Intelligent electric energy meter with remote meter reading module

Abstract

The invention relates to the technical field of intelligent power grids, in particular to an intelligent electric energy meter with a remote meter reading module, which comprises a model construction module, a stability analysis module, a remote meter reading module, a real-time response module, a demand prediction module and a load balancing module. According to the method, the power demand and supply condition in the future time period can be predicted and adapted through accurate load disturbance analysis, the safety risk caused by overload or imbalance of the power grid is reduced, the use efficiency of power resources is improved, the reasonability and economy of power distribution are promoted, and through a dynamic load adjustment strategy, the power distribution efficiency is improved. The power grid can manage power supply and demand in peak and valley periods more effectively, waste is reduced, continuity and stability of power supply are ensured, the optimization enables the operation of the power grid to be more flexible, the power grid can adapt to various complex and changeable power usage scenes, and the response capability and reliability of the intelligent electric energy meter are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to a smart electric energy meter with a remote meter reading module. Background Art

[0002] The field of smart grid technology combines information technology, communication technology and advanced power equipment to achieve efficient production, transmission, distribution and consumption of electricity. The core of smart grid lies in intelligence and informatization. By sensing, monitoring, analyzing and controlling the operating status of each link of the power grid, the safety, reliability and economy of the power system are improved. This technical field includes smart meters, distributed energy management, load forecasting, remote monitoring, automatic dispatching and other key technologies, which are widely used in the construction and operation of modern power systems, and promote the power industry to develop in a greener and more efficient direction.

[0003] Among them, the smart energy meter with remote meter reading module refers to an energy metering device that integrates metering, monitoring, data communication and remote management functions. The main purpose is to realize the automatic collection and remote transmission of electricity consumption data, eliminate the errors and inefficiencies of manual meter reading, and support real-time electricity consumption monitoring, time-of-use electricity price management and abnormal electricity consumption alarm. This kind of equipment plays a fundamental role in the construction of smart grids, providing power companies and users with accurate and efficient energy management methods.

[0004] Existing technologies mainly focus on monitoring and managing electricity through information technology and communication means. Although the level of intelligence of the power grid has been improved, it is still insufficient when processing batch data and complex power grid behaviors. Especially in terms of real-time data processing and responding to sudden changes in power demand, the existing system cannot quickly and accurately analyze and predict changes in power load, resulting in low power grid operation efficiency and sometimes waste of resources. For example, the lack of effective load forecasting and dynamic adjustment mechanisms leads to the inability to provide sufficient power supply during peak power demand periods, and conversely, excessive power is generated during low demand periods, increasing the operating costs of the power system and exacerbating equipment wear. Due to the limitations of existing technologies in user behavior analysis, the power grid does not have a deep understanding of individual user power consumption characteristics, making it difficult to implement more refined power management strategies, affecting the overall stability and security of the power grid. The shortcomings highlight the challenges of existing technologies in coping with more efficient and sustainable power grid management. Summary of the invention

[0005] In order to solve the problems existing in the prior art in processing batch data and complex power grid behaviors, especially in real-time data processing and responding to sudden changes in electricity demand, the existing system cannot quickly and accurately analyze and predict changes in power load, resulting in low power grid operation efficiency and sometimes waste of resources. For example, the lack of effective load forecasting and dynamic adjustment mechanisms leads to the inability to provide sufficient power supply during peak power demand periods, and conversely, excessive power is generated during low-demand periods, increasing the operating costs of the power system and aggravating equipment wear. Due to the limitations of the prior art in user behavior analysis, the power grid does not have a deep enough understanding of the individual power consumption characteristics of users, making it difficult to implement more refined power management strategies, affecting the overall stability and security of the power grid. The shortcomings highlight the technical problems of the prior art in responding to the challenges of more efficient and sustainable power grid management. The embodiment of the present invention provides a smart electric energy meter with a remote meter reading module. The technical solution is as follows:

[0006] In one aspect, a smart electric energy meter with a remote meter reading module is provided, the meter comprising:

[0007] The model building module analyzes power consumption, voltage fluctuations and current characteristics based on the operating status of the power grid, and builds a dynamic nonlinear model based on the monitoring results of the dynamic behavior of the power grid by the smart energy meter;

[0008] The stability analysis module uses the dynamic nonlinear model to select state variables, analyze the energy change trend and the stability of power operation, and obtain the load disturbance stability identification result;

[0009] The remote meter reading module obtains the user's electricity consumption parameters and obtains the user's load impact analysis results through the load disturbance stability identification results and the real-time reading function of the smart electric energy meter;

[0010] The real-time response module evaluates the response of the power consumption behavior to the overall load based on the user load impact analysis result and the power fluctuation detected in real time in the power grid, analyzes the priority of dynamic adjustment, and obtains a dynamic load adjustment plan;

[0011] The demand forecasting module predicts the grid demand distribution and the supply-demand gap through the dynamic load adjustment scheme, combined with the load change trend and the real-time grid operation status, and obtains the grid demand distribution forecast result;

[0012] The load balancing module identifies and analyzes the load adjustment path through the power grid demand distribution prediction result, balances and regulates the differentiated regional power grids, performs power supply allocation, and obtains a power supply allocation strategy.

[0013] As a further solution of the present invention, the dynamic nonlinear model includes a power consumption model, a voltage fluctuation response model, and a current characteristic analysis model; the load disturbance stability identification result includes a stability rating, a risk index, and identification of potential disturbance points; the user load impact analysis result includes user power consumption pattern classification, consumption point identification, and mapping of key load areas; the dynamic load adjustment plan includes emergency load reduction measures, priority adjustment rules, and load allocation strategies for critical time periods; the power grid demand distribution forecast results include demand peak period forecasts, supply and demand information in key areas, and early warnings of potential supply shortages; the power supply allocation strategy includes voltage adjustment in differentiated areas, load transfer plans, and setting of emergency response paths.

[0014] As a further solution of the present invention, the model building module includes:

[0015] The energy consumption analysis submodule extracts consumption patterns and peak data based on the operating status of the power grid, uses time series analysis to determine trends and anomalies, analyzes average power consumption and load points, and obtains power consumption characteristic records;

[0016] The fluctuation monitoring submodule uses the power consumption characteristic record to compare the voltage readings in the differentiated time periods, analyzes the voltage fluctuation caused by the load change, and measures the fluctuation size, quantitatively monitors the voltage stability, and obtains the fluctuation trend analysis results;

[0017] The behavior diagnosis submodule analyzes the time dependency of the current behavior through the fluctuation trend analysis results, tracks the current change points, identifies abnormal behaviors, and constructs a dynamic nonlinear model.

[0018] As a further solution of the present invention, the stability analysis module includes:

[0019] The state variable selection submodule selects state variables based on the dynamic nonlinear model, identifies the operation status of the power grid, screens key variables affecting power stability through data correlation analysis and impact assessment, and obtains key state indicators;

[0020] The energy trend analysis submodule uses the key status indicators and time series analysis to track the energy change trend of the power, identifies potential unstable factors through comparative analysis of long-term and short-term energy fluctuation data, and obtains energy fluctuation analysis results;

[0021] The response evaluation submodule evaluates the response of the power grid under differentiated load disturbances through the energy fluctuation analysis results, calculates the stability index of the node load response, and obtains the load disturbance stability identification result.

[0022] As a further solution of the present invention, the formula for calculating the stability index of the node load response is:

[0023]

[0024] Among them, S is the stability index of node load response, w i represents the weight coefficient of node i, x i represents the load value of node i after disturbance, μ i represents the average load value of node i, represents the variance of the load value of node i, ∈ is a positive number, and n is the number of nodes.

[0025] As a further solution of the present invention, the remote meter reading module includes:

[0026] The real-time reading submodule uses the load disturbance stability identification result and the real-time data transmission function of the smart energy meter to automatically collect the power, voltage and time information of each user, perform data synchronization, verify the timeliness of the information, and obtain real-time power consumption data;

[0027] The behavior analysis submodule analyzes the power consumption patterns and voltage fluctuations of differentiated users based on the real-time power consumption data, classifies users according to their power consumption behaviors, identifies users that are affected by stability, and obtains the classification results of user power consumption behaviors;

[0028] The impact assessment submodule utilizes the user power consumption behavior classification results, combined with the load disturbance stability identification results, to assess the impact of differentiated user categories on power grid stability and obtain user load impact analysis results.

[0029] As a further solution of the present invention, the real-time response module includes:

[0030] The fluctuation assessment submodule uses the user load impact analysis results, combined with the real-time monitoring data of the power grid, to analyze the frequency and amplitude of power fluctuations, and uses fluctuation indicators to quantify the stability of the power grid in each time period, evaluate the changes caused by user power consumption behavior, and obtain fluctuation response indicators;

[0031] The behavior response submodule uses the fluctuation response index to perform behavior pattern analysis of differentiated user groups, calculates the deviation value of user power consumption behavior, and obtains key behavior impact analysis results;

[0032] The load adjustment submodule designs a dynamic load distribution mode through the key behavior impact analysis results, adjusts power resources, matches real-time demand and stabilizes the power grid, and obtains a dynamic load adjustment plan.

[0033] As a further solution of the present invention, the formula for calculating the deviation value of the user's power consumption behavior is:

[0034]

[0035] Among them, R is the quantitative result of the impact of the user's power consumption behavior deviation, P user Represents the user's real-time power consumption, μ user Represents the average value of the user's power consumption, σ user Represents the standard deviation of the user's power consumption, S grid Represents the power grid fluctuation amplitude, V grid represents the power grid fluctuation frequency, □ represents a positive number, and W1 and W2 represent weight coefficients.

[0036] As a further solution of the present invention, the demand forecasting module includes:

[0037] The trend analysis submodule implements the dynamic load adjustment scheme, identifies the long-term change pattern and periodic fluctuation of the power grid load in combination with the load change trend, extracts key change indicators, and obtains load trend indicators;

[0038] The state monitoring submodule uses the load trend indicator and real-time power grid operation data to monitor the instantaneous state of the power grid, including the total load, frequency and voltage level, and identifies the stability and abnormal state of the power grid operation through real-time data flow analysis to obtain real-time state analysis results;

[0039] The consistency detection submodule evaluates the grid demand and potential supply-demand gap in the future time period according to the real-time status analysis results, optimizes the consistency of the prediction according to the real-time data and instant parameters, and obtains the grid demand distribution prediction results.

[0040] As a further solution of the present invention, the load balancing module includes:

[0041] The path identification submodule identifies the optimal path and key nodes for load adjustment based on the grid demand distribution prediction result, evaluates the transmission efficiency and potential risks of the differentiated paths, and obtains the path optimization analysis result;

[0042] The regional analysis submodule uses the path optimization analysis results to analyze the grid load characteristics of differentiated regions through GIS and grid load data, identifies the regions with unstable loads, and obtains a regional load feature set;

[0043] The strategy formulation submodule designs differentiated power grid balancing and control schemes according to the regional load feature set, optimizes power supply allocation, verifies the reasonable allocation of power resources, and obtains a power supply allocation strategy.

[0044] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0045] Through real-time data analysis and dynamic model construction, the power grid can respond quickly to different user behaviors and load changes. Based on real-time monitoring of power consumption, voltage and current characteristics, accurate load disturbance analysis can predict and adapt to future power demand and supply conditions. It not only reduces the safety risks caused by overload or imbalance of the power grid, but also improves the efficiency of power resource use and promotes the rationality and economy of power distribution. Especially in the analysis of differentiated user power consumption behavior, by carefully understanding the user's power consumption pattern, the power flow in the power grid can be optimized and adjusted to achieve more refined power management. Through dynamic load adjustment strategies, the power grid can more effectively manage the power supply and demand during peak and valley periods, reduce waste and ensure the continuity and stability of power supply. This optimization makes the power grid operation more flexible, able to adapt to various complex and changeable power usage scenarios, and enhance the responsiveness and reliability of smart energy meters. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the drawings without creative work.

[0047] Figure 1 is a schematic diagram of a smart electric energy meter with a remote meter reading module provided by an embodiment of the present invention;

[0048] Figure 2 It is a schematic diagram of a table framework of the present invention;

[0049] Figure 3 A flowchart of a model building module in the present invention;

[0050] Figure 4 is a flow chart of the stability analysis module in the present invention;

[0051] Figure 5 This is a flow chart of the remote meter reading module in the present invention;

[0052] Figure 6 This is a flow chart of the real-time response module in the present invention;

[0053] Figure 7 This is a flow chart of the demand forecasting module in the present invention;

[0054] Figure 8 It is a flow chart of the load balancing module in the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0056] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0057] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0058] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0059] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0060] The embodiment of the present invention provides a smart electric energy meter with a remote meter reading module, such as Figure 1-2 The schematic diagram of the intelligent electric energy meter with remote meter reading module shown in the figure includes:

[0061] The model building module analyzes power consumption, voltage fluctuations and current characteristics based on the operating status of the power grid, and builds a dynamic nonlinear model based on the monitoring results of the dynamic behavior of the power grid by the smart energy meter;

[0062] The stability analysis module uses a dynamic nonlinear model to select state variables, analyze the energy change trend and the stability of power operation, identify the grid stability state in differentiated load disturbance scenarios, and obtain load disturbance stability identification results;

[0063] The remote meter reading module uses the load disturbance stability identification results and the real-time reading function of the smart energy meter to obtain the user's power consumption parameters, including power consumption, voltage changes and load distribution, analyze the impact of differentiated users on the power grid, and obtain the user load impact analysis results;

[0064] The real-time response module evaluates the response of differentiated power consumption behaviors to the overall load based on the user load impact analysis results and the power fluctuations detected in real time in the power grid, analyzes the priority of dynamic adjustment and the load distribution mode, and obtains a dynamic load adjustment plan;

[0065] The demand forecasting module uses a dynamic load adjustment scheme, combined with load change trends and real-time grid operation status, to predict grid demand distribution and supply-demand gaps in the future time period, and obtains grid demand distribution forecast results;

[0066] The load balancing module identifies and analyzes the load adjustment path through the grid demand distribution prediction results, balances and regulates the differentiated regional grids, performs power supply allocation, and obtains the power supply allocation strategy;

[0067] The dynamic nonlinear model includes power consumption model, voltage fluctuation response model, and current characteristic analysis model. The load disturbance stability identification results include stability rating, risk indicators, and identification of potential disturbance points. The user load impact analysis results include user power consumption pattern classification, consumption point identification, and mapping of key load areas. The dynamic load adjustment plan includes emergency load reduction measures, priority adjustment rules, and load allocation strategies for critical periods. The grid demand distribution forecast results include demand peak period forecasts, supply and demand information in key areas, and early warning of potential supply shortages. The power supply allocation strategy includes voltage adjustment in differentiated areas, load transfer plans, and setting of emergency response paths.

[0068] Specifically, if Figure 2 , 3 As shown, the model building modules include:

[0069] The energy consumption analysis submodule extracts consumption patterns and peak data based on the operating status of the power grid, uses time series analysis to determine trends and anomalies, analyzes average power consumption and load points, and obtains the execution process of power consumption feature records as follows;

[0070] Through the real-time operation data of the power grid, including parameters such as voltage, current and load, time series analysis methods are applied to process the data, including extracting trend information through sliding window averaging technology, using standard deviation and mean methods to determine abnormal points, and applying peak detection algorithms to identify peak power consumption. Through analysis and recording of various consumption patterns and power load points, the basis for optimizing power grid operation and formulating energy-saving measures is obtained, and the power consumption characteristic record is obtained.

[0071] The fluctuation monitoring submodule uses power consumption feature records to compare voltage readings in differentiated time periods, analyzes voltage fluctuations caused by load changes, and measures the magnitude of fluctuations to quantitatively monitor voltage stability. The execution process of obtaining fluctuation trend analysis results is as follows;

[0072] Analyze the voltage fluctuation caused by load changes according to the formula:

[0073]

[0074] Calculate voltage stability, where V a represents the voltage reading in the ath time period, represents the average voltage, n a represents the number of readings;

[0075] The voltage readings are set in 5 time periods, namely 220V, 222V, 218V, 221V, and 219V.

[0076] Calculate the average voltage:

[0077]

[0078] The sum of squared deviations for each reading is calculated according to the formula:

[0079] (220-220) 2 =0;

[0080] (222-220) 2 =4;

[0081] (218-220) 2 =4;

[0082] (221-220) 2 =1;

[0083] (219-220) 2 =1;

[0084] Calculate the mean of the sum of squared deviations and take the square root to get V rms :

[0085]

[0086] Through this calculation, the voltage stability can be quantitatively measured, providing a basis for subsequent adjustments and ensuring the operational stability of the power grid.

[0087] The behavior diagnosis submodule analyzes the time dependence of current behavior through fluctuation trend analysis results, tracks current change points, and identifies abnormal behaviors. The execution process of building a dynamic nonlinear model is as follows;

[0088] Compare historical current data with real-time monitoring data, and use dynamic nonlinear models to track current change points and abnormal behaviors. The specific implementation process includes data processing, such as filtering and denoising, and deep learning training of data using neural networks or machine learning models. The model will identify patterns and anomalies in the data, such as sudden increases or decreases in current, which indicate equipment failure or illegal operation. Through continuous learning and adjustment of the model, abnormal current behavior in the power grid can be monitored and diagnosed in real time, which provides strong technical support for the safe operation and maintenance of the power grid and builds a dynamic nonlinear model.

[0089] Specifically, if Figure 2 , 4 As shown, the stability analysis module includes:

[0090] The state variable selection submodule selects state variables based on the dynamic nonlinear model, identifies the operation status of the power grid, and screens the key variables affecting power stability through data correlation analysis and impact assessment. The execution process of obtaining key state indicators is as follows;

[0091] Based on the dynamic nonlinear model, batch real-time and historical power grid data are collected and analyzed. Through data mining techniques such as correlation analysis and principal component analysis, the key state variables of power grid operation are determined. The variables include voltage, current, frequency and temperature. The screening of key variables is based on the degree of influence on the stability of the power grid. This process ensures that only the most influential variables are selected for further operation status evaluation. Through key indicators, abnormal or potential unstable factors in the power grid can be quickly identified and key status indicators can be obtained.

[0092] The energy trend analysis submodule uses key status indicators and time series analysis to track the energy change trend of electricity. Through comparative analysis of long-term and short-term energy fluctuation data, it identifies potential unstable factors and obtains the execution process of energy fluctuation analysis results as follows;

[0093] By analyzing and processing the historical and real-time data of the power grid, and applying time series analysis methods such as the autoregressive moving average (ARMA) model, we can track and predict the changing trends of electric energy. This process involves decomposing energy data into seasonal and trend components to more accurately understand the long-term and short-term changes in energy. It also compares long-term and short-term data to identify potential energy fluctuations caused by load changes, weather conditions or external factors. Through analysis, factors that lead to power grid instability can be discovered and prevented in a timely manner, and energy fluctuation analysis results can be obtained.

[0094] The response evaluation submodule evaluates the response of the power grid under differentiated load disturbances through the energy fluctuation analysis results, calculates the stability index of the node load response, and obtains the load disturbance stability identification results. The execution process is as follows;

[0095] The formula for calculating the stability index of the node load response is:

[0096]

[0097] Among them, S is the stability index of node load response, w i represents the weight coefficient of node i, x i represents the load value of node i after disturbance, μ i represents the average load value of node i, represents the variance of the load value of node i, ∈ is a positive number, and n is the number of nodes;

[0098] Parameter meaning and setting value:

[0099] w i The weight coefficient of node i reflects the importance of the node in the overall grid stability. The weight is set according to the node's historical fault data and its position in the grid. For example, node i is an important distribution station with a higher weight.

[0100] x i represents the real-time load value of node i after the load disturbance, which is obtained through the real-time monitoring system of the power grid;

[0101] μ i represents the historical average load value of node i, obtained through historical data analysis;

[0102] It represents the load fluctuation variance of node i, which is used to measure the stability of the node load and is obtained through statistical analysis of historical load data;

[0103] ∈ is a small positive number used to ensure that the denominator is non-zero, set to 0.01 to increase the calculation stability of the formula;

[0104] The data for setting three nodes is as follows:

[0105] Node 1: w1=0.5, x1=100, μ1=95,

[0106] Node 2: w2=0.3, x2=103, μ2=100,

[0107] Node 3: w3=0.2, x3=98, μ3=99,

[0108] Substitute the parameters into the formula for calculation:

[0109]

[0110] The result of 0.87 shows that the power grid has a higher stability response under the current load disturbance. The numerical result of 0.87 can be used to judge the real-time stability level of the power grid by comparing with the historical stability index. This value can be compared with the stability value of the node to determine whether the power grid operation needs to be adjusted or optimized.

[0111] Specifically, if Figure 2 , 5 As shown, the remote meter reading module includes:

[0112] The real-time reading submodule uses the load disturbance stability identification results and the real-time data transmission function of the smart energy meter to automatically collect the power, voltage and time information of each user, synchronize the data, verify the timeliness of the information, and obtain the real-time power consumption data. The execution process is as follows;

[0113] Through the function of smart electricity meters, the power consumption, voltage and time information of each user are automatically collected. The process includes the configuration of smart meters to ensure real-time transmission of data, and ensures the synchronization and timeliness of data through network transmission. The data includes voltage peak, current change, etc. This process not only involves data collection, but also includes checking the integrity and accuracy of data through data verification procedures. The analysis of real-time data can help power companies better understand users' electricity demand, optimize resource allocation and power supply plans, ensure the efficiency and safety of power grid operation, provide information for further data analysis and decision-making, and obtain real-time electricity consumption data.

[0114] The behavior analysis submodule analyzes the power consumption patterns and voltage fluctuations of differentiated users based on real-time power consumption data, classifies users according to their power consumption behaviors, identifies users that affect stability, and obtains the execution process of the user power consumption behavior classification results as follows;

[0115] Through data analysis techniques, such as cluster analysis and pattern recognition, we can identify users' electricity consumption patterns and voltage fluctuations. The technology helps determine how user behavior affects the stability of the power grid. For example, by analyzing the daily periodic changes in electricity consumption and voltage fluctuations, users can be classified into high-risk and low-risk users. The classification is based on the user's electricity consumption behavior and the grid's response to the behavior. This process analyzes and compares data from different user groups to identify behavioral patterns that lead to grid instability, provide a basis for formulating targeted management strategies and optimizing grid operation, and obtain classification results for user electricity consumption behavior.

[0116] The impact assessment submodule uses the classification results of user power consumption behavior and combines the load disturbance stability identification results to evaluate the impact of differentiated user categories on grid stability. The execution process of obtaining the user load impact analysis results is as follows;

[0117] To evaluate the impact of differentiated user categories on grid stability, follow the formula:

[0118]

[0119] Calculate the user load impact analysis results, where S is the quantitative value of the impact of user category on power grid stability, U b represents the influence coefficient of the electricity consumption behavior of the b-th user on the grid stability, P b represents the power consumption probability of the b-th user, and k represents the total number of user categories;

[0120] It is assumed that there are three types of users, and the influence coefficient of each type of user on the grid stability is:

[0121] U1=0.3, U2=0.5, U3=0.2;

[0122] The power consumption probabilities are:

[0123] P1=0.4, P2=0.35, P3=0.25;

[0124] The calculation in the formula is:

[0125] S=(0.3·0.4)+(0.5·0.35)+(0.2·0.25)

[0126] =0.12+0.175+0.05

[0127] =0.345

[0128] Through this calculation, the overall impact of different user categories on grid stability can be quantitatively evaluated, providing a basis for formulating more effective load regulation strategies.

[0129] Specifically, if Figure 2 , 6 As shown, the real-time response module includes:

[0130] The fluctuation assessment submodule uses the user load impact analysis results, combined with the real-time monitoring data of the power grid, to analyze the frequency and amplitude of power fluctuations, and uses the fluctuation index to quantify the stability of the power grid in each time period, evaluate the changes caused by the user's power consumption behavior, and obtain the execution process of the fluctuation response index as follows;

[0131] By integrating the results of user load impact analysis and real-time monitoring data of the power grid, and using advanced data analysis techniques such as Fourier transform to analyze the frequency of power fluctuations and standard deviation to calculate the amplitude of fluctuations, the technology helps to accurately quantify the stability indicators of the power grid in different time periods. By evaluating the fluctuation indicators, it is possible to analyze how user electricity consumption behavior affects the stability of the power grid. This analysis process not only identifies the main factors causing power fluctuations, but also evaluates the actual impact of the factors through quantitative methods, so that operators can take corresponding measures to adjust or optimize power grid operations and obtain fluctuation response indicators.

[0132] The behavior response submodule uses the fluctuation response index to analyze the behavior patterns of differentiated user groups, calculates the deviation value of user power consumption behavior, and obtains the key behavior impact analysis results. The execution process is as follows;

[0133] The formula for calculating the deviation value of the user's power consumption behavior is:

[0134]

[0135] Among them, R is the quantitative result of the impact of the user's power consumption behavior deviation, P user Represents the user's real-time power consumption, μ user Represents the average value of the user's power consumption, σ user Represents the standard deviation of the user's power consumption, S grid Represents the power grid fluctuation amplitude, V grid represents the power grid fluctuation frequency, □ represents a positive number, W1 and W2 represent weight coefficients;

[0136] Parameter meaning and setting value:

[0137] P user The user's real-time power consumption, which can be obtained through real-time monitoring of smart meters, for example, 22 kilowatts;

[0138] μ user The historical average power consumption of users is calculated based on the power consumption data over the past period of time and is set to 18 kilowatts.

[0139] σ user The standard deviation of the user's power consumption indicates the degree of fluctuation of power consumption. It is calculated based on historical data and is set to 4 kilowatts.

[0140] S grid is the power grid fluctuation amplitude, obtained through monitoring of power grid operation data, and is set to 0.5;

[0141] V grid is the grid fluctuation frequency, which is also obtained through grid data monitoring and is set to 0.1;

[0142] ∈ is a small positive number used to ensure that the denominator is non-zero, set to 0.01;

[0143] W1 and W2 are weight coefficients, which are adjusted according to the importance of historical data analysis. W1 is set to 0.7, reflecting the importance of user behavior deviation;

[0144] W2 is set to 0.3, reflecting the importance of grid fluctuation sensitivity;

[0145] Substitute the parameters into the formula for calculation:

[0146]

[0147] The result 1.339 shows that the combined impact of user behavior deviation and grid fluctuation is above medium, indicating that the user's influence on grid stability is relatively obvious. This value is higher than 1, indicating that there is a clear correlation between user electricity consumption behavior and grid fluctuation, which has reference value for the adjustment of grid stability and operation strategy.

[0148] The load adjustment submodule designs a dynamic load distribution mode based on the key behavior impact analysis results, adjusts power resources, matches real-time demand and stabilizes the power grid, and obtains the execution process of the dynamic load adjustment scheme as follows;

[0149] Design a targeted dynamic load distribution mode. This process includes evaluating the total load demand of the current power grid and the power supply status of each region, and optimizing and calculating the most appropriate resource allocation plan through algorithms. The algorithms include linear programming or multi-objective optimization to ensure that while meeting real-time needs, the operating efficiency and stability of the power grid are maximized. Time factors and geographical distribution are also taken into account to dynamically adjust power resources. For example, power supply to certain areas can be increased during peak demand periods, while it can be reduced during off-peak periods. Through this dynamic adjustment, the power grid can respond more flexibly to various emergencies, ensure the stable operation of the power grid under different conditions, optimize energy consumption, improve overall energy efficiency, and obtain a dynamic load adjustment plan.

[0150] Specifically, if Figure 2 , 7 As shown, the demand forecasting module includes:

[0151] The trend analysis submodule implements the dynamic load adjustment scheme, combines the load change trend, identifies the long-term change pattern and periodic fluctuation of the power grid load, extracts key change indicators, and obtains the execution process of the load trend indicator as follows;

[0152] By identifying the long-term change patterns and periodic fluctuations of the grid load, data analysis is performed. By analyzing the historical grid operation data, the main load growth points and load reduction points are identified. The data points are provided by the historical grid operation data and time series analysis, and the key change indicators are determined through statistical analysis. Each change indicator reflects a specific state in the operation of the grid, such as peak load time, minimum load time, and load value at a point in time. It is also necessary to combine external factors such as climate change and economic activities. External factors are considered through correlation analysis and regression models. The variables added to the analysis model, such as temperature, industrial output, etc., are all accurately collected and processed to ensure the accuracy of the data and the effectiveness of the analysis, and to obtain load trend indicators.

[0153] The state monitoring submodule uses load trend indicators and real-time grid operation data to monitor the immediate state of the grid, including total load, frequency, and voltage levels. It identifies the stability and abnormal state of grid operation through real-time data stream analysis, and obtains the execution process of real-time state analysis results as follows;

[0154] Using real-time grid operation data, according to the formula:

[0155] L=∑(V i ·F i )

[0156] Calculate the total load L, where V i represents the voltage of the ith node, F i represents the frequency of the ith node, and the total load is obtained by multiplying the two together;

[0157] The specific number of nodes is set to 3, i.e., i = 1, 2, 3. The voltage and frequency of each node are collected in real time through sensors, for example:

[0158] V1=230V, V2=230V, V3=230V;

[0159] F1=50Hz, F2=49.8Hz, F3=50.2Hz;

[0160] The calculation in the formula is:

[0161] L=(230V·50Hz)+(230V·49.8Hz)+(230V·50.2Hz)

[0162] =34500VA+11454VA+11546VA

[0163] =34500VA

[0164] Through this calculation, the power grid status can be tracked in real time. If the frequency or voltage is abnormal, an alarm will be issued immediately and the problem area will be identified.

[0165] The consistency detection submodule evaluates the grid demand and potential supply-demand gap in the future time period based on the real-time status analysis results, optimizes the consistency of the prediction based on the real-time data and instant parameters, and obtains the grid demand distribution prediction results. The execution process is as follows;

[0166] Evaluate the demand of the power grid and the potential supply-demand gap. During the execution process, compare and analyze the supply and demand of the power grid under similar conditions through historical data. The data includes the historical total load, the historical highest peak and valley power, and the time points of occurrence. Through historical data analysis, it is possible to predict the demand trend of the power grid in the next time period. Combined with the current real-time data, such as the current total load, frequency and voltage level, through data processing algorithms such as time series analysis, regression analysis and pattern recognition technology, comprehensively evaluate the future operation status and demand distribution of the power grid. In this process, continuously compare the prediction results with real-time data to optimize the consistency of the prediction, realize dynamic adjustment of the potential supply-demand gap, and obtain the prediction results of the power grid demand distribution.

[0167] Specifically, if Figure 2 , 8 As shown, the load balancing module includes:

[0168] The path identification submodule identifies the optimal path and key nodes for load adjustment based on the grid demand distribution prediction results, evaluates the transmission efficiency and potential risks of differentiated paths, and obtains the execution process of the path optimization analysis results as follows;

[0169] Identify the optimal path and key nodes for load adjustment according to the formula:

[0170] P = ∑(e ij ·r ij )

[0171] Calculate the transmission efficiency P, where e ij represents the grid efficiency from node i to node j, r ij represents the risk coefficient from node i to node j. The two are multiplied and added together to get the total transmission efficiency;

[0172] The specific node pairs are set to 3 pairs, namely (i, j) = (1, 2), (2, 3), (3, 1). The grid efficiency and risk coefficient of each pair of nodes are calculated through the historical data and real-time monitoring data of the grid, for example:

[0173] e 12 =0.95, e 23 =0.90, e 31 =0.92;

[0174] r 12 =0.05, r 23 =0.07, r 31 =0.06;

[0175] The calculation in the formula is:

[0176] P=(0.95·0.05)+(0.90·0.07)+(0.92·0.06)

[0177] =0.0475+0.063+0.0552

[0178] =0.1657

[0179] Through this calculation, the transmission efficiency and potential risks of each path can be evaluated, grid operation and adjustment strategies can be optimized, and efficient and stable power transmission can be ensured.

[0180] The regional analysis submodule uses the path optimization analysis results, GIS and power grid load data to analyze the grid load characteristics of differentiated regions, identify areas with unstable loads, and obtain the execution process of the regional load feature set as follows;

[0181] Perform regional power grid load characteristic analysis. This process involves obtaining geographic information parameters from GIS data, combining it with real-time load data of power grid operation, analyzing the load characteristics of each region. The characteristics include load peaks, valleys, time points and geographical locations, distinguishing between areas of load stability and load volatility, and analyzing the causes of unstable load areas, such as connection problems, equipment aging, or external factors such as weather and social activities. The analysis results help further optimize the structure and operation strategy of the power grid and obtain a set of regional load characteristics.

[0182] The strategy formulation submodule designs differentiated grid balance control schemes based on the regional load feature set, optimizes power supply allocation, verifies the reasonable allocation of power resources, and obtains the execution process of the power supply allocation strategy as follows;

[0183] Design a grid balancing control plan. The design process includes analyzing and comparing the power supply demand in different regions, considering the reliability and transmission efficiency of the regional power grid connection, and using data analysis methods such as linear programming and network flow analysis to determine the optimal configuration of power supply. The design also needs to consider various potential risk factors such as equipment failure rate and environmental impact to ensure the rational allocation of power resources and the stable operation of the power grid. The effectiveness of the control plan is verified through actual application scenario testing to obtain a power supply allocation strategy.

[0184] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A smart electric energy meter with a remote meter reading module, characterized in that: The table includes: The model building module analyzes power consumption, voltage fluctuations and current characteristics based on the operating status of the power grid, and builds a dynamic nonlinear model based on the monitoring results of the dynamic behavior of the power grid by the smart energy meter; The stability analysis module uses the dynamic nonlinear model to select state variables, analyze the energy change trend and the stability of power operation, and obtain the load disturbance stability identification result; The remote meter reading module obtains the user's electricity consumption parameters and obtains the user's load impact analysis results through the load disturbance stability identification results and the real-time reading function of the smart electric energy meter; The real-time response module evaluates the response of the power consumption behavior to the overall load based on the user load impact analysis result and the power fluctuation detected in real time in the power grid, analyzes the priority of dynamic adjustment, and obtains a dynamic load adjustment plan; The demand forecasting module predicts the grid demand distribution and the supply-demand gap through the dynamic load adjustment scheme, combined with the load change trend and the real-time grid operation status, and obtains the grid demand distribution forecast result; The load balancing module identifies and analyzes the load adjustment path through the power grid demand distribution prediction result, balances and regulates the differentiated regional power grids, performs power supply allocation, and obtains a power supply allocation strategy.

2. The smart electric energy meter with remote meter reading module according to claim 1, characterized in that: The dynamic nonlinear model includes a power consumption model, a voltage fluctuation response model, and a current characteristic analysis model. The load disturbance stability identification result includes a stability rating, a risk index, and identification of potential disturbance points. The user load impact analysis result includes user power consumption pattern classification, consumption point identification, and mapping of key load areas. The dynamic load adjustment plan includes emergency load reduction measures, priority adjustment rules, and load allocation strategies for critical time periods. The power grid demand distribution forecast results include demand peak period forecasts, supply and demand information in key areas, and early warnings of potential supply shortages. The power supply allocation strategy includes voltage adjustment in differentiated areas, load transfer plans, and setting of emergency response paths.

3. The smart electric energy meter with remote meter reading module according to claim 1, characterized in that: The model building module includes: The energy consumption analysis submodule extracts consumption patterns and peak data based on the operating status of the power grid, uses time series analysis to determine trends and anomalies, analyzes average power consumption and load points, and obtains power consumption characteristic records; The fluctuation monitoring submodule uses the power consumption characteristic record to compare the voltage readings in the differentiated time periods, analyzes the voltage fluctuation caused by the load change, and measures the fluctuation size, quantitatively monitors the voltage stability, and obtains the fluctuation trend analysis results; The behavior diagnosis submodule analyzes the time dependency of the current behavior through the fluctuation trend analysis results, tracks the current change points, identifies abnormal behaviors, and constructs a dynamic nonlinear model.

4. The smart electric energy meter with a remote meter reading module according to claim 1, characterized in that: The stability analysis module includes: The state variable selection submodule selects state variables based on the dynamic nonlinear model, identifies the operation status of the power grid, screens key variables affecting power stability through data correlation analysis and impact assessment, and obtains key state indicators; The energy trend analysis submodule uses the key status indicators and time series analysis to track the energy change trend of the power, identifies potential unstable factors through comparative analysis of long-term and short-term energy fluctuation data, and obtains energy fluctuation analysis results; The response evaluation submodule evaluates the response of the power grid under differentiated load disturbances through the energy fluctuation analysis results, calculates the stability index of the node load response, and obtains the load disturbance stability identification result.

5. The smart electric energy meter with remote meter reading module according to claim 4, characterized in that: The formula for calculating the stability index of the node load response is: Among them, S is the stability index of node load response, w i represents the weight coefficient of node i, x i represents the load value of node i after disturbance, μ i represents the average load value of node i, represents the variance of the load value of node i, ∈ is a positive number, and n is the number of nodes.

6. The smart electric energy meter with remote meter reading module according to claim 1, characterized in that: The remote meter reading module comprises: The real-time reading submodule uses the load disturbance stability identification result and the real-time data transmission function of the smart energy meter to automatically collect the power, voltage and time information of each user, perform data synchronization, verify the timeliness of the information, and obtain real-time power consumption data; The behavior analysis submodule analyzes the power consumption patterns and voltage fluctuations of differentiated users based on the real-time power consumption data, classifies users according to their power consumption behaviors, identifies users that are affected by stability, and obtains the classification results of user power consumption behaviors; The impact assessment submodule utilizes the user power consumption behavior classification results, combined with the load disturbance stability identification results, to assess the impact of differentiated user categories on power grid stability and obtain user load impact analysis results.

7. The smart electric energy meter with a remote meter reading module according to claim 1, characterized in that: The real-time response module includes: The fluctuation assessment submodule uses the user load impact analysis results, combined with the real-time monitoring data of the power grid, to analyze the frequency and amplitude of power fluctuations, and uses fluctuation indicators to quantify the stability of the power grid in each time period, evaluate the changes caused by user power consumption behavior, and obtain fluctuation response indicators; The behavior response submodule uses the fluctuation response index to perform behavior pattern analysis of differentiated user groups, calculates the deviation value of user power consumption behavior, and obtains key behavior impact analysis results; The load adjustment submodule designs a dynamic load distribution mode through the key behavior impact analysis results, adjusts power resources, matches real-time demand and stabilizes the power grid, and obtains a dynamic load adjustment plan.

8. The smart electric energy meter with remote meter reading module according to claim 7, characterized in that: The formula for calculating the deviation value of the user's power consumption behavior is: Among them, R is the quantitative result of the impact of the user's power consumption behavior deviation, P user Represents the user's real-time power consumption, μ user Represents the average value of the user's power consumption, σ user Represents the standard deviation of the user's power consumption, S grid Represents the power grid fluctuation amplitude, V grid represents the power grid fluctuation frequency, □ represents a positive number, and W1 and W2 represent weight coefficients.

9. The smart electric energy meter with a remote meter reading module according to claim 1, characterized in that: The demand forecasting module comprises: The trend analysis submodule implements the dynamic load adjustment scheme, identifies the long-term change pattern and periodic fluctuation of the power grid load in combination with the load change trend, extracts key change indicators, and obtains load trend indicators; The state monitoring submodule uses the load trend indicator and real-time power grid operation data to monitor the instantaneous state of the power grid, including the total load, frequency and voltage level, and identifies the stability and abnormal state of the power grid operation through real-time data flow analysis to obtain real-time state analysis results; The consistency detection submodule evaluates the grid demand and potential supply-demand gap in the future time period according to the real-time status analysis results, optimizes the consistency of the prediction according to the real-time data and instant parameters, and obtains the grid demand distribution prediction results.

10. The smart electric energy meter with remote meter reading module according to claim 1, characterized in that: The load balancing module comprises: The path identification submodule identifies the optimal path and key nodes for load adjustment based on the grid demand distribution prediction result, evaluates the transmission efficiency and potential risks of the differentiated paths, and obtains the path optimization analysis result; The regional analysis submodule uses the path optimization analysis results to analyze the grid load characteristics of differentiated regions through GIS and grid load data, identifies the regions with unstable loads, and obtains a regional load feature set; The strategy formulation submodule designs differentiated power grid balancing and control schemes according to the regional load feature set, optimizes power supply allocation, verifies the reasonable allocation of power resources, and obtains a power supply allocation strategy.

Images