maximize energy utilization and optimize economic benefits. In the end, this system improves the energy management level of the factory and realizes intelligent and refined power management.

[0107] Example 5

[0108] This embodiment is explained in Example 4. Please refer to Figure 1 ,Specifically: the electricity purchase cost evaluation module includes the electricity purchase cost calculation unit and the ,benefit evaluation optimization unit;

[0109] The electricity purchase cost calculation unit is used to perform associated calculations based on the predicted electricity price Cgrid(t) at time t and the electricity purchase energy value Egrid(t) at time t to obtain the total electricity purchase cost Cost(t) at time t;

[0110] The total electricity purchase cost Cost(t) at time t is calculated by the following algorithm formula:

[0111]

[0112] Where dt represents the time integral, and the total power consumption of the project within a period of time is calculated by integrating the purchased power energy value Egrid with time.

[0113] The benefit evaluation and optimization unit is used to preset the control threshold T based on the average benefit of factory electricity consumption, and then compare and evaluate it with the total electricity purchase cost Cost(t) obtained at time t, and analyze the control status of the factory meter and distributed energy management system within time t;

[0114] The specific evaluation plan is as follows:

[0115] When the total electricity purchase cost Cost(t) at time t> the control threshold T, it means that the total amount of electricity purchased by the factory system exceeds the average benefit during the time period t. At this time, an alarm is generated through the power meter to remind relevant staff that there is an abnormality in distributed energy;

[0116] Indicates that during this time period, the system purchased a large amount of electricity to meet user needs. This may be due to insufficient generation of distributed energy or failure to effectively utilize it, causing the system to rely on the grid to purchase more electricity. This may mean that the system has a high economic cost and needs to further optimize the scheduling strategy of distributed energy to reduce dependence on grid power and thus reduce costs;

[0117] When the total electricity purchase cost Cost(t) at time t> the control threshold T, it means that the factory system successfully utilizes distributed energy in the time period t, and there is no need to generate an alarm;

[0118] It means that the system successfully utilizes distributed energy and reduces the purchase of grid electricity, so the cost of purchasing electricity is lower. This shows that the system is more economically efficient and can effectively utilize renewable energy and self-generated energy to achieve the goal of energy conservation and emission reduction.

[0119] In this embodiment, the system consists of an electricity purchase cost calculation unit and a benefit evaluation and optimization unit through an electricity purchase cost evaluation module. By real-time prediction of electricity prices and electricity purchase energy, the total electricity purchase cost is accurately calculated, and benefit evaluation and optimization are performed. The electricity purchase cost calculation unit combines the predicted electricity price and electricity purchase energy to calculate the total electricity purchase cost Cost(t) within a specific time period, thereby accurately reflecting the factory's electricity purchase expenditure. The benefit evaluation and optimization unit compares and evaluates the total electricity purchase cost according to the preset control threshold T, judges the control status of the system within a specific time period, and generates corresponding alarms or optimization suggestions. This module effectively improves the accuracy and decision-making efficiency of electricity cost management. Compared with traditional means, the system can monitor and evaluate the electricity purchase cost in real time, identify the cost exceeding the standard in time, and prompt relevant staff to make adjustments and optimizations to avoid unnecessary electricity expenditures. Through accurate cost calculation and benefit evaluation, the system helps the factory optimize the scheduling strategy of distributed energy, reduce dependence on grid power, reduce operating costs, and maximize economic benefits. Finally, this module improves the energy management efficiency of the factory, promotes energy conservation, emission reduction and sustainable development.

[0120] Example 6

[0121] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the user feedback interaction module includes the front-end ,interaction unit and the suggestion optimization unit;

[0122] The front-end interactive unit is used to build a monitoring interactive interface, which displays the power consumption and distributed energy generation of the factory in the form of charts and real-time data updates, including real-time curves of power consumption and distributed energy generation, and integrates with the time series database to build a front-end search window to connect with the API application query interface in the time series database, and set the user query historical data function, which specifically allows viewing historical power consumption and generation by date range and time granularity;

[0123] The optimization unit is used to integrate the data analysis unit to analyze historical data, identify the factory's power consumption pattern and distributed energy generation pattern, generate energy-saving suggestions and energy optimization suggestions based on the analysis results, and display reminders through the monitoring interactive interface, such as peak-time power consumption reminders and photovoltaic system maintenance suggestions.

[0124] In this embodiment, the system realizes efficient interaction between users and the system by constructing an intuitive monitoring interface and providing energy-saving optimization suggestions. The front-end interactive unit displays the power consumption and distributed energy generation of the factory in the form of charts and real-time data updates, and provides a historical data query function, so that users can easily view and analyze the power usage in different periods. The suggestion optimization unit generates energy-saving and energy optimization suggestions based on the data analysis results, and reminds users of important information such as peak power consumption periods and photovoltaic system maintenance through the monitoring interface. This module significantly improves the user's participation and decision-making ability. Users can monitor the power usage of the factory in real time, and promptly discover anomalies and optimization opportunities, which is more intuitive and efficient than traditional means. By providing personalized energy-saving suggestions and optimization solutions, it helps users optimize energy management strategies, reduce electricity costs, and achieve energy conservation and emission reduction goals. Finally, the user feedback interaction module enhances the operability and user experience of the system, and promotes the intelligence and refinement of factory energy management.

[0125] Example 7

[0126] See also Figure 1 and Figure 2 , a cloud collaborative control method for an electric energy meter comprises the following steps:

[0127] S1. First, collect the electricity consumption data of the energy meter and the power generation data of the distributed energy in real time, and upload the collected electricity consumption data and power generation data to the cloud server through the wireless network;

[0128] S2. The collected electricity consumption data and power generation data are then used to establish a database on the cloud server to store the historical data of electricity meters and distributed energy;

[0129] S3. By constructing an algorithm formula, based on the electricity consumption data and power generation data, the electricity consumption energy Euse and the power generation energy Egen are calculated, and the difference calculation is performed to obtain the purchased electricity energy value Egrid, and then the preset purchase electricity evaluation threshold G is used to compare and evaluate the purchase electricity demand;

[0130] S4, recalculate the total electricity purchase cost Cost(t) at time t, and perform a secondary comparison between the preset control threshold T and the obtained total electricity purchase cost Cost(t) to evaluate and analyze the electricity purchase situation of the distributed system operation and generate optimization measures;

[0131] S5. Finally, a user interaction platform is built to display real-time electricity consumption and power generation, and provide energy-saving suggestions and distributed energy use suggestions.

[0132] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A cloud-based collaborative control system for electric energy meters, characterized in that: It includes power data acquisition module, data processing and analysis module, optimization algorithm module, power purchase cost assessment module and user feedback interaction module; The electric energy data collection module is used to collect the power consumption data of the electric energy meter and the power generation data of the distributed energy in real time, and upload the collected power consumption data and power generation data to the cloud server through the wireless network; The data processing and analysis module is used to establish a database on the cloud server to store the historical data of the electric energy meter and distributed energy sources with the collected power consumption data and power generation data; The optimization algorithm module is used to construct an algorithm formula, calculate and obtain the electricity consumption energy Euse and the power generation energy Egen according to the electricity consumption data and the power generation data, and perform difference calculation to obtain the power purchase energy value Egrid, and then perform comparative evaluation and analysis of the power purchase demand by using the preset power purchase evaluation threshold G; The electricity purchase cost evaluation module is used to calculate the total electricity purchase cost Cost(t) at time t, and to perform a secondary comparison between the preset control threshold T and the obtained total electricity purchase cost Cost(t) to evaluate and analyze the electricity purchase situation of the distributed system operation and generate optimization measures; The user feedback interaction module is used to build a user interaction platform, display real-time electricity consumption and power generation, and provide energy-saving suggestions and distributed energy use suggestions.

2. The cloud-based collaborative control system for electric energy meters according to claim 1 is characterized in that: The data acquisition module includes an electric equipment acquisition unit, a power generation data acquisition unit, an electricity price data acquisition unit and a data transmission unit; The power consumption equipment collection unit is used to install an integrated power consumption sensor group inside the power meter to collect power consumption data of the power meter every 30 minutes at a fixed period. The integrated sensor group includes a current sensor and a voltage sensor. The power consumption data includes current I and voltage V. The power generation data acquisition unit is used to install a power generation integrated sensor on the distributed power generation equipment, and collect the power generation data of the distributed power generation equipment every 30 minutes at a fixed period. The power generation integrated sensor includes an inverter, and the power generation data includes a DC current Idc, a DC voltage Vdc and an inverter efficiency N inv ; The electricity price data acquisition unit obtains the current electricity price Cgrid in real time through the API application program interface provided by the power company, and uses the moving time series average method based on the historical electricity price Cgrid records to identify the long-term trend in the electricity price data, and combines the seasonal STL decomposition to decompose the time series into trend, seasonal and residual parts, identify the periodic electricity price change pattern, and predict the electricity price Cgrid(t) at time t; The data transmission unit is used to package the power consumption data and power generation data collected a fixed number of times 6 times, and transmit the real-time collected power consumption data and power generation data to the cloud server by using Zigbee wireless communication technology and Modbus technology.

3. The cloud-based collaborative control system for electric energy meters according to claim 2 is characterized in that: The data processing and analysis module includes a data storage unit, a data preprocessing unit and a data analysis unit; The data storage unit constructs a time series database through InfluxDB and TimescaleDB, classifies and stores the real-time collected power consumption data and power generation data, as well as the historical power consumption data and power generation data, and sets an API application query interface to extract relevant power consumption data and power generation data in different time periods in real time; The data preprocessing unit is used to detect abnormal values ​​of the power consumption data and power generation data in the time series database using statistical methods and machine learning algorithms, and make corrections according to actual conditions, while using linear interpolation and polynomial interpolation to fill in missing data points; The data analysis unit is used to use a cluster analysis algorithm to analyze the patterns in the electricity consumption data and the power generation data and the correlation between the data, and at the same time use regression analysis and neural networks as a basic framework to build prediction models and classification models, perform trend analysis and periodicity analysis on historical data, and predict future factory electricity consumption, power generation and electricity price trends.

4. The cloud-based collaborative control system for electric energy meters according to claim 2 is characterized in that: The optimization algorithm module includes an electricity consumption energy calculation unit, a power generation energy calculation unit and an electricity purchase analysis unit; The electric energy calculation unit is used to construct an electric energy calculation formula, and substitute the real-time collected electric energy data set into the formula to calculate and analyze the current factory electric energy Euse; The electric energy Euse is calculated by the following electric energy calculation formula: Wherein, t0 represents the initial time, t represents the end time, Euse(t) represents the electrical energy Euse at time t, V(t) represents the voltage V at time t, I(t) represents the current I at time t, V(t)×I(t) represents the electrical power, and dt represents the integral of the electrical time.

5. The cloud-based collaborative control system for electric energy meters according to claim 4 is characterized in that: The power generation energy calculation unit is used to construct a power generation energy calculation formula, and substitute the power generation data collected in real time into the formula to calculate and obtain the power generation energy Egen of the distributed power generation equipment; The power generation energy Egen is obtained by the following power generation energy calculation formula: Wherein, Egen(t) represents the generated energy Egen at time t, Vdc(t) represents the DC voltage Vdc at time t, Idc(t) represents the DC current Idc at time t, and dt represents the integral of the generating time.

6. The cloud-based collaborative control system for electric energy meters according to claim 4 is characterized in that: The electricity purchase analysis unit includes an electricity purchase calculation unit and an electricity purchase evaluation unit; The electricity purchase calculation unit is used to calculate the difference between the electricity consumption energy Euse(t) and the power generation energy Egen(t) obtained at the same time t, and obtain the electricity purchase energy value Egrid; The purchased electricity energy value Egrid is obtained by the following calculation formula: Egrid(t)=Euse(t)-Egen(t); In the formula, Egrid(t) represents the purchased electricity energy value Egrid at time t; The power purchase evaluation unit is used to preset a power purchase evaluation threshold G based on the power consumption baseline of the factory, and then compare and evaluate the power purchase energy value Egrid(t) obtained at time t to analyze the power purchase energy required by the current factory; The specific evaluation plan is as follows: When the purchased electricity energy value Egrid(t) at time t ≥ the purchased electricity evaluation threshold G, it means that the current purchased electricity demand is zero; When the electricity purchase energy value Egrid(t) at time t is less than the electricity purchase evaluation threshold G, it means that electricity purchase is currently required.

7. The cloud-based collaborative control system for electric energy meters according to claim 6 is characterized in that: The electricity purchase cost evaluation module includes an electricity purchase cost calculation unit and a benefit evaluation optimization unit; The electricity purchase cost calculation unit is used to perform associated calculations based on the predicted electricity price Cgrid(t) at time t and the electricity purchase energy value Egrid(t) at time t to obtain the total electricity purchase cost Cost(t) at time t; The total electricity purchase cost Cost(t) at time t is calculated by the following algorithm formula: Where dt represents the time integral, and the total power consumption of the project within a period of time is calculated by integrating the purchased power energy value Egrid with time.

8. The cloud-based collaborative control system for electric energy meters according to claim 7 is characterized in that: The benefit evaluation and optimization unit is used to preset a control threshold T based on the average benefit of factory electricity consumption, and then compare and evaluate it with the total electricity purchase cost Cost(t) obtained at time t, and analyze the control status of the factory meter and the distributed energy management system at time t; The specific evaluation plan is as follows: When the total electricity purchase cost Cost(t) at time t> the control threshold T, it means that the total amount of electricity purchased by the factory system exceeds the average benefit during the time period t. At this time, an alarm is generated through the power meter to remind relevant staff that there is an abnormality in distributed energy; When the total electricity purchase cost Cost(t) at time t> the control threshold T, it means that the factory system successfully utilizes distributed energy within the time period t, and there is no need to generate an alarm.

9. The cloud-based collaborative control system for electric energy meters according to claim 1, characterized in that: The user feedback interaction module includes a front-end interaction unit and a suggestion optimization unit; The front-end interactive unit is used to construct a monitoring interactive interface, display the power consumption and distributed energy generation of the factory in the form of charts and real-time data updates, specifically including the real-time curve of power consumption and the real-time curve of distributed energy generation, and integrate with the time series database to construct a front-end search window and set a user query historical data function; The suggestion optimization unit is used to integrate the data analysis unit to analyze historical data, identify the factory's power consumption pattern and distributed energy generation pattern, generate energy-saving suggestions and energy optimization suggestions based on the analysis results, and display reminders through the monitoring interactive interface.

10. A cloud-based collaborative control method for an electric energy meter, applied to an electric energy meter cloud-based collaborative control system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1. First, collect the electricity consumption data of the energy meter and the power generation data of the distributed energy in real time, and upload the collected electricity consumption data and power generation data to the cloud server through the wireless network; S2. The collected electricity consumption data and power generation data are then used to establish a database on the cloud server to store the historical data of electricity meters and distributed energy; S3. By constructing an algorithm formula, based on the electricity consumption data and power generation data, the electricity consumption energy Euse and the power generation energy Egen are calculated, and the difference calculation is performed to obtain the purchased electricity energy value Egrid, and then the preset purchase electricity evaluation threshold G is used to compare and evaluate the purchase electricity demand; S4, recalculate the total electricity purchase cost Cost(t) at time t, and perform a secondary comparison between the preset control threshold T and the obtained total electricity purchase cost Cost(t) to evaluate and analyze the electricity purchase situation of the distributed system operation and generate optimization measures; S5. Finally, a user interaction platform is built to display real-time electricity consumption and power generation, and provide energy-saving suggestions and distributed energy use suggestions.

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