Mobile energy storage vehicle scheduling optimization method based on charging station demand and traffic network balance

Abstract

The invention relates to a mobile energy storage vehicle scheduling optimization method based on charging station requirements and traffic network balance, which belongs to the technical problem of energy scheduling and comprises the following steps: S1, establishing an objective function, and solving a charging station corresponding to the minimum scheduling cost of a certain mobile energy storage vehicle; s2, obtaining the electric quantity gap value of each charging station, recording the charging station with the mobile energy storage vehicle combination capable of filling the electric quantity gap as an optimal charging station, and otherwise, recording the charging station as a poor charging station; s3, selecting a combination with the minimum scheduling cost and scheduling the combination to the optimal charging station; s4, the mobile energy storage vehicles of other combinations with the minimum non-scheduling cost under all the optimal charging stations are classified into a set Q; and S5, sorting the inferior charging stations, and sequentially distributing the mobile energy storage vehicles in the set Q to each inferior charging station according to the electric quantity gap from large to small. According to the method, part of the mobile energy storage vehicles of the optimal charging station can be dispatched to the inferior charging station, the electric quantity of the inferior charging station is filled up, and dispatching is more reasonable.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy dispatching, and in particular relates to a method for optimizing dispatching of mobile energy storage vehicles based on charging station demand and traffic network balance. Background Art

[0002] With the development of electric vehicles, the demand for power supply of charging stations is also increasing. During peak hours of electricity consumption, charging stations will have power gaps, resulting in insufficient power at charging stations. When charging stations are short of power, mobile energy storage vehicles can be used as a way to supply power to charging stations. Mobile energy storage vehicles can travel between charging stations that need power. When a charging station has a power gap, the mobile energy storage vehicle's own power can be used to supplement the power of the charging station.

[0003] The current method of dispatching mobile energy storage vehicles to charging stations is to take the lowest dispatching cost of mobile energy storage vehicles to charging stations as the goal, and by setting constraints, iteratively calculate the charging station with the lowest dispatching cost for a mobile energy storage vehicle, so that the mobile energy storage vehicle can go to the charging station with the lowest dispatching cost accordingly. For example: Charging station A and charging station B have a power shortage and need mobile energy storage vehicles to supplement the power. There are four mobile energy storage vehicles that can supply power to charging station A or charging station B. After calculation by the existing dispatching method, it is assumed that the dispatching cost of the first three mobile energy storage vehicles to charging station A is lower than the dispatching cost to charging station B; the dispatching cost of the last mobile energy storage vehicle to charging station B is lower than the dispatching cost to charging station A. According to the existing dispatching method design, the first three mobile energy storage vehicles are dispatched to charging station A, and the last mobile energy storage vehicle is dispatched to charging station B.

[0004] However, in reality, 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. For example, the first three mobile energy storage vehicles all went 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 did not play a role in power supply at charging station A, resulting in unreasonable energy distribution of the mobile energy storage vehicle at charging station A. Summary of the invention

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

[0006] A method for optimizing the dispatching of mobile energy storage vehicles based on charging station demand and traffic network balance of the present invention comprises the following steps:

[0007] S1: Establish the objective function, set the traffic network constraints, charging station and mobile energy storage vehicle constraints, and solve the charging station corresponding to the minimum dispatch cost of a mobile energy storage vehicle through iterative solution; record the mobile energy storage vehicle under the charging station with the minimum dispatch cost;

[0008] S2: The dispatch center obtains the power gap value of each charging station, and calculates the combination of mobile energy storage vehicles that can fill the power gap of a charging station for the mobile energy storage vehicles under a certain charging station; the charging station with a combination of mobile energy storage vehicles that can fill the power gap is recorded as a good charging station, and the charging station without a combination of mobile energy storage vehicles that can fill the power gap is recorded as a bad charging station; the meaning of filling is: the total 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, according to step S1, the dispatching cost of each combination under the superior charging station is calculated, and the combination with the smallest dispatching cost is selected and dispatched to the superior charging station; for any inferior charging station, the mobile energy storage vehicle under the inferior charging station is dispatched to the corresponding inferior charging station;

[0010] S4: All other combinations of mobile energy storage vehicles with the lowest non-dispatch cost under all the optimal charging stations are assigned to the set Q;

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

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

[0013] S5.1: Sort the inferior charging stations from large to small according to the power gap, and record them as set C after sorting; suppose there are H inferior charging stations in total, and set C is represented by: C = {B1, B2, ..., Bj, ..., BH}; j∈[1,H]; H is a positive integer;

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

[0015] S5.3: Dispatch the mobile energy storage vehicles in set Q to the inferior charging station B1 in order of dispatch cost from small to large, and update set Q once each time a mobile energy storage vehicle is dispatched; updating set Q means: dispatching a mobile energy storage vehicle to an inferior charging station, then deleting the mobile energy storage vehicle from set Q;

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

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

[0018] S5.5: If there are mobile energy storage vehicles in set Q and there are inferior charging stations in set C, continue to recalculate the dispatching costs of all mobile energy storage vehicles in set Q to Bj in set C in turn; and reorder the mobile energy storage vehicles in set Q according to the dispatching costs to Bj from small to large;

[0019] Dispatch mobile energy storage vehicles to the Bj inferior charging stations in set C in order of dispatch cost from small to large; update set Q once each time a mobile energy storage vehicle is dispatched;

[0020] If there is no mobile energy storage vehicle in set Q, or there is no inferior charging station 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 in sequence according to the method in S5.5 until j=H or there are no mobile energy storage vehicles in the set Q, and stop dispatching mobile energy storage vehicles to Bj.

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

[0023] minT ij =M ij +C ij (B-1)

[0024] Where: M ij It is represented as the travel cost from the i-th mobile energy storage vehicle to the j-th charging station; C ij It is represented as the discharge cost of the i-th mobile energy storage vehicle to the j-th charging station; T ij is the dispatch cost of the i-th mobile energy storage vehicle to the j-th 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] Furthermore, in step S5.2, if the mobile energy storage vehicle in the 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.

[0027] Beneficial effects:

[0028] This solution can calculate the dispatching cost of the mobile energy storage vehicles under a certain charging station in the form of combination, thereby dividing the charging stations into superior charging stations and inferior charging stations, and then dispatching the mobile energy storage vehicles of other combinations with non-minimum dispatching costs under the superior charging stations to inferior charging stations, making the allocation of mobile energy storage vehicles more reasonable.

[0029] The constraint of traffic network balance is adopted to reduce travel time. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flowchart of the whole method;

[0031] Figure 2 It is the traffic network diagram of Nguyen-Dupuis 13-node traffic network example;

[0032] Figure 3 It is the scheduling time curve diagram of the existing scheduling method and the scheduling method of this scheme. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] Embodiment 1: A method for optimizing the dispatch of mobile energy storage vehicles based on charging station demand and traffic network balance, comprising the following steps:

[0035] S1: Establish the objective function, set the traffic network constraints, charging station and mobile energy storage vehicle constraints, and solve the charging station corresponding to the minimum dispatch cost of a mobile energy storage vehicle through iterative solution; record the mobile energy storage vehicle under the charging station with the minimum dispatch cost.

[0036] The specific steps include:

[0037] S1.1: Objective function:

[0038] minT ij =M ij +C ij (B-2)

[0039] Where: M ij It is represented as the travel cost from the i-th mobile energy storage vehicle to the j-th charging station; C ij It is represented as the discharge cost of the i-th mobile energy storage vehicle to the j-th charging station; T ijis the dispatch cost of the i-th mobile energy storage vehicle to the j-th charging station.

[0040] S1.2: Transportation network constraints.

[0041] Specifically include:

[0042] S1.2.1: Travel time constraints;

[0043] The road resistance model uses the Bureau of Public Roads (BPR) function:

[0044]

[0045] Where: is the actual travel time of the conventional road section a; is the free travel time on the regular road section a; is the traffic flow on the regular road section a; is the traffic flow capacity on the conventional road section a; For regular road sections;

[0046] S1.2.2: Traffic flow conservation constraints

[0047]

[0048] Where: Respectively represent the coupling relationship between the driving section and the path of GV (gasoline vehicle) and EV (electric vehicle), which are 0-1 variables; are the traffic volumes of GV and EV on charging path k, respectively; charging path k is a path consisting of multiple continuous road sections leading to the charging station; is the traffic flow on the regular road section a, where the regular road section means: a road section without a charging station; multiple consecutive regular road sections constitute a regular path m; is the EV flow on the charging section b, that is, the number of EVs going to the charging station. The charging section is the section containing the charging station; h g 、h e are the penetration rates of GV and EV, respectively; is the charging section collection; q rs is the total traffic demand; are the conventional path set and the charging path set respectively; rs represents the start-end point pair.

[0049] S1.2.3: Transportation network cost constraints

[0050]

[0051] Where: w is the travel cost per unit time, recorded as 0.17$ / min; To represent the set of GV and EV paths between the origin-destination pairs; For congestion charges. It represents the coupling relationship between the EV driving section and the path, which is a 0-1 variable.

[0052] S1.2.4: Transportation network complementarity constraints

[0053]

[0054] Where: are the minimum driving costs of GV and EV between the start-end point pair respectively; 0￡a^b 30 means a 30, b 30 and ab=0.

[0055] S1.3: Constraints on charging stations and mobile energy storage vehicles

[0056] Specifically include:

[0057] S1.3.1: Charging station power demand constraints

[0058]

[0059] Where: T i is the average operating time of the i-th charging station in a day; is the discharge power of the i-th charging station; D EV is the total daily charging demand of EV users in the region; M is the number of charging stations.

[0060] S1.3.3: Mobile energy storage vehicle energy storage operation constraints

[0061] 0￡P dis,t £P dis,max (B-12)

[0062] E t+1 =E t -P dis,t Δt / h dis (B-13)

[0063] 0.1E N ￡E t £0.9E N (B-14)

[0064] E r =E t -lΔr (B-15)

[0065] 0.5E N ￡E r £0.9E N (B-16) Where: P dis,max is the maximum discharge power of the mobile energy storage vehicle; Pdis,t is the discharge power of the mobile energy storage vehicle at time t, E t+1 h is the power of the mobile energy storage vehicle at time t+1; dis are the discharge efficiency of the mobile energy storage vehicle; Δt is the time interval between adjacent time periods; E N is the rated capacity of the mobile energy storage vehicle; E r is the amount of electricity when the mobile energy storage vehicle arrives at the charging station; l is the moving distance; Δr is the power consumption per unit distance of the mobile energy storage vehicle.

[0066] S1.3.5: Cost constraints of mobile energy storage vehicles

[0067] T ij £c j (E i,t+1 -E i,t )(B-17)

[0068] Where: c j is the electricity price of charging station j; E i,t+1 、E i,t are the power of the i-th mobile energy storage vehicle at time t+1 and time t respectively; T ij is the dispatch cost of the i-th mobile energy storage vehicle to the j-th charging station.

[0069] Through step S1, the minimum dispatching cost of a mobile energy storage vehicle to the corresponding charging station is iteratively calculated, and the charging station corresponding to the minimum dispatching cost of the mobile energy storage vehicle can be obtained. The mobile energy storage vehicle under each charging station is recorded.

[0070] Example:

[0071] For example: Assume that there are charging stations A, B and C; there are 9 mobile energy storage vehicles, recorded as vehicle one, vehicle two, vehicle three, vehicle four, vehicle five, vehicle six, vehicle seven, vehicle eight and vehicle nine.

[0072] By calculating the dispatching cost from each mobile energy storage vehicle to each charging station in step S1, and then comparing the dispatching costs from each mobile energy storage vehicle to each charging station, the charging station corresponding to the minimum dispatching cost of each mobile energy storage vehicle can be obtained.

[0073] Assuming that after the calculation of S1, the dispatching cost of car 1, car 2, and car 3 going to charging station A is smaller than the dispatching cost of car 1, car 2, and car 3 going to charging station B and charging station C, then the dispatching cost of car 1, car 2, and car 3 going to charging station A is the smallest, and car 1, car 2, and car 3 are recorded under charging station A; similarly, assuming that the dispatching cost of car 4, car 5, and car 6 going to charging station B is the smallest, car 4, car 5, and car 6 are recorded under charging station B; the dispatching cost of car 7, car 8, and car 9 going to charging station C is the smallest, and car 7, car 8, and car 9 are recorded under charging station C.

[0074] S2: The dispatch center obtains the power gap value of each charging station, and calculates the mobile energy storage vehicle combination that can fill the power gap of a charging station for the mobile energy storage vehicles under the charging station; the charging station with a mobile energy storage vehicle combination that can fill the power gap is recorded as a good charging station, and the charging station without a mobile energy storage vehicle combination that can fill the power gap is recorded as a bad charging station;

[0075] Continuing with the example in S1, assume that for charging station A, there are three mobile energy storage vehicle combinations that can fill the power gap of charging station A, namely:

[0076] Combination 1: car 1 + car 2;

[0077] Combination 2: car one + car three;

[0078] Combination three: car one + car two + car three;

[0079] The charging station of the mobile energy storage vehicle combination that fills the power gap is called the best charging station. Then charging station A is the best charging station. The meaning of filling is: the total power of all mobile energy storage vehicles in the combination is greater than or equal to the power gap value of the charging station;

[0080] For charging station B: there is no combination that can fill its power gap. That is, the total power of car 4, car 5, and car 6 is less than the power gap of charging station B. The charging station without a mobile energy storage car combination that fills the power gap is called an inferior charging station. Then charging station B is an inferior charging station.

[0081] For charging station C: Assume that there are two combinations that can fill the power gap of charging station C, namely combination 4: car 7 + car 8; combination 5: car 7 + car 9. Charging station C is the best charging station.

[0082] S3: For any optimal charging station, calculate the dispatching cost of each combination in the optimal charging station, select the combination with the smallest dispatching cost and dispatch it to the optimal charging station; for any inferior charging station, dispatch all mobile energy storage vehicles under the inferior charging station to the corresponding inferior charging station;

[0083] For example, for charging station A, the dispatching costs of combination 1, combination 2, and combination 3 are calculated respectively, and the combination with the smallest dispatching cost is selected, because the dispatching costs of vehicle 1, vehicle 2, and vehicle 3 are T ij All of them are obtained in step S1, so the dispatching costs of combination 1, combination 2 and combination 3 can be calculated respectively by accumulating the dispatching costs of mobile energy storage vehicles. Assuming that after calculation and comparison, the combination with the smallest dispatching cost of charging station A is combination 1, the dispatching center will dispatch multiple mobile energy storage vehicles in combination 1 to charging station A.

[0084] Similarly, for charging station C, the combination with the lowest dispatch cost is also calculated and compared. Assuming that the combination with the lowest dispatch 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.

[0085] For charging station B, since there is no combination that can fill the power gap of charging station B, the 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.

[0086] S4: All other combinations of mobile energy storage vehicles with the smallest non-dispatch cost among all the optimal charging stations are assigned to the set Q.

[0087] For example, because combination 1 is dispatched to charging station A and combination 4 is dispatched to charging station C, after charging station A removes cars 1 and 2 from combination 1, only car 3 is left under charging station A; similarly, after charging station C removes cars 7 and 8 from combination 4, only car 9 is left under charging station C.

[0088] Then the set Q is expressed as:

[0089] Q = {Rook 3, Rook 9};

[0090] S5: Sort the inferior charging stations according to the size of the power gap, and assign the mobile energy storage vehicles in the set Q to the inferior charging stations until all the mobile energy storage vehicles are assigned or until all the inferior charging stations have no power gap.

[0091] The specific steps include:

[0092] S5.1: Ranking of inferior charging stations.

[0093] Assume that there are H inferior charging stations in total, which are sorted from large to small according to the power gap. The sorted charging stations are recorded as set C, which is expressed as: C = {B1, B2, ..., Bj, ..., BH}; j∈[1,H]; Bj represents the jth inferior charging station.

[0094] S5.2: According to step S1, the dispatching cost of dispatching all mobile energy storage vehicles in set Q to the inferior charging station B1 is calculated; the mobile energy storage vehicles in set Q are sorted in ascending order according to the dispatching cost; if the mobile energy storage vehicle in set Q cannot be dispatched to a certain inferior charging station, such as the distance between the mobile energy storage vehicle and the inferior charging station is too far, and the power of the mobile energy storage vehicle is insufficient to move it to the inferior charging station, the dispatching cost of dispatching the mobile energy storage vehicle to the inferior charging station is regarded as infinite. If the dispatching cost of the mobile energy storage vehicle is infinite, the mobile energy storage vehicle is not dispatched to the charging station.

[0095] S5.3: Dispatch the mobile energy storage vehicles in set Q to the inferior charging station B1 in order of dispatch cost from small to large, and update set Q once each time a mobile energy storage vehicle is dispatched; updating set Q means: dispatching a mobile energy storage vehicle to an inferior charging station, then deleting the mobile energy storage vehicle from set Q;

[0096] If the inferior charging station B1 fills the power gap, or there is no mobile energy storage vehicle in the set Q, stop dispatching mobile energy storage vehicles to B1;

[0097] S5.4: After stopping dispatching the mobile energy storage vehicle to the inferior charging station B1, update the inferior charging stations in the set C; the meaning of updating the set C is: if the charging station fills the power gap, delete the inferior charging station from the set C; if the charging station does not fill the power gap, do not delete the inferior charging station from the set C;

[0098] S5.5: If there are mobile energy storage vehicles in set Q and there are inferior charging stations in set C, recalculate the dispatching costs of all mobile energy storage vehicles in set Q to Bj in set C in turn, and reorder the mobile energy storage vehicles in set Q from small to large according to the dispatching costs to Bj;

[0099] Dispatch mobile energy storage vehicles to the Bj inferior charging stations in set C in order of dispatch cost from small to large; update set Q once each time a mobile energy storage vehicle is dispatched;

[0100] If there is no mobile energy storage vehicle in set Q, or there is no inferior charging station in set C, stop dispatching mobile energy storage vehicles to Bj.

[0101] 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 the set Q, and stop dispatching mobile energy storage vehicles to Bj.

[0102] Embodiment 2:

[0103] Example description:

[0104] The present invention adopts the Nguyen-Dupuis 13-node traffic network example, such as Figure 2The traffic network parameters shown are as follows: Assume that there are 2,000 vehicles in the traffic network, of which 200 electric vehicles need to be charged, and the traffic network contains 10 mobile energy storage vehicles and 3 charging stations; assume that each electric vehicle charges 40kWh on average, and 10% of the expected charging amount of the electric vehicle is used as the power gap of the charging station. The power data of mobile energy storage is shown in Table 2, and the average discharge time is 1h. The discharge cost of the three charging stations is set to 0.5$ / (kWh), and the unit time cost of the travel user is 0.17$ / min. The traditional scheduling method approximately assumes that the traffic flow of each section is the same, and the average travel time of each section is recorded as 15min. The present invention is implemented through the GAMS optimization platform, and the 13-node traffic network uses the IPOPT solver to solve the NLP problem.

[0105] After calculation, the distribution of mobile energy storage vehicles at each charging station is shown in Table 1.

[0106] Table 1 Distribution of mobile energy storage vehicles at each charging station

[0107]

[0108] Table 2: Mobile energy storage vehicle power data and nodes

[0109]

[0110]

[0111] From the analysis of Table 1, it can be seen that this example compares the traditional scheduling method that only aims to meet the needs of charging stations. The comparison finds that if the goal is only to meet the needs of charging stations, the charging station may dispatch more mobile energy storage vehicles from more distant nodes to ensure the supply of electricity. This may not only lead to an excessive number of mobile energy storage vehicles being dispatched, but also may be repeatedly dispatched, which is not in line with the principle of economy. The method of the present invention takes this repeated call into account and recombines the mobile energy storage vehicles. As can be seen from Table 1, whether it is the distribution of mobile energy storage vehicles in charging stations or the purpose of low operating costs, it can be reflected that this method is more reasonable than the existing methods.

[0112] Compared with the traditional dispatching method, this method also introduces the constraint of traffic network balance. By introducing traffic congestion fees to alleviate traffic congestion, it reduces traffic costs and travel time, and provides support for mobile energy storage vehicles to reach charging stations at low cost and high efficiency. However, the traditional dispatching method cannot be interconnected with the traffic network, resulting in higher traffic costs for mobile energy storage vehicles. Therefore, by comparison, the dispatching cost and dispatching time of each charging station are Figure 3 shown.

[0113] from Figure 3It can be seen that traffic network balance can alleviate traffic congestion by levying traffic congestion fees, shortening the travel time of each road section, thereby reducing the dispatch time of mobile energy storage vehicles to various charging stations.

[0114] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A mobile energy storage vehicle scheduling optimization method based on charging station demand and traffic network balance, characterized in that: The following steps are involved: S1: Establish the objective function, set the traffic network constraints, charging station and mobile energy storage vehicle constraints, and solve the charging station corresponding to the minimum dispatch cost of a mobile energy storage vehicle through iterative solution; record the mobile energy storage vehicle under the charging station with the minimum dispatch cost; S2: The dispatch center obtains the power gap value of each charging station, and calculates the combination of mobile energy storage vehicles that can fill the power gap of a charging station for the mobile energy storage vehicles under a certain charging station; the charging station with a combination of mobile energy storage vehicles that can fill the power gap is recorded as a good charging station, and the charging station without a combination of mobile energy storage vehicles that can fill the power gap is recorded as a bad charging station; the meaning of filling is: the total power of all mobile energy storage vehicles in the combination is greater than or equal to the power gap value of the charging station; S3: For any superior charging station, according to step S1, the dispatching cost of each combination under the superior charging station is calculated, and the combination with the smallest dispatching cost is selected and dispatched to the superior charging station; for any inferior charging station, the mobile energy storage vehicle under the inferior charging station is dispatched to the corresponding inferior charging station; S4: All other combinations of mobile energy storage vehicles with the lowest non-dispatch cost under all the optimal charging stations are assigned to the set Q; S5: Sort the inferior charging stations according to the size of the power gap, and assign the mobile energy storage vehicles in the set Q to the inferior charging stations until all the mobile energy storage vehicles are assigned or until all the inferior charging stations have no power gap.

2. A mobile energy storage vehicle scheduling optimization method based on charging station demand and traffic network balance according to claim 1, characterized in that: Step S5 includes the following steps: S5.1: Sort the inferior charging stations from large to small according to the power gap, and record them as set C after sorting; suppose there are H inferior charging stations in total, and set C is represented by: C = {B1, B2, ..., Bj, ..., BH}; j∈[1,H]; H is a positive integer; S5.2: Calculate the dispatching cost of all mobile energy storage vehicles in set Q to the inferior charging station B1; then sort the mobile energy storage vehicles in set Q from small to large according to the dispatching cost; S5.3: Dispatch the mobile energy storage vehicles in set Q to the inferior charging station B1 in order of dispatch cost from small to large, and update set Q once each time a mobile energy storage vehicle is dispatched; updating set Q means: dispatching a mobile energy storage vehicle to an inferior charging station, then deleting the mobile energy storage vehicle from set Q; If the inferior charging station B1 fills the power gap, or there is no mobile energy storage vehicle in the set Q, stop dispatching mobile energy storage vehicles to B1; S5.4: After stopping dispatching the mobile energy storage vehicle to the inferior charging station B1, update the inferior charging stations in the set C; the meaning of updating the set C is: if the charging station fills the power gap, delete the inferior charging station from the set C; if the charging station does not fill the power gap, do not delete the inferior charging station from the set C; S5.5: If there are mobile energy storage vehicles in set Q and there are inferior charging stations in set C, continue to recalculate the dispatching costs of all mobile energy storage vehicles in set Q to Bj in set C in turn; and reorder the mobile energy storage vehicles in set Q according to the dispatching costs to Bj from small to large; Dispatch mobile energy storage vehicles to the Bj inferior charging stations in set C in order of dispatch cost from small to large; update set Q once each time a mobile energy storage vehicle is dispatched; If there is no mobile energy storage vehicle in set Q, or there is no inferior charging station 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 the set Q, and stop dispatching mobile energy storage vehicles to Bj.

3. A method for optimizing the scheduling of mobile energy storage vehicles based on charging station demand and traffic network balance according to claim 1, characterized in that: In step S1, the objective function is: minT ij =M ij +C ij (A-1) Where: M ij It is represented as the travel cost from the i-th mobile energy storage vehicle to the j-th charging station; C ij It is represented as the discharge cost of the i-th mobile energy storage vehicle to the j-th charging station; T ij is the dispatch cost of the i-th mobile energy storage vehicle to the j-th charging station.

4. A method for optimizing the dispatch of mobile energy storage vehicles based on charging station demand and traffic network balance according to 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.

5. A method for optimizing the dispatch of mobile energy storage vehicles based on charging station demand and traffic network balance according to claim 2, characterized in that: In step S5.2, if the mobile energy storage vehicle in the 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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