A power grid remote electricity charge management method

Abstract

The application relates to the technical field of power grid dispatching, and discloses a power grid remote electricity charge management method, which comprises the following steps: collecting current user electric energy use data and current environment data; constructing a total electricity consumption prediction model through the collected current user electric energy use data and current environment data, analyzing real-time electric energy use data and current environment data, and generating a dispatching strategy; generating corresponding power grid dispatching instructions according to the dispatching strategy, optimizing the power distribution network, and performing real-time demand response management; and generating a user electricity consumption bill based on a charging rule. Through real-time adjustment of power generation, power transmission lines and user demand response, combined with strategy optimization and artificial intelligence model updating, the power grid can realize efficient and flexible dispatching, guarantee the stability and economy of power supply, and promote renewable energy consumption and user participation.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching technology, and in particular to a method for remote electricity fee management of power grids. Background Technology

[0002] Currently, smart grid technology is a key component of power system modernization. By integrating advanced information and communication technologies and intelligent control technologies, it achieves intelligent, efficient, and reliable power systems. The goals of a smart grid are to optimize power resource allocation, improve grid operating efficiency and reliability, promote the integration of new energy sources and electric vehicles, and enhance user participation and interactivity. With increasing energy demand and growing environmental awareness, smart grids have become an inevitable trend in the future development of power systems.

[0003] Faced with increasingly complex and dynamic electricity demands, improving the flexibility, rapid response capability, and self-healing capability of the power grid is a major challenge for the current power system. Although artificial intelligence and big data have broad application prospects in the power system, how to efficiently and accurately utilize these technologies for load forecasting, fault diagnosis, and other tasks still requires in-depth research. Summary of the Invention

[0004] The purpose of this invention is to enable the power grid to achieve efficient and flexible dispatch by adjusting power generation, transmission lines and user demand response in real time, combined with strategy optimization and artificial intelligence model updates, thereby ensuring the stability and economy of power supply, while promoting the consumption of renewable energy and user participation.

[0005] To achieve the above objectives, the present invention provides a method for remote electricity fee management in power grids, comprising:

[0006] Collect current user's electricity usage data and current environmental data;

[0007] Construct a total electricity consumption prediction model and generate scheduling strategies based on current user electricity usage data and environmental data;

[0008] Generate corresponding power grid dispatching instructions based on the dispatching strategy, and correct the operating parameters of the distribution network based on the power grid dispatching instructions;

[0009] Electricity bills for users are generated based on billing rules.

[0010] In some embodiments of the present invention, constructing a total electricity consumption prediction model includes:

[0011] Acquire historical energy data, including historical energy data from the power supply side and historical energy data from the power consumption side;

[0012] The historical power data at the power supply end includes: historical voltage data U, historical current data I, and historical power factor. Generate the total power consumption E1 for the first stage;

[0013] Compare the total electricity consumption E in the first phase with the historical electricity consumption E at the consumer end. S Determine the total transmission loss Q;

[0014] Calculate the total transmission loss Q over different time periods. W ;

[0015] Combined with the total transmission loss Q in different time periods W Thus, the total electricity consumption in the second phase, E2, is obtained.

[0016] The total power transmission loss was classified into different levels to obtain a prediction model for total power consumption.

[0017] In some embodiments of the present invention, determining the total transmission loss Q includes:

[0018] Obtain historical user electricity usage data, including:

[0019] User historical voltage data U, U=[u1,u2…u t …u n ];

[0020] User historical current data I, I=[i1,i2…i t …i n ];

[0021] User historical power factor

[0022]

[0023] Among them, u t i represents the historical voltage data value for time period t. t This represents the historical current data value for time period t. Let represent the historical power factor for time period t, n represent the last time period of the historical data, and T represent the time period length;

[0024] The total electricity demand of users is predicted based on historical electricity usage data, as shown in the following formula:

[0025]

[0026] Obtain the historical power consumption data ES at the power consumption end, and then obtain the total transmission loss Q, Q=|E1-ES|.

[0027] In some embodiments of the present invention, the total transmission loss Q is calculated over different time periods. W ,include:

[0028] Based on historical environmental data, a temperature range was selected and then equally divided to obtain different temperature sub-ranges. The total transmission loss for each time period corresponding to each temperature sub-range was calculated, and the results were statistically analyzed and sorted in ascending order to obtain Q. W =[Q1,Q2,…Q i …,Q n ];

[0029] Among them, Q W This represents the total transmission loss for different temperature sub-ranges corresponding to different time periods; Q1 represents the total transmission loss for the first segment, Q i This represents the total transmission loss of the i-th segment, and n represents the number of time segments;

[0030] In some embodiments of the present invention, the total transmission loss Q over different time periods is considered. W The total electricity consumption E2 for the second stage is obtained, including:

[0031] By repeatedly calculating the total transmission loss for different user electricity usage data over corresponding time periods and taking the average value, an estimated value Q' of the impact of different temperatures on the total transmission loss is generated. W The estimated value Q' of the impact of different temperatures on total transmission losses. W The total electricity consumption for the second stage is E2, where E2 = E1 + Q'. W .

[0032] In some embodiments of the present invention, generating a scheduling policy includes:

[0033] Input the current user's electricity usage data and environmental data into the total electricity consumption prediction model, and output the current user's total electricity consumption in the second stage. By classifying the user type and the electricity consumption growth rate k of different user types, the total electricity consumption in the third stage E3 is obtained, E3 = E2 × (1 + k).

[0034] Different scheduling strategies are formulated based on the total electricity consumption E3 in the third phase.

[0035] In some embodiments of the present invention, calculating the electricity consumption growth rate k based on user type includes:

[0036] Acquire historical electricity usage data and behavioral pattern data to generate multiple influencing factors;

[0037] Multiple user categories were constructed based on the different value ranges of the influencing factors, and the average electricity consumption growth rate of each user category was calculated.

[0038] In some embodiments of the present invention, different scheduling strategies are formulated based on the total electricity consumption E3 in the third stage, including:

[0039] The overall power distribution in the current test area is generated by using the total power consumption E3 in the third stage.

[0040] Based on the overall electricity distribution within the current testing area, the electricity consumption areas are incrementally classified to generate electricity consumption zones representing different electricity consumption levels, including:

[0041] Level 1 power consumption zone, Level 2 power consumption zone, and Level 3 power consumption zone;

[0042] Different power consumption dispatch strategies are generated for different power consumption areas based on different power consumption levels.

[0043] In some embodiments of the present invention, different power dispatching strategies are generated for different power consumption areas based on different power consumption levels, including:

[0044] Primary scheduling strategy and secondary scheduling strategy;

[0045] Among them, the first-level dispatch strategy is to adjust the power supply parameters of different power consumption areas based on different power consumption levels;

[0046] For areas with high electricity consumption, reduce the current total power supply;

[0047] For secondary power consumption areas, the current total power supply will be maintained;

[0048] Increase the current total power supply for Level 3 electricity consumption areas;

[0049] The secondary dispatch strategy involves formulating different demand response policies based on the different types of users in the electricity consumption area, thereby adjusting the users' electricity consumption and electricity consumption periods.

[0050] In some embodiments of the present invention, generating a user's electricity bill based on billing rules includes:

[0051] Based on the user's real-time power consumption estimate E3 and the billing rules, calculate the remaining usage time of the user's current power consumption;

[0052] Users are categorized into different levels based on their remaining usage time;

[0053] Send remaining usage time and demand response policies to users of different levels.

[0054] Compared with existing technologies, the remote electricity fee management method for power grids according to embodiments of the present invention has the following advantages:

[0055] By analyzing historical voltage data, current data, and power factor, the constructed total electricity consumption prediction model can more accurately predict the total electricity consumption in the current time period, thereby providing more precise data support for power grid dispatch.

[0056] By comparing historical electricity consumption with predicted total electricity consumption, the total transmission loss can be accurately calculated, and the loss changes over different time periods can be further analyzed. This helps grid operators identify loss characteristics and take measures to reduce losses.

[0057] By combining electricity consumption forecasting models, the power grid can monitor and manage power quality in real time at different times, ensuring the stability and reliability of power supply.

[0058] Effective management and optimized scheduling of transmission losses can reduce energy waste, support the implementation of energy conservation and emission reduction policies, and align with the goals of sustainable development.

[0059] Accurate electricity usage forecasting and real-time monitoring can help grid operators provide more personalized services, such as dynamic pricing and demand response incentives, thereby improving user satisfaction.

[0060] The total electricity consumption forecasting model can automatically adjust according to the power grid's operating status and changes in the external environment, thereby improving the power grid's response to emergencies and its self-recovery capabilities. Attached Figure Description

[0061] Figure 1 This is a flowchart of a remote electricity fee management method for power grids provided in an embodiment of the present invention. Detailed Implementation

[0062] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0063] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0064] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0065] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0066] like Figure 1 As shown, a preferred embodiment of the present invention provides a method for remote electricity fee management in a power grid, comprising:

[0067] Collect current user's electricity usage data and current environmental data;

[0068] Construct a total electricity consumption prediction model and generate scheduling strategies based on current user electricity usage data and environmental data;

[0069] Generate corresponding power grid dispatching instructions based on the dispatching strategy, and correct the operating parameters of the distribution network based on the power grid dispatching instructions;

[0070] Electricity bills for users are generated based on billing rules.

[0071] In this embodiment, billing management is the process by which service providers calculate fees based on user service usage, typically involving data collection, billing rule application, bill generation, and customer payment; including:

[0072] Real-time monitoring and recording of usage: Use devices such as smart meters and network traffic meters to monitor and record users' service usage in real time, such as electricity consumption, data usage, and call time; Environmental and status information: Information on factors that affect billing, such as time, location, and service status, also needs to be collected.

[0073] Data cleaning and verification: Clean the collected data, remove outliers and erroneous data to ensure data accuracy; Data transformation: Convert the raw data into a format that the billing system can recognize, ready for billing calculation; Apply billing rules: Calculate the cost based on factors such as service type, usage, and time, using preset billing rules.

[0074] Bill generation: Based on the calculated costs, generate a detailed bill, including information such as service items, usage, unit price, and total cost; Bill review: Review the generated bills to ensure the accuracy and reasonableness of the billing results.

[0075] Billing notification: Send billing information to customers via email, SMS, or mail; Payment management: Provide multiple payment channels (such as online payment, bank transfer, etc.), track payment status, and process unpaid bills.

[0076] Consumer behavior analysis: Analyze user consumption data to identify consumption patterns and trends, providing a basis for optimizing billing strategies. Billing strategy adjustment: Adjust billing rules and optimize service pricing based on market feedback and data analysis results.

[0077] Specifically, in a smart grid, the process is as follows:

[0078] Smart meters collect data in real time: Smart meters record users' electricity consumption in real time.

[0079] Data uploaded to the server: Electricity meter data is uploaded to the billing system server via the network.

[0080] Billing engine processing: The billing engine on the server calculates the cost based on the electricity pricing policy and the user's electricity consumption data.

[0081] Billing generation and notification: Generate monthly bills and send them to users electronically.

[0082] User payment: Users pay their electricity bills online or via bank transfer.

[0083] By using smart meters and automated billing systems, remote billing management can be achieved, improving efficiency, reducing operating costs, and providing users with more transparent and convenient billing services.

[0084] In some embodiments of this application, a total electricity consumption prediction model is constructed, including:

[0085] Acquire historical energy data, including historical energy data from the power supply side and historical energy data from the power consumption side;

[0086] The historical power data at the power supply end includes: historical voltage data U, historical current data I, and historical power factor. Generate the total power consumption E1 for the first stage;

[0087] Compare the total electricity consumption E in the first phase with the historical electricity consumption E at the consumer end. S Determine the total transmission loss Q;

[0088] Calculate the total transmission loss Q over different time periods. W ;

[0089] Combined with the total transmission loss Q in different time periods W Thus, the total electricity consumption in the second phase, E2, is obtained.

[0090] The total power transmission loss was classified into different levels to obtain a prediction model for total power consumption.

[0091] In some embodiments of this application, determining the total transmission loss Q includes:

[0092] Obtain historical user electricity usage data, including:

[0093] User historical voltage data U, U=[u1,u2…u t …u n ];

[0094] User historical current data I, I=[i1,i2…i t …i n ];

[0095] User historical power factor

[0096]

[0097] Among them, u t i represents the historical voltage data value for time period t. t This represents the historical current data value for time period t. Let represent the historical power factor for time period t, n represent the last time period of the historical data, and T represent the time period length;

[0098] The total electricity demand of users is predicted based on historical electricity usage data, as shown in the following formula:

[0099]

[0100] Obtain the historical power consumption data ES at the power consumption end, and then obtain the total transmission loss Q, Q=|E1-ES|.

[0101] In this embodiment, the first stage is to predict the total electricity consumption E1.

[0102] Data preprocessing: Standardize historical data to ensure data quality.

[0103] Historical data is used to predict energy consumption for each time period in the model.

[0104] Total electricity consumption E1: The predicted electricity consumption for each time period is added together to obtain the total electricity consumption E1 for the first stage.

[0105] Phase Two: Determining the Total Transmission Loss Q

[0106] Query the historical electricity consumption data (ES) of the electricity consumer: retrieve the historical electricity consumption data (ES) of the electricity consumer from the historical power supply information registry.

[0107] Calculate the total transmission loss Q using the formula Q = ES -E1 is used to calculate the total transmission loss.

[0108] Example Results

[0109] Assuming E1 is 1000kWh and ES is 1050kWh, the total transmission loss Q is 50kWh.

[0110] During the day, the QW is 10kWh from 10 am to 12 pm, 20kWh from 5 pm to 7 pm, and the average QW is 5kWh at other times.

[0111] Based on the loss pattern, E2 is predicted to be 1020kWh, which is closer to the actual power consumption.

[0112] Through this series of steps, the total electricity consumption prediction model can more accurately predict electricity usage, assisting the power grid in refined management, reducing losses, and improving efficiency.

[0113] In some embodiments of this application, the total transmission loss Q within different time periods is calculated. W ,include:

[0114] Based on historical environmental data, a temperature range was selected and then equally divided to obtain different temperature sub-ranges. The total transmission loss for each time period corresponding to each temperature sub-range was calculated, and the results were statistically analyzed and sorted in ascending order to obtain Q. W =[Q1,Q2,…Q i …,Q n ];

[0115] Among them, Q W This represents the total transmission loss for different temperature sub-ranges corresponding to different time periods; Q1 represents the total transmission loss for the first segment, Q i This represents the total transmission loss of the i-th segment, and n represents the number of time segments;

[0116] In this embodiment, a temperature range of -20 to 50 degrees Celsius is selected, with each temperature range being 10 degrees Celsius. Time periods are divided based on different temperature ranges, and the total transmission loss Q in different time periods is calculated. W Q W = [Q1,Q2,Q3,Q4,Q5,Q6,Q7];

[0117] Where Q1 represents the total transmission loss at -20 to -10 degrees Celsius, Q2 represents the total transmission loss at -10 to 0 degrees Celsius, Q3 represents the total transmission loss at 0 to 10 degrees Celsius, Q4 represents the total transmission loss at 10 to 20 degrees Celsius, Q5 represents the total transmission loss at 20 to 30 degrees Celsius, Q6 represents the total transmission loss at 30 to 40 degrees Celsius, and Q7 represents the total transmission loss at 40 to 50 degrees Celsius.

[0118] In some embodiments of the present invention, the total transmission loss Q over different time periods is considered. W The total electricity consumption E2 for the second stage is obtained, including:

[0119] By repeatedly calculating the total transmission loss for different user electricity usage data over corresponding time periods and taking the average value, an estimated value Q' of the impact of different temperatures on the total transmission loss is generated. W The estimated value Q' of the impact of different temperatures on total transmission losses. W The total electricity consumption for the second stage is E2, where E2 = E1 + Q'. W .

[0120] In this embodiment, it is assumed that the total electricity consumption E1 obtained by the prediction model during a certain historical period is 1000kWh.

[0121] QW Calculation: Assuming that the total transmission losses are calculated in different temperature ranges, the total transmission losses are Q1 = 10kWh, Q2 = 15kWh, Q3 = 18kWh, Q4 = 20kWh, Q5 = 25kWh, Q6 = 30kWh, and Q7 = 35kWh.

[0122] Q'W Calculation: For each temperature range, 10 historical data calculations are performed to obtain the loss value, which corresponds to Q' within a specific temperature range. W If the total electricity consumption in the second phase is 20 kWh, then the total electricity consumption E2 is calculated as follows:

[0123] E2=1000kWh+20kWh=1020kWh;

[0124] Through the above steps, not only is the forecast of electricity consumption taken into account, but also the impact of temperature on the total transmission loss, resulting in a more accurate second-stage total electricity consumption forecast E2, providing more detailed and accurate data support for smart grid management.

[0125] In some embodiments of the present invention, generating a scheduling policy includes:

[0126] Input the current user's electricity usage data and environmental data into the total electricity consumption prediction model, and output the current user's total electricity consumption in the second stage. By classifying the user type and the electricity consumption growth rate k of different user types, the total electricity consumption in the third stage E3 is obtained, E3 = E2 × (1 + k).

[0127] Different scheduling strategies are formulated based on the total electricity consumption E3 in the third phase.

[0128] In some embodiments of the present invention, calculating the electricity consumption growth rate k based on user type includes:

[0129] Acquire historical electricity usage data and behavioral pattern data to generate multiple influencing factors;

[0130] Multiple user categories were constructed based on the different value ranges of the influencing factors, and the average electricity consumption growth rate of each user category was calculated.

[0131] In this embodiment, by understanding the electricity consumption behavior and demand growth trends of different user types, the power grid company can provide more personalized services, such as demand response procedures and energy efficiency consulting, to improve user satisfaction.

[0132] Electricity market competitiveness: Through refined management and intelligent dispatch, power grid companies can enhance their competitiveness in the electricity market, attract more users, and expand their market share.

[0133] Power grid stability: Electricity consumption growth rate analysis based on user type helps power grid companies prevent and mitigate load fluctuations, reduce power grid failures, and improve the stability and reliability of power supply.

[0134] In some embodiments of the present invention, different scheduling strategies are formulated based on the total electricity consumption E3 in the third stage, including:

[0135] The overall power distribution in the current test area is generated by using the total power consumption E3 in the third stage.

[0136] Based on the overall electricity distribution within the current testing area, the electricity consumption areas are incrementally classified to generate electricity consumption zones representing different electricity consumption levels, including:

[0137] Level 1 power consumption zone, Level 2 power consumption zone, and Level 3 power consumption zone;

[0138] Different power consumption dispatch strategies are generated for different power consumption areas based on different power consumption levels.

[0139] In some embodiments of this application,

[0140] In some embodiments of the present invention, different power dispatching strategies are generated for different power consumption areas based on different power consumption levels, including:

[0141] Primary scheduling strategy and secondary scheduling strategy;

[0142] Among them, the first-level dispatch strategy is to adjust the power supply parameters of different power consumption areas based on different power consumption levels;

[0143] For areas with high electricity consumption, reduce the current total power supply;

[0144] For secondary power consumption areas, the current total power supply will be maintained;

[0145] Increase the current total power supply for Level 3 electricity consumption areas;

[0146] The secondary dispatch strategy involves formulating different demand response policies based on the different types of users in the electricity consumption area, thereby adjusting the users' electricity consumption and electricity consumption periods.

[0147] In this embodiment, the power dispatching strategy includes:

[0148] For areas with peak electricity demand, increase power generation or dispatch backup power sources;

[0149] For areas with stable or low electricity demand, the grid load is monitored in real time, the load on transmission lines is dynamically adjusted, and users are guided to adjust their electricity consumption behavior through price signals or incentive measures, thereby encouraging users to use electricity and smoothing the grid load curve.

[0150] In this embodiment, the urban power grid experiences peak electricity consumption from 17:00 to 21:00 on weekdays, and low electricity consumption from 21:00 to 8:00 the next day, with the remaining time periods being stable electricity consumption periods.

[0151] Peak electricity demand period: From 17:00 to 21:00, the power grid dispatch center increases the output of thermal power plants and calls on gas turbines as a fast-response power source to meet the electricity demand during peak hours.

[0152] During periods of stable electricity demand, the load on transmission lines is dynamically adjusted using intelligent algorithms to ensure stable grid operation. At the same time, the power generation of renewable energy is monitored, and the amount of renewable energy connected to the grid is adjusted in a timely manner.

[0153] Off-peak electricity consumption period: During off-peak electricity consumption periods, time-of-use pricing strategies, such as lowering electricity prices, can be used to guide users to shift some of their electricity consumption (such as charging electric vehicles) to this period, while increasing the proportion of electricity from renewable energy sources such as wind and solar power to the grid.

[0154] By implementing the above strategies, the power grid can effectively cope with the electricity demand in different electricity consumption cycles, achieve optimal allocation of power resources, ensure the stability and economy of power supply, and at the same time promote the utilization of renewable energy and drive the green transformation of the energy structure.

[0155] In some embodiments of this application, corresponding power grid dispatch instructions are generated according to the dispatch strategy, including:

[0156] Based on the parsed strategy, the power grid dispatch instructions in this embodiment are generated, including:

[0157] Adjust power generation, optimize transmission lines, start or shut down energy storage systems, and adjust user demand response plans;

[0158] Among them, adjusting power generation: dynamically adjusting the output power of various types of generator sets based on load forecasts and power generation capacity;

[0159] Optimize transmission lines: Adjust line loads and optimize power transmission paths based on real-time grid conditions;

[0160] Demand response: Sending demand response signals to users to encourage or incentivize them to adjust their electricity consumption behavior and guiding industrial users to produce during off-peak hours through pricing mechanisms.

[0161] In some embodiments of this application, the power distribution network is optimized and demand response management is implemented, including:

[0162] Collect data during instruction execution, perform in-depth analysis, and identify optimization points; optimize strategies: based on data analysis results, optimize scheduling strategies and instruction generation algorithms to improve prediction accuracy and scheduling efficiency; utilize artificial intelligence technology to update historical data during model building and adjust parameters.

[0163] In some embodiments of the present invention, generating a user's electricity bill based on billing rules includes:

[0164] Based on the user's real-time power consumption estimate E3 and the billing rules, calculate the remaining usage time of the user's current power consumption;

[0165] Users are categorized into different levels based on their remaining usage time;

[0166] Send remaining usage time and demand response policies to users of different levels.

[0167] In this embodiment, power generation is adjusted by dynamically adjusting the output power of each type of generator set based on load forecasts and power generation capacity; thermal power and hydropower serve as baseload power sources, and their base output is adjusted based on the predicted total load demand; wind power and photovoltaic power are adjusted in real time based on the forecast of renewable energy power generation according to weather and sunlight conditions.

[0168] Optimize transmission lines: Adjust line loads and optimize power transmission paths based on real-time grid status; Line load: Adjust power distribution between lines through real-time monitoring to avoid overload and improve transmission efficiency; Power transmission path: Optimize power transmission paths and reduce losses based on grid topology and real-time load; Start or shut down energy storage systems: Start or shut down battery energy storage systems to smooth grid load based on real-time demand and forecasts; Demand response management: Send demand response signals to users and guide them to consume electricity during off-peak hours through pricing mechanisms; Pricing mechanism: Implement time-of-use pricing strategies to encourage users to consume electricity during off-peak hours; Industrial users: Send demand response signals to industrial users to encourage them to schedule production during off-peak hours.

[0169] Strategy optimization and intelligent iteration

[0170] Data Collection and Analysis: Command Execution Data: Collect detailed data during the execution of dispatch commands, including power generation adjustments, line load changes, energy storage system status, and user response status; In-depth Analysis: Utilize big data and artificial intelligence technologies to conduct in-depth analysis of the collected data and identify optimization points in the dispatch process.

[0171] Strategy optimization: Dispatch strategy optimization: Adjust the optimization strategies for power generation and transmission lines based on data analysis results; Command generation algorithm optimization: Optimize the command generation algorithm to improve the accuracy and efficiency of dispatch commands; Artificial intelligence model update: Historical data update: Regularly update historical data during the model building process to reflect the latest power grid operation status and user behavior; Parameter adjustment: Adjust algorithm parameters according to model performance to improve the accuracy of prediction and dispatch.

[0172] The user's electricity bill is generated based on the billing rules, including:

[0173] Based on the user's real-time estimated power consumption value E3 and the billing rules, the system calculates the usage time of the user's current power balance. Based on the length of the usage time, users are classified into different levels, and different reminders and power allocation strategies are implemented for different levels of users.

[0174] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make several improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.

Claims

1. A method for remote electricity fee management in a power grid, characterized in that, include: Collect current user's electricity usage data and current environmental data; Acquire historical energy data, including historical energy data from the power supply side and historical energy data from the power consumption side; The historical power data of the power supply side includes: acquiring historical voltage data U, historical current data I, and historical power factor cos(φ); and generating the total power consumption E1 for the first stage. By comparing the total electricity consumption E1 in the first phase with the historical electricity acquisition ES at the power consumption end, the total transmission loss Q is determined. Based on historical environmental data, a temperature range was selected and then equally divided to obtain different temperature sub-ranges. The total transmission loss for each time period corresponding to each temperature sub-range was calculated, and the results were statistically analyzed and sorted in ascending order to obtain Q. W =[Q1,Q2,…Q i …,Q n ]; Among them, Q W This represents the total transmission loss for different temperature sub-ranges corresponding to different time periods; Q1 represents the total transmission loss for the first segment, Q i This represents the total transmission loss of the i-th segment, and n represents the number of time segments; By repeatedly calculating the total transmission loss for different user electricity usage data over corresponding time periods and taking the average value, an estimated value Q' of the impact of different temperatures on the total transmission loss is generated. W The estimated value Q' of the impact of different temperatures on total transmission losses. W The total electricity consumption for the second stage is E2, where E2 = E1 + Q'. W ; The total transmission loss is then classified into different levels to obtain a total electricity consumption prediction model. Input the current user's electricity usage data and environmental data into the total electricity consumption prediction model, and output the current user's total electricity consumption in the second stage. By classifying the user type and the electricity consumption growth rate k of different user types, the total electricity consumption in the third stage E3 is obtained, E3=E2×(1+k). Different scheduling strategies are formulated based on the total electricity consumption E3 in the third phase; Generate corresponding power grid dispatching instructions based on the dispatching strategy, and correct the operating parameters of the distribution network based on the power grid dispatching instructions; Electricity bills for users are generated based on billing rules.

2. The remote electricity fee management method for power grids as described in claim 1, characterized in that, Determining the total transmission loss Q includes: Obtain historical user electricity usage data, including: User historical voltage data U, U=[u1,u2…u t …u n ]; User historical current data I, I=[i1,i2…i t …i n ]; User's historical power factor cos(φ), cos(φ)=[cos(φ1),cos(φ2)…cos(φ t )…cos(φ n )]; Among them, u t i represents the historical voltage data value for time period t. t Represents the historical current data value for time period t, cos(φ) t ) represents the historical power factor in time period t, n represents the last time period of the historical data, and T represents the length of the time period; The total electricity demand of users is predicted based on historical electricity usage data, as shown in the following formula: ； Obtain the historical power consumption data ES at the power consumption end, and then obtain the total transmission loss Q, Q=|E1-ES|.

3. The remote electricity fee management method for power grids as described in claim 1, characterized in that, Calculate the electricity consumption growth rate k based on user type, including: Acquire historical electricity usage data and behavioral pattern data to generate multiple influencing factors; Multiple user categories were constructed based on the different value ranges of the influencing factors, and the average electricity consumption growth rate of each user category was calculated.

4. The remote electricity fee management method for power grids as described in claim 3, characterized in that, Different scheduling strategies are formulated based on the total electricity consumption E3 in the third phase, including: The overall power distribution in the current test area is generated by using the total power consumption E3 in the third stage. Based on the overall electricity distribution within the current testing area, the electricity consumption areas are incrementally classified to generate electricity consumption zones representing different electricity consumption levels, including: Level 1 power consumption zone, Level 2 power consumption zone, and Level 3 power consumption zone; Different power consumption dispatch strategies are generated for different power consumption areas based on different power consumption levels.

5. The remote electricity fee management method for power grids as described in claim 4, characterized in that, Based on different electricity consumption levels, different electricity dispatching strategies are generated for different electricity consumption areas, including: Primary scheduling strategy and secondary scheduling strategy; Among them, the first-level dispatch strategy is to adjust the power supply parameters of different power consumption areas based on different power consumption levels; For areas with high electricity consumption, reduce the current total power supply; For secondary power consumption areas, the current total power supply will be maintained; Increase the current total power supply for Level 3 electricity consumption areas; The secondary dispatch strategy involves formulating different demand response policies based on the different types of users in the electricity consumption area, thereby adjusting the users' electricity consumption and electricity consumption periods.

6. The remote electricity fee management method for power grids as described in claim 5, characterized in that, The user's electricity bill is generated based on the billing rules, including: Based on the user's real-time power consumption estimate E3 and the billing rules, calculate the remaining usage time of the user's current power consumption; Users are categorized into different levels based on their remaining usage time; Send remaining usage time and demand response policies to users of different levels.

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