A mobile energy storage vehicle scheduling optimization method based on charging station demand and traffic network equilibrium

Abstract

The application is a mobile energy storage vehicle scheduling optimization method based on charging station demand and traffic network balance, belonging to the technical problem of energy scheduling, comprising the following steps: S1: establishing a target function to solve the charging station corresponding to the minimum scheduling cost of a mobile energy storage vehicle; S2: obtaining the power gap value of each charging station, and recording the charging station that can fill the power gap mobile energy storage vehicle combination as a superior charging station, and vice versa as an inferior charging station; S3: selecting the combination with the minimum scheduling cost and scheduling to the superior charging station; S4: all mobile energy storage vehicles of other combinations with non-minimum scheduling cost under all superior charging stations are attributed to set Q; S5: the inferior charging stations are sorted, and the mobile energy storage vehicles in set Q are sequentially distributed to each inferior charging station according to the power gap from large to small. The method can schedule part of the mobile energy storage vehicles of the superior charging station to the inferior charging station, fill the power of the inferior charging station, and make the scheduling more reasonable.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of energy scheduling, and particularly relates to a mobile energy storage vehicle scheduling optimization method based on charging station demand and traffic network balance. BACKGROUND

[0002] With the development of electric vehicles, the demand for power supply of charging stations is also increasing. During the peak period of electricity consumption, the charging stations will have a power gap, and the charging stations will be in a situation of insufficient power. When the charging stations are in a situation of insufficient power, the mobile energy storage vehicle can be used as a way to supply power to the charging stations. The mobile energy storage vehicle can move between charging stations that need power. When the charging stations have a power gap, the power of the mobile energy storage vehicle itself can supplement the power of the charging stations.

[0003] The current method of scheduling mobile energy storage vehicles to charging stations is to minimize the scheduling cost of the mobile energy storage vehicles to the charging stations. By setting constraint conditions, the scheduling cost of a mobile energy storage vehicle to a charging station is iteratively calculated, and the mobile energy storage vehicle is sent to the charging station with the lowest scheduling cost. For example, charging station A and charging station B have a power gap and need mobile energy storage vehicles to supplement power. There are currently four mobile energy storage vehicles that can supply power to charging station A or charging station B. After the current scheduling method is calculated, it is assumed that the scheduling cost of the first three mobile energy storage vehicles to charging station A is lower than that to charging station B. The scheduling cost of the last mobile energy storage vehicle to charging station B is lower than that to charging station A. According to the current scheduling method, the first three mobile energy storage vehicles are scheduled to charging station A, and the last mobile energy storage vehicle is scheduled to charging station B.

[0004] However, in actual situations, the power gap of a charging station is limited. If too many mobile energy storage vehicles go to the charging station, the total power of all mobile energy storage vehicles will far exceed the power gap of the charging station, causing the power of the mobile energy storage vehicles to accumulate at the charging station. As mentioned above, the first three mobile energy storage vehicles all go to charging station A, but charging station A may only need two mobile energy storage vehicles to fill the power gap. The extra mobile energy storage vehicle does not play a role in supplying power at charging station A, causing unreasonable energy distribution of the mobile energy storage vehicle at charging station A. SUMMARY

[0005] The mobile energy storage vehicle scheduling optimization method based on charging station demand and traffic network balance of the application can rationalize the scheduling method of mobile energy storage vehicles, avoid the accumulation of mobile energy storage vehicles at a charging station, and cause unreasonable energy distribution.

[0006] The mobile energy storage vehicle scheduling optimization method based on charging station demand and traffic network balance of the application comprises the following steps:

[0007] S1: Establish a target function, set traffic network constraints, charging station and mobile energy storage vehicle constraints, solve the minimum scheduling cost of a mobile energy storage vehicle corresponding to the charging station by iteration; record the mobile energy storage vehicle at the charging station with the minimum scheduling cost;

[0008] S2: Obtain the power gap value of each charging station from the scheduling center, calculate the mobile energy storage vehicle combination that can fill the power gap of the charging station for the mobile energy storage vehicle under the charging station; the charging station with the mobile energy storage vehicle combination that can fill the power gap is recorded as a superior charging station, and the charging station without the mobile energy storage vehicle combination that can fill the power gap is recorded as an inferior charging station; the meaning of filling is that the sum of the power of all mobile energy storage vehicles in the combination is greater than or equal to the power gap value of the charging station;

[0009] S3: For any superior charging station, calculate the scheduling cost of each combination under the superior charging station according to step S1, select the combination with the minimum scheduling cost and schedule it to the superior charging station; for any inferior charging station, schedule the mobile energy storage vehicle under the inferior charging station to the corresponding inferior charging station;

[0010] S4: All mobile energy storage vehicles under the superior charging station, other than the combination with the minimum scheduling cost, are included in set Q;

[0011] S5: Sort the inferior charging stations by power gap size, and distribute the mobile energy storage vehicles in set Q to the inferior charging stations until all mobile energy storage vehicles are distributed or until all inferior charging stations have no power gap.

[0012] Further, step S5 includes the following steps:

[0013] S5.1: Sort the inferior charging stations by power gap from large to small, and sort them as set C; let there be H inferior charging stations, and set C is represented as: C={B1, B2, …, Bj, …, BH}; ; H is a positive integer;

[0014] S5.2: Calculate the scheduling cost of all mobile energy storage vehicles in set Q to inferior charging station B1; then sort the mobile energy storage vehicles in set Q by scheduling cost from small to large;

[0015] S5.3: Schedule the mobile energy storage vehicles in set Q to inferior charging station B1 in order of scheduling cost from small to large, and update set Q once for each mobile energy storage vehicle; the meaning of updating set Q is that when a mobile energy storage vehicle is scheduled to an inferior charging station, the mobile energy storage vehicle is deleted from set Q;

[0016] If the inferior charging station B1 fills the power gap or there is no mobile energy storage vehicle in set Q, stop scheduling mobile energy storage vehicles to B1;

[0017] S5.4: After stopping the dispatch of mobile energy storage vehicles to the inferior charging station B1, update the inferior charging stations in set C; the meaning of updating set C is: if the charging station fills the power gap, the inferior charging station is deleted from set C; if the charging station does not fill the power gap, the inferior charging station is not deleted from set C.

[0018] S5.5: If there are mobile energy storage vehicles in set Q and there are substandard charging stations in set C, continue to recalculate the scheduling costs of all mobile energy storage vehicles in set Q to Bj in set C; and reorder the mobile energy storage vehicles in set Q in ascending order of scheduling costs to Bj.

[0019] Based on the scheduling cost from smallest to largest, mobile energy storage vehicles are scheduled to inferior charging stations Bj in set C in turn; each time a mobile energy storage vehicle is scheduled, set Q is updated once;

[0020] If there are no mobile energy storage vehicles in set Q, or no inferior charging stations in set C, stop dispatching mobile energy storage vehicles to Bj;

[0021] S5.6: Repeat S5.5, and dispatch mobile energy storage vehicles to Bj sequentially in the manner described in S5.5, until j=H or there are no mobile energy storage vehicles in set Q, then stop dispatching mobile energy storage vehicles to Bj.

[0022] Furthermore, in step S1, the objective function is:

[0023]

[0024] In the formula: M ij Represented as the first i Mobile energy storage vehicle to the first j The cost of travel to a charging station; C ij Represented as the first i Mobile energy storage vehicle to the first j The discharge cost of a single charging station; T ij For the first i Mobile energy storage vehicle to the first j The scheduling cost of each charging station.

[0025] Furthermore, in step S1, the traffic network constraints include: travel time constraints, traffic flow conservation constraints, traffic network cost constraints, and traffic network complementarity constraints; the charging station and mobile energy storage vehicle constraints include: charging station power demand constraints, mobile energy storage vehicle energy storage operation constraints, and mobile energy storage vehicle cost constraints.

[0026] Further, in step S5.2, if the mobile energy storage vehicle in set Q cannot be dispatched to a certain inferior charging station, the dispatching cost of dispatching the mobile energy storage vehicle to the inferior charging station is regarded as infinite, and if the dispatching cost of the mobile energy storage vehicle is infinite, the mobile energy storage vehicle is not dispatched to the charging station. Advantages

[0027] The scheme can calculate the dispatching cost of the mobile energy storage vehicle under a certain charging station in a combined form, thereby classifying the charging stations into superior charging stations and inferior charging stations, and dispatching other mobile energy storage vehicles under the superior charging stations to the inferior charging stations in other combinations of non-minimal dispatching cost, so that the allocation of the mobile energy storage vehicles is more reasonable.

[0028] The traffic network equilibrium constraint is adopted, and the travel time is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a flowchart of the whole method;

[0030] Figure 2 is a traffic network diagram of an Nguyen-Dupuis 13-node traffic network example;

[0031] Figure 3 is a dispatching time curve diagram of the existing dispatching method and the dispatching method of the scheme. DETAILED DESCRIPTION

[0032] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be described below in detail with reference to the drawings, obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0033] Embodiment 1: as shown in the figure, a mobile energy storage vehicle dispatching optimization method based on charging station demand and traffic network equilibrium comprises the following steps: Figure 1

[0034] S1: establishing a target function, setting traffic network constraints, charging station and mobile energy storage vehicle constraints, and solving the charging station corresponding to the minimum dispatching cost of a mobile energy storage vehicle through iteration; recording the mobile energy storage vehicle at the charging station with the minimum dispatching cost.

[0035] Specifically, the following steps are included:

[0036] S1.1: target function:

[0037]

[0038] In the formula:​M ij Represented as the first i Mobile energy storage vehicle to the first j The cost of travel to a charging station; C ij Represented as the first i Mobile energy storage vehicle to the first j The discharge cost of a single charging station; T ij For the first i Mobile energy storage vehicle to the first j The scheduling cost of each charging station.

[0039] S1.2: Transportation network constraints.

[0040] Specifically, it includes:

[0041] S1.2.1: Passage time constraint;

[0042] The road resistance model uses the Bureau of Public Roads (BPR) function:

[0043]

[0044] In the formula: Regular road section Actual travel time; Regular road section Free passage time on; Regular road section Traffic flow on the road; Regular road section Traffic flow capacity; This is a collection of regular road sections;

[0045] S1.2.2: Traffic flow conservation constraint;

[0046]

[0047] In the formula: The coupling relationship between the road segments and paths of GV (gasoline vehicles) and EV (electric vehicles) is represented by 0-1 variables respectively. Charging path k Traffic flow of GV and EV vehicles; charging routes k A path consisting of multiple consecutive road segments leading to a charging station; Regular road section a Traffic flow on the road, regular road segment means: road segment excluding charging station; multiple consecutive regular road segments form a regular path m; Charging section bEV traffic, i.e. the number of EVs heading to charging stations, and charging segments are segments of roads containing charging stations. These represent the penetration rates of GV and EV, respectively. This is a collection of charging routes; Total traffic demand; These are the set of regular paths and the set of charging paths, respectively. This indicates a start-end pair.

[0048] S1.2.3: Transportation network cost constraints;

[0049]

[0050] In the formula: The cost of travel per unit time is denoted as $0.17 / min; To represent the set of GV and EV paths between origin-end pairs; For congestion charges. This represents the coupling relationship between the EV driving segment and the path, and is a 0-1 variable.

[0051] S1.2.4: Complementary constraints of transportation networks;

[0052]

[0053] In the formula: These are the minimum driving costs for GV and EV respectively between the start and end points; represent and .

[0054] S1.3: Constraints on charging stations and mobile energy storage vehicles;

[0055] Specifically, it includes:

[0056] S1.3.1: Charging station power demand constraints;

[0057]

[0058] In the formula: The first in a day i Average operating time of each charging station; For the first i Discharge power of each charging station; The total daily charging needs of EV users in the region; M This represents the number of charging stations.

[0059] S1.3.3: Energy storage operation constraints for mobile energy storage vehicles;

[0060]

[0061] In the formula: This represents the maximum discharge power of the mobile energy storage vehicle. Let be the discharge power of the mobile energy storage vehicle at time t. for The energy storage vehicle's power is constantly being moved; These are the discharge efficiency of the mobile energy storage vehicle; The time interval between adjacent time periods; The rated power of the mobile energy storage vehicle; The charge level when the mobile energy storage vehicle arrives at the charging station; The distance traveled; This refers to the power consumption per unit distance of a mobile energy storage vehicle.

[0062] S1.3.5: Cost constraints of mobile energy storage vehicles;

[0063]

[0064] In the formula: For charging stations j Electricity price; The first i Mobile energy storage vehicles in t +1 and t Battery level at any given moment; For the first i Mobile energy storage vehicle to the first j The scheduling cost of each charging station.

[0065] Step S1 iteratively calculates the minimum scheduling cost from a mobile energy storage vehicle to its corresponding charging station, and obtains the charging station corresponding to the minimum scheduling cost of the mobile energy storage vehicle. The mobile energy storage vehicles at each charging station are recorded.

[0066] For example:

[0067] For example: Suppose there are charging stations A, B and C; and a total of 9 mobile energy storage vehicles, denoted as vehicle 1, vehicle 2, vehicle 3, vehicle 4, vehicle 5, vehicle 6, vehicle 7, vehicle 8 and vehicle 9.

[0068] By calculating the scheduling cost from each mobile energy storage vehicle to each charging station in step S1, and then comparing the scheduling costs from each mobile energy storage vehicle to each charging station, the charging station corresponding to the minimum scheduling cost of each mobile energy storage vehicle can be obtained.

[0069] Assuming that after calculation by S1, the scheduling cost of vehicles 1, 2, and 3 going to charging station A is less than the scheduling cost of vehicles 1, 2, and 3 going to charging stations B and C, then the scheduling cost of vehicles 1, 2, and 3 going to charging station A is the minimum, so vehicles 1, 2, and 3 are recorded under charging station A. Similarly, assuming that the scheduling cost of vehicles 4, 5, and 6 going to charging station B is the minimum, vehicles 4, 5, and 6 are recorded under charging station B; and that the scheduling cost of vehicles 7, 8, and 9 going to charging station C is the minimum, so vehicles 7, 8, and 9 are recorded under charging station C.

[0070] S2: The dispatch center obtains the power shortage value of each charging station, calculates the combination of mobile energy storage vehicles that can fill the power shortage of a certain charging station, and records the charging station with a combination of mobile energy storage vehicles that can fill the power shortage as a superior charging station, and the charging station without a combination of mobile energy storage vehicles that can fill the power shortage as a inferior charging station.

[0071] Continuing with the example in S1, suppose that for charging station A: there are three mobile energy storage vehicle combinations that can fill the power shortage of charging station A, namely:

[0072] Combination 1: Car 1 + Car 2;

[0073] Combination 2: Car 1 + Car 3;

[0074] Combination 3: Car 1 + Car 2 + Car 3;

[0075] Let the charging station that has a mobile energy storage vehicle combination that can fill the power gap be called the "superior charging station". Then charging station A is the superior charging station. Filling the gap means that the total power of all mobile energy storage vehicles in the combination is greater than or equal to the power gap of the charging station.

[0076] For charging station B: there is no combination that can fill its power gap. That is, the total power of vehicles four, five, and six is ​​less than the power gap of charging station B. A charging station without a mobile energy storage vehicle combination to fill its power gap is denoted as a substandard charging station. Therefore, charging station B is a substandard charging station.

[0077] For charging station C: Assume there are two combinations that can fill the power shortage of charging station C, namely combination four: vehicle seven + vehicle eight; combination five: vehicle seven + vehicle nine. Charging station C is the superior charging station.

[0078] S3: For any superior charging station, calculate the scheduling cost of each combination in the superior charging station, select the combination with the lowest scheduling cost and schedule it to the superior charging station; for any inferior charging station, schedule all mobile energy storage vehicles under the inferior charging station to the corresponding inferior charging station.

[0079] For example, for charging station A, calculate the scheduling costs of combination one, combination two, and combination three respectively, and select the combination with the lowest scheduling cost, because the scheduling costs of vehicle one, vehicle two, and vehicle three are... T ij All of these costs are calculated in step S1. Therefore, the scheduling costs of combination one, combination two, and combination three can be calculated by accumulating the scheduling costs of mobile energy storage vehicles. Assuming that combination one has the lowest scheduling cost for charging station A after calculation and comparison, the dispatch center will dispatch multiple mobile energy storage vehicles in combination one to charging station A.

[0080] Similarly, for charging station C, the combination with the lowest scheduling cost is calculated and compared. Assuming that the combination with the lowest scheduling cost for charging station C is combination four: vehicle seven + vehicle eight, the dispatch center will dispatch multiple mobile energy storage vehicles in combination four to charging station B.

[0081] For charging station B, since there is no combination that can fill the power gap of charging station B, multiple mobile energy storage vehicles under charging station B are directly dispatched to charging station B, that is, vehicles four, five and six are dispatched to charging station B.

[0082] S4: Among all the best charging stations, the mobile energy storage vehicles with the lowest non-scheduling costs are grouped into set Q.

[0083] For example, because vehicle 1 is dispatched to charging station A and vehicle 2 is dispatched to charging station C, after removing vehicle 1 and vehicle 2 from vehicle 1, only vehicle 3 remains at charging station A; similarly, after removing vehicle 7 and vehicle 8 from vehicle 4, only vehicle 9 remains at charging station C.

[0084] Then set Q is represented as:

[0085] Q = {Car 3, Car 9};

[0086] S5: Sort the inferior charging stations according to the size of their power shortage, and allocate the mobile energy storage vehicles in set Q to the inferior charging stations until all mobile energy storage vehicles have been allocated or until all inferior charging stations have no power shortage.

[0087] Specifically, the following steps are included:

[0088] S5.1: Ranking of inferior charging stations.

[0089] Assume there are a total of H substandard charging stations. They are sorted from largest to smallest in terms of power shortage, and the sorted result is denoted as set C. Set C is represented as: C = {B1, B2, ..., Bj, ..., BH}. Bj represents the j-th substandard charging station.

[0090] S5.2: Following the method in step S1, calculate the scheduling cost of dispatching all mobile energy storage vehicles in set Q to inferior charging station B1; sort the mobile energy storage vehicles in set Q in ascending order of scheduling cost; if a mobile energy storage vehicle in set Q cannot be dispatched to a certain inferior charging station, such as if the distance between the mobile energy storage vehicle and the inferior charging station is too far, or the power of the mobile energy storage vehicle is insufficient to move to the inferior charging station, then the scheduling cost of dispatching the mobile energy storage vehicle to the inferior charging station is considered to be infinite. If the scheduling cost of the mobile energy storage vehicle is infinite, then the mobile energy storage vehicle is not dispatched to the charging station.

[0091] S5.3: Dispatch the mobile energy storage vehicles in set Q to the inferior charging station B1 in order of dispatch cost from smallest to largest. Update set Q once for each mobile energy storage vehicle dispatched. The meaning of updating set Q is: if a mobile energy storage vehicle is dispatched to the inferior charging station, then the mobile energy storage vehicle is deleted from set Q.

[0092] If the inferior charging station B1 fills the power gap, or if there is no mobile energy storage vehicle in the set Q, then the dispatch of mobile energy storage vehicles to B1 will stop.

[0093] S5.4: After stopping the dispatch of mobile energy storage vehicles to the inferior charging station B1, update the inferior charging stations in set C; the meaning of updating set C is: if the charging station fills the power gap, the inferior charging station is deleted from set C; if the charging station does not fill the power gap, the inferior charging station is not deleted from set C.

[0094] S5.5: If there are mobile energy storage vehicles in set Q and there are substandard charging stations in set C; recalculate the scheduling costs of all mobile energy storage vehicles in set Q to Bj in set C, and reorder the mobile energy storage vehicles in set Q in ascending order of scheduling costs to Bj.

[0095] Based on the scheduling cost from smallest to largest, mobile energy storage vehicles are scheduled to inferior charging stations Bj in set C in turn; each time a mobile energy storage vehicle is scheduled, set Q is updated once;

[0096] If there are no mobile energy storage vehicles in set Q, or no inferior charging stations in set C, stop dispatching mobile energy storage vehicles to Bj.

[0097] S5.6: Repeat S5.5, and dispatch mobile energy storage vehicles to Bj sequentially in the manner described in S5.5, until j=H or there are no mobile energy storage vehicles in set Q, then stop dispatching mobile energy storage vehicles to Bj.

[0098] Example 2:

[0099] Example explanation:

[0100] This invention uses a 13-node traffic network example from Nguyen–Dupuis, such as... Figure 2The traffic network parameters are as follows: Assume there are 2000 vehicles in the network, of which 200 are electric vehicles requiring charging. The network includes 10 mobile energy storage vehicles and 3 charging stations. Assume an average charge of 40 kWh per electric vehicle, with a 10% charge gap for the charging stations based on the expected charge of electric vehicles. The energy data for the mobile energy storage vehicles is shown in Table 2. The average discharge time is 1 hour. The discharge cost of the three charging stations is set at $0.5 / (kWh), and the unit time cost for travelers is $0.17 / min. Traditional scheduling methods approximate the same traffic flow across all road segments, with an average travel time of 15 minutes. This invention is implemented using the GAMS optimization platform, and the 13-node traffic network is solved using the IPOPT solver for NLP problems.

[0101] The distribution of mobile energy storage vehicles at each charging station is shown in Table 1.

[0102] Table 1. Distribution of mobile energy storage vehicles at various charging stations

[0103]

[0104] Table 2: Power Data of Mobile Energy Storage Vehicles and Their Location

[0105]

[0106] Analysis of Table 1 shows that this example compares with traditional scheduling methods that only aim to meet the needs of charging stations. The comparison reveals that focusing solely on meeting charging station needs might lead to charging stations dispatching more mobile energy storage vehicles from more distant nodes to ensure power supply. This could result in an excessive number of mobile energy storage vehicles being dispatched and duplicated, violating economic principles. The method of this invention considers this duplicate dispatching scenario and reorganizes the mobile energy storage vehicles. As can be seen from Table 1, both the distribution of mobile energy storage vehicles at charging stations and the goal of lower operating costs demonstrate that this method is more reasonable than existing methods.

[0107] Compared to traditional scheduling methods, this approach incorporates the constraint of traffic network equilibrium. By introducing congestion pricing, it alleviates traffic congestion, reduces transportation costs and travel time, and supports the low-cost and efficient arrival of mobile energy storage vehicles at charging stations. Traditional scheduling methods, however, cannot achieve interconnection with the transportation network, leading to increased transportation costs for mobile energy storage vehicles. Therefore, a comparison reveals the scheduling costs and times for each charging station. Figure 3 As shown.

[0108] from Figure 3 It can be seen that traffic network balancing can alleviate traffic congestion by levying congestion fees, shorten the travel time on each road segment, and thus reduce the dispatch time for mobile energy storage vehicles to various charging stations.

[0109] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for optimizing the scheduling of mobile energy storage vehicles based on charging station demand and traffic network equilibrium, characterized in that, Includes the following steps: S1: Establish the objective function, set constraints on the transportation network, charging stations, and mobile energy storage vehicles, and solve iteratively to find the charging station corresponding to the minimum scheduling cost of a certain mobile energy storage vehicle; record the mobile energy storage vehicle under the charging station with the minimum scheduling cost. S2: The dispatch center obtains the power shortage value of each charging station. For mobile energy storage vehicles under a certain charging station, it calculates the combination of mobile energy storage vehicles that can fill the power shortage of the charging station. Charging stations with mobile energy storage vehicle combinations that can fill the power shortage are marked as excellent charging stations, and charging stations without such combinations are marked as poor charging stations. Filling means that the total power of all mobile energy storage vehicles in the combination is greater than or equal to the power shortage value of the charging station. S3: For any superior charging station, according to step S1, calculate the scheduling cost of each combination under the superior charging station, select the combination with the lowest scheduling cost and schedule it to the superior charging station; for any inferior charging station, schedule the mobile energy storage vehicle under the inferior charging station to the corresponding inferior charging station. S4: Under all the best charging stations, the mobile energy storage vehicles with the lowest non-scheduling cost are grouped into set Q; S5: Sort the inferior charging stations according to the size of their power shortage, and allocate the mobile energy storage vehicles in set Q to the inferior charging stations until all mobile energy storage vehicles have been allocated or until all inferior charging stations have no power shortage. Step S5 includes the following steps: S5.1: Sort the inferior charging stations in descending order of their power shortage, and denote the sorted set as C; Suppose there are H inferior charging stations in total, and the set C is represented as: C = {B1, B2, ..., Bj, ..., BH}; j∈[1,H]; H is a positive integer; S5.2: Calculate the scheduling cost of dispatching all mobile energy storage vehicles in set Q to inferior charging station B1; then sort the mobile energy storage vehicles in set Q in ascending order of scheduling cost. S5.3: Dispatch the mobile energy storage vehicles in set Q to the inferior charging station B1 in order of dispatch cost from smallest to largest. Update set Q once for each mobile energy storage vehicle dispatched. The meaning of updating set Q is: if a mobile energy storage vehicle is dispatched to the inferior charging station, then the mobile energy storage vehicle is deleted from set Q. If the inferior charging station B1 fills the power gap, or if there is no mobile energy storage vehicle in the set Q, then the dispatch of mobile energy storage vehicles to B1 will stop. S5.4: After stopping the dispatch of mobile energy storage vehicles to the inferior charging station B1, update the inferior charging stations in set C; the meaning of updating set C is: if the charging station fills the power gap, the inferior charging station is deleted from set C; if the charging station does not fill the power gap, the inferior charging station is not deleted from set C. S5.5: If there are mobile energy storage vehicles in set Q and there are substandard charging stations in set C, continue to recalculate the scheduling costs of all mobile energy storage vehicles in set Q to Bj in set C; and reorder the mobile energy storage vehicles in set Q in ascending order of scheduling costs to Bj. Based on the scheduling cost from smallest to largest, mobile energy storage vehicles are scheduled to inferior charging stations Bj in set C in turn; each time a mobile energy storage vehicle is scheduled, set Q is updated once; If there are no mobile energy storage vehicles in set Q, or no inferior charging stations in set C, stop dispatching mobile energy storage vehicles to Bj; S5.6: Repeat S5.5, and dispatch mobile energy storage vehicles to Bj in sequence according to the method in S5.5, until j = H or there are no mobile energy storage vehicles in set Q, then stop dispatching mobile energy storage vehicles to Bj.

2. The method for optimizing the scheduling of mobile energy storage vehicles based on charging station demand and traffic network equilibrium as described in claim 1, characterized in that, In step S1, the objective function is: min T ij =M ij +C ij Where: M ij Let C represent the distance cost from the i-th mobile energy storage vehicle to the j-th charging station; ij Let T represent the discharge cost of the i-th mobile energy storage vehicle to the j-th charging station; ij Let represent the dispatch cost from the i-th mobile energy storage vehicle to the j-th charging station.

3. The method for optimizing the scheduling of mobile energy storage vehicles based on charging station demand and traffic network equilibrium as described in claim 1, characterized in that, In step S1, the traffic network constraints include: travel time constraints, traffic flow conservation constraints, traffic network cost constraints, and traffic network complementarity constraints; the charging station and mobile energy storage vehicle constraints include: charging station power demand constraints, mobile energy storage vehicle energy storage operation constraints, and mobile energy storage vehicle cost constraints.

4. The method for optimizing the scheduling of mobile energy storage vehicles based on charging station demand and traffic network equilibrium as described in claim 2, characterized in that, In step S5.2, if the mobile energy storage vehicle in set Q cannot be dispatched to a certain inferior charging station, the dispatch cost of dispatching the mobile energy storage vehicle to the inferior charging station is regarded as infinite. If the dispatch cost of the mobile energy storage vehicle is infinite, the mobile energy storage vehicle is not dispatched to the charging station.

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