CN117207819A - Electric vehicle charging guiding method and system based on hybrid linear integer programming model - Google Patents
Electric vehicle charging guiding method and system based on hybrid linear integer programming model Download PDFInfo
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Abstract
The invention relates to an electric vehicle charging guiding method and system based on a hybrid linear integer programming model, wherein the method comprises the following steps: s1, collecting all charging stations in an area and charging pile information in the charging stations at the same time, and preprocessing vehicle information; s2, guiding an optimization function of an electric vehicle target charging station based on a hybrid linear integer programming model at the charging application processing moment of an electric vehicle user in the region, and finishing off-station guiding; s3, after the electric vehicle guided in the step S2 arrives at the charging station, the cloud server completes in-station arrangement of the electric vehicle according to a result given by guiding and based on an in-electric vehicle target charging station dispatching optimization function of the hybrid linear integer programming model; s4, executing the step S1, and preparing for charging application processing of the electric automobile user in the next time zone. The method has the beneficial effects of minimizing the waiting time of charging of the user and saving the time cost of the user.
Description
[ field of technology ]
The invention relates to the technical field of electric vehicles, in particular to an electric vehicle charging guiding method and system based on a hybrid linear integer programming model.
[ background Art ]
Electric vehicles are gradually replacing traditional fuel vehicles due to the low-carbon and environment-friendly properties of the electric vehicles. However, electric vehicles are not as simple and fast as traditional fuel vehicles in the aspect of supplementing energy, so that the problems of difficult charging and slow charging caused by the electric vehicles become research hot spots.
At present, the mode that adopts carries out orderly guide to electric automobile user, avoids the unordered negative effect that charges of electric automobile to cause. The consideration of the electric automobile user comprises two aspects of user behavior characterization and user cost saving. The self behavior depiction of the user is to accurately depict the driving habit or the driving habit of the user, further predict the next charging behavior and charging time of the user, and therefore achieve the guiding strategy of planning charging in advance. After considering the user behavior, the user is guided in order in economic or time interests, and is guided by price stimulation, but none of the above strategies achieves optimal results.
A hybrid linear integer programming model (mixed integer linear programming, abbreviated as MILP) is a special mathematical programming in which all or a portion of the variables to be solved must take integer values. Linear programming model (Linear Programming, LP): meaning that the objective function is linear, so the constraint is also linear, and the decision variable can take any real number; if some of the decision variable requirements in the linear programming problem must be integers, then the programming problem at this time is converted into a mixed integer linear programming problem, that is, the optimization problem is not only conditional but also integer constrained.
Aiming at the technical problem that the prior ordered guiding of the electric automobile user can not reach the optimal result, the invention improves the electric automobile charging guiding method and system.
The references are as follows:
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[ invention ]
The invention aims to provide an electric automobile charging guiding method which can minimize the waiting time of charging of a user and save the time cost of the user.
In order to achieve the above purpose, the technical scheme adopted by the invention is an electric vehicle charging guiding method based on a hybrid linear integer programming model, comprising the following steps:
s1, a cloud server collects all charging stations in an area and charging pile information in the charging stations at the same time, and carries out vehicle information preprocessing on electric vehicles sending out charging applications;
s2, at the charging application processing time of the electric vehicle user in the area, the cloud server guides an optimization function based on the electric vehicle target charging station of the hybrid linear integer programming model according to the time of the electric vehicle reaching different charging stations and the different charging time of the charging pile in the different charging stations, so as to reduce the charging waiting time of the electric vehicle user, guide all electric vehicle groups meeting constraint conditions to the charging stations at one time, determine the starting charging time and the charging time of each electric vehicle, and finish off-station guiding;
s3, after the electric vehicle guided in the step S2 arrives at the charging station, the cloud server starts charging on the charging pile or enters a waiting queue in the charging station according to the electric vehicle target charging station scheduling optimization function based on the mixed linear integer programming model according to the result given by the guiding, and the electric vehicles entering the charging station are sequentially arranged according to the shortest waiting time and the principle of not replacing the charging pile, so that the in-station arrangement of the electric vehicles is completed;
s4, executing the step S1, and preparing for charging application processing of the electric automobile user in the next time zone.
Preferably, in the method for guiding electric vehicle charging based on the hybrid linear integer programming model, step S2: the cloud server divides the time sequence of the electric vehicle charging scheduling period into a plurality of time windows, the electric vehicle charging scheduling period comprises an electric vehicle running state time window, an electric vehicle waiting charging state time window, an electric vehicle charging state time window and an electric vehicle completion charging state time window, and step S2 enables the sum of all the electric vehicle charging scheduling period time windows to be minimum, so that the optimization purpose is achieved.
Preferably, the object, spatial domain and temporal domain guided by the hybrid linear integer programming model are defined as follows:
S EV ＝{1，2，3，...，N EV } (1)
S sta ＝{1，2，3，...，N sta } (2)
S T ＝{1，2，3，...，N T } (4)
wherein, the formula (1) defines an electric automobile set S for issuing a charging application EV Equation (2) defines the set S of all charging stations in the area sta Equation (3) defines the set of charging posts within charging station iEquation (4) defines a set S of discrete time windows within a charge schedule period T In total N in the region EV Electric vehicleIssue charging application and N sta The charging schedule period is equally divided into N T A time window.
Preferably, in the method for guiding electric vehicle charging based on the hybrid linear integer programming model, step S2: the hybrid linear integer programming model clearly shows the optimization effect of the electric vehicle target charging station guiding optimization function through three 0-1 variables; the three types of 0-1 variables comprise an electric vehicle charging station selection variable, an electric vehicle charging start time window variable and an electric vehicle charging state variable.
Preferably, the method comprises the steps of,
the electric vehicle selects charging station variable x k，i Defined as the electric vehicle being guided into one of the charging stations that is reachable within the remaining mileage, locking of the electric vehicle in the charging station is accomplished,
x k，i ∈{0，1} k∈S EV ，i∈S sta (5)
when electric vehicle k is successfully guided to charging station i, x k，i =1, otherwise x k，i ＝0；
The variable y of the charging starting time window of the electric automobile i，t Is defined as whether a time window position for starting charging can exist in the charging schedule period when the charging process is continuously started after the electric automobile is successfully guided into the charging station,
y k，t ∈{0，1} k∈S EV ，t∈S (6)
if y k，t =1, indicating that electric car k starts charging in the t-th time window; otherwise, there is no time window for charging to start, i.e.
The electric automobile charging state variable u k,t Defined as having a time window of electric vehicle state of charge that is continuous in time after guiding the electric vehicle to the charging station and giving a start charging window,
u k，t ∈{0,1} k∈S EV ,t∈S T (7)
wherein u is k，t And setting 1 below the electric vehicle charging state time window, and setting 0 in the electric vehicle running state time window, the electric vehicle waiting charging state time window and the electric vehicle charging state completion time window.
Preferably, the method comprises the steps of,
the electric vehicle target charging station guidance optimization function is defined as follows:
where Aux is a one-dimensional row vector, representing a time auxiliary matrix, specifically expressed as aux= [1,2, ], N T ]'M f For a sufficiently large penalty factor, the value can be N T 。
Preferably, the method comprises the steps of,
the constraint conditions of the electric vehicle target charging station guiding optimization function comprise electric vehicle constraint and charging station capacity constraint; the electric vehicle constraint comprises selective constraint of the electric vehicle, driving mileage constraint of the electric vehicle, charging duration constraint of the electric vehicle, time constraint of starting charging of the electric vehicle and continuous charging constraint of the electric vehicle;
the selective constraint of the electric automobile means that the electric automobile can only be guided into one charging station in one charging schedule period, or the electric automobile is empty in the charging schedule period, cannot select the charging station, enters a waiting queue, waits for the re-guiding of the subsequent charging schedule period,
the driving mileage constraint of the electric automobile means that the electric automobile cannot go to any charging station in the area under the condition of residual charge quantity,
in the method, in the process of the invention,representing the maximum driving mileage of the electric vehicle k; />The initial charge amount of the electric automobile k at the time of issuing the application is shown; zeta type EV The power consumption of the unit mileage of the electric automobile is represented; />Indicating the distance of the electric vehicle k to the charging station i;
the constraint of the charging time length of the electric automobile means that when the electric automobile goes to different charging stations, the electric quantity supplemented in the charging stations is different due to different distances,
wherein T is ch，k，i The charging time of the electric automobile k at the charging station o is represented; soc tar Representing a target charge amount of the electric vehicle; c (C) max Representing a maximum battery capacity of the electric vehicle; p (P) i The charging power of the charging post in the charging station i is shown.
The time constraint of starting charging of the electric automobile means that the electric automobile can start charging only after reaching a charging station,
in the method, in the process of the invention,indicating the time when the electric vehicle k arrives at the charging station i;
the continuous charging constraint of the electric automobile means that the electric automobile is charged continuously for a period of time after the electric automobile starts to be charged until the established charging time is finished,
formula (15) is used for judging u k，t When y is the value of k，t′ When=1, u k，t The time window on the same time slice takes a value of 1 as well, and is set at T' ∈ [ T-min (T, T) ch，k，i )，t]The interval is continuously provided with 1, so that continuous charging of the electric automobile is ensured;
the charging station capacity constraint means that the number of electric vehicles which can be charged at the same time must be strictly less than or equal to the number of charging stations because of the limited number of charging posts present in the charging station,
equation (16) defines the sum of the continuous charging variables of the electric vehicles selected from the same charging station in the time section to be less than or equal to the number of charging piles in the charging station.
Preferably, the method for guiding the electric vehicle charging based on the hybrid linear integer programming model includes step S3: the hybrid linear integer programming model uses three-dimensional 0-1 variable electric vehicle charging station internal charging state variable v k,j,t Clearly showing the optimizing effect of the dispatching optimizing function in the electric vehicle charging station; the state of charge variable in the electric vehicle charging station intuitively represents which charging post the electric vehicle is charged on in the charging station and the charging time window,
when v k,j,t When=1, it indicates that electric car k is in a charged state in the t-th time window on charging pile j.
Preferably, the method comprises the steps of,
the dispatching optimization function in the electric vehicle charging station is defined as follows:
after fixing electric automobile k, pair v k，j，t After square is taken, accumulating each row to obtain the maximum value, and ensuring that the charging process of the electric automobile is kept on the same charging pile as much as possible;
the constraint conditions of the scheduling optimization function in the electric vehicle charging station comprise the scheduling constraint in the charging station;
the scheduling constraint in the charging station refers to a three-dimensional 0-1 variable charging state variable v in the charging station of the electric vehicle at the charging position of the electric vehicle k，j，t The sum in the column direction is equal to the electric vehicle state of charge variable,
and the charging car selecting the same charging station cannot exceed the maximum space and time capacity of the charging station,
the sum of the charging position variables of the electric vehicles which are restricted to select the same charging station is required to be strictly less than or equal to 1, and the situation that two electric vehicles are charged simultaneously on one charging pile in one time window does not occur.
It is still another object of the present invention to provide an electric vehicle charging guide system that minimizes user charging waiting time and saves user time costs.
In order to achieve the above object, the present invention adopts a technical scheme that an electric vehicle charging guiding system based on a hybrid linear integer programming model comprises a cloud server, a plurality of electric vehicles and a plurality of charging stations, wherein each charging station comprises a plurality of charging piles for executing the above electric vehicle charging guiding method based on the hybrid linear integer programming model.
The electric vehicle charging guiding method and system based on the hybrid linear integer programming model have the following beneficial effects: by establishing the hybrid linear integer programming model, the charging waiting time of the electric automobile user is minimized, the average charging waiting time of the electric automobile user is effectively reduced, the time cost of the user is saved, and the validity and superiority of the strategy based on the hybrid linear integer programming model are provided by the real data verification under different charging scenes: 1. the average charging waiting time of the electric automobile user can be effectively reduced, and the charging facility utilization rate of each charging station can be well balanced; 2) The method has the advantages that the situation of different scenes in the electric automobile charging guiding process is considered, the guiding results of the test area in two different scenes are verified, and the proposed strategy can be suitable for various scenes.
[ description of the drawings ]
Fig. 1 is a schematic diagram of an electric vehicle timing model.
Fig. 2 is a basic structural diagram of a guiding optimization model of an electric vehicle reaching a charging station and a dispatching optimization model of the electric vehicle in the charging station.
Fig. 3 is a flowchart of an electric vehicle charging guidance method based on a hybrid linear integer programming model.
FIG. 4 is a 0-1 variable constraint relationship diagram of an electric vehicle starting charging and an electric vehicle state of charge.
Fig. 5 is an example charging station profile.
Fig. 6 is a diagram of the guiding results of 80 electric vehicles.
Fig. 7 is a schematic diagram illustrating three exemplary vehicle charging modes.
Fig. 8 is a schematic diagram of an example charging station service strength.
Fig. 9 is a graph of average waiting time results for an electric vehicle user in different vehicle permeability scenarios.
Fig. 10 is a graph of charging station service intensity in different occupancy level scenarios.
[ detailed description ] of the invention
The invention is further described below with reference to examples and with reference to the accompanying drawings.
Examples
The embodiment realizes an electric vehicle charging guiding method based on a hybrid linear integer programming model.
In order to overcome the defects in the prior art, the embodiment provides an electric vehicle charging guiding method based on a hybrid linear integer programming model. By establishing the MILP model, the charging waiting time of the electric automobile user is minimized, and the time cost of the user is saved.
The technical solution of this embodiment is as follows:
1. charging guide framework of electric automobile
1.1, electric automobile time sequence model
The embodiment aims to reduce the charging waiting time of the electric automobile user, so that a time sequence model is built for the electric automobile, and the charging waiting time of the user is minimized through the characteristics of the MILP model.
Fig. 1 is a schematic diagram of an electric vehicle timing model. As shown in fig. 1, an electric vehicle is divided into a plurality of time windows in a time sequence within a given scheduling period, and the vehicle states of the electric vehicle within the different time windows are represented by boxes with different colors. The method of equivalent mathematical model is used in the embodiment, so that the sum of boxes (time windows) of all electric vehicles going to the charging station is minimized, and the optimization purpose is achieved.
1.2 charging guide frame
Fig. 2 is a basic structural diagram of a guiding optimization model of an electric vehicle reaching a charging station and a dispatching optimization model of the electric vehicle in the charging station. As shown in fig. 2, the MILP model of the present embodiment is divided into a guiding optimization model of an electric vehicle reaching a charging station and a dispatching optimization model of the electric vehicle in the charging station.
For guiding electric vehicles outside the charging station, all electric vehicle groups meeting the same constraint conditions are guided into the same charging station at one time in one scheduling period, so that the guiding target outside the charging station is completed. The arrangement and guidance of electric vehicles in a charging station means that vehicles coming into the station are arranged on a charging pile according to the shortest waiting time, and the vehicles coming into the station are charged without changing the charging pile in the charging time as much as possible [16] The user can simplify the charging process and avoid the trouble caused by repeated pile replacement.
1.3, charging guide specific procedure
Fig. 3 is a flowchart of an electric vehicle charging guidance method based on a hybrid linear integer programming model. As shown in fig. 3, after the guidance is started, the electric vehicle sends a charging demand, uploads its real-time information to the cloud system, and the charging station uploads the charging pile information in the station, and both information is processed in the cloud system. Then, the system outputs a guiding result to the vehicle user, and a charging pile time sequence using sequence in the charging station output station.
The specific guiding flow is as follows:
1) When an electric automobile user in the area sends out a charging application, the cloud system collects charging pile information in all charging stations in the area at the same time, and carries out vehicle information preprocessing on the electric automobile sending out the charging application.
2) According to the time when the electric vehicles reach different charging stations and the different charging time in the different charging stations, the system guides all the electric vehicle groups meeting the constraint conditions to the charging stations at one time, and confirms the starting charging time and the charging time of each electric vehicle, so as to finish off-station guiding.
3) And after the vehicle arrives at the charging station, according to the result given by the system guidance, the vehicles are sequentially arranged on the charging piles to start charging or enter a waiting queue in the station to finish the in-station arrangement of the electric vehicle.
2. Electric vehicle target charging station guiding optimization model (electric vehicle arrival charging station guiding optimization model)
In order to better show the guiding optimization model of the electric automobile reaching the charging station, three types of 0-1 variables are defined in the embodiment, and the optimization effect of the guiding model can be clearly shown through the mathematical model represented by the three types of 0-1 variables.
2.1, three classes of 0-1 variables
Before defining the boot variables, the scope of the boot, i.e. the object, spatial domain and temporal domain of the boot, is first defined as follows:
S EV ＝{1，2，3，...，N EV } (1)
S sta ＝{1，2，3，...，N sta } (2)
S T ＝{1，2，3，，...，N T } (4)
formulas (1) - (4) respectively define an electric automobile set S for issuing a charging application EV Set S of all charging stations in an area sta Charging pile set in charging station iDiscrete time window set S within a scheduling period T . In total N in the region EV The electric automobile sends out charging application and N sta And (3) charging stations. The scheduling period is equally divided into N T A time window.
2.2.1 electric vehicle charging station selection variable
The electric vehicle is guided into a charging station which is reachable within the remaining range, whereby the 0-1 variable x of the electric vehicle selection charging station is defined k，i 。
x k，i ∈{0，1} k∈S EV ，i∈S sta (5)
When electric vehicle k is successfully guided to charging station i, x k，i =1, otherwise x k，i =0. This variable completes the locking of the electric vehicle in the charging station.
2.2.2. time window variable for starting charging of electric automobile
After the electric vehicle has been successfully guided into the charging station, a time window position for starting the charging should be present when the charging process continues to start. Thus defining the 0-1 variable y for the electric vehicle to start charging i，t ：
y k，t ∈{0，1} k∈S EV ，t∈S T (6)
If y k，t =1, indicating that electric car k starts charging in the t-th time window; otherwise, there is no time window for charging to start, i.e.The variable indicates whether the electric automobile can have a time window position for starting charging in the scheduling period.
2.2.3 State of Charge variable of electric automobile
After guiding the electric vehicle to the charging station and giving a start charging window, the electric vehicle should have a charging state that is continuous in time. 0-1 variable u defining electric vehicle charging state k,t ：
u k，t ∈{0，1} k∈S EV ，t∈S T (7)
FIG. 4 is a 0-1 variable constraint relationship diagram of an electric vehicle starting charging and an electric vehicle state of charge. As shown in fig. 4, a 0-1 variable of the state of charge of the electric vehicle represents the state of continuous charge of the electric vehicle with one continuous line of 1, and y k,t And u k,t There is a constraint relationship as shown in fig. 4; wherein u is k，t The charging state of the electric automobile is set to 1, and the running state, the waiting charging state and the ending charging state are all set to 0.
y k,t And u k,t The first 1 position of (2) should be at the same time window position, which indicates that the electric automobile starts charging in the time window; and u k，t After starting charging, setting 1 continuously in the charging time period until the charging is finished, so as to represent that the electric automobile is in a time dimensionIs a continuous state of charge of (c).
2.2 objective function
To minimize the charge latency for an electric vehicle user, the objective function is defined as follows:
wherein: aux is a one-dimensional row vector representing a time auxiliary matrix, specifically denoted ax= [1,2, ], N T ]'M f For a sufficiently large penalty factor, the value can be N T 。
The first part of the objective function indicates that a charging starting time window of the electric automobile needs to be as far ahead as possible, so that the aim of shortening the waiting time of a user is achieved; the second part indicates that in the present scheduling period, if the user does not have a time window for starting charging, a penalty coefficient is introduced to make the user have a time window for starting charging as much as possible in the scheduling period, otherwise, penalty is performed.
2.3 constraint conditions
2.3.1 electric automobile restraint
(1) Selective restraint of electric vehicle
In a dispatch cycle, the electric vehicle can only be guided into one charging station, or the electric vehicle is empty in the present cycle, and the charging station cannot be selected. Under the condition of empty wheels, the electric automobile does not have a corresponding charging start time window, and the electric automobile is considered to be unguided in the dispatching cycle, and the electric automobile enters a waiting queue to wait for the reboot of the following dispatching cycle.
(2) Mileage constraint of electric automobile
Electric vehicles have mileage constraints, i.e., cannot go to any charging station in the area with the amount of charge remaining. Thus giving the maximum mileage constraint of an electric car:
wherein:representing the maximum driving mileage of the electric vehicle k; />The initial charge amount of the electric automobile k at the time of issuing the application is shown; zeta type EV The power consumption of the unit mileage of the electric automobile is represented; />Indicating the distance of the electric vehicle k to the charging station i. Equation (12) limits the guided electric vehicle to be able to reach a given charging station.
(3) Charging duration constraint of electric automobile
When an electric vehicle travels to different charging stations, the electric quantity supplied in the charging stations is different due to different distances.
Wherein: t (T) ch,k,i The charging time of the electric automobile k at the charging station i is represented; soc tar Representing a target charge amount of the electric vehicle; c (C) max Representing a maximum battery capacity of the electric vehicle; p (P) i The charging power of the charging post in the charging station i is shown.
(4) Time constraint for electric vehicle to start charging:
meanwhile, the electric automobile can start charging after reaching a charging station:
wherein:the time when electric vehicle k arrives at charging station i is indicated. Equation (14) indicates that the electric vehicle should start charging after reaching the charging station by the equivalent of the mathematical model.
(5) Continuous charging constraint of electric automobile:
after the electric automobile starts to charge, the electric automobile is charged continuously for a period of time until the preset charging time is finished. Thus, constraints are given on continuous charging of the electric vehicle.
Formula (15) is used for judging u k，t Is a value of (a). When y is k，t′ When=1, u k，t The time window on the same time slice takes a value of 1 as well, and is set at T' e [ -min (T, T) ch，k，i )，t]And 1 is continuously arranged in the interval, so that continuous charging of the electric automobile is ensured.
2.3.2 charging station Capacity constraints
Because of the limited number of piles present in the charging station, the number of vehicles that can be charged simultaneously must be strictly less than or equal to the number of piles of the charging station,
the constraint purpose is achieved by the fact that the sum of continuous charging variables of the electric vehicles with the same charging station in a time section is smaller than or equal to the number of charging piles in the charging station.
3. Electric vehicle charging station internal dispatching optimization model (electric vehicle charging station internal dispatching optimization model)
And (5) a station scheduling model of the charging station, and giving a corresponding charging pile of the electric vehicle in the charging process as a final result. And combining a guide model outside the charging station to give three results of the charging station, the charging pile and the charging time for the electric vehicle to go.
3.1 State of Charge variable in electric vehicle charging station
In order to more intuitively represent the charging position and the charging time window of an electric vehicle in a charging station, a three-dimensional 0-1 variable v for the charging state of the electric vehicle in the charging station reaching the charging station is defined k，j，t 。
Through the three-dimensional 0-1 variable, it can be clearly seen which charging pile the electric automobile charges in different time windows. When v k，j，t When=1, it indicates that electric car k is in a charged state in the t-th time window on charging pile j.
3.2 objective function
In the foregoing model, the objective function makes the charging waiting time of the user shortest, and gives the position of the charging time of the user on the time sequence, but fails to give the specific position of the electric vehicle for charging in the station, and if the charging pile is frequently replaced in the charging process, the charging experience of the user is greatly reduced, so the objective function is defined as that the charging pile is not replaced as much as possible in the charging process of the electric vehicle user, and the charging process is simplified.
The objective function is equivalent by using a mathematical model, and v is calculated after the electric automobile k is fixed k，j，t After squaring the sum of the rows, the maximum value is obtained by accumulating the rows, and the equivalent method of the mathematical model is used for protectingThe charging process of the electric automobile is kept on the same charging pile as much as possible, and the aim that the electric automobile does not replace the charging pile in the charging station as much as possible is achieved.
3.3, scheduling constraints within charging station
The state of charge of the electric vehicle is given by the first partial model, so the three-dimensional 0-1 variable of the charging position of the electric vehicle is in the column direction and should be equal to the 0-1 variable of the state of the electric vehicle.
/>
The method ensures that the results of the guiding model keep the unified guiding results of the vehicles in the front model and the rear model after the guiding model is guided into the dispatching model.
Second, the charging cars of the same charging station are selected not to exceed the maximum space and time capacity of the charging station.
The sum of the charging position variables of the electric vehicles selecting the same charging station is restricted to be strictly less than or equal to 1, otherwise, in a time window, two electric vehicles are charged on one charging pile at the same time.
4. Calculation case analysis
4.1, setting of calculation examples
Fig. 5 is an example charging station profile. As shown in fig. 5, in order to verify the feasibility and effectiveness of the policy proposed in this embodiment, the actual data of a certain region in the Shanghai is selected for verification. The scheduling period is set to 9:00-10:00, the total scheduling time length is 1 hour, and the time window delta t is 5 minutes. The total of 8 charging stations and 60 charging piles are in the area, and specific geographic diagrams and charging station information are shown in fig. 5 and table 1. The electric vehicle data is given by a hundred-degree map data interface.
Table 1 charging station information
4.2 analysis of results
Fig. 6 is a diagram of the guiding results of 80 electric vehicles. As shown in fig. 6, an optimized guidance simulation was performed on the electric vehicles, and guidance results of 80 electric vehicles were drawn.
Specific guidance results are shown in table 2.
Table 2 charging guidance results under different guidance strategies
Charging station service intensity [18] The method is used for indicating the utilization rate of the guiding strategy to the charging facilities in the charging station, and the calculation method is shown in the formula (21):
/>
in the method, in the process of the invention,indicating the charging capacity in charging station i +.>Indicating the charge capacity of all charging stations in the area. Wherein (1)>The ratio of the number of electric vehicles going to the charging station i to the total number of electric vehicles in the area is represented; />The ratio of the charge capacity in charging station i to the total charge capacity of all charging stations in the area is represented. The closer the value of the charging service intensity is to 1, the more the number of electric vehicles guided to the charging station matches the charging capacity of the charging station.
And after the service intensity of all the charging stations is calculated, calculating the variance value of the service intensity of the charging stations to obtain the service intensity variance of the charging stations. The smaller the variance is, the smaller the charging facility utilization rate difference between different charging stations is, and the guiding result is more balanced and reasonable.
As can be seen from table 2, the average waiting time of the electric vehicle optimizing the guidance strategy is reduced by 19.65 minutes compared to the nearby guidance strategy, the service strength variance of the charging station is reduced to 1.8% of the nearby guidance strategy, and the average driving distance and driving time are increased by 153.54 meters and 2.47 minutes compared to the nearby guidance strategy.
Fig. 7 is a schematic diagram illustrating three exemplary vehicle charging modes. As shown in fig. 7, three typical vehicle guidance results are drawn based on the guidance results of fig. 6. After the No. 28 vehicle arrives at the charging station, the vehicle directly starts to charge to a set duration without waiting time because of an idle charging pile in the charging station, and leaves the charging station after the charging is completed in the dispatching period; after the vehicle No. 1 arrives at the charging station, the charging piles are fully occupied by other vehicles, so that the vehicles enter a queuing waiting state, and charging is started again after waiting is finished, and charging can still be completed and leave the charging station in the dispatching period; the vehicle No. 41 waits and starts charging after reaching the charging station, but the charging cannot be completed in the dispatching cycle, and the charging is continued until the following dispatching cycle.
Fig. 8 is a schematic diagram of an example charging station service strength. As shown in fig. 8, charging station service intensities under two guidance policies, namely a nearby guidance policy and an optimized guidance policy, are plotted in a scheduling period. The variance values of the nearby guiding strategy and the proposed optimized guiding strategy are 0.198 and 0.0036 respectively, so that the proposed strategy is better than the nearby guiding strategy in service intensity variance of charging stations, and the fact that the proposed strategy can better balance the charging facility utilization rate of each charging station is verified.
4.3 comparative examples
(1) Different vehicle permeability scenarios
Fig. 9 is a graph of average waiting time results for an electric vehicle user in different vehicle permeability scenarios. As shown in fig. 8, the scenes of setting different vehicle permeabilities are that there are 60, 70, 80, 90 and 100 electric vehicles waiting for guidance in the guidance area and the dispatch period, respectively, and the guidance results are shown in fig. 9.
And comparing the proposed strategy with the nearby guiding strategy under the guiding scene without the initial occupation of the charging pile, wherein the specific guiding result is shown in a table 3. The average waiting time of the optimized guidance strategy in different vehicle permeability scenarios is lower than that of the nearby guidance strategy.
Table 3 guiding results in different vehicle permeability scenarios
In a permeability scene of 60 electric vehicles, the number of charging piles in the area is 60, so that the guiding result is that one charging pile is matched with one electric vehicle, the service intensity value of each charging station is consistent, and the variance is 0. As vehicle permeability increases, the average waiting time of the nearby boot strategy increases from 21.56 minutes to 41 minutes, while the proposed strategy increases from 2.75 minutes to 12.4 minutes. And in different vehicle permeability scenes, the service strength variance of the charging station of the proposed strategy is also superior to that of the nearby guiding strategy.
(2) Scene with different initial occupation degrees of charging piles
Fig. 10 is a graph of charging station service intensity in different occupancy level scenarios. As shown in fig. 10, in this scenario, the number of electric vehicles is set to 80, and the charging piles in the partial charging stations have the situation that the time window is occupied in the initial time window of the scheduling period; three occupancy levels were set, namely light occupancy, medium occupancy and heavy occupancy, and the occupied pile number ratios were 30%, 60% and 90%, respectively, and the guidance results are shown in fig. 10.
TABLE 4 guiding results under different occupancy scenarios
In the unoccupied case, the variance of the charging station service intensity of the nearby guidance is 0.198, and the variance of the optimized guidance is 0.036. As shown in table 4, the average waiting time was increased by about 2 minutes each time as the occupancy level was increased, and the total charge amount tended to decrease, and the charging station service intensity variance was not greatly fluctuated. The proposed optimization strategy is thus shown to be able to equalize the charging facility utilization of the individual charging stations even at different degrees of occupancy.
5. Conclusion(s)
The method of the embodiment effectively reduces the average charging waiting time of the electric automobile user. The effectiveness and superiority of the proposed strategy are verified by the real data of a certain area of the Shanghai under different charging scenes. The following conclusions can be drawn from the simulation results:
1) The provided strategy can effectively reduce the average charging waiting time of the electric automobile user and can well balance the charging facility utilization rate of each charging station.
2) The provided charging guiding strategy considers different scene conditions in the charging guiding process of the electric automobile, and the guiding results of the test area in two different scenes prove that the provided strategy can be suitable for various scenes.
It will be appreciated by those of ordinary skill in the art that all or part of the steps of implementing the above embodiments may be implemented by hardware, or may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, where the storage medium may be a magnetic disk, an optical disc, a Read-Only Memory (ROM), a random access Memory (Random Acess Memory, RAM), or the like.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and additions to the present invention may be made by those skilled in the art without departing from the principles of the present invention and such modifications and additions are to be considered as well as within the scope of the present invention.
Claims (10)
1. An electric vehicle charging guiding method based on a hybrid linear integer programming model is characterized by comprising the following steps of:
s1, a cloud server collects all charging stations in an area and charging pile information in the charging stations at the same time, and carries out vehicle information preprocessing on electric vehicles sending out charging applications;
s2, at the charging application processing time of the electric vehicle user in the area, the cloud server guides an optimization function based on the electric vehicle target charging station of the hybrid linear integer programming model according to the time of the electric vehicle reaching different charging stations and the different charging time of the charging pile in the different charging stations, so as to reduce the charging waiting time of the electric vehicle user, guide all electric vehicle groups meeting constraint conditions to the charging stations at one time, determine the starting charging time and the charging time of each electric vehicle, and finish off-station guiding;
s3, after the electric vehicle guided in the step S2 arrives at the charging station, the cloud server starts charging on the charging pile or enters a waiting queue in the charging station according to the electric vehicle target charging station scheduling optimization function based on the mixed linear integer programming model according to the result given by the guiding, and the electric vehicles entering the charging station are sequentially arranged according to the shortest waiting time and the principle of not replacing the charging pile, so that the in-station arrangement of the electric vehicles is completed;
s4, executing the step S1, and preparing for charging application processing of the electric automobile user in the next time zone.
2. The electric vehicle charging guidance method based on the hybrid linear integer programming model according to claim 1, characterized by step S2: the cloud server divides the time sequence of the electric vehicle charging scheduling period into a plurality of time windows, the electric vehicle charging scheduling period comprises an electric vehicle running state time window, an electric vehicle waiting charging state time window, an electric vehicle charging state time window and an electric vehicle completion charging state time window, and step S2 enables the sum of all the electric vehicle charging scheduling period time windows to be minimum, so that the optimization purpose is achieved.
3. The electric vehicle charging guidance method based on the hybrid linear integer programming model according to claim 2, characterized in that: the object, spatial domain and temporal domain guided by the hybrid linear integer programming model are defined as follows:
S EV ＝{1，2，3，...，N EV } (1)
S sta ＝{1，2，3，...，N sta } (2)
S T ＝{1，2，3，...，N T } (4)
wherein, the formula (1) defines an electric automobile set S for issuing a charging application EV Equation (2) defines the set S of all charging stations in the area sta Equation (3) defines the set of charging posts within charging station iEquation (4) defines a set S of discrete time windows within a charge schedule period T In total N in the region EV The electric automobile sends out charging application and N sta The charging schedule period is equally divided into N T A time window.
4. The electric vehicle charging guidance method based on the hybrid linear integer programming model according to claim 3, wherein step S2: the hybrid linear integer programming model clearly shows the optimization effect of the electric vehicle target charging station guiding optimization function through three 0-1 variables; the three types of 0-1 variables comprise an electric vehicle charging station selection variable, an electric vehicle charging start time window variable and an electric vehicle charging state variable.
5. The electric vehicle charging guidance method based on the hybrid linear integer programming model according to claim 4, wherein:
the electric vehicle selects charging station variable x k，i Defined as the electric vehicle being guided into one of the charging stations that is reachable within the remaining mileage, locking of the electric vehicle in the charging station is accomplished,
x k，i ∈{0，1}k∈S EV ，i∈S sta (5)
when electric vehicle k is successfully guided to charging station i, x k，i =1, otherwise x k，i ＝0；
The variable y of the charging starting time window of the electric automobile i，t Is defined as whether a time window position for starting charging can exist in the charging schedule period when the charging process is continuously started after the electric automobile is successfully guided into the charging station,
y k，t ∈{0，1}k∈S EV ，t∈S T (6)
if y k，t =1, indicating that electric car k starts charging in the t-th time window; otherwise, there is no time window for charging to start, i.e.
The electric automobile charging state variable u k，t Defined as having a time window of electric vehicle state of charge that is continuous in time after guiding the electric vehicle to the charging station and giving a start charging window,
u k，t ∈{0，1}k∈S EV ，t∈S T (7)
wherein u is k，t And setting 1 below the electric vehicle charging state time window, and setting 0 in the electric vehicle running state time window, the electric vehicle waiting charging state time window and the electric vehicle charging state completion time window.
6. The electric vehicle charging guidance method based on the hybrid linear integer programming model according to claim 5, wherein:
the electric vehicle target charging station guidance optimization function is defined as follows:
where Aux is a one-dimensional row vector, representing a time auxiliary matrix, specifically expressed as aux= [1,2, ], N T ]′；M f For a sufficiently large penalty factor, the value can be N T 。
7. The electric vehicle charging guidance method based on the hybrid linear integer programming model according to claim 6, wherein:
the constraint conditions of the electric vehicle target charging station guiding optimization function comprise electric vehicle constraint and charging station capacity constraint; the electric vehicle constraint comprises selective constraint of the electric vehicle, driving mileage constraint of the electric vehicle, charging duration constraint of the electric vehicle, time constraint of starting charging of the electric vehicle and continuous charging constraint of the electric vehicle;
the selective constraint of the electric automobile means that the electric automobile can only be guided into one charging station in one charging schedule period, or the electric automobile is empty in the charging schedule period, cannot select the charging station, enters a waiting queue, waits for the re-guiding of the subsequent charging schedule period,
the driving mileage constraint of the electric automobile means that the electric automobile cannot go to any charging station in the area under the condition of residual charge quantity,
in the method, in the process of the invention,representing the maximum driving mileage of the electric vehicle k; />The initial charge amount of the electric automobile k at the time of issuing the application is shown; zeta type EV The power consumption of the unit mileage of the electric automobile is represented; />Indicating the distance of the electric vehicle k to the charging station i;
the constraint of the charging time length of the electric automobile means that when the electric automobile goes to different charging stations, the electric quantity supplemented in the charging stations is different due to different distances,
wherein T is ch，k，i The charging time of the electric automobile k at the charging station i is represented; soc tar Representing a target charge amount of the electric vehicle; c (C) max Representing a maximum battery capacity of the electric vehicle; p (P) i The charging power of the charging post in the charging station i is shown.
The time constraint of starting charging of the electric automobile means that the electric automobile can start charging only after reaching a charging station,
in the method, in the process of the invention,indicating the time when the electric vehicle k arrives at the charging station i;
the continuous charging constraint of the electric automobile means that the electric automobile is charged continuously for a period of time after the electric automobile starts to be charged until the established charging time is finished,
formula (15) is used for judging u k，t When y is the value of k，t′ When=1, u k，t The time window on the same time slice takes a value of 1 as well, and is set at T' ∈ [ T-min (T, T) ch，k，i )，t]The interval is continuously provided with 1, so that continuous charging of the electric automobile is ensured;
the charging station capacity constraint means that the number of electric vehicles which can be charged at the same time must be strictly less than or equal to the number of charging stations because of the limited number of charging posts present in the charging station,
equation (16) defines the sum of the continuous charging variables of the electric vehicles selected from the same charging station in the time section to be less than or equal to the number of charging piles in the charging station.
8. The electric vehicle charging guidance method based on the hybrid linear integer programming model according to claim 7, wherein step S3: the hybrid linear integer programming model uses three-dimensional 0-1 variable electric vehicle charging station internal charging state variable v k，j，t Clearly showing the optimizing effect of the dispatching optimizing function in the electric vehicle charging station; the state of charge variable in the electric vehicle charging station intuitively represents which charging post the electric vehicle is charged on in the charging station and the charging time window,
when v k，j，t When=1, it indicates that electric car k is in a charged state in the t-th time window on charging pile j.
9. The electric vehicle charging guidance method based on the hybrid linear integer programming model according to claim 8, wherein:
the dispatching optimization function in the electric vehicle charging station is defined as follows:
after fixing electric automobile k, pair v k，j，t After square is taken, accumulating each row to obtain the maximum value, and ensuring that the charging process of the electric automobile is kept on the same charging pile as much as possible;
the constraint conditions of the scheduling optimization function in the electric vehicle charging station comprise the scheduling constraint in the charging station;
the scheduling constraint in the charging station refers to a three-dimensional 0-1 variable charging state variable v in the charging station of the electric vehicle at the charging position of the electric vehicle k，j，t The sum in the column direction is equal to the electric vehicle state of charge variable,
and the charging car selecting the same charging station cannot exceed the maximum space and time capacity of the charging station,
the sum of the charging position variables of the electric vehicles which are restricted to select the same charging station is required to be strictly less than or equal to 1, and the situation that two electric vehicles are charged simultaneously on one charging pile in one time window does not occur.
10. Electric automobile guidance system that charges based on mixed linear integer programming model, including high in the clouds server, a plurality of electric automobile and a plurality of charging station, the charging station includes a plurality of electric piles, its characterized in that: an electric vehicle charging guidance method based on a hybrid linear integer programming model for performing any one of claims 1 to 9.
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