A method for predicting electricity sales in a power grid and an electricity sales management system

Abstract

This invention discloses a method and management system for predicting electricity sales in a power grid, relating to the field of power grid management technology. The method involves acquiring the power grid coverage area through a data acquisition terminal, marking each area to form an initial area list, obtaining regional electricity consumption data, performing spatiotemporal analysis on the data, constructing a regional electricity consumption impact index based on the time-series analysis results, and constructing a regional electricity consumption fluctuation factor based on the spatiotemporal analysis results. The method then dynamically obtains the predicted electricity consumption for each region by combining the electricity consumption impact index and the electricity consumption fluctuation factor, and finally sums the predicted electricity consumption of all regions to obtain the predicted electricity sales. This prediction method, by dividing the power grid coverage area, analyzing data for each region separately, dynamically obtaining the predicted electricity consumption for each region, and then summing the predicted electricity consumption of all regions to obtain the predicted electricity sales, helps improve the accuracy and precision of predicting power grid electricity sales.

Description

Technical Field

[0001] This invention relates to the field of power grid management technology, specifically to a method for predicting power grid sales volume and a power sales management system. Background Technology

[0002] Power grid management refers to the process of comprehensively coordinating and controlling all aspects of power system operation, maintenance, dispatching and optimization. It aims to ensure the safe, stable, economical and efficient operation of the power system. Power grid management involves all aspects of power production, transmission, distribution and use, and is a core component of the modern energy system.

[0003] The existing technology has the following drawbacks:

[0004] The current power grid electricity sales volume is set based on the electricity sales volume of a previous period. However, in actual applications, since the power grid usually covers a certain area and there are factors in the area that affect electricity consumption, setting the electricity sales volume based on the electricity sales volume of a previous period may result in problems such as overestimating or underestimating the electricity sales volume.

[0005] Based on this, the present invention proposes a method and management system for predicting electricity sales in the power grid. By dividing the power grid coverage area, analyzing the data of each area separately, dynamically obtaining the predicted electricity consumption of the area, and then summing the predicted electricity consumption of all areas to obtain the predicted electricity sales of the power grid, it is beneficial to improve the accuracy and precision of predicting electricity sales in the power grid. Summary of the Invention

[0006] The purpose of this invention is to provide a method for predicting electricity sales in a power grid and an electricity sales management system to address the shortcomings in the prior art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting electricity sales in a power grid, the prediction method comprising the following steps:

[0008] The data acquisition terminal obtains the power grid coverage area and marks each area to form an initial area list;

[0009] After obtaining regional electricity consumption data, spatiotemporal analysis is performed on the electricity consumption data. Based on the results of the regional electricity consumption time series analysis, a regional electricity consumption impact index is constructed, and based on the results of the regional electricity consumption spatiotemporal analysis, a regional electricity consumption fluctuation factor is constructed.

[0010] The predicted electricity consumption for a region is obtained by combining the electricity consumption impact index with the electricity consumption fluctuation factor. The predicted electricity consumption of all regions is then summed to obtain the predicted electricity sales of the power grid.

[0011] In a preferred embodiment, a regional electricity consumption impact index is constructed based on the time-series analysis results of regional electricity consumption, including the following steps:

[0012] Obtain regional electricity consumption data, which includes the electricity consumption change index and the industrial output change index;

[0013] The electricity consumption change index and the industrial output change index are normalized, and their value ranges are mapped to [0,1].

[0014] The electricity consumption impact index is obtained by summing the normalized electricity consumption change index and the industrial output change index.

[0015] In a preferred embodiment, the predicted electricity consumption of a region is dynamically obtained by combining the electricity consumption impact index with the electricity consumption fluctuation factor, including the following steps:

[0016] The change coefficient for the region is obtained by multiplying the electricity consumption impact index by the electricity consumption fluctuation factor. The current electricity consumption in the region is then obtained, and the predicted electricity consumption for the region is obtained by adjusting the current electricity consumption using the change coefficient. The expression is as follows: In the formula, To predict electricity consumption, This represents the current electricity consumption. The coefficient of variation, The threshold for change;

[0017] After obtaining the predicted electricity consumption for each region, sum the predicted electricity consumption for all regions to obtain the predicted electricity sales of the power grid.

[0018] In a preferred embodiment, the electricity consumption fluctuation factor of the region is constructed based on the spatiotemporal analysis results of the region's electricity consumption, including the following steps:

[0019] The number of nodes in the region is obtained, and the electricity consumption of each node at multiple time points is obtained. The average electricity consumption of the nodes is calculated, and the standard deviation of electricity consumption and the cumulative electricity consumption coefficient of the region are calculated based on the average electricity consumption.

[0020] The electricity consumption fluctuation factor is calculated by combining the standard deviation of electricity consumption and the cumulative coefficient of electricity consumption. The expression is as follows: In the formula, This is the cumulative electricity consumption factor. The standard deviation of electricity consumption For power fluctuation factor, , It is the adjustment coefficient, and , All are greater than 0.

[0021] In a preferred embodiment, the standard deviation of electricity consumption in the region is calculated based on the average electricity consumption, expressed as: In the formula, The standard deviation of electricity consumption For the number of nodes, For the first Average power consumption of each node The average electricity consumption is the mean. The larger the standard deviation of electricity consumption, the greater the overall fluctuation of electricity consumption in the region, which means that the reliability of information in the region is reduced.

[0022] The cumulative electricity consumption coefficient for a region is calculated based on the average electricity consumption, and the expression is as follows:

[0023] In the formula, This is the cumulative electricity consumption factor. For the number of nodes, For the first Average power consumption of each node Let the time interval be the integral. Indicates time The sum of the average electricity consumption of all nodes in the time region.

[0024] In a preferred embodiment, the processing logic of the electricity consumption change index is as follows: within the monitoring period, the electricity consumption change rate at multiple time points is obtained, and the electricity consumption change rate at multiple time points is summed to obtain the electricity consumption change index. For the current time point, the logic for obtaining the electricity consumption change rate is as follows: the electricity consumption difference is obtained by subtracting the electricity consumption at the previous time point from the current time point, the interval duration is obtained by subtracting the previous time point from the current time point, and the electricity consumption change index is obtained by dividing the electricity consumption difference by the interval duration.

[0025] The industrial output change index is obtained by summing the industrial output change rates at multiple time points within the monitoring period. For the current time point, the logic for obtaining the industrial output change rate is as follows: the difference in electricity consumption is obtained by subtracting the industrial output value of the previous time point from the current time point's industrial output value; the interval duration is obtained by subtracting the previous time point from the current time point; and the industrial output change index is obtained by dividing the electricity consumption difference by the interval duration.

[0026] A power grid electricity sales management system includes a data acquisition module, a forecasting module, and a management module;

[0027] Data acquisition module: Acquires the power grid coverage area and marks each area to form an initial area list.

[0028] Forecasting module: Acquire regional electricity consumption data, perform spatiotemporal analysis on the electricity consumption data, construct the regional electricity consumption impact index based on the regional electricity consumption time series analysis results, construct the regional electricity consumption fluctuation factor based on the regional electricity consumption spatiotemporal analysis results, combine the electricity consumption impact index and the electricity consumption fluctuation factor to dynamically obtain the regional predicted electricity consumption, and sum the predicted electricity consumption of all regions to obtain the grid predicted electricity sales.

[0029] Management module: Based on the power grid's predicted electricity sales volume and the relationship between the power grid's energy storage capacity and electricity prices, it generates corresponding management strategies.

[0030] In a preferred embodiment, after the management module obtains the grid's predicted electricity sales, it manages the electricity sales based on the comparison between the predicted electricity sales and the grid's storage capacity. If the predicted electricity sales are greater than the grid's storage capacity, it is analyzed that the current storage capacity is insufficient to support the electricity sales, and electricity can be transferred from other grid sub-networks for compensation. If the predicted electricity sales are less than or equal to the grid's storage capacity, it is analyzed that the current storage capacity is sufficient to support the electricity sales.

[0031] When the current electricity storage capacity is sufficient to support electricity sales, the current electricity price is obtained. If the current electricity price is less than the preset price threshold, the excess electricity is stored. If the current electricity price is greater than or equal to the preset price threshold, the excess electricity can be sold. The system then checks whether there is a demand for electricity compensation in other regions. If not, the excess electricity is stored. If so, the excess electricity is sold to other regions.

[0032] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0033] This invention acquires the power grid coverage area through a data acquisition terminal, marks each area to form an initial area list, obtains regional electricity consumption data, performs spatiotemporal analysis on the electricity consumption data, constructs a regional electricity consumption impact index based on the regional electricity consumption time series analysis results, constructs a regional electricity consumption fluctuation factor based on the regional electricity consumption spatiotemporal analysis results, and dynamically obtains the predicted electricity consumption of the region by combining the electricity consumption impact index and the electricity consumption fluctuation factor. Finally, the predicted electricity consumption of all regions is summed to obtain the predicted power grid sales volume. This prediction method, by dividing the power grid coverage area, analyzing the data of each region separately, dynamically obtaining the predicted electricity consumption of the region, and then summing the predicted electricity consumption of all regions to obtain the predicted power grid sales volume, helps to improve the accuracy and precision of the predicted power grid sales volume.

[0034] This invention acquires regional electricity consumption data through a prediction module, performs spatiotemporal analysis on the data, constructs a regional electricity consumption impact index based on the time-series analysis results, and constructs a regional electricity consumption fluctuation factor based on the spatiotemporal analysis results. The electricity consumption impact index and fluctuation factor are combined to dynamically obtain the region's predicted electricity consumption. The predicted electricity consumption of all regions is summed to obtain the grid's predicted electricity sales. This predicted electricity sales is sent to a management module, which generates corresponding management strategies based on the relationship between grid storage capacity and electricity prices. This enables the grid to manage electricity sales based on predicted electricity sales, improving the efficiency of grid electricity sales management. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0036] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0038] Example 1: Please refer to Figure 1 As shown in this embodiment, a method for predicting electricity sales in a power grid includes the following steps:

[0039] The data acquisition unit obtains the power grid coverage area and marks each area to form an initial area list. After obtaining the regional electricity consumption data, it performs spatiotemporal analysis on the electricity consumption data. Based on the time series analysis results of regional electricity consumption, it constructs the regional electricity consumption impact index and the regional electricity consumption fluctuation factor. It combines the electricity consumption impact index and the electricity consumption fluctuation factor to dynamically obtain the predicted electricity consumption of the region. Finally, it sums up the predicted electricity consumption of all regions to obtain the predicted power grid sales volume.

[0040] This application acquires the power grid coverage area through a data acquisition terminal, marks each area to form an initial area list, obtains regional electricity consumption data, performs spatiotemporal analysis on the electricity consumption data, constructs a regional electricity consumption impact index based on the regional electricity consumption time series analysis results, constructs a regional electricity consumption fluctuation factor based on the regional electricity consumption spatiotemporal analysis results, and dynamically obtains the predicted electricity consumption of the region by combining the electricity consumption impact index and the electricity consumption fluctuation factor. Finally, the predicted electricity consumption of all regions is summed to obtain the predicted power grid sales volume. This prediction method, by dividing the power grid coverage area, analyzing the data of each region separately, dynamically obtaining the predicted electricity consumption of the region, and then summing the predicted electricity consumption of all regions to obtain the predicted power grid sales volume, helps to improve the accuracy and precision of the predicted power grid sales volume.

[0041] Example 2: The acquisition terminal obtains the power grid coverage area and marks each area to form an initial area list, including the following steps:

[0042] Power grid topology data acquisition: Obtain regional division and power network structure information from the power grid GIS system or dispatch center database.

[0043] Sensor data acquisition: Electricity consumption data is collected through smart meters and substation monitoring devices within the area to determine the area boundaries and nodes.

[0044] External data integration: Obtain matching data on administrative divisions, user distribution, and regional power grid coverage.

[0045] Region name (e.g., region A, B, C).

[0046] Regional coordinate range or boundary information.

[0047] Key facilities (substations, load centers) within the region.

[0048] Store the information for each region in a uniform format, for example:

[0049] Area Name: Area A;

[0050] Coordinate range: [(x1,y1),(x2,y2),...];

[0051] Load center: Substation A1;

[0052] Electricity consumption type: Mainly industrial;

[0053] Based on the power grid coverage area, the entire network is divided into multiple independent regions, each corresponding to a load center or power supply node.

[0054] Marking method:

[0055] Area number: Assign a unique number to each area (e.g., A1, B2).

[0056] Feature tagging: Tagging based on regional characteristics.

[0057] Priority marking: Classified according to information such as the importance of regional power supply and load size (e.g., core area, secondary area).

[0058] Compile all area names, boundaries, and labeling information into a complete list, for example:

[0059] ;

[0060] Table 1

[0061] The initial list of regions is saved to a database or distributed storage system for use by subsequent prediction models and scheduling systems.

[0062] After obtaining regional electricity consumption data, spatiotemporal analysis is performed on the data. Based on the time-series analysis results, a regional electricity consumption impact index is constructed, including the following steps:

[0063] Historical electricity consumption records: historical data from smart meters, substations, or electricity load monitoring systems within the region.

[0064] Time granularity: Data at the daily, hourly, or minute level.

[0065] Spatial distribution: Covers all defined areas.

[0066] Obtain regional electricity consumption data, which includes the electricity consumption change index and the industrial output change index;

[0067] The electricity consumption change index and the industrial output change index are normalized, and their value ranges are mapped to [0,1]. The electricity consumption impact index is obtained by summing the normalized electricity consumption change index and the industrial output change index.

[0068] The processing logic for the electricity consumption change index is as follows: within the monitoring period, the electricity consumption change rate at multiple time points is obtained, and the electricity consumption change rate at multiple time points is summed to obtain the electricity consumption change index. For the current time point, the logic for obtaining the electricity consumption change rate is as follows: the electricity consumption difference is obtained by subtracting the electricity consumption at the previous time point from the current time point, the interval duration is obtained by subtracting the previous time point from the current time point, and the electricity consumption change index is obtained by dividing the electricity consumption difference by the interval duration.

[0069] Industrial output change index: The industrial output change rate is obtained at multiple time points within the monitoring period. The industrial output change rate at multiple time points is summed to obtain the industrial output change index. For the current time point, the logic for obtaining the industrial output change rate is as follows: the difference in electricity consumption is obtained by subtracting the industrial output value at the previous time point from the current time point's industrial output value; the interval duration is obtained by subtracting the previous time point from the current time point; and the industrial output change index is obtained by dividing the electricity consumption difference by the interval duration.

[0070] 1) Relationship between electricity consumption change index and future regional electricity consumption changes

[0071] A higher electricity consumption change index indicates a faster rate of change in regional electricity consumption, with a significant increase or decrease in electricity demand within a short period. A positive and large index indicates rapid growth in regional electricity consumption, which may continue to increase in the future. A negative index with a large absolute value indicates a rapid decline in regional electricity consumption, which may show a decreasing trend in the future.

[0072] A smaller value for the electricity consumption change index indicates that the region's electricity consumption is relatively stable, and electricity demand fluctuates little. Future electricity consumption is likely to remain stable with minimal fluctuations.

[0073] 2) Relationship between industrial output value change index and future regional electricity consumption changes

[0074] A higher industrial output change index indicates more rapid changes in industrial activity, potentially leading to a significant increase or decrease in industrial electricity demand. A large positive index indicates rapid growth in industrial output, which may drive up industrial electricity demand. Conversely, a large negative index indicates a rapid decline in industrial output, which may result in a decrease in industrial electricity demand.

[0075] A smaller industrial output change index indicates more stable industrial activity and a smaller impact on changes in electricity demand. Future industrial electricity consumption is likely to remain stable without significant fluctuations.

[0076] Based on the spatiotemporal analysis results of regional electricity consumption, a regional electricity consumption fluctuation factor is constructed, including the following steps:

[0077] Obtain the number of nodes in the region (each node can be a residential area, factory, hospital, etc.), obtain the electricity consumption of each node at multiple time points, calculate the average electricity consumption of the nodes, and calculate the standard deviation of electricity consumption for the region based on the average electricity consumption. The expression is:

[0078] In the formula, The standard deviation of electricity consumption For the number of nodes, For the first Average power consumption of each node The average electricity consumption is the mean. The larger the standard deviation of electricity consumption, the greater the overall fluctuation of electricity consumption in the region, which means that the reliability of information in the region is reduced.

[0079] The cumulative electricity consumption coefficient for a region is calculated based on the average electricity consumption, and the expression is as follows:

[0080] In the formula, This is the cumulative electricity consumption factor. For the number of nodes, For the first Average power consumption of each node The time interval for integration (e.g., from the beginning to the end of a day, or any time period within a week). Indicates time The sum of the average electricity consumption of all nodes in the region at a given time is the cumulative electricity consumption coefficient. The larger the cumulative electricity consumption coefficient, the greater the increase in the overall electricity consumption of the region, which means that the information reliability of the region is increased.

[0081] The electricity consumption fluctuation factor is calculated by combining the standard deviation of electricity consumption and the cumulative coefficient of electricity consumption. The expression is as follows: In the formula, This is the cumulative electricity consumption factor. The standard deviation of electricity consumption For power fluctuation factor, , It is the adjustment coefficient, and , All are greater than 0.

[0082] The predicted electricity consumption for a region is dynamically obtained by combining the electricity consumption impact index with the electricity consumption fluctuation factor, including the following steps:

[0083] The change coefficient for the region is obtained by multiplying the electricity consumption impact index by the electricity consumption fluctuation factor. The current electricity consumption for the region (which can be within a week or within 24 hours) is then obtained. The predicted electricity consumption for the region is obtained by adjusting the current electricity consumption using the change coefficient. The expression is as follows:

[0084] In the formula, To predict electricity consumption, This represents the current electricity consumption. The coefficient of variation, The change threshold is used in the above algorithm to analyze whether the electricity consumption in the region is increasing or decreasing. When the change coefficient is greater than or equal to the change threshold, the electricity consumption in the region increases; when the change coefficient is less than the change threshold, the electricity consumption in the region decreases.

[0085] After obtaining the predicted electricity consumption for each region, sum the predicted electricity consumption for all regions to obtain the predicted electricity sales of the power grid.

[0086] Example 3: The power grid sales management system described in this example includes a data acquisition module, a forecasting module, and a management module;

[0087] Data acquisition module: Acquires the power grid coverage area, marks each area to form an initial area list, and sends the initial area list to the prediction module.

[0088] Forecasting module: acquires regional electricity consumption data, performs spatiotemporal analysis on the electricity consumption data, constructs regional electricity consumption impact index based on the time series analysis results of regional electricity consumption, constructs regional electricity consumption fluctuation factor based on the spatiotemporal analysis results of regional electricity consumption, combines the electricity consumption impact index and the electricity consumption fluctuation factor to dynamically obtain the predicted electricity consumption of the region, sums the predicted electricity consumption of all regions to obtain the grid predicted electricity sales, and sends the grid predicted electricity sales to the management module.

[0089] Management module: Based on the power grid's predicted electricity sales volume and the relationship between the power grid's energy storage capacity and electricity prices, it generates corresponding management strategies.

[0090] After obtaining the grid's predicted electricity sales, the management module manages the electricity sales based on the comparison between the predicted electricity sales and the grid's storage capacity. If the predicted electricity sales are greater than the grid's storage capacity, it is analyzed that the current storage capacity is insufficient to support the electricity sales, and electricity can be transferred from other grid sub-networks for compensation. If the predicted electricity sales are less than or equal to the grid's storage capacity, it is analyzed that the current storage capacity is sufficient to support the electricity sales.

[0091] When the current electricity storage capacity is sufficient to support the electricity sales, the current electricity price is obtained. If the current electricity price is less than the preset price threshold, the excess electricity is stored. If the current electricity price is greater than or equal to the preset price threshold, the excess electricity can be sold. The system then checks whether there is a demand for electricity compensation in other regions. If not, the excess electricity is stored. If so, the excess electricity is sold to other regions.

[0092] It should be noted that in this application, if the current electricity price is less than a preset price threshold, the excess electricity is stored. If the current electricity price is greater than or equal to the preset price threshold, the excess electricity can be sold. The analysis is only performed when the electricity is in non-emergency use (such as industrial use). If other areas are in emergency use (such as disaster relief use), the electricity is directly compensated to other areas.

[0093] This application acquires regional electricity consumption data through a prediction module, performs spatiotemporal analysis on the data, constructs a regional electricity consumption impact index based on the time-series analysis results, and constructs a regional electricity consumption fluctuation factor based on the spatiotemporal analysis results. The electricity consumption impact index and fluctuation factor are combined to dynamically obtain the region's predicted electricity consumption. The predicted electricity consumption of all regions is summed to obtain the grid's predicted electricity sales. This predicted electricity sales is then sent to a management module. The management module generates corresponding management strategies based on the relationship between grid storage capacity and electricity prices. This enables the grid to manage electricity sales based on predicted electricity sales, improving the efficiency of grid electricity sales management.

[0094] The following are examples of application scenarios for the power grid sales management system in this application:

[0095] Scenario 1: Peak electricity consumption management in large urban areas

[0096] Background: A power grid covers multiple large areas of city A (e.g., area 1, area 2, and area 3), each including residential areas, commercial areas, and industrial parks. Recently, due to high temperatures, peak residential electricity consumption has coincided with increased industrial load, leading to a surge in overall electricity demand in city A. The power grid needs to predict electricity consumption trends in each area and formulate control strategies.

[0097] Data Acquisition Module: The system acquires the coverage areas of Region 1, Region 2, and Region 3, and marks residential areas, commercial areas, and industrial parks within each region. It acquires electricity consumption data for each region at multiple time points, such as: Region 1: Residential areas 50%, Commercial areas 30%, Industrial parks 20%. Region 2: Residential areas 40%, Commercial areas 40%, Industrial parks 20%. Region 3: Residential areas 30%, Commercial areas 20%, Industrial parks 50%.

[0098] Forecasting Module: Performs spatiotemporal analysis of electricity consumption in each major region, constructing an electricity consumption impact index and fluctuation factor. Predicts peak electricity consumption periods for each region: Region 1 experiences a rapid increase in electricity consumption from 6 PM to 10 PM (peak residential electricity consumption). Region 3 experiences increased electricity demand from 9 AM to 4 PM (peak industrial load). Dynamically combining the impact index and fluctuation factor, the predicted electricity consumption for each major region is calculated. The predicted electricity consumption for all major regions is then aggregated to obtain the overall predicted electricity sales for the power grid.

[0099] Management Module: Analyzes and compares predicted electricity sales with current grid storage capacity. If predicted sales exceed grid storage, compensation is obtained from neighboring grids (e.g., City B). If predicted sales are less than grid storage, storage capacity is dynamically allocated: excess capacity is stored during off-peak periods (e.g., Region 2), and priority is given to dispatching power during peak periods (e.g., Regions 1 and 3). The grid successfully balances electricity demand across large regions, avoiding power shortages caused by insufficient storage during peak hours and improving electricity management efficiency.

[0100] Scenario 2: Emergency power allocation between large areas

[0101] Background: Due to a natural disaster, some power supply facilities in a large region (Region 2) were damaged, resulting in severe power shortages in commercial areas and industrial parks, requiring emergency power replenishment. Meanwhile, other large regions (Regions 1 and 3) have relatively stable power supply and surplus stored power.

[0102] Data Acquisition Module: The system acquires the emergency power demand of Area 2 and marks it as a priority compensation area, especially the power consumption of the industrial park (accounting for 50% of the power consumption in Area 2). At the same time, it acquires the power storage status of Area 1 and Area 3, and finds that 30% of the power storage in these two areas can be used for allocation.

[0103] Prediction module: Analyzes the emergency power demand in Area 2 and dynamically adjusts compensation priorities based on the high power consumption characteristics of the industrial park. Calculates the power storage release strategy for Areas 1 and 3: Prioritizes power delivery to the industrial park in Area 2 without affecting the normal power consumption of their own residential and commercial areas.

[0104] Management Module: This module allocates excess electricity storage in Region 1 and Region 3 to compensate for emergency needs in Region 2. Priority is given to meeting the electricity needs of the industrial park in Region 2, followed by supplementing the commercial area. If storage is insufficient, electricity is purchased from a neighboring power grid (e.g., City B) by increasing inter-regional electricity prices to further supplement the supply. The industrial park in Region 2 receives timely power support, ensuring normal production and repair operations during emergencies. The excess electricity in Regions 1 and 3 is allocated rationally without affecting their own normal electricity needs.

[0105] Scenario 3: Optimization of Off-Peak Electricity Pricing for Energy Storage and Cross-Regional Electricity Sales

[0106] Background: In a city's power grid, the industrial park in Region 3 experienced a decrease in electricity demand due to holiday shutdowns, resulting in a large amount of unused stored electricity. Meanwhile, the power grids in Region 1 and the neighboring city C predict a significant increase in future electricity demand.

[0107] Data Acquisition Module: The system acquires the stored electricity and current electricity price of Industrial Park Area 3. It finds that the current electricity price is below a preset threshold (during off-peak hours). It acquires the predicted electricity consumption for Area 1, discovering that residential electricity demand will increase rapidly at night. It acquires the predicted data for City C, showing a significant demand for electricity in the industrial park the following day.

[0108] Forecasting module: Dynamically analyzes the storage capacity and electricity price in Region 3, finding no high electricity demand in the region in the short term. Based on the electricity demand of Region 1 and City C, predicts their peak electricity consumption times and compensation needs.

[0109] Management Module: During off-peak electricity pricing periods, excess electricity storage in Region 3 is stored. During peak electricity consumption periods in Region 1, some stored electricity is released to meet residential electricity demand. During peak hours in the industrial park of City C, the remaining stored electricity is sold at a higher price, maximizing cross-regional transaction revenue. The stored electricity in Region 3 is utilized effectively, and revenue is optimized through cross-regional electricity sales. The electricity needs of Region 1 and City C are guaranteed, avoiding potential power shortages.

[0110] Based on the above examples, it can be seen that by adjusting the electricity consumption characteristics of residential areas, commercial areas, and industrial parks within a large region, the following can be achieved: dynamically predicting electricity demand in each region and improving electricity efficiency within the region; rationally allocating surplus electricity storage to ensure power supply in emergencies; and optimizing revenue through off-peak electricity pricing for electricity storage and cross-regional electricity sales to maximize the utilization of grid resources.

[0111] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0112] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0113] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for predicting electricity sales in a power grid, characterized in that: The prediction method includes the following steps: The data acquisition terminal obtains the power grid coverage area and marks each area to form an initial area list; After obtaining regional electricity consumption data, spatiotemporal analysis is performed on the electricity consumption data. Based on the results of the regional electricity consumption time series analysis, a regional electricity consumption impact index is constructed, and based on the results of the regional electricity consumption spatiotemporal analysis, a regional electricity consumption fluctuation factor is constructed. The predicted electricity consumption of a region is dynamically obtained by combining the electricity consumption impact index with the electricity consumption fluctuation factor, and the predicted electricity sales of the power grid are obtained by summing the predicted electricity consumption of all regions. Based on the time-series analysis results of regional electricity consumption, a regional electricity consumption impact index is constructed, including the following steps: Obtain regional electricity consumption data, which includes the electricity consumption change index and the industrial output change index; The electricity consumption change index and the industrial output change index are normalized, and their value ranges are mapped to [0,1]. The electricity consumption impact index is obtained by summing the normalized electricity consumption change index and the industrial output change index. Based on the spatiotemporal analysis results of regional electricity consumption, a regional electricity consumption fluctuation factor is constructed, including the following steps: The number of nodes in the region is obtained, and the electricity consumption of each node at multiple time points is obtained. The average electricity consumption of the nodes is calculated, and the standard deviation of electricity consumption and the cumulative electricity consumption coefficient of the region are calculated based on the average electricity consumption. The electricity consumption fluctuation factor is calculated by combining the standard deviation of electricity consumption and the cumulative coefficient of electricity consumption. The expression is as follows: In the formula, This is the cumulative electricity consumption factor. The standard deviation of electricity consumption For power fluctuation factor, , It is the adjustment coefficient, and , All are greater than 0.

2. The method for predicting electricity sales in a power grid according to claim 1, characterized in that: The predicted electricity consumption for a region is dynamically obtained by combining the electricity consumption impact index with the electricity consumption fluctuation factor, including the following steps: The change coefficient for the region is obtained by multiplying the electricity consumption impact index by the electricity consumption fluctuation factor. The current electricity consumption in the region is then obtained, and the predicted electricity consumption for the region is obtained by adjusting the current electricity consumption using the change coefficient. The expression is as follows: In the formula, To predict electricity consumption, This represents the current electricity consumption. The coefficient of variation, The threshold for change; After obtaining the predicted electricity consumption for each region, sum the predicted electricity consumption for all regions to obtain the predicted electricity sales of the power grid.

3. The method for predicting electricity sales in a power grid according to claim 2, characterized in that: The standard deviation of electricity consumption in a region is calculated based on the average electricity consumption, expressed as follows: In the formula, The standard deviation of electricity consumption For the number of nodes, For the first Average power consumption of each node The average electricity consumption is the mean. The larger the standard deviation of electricity consumption, the greater the overall fluctuation of electricity consumption in the region, which means that the reliability of information in the region is reduced. The cumulative electricity consumption coefficient for a region is calculated based on the average electricity consumption, and the expression is as follows: In the formula, This is the cumulative electricity consumption factor. For the number of nodes, For the first Average power consumption of each node Let the time interval be the integral. Indicates time The sum of the average electricity consumption of all nodes in the time region.

4. The method for predicting electricity sales in a power grid according to claim 1, characterized in that: The processing logic of the electricity consumption change index is as follows: within the monitoring period, the electricity consumption change rate at multiple time points is obtained, and the electricity consumption change rate at multiple time points is summed to obtain the electricity consumption change index. For the current time point, the logic for obtaining the electricity consumption change rate is as follows: the electricity consumption difference is obtained by subtracting the electricity consumption at the previous time point from the current time point, the interval duration is obtained by subtracting the previous time point from the current time point, and the electricity consumption change index is obtained by dividing the electricity consumption difference by the interval duration. The industrial output change index is obtained by summing the industrial output change rates at multiple time points within the monitoring period. For the current time point, the logic for obtaining the industrial output change rate is as follows: the difference in electricity consumption is obtained by subtracting the industrial output value of the previous time point from the current time point's industrial output value; the interval duration is obtained by subtracting the previous time point from the current time point; and the industrial output change index is obtained by dividing the electricity consumption difference by the interval duration.

5. A power grid electricity sales management system, implemented based on the prediction method according to any one of claims 1-4, characterized in that: It includes a data acquisition module, a prediction module, and a management module; Data acquisition module: Acquires the power grid coverage area and marks each area to form an initial area list. Forecasting module: Acquire regional electricity consumption data, perform spatiotemporal analysis on the electricity consumption data, construct the regional electricity consumption impact index based on the regional electricity consumption time series analysis results, construct the regional electricity consumption fluctuation factor based on the regional electricity consumption spatiotemporal analysis results, combine the electricity consumption impact index and the electricity consumption fluctuation factor to dynamically obtain the regional predicted electricity consumption, and sum the predicted electricity consumption of all regions to obtain the grid predicted electricity sales. Management module: Based on the power grid's predicted electricity sales volume and the relationship between the power grid's energy storage capacity and electricity prices, it generates corresponding management strategies.

6. The power grid sales management system according to claim 5, characterized in that: After obtaining the grid's predicted electricity sales, the management module manages the electricity sales based on the comparison between the predicted electricity sales and the grid's storage capacity. If the predicted electricity sales are greater than the grid's storage capacity, it is analyzed that the current storage capacity is insufficient to support the electricity sales, and electricity can be transferred from other grid sub-networks for compensation. If the predicted electricity sales are less than or equal to the grid's storage capacity, it is analyzed that the current storage capacity is sufficient to support the electricity sales. When the current electricity storage capacity is sufficient to support electricity sales, the current electricity price is obtained. If the current electricity price is less than the preset price threshold, the excess electricity is stored. If the current electricity price is greater than or equal to the preset price threshold, the excess electricity can be sold. The system then checks whether there is a demand for electricity compensation in other regions. If not, the excess electricity is stored. If so, the excess electricity is sold to other regions.

Images