Electric vehicle charging process optimization scheduling method based on queue theory

Abstract

The invention discloses an electric vehicle charging process optimization scheduling method based on a queue theory. The charging process of the electric vehicles is divided into an input process, a queuing process and a service process, and when the electric vehicles access a power grid according to the probability of discrete Poisson distribution, the queuing process is started. When the number of the electric vehicles connected to the power grid is small or the load of the power grid is low, the electric vehicles can be directly charged without waiting; and when the number of the electric vehicles accessed to the power grid is large or the load of the power grid is at a peak value, available power resources of the power grid are limited, and queuing waiting is needed. By optimizing the queuing process, the efficiency and fairness of the charging process of the electric vehicles can be effectively considered, and the queuing length and waiting time of the electric vehicles in the charging station can be greatly reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle charging, and specifically refers to an electric vehicle charging process optimization scheduling method based on queue theory. Background Art

[0002] As a new mode of transportation, electric vehicles are gaining widespread adoption due to their energy-saving, environmentally friendly, and low-carbon nature. According to relevant statistics, my country's electric vehicle sales reached 12.866 million units in 2024, a year-on-year increase of 35.5%, ranking first in the world for nine consecutive years. The construction of electric vehicle charging stations has also entered a period of rapid development. By February 2025, the total number of public charging stations in China will reach 3.83 million, accounting for 70% of the global total.

[0003] However, some issues have arisen during the operation of charging stations. Unlike traditional fuel vehicles, which typically refuel in about five minutes, electric vehicles can charge to 80% in as little as 30 minutes. This can lead to long queues for electric vehicles arriving at charging stations. Electric vehicle charging is typically arranged on a first-come, first-served basis. However, due to the varying charging requirements of different electric vehicles and the varying remaining power levels upon arrival, this arrangement can lead to long queues of electric vehicles, resulting in extended wait times for electric vehicle owners and inefficient charging station utilization, impacting the normal operation of charging stations. Summary of the Invention

[0004] The purpose of the present invention is to provide an electric vehicle charging process optimization scheduling method based on queue theory, which can reduce the waiting time of electric vehicles in queues and improve the charging efficiency of charging stations.

[0005] To achieve the above objectives, the present invention provides an electric vehicle charging process optimization scheduling method based on queue theory, which divides the electric vehicle charging process into input process, queuing process, and service process, and applies arrival rules, queuing rules, and charging service rules respectively.

[0006] As a further solution of the present invention, the input process, i.e., the input process of electric vehicles arriving at the power station and queuing, is set to a discrete Poisson distribution, and its probability density function is as follows:

[0007]

[0008] In formula (1), k is the number of electric vehicles requesting charging within time t; parameter λ1 is the expected number of electric vehicles requesting charging within time t;

[0009] The queuing process is that when electric vehicles are connected to the power grid, the service capacity of the power grid is mainly affected by the service resources of the queuing system, that is, the power resources P of the power grid. res Constraint, its expression is:

[0010] P res =P all -P load -P charge (2)

[0011] In formula (2), P all is the upper limit of grid load power; P load is the current power grid load power; P charge is the total charging power of n electric vehicles being charged in the power grid, which can be obtained by the following formula:

[0012]

[0013] In formula (3), P i charge is the charging power of the i-th electric vehicle; T i start 、T i end are the start time and end time of charging for the i-th electric vehicle respectively;

[0014] In the service process, each electric vehicle entering the service process is charged independently at each charging pile and leaves the service process after being fully charged. In this process, if there is no interference, the charging time T C for:

[0015]

[0016] In formula (4), The expected state of charge set for the owner of the i-th electric vehicle; is the initial charging state of the i-th electric vehicle; C i The battery capacity of electric vehicles.

[0017] As a further solution of the present invention, the queuing process is optimized and the queues are divided into high-level queues and low-level queues, including the following steps:

[0018] Step 1: Calculate the number of electric vehicles that need to queue according to formula (5). The formula is as follows:

[0019] n=n HPEV,arr +n LPEV,arr -n HPEV,dep -n LPEV,dep +n HPEV,back +n LPEV,back (5)

[0020] In formula (5), n HPEV,arr represents the number of electric vehicles that arrive at the charging station and choose the high-level queue; n LPEV,arr represents the number of electric vehicles that arrive at the charging station and choose the low-level queue; n HPEV,dep represents the number of electric vehicles that leave the high-level queue and enter the charging process; n LPEV,dep represents the number of electric vehicles that leave the low-level queue and enter the charging process; n HPEV,back represents the number of electric vehicles that return to the high-level queue from the charging process; n LPEV,back represents the number of EVs that are withdrawn from the charging process and returned to the lower-level queue;

[0021] Step 2: Based on different user needs, the queues are divided into high-service and low-service levels. The high-service level queue has the right to enter the service process first, but needs to pay a higher price than other queues. Therefore, electric vehicle users need to choose the appropriate queue according to their own needs;

[0022] n HPEV,arr =α·n PEV,x (6)

[0023] n LPEV,arr =(1-α)·n PEV,x (7)

[0024] In formulas (6) and (7), n PEV,x is the xth electric car that arrives, and its arrival probability conforms to the discrete Poisson distribution. α represents the proportion of users who choose the high-level queue, and 1-α represents the proportion of users who choose the low-level queue.

[0025] Step 3: Calculate the net power P in the grid used for charging electric vehicles according to formula (8): NET , the formula is as follows:

[0026]

[0027] In formula (6), P all Indicates the upper limit of grid load power, P load Indicates the current power consumption of the grid load; Indicates the total charging power of high-level electric vehicles; Indicates the total charging power of low-level electric vehicles;

[0028] Step 4: Determine the net power P of the grid NET >0 is satisfied?

[0029] Step 5: If the net power of the grid P NET>0, indicating that the grid has available power resources and can accept new electric vehicles for charging. The number of new electric vehicles allowed at this time is the number of electric vehicles that leave the queue and enter the charging process n PEV,dep It can be obtained by the following formula:

[0030]

[0031] λ HPEV ,λ LPEV Represent the departure weight coefficients of high-level and low-level electric vehicles, λ HPEV Greater than λ LPEV ;

[0032] The algorithm jumps to step 3 and recalculates the net power P in the grid for charging electric vehicles. NET ;

[0033] Step 6: If the net power of the grid P NET <0, indicating that the power grid is overloaded. To avoid this phenomenon, the charging process of low-level electric vehicles is first interrupted;

[0034] Step 7: Determine whether all low-level electric vehicles have returned to the corresponding queues;

[0035] Step 8: If the conditions are not met, the corresponding number of low-level electric vehicles return to the corresponding queue, the number of which is n LPEV,back It can be obtained by the following formula:

[0036]

[0037] The algorithm jumps to step 2;

[0038] Step 9: If the conditions are met, that is, all low-level electric vehicles return to the corresponding queues, then the situation of limited net charging power in the power grid cannot be alleviated, that is, P NET <0, the charging process of high-level electric vehicles needs to be interrupted and returned to the corresponding queue;

[0039] Step 10: Determine whether all high-level electric vehicles have returned to the corresponding queues;

[0040] Step 11: If the conditions are not met, the corresponding number of high-level electric vehicles return to the corresponding queue, the number of which is n HPEV,back It can be obtained by the following formula:

[0041]

[0042] The algorithm jumps to step 2;

[0043] Step 12: If the conditions are met, that is, all high-level electric vehicles are returned to the corresponding queues, the optimization ends, and the number of electric vehicles that need to queue needs to be recalculated, and the high and low-level queues are selected;

[0044] The algorithm returns to step 1.

[0045] As a further solution of the present invention: the goal of the optimization process is to minimize the number of electric vehicles waiting in line, and the constraint condition is the net power of the grid P NET The fluctuation is minimal.

[0046] Compared with existing technologies, the present invention has the following beneficial effects: It divides the electric vehicle charging process into the input process, the queuing process, and the service process. When an electric vehicle is connected to the grid or microgrid according to the probability of a discrete Poisson distribution, it enters the queuing process. When the number of electric vehicles connected to the grid is small or the grid load is low, electric vehicles can charge directly without waiting. However, when the number of electric vehicles connected to the grid is large or the grid load is at its peak, available grid power resources are limited, and queuing is required. This queuing process is optimized to minimize the number of electric vehicles waiting in line and the fluctuation of the net grid power, thereby improving the charging efficiency of the charging station. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a structural diagram of an electric vehicle charging process optimization scheduling method based on queue theory in the present invention.

[0048] Figure 2 The present invention provides a flow chart of optimizing the scheduling of electric vehicle queuing process based on queue theory.

[0049] Figure 3 The present invention provides an optimized scheduling model for electric vehicle queuing process based on queue theory.

[0050] Figure 4 This is a comparison diagram of the grid load fluctuation changes of the algorithm of the present invention and the disordered scheduling algorithm, FCFS algorithm, and PAD algorithm, where (a) is the grid load fluctuation change diagram under weekdays; (b) is the grid load fluctuation change diagram under rest days. DETAILED DESCRIPTION

[0051] The present invention will be further described below with reference to the accompanying drawings.

[0052] like Figure 1As shown, the present invention divides the electric vehicle charging process into three parts: the input process, the queuing process, and the service process. The input process refers to the electric vehicle's arrival at the charging station, which takes into account arrival rules; the queuing process refers to the process when the electric vehicle enters the queue and waits for charging, which takes into account queuing rules; and the service process refers to the electric vehicle's initial charging service, which takes into account charging service rules. Of these rules, the queuing rule is the most important. The electric vehicle queuing process is an optimization problem, the optimization objective of which is to minimize the number of electric vehicles waiting in the queue, and the constraint is to minimize fluctuations in the grid's net power, PNET. Considering the varying charging needs of different electric vehicles, and to balance fairness and efficiency, the present invention divides the charging process into high and low service levels based on user needs. Queues in higher levels have priority access to the service process, but they must pay a higher price than other queues. Electric vehicle owners need to choose the appropriate queue based on their needs. Furthermore, if grid resources are insufficient, the system will implement a return rule to return some electric vehicles currently in service to the queue and wait again for charging.

[0053] All electric vehicles arriving at a charging station ready to receive charging services are considered a "queue." The electric vehicle charging process mainly consists of three parts: the input process, the queue process, and the service process. Arrival rules, queue rules, and charging service rules apply to them respectively.

[0054] ① Input process

[0055] Since most electric vehicles are used for commuting during the day and will go to charging stations to charge after get off work in the evening, the input process of electric vehicles arriving at charging stations and queuing can be set as a discrete Poisson distribution process, and its probability density function is shown as follows:

[0056]

[0057] In formula (1), k is the number of electric vehicles requesting charging within time t; parameter λ1 is the expected number of electric vehicles requesting charging within time t.

[0058] ②Queuing process

[0059] When electric vehicles are connected to the grid, the grid's service capacity is mainly affected by the queuing system's service resources, that is, the grid power resources P res Constraint, its expression is:

[0060] P res =P all -P load -P charge (2)

[0061] In formula (2), P all is the upper limit of grid load power; Pload is the current power grid load power; P charge is the total charging power of n electric vehicles being charged in the power grid, which can be obtained by the following formula:

[0062]

[0063] In formula (3), P i charge is the charging power of the i-th electric vehicle; T i start 、T i end are the start time and end time of charging for the i-th electric vehicle respectively.

[0064] Therefore, once the upper limit of the local electric vehicle grid load power P is obtained all , Current grid load power P load , the total charging power P of n electric vehicles being charged in the power grid charge , we can calculate the power resource P of the power grid res , and then find the number of electric vehicles that can provide services and the number that need to wait. During the queuing process, electric vehicles wait according to certain queuing rules and enter the service process in turn when the power conditions are met. During this period, the load power of the power grid will fluctuate, or some electric vehicles may complete charging and leave the service process, which will cause the power resource P of the power grid to fluctuate. pro There are frequent changes, and when such changes cannot satisfy all electric vehicles in the service process, some electric vehicles in the service process will return to the queue and continue to wait in line for charging.

[0065] ③Service process

[0066] Each electric vehicle entering the service process is charged independently at each charging pile and leaves the service process after being fully charged. During this process, if there is no interference, the charging time T C for:

[0067]

[0068] In formula (4), The expected state of charge set for the owner of the i-th electric vehicle; is the initial charging state of the i-th electric vehicle; C i The battery capacity of electric vehicles.

[0069] Among these rules, the queuing rule is the most important. The electric vehicle queuing process is an optimization problem, whose optimization goal is to minimize the number of electric vehicles waiting in line, and the constraint condition is the net power of the grid P NETMinimize fluctuations. Considering the varying charging needs of different EVs, and to balance fairness and efficiency, we categorize users into high and low service levels based on their needs. Generally speaking, higher-level queues enjoy priority access, but pay a higher price than other queues. EV owners should choose the appropriate queue based on their needs. Furthermore, if grid resources are insufficient, the system will implement a rollback policy to return some EVs currently in service to the queue to re-queue for charging.

[0070] The following combination Figure 2 Further explanation is provided.

[0071] Step 1: Calculate the number of electric vehicles that need to queue according to formula (5). The formula is as follows:

[0072] n=n HPEV,arr +n LPEV,arr -n HPEV,dep -n LPEV,dep +n HPEV,back +n LPEV,back (5)

[0073] In formula (5), n HPEV,arr represents the number of electric vehicles that arrive at the charging station and choose the high-level queue; n LPEV,arr represents the number of electric vehicles that arrive at the charging station and choose the low-level queue; n HPEV,dep represents the number of electric vehicles that leave the high-level queue and enter the charging process; n LPEV,dep represents the number of electric vehicles that leave the low-level queue and enter the charging process; n HPEV,back represents the number of electric vehicles that return to the high-level queue from the charging process; n LPEV,back Indicates the number of electric vehicles that are withdrawn from the charging process and returned to the lower-level queue queue.

[0074] Step 2: To balance fairness and efficiency, this invention divides the queues into high and low service levels based on user needs. Generally speaking, high-level queues have priority access to the service process, but they must pay a higher price than other queues. Therefore, electric vehicle users need to choose the appropriate queue based on their needs.

[0075] n HPEV,arr =α·n PEV,x (6)

[0076] n LPEV,arr =(1-α)·n PEV,x (7)

[0077] In formulas (6) and (7), n PEV,xis the xth electric car that arrives, and its arrival probability conforms to the discrete Poisson distribution. α represents the proportion of users choosing high-level queues, and 1-α represents the proportion of users choosing low-level queues.

[0078] Step 3: Calculate the net power P in the grid used for charging electric vehicles according to formula (8): NET , the formula is as follows:

[0079]

[0080] In formula (6), P all Indicates the upper limit of grid load power, P load Indicates the current power consumption of the grid load; Indicates the total charging power of high-level electric vehicles; Indicates the total charging power of low-level electric vehicles.

[0081] Step 4: Determine the net power P of the grid NET >0 is satisfied.

[0082] Step 5: If the net power of the grid P NET >0, indicating that the grid has available power resources and can accept new electric vehicles for charging. The number of new electric vehicles allowed at this time is the number of electric vehicles that leave the queue and enter the charging process n PEV,dep It can be obtained by the following formula:

[0083]

[0084] λ HPEV ,λ LPEV Represent the departure weight coefficients of high-level and low-level electric vehicles respectively. Generally speaking, λ HPEV Greater than λ LPEV .

[0085] The algorithm jumps to step 3 and recalculates the net power P in the grid for charging electric vehicles. NET ;

[0086] Step 6: If the net power of the grid P NET <0, indicating that the power grid is overloaded. To avoid this phenomenon, the present invention first interrupts the charging process of low-level electric vehicles.

[0087] Step 7: Determine whether all low-level electric vehicles have returned to the corresponding queues.

[0088] Step 8: If the conditions are not met, the corresponding number of low-level electric vehicles return to the corresponding queue, the number of which is n LPEV,back It can be obtained by the following formula:

[0089]

[0090] The algorithm jumps to step 2.

[0091] Step 9: If the conditions are met, that is, all low-level electric vehicles return to the corresponding queues, then the situation of limited net charging power in the power grid cannot be alleviated, that is, P NET <0, the charging process of the high-level electric vehicle needs to be interrupted and returned to the corresponding queue.

[0092] Step 10: Determine whether all high-level electric vehicles have returned to the corresponding queues.

[0093] Step 11: If the conditions are not met, the corresponding number of high-level electric vehicles return to the corresponding queue, the number of which is n HPEV,back It can be obtained by the following formula:

[0094]

[0095] The algorithm jumps to step 2.

[0096] Step 12: If the conditions are met, that is, all high-level electric vehicles are returned to the corresponding queues, the optimization ends at this time, and the number of electric vehicles that need to queue needs to be recalculated and the high and low level queues are selected.

[0097] The algorithm returns to step 1.

[0098] Example:

[0099] In order to verify the optimization effect, the present invention builds an electric vehicle charging process optimization scheduling model, which consists of an input module, a queuing module and a charging service optimization scheduling module. Figure 3 shown.

[0100] Input Module

[0101] Considering the charging habits of electric vehicles, the arrival of an electric vehicle at a charging station can be treated as a discrete event. Based on this, when starting the simulation, the Create module in the Item library can be used to generate entities (Activities) representing electric vehicles according to Equation (1). The number of electric vehicles can be changed at will. The Equation (I) module is used to perform initial assignments on the electric vehicles, setting the electric vehicle number, the location of the electric vehicle, the current charge level of the electric vehicle, the time of arrival at the charging station, and dynamically updating parameters.

[0102] Queuing Module

[0103] In the queuing module, the highest and lowest levels of electric vehicles in the queue are compared through the Decision module, and the low-level electric vehicles are removed from the queue through the Preemption operation according to the above algorithm. This enables high-level electric vehicles to preempt the service of low-level electric vehicles under the condition of limited available power of electric vehicles. The electric vehicles removed from the queue return to the queue through the Merge module to wait. To ensure fairness, the electric vehicles removed from the queue will be given a certain priority level so that they can be at the head of the queue when they return to the queue and re-enter the service process as soon as possible.

[0104] Charging service optimization scheduling module

[0105] In the charging service optimization scheduling module, these scheduling algorithms are implemented through the selection module ("Select Item"), which determines which input to select based on the value received by the Select port. The value of the Select port is obtained by programming through the Equation module.

[0106] The algorithm proposed in the present invention and the most commonly used first-come, first-served algorithm were used in the optimization scheduling model for the electric vehicle charging process for comparison. The model was run continuously for 24 hours on weekends and weekdays.

[0107] Grid load fluctuations and changes

[0108] The changes in grid load fluctuations under different control algorithms are as follows: Figure 4 As shown. Figure 4 In the figure, the disordered scheduling algorithm, FCFS algorithm, PAD algorithm, and the algorithm of the present invention are respectively shown. The thin black dotted line represents the optimal operating point of the power grid, and the thick black solid line represents the upper limit of the original load power of the power grid in the area.

[0109] If electric vehicles are charged in an unordered manner, that is, if their charging service process is not optimized and scheduled, when a large number of electric vehicles are connected to the power grid and charged at the same time, their charging process will show obvious time concentration, which will cause a significant increase in the load on the power grid. Figure 4As can be seen from (a), during the peak period of electricity consumption from 15:00 to 23:00, the grid load is above the grid economic operation point for a long time, and even exceeds the grid load upper limit from 17:00 to 21:00. In order to make up for the contradiction between power supply and load demand, new power generation resources need to be added, which will inevitably increase the grid power generation cost. If the gap is too large, it will also have a great impact on the safety of grid equipment. During the low electricity consumption period from 3:00 to 8:00 in the morning, since most electric vehicles finish charging at this time, the load has no significant change compared to usual times, which makes the peak-to-valley ratio further increase and the economy of the grid operation worse. The FCFS algorithm, PAD algorithm and the algorithm of the present invention are used to optimize the scheduling of the charging service process of electric vehicles. Figure 4 It can be seen from (a) and (b) that when electric vehicles are charged in a centralized manner at night, their grid load mainly operates near the optimal grid operation point of 2400KW, which prevents the grid from operating at high load for a long time; in addition, the charging time of some electric vehicles is scheduled to after 23:00 at night, which plays a role in regulating the peak-to-valley ratio of the grid. This shows that the three algorithms can all play a role in smoothing the impact of the peak of electric vehicle charging on the grid load and regulating the peak-to-valley ratio of the grid. Judging from the performance of the three algorithms, the charging curve of electric vehicles using the segmented control power weight factor allocation algorithm for charging optimization scheduling is smoother, and the grid load works at the optimal grid operation point for a longer time during the charging peak period. In addition, there are more electric vehicles charging at midnight, which is Figure 4 As can be seen from the grid load values ​​from 23:00 to 2:00 the next day shown in (a) and (b), the higher the load value, the more electric vehicles are involved in the dispatch, which can make full use of the excess electricity generated by renewable energy at night and effectively reduce the power generation cost of the grid.

[0110] Electric vehicle charging waiting time

[0111] For electric car owners, they are more concerned about the length of the charging wait time. The average queue length, maximum queue length, average waiting time, and maximum waiting time of the proposed algorithm on weekdays and weekends are compared with the first-come, first-served algorithm. The comparison results are shown in Table 1.

[0112] Table 1 Comparison of electric vehicle charging waiting time under different control algorithms

[0113]

[0114] As can be seen from Table 1, whether on weekdays or weekends, the algorithm proposed in this invention outperforms the first-come-first-served algorithm in key indicators such as average queue length, maximum queue length, average waiting time and maximum waiting time.

Claims

1. A method for optimizing the charging process of electric vehicles based on queue theory, characterized in that: The electric vehicle charging process is divided into input process, queuing process and service process, and arrival rules, queuing rules and charging service rules are applied respectively.

2. The method for optimizing the charging process of electric vehicles based on queue theory according to claim 1, characterized in that: The input process is the input process of electric vehicles arriving at the power station and queuing. The input process is set to a discrete Poisson distribution, and its probability density function is as follows: In formula (1), k is the number of electric vehicles requesting charging within time t; parameter λ1 is the expected number of electric vehicles requesting charging within time t; The queuing process is when electric vehicles are connected to the grid, and the grid service capacity is affected by the queuing process service resources, that is, the grid power resources P res Constraint, its expression is: P res =P all -P load -P charge (2) In formula (2), P all is the upper limit of grid load power; P load is the current power grid load power; P charge is the total charging power of n electric vehicles being charged in the power grid, which can be obtained by the following formula: In formula (3), is the charging power of the i-th electric vehicle; are the start time and end time of charging for the i-th electric vehicle respectively; In the service process, each electric vehicle entering the service process is charged independently at each charging pile and leaves the service process after being fully charged. In this process, if there is no interference, the charging time T C for: In formula (4), The expected state of charge set for the owner of the i-th electric vehicle; is the initial charging state of the i-th electric vehicle; C i The battery capacity of electric vehicles.

3. The method for optimizing the charging process of electric vehicles based on queue theory according to claim 2, characterized in that: The queuing process is optimized and the queues are divided into high-level queues and low-level queues, including the following steps: Step 1: Calculate the number of electric vehicles that need to queue according to formula (5). The formula is as follows: n=n HPEV,arr +n LPEV,arr -n HPEV,dep -n LPEV,dep +n HPEV,back +n LPEV,back (5) In formula (5), n HPEV,arr represents the number of electric vehicles that arrive at the charging station and choose the high-level queue; n LPEV,arr represents the number of electric vehicles that arrive at the charging station and choose the low-level queue; n HPEV,dep represents the number of electric vehicles that leave the high-level queue and enter the charging process; n LPEV,dep represents the number of electric vehicles that leave the low-level queue and enter the charging process; n HPEV,back represents the number of electric vehicles that return to the high-level queue from the charging process; n LPEV,back represents the number of EVs that are withdrawn from the charging process and returned to the lower-level queue; Step 2: Based on different user needs, the queues are divided into high-service and low-service levels. The high-service level queue has the right to enter the service process first, but needs to pay a higher price than other queues. Therefore, electric vehicle users need to choose the appropriate queue according to their own needs; n HPEV,arr =α·n PEV,x (6) n LPEV,arr =(1-α)·n PEV,x (7) In formulas (6) and (7), n PEV,x is the xth electric car that arrives, and its arrival probability conforms to the discrete Poisson distribution. α represents the proportion of users who choose the high-level queue, and 1-α represents the proportion of users who choose the low-level queue. Step 3: Calculate the net power P in the grid used for charging electric vehicles according to formula (8): NET , the formula is as follows: In formula (6), Indicates the total charging power of high-level electric vehicles; Indicates the total charging power of low-level electric vehicles; Step 4: Determine the net power P of the grid NET >0 is satisfied? Step 5: If the net power of the grid P NET >0, indicating that the grid has available power resources and can accept new electric vehicles for charging. The number of new electric vehicles allowed at this time is the number of electric vehicles that leave the queue and enter the charging process n PEV,dep It can be obtained by the following formula: λ HPEV ,λ LPEV Represent the departure weight coefficients of high-level and low-level electric vehicles, λ HPEV Greater than λ LPEV ; The algorithm jumps to step 3 and recalculates the net power P in the grid for charging electric vehicles. NET ; Step 6: If the net power of the grid P NET <0, indicating that the power grid is overloaded. To avoid this phenomenon, the charging process of low-level electric vehicles is first interrupted; Step 7: Determine whether all low-level electric vehicles have returned to the corresponding queues; Step 8: If the conditions are not met, the corresponding number of low-level electric vehicles return to the corresponding queue, the number of which is n LPEV,back It can be obtained by the following formula: The algorithm jumps to step 2; Step 9: If the conditions are met, that is, all low-level electric vehicles return to the corresponding queues, then the situation of limited net charging power in the power grid cannot be alleviated, that is, P NET <0, the charging process of high-level electric vehicles needs to be interrupted and returned to the corresponding queue; Step 10: Determine whether all high-level electric vehicles have returned to the corresponding queues; Step 11: If the conditions are not met, the corresponding number of high-level electric vehicles return to the corresponding queue, the number of which is n HPEV,back It can be obtained by the following formula: The algorithm jumps to step 2; Step 12: If the conditions are met, that is, all high-level electric vehicles are returned to the corresponding queues, the optimization ends, and the number of electric vehicles that need to queue needs to be recalculated, and the high and low-level queues are selected; The algorithm returns to step 1.

4. The method for optimizing the charging process of electric vehicles based on queue theory according to claim 3 is characterized in that: The goal of the optimization process is to minimize the number of electric vehicles waiting in line, and the constraint is the net power of the grid P NET The fluctuation is minimal.

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