Charging station pricing method for controlling load balance of power grid based on constant current-constant voltage charging characteristic difference

Abstract

The invention discloses a charging station pricing method for controlling power grid load balance based on constant current-constant voltage charging characteristic difference, and the method comprises the steps: constructing an electric vehicle energy consumption model adaptive to the operation characteristics of an expressway, comprising a highway network sub-model, an electric vehicle fusion energy consumption model sub-model, a charging time calculation sub-model and a charging station queuing analysis sub-model. Constructing a constant-current stage pricing sub-model and a constant-voltage stage pricing sub-model based on the constant-current and constant-voltage stage charging characteristics of the electric vehicle as a constant-current-constant-voltage two-stage charging pricing model of the electric vehicle; based on the constant current-constant voltage two-stage charging pricing model of the electric vehicle, constructing an operator optimization model; and on the basis of an electric vehicle energy consumption model and an electric vehicle two-stage charging pricing model, a user charging decision optimization model is constructed, an optimal charging path with the lowest user charging comprehensive cost and the charging amount and average queuing time of each charging station in different time periods are solved, and collaborative optimization of the user and a power grid is realized.

Description

Technical Field

[0001] This invention relates to the field of power system optimization technology, and in particular to a charging station pricing method based on the difference in constant current-constant voltage charging characteristics to control grid load balance. Background Technology

[0002] With the widespread adoption of electric vehicles (EVs), highway charging stations provide range support for long-distance travel, but also cause charging congestion, especially during holidays when the surge in charging demand and insufficient charging infrastructure exacerbate the problem. Compared to traditional gasoline vehicles, EVs have superior environmental benefits, leading countries to vigorously promote their adoption. This has resulted in a closer connection between transportation and power grids. Simultaneously, the rapid growth of EVs affects traffic flow distribution; without guidance on EV charging behavior, highway congestion could worsen. Therefore, implementing highway charging guidance is crucial for improving travel efficiency and optimizing the EV charging experience.

[0003] Existing technologies for guiding electric vehicle users to adjust their charging methods mostly focus on price incentives to influence user charging behavior. These include setting lower highway tolls, proposing real-time pricing strategies for charging stations, implementing differentiated electricity prices for different time periods, and segmenting peak hours into off-peak and peak-peak intervals to guide charging load shifts or adjust user charging behavior. Other technologies focus on user responses after charging prices are determined. For example, users can adjust charging times to respond to electricity prices during short trips, while during long-distance travel, users consider factors such as queuing time, charging time, and electricity prices when choosing charging stations and planning routes. Still other technologies compare the impact of different charging algorithms on queuing time, demonstrating that algorithms utilizing global information can shorten queuing times.

[0004] However, existing technologies still have the following shortcomings: 1) The power variation characteristics of electric vehicle charging stage are not fully considered: the charging power will decrease in the later stage of electric vehicle charging, but most studies do not consider this charging power decay characteristic when guiding users' charging decisions; when guiding electric vehicle users to charge, only users in the constant current charging stage are targeted, while users in the constant voltage charging stage (where power decay occurs) are ignored.

[0005] 2) Insufficient research on multi-stage guidance in scenarios of insufficient charging capacity: Existing charging guidance strategies mostly assume "sufficient charging capacity on highways" and conduct balanced guidance of charging demand. However, the problem of insufficient charging capacity at highway service areas during holidays is particularly prominent, and existing research has not sufficiently explored how to implement a second stage of guidance based on balanced guidance in this scenario. In this case, if the traditional guidance method of "assuming constant charging power and focusing only on balance" is still adopted, the scheduling potential of charging strategies will be significantly reduced, making it difficult for charging operators to achieve better results in reducing grid load and alleviating charging congestion, and for electric vehicle users to achieve better results in improving charging satisfaction and reducing charging costs. Summary of the Invention

[0006] Technical Objective: To address the shortcomings of existing pricing strategies, which often rely solely on time periods or uniform electricity prices and fail to consider the long charging times and slow speeds during constant-voltage charging phases, the lack of control over low-speed charging exacerbates long queues at highway charging stations during holidays, insufficient consideration of charging power attenuation in highway electric vehicle charging guidance, and the absence of multi-stage guidance in scenarios with insufficient capacity. These deficiencies lead to low charging station operational efficiency and significant grid load fluctuations. This invention discloses a charging station pricing method based on the difference in constant-current and constant-voltage charging characteristics to control grid load balance. By establishing a multi-stage charging guidance mechanism and combining charging power variation characteristics with a dynamic adjustment strategy for charging station capacity, this invention achieves more precise spatiotemporal distribution optimization of charging load, thereby improving charging station operational efficiency, smoothing the grid load curve, and controlling grid load balance. Simultaneously, it improves charging efficiency and user satisfaction at charging stations in highway service areas during holidays, alleviates charging congestion, and reduces user charging costs.

[0007] Technical solution: To achieve the above technical objectives, the present invention adopts the following technical solution.

[0008] A charging station pricing method based on the difference in constant current-constant voltage charging characteristics for controlling grid load balancing includes: An electric vehicle energy consumption model adapted to the operating characteristics of highways is constructed, comprising four major models: a highway network sub-model, an electric vehicle integrated energy consumption model sub-model, a charging time calculation sub-model, and a charging station queuing analysis sub-model. The electric vehicle energy consumption model allocates charging load based on road network accessibility, using the road network coverage of charging stations as a benchmark to ensure that the load distribution accurately matches the actual road network usage. Among them, the highway network sub-model provides basic route data, the electric vehicle integrated energy consumption sub-model calculates the energy consumption of different routes, the charging time calculation sub-model calculates the charging time by combining the energy consumption results of the electric vehicle integrated energy consumption sub-model, and the charging station queuing analysis sub-model determines the queuing situation based on the charging time and traffic flow, providing basic spatiotemporal data support for subsequent pricing work. A two-stage charging pricing model for electric vehicles, based on constant current and constant voltage, is constructed, including: a constant current stage pricing sub-model and a constant voltage stage pricing sub-model based on the charging characteristics of electric vehicles in constant current and constant voltage stages; the constant current stage pricing sub-model sets a benchmark electricity price based on the differences in charging volume at different charging stations during different time periods; the constant voltage stage pricing sub-model then dynamically adjusts the electricity price based on the charging power attenuation characteristics of electric vehicles in constant voltage stages, according to the benchmark electricity price. Optimization of charging station operators: Based on the constant current-constant voltage two-stage charging pricing model for electric vehicles, an operator optimization model is constructed, including: considering the impact of user charging choices on load distribution, the charging station operator adjusts its own electricity price in real time through the constant current-constant voltage two-stage charging pricing model for electric vehicles, aiming to minimize the total charging load difference, to achieve spatiotemporal joint equilibrium of charging load, and to achieve load balance of the control grid; the charging station operator first collects the equilibrium data to achieve load balance of the control grid. During the data preparation phase, the charging station operator first collects the actual charging volume data of the charging station for 24 time periods throughout the day, and then determines the initial values ​​of constant current and constant voltage electricity prices based on the electric vehicle constant current-constant voltage two-stage charging pricing model. After entering the optimization process, the charging station operator aims to minimize the difference in total charging load and uses a tabu search algorithm to iteratively adjust the initial electricity price in multiple rounds. During the iteration process, the charging path obtained by the electric vehicle user-side decision optimization model under different electricity prices is used to obtain the charging volume results of each charging station, and the electricity price for each time period is output. The result with the smallest difference in charging volume among the charging stations is selected as the optimal electricity price and output. Electric vehicle user-side decision optimization: Based on the electric vehicle energy consumption model and the electric vehicle two-stage charging pricing model, a user charging decision optimization model is constructed to solve for the optimal charging path with the lowest overall charging cost for users, as well as the charging volume and average queuing time of each charging station at different times, thereby achieving collaborative optimization between users and the power grid.

[0009] The core of this invention is to compare the impact of pricing strategies that differentiate charging stages versus those that do not on user charging choices. It constructs a user behavior model based on highway range characteristics, quantifying user behavior in different scenarios using statistical data, including anxiety thresholds based on remaining battery power and preference for different queuing times. During model operation, it comprehensively calculates the cost of different charging options by combining multiple types of data: energy consumption matching data based on highway accessibility (whether remaining battery power can support driving to the next station); charging station load status data, including differences in queuing times at each station; and two-stage electricity price information. Finally, it only releases key information such as electricity prices and queuing times for each charging station to users, allowing them to choose the optimal charging route based on the overall charging cost. By differentiating pricing for different charging stages, it guides users to switch to stations or time periods with lower overall costs, thus balancing the overall charging load.

[0010] Beneficial effects: 1. This invention first considers the energy consumption characteristics of electric vehicles while driving on highways and establishes a queuing model for highway charging stations. It also clarifies the power decay patterns and corresponding charging time characteristics during the constant-current charging phase (fast charging) and constant-voltage charging phases. By calculating the average critical SOC value set by the charging time calculation sub-model, the differences in duration and power between the two phases are quantified, addressing the problem of traditional pricing ignoring the characteristics of different charging phases. Secondly, based on the differences in charging volume at different times of each charging station, a first-stage charging strategy is proposed to achieve an initial balance of charging volume in both time and space dimensions, laying the foundation for improving the operational efficiency of charging stations and smoothing the load curve. Finally, a second-stage charging strategy is formulated based on the power decay characteristics of electric vehicles in the later stages of charging. To further optimize the allocation of charging resources and strengthen support for the efficient operation and load balancing of charging stations, the algorithm uses a tabu search algorithm to determine two-stage electricity prices, aiming to balance the charging load of different charging stations at different times. The algorithm ensures that the output electricity price can accurately minimize the difference in total charging load by reasonably setting the number of iterations. With the goal of minimizing the comprehensive charging cost for electric vehicle users, the algorithm uses Dijkstra's algorithm to solve the charging decision path. The algorithm considers the queuing time of charging stations, highway driving energy consumption, and air conditioning energy consumption when solving the problem, thereby improving the adaptability of path planning to actual scenarios. Ultimately, this achieves the effect of improving the operating efficiency of charging stations and smoothing the load curve, while also alleviating charging congestion and reducing user charging costs. 2. In the electric vehicle energy consumption model of this invention, a differentiated electricity price is formulated considering the differences in charging characteristics between the constant current and constant voltage stages of electric vehicles: An electricity price optimization model is constructed based on the charging power curves, energy consumption patterns, and charging time proportions of the vehicle during the constant current stage (high power, high energy consumption) and the constant voltage stage (low power, low energy consumption). This electricity price has a stable gradient pricing structure, achieving the effects of reducing user charging costs and promoting grid load balance. By employing a gradient design with low electricity prices during the constant current phase and high electricity prices during the constant voltage phase, the gradient difference is adjusted according to the load characteristics during peak, off-peak, and valley periods. This adapts to the grid load regulation needs at different times, allowing users to calculate charging costs in advance based on their charging needs. While meeting charging demands, users can proactively avoid peak and high-price periods and prioritize charging during the constant current phase, achieving the dual goals of meeting charging needs and reducing charging costs. Leveraging the stability of the electricity pricing scheme, users focus more on charging prices, remaining capacity, and waiting time when making charging decisions, avoiding hesitation caused by real-time price fluctuations. This promotes the dispersion of charging demand from popular service areas and peak hours to less popular service areas and off-peak hours, maintaining the charging load in each service area within a relatively stable range at different times. Through load fluctuation control, it ensures that load distribution meets the dual requirements of safe grid operation and efficient charging station operation. This electricity pricing scheme can reduce the peak-period impact of charging load on the grid and improve the grid's load balancing capacity. Simultaneously, it considers the operating revenue of charging stations and the charging costs for users, achieving a win-win situation for the grid, operators, and users. It is suitable for load regulation in highway charging station scenarios. Attached Figure Description

[0011] Figure 1 A schematic diagram of a charging strategy framework considering the characteristics of different charging stages is shown in an embodiment of the present invention. Figure 2 This diagram illustrates the charging characteristic curves of an electric vehicle according to an embodiment of the present invention. Figure 3 This diagram illustrates the EV user charging decision-making process according to an embodiment of the present invention. Figure 4 A flowchart illustrating a method according to an embodiment of the present invention is shown. Detailed Implementation

[0012] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0013] Example As attached Figure 1and attached Figure 4 As shown, in order to achieve the above goals of improving the operating efficiency of charging stations and smoothing the power grid load curve, this embodiment discloses a charging station pricing method based on the difference in constant current-constant voltage charging characteristics to control the load balance of the power grid, including the following steps: S1. Construct an electric vehicle energy consumption model adapted to the operating characteristics of highways, comprising four main models: a highway network sub-model, an electric vehicle integrated energy consumption sub-model, a charging time calculation sub-model, and a charging station queuing analysis sub-model. The data from each sub-model are interconnected to provide basic energy consumption parameters. The electric vehicle energy consumption model is based on dynamic data from the highway network, including road gradients and real-time traffic flow. The electric vehicle energy consumption model allocates charging load based on road network accessibility, using the road network coverage of charging stations as a benchmark to ensure accurate matching of load distribution with actual road network usage. Specifically, the highway network sub-model provides basic path data, the electric vehicle integrated energy consumption sub-model calculates energy consumption for different paths, the charging time calculation sub-model calculates charging time based on the energy consumption results from the electric vehicle integrated energy consumption sub-model, and the charging station queuing analysis sub-model derives queuing information based on charging time and traffic flow, providing spatiotemporal data support for subsequent pricing.

[0014] (1) The content of the expressway network sub-model includes: The highway network sub-model performs joint spatial-temporal optimization, moving beyond simple allocation of charging load to a comprehensive approach that considers the actual characteristics of the highway network. This process involves several steps: First, a network topology is built around highway entrances and exits as core nodes. Node placement is tailored to the distribution of service areas along the highway, ensuring coverage of all service areas with charging capabilities. Charging is achieved through charging stations within these service areas, forming a network framework that matches the actual operational scenario and covers the entire road segment. This fully reflects the actual impact of the network topology on charging load distribution. Next, based on the daily driving habits of electric vehicle owners, especially their holiday travel preferences, a time-segmented OD matrix is ​​used to statistically analyze traffic flow at different times and from different origins to destinations. The OD matrix statistical period covers multiple complete holidays, ensuring the data fully reflects the traffic flow characteristics and path selection patterns during peak holiday travel. Furthermore, the OD matrix is ​​transformed into an OD probability matrix, intuitively reflecting the path dependence characteristics of vehicle owners.

[0015] Vehicle travel routes have a significant impact on the spatiotemporal distribution of electric vehicle charging load. Highways typically employ a closed management model, allowing vehicles to enter and exit only through a limited number of entrances and exits. Based on the OD (Original Design Location) analysis concept, these entrances and exits are treated as road network nodes, constructing a highway OD matrix. Dividing the day into 24 time periods using hours as the unit of time, the OD matrix then consists of 24 sub-matrices. Composition. Among them... mThe number of road network nodes. T The values ​​are 1, 2, ..., 24. express T Traffic flow between the start and end points of a time period. OD matrix. It can be transformed into an OD probability matrix. The OD probability matrix can more intuitively reflect the probability distribution of electric vehicle travel routes on highways during holidays.

[0016] , in, Let T be the probability that an electric vehicle travels from node i to node j during time period T. ; Let T be the number of electric vehicles that travel from node i to node j during time period T.

[0017] There is a certain distance between the electric vehicle's origin and the highway toll station. Considering the energy loss during this journey and the fact that drivers tend to keep the vehicle at a higher state of charge before entering the highway, it is assumed that the initial state of charge (SOC) of the electric vehicle upon arrival at the toll station follows a normal distribution. .

[0018] , in, The initial battery level of the electric vehicle upon arrival at the highway entrance; The expected battery level of the electric vehicle upon arrival at the highway entrance; The standard deviation is denoted as .

[0019] (2) The content of the electric vehicle integrated energy consumption sub-model includes: The electric vehicle energy consumption model sub-model considers both time-dimensional differences, such as energy consumption changes caused by different driving speeds at different times, and spatial-dimensional factors, such as energy consumption differences caused by different road terrain features.

[0020] The energy consumption of an electric vehicle is calculated based on air resistance, gradient resistance, and rolling resistance during its operation. The formula is as follows: , in, The energy consumed by electric vehicles to drive The power consumed by an electric vehicle to overcome resistance. For electric vehicles on the road section s average driving speed Where F is the vehicle's transmission efficiency, and F is the sum of air resistance, gradient resistance, and rolling resistance. The air drag coefficient, For the density of air, SLet V be the vehicle's frontal area, V be the vehicle's speed, m be the vehicle's weight, and g be the gravitational coefficient. For road section s The altitude of the starting point For road section s End point altitude For road section s distance, denoted as the rolling resistance coefficient, and x as the gradient angle of the highway.

[0021] The energy consumption of an electric vehicle's onboard air conditioning system is calculated based on its energy consumption under different weather temperature conditions. The formula is as follows: , in, The energy consumption for using air conditioning in an electric vehicle, where P is the power required to maintain a stable interior temperature. Let A be the thermal conductivity coefficient of the electric vehicle body, A be the body area, and T be the air conditioning operating temperature. The outside temperature of the vehicle. The heat load transfer coefficient of the vehicle's air conditioning system. This is the mechanical transmission coefficient of the air conditioner.

[0022] (3) The charging time calculation sub-model includes: The charging time of an electric vehicle is calculated based on the power and battery capacity at different charging stages. The calculation distinguishes between the constant current and constant voltage charging stages, and sets the critical SOC average value for both stages. The formula is as follows: , in, Charging time for electric vehicles; Target SOC for charging electric vehicles; SOC (State of Charge) when an electric vehicle begins charging; The value of SOC represents the critical state of charge (SOC) during the constant current and constant voltage phases of various types of electric vehicles; E represents the battery capacity of the electric vehicle. The charging power for electric vehicles during the constant current phase; This refers to the charging power during the constant current phase of an electric vehicle.

[0023] When calculating the charging time of electric vehicles, it is necessary to distinguish between the constant current stage and the constant voltage stage of electric vehicle charging. In combination with the "rapid replenishment" demand of highway charging users, the critical SOC average value of the two stages is set. For the rapid replenishment demand, in the user charging decision optimization model in the electric vehicle user-side decision optimization, when the user's target SOC may enter the constant voltage stage, the user is prompted with the trend of constant current and constant voltage charging power change.

[0024] As attached Figure 2 As shown, the charging time calculation sub-model and charging stage characteristic analysis provide key technical support for improving the turnover efficiency of charging stations. By accurately distinguishing the differences in charging time and power between the constant current stage and the constant voltage stage, and clarifying the characteristics of high charging efficiency and short charging time in the constant current stage, and low charging efficiency and long charging time in the constant voltage stage, users can be effectively guided to prioritize completing their main charging needs in the high-efficiency constant current stage, reducing the charging time in the low-efficiency constant voltage stage. This shortens the time a single electric vehicle occupies a charging pile, accelerating the service turnover of charging stations. Simultaneously, based on the clear definition of charging stages in this model, operators can more accurately formulate electricity pricing and service strategies for different charging stages, further promoting the concentration of user charging behavior in the efficient constant current stage, ultimately increasing the number of vehicles served by charging piles per unit time and significantly improving the turnover efficiency of charging stations.

[0025] (4) The charging station queuing analysis sub-model includes: establishing a charging station queuing analysis sub-model based on event-driven queues and calculating the average queuing time of electric vehicles; In this model, considering the "reservation-arrival-charging" process characteristics of vehicles on highways, charging station queues are divided into three types: Queue 1: Currently charging; Queue 2: Currently queuing; Queue 3: Vehicles that have submitted charging requests but have not yet arrived at the charging station. The arrival time of vehicles in Queue 3 is related to highway congestion, and there is a possibility of arrival delay. The submission of a charging request by an electric vehicle is defined as an event. The arrival of electric vehicles at charging stations is an event. Electric vehicles begin charging as an event The electric vehicle leaving the charging station is an event The set of all times is: , For charging stations s , Indicates charging station s Is it at the moment The status variable that receives a charging request from an electric vehicle is set to 1 if yes, and 0 otherwise. , and These represent whether the electric vehicle is at time. The status variables for arriving at a charging station, starting charging, and leaving a charging station are 1 if they are, and 0 if they are not. It can be represented as: , Among them, T Rq The set of events that trigger a charging request for an electric vehicle; , and These four variables have the same representational relationship and satisfy the following: , definition , and They are time points Next s The number of electric vehicles in queues 1, 2, and 3 of each charging station. For a moment Next s The total number of electric vehicles in the queue of each charging station satisfies: , At time 1, the number of electric vehicles in each queue is: , in, For a moment Next, the s The number of electric vehicles in charging station queue 1 (currently charging). For a moment Next, the s The number of electric vehicles in charging station queue 2 (currently queuing), For a moment Next, the s The number of electric vehicles in charging station queue 3 (requests sent but not yet received). For a moment Next, the s The total number of electric vehicles in the charging station queue; For charging stations s At any moment The number of charging requests received from electric vehicles. For charging stations s At any moment The number of electric vehicles arriving at charging stations For charging stations s At any moment The number of electric vehicles that have started charging For charging stations s At any moment The number of electric vehicles leaving the charging station.

[0026] Section, charging station s The queuing time is: , in, For charging stations s The number of charging stations, For electric vehicles k At the charging station s The target charging amount For electric vehicles k At the charging station s The initial charge, For electric vehicles k At the charging station s The charging rate.

[0027] The charging time calculation sub-model and the charging station queuing analysis sub-model provide key technical support for improving the turnover efficiency of charging stations. By accurately distinguishing the differences in charging time and power between the constant current stage and the constant voltage stage, and clarifying the characteristics of high charging efficiency and short charging time in the constant current stage, and low charging efficiency and long charging time in the constant voltage stage, users can be effectively guided to prioritize completing their main charging needs in the high-efficiency constant current stage, reducing the charging time in the low-efficiency constant voltage stage. This shortens the time a single electric vehicle occupies a charging pile, accelerating the service turnover of charging piles. Simultaneously, based on the clear definition of charging stages by this model, operators can more accurately formulate electricity pricing and service strategies for different charging stages, further promoting the concentration of user charging behavior in the efficient constant current stage, ultimately increasing the number of vehicles served by charging piles per unit time and significantly improving the turnover efficiency of charging stations.

[0028] S1 analyzes the changes in battery state under different temperature environments and the differences in energy consumption caused by high-speed dynamic driving. At the same time, it combines the charging characteristic curve to analyze the changing laws of constant current power peak and constant voltage power decay rate, and establishes an electric vehicle charging model. This provides quantitative data support for subsequent precise control of charging pile use and balancing of grid load, laying the foundation for improving the operating efficiency of charging stations and smoothing the grid load curve.

[0029] S2. Construct a two-stage charging pricing model for electric vehicles, including: constructing a constant current stage pricing sub-model and a constant voltage stage pricing sub-model based on the charging characteristics of electric vehicles in the constant current and constant voltage stages; the constant current stage pricing sub-model sets a benchmark electricity price based on the differences in charging volume at different charging stations during different time periods; the constant voltage stage pricing sub-model then dynamically adjusts the electricity price based on the charging power attenuation characteristics of electric vehicles in the constant voltage stage, on the basis of the benchmark electricity price.

[0030] In this embodiment, the constant current stage pricing sub-model no longer uses a uniform benchmark price. Instead, it first collects charging volume data for all charging stations over several consecutive days, and sets a benchmark price based on the differences in charging volume at different charging stations during different time periods. This price difference guides car owners to distribute their vehicles reasonably among different stations, thereby balancing the charging volume at different stations during different time periods. The constant voltage stage pricing sub-model dynamically adjusts the price based on the power attenuation characteristics of electric vehicles charging during constant voltage stages. When the grid load is high during peak hours, the constant voltage price is moderately increased based on the constant current price to guide car owners to complete charging during the more efficient constant current stage. When the grid load is low during off-peak hours, the constant voltage price is slightly increased based on the constant current price, which neither suppresses normal charging demand nor causes excessive occupation of charging pile resources during the constant voltage stage. The resulting two-stage pricing system, which distinguishes the charging characteristics of different stages, can accurately match the charging characteristics of high efficiency with constant current and low efficiency with constant voltage, while also taking into account the load differences of different stations and different time periods.

[0031] The constant current phase pricing sub-model must be based on the "time-based charging volume differences at highway charging stations" and must meet the following prerequisites: First, collect charging volume data for all charging stations within the same highway segment for seven consecutive days, remove outliers, and then determine the charging volume range for each time period; the constant current phase pricing sub-model sets a benchmark electricity price based on the time-based charging volume differences of different charging stations, using the following formula: , Where W is the benchmark electricity price, i.e., the initial value of the constant current charging price for each charging station at each time period; L is the lower limit of the charging price; U is the upper limit of the charging price; and C is the actual charging volume of each charging station at each time period. The minimum charging amount for all charging stations during this period. The highest charging volume for all charging stations during that time period.

[0032] The constant-voltage pricing sub-model dynamically adjusts the electricity price based on the power decay characteristics during electric vehicle charging. This includes classifying each charging station's 24 time periods into peak, flat, and valley categories according to the membership degree of the charging volume. The calculation of the membership degree involves dividing the charging volume of different charging stations across the 24 time periods of the day using membership functions to differentiate between high and low charging load levels. Two types of membership functions are involved: a skewed semi-trapezoidal membership function and a skewed semi-trapezoidal membership function. The skewed semi-trapezoidal membership function represents high load, while the skewed semi-trapezoidal membership function represents low load. The formula is as follows: , in, It is a semi-trapezoidal membership function with a large size. It is a semi-trapezoidal membership function with a small size. M This represents the maximum charging volume at all charging stations for each time period.N This represents the minimum charging volume at all charging stations during all time periods. x This refers to the charging volume at different charging stations at different times.

[0033] By inputting the charging volume of different charging stations at different times into the membership function, the membership value can be calculated. The result of the skewed semi-trapezoidal membership function represents the degree to which the charging volume of that time period approaches the highest load of the day; a higher value indicates that the charging load is closer to the peak. Conversely, the result of the skewed semi-trapezoidal membership function represents the degree to which the charging volume of that time period approaches the lowest load of the day; a higher value indicates that the charging load is closer to the valley value. This invention clarifies the time period type corresponding to different load levels by setting a unified membership degree determination threshold, thereby dividing peak, flat, and valley periods. The formula for dividing peak, flat, and valley periods is as follows: , in, t For time period, The membership values ​​obtained by solving the membership function. The membership threshold is defined as follows: A represents the peak period, B represents the normal period, and C represents the valley period; f1(t) and f2(t) are the skewed semi-trapezoidal membership functions for the respective time periods. t The calculated values ​​of the partial semi-trapezoidal membership function and the calculated values ​​of the partial semi-trapezoidal membership function; The formula shows that, based on the skewed membership function The result obtained is greater than or equal to When this period is defined as a peak period, it is determined according to the smaller membership function. The result obtained is greater than or equal to When the time period is specified, it is considered a valley period; otherwise, it is considered a normal period.

[0034] The two-stage electricity pricing formula is formulated by combining the peak, flat, and valley periods determined by membership degree calculation with the differences in charging efficiency of electric vehicles at different stages: , in, This represents the initial price for charging during the constant voltage phase. W The initial value for constant current charging price is set by the first-stage pricing sub-model based on the differences in charging volume at different charging stations during different time periods. This represents the average battery capacity used during the constant current phase for different types of electric vehicles. This represents the average battery capacity occupied by different types of electric vehicles. This is the average constant current charging time for different types of electric vehicles at the current charging station. This is the average charging time during the constant voltage phase for different types of electric vehicles at the current charging station. This refers to the grid load regulation coefficients for peak, off-peak, and valley periods. Most existing studies neglect power attenuation during the constant voltage phase when calculating charging time. Ignoring this phenomenon leads to inaccurate estimations of the charging time required for electric vehicles, hindering the effective control of grid load balancing.

[0035] The electric vehicle constant current-constant voltage two-stage charging pricing model of the present invention achieves the goals of improving the turnover efficiency of charging stations and smoothing the power grid load curve from two dimensions: spatiotemporal distribution and charging stage. The two-stage pricing divides peak, flat and valley periods through membership functions and sets electricity prices based on the differences in charging volume among charging stations. This can guide users to transfer to charging stations in valley periods and low-load service areas with low charging volume, avoiding the problem of equipment idleness and overload operation of charging stations in different service areas at different times, and improving the overall turnover efficiency. At the same time, the dispersed distribution of load in the spatiotemporal dimension can effectively reduce the peak load of the power grid during peak periods and supplement the valley load during valley periods, thus promoting the smoothing of the load curve. The first-stage pricing is based on the characteristics of charging power decay. By setting different electricity prices for constant current and constant voltage stages, it guides users to reduce the charging time during the constant voltage stage, which is inefficient and time-consuming, thereby shortening the service cycle of charging piles and further accelerating the turnover of equipment. In addition, the unstable charging power during the constant voltage stage can easily cause grid load fluctuations. Reducing charging behavior during this stage can reduce the load impact on the grid caused by inefficient and fluctuating charging, achieve a smooth load curve, and ultimately achieve the technical effect of efficient turnover of charging stations and balanced grid load.

[0036] Based on differences in charging demand and queuing time, and considering the power differences and charging power decay patterns of different charging stations, S2 proposes a constant current phase pricing scheme that takes into account the differences in charging stations. This scheme increases the constant current electricity price for service areas with low charging volume and decreases the constant current electricity price for service areas with high charging volume, while also considering the constant voltage phase pricing scheme that takes into account charging power decay. Based on the membership degree of the time periods and the efficiency differences of electric vehicles at different charging stages, the corresponding constant voltage charging price is increased on top of the constant current phase pricing scheme. This pricing scheme aims to reduce inefficient occupancy of charging piles, balance the charging load of different stations at different times, and directly contribute to improving the operational efficiency of charging stations and smoothing the grid load curve.

[0037] S3. Optimization of charging station operators: Based on the constant current-constant voltage two-stage charging pricing model for electric vehicles, an operator optimization model is constructed, including: considering the impact of user charging selection behavior on load distribution, the charging station operator adjusts its own electricity price in real time through the constant current-constant voltage two-stage charging pricing model for electric vehicles, with the goal of minimizing the total charging load difference, to achieve spatiotemporal joint equilibrium of charging load and realize the control of grid load equilibrium. During the data preparation phase, the charging station operator first collects actual charging volume data for all charging stations across 24 time periods throughout the day. Then, based on the electric vehicle constant current-constant voltage two-stage charging pricing model, the initial values ​​of constant current and constant voltage electricity prices are determined. In the optimization process, the charging station operator aims to minimize the total charging load difference. Using a tabu search algorithm, the initial electricity price is iteratively adjusted multiple times. During the iteration, the charging path obtained from the electric vehicle user-side decision optimization model under different electricity prices is used to derive the charging volume results for each charging station. The electricity price for each time period is then output, and the result with the smallest charging volume difference among all charging stations is selected as the optimal electricity price and output. The tabu search algorithm uses a tabu list to avoid invalid searches and sets a maximum number of iterations to ensure that the output electricity price can reduce the load difference between stations within each time period to the target range. This invention uniquely applies tabu search to the electricity price optimization scenario. By using a tabu table to record and avoid repeatedly adjusting ineffective solutions, it avoids getting trapped in local optima. Through multiple iterations, it gradually reduces the charging volume differences between different stations and time periods, quickly finding the optimal electricity price that minimizes the charging volume differences across all charging stations and time periods. With operator profits as the core constraint, it sets reasonable upper and lower limits for electricity prices to ensure that the electricity price covers operating costs and maintains reasonable revenue. If the electricity price at a certain station exceeds the set range, it is automatically adjusted to the reasonable range, which neither harms the operator's interests nor hinders the effective guidance of load distribution through price signals.

[0038] The optimization objective of the operator's optimization model is to minimize the difference in total charging load, i.e., the objective function is... The total charging load variation includes the standard deviation of charging volume during different time periods at each charging station. and the standard deviation of charging volume between charging stations within the service area during different time periods The summation is the core of achieving this goal. The constant-current and constant-voltage electricity prices for S charging stations across 24 time periods are the main factors. These prices are iteratively adjusted using a tabu search algorithm until the objective function is achieved. When the minimum value is reached, the electricity price at that point is the optimal electricity price.

[0039] Due to the objective function The significance is to measure the degree of spatiotemporal unevenness of charging load. A smaller value indicates a more stable charging load across different service areas at different times. This means the power grid doesn't need to cope with situations where some areas are overloaded while others are idle, thus reducing the impact on the power grid caused by charging load fluctuations. Simultaneously, load balancing also reduces charging pile vacancy rates and queuing congestion, achieving the core goal of improving charging station operational efficiency. The objective function of the operator's optimization model is: , Among them, T ba This represents the total charging load difference, which is the sum of the differences in charging volume within each charging station during a given time period and the differences in charging volume between charging stations during different time periods. For charging stations s Standard deviation of charge amount over all time periods For time period t Standard deviation of charging volume at all charging stations For charging stations s During the period t Total charging capacity, For charging stations s The average total charge over 24 time periods For time period t The average total charging capacity of the S charging stations in this embodiment; S Take 12.

[0040] The core of the operator optimization model is to "minimize the total charging load difference". The physical meaning of this objective function is to avoid overload or idle power supply in highway service areas by balancing the charging volume fluctuations of each charging station during the same period and the load distribution of different charging stations during the same period. The charging volume fluctuations of each charging station during the same period are like the load difference between the morning peak and noon of a certain charging station. The load distribution of different charging stations during the same period is like the load difference between charging station A and charging station B during the same period.

[0041] The constraints include operator profit constraints, upper and lower limits of electricity prices at different stages, and charging volume constraints for charging stations in a single time period. The constant voltage electricity price is higher than the constant current electricity price to guide users away from the constant voltage stage where charging efficiency is low. The specific constraints are as follows: The operator's profit constraint formula includes: , in, For charging stations s During the period t The constant current electricity price, For charging stations s During the period t The constant voltage electricity price, constant current electricity price, and constant voltage electricity price are all optimized during the iterative solution process; For charging stations s During the period t The cost of electricity, For charging stations s During the period t The constant current charging amount, For charging stations s During the period t The constant voltage charging amount.

[0042] The formulas for the upper and lower limits of electricity prices at different stages include: , in, The lowest price for constant current electricity. The highest price for constant current electricity. The lowest electricity price under constant voltage. This represents the maximum electricity price for constant voltage. The minimum and maximum electricity prices for constant current / constant voltage are preset values ​​and can be set based on the results of a survey of electric vehicles.

[0043] The formula for the charging volume constraint of a charging station in a single time period includes: , in, For charging stations s The maximum amount of charge it can withstand in a single time period.

[0044] The operator optimization model balances the charging load of different charging piles at different times while simultaneously achieving the technical goal of smoothing the power grid load curve. On the one hand, the design of minimizing the charging volume difference in different service areas at different times in the objective function can significantly reduce the fluctuation of charging load in the time and space dimensions of charging stations in each service area, avoiding load shocks to the power grid caused by sudden increases or decreases in charging volume in local service areas or at local times, and promoting a uniform distribution of charging load in the time and space dimensions. On the other hand, by reasonably adjusting the electricity price through the charging load data of each charging station, the peak load of a single charging station can be controlled, preventing the overload of charging load in local areas from causing instantaneous pressure on the power grid. At the same time, the differentiation of electricity prices at different stages provides space to guide the charging behavior of electric vehicle users. By reasonably setting the electricity price difference, users can be guided to complete their main charging needs in the constant current stage with high charging efficiency and stable power output, reducing the charging time in the constant voltage stage with low charging efficiency and significant power decay, reducing the load fluctuations of the power grid caused by inefficient and unstable charging, and ultimately achieving a smooth power grid load curve and ensuring the stability of power grid operation.

[0045] S4. Electric Vehicle User-Side Decision Optimization: Based on the electric vehicle energy consumption model, a user charging decision optimization model is constructed to find the optimal charging path with the lowest overall charging cost, as well as the charging volume and average queuing time at different charging stations during different time periods, achieving collaborative optimization between users and the power grid. The user charging decision optimization model compares the impact of pricing strategies that differentiate charging stages versus those that do not, on user charging choices. This includes: constructing an electric vehicle user charging decision behavior model based on highway range characteristics; quantifying user behavior at different decision stages using statistical data, including anxiety thresholds based on remaining battery power and selection tendencies under different queuing times; during the operation of the user charging decision optimization model, the cost of different charging choices is calculated by combining multiple types of data, including: energy consumption matching data based on highway reachability (whether the remaining battery power can support driving to the next station); charging station load status data, including differences in queuing times at each station; and two-stage electricity price information; finally, only key information such as electricity prices and queuing times at each charging station is released to users, allowing them to choose the optimal charging path based on the overall charging cost. By differentiating pricing for different charging stages, users are guided to switch to stations or time periods with lower overall costs, thus balancing the overall charging load.

[0046] In optimizing user decisions for electric vehicles, the impact of pricing strategies that differentiate charging stages versus those that don't is compared on user charging choices. A user behavior model based on highway range characteristics is constructed, quantifying user behavior in different scenarios using statistical data, including anxiety thresholds based on remaining battery power and preference for different queue lengths. During model operation, the cost of different charging options is calculated by combining multiple data sources: energy consumption matching data based on highway accessibility (whether remaining battery power can support driving to the next station); charging station load status data, including differences in queue lengths at each station; and two-stage electricity price information. Finally, only key information such as electricity prices and queue times at each charging station is released to users, allowing them to choose the optimal charging route based on the overall charging cost. By differentiating pricing for different charging stages, users are guided to switch to stations or time periods with lower overall costs, thus balancing the overall charging load.

[0047] The core of the user charging decision optimization model is "charging decision analysis based on battery anxiety and scenario classification", which includes: quantification of battery anxiety and triggering conditions. A battery anxiety coefficient θ is introduced to represent the degree of user charging demand. The value of θ is 0.2, that is, when the remaining battery power of the electric vehicle is less than 20% of the rated battery power, the user generates a charging demand.

[0048] S4 specifically includes: S41, as attached Figure 3As shown, the energy consumption calculated by the fusion energy consumption sub-model of electric vehicles is compared with the remaining battery power of electric vehicle users. Four charging decision stages are defined for highway driving scenarios to determine the charging decision behavior of electric vehicle users. Battery anxiety quantification and triggering conditions are introduced into the four charging decision stages in the electric vehicle user charging decision behavior model. The electric vehicle user charging decision behavior model includes: Phase 1: When the electric vehicle user's current remaining battery power is insufficient to reach the next charging station, the user will charge at the current charging station until the necessary battery power allows them to reach the next charging station. The binary variable calculation formula for Phase 1 is as follows: , Among them, binary variables Indicates whether the electric vehicle owner is charging at the current service area. This indicates that the user is charging in the current service area. This indicates that the user is not charging in the current service area. Energy consumption to support the remaining battery power of electric vehicles. The energy consumed by electric vehicles to drive Energy consumption for using air conditioning in electric vehicles.

[0049] Phase 2: When an electric vehicle user charges at the current charging station to the necessary level to reach the next charging station, if the remaining battery level is lower than the set anxiety level, the user will continue charging at the current charging station until the anxiety level is reached. Phase 2 introduces a battery anxiety coefficient. It is defined as the ratio of the remaining battery power when the user starts considering charging to the total battery power of the electric vehicle. In this embodiment, it is set as follows: The formula for calculating binary variables in stage 2 is: , Among them, binary variables Indicates whether the electric vehicle owner is charging at the current service area. This indicates that the user is charging in the current service area. This indicates that the user is not charging in the current service area, and E represents the electric vehicle's battery capacity.

[0050] Phase 3: When an electric vehicle user's battery level at the current charging station reaches the anxiety level, the user faces two choices: (1) continue to the next charging station, with the total charging cost being the travel time between the two charging stations; (2) charge at the current charging station, with the total charging cost being the price cost, charging time cost, and queuing time cost of charging at the current charging station. j The formula for charging price and the formula for calculating binary variables in stage 3 are as follows: , in, Charging time for electric vehicles; Target SOC for charging electric vehicles; SOC (State of Charge) when an electric vehicle begins charging; The charging price during the constant current phase at charging station j. Price coefficient converted to time; Let j be the queuing time at charging station j. To determine the overall charging cost during the constant current phase at charging station j, To calculate the overall charging cost during the constant current phase at charging station j+1; binary variable. Indicates whether the electric vehicle owner is charging at the current service area. This indicates that the user is charging in the current service area. This indicates that the user is not charging in the current service area. Electric vehicle users can choose the charging route with the lowest overall cost by comparing the charging costs of different charging stations.

[0051] Phase 4: If an electric vehicle user chooses to charge at the current charging station until the constant current phase ends, they will face two choices by comparing the constant voltage charging cost with that of the next service area: (1) decide to continue charging at the current charging station until the constant voltage phase ends; (2) decide to go to the next charging station to charge. The formula for calculating the price cost of electric vehicle users charging at charging station j and the formula for calculating the binary variables in stage 4 are as follows: , in, For charging stations j The price of charging during the constant voltage phase; The average critical SOC of various types of electric vehicles during the constant current and constant voltage stages; The charging power for electric vehicles during the constant current phase. For charging stations j The overall charging cost during the constant voltage phase; binary variables Indicates whether the electric vehicle owner is charging at the current service area. This indicates that the user is charging in the current service area. This indicates that the user is not charging in the current service area.

[0052] S42. Based on the electric vehicle energy consumption model, an optimization model for user charging decisions is constructed to solve for the optimal charging path with the lowest overall charging cost, as well as the charging volume and average queuing time at different times for each charging station, thus achieving collaborative optimization between users and the power grid. In solving for the optimal charging path, the electric vehicle user first obtains data such as constant current and constant voltage electricity prices, queuing time, and driving time in each service area. With the goal of minimizing the overall charging cost, the above cost formula is substituted into the Dijkstra algorithm for solution. Finally, the optimal charging path and the charging volume and average queuing time at different times for each service area are output, thus achieving collaborative optimization of charging station operation efficiency and user experience.

[0053] For electric vehicle users, based on the constant current and constant voltage charging prices at different charging stations at various times, and with the goal of minimizing the overall charging cost, the Dijkstra algorithm is used to find the optimal charging path with the lowest overall charging cost for the user, along with the corresponding charging volume and average queuing time at each charging station at different times. This aims to improve the operational efficiency of charging stations. The objective function of the user charging decision optimization model is: , in, For the overall cost of charging, The total price for charging at a charging station. The cost of queuing time at charging stations, Let P be the charging time cost, P be the electric vehicle's travel path, and p be the corresponding electricity price at the charging station node. Let K be the energy consumption of the electric vehicle on the k-th road segment. The energy consumption of the electric vehicle's onboard air conditioning on road segment k is given. The driving energy consumption and onboard air conditioning energy consumption are calculated based on the electric vehicle energy consumption model, where n is the total number of roads. The charging power for electric vehicles during the constant current phase. The charging power for electric vehicles during the constant voltage phase; To reach the charging station s The corresponding hourly period Used to determine whether an electric vehicle user has entered the constant voltage charging stage; For charging stations s Queue length For charging stations s The constant current charging time, For charging stations s The constant voltage phase charging duration; constraints include charging period constraints and duration non-negative constraints.

[0054] The constraints include charging period constraints and duration non-negativity constraints. These two types of constraints need to be set in conjunction with the travel characteristics of highway users; specifically as follows: The charging time constraint needs to match the user's highway travel schedule. The formula for the charging time constraint is: , The non-negative duration constraint ensures the physical rationality of charging and queuing times, as shown in the formula: , Among them, t sta (s) represents the time when the electric vehicle arrives at charging station s. This time needs to be associated with the travel time on the highway segment. The association logic is as follows: if the user departs from service area A and it takes 1.5 hours to travel to service area B, and the departure time from service area A is 10:00, then tsta (B) is approximately 11:30, ensuring that the time parameter conforms to the actual travel logic.

[0055] The process of solving the user-side path P can improve the turnover efficiency of charging piles by guiding user behavior: the inclusion of queuing time cost in the objective function will encourage users to prioritize charging nodes with shorter queuing times, reducing idle losses caused by the accumulation of waiting vehicles; changes in charging prices at different stages can guide users to avoid the constant-pressure stage with low charging efficiency and long charging time, shortening the duration of a single charge and accelerating the turnover of charging piles. At the same time, prices at different charging times can guide users to charge during off-peak hours, avoiding the problem of charging piles operating under overload during peak hours and being idle during off-peak hours, achieving a balanced distribution of charging load over time, further ensuring the continuous and efficient operation of charging piles, and ultimately improving the overall turnover efficiency of charging stations.

[0056] In this invention, the operator's goal is to minimize the difference in charging load at charging stations in different service areas at different times, thereby reducing load fluctuations. The solution is to calculate the electricity price at charging stations in different service areas at different times. The user, on the other hand, aims to minimize their overall charging cost by finding a suitable charging route and the corresponding charging volume and average queuing time at charging stations in different service areas at different times.

[0057] In this invention, since energy consumption, queuing, and charging strategies need to be optimized collaboratively during electric vehicle charging, and the constant current and constant voltage stages of charging have significantly different characteristics, this invention constructs a system framework that integrates an energy consumption model, a charging time model, a charging station queuing model, and a two-stage charging pricing system. The highway electric vehicle energy consumption model is one of the key elements supporting user charging decisions. The staged charging time model calculates the charging time for each stage of the constant current and constant voltage stages to provide time support. The highway charging station queuing time model calculates queuing time to provide queue status feedback. The charging strategy, centered around the goals of operators and users, achieves bidirectional optimization and data interaction through different algorithms. With the goal of minimizing the difference in charging volume across all time periods in each service area, and relying on the initial values ​​of charging volume and constant current and constant voltage electricity prices for each service area at each time period, a tabu search algorithm is used to optimize the constant current and constant voltage electricity prices for different time periods in each service area. This guides the charging load to be evenly distributed in time and space. With the goal of minimizing the overall charging cost for users, the Dijkstra algorithm is used to optimize charging navigation and scheduling services by integrating factors such as charging price, queuing time, and energy consumption. This provides efficient charging decision support for electric vehicle users. At the same time, data such as charging volume and average queuing time at different time periods in each service area are fed back into the algorithm process to continuously support strategy formulation and ensure that the charging strategy is adapted to the charging needs of different scenarios.

[0058] This invention also discloses a method for calculating user satisfaction based on different types of electric vehicle users, using the following formula: , in, The overall user satisfaction score indicates that the higher the score, the greater the user's overall satisfaction with the charging guidance strategy. , , and The weights for time, power consumption, cost, and distance are respectively determined to satisfy... ; For time satisfaction, The time after execution according to the guidance strategy. The total time expected by the user; For charging power satisfaction, For the user's expected charging amount, The current state of charge, The desired state of charge; Satisfaction with charging prices. The charging cost after the guided strategy is executed. Expected charging cost; Satisfaction with the distance to charging stations. L The scheduling distance after execution according to the guidance strategy. The maximum scheduling distance acceptable to the user.

[0059] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A charging station pricing method based on the difference in constant current-constant voltage charging characteristics for controlling grid load balancing, characterized in that, The methods include: An electric vehicle energy consumption model adapted to the operating characteristics of highways is constructed, comprising four major models: a highway network sub-model, an electric vehicle integrated energy consumption model sub-model, a charging time calculation sub-model, and a charging station queuing analysis sub-model. The electric vehicle energy consumption model allocates charging load based on road network accessibility, using the road network coverage of charging stations as a benchmark to ensure that the load distribution accurately matches the actual road network usage. Among them, the highway network sub-model provides basic route data, the electric vehicle integrated energy consumption sub-model calculates the energy consumption of different routes, the charging time calculation sub-model calculates the charging time by combining the energy consumption results of the electric vehicle integrated energy consumption sub-model, and the charging station queuing analysis sub-model determines the queuing situation based on the charging time and traffic flow, providing basic spatiotemporal data support for subsequent pricing work. A two-stage charging pricing model for electric vehicles, based on constant current and constant voltage, is constructed, including: a constant current stage pricing sub-model and a constant voltage stage pricing sub-model based on the charging characteristics of electric vehicles in constant current and constant voltage stages; the constant current stage pricing sub-model sets a benchmark electricity price based on the differences in charging volume at different charging stations during different time periods; the constant voltage stage pricing sub-model then dynamically adjusts the electricity price based on the charging power attenuation characteristics of electric vehicles in constant voltage stages, according to the benchmark electricity price. Optimization of charging station operators: Based on the constant current-constant voltage two-stage charging pricing model for electric vehicles, an operator optimization model is constructed, including: considering the impact of user charging choices on load distribution, the charging station operator adjusts its own electricity price in real time through the constant current-constant voltage two-stage charging pricing model for electric vehicles, aiming to minimize the total charging load difference, to achieve spatiotemporal joint equilibrium of charging load, and to achieve load balance of the control grid; the charging station operator first collects the equilibrium data to achieve load balance of the control grid. During the data preparation phase, the charging station operator first collects the actual charging volume data of the charging station for 24 time periods throughout the day, and then determines the initial values ​​of constant current and constant voltage electricity prices based on the electric vehicle constant current-constant voltage two-stage charging pricing model. After entering the optimization process, the charging station operator aims to minimize the difference in total charging load and uses a tabu search algorithm to iteratively adjust the initial electricity price in multiple rounds. During the iteration process, the charging path obtained by the electric vehicle user-side decision optimization model under different electricity prices is used to obtain the charging volume results of each charging station, and the electricity price for each time period is output. The result with the smallest difference in charging volume among the charging stations is selected as the optimal electricity price and output. Electric vehicle user-side decision optimization: Based on the electric vehicle energy consumption model and the electric vehicle two-stage charging pricing model, a user charging decision optimization model is constructed to solve for the optimal charging path with the lowest overall charging cost for users, as well as the charging volume and average queuing time of each charging station at different times, thereby achieving collaborative optimization between users and the power grid.

2. The charging station pricing method based on the difference in constant current-constant voltage charging characteristics for controlling grid load balancing, as described in claim 1, is characterized in that... The high-speed road network sub-model performs joint space-time optimization and realizes charging load allocation by combining the actual characteristics of the highway road network. The implementation process includes: building a road network topology structure with highway entrances and exits as core nodes; based on the daily driving habits of electric vehicle owners, counting traffic flow at different times and different origin-end points through time-segmented OD matrices; and finally converting the OD matrix into an OD probability matrix. In the sub-model of the electric vehicle integrated energy consumption model, the driving energy consumption of the electric vehicle is calculated based on the air resistance, slope resistance and rolling resistance during the driving process; the energy consumption of the electric vehicle's on-board air conditioning system is calculated based on the energy consumption values ​​of the electric vehicle's on-board air conditioning system under different weather and temperature conditions. The charging time calculation sub-model includes: The charging time of an electric vehicle is calculated based on the power and battery capacity at different charging stages. The calculation distinguishes between the constant current and constant voltage charging stages, and sets the critical SOC average value for both stages. The formula is as follows: ， in, Charging time for electric vehicles; Target SOC for charging electric vehicles; SOC (State of Charge) when an electric vehicle begins charging; The value of SOC represents the critical state of charge (SOC) during the constant current and constant voltage phases of various types of electric vehicles; E represents the battery capacity of the electric vehicle. The charging power for electric vehicles during the constant current phase; The charging power for electric vehicles during the constant current phase; The charging station queuing analysis sub-model is established based on the event-driven queue to calculate the average queuing time of electric vehicles. Combining the process characteristics of "reservation-arrival-charging" of highway vehicles, the charging station queue is divided into three types: queue 1 indicates that the vehicle is charging, queue 2 indicates that the vehicle is queuing, and queue 3 indicates that the vehicle has made a charging request but has not yet arrived at the charging station. At time 1, the number of electric vehicles in each queue is: ， in, , and They are time points Next s The number of electric vehicles in queues 1, 2, and 3 of each charging station. For a moment Next s The total number of electric vehicles in the charging station queue. For a moment Next, the s The number of electric vehicles in charging station queue 1 For a moment Next, the s The number of electric vehicles in charging station queue 2 For a moment Next, the s The number of electric vehicles in charging station queue 3. For a moment Next, the s The total number of electric vehicles in the charging station queue; For charging stations s At any moment The number of charging requests received from electric vehicles. For charging stations s At any moment The number of electric vehicles arriving at charging stations For charging stations s At any moment The number of electric vehicles that have started charging For charging stations s At any moment The number of electric vehicles leaving the charging station; Section, charging station s The queuing time is: ， in, For charging stations s The number of charging stations, For electric vehicles k At the charging station s The target charging amount For electric vehicles k At the charging station s The initial charge, For electric vehicles k At the charging station s The charging rate.

3. The charging station pricing method based on the difference in constant current-constant voltage charging characteristics for controlling grid load balancing, as described in claim 1, is characterized in that: The constant current stage pricing sub-model sets a benchmark electricity price based on the differences in charging volume at different charging stations during different time periods. The formula is as follows: ， Where W is the benchmark electricity price, i.e., the initial value of the constant current charging price for each charging station at each time period; L is the lower limit of the charging price; U is the upper limit of the charging price; and C is the actual charging volume of each charging station at each time period. The minimum charging amount for all charging stations during this period. The highest charging volume for all charging stations during that time period.

4. The charging station pricing method based on the difference in constant current-constant voltage charging characteristics for controlling grid load balancing, as described in claim 1, is characterized in that: The constant-voltage stage pricing sub-model dynamically adjusts the electricity price based on the power decay characteristics during electric vehicle charging. This includes classifying each charging station's 24 time periods into peak, flat, and valley categories according to the membership degree of the charging volume. The calculation of the membership degree involves dividing the charging volume of different charging stations across the 24 time periods of the day using membership functions to differentiate between high and low charging load levels. Two types of membership functions are involved: a skewed semi-trapezoidal membership function and a skewed semi-trapezoidal membership function. The skewed semi-trapezoidal membership function represents high load, while the skewed semi-trapezoidal membership function represents low load. The formula includes: ， in, It is a semi-trapezoidal membership function with a large size. It is a semi-trapezoidal membership function with a small size. M This represents the maximum charging volume at all charging stations for each time period. N This represents the minimum charging volume at all charging stations during all time periods. x The charging amount at different charging stations at different times; By inputting the charging volume of different charging stations at different times into the membership function, the membership value is calculated, and then the peak, flat, and valley periods are divided. The formula is as follows: ， in, t For time period, The membership values ​​obtained by solving the membership function. The membership threshold is defined as follows: A represents the peak period, B represents the normal period, and C represents the valley period; f1(t) and f2(t) are the skewed semi-trapezoidal membership functions for the respective time periods. t The calculated values ​​of the partial semi-trapezoidal membership function and the calculated values ​​of the partial semi-trapezoidal membership function; The two-stage electricity pricing formula is formulated by combining the peak, flat, and valley periods determined by membership degree calculation with the differences in charging efficiency of electric vehicles at different stages: ， in, This represents the initial price for charging during the constant voltage phase. W The initial value for constant current charging price is set by the first-stage pricing sub-model based on the differences in charging volume at different charging stations during different time periods. This represents the average battery capacity used during the constant current phase for different types of electric vehicles. This represents the average battery capacity occupied by different types of electric vehicles. This is the average constant current charging time for different types of electric vehicles at the current charging station. This is the average charging time during the constant voltage phase for different types of electric vehicles at the current charging station. This refers to the power grid load regulation coefficients for the three different time periods: peak, flat, and valley.

5. The charging station pricing method based on the difference in constant current-constant voltage charging characteristics for controlling grid load balancing, as described in claim 1, is characterized in that: The objective function of the operator optimization model is: ， Among them, T ba This represents the total charging load difference, which is the sum of the differences in charging volume within each charging station during a given time period and the differences in charging volume between charging stations during different time periods. For charging stations s Standard deviation of charge amount over all time periods For time period t Standard deviation of charging volume at all charging stations For charging stations s During the period t Total charging capacity, For charging stations s The average total charge over 24 time periods For time period t The average total charging volume of the S charging stations is calculated; the constraints include operator profit constraints, upper and lower limits of electricity prices at different stages, and charging volume constraints of charging stations in a single time period.

6. The charging station pricing method based on the difference in constant current-constant voltage charging characteristics for controlling grid load balancing, as described in claim 5, is characterized in that: The operator's profit constraint formula includes: ， in, For charging stations s During the period t The constant current electricity price, For charging stations s During the period t The constant voltage electricity price, constant current electricity price, and constant voltage electricity price are all optimized during the iterative solution process; For charging stations s During the period t The cost of electricity, For charging stations s During the period t The constant current charging amount, For charging stations s During the period t Constant voltage charging quantity; The formulas for the upper and lower limits of electricity prices at different stages include: ， in, The lowest price for constant current electricity. The highest price for constant current electricity. The lowest electricity price under constant voltage. The highest electricity price under constant voltage; The formula for the charging volume constraint of a charging station in a single time period includes: ， in, For charging stations s The maximum amount of charge it can withstand in a single time period.

7. The charging station pricing method based on the difference in constant current-constant voltage charging characteristics for controlling grid load balancing as described in claim 1, characterized in that: Electric vehicle user-side decision optimization includes: The energy consumption calculated by the electric vehicle fusion energy consumption sub-model is compared with the remaining power of the electric vehicle user. Four charging decision stages are divided for the highway range scenario to determine the charging decision behavior of electric vehicle users. Power anxiety quantification and triggering conditions are introduced into the four charging decision stages in the electric vehicle user charging decision behavior model. Based on the electric vehicle energy consumption model, electric vehicle users can construct a user charging decision optimization model to solve for the optimal charging path with the lowest overall charging cost, as well as the charging volume and average queuing time of each charging station at different times, thereby achieving collaborative optimization between users and the power grid.

8. The charging station pricing method based on the difference in constant current-constant voltage charging characteristics for controlling grid load balancing, as described in claim 7, is characterized in that: Electric vehicle user charging decision behavior models include: Phase 1: When the electric vehicle user's current remaining battery power is insufficient to reach the next charging station, the user will charge at the current charging station until the necessary battery power allows them to reach the next charging station. The binary variable calculation formula for Phase 1 is as follows: ， Among them, binary variables Indicates whether the electric vehicle owner is charging at the current service area. This indicates that the user is charging in the current service area. This indicates that the user is not charging in the current service area. Energy consumption to support the remaining battery power of electric vehicles. The energy consumed by electric vehicles to drive Energy consumption for using air conditioning in electric vehicles; Phase 2: When an electric vehicle user charges at the current charging station to the necessary level to reach the next charging station, if the remaining battery level is lower than the set anxiety level, the user will continue charging at the current charging station until the anxiety level is reached. Scenario 2 introduces a battery anxiety coefficient. The formula for calculating the binary variable in stage 2 is: ， Among them, binary variables Indicates whether the electric vehicle owner is charging at the current service area. This indicates that the user is charging in the current service area. This indicates that the user is not charging in the current service area; E represents the electric vehicle's battery capacity. Phase 3: When an electric vehicle user's battery level at the current charging station reaches the anxiety level, the user faces two choices: (1) continue to the next charging station, with the total charging cost being the travel time between the two charging stations; (2) charge at the current charging station, with the total charging cost being the price cost, charging time cost, and queuing time cost of charging at the current charging station. j The formula for charging price and the formula for calculating binary variables in stage 3 are as follows: ， in, Charging time for electric vehicles; Target SOC for charging electric vehicles; SOC (State of Charge) when an electric vehicle begins charging; The charging price during the constant current phase at charging station j. Price coefficient converted to time; Let j be the queuing time at charging station j. To determine the overall charging cost during the constant current phase at charging station j, To calculate the overall charging cost during the constant current phase at charging station j+1; binary variable. Indicates whether the electric vehicle owner is charging at the current service area. This indicates that the user is charging in the current service area. This indicates that the user is not charging in the current service area. Electric vehicle users can choose the charging route with the lowest overall cost by comparing the charging costs of different charging stations. Phase 4: If an electric vehicle user chooses to charge at the current charging station until the constant current phase ends, they will face two choices by comparing the constant voltage charging cost with that of the next service area: (1) decide to continue charging at the current charging station until the constant voltage phase ends; (2) decide to go to the next charging station to charge. The formula for calculating the price cost of electric vehicle users charging at charging station j and the formula for calculating the binary variables in stage 4 are as follows: ， in, For charging stations j The price of charging during the constant voltage phase; The average critical SOC of various types of electric vehicles during the constant current and constant voltage stages; The charging power for electric vehicles during the constant current phase. For charging stations j The overall charging cost during the constant voltage phase; binary variables Indicates whether the electric vehicle owner is charging at the current service area. This indicates that the user is charging in the current service area. This indicates that the user is not charging in the current service area.

9. A charging station pricing method for controlling grid load balancing based on the difference in constant current-constant voltage charging characteristics, as described in claim 7, is characterized in that: The objective function of the user charging decision optimization model is: ， in, For the overall cost of charging, The total price for charging at a charging station. The cost of queuing time at charging stations, Let P be the charging time cost, P be the electric vehicle's travel path, and p be the corresponding electricity price at the charging station node. Let K be the energy consumption of the electric vehicle on the k-th road segment. The energy consumption of the electric vehicle's onboard air conditioning on road segment k is given. The driving energy consumption and onboard air conditioning energy consumption are calculated based on the electric vehicle energy consumption model, where n is the total number of roads. The charging power for electric vehicles during the constant current phase. The charging power for electric vehicles during the constant voltage phase; To reach the charging station s The corresponding hourly period Used to determine whether an electric vehicle user has entered the constant voltage charging stage; For charging stations s Queue length For charging stations s The constant current charging time, For charging stations s The constant voltage phase charging duration; constraints include charging period constraints and duration non-negative constraints.

10. A charging station pricing method for controlling grid load balancing based on the difference in constant current-constant voltage charging characteristics, as described in claim 9, is characterized in that: The model's two types of constraints are set in conjunction with the travel characteristics of highway users; the charging time constraint needs to match the user's highway travel time plan, and the formula is: ， The non-negative duration constraint ensures the physical rationality of charging and queuing times, as shown in the formula: ， Among them, t sta (s) represents the time when the electric vehicle arrives at the charging station s.

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