CN114021795A - Charging station planning method and system considering charging requirements of electric vehicle - Google Patents

Charging station planning method and system considering charging requirements of electric vehicle Download PDF

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CN114021795A
CN114021795A CN202111256635.4A CN202111256635A CN114021795A CN 114021795 A CN114021795 A CN 114021795A CN 202111256635 A CN202111256635 A CN 202111256635A CN 114021795 A CN114021795 A CN 114021795A
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苏粟
梁方
李玉璟
王陆飞
董刚
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Beijing Jiaotong University
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Abstract

The invention relates to a charging station planning method and a charging station planning system considering the charging demand of an electric vehicle, wherein the method analyzes the characteristics of travel and charging behaviors based on the real data of non-operating vehicles, and fully considers the attraction of a specific charging place to non-operation vehicle users, more practical charging demand space-time distribution prediction is carried out, thereby establishing a non-operation vehicle charging demand space-time model, establishing an operation vehicle charging demand space-time distribution prediction model by utilizing the trajectory data of the operation vehicle, further fusing the two models, and the charging demand prediction result output by the fused model is applied to a charging station planning construction scheme, the invention considers the space-time distribution of the charging demands of different types of vehicles, ensures the normal and high-efficiency operation of the urban electric automobile system, and improves the practicability and the application range of the charging station planning.

Description

Charging station planning method and system considering charging requirements of electric vehicle
Technical Field
The invention relates to the technical field of charging station planning, in particular to a charging station planning method and system considering the charging requirement of an electric vehicle.
Background
With the increasing pressure of energy shortage and environmental pollution, the electric automobile industry is paid attention to and developed vigorously, and the development of electric automobiles is an important measure for coping with energy crisis in China and is an effective way for realizing carbon peak reaching. The electric automobile production and sale quantity and the conservation quantity continuously live at the top of the world for five years since 2015 in China, and the new energy automobile sales quantity is about 20% of the total automobile sales quantity by 2025. In 2035 years, the quantity of new energy vehicles in China is estimated to exceed 1.6 hundred million, wherein the electric capacity of pure electric vehicles exceeds nine years, and all public vehicles are electrically driven, so that the electric automobile industry in China has wide prospect.
The charging facility serves as an operation foundation of the electric automobile, and reasonable large-scale construction of the charging facility is a precondition for popularization and development of the electric automobile and must be adapted to development of the electric automobile. However, different from the well-developed situation of the electric automobile and the charging infrastructure industry, the unreasonable layout of the charging station causes the problems of difficult charging for users, unbalanced resource distribution of charging facilities and the like, accordingly, the benefit of charging operators is also affected, and the development of the electric automobile industry is greatly not facilitated. Therefore, the problem still needs to be solved at the present stage to provide a method for planning an electric vehicle charging station that takes account of the charging cost of the user, the convenience and the operator profit.
The existing research processes travel track data and operation data of taxis, net car appointment and city residents through a data mining technology, and the travel rule of city residents is mined, and the travel track data is applied to the planning of charging stations of taxis, net car appointment and other operation type vehicles, so that the construction capacity of the charging stations is optimally designed through the planning result, but the research is the planning and design of the charging stations performed on the operation vehicles, and non-operation vehicles such as private cars are not analyzed, so that the planning result of the charging stations does not have applicability.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a charging station planning method and a charging station planning system considering the charging requirement of an electric vehicle.
In order to achieve the purpose, the invention provides the following scheme:
a charging station planning method considering the charging demand of an electric vehicle comprises the following steps:
acquiring trip chain data and charging scene data of non-operating vehicles;
determining travel characteristics of the non-operating vehicle according to the travel chain data; the travel characteristics include: the probability of different trip chains, the probability of departure time, the probability of destination and the probability of arrival time;
determining charging characteristics of the non-operating vehicle according to the charging scene data; the charging feature includes: charging frequency, destination charging behavior characteristics and attractiveness of different functional areas of the non-operating vehicle to the non-operating vehicle of different trip chains;
establishing a non-operation vehicle charging demand space-time model according to the travel characteristics and the charging characteristics;
establishing an operating vehicle charging demand space-time distribution model based on the trajectory data of the operating vehicle order;
fusing the non-operation vehicle charging demand space-time model and the operation vehicle charging demand space-time distribution model to obtain an electric vehicle charging demand space-time distribution model;
constructing an objective function of a charging station location and volume-fixing planning model based on the annual construction and operation cost of the charging station, the annual time consumption cost of an electric vehicle user from a charging demand point to the charging station and the annual queuing waiting time cost of the electric vehicle user; the electric vehicle users comprise users of the non-operating vehicle and users of the operating vehicle;
and solving the objective function by adopting a particle swarm algorithm according to the electric vehicle charging demand space-time distribution model, and determining the positions of the charging stations and the configuration number of chargers in each charging station.
Preferably, the determining the travel characteristics of the non-operating vehicle according to the travel chain data includes:
performing statistical analysis according to the trip chain data to obtain the probabilities of the different trip chains, the probability of the departure time and the time consumption distribution of single trip;
and simulating the probability, the departure time probability and the single trip time consumption distribution based on a Monte Carlo method to obtain the destination probability and the arrival time probability.
Preferably, the determining the charging characteristics of the non-operating vehicle according to the charging scenario data includes:
performing probability distribution simulation on the charging frequency of the non-operating vehicles in the public piles within preset time according to the charging scene data to obtain an expectation; the expectation is the average public pile charging frequency of each person in the preset time; sampling and simulating the expectation based on a Monte Carlo method to obtain the charging frequency of the non-operating vehicles using the charging piles within each day;
analyzing the charging scene data to obtain destination charging behavior characteristics of the non-operating vehicles in each trip chain;
and counting the charging interest points of each functional area in the charging scene data, and performing weight analysis according to the number of the charging interest points corresponding to different functional areas to obtain the attraction of the different functional areas to the non-operating vehicle.
Preferably, the establishing a non-operating vehicle charging demand space-time model according to the travel characteristics and the charging characteristics includes:
acquiring the total number M of the vehicles of the non-operation vehicles;
performing charging simulation on the ith non-operating vehicle, and extracting a trip chain of the non-operating vehicle according to the probability of different trip chains;
extracting a destination of the non-operating vehicle on the trip chain, which is charged after the trip is finished, based on the destination charging behavior characteristics and the destination probability of the non-operating vehicle;
extracting a charging area of the non-operating vehicle on the trip chain based on the attraction, and extracting a road node in the charging area, wherein the road node is a specific charging place of the ith non-operating vehicle;
extracting a travel arrival time of the destination charging behavior of the ith non-operation vehicle based on the arrival time probability and the departure time probability, wherein the travel arrival time is the specific charging time of the ith private car;
and writing the specific charging place and the specific charging time into a non-operating vehicle quick-charging demand set, judging whether i is equal to M, if not, judging that i is equal to i +1, returning to the step of performing charging simulation on the ith non-operating vehicle, and if so, determining the non-operating vehicle charging demand spatio-temporal model according to the non-operating vehicle quick-charging demand set.
Preferably, the objective function of the charging station siting volume planning model is represented as:
minF＝αF1+β(F2+F3)nyear；
wherein F is the annual comprehensive cost; f1Establishing operating costs for the charging station year; f2The time-consuming cost per year for the electric vehicle user from a charging demand point to a charging station; f3Annual queuing latency costs for said users; n isyearPlanning the age for the charging station, wherein alpha is a first trade-off benefit coefficient, and beta is a second trade-off benefit coefficient;
Figure BDA0003323759660000041
n is the number of planned charging stations; qiCharging the number of motors in the charging station No. i; r is0The current rate is the current rate; m is the depreciation age of the charging station, C (Q)i) Constructing an investment cost function for the charging station No. i; u (Q)i) A yearly operation cost function for charging station number i;
Figure BDA0003323759660000042
j is a set of points of demand for charging,
Figure BDA0003323759660000043
a charging demand point set for selecting the charging station No. i for charging;
Figure BDA0003323759660000044
a j charging demand user who selects the i charging station for charging travels to a road set which passes by the i charging station in the charging process; phi is the coefficient of the passing road, dkIs k road length; v. ofktThe vehicle passing speed of the k road at the time t; f. ofwConverting the time cost into a coefficient for an electric vehicle user;
Figure BDA0003323759660000045
Figure BDA0003323759660000046
charging queuing waiting time of the ith charging station within the time t;
Figure BDA0003323759660000047
the number of electric vehicles charged in the moment t for the charging station No. i; t e [ t ∈ ]0,te]Representing the time of travel, t, of a simulated electric vehicle0Indicating the start time of travel, teIndicating the end of travel time.
Preferably, the solving the objective function by using a particle swarm algorithm according to the electric vehicle charging demand space-time distribution model to determine the charging station positions and the configuration number of chargers in each charging station specifically includes:
and solving the objective function by adopting a particle swarm algorithm for improving the self-adaptive inertia weight according to the electric vehicle charging demand space-time distribution model, and determining the positions of the charging stations and the configuration quantity of chargers in each charging station.
A charging station planning system that considers electric vehicle charging needs, comprising:
the acquisition module is used for acquiring trip chain data and charging scene data of the non-operating vehicle;
the first characteristic determining module is used for determining the travel characteristic of the non-operation vehicle according to the travel chain data; the travel characteristics include: the probability of different trip chains, the probability of departure time, the probability of destination and the probability of arrival time;
the second characteristic determining module is used for determining the charging characteristics of the non-operating vehicle according to the charging scene data; the charging feature includes: charging frequency, destination charging behavior characteristics and attractiveness of different functional areas of the non-operating vehicle to the non-operating vehicle of different trip chains;
the first model establishing module is used for establishing a non-operation vehicle charging demand space-time model according to the travel characteristic and the charging characteristic;
the second model establishing module is used for establishing a space-time distribution model of the charging demand of the operating vehicle based on the track data of the operating vehicle order;
the fusion module is used for fusing the non-operation vehicle charging demand space-time model and the operation vehicle charging demand space-time distribution model to obtain an electric vehicle charging demand space-time distribution model;
the target function construction module is used for constructing a target function of a charging station location and volume planning model based on the annual construction and operation cost of the charging station, the annual time consumption cost of an electric vehicle user from a charging demand point to the charging station and the annual queuing waiting time cost of the electric vehicle user; the electric vehicle users comprise users of the non-operating vehicle and users of the operating vehicle;
and the charging station planning module is used for solving the objective function by adopting a particle swarm algorithm according to the space-time distribution model of the charging demand of the electric automobile and determining the positions of the charging stations and the configuration number of chargers in each charging station.
Preferably, the first feature determination module specifically includes:
the first statistical unit is used for carrying out statistical analysis according to the trip chain data to obtain the probabilities of different trip chains, the probability of the departure time and the time consumption distribution of single trip;
a first simulation unit, configured to simulate the probability, the departure time probability, and the single trip time consumption distribution based on a monte carlo method, so as to obtain the destination probability and the arrival time probability.
Preferably, the second feature determination module specifically includes:
the second simulation unit is used for performing probability distribution simulation on the charging frequency of the non-operating vehicles in the public pile within the preset time according to the charging scene data to obtain an expectation; the expectation is the average public pile charging frequency of each person in the preset time; sampling and simulating the expectation based on a Monte Carlo method to obtain the charging frequency of the non-operating vehicles using the charging piles within each day;
the analysis unit is used for analyzing the charging scene data to obtain destination charging behavior characteristics of the non-operating vehicles in each trip chain;
and the second statistical unit is used for counting the charging interest points of each functional area in the charging scene data, and performing weight analysis according to the number of the charging interest points corresponding to different functional areas to obtain the attraction of the different functional areas to the non-operating vehicle.
Preferably, the first model building module specifically includes:
an acquisition unit configured to acquire a total number M of vehicles of the non-operating vehicles;
a third simulation unit, configured to perform charging simulation on the ith non-operating vehicle, and extract a trip chain of the non-operating vehicle according to the probabilities of different trip chains;
a first extraction unit, configured to extract a destination to which the non-operating vehicle on the travel chain charges after a travel is finished, based on the destination charging behavior feature and the destination probability of the non-operating vehicle;
a second extraction unit, configured to extract a charging area of the non-operating vehicle on the travel chain based on the attraction force, and extract a road node in the charging area, where the road node is a specific charging location of an ith non-operating vehicle;
a third extraction unit configured to extract a trip arrival time of a destination charging behavior of an ith non-operated vehicle, which is a specific charging time of an ith private car, based on the arrival time probability and the departure time probability;
and the judging unit is used for writing the specific charging place and the specific charging time into a non-operating vehicle quick-charging demand set, judging whether i is equal to M or not, if not, judging that i is i +1, returning to the step of performing charging simulation on the ith non-operating vehicle, and if so, determining the non-operating vehicle charging demand spatio-temporal model according to the non-operating vehicle quick-charging demand set.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a charging station planning method and a charging station planning system considering the charging requirement of an electric vehicle, wherein the method comprises the following steps: acquiring trip chain data and charging scene data of non-operating vehicles; determining travel characteristics of the non-operating vehicle according to the travel chain data; the travel characteristics include: the probability of different trip chains, the probability of departure time, the probability of destination and the probability of arrival time; determining charging characteristics of the non-operating vehicle according to the charging scene data; the charging feature includes: charging frequency, destination charging behavior characteristics and attractiveness of different functional areas of the non-operating vehicle to the non-operating vehicle of different trip chains; establishing a non-operation vehicle charging demand space-time model according to the travel characteristics and the charging characteristics; establishing an operating vehicle charging demand space-time distribution model based on the trajectory data of the operating vehicle order; fusing the non-operation vehicle charging demand space-time model and the operation vehicle charging demand space-time distribution model to obtain an electric vehicle charging demand space-time distribution model; constructing an objective function of a charging station location and volume-fixing planning model based on the annual construction and operation cost of the charging station, the annual time consumption cost of an electric vehicle user from a charging demand point to the charging station and the annual queuing waiting time cost of the electric vehicle user; the electric vehicle users comprise users of the non-operating vehicle and users of the operating vehicle; and solving the objective function by adopting a particle swarm algorithm according to the electric vehicle charging demand space-time distribution model, and determining the positions of the charging stations and the configuration number of chargers in each charging station. Aiming at the characteristics of blindness and unbalanced layout of the existing site selection and volume determination planning construction of the charging station, the invention provides a method for planning the charging station by fully considering the function types and the vehicle characteristics of urban electric vehicles, considering the space-time distribution of the charging demands of different types of vehicles, integrating the charging demands of non-operation vehicles and operation electric vehicles, and balancing the cost of operators of the charging station and the charging cost and convenience of electric vehicle users. Therefore, the normal and high-efficiency operation of the urban electric automobile system is ensured by determining the proper charging station position and the configuration of the charger in the station, and the practicability and the application range of the charging station planning are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a first schematic flow chart of a method of planning a charging station according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method of planning a charging station according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of distribution of departure times of different trip chains in the embodiment of the present invention;
fig. 4 is a schematic diagram of distribution of arrival times of different trip chains in the embodiment of the present invention;
FIG. 5 is a schematic diagram of a weekly pole charging frequency for a user of an electric personal vehicle in an embodiment provided by the present invention;
FIG. 6 is a schematic diagram of a weekly pole charging frequency for a user of an electric personal vehicle in an embodiment provided by the present invention;
FIG. 7 is a flowchart of a model for predicting the temporal-spatial distribution of the charging demand of an electric vehicle according to an embodiment of the present invention;
FIG. 8 is a flow chart of an improved particle swarm algorithm in an embodiment provided by the present invention;
fig. 9 is a schematic diagram of the total cost for planning different numbers of charging stations in the embodiment provided by the present invention;
FIG. 10 shows the result of a charging station planning in an embodiment of the present invention;
fig. 11 is a block diagram of a charging station planning system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The terms "first," "second," "third," and "fourth," etc. in the description and claims of this application and in the accompanying drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, the inclusion of a list of steps, processes, methods, etc. is not limited to only those steps recited, but may alternatively include additional steps not recited, or may alternatively include additional steps inherent to such processes, methods, articles, or devices.
The invention aims to provide a charging station planning method and a charging station planning system considering the charging requirements of electric vehicles, which ensure the normal and efficient operation of an urban electric vehicle system and improve the practicability and the application range of the charging station planning by determining a proper charging station position and the configuration of a charger in a station.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 and fig. 2 are a first method flow diagram and a first method flow diagram of a charging station planning method in an embodiment of the present invention, and as shown in fig. 1 and fig. 2, the present invention provides a charging station planning method considering a charging requirement of an electric vehicle, including:
step 100: acquiring trip chain data and charging scene data of non-operating vehicles;
step 200: determining travel characteristics of the non-operating vehicle according to the travel chain data; the travel characteristics include: the probability of different trip chains, the probability of departure time, the probability of destination and the probability of arrival time;
step 300: determining charging characteristics of the non-operating vehicle according to the charging scene data; the charging feature includes: charging frequency, destination charging behavior characteristics and attractiveness of different functional areas of the non-operating vehicle to the non-operating vehicle of different trip chains;
step 400: establishing a non-operation vehicle charging demand space-time model according to the travel characteristics and the charging characteristics;
step 500: establishing an operating vehicle charging demand space-time distribution model based on the trajectory data of the operating vehicle order;
step 600: fusing the non-operation vehicle charging demand space-time model and the operation vehicle charging demand space-time distribution model to obtain an electric vehicle charging demand space-time distribution model;
step 700: constructing an objective function of a charging station location and volume-fixing planning model based on the annual construction and operation cost of the charging station, the annual time consumption cost of an electric vehicle user from a charging demand point to the charging station and the annual queuing waiting time cost of the electric vehicle user; the electric vehicle users comprise users of the non-operating vehicle and users of the operating vehicle;
step 800: and solving the objective function by adopting a particle swarm algorithm according to the electric vehicle charging demand space-time distribution model, and determining the positions of the charging stations and the configuration number of chargers in each charging station.
Preferably, the step 200 comprises:
performing statistical analysis according to the trip chain data to obtain the probabilities of the different trip chains, the probability of the departure time and the time consumption distribution of single trip;
and simulating the probability, the departure time probability and the single trip time consumption distribution based on a Monte Carlo method to obtain the destination probability and the arrival time probability.
In the present embodiment, an electric private car is set as a research object of a non-operating vehicle, and an electric taxi is set as a research object of an operating vehicle.
Fig. 3 and 4 are schematic diagrams of departure time distributions of different trip chains and distribution of arrival time distributions of different trip chains in the embodiment of the present invention, respectively, as shown in fig. 3 and 4, hbt (h) in the drawings represents a trip with a home as a starting point, wbt (w) represents a trip with a work unit (school) as a starting point, and OBT represents a trip with an entertainment and leisure place as a starting point. Finally, based on the research result data, the following characteristics are used to describe the travel characteristics of the users of the electric private cars in the region: the probability (number of outgoing lines) of different trip chains, the probability (departure space-time distribution) of departure time of different trip chains, the probability (destination space distribution) of destination of different trip chains, and the probability (destination time distribution) of arrival time of different trip chains. As shown in table 1, table 1 shows the trip chain and the occupancy of the target region.
TABLE 1
Figure BDA0003323759660000101
Specifically, in the table, H denotes a home, W denotes a work (learning) place, S denotes a vegetable buying and shopping place, and L denotes a social entertainment place.
Preferably, the step 300 comprises:
performing probability distribution simulation on the charging frequency of the non-operating vehicles in the public piles within preset time according to the charging scene data to obtain an expectation; the expectation is the average public pile charging frequency of each person in the preset time; sampling and simulating the expectation based on a Monte Carlo method to obtain the charging frequency of the non-operating vehicles using the charging piles within each day;
analyzing the charging scene data to obtain destination charging behavior characteristics of the non-operating vehicles in each trip chain;
and counting the charging interest points of each functional area in the charging scene data, and performing weight analysis according to the number of the charging interest points corresponding to different functional areas to obtain the attraction of the different functional areas to the non-operating vehicle.
Fig. 5 and 6 are schematic diagrams of charging frequency of a weekly public post of an electric private car user and charging frequency of a weekly public post of an electric private car user in an embodiment provided by the present invention, and as shown in fig. 5 and 6, in the present embodiment, 310 effective samples investigated on a charging scene of a public charging pile are subjected to probability analysis to obtain probability distribution of charging frequency of the weekly public post and probability distribution of the electric private car user at a charging place in a public place, which are shown in fig. 5 and 6, and based on a principle that charging interval times are the same, the electric private car users are obtained by monte carlo sampling, about 30% of the electric private car users can use the public charging pile to supply electric energy within a certain day, charging destination behavior characteristics of the electric private car users are shown in table 2, and table 2 is charging destination behavior characteristics of the electric private car users; through statistics and processing of relevant POIs in each functional area and the relevant attribute mixed area, the probability of the electric vehicle user arriving in the area and generating the charging requirement is obtained.
TABLE 2
Figure BDA0003323759660000111
Specifically, most of existing researches predict the charging demand of the electric private car based on a method for travel simulation and charging judgment of a travel chain, however, the number of times of travel and the travel distance of the electric private car in one day are not in a same level with that of a taxi, and the initial state of charge (SOC) has strong uncertainty. Therefore, the embodiment develops research on the problems of the charging frequency of a week when the electric private car user selects the public stake for charging, the selection habit of the charging scene and the like in the public charging station. The quick charging demand of the electric private car is predicted by analyzing the time-space characteristics of the charging behavior of the electric private car, the charging psychology and the distribution of the travel destination. Wherein the charging characteristics specifically include:
(1) frequency of charging
And (3) simulating the charging frequency probability distribution of the public piles of the electric private car users every week by the research data, and calculating the expectation, namely the average charging frequency of the public piles of each person every week. Based on the principle that the charging interval time is the same, the electric private car users who average have the probability in a certain day can use the public charging pile to supply the electric quantity through Monte Carlo sampling simulation.
(2) Destination charging behavior characterization
And simulating the probability distribution of the electric private car users in the charging places of the public places by the research data. If the charging probability of the public parking lot in the residential area is known to be X times that of the public parking lot in the office area, the charging probability of an electric private car user with the public charging pile charging requirement and the trip chain in the H-W-H mode is 1/(X +1) at the office place, the charging probability after returning home is X/(X +1), and the charging probability of other trip chains at the office place is calculated in the same way to obtain the destination charging behavior characteristic of the electric private car user. The destination charging behavior characteristics of the electric private car users in each trip chain are analyzed, and the charging behavior space-time characteristics of the electric private car users can be obtained by combining the trip characteristics, so that an electric private car model is established.
(3) The 'attraction' of different areas of each functional area to electric vehicle users is refined "
The 'attraction' of each functional area to the electric private car user is quantitatively described based on Point of Interest (POI) information so as to more practically carry out the electric private car charging demand.
1) Residential area
The present embodiment quantitatively describes the possibility that the electric vehicle reaches and is charged in each residential area by counting the number of POIs within 1000 meters of each residential area and the residential attribute mixed functional area. The score of the residential area a with the largest number of POIs in the range is set to be 100, and the scores of other residential areas are linearly related to the number of POIs (if the number of POIs in the range of the residential area B is 80% of the number of POIs in the residential area a, the arrival/charging possibility score of the electric automobile in the area is 80%). The score of the residential attribute mixed area needs to be multiplied by a certain coefficient, the coefficient is the frequency density ratio of the residential POI in the area, and the calculation formula is
Figure BDA0003323759660000131
In the formula, ciThe ratio of the frequency density sum of the ith POI to the frequency density sum of all POIs in the grid is obtained; d is the sum of the frequency densities of various POIs in the grid; diThe sum of the frequency densities of the i-th POI in the grid. diThe calculation formula of (2) is as follows:
Figure BDA0003323759660000132
in the formula, alphaiWeight of the i-th POI; n isiThe number of the ith POI in the grid; and S is the area of the grid.
The higher the score, the greater the probability that the electric vehicle will generate a charging demand in that residential zone.
2) Office area
Under the influence of unit properties, the number of cars used during traveling from a gateway unit, a national enterprise collective unit to an individual and private department is 1.28 times that of cars used during traveling of residents on duty. When relevant POI in the office area and the relevant attribute mixed area are counted and processed, the POI value of the organ and public institution and enterprise collective unit is assumed to be Y, and the POI value of the individual and private unit is assumed to be 1.28Y. For some enterprise company POIs with difficult property identification, core characters in the enterprise directory of the enterprise need to be extracted, and then the core characters are compared with the enterprise POIs of the company to search, so that the property of the company is authenticated. The higher the score, the greater the probability that the electric vehicle will generate a charging demand in the office-like area.
3) Recreation and leisure area
Every time the commercial accessibility of the residential community is increased by one unit, the trip occurrence rate of the private car is increased to 2 times, and due to the improvement of the commercial accessibility, the frequency of completing activities such as buying vegetables, buying living goods, eating, entertainment and the like by driving the private car to a nearby business center is increased. The number of the residential POIs in the range of 1000 meters in the entertainment and leisure area is counted, and the probability of the arrival/generation of the charging demand of the electric vehicle user in the area is quantitatively described through the number of the residential POIs in the same method as that of the residential area. The higher the score, the greater the probability that the electric vehicle will generate a charging demand in the recreational area.
Preferably, the step 400 comprises:
acquiring the total number M of the vehicles of the non-operation vehicles;
performing charging simulation on the ith non-operating vehicle, and extracting a trip chain of the non-operating vehicle according to the probability of different trip chains;
extracting a destination of the non-operating vehicle on the trip chain, which is charged after the trip is finished, based on the destination charging behavior characteristics and the destination probability of the non-operating vehicle;
extracting a charging area of the non-operating vehicle on the trip chain based on the attraction, and extracting a road node in the charging area, wherein the road node is a specific charging place of the ith non-operating vehicle;
extracting a travel arrival time of the destination charging behavior of the ith non-operation vehicle based on the arrival time probability and the departure time probability, wherein the travel arrival time is the specific charging time of the ith private car;
and writing the specific charging place and the specific charging time into a non-operating vehicle quick-charging demand set, judging whether i is equal to M, if not, judging that i is equal to i +1, returning to the step of performing charging simulation on the ith non-operating vehicle, and if so, determining the non-operating vehicle charging demand spatio-temporal model according to the non-operating vehicle quick-charging demand set.
Specifically, the embodiment performs space-time distribution prediction on the private car charging demand, and the specific steps are as follows:
1) setting the total number M of the electric private cars with the fast charging requirement of the public charging pile at the same day;
2) performing charging simulation on a first private car, and extracting a trip chain of the electric private car based on the duty ratio of each trip chain;
3) extracting a destination of the electric private car user for charging after the travel is finished based on the destination charging behavior characteristics of the electric private car user;
4) based on the 'attraction' of different areas with the same functional attributes to electric private car users, extracting a charging area, and extracting road nodes in the area to serve as a specific charging place of a first private car;
5) extracting the travel arrival time of the first private car destination charging behavior based on different trip chain arrival time probabilities (destination time distribution) as specific charging time of the first private car;
6) recording the time and the place, and writing the time and the place into a quick charging demand set of the electric automobile; and judging whether i is equal to M, if not, making i equal to i +1, and entering the step 2), otherwise, fusing the private car and operating vehicle charging demand space-time distribution model, and ending.
Further, the step 500 includes:
step 501: acquiring track data of an operating vehicle order; the trajectory data comprises a plurality of trajectory points;
step 502: carrying out data cleaning on the track data to obtain first processing track data;
step 503: correcting the deviation point in the first processing track data to obtain second processing track data; the deviation points are track points with the distance from the road in the traffic network outside a first set distance;
step 504: obtaining an OD point pair trip set, a vehicle passing speed set and a vehicle running track set according to the second processing track data;
step 505: and simulating the operation of the operation vehicle according to the OD point pair travel set, the vehicle passing speed set and the vehicle running track set, and establishing an operation vehicle charging demand space-time distribution model according to the operation of the simulated operation vehicle.
Optionally, the step 502 includes:
arranging the track data into a track set according to a time sequence by taking the order as a unit;
deleting data which are not in the set area range in the track set to obtain first cleaning data;
deleting the repeated track data of the same order in the first cleaning data within a second set distance to obtain second cleaning data;
deleting the track data with the instantaneous speed exceeding 120 km/h in the second cleaning data to obtain third cleaning data;
deleting the abnormal deviation points in the third cleaning data to obtain fourth cleaning data; an included angle between a connecting line of the abnormal deviation point and the track point at the previous moment and a connecting line of the abnormal deviation point and the track point at the next moment is an acute angle;
and deleting the order data of which the track point is less than 10, the order duration is less than 1 minute or the order distance is less than 800 meters in the order in the fourth cleaning data to obtain the first processing track data.
Specifically, the step 503 includes:
selecting 4-6 roads with the shortest distance between each road in the traffic network and the deviation point as a first road section set to be confirmed;
taking the deviation point as a starting point of a temporary segment, taking a track point at the next moment as an end point of the temporary segment, and screening out two roads with the smallest difference with the absolute value of the slope of the temporary segment from the road section set to be confirmed as a second road section set to be confirmed;
selecting a road with the shortest distance to the deviation point from the second road section set to be confirmed as an attribution road of the deviation point;
and vertically projecting the deviation point to the position of the home road as a corrected position of the deviation point.
Optionally, the step 504 includes:
calculating the instantaneous speed of each track point according to the second processing track data to obtain the vehicle passing speed of each road at different moments, wherein the vehicle passing speeds of each road at different moments form the vehicle passing speed set;
extracting a starting position and an end position of each order in the second processing track data to obtain an OD point pair trip set;
and identifying the vehicle track based on a map matching method, extracting a set of all traffic nodes between each order starting position and each order ending position in the second processing track data, wherein the set of all traffic nodes between the order starting position and the order ending position forms the vehicle running track set.
Specifically, the step 505 includes:
initializing parameters of an operating vehicle; the parameters of the operating vehicles comprise the total number of the operating vehicles, the number of the operating vehicles in each shift under the working mode of one shift or two shifts, the working starting time and the working ending time of each operating vehicle and the initial electric quantity of each operating vehicle;
initializing an operating vehicle charging demand set;
numbering the operating vehicles according to the starting working time of the operating vehicles; the number is denoted by j;
simulating the operation of the jth operating vehicle;
judging whether the jth operating vehicle generates a charging demand or not according to the battery electric quantity of the jth operating vehicle at the current moment;
if the jth operating vehicle generates a charging demand, adding the moment and the position of the charging demand into the operating vehicle charging demand set, so that the jth operating vehicle stops operating within a first set time, adding 1 to the j value, and returning to the step of simulating the operation of the jth operating vehicle;
if the jth operating vehicle does not generate the charging demand, enabling the jth operating vehicle to patrol in the traffic network until an order is received;
extracting a starting point and a terminal point in the order, and leading the jth operating vehicle to the starting point to receive passengers;
after a jth operating vehicle receives a passenger, deleting an OD point pair of a current order of a trip set from the OD point pair, recording the current time, the current-time battery electric quantity and the current-time position, taking a historical travel track of the current order in the vehicle travel track set as a current travel track, extracting a road travel speed corresponding to the current travel track in the vehicle travel speed set, and calculating the travel time of the current order according to the current travel track and the corresponding road travel speed;
when the current order reaches the terminal, updating the current time, the current battery power and the current time position;
judging whether the jth operating vehicle reaches the working finishing time or not;
if the jth operating vehicle reaches the working ending time, stopping the operation of the jth operating vehicle within a second set time, and judging whether j is equal to the total number of the operating vehicles;
if the jth operating vehicle does not reach the working ending time, judging whether j is equal to the total number of the operating vehicles;
if j is not equal to the total number of the operating vehicles, adding 1 to the value of j, and returning to the step of simulating the operation of the jth operating vehicle;
if j is equal to the total number of the operating vehicles, the simulation operation is finished;
and determining an operating vehicle charging demand space-time distribution model according to the operating vehicle charging demand set.
Fig. 7 is a flowchart of a model for predicting the temporal-spatial distribution of the charging demands of the electric vehicle in the embodiment of the present invention, and as shown in fig. 7, the model for predicting the temporal-spatial distribution of the charging demands of the electric vehicle in step 600 is the model for predicting the temporal-spatial distribution of the charging demands of the private car and the operating vehicle in fig. 7.
Preferably, the objective function of the charging station siting volume planning model is represented as:
min F＝αF1+β(F2+F3)nyear；
wherein F is the annual comprehensive cost; f1Establishing operating costs for the charging station year; f2The time-consuming cost per year for the electric vehicle user from a charging demand point to a charging station; f3Annual queuing latency costs for said users; n isyearPlanning the age for the charging station, wherein alpha is a first trade-off benefit coefficient, and beta is a second trade-off benefit coefficient;
Figure BDA0003323759660000181
n is the number of planned charging stations; qiCharging the number of motors in the charging station No. i; r is0The current rate is the current rate; m is the depreciation age of the charging station, C (Q)i) Constructing an investment cost function for the charging station No. i; u (Q)i) A yearly operation cost function for charging station number i;
Figure BDA0003323759660000182
j is a set of points of demand for charging,
Figure BDA0003323759660000183
a charging demand point set for selecting the charging station No. i for charging;
Figure BDA0003323759660000184
a j charging demand user who selects the i charging station for charging travels to a road set which passes by the i charging station in the charging process; phi is the coefficient of the passing road, dkIs k road length; v. ofktThe vehicle passing speed of the k road at the time t; f. ofwConverting the time cost into a coefficient for an electric vehicle user;
Figure BDA0003323759660000185
Figure BDA0003323759660000186
charging queuing waiting time of the ith charging station within the time t;
Figure BDA0003323759660000187
the number of electric vehicles charged in the moment t for the charging station No. i; t e [ t ∈ ]0,te]Representing the time of travel, t, of a simulated electric vehicle0Indicating the start time of travel, teIndicating the end of travel time.
Specifically, a space-time distribution model of the charging demand of the electric vehicle is established based on the above process, and necessary input data is provided for modeling of the charging station planning. For cities with relatively stable population and developed road infrastructure, travel modes and transportation systems in the cities tend to be consistent and stable. Under the predictable future condition, the evolution condition of charging space-time distribution can be estimated by utilizing the existing taxi track according to the market penetration rate of the electric automobile, and the charging station network is reconstructed or expanded, and the method specifically comprises the following steps:
(1) annual construction and operation cost of charging station
The cost mainly comprises the expenses of civil engineering, power grid facilities, a charger, related protection equipment and the like during early construction, and the daily maintenance of the equipment, the labor expense and the like after the equipment is put into operation. The construction supporting facility cost, the equipment protection and the labor cost of the charging station are closely related to the scale of the charging station, and the number of the charging motors in the charging station determines the scale of the charging station, so that the construction investment cost and the annual operation cost are defined as functions related to the number of chargers, specifically:
Figure BDA0003323759660000191
Figure BDA0003323759660000192
U(Qi)＝0.1×C(Qi)；
in the formula, N is the number of the planned charging stations; qiCharging the number of motors in the charging station I; r is0The current rate is the current rate; m is the depreciation age of the charging station; c (Q)i) Constructing an investment cost function for the charging station; u (Q)i) Taking 10% of the construction investment cost of the charging station in the embodiment as a charging station annual operation cost function; o is the civil engineering cost for building the charging station; q is the unit price of the charger; and e is the equivalent coefficient of the charging machine supporting facility cost.
(2) Annual time-consuming cost of electric vehicle users from charging demand points to charging stations
This cost can be expressed as the following equation:
Figure BDA0003323759660000193
wherein J is a set of charging demand points, JCSiThe method comprises the steps that a charging demand point set for charging by selecting a charging station I is selected, namely, electric vehicle users in the set all select the charging station I to charge; eJCSiTo select charging number iThe method comprises the steps that a j number charging demand user who performs charging in a station goes to a path set which passes in the way of charging of a charging station; phi is a coefficient of passing a road, phi is equal to 1 and represents that the vehicle travels the whole road, and phi is equal to 0.5 and represents that the vehicle travels only the half road; dkIs k road length; v. ofktThe vehicle passing speed of the k road at the time t.
(3) Annual queuing waiting time cost of electric vehicle users
The electric vehicle users may encounter a queuing waiting phenomenon in the charging process, the arrival time interval of the electric vehicle users in the public charging station obeys negative exponential distribution, the charging duration obeys multi-Gaussian distribution, and the electric vehicle users accord with an M/G/k queuing model. The customer arrival in the M/G/k queuing model is subject to the Poisson distribution with the parameter lambda, the service duration is the general distribution G, and the expectation of the general distribution G is ETVariance is VTAverage latency of the M/G/k queuing model
Figure BDA0003323759660000194
The approximate calculation formula of (c) is:
Figure BDA0003323759660000201
in the formula, k is the number of chargers; λ is the number of electric vehicles arriving at the charging station per unit time.
Average queue length of
Figure BDA0003323759660000202
In the formula (I), the compound is shown in the specification,
Figure BDA0003323759660000203
the number of the electric automobiles waiting in a queue in unit time is shown.
The average captain is:
Figure BDA0003323759660000204
in the formula (I), the compound is shown in the specification,
Figure BDA0003323759660000205
is electricity per unit timeThe total number of the electric vehicles, namely the number of the electric vehicles waiting in line and being charged; mu is the number of the electric vehicles which are served by each charger in unit time on average.
The annual waiting time cost of the electric vehicle user is expressed as:
Figure BDA0003323759660000206
in the formula (I), the compound is shown in the specification,
Figure BDA0003323759660000207
charging queuing waiting time of the charging station I within the time period t;
Figure BDA0003323759660000208
the number of electric vehicles served by charging station number i in the time period t. 22 denotes a travel start time, specifically te [8,22 ]]And means the travel time period from eight points earlier to 22 points later.
The constraint conditions are as follows:
Figure BDA0003323759660000209
the invention assumes that the electric vehicle user does not have secondary selection behavior during charging, namely station change charging is not selected, so that the queuing waiting time of the electric vehicle user is ensured to be within a certain range, WmaxThe maximum endurance time of the electric automobile user to waiting in line is provided.
The invention describes the benefits of an operator side by using the construction and operation cost of the charging station, describes the benefits of an electric vehicle user side by using the time consumption cost from a charging demand point to the charging station and the queuing and waiting time cost in the charging process, and establishes an optimal location and volume planning model of the charging station by taking the total cost and the minimum cost as the targets, wherein the target function is as follows:
min F＝αF1+β(F2+F3)nyear；
in the formula: f is the annual comprehensive cost; f1The annual construction and operation cost of the charging station is saved; f2Charging for userThe time-consuming cost of the electricity demand point on the way to the target charging station; f3Annual queuing waiting time cost for the user; n isyearSince α and β are coefficients for balancing the interests of both the charging stations for the planned age of the charging station, the embodiment places more importance on the public service function of the charging station, and therefore, the interests of the user are slightly skewed, where α is 0.8 and β is 1.2.
Preferably, the solving the objective function by using a particle swarm algorithm according to the electric vehicle charging demand space-time distribution model to determine the charging station positions and the configuration number of chargers in each charging station specifically includes:
and solving the objective function by adopting a particle swarm algorithm for improving the self-adaptive inertia weight according to the electric vehicle charging demand space-time distribution model, and determining the positions of the charging stations and the configuration quantity of chargers in each charging station.
In this embodiment, according to a charging station location and volume planning model, an improved particle swarm algorithm is applied to solve an optimal station location and the configuration of the number of chargers in the station, and an algorithm flow is shown in fig. 8. The basic parameter settings of this example are shown in table 3, and when the number of charging stations is 6 to 13, the trend of the overall cost change is shown in fig. 9. The results of the optimal planning of the in-area charging stations are shown in fig. 10, and the detailed information of each planned charging station is shown in table 4. Table 3 is a charging station basic parameter setting table, and table 4 is a charging station optimal planning result indication table.
TABLE 3
Figure BDA0003323759660000211
TABLE 4
Figure BDA0003323759660000221
Fig. 11 is a module connection diagram of the charging station planning system in the embodiment of the present invention, as shown in fig. 11, the embodiment further provides a charging station planning system considering a charging requirement of an electric vehicle, including:
the acquisition module is used for acquiring trip chain data and charging scene data of the non-operating vehicle;
the first characteristic determining module is used for determining the travel characteristic of the non-operation vehicle according to the travel chain data; the travel characteristics include: the probability of different trip chains, the probability of departure time, the probability of destination and the probability of arrival time;
the second characteristic determining module is used for determining the charging characteristics of the non-operating vehicle according to the charging scene data; the charging feature includes: charging frequency, destination charging behavior characteristics and attractiveness of different functional areas of the non-operating vehicle to the non-operating vehicle of different trip chains;
the first model establishing module is used for establishing a non-operation vehicle charging demand space-time model according to the travel characteristic and the charging characteristic;
the second model establishing module is used for establishing a space-time distribution model of the charging demand of the operating vehicle based on the track data of the operating vehicle order;
the fusion module is used for fusing the non-operation vehicle charging demand space-time model and the operation vehicle charging demand space-time distribution model to obtain an electric vehicle charging demand space-time distribution model;
the target function construction module is used for constructing a target function of a charging station location and volume planning model based on the annual construction and operation cost of the charging station, the annual time consumption cost of an electric vehicle user from a charging demand point to the charging station and the annual queuing waiting time cost of the electric vehicle user; the electric vehicle users comprise users of the non-operating vehicle and users of the operating vehicle;
and the charging station planning module is used for solving the objective function by adopting a particle swarm algorithm according to the space-time distribution model of the charging demand of the electric automobile and determining the positions of the charging stations and the configuration number of chargers in each charging station.
Preferably, the first feature determination module specifically includes:
the first statistical unit is used for carrying out statistical analysis according to the trip chain data to obtain the probabilities of different trip chains, the probability of the departure time and the time consumption distribution of single trip;
a first simulation unit, configured to simulate the probability, the departure time probability, and the single trip time consumption distribution based on a monte carlo method, so as to obtain the destination probability and the arrival time probability.
Preferably, the second feature determination module specifically includes:
the second simulation unit is used for performing probability distribution simulation on the charging frequency of the non-operating vehicles in the public pile within the preset time according to the charging scene data to obtain an expectation; the expectation is the average public pile charging frequency of each person in the preset time; sampling and simulating the expectation based on a Monte Carlo method to obtain the charging frequency of the non-operating vehicles using the charging piles within each day;
the analysis unit is used for analyzing the charging scene data to obtain destination charging behavior characteristics of the non-operating vehicles in each trip chain;
and the second statistical unit is used for counting the charging interest points of each functional area in the charging scene data, and performing weight analysis according to the number of the charging interest points corresponding to different functional areas to obtain the attraction of the different functional areas to the non-operating vehicle.
Preferably, the first model building module specifically includes:
an acquisition unit configured to acquire a total number M of vehicles of the non-operating vehicles;
a third simulation unit, configured to perform charging simulation on the ith non-operating vehicle, and extract a trip chain of the non-operating vehicle according to the probabilities of different trip chains;
a first extraction unit, configured to extract a destination to which the non-operating vehicle on the travel chain charges after a travel is finished, based on the destination charging behavior feature and the destination probability of the non-operating vehicle;
a second extraction unit, configured to extract a charging area of the non-operating vehicle on the travel chain based on the attraction force, and extract a road node in the charging area, where the road node is a specific charging location of an ith non-operating vehicle;
a third extraction unit configured to extract a trip arrival time of a destination charging behavior of an ith non-operated vehicle, which is a specific charging time of an ith private car, based on the arrival time probability and the departure time probability;
and the judging unit is used for writing the specific charging place and the specific charging time into a non-operating vehicle quick-charging demand set, judging whether i is equal to M or not, if not, judging that i is i +1, returning to the step of performing charging simulation on the ith non-operating vehicle, and if so, determining the non-operating vehicle charging demand spatio-temporal model according to the non-operating vehicle quick-charging demand set.
The invention has the following beneficial effects:
(1) according to the invention, the GPS track data of the operating vehicle is utilized, the conditions such as the operating characteristic and the charging characteristic are comprehensively considered, and the electric taxi charging demand space-time distribution prediction model is established through real-time traffic simulation, order behavior reproduction, charging behavior simulation and the like, so that the error caused by the fact that the traditional method only utilizes the travel starting point and the destination to perform charging behavior simulation is overcome.
(2) The invention analyzes the characteristics of traveling and charging behaviors based on real data of the private car, fully considers the attraction of a specific charging place to a private car user, performs more practical space-time distribution prediction of charging demands, and further applies the prediction results of the charging demands of the private car user and the charging place user to a charging station planning construction scheme. The normal and high-efficiency operation of the urban electric automobile system is ensured, and the practicability and the application range of the charging station planning are improved.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (10)

1. A charging station planning method considering the charging demand of an electric vehicle is characterized by comprising the following steps:
acquiring trip chain data and charging scene data of non-operating vehicles;
determining travel characteristics of the non-operating vehicle according to the travel chain data; the travel characteristics include: the probability of different trip chains, the probability of departure time, the probability of destination and the probability of arrival time;
determining charging characteristics of the non-operating vehicle according to the charging scene data; the charging feature includes: charging frequency, destination charging behavior characteristics and attractiveness of different functional areas of the non-operating vehicle to the non-operating vehicle of different trip chains;
establishing a non-operation vehicle charging demand space-time model according to the travel characteristics and the charging characteristics;
establishing an operating vehicle charging demand space-time distribution model based on the trajectory data of the operating vehicle order;
fusing the non-operation vehicle charging demand space-time model and the operation vehicle charging demand space-time distribution model to obtain an electric vehicle charging demand space-time distribution model;
constructing an objective function of a charging station location and volume-fixing planning model based on the annual construction and operation cost of the charging station, the annual time consumption cost of an electric vehicle user from a charging demand point to the charging station and the annual queuing waiting time cost of the electric vehicle user; the electric vehicle users comprise users of the non-operating vehicle and users of the operating vehicle;
and solving the objective function by adopting a particle swarm algorithm according to the electric vehicle charging demand space-time distribution model, and determining the positions of the charging stations and the configuration number of chargers in each charging station.
2. The charging station planning method according to claim 1, wherein the determining the travel characteristics of the non-operating vehicle from the travel chain data comprises:
performing statistical analysis according to the trip chain data to obtain the probabilities of the different trip chains, the probability of the departure time and the time consumption distribution of single trip;
and simulating the probability, the departure time probability and the single trip time consumption distribution based on a Monte Carlo method to obtain the destination probability and the arrival time probability.
3. The charging station planning method of claim 1, wherein said determining the charging characteristics of the non-operating vehicle from the charging scenario data comprises:
performing probability distribution simulation on the charging frequency of the non-operating vehicles in the public piles within preset time according to the charging scene data to obtain an expectation; the expectation is the average public pile charging frequency of each person in the preset time; sampling and simulating the expectation based on a Monte Carlo method to obtain the charging frequency of the non-operating vehicles using the charging piles within each day;
analyzing the charging scene data to obtain destination charging behavior characteristics of the non-operating vehicles in each trip chain;
and counting the charging interest points of each functional area in the charging scene data, and performing weight analysis according to the number of the charging interest points corresponding to different functional areas to obtain the attraction of the different functional areas to the non-operating vehicle.
4. The charging station planning method of claim 1, wherein the building a non-operational vehicle charging demand spatiotemporal model according to the travel characteristics and the charging characteristics comprises:
acquiring the total number M of the vehicles of the non-operation vehicles;
performing charging simulation on the ith non-operating vehicle, and extracting a trip chain of the non-operating vehicle according to the probability of different trip chains;
extracting a destination of the non-operating vehicle on the trip chain, which is charged after the trip is finished, based on the destination charging behavior characteristics and the destination probability of the non-operating vehicle;
extracting a charging area of the non-operating vehicle on the trip chain based on the attraction, and extracting a road node in the charging area, wherein the road node is a specific charging place of the ith non-operating vehicle;
extracting a travel arrival time of the destination charging behavior of the ith non-operation vehicle based on the arrival time probability and the departure time probability, wherein the travel arrival time is the specific charging time of the ith private car;
and writing the specific charging place and the specific charging time into a non-operating vehicle quick-charging demand set, judging whether i is equal to M, if not, judging that i is equal to i +1, returning to the step of performing charging simulation on the ith non-operating vehicle, and if so, determining the non-operating vehicle charging demand spatio-temporal model according to the non-operating vehicle quick-charging demand set.
5. The charging station planning method of claim 1, wherein the objective function of the charging station siting volume planning model is represented as:
minF＝αF1+β(F2+F3)nyear；
wherein F is the annual comprehensive cost; f1Establishing operating costs for the charging station year; f2From the charging demand point to the electric vehicle userThe annual cost of charging stations; f3Annual queuing latency costs for said users; n isyearPlanning the age for the charging station, wherein alpha is a first trade-off benefit coefficient, and beta is a second trade-off benefit coefficient;
Figure FDA0003323759650000031
n is the number of planned charging stations; qiCharging the number of motors in the charging station No. i; r is0The current rate is the current rate; m is the depreciation age of the charging station, C (Q)i) Constructing an investment cost function for the charging station No. i; u (Q)i) A yearly operation cost function for charging station number i;
Figure FDA0003323759650000032
j is a set of points of demand for charging,
Figure FDA0003323759650000033
a charging demand point set for selecting the charging station No. i for charging;
Figure FDA0003323759650000034
a j charging demand user who selects the i charging station for charging travels to a road set which passes by the i charging station in the charging process; phi is the coefficient of the passing road, dkIs k road length; v. ofktThe vehicle passing speed of the k road at the time t; f. ofwConverting the time cost into a coefficient for an electric vehicle user;
Figure FDA0003323759650000035
Figure FDA0003323759650000036
charging station for No. i at time tCharging queuing waiting time within a moment;
Figure FDA0003323759650000037
the number of electric vehicles charged in the moment t for the charging station No. i; t e [ t ∈ ]0,te]Representing the time of travel, t, of a simulated electric vehicle0Indicating the start time of travel, teIndicating the end of travel time.
6. The charging station planning method according to claim 1, wherein the solving of the objective function by using a particle swarm algorithm according to the electric vehicle charging demand space-time distribution model to determine the charging station positions and the configuration number of chargers in each charging station specifically comprises:
and solving the objective function by adopting a particle swarm algorithm for improving the self-adaptive inertia weight according to the electric vehicle charging demand space-time distribution model, and determining the positions of the charging stations and the configuration quantity of chargers in each charging station.
7. A charging station planning system that considers the charging requirements of electric vehicles, comprising:
the acquisition module is used for acquiring trip chain data and charging scene data of the non-operating vehicle;
the first characteristic determining module is used for determining the travel characteristic of the non-operation vehicle according to the travel chain data; the travel characteristics include: the probability of different trip chains, the probability of departure time, the probability of destination and the probability of arrival time;
the second characteristic determining module is used for determining the charging characteristics of the non-operating vehicle according to the charging scene data; the charging feature includes: charging frequency, destination charging behavior characteristics and attractiveness of different functional areas of the non-operating vehicle to the non-operating vehicle of different trip chains;
the first model establishing module is used for establishing a non-operation vehicle charging demand space-time model according to the travel characteristic and the charging characteristic;
the second model establishing module is used for establishing a space-time distribution model of the charging demand of the operating vehicle based on the track data of the operating vehicle order;
the fusion module is used for fusing the non-operation vehicle charging demand space-time model and the operation vehicle charging demand space-time distribution model to obtain an electric vehicle charging demand space-time distribution model;
the target function construction module is used for constructing a target function of a charging station location and volume planning model based on the annual construction and operation cost of the charging station, the annual time consumption cost of an electric vehicle user from a charging demand point to the charging station and the annual queuing waiting time cost of the electric vehicle user; the electric vehicle users comprise users of the non-operating vehicle and users of the operating vehicle;
and the charging station planning module is used for solving the objective function by adopting a particle swarm algorithm according to the space-time distribution model of the charging demand of the electric automobile and determining the positions of the charging stations and the configuration number of chargers in each charging station.
8. The charging station planning system of claim 7, wherein the first characteristic determination module specifically comprises:
the first statistical unit is used for carrying out statistical analysis according to the trip chain data to obtain the probabilities of different trip chains, the probability of the departure time and the time consumption distribution of single trip;
a first simulation unit, configured to simulate the probability, the departure time probability, and the single trip time consumption distribution based on a monte carlo method, so as to obtain the destination probability and the arrival time probability.
9. The charging station planning system of claim 7, wherein the second characteristic determination module specifically comprises:
the second simulation unit is used for performing probability distribution simulation on the charging frequency of the non-operating vehicles in the public pile within the preset time according to the charging scene data to obtain an expectation; the expectation is the average public pile charging frequency of each person in the preset time; sampling and simulating the expectation based on a Monte Carlo method to obtain the charging frequency of the non-operating vehicles using the charging piles within each day;
the analysis unit is used for analyzing the charging scene data to obtain destination charging behavior characteristics of the non-operating vehicles in each trip chain;
and the second statistical unit is used for counting the charging interest points of each functional area in the charging scene data, and performing weight analysis according to the number of the charging interest points corresponding to different functional areas to obtain the attraction of the different functional areas to the non-operating vehicle.
10. The charging station planning system of claim 7, wherein the first model building module specifically comprises:
an acquisition unit configured to acquire a total number M of vehicles of the non-operating vehicles;
a third simulation unit, configured to perform charging simulation on the ith non-operating vehicle, and extract a trip chain of the non-operating vehicle according to the probabilities of different trip chains;
a first extraction unit, configured to extract a destination to which the non-operating vehicle on the travel chain charges after a travel is finished, based on the destination charging behavior feature and the destination probability of the non-operating vehicle;
a second extraction unit, configured to extract a charging area of the non-operating vehicle on the travel chain based on the attraction force, and extract a road node in the charging area, where the road node is a specific charging location of an ith non-operating vehicle;
a third extraction unit configured to extract a trip arrival time of a destination charging behavior of an ith non-operated vehicle, which is a specific charging time of an ith private car, based on the arrival time probability and the departure time probability;
and the judging unit is used for writing the specific charging place and the specific charging time into a non-operating vehicle quick-charging demand set, judging whether i is equal to M or not, if not, judging that i is i +1, returning to the step of performing charging simulation on the ith non-operating vehicle, and if so, determining the non-operating vehicle charging demand spatio-temporal model according to the non-operating vehicle quick-charging demand set.
CN202111256635.4A 2021-10-27 2021-10-27 Charging station planning method and system considering charging requirements of electric vehicle Pending CN114021795A (en)

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CN114925882B (en) * 2022-04-14 2023-04-07 中国科学院地理科学与资源研究所 New energy charging pile distribution evaluation method and device
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