Electric vehicle charging station energy management method considering vehicle network interaction

Abstract

The invention relates to an electric vehicle charging station energy management method considering vehicle-network interaction, which is characterized in that charging modes are divided into two types, the first type is a quick charging mode which does not consider the vehicle-network interaction and charges at constant maximum charging power, and the second type is an elastic charging mode which considers the vehicle-network interaction and is flexibly adjusted according to an in-station load; the method comprises the steps of designing a charging price in a rapid charging mode and an elastic charging price in an elastic charging mode, collecting baseline data according to mode selection of a user under a reference price, modeling a naive Bayesian user behavior probability model according to a historical data set, obtaining a user price behavior model according to the baseline data, and obtaining a user price behavior model according to the baseline data. And according to the charging station operation cost and the user behavior model in different scenes, optimizing a user charging curve, formulating a charging electricity price, and updating the behavior model according to user selection. The users are stimulated to participate in vehicle-network interaction through a reasonable energy management mechanism, and mutual benefit of the power grid, the charging station operator and the users is achieved.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicle operation and optimal control, and in particular to an energy management method for an electric vehicle charging station taking vehicle-grid interaction into consideration. Background Art

[0002] With the popularity of electric vehicles, charging infrastructure has received more and more attention. Compared with traditional fuel vehicles, electric vehicle charging has higher flexibility and economic value, which has raised a series of issues about the energy management of charging stations. Energy management strategies affect both the economic benefits of charging stations and the charging decisions of electric vehicle owners. Reasonable energy management mechanisms can motivate users to participate in vehicle-grid interaction, which is of great significance to the safe and stable operation of the distribution network in the substation area, and at the same time bring considerable benefits to both users and charging stations.

[0003] Dynamic pricing, time-of-day pricing, and demand-based pricing models are some of the existing strategies that take into account multiple factors such as grid load, electricity price fluctuations, and user demand. The right energy management strategy can balance grid load, reduce peak demand, and encourage users to charge during off-peak hours. Traditional charging station energy management strategies mostly only consider promoting orderly charging of electric vehicles, and focus on the revenue of charging station operators, ignoring other entities such as the grid and users. With the development of vehicle-grid interaction and V2G technology, electric vehicle users have transformed from consumers to prosumers. It is urgent to conduct in-depth research on charging station energy management strategies and propose economically sustainable energy management methods. Summary of the invention

[0004] The purpose of the present invention is to provide an electric vehicle charging station energy management method that encourages users to participate in vehicle-grid interaction and achieves mutual benefit among the power grid, charging station operators and users and takes vehicle-grid interaction into consideration.

[0005] The technical solution of the present invention is:

[0006] An energy management method for electric vehicle charging stations taking into account vehicle-grid interaction is proposed. The charging modes are divided into two categories. The first category is a fast charging mode that does not consider vehicle-grid interaction and charges at a constant maximum charging power. The second category is a flexible charging mode that considers vehicle-grid interaction and is flexibly adjusted according to the load in the station. The charging price under the fast charging mode and the flexible charging price under the flexible charging mode are designed. Baseline data are collected based on the user's mode selection under the benchmark price. A naive Bayes user behavior probability model is established based on the historical data set. Then, according to the declaration requirements of new users, the charging station operation cost function and model constraints under different billing scenarios are calculated. The power battery degradation cost caused by V2G and the fluctuation risk of the distribution network in the substation area are simultaneously optimized and reduced. Finally, the solver is used to solve the optimal charging price and optimal charging control strategy for the current user, and the dispatching control curve in the station is updated.

[0007] Preferably, the Naive Bayes user behavior probability model uses the following mathematical model to determine parameters:

[0008]

[0009] In the formula For the price of choosing flexible and fast charging modes,

[0010] p FLEX ,p ASAP and p LEAVE are the probabilities of selecting flexible charging mode, fast charging mode and leaving the charging station respectively;

[0011] Given flexibility, fast charging mode and the choice of leaving the charging station, the price is observed The probability of , which is estimated from the historical data set;

[0012] π FLEX , π ASAP , π LEAVE are the prior probabilities of choosing flexibility, fast charging option and leaving, respectively;

[0013] is the normalization constant

[0014] FLEX is the flexible charging mode and ASAP is the fast charging mode.

[0015] Preferably, the user price behavior model is used to calculate the optimization objective function of each user scenario. The implementation method is as follows: when a new user arrives at the charging station, he reports the charging power and the planned departure time to the station operator. The operator obtains the cost function of the charging station operator based on the requirements submitted by the user, and is characterized as the following optimization function that minimizes the cost function:

[0016]

[0017] In the formula, is the expected cost of the operator, Pr(m=i) is the probability of choosing the i-th charging mode, and f i is the cost function for selecting the i-th charging mode, p FLEX and p ASAP When selecting the flexible charging mode and the fast charging mode, the corresponding charging control strategy, x FLEX and x ASAP These are the prices for selecting flexible charging mode and fast charging mode respectively.

[0018] Preferably, the charging station minimizes the cost function optimization function to calculate the charging cost of the elastic scenario and the physical-electrical constraints of the station; each time the optimization is performed, the difference between the current optimization trigger time point and the departure time of the latest known user leaving the station is taken as the optimization control interval, and τ is used to represent the local time in the interval, and t is used to represent the global optimization time, and the following cost function and constraints are obtained: Given the charging energy demand declared by a new user n and the vehicle departure time T n , considering the charging status of the current user and other users in the station, design the following cost function

[0019]

[0020] In the formula is the set of all users who choose the flexible charging mode in the charging station. is the set of all users who choose fast charging mode in the charging station. is the instantaneous elastic charging power for new user n at time τ, is the elastic charging price for new user n, Δt is the minimum calculation control interval of the proposed algorithm, is the charging and discharging power of the ith electric vehicle in flexible charging mode at time τ, c τ is the marginal electricity price of the distribution network node of the electric vehicle charging station at time τ, E f,i is the power battery capacity of the i-th vehicle, E f,n is the power battery capacity of new user n, is the charging settlement price of the i-th vehicle, is the charging settlement price of the jth vehicle, T i The corresponding time for leaving the charging station, N i is the total number of charge and discharge cycles that the battery can be rated for before its capacity drops below an acceptable threshold, N n is the total number of charge and discharge cycles that a new user n battery can be rated for before its capacity drops below an acceptable threshold, C b,i is the battery replacement cost, C b,n For, c D is the power fluctuation penalty factor, D is the maximum calculated total load within the control interval, and p max is the maximum charging power of the user's vehicle model. The calculation logic of D is as follows:

[0021]

[0022] In the formula, p i,τ is the charging power of the i-th electric vehicle user at time τ, A τ is the total load of the whole station at time τ, τ0 is the current time, T i is the remaining charging time of the i-th user, Tmax is the maximum remaining charging time for all users in the station, D0 is the maximum load of the station in the historical time window, and D τ is the maximum load expected to be observed within the control time window;

[0023] In formula (4), I i,τ is a zero-one control variable, and its integer variable control logic is as follows:

[0024]

[0025] In the formula, is the maximum charging power, is the maximum discharge power;

[0026] For the flexible charging mode, the charging station controls the charging time and power in each period. The operator only guarantees that the declared energy requirements are completed before the user's declared departure time. Therefore, the following energy constraints are met:

[0027]

[0028] In the formula, is the initial cumulative charging amount of the i-th user, e i,t is the cumulative charging amount of the i-th user at time t, is the set of all elastic users, Δt is the charge-discharge interval, p i,t is the charging and discharging power of the i-th user at time t, is the total amount of charge of the i-th user at the time of leaving, η is the charge and discharge efficiency, and different efficiency factors are selected according to the current charge or discharge, SOC i,t is the SOC of user i at time t, SOC i,min and SOC i,max Respectively represent their minimum and maximum allowed values, is the total capacity of the power battery of the i-th user, is the total reported charging power of the i-th user.

[0029] Preferably, the charging cost of the fast scenario and the physical-electrical constraints of the station are calculated by using the established charging station minimization cost function optimization function; for the fast mode, the charging power of the new user will maintain the maximum charging power constant until the battery is full or the user leaves, and for the remaining flexible users in the station, the charging curve is re-optimized and adjusted according to the overall charging station load after the new user is added. The charging cost function in this mode is

[0030]

[0031] At the same time, the following energy constraints need to be met

[0032]

[0033] In the formula, is the set of all fast mode users, T j The charging stop time should be less than or equal to the departure time declared by the user. is the required electricity declared by user j, p j,t is the charging power of user j at time t, e j,t is the cumulative charging amount of user j at time t.

[0034] Preferably, the charging cost of the departure scenario and the physical-electrical constraints of the station are optimized by using the established charging station minimization cost function; if the user rejects the fast and flexible solutions and chooses to leave, the charging operation cost of the corresponding charging station is f LEAVE Set to 0 to impose no constraints on the optimization problem.

[0035] Preferably, in addition to the constraints contained in equations (4)-(6) and (8), the optimization problem in equation (2) also considers the coupling relationship between the price and the selection probability in equation (1) and includes the following constraints:

[0036] p FLEX +p ASAP +p LEAVE =1

[0037] 0≤p FLEX ,p ASAP ,p LEAVE ≤1 (9)

[0038] x≥0.

[0039] Preferably, utilizing the established optimization function for minimizing the cost function of the charging station, when implementing the calculation, the following programming methods based on targets and classes are designed: designing user classes and station information classes, and initializing a new target according to the class in each embodiment; the user class includes four methods: event generation, selection function, user information, and adding selection results; the class summary includes three methods: data analysis, checking the availability of charging piles, and profit calculation.

[0040] Preferably, the elastic charging mode or fast charging mode is used together with the established charging station minimization cost function optimization function to solve the optimization problem, and the software interface options and user behavior model are updated according to real-time data, and the solver is called to solve the optimization problem; the charging station operator provides users with electricity prices corresponding to different charging options through the software interface for users to choose, and the price and user selection data are recorded in the operator's database. When the data is collected, the naive Bayes user behavior probability model calculation formula is repeated according to the existing data to update the user behavior model and guide the formulation of future development plans for the station.

[0041] Preferably, the established optimization function for minimizing the cost function of the charging station is used to establish a scenario-based optimization problem, determine the optimal control range of the charging station, balance the calculation cost and ensure the rationality of the price and charging curve scheduling, D is the maximum total calculation load within the control range, and the calculation logic is calculated according to formula (4) and formula (5).

[0042] Beneficial effects of the present invention:

[0043] 1. Design two charging modes to enhance users’ enthusiasm for participating in vehicle-grid interaction through energy management methods and economic incentives, allowing users to choose the most suitable charging method according to their needs and time flexibility.

[0044] 2. The naive Bayes probability model is used to model user behavior. This method can better predict user choices and thus set more reasonable electricity prices.

[0045] 3. For users, the two proposed energy management strategies that consider vehicle-grid interaction can reduce user charging costs and slow down battery degradation; for charging station owners / operators, the proposed method can reduce the overall operating costs of charging stations; for the power grid, the proposed method can smooth out load fluctuations caused by the access of a large number of charging loads in the substation area and promote the consumption of renewable energy.

[0046] 4. While determining the optimal energy management solution, this method also achieves the synchronous solution of the scheduling control solution within the charging station, improving the integration and robustness of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 Determine the method diagram for optimizing the control interval;

[0048] Figure 2 Different charging modes and software interface diagrams designed for this purpose;

[0049] Figure 3 Select the resulting graph for historical baseline data and user price-behavior;

[0050] Figure 4 A probability graph showing the relationship between the user's choice of "leave" and the electricity price;

[0051] Figure 5 This is a typical value diagram of marginal electricity price curve of charging station distribution network node;

[0052] Figure 6 Provide charging station users with charging process statistics and information collection charts;

[0053] Figure 7 This is a graph showing the impact of different numbers of charging piles on electricity purchase costs, carbon emissions, and station revenue;

[0054] Figure 8 This is a diagram showing the impact of different numbers of charging piles on user selection;

[0055] Fig. 9 Construct a structural diagram for a digital twin of the proposed optimization pricing method;

[0056] Fig.10 A graph showing the availability of charging stations at the station at different times of the day;

[0057] Fig.11 This is a comparison chart of charging load and price for different options when a new user arrives at the charging station at 8 a.m.;

[0058] Fig.12 This is a comparison chart of the benefits of traditional pricing and the proposed energy management method. DETAILED DESCRIPTION

[0059] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0060] The present invention provides a flexible energy management strategy for charging stations that takes vehicle-grid interaction into consideration. First, two basic charging modes are designed, and baseline data is collected. A naive Bayesian user behavior probability model is established using statistical learning methods. Then, according to the declaration requirements of new users, the charging station operating cost function and model constraints under different billing scenarios are calculated. The power battery degradation cost caused by V2G and the fluctuation risk of the distribution network in the substation area are simultaneously optimized. Finally, the solver is used to solve the optimal charging price and optimal charging control strategy for the current user, and the in-station dispatch control curve is updated. The proposed method aims to improve the operating efficiency of charging stations while encouraging users to participate in vehicle-grid interaction through a reasonable price mechanism, so as to achieve mutual benefit among the power grid, charging station operators and users.

[0061] To make the purpose, technical solutions and advantages of the present invention more clear, the following will be combined with the attached Figure 1-12 The following description will be given by way of specific examples.

[0062] S1. Propose a flexible energy management strategy for charging stations that takes into account vehicle-grid interaction. Consider the following typical embodiment: a charging station with 8 charging piles, each with a power of high and low (high power 6.6kW, low power 3.3kW, which are typical values ​​of charging power for existing market models). First, set the "fast" and "flexible" charging modes, and initialize the charging electricity prices of the two modes to a unified price (for the "elastic" mode, convert it to the charging price per unit time). In this embodiment, the initial value is 1 yuan (100 points) per hour, which can actually be determined based on the price of electricity purchased by the charging station from the power grid. Operate the charging station for a period of time, collect baseline data, analyze user behavior-price characteristics, and use statistical learning methods to establish a naive Bayes user behavior probability model. The software interfaces of the two designed charging methods are attached. Figure 2 The results of the baseline data collection are attached. Figure 3 .

[0063] S2. The Naive Bayesian user behavior probability model described in "S1" uses the following mathematical model to determine parameters:

[0064]

[0065] In the formula For the price of selecting the “flexible” and “fast” charging modes, p FLEX ,p ASAP and p LEAVE are the probabilities of selecting “flexible”, “fast” charging mode and leaving the charging station, respectively; For the “flexible” and “fast” charging modes and when leaving the charging station, the price is observed The probability of π is estimated from the historical data set; FLEX , π ASAP , π LEAVE are the prior probabilities of choosing the “flexible”, “fast” charging option and leaving, respectively. is a normalization constant. For example, for the embodiment described above, the following Figure 4 of users choose to “leave” - price action probability model.

[0066] S3. Using the user price-behavior model established in 'S2', calculate the optimization objective function of each user scenario. The implementation method is as follows: When a new user arrives at the charging station, he reports the charging power and planned departure time to the station operator. Based on the requirements submitted by the user, the operator can obtain the cost function of the charging station operator, which can be represented as the following cost function optimization problem of minimizing the cost function:

[0067]

[0068] In the formula, is the expected cost of the operator, Pr(m=i) is the probability of choosing the i-th charging mode, and f i is the cost function for selecting the i-th charging mode, p FLEX and p ASAP The corresponding charging control strategy when selecting "elastic" and "fast" charging modes.

[0069] S4. Using the charging station minimization cost function optimization problem established in 'S3', calculate the charging cost and physical-electrical constraints of the "elastic" scenario. Each time the optimization is performed, the difference between the current optimization trigger time point and the departure time of the latest known user leaving the station is taken as the optimization control interval, and τ is used to represent the local time in the interval, and t is used to represent the global optimization time, and the following cost function and constraints are obtained: Given the charging energy demand reported by a new user n and the vehicle departure time T n , considering the charging status of the current user and other users in the station, design the following cost function

[0070]

[0071] In the formula, is the charging and discharging power of the ith electric vehicle in the “elastic” charging mode at time τ, c τ is the marginal electricity price of the distribution network node of the electric vehicle charging station at time τ. Its typical value can be seen in the attached Figure 5 . E f,i is the power battery capacity of the i-th vehicle, is the charging settlement price of the i-th vehicle, T i The corresponding time for leaving the charging station, N i is the total number of charge and discharge cycles that the battery can be rated for before its capacity drops below an acceptable threshold, C b,i is the battery replacement cost, c D is the power fluctuation penalty factor, D is the maximum calculated total load within the control interval, and the calculation logic is as follows:

[0072]

[0073] In the formula, A τ is the total load of the whole station at time τ, τ0 is the current time, T i is the remaining charging time of the i-th user, and D0 is the maximum load of the station in the historical time window.

[0074] In formula (4), I i,τ is a zero-one control variable, and its integer variable control logic is as follows:

[0075]

[0076] In the formula, is the maximum charging power, is the maximum discharge power.

[0077] For the "flexible" mode, the charging station can control the charging time and power in each period. The operator only needs to ensure that the declared energy requirements are completed before the user's declared departure time. Therefore, the following energy constraints need to be met:

[0078]

[0079] In the formula, is the initial cumulative charging amount of the i-th user, e i,t is the cumulative charging amount of the i-th user at time t, is the set of all elastic users, Δt is the charge-discharge interval, p i,t is the charging and discharging power of the i-th user at time t, is the total amount of charge of the i-th user at the time of leaving, η is the charge and discharge efficiency, and different efficiency factors can be selected according to the current charge or discharge, SOC i,t is the SOC of user i at time t, SOC i,min and SOCi, max Respectively represent their minimum and maximum allowed values, is the total capacity of the power battery of the i-th user. For the user’s charging process and declaration information table, see the attached Figure 6 .

[0080] S5. Using the optimization problem of minimizing the cost function of the charging station established in 'S3', calculate the charging cost and physical-electrical constraints of the "fast" scenario. For the "fast" mode, the charging power of the new user will maintain the maximum charging power constant until the battery is full or the user leaves. For the remaining "elastic" users in the station, it is necessary to re-optimize and adjust the charging curve according to the overall charging station load after adding the new user. The charging cost function in this mode is

[0081]

[0082] At the same time, the following energy constraints need to be met

[0083]

[0084] In the formula, is the set of all “fast” mode users, T j This is the charging stop time, which should be less than or equal to the departure time declared by the user.

[0085] S6. Using the optimization problem of minimizing the cost function of the charging station established in 'S3', the charging cost of the "leave" scenario and the physical-electrical constraints of the station. If the user rejects the "fast" and "flexible" options and chooses to leave, the charging operation cost of the corresponding charging station is f LEAVE Set to 0 to impose no constraints on the optimization problem.

[0086] S7. Using the optimization problem of minimizing the cost function of the charging station established in 'S3', design the following programming methods based on targets and classes when implementing the calculation: Design the Driver class and the Station Information class, and initialize a new target according to the class in each embodiment, where the Driver class needs to include four methods: Session Generation, Choice Function, User Info, and Choice Append. The Station Information class is summarized, including three methods: Data Analysis, Check Poles, and Revenue Calculation.

[0087] S8. Utilize the flexible charging mode and fast charging mode described in 'S1' and the charging station minimization cost function optimization problem established in 'S3' to solve the optimization problem, and update the software interface options and user behavior model based on real-time data. Call the solver to solve the above optimization problem. The charging station operator provides users with electricity prices corresponding to different charging options through the software interface for users to choose from. The operator records the price and user selection data in the database. When a certain amount of data is collected, step 2 is repeated based on the existing data to update the user behavior model and guide the formulation of future development plans for the station. For example, based on the results of data analysis, examine indicators such as the number of charging piles and energy utilization rate at the station to guide the upgrade of station facilities. An example of sensitivity analysis is attached. Figure 7 With attached Figure 8 An example of the architecture of the proposed embodiment is shown in the attached Fig. 9 .

[0088] S9. For the eight charging pile charging station embodiments described in 'S1', a continuous time simulation is performed to obtain the availability of charging piles in different time periods. See attached Fig.10 , where "occupied" means that the charging pile is being used during this period. Further, taking the 8 am period as an example, if a new user arrives at the charging station at this time and reports to the charging station a charging time of 4 hours and a total charging power of 7.56 kW·h, then the following can be obtained: Fig.11 The optimization results are shown in the table below. Fig.11It is divided into 3 sub-graphs, where the solid lines represent the charging power and the dotted lines represent the charging price. Figure 1 , 2 If the user chooses "fast" or "flexible" charging mode, the corresponding user charging curve and the charging curve of other users in the station are shown. It can be seen that if the user chooses "flexible", the charging price is 121.05 cents per hour, which is lower than the 129.97 cents per hour of "fast", and the user's charging cost is reduced. Figure 3 is the total load in the charging station. The pink line represents the total load after the user chooses "elastic" charging, and the black line represents the total load in "fast" mode. It can be seen that the "elastic" mode reduces the maximum total load in the station from 26kW to 23kW, which is conducive to smoothing the load fluctuations in the distribution network area and ensuring the safe and stable operation of the distribution network. Finally, the benefits of the charging station operator / owner during this period are examined. The various costs and benefits are shown in the attached Fig.12 It can be seen that the proposed pricing method reduces the operating cost of the charging station and increases the total revenue of the charging station, and ultimately improves the net profit of the operator.

[0089] S10. Using the optimization problem of minimizing the cost function of the charging station established in 'S3', establish a scenario-based optimization problem to determine the optimal control interval of the charging station to balance the computing cost and ensure the rationality of the price and charging curve scheduling. D is the maximum total computing load within the control interval, and the calculation logic is as follows:

[0090]

[0091] In the formula, A τ is the total load of the whole station at time τ, τ0 is the current time, T i is the remaining charging time of the i-th user, and D0 is the maximum load of the station in the historical time window.

[0092] In formula (4), I i，τ is a zero-one control variable, and its integer variable control logic is as follows:

[0093]

[0094] In the formula, is the maximum charging power, is the maximum discharge power. The method for determining the optimal control interval can be found in the attached Figure 1 .

[0095] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. An energy management method for electric vehicle charging stations taking into account vehicle-grid interaction, wherein charging modes are divided into two categories, the first category being a fast charging mode that does not take into account vehicle-grid interaction and charges at a constant maximum charging power, and the second category being a flexible charging mode that takes into account vehicle-grid interaction and is flexibly adjusted according to the load in the station, wherein the method is characterized in that: Design the fast charging price under the fast charging mode and the flexible charging price under the flexible charging mode. Collect baseline data based on the user's mode selection under the benchmark price. Establish a naive Bayesian user behavior probability model based on the historical data set. Then, calculate the charging station operation cost function and model constraints under different billing scenarios based on the declaration requirements of new users. Simultaneously optimize and reduce the power battery degradation cost caused by V2G and smooth the fluctuation risk of the distribution network in the substation. Finally, use the solver to solve the current user's optimal charging price and optimal charging control strategy, and update the station scheduling control curve.

2. The method for energy management of electric vehicle charging stations taking into account vehicle-grid interaction according to claim 1, characterized in that: The Naive Bayes user behavior probability model uses the following mathematical model to determine parameters: In the formula For the price of choosing flexible and fast charging modes, p FLEX , p ASAP and p LEAVE are the probabilities of selecting flexible charging mode, fast charging mode and leaving the charging station respectively; Given flexibility, fast charging mode and the choice of leaving the charging station, the price is observed The probability of , which is estimated from the historical data set; π FLEX , π ASAP , π LEAVE are the prior probabilities of choosing flexibility, fast charging option and leaving, respectively; is the normalization constant; FLEX is the flexible charging mode and ASAP is the fast charging mode.

3. The energy management method for electric vehicle charging stations taking into account vehicle-grid interaction according to claim 2, characterized in that: The user price behavior model is used to calculate the optimization objective function of each user scenario. The implementation method is as follows: when a new user arrives at the charging station, he reports the charging power and planned departure time to the station operator. The operator obtains the cost function of the charging station operator based on the requirements submitted by the user, and represents it as the following optimization function that minimizes the cost function: In the formula, is the expected cost of the operator, Pr(m=i) is the probability of choosing the i-th charging mode, and f i is the cost function for selecting the i-th charging mode, p FLEX and p ASAP When selecting the flexible charging mode and the fast charging mode, the corresponding charging control strategy, x FLEX and x ASAP These are the prices for selecting flexible charging mode and fast charging mode respectively.

4. The method for energy management of electric vehicle charging stations taking into account vehicle-grid interaction according to claim 3, characterized in that: The charging station minimizes the cost function optimization function to calculate the charging cost of the elastic scenario and the physical-electrical constraints of the station. Each time the optimization is performed, the difference between the current optimization trigger time point and the departure time of the latest known user leaving the station is taken as the optimization control interval, and τ is used to represent the local time in the interval, and t is used to represent the global optimization time. The following cost function and constraints are obtained: Given the charging energy demand declared by a new user n and the vehicle departure time T n , considering the charging status of the current user and other users in the station, the following cost function is designed: In the formula is the set of all users who choose the flexible charging mode in the charging station. is the set of all users who choose fast charging mode in the charging station. is the instantaneous elastic charging power for new user n at time τ, is the elastic charging price for new user n, Δt is the minimum calculation control interval of the proposed algorithm, is the charging and discharging power of the ith electric vehicle in flexible charging mode at time τ, c τ is the marginal electricity price of the distribution network node of the electric vehicle charging station at time τ, E f，i is the power battery capacity of the i-th vehicle, E f，n is the power battery capacity of new user n, is the charging settlement price of the i-th vehicle, is the charging settlement price of the jth vehicle, T i The corresponding time for leaving the charging station, N i is the total number of charge and discharge cycles that the battery can be rated for before its capacity drops below an acceptable threshold, N n is the total number of charge and discharge cycles that a new user n battery can be rated for before its capacity drops below an acceptable threshold, C b，i is the battery replacement cost, C b，n For, c D is the power fluctuation penalty factor, D is the maximum calculated total load within the control interval, and p max is the maximum charging power of the user's vehicle model. The calculation logic of D is as follows: In the formula, p i，τ is the charging power of the i-th electric vehicle user at time τ, A τ is the total load of the whole station at time v, τ0 is the current time, T i is the remaining charging time of the i-th user, T max is the maximum remaining charging time for all users in the station, D0 is the maximum load of the station in the historical time window, and D τ is the maximum load expected to be observed within the control time window; In formula (4), I i，τ is a zero-one control variable, and its integer variable control logic is as follows: In the formula, is the maximum charging power, is the maximum discharge power; For the flexible charging mode, the charging station controls the charging time and power in each period. The operator only guarantees that the declared energy requirements are completed before the user's declared departure time. Therefore, the following energy constraints are met: In the formula, is the initial cumulative charging amount of the i-th user, e i，t is the cumulative charging amount of the i-th user at time t, is the set of all elastic users, Δt is the charge-discharge interval, p i，t is the charging and discharging power of the i-th user at time t, is the total amount of charge of the i-th user at the time of leaving, η is the charge and discharge efficiency, and different efficiency factors are selected according to the current charge or discharge, SOC i，t is the SOC of user i at time t, SOC i，min and SOC i，max Respectively represent their minimum and maximum allowed values, is the total capacity of the power battery of the i-th user, is the total reported charging power of the i-th user.

5. The method for energy management of electric vehicle charging stations taking into account vehicle-grid interaction according to claim 4, characterized in that: The charging cost and physical-electrical constraints of the fast scenario are calculated using the established charging station minimization cost function optimization function. For the fast mode, the charging power of the new user will maintain the maximum charging power constant until the battery is full or the user leaves. For the remaining flexible users in the station, the charging curve is re-optimized and adjusted according to the overall charging station load after the new user is added. The charging cost function in this mode is: At the same time, the following energy constraints need to be met In the formula, is the set of all fast mode users, T j The charging stop time should be less than or equal to the departure time declared by the user. is the required electricity declared by user j, p j，t is the charging power of user j at time t, e j，t is the cumulative charging amount of user j at time t.

6. The method for energy management of electric vehicle charging stations taking into account vehicle-grid interaction according to claim 5, characterized in that: The established charging station minimizes the cost function optimization function, the charging cost of the departure scenario and the physical-electrical constraints of the station; if the user rejects the fast and flexible solutions and chooses to leave, the charging operation cost of the corresponding charging station is f LEAVE Set to 0 to impose no constraints on the optimization problem.

7. The method for energy management of electric vehicle charging stations taking into account vehicle-grid interaction according to claim 6, characterized in that: In addition to the constraints contained in equations (4)-(6) and (8), the optimization problem in equation (2) also considers the coupling relationship between price and selection probability in equation (1) and includes the following constraints:

8. The method for energy management of electric vehicle charging stations taking into account vehicle-grid interaction according to claim 3, characterized in that: Using the established charging station cost minimization optimization function, the following target and class-based programming methods are designed when implementing the calculation: design user classes and station information classes, and initialize a new target according to the class in each embodiment; the user class includes four methods: event generation, selection function, user information, and adding selection results; the class summary includes three methods: data analysis, checking charging pile availability, and profit calculation.

9. The method for energy management of electric vehicle charging stations taking into account vehicle-grid interaction according to claim 3, characterized in that: The elastic charging mode or the fast charging mode is used together with the established optimization function for minimizing the cost function of the charging station to solve the optimization problem, and the software interface options and the user behavior model are updated according to the real-time data, and the solver is called to solve the optimization problem; the charging station operator provides the user with electricity prices corresponding to different charging options through the software interface for the user to choose, and the price and user selection data are recorded in the operator's database. When the data is collected, the naive Bayes user behavior probability model calculation formula is repeated according to the existing data to update the user behavior model.

10. The method for energy management of electric vehicle charging stations taking into account vehicle-grid interaction according to claim 7, characterized in that: Using the established optimization function of minimizing the cost function of the charging station, a scenario-based optimization problem is established to determine the optimal control interval of the charging station, balance the calculation cost and ensure the rationality of the price and charging curve scheduling. D is the maximum total calculation load in the control interval, and the calculation logic is calculated according to formula (4) and formula (5).

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