CN106056255A - Space-time joint scheduling ordered charging method and device - Google Patents

Abstract

The invention provides a space-time joint scheduling ordered charging method and device. The method comprises the steps of: standardizing a plurality of target functions related to time scheduling and space scheduling of an electric vehicle respectively; normalizing the plurality of target functions, converting a multi-target optimization problem into a single-target optimization problem, and working out the departure time when the electric vehicle is driven to a charging station and the charging expense. The method and the device solve the problem that the charging station overloads or the charging resource of the charging station is wasted due to the charging load imbalance of the charging station, and improve the space-time utilization rate of the charging station.

Description

Time-space joint scheduling ordered charging method and device

Technical Field

The invention relates to the field of electric vehicle charging scheduling, in particular to a time-space joint scheduling ordered charging method and device.

Background

Under the condition that the electric automobile develops to a certain market scale, the influence factors of the charging load of the electric automobile can be summarized into four aspects, namely the type and the number of the electric automobiles, the running characteristics of the electric automobile, the charging behavior habits of electric automobile users, the type of charging facilities and the construction layout.

At present, the electric vehicle is lack of scheduling for charging, and a user randomly selects time and selects a charging station nearby for charging, so that the charging stations in a charging peak period and a densely populated area run in an overload mode, and charging resources of the charging stations with relatively sparse population and charging valleys are wasted.

Although the related art can adopt the peak-shifting electricity price and adopt different charging prices for different charging stations to enable a user to autonomously select the charging time and the charging stations so as to balance the charging load of each charging station in each time interval, the selection depends on the subjective judgment of the user, and is lack of controllability and not ideal in the effect of balancing the charging load.

Aiming at the problem that charging resources of a charging station are wasted or overload operation of the charging station is caused due to unbalanced charging load of the charging station in the related technology, an effective solution is not provided yet.

Disclosure of Invention

The invention provides a time-space combined scheduling ordered charging method and a time-space combined scheduling ordered charging device, which at least solve the problem that charging stations run in an overload mode or charging resources of the charging stations are wasted due to unbalanced charging loads of the charging stations.

According to an aspect of the present invention, there is provided a space-time joint scheduling ordered charging method, including:

respectively carrying out standardized processing on a plurality of objective functions respectively related to time scheduling and space scheduling of the electric vehicle:

$F=min({\mathrm{\λ}}_1\frac{F_1}{F_{1max}}+{\mathrm{\λ}}_2\frac{F_2}{F_{2max}}+...);$

in the formula: f_1max、F_2maxThe target function corresponding to the original load curve before dispatching; lambda [ alpha ]₁、λ₂As an objective function F₁、F₂Corresponding weight coefficient, and λ₁+λ₂+…＝1；

And carrying out normalization processing on the plurality of objective functions, converting the multi-objective optimization problem into a single-objective optimization problem, and solving the departure time and the charging cost of the electric vehicle moving to the charging station.

Optionally, the plurality of objective functions comprises:

the method comprises the following steps of taking the space-time utilization rate of a charging facility as an objective function, distributing charging vehicles according to time, and averaging the time characteristics of the charging vehicles, wherein the objective function of the space-time utilization rate is as follows:

wherein,

time utilization

Space utilization rate

Wherein T is the total duration, M is the total number of vehicles that need to be charged in the time duration inner region of T, N is the number of charging stations that drop into operation in the time duration inner region of T, C_nNumber of charging piles, P, for the nth charging station_mCharging power for the mth vehicle in the charging station; x is the number of_mntThe three-dimensional decision variable represents a charging decision variable of the mth vehicle at the tth moment in the charging station n, and the variable value is 0 or 1.

Optionally, the plurality of objective functions comprises:

the algorithm for improving the load distribution of the regional power distribution network to minimize the operation loss of the regional power distribution network is as follows:

$F_2=min\underset{t=1}{\overset{T}{\Σ}}(P_{loss,t}^2+Q_{loss,t}^2);$

wherein, P_loss,t，Q_loss,tRespectively the active loss and the reactive loss of the regional power distribution network power flow at the t moment.

Optionally, the plurality of objective functions comprises:

an algorithm that minimizes electric vehicle user charging service time, wherein an objective function of electric vehicle user charging service time is:

$F_3=min\underset{m=1}{\overset{M}{\Σ}}{\mathrm{TT}}_{mn,onway}+{\mathrm{TT}}_{mn,wait}+{\mathrm{TT}}_{mn,c};$

wherein TT_mn,onway，TT_mn,wait，TT_mn,cThe time period T is the time taken by the mth vehicle to travel to the charging station n, the waiting time for charging at the charging station n, and the charging time.

Optionally, the plurality of objective functions comprises:

an algorithm for making the charging service cost of the electric vehicle user the most economical, wherein the objective function of the charging service cost of the electric vehicle user is as follows:

$F_4=min\underset{t=1}{\overset{T}{\Σ}}{c}_t\left(\underset{n=1}{\overset{N}{\Σ}}{c}_n\underset{m=1}{\overset{M}{\Σ}}{x}_{mnt}{P}_m\mathrm{\Δ}t\right);$

wherein, c_tAs a time-varying unit time of electricity prices, c_nCharging unit electricity prices for different charging stations, T is total duration, M is total number of vehicles needing to be charged in the time duration inner area of T, N is number of charging stations put into operation in the time duration inner area of T, P_mCharging power for the mth vehicle in the charging station; x is the number of_mntThe three-dimensional decision variable represents a charging decision variable of the mth vehicle at the tth moment in the charging station n, and the variable value is 0 or 1.

According to another aspect of the present invention, there is also provided a space-time joint scheduling ordered charging apparatus, including:

the normalization module is used for respectively carrying out normalization processing on a plurality of objective functions respectively related to time scheduling and space scheduling of the electric vehicle:

$F=min({\mathrm{\λ}}_1\frac{F_1}{F_{1max}}+{\mathrm{\λ}}_2\frac{F_2}{F_{2max}}+...);$

in the formula: f_1max、F_2maxThe target function corresponding to the original load curve before dispatching; lambda [ alpha ]₁、λ₂As an objective function F₁、F₂Corresponding weight coefficient, and λ₁+λ₂+…＝1；

And the optimization module is used for carrying out normalization processing on the plurality of objective functions, converting the multi-objective optimization problem into a single-objective optimization problem and solving the departure time and the charging cost of the electric vehicle driving to the charging station.

According to the invention, a plurality of objective functions respectively related to the time scheduling and the space scheduling of the electric vehicle are respectively subjected to standardization processing; the method has the advantages that normalization processing is carried out on the multiple objective functions, the multi-objective optimization problem is converted into the single-objective optimization problem, the starting time and the charging cost of the electric vehicle moving to the charging station are solved, the problem that the charging station runs in an overload mode or charging resources of the charging station are wasted due to unbalanced charging load of the charging station is solved, and the space-time utilization rate of the charging station is improved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:

FIG. 1 is a flow chart of a spatio-temporal joint scheduling ordered charging method according to an embodiment of the present invention;

FIG. 2 is a schematic structural diagram of a spatio-temporal joint scheduling ordered charging apparatus according to an embodiment of the present invention;

fig. 3 is a flow chart of an improved particle swarm algorithm according to a preferred embodiment of the present invention.

Detailed Description

The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.

In an embodiment of the present invention, a spatio-temporal joint scheduling ordered charging method is provided, fig. 1 is a flowchart of the spatio-temporal joint scheduling ordered charging method according to the embodiment of the present invention, as shown in fig. 1, the flowchart includes the following steps:

step S101, respectively carrying out standardization processing on a plurality of objective functions respectively related to time scheduling and space scheduling of the electric vehicle:

$F=\min ({\mathrm{\λ}}_1\frac{F_1}{F_{1\max }}+{\mathrm{\λ}}_2\frac{F_2}{F_{2\max }}+...);$

in the formula: f_1max、F_2maxThe target function corresponding to the original load curve before dispatching; lambda [ alpha ]₁、λ₂As an objective function F₁、F₂Corresponding weight coefficient, and λ₁+λ₂+…＝1；

And S102, carrying out normalization processing on the plurality of objective functions, converting the multi-objective optimization problem into a single-objective optimization problem, and solving the departure time and the charging cost of the electric vehicle driving to the charging station.

Through the steps, the departure time and the charging cost of the electric vehicle moving to the charging station are calculated by adopting a time-space combined scheduling ordered charging method and combining the objective functions of time scheduling and space scheduling, the problem that the charging station runs in an overload mode or charging resources of the charging station are wasted due to unbalanced charging load of the charging station is solved, and the time-space utilization rate of the charging station is improved.

Alternatively, after calculating the departure time and the charging fee for the electric vehicle traveling to the charging station, the charging station identification, the departure time, and the charging fee of the charging station may be transmitted to the electric vehicle.

Optionally, the spatio-temporal joint scheduling ordered charging method includes: according to historical charging data of a plurality of charging stations, counting charging load characteristics of the plurality of charging stations; and calculating the charging station which provides charging service for the electric vehicle from the plurality of charging stations by taking the characteristic peak clipping and valley leveling of the charging load as one of the optimization targets.

Optionally, the spatio-temporal joint scheduling ordered charging method includes: the method comprises the following steps of taking the space-time utilization rate of a charging facility as an objective function, distributing charging vehicles according to time, and averaging the time characteristics of the charging vehicles, wherein the objective function of the space-time utilization rate is as follows:

wherein,

time utilization

Space utilization rate

Wherein T is the total duration, M is the total number of vehicles that need to be charged in the time duration inner region of T, N is the number of charging stations that drop into operation in the time duration inner region of T, C_nNumber of charging piles, P, for the nth charging station_mCharging power for the mth vehicle in the charging station; x is the number of_mntThe three-dimensional decision variable represents a charging decision variable of the mth vehicle at the tth moment in the charging station n, and the variable value is 0 or 1.

Optionally, the spatio-temporal joint scheduling ordered charging method includes: the algorithm for improving the load distribution of the regional power distribution network to minimize the operation loss of the regional power distribution network is as follows:

$F_2=min\underset{t=1}{\overset{T}{\Σ}}(P_{loss,t}^2+Q_{loss,t}^2);$

wherein, P_loss,t，Q_loss,tRespectively the active loss and the reactive loss of the regional power distribution network power flow at the t moment.

Optionally, the spatio-temporal joint scheduling ordered charging method includes: an algorithm that minimizes electric vehicle user charging service time, wherein an objective function of electric vehicle user charging service time is:

$F_3=min\underset{m=1}{\overset{M}{\Σ}}{\mathrm{TT}}_{mn,onway}+{\mathrm{TT}}_{mn,wait}+{\mathrm{TT}}_{mn,c};$

wherein TT_mn,onway，TT_mn,wait，TT_mn,cRespectively means the waiting time of the mth vehicle to the charging station n in the time length of T and the charging in the charging station nTime and charging time.

Optionally, the spatio-temporal joint scheduling ordered charging method includes: an algorithm for making the charging service cost of the electric vehicle user the most economical, wherein the objective function of the charging service cost of the electric vehicle user is as follows:

$F_4=min\underset{t=1}{\overset{T}{\Σ}}{c}_t\left(\underset{n=1}{\overset{N}{\Σ}}{c}_n\underset{m=1}{\overset{M}{\Σ}}{x}_{mnt}{P}_m\mathrm{\Δ}t\right);$

wherein, c_tAs a time-varying unit time of electricity prices, c_nCharging unit electricity prices for different charging stations, T is total duration, M is total number of vehicles needing to be charged in the time duration inner area of T, N is number of charging stations put into operation in the time duration inner area of T, P_mCharging power for the mth vehicle in the charging station; x is the number of_mntThe three-dimensional decision variable represents a charging decision variable of the mth vehicle at the tth moment in the charging station n, and the variable value is 0 or 1.

Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.

The present embodiment further provides a space-time joint scheduling ordered charging device, which is used to implement the foregoing embodiments and preferred embodiments, and the description of the space-time joint scheduling ordered charging device is omitted for brevity. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.

Fig. 2 is a schematic structural diagram of a spatio-temporal joint scheduling ordered charging device according to an embodiment of the invention. As shown in fig. 2, the apparatus includes: a normalization module 21 and an optimization module 22, wherein,

the normalization module 21 is configured to perform normalization processing on a plurality of objective functions respectively related to time scheduling and space scheduling of the electric vehicle; and the optimization module 22 is coupled to the normalization module 21 and is used for performing normalization processing on the plurality of objective functions, converting the multi-objective optimization problem into a single-objective optimization problem, and solving the departure time and the charging cost of the electric vehicle moving to the charging station.

It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in a plurality of processors.

The embodiment of the present invention also provides software for executing the technical solutions described in the above embodiments and preferred embodiments.

The embodiment of the invention also provides a storage medium. In the present embodiment, the storage medium described above may be configured to store program code for performing the steps of:

step S101, respectively carrying out standardization processing on a plurality of objective functions respectively related to time scheduling and space scheduling of the electric vehicle:

$F=min({\mathrm{\λ}}_1\frac{F_1}{F_{1max}}+{\mathrm{\λ}}_2\frac{F_2}{F_{2max}}+...);$

in the formula: f_1max、F_2maxThe target function corresponding to the original load curve before dispatching; lambda [ alpha ]₁、λ₂As an objective function F₁、F₂Corresponding weight coefficient, and λ₁+λ₂+…＝1；

And S102, carrying out normalization processing on the plurality of objective functions, converting the multi-objective optimization problem into a single-objective optimization problem, and solving the departure time and the charging cost of the electric vehicle driving to the charging station.

Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.

Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.

In order that the description of the embodiments of the invention will be more apparent, reference is now made to the preferred embodiments for illustration.

Establishment of space-time scheduling model

(1) Time utilization rate of charging facility of charging station is improved

Due to the operation characteristics of the electric automobile, the efficiency of the charging equipment is unbalanced in the whole time range, so that vehicles to be charged are intensively selected to be charged in a certain time period, the time utilization rate of the charging equipment is reduced, and the load characteristic of a power distribution network is also negatively influenced. From the perspective of a charging station operator, the charging facilities are improved in time utilization rate as an objective function, the charging vehicles are distributed according to time, the time characteristics of the charging vehicles are averaged, imbalance of charging load of the charging station in time can be reduced, and the utilization rate of charging equipment in the charging station is higher. The objective function is:

$F_1=min\sqrt{F_{1,time}+F_{1,space}}---(1-1)$

$F_{1,time}=\underset{t=1}{\overset{T}{\Σ}}{(\underset{m=1}{\overset{M}{\Σ}}{x}_{mnt}{P}_m-\frac{1}{T}\underset{t=1}{\overset{T}{\Σ}}\underset{m=1}{\overset{M}{\Σ}}{x}_{mnt}{P}_m)}^2---(1-2)$

$F_{1,space}=\underset{n=1}{\overset{N}{\Σ}}{(\underset{m=1}{\overset{M}{\Σ}}{x}_{mn}{P}_m-\frac{C_n}{\underset{j=1}{\overset{N}{\Σ}}{C}_n}\underset{m=1}{\overset{M}{\Σ}}{P}_m)}^2---(1-3)$

in the formula: x is the number of_mnt-a three-dimensional decision variable representing the charging decision variable of the mth vehicle at the time t within the charging station n, the variable value being 0 or 1;

(2) improving load distribution in regional distribution networks

The aim of optimizing the load of the regional distribution network is to improve the minimum running loss of the regional distribution network.

$F_2=min\underset{t=1}{\overset{T}{\Σ}}(P_{loss,t}^2+Q_{loss,t}^2)---(1-4)$

Wherein, P_loss,t,Q_loss,tRespectively the active loss and the reactive loss of the regional power distribution network power flow at the t moment.

(3) Minimizing user charging service time for electric vehicles

$F_3=min\underset{m=1}{\overset{M}{\Σ}}{\mathrm{TT}}_{mn,onway}+{\mathrm{TT}}_{mn,wait}+{\mathrm{TT}}_{mn,c}---(1-5)$

Wherein TT_mn,onway,TT_mn,wait,TT_mn,cThe time period T is the time taken by the mth vehicle to travel to the charging station n, the waiting time for charging at the charging station n, and the charging time.

(4) Charging service cost of electric vehicle user is most saved

$F_4=min\underset{t=1}{\overset{T}{\Σ}}{c}_t\left(\underset{n=1}{\overset{N}{\Σ}}{c}_n\underset{m=1}{\overset{M}{\Σ}}{x}_{mnt}{P}_m\mathrm{\Δ}t\right)---(1-6)$

(5) Multi-objective optimization objective function processing method

Since a plurality of objective functions are obtained by analyzing from different angles, the objective functions need to be normalized, a multi-objective optimization problem is converted into a single-objective optimization problem, and the objective functions are converted. The present study employed a linear weighted sum method.

Since the dimensions of the multiple objects are different, each object function needs to be normalized as shown in the following formula:

$F=min({\mathrm{\λ}}_1\frac{F_1}{F_{1max}}+{\mathrm{\λ}}_2\frac{F_2}{F_{2max}}+...)---(1-7)$

in the formula: f_1max、F_2max-an objective function corresponding to the original load curve before adjustment;

λ₁、λ₂-an objective function F₁、F₂Corresponding weight coefficient, and λ₁+λ₂+…＝1。

Example analysis

Example arrangement

Firstly, the relevant data are set:

1) the electric vehicle considered in the research is a taxi, biddie 6, the battery capacity is 200Ah, the charging is rapid, the charging current multiplying power is 0.5C, the charging power Pm is 30kW, and the electric vehicle is charged to full charge within 2 hours. The charging time is calculated according to the process that the SOC is charged to 100%.

2) The number of the electric vehicles to be charged is 100, namely, M is 100.

3) Since the average speed per hour of urban road traffic is 40km/h, v is 40km/h in this study.

4) The charging distance from the electric automobile to the charging station is expected to be 5km (service radius of a gas station), and the value is randomly selected in normal distribution N (5,1) with the standard deviation of 1 km.

5) 8 charging stations are selected as scheduling objects, that is, N is 8, and the configuration of the specific charging pile is shown in table 1.

6) The initial charging time setting refers to the initial charging time of a charging station in a certain city in the south.

Table 1 charging station parameter settings

Example flow

The particle number is taken as 50, the maximum iteration number is taken as 300, the acceleration factor c1 is obtained, the acceleration factor c2 is 1.49445, the acceleration factor r1 is obtained, the acceleration factor r2 is 0.5, and the weight coefficient corresponding to the objective function is 0.5. The flow chart is shown in fig. 3.

Example analysis

And respectively carrying out optimization calculation according to the three strategies of the ordered charging time scheduling strategy, the ordered charging space scheduling strategy and the ordered charging space-time combined scheduling strategy, and obtaining different optimization results within a time range and a space range. The optimization result shows that the charging load condition which is not optimized is worst, and the load has obvious charging peak; meanwhile, the ordered charging time optimization strategy and the space-time combined optimization strategy can play an optimization role, and the charging load characteristic is improved. In addition, compared with the optimization result of the space-time joint optimization strategy on the load, the effect of the ordered charging time optimization strategy on the load optimization in the time range is more obvious.

The optimization result shows that the space and joint optimization strategy has a certain optimization effect on the spatial distribution of the electric automobile load, and the space optimization effect is better.

Generally, a single ordered time and space optimization strategy has a good effect on the corresponding time and space optimization control, and the space-time joint optimization needs to take optimization indexes in both time and space into consideration, so that an optimal solution is more difficult to obtain.

It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. A time-space joint scheduling ordered charging method is characterized by comprising the following steps:

respectively carrying out standardized processing on a plurality of objective functions respectively related to time scheduling and space scheduling of the electric vehicle:

$F=min({\mathrm{\λ}}_1\frac{F_1}{F_{1max}}+{\mathrm{\λ}}_2\frac{F_2}{F_{2max}}+...);$

in the formula: f_1max、F_2maxThe target function corresponding to the original load curve before dispatching; lambda [ alpha ]₁、λ₂As an objective function F₁、F₂Corresponding weight coefficient, and λ₁+λ₂+…＝1；

And carrying out normalization processing on the plurality of objective functions, converting the multi-objective optimization problem into a single-objective optimization problem, and solving the departure time and the charging cost of the electric vehicle moving to the charging station.

2. The method of claim 1, wherein the plurality of objective functions comprises:

the method comprises the following steps of taking the space-time utilization rate of a charging facility as an objective function, distributing charging vehicles according to time, and averaging the time characteristics of the charging vehicles, wherein the objective function of the space-time utilization rate is as follows:

wherein,

time utilization

Space utilization rate

Wherein T is the total duration, M is the total number of vehicles that need to be charged in the time duration inner region of T, N is the number of charging stations that drop into operation in the time duration inner region of T, C_nNumber of charging piles, P, for the nth charging station_mCharging power for the mth vehicle in the charging station; x is the number of_mntThe three-dimensional decision variable represents a charging decision variable of the mth vehicle at the tth moment in the charging station n, and the variable value is 0 or 1.

3. The method of claim 1, wherein the plurality of objective functions comprises:

the algorithm for improving the load distribution of the regional power distribution network to minimize the operation loss of the regional power distribution network is as follows:

$F_2=min\underset{t=1}{\overset{T}{\Σ}}(P_{loss,t}^2+Q_{loss,t}^2);$

wherein, P_loss,t，Q_loss,tRespectively the active loss and the reactive loss of the regional power distribution network power flow at the t moment.

4. The method of claim 1, wherein the plurality of objective functions comprises:

an algorithm that minimizes electric vehicle user charging service time, wherein an objective function of electric vehicle user charging service time is:

$F_3=min\underset{m=1}{\overset{M}{\Σ}}{\mathrm{TT}}_{mn,onway}+{\mathrm{TT}}_{mn,wait}+{\mathrm{TT}}_{mn,c};$

wherein TT_mn,onway，TT_mn,wait，TT_mn,cThe time period T is the time taken by the mth vehicle to travel to the charging station n, the waiting time for charging at the charging station n, and the charging time.

5. The method of claim 1, wherein the plurality of objective functions comprises:

an algorithm for making the charging service cost of the electric vehicle user the most economical, wherein the objective function of the charging service cost of the electric vehicle user is as follows:

$F_4=min\underset{t=1}{\overset{T}{\Σ}}{c}_t\left(\underset{n=1}{\overset{N}{\Σ}}{c}_n\underset{m=1}{\overset{M}{\Σ}}{x}_{mnt}{P}_m\mathrm{\Δ}t\right);$

wherein, c_tAs a time-varying unit time of electricity prices, c_nCharging unit electricity prices for different charging stations, T is total duration, M is total number of vehicles needing to be charged in the time duration inner area of T, N is number of charging stations put into operation in the time duration inner area of T, P_mCharging power for the mth vehicle in the charging station; x is the number of_mntThe three-dimensional decision variable represents a charging decision variable of the mth vehicle at the tth moment in the charging station n, and the variable value is 0 or 1.

6. A spatio-temporal joint scheduling ordered charging device is characterized by comprising:

the normalization module is used for respectively carrying out normalization processing on a plurality of objective functions respectively related to time scheduling and space scheduling of the electric vehicle:

$F=min({\mathrm{\λ}}_1\frac{F_1}{F_{1max}}+{\mathrm{\λ}}_2\frac{F_2}{F_{2max}}+...);$

in the formula: f_1max、F_2maxThe target function corresponding to the original load curve before dispatching; lambda [ alpha ]₁、λ₂As an objective function F₁、F₂Corresponding weight coefficient, and λ₁+λ₂+…＝1；

And the optimization module is used for carrying out normalization processing on the plurality of objective functions, converting the multi-objective optimization problem into a single-objective optimization problem and solving the departure time and the charging cost of the electric vehicle driving to the charging station.