CN112590598B - Optimal configuration method and system for mobile charging vehicle - Google Patents

Optimal configuration method and system for mobile charging vehicle Download PDF

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Publication number
CN112590598B
CN112590598B CN202011453519.7A CN202011453519A CN112590598B CN 112590598 B CN112590598 B CN 112590598B CN 202011453519 A CN202011453519 A CN 202011453519A CN 112590598 B CN112590598 B CN 112590598B
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mobile charging
charging vehicle
node
vehicle
mobile
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CN112590598A (en
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周开乐
刘璐
陆信辉
丁涛
杨善林
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Hefei University of Technology
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    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
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    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
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    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/66—Data transfer between charging stations and vehicles
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60P—VEHICLES ADAPTED FOR LOAD TRANSPORTATION OR TO TRANSPORT, TO CARRY, OR TO COMPRISE SPECIAL LOADS OR OBJECTS
    • B60P3/00—Vehicles adapted to transport, to carry or to comprise special loads or objects
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Abstract

The invention provides an optimal configuration method and system for a mobile charging vehicle, and relates to the technical field of new energy automobiles. According to the technical scheme, a plurality of groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicles are firstly obtained; then, an optimal scheduling model is obtained based on the charging demand information data and the node information data, and constraint conditions of the optimal scheduling model are determined; and solving the optimal scheduling model by using the configuration data, and determining an optimal configuration scheme based on a solving result. The technical scheme effectively solves the problem of optimal configuration of the mobile charging vehicle, has strong adaptability to flexible and changeable electric vehicle charging requirements, and can improve the economical efficiency and the effectiveness of the operation of the mobile charging vehicle.

Description

Optimal configuration method and system for mobile charging vehicle
Technical Field
The invention relates to the technical field of new energy automobiles, in particular to a mobile charging car optimal configuration method and system.
Background
In recent years, although the electric automobile industry is rapidly developed, the development speed of the electric automobile industry is still limited by the limitation of the energy storage battery technology, and the endurance mileage is still one of important factors influencing the popularization of the electric automobile. Currently, electric vehicles are charged mainly through a fixed charging station, but the fixed charging station has long construction period, high investment cost and poor flexibility. Compared with a fixed charging station, the mobile charging vehicle adopts a form of receiving charging demand information in advance and then going to the gate for service, and can be more flexibly adapted to various changes of charging demands. In order to better reduce the cost and realize larger benefits, the mobile charging vehicle needs to be optimized. Currently, the optimization of the mobile charging car comprises two aspects of optimization of the dispatching and optimization of the configuration of the mobile charging car.
The optimization of the mobile charging vehicles is mostly focused on the problem of optimizing and dispatching the mobile charging vehicles, and the problem is converted into the problem of planning the path of the electric vehicle with a time window, and the type of research is to optimize the operation strategy of the mobile charging vehicles on the basis of the configuration determination of the mobile charging vehicles; the optimization configuration research of the fixed charging station is mainly to optimize the site selection, volume fixing, number of charging piles, renewable energy source matching, energy storage feasibility and the like of the charging station by combining charging demand prediction data, charging history data, geographical planning information and the like based on the economy of both the fixed charging station operator and the electric vehicle or the economy of the power grid.
Therefore, the current optimization research of the mobile charging vehicle is more focused on optimizing the operation strategy of the mobile charging vehicle, and the optimization configuration is not considered; the fixed charging station has the characteristics of poor mobility, large scale, fixed charging requirement, incapability of fully adapting to the mobile charging vehicle by the method for optimally configuring the fixed charging station, high requirement on the volume and the quality of energy storage equipment, certain randomness of the charging requirement and the like. In conclusion, the prior art cannot perform configuration optimization on the mobile charging vehicle.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a method and a system for optimizing the configuration of a mobile charging vehicle, which solve the problem that the prior art cannot optimize the configuration of the mobile charging vehicle.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme:
in a first aspect, the present invention firstly proposes a mobile charging vehicle optimizing configuration method, which includes:
acquiring a plurality of groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicles;
acquiring an optimal scheduling model based on the charging demand information data and the node information data, and determining constraint conditions of the optimal scheduling model;
and solving the optimal scheduling model by using the configuration data, and determining an optimal configuration scheme based on a solving result.
Preferably, the configuration data includes: battery capacity configuration data and fleet proportioning configuration data; the charging demand information data includes: predetermined charging location, required power, time window, and charging mode data; the node information data includes: electric vehicle position data, center station position data, and stationary charging station position data.
Preferably, the objective function of the optimal scheduling model is:
max profit＝I-C 1 -C 2 -C 3 -C 4
wherein, I represents the total income of the mobile charging vehicle operators; c (C) 1 Representing the dispatch cost of the mobile charging vehicle; c (C) 2 The running cost of the mobile charging vehicle to and from the fixed charging station is represented; c (C) 3 The service cost of charging the electric automobile by the mobile charging vehicle is represented; c (C) 4 And (5) representing the punishment cost of the mobile charging vehicle for violating the time window.
Preferably, the total income I of the mobile charging vehicle operators is as follows:
dispatch cost C of mobile charging vehicle 1 ：
Running cost C of mobile charging vehicle to and from fixed charging station 2 ：
Service cost C for charging electric automobile by mobile charging vehicle 3 ：
Punishment cost C of mobile charging vehicle violating time window 4 ：
Wherein n is the total number of mobile charging vehicles; m is the total number of the electric automobile nodes and the center station nodes; x is x i,j As a decision variable, when x i,j When the value is 1, the i mobile charging vehicle access node j is indicated; when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; alpha i,k Charging service charge for the mobile charging vehicle i; k represents a charging mode; RN (RN) j Representing the required electric quantity of the node j; p is p E The unit electricity price for charging the electric automobile for the mobile charging vehicle; x is x i As a decision variable, when x i When the value is 1, the ith mobile charging vehicle is dispatched; when x is i When the value is 0, the ith mobile charging vehicle is not dispatched; epsilon i Dispatch cost for the ith mobile charging vehicle; q is the total number of stationary charging stations;as decision variables, when->When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when->When the value is 0, the mobile charging car i is visitedAfter the node j, the electric energy is not supplemented to the fixed charging station; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; omega j,z As a decision variable, when ω j,z When the value is 1, replenishing electric energy at the fixed charging station z; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1; θ is the unit mileage energy consumption of the mobile charging vehicle; p is p M The unit electricity price for charging the mobile charging vehicle for the fixed charging station; d, d i,j Distance between the mobile charging vehicle i and the node j; c (C) M The battery capacity of the mobile charging vehicle is; t (T) M The total cycle times of the mobile charging vehicle in the life cycle of the battery are set; gamma is the replacement cost of the mobile charging vehicle battery; mu (mu) k The loss coefficient of the mobile charging vehicle to the battery is calculated when the mobile charging vehicle discharges in the kth charging service mode; inner time window- >The node j is a soft time window and represents an optimal time interval for the mobile charging vehicle i to reach; outer time window->The time window is a hard time window, and represents the maximum time interval that the mobile charging vehicle i acceptable by the node j reaches; />The time when the mobile charging vehicle i reaches the node j; />Are all decision variables, and when the values of the decision variables take 1, the decision variables respectively indicate that the mobile charging car i is at +.>Reaches node j within a certain time interval; when the decision variable is 0, respectively indicating that the mobile charging vehicle i cannot arrive in the corresponding time interval; c e 、c l 、c min Respectively is movedThe dynamic charging vehicle is-> Penalty cost per unit time when node j is reached.
Preferably, the constraint condition includes: time window constraint of nodes:
arrival time constraint of mobile charging vehicle:
the interval constraint of the arrival time of the mobile charging vehicle:
the electric automobile accepts service constraints:
and (3) restraining the charge and discharge amount of the mobile charging vehicle:
and (3) constraint of the range of the residual electric quantity of the mobile charging vehicle:
electric automobile demand electricity quantity constraint:
0≤RN j ≤C E
wherein,respectively the earliest and latest time points of the outer time window of the node j; /> Respectively the earliest and latest time points of the inner time window of the node j; />The time when the mobile charging vehicle i reaches the node j+1; />The time when the mobile charging vehicle i reaches the node j; r is (r) k The discharging power of the kth mode for charging the electric automobile for the mobile charging vehicle; x is x i,j+1 As a decision variable, when x i,j+1 When the value is 1, the access node j+1 of the ith mobile charging vehicle is indicated; when x is i,j+1 When the value is 0, the ith mobile charging vehicle does not access the node j+1; />As decision variables, when->When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when->When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement electric energy after accessing the node j; d, d i,j+1 Distance between mobile charging car i and node j+1; omega j,z As a decision variable, when ω j,z The value of 1 is expressed at the fixed charging station zSupplementing electric energy; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; r is (r) M Charging power of the mobile charging vehicle at a fixed charging station; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1; v is the running speed of the mobile charging vehicle; RN (RN) j The required electric quantity of the node j is; RM (RM) i,j After the mobile charging vehicle i accesses the node j, supplementing electric quantity at a fixed charging station; lambda (lambda) i,j 、/>All are decision variables, when lambda i,j The value is 1, which indicates that the mobile charging car i is in +. >Reaching the node j in the time interval; when the decision variable is 0, respectively indicating that the mobile charging vehicle i cannot arrive in the corresponding time interval; n is the total number of mobile charging cars; x is x i,j As a decision variable, when x i, j represents the access node j of the ith mobile charging vehicle when the value of j is 1, and when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; m is the total number of the electric automobile nodes and the center station nodes; q is the total number of stationary charging stations; x is x i,j As a decision variable, when x i,j When the value is 1, the access node j of the ith mobile charging vehicle is represented, and when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; d, d i,j Distance between the mobile charging vehicle i and the node j; θ is the unit mileage energy consumption of the mobile charging vehicle; c (C) M The battery capacity of the mobile charging vehicle is; c (C) E Indicating the battery capacity of the mobile charging vehicle.
In a second aspect, the present invention provides a mobile charging vehicle optimal configuration system, the system comprising:
the data acquisition module is used for acquiring a plurality of groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicles;
the model acquisition module is used for acquiring an optimal scheduling model based on the charging demand information data and the node information data and determining constraint conditions of the optimal scheduling model;
And the configuration scheme determining module is used for solving the optimal scheduling model by utilizing the configuration data and determining an optimal configuration scheme based on a solving result.
Preferably, the configuration data acquired by the data acquisition module includes: battery capacity configuration data and fleet proportioning configuration data; the charging demand information data includes: predetermined charging location, required power, time window, and charging mode data; the node information data includes: electric vehicle position data, center station position data, and stationary charging station position data.
Preferably, the objective function of the optimized scheduling model constructed by the model acquisition module is as follows:
max profit＝I-C 1 -C 2 -C 3 -C 4
wherein, I represents the total income of the mobile charging vehicle operators; c (C) 1 Representing the dispatch cost of the mobile charging vehicle; c (C) 2 The running cost of the mobile charging vehicle to and from the fixed charging station is represented; c (C) 3 The service cost of charging the electric automobile by the mobile charging vehicle is represented; c (C) 4 And (5) representing the punishment cost of the mobile charging vehicle for violating the time window.
Preferably, the total income I of the mobile charging vehicle operators is as follows:
dispatch cost C of mobile charging vehicle 1 ：
Running cost C of mobile charging vehicle to and from fixed charging station 2 ：
Service cost C for charging electric automobile by mobile charging vehicle 3 ：
Punishment cost C of mobile charging vehicle violating time window 4 ：
Wherein n is the total number of mobile charging vehicles; m is the total number of the electric automobile nodes and the center station nodes; x is x i,j As a decision variable, when x i,j When the value is 1, the i mobile charging vehicle access node j is indicated; when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; alpha i,k Charging service charge for the mobile charging vehicle i; k represents a charging mode; RN (RN) j Representing the required electric quantity of the node j; p is p E The unit electricity price for charging the electric automobile for the mobile charging vehicle; x is x i As a decision variable, when x i When the value is 1, the ith mobile charging vehicle is dispatched; when x is i When the value is 0, the ith mobile charging vehicle is not dispatched; epsilon i Dispatch cost for the ith mobile charging vehicle; q is the total number of stationary charging stations;as decision variables, when->When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when->When the value is 0, the mobile charging vehicle i does not go to the fixed charging station for recharging after accessing the node jEnergy is available; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; omega j,z As a decision variable, when ω j,z When the value is 1, replenishing electric energy at the fixed charging station z; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1; θ is the unit mileage energy consumption of the mobile charging vehicle; p is p M The unit electricity price for charging the mobile charging vehicle for the fixed charging station; d, d i,j Distance between the mobile charging vehicle i and the node j; c (C) M The battery capacity of the mobile charging vehicle is; t (T) M The total cycle times of the mobile charging vehicle in the life cycle of the battery are set; gamma is the replacement cost of the mobile charging vehicle battery; mu (mu) k The loss coefficient of the mobile charging vehicle to the battery is calculated when the mobile charging vehicle discharges in the kth charging service mode; inner time window->The node j is a soft time window and represents an optimal time interval for the mobile charging vehicle i to reach; outer time window->The time window is a hard time window, and represents the maximum time interval that the mobile charging vehicle i acceptable by the node j reaches; />The time when the mobile charging vehicle i reaches the node j; />Are all decision variables, and when the values of the decision variables take 1, the decision variables respectively indicate that the mobile charging car i is at +.>Reaches node j within a certain time interval; when the decision variable is 0, respectively indicating that the mobile charging vehicle i cannot arrive in the corresponding time interval; c e 、c l 、c min The movable charging vehicles are at ∈> Penalty cost per unit time when node j is reached.
Preferably, the constraint condition includes: time window constraint of nodes:
arrival time constraint of mobile charging vehicle:
the interval constraint of the arrival time of the mobile charging vehicle:
the electric automobile accepts service constraints:
and (3) restraining the charge and discharge amount of the mobile charging vehicle:
and (3) constraint of the range of the residual electric quantity of the mobile charging vehicle:
electric automobile demand electricity quantity constraint:
0≤RN j ≤C E
wherein,respectively the earliest and latest time points of the outer time window of the node j; /> Respectively the earliest and latest time points of the inner time window of the node j; />The time when the mobile charging vehicle i reaches the node j+1; />The time when the mobile charging vehicle i reaches the node j; r is (r) k The discharging power of the kth mode for charging the electric automobile for the mobile charging vehicle; x is x i, j + 1 is a decision variable, when x i,j+1 When the value is 1, the access node j+1 of the ith mobile charging vehicle is indicated; when x is i,j+1 When the value is 0, the ith mobile charging vehicle does not access the node j+1; />As decision variables, when->When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when->When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement electric energy after accessing the node j; d, d i,j+1 Distance between mobile charging car i and node j+1; omega j,z As a decision variable, when ω j,z When the value is 1, replenishing electric energy at the fixed charging station z; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; r is (r) M Charging power of the mobile charging vehicle at a fixed charging station; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1; v is the running speed of the mobile charging vehicle; RN (RN) j The required electric quantity of the node j is; RM (RM) i,j After the mobile charging vehicle i accesses the node j, supplementing electric quantity at a fixed charging station; lambda (lambda) i,j 、/>All are decision variables, when lambda i,j The value is 1, which indicates that the mobile charging car i is in +.>Reaching the node j in the time interval; when the decision variable is 0, respectively indicating that the mobile charging vehicle i cannot arrive in the corresponding time interval; n is the total number of mobile charging cars; x is x i,j As a decision variable, when x i,j When the value is 1, the access node j of the ith mobile charging vehicle is represented, and when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; m is the total number of the electric automobile nodes and the center station nodes; q is the total number of stationary charging stations; x is x i,j As a decision variable, when x i,j When the value is 1, the access node j of the ith mobile charging vehicle is represented, and when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; d, d i,j Distance between the mobile charging vehicle i and the node j; θ is the unit mileage energy consumption of the mobile charging vehicle; c (C) M The battery capacity of the mobile charging vehicle is; c (C) E Indicating the battery capacity of the mobile charging vehicle.
(III) beneficial effects
The invention provides a mobile charging vehicle optimal configuration method and a system. Compared with the prior art, the method has the following beneficial effects:
according to the method, the mobile charging vehicle optimizing and scheduling model is built based on the charging demand information data and the node information data of the mobile charging vehicles by acquiring multiple groups of mobile charging vehicle configuration data, then the acquired multiple groups of mobile charging vehicle configuration data are brought into the optimizing and scheduling model to carry out operation and solution, and finally the accurate data of optimizing and configuring are acquired, so that the optimal mobile charging vehicle configuration scheme is determined. The technical scheme effectively solves the problem of optimal configuration of the mobile charging vehicle, and has strong adaptability to flexible and changeable charging requirements of the electric vehicle.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a mobile charging vehicle optimizing configuration method according to an embodiment of the application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application are clearly and completely described, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The embodiment of the application solves the problem that the prior art cannot optimally configure the mobile charging vehicle by providing the mobile charging vehicle optimal configuration method and system, and achieves the aim of improving the running economy and effectiveness of the mobile charging vehicle.
The technical scheme in the embodiment of the application aims to solve the technical problems, and the overall thought is as follows:
in order to solve the problem that the configuration of the mobile charging vehicles cannot be optimized in the prior art, the technical scheme comprises the steps of firstly obtaining a plurality of groups of mobile charging vehicle configuration data, then constructing an optimal scheduling model according to the total income and each cost of the mobile charging vehicles, substituting the plurality of groups of mobile charging vehicle configuration data into the optimal scheduling model for optimal scheduling, and finally obtaining the corresponding group of mobile charging vehicle configuration data when the optimal scheduling model obtains an optimal scheduling result as the optimal configuration of the mobile charging vehicles.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
Example 1:
in a first aspect, the present invention firstly proposes a mobile charging vehicle optimizing configuration method, which includes:
s1, acquiring a plurality of groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicles;
s2, acquiring an optimal scheduling model based on the charging demand information data and the node information data, and determining constraint conditions of the optimal scheduling model;
and S3, solving the optimal scheduling model by using the configuration data, and determining an optimal configuration scheme based on a solving result.
Therefore, the optimal mobile charging vehicle configuration scheme is determined by acquiring multiple groups of mobile charging vehicle configuration data, constructing the mobile charging vehicle optimal scheduling model based on the charging demand information data and the node information data of the mobile charging vehicles, and then bringing the acquired multiple groups of mobile charging vehicle configuration data into the optimal scheduling model for calculation and solution, and finally acquiring the accurate data of optimal configuration. The technical scheme effectively solves the problem of optimal configuration of the mobile charging vehicle, and has strong adaptability to flexible and changeable charging requirements of the electric vehicle.
In the above method of the embodiment of the present invention, the acquired configuration data includes: battery capacity configuration data and fleet proportioning configuration data; the charging demand information data includes: predetermined charging location, required power, time window, and charging mode data; the node information data includes: electric vehicle position data, center station position data, and stationary charging station position data.
In addition, in the embodiment of the invention, in order to obtain the optimal configuration of the mobile charging vehicle, a preferred processing mode is to construct an optimal scheduling model from the consideration of the maximization of the total profit of the mobile charging vehicle operator, wherein the objective function of the optimal scheduling model is as follows:
max profit＝I-C 1 -C 2 -C 3 -C 4
wherein, I represents the total income of the mobile charging vehicle operators; c (C) 1 Representing the dispatch cost of the mobile charging vehicle; c (C) 2 The running cost of the mobile charging vehicle to and from the fixed charging station is represented; c (C) 3 The service cost of charging the electric automobile by the mobile charging vehicle is represented; c (C) 4 And (5) representing the punishment cost of the mobile charging vehicle for violating the time window.
In practice, constructing each item of data of the optimal scheduling model includes: total income I of the mobile charging vehicle operators:
dispatch cost C of mobile charging vehicle 1 ：
Running cost C of mobile charging vehicle to and from fixed charging station 2 ：
Service cost C for charging electric automobile by mobile charging vehicle 3 ：
Punishment cost C of mobile charging vehicle violating time window 4 ：
Wherein n is the total number of mobile charging vehicles; m is the total number of the electric automobile nodes and the center station nodes; x is x i,j As a decision variable, when x i,j When the value is 1, the i mobile charging vehicle access node j is indicated; when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; alpha i,k Charging service charge for the mobile charging vehicle i; k represents a charging mode; RN (RN) j Representing the required electric quantity of the node j; p is p E The unit electricity price for charging the electric automobile for the mobile charging vehicle; x is x i As a decision variable, when x i When the value is 1, the ith mobile charging vehicle is dispatched; when x is i When the value is 0, the ith mobile charging vehicle is not dispatched; epsilon i Dispatch cost for the ith mobile charging vehicle; q is the total number of stationary charging stations;as decision variables, when->When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when->When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement electric energy after accessing the node j; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; omega j,z As a decision variable, when ω j,z When the value is 1, replenishing electric energy at the fixed charging station z; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1; θ is the unit mileage energy consumption of the mobile charging vehicle; p is p M The unit electricity price for charging the mobile charging vehicle for the fixed charging station; d, d i,j Distance between the mobile charging vehicle i and the node j; c (C) M The battery capacity of the mobile charging vehicle is; t (T) M The total cycle times of the mobile charging vehicle in the life cycle of the battery are set; gamma is the replacement cost of the mobile charging vehicle battery; mu (mu) k The loss coefficient of the mobile charging vehicle to the battery is calculated when the mobile charging vehicle discharges in the kth charging service mode; inner time window->The node j is a soft time window and represents an optimal time interval for the mobile charging vehicle i to reach; outer time window->The time window is a hard time window, and represents the maximum time interval that the mobile charging vehicle i acceptable by the node j reaches; />The time when the mobile charging vehicle i reaches the node j; />Are all decision variables, and when the values of the decision variables take 1, the decision variables respectively indicate that the mobile charging car i is at +.>Reaches node j within a certain time interval; when the decision variable is 0, respectively indicating that the mobile charging vehicle i cannot arrive in the corresponding time interval; c e 、c l 、c min The movable charging vehicles are at ∈> Penalty cost per unit time when node j is reached.
In addition, in the embodiment of the present invention, in order to fully consider the possible limitations of the mobile charging vehicle during actual operation, so as to avoid the influence of these limitations on the optimization result, a preferred processing manner is that the set constraint conditions include: the constraint conditions include: time window constraint of nodes:
arrival time constraint of mobile charging vehicle:
the interval constraint of the arrival time of the mobile charging vehicle:
the electric automobile accepts service constraints:
and (3) restraining the charge and discharge amount of the mobile charging vehicle:
and (3) constraint of the range of the residual electric quantity of the mobile charging vehicle:
electric automobile demand electricity quantity constraint:
0≤RN j ≤C E
wherein,respectively the earliest and latest time points of the outer time window of the node j; /> Respectively the earliest and latest time points of the inner time window of the node j; />The time when the mobile charging vehicle i reaches the node j+1; />The time when the mobile charging vehicle i reaches the node j; r is (r) k The discharging power of the kth mode for charging the electric automobile for the mobile charging vehicle; x is x i,j+1 As a decision variable, when x i,j+1 When the value is 1, the access node j+1 of the ith mobile charging vehicle is indicated; when x is i,j+1 When the value is 0, the ith mobile charging vehicle does not access the node j+1; />As decision variables, when->When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when- >When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement electric energy after accessing the node j; d, d i,j+1 Distance between mobile charging car i and node j+1; omega j,z As a decision variable, when ω j,z When the value is 1, replenishing electric energy at the fixed charging station z; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; r is (r) M Charging power of the mobile charging vehicle at a fixed charging station; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1; v is the running speed of the mobile charging vehicle; RN (RN) j The required electric quantity of the node j is; RM (RM) i,j After the mobile charging vehicle i accesses the node j, supplementing electric quantity at a fixed charging station; lambda (lambda) i,j 、/>All are decision variables, when lambda i,j The value is 1, which indicates that the mobile charging car i is in +.>Reaching the node j in the time interval; when the decision variable is 0, respectively indicating that the mobile charging vehicle i cannot arrive in the corresponding time interval; n is the total number of mobile charging cars; x is x i,j As a decision variable, when x i,j When the value is 1, the access node j of the ith mobile charging vehicle is represented, and when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; m is the total number of the electric automobile nodes and the center station nodes; q is the total number of stationary charging stations; x is x i,j As a decision variable, when x i,j When the value is 1, the access node j of the ith mobile charging vehicle is represented, and when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; d, d i,j Distance between the mobile charging vehicle i and the node j; θ is the unit mileage energy consumption of the mobile charging vehicle; c (C) M The battery capacity of the mobile charging vehicle is; c (C) E Indicating the battery capacity of the mobile charging vehicle.
The implementation of one embodiment of the present invention will be described in detail below in conjunction with an explanation of specific steps.
Fig. 1 is a flowchart of an optimization configuration method of a mobile charging vehicle, referring to fig. 1, the optimization configuration method of the mobile charging vehicle specifically includes the following steps:
s1, acquiring a plurality of groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicles.
And combining market conditions of the mobile charging vehicles, and acquiring multiple groups of mobile charging vehicle configuration data from mobile charging vehicle manufacturers as much as possible. The configuration data includes battery capacity configuration data and fleet proportioning configuration data, wherein the battery capacityConfiguration data represents battery capacity C of each group of mobile charging cars M The fleet proportioning configuration data represents the number n of different types of mobile charging vehicles 1 、n 2 、n 3 . Wherein n is 1 、n 2 、n 3 The number of the mobile charging vehicles which can only provide low-power charging service, the number of the mobile charging vehicles which can only provide high-power charging service and the number of the mobile charging vehicles which can provide high-power charging service and low-power charging service are respectively represented.
And acquiring electric vehicle charging requirement information in the future 24 hours, wherein the electric vehicle charging requirement information comprises a preset charging position LN, a required electric quantity RN, a time window TW and a charging mode MN. Determining node information to be accessed of the mobile charging vehicle, determining the position of a central station and the position of a fixed charging station, converting the electric vehicle and the central station into nodes to be accessed to form m nodes, wherein the nodes with the number of 1 and the number of m are set as the central station, the nodes with the number of 2-m-1 are set as electric vehicles with charging requirements, and the electric vehicles are respectively numbered 2-m-1 according to the sequence in which the charging requirement information of the electric vehicles is acquired. The fixed charging stations are individually numbered 1-q.
Taking node j as an example, the predetermined charging position coordinates of node j are (LNx j ,LNy j ) The charge demand power is RN j The inner time window isThe outer time window is->The selected charging mode is MN j The charging demand information of all nodes can be expressed as:
Charging position information:
the required electric quantity information:
RN＝[RN 1 … RN j … RN m ] T
time window information:
charging mode information:
MN＝[MN 1 … MN j … MN m ] T 。
s2, acquiring an optimal scheduling model based on the charging demand information data and the node information data, and determining constraint conditions of the optimal scheduling model.
The total income of the mobile charging vehicle operator is equal to the charged charging service fee and electricity fee, and the model formula can be expressed as follows:
wherein n is the total number of mobile charging cars and satisfies n=n 1 +n 2 +n 3 M is the total number of the electric automobile nodes and the center station nodes; x is x i,j For deciding whether the mobile charging vehicle i accesses the decision variable of the node j, when x i,j When the value is 1, the i mobile charging vehicle access node j is indicated; when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; alpha i,k For charging service charge of the mobile charging vehicle i, alpha is determined according to the type and charging mode of the mobile charging vehicle i,k The values are also different, wherein k=1, 2 respectively represent a low-power charging mode and a high-power charging mode; RN (RN) j The required electric quantity of the node j is; p is p E And charging the electric car for the mobile charging car. Since the mobile charging vehicle starts from the central station and returns to the central station after all services are finished, the start and stop nodes of the access sequence are both central stations, and the central station does not need to be charged from the mobile charging vehicle, so that the charge demand electric quantity is 0 when the nodes are central stations, for example, when the mobile charging vehicle accesses the nodes 1 (j=1) and m (j=m), the nodes And the charge demand power of (2) is 0.
Each cost of the mobile charging vehicle comprises: the cost of dispatching the mobile charging vehicle is the cost of dispatching the mobile charging vehicle, including the use cost of the mobile charging vehicle and the salary of a driver, and the dispatching cost of a mobile charging vehicle is represented by a fixed value.
Wherein n is the total number of mobile charging vehicles; x is x i To determine whether the ith mobile charging car is dispatched or not, when x is i When the value is 1, the ith mobile charging vehicle is dispatched; when x is i When the value is 0, the ith mobile charging vehicle is not dispatched; epsilon i The dispatching cost for the ith mobile charging vehicle comprises the use cost of the mobile charging vehicle and the payroll of a driver, and epsilon is according to the different types of the mobile charging vehicles i The value is different, and the value does not change along with running time, running distance and the like.
The cost of travel of the mobile charging vehicle to and from the stationary charging station includes the cost of travel from the current location of the electric vehicle to the selected stationary charging station and from the stationary charging station to the next node in the access sequence.
Wherein n is the total number of mobile charging vehicles; m is the total number of the electric automobile nodes and the center station nodes; q is the total number of stationary charging stations; x is x i,j As a decision variable, when x i,j When the value is 1, the i mobile charging vehicle access node j is indicated; when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j;for the decision variables for deciding whether the mobile charging vehicle i, after access to the node j, is going to the stationary charging station for supplementing electric energy, if ∈ ->When the value is 1, the mobile charging vehicle i is led to go to the fixed charging station to supplement electric energy after accessing the node j; when->When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement electric energy after accessing the node j; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; omega j,z To make a decision as to whether the mobile charging vehicle is going to the stationary charging station z after accessing node j, the decision variable is made as omega j,z When the value is 1, the electric energy is supplemented at the fixed charging station z; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1; θ is the unit mileage energy consumption of the mobile charging vehicle; p is p M And charging the mobile charging vehicle for the fixed charging station.
The service cost of the mobile charging vehicle for charging the electric vehicle comprises the running cost of each node in the access sequence, the electric energy cost for charging the electric vehicle and the loss cost of the battery of the mobile charging vehicle.
Wherein n is the total number of mobile charging vehicles; m is the total number of the electric automobile nodes and the center station nodes; x is x i,j As a decision variable, when x i,j When the value is 1, the i mobile charging vehicle access node j is indicated; when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; d, d i,j Distance between the mobile charging vehicle i and the node j; θ is the unit mileage energy consumption of the mobile charging vehicle; p is p M The unit electricity price for charging the mobile charging vehicle for the fixed charging station; RN (RN) j Representing the required electric quantity of the node j; c (C) M The battery capacity of the mobile charging vehicle is; t (T) M Generating for mobile charging vehicle batteryTotal number of cycles in a life cycle; gamma is the replacement cost of the mobile charging vehicle battery; mu (mu) k The loss coefficient of the mobile charging vehicle to the battery is measured when the mobile charging vehicle discharges in the kth charging service mode.
The punishment cost of the mobile charging vehicle violating the time window is the punishment cost when the time of the mobile charging vehicle reaching the node violating the node time window, and when the mobile charging vehicle arrives in the node soft time window, the punishment cost violating the time window does not exist; the cost includes two types of waiting cost arriving earlier than the time window and late cost arriving later than the time window, and the penalty cost per unit time arriving in different time intervals is different.
Wherein, the inner time windowThe node j is a soft time window and represents an optimal time interval for the mobile charging vehicle i to reach; outer time window->The time window is a hard time window, and represents the maximum time interval that the mobile charging vehicle i acceptable by the node j reaches; />The time when the mobile charging vehicle i reaches the node j; />For deciding the decision variables of the time interval when the mobile charging vehicle i reaches the node j, when the value of the decision variables is 1, the decision variables indicate that the mobile charging vehicle i is in the node jReaches node j within a certain time interval; when the decision variable is 0, the mobile charging vehicle i cannot arrive in the corresponding time interval; c e 、c l 、c min Respectively the movable charging vehicles are atPunishment cost per unit time when reaching the node j; when the mobile charging vehicle is at->When the node is reached, the electric automobile cancels the order, and the punishment cost of the mobile charging vehicle in delay is the due income of the order; p is p E The unit electricity price for charging the electric automobile for the mobile charging vehicle; alpha i,k For charging service charge of the mobile charging vehicle i, alpha is determined according to the type and charging mode of the mobile charging vehicle i,k The values are also different, wherein k=1, 2 respectively represent a low-power charging mode and a high-power charging mode; RN (RN) j Is the required power of node j.
In summary, the total profit of the mobile charging vehicle can be expressed as:
max profit＝I-C 1 -C 2 -C 3 -C 4 。
the final purpose of the technical scheme is to obtain the battery capacity configuration data and the configuration data of different types of vehicle proportions when the total profit of the mobile charging vehicle is maximum, so that the formula is used as an objective function of an optimal scheduling model.
Determining constraint conditions of the objective function of the optimal scheduling model specifically comprises the following steps:
time window constraint of nodes: the optimal scheduling of the mobile charging vehicle is static optimal scheduling in the future, so that the time windows of all access nodes are within 24 hours in the future, and the time windows can be expressed as follows:
wherein,respectively the earliest and latest time points of the outer time window of the node j，/> Respectively the earliest and latest time points of the inner time window of the node j; when the node is the central site, the inner and outer time windows are 0,24]I.e.And->
Arrival time constraint of mobile charging vehicle: the time when the mobile charging vehicle arrives at the node j+1 is the time when the mobile charging vehicle arrives at the node j plus the travel time when the mobile charging vehicle serves the node j and goes to the node j+1, or the sum of the charging time of the previous charging power supply to the fixed charging station z and the travel time when the mobile charging vehicle goes to the node j+1 from the fixed charging station z can be expressed as follows:
Wherein,for moving charging car i to the moment when node j+1,/is reached>The time when the mobile charging vehicle i reaches the node j; RN (RN) j Representing the required electric quantity of the node j; r is (r) k The discharging power of the kth mode for charging the electric automobile for the mobile charging vehicle; x is x i,j+1 As a decision variable, when x i,j+1 When the value is 1, the access node j+1 of the ith mobile charging vehicle is indicated; when x is i,j+1 The value of 0 represents the ithThe mobile charging vehicle does not access the node j+1; />As decision variables, when->When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when->When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement electric energy after accessing the node j; d, d i,j+1 Distance between mobile charging car i and node j+1; omega j,z As a decision variable, when ω j,z When the value is 1, replenishing electric energy at the fixed charging station z; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; r is (r) M Charging power of the mobile charging vehicle at a fixed charging station; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1; v is the running speed of the mobile charging vehicle; RM (RM) i,j After the mobile charging vehicle i accesses the node j, supplementing electric quantity at a fixed charging station; r is (r) M The charging power of the mobile charging vehicle at the fixed charging station.
The interval constraint of the arrival time of the mobile charging vehicle: the time of the mobile charging vehicle reaching the node is unique in the interval, and the interval is expressed as follows:
wherein lambda is i,j 、All are decision variables, when lambda i,j The value is 1, which indicates that the mobile charging car i is in +.>Reaching the node j in the time interval; when the decision variable is 0, the mobile charging cars i are respectively indicated to be not arrived in the corresponding time interval. When the mobile charging vehicle is at the inner layer time windowWhen node j arrives internally, the time window is not violated, so the penalty cost of violating the time window is not generated.
The electric automobile accepts service constraints: all nodes except the central node can only be served by one mobile charging car, and the formula is as follows:
wherein n is the total number of mobile charging vehicles; x is x i,j As a decision variable, when x i,j When the value is 1, the access node j of the ith mobile charging vehicle is represented, and when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j;
and (3) restraining the charge and discharge amount of the mobile charging vehicle: if the mobile charging vehicle supplements electric energy in the middle, the sum of the initial electric quantity and the middle supplementing electric quantity is equal to the total electric quantity consumed by completing all the services and returning to the central station; if the mobile charging vehicle is not supplementing electric energy in the middle, the initial electric quantity is not less than the total electric quantity consumed by the mobile charging vehicle, and the initial electric quantity is expressed as follows by a formula:
/>
Wherein m is the total number of the electric automobile nodes and the center station nodes; q is the total number of stationary charging stations; x is x i,j As a decision variable, when x i,j When the value is 1, the access node j of the ith mobile charging vehicle is represented, and when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; RN (RN) j Representing the required electric quantity of the node j; d, d i,j Distance between the mobile charging vehicle i and the node j; theta is the unit of the mobile charging vehicleMileage energy consumption; RM (RM) i,j After the mobile charging vehicle i accesses the node j, supplementing electric quantity at a fixed charging station; omega j,z As a decision variable, when ω j,z When the value is 1, replenishing electric energy at the fixed charging station z; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1;as decision variables, when->When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when->When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement electric energy after accessing the node j; c (C) M The battery capacity of the mobile charging vehicle is;
and (3) constraint of the range of the residual electric quantity of the mobile charging vehicle: the remaining capacity of the battery of the mobile charging vehicle is always larger than zero and smaller than the battery capacity of the mobile charging vehicle, and the remaining capacity of the battery of the mobile charging vehicle is expressed as follows by the formula:
Wherein m is the total number of the electric automobile nodes and the center station nodes; q is the total number of stationary charging stations; c (C) M The battery capacity of the mobile charging vehicle is; x is x i,j As a decision variable, when x i,j When the value is 1, the access node j of the ith mobile charging vehicle is represented, and when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; RN (RN) j Representing the required electric quantity of the node j; d, d i,j Distance between the mobile charging vehicle i and the node j; θ is the unit mileage energy consumption of the mobile charging vehicle;as decision variables, when->When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when->When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement electric energy after accessing the node j; RM (RM) i,j After the mobile charging vehicle i accesses the node j, supplementing electric quantity at a fixed charging station; omega j,z As a decision variable, when ω j,z When the value is 1, replenishing electric energy at the fixed charging station z; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1;
electric automobile demand electricity quantity constraint: the charge demand electric quantity at the node is more than 0 and less than the battery capacity of the electric automobile at the node; the battery capacity of the center site is 0.
0≤RN j ≤C E
Wherein, RN j Representing the required electric quantity of the node j; c (C) E Indicating the battery capacity of the mobile charging vehicle.
And S3, solving the optimal scheduling model by using the configuration data, and determining an optimal configuration scheme based on a solving result.
And substituting the obtained configuration data of each group of mobile charging vehicles into the optimal scheduling model, performing optimal scheduling based on an objective function of the optimal scheduling model, determining a group of mobile charging vehicle configuration data with the maximum total profit of the mobile charging vehicle operators as an optimal configuration scheme, and feeding back the optimal configuration scheme (comprising optimal battery capacity configuration and optimal quantity ratio of mobile charging vehicles in the fleet, wherein the optimal quantity ratio of mobile charging vehicles can provide high-power charging service, low-power charging service and high-power charging service) to the mobile charging vehicle operators.
Example 2:
in a second aspect, the invention further provides a mobile charging vehicle optimal configuration system, which comprises:
the data acquisition module is used for acquiring a plurality of groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicles;
the model acquisition module is used for acquiring an optimal scheduling model based on the charging demand information data and the node information data and determining constraint conditions of the optimal scheduling model;
And the configuration scheme determining module is used for solving the optimal scheduling model by utilizing the configuration data and determining an optimal configuration scheme based on a solving result.
Preferably, the configuration data acquired by the data acquisition module includes: battery capacity configuration data and fleet proportioning configuration data; the charging demand information data includes: predetermined charging location, required power, time window, and charging mode data; the node information data includes: electric vehicle position data, center station position data, and stationary charging station position data.
Preferably, the objective function of the optimized scheduling model constructed by the model acquisition module is as follows:
max profit＝I-C 1 -C 2 -C 3 -C 4
wherein, I represents the total income of the mobile charging vehicle operators; c (C) 1 Representing the dispatch cost of the mobile charging vehicle; c (C) 2 The running cost of the mobile charging vehicle to and from the fixed charging station is represented; c (C) 3 The service cost of charging the electric automobile by the mobile charging vehicle is represented; c (C) 4 And (5) representing the punishment cost of the mobile charging vehicle for violating the time window.
Preferably, the total income I of the mobile charging vehicle operators is as follows:
dispatch cost C of mobile charging vehicle 1 ：
Running cost C of mobile charging vehicle to and from fixed charging station 2 ：
Service cost C for charging electric automobile by mobile charging vehicle 3 ：
Punishment cost C of mobile charging vehicle violating time window 4 ：
Wherein n is the total number of mobile charging vehicles; m is the total number of the electric automobile nodes and the center station nodes; x is x i,j As a decision variable, when x i,j When the value is 1, the i mobile charging vehicle access node j is indicated; when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; alpha i,k Charging service charge for the mobile charging vehicle i; k represents a charging mode; RN (RN) j Representing the required electric quantity of the node j; p is p E The unit electricity price for charging the electric automobile for the mobile charging vehicle; x is x i As a decision variable, when x i When the value is 1, the ith mobile charging vehicle is dispatched; when x is i When the value is 0, the dispatch of the first item is not indicated i The vehicle moves a charging vehicle; epsilon i Dispatch cost for the ith mobile charging vehicle; q is the total number of stationary charging stations;as decision variables, when->When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when->When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement electric energy after accessing the node j; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; omega j,z As a decision variable, when ω j，z When the value is 1, replenishing electric energy at the fixed charging station z; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1; θ is the unit mileage energy consumption of the mobile charging vehicle; p is p M The unit electricity price for charging the mobile charging vehicle for the fixed charging station; d, d i,j Distance between the mobile charging vehicle i and the node j; c (C) M The battery capacity of the mobile charging vehicle is; t (T) M The total cycle times of the mobile charging vehicle in the life cycle of the battery are set; gamma is the replacement cost of the mobile charging vehicle battery; mu (mu) k The loss coefficient of the mobile charging vehicle to the battery is calculated when the mobile charging vehicle discharges in the kth charging service mode; inner time window->The node j is a soft time window and represents an optimal time interval for the mobile charging vehicle i to reach; outer time window->The time window is a hard time window, and represents the maximum time interval that the mobile charging vehicle i acceptable by the node j reaches; />The time when the mobile charging vehicle i reaches the node j; />Are all decision variables, and when the values of the decision variables take 1, the decision variables respectively indicate that the mobile charging car i is at +.>Reaches node j within a certain time interval; when the decision variable is 0, respectively indicating that the mobile charging vehicle i cannot arrive in the corresponding time interval; c e 、c l 、c min The movable charging vehicles are at ∈> Penalty cost per unit time when node j is reached.
Preferably, the constraint condition includes: time window constraint of nodes:
arrival time constraint of mobile charging vehicle:
the interval constraint of the arrival time of the mobile charging vehicle:
the electric automobile accepts service constraints:
and (3) restraining the charge and discharge amount of the mobile charging vehicle:
and (3) constraint of the range of the residual electric quantity of the mobile charging vehicle:
electric automobile demand electricity quantity constraint:
0≤RN j ≤C E
wherein,respectively the earliest and latest time points of the outer time window of the node j; /> Respectively the earliest and latest time points of the inner time window of the node j; />The time when the mobile charging vehicle i reaches the node j+1; />The time when the mobile charging vehicle i reaches the node j; r is (r) k The discharging power of the kth mode for charging the electric automobile for the mobile charging vehicle; x is x i,j+1 As a decision variable, when x i,j+1 When the value is 1, the access node j+1 of the ith mobile charging vehicle is indicated; when x is i,j+1 When the value is 0, the ith mobile charging vehicle does not access the node j+1; />As decision variables, when->Take the value of1, the mobile charging vehicle i goes to a fixed charging station to supplement electric energy after accessing a node j; when->When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement electric energy after accessing the node j; d, d i,j+1 Distance between mobile charging car i and node j+1; omega j,z As a decision variable, when ω j,z When the value is 1, replenishing electric energy at the fixed charging station z; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; r is (r) M Charging power of the mobile charging vehicle at a fixed charging station; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1; v is the running speed of the mobile charging vehicle; RN (RN) j The required electric quantity of the node j is; RM (RM) i,j After the mobile charging vehicle i accesses the node j, supplementing electric quantity at a fixed charging station; lambda (lambda) i,j 、/>All are decision variables, when lambda i,j The value is 1, which indicates that the mobile charging car i is in +.>Reaching the node j in the time interval; when the decision variable is 0, respectively indicating that the mobile charging vehicle i cannot arrive in the corresponding time interval; n is the total number of mobile charging cars; x is x i,j As a decision variable, when x i,j When the value is 1, the access node j of the ith mobile charging vehicle is represented, and when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; m is the total number of the electric automobile nodes and the center station nodes; q is the total number of stationary charging stations; x is x i,j As a decision variable, when x i,j When the value is 1, the access node j of the ith mobile charging vehicle is represented, and when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; d, d i,j Distance between the mobile charging vehicle i and the node j; theta is the unit of the mobile charging vehicleMileage energy consumption; c (C) M The battery capacity of the mobile charging vehicle is; c (C) E Indicating the battery capacity of the mobile charging vehicle.
It can be understood that the mobile charging vehicle optimal configuration system provided by the embodiment of the invention corresponds to the mobile charging vehicle optimal configuration method, and the explanation, the examples, the beneficial effects and the like of the relevant content can refer to the corresponding content in the mobile charging vehicle optimal configuration method, and are not repeated here.
In summary, compared with the prior art, the method has the following beneficial effects:
1. according to the method, multiple groups of mobile charging vehicle configuration data are obtained, a mobile charging vehicle optimizing and scheduling model is built based on charging demand information data and node information data of the mobile charging vehicles, then the obtained multiple groups of mobile charging vehicle configuration data are brought into the optimizing and scheduling model to be calculated and solved, and finally accurate data of optimizing configuration are obtained, so that an optimal mobile charging vehicle configuration scheme is determined. The technical scheme effectively solves the problem of optimal configuration of the mobile charging vehicle, and has strong adaptability to flexible and changeable charging requirements of the electric vehicle;
2. According to the invention, the configuration optimization is carried out on the battery capacity configuration and the fleet proportioning configuration of the mobile charging vehicles, so that the high cost of the mobile charging vehicles caused by the overhigh battery capacity can be reduced, and the different requirements of the electric vehicle charging are effectively met by reasonable proportioning of the quantity of the mobile charging vehicles;
3. in the invention, in the process of optimizing and dispatching the mobile charging vehicle optimizing and dispatching model, the constraint of the soft time window and the hard time window is set, the punishment cost of the mobile charging vehicle against the time window is fully considered, and the optimizing result is more accurate.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (4)

1. The mobile charging vehicle optimal configuration method is characterized by comprising the following steps of:
acquiring a plurality of groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicles; the configuration data includes: battery capacity configuration data and fleet proportioning configuration data; the charging demand information data includes: predetermined charging location, required power, time window, and charging mode data; the node information data includes: electric vehicle position data, center station position data, and stationary charging station position data;
acquiring an optimal scheduling model based on the charging demand information data and the node information data, and determining constraint conditions of the optimal scheduling model; the objective function of the optimal scheduling model is as follows:
max profit＝I-C 1 -C 2 -C 3 -C 4
Wherein, I represents the total income of the mobile charging vehicle operators; c (C) 1 Representing the dispatch cost of the mobile charging vehicle; c (C) 2 Indicating the movement of a mobile charging vehicle to and from a stationary charging stationRunning cost; c (C) 3 The service cost of charging the electric automobile by the mobile charging vehicle is represented; c (C) 4 The punishment cost of the mobile charging vehicle violating the time window is represented;
solving the optimal scheduling model by using the configuration data, and determining an optimal configuration scheme based on a solving result;
wherein, mobile charging car operator total income I:
dispatch cost C of mobile charging vehicle 1 ：
Running cost C of mobile charging vehicle to and from fixed charging station 2 ：
Service cost C for charging electric automobile by mobile charging vehicle 3 ：
Punishment cost C of mobile charging vehicle violating time window 4 ：
Wherein n is the total number of mobile charging vehicles; m is the total number of the electric automobile nodes and the center station nodes; x is x i,j As a decision variable, when x i,j When the value is 1, the i mobile charging vehicle access node j is indicated; when x is i,j When the value is 0Indicating that the ith mobile charging vehicle does not access the node j; alpha i,k Charging service charge for the mobile charging vehicle i; k represents a charging mode; RN (RN) j Representing the required electric quantity of the node j; p is p E The unit electricity price for charging the electric automobile for the mobile charging vehicle; x is x i As a decision variable, when x i When the value is 1, the ith mobile charging vehicle is dispatched; when x is i When the value is 0, the ith mobile charging car epsilon is not dispatched i The method comprises the steps of carrying out a first treatment on the surface of the Dispatch cost for the ith mobile charging vehicle; q is the total number of stationary charging stations;as decision variables, when->When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when->When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement electric energy after accessing the node j; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; omega j,z As a decision variable, when ω j,z When the value is 1, replenishing electric energy at the fixed charging station z; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1; θ is the unit mileage energy consumption of the mobile charging vehicle; p is p M The unit electricity price for charging the mobile charging vehicle for the fixed charging station; d, d i,j Distance between the mobile charging vehicle i and the node j; c (C) M The battery capacity of the mobile charging vehicle is; t (T) M The total cycle times of the mobile charging vehicle in the life cycle of the battery are set; gamma is the replacement cost of the mobile charging vehicle battery; mu (mu) k The loss coefficient of the mobile charging vehicle to the battery is calculated when the mobile charging vehicle discharges in the kth charging service mode; inner time window- >The node j is a soft time window and represents an optimal time interval for the mobile charging vehicle i to reach; outer time window->The time window is a hard time window, and represents the maximum time interval that the mobile charging vehicle i acceptable by the node j reaches; />The time when the mobile charging vehicle i reaches the node j; />Are all decision variables, and when the values of the decision variables take 1, the decision variables respectively indicate that the mobile charging car i is at +.>Reaches node j within a certain time interval; when the decision variable is 0, respectively indicating that the mobile charging vehicle i cannot arrive in the corresponding time interval; c e 、c l 、c min The movable charging vehicles are at ∈> Penalty cost per unit time when node j is reached.
2. The method of claim 1, wherein the constraint comprises: time window constraint of nodes:
arrival time constraint of mobile charging vehicle:
the interval constraint of the arrival time of the mobile charging vehicle:
the electric automobile accepts service constraints:
and (3) restraining the charge and discharge amount of the mobile charging vehicle:
and (3) constraint of the range of the residual electric quantity of the mobile charging vehicle:
electric automobile demand electricity quantity constraint:
0≤RN j ≤C E
wherein,respectively the earliest and latest time points of the outer time window of the node j; /> Respectively the earliest and latest time points of the inner time window of the node j; />The time when the mobile charging vehicle i reaches the node j+1; / >The time when the mobile charging vehicle i reaches the node j; r is (r) k The discharging power of the kth mode for charging the electric automobile for the mobile charging vehicle; x is x i,j+1 As a decision variable, when x i,j+1 When the value is 1, the access node j+1 of the ith mobile charging vehicle is indicated; when x is i,j+1 When the value is 0, the ith mobile charging vehicle does not access the node j+1; />As decision variables, when->When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when->When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement electric energy after accessing the node j; d, d i,j+1 Distance between mobile charging car i and node j+1; omega j,z As a decision variable, when ω j,z When the value is 1, replenishing electric energy at the fixed charging station z; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; r is (r) M Charging power of the mobile charging vehicle at a fixed charging station; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1; v is the running speed of the mobile charging vehicle; RN (RN) j The required electric quantity of the node j is; RM (RM) i,j After the mobile charging vehicle i accesses the node j, supplementing electric quantity at a fixed charging station; lambda (lambda) i,j 、/>All are decision variables, when lambda i,j The value is 1, which indicates that the mobile charging car i is in +.>Reaching the node j in the time interval; when the decision variable is 0, respectively indicating that the mobile charging vehicle i cannot arrive in the corresponding time interval; n is the total number of mobile charging cars; x is x i,j As a decision variable, when x i,j When the value is 1, the access node j of the ith mobile charging vehicle is represented, and when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; m is the total number of the electric automobile nodes and the center station nodes; q is the total number of stationary charging stations; x is x i, j is a decision variable, when x i,j When the value is 1, the access node j of the ith mobile charging vehicle is represented, and when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; d, d i,j Distance between the mobile charging vehicle i and the node j; θ is the unit mileage energy consumption of the mobile charging vehicle; c (C) M The battery capacity of the mobile charging vehicle is; c (C) E Indicating the battery capacity of the mobile charging vehicle.
3. An optimal configuration system for a mobile charging vehicle, the system comprising:
the data acquisition module is used for acquiring a plurality of groups of mobile charging vehicle configuration data, electric vehicle charging demand information data and node information data to be accessed by the mobile charging vehicles; the configuration data includes: battery capacity configuration data and fleet proportioning configuration data; the charging demand information data includes: predetermined charging location, required power, time window, and charging mode data; the node information data includes: electric vehicle position data, center station position data, and stationary charging station position data; the model acquisition module is used for acquiring an optimal scheduling model based on the charging demand information data and the node information data and determining constraint conditions of the optimal scheduling model; the objective function of the optimal scheduling model is as follows:
max profit＝I-C 1 -C 2 -C 3 -C 4
Wherein, I represents the total income of the mobile charging vehicle operators; c (C) 1 Representing the dispatch cost of the mobile charging vehicle; c (C) 2 The running cost of the mobile charging vehicle to and from the fixed charging station is represented; c (C) 3 The service cost of charging the electric automobile by the mobile charging vehicle is represented; c (C) 4 The punishment cost of the mobile charging vehicle violating the time window is represented;
the configuration scheme determining module is used for solving the optimal scheduling model by utilizing the configuration data and determining an optimal configuration scheme based on a solving result;
wherein, mobile charging car operator total income I:
dispatch cost C of mobile charging vehicle 1 ：
Running cost C of mobile charging vehicle to and from fixed charging station 2 ：
Service cost C for charging electric automobile by mobile charging vehicle 3 ：
Punishment cost C of mobile charging vehicle violating time window 4 ：
Wherein n is the total number of mobile charging vehicles; m is the total number of the electric automobile nodes and the center station nodes; x is x i, j is a decision variable, when x i, j represents an access node j of the ith mobile charging vehicle when the value of j is 1; when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; alpha i,k Charging service charge for the mobile charging vehicle i; k represents a charging mode; RN (RN) j Representing the required electric quantity of the node j; p is p E The unit electricity price for charging the electric automobile for the mobile charging vehicle; x is x i As a decision variable, when x i When the value is 1, the ith mobile charging vehicle is dispatched; when x is i When the value is 0, the ith mobile charging car epsilon is not dispatched i The method comprises the steps of carrying out a first treatment on the surface of the Dispatch cost for the ith mobile charging vehicle; q is the total number of stationary charging stations;as decision variables, when->When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when->When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement electric energy after accessing the node j; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; omega j,z As a decision variable, when ω j,z When the value is 1, replenishing electric energy at the fixed charging station z; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1; θ is the unit mileage energy consumption of the mobile charging vehicle; p is p M The unit electricity price for charging the mobile charging vehicle for the fixed charging station; d, d i,j Distance between the mobile charging vehicle i and the node j; c (C) M The battery capacity of the mobile charging vehicle is; t (T) M The total cycle times of the mobile charging vehicle in the life cycle of the battery are set; gamma is the replacement cost of the mobile charging vehicle battery; mu (mu) k The loss coefficient of the mobile charging vehicle to the battery is calculated when the mobile charging vehicle discharges in the kth charging service mode; inner time window- >The node j is a soft time window and represents an optimal time interval for the mobile charging vehicle i to reach; outer time window->The time window is a hard time window, and represents the maximum time interval that the mobile charging vehicle i acceptable by the node j reaches; />The time when the mobile charging vehicle i reaches the node j; />Are all decision variables, and when the values of the decision variables take 1, the decision variables respectively indicate that the mobile charging car i is at +.>Reaches node j within a certain time interval; when the decision variable is 0, respectively indicating that the mobile charging vehicle i cannot arrive in the corresponding time interval; c e 、c l 、c min The movable charging vehicles are at ∈> Penalty cost per unit time when node j is reached.
4. The system of claim 3, wherein the constraint comprises: time window constraint of nodes:
arrival time constraint of mobile charging vehicle:
the interval constraint of the arrival time of the mobile charging vehicle:
the electric automobile accepts service constraints:
and (3) restraining the charge and discharge amount of the mobile charging vehicle:
and (3) constraint of the range of the residual electric quantity of the mobile charging vehicle:
electric automobile demand electricity quantity constraint:
0≤RN j ≤C E
wherein,respectively the earliest and latest time points of the outer time window of the node j; /> Respectively the earliest and latest time points of the inner time window of the node j; />The time when the mobile charging vehicle i reaches the node j+1; / >The time when the mobile charging vehicle i reaches the node j; r is (r) k The discharging power of the kth mode for charging the electric automobile for the mobile charging vehicle; x is x i,j+1 As a decision variable, when x i,j+1 When the value is 1, the access node j+1 of the ith mobile charging vehicle is indicated; when x is i,j+1 When the value is 0, the ith mobile charging vehicle does not access the node j+1; />As decision variables, when->When the value is 1, the mobile charging vehicle i goes to the fixed charging station to supplement electric energy after accessing the node j; when->When the value is 0, the mobile charging vehicle i does not go to the fixed charging station to supplement electric energy after accessing the node j; d, d i,j+1 Distance between mobile charging car i and node j+1; omega j,z As a decision variable, when ω j,z When the value is 1, replenishing electric energy at the fixed charging station z; when omega j,z When the value is 0, the electric energy is not supplemented at the fixed charging station z; r is (r) M Charging power of the mobile charging vehicle at a fixed charging station; d, d i,z The distance between the current position of the mobile charging vehicle and the fixed charging station z is set; d, d z,j+1 Is the distance between the fixed charging station z and the node j+1; v is the running speed of the mobile charging vehicle; RN (RN) j The required electric quantity of the node j is; RM (RM) i,j After the mobile charging vehicle i accesses the node j, supplementing electric quantity at a fixed charging station; lambda (lambda) i,j 、/>All are decision variables, when lambda i,j The value is 1, which respectively indicates that the mobile charging car i is inReaching the node j in the time interval; when the decision variable is 0, respectively indicating that the mobile charging vehicle i cannot arrive in the corresponding time interval; n is the total number of mobile charging cars; x is x i,j As a decision variable, when x i,j When the value is 1, the access node j of the ith mobile charging vehicle is represented, and when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; m is the total number of the electric automobile nodes and the center station nodes; q is the total number of stationary charging stations; x is x i,j As a decision variable, when x i,j When the value is 1, the access node j of the ith mobile charging vehicle is represented, and when x is i,j When the value is 0, the ith mobile charging vehicle does not access the node j; d, d i,j Distance between the mobile charging vehicle i and the node j; θ is the unit mileage energy consumption of the mobile charging vehicle; c (C) M The battery capacity of the mobile charging vehicle is; c (C) E Indicating the battery capacity of the mobile charging vehicle. />
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