Method, device, equipment and medium for determining charging strategy of charging pile

Abstract

The present application relates to the technical field of charging piles, and in particular to a method, apparatus, device, and medium for determining a charging strategy for a charging pile. The method comprises: the present application obtains the regional scale (including the number of charging piles and construction costs) and regional location of the charging area, and predicts the number of users in each sub-period in the future cycle based on the number of charging piles and regional location, that is, the number of charging vehicles, and further predicts the number of kilowatt-hours of electricity purchased. When setting the billing standard, the busyness of the sub-period is taken into consideration to make the billing more reasonable. Sub-periods with high busyness correspond to higher billing standards, which helps to encourage users to charge during non-peak hours. By calculating the busyness of each sub-period and formulating a charging sub-strategy accordingly, a portion of price-sensitive users are guided to change their charging habits through price leverage and charge during low-price periods, thus avoiding the phenomenon of charging queues during peak hours to a certain extent and enhancing user trust and satisfaction.

Description

Technical Field

[0001] The present application relates to the technical field of charging piles, and in particular to a method, device, equipment and medium for determining a charging strategy for a charging pile. Background Art

[0002] With the booming electric vehicle industry in my country, the market share of electric vehicles continues to rise. This trend has directly led to the rapid development of various charging stations as key infrastructure. As a key means of energy replenishment for electric vehicles, the widespread deployment and efficient operation of charging piles are of immeasurable value in promoting the widespread adoption and in-depth application of electric vehicles.

[0003] These charging stations are often deployed in clusters, creating convenient and efficient charging zones. Most are located in high-traffic areas such as residential communities, parking lots in shopping malls and office buildings, and highway service areas, precisely catering to users' daily commutes and long-distance travel needs. However, charging demand fluctuates significantly in these popular locations: during specific periods, electric vehicles flock to charge, straining resources and creating frequent queues. Some users even miss out on charging opportunities, which in turn diminishes the charging experience for electric vehicle users in these areas. Summary of the Invention

[0004] In order to improve the charging experience of electric vehicle users in the charging area, the present application provides a charging strategy determination method, device, equipment and medium for a charging pile.

[0005] In a first aspect, the present application provides a method for determining a charging strategy for a charging pile, which adopts the following technical solution:

[0006] A method for determining a charging strategy for a charging pile, comprising:

[0007] Obtain the regional scale and location of the charging area, where the regional scale includes the number of charging piles and construction costs;

[0008] Determining, based on the number of charging piles and the location of the area, a user volume corresponding to each sub-period of the charging area in a future period, where the user volume is the number of charging vehicles that use the charging piles to charge in a sub-period of the future period;

[0009] Based on the number of users, predict the amount of electricity purchased in each sub-period of the charging area in the future period;

[0010] Predicting the maintenance cost of the charging area in a future period and the corresponding grid load distribution in the future period, and determining the basic fee corresponding to the charging area based on the grid load distribution, the purchased electricity corresponding to each sub-period, and the maintenance cost;

[0011] Determining the weights of the grid load distribution and the user quantity;

[0012] Based on the load distribution of the power grid and the weights of the user quantities, the busyness corresponding to each sub-period is calculated;

[0013] Based on the busyness corresponding to each sub-cycle, the construction cost and the basic cost, the billing standard corresponding to each sub-cycle is determined, and the charging sub-strategy corresponding to each sub-cycle in the future cycle is determined, and the corresponding charging operation is performed based on the billing standard and the charging sub-strategy corresponding to each sub-cycle.

[0014] By adopting the above technical solution, by obtaining the regional scale (including the number of charging piles and construction costs) and regional location of the charging area, we can achieve accurate grasp of the basic information of the charging area, which will help to formulate more refined management and operation strategies later. Based on the number of charging piles and regional location, the user volume of each sub-cycle in the future cycle (that is, the number of charging vehicles) is predicted. This helps to prepare resources in advance, avoid supply and demand imbalances, and improve user satisfaction. Further forecasting the purchased electricity volume helps to rationalize electricity procurement, reduce costs, and ensure the stability of the power supply. When setting billing standards, the busyness of the sub-cycle is taken into account, making billing more reasonable. Sub-cycles with high busyness correspond to higher billing standards, which helps to encourage users to charge during off-peak hours. By calculating the busyness of each sub-cycle and formulating charging sub-strategies accordingly, dynamic monitoring and adjustment of the operating status of the charging area are achieved. Through price leverage, some price-sensitive users are guided to change their charging habits and charge during off-peak hours when prices are lower. To a certain extent, this avoids charging queues during peak hours, reduces the churn rate of charging demand and improves user satisfaction. Charging operations are closely linked to billing standards and charging sub-strategies, ensuring the fairness and transparency of charging and enhancing user trust and satisfaction.

[0015] In one possible implementation, determining the number of users corresponding to each sub-period in the charging area in a future period based on the number of charging piles and the location of the area includes:

[0016] Obtaining a charging demand source corresponding to the regional location, where the charging demand source is used to represent vehicles passing through the regional location and having charging demand;

[0017] Determining whether the type corresponding to the charging demand source is a stable type, so as to determine whether there are primary users and secondary users in the regional location;

[0018] If there are primary users and secondary users in the area, obtain historical charging data corresponding to the charging area, wherein the historical charging data includes the number of historical charging users corresponding to each historical period and the historical charging times and historical charging times corresponding to each historical charging user;

[0019] Dividing the historical period into a plurality of historical sub-periods based on the historical charging data;

[0020] The future cycle is divided into multiple sub-cycles based on the start and end times corresponding to each historical sub-cycle, and the number of users corresponding to each sub-cycle in the charging area in the future cycle is determined based on the historical number of charging users corresponding to each historical sub-cycle.

[0021] By adopting the above technical solution, by obtaining the source of charging demand corresponding to the regional location and determining whether the type of charging demand source is a stable type, it is helpful to distinguish between primary users and secondary users. Primary users may be a fixed user group that frequently charges in the area, while secondary users may be temporary users who occasionally pass by. This helps to more finely analyze user behavior and formulate more targeted operation strategies. Obtaining historical charging data corresponding to the charging area, including the historical number of charging users, historical charging times, and historical charging times, provides a reliable data basis for predicting future user volume. Dividing both historical and future cycles into multiple sub-cycles helps to more carefully analyze user charging needs in different time periods, and can also provide important reference for resource allocation, power procurement, and charging strategies in the charging area.

[0022] In a possible implementation, based on the historical charging data, the historical period is divided into a plurality of historical sub-periods, including:

[0023] From the historical charging users, select historical charging users whose historical charging times are greater than a charging times threshold as first historical charging users, and select historical charging users whose historical charging times have a regularity greater than a regularity threshold as second historical charging users;

[0024] Determine the first historical charging user and the second historical charging user as primary users, and determine the historical charging users other than the primary users as secondary users;

[0025] The historical period is divided into a plurality of historical sub-periods based on the historical charging times corresponding to the primary user and the historical charging times corresponding to the secondary user.

[0026] By adopting the above technical solution, users with a high number of historical charging times and regular charging times are screened out as main users. The behavior patterns of these users are more stable and representative. Dividing historical sub-cycles based on the data of these users can more accurately reflect the cyclical changes in electric vehicle charging behavior, providing a more reliable basis for subsequent analysis and prediction. At the same time, understanding the distribution of charging demand in different historical sub-cycles will help charging station operators better plan the layout and capacity of charging facilities to meet the charging demand in different time periods. They can also adjust charging price strategies or optimize charging service processes based on the charging behavior characteristics in historical sub-cycles to improve user experience and operational efficiency.

[0027] In one possible implementation, determining the basic fee corresponding to the charging area based on the grid load distribution, the number of purchased kilowatt-hours corresponding to each sub-cycle, and the maintenance fee includes:

[0028] Based on the load distribution of the power grid, the future time period is divided into peak period, normal period and valley period;

[0029] Based on the electricity purchase amount corresponding to each sub-period, a first electricity purchase amount corresponding to the peak period, a second electricity purchase amount corresponding to the normal period, and a third electricity purchase amount corresponding to the valley period are calculated;

[0030] Predict the unit electricity prices corresponding to peak, normal and off-peak periods in the future cycle;

[0031] Based on Formula 1: M = M1 + C, calculate the basic fee corresponding to the charging area;

[0032] Where M is the basic cost of the charging area in the future cycle, M1 is the electricity purchase cost of the charging area in the future cycle, and C is the maintenance cost;

[0033] Among them, based on formula 2: M1=∑ t∈所有时段 ((E t,p *P p +E t,N *P N +E t,L *P L )*F(U t )) and predicting the electricity purchase cost of the charging area in the future cycle;

[0034] Among them, M1 is the electricity purchase cost of the charging area in the future cycle, E t,p is the first purchased electricity quantity, E t,N is the second purchased electricity quantity, E t,L is the third purchased electricity quantity, P p is the unit electricity price during peak hours, P Nis the unit electricity price during normal times, P L is the unit electricity price during the off-peak period, F(U t ) is a discount or markup factor related to the number of users;

[0035] Based on formula 3: Calculate discount or markup factors related to user volume;

[0036] Among them, F(U t ) is the discount or markup factor related to the number of users, U threshold is the first threshold corresponding to the number of users, U max is the second threshold corresponding to the number of users.

[0037] By adopting this technical solution, which divides future time periods into peak, average, and off-peak periods and calculates the amount of electricity purchased for each period, the cost of electricity purchased in the charging area can be more accurately estimated. By introducing discounts or price increases based on user volume, this solution can incentivize users to charge during different times, thereby balancing the grid load. When the charging area has a low user volume, discounts are offered to attract users; when the user volume is high, price increases are used to limit charging demand and avoid excessive pressure on the grid, helping to achieve a reasonable distribution of charging demand and smooth grid load operation.

[0038] In a possible implementation, when there are no primary users and secondary users in the area, determining the number of users corresponding to each sub-period in the charging area in a future period based on the number of charging piles and the area location includes:

[0039] Based on the number of charging piles, at least one target charging area is selected from the remaining charging areas, where the number of charging piles in the target charging area is the same as the number of charging piles corresponding to the charging area, and there are no primary users or secondary users in the target charging area;

[0040] Obtaining first historical data corresponding to the target charging area and second historical data corresponding to the charging area, wherein the first historical data includes historical usage times of each charging pile in the target charging area, and the second historical data includes historical usage times of each charging pile in the charging area;

[0041] Dividing the future period into at least two sub-periods based on each historical usage moment included in the first historical data and each historical usage moment included in the second historical data;

[0042] Determine the date label corresponding to each sub-period;

[0043] Based on the date tag corresponding to each sub-cycle and the historical usage time of each charging pile, the number of users corresponding to each sub-cycle in the charging area in the future cycle is determined.

[0044] By employing this technical solution, target charging areas with the same number of charging piles and no primary or secondary users are selected from the remaining charging areas. This allows for cross-regional data comparison, helping to identify charging behavior patterns under similar conditions and providing a valuable reference for predicting user volume in the current charging area. Dividing the future cycle into at least two sub-cycles based on historical usage times and determining a date tag for each sub-cycle helps more accurately capture cyclical changes in charging behavior, leading to more accurate predictions of user volume. By combining the date tags of each sub-cycle with the historical usage times of the charging piles, the solution can more comprehensively account for various factors that influence user volume, such as the difference between weekdays and weekends and the impact of holidays. This comprehensive consideration helps improve the accuracy of user volume predictions.

[0045] In one possible implementation, determining the weights of the grid load distribution and the user quantity includes:

[0046] Establishing a first judgment matrix based on the grid load sub-distribution corresponding to each sub-period in the future period;

[0047] Establishing a second judgment matrix based on the user quantity corresponding to each sub-period;

[0048] The maximum eigenvalue and eigenvector corresponding to each of the first judgment matrix and the second judgment matrix are calculated to obtain the weights of the power grid load distribution and the user quantity.

[0049] By adopting the above technical solution, the weights are determined by establishing a first judgment matrix (based on the grid load sub-distribution) and a second judgment matrix (based on the number of users), and calculating the eigenvectors corresponding to their respective maximum eigenvalues. This is scientific and objective, avoiding the deviation caused by subjective judgment, making the determination of weights more accurate and reliable, and helping to more comprehensively evaluate their impact on the operation of the charging area, providing more comprehensive information support for subsequent decision-making.

[0050] In a possible implementation, determining a billing standard corresponding to each sub-period based on the busyness corresponding to each sub-period, the completion fee, and the basic fee includes:

[0051] Determine the estimated usage time corresponding to the charging area;

[0052] Determining a construction sub-cost corresponding to each sub-period in the future period based on the expected usage duration and the construction cost;

[0053] Determine the proportion of the allocated construction costs and basic costs corresponding to each sub-period to obtain the cost allocation factor corresponding to each sub-period;

[0054] Based on formula 4: Get the billing standard corresponding to each sub-period;

[0055] Among them, S i is the cost per kilowatt-hour of electricity corresponding to the sub-period, E i The amount of electricity purchased corresponding to the sub-period, S base is the sum of the basic cost corresponding to the sub-period and the construction cost, F a F is the busyness level, b is the cost sharing factor, and α and β are both constants.

[0056] By adopting this technical solution, which factors in the busyness, construction costs, and base fees of each sub-cycle, we can more accurately reflect the cost variations of charging services over different time periods, thereby providing users with more fair and reasonable charging rates. Busyness, as a key factor in billing standards, can incentivize users to charge during off-peak hours, thereby balancing the grid load and reducing pressure during peak hours.

[0057] In a second aspect, the present application provides a device for determining a charging strategy for a charging pile, which adopts the following technical solution:

[0058] A device for determining a charging strategy for a charging pile, comprising:

[0059] An acquisition module is used to obtain the regional scale and regional location of the charging area, wherein the regional scale includes the number of charging piles and the construction cost;

[0060] A user quantity determination module is configured to determine the user quantity corresponding to each sub-period of the charging area in a future cycle based on the number of charging piles and the location of the area, wherein the user quantity is the number of charging vehicles charged using the charging piles in a sub-period of the future cycle;

[0061] A prediction module, configured to predict the amount of electricity purchased in each sub-period of the charging area in a future period based on the number of users;

[0062] a fee determination module, configured to predict the maintenance fee of the charging area in a future period and the corresponding grid load distribution in the future period, and determine the basic fee corresponding to the charging area based on the grid load distribution, the purchased electricity corresponding to each sub-period, and the maintenance fee;

[0063] A weight determination module, used to determine the weights of the power grid load distribution and the user quantity;

[0064] a calculation module, configured to calculate a busyness corresponding to each sub-period based on a first weight corresponding to the grid load distribution and a second weight corresponding to the user quantity;

[0065] The strategy determination module is used to determine the billing standard corresponding to each sub-cycle based on the busyness corresponding to each sub-cycle, the construction cost and the basic cost, so as to obtain the charging sub-strategy corresponding to each sub-cycle in the future cycle, and perform the corresponding charging operation based on the billing standard and the charging sub-strategy corresponding to each sub-cycle, wherein the greater the busyness of the sub-cycle, the higher the corresponding billing standard.

[0066] In a third aspect, the present application provides an electronic device, which adopts the following technical solution:

[0067] An electronic device, comprising:

[0068] at least one processor;

[0069] Memory;

[0070] At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the charging pile charging strategy determination method described in the first aspect above.

[0071] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0072] A computer-readable storage medium includes: storing a computer program that can be loaded by a processor and execute the charging pile charging strategy determination method described in the first aspect above.

[0073] In summary, the present application includes the following beneficial technical effects: by obtaining the regional scale (including the number of charging piles and construction costs) and regional location of the charging area, accurate grasp of the basic information of the charging area is achieved, which is helpful for the subsequent formulation of more refined management and operation strategies. Predicting the number of users in each sub-cycle in the future cycle based on the number of charging piles and regional location, that is, the number of charging vehicles, helps to prepare resources in advance, avoid imbalance between supply and demand, and improve user satisfaction. Further predicting the number of purchased electricity will help to reasonably arrange electricity procurement, reduce costs, and ensure the stability of power supply. When setting the billing standard, the busyness of the sub-cycle is taken into consideration to make the billing more reasonable. Sub-cycles with high busyness correspond to higher billing standards, which helps to encourage users to charge during off-peak hours. By calculating the busyness of each sub-cycle and formulating charging sub-strategies accordingly, dynamic monitoring and adjustment of the operating status of the charging area are achieved. Through price leverage, some price-sensitive users are guided to change their charging habits and charge during off-peak hours when prices are lower. To a certain extent, this avoids charging queues during peak hours, reduces the churn rate of charging demand and improves user satisfaction. Charging operations are closely linked to billing standards and charging sub-strategies, ensuring the fairness and transparency of charging and enhancing user trust and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a schematic diagram of the connection and interaction between an electronic device and a charging pile in a charging area provided by an embodiment of the present application;

[0075] Figure 2 This is a flow chart of a method for determining a charging strategy for a charging pile provided in an embodiment of the present application;

[0076] Figure 3 is a block diagram of a device for determining a charging strategy for a charging pile provided in an embodiment of the present application;

[0077] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0078] The following is combined with Figure 1 -Attached Figure 4 This application is described in further detail.

[0079] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0080] In order to facilitate understanding of the technical solutions proposed in this application, several elements that will be introduced in the description of this application are first introduced here. It should be understood that the following introduction is only for the convenience of understanding these elements, so as to understand the content of the embodiments of this application, and does not necessarily cover all possible situations.

[0081] A charging area is an area containing multiple charging piles, generally located in residential areas, shopping malls, office buildings or highway service areas, so as to provide electricity for new energy vehicles (hereinafter referred to as charging vehicles).

[0082] A charging station is a device that provides DC or AC power to charging vehicles. It can be fixed to the ground. The input end of the charging station is connected to the AC power grid, and the output end is connected to the electric vehicle through a charging plug, realizing the transmission of electrical energy. Most charging stations have multiple functions such as display, card swiping, billing, and charging information printing. The owner of the charging vehicle can select the charging time by scanning a code or swiping a card.

[0083] Charging zones are often precisely aligned with users' daily commutes and long-distance travel needs. However, charging demand fluctuates significantly in these popular areas: during specific periods, charging vehicles flock to the area, competing to charge, straining resources and creating frequent queues. Some users even miss out on charging opportunities, which in turn diminishes the charging experience for those who use the area.

[0084] In view of this, the embodiment of the present application provides a method for determining a charging strategy of a charging pile, such as Figure 1 As shown, the electronic device establishes connections with each charging pile in the charging area respectively, and the electronic device can obtain the charging information of each charging pile. The electronic device can also control the charging billing information corresponding to each charging pile.

[0085] The embodiment of the present application provides a method for determining a charging strategy of a charging pile, such as Figure 2 As shown, the method provided in the embodiment of the present application is performed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. The method includes steps S201 to S207, wherein: Step S201, obtain the regional scale and regional location of the charging area.

[0086] Among them, the regional scale includes the number of charging piles and construction costs.

[0087] For any charging area, the corresponding charging location, construction cost and number of charging piles of the charging area are all stored in the charging database. The charging scale and area location of the charging area to be queried can be obtained from the charging database.

[0088] Step S202: Based on the number of charging piles and the location of the area, determine the number of users corresponding to each sub-period in the charging area in the future period.

[0089] Among them, the user volume is the number of charging vehicles charged using charging piles in a sub-period of the future cycle. The future cycle can be one day or the next twelve hours, which is not limited in the embodiment of the present application. Specifically, the future cycle can be evenly divided into multiple sub-periods, or based on the number of charging piles and regional locations, a curve of the user volume corresponding to each charging area in each historical period over time can be obtained, and according to each curve, the period is divided into a time range when users appear in a concentrated manner and a time range when users do not appear in a concentrated manner, so as to divide the future cycle into multiple sub-periods. More specifically, based on the curve of the user volume corresponding to each sub-period over time, the time range where most of the user volume is located is identified, and the future cycle is divided into multiple sub-periods according to the time range. For example, the future period is 6:00:00-18:00:00, and the time ranges when most users are present are 8:00:00-9:00:00 and 14:00:00-16:00:00. The future period is divided into five sub-periods, namely 6:00:00-8:00:00, 8:00:00-9:00:00, 9:00:00-14:00:00, 14:00:00-16:00:00, and 16:00:00-18:00:00.

[0090] After obtaining a curve showing the time-varying user volume for each charging area in each historical period, divide each historical period into multiple sub-periods, and obtain the historical user volume for each sub-period. Calculate the average of the historical user volume for each sub-period to determine the user volume for each sub-period in the future period.

[0091] Step S203: Based on the number of users, predict the amount of electricity purchased in the charging area corresponding to each sub-period in the future period.

[0092] Each charging vehicle corresponds to a battery capacity, which is the amount of electricity required to fully charge the charging vehicle. The average battery capacity of the charging vehicles is obtained as the battery capacity of each charging vehicle.

[0093] Specifically, based on the battery capacity of each charging vehicle and the number of users corresponding to each sub-cycle in the future cycle, the power provided to the charging vehicle corresponding to each sub-cycle is calculated, thereby obtaining the purchased power kWh corresponding to each sub-cycle in the charging area in the future cycle.

[0094] Step S204: predict the maintenance cost of the charging area in the future period and the grid load distribution corresponding to the future period, and determine the basic cost corresponding to the charging area based on the grid load distribution, the purchased electricity corresponding to each sub-period, and the maintenance cost.

[0095] The grid load distribution can be presented in the form of a grid load distribution curve, which is a graphical representation of the time-varying changes in various types of power loads in a power system. The horizontal axis of the grid load distribution curve typically represents time. In a load curve, time can be divided into hourly, half-hourly, or shorter intervals to illustrate how the power load changes over time. The vertical axis of the grid load distribution curve represents the load value, which typically refers to active power or reactive power. Active power is the power actually consumed by the grid and is used to perform work, such as driving motors, heaters, and other equipment. Reactive power is the power used to establish and maintain the electromagnetic field in the grid. It does not directly consume energy, but is crucial for the stable operation of the grid. The load value on the vertical axis shows the grid load at a specific point in time and is an important basis for power system scheduling and planning.

[0096] Specifically, multiple historical load distribution curves corresponding to regional locations in future periods are obtained. Using the ARIMA model, these historical load distribution curves are used to identify patterns in the time series. The power grid load distribution curves for future periods are then predicted to obtain the power grid load distribution corresponding to the future periods. The time interval between the historical period and the future period corresponding to each historical load distribution curve is no greater than a time interval threshold.

[0097] The process of predicting the maintenance costs of the charging area in the future cycle can be: obtaining the damage records of the charging piles and the maintenance costs corresponding to each damage, calculating the damage frequency of each charging pile (number of damages / total number of days), and based on the average damage frequency = Σ(charge pile damage frequency) / number of charging piles, averaging the damage frequencies of all charging piles to obtain the average damage frequency of the charging piles. Based on the average maintenance cost = Σ(maintenance cost) / number of repairs, average all maintenance costs to obtain the average maintenance cost. Based on the number of charging piles in the charging area, the predicted number of damages in the future cycle is calculated, and based on the predicted number of damages = average damage frequency * number of charging piles * number of days in the future cycle and the maintenance cost = predicted number of damages * average maintenance cost, the maintenance costs of the charging area in the future cycle are calculated.

[0098] Furthermore, the electricity price table corresponding to the grid load distribution is obtained. Based on the purchased kWh and the grid load distribution corresponding to each sub-cycle, the electricity purchase fee for each sub-cycle is calculated. The electricity purchase fees for each sub-cycle are summed to obtain the electricity purchase fee for the charging area in the future cycle. Furthermore, based on the electricity purchase fee and maintenance fee for the charging area in the future cycle, the basic fee corresponding to the charging area is obtained.

[0099] Step S205: Determine the weights of the grid load distribution and the number of users.

[0100] The analytic hierarchy process (AHP) can be used to determine the weights assigned to grid load distribution and user volume. Specifically, a hierarchical model is constructed. The objective layer determines the goal of "assessing the busyness of the grid sub-cycle," and the criterion layer sets two main criteria: criterion A is the grid load distribution, and criterion B is the user volume. Different sub-cycles are used as elements of the solution layer.

[0101] For each criterion (A and B) and the elements of the solution layer, a judgment matrix is ​​constructed. Constructing the judgment matrix of criterion A (grid load distribution): Compare the load fluctuations corresponding to different sub-periods and use the 1-9 scaling method to construct the judgment matrix.

[0102] Construct a judgment matrix for criterion B (user volume): Compare the relative importance of different sub-periods in terms of user volume, also using a 1-9 scale. Relative importance is based on the historical user volume within each sub-period; the greater the historical user volume, the greater the importance of the sub-period.

[0103] For each judgment matrix, the eigenvalue method is used to calculate the maximum eigenvalue and eigenvector corresponding to each judgment matrix. The elements of this eigenvector are the weights of the corresponding sub-periods. The weights are normalized so that the sum of the weights of all sub-periods under each criterion is 1. Based on the relative importance of the criterion layers (A and B), a total weight is assigned to criteria A and B (for example, A: 0.6, B: 0.4) to obtain the weights of the grid load distribution and the number of users.

[0104] Step S206: The power grid load distribution and the weights of the user quantities are used to calculate the busyness corresponding to each sub-period.

[0105] The busyness is used to characterize the energy supply tension in the charging area. The tighter the energy supply, the busier the corresponding busyness.

[0106] Specifically, the collected grid load data and user volume data of each sub-cycle are standardized to eliminate dimensional differences. Then, the weight of each sub-cycle is multiplied by the standardized grid load and user volume data, and the grid load weighted value and user volume weighted value of each sub-cycle are added together to obtain the busyness value of the sub-cycle.

[0107] Step S207: Based on the busyness, construction cost, and basic cost corresponding to each sub-cycle, determine the billing standard corresponding to each sub-cycle, and determine the charging sub-strategy corresponding to each sub-cycle in the future cycle, and perform the corresponding charging operation based on the billing standard and charging sub-strategy corresponding to each sub-cycle.

[0108] The busier the sub-cycle is, the higher the corresponding billing standard is. The billing standard is the fee charged after providing unit electricity.

[0109] Specifically, the average usage time of each charging area is obtained to obtain the estimated usage time corresponding to the charging area. Based on the estimated usage time and the construction cost, the construction sub-cost corresponding to the future period is calculated.

[0110] Furthermore, based on the construction sub-cost and base cost corresponding to the future cycle, the expenses of the charging area in the future cycle are calculated, and the corresponding profit rate of the charging area is obtained. Based on the expenses and profit rate in the future cycle, the income for the future cycle is calculated based on the formula: income = profit rate * number of purchased kilowatt-hours + expenses. The profit rate is the ratio of the difference between the fee collected and the expenses for providing a unit of electricity to the expenses. Specifically, the profit rate for each sub-cycle is determined based on the busyness level corresponding to each sub-cycle, and the income for the future cycle is calculated based on the profit rate and the number of purchased kilowatt-hours for each sub-cycle. More specifically, the profit rate for each sub-cycle is calculated based on the formula: profit rate = busyness level corresponding to the sub-cycle / Σ(busyness level corresponding to each sub-cycle) * profit rate. The sub-income for each sub-cycle is then calculated based on the formula: sub-income for each sub-cycle = profit rate for the sub-cycle * number of purchased kilowatt-hours for the sub-cycle. The sub-expense for each sub-cycle is calculated based on the formula: sub-expense / future cycle * the expenses of the charging area in the future cycle. Furthermore, for each sub-period, the billing standard corresponding to the sub-period = (sub-expenditure + sub-income) / the number of purchased electricity corresponding to the sub-period.

[0111] After obtaining the billing standard corresponding to each sub-cycle, the electronic device generates a charging sub-strategy including the start and end time of each sub-cycle and the billing standard corresponding to each cycle, and sends instructions to each charging pile in the charging area to control the charging pile to charge according to the corresponding charging sub-strategy.

[0112] The embodiment of the present application provides a method for determining a charging strategy for a charging pile. By obtaining the regional scale (including the number of charging piles and construction costs) and regional location of the charging area, it is possible to accurately grasp the basic information of the charging area, which is helpful for the subsequent formulation of more refined management and operation strategies. Predicting the number of users in each sub-cycle in the future cycle based on the number of charging piles and regional location, that is, the number of charging vehicles, helps to prepare resources in advance, avoid imbalances between supply and demand, and improve user satisfaction. Further predicting the number of purchased electricity will help to reasonably arrange electricity procurement, reduce costs, and ensure the stability of power supply. When setting the billing standard, the busyness of the sub-cycle is taken into consideration to make the billing more reasonable. Sub-cycles with high busyness correspond to higher billing standards, which helps to encourage users to charge during off-peak hours. By calculating the busyness of each sub-cycle and formulating charging sub-strategies accordingly, dynamic monitoring and adjustment of the operating status of the charging area are achieved. Through price leverage, some price-sensitive users are guided to change their charging habits and charge during off-peak hours when prices are lower. To a certain extent, this avoids charging queues during peak hours, reduces the churn rate of charging demand and improves user satisfaction. Charging operations are closely linked to billing standards and charging sub-strategies, ensuring the fairness and transparency of charging and enhancing user trust and satisfaction.

[0113] In one possible implementation of the embodiment of the present application, in step S102 above, determining the number of users corresponding to each sub-period in the charging area in the future period based on the number of charging piles and the regional location includes:

[0114] Obtain the charging demand source corresponding to the regional location. The charging demand source is used to represent vehicles passing through the regional location and having charging needs.

[0115] Determine whether the type corresponding to the charging demand source is a stable type to determine whether there are primary users and secondary users in the regional location;

[0116] If there are primary and secondary users in the area, obtain the historical charging data corresponding to the charging area. The historical charging data includes the number of historical charging users corresponding to each historical period, the number of historical charging times and the historical charging time corresponding to each historical charging user.

[0117] Based on historical charging data, the historical cycle is divided into multiple historical sub-cycles;

[0118] Based on the start and end times corresponding to each historical sub-cycle, the future cycle is divided into multiple sub-cycles, and based on the historical number of charging users corresponding to each historical sub-cycle, the number of users corresponding to each sub-cycle in the charging area in the future cycle is determined.

[0119] When a charging area is located near a residential area, shopping mall, or office building, these locations have a fixed flow of people, and therefore, the number of vehicles charging in the charging area is also relatively fixed. Therefore, based on the charging area's regional location, the source of charging demand corresponding to the charging area can be determined to predict the number of users corresponding to each sub-cycle in the charging area in the future. Specifically, based on the charging area's regional location, the charging demand source corresponding to the regional location is obtained, and the type of the charging demand source is determined to be stable to determine whether there are primary and secondary users in the regional location. That is, if the charging demand source is stable, there are primary and secondary users in the regional location; if the charging demand source is unstable, there are no primary or secondary users in the regional location. More specifically, it is determined whether there are places with a fixed flow of people within a certain range of the regional location. If so, the type of charging demand source corresponding to the regional location is determined to be stable; if not, the type of charging demand source corresponding to the regional location is determined to be unstable.

[0120] Furthermore, if the area has both primary and secondary users, historical charging data for that charging area is obtained, including the number of charging users, the number of charges per user, and the time of charge. This historical data is then divided into multiple historical sub-periods based on time (e.g., days or hours) to analyze charging behavior patterns within these time periods. Future periods are also divided into corresponding sub-periods based on the start and end times of these historical sub-periods.

[0121] Furthermore, statistical analysis is performed on the user volume for each historical sub-period to identify trends in user volume. Based on this historical data, a prediction model is constructed and trained to produce a trained prediction model. Time parameters for future periods are input into the trained prediction model to output the predicted user volume for each sub-period. For example, an ARIMA model or LSTM neural network can be used to predict the user volume for each future sub-period.

[0122] A possible implementation of the embodiment of the present application is to divide the historical cycle into multiple historical sub-cycles based on the historical charging data in the above embodiment, including:

[0123] From the historical charging users, select historical charging users whose historical charging times are greater than a charging times threshold as first historical charging users, and select historical charging users whose historical charging times have a regularity greater than a regularity threshold as second historical charging users;

[0124] Determine the first historical charging user and the second historical charging user as primary users, and determine the historical charging users other than the primary user as secondary users;

[0125] The historical period is divided into a plurality of historical sub-periods based on the historical charging times corresponding to the primary user and the historical charging times corresponding to the secondary user.

[0126] Specifically, historical charging data is traversed to obtain the number of charges for each historical charging user. Users whose charge times exceed a charging threshold (e.g., more than 5 times per month) are selected as the first historical charging users. For each historical charging user, their historical charging times are analyzed and the standard deviation of charging times is calculated to determine the degree of regularity for that historical charging user. Furthermore, after obtaining the degree of regularity for each historical charging user, users whose regularity exceeds a regularity threshold (e.g., the standard deviation is less than the corresponding threshold) are selected as the second historical charging users.

[0127] The primary user set is obtained by taking the union of the first and second historical charging users, and the remaining historical charging users are identified as secondary users. A statistical analysis of the primary users' historical charging times is performed to identify the main charging time periods (e.g., 7:00 PM to 10:00 PM, 8:00 AM to 10:00 AM, etc.). Based on the distribution of the primary users' charging time periods, the historical cycle is divided into multiple historical sub-cycles.

[0128] A possible implementation of the embodiment of the present application, in the above embodiment, determines the basic fee corresponding to the charging area based on the grid load distribution, the number of purchased kilowatt-hours corresponding to each sub-cycle, and the maintenance fee, including:

[0129] Based on the load distribution of the power grid, the future time period is divided into peak period, normal period and valley period;

[0130] Based on the electricity purchase amount corresponding to each sub-period, a first electricity purchase amount corresponding to the peak period, a second electricity purchase amount corresponding to the normal period, and a third electricity purchase amount corresponding to the off-peak period are calculated;

[0131] Predict the unit electricity prices corresponding to peak, normal and off-peak periods in the future cycle;

[0132] Based on Formula 1: M = M1 + C, calculate the basic fee corresponding to the charging area;

[0133] Among them, M is the basic cost of the charging area in the future cycle, M1 is the electricity purchase cost of the charging area in the future cycle, and C is the maintenance cost;

[0134] Among them, based on formula 2: M1=∑ t∈所有时段 ((E t,p *P p+E t,N *P N +E t,L *P L )*F(U t )) to predict the electricity purchase cost of the charging area in the future cycle;

[0135] Among them, M1 is the electricity purchase cost of the charging area in the future cycle, E t,p is the first purchased electricity quantity, E t,N is the second purchased electricity quantity, E t,L is the third purchased electricity quantity, P p is the unit electricity price during peak hours, P N is the unit electricity price during normal times, P L is the unit electricity price during the off-peak period, F(U t ) is a discount or markup factor related to the number of users;

[0136] Based on formula 3: Calculate discount or markup factors related to user volume;

[0137] Among them, F(U t ) is the discount or markup factor related to the number of users, U threshold is the first threshold corresponding to the number of users, U max is the second threshold corresponding to the number of users.

[0138] Among them, peak period: the time period when the load is higher than the average load by a certain percentage (such as more than 120% of the average load). Normal period: the time period when the load is between the peak period and the valley period. Valley period: the time period when the load is lower than the average load by a certain percentage (such as less than 80% of the average load). Specifically, after obtaining the grid load distribution in the future cycle, the future time period is divided into peak period, normal period, and valley period. Based on the overlapping time period of the sub-cycle with the peak period, normal period, and valley period, and the purchased electricity kilowatt-hour corresponding to each sub-cycle, the first purchased electricity kilowatt-hour corresponding to the peak period, the second purchased electricity kilowatt-hour corresponding to the normal period, and the third purchased electricity kilowatt-hour corresponding to the valley period are obtained. More specifically, the first electricity purchase number = the duration corresponding to the overlapping time period of the sub-cycle and the peak period / the duration corresponding to the sub-cycle * the number of electricity purchased corresponding to the sub-cycle. Similarly, the second electricity purchase number = the duration corresponding to the overlapping time period of the sub-cycle and the normal period / the duration corresponding to the sub-cycle * the number of electricity purchased corresponding to the sub-cycle. The third electricity purchase number = the duration corresponding to the overlapping time period of the sub-cycle and the off-peak period / the duration corresponding to the sub-cycle * the number of electricity purchased corresponding to the sub-cycle.

[0139] Obtain the unit electricity prices corresponding to the peak period, normal period and valley period in the current cycle, and use them as the unit electricity prices corresponding to the peak period, normal period and valley period in the future cycle, so as to obtain the predicted unit electricity prices corresponding to the peak period, normal period and valley period in the future cycle.

[0140] Furthermore, the basic cost corresponding to the charging area is calculated based on Formula 1: M = M1 + C. Here, M is the basic cost of the charging area in the future cycle, M1 is the electricity purchase cost of the charging area in the future cycle, and C is the maintenance cost.

[0141] Among them, the electricity purchase cost of the charging area in the future cycle can be based on Formula 2: M1 = ∑ t∈所有时段 ((E t,p *P p +E t,N *P N +E t,L *P L )*F(U t )) is predicted. Among them, M1 is the electricity purchase cost of the charging area in the future cycle, E t,p is the electricity purchased during peak hours, E t,N is the electricity purchased during normal times, E t,L is the electricity purchased during off-peak hours, P p is the unit electricity price during peak hours, P N is the unit electricity price during normal times, P L is the unit electricity price during the off-peak period, F(U t ) is a discount or markup factor related to the number of users.

[0142] The discount or price increase factor related to user volume in Formula 2 can be based on Formula 3: Calculated. Among them, F(U t ) is the discount or markup factor related to the number of users, U threshold is the first threshold corresponding to the number of users, U max The first threshold is the lower limit of the number of users, and the second threshold is the upper limit of the number of users. It can be a value input by the administrator or set according to actual conditions, and this embodiment of the application is not limited to this.

[0143] A possible implementation of the embodiment of the present application is to determine, when there are no primary users and secondary users in the regional location, the number of users corresponding to the charging area in each sub-period in the future cycle based on the number of charging piles and the regional location, including: based on the number of charging piles, screening at least one target charging area from the remaining charging areas, the number of charging piles in the target charging area being the same as the number of charging piles corresponding to the charging area, and the target charging area being free of primary users and secondary users; obtaining first historical data corresponding to the target charging area and second historical data corresponding to the charging area, the first historical data including historical usage time of each charging pile in the target charging area, and the second historical data including historical usage time of each charging pile in the charging area;

[0144] Dividing the future period into at least two sub-periods based on each historical usage moment included in the first historical data and each historical usage moment included in the second historical data;

[0145] Determine the date label corresponding to each sub-period;

[0146] Based on the date tag corresponding to each sub-cycle and the historical usage time of each charging pile, the number of users corresponding to each sub-cycle in the charging area in the future cycle is determined.

[0147] When there are no places with a constant flow of people within a certain range of the location of the area, that is, when the type of charging demand source corresponding to the location of the area is unstable, historical data of the charging area can be obtained and the user volume corresponding to the charging area can be predicted based on this historical data. Specifically, based on the number of charging piles in the charging area, an area with the same number of charging piles is searched from other charging areas in the charging database as the target charging area. For each selected target charging area and the current charging area, first historical data containing the historical usage time of each charging pile corresponding to each target charging area is obtained, and second historical data containing the historical usage time of each charging pile corresponding to the charging area is obtained.

[0148] Furthermore, based on the usage moments in the historical data, cluster analysis is used to analyze the periodic patterns of charging pile usage (such as the difference between weekdays and weekends, seasonal changes, etc.), and the future cycle is divided into several sub-cycles. One or more date labels are assigned to each sub-cycle. These labels are used to identify the date range or characteristics corresponding to the sub-cycle (such as "weekdays", "weekends", "holidays", etc.).

[0149] Furthermore, based on the date tag of each sub-cycle and the historical usage time of the charging pile, the user volume in the current charging area in each future sub-cycle is predicted.

[0150] For each sub-cycle, the average number of times the charging piles under the same date label in the target charging area are used is counted as the number of times the charging piles are used corresponding to each sub-cycle, and the sum of the number of times each charging pile is used is calculated to obtain the number of users corresponding to the charging area in the future cycle.

[0151] In a possible implementation of the embodiment of the present application, in step S205, determining the weights of the grid load distribution and the number of users includes:

[0152] Establishing a first judgment matrix based on the grid load sub-distribution corresponding to each sub-period in the future period;

[0153] Based on the number of users corresponding to each sub-period, a second judgment matrix is ​​established;

[0154] The maximum eigenvalue and eigenvector corresponding to the first judgment matrix and the second judgment matrix are calculated to obtain the weights of the power grid load distribution and the user quantity.

[0155] Specifically, the process of constructing the first judgment matrix is ​​as follows: Assuming that the future period is divided into n subperiods, an n*n matrix is ​​created, where both rows and columns represent different subperiods, and the elements aij in the matrix represent the importance of the grid load distribution in subperiod i relative to subperiod j. For example, if the grid load distribution in subperiod 1 is slightly more important than that in subperiod 2, then a12 might be 3; if the two are equally important, then a12 = a21 = 1.

[0156] The process of constructing the second judgment matrix is ​​as follows: Create an n*n matrix whose elements represent the relative importance of the number of users in different sub-periods. Use the same scaling method as the first judgment matrix to determine the element values.

[0157] Furthermore, mathematical methods (such as the power method, Jacobi method, etc.) are used to calculate all eigenvalues ​​of the matrix, and the largest eigenvalue is found among all the eigenvalues, denoted as λmax, and the eigenvector corresponding to λmax is found. Each element in the eigenvector represents the weight of the corresponding sub-period.

[0158] Furthermore, to ensure the rationality of the judgment matrix, a consistency check is usually required. Specifically, the consistency index (CI) is calculated, the random consistency ratio (RI) is found, and the consistency ratio (CR = CI / RI) is calculated. If the CR is less than 0.1, the consistency of the judgment matrix is ​​considered acceptable.

[0159] If the consistency of the first judgment matrix and the second judgment matrix is ​​acceptable, each element in the eigenvector is normalized so that the sum of all elements is equal to 1. The vector obtained after normalization is the weight of the grid load distribution and the user volume.

[0160] A possible implementation of the embodiment of the present application, in the above embodiment, determines the billing standard corresponding to each sub-period based on the busyness, construction cost, and basic cost corresponding to each sub-period, including:

[0161] Determine the estimated usage time corresponding to the charging area;

[0162] Based on the expected usage time and construction cost, determine the corresponding construction sub-cost for each sub-cycle in the future cycle;

[0163] Determine the proportion of the allocated construction costs and basic costs corresponding to each sub-period to obtain the cost allocation factor corresponding to each sub-period;

[0164] Based on formula 4: Get the billing standard corresponding to each sub-period;

[0165] Among them, S i is the cost per kilowatt-hour of electricity corresponding to the sub-period, E i The amount of electricity purchased corresponding to the sub-period, S base is the sum of the basic cost corresponding to the sub-period and the construction cost, F a F is the busyness level, b is the cost sharing factor, and α and β are both constants.

[0166] The cost allocation factor is the coefficient for allocating the total cost to each sub-period.

[0167] Specifically, the average usage time of each charging area is obtained to obtain the estimated usage time corresponding to the charging area. Based on the estimated usage time and the construction cost, the construction sub-cost corresponding to the future period is calculated.

[0168] For grid load, the load factor for each sub-cycle can be calculated based on the ratio of the average load of the sub-cycle to the average load of the entire cycle, that is, the load factor of each sub-cycle = the average load of the sub-cycle / the average load of the future cycle. The usage frequency of each sub-cycle can also be calculated based on the usage frequency of the sub-cycle = the number of times the sub-cycle is used / the number of times the future cycle is used. The usage number of the sub-cycle is the sum of the number of times each charging station in the charging area is used during the sub-cycle, and the usage number in the future cycle is the sum of the number of times each charging station in the charging area is used during the future cycle.

[0169] Furthermore, the usage frequency of the sub-period and the average of the load factor are used as the allocation ratio, and the cost allocation factor of each sub-period is obtained based on the cost allocation factor of the sub-period = the allocation ratio of the sub-period / the sum of the allocation ratios of each sub-period.

[0170] After obtaining the cost allocation factor corresponding to each sub-period, we can use Formula 4: Calculate the billing standard corresponding to each sub-period, where S i is the cost per kilowatt-hour of electricity corresponding to the sub-period, E i The amount of electricity purchased corresponding to the sub-period, S base is the sum of the basic cost corresponding to the sub-period and the construction cost, F a F is the busyness level, b is the cost allocation factor, and α and β are both constants. For example, assume Sbase = 100 (yuan / sub-period), α = 0.5, β = 0.3, the busyness factor Fbi of a sub-period = 0.8, and the cost allocation factor Fci = 0.2. Then: Si = 100 × (1 + 0.5 × 0.8 + 0.3 × 0.2) = 100 × (1 + 0.4 + 0.06) = 100 × 1.46 = 146 (yuan / sub-period).

[0171] The above embodiment introduces a method for determining a charging strategy for a charging pile from the perspective of a method flow. The following embodiment introduces a device for determining a charging strategy for a charging pile from the perspective of a virtual module or a virtual unit. For details, please refer to the following embodiment.

[0172] See also Figure 3 The charging pile charging strategy determination device 30 may specifically include: an acquisition module 301, a user quantity determination module 302, a prediction module 303, a cost determination module 304, a weight determination module 305, a calculation module 306, and a strategy determination module 307. Specifically:

[0173] A charging pile charging strategy determination device 30, comprising:

[0174] An acquisition module 301 is used to obtain the regional scale and location of the charging area, where the regional scale includes the number of charging piles and construction costs;

[0175] A user quantity determination module 302 is configured to determine the user quantity corresponding to each sub-period in the charging area in a future period based on the number of charging piles and the area location. The user quantity is the number of charging vehicles that use the charging piles to charge in a sub-period in the future period.

[0176] Prediction module 303 is used to predict the amount of electricity purchased in each sub-cycle of the charging area in the future cycle based on the number of users. Cost determination module 304 is used to predict the maintenance cost of the charging area in the future cycle and the grid load distribution corresponding to the future cycle, and determine the basic cost corresponding to the charging area based on the grid load distribution, the amount of electricity purchased in each sub-cycle, and the maintenance cost.

[0177] The weight determination module 305 is used to determine the weights of the grid load distribution and the number of users;

[0178] A calculation module 306 is configured to calculate the busyness corresponding to each sub-period based on the grid load distribution and the weights of the user quantities;

[0179] The strategy determination module 307 is used to determine the billing standard corresponding to each sub-cycle based on the busyness, construction cost and basic cost corresponding to each sub-cycle, and determine the charging sub-strategy corresponding to each sub-cycle in the future cycle, and perform corresponding charging operations based on the billing standard and charging sub-strategy corresponding to each sub-cycle.

[0180] In one possible implementation of the embodiment of the present application, the user quantity determination module 302 is specifically configured to:

[0181] Obtain the charging demand source corresponding to the regional location. The charging demand source is used to represent vehicles passing through the regional location and having charging needs.

[0182] Determine whether the type corresponding to the charging demand source is a stable type to determine whether there are primary users and secondary users in the regional location;

[0183] If there are primary and secondary users in the area, obtain the historical charging data corresponding to the charging area. The historical charging data includes the number of historical charging users corresponding to each historical period, the number of historical charging times and the historical charging time corresponding to each historical charging user.

[0184] Based on historical charging data, the historical cycle is divided into multiple historical sub-cycles;

[0185] Based on the start and end times corresponding to each historical sub-cycle, the future cycle is divided into multiple sub-cycles, and based on the historical number of charging users corresponding to each historical sub-cycle, the number of users corresponding to each sub-cycle in the charging area in the future cycle is determined.

[0186] In one possible implementation of the embodiment of the present application, when the user quantity determination module 302 divides a historical period into multiple historical sub-periods based on historical charging data, it is specifically configured to:

[0187] From the historical charging users, select historical charging users whose historical charging times are greater than a charging times threshold as first historical charging users, and select historical charging users whose historical charging times have a regularity greater than a regularity threshold as second historical charging users;

[0188] Determine the first historical charging user and the second historical charging user as primary users, and determine the historical charging users other than the primary user as secondary users;

[0189] The historical period is divided into a plurality of historical sub-periods based on the historical charging times corresponding to the primary user and the historical charging times corresponding to the secondary user.

[0190] In one possible implementation of the embodiment of the present application, the fee determination module 304, when determining the basic fee corresponding to the charging area based on the grid load distribution, the number of purchased kilowatt-hours corresponding to each sub-cycle, and the maintenance fee, is specifically configured to: divide the future time period into a peak period, a normal period, and a valley period based on the grid load distribution;

[0191] Based on the electricity purchase amount corresponding to each sub-period, a first electricity purchase amount corresponding to the peak period, a second electricity purchase amount corresponding to the normal period, and a third electricity purchase amount corresponding to the off-peak period are calculated;

[0192] Predict the unit electricity prices corresponding to peak, normal and off-peak periods in the future cycle;

[0193] Based on Formula 1: M = M1 + C, calculate the basic fee corresponding to the charging area;

[0194] Among them, M is the basic cost of the charging area in the future cycle, M1 is the electricity purchase cost of the charging area in the future cycle, and C is the maintenance cost;

[0195] Among them, based on formula 2: M1=∑ t∈所有时段 ((E t,p *P p +E t,N *P N +E t,L *P L )*F(U t )) to predict the electricity purchase cost of the charging area in the future cycle;

[0196] Among them, M1 is the electricity purchase cost of the charging area in the future cycle, E t,p is the first purchased electricity quantity, E t,N is the second purchased electricity quantity, E t,L is the third purchased electricity quantity, P p is the unit electricity price during peak hours, P N is the unit electricity price during normal times, P L is the unit electricity price during the off-peak period, F(U t ) is a discount or markup factor related to the number of users;

[0197] Based on formula 3: Calculate discount or markup factors related to user volume;

[0198] Among them, F(U t ) is the discount or markup factor related to the number of users, U threshold is the first threshold corresponding to the number of users, U max is the second threshold corresponding to the number of users.

[0199] In one possible implementation of the embodiment of the present application, when there are no primary users or secondary users in the regional location, the user quantity determination module 302 determines the user quantity corresponding to each sub-period in the charging area in the future period based on the number of charging piles and the regional location, specifically for:

[0200] Based on the number of charging piles, at least one target charging area is selected from the remaining charging areas, where the number of charging piles in the target charging area is the same as the number of charging piles corresponding to the charging area, and there are no primary users or secondary users in the target charging area; first historical data corresponding to the target charging area and second historical data corresponding to the charging area are obtained, where the first historical data includes historical usage time of each charging pile in the target charging area, and the second historical data includes historical usage time of each charging pile in the charging area;

[0201] Dividing the future period into at least two sub-periods based on each historical usage moment included in the first historical data and each historical usage moment included in the second historical data;

[0202] Determine the date label corresponding to each sub-period;

[0203] Based on the date tag corresponding to each sub-cycle and the historical usage time of each charging pile, the number of users corresponding to each sub-cycle in the charging area in the future cycle is determined.

[0204] In one possible implementation of the embodiment of the present application, the weight determination module 305 is specifically configured to:

[0205] Establishing a first judgment matrix based on the grid load sub-distribution corresponding to each sub-period in the future period;

[0206] Based on the number of users corresponding to each sub-period, a second judgment matrix is ​​established;

[0207] The maximum eigenvalue and eigenvector corresponding to the first judgment matrix and the second judgment matrix are calculated to obtain the weights of the power grid load distribution and the user quantity.

[0208] In one possible implementation of the embodiment of the present application, the policy determination module 307 determines the billing standard corresponding to each sub-period based on the busyness, construction cost, and basic cost corresponding to each sub-period, specifically for:

[0209] Determine the estimated usage time corresponding to the charging area;

[0210] Based on the expected usage time and construction cost, determine the corresponding construction sub-cost for each sub-cycle in the future cycle;

[0211] Determine the proportion of the allocated construction costs and basic costs corresponding to each sub-period to obtain the cost allocation factor corresponding to each sub-period;

[0212] Based on formula 4: Get the billing standard corresponding to each sub-period;

[0213] Among them, S i is the cost per kilowatt-hour of electricity corresponding to the sub-period, E i The amount of electricity purchased corresponding to the sub-period, S base is the sum of the basic cost corresponding to the sub-period and the construction cost, F a F is the busyness level, b is the cost sharing factor, and α and β are both constants.

[0214] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0215] See also Figure 4 , the embodiment of the present application also introduces an electronic device from the perspective of a physical device, such as Figure 4 As shown, Figure 4 The electronic device 40 shown includes a processor 401 and a memory 403. The processor 401 and the memory 403 are connected, for example, via a bus 402. Optionally, the electronic device 40 may further include a transceiver 404. It should be noted that in actual applications, the number of transceivers 404 is not limited to one, and the structure of the electronic device 40 does not constitute a limitation on the embodiments of the present application.

[0216] Processor 401 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 401 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0217] The bus 402 may include a path for transmitting information between the above components. The bus 402 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 402 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0218] The memory 403 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0219] The memory 403 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 401. The processor 401 is used to execute the application code stored in the memory 403 to implement the content shown in the above method embodiment.

[0220] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc., and can also be servers, etc. Figure 4 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0221] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0222] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0223] The above are only some of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining a charging strategy for a charging pile, characterized in that: The method comprises: Obtain the regional scale and location of the charging area, where the regional scale includes the number of charging piles and construction costs; Determining, based on the number of charging piles and the location of the area, a user volume corresponding to each sub-period of the charging area in a future period, where the user volume is the number of charging vehicles that use the charging piles to charge in a sub-period of the future period; Based on the number of users, predict the amount of electricity purchased in each sub-period of the charging area in the future period; Predicting the maintenance cost of the charging area in a future period and the corresponding grid load distribution in the future period, and determining the basic fee corresponding to the charging area based on the grid load distribution, the purchased electricity corresponding to each sub-period, and the maintenance cost; Determining the weights of the grid load distribution and the user quantity; Calculating the busyness corresponding to each sub-period based on the grid load distribution, the number of users, and the weights of the grid load distribution and the number of users; Based on the busyness corresponding to each sub-cycle, the construction cost and the basic cost, the billing standard corresponding to each sub-cycle is determined, and the charging sub-strategy corresponding to each sub-cycle in the future cycle is determined, and the corresponding charging operation is performed based on the billing standard and the charging sub-strategy corresponding to each sub-cycle.

2. The method for determining a charging strategy for a charging pile according to claim 1, wherein: The determining, based on the number of charging piles and the location of the area, the number of users corresponding to each sub-period in the charging area in a future period includes: Obtaining a charging demand source corresponding to the regional location, where the charging demand source is used to represent vehicles passing through the regional location and having charging demand; Determining whether the type corresponding to the charging demand source is a stable type, so as to determine whether there are primary users and secondary users in the regional location; If there are primary users and secondary users in the area, obtain historical charging data corresponding to the charging area, wherein the historical charging data includes the number of historical charging users corresponding to each historical period and the historical charging times and historical charging times corresponding to each historical charging user; Dividing the historical period into a plurality of historical sub-periods based on the historical charging data; The future cycle is divided into multiple sub-cycles based on the start and end times corresponding to each historical sub-cycle, and the number of users corresponding to each sub-cycle in the charging area in the future cycle is determined based on the historical number of charging users corresponding to each historical sub-cycle.

3. The method for determining a charging strategy for a charging pile according to claim 2, wherein: The dividing the historical period into a plurality of historical sub-periods based on the historical charging data includes: From the historical charging users, select historical charging users whose historical charging times are greater than a charging times threshold as first historical charging users, and select historical charging users whose historical charging times have a regularity greater than a regularity threshold as second historical charging users; Determine the first historical charging user and the second historical charging user as primary users, and determine the historical charging users other than the primary users as secondary users; The historical period is divided into a plurality of historical sub-periods based on the historical charging times corresponding to the primary user and the historical charging times corresponding to the secondary user.

4. The method for determining a charging strategy for a charging pile according to claim 2 or 3, wherein: The determining of the basic fee corresponding to the charging area based on the grid load distribution, the purchased electricity quantity corresponding to each sub-cycle, and the maintenance fee includes: Based on the load distribution of the power grid, the future time period is divided into peak period, normal period and valley period; Based on the electricity purchase amount corresponding to each sub-period, a first electricity purchase amount corresponding to the peak period, a second electricity purchase amount corresponding to the normal period, and a third electricity purchase amount corresponding to the valley period are calculated; Predict the unit electricity prices corresponding to peak, normal and off-peak periods in the future cycle; Based on Formula 1: M = M1 + C, calculate the basic fee corresponding to the charging area; Where M is the basic cost of the charging area in the future cycle, M1 is the electricity purchase cost of the charging area in the future cycle, and C is the maintenance cost; Among them, based on formula 2: M1=∑ t∈所有时段 ((E t,p *P p +E t,N *P N +E t,L *P L )*F(U t )) and predicting the electricity purchase cost of the charging area in the future cycle; Among them, M1 is the electricity purchase cost of the charging area in the future cycle, E t,p is the first purchased electricity quantity, E t,N The second purchased electricity quantity, E t,L is the third purchased electricity quantity, P p is the unit electricity price during peak hours, P N is the unit electricity price during normal times, P L is the unit electricity price during the off-peak period, F(U t ) is a discount or markup factor related to the number of users; Based on formula 3: Calculate discount or markup factors related to user volume; Among them, F(U t ) is the discount or markup factor related to the number of users, U threshold is the first threshold corresponding to the number of users, U max is the second threshold corresponding to the number of users.

5. The method for determining a charging strategy for a charging pile according to claim 2, wherein: When there are no primary users and secondary users in the regional location, determining the number of users corresponding to each sub-period in the charging area in a future period based on the number of charging piles and the regional location includes: Based on the number of charging piles, at least one target charging area is selected from the remaining charging areas, where the number of charging piles in the target charging area is the same as the number of charging piles corresponding to the charging area, and there are no primary users or secondary users in the target charging area; Obtaining first historical data corresponding to the target charging area and second historical data corresponding to the charging area, wherein the first historical data includes historical usage times of each charging pile in the target charging area, and the second historical data includes historical usage times of each charging pile in the charging area; Dividing the future period into at least two sub-periods based on each historical usage moment included in the first historical data and each historical usage moment included in the second historical data; Determine the date label corresponding to each sub-period; Based on the date tag corresponding to each sub-cycle and the historical usage time of each charging pile, the number of users corresponding to each sub-cycle in the charging area in the future cycle is determined.

6. The method for determining a charging strategy for a charging pile according to claim 1, wherein: Determining the weights of the grid load distribution and the user quantity includes: Establishing a first judgment matrix based on the grid load sub-distribution corresponding to each sub-period in the future period; Establishing a second judgment matrix based on the user quantity corresponding to each sub-period; The maximum eigenvalue and eigenvector corresponding to each of the first judgment matrix and the second judgment matrix are calculated to obtain the weights of the power grid load distribution and the user quantity.

7. The method for determining a charging strategy for a charging pile according to claim 6, wherein: The determining of the charging standard corresponding to each sub-cycle based on the busyness level corresponding to each sub-cycle, the construction fee, and the basic fee includes: determining an estimated usage time corresponding to the charging area; Determining a construction sub-cost corresponding to each sub-period in the future period based on the expected usage duration and the construction cost; Determine the proportion of the allocated construction costs and basic costs corresponding to each sub-period to obtain the cost allocation factor corresponding to each sub-period; Based on formula 4: Get the billing standard corresponding to each sub-period; Among them, S i is the cost per kilowatt-hour of electricity corresponding to the sub-period, E i is the amount of electricity purchased corresponding to the sub-period, S base is the sum of the basic cost corresponding to the sub-period and the construction cost, F a F is the busyness level, b is the cost sharing factor, and α and β are both constants.

8. A device for determining a charging strategy for a charging pile, characterized in that: include: An acquisition module is used to obtain the regional scale and regional location of the charging area, wherein the regional scale includes the number of charging piles and the construction cost; A user quantity determination module is configured to determine the user quantity corresponding to each sub-period of the charging area in a future cycle based on the number of charging piles and the location of the area, wherein the user quantity is the number of charging vehicles charged using the charging piles in a sub-period of the future cycle; A prediction module, configured to predict the amount of electricity purchased in each sub-period of the charging area in a future period based on the number of users; a fee determination module, configured to predict the maintenance fee of the charging area in a future period and the corresponding grid load distribution in the future period, and determine the basic fee corresponding to the charging area based on the grid load distribution, the purchased electricity corresponding to each sub-period, and the maintenance fee; A weight determination module, used to determine the weights of the power grid load distribution and the user quantity; a calculation module, configured to calculate a busyness corresponding to each sub-period based on the grid load distribution, the number of users, and the weights of the grid load distribution and the number of users; The strategy determination module is used to determine the billing standard corresponding to each sub-cycle based on the busyness corresponding to each sub-cycle, the construction cost and the basic cost, and determine the charging sub-strategy corresponding to each sub-cycle in the future cycle, and perform corresponding charging operations based on the billing standard and the charging sub-strategy corresponding to each sub-cycle.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the charging pile charging strategy determination method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is instructed to execute the method for determining a charging strategy for a charging pile according to any one of claims 1 to 7.

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