CN112308309A - Intelligent electric vehicle charging guiding method based on path optimization - Google Patents
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Abstract
The invention relates to an electric vehicle charging intelligent guiding method based on path optimization. The three attributes of electric vehicle charging are analyzed, namely a traffic network, a charging station and a power distribution network. And optimally solving the charging behavior scheduling problem of the electric automobile by using the three attributes. And solving the optimal solution of the objective function to realize the optimization of scheduling by establishing an optimization objective function. The invention can effectively improve the traffic jam rate and the power supply pressure of the power distribution network end.
Description
Technical Field
The invention belongs to the technical field of electric power and communication engineering, and particularly relates to an electric vehicle charging intelligent guiding method based on path optimization.
Background
At present, the great development of electric automobiles can cause power grid operation impact and difficulty in vehicle operation scheduling. In a general charging scheduling method, the driving route of the electric automobile is not well considered, so that the final scheduling effect is poor, and the traffic end and the point network end are not obviously improved. If the charging dispatching of the electric automobile only considers the operation condition of the power grid, but neglects the reasonable operation of the traffic network and the charging station, the rigor of intelligent guidance is lost. The problem caused by the charging behavior is not sufficiently reflected, and the problem cannot be really solved.
Aiming at the problem, an intelligent electric vehicle charging guiding method based on charging path planning is provided, and the problem of electric vehicle charging scheduling is solved. The invention analyzes the operation condition of a power grid from the aspect of the charging requirement of an electric vehicle and provides a method for optimizing a charging path from the aspects of electric vehicles, traffic networks, charging stations and power distribution networks. The method solves the problem by establishing a multi-objective optimization function and constraint conditions, so that the optimal path planning is obtained. The conclusion can be drawn through simulation, and the method provided by the invention can effectively relieve the charging guide pressure.
Disclosure of Invention
The invention aims to provide an electric vehicle charging intelligent guiding method based on path optimization, which can effectively improve the traffic jam rate and the power supply pressure of a power distribution network end by establishing an objective function and intelligently guiding the charging of an electric vehicle based on the path optimization.
In order to achieve the purpose, the technical scheme of the invention is as follows: an electric vehicle charging intelligent guiding method based on path optimization comprises the following steps:
step A, establishing an optimization objective function:
establishing an optimization objective function, and introducing three necessary attributes to solve the objective function:
Y＝min(ω1yt+ω2yc+ω3yp)
in the formula, yt、yc、yPEach representing three properties, ω, of the objective function1、ω2、ω3A weight value representing a corresponding attribute of the objective function;
b, scheduling attribute analysis:
the traffic dredging degree near the charging station can be judged according to the passing time of the electric automobile:
wherein t isr{ a, b } (i) represents the time when the vehicle passes through the road section (a, b) at different moments, and t { a, b } (i) represents the passing time when the vehicle passes through the road section (a, b) under the condition that only traffic is considered without considering other factors;
the electric automobile can plan a path according to the distance to the optimal charging station, different paths reach different charging stations j, and the required time tjAt different times tjMake an estimate to be N (j, t)j) The optimal number of vehicles at the charging station is the number of vehicles charged at each charging station under the condition that no queuing waiting exists, namely NG(j，tj) Charging station index ycThe ratio of the two is:
restraining load supply of the charging station, ensuring normal operation of the power distribution network, and setting an optimized target attribute as a natural charging station load estimated value L (j, t)j) And an optimal charging station load estimate LG(j，tj) The ratio of (a) to (b), namely:
step C, establishing a speed constraint condition:
and (3) restraining the traffic condition near the charging station: in the preset distance range of the charging station, if the running speed of the electric automobile is below 30% of the speed limit of the road section, the congestion is serious, the electric automobile is prohibited to pass through the paths, and other suitable paths are selected; otherwise, the running speed is more than 30% of the speed limit, the electric automobile can normally pass, the path in the range can be taken as a recommended path, and the running speed is set as follows:
v(a，b)≥30％vmax{a，b}
wherein v ismax(a, b) is the speed limit of the corresponding road section;
step D, establishing a remaining mileage constraint condition:
let Mr(i) For the remaining mileage of the electric vehicle, ME(i) For the endurance mileage, for the rationality, when the remaining mileage of the electric vehicle i is lower than 30% of the remaining endurance of the battery of the electric vehicle, the electric vehicle needs to be charged:
Mr(i)≤0.3ME(i)
step E, establishing a planning path constraint condition:
the remaining mileage of the electric vehicle needs to be less than the path length M of the planned charging stationd(i)：
Mr(i)≤Md(i)
Step F, establishing a power distribution network constraint condition:
in order to ensure the normal operation of the power distribution network, the network loss rate and the voltage deviation rate of the power grid are set within a normal range, and the network loss rate is set as follows:
Ploss(k，t)＜0.72％
the voltage offset ratio is:
Vshift(k，t)＜0.72％
g, setting a weight coefficient:
setting an initial weight coefficient: the weight coefficient of traffic network or distribution network is increased in the traffic peak or charging peak period, and the weight of distribution network and charging station is reduced: omega1∶ω2∶ω37: 2: 1; when the traffic peak and the charging peak coincide and the power distribution network operates normally, the weight coefficient omega of the traffic network is increased1∶ω2∶ω37: 1: 2; when the voltage deviation of the power distribution network reaches the upper limit, the weight coefficient of the power distribution network is increased: omega1∶ω2∶ω3＝1∶1∶8；
Step H, guiding flow determination:
through the steps, the optimal objective function is solved, the information of the traffic, the charging stations and the power distribution network is updated, the driving path of the electric vehicle is solved according to the constraint conditions, the optimal charging station is recommended, whether the charging station is in the range of the remaining mileage of the electric vehicle or not is judged, and if the charging station is in the range of the remaining mileage of the electric vehicle, the vehicle information is updated; if not, the route is planned again, and the charging stations are screened again.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention provides an electric vehicle charging intelligent guiding method based on path optimization aiming at the problems of traffic network congestion increase and power distribution network pressure increase caused by electric vehicle charging behaviors, and effectively reduces the congestion rate of a traffic network end and the operating pressure of a power distribution network.
2. A reasonable optimization objective function is established, analysis is carried out according to three attributes of the optimization function, traffic, power grid and charging station constraint conditions are considered respectively, the optimal solution of the objective function is obtained under the constraint conditions, the optimal intelligent guide path is obtained, and the congestion rate is reduced efficiently.
Drawings
Fig. 1 is a schematic flow chart of an intelligent electric vehicle guiding method according to the present invention.
FIG. 2 is a diagram illustrating an example of an electric vehicle dispatch.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
The key factors of intelligent guiding of electric automobile charging are a traffic network and a power distribution network, and intelligent guiding is significant only when normal operation of the traffic network and the power distribution network is realized. The invention analyzes three attributes of electric vehicle charging, namely a traffic network, a charging station and a power distribution network. And optimally solving the charging behavior scheduling problem of the electric automobile by using the three attributes. And solving the optimal solution of the objective function to realize the optimization of scheduling by establishing an optimization objective function.
As shown in fig. 1, the invention provides an electric vehicle charging intelligent guidance method based on path optimization, which includes the following steps:
step A, establishing an optimization objective function:
establishing an optimization objective function, and introducing three necessary attributes to solve the objective function:
Y＝min(ω1yt+ω2yc+ω3yp)
in the formula, yt、yc、yPEach representing three properties, ω, of the objective function1、ω2、ω3A weight value representing a corresponding attribute of the objective function;
b, scheduling attribute analysis:
one of the rationality determinations of the schedule is the length of time for the electric vehicle to reach the charging station. If the electric automobile can arrive at the charging station more quickly, the traffic network is dredged. The traffic dredging degree near the charging station can be judged according to the passing time of the electric automobile:
wherein t isr{ a, b } (i) represents the time when the vehicle passes through the road section (a, b) at different moments, and t { a, b } (i) represents the passing time when the vehicle passes through the road section (a, b) under the condition that only traffic is considered without considering other factors;
the charging station has the problem that charging queues wait, and the owner charges for a long time, so the number of electric vehicles in the charging station needs to be controlled. The electric automobile can plan a path according to the distance to the optimal charging station, different paths reach different charging stations j, and the required time tjAt different times tjMake an estimate to be N (j, t)j) The optimal number of vehicles at the charging station is the number of vehicles charged at each charging station under the condition that no queuing waiting exists, namely NG(j，tj) Charging station index ycThe ratio of the two is:
in addition to this, the performance of the distribution network is the most important scheduling attribute. It is only meaningful to ensure a normal operation scheduling scheme of the power distribution network. Restraining load supply of the charging station, ensuring normal operation of the power distribution network, and setting an optimized target attribute as a natural charging station load estimated value L (j, t)j) And an optimal charging station load estimate LG(j，tj) The ratio of (a) to (b), namely:
step C, establishing a speed constraint condition:
in order to obtain a solution to the objective function, a constraint condition needs to be set for the function. Specific constraints are set for different objective functions. And (3) restraining the traffic condition near the charging station: in the preset distance range of the charging station, if the running speed of the electric automobile is below 30% of the speed limit of the road section, the congestion is serious, the electric automobile is prohibited to pass through the paths, and other suitable paths are selected; otherwise, the running speed is more than 30% of the speed limit, the electric automobile can normally pass, the path in the range can be taken as a recommended path, and the running speed is set as follows:
v(a，b)≥30％vmax{a，b}
wherein v ismax(a, b) is the speed limit of the corresponding road section;
step D, establishing a remaining mileage constraint condition:
let Mr(i) For the remaining mileage of the electric vehicle, ME(i) For the endurance mileage, for the rationality, when the remaining mileage of the electric vehicle i is lower than 30% of the remaining endurance of the battery of the electric vehicle, the electric vehicle needs to be charged:
Mr(i)≤0.3ME(i)
step E, establishing a planning path constraint condition:
the remaining mileage of the electric vehicle needs to be less than the path length M of the planned charging stationd(i)：
Mr(i)≤Md(i)
Step F, establishing a power distribution network constraint condition:
in order to ensure the normal operation of the power distribution network, the network loss rate and the voltage deviation rate of the power grid are set within a normal range, and the network loss rate is set as follows:
Ploss(k，t)＜0.72％
the voltage offset ratio is:
Vshift(k，t)＜0.72％
g, setting a weight coefficient:
setting an initial weight coefficient: the weight coefficient of traffic network or distribution network is increased in the traffic peak or charging peak period, and the weight of distribution network and charging station is reduced: omega1∶ω2∶ω37: 2: 1; when the traffic peak and the charging peak coincide and the power distribution network operates normally, the weight coefficient omega of the traffic network is increased1∶ω2∶ω37: 1: 2; when the voltage deviation of the power distribution network reaches the upper limit, the weight coefficient of the power distribution network is increased: omega1∶ω2∶ω3＝1∶1∶8；
Step H, guiding flow determination:
through the steps, the optimal objective function is solved, the information of the traffic, the charging stations and the power distribution network is updated, the driving path of the electric vehicle is solved according to the constraint conditions, the optimal charging station is recommended, whether the charging station is in the range of the remaining mileage of the electric vehicle or not is judged, and if the charging station is in the range of the remaining mileage of the electric vehicle, the vehicle information is updated; if not, the route is planned again, and the charging stations are screened again.
Example 1:
the charging scheduling is performed during the charging peak period from 7 to 9 points in a day, and the obtained charging scheduling result is shown in fig. 2.
Selecting a traffic network in a certain area of Beijing as a simulation object. According to the general travel rule and the charging rule of citizens, the travel time and the charging time are considered in a combined manner.
The electric automobiles with different quantities are introduced into the system according to the difference of travel laws. The peak time is 2500 vehicles per 5 minutes, the power consumption of the vehicles is 15-20 kwh, and the quick charging power is 25-60 khw.
And selecting the traffic jam rate and the charging quantity of the electric automobiles of the charging station as evaluation parameters according to the simulation result. To achieve reasonable charging scheduling, the traffic congestion rate near the charging stations should be reduced as much as possible:
wherein N isc(T) represents the number of congested roads at time T and the congestion rate Rc(t) is the ratio of the number of congested roads to the total number. It can be known that the larger the congestion rate is, the worse the charging schedule of the electric vehicle is. To achieve a reasonable charge schedule, the traffic near the charging station needs to be calculated. And obtaining the congestion condition in the peak period through analog simulation. In addition, the optimization result is compared with the natural congestion rate without using the scheduling scheme of the application,a result is formed.
As shown in fig. 2, when the intelligent guidance scheme of the present invention is not used during the peak period of 7:00 to 9:00, the natural congestion rate reaches 7.98% at the maximum, 2.31% at the minimum, and 5.62% on average. By optimizing the congestion rate through the guided charging scheme herein, a substantial reduction in congestion rate can be seen. The optimized congestion rate is 1.31% at most and 0 at least, and the congestion rate is 0 most of the time, which indicates that the traffic is very smooth. The average value of the congestion rate after optimization is 0.24%.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.
Claims (1)
1. An electric vehicle charging intelligent guiding method based on path optimization is characterized by comprising the following steps:
step A, establishing an optimization objective function:
establishing an optimization objective function, and introducing three necessary attributes to solve the objective function:
Y＝min(ω1yt+ω2yc+ω3yp)
in the formula, yt、yc、yPEach representing three properties, ω, of the objective function1、ω2、ω3A weight value representing a corresponding attribute of the objective function;
b, scheduling attribute analysis:
the traffic dredging degree near the charging station can be judged according to the passing time of the electric automobile:
wherein t isr{ a, b } (i) represents the time at which the vehicle passes through the road segment (a, b) at different times, and t { a, b } (i) represents the time at which the vehicle passes through the road segment (a, b) under the condition that only traffic is considered, regardless of other factors,b) a passage time of the link;
the electric automobile can plan a path according to the distance to the optimal charging station, different paths reach different charging stations j, and the required time tjAt different times tjMake an estimate to be N (j, t)j) The optimal number of vehicles at the charging station is the number of vehicles charged at each charging station under the condition that no queuing waiting exists, namely NG(j，tj) Charging station index ycThe ratio of the two is:
restraining load supply of the charging station, ensuring normal operation of the power distribution network, and setting an optimized target attribute as a natural charging station load estimated value L (j, t)j) And an optimal charging station load estimate LG(j，tj) The ratio of (a) to (b), namely:
step C, establishing a speed constraint condition:
and (3) restraining the traffic condition near the charging station: in the preset distance range of the charging station, if the running speed of the electric automobile is below 30% of the speed limit of the road section, the congestion is serious, the electric automobile is prohibited to pass through the paths, and other suitable paths are selected; otherwise, the running speed is more than 30% of the speed limit, the electric automobile can normally pass, the path in the range can be taken as a recommended path, and the running speed is set as follows:
v(a，b)≥30％vmax{a，b}
wherein v ismax(a, b) is the speed limit of the corresponding road section;
step D, establishing a remaining mileage constraint condition:
let Mr(i) For the remaining mileage of the electric vehicle, ME(i) For the continuation of the journey mileage and for the rationality, the remaining mileage of the electric automobile iWhen the residual battery life of the electric automobile is less than 30%, the electric automobile needs to be charged:
Mr(i)≤0.3ME(i)
step E, establishing a planning path constraint condition:
the remaining mileage of the electric vehicle needs to be less than the path length M of the planned charging stationd(i)：
Mr(i)≤Md(i)
Step F, establishing a power distribution network constraint condition:
in order to ensure the normal operation of the power distribution network, the network loss rate and the voltage deviation rate of the power grid are set within a normal range, and the network loss rate is set as follows:
Ploss(k，t)＜0.72％
the voltage offset ratio is:
Vshift(k，t)＜0.72％
g, setting a weight coefficient:
setting an initial weight coefficient: the weight coefficient of traffic network or distribution network is increased in the traffic peak or charging peak period, and the weight of distribution network and charging station is reduced: omega1∶ω2∶ω37: 2: 1; when the traffic peak and the charging peak coincide and the power distribution network operates normally, the weight coefficient omega of the traffic network is increased1∶ω2∶ω37: 1: 2; when the voltage deviation of the power distribution network reaches the upper limit, the weight coefficient of the power distribution network is increased: omega1∶ω2∶ω3＝1∶1∶8；
Step H, guiding flow determination:
through the steps, the optimal objective function is solved, the information of the traffic, the charging stations and the power distribution network is updated, the driving path of the electric vehicle is solved according to the constraint conditions, the optimal charging station is recommended, whether the charging station is in the range of the remaining mileage of the electric vehicle or not is judged, and if the charging station is in the range of the remaining mileage of the electric vehicle, the vehicle information is updated; if not, the route is planned again, and the charging stations are screened again.
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| CN114117703A (en) * | 2021-11-30 | 2022-03-01 | 南方电网能源发展研究院有限责任公司 | Charging station configuration method and device based on coupling of traffic network and power distribution network |
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Application publication date: 20210202 |