CN113033911A - Charging and battery-changing station facility configuration and cost optimization method - Google Patents

Charging and battery-changing station facility configuration and cost optimization method Download PDF

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CN113033911A
CN113033911A CN202110394247.6A CN202110394247A CN113033911A CN 113033911 A CN113033911 A CN 113033911A CN 202110394247 A CN202110394247 A CN 202110394247A CN 113033911 A CN113033911 A CN 113033911A
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charging
behavior model
facility
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胡兰馨
岳飞扬
沈灿然
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Shanghai Dianji University
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Abstract

The invention discloses a charging and replacing power station facility configuration and cost optimization method, which comprises the following steps: establishing a behavior model of the electric automobile in the battery charging and replacing station, and giving out constraint conditions of the behavior model; acquiring a user arrival rate, a user leaving rate, facility service intensity and a facility utilization rate of the behavior model based on basic parameters of the behavior model; and optimizing the facility utilization rate to obtain an optimal curve of the facility utilization rate. The invention can effectively improve the utilization efficiency of the charging and battery replacing facilities, increases the satisfaction degree of users by setting the quantity proportion between the charging facilities and the battery replacing facilities, provides a basis for the yield expansion and the facility expansion of related enterprises and promotes the industrial development.

Description

Charging and battery-changing station facility configuration and cost optimization method
Technical Field
The invention relates to the field of facility planning of charging and replacing stations, in particular to a charging and replacing station facility configuration and cost optimization method based on a queuing theory.
Background
At present, electromotion, networking, intellectualization and sharing are becoming development trends and trends of the automobile industry, and the new energy automobile sales account for more than 40% of the automobile sales in the same year around 2030 years. Aiming at two bottlenecks of endurance mileage and matched infrastructure construction which restrict the development of new energy automobiles, particularly electric automobiles, the construction of a charging and replacing network is greatly promoted, and the service level of the charging infrastructure is improved.
Disclosure of Invention
The invention aims to solve the technical problem of how to promote the construction of a charging and switching power network and improve the service level of a charging and sending-out facility, and provides a charging and switching power station facility configuration and cost optimization method.
The invention solves the technical problems through the following technical scheme:
a charging and swapping station facility configuration and cost optimization method comprises the following steps:
establishing a behavior model of the electric automobile in the battery charging and replacing station, and giving out constraint conditions of the behavior model;
acquiring a user arrival rate, a user leaving rate, facility service intensity and a facility utilization rate of the behavior model based on basic parameters of the behavior model;
and optimizing the facility utilization rate to obtain an optimal curve of the facility utilization rate.
Further, according to the number of the electric automobiles, the probability of the electric automobiles reaching the charging and replacing station and the probability of the electric automobiles leaving after completing the service, the behavior model is established.
Preferably, the behavior model is established based on an M/M/N/k/∞/FCFS queuing model, wherein parameters M and M respectively represent that the interval time and service time of the electric vehicle user reaching the charging and conversion station in the behavior model obey negative exponential distribution, N represents the number of charging and conversion facilities, k represents the number of potential users at most simultaneously accommodated, infinity represents the number of serviceable users, and FCFS represents that the behavior model obeys a service rule from first to first.
Further, the basic parameters include: average number of electric vehicles in the queue, average waiting time of users, average time of stay of users in the station, average number of devices in use, and average utilization rate of facilities in the station.
Furthermore, the constraint condition is a constraint condition according with the safety of the power grid; optimizing the utility ratio using marginal cost theory.
On the basis of the common knowledge in the field, the above preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows: the invention can effectively improve the utilization efficiency of the charging and battery replacing facilities, increases the satisfaction degree of users by setting the quantity proportion between the charging facilities and the battery replacing facilities, provides a basis for the yield expansion and the facility expansion of related enterprises and promotes the industrial development.
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Fig. 1 is a flowchart of a method in an embodiment of a method for facility configuration and cost optimization of a charging and swapping station according to the present invention;
fig. 2 is a flow chart of a charging and swapping behavior model in an embodiment of a charging and swapping station facility configuration and cost optimization method of the present invention;
fig. 3 is a schematic diagram of an optimal curve in an embodiment of a facility configuration and cost optimization method for a charging and swapping station according to the present invention.
Detailed Description
To facilitate an understanding of the present application, the present application will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present application are shown in the drawings. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Fig. 1 is a flowchart of a method in an implementation of a charging and swapping facility configuration and cost optimization method of the present invention:
s01: establishing a behavior model of the electric automobile in the battery charging and replacing station, and giving out constraint conditions of the behavior model;
in one example, the number n of electric vehicles, the number c of facilities within a station that can provide a charging and battery replacing service, the probability λ of the electric vehicle reaching the charging and battery replacing station, and the probability μ of the electric vehicle leaving after completing the service are determined, and a corresponding electric vehicle charging and battery replacing behavior model is established according to constraints such as power grid security, and a schematic diagram of the behavior model is shown in fig. 2:
the objective function is: min c
The state probability equilibrium equation of the behavior model is as follows:
Figure BDA0003017930510000031
wherein, PnThe probability that n electric vehicles are in the charging service is represented; c represents the number of facilities which can provide the charging and battery replacing service in the station, and n represents the number of electric vehicles which are receiving the charging and battery replacing service in the station; when n is more than or equal to 1 and less than or equal to c, the fact that an electric automobile is receiving the battery charging and replacing service in the current station is shown, and c-n vacant positions still exist; when n is>And c, all the c charging and replacing equipment in the current station are in service, namely the service quota in the station is full, and n-c electric vehicles are still in a queue and in a service waiting state.
The constraints of the behavioral model are as follows:
(1) the user average latency probability constraint, whose probability needs to be greater than the probability required to reach the average latency:
P{Wq≤tq}＝1-P{Wq>tq}＝1-Pq>pq
in the above formula WqIndicates the current user waiting time, tqDenotes the average latency, P { W }q≤tqDenotes the average waiting time probability of the user, pqRepresenting the probability required to reach an average latency.
(2) The probability constraint for dwell time in the subscriber station, whose probability needs to be greater than the probability required to reach the average dwell time:
P{W≤t}＝1-P{W>t}＝1-P>p
wherein, W represents the current user stay time, t represents the average stay time, P { W ≦ t } represents the user average stay time probability, and P represents the probability required by the main road average stay time.
(3) User service time constraints, the minimum time required for in-station stay is greater than the service time:
t>tmin
where t represents the average residence time of the user, tminRepresenting the minimum time required to service the user.
(4) Service strength constraints, ensure that the system does not form an infinite queue:
ρ<1
where p is the service strength.
S02: acquiring a user arrival rate, a user leaving rate, facility service intensity and a facility utilization rate of the behavior model based on basic parameters of the behavior model;
in one example, the base parameters include: average number of electric vehicles in the queue, average waiting time of users, average time of stay of users in the station, average number of devices in use, average utilization rate of facilities in the station, and the like. And solving the user arrival rate, the user leaving rate, the facility service intensity and the facility utilization rate in the behavior model based on the basic parameters.
S03: and optimizing the facility utilization rate to obtain an optimal curve of the facility utilization rate.
In one example, the electric vehicle charging and battery replacing behavior model established by the charging and battery replacing power station facility configuration and cost optimization method conforms to the power grid safety constraint. The electric vehicle battery charging and replacing behavior model is established based on an M/M/N/k/∞/FCFS queuing model, the meaning of the electric vehicle battery charging and replacing behavior model is that the interval time and service time of electric vehicle users arriving at a battery charging and replacing station in the model obey negative exponential distribution, N battery charging and replacing facilities are shared, k potential users are accommodated at most simultaneously, the number of users which can be served is unlimited, and the rule of first-come first-serve is obeyed. The charging and battery-replacing power station facility configuration and cost optimization method comprises the principle of charging or battery-replacing mode allocation for users based on the current service intensity. According to the marginal cost theory, a facility utilization rate optimal curve in a corresponding state is solved, as shown in fig. 3, the abscissa in the graph represents the facility number, the ordinate represents the utilization rate, and the curve cluster represents a relationship curve between the utilization rate and the facility number under different conditions along with the rise of the service intensity. And calculating the optimal capacity c under the corresponding service intensity and the optimal utilization rate u of the facility through an objective function, and finally connecting the point sets to obtain an optimal curve.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.

Claims (5)

1. A charging and replacing power station facility configuration and cost optimization method is characterized by comprising the following steps:
establishing a behavior model of the electric automobile in the battery charging and replacing station, and giving out constraint conditions of the behavior model;
acquiring a user arrival rate, a user leaving rate, facility service intensity and a facility utilization rate of the behavior model based on basic parameters of the behavior model;
and optimizing the facility utilization rate to obtain an optimal curve of the facility utilization rate.
2. The charging and swapping station facility configuration and cost optimization method of claim 1, wherein the behavior model is established according to the number of the electric vehicles, the probability of the electric vehicles arriving at the charging and swapping station, and the probability of the electric vehicles leaving after completing the service.
3. The charging and swapping power station facility configuration and cost optimization method of claim 2, wherein the behavior model is established based on an M/M/N/k/∞/FCFS queuing model, wherein parameters M and M respectively represent a negative exponential distribution of an interval time and a service time of an electric vehicle user arriving at the charging and swapping power station in the behavior model, N represents the number of charging and swapping facilities, k represents the number of potential users at most simultaneously, infinity represents the number of serviceable users, and FCFS represents that the behavior model complies with a first-to-first service rule.
4. The charging and swapping station facility configuration and cost optimization method of claim 1, wherein the basic parameters comprise: average number of electric vehicles in the queue, average waiting time of users, average time of stay of users in the station, average number of devices in use, and average utilization rate of facilities in the station.
5. The charging and swapping station facility configuration and cost optimization method according to any one of claims 1 to 4, wherein the constraint condition is a constraint condition meeting power grid safety; optimizing the utility ratio using marginal cost theory.
CN202110394247.6A 2021-04-13 2021-04-13 Charging and battery-changing station facility configuration and cost optimization method Pending CN113033911A (en)

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Citations (6)

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Publication number Priority date Publication date Assignee Title
CN106779176A (en) * 2016-11-25 2017-05-31 北京交通大学 Electric taxi fills electrically-charging equipment configuration and constant volume method in station soon
CN106849065A (en) * 2017-03-06 2017-06-13 四川大学 A kind of power distribution network multiple target collaborative planning method of meter and charging station
CN108334991A (en) * 2018-02-12 2018-07-27 清华大学 A kind of electric automobile charging station method and system for planning
CN110705746A (en) * 2019-08-27 2020-01-17 北京交通大学 Optimal configuration method for electric taxi quick charging station
EP3700044A1 (en) * 2017-10-20 2020-08-26 Nio Nextev Limited Method and apparatus for determining battery charging strategy of battery swap station
CN112381398A (en) * 2020-11-13 2021-02-19 国网山东省电力公司青岛供电公司 Electric vehicle charging station site selection method and system considering limited trip of user

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Publication number Priority date Publication date Assignee Title
CN106779176A (en) * 2016-11-25 2017-05-31 北京交通大学 Electric taxi fills electrically-charging equipment configuration and constant volume method in station soon
CN106849065A (en) * 2017-03-06 2017-06-13 四川大学 A kind of power distribution network multiple target collaborative planning method of meter and charging station
EP3700044A1 (en) * 2017-10-20 2020-08-26 Nio Nextev Limited Method and apparatus for determining battery charging strategy of battery swap station
CN108334991A (en) * 2018-02-12 2018-07-27 清华大学 A kind of electric automobile charging station method and system for planning
CN110705746A (en) * 2019-08-27 2020-01-17 北京交通大学 Optimal configuration method for electric taxi quick charging station
CN112381398A (en) * 2020-11-13 2021-02-19 国网山东省电力公司青岛供电公司 Electric vehicle charging station site selection method and system considering limited trip of user

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Application publication date: 20210625