A power grid management and control method and device and an intelligent remote meter

Abstract

The application discloses a power grid management and control method and device and an intelligent remote electric meter in the technical field of power system automation, and relates to the technical field of power grid management and control.The method comprises the following steps: acquiring a current multi-dimensional index function of a power grid, wherein the multi-dimensional index function comprises an electricity cost function, a carbon emission function and a load imbalance function, and each variable in the functions is an electricity strategy variable; constructing a target optimization model based on the multi-dimensional index function, wherein the objective function of the target optimization model is a function for comprehensively optimizing the function values in the multi-dimensional index function; performing optimization and solution on the target optimization model to obtain the values of each electricity strategy variable, determining an optimal electricity strategy based on the values of each electricity strategy variable, and realizing the management and control of the power grid based on the optimal electricity strategy. Thus, the cost can be effectively reduced while ensuring the optimization of the electricity operation scheme, the data docking method is convenient, and the real-time acquisition of a dynamic carbon emission factor can be realized.

Description

Technical Field

[0001] This invention relates to the field of power system automation technology, and more specifically, to a power grid management method, device, and smart remote meter. Background Technology

[0002] While existing remote-reading meters possess basic data acquisition and transmission functions, they still have significant shortcomings in intelligent power grid management.

[0003] In terms of electricity consumption management, existing technologies typically involve manually setting electricity consumption strategies based on electricity consumption data collected from remote meters and actual conditions, and then using these strategies to manage electricity consumption. However, this approach requires experienced staff, resulting in high labor costs, and is easily influenced by the staff's supervisory factors, which may prevent the electricity consumption strategies from achieving optimal results. Summary of the Invention

[0004] The purpose of this invention is to provide a power grid management method, device, and smart remote meter that can effectively reduce costs while ensuring the optimization of power operation schemes.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A power grid management method, applied to a smart remote meter, the power grid management method comprising:

[0007] Obtain the current multi-dimensional indicator functions of the power grid; wherein, the multi-dimensional indicator functions include the electricity cost function, the carbon emission function, and the load imbalance function, and each variable in the function is an electricity consumption strategy variable;

[0008] A target optimization model is constructed based on the multi-dimensional index function; wherein, the objective function of the target optimization model is the function that makes the comprehensive value of each function in the multi-dimensional index function optimal.

[0009] The target optimization model is optimized and solved to obtain the values ​​of each electricity consumption strategy variable. The optimal electricity consumption strategy is determined based on the values ​​of each electricity consumption strategy variable, and the power grid is controlled based on the optimal electricity consumption strategy.

[0010] Preferably, the load imbalance function is expressed as:

[0011]

[0012] Among them, D total The overall load imbalance is represented by the value of the load imbalance function, D. region D represents the degree of load imbalance between spatial partitions. time L represents the load imbalance within a time segment.i This represents the real-time load of the i-th partition. L represents the average load across all partitions, where n represents the number of partitions. max L min L avg These represent the peak, trough, and mean load values ​​within the time segments, respectively, with α and β being the spatial and temporal weighting coefficients, respectively.

[0013] Preferably, the carbon emission function is expressed as:

[0014]

[0015] Where E represents carbon emissions, and its value is the value of the carbon emissions function, λ t L represents the dynamic factor of carbon emissions within a time segment t. t This represents the total load within time segment t, where T represents the total time period consisting of all time segments.

[0016] Preferably, the process of obtaining the dynamic factor of carbon emissions includes:

[0017] Obtain the power consumption structure data of the power grid within the corresponding time segment;

[0018] Based on the power consumption structure data of the power grid, determine the proportion of each type of power source in the power grid;

[0019] Obtain the baseline carbon emission factors corresponding to each type of power source, multiply the proportion of each type of power source by the corresponding baseline carbon emission factor, and sum the results of the multiplication to obtain the dynamic carbon emission factor.

[0020] Preferably, the electricity cost function is expressed as:

[0021]

[0022] Where C represents the cost of electricity, and its value is the value of the electricity cost function, p t L represents the electricity price within a time segment t. t φ represents the total load within time segment t, where T represents the total time period consisting of all time segments. k I represents the compensation unit price for the k-th type of interruptible load. k Let K represent the interruption decision variable for the k-th type of interruptible load, where K represents the number of interruptible load categories.

[0023] Preferably, after implementing control over the power grid based on the optimal power consumption strategy, the method further includes:

[0024] The system monitors the electricity cost, carbon emissions, and load imbalance of the power grid in real time, and when the change value of at least one of the monitored data reaches the corresponding threshold, it executes the step of obtaining the current multi-dimensional index function of the power grid.

[0025] Preferred options also include:

[0026] The system collects electricity consumption data from the power grid in real time and analyzes the power quality of the power grid based on the collected electricity consumption data; wherein the electricity consumption data includes voltage, current, power, and electricity consumption.

[0027] The power consumption data and power quality are transmitted to the cloud or local management platform based on the preset LoRa wireless communication module.

[0028] Preferred options also include:

[0029] The power grid is monitored in real time;

[0030] If an electrical abnormality or equipment failure is detected in the power grid, an alarm will be triggered in a preset manner; if the load power in the power grid exceeds a preset power threshold, the load will be reduced or a power outage will be triggered.

[0031] A power grid management device is applied to a smart remote meter, the power grid management device comprising:

[0032] The acquisition module is used to: acquire the current multi-dimensional indicator functions of the power grid; wherein, the multi-dimensional indicator functions include the electricity cost function, the carbon emission function, and the load imbalance function, and all variables in the functions are electricity consumption strategy variables;

[0033] The construction module is used to: construct a target optimization model based on the multi-dimensional index function; wherein, the objective function of the target optimization model is a function that makes the comprehensive value of each function in the multi-dimensional index function optimal;

[0034] The control module is used to: optimize and solve the target optimization model to obtain the values ​​of various electricity consumption strategy variables, determine the optimal electricity consumption strategy based on the values ​​of various electricity consumption strategy variables, and implement control over the power grid based on the optimal electricity consumption strategy.

[0035] A smart remote electricity meter is installed at a designated point in the power grid and includes the power grid management device as described above.

[0036] This invention provides a power grid management method, device, and smart remote meter. The method includes: acquiring a multi-dimensional index function of the current power grid, wherein the multi-dimensional index function includes an electricity cost function, a carbon emission function, and a load imbalance function, and each variable in the function is an electricity consumption strategy variable; constructing a target optimization model based on the multi-dimensional index function, wherein the objective function of the target optimization model is the function that optimizes the comprehensive values ​​of each function in the multi-dimensional index function; optimizing and solving the target optimization model to obtain the values ​​of each electricity consumption strategy variable; determining the optimal electricity consumption strategy based on the values ​​of each electricity consumption strategy variable; and implementing power grid management based on the optimal electricity consumption strategy. This invention, through multi-dimensional data fusion modeling and a global optimization algorithm, can simultaneously coordinate the conflicting relationships of multiple dimensions such as electricity cost, carbon emissions, and load imbalance in a single calculation, achieving collaborative optimization of power grid operating parameters under multi-dimensional constraints. The optimization results of these multiple dimensions mutually restrict and promote each other, forming a globally optimal power grid operation scheme. The entire process is automatic, requiring no manual intervention, thereby effectively reducing costs while ensuring the optimization of the electricity consumption operation scheme. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0038] Figure 1 A flowchart of a power grid management method provided in an embodiment of the present invention;

[0039] Figure 2 This is a schematic diagram of a power grid control device provided in an embodiment of the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] To address the technical problems existing in current technologies, the inventors studied the coupling relationships among power grid operating parameters and discovered that electricity costs, carbon emissions, and load balance exhibit nonlinear correlation characteristics, and adjusting a single variable can lead to the deterioration of other indicators. Based on this, they proposed a multi-dimensional data fusion model to establish a comprehensive optimization objective function, and then used mathematical methods to solve for the multi-objective equilibrium point. Furthermore, intelligent optimization algorithms are employed to handle high-dimensional decision variables, enabling the model to adapt to dynamically changing power grid environments.

[0042] Please see Figure 1 The diagram illustrates a flowchart of a power grid management method provided by an embodiment of the present invention, applied to a smart remote meter. The power grid management method includes:

[0043] S11: Obtain the current multi-dimensional indicator function of the power grid; wherein, the multi-dimensional indicator function includes the electricity cost function, the carbon emission function and the load imbalance function, and all variables in the function are electricity consumption strategy variables.

[0044] It should be noted that multi-dimensional index functions refer to a set of functions that can simultaneously reflect the economic efficiency, environmental protection, and operational stability of the power grid. These include electricity cost functions, carbon emission functions, and load imbalance functions, which are used to calculate the electricity cost, carbon emission, and load imbalance of the power grid, respectively.

[0045] Among these, electricity cost refers to the expense of using electricity from the power grid, carbon emissions are the amount of carbon emitted during the power grid's electricity consumption process, and load imbalance is the load balance of the power grid in different spaces and at different times. Furthermore, the variables used in the multi-dimensional index function to calculate electricity cost, carbon emissions, and load imbalance are all electricity consumption strategy variables, which are controllable power grid operation parameters, such as power grid load and interruptible load dispatching. Adjusting these variables directly affects electricity cost, carbon emissions, and load distribution.

[0046] S12: Construct a target optimization model based on the multi-dimensional index function; wherein, the objective function of the target optimization model is the function that makes the comprehensive value of each function in the multi-dimensional index function optimal.

[0047] The objective optimization model refers to transforming a multi-objective optimization problem into a solvable mathematical model. Specifically, the objective function can be constructed using the weighted summation method or the Pareto optimal method. Constraints reflect the physical limitations and operating specifications of the power grid (such as power grid safety thresholds, minimum guaranteed power usage time for users, and equipment start-up and shutdown frequency limits) to ensure the feasibility of the optimization results.

[0048] The objective function of the target optimization model is constructed as a weighted combination of three indicators: electricity cost, carbon emissions, and load imbalance. The weight coefficients can be dynamically configured according to the needs of the grid operator. For example, it can be expressed as: Min(a×electricity cost+b×carbon emissions+c×load imbalance), where a, b, and c are the weight coefficients.

[0049] S13: Optimize and solve the target optimization model to obtain the values ​​of each electricity consumption strategy variable, determine the optimal electricity consumption strategy based on the values ​​of each electricity consumption strategy variable, and implement the control of the power grid based on the optimal electricity consumption strategy.

[0050] In the solution process, genetic algorithms or particle swarm optimization algorithms can be used to handle high-dimensional nonlinear optimization problems, obtaining the optimal combination of power consumption strategy variables that maximizes the comprehensive index through iterative calculations. Based on the calculated combination of power consumption strategy variables, the corresponding power consumption strategy is finally generated. For example, the grid load can be set as the load target value in the power consumption strategy, and whether or not interruptible loads are interrupted can be set as the interruptible load control target in the power consumption strategy. Furthermore, the setting scheme for zoned time-of-use management can be determined based on the combination of power consumption strategy variables, etc. Finally, the power grid operation state is optimized by executing this strategy through electronic power equipment.

[0051] This solution, through multi-dimensional data fusion modeling and global optimization algorithms, can simultaneously coordinate the conflicting relationships between multiple dimensions of objectives, such as electricity cost, carbon emissions, and load imbalance, in a single calculation. This enables the collaborative optimization of power grid operating parameters under multi-dimensional constraints. The optimization results of these multiple dimensions are mutually restrictive and mutually reinforcing, forming a globally optimal power grid operation scheme. Moreover, the entire process is automated and requires no manual intervention, thereby effectively reducing costs while ensuring the optimization of the electricity operation scheme.

[0052] The power grid management method provided in this embodiment of the invention, wherein the load imbalance function can be expressed as:

[0053]

[0054] Among them, D total The overall load imbalance is represented by the value of the load imbalance function, D. region D represents the degree of load imbalance between spatial partitions. time L represents the load imbalance within a time segment (the segment where power consumption strategy optimization needs to be implemented; this could be a single segment, several segments, or the total time period consisting of all segments). i This represents the real-time load of the i-th partition. L represents the average load across all partitions, where n represents the number of partitions. max L min L avgThese represent the peak, trough, and mean load values ​​within the time segments, respectively, with α and β being the spatial and temporal weighting coefficients, respectively.

[0055] The electricity cost function can be expressed as:

[0056]

[0057] Where C represents the cost of electricity, and its value is the value of the electricity cost function, p t L represents the electricity price within a time segment t. t φ represents the total load within time segment t, where T represents the total time period consisting of all time segments. k I represents the compensation unit price for the k-th type of interruptible load. k Let K represent the interruption decision variable for the k-th type of interruptible load, where K represents the number of interruptible load categories.

[0058] The carbon emission function can be expressed as:

[0059]

[0060] Where E represents carbon emissions, and its value is the value of the carbon emissions function, λ t L represents the dynamic factor of carbon emissions within a time segment t. t This represents the total load within time segment t, where T represents the total time period consisting of all time segments.

[0061] It should be noted that, in this embodiment of the invention, the power grid can be divided into different physical / logical regions according to physical / logical characteristics to obtain multiple partitions, and time-sharing and partitioned management of the power grid can be achieved based on these multiple partitions. Furthermore, this application sets the load imbalance as a combination of load imbalance in spatial partitions and load imbalance in time segments. The load imbalance between partitions refers to the difference in load distribution between different partitions of the power grid, which can be specifically achieved by calculating the deviation between the real-time load and the average load of each partition, for example, by using statistical methods such as variance or standard deviation to quantify the spatial distribution difference. The load imbalance within a time segment refers to the load fluctuation characteristics within a certain time period, which can be specifically achieved by analyzing the difference between the peak, valley, and average load values ​​within the time period, for example, by using the ratio of the peak-valley difference to the average value to characterize the time distribution difference. The total load imbalance is obtained by weighted summing of the load imbalance between spatial partitions and the load imbalance within a time segment.

[0062] Specifically, the calculation of load imbalance between time zones is based on the power grid topology, dividing the grid into multiple logical or physical zones. By comparing the real-time load of each zone with the overall average load, areas with excessively high or low loads are identified. The calculation of load imbalance within time segments is based on time-series data, dividing the operating cycle into multiple time periods. By analyzing the peak-valley fluctuations of the load within each time period, the patterns of load changes over time are revealed. Finally, the spatial zone differences and the temporal segment differences are weighted or fused to obtain a comprehensive index reflecting the overall load status of the power grid. Thus, through this dual-dimensional calculation by zone and segment, the degree of load imbalance can be accurately identified, providing more granular data support for dynamically adjusting electricity consumption strategies.

[0063] In this embodiment of the invention, the electricity cost includes the cost derived from the total load and electricity price over the total time period, and also includes the cost derived from the compensation unit price and interruption decision variables for different types of interruptible loads. The interruption decision variable takes the value 0 or 1; a value of 0 indicates that the corresponding type of interruptible load has been interrupted and not connected to the grid, while a value of 1 indicates that the corresponding type of interruptible load has been connected to the grid. The compensation electricity price can be preset according to actual needs. Thus, the electricity cost of the grid is represented by the sum of the conventional cost and the compensation cost, thereby improving the accuracy of the electricity cost. In this embodiment of the invention, carbon emissions are set as the carbon emissions emitted over the total time period.

[0064] In one specific implementation, embodiments of the present invention can perform regional hierarchical management after implementing physical or logical partitioning, thereby achieving multi-level power usage permissions and data isolation. For example, in the management logic, the hierarchical architecture can support a three-level management mode of "master table - sub-table - sub-device" (e.g., park → building → floor). Permission allocation can set multiple role permissions such as administrator, operator, and visitor (e.g., only view, control, configuration). Data isolation can set independent statistics for power usage data in each area and prohibit cross-area access. In addition, it can automatically control the on / off of circuits according to a preset time strategy, supporting time-sharing and zoned management. The time setting can support daily, weekly, and holiday cycle modes with an accuracy of ±1 second. It can be linked with other devices (e.g., air conditioners, lighting) to execute energy-saving strategies, and can also be remotely manually intervened to immediately cancel the timed strategy.

[0065] An embodiment of the present invention provides a power grid management method, the process of obtaining the dynamic factor of carbon emissions may include:

[0066] Obtain the power consumption structure data of the power grid within the corresponding time segment;

[0067] Based on the power consumption structure data of the power grid, determine the proportion of each type of power source in the power grid;

[0068] Obtain the baseline carbon emission factors corresponding to each type of power source, multiply the proportion of each type of power source by the corresponding baseline carbon emission factor, and sum the results of the multiplication to obtain the dynamic carbon emission factor.

[0069] Among them, electricity consumption structure data refers to the real-time proportion of various types of power sources in the power grid. Specifically, this can be obtained through generation-side data collected by smart remote meters or the power grid dispatch system, reflecting the current energy composition ratio of the power grid. The carbon emission dynamic factor refers to the carbon emission coefficient dynamically adjusted according to the real-time electricity consumption structure. Specifically, it can be obtained by weighted summing the benchmark carbon emission factors of various power sources with their real-time proportions, used to quantify the carbon emission intensity per unit of electricity consumption under different energy structures.

[0070] Specifically, by collecting real-time data on the power generation proportions of different types of power sources such as thermal power, wind power, and photovoltaic power in the power grid, and combining this with preset benchmark carbon emission factors for each type of power source, a dynamically changing carbon emission factor is calculated. For example, if thermal power accounts for 60%, wind power for 30%, and photovoltaic power for 10% in a certain period, then the dynamic factor is the sum of the thermal power benchmark factor multiplied by 0.6, the wind power benchmark factor multiplied by 0.3, and the photovoltaic benchmark factor multiplied by 0.1. Subsequently, multiplying the dynamic carbon emission factor by the total electricity consumption of the power grid yields the total carbon emissions of the power grid for that period. This process, by dynamically tracking changes in the energy structure, achieves accurate dynamic accounting of the power grid's carbon emissions, solving the problem of carbon emission estimation errors caused by traditional methods that ignore real-time changes in the energy structure.

[0071] In addition, this embodiment of the invention may also include a data interface function. Specifically, when the smart remote meter receives data transmitted from the outside world (such as a power grid dispatching system or other systems), it identifies the protocol type (such as DL / T645, IEC 61850) of the input data (received data), and converts the original protocol type format of the input data into a data format that the smart remote meter can recognize (i.e., the data format of the protocol type required by the smart remote meter when processing data, such as JSONSchema or CIM model), and then hands it over to the smart remote meter for corresponding processing; for example, parsing the Modbus message of the power grid dispatching system, obtaining its "power setpoint" value (such as 5000), converting it into {"cmd":"set_power","value":5.0} (unit kW) that the smart remote meter can recognize, and then handing it over to the smart remote meter for processing. When a smart remote meter needs to transmit data to the outside world, the output data (the data to be transmitted) is first converted into a data format corresponding to the protocol type that the target system (the system that needs to receive the output data) can recognize before being output, allowing the target system to quickly parse the output data. For example, if the output data of the smart remote meter is [0x01, 0x90, 0x12, ...] (representing a voltage of 220V) based on the DL / T645 protocol, then this data is converted to {"Voltage":220,"unit":"V"} before being output to the corresponding system. Simultaneously, each protocol can be independently encapsulated as a plugin (such as a dynamic link library or Docker container), supporting hot-swapping, thus enabling compatibility with corresponding protocols according to actual scenario needs. This gives it strong data interface capabilities, allowing for rapid connection to data from power grid dispatching systems or other systems.

[0072] In addition, the embodiments of the present invention can also provide parameter configuration functions. Specifically, any parameter used in the method of the embodiments of the present invention, including dynamic carbon emission factor, carbon emission calculation formula, etc., can be automatically configured in real time through the corresponding interface or options when needed, thereby giving it strong parameter configuration capabilities and making it convenient to set various parameters.

[0073] It should be noted that the above data interface functions and parameter configuration functions can all be implemented by the management system set inside the smart remote meter. This management system can be a power grid control device, a module set inside the power grid control device, or a part that is parallel to the power grid control device. No specific limitation is made here.

[0074] In order to make the optimized electricity consumption strategy real-time and forward-looking when implementing the electricity consumption strategy in the embodiments of the present invention, the values ​​of the fixed parameters used in the multi-dimensional function can be the values ​​that the parameters should have in the predicted total future time period. For example, the settings of time-sharing zones, electricity prices, electricity consumption structure data, and various weight coefficients can all be predicted. Moreover, the prediction method can be any method that can achieve the prediction purpose, such as deep learning-based implementation, linear model-based implementation, etc., all of which are within the protection scope of the present invention.

[0075] The power grid management method provided in this embodiment of the invention, after managing the power grid based on the optimal power consumption strategy, may further include:

[0076] The system monitors the electricity cost, carbon emissions, and load imbalance of the power grid in real time, and when the change value of at least one of the monitored data reaches the corresponding threshold, it executes the step of obtaining the current multi-dimensional index function of the power grid.

[0077] Real-time monitoring refers to the continuous collection of relevant values ​​of various electricity consumption strategy variables through smart remote meters deployed in the power grid, and the calculation of real-time data on electricity costs, carbon emissions, and load imbalance according to the above formula. Its function is to continuously track the changing trends of the power grid's operating status. Reaching corresponding threshold values ​​means that dynamic critical ranges for electricity costs, carbon emissions, and load imbalance are pre-set, which can be implemented through a sliding window algorithm or a dynamic threshold adjustment model. Its function is to accurately identify abnormal fluctuations or trend deviations in the power grid's operating status. The step of obtaining the current multi-dimensional indicator function of the power grid refers to triggering the restart of the electricity consumption strategy optimization process when any monitored indicator exceeds the threshold. This can be implemented using an event-driven task scheduling mechanism, which forms a closed-loop feedback control link to ensure the dynamic response capability of the strategy optimization process. Since the fixed values ​​(such as weighting coefficients) in the multi-dimensional indicator function may change, the electricity consumption strategy optimization process is restarted from the step of obtaining the current multi-dimensional indicator function of the power grid.

[0078] Specifically, during grid operation, electricity costs may deviate from expectations due to fluctuations in electricity prices or changes in equipment efficiency; carbon emissions may fluctuate due to dynamic adjustments in the energy structure; and load imbalance may be exacerbated by changes in electricity load distribution. By monitoring these three indicators in real time, the system automatically triggers a strategy optimization process when it detects that the fluctuation in electricity costs exceeds a preset percentage, the cumulative increase in carbon emissions exceeds environmental capacity limits, or the load imbalance deviates from a safe range. For example, if the load imbalance continues to rise and exceeds a threshold for three consecutive sampling periods, the system immediately re-acquires the multi-dimensional indicator function and generates a new electricity consumption strategy based on the updated function, thereby avoiding the risk of equipment overload caused by uneven load distribution. Furthermore, the strategy optimization process can be triggered periodically or immediately as needed to meet the requirements of different scenarios. In one specific implementation, detailed energy consumption reports can also be obtained based on the above indicators, including automatically generating multi-dimensional electricity consumption analysis reports, supporting carbon emission accounting, and the time dimension of the report content can include daily, weekly, monthly, and yearly electricity consumption trend charts, peak-valley-flat electricity consumption ratios, and carbon emission data can be based on second-level electricity consumption data and automatically converted according to the regional power grid carbon emission dynamic factor (such as 0.5kg CO2 / kWh). The economic analysis includes electricity cost estimates (requiring input of the electricity price model) and energy-saving potential tips. The output format can be Excel, PDF, or CSV, and it supports API integration with financial systems.

[0079] The embodiments of the present invention can update and optimize strategies in a timely manner when the power grid operating parameters change significantly, ensuring that electricity costs, carbon emissions and load balance are always maintained in an optimal state. This effectively solves the problem of strategy failure caused by the dynamic characteristics of the power grid, and at the same time reduces the continuous consumption of computing resources through the threshold-triggered on-demand optimization mechanism.

[0080] The power grid management method provided in this embodiment of the invention may further include:

[0081] The system collects electricity consumption data from the power grid in real time and analyzes the power quality of the power grid based on the collected electricity consumption data; wherein the electricity consumption data includes voltage, current, power, and electricity consumption.

[0082] The power consumption data and power quality are transmitted to the cloud or local management platform based on the preset LoRa wireless communication module.

[0083] In this embodiment of the invention, a smart remote meter can be used to achieve remote real-time meter reading. Through a LoRa wireless communication module, data such as voltage, current, power, and electricity consumption collected by the smart remote meter are collected and transmitted in real time, supporting second-level data refresh. Its communication distance can reach 2-5km in urban environments (depending on obstacle density). The data frequency can be set to a default of 5 seconds / time, supports custom collection intervals, and is compatible with multiple protocols, such as Modbus and DL / T645.

[0084] It should be noted that electricity consumption data refers to parameters such as voltage, current, power, and electricity consumption generated during power grid operation. This data can be collected in real-time using smart meters, providing fundamental data support for power quality analysis. Power quality analysis involves dynamically assessing factors such as harmonic content, voltage fluctuations, and frequency deviations during power grid operation. This can be achieved using Fast Fourier Transform (FFT) or Wavelet Transform (WFT) algorithms to identify potential anomalies in the power grid. Additionally, the LoRa wireless communication module is a low-power, long-range wireless transmission device based on spread spectrum technology. It can be implemented using half-duplex communication mode and adaptive rate adjustment mechanisms, ensuring the stability and real-time performance of data transmission in complex power environments.

[0085] Specifically, smart remote-reading meters acquire real-time data on grid voltage, current, power, and electricity consumption through built-in sensors. This data is input into a power quality analysis module for dynamic processing. For example, voltage data, after being processed by a harmonic analysis algorithm, yields the total harmonic distortion (THD) index; current data, through waveform decomposition, identifies three-phase imbalance. The results of power quality analysis, along with the acquired electricity consumption data, are transmitted to a cloud or local platform via a LoRa wireless communication module, supporting second-level data refresh. It should be noted that data compression technology can be used during transmission to reduce bandwidth consumption, while forward error correction coding enhances anti-interference capabilities. Thus, the cloud or local platform can simultaneously acquire raw electricity consumption data and power quality assessment results, providing multi-dimensional decision-making support for grid control strategies. In one specific implementation, power quality monitoring can include harmonic analysis (THD≤5%) and voltage fluctuation recording, conforming to GB / T 14549 standards. It can also be equipped with anti-theft functions, including cover-opening alarms, magnetic field interference detection, and reverse current monitoring. Furthermore, it can achieve OTA remote upgrades, with firmware and algorithms updated via cloud push without disassembling the device.

[0086] This invention integrates real-time analysis functionality at the meter level, synchronously transmitting power quality assessment results and raw electricity consumption data. This reduces the platform's computational load, avoids delays caused by the separation of data transmission and analysis, and enables the management platform to make decisions directly based on preprocessed results, improving grid management efficiency. Furthermore, traditional wireless communication methods such as GPRS are susceptible to electromagnetic interference in areas with dense power equipment. LoRa wireless communication modules, through spread spectrum technology and adaptive rate adjustment, significantly improve transmission distance and anti-interference capabilities at the same power consumption, ensuring data integrity and real-time performance in the complex electromagnetic environment of the power system, providing a reliable data foundation for rapid response to abnormal grid conditions.

[0087] The power grid management method provided in this embodiment of the invention may further include:

[0088] The power grid is monitored in real time;

[0089] If an electrical abnormality or equipment failure is detected in the power grid, an alarm will be triggered in a preset manner; if the load power in the power grid exceeds a preset power threshold, the load will be reduced or a power outage will be triggered.

[0090] Real-time monitoring refers to the continuous collection of power grid operating parameters, which can include electricity consumption data such as voltage, current, and power, as well as equipment operating status data. This can be achieved through the built-in current transformers, voltage sensors, and power analysis modules in smart meters, providing a real-time data foundation for anomaly detection. Preset alarm methods trigger different alarm signals based on the type of anomaly. This can be achieved using audible and visual alarms, SMS notification modules, or cloud platform push notifications, used to differentiate fault levels and notify maintenance personnel for timely handling. Load reduction or triggered power outage refers to adjusting load power by controlling relays or smart circuit breakers. This can be achieved by phased disconnection of unnecessary loads or direct disconnection of the main circuit, used to avoid equipment damage or fire risks caused by overload.

[0091] Specifically, the system continuously collects grid operating parameters and calculates real-time load power using a power analysis module. When voltage fluctuations exceed thresholds, current rises abnormally, or equipment communication is interrupted, an audible and visual alarm is triggered, and an alarm message is sent to the management platform via a wireless communication module. If the load power continues to exceed a preset threshold, the power supply circuit for non-critical loads is cut off first. If the power still cannot return to a safe range, the main circuit breaker is controlled to disconnect the power supply. This forms a tiered protection mechanism that can issue early warnings in the early stages of anomalies and proactively intervene in overload situations, avoiding the escalation of faults caused by traditional passive monitoring.

[0092] In one specific implementation, alarm types for electrical anomalies and faulty equipment faults can include electrical anomalies (such as overvoltage (>440V), undervoltage (<300V), overcurrent (>80A), three-phase imbalance (>20%), leakage current (>30mA)) and equipment faults (such as communication interruption, memory error, clock anomaly). Alarm methods can include platform pop-ups, SMS, APP push, and email (supporting multi-level contact grouping). In addition, alarm history storage can be implemented, such as retaining the most recent 1000 records, and exporting and analyzing them can be supported. In another specific implementation, threshold management is implemented for the power of the load device to prevent over-consumption. The threshold range of the load power can be set to be continuously adjustable from 0 to 80A with a step accuracy of 0.1A. It supports remote real-time modification of the threshold without on-site operation. When the limit is exceeded, the load is reduced first. If the limit is exceeded for a continuous period, the power is cut off. When the load power exceeds the set threshold, the circuit is automatically cut off and the event can be recorded. The response time is ≤0.1 seconds (hardware-level protection). It supports remote reset or local button reset and can realize hierarchical protection, such as a 10-second delay for short-term overload (e.g., 120% load) and immediate power cut-off for severe overload (e.g., 150% load).

[0093] This invention provides real-time monitoring of the power grid's operation, rapidly issuing alarm signals when electrical anomalies or equipment failures occur, thus shortening fault response time. It also automatically performs load reduction or power outage operations when load power exceeds limits, effectively preventing safety accidents caused by line overheating or equipment overload. Furthermore, a tiered processing strategy reduces the impact of unnecessary power outages on power supply continuity. Therefore, this invention not only issues alarms upon detecting anomalies but also executes load reduction or power outage operations according to preset strategies, solving the lag problem of traditional technologies that rely solely on manual response and reducing the risk of electrical fires and equipment damage.

[0094] This invention also provides a power grid management device, such as... Figure 2 As shown, the power grid management device, applied to smart remote meters, may include:

[0095] The acquisition module 11 is used to: acquire the current multi-dimensional indicator function of the power grid; wherein, the multi-dimensional indicator function includes the electricity cost function, the carbon emission function and the load imbalance function, and each variable in the function is an electricity consumption strategy variable;

[0096] Construction module 12 is used to: construct a target optimization model based on the multi-dimensional index function; wherein, the objective function of the target optimization model is a function that makes the comprehensive value of each function in the multi-dimensional index function optimal;

[0097] The control module 13 is used to: optimize and solve the target optimization model to obtain the values ​​of various power consumption strategy variables, determine the optimal power consumption strategy based on the values ​​of various power consumption strategy variables, and implement control over the power grid based on the optimal power consumption strategy.

[0098] This invention also provides a smart remote transmission meter, characterized in that it is installed at a designated point in the power grid and includes the power grid management device as described in the above embodiments.

[0099] The smart remote meter can be a smart three-phase four-wire rail-mounted remote metering meter, and other settings can be made according to actual needs, all of which are within the protection scope of this invention. The smart remote meter is installed at a designated location in the power grid where electricity data collection is required, and then the built-in power grid management device at the corresponding location realizes the power grid management function.

[0100] In addition, the smart remote transmission meter in this embodiment of the invention is classified as active power level 1, with a voltage of 3*380V, a current of 3*5 (80A), a frequency of 50HZ, an LED display, a DIN rail mounting method, and communication based on a LoRa wireless communication module. Its built-in algorithm includes real-time generation of second-level carbon emission data. It has an environmental adaptability of operating temperature from -25℃ to +70℃, an IP54 protection rating (dustproof and splashproof), AES-128 encrypted communication for data security, optional support for the national cryptographic SM4 algorithm, a design life of 15 years, and has passed CE, RoHS, and CPA metrological certifications.

[0101] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A power grid control method, characterized in that, The power grid management method, applied to smart remote meters, includes: Obtain the current multi-dimensional indicator functions of the power grid; wherein, the multi-dimensional indicator functions include the electricity cost function, the carbon emission function, and the load imbalance function, and each variable in the function is an electricity consumption strategy variable; A target optimization model is constructed based on the multi-dimensional index function; wherein, the objective function of the target optimization model is the function that makes the comprehensive value of each function in the multi-dimensional index function optimal. The target optimization model is optimized and solved to obtain the values ​​of each electricity consumption strategy variable. The optimal electricity consumption strategy is determined based on the values ​​of each electricity consumption strategy variable, and the power grid is controlled based on the optimal electricity consumption strategy. The load imbalance function is expressed as follows: ， ， ， in, This represents the overall load imbalance degree, and its value is the value of the load imbalance degree function. This indicates the degree of load imbalance between spatial partitions. Indicates the degree of load imbalance within a time segment. This represents the real-time load of the i-th partition. The average load across all partitions is represented by , and the number of partitions is represented by . , These represent the peak, trough, and average load values ​​within the time segments, respectively. , These are the spatial weighting coefficient and the time weighting coefficient, respectively. The electricity cost function is expressed as follows: ， in, This represents the cost of electricity, and its value is the value of the electricity cost function. This represents the electricity price within a time segment t. This represents the total load within the time segment t. T This represents the total time period composed of all time segments. This represents the compensation unit price for the k-th type of interruptible load. This represents the interruption decision variable for the k-th type of interruptible load. K This indicates the number of interruptible load categories.

2. The method according to claim 1, characterized in that, The carbon emission function is expressed as follows: ， in, This represents carbon emissions, and its value is a function of the stated carbon emissions. Indicates time segments t Dynamic factors of carbon emissions within the region. Indicates time segments t Total load within, T This represents the total time period composed of all time segments.

3. The method according to claim 2, characterized in that, The process of obtaining the dynamic factor of carbon emissions includes: Obtain the power consumption structure data of the power grid within the corresponding time segment; Based on the power consumption structure data of the power grid, determine the proportion of each type of power source in the power grid; Obtain the baseline carbon emission factors corresponding to each type of power source, multiply the proportion of each type of power source by the corresponding baseline carbon emission factor, and sum the results of the multiplication to obtain the dynamic carbon emission factor.

4. The method according to any one of claims 1 to 3, characterized in that, After implementing the control of the power grid based on the optimal power consumption strategy, the method further includes: The system monitors the electricity cost, carbon emissions, and load imbalance of the power grid in real time, and when the change value of at least one of the monitored data reaches the corresponding threshold, it executes the step of obtaining the current multi-dimensional index function of the power grid.

5. The method according to claim 4, characterized in that, Also includes: The system collects electricity consumption data from the power grid in real time and analyzes the power quality of the power grid based on the collected electricity consumption data; wherein the electricity consumption data includes voltage, current, power, and electricity consumption. The power consumption data and power quality are transmitted to the cloud or local management platform based on the preset LoRa wireless communication module.

6. The method according to claim 5, characterized in that, Also includes: The power grid is monitored in real time; If an electrical anomaly or equipment failure is detected in the power grid, an alarm will be triggered in a preset manner; If the load power in the power grid is detected to exceed a preset power threshold, the load will be reduced or a power outage will be triggered.

7. A power grid control device, characterized in that, The power grid management device, applied to smart remote meters, includes: The acquisition module is used to: acquire the current multi-dimensional indicator functions of the power grid; wherein, the multi-dimensional indicator functions include the electricity cost function, the carbon emission function, and the load imbalance function, and all variables in the functions are electricity consumption strategy variables; The construction module is used to: construct a target optimization model based on the multi-dimensional index function; wherein, the objective function of the target optimization model is a function that makes the comprehensive value of each function in the multi-dimensional index function optimal; The control module is used to: optimize and solve the target optimization model to obtain the values ​​of various electricity consumption strategy variables, determine the optimal electricity consumption strategy based on the values ​​of various electricity consumption strategy variables, and implement control over the power grid based on the optimal electricity consumption strategy; The load imbalance function is expressed as follows: ， ， ， in, This represents the overall load imbalance degree, and its value is the value of the load imbalance degree function. This indicates the degree of load imbalance between spatial partitions. Indicates the degree of load imbalance within a time segment. This represents the real-time load of the i-th partition. This represents the average load across all partitions. Indicates the number of partitions. , , These represent the peak, trough, and average load values ​​within the time segments, respectively. , These are the spatial weighting coefficient and the time weighting coefficient, respectively. The electricity cost function is expressed as follows: ， in, This represents the cost of electricity, and its value is the value of the electricity cost function. This represents the electricity price within a time segment t. This represents the total load within the time segment t. T This represents the total time period composed of all time segments. This represents the compensation unit price for the k-th type of interruptible load. This represents the interruption decision variable for the k-th type of interruptible load. K This indicates the number of interruptible load categories.

8. A smart remote transmission meter, characterized in that, It is located at a designated point in the power grid and includes the power grid management device as described in claim 7.

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