An electric vehicle charging process optimization scheduling method based on queue theory

Abstract

This invention discloses an optimized scheduling method for electric vehicle (EV) charging based on queue theory. The EV charging process is divided into an input process, a queuing process, and a service process. When an EV connects to the power grid with a discrete Poisson probability distribution, it enters the queuing process. When the number of EVs connected to the grid is small or the grid load is low, EVs can charge directly without waiting. However, when the number of EVs connected to the grid is large or the grid load is at its peak, available grid power resources are limited, requiring queuing. This invention optimizes the queuing process, effectively balancing efficiency and fairness in the EV charging process, significantly reducing queue length and waiting time at charging stations.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle charging technology, specifically referring to an optimized scheduling method for the electric vehicle charging process based on queue theory. Background Technology

[0002] Electric vehicles, as a new mode of transportation, have been widely adopted due to their energy-saving, environmentally friendly, and low-carbon advantages. 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 stage of rapid development. As of February 2025, the total number of public charging piles in China reached 3.83 million, accounting for 70% of the global total.

[0003] However, some problems have also arisen during the operation of charging stations. Unlike refueling traditional gasoline cars, which typically takes only about 5 minutes, electric vehicles can be charged to 80% in as little as 30 minutes. This easily leads to long queues for electric vehicles upon arrival at charging stations. Generally, the current charging arrangements for electric vehicles mostly follow the traditional first-come, first-served principle. However, since different electric vehicles have different charging needs and varying remaining battery levels upon arrival at the charging station, this arrangement results in a large number of electric vehicles queuing for charging. This causes excessively long waiting times for electric vehicle owners and low utilization rates at charging stations, thus affecting their normal operation. Summary of the Invention

[0004] The purpose of this invention is to provide an optimized scheduling method for electric vehicle charging based on queue theory, which can reduce the queuing time of electric vehicles and improve the charging efficiency of charging stations.

[0005] To achieve the above objectives, this invention provides an optimized scheduling method for electric vehicle charging process based on queue theory. The electric vehicle charging process is divided into an input process, a queuing process, and a service process, and arrival rules, queuing rules, and charging service rules are applied to each process respectively.

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

[0007]

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

[0009] The queuing process refers to the fact that after an electric vehicle connects to the power grid, the grid's service capacity is mainly affected by the queuing system's service resources, namely, the grid power resources P. res The constraint, its expression is:

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

[0011] In equation (2), P all P represents the upper limit of the grid load power; load P represents the current power consumption of the power grid load. charge The total charging power for n electric vehicles currently charging in the power grid can be obtained from the following formula:

[0012]

[0013] In equation (3), P i charge The charging power of the i-th electric vehicle; T i start T i end Let be 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 charges independently at its respective charging station and leaves the service process after being fully charged. During this process, if there are no disturbances, the charging time T is... C for:

[0015]

[0016] In equation (4), The desired charging state set for the owner of the i-th electric vehicle; C represents the initial charging state of the i-th electric vehicle; i This refers to the battery capacity of electric vehicles.

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

[0018] Step 1: Calculate the number of electric vehicles that need to queue according to formula (5), 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 equation (5), n HPEV,arr This represents the number of electric vehicles that arrive at the charging station and select a higher-level queue; n LPEV,arr This represents the number of electric vehicles that arrive at the charging station and select a lower-level queue; n HPEV,dep This represents the number of electric vehicles that leave the high-level queue and enter the charging process; n LPEV,dep This represents the number of electric vehicles that leave the lower-level queue and enter the charging process; n HPEV,back This represents the number of electric vehicles that have returned to a higher-level queue after the charging process; n LPEV,back This indicates the number of electric vehicles that have returned to a lower-level queue after the charging process.

[0021] Step 2: Based on different user needs, users are divided into high-service levels and low-service levels. High-level queues have priority access to the service process, but they need 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 equations (6) and (7), n PEV,x For the xth electric vehicle to arrive, its arrival probability follows a discrete Poisson distribution, where α represents the proportion of users choosing the high-level queue and 1-α represents the proportion of users choosing the low-level queue.

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

[0026]

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

[0028] Step 4: Determine the net power P of the power grid NET Does >0 satisfy the condition?

[0029] Step 5: If the net power of the grid P NETA value greater than 0 indicates that the power grid has available power resources and can accept new electric vehicles for charging. The number of new electric vehicles that can be added at this time is the number of electric vehicles leaving the queue and entering the charging process, n. PEV,dep It can be obtained from the following formula:

[0030]

[0031] λ HPEV , λ LPEV λ represents the departure weighting coefficients for high- and low-level electric vehicles, respectively. HPEV It must be greater than λ LPEV ;

[0032] The algorithm jumps to step three to recalculate the net grid power P used for electric vehicle charging. NET ;

[0033] Step Six: If the net power of the grid P NET A value less than 0 indicates an overload in the power grid. To avoid this, the charging process of low-grade electric vehicles should be interrupted first.

[0034] Step 7: Determine whether all lower-level electric vehicles have returned to their respective queues;

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

[0036]

[0037] The algorithm jumps to step two;

[0038] Step Nine: If the condition is met, i.e., all low-level electric vehicles return to their respective queues, this still cannot alleviate the situation of limited net charging power in the grid, i.e., P NET If the value is less than 0, the charging process of the high-level electric vehicle needs to be interrupted and returned to the corresponding queue.

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

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

[0041]

[0042] The algorithm jumps to step two;

[0043] Step 12: If the condition is met, that is, all high-level electric vehicles return to the corresponding queue, the optimization ends. It is necessary to recalculate the number of electric vehicles that need to queue and select the high- and low-level queues.

[0044] The algorithm returns to step one.

[0045] As a further aspect of the present invention: the objective of the optimization process is to minimize the number of electric vehicles waiting in the queue, with the constraint being the net power of the power grid P. NET The fluctuation is the smallest.

[0046] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention divides the electric vehicle charging process into an input process, a queuing process, and a service process. When an electric vehicle connects to the power grid or microgrid according to a discrete Poisson distribution probability, it enters the queuing process. When the number of electric vehicles connected to the power 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 power grid is large or the grid load is at its peak, the available grid power resources are limited, and queuing is required. Optimizing the queuing process minimizes the number of electric vehicles waiting in the queue and minimizes the net power fluctuation of the power grid, thereby improving the charging efficiency of the charging station. Attached Figure Description

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

[0048] Figure 2 This is a flowchart illustrating the optimized scheduling of electric vehicle queuing processes based on queue theory, according to the present invention.

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

[0050] Figure 4 The graph shows a comparison of the power grid load fluctuation changes between the algorithm of this invention and the disordered scheduling algorithm, FCFS algorithm, and PAD algorithm, where (a) is the power grid load fluctuation change graph under weekday conditions; and (b) is the power grid load fluctuation change graph under rest day conditions. Detailed Implementation

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

[0052] like Figure 1As shown, this 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 arriving at the charging station, considering arrival rules; the queuing process refers to the electric vehicle entering the queue and waiting to be charged, considering queuing rules; and the service process refers to the electric vehicle beginning to receive charging service, considering charging service rules. Among these rules, the queuing rule is the most important. The electric vehicle queuing process is an optimization problem, with the optimization objective being to minimize the number of electric vehicles waiting in the queue, and the constraint being to minimize the fluctuation of the grid's net power PNET. Considering the differences in charging needs among different electric vehicles, to balance fairness and efficiency, this invention divides users into high and low service levels based on their needs. Higher-level queues have priority in entering the service process but require a higher price than other queues. Electric vehicle owners need to choose the appropriate queue based on their own needs. Furthermore, if there is insufficient available grid resources, the system will use a backlog rule to return some electric vehicles currently in the service process to the queue to re-queue for charging.

[0053] All electric vehicles arriving at a charging station to receive charging services are considered as a "queue". The electric vehicle charging process mainly consists of three parts: the input process, the queuing process, and the service process, which are respectively governed by arrival rules, queuing rules, and charging service rules.

[0054] ① Input process

[0055] Since the vast majority of electric vehicles are mainly used for commuting during the day, they will choose to go to charging stations to charge after get off work in the evening. The input process of electric vehicles queuing at charging stations can be set as a discrete Poisson distribution process, and its probability density function is shown in the following equation:

[0056]

[0057] In equation (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 power grid, the grid's service capacity is mainly affected by the queuing system's service resources, i.e., the grid's power resources P. res The constraint, its expression is:

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

[0061] In equation (2), P all P represents the upper limit of the grid load power;load P represents the current power consumption of the power grid load. charge The total charging power for n electric vehicles currently charging in the power grid can be obtained from the following formula:

[0062]

[0063] In equation (3), P i charge The charging power of the i-th electric vehicle; T i start T i end Let be the start time and end time of charging for the i-th electric vehicle, respectively.

[0064] Therefore, once the local grid load power limit P for electric vehicles is obtained... all Current power consumption P of the power grid load load The total charging power P of n electric vehicles currently charging in the power grid charge Then the power resource P of the power grid can be calculated. res This allows us to determine the number of electric vehicles available for service and the number that need to wait. During the queuing process, electric vehicles wait according to certain queuing rules and enter the service process sequentially when the power conditions are met. During this period, fluctuations in the grid load power or the departure of some electric vehicles after charging can lead to changes in the grid power resource P. pro Frequent changes occur, and when these changes cannot meet the needs of all electric vehicles in the service process, some electric vehicles in the service process will return to the queue to continue waiting for charging.

[0065] ③ Service process

[0066] Each electric vehicle entering the service process charges independently at its respective charging station and leaves the service process after being fully charged. During this process, if there are no disturbances, its charging time T is [not specified]. C for:

[0067]

[0068] In equation (4), The desired charging state set for the owner of the i-th electric vehicle; C represents the initial charging state of the i-th electric vehicle; i This refers to the battery capacity of electric vehicles.

[0069] Of these rules, the queuing rule is the most important. The electric vehicle queuing process is an optimization problem, the objective of which is to minimize the number of electric vehicles waiting in the queue, with the constraint being the net power of the grid, P. NETThe fluctuation is minimal. Considering the varying charging needs of different electric vehicles, to balance fairness and efficiency, we divide users into high and low service levels based on their needs. Generally, higher-level queues have priority access to the service process but require a higher price. Electric vehicle owners need to choose the appropriate queue based on their own needs. Furthermore, if there is insufficient available grid resources, the system will use a fallback rule to return some electric vehicles currently in service to the queue to rejoin the charging queue.

[0070] The following is combined Figure 2 Further explanation is needed.

[0071] Step 1: Calculate the number of electric vehicles that need to queue according to formula (5), 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 equation (5), n HPEV,arr This represents the number of electric vehicles that arrive at the charging station and select a higher-level queue; n LPEV,arr This represents the number of electric vehicles that arrive at the charging station and select a lower-level queue; n HPEV,dep This represents the number of electric vehicles that leave the high-level queue and enter the charging process; n LPEV,dep This represents the number of electric vehicles that leave the lower-level queue and enter the charging process; n HPEV,back This represents the number of electric vehicles that have returned to a higher-level queue after the charging process; n LPEV,back This indicates the number of electric vehicles that have been decommissioned from the charging process and returned to a lower-level queue.

[0074] Step Two: To balance fairness and efficiency, this invention categorizes users into high and low service levels based on their needs. Generally, higher-level queues have priority access to the service process but require a higher price. Therefore, electric vehicle users need to choose the appropriate queue based on their individual needs.

[0075] n HPEV,arr =α·n PEV,x (6)

[0076] n LPEV,arr = (1-α)·n PEV,x (7)

[0077] In equations (6) and (7), n PEV,xLet x be the electric vehicle that arrives. Its arrival probability follows a discrete Poisson distribution. α represents the proportion of users choosing the high-level queue, and 1-α represents the proportion of users choosing the low-level queue.

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

[0079]

[0080] In equation (6), P all P represents the upper limit of the power grid load. load This indicates the current power consumption of the power grid load; This indicates the total charging power of high-level electric vehicles; This indicates the total charging power of a low-level electric vehicle.

[0081] Step 4: Determine the net power P of the power grid NET Does >0 satisfy the condition?

[0082] Step 5: If the net power of the grid P NET A value greater than 0 indicates that the power grid has available power resources and can accept new electric vehicles for charging. The number of new electric vehicles that can be added at this time is the number of electric vehicles leaving the queue and entering the charging process, n. PEV,dep It can be obtained from the following formula:

[0083]

[0084] λ HPEV , λ LPEV These represent the departure weighting coefficients for high- and low-level electric vehicles, respectively. Generally, λ HPEV It must be greater than λ LPEV .

[0085] The algorithm jumps to step three to recalculate the net grid power P used for electric vehicle charging. NET ;

[0086] Step Six: If the net power of the grid P NET A value less than 0 indicates an overload in the power grid. To avoid this, the present invention first interrupts the charging process of low-level electric vehicles.

[0087] Step 7: Determine whether all lower-level electric vehicles have returned to their respective queues.

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

[0089]

[0090] The algorithm jumps to step two.

[0091] Step Nine: If the condition is met, i.e., all low-level electric vehicles return to their respective queues, this still cannot alleviate the situation of limited net charging power in the grid, i.e., P NET If the value is less than 0, the charging process for high-level electric vehicles needs to be interrupted and the vehicles returned to the corresponding queue.

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

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

[0094]

[0095] The algorithm jumps to step two.

[0096] Step 12: If the condition is met, that is, all high-level electric vehicles return to the corresponding queue, the optimization ends. It is necessary to recalculate the number of electric vehicles that need to queue and select the high- and low-level queues.

[0097] The algorithm returns to step one.

[0098] Example:

[0099] To verify the optimization effect, this invention establishes an optimized scheduling model for the electric vehicle charging process. This model consists of an input module, a queuing module, and a charging service optimized scheduling module, as follows: Figure 3 As 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 an entity (Activity) representing an electric vehicle according to Equation (1). The number of electric vehicles can be changed arbitrarily. The Equation (I) module is used to initially assign values ​​to the electric vehicles, setting their number, location, current battery level, and arrival time at the charging station, and dynamically updating the parameters.

[0102] Queuing module

[0103] In the queuing module, the highest and lowest priority electric vehicles in the queue are compared by the Decision module. Based on the algorithm above, the lower priority electric vehicles are removed from the queue through the Preemption operation. This enables higher priority electric vehicles to preempt services from lower priority electric vehicles when the available power of electric vehicles is limited. Electric vehicles removed from the queue return to the queue through the Merge module to wait. To ensure fairness, the removed electric vehicles will be given a certain priority level so that they will 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 all implemented through the selection module ("Select Item"). The selection of which input is determined based on the value received by the Select port. The value of the Select port is obtained by writing a program through the Equation module.

[0106] The algorithm proposed in this invention and the most commonly used first-come, first-served (FFS) algorithm are compared in the electric vehicle charging process optimization scheduling model. The model runs continuously for 24 hours on both rest days and weekdays.

[0107] Power grid load fluctuations

[0108] The changes in power grid load under different algorithm control are as follows: Figure 4 As shown. In Figure 4 The diagrams show the disordered scheduling algorithm, the FCFS algorithm, the PAD algorithm, and the algorithm of this invention. The thin black dashed 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 that area.

[0109] If electric vehicles are charged haphazardly—that is, without optimized scheduling of their charging services—a significant concentration of charging activity will occur when a large number of electric vehicles connect to the grid simultaneously, leading to a substantial increase in grid load. Figure 4As shown in (a), during peak electricity consumption periods (15:00-23:00), the grid load remains above the economic operating point for an extended period, exceeding the grid load ceiling from 17:00-21:00. To compensate for the imbalance between power supply and load demand, additional power generation resources are needed, inevitably increasing grid generation costs. If the shortfall is too large, it can significantly impact the safety of grid equipment. Conversely, during the off-peak period (3:00-8:00 AM), most electric vehicles finish charging, resulting in minimal load change compared to normal times. This further widens the peak-to-valley ratio, deteriorating the economic efficiency of grid operation. The FCFS algorithm, PAD algorithm, and the algorithm of this invention are used to optimize the scheduling of electric vehicle charging services. Figure 4 As can be seen from (a) and (b), when electric vehicles are charged intensively at night, their grid load mainly operates near the grid's optimal operating point of 2400 kW, avoiding prolonged high-load operation of the grid. Furthermore, scheduling the charging time of some electric vehicles to after 11:00 PM helps regulate the peak-to-valley ratio of the grid. This indicates that all three algorithms can mitigate the impact of peak electric vehicle charging on the grid load and regulate the peak-to-valley ratio. From the performance of these three algorithms, the charging curve of electric vehicles using the segmented control power weight factor allocation algorithm for optimized charging scheduling is more stable, the grid load operates at the grid's optimal operating point for a longer period during peak charging periods, and a larger number of electric vehicles are charging at midnight. Figure 4 As shown in (a) and (b), the grid load values ​​from 23:00 to 2:00 the next day indicate that the higher the load value, the more electric vehicles are participating in the dispatch. This allows for full utilization of the surplus electricity generated by renewable energy at night, which can effectively reduce the grid's power generation costs.

[0110] Electric vehicle charging wait time

[0111] For electric vehicle owners, the length of charging wait time is a primary concern. The algorithm proposed in this invention is compared with the first-come, first-served algorithm in terms of average queue length, maximum queue length, average waiting time, and maximum waiting time on weekdays and weekends. The comparison results are shown in Table 1.

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

[0113]

[0114] As can be seen from Table 1, regardless of whether it is a weekday or a weekend, 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 scheduling of electric vehicle charging processes based on queue theory, characterized in that, The electric vehicle charging process is divided into an input process, a queuing process, and a service process, and arrival rules, queuing rules, and charging service rules are applied respectively. The input process refers to the process of electric vehicles arriving at the power station and queuing. This input process is assumed to be a discrete Poisson distribution, and its probability density function is as follows: (1); In equation (1), k for t The number of electric vehicles requesting charging within a given time period; parameters for t The expected number of electric vehicles requesting charging within a given time period; The queuing process refers to the impact of queuing service resources, i.e., grid power resources, on the grid's service capacity after an electric vehicle connects to the grid. The constraint, its expression is: (2); In equation (2), This represents the upper limit of the power grid load. This represents the current power consumption of the power grid load. Charging in the power grid n Total charging power of electric vehicles in Taiwan; In this service process, each electric vehicle entering the service area charges independently at its respective charging station and leaves the service area after being fully charged. During this process, if there are no disturbances, the charging time is [not specified]. for: (3); In equation (3), For the first i The desired charging status set by the owner of an electric vehicle; For the first i The initial charging state of an electric vehicle; For electric vehicle battery capacity; For the first i The charging power of an electric vehicle; Optimize the queuing process by dividing queues into high-level and low-level queues, including the following steps: Step 1: Calculate the number of electric vehicles that need to queue according to formula (4), as follows: (4); In equation (4), This indicates the number of electric vehicles that have arrived at the charging station and selected a higher-level queue. This indicates the number of electric vehicles that arrive at the charging station and select a lower-level queue. This indicates the number of electric vehicles that have left the high-level queue and entered the charging process; This indicates the number of electric vehicles that have left the lower-level queue and entered the charging process; This indicates the number of electric vehicles that have returned to a higher-level queue after the charging process. This indicates the number of electric vehicles that have returned to a lower-level queue after the charging process. Step 2: Based on different user needs, users are divided into high-service levels and low-service levels. High-level queues have priority access to the service process, but they need to pay a higher price than other queues. Therefore, electric vehicle users need to choose the appropriate queue according to their own needs. (5); (6); In equations (5) and (6), For the first time to arrive x The arrival probability of a vehicle follows a discrete Poisson distribution. This indicates the percentage of users who choose higher-level queues. This indicates the proportion of users who selected lower-level queues; Step 3: Calculate the net grid power used for electric vehicle charging according to equation (7). P NET The formula is as follows: (7); In equation (7), This indicates the total charging power of high-level electric vehicles; This indicates the total charging power of low-level electric vehicles; Step 4: Determine the net power of the power grid P NET Does >0 satisfy the condition? Step 5: If the net power of the grid P NET A value greater than 0 indicates that the power grid has available power resources and can accept new electric vehicles for charging. The number of new electric vehicles that can be added at this time is the number of electric vehicles that leave the queue and enter the charging process. It can be obtained from the following formula: (8); (9); (10); , These represent the departure weighting coefficients for high- and low-grade electric vehicles, respectively. Greater than ; The algorithm jumps to step three to recalculate the net grid power used for electric vehicle charging. P NET ; Step Six: If the net power of the grid P NET A value less than 0 indicates an overload in the power grid. To avoid this, the charging process of low-grade electric vehicles should be interrupted first. Step 7: Determine whether all lower-level electric vehicles have returned to their respective queues; Step 8: If the conditions are not met, the corresponding number of lower-level electric vehicles will return to the corresponding queue. It can be obtained from the following formula: (11); The algorithm jumps to step two; Step Nine: If the condition is met, i.e., all lower-level electric vehicles return to their respective queues, this still cannot alleviate the situation of limited net charging power in the power grid. If so, 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 their respective queues; Step 11: If the conditions are not met, the corresponding number of high-level electric vehicles will return to the corresponding queue. It can be obtained from the following formula: (12); The algorithm jumps to step two; Step 12: If the condition is met, that is, all high-level electric vehicles return to the corresponding queue, the optimization ends. It is necessary to recalculate the number of electric vehicles that need to queue and select the high- and low-level queues. The algorithm returns to step one.

2. The electric vehicle charging process optimization scheduling method based on queue theory according to claim 1, characterized in that, The goal of the optimization process is to minimize the number of electric vehicles waiting in the queue, with the constraint being the net power of the grid. P NET The fluctuation is the smallest.

Images