CN117035185A - Electric vehicle charging station layout optimization method and system based on dynamic charging demand - Google Patents
Electric vehicle charging station layout optimization method and system based on dynamic charging demand Download PDFInfo
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Abstract
The application discloses an electric vehicle charging station layout optimization method and system based on dynamic charging requirements, wherein the method comprises the following steps: acquiring travel characteristic parameters and charging characteristic parameters according to the travel track of the electric automobile; carrying out travel division on the running track of the electric automobile, and building a user travel chain; performing user travel activity simulation based on the characteristic parameters and the travel chain to obtain a space-time distribution result of the charging demand; establishing a charging station layout optimization model with minimum comprehensive cost as a target and considering carbon emission; based on the space-time distribution result of the charging demand, solving a charging station layout optimization model by using a whale optimization algorithm to obtain a final layout scheme. According to the application, travel and charging behaviors are simulated by utilizing the Monte Carlo method by analyzing the vehicle running track and the charging influence factors, and meanwhile, the position distribution and the charging demand distribution of the charging stations are considered, so that the reasonable distribution of the charging stations is facilitated, and the charging satisfaction degree of users and the profit of enterprises are improved.
Description
Technical Field
The application belongs to the field of traffic planning, and particularly relates to an electric vehicle charging station layout optimization method and system based on dynamic charging requirements.
Background
The electric automobile meets the rapid development stage by virtue of low pollution, zero (low) emission, high energy conversion efficiency and the like. In the technological surge of rapid development of electric vehicles, a charging station is taken as an important component in a charging facility, and the site selection layout of the charging station becomes an important ring of the urban scientific development layout. Because the electric automobile in the present stage has insufficient endurance mileage, the battery problem cannot be developed in a breakthrough manner in a short time, and the mileage anxiety problem is more serious, so that it is important to develop accurate demand prediction and reasonable site layout research.
The overall service efficiency of the public charging piles in the current cities is low, and the main reason is that the development of the charging infrastructure is uncoordinated and unbalanced, so that the increasing charging demands are difficult to meet. In the early stage of development and construction of a charging station, the problem that vehicles are concerned and development of supporting facilities such as the charging station is ignored is common, so that the situation that whether a vehicle exists or not or whether a pile exists frequently occurs, and reasonable distribution and utilization of charging resources cannot be realized. The charging requirement of the electric automobile is related to not only daily travel of a user, but also uncertainty factors such as weather, road conditions, vehicle states and the like, so that the control and the dispatching of the power grid can be greatly challenged. Meanwhile, the charging requirement of the electric automobile is different from the general power requirement, the electric automobile has not only space uncertainty but also time uncertainty, and the determination of the charging capacity and the site selection of facilities are challenged.
At present, when the charging demand is predicted and analyzed, prediction research is performed based on a charging station terminal, and the prediction effect is better when the model characteristic analysis is thorough and accurate, similar to the traditional power load prediction method. With the increasing types and the increasing conservation amount of electric vehicles, the accuracy and effectiveness of model establishment are greatly tested. And because the charging behavior of the user can be influenced by various factors such as weather, road conditions and the like, the travel characteristics and the charging characteristics of different types of vehicles have large differences, and when the vehicle is expanded to a wider research area and a more complex real scene, the research method based on the charging station terminal is not suitable any more. On the other hand, the site selection layout of the current electric vehicle charging station is mostly studied independently from the charging demand prediction, and when site selection models are established, the site selection models are mostly developed from the aspects of construction and operation costs, enterprise profits, land costs and the like, carbon emission generated by the electric vehicle during charging activities is not considered, the practicability and the comprehensiveness of the models are still to be further improved, and meanwhile, the problem of determining the positions of new charging stations by combining the distribution conditions of the existing charging stations is lacked.
Disclosure of Invention
In order to solve the problems, the application provides an electric vehicle charging station layout optimization method and system based on dynamic charging requirements, which are used for determining the space-time distribution condition of charging loads in a research area based on actual electric vehicle driving track data and realizing quick determination of charging station locating and sizing by considering carbon emission and cost.
The technical scheme adopted for solving the technical problems is as follows:
in one aspect, the electric vehicle charging station layout optimization method based on dynamic charging requirements provided by the embodiment of the application comprises the following steps:
acquiring travel characteristic parameters and charging characteristic parameters according to the travel track of the electric automobile;
carrying out travel division on the running track of the electric automobile, and building a user travel chain;
performing user travel activity simulation based on the characteristic parameters and the travel chain to obtain a space-time distribution result of the charging demand;
establishing a charging station layout optimization model with minimum comprehensive cost as a target and considering carbon emission;
based on the space-time distribution result of the charging demand, solving a charging station layout optimization model by using a whale optimization algorithm to obtain a final layout scheme.
On the other hand, the electric vehicle charging station layout optimization system based on the dynamic charging requirement provided by the embodiment of the application comprises:
the characteristic extraction module is used for acquiring travel characteristic parameters and charging characteristic parameters according to the running track of the electric automobile;
the travel chain building module is used for carrying out travel division on the travel track of the electric automobile and building a user travel chain;
the trip simulation module is used for carrying out user trip activity simulation based on the characteristic parameters and the trip chain to obtain a space-time distribution result of the charging demand;
the model building module is used for building a charging station layout optimization model taking the minimum comprehensive cost as a target and considering the carbon emission;
and the optimization solving module is used for solving the charging station layout optimization model by using a whale optimization algorithm based on the space-time distribution result of the charging demand, so as to obtain a final layout scheme.
The technical scheme of the embodiment of the application has the following beneficial effects:
(1) By analyzing the running track of the vehicle and the charging influence factors, the dynamic travel process of the electric vehicle and the charging behavior of a user are simulated by utilizing Monte Carlo simulation, so that the number of model parameters is reduced, and the usability of the model is improved;
(2) The combined application of the charging demand prediction and the site selection layout is realized, the position distribution of the charging stations is considered, the charging demand of the electric automobile is considered, the reasonable distribution of the charging demands is realized, the charging satisfaction degree of users and the profit of enterprises are improved, and meanwhile, the carbon emission is considered, so that a decision reference is provided for verifying the environmental protection of the electric automobile;
(3) Potential charging demand points are excavated, and the position of a new charging station can be rapidly determined on the basis of distribution of existing charging stations.
Drawings
FIG. 1 is a flowchart illustrating a method of optimizing an electric vehicle charging station layout based on dynamic charging demand, according to an exemplary embodiment;
FIG. 2 is a schematic diagram illustrating an electric vehicle charging process according to an exemplary embodiment;
FIG. 3 is a graph illustrating a change in charging demand of an electric vehicle within a charging station, according to an exemplary embodiment;
FIG. 4 is a diagram illustrating an in-situ generated charge demand change for an electric vehicle according to an exemplary embodiment;
FIG. 5 is a graph illustrating a change in total charge demand generated by an electric vehicle according to an exemplary embodiment;
FIG. 6 is a spatial distribution diagram illustrating an electric vehicle charging demand according to an exemplary embodiment;
FIG. 7 is a graph of clustering results shown in accordance with an exemplary embodiment;
FIG. 8 is a graph illustrating a layout optimization model convergence curve in accordance with an example embodiment;
FIG. 9 is a charging station final layout result shown according to an example embodiment;
FIG. 10 is a schematic diagram illustrating an electric vehicle charging station layout optimization system based on dynamic charging requirements, according to an example embodiment.
Detailed Description
The application is further illustrated by the following examples in conjunction with the accompanying drawings:
in order to clearly illustrate the technical features of the present solution, the present application will be described in detail below with reference to the following detailed description and the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different structures of the application. In order to simplify the present disclosure, components and arrangements of specific examples are described below. Furthermore, the present application may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and processes are omitted so as to not unnecessarily obscure the present application.
As shown in fig. 1, the method for optimizing the layout of the electric vehicle charging station based on the dynamic charging requirement provided by the embodiment of the application comprises the following steps:
step one: and acquiring travel characteristic parameters and charging characteristic parameters according to the travel track of the electric automobile.
In order to avoid interference of noise data in the original data set to experimental results, the original data needs to be preprocessed: performing time format conversion on the running track data of the electric automobile, cleaning the damaged data, and deleting the following data in the cleaning process: data containing missing values, data not within the study range, data with passenger distance less than 500 meters, data with resting time less than 60 seconds, etc. As shown in table 1, the data fields mainly used include a vehicle number, time, longitude, latitude, passenger carrying state, speed, etc.;
table 1 description of electric vehicle trip data format
| Field name | Field type | Data example |
| vehicleid (vehicle number) | Varchar | 0 |
| Longitude (Longitude) | Flout64 | 114.031799 |
| Latitude (Latitude) | Flout64 | 22.524799 |
| Time (Time) | Datatime | 2014-10-22T02:54:30.000Z |
| Speed (Speed) | Int64 | 42 |
| Load (passenger state) | Int64 | 0 |
Acquiring travel characteristic parameters and charging characteristic parameters after data processing, wherein the travel characteristic parameters and the charging characteristic parameters at least comprise the model number of a vehicle, the state of charge of a battery, the starting travel time and the charging time;
fitting the travel characteristic parameters and the charging characteristic parameters by Matlab to obtain probability density functions of each parameter, and meanwhile, assuming that the battery charge state at the initial travel time of the electric automobile obeys normal distribution N SOC (0.8,0.1)。
The obtaining of the charging time length needs to be achieved by extracting a time period meeting the charging requirement, and the occurrence of a charging event needs to be achieved: the charging action occurs in an empty state; charging time is at least 15 minutes; the charging mode is fast charging. The charging duration is checked for data distribution by means of MATLAB and data fitting is performed, and the fitted curve expression is as follows:
and the final charge duration Act is the charge duration t obtained by comparing the probability density functions c And an acceptable maximum charge time t need To determine:
tc＝f(x)/60
C use ＝C p0 -C p(t)
wherein C is p(t) C is the electric quantity at time t use P is the consumed electric quantity of the electric automobile charg To charge the pile power, P charg ＝60kW，t need The time required for the car to go from the current charge to full charge.
Step two: and carrying out travel division on the running track of the electric automobile, and building a user travel chain.
Carrying out travel division according to the travel track data of the electric automobile to obtain the start and stop points of each section of travel of a user; the form track data of the operation vehicle comprises a passenger carrying state of the vehicle, when the value of the passenger carrying state in the vehicle track data is 0, the operation vehicle is represented as an idle state, and when the value of the passenger carrying state is 1, the operation vehicle is represented as the passenger carrying state, and the starting and stopping points of each section of travel of the operation vehicle are obtained according to the passenger carrying state of the vehicle; for private cars of ordinary users, a long interval time often exists between each section of travel, and in this embodiment, the private car travel starting point can be divided by the vehicle residence time;
acquiring a travel track omega of the electric automobile in one day according to a start and stop point of a user travel:
Ω＝{(x 1 ,y 1 ,t 1 ),(x 2 ,y 2 ,t 2 ),(x 3 ,y 3 ,t 3 ),...,(x n ,y n ,t n )}
wherein, (x) i ,y i ,t i ) The i-th point, x, representing the trajectory Ω i And y i Respectively representing the longitude and latitude of point i, t i Time representing point i;
and building a user travel chain according to the travel tracks of all the electric vehicles.
Step three: and carrying out user travel activity simulation based on the characteristic parameters and the travel chain to obtain a space-time distribution result of the charging demand.
And predicting the charging requirement by using a Monte Carlo method, wherein the experimental time is set to be ten days. The data information to be recorded in the simulation process includes: (1) the number of trips of the vehicle when the current destination is reached; (2) a remaining amount of electricity of the vehicle after each stroke is completed; (3) charging duration and supplementary electric quantity; (4) vehicle location at which the charge demand is generated. For better analysis of the influence of vehicles of different scales on the charging requirement, the number of electric vehicles is set as follows: 50, 100, 150 and 200.
Randomly generating a vehicle model, an initial trip time and a battery residual capacity of each electric automobile according to probability density functions of trip characteristics and charging characteristic parameters;
according to a travel chain of a user, an initial position and a destination are allocated to each electric automobile, a get-on and get-off point of the next journey is randomly generated after the electric automobile reaches the destination, meanwhile, the residual electric quantity of the automobile is calculated, and whether a charging requirement is triggered or not is judged;
generally, a driver does not charge after the vehicle power is completely consumed, so when the power of the electric vehicle is lower than a set threshold power at a certain time, the charging requirement is triggered:
C p(t) ≤ε·C P0
wherein C is p(t) For the electric quantity at the moment t, epsilon is a user mileage anxiety coefficient, epsilon obeys uniform distribution, and the probability density function is as follows:
wherein a is the lower boundary of the value interval, b is the upper boundary of the value interval, a and b together form a value interval, and the values of a and b are selected according to actual needs.
When the charging demand is triggered, the electric automobile can search for a charging station nearby, but in the actual situation, the site selection construction of part of sites is unreasonable, so that the situation that the electric automobile needs to be charged but the position of the electric automobile is far away from the charging station can occur, and in order to improve the unreasonable situation, when the charging demand is generated, the electric automobile is set to be charged towards the charging station if the distance between the electric automobile and the charging station closest to the electric automobile is less than 3 km; otherwise, charging demand points are generated at the current position of the vehicle, and quick charging load time-space information is determined, so that a choice is provided for site selection construction of a next newly-added charging station, and the current situation that the current charging station construction is unreasonable is improved.
As shown in fig. 2, there are 4 vehicles running on the road in fig. 2, CS1, CS2 and CS3 are charging stations in the figure, CS1 and CS3 are closely spaced, and circles with different colors in the figure represent service ranges of different charging stations. S1 represents a charging service range of a charging station, and S2 represents a distance between two charging stations. While traveling, when the driver finds that the range of the vehicle is small and it is difficult to meet the following trip, a charging demand is generated. Generally, when the charging demand is triggered, the user selects the charging station closest to the user to charge, and fast charge time-space information is generated. When electric vehicles Car1 and Car2 want to be charged, they can choose to go to charging station CS2 for charging because they are within service range of charging station CS2. If the electric Car3 generates a charging demand, the distance from Car3 to CS1 or CS2 is the same, so that it can select either CS1 or CS2 when selecting a charging station. When Car4 generates a charging demand, three charging stations in the figure are far away, and the charging stations CS1 and CS2 are found to be unreasonably and objectively selected. Because CS1 and CS2 are nearer, make the crossing part charge the resource abundant of orange circle and blue circle in the figure, very big satisfied the charge demand in this place. However, the charging resource distribution is uneven, and the charging of the user is difficult, which is disadvantageous for the charging of the electric Car4 and the nearby vehicles. To improve this unreasonable situation, it is provided herein that when the electric vehicle generates a charging demand, if the electric vehicle is at a distance from its nearest charging station that is less than S1 at this time, charging is carried out to the charging station; otherwise, the current location of the automobile is considered as a potential charging demand point and the quick charging load time-space information is determined. Meanwhile, the distance between the two charging stations is set to be not smaller than S2, so that excessive concentration of charging resources is avoided, and the current situation that the construction of the current charging station is unreasonable is improved.
Repeating the above process to simulate the travel of all electric vehicles for one day, so as to obtain the time-space distribution of the charging requirement for one day, and changing the charging requirement for 24 hours when the vehicle scale is 100 vehicles is shown in fig. 3-5.
According to the space-time distribution result of the charging demands, the charging load demands of all charging stations are obtained, the positions of the charging stations can be adjusted, and for a charging station with the demand of 0 or lower, the charging stations with the charging load demands lower than a threshold value can be eliminated due to unreasonable address selection;
besides the charging stations, the places where the charging demands are generated can be at the current positions of the vehicles, the positions of the electric vehicles which trigger the charging demands and are more than the set threshold from the nearest charging station are potential charging demand points which are not listed in a charging plan, the triangle positions shown in fig. 6 are the charging demand points, the potential charging demand points are clustered into k categories by using a k-means clustering method to generate k new charging station positions, k values of k means are determined by using an elbow method, the clustering result of the charging demand points in fig. 6 is shown in fig. 7, and the triangle positions in fig. 7 are finally determined positions where the new charging stations can be built.
Step four: and (3) establishing a charging station layout optimization model with the minimum comprehensive cost as a target and considering the carbon emission.
Most of researches mainly consider the influence of economic factors on site selection in the process of establishing a site selection model, and lack actual research and analysis on the energy saving and emission reduction effects of the electric automobile. Therefore, the application establishes an addressing model taking the minimum comprehensive cost (annual construction and operation cost, time cost and punishment value) of the whole society as a target, and brings two main benefits of enterprises (annual construction and operation cost) and users (time cost) into the model and brings the charging service range, the charging coverage rate, the charging station scale and the like into addressing constraint conditions. Compared with other site selection models, the site selection model has the advantages that on one hand, the cost calculation is more careful and accurate, the interests of enterprises and users are considered, on the other hand, the important consideration standard of the carbon emission is taken into consideration, the influence of different power systems on the carbon emission can be better understood, and the carbon emission generated by the electric automobile and the fuel oil automobile under the same driving mileage can be analyzed.
The objective function of the charging station layout optimization model includes annual construction and operation costs F C1 Cost of time F C2 And penalty value F C3 ：
minF＝F C1 +F C2 +F C3
Annual construction and operation costs F C1 The method comprises year construction cost Con and year operation cost Cop, wherein the year construction cost mainly comprises the cost of charging piles, lands, transformers and the like, and the objective function formula is as follows:
wherein i is the charging station serial number, con i And Cop i Respectively representing the annual construction cost and the annual operation cost of the charging station i, r0 is the discount rate, nyear is the depreciation age, cg is the fixed investment cost, nchar is the number of charging piles in the station,the equivalent investment coefficient of the equipment cost is epsilon, the price of a single charging pile, gamma is the conversion coefficient of the manual operation and maintenance cost of the equipment, and gamma=0.1;
time cost mainly considers the cost spent by the user on the way to the charging station, time cost F C2 The target function of (2) is:
F C2 ＝365·costF·λ
the costF is the distance between each charging demand point and the nearest charging station, and λ is the cost spent by the electric vehicle for each kilometer;
the penalty value represents the cost due to the unsatisfied constraint, which can be applied to sites in the optimization processLimiting, mainly restricting the site selection from three specific layers: the distance from the charging demand station to the charging service station providing charging service for the charging demand station is not greater than S1, if the distance between the charging demand point and the corresponding charging service station exceeds S1 in a certain demand allocation relation, the distance value is stored in the set S a In C a Is set S a The sum of all distance values in (a); the distance between the charging service stations is not less than S2, and in the set of charging service stations, if the distance between two service stations exceeds S2, the stations are stored in the set S b In C b Representation set S b The number of medium stations; the charging service station should have the capability of simultaneously providing charging service for all demand points, i.e. the number of charging piles in the service station should be not less than the number of vehicles charged simultaneously, C c The specific values of S1 and S2 are selected according to the actual situation to obtain a punishment value F C3 The target function of (2) is:
wherein C is a C is the sum of distance values exceeding a set value between all charging demand points and the nearest charging station b For the number of stations with the distance between two charging stations exceeding the set value, C c Is the number of charging stations that cannot meet the charging load demand.
The constraint condition constraint of the charging station layout optimization model mainly comprises two aspects: distance, charging demand allocation:
the charging demand point should be within the service range of the nearest charging station:
d ij ≤S1
wherein d ij For the distance between the charging service station i and the charging demand point j, S1 is the service range set by the charging station;
the distance between charging service stations should satisfy:
wherein d i1-i2 S2 is a distance threshold value between two charging stations;
in addition to considering the cost problem, the carbon emission of the electric automobile on the way to the charging station is taken as an important consideration standard, and the carbon emission of the electric automobile is as follows:
Carbon ijk ＝ω·d ijk ·W a
wherein d ijk Representing the distance of the vehicle k from the start point i to the end point j, W a Representing the power consumption rate of the electric vehicle. Omega represents CO of a vehicle when using electric energy 2 Emission rate, ζ is CO of a vehicle using gasoline 2 Emission rate.
Step five: based on the space-time distribution result of the charging demand, solving a charging station layout optimization model by using a whale optimization algorithm to obtain a final layout scheme.
Initializing related parameters of an algorithm, and randomly generating a first generation population;
when the layout optimization of the charging station is carried out, not only the position of the charging station is required to be considered, but also the charge demand load quantity of the charging station is required to be considered according to the vehicle charging time Act, so that the minimum load demand quantity and the maximum load demand quantity are determined according to the charge demand space-time distribution result, a layout scheme is worked out, and the maximum demand load in one day is CL according to the charge demand prediction result max Minimum CL min The battery capacity of the electric automobile is BC, and the minimum construction quantity of the charging station is station min ＝CL min BC, the maximum construction quantity of charging stations is station max ＝CL max /BC. Solving the number of charging stations from station by using whale optimization algorithm min To station max The objective function value and each cost change of the site selection model are sequentially taken;
in the solving process, firstly setting the number of whales and the maximum iteration number of the algorithm, and initializing the position information. The fitness of each whale is then calculated, and the current optimal whale position is found and retained. Parameters a, p and coefficient vector A, C in the algorithm are calculated. Judging whether the probability p is less than 50%, and if not, adopting a bubble net food catching mechanism to update the position. If yes, judging whether the absolute value of the coefficient vector A is smaller than 1, and if yes, surrounding the prey; otherwise, searching the hunting object globally at random. And (3) after the position update is finished, calculating the fitness of each whale, comparing the fitness with the position of the optimal whale reserved previously, and if the fitness is superior to the position of the optimal whale, replacing the whale with a new optimal solution. Judging whether the current calculation reaches the maximum iteration times, if so, obtaining an optimal solution, ending the calculation, and otherwise, entering the next iteration. The model solving results are shown in table 2, and it can be found that when the addressing scheme is to construct 19 charging stations, the objective function value is the smallest, the convergence curve of the model is shown in fig. 8, and the charging station layout optimizing result is shown in fig. 9.
Table 2 charging station costs
After solving the site selection model, analyzing what value is measured by the number of charging stations, wherein the objective function value is the smallest, and simultaneously calculating the total driving mileage and carbon emission of the electric vehicle under the scheme. And then calculating the carbon emission of the fuel oil vehicle under the same driving mileage, and carrying out comparative analysis. When 19 charging stations are constructed, the electric vehicle generates 76.13818kg of carbon emissions from going to the charging stations. And the carbon emission amount generated when the fuel oil vehicle is used for replacing the electric vehicle to travel the same distance is calculated as follows: 80.9974kg. Clearly, electric vehicles have a positive effect on reducing carbon emissions.
As shown in fig. 10, an electric vehicle charging station layout optimization system based on dynamic charging requirements according to an embodiment of the present application includes:
the characteristic extraction module is used for acquiring travel characteristic parameters and charging characteristic parameters according to the running track of the electric automobile;
the travel chain building module is used for carrying out travel division on the travel track of the electric automobile and building a user travel chain;
the trip simulation module is used for carrying out user trip activity simulation based on the characteristic parameters and the trip chain to obtain a space-time distribution result of the charging demand;
the model building module is used for building a charging station layout optimization model taking the minimum comprehensive cost as a target and considering the carbon emission;
and the optimization solving module is used for solving the charging station layout optimization model by using a whale optimization algorithm based on the space-time distribution result of the charging demand, so as to obtain a final layout scheme.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, which is intended to be covered by the claims.
Claims (10)
1. An electric vehicle charging station layout optimization method based on dynamic charging requirements is characterized by comprising the following steps:
acquiring travel characteristic parameters and charging characteristic parameters according to the travel track of the electric automobile;
carrying out travel division on the running track of the electric automobile, and building a user travel chain;
performing user travel activity simulation based on the characteristic parameters and the travel chain to obtain a space-time distribution result of the charging demand;
establishing a charging station layout optimization model with minimum comprehensive cost as a target and considering carbon emission;
based on the space-time distribution result of the charging demand, solving a charging station layout optimization model by using a whale optimization algorithm to obtain a final layout scheme.
2. The method for optimizing the layout of an electric vehicle charging station based on dynamic charging requirements according to claim 1, wherein obtaining travel characteristic parameters and charging characteristic parameters according to a travel track of an electric vehicle comprises:
converting the time format of the running track data of the electric automobile, and cleaning the damaged data;
acquiring travel characteristic parameters and charging characteristic parameters after data processing, wherein the travel characteristic parameters and the charging characteristic parameters at least comprise the model number of a vehicle, the state of charge of a battery, the starting travel time and the charging time;
and carrying out fitting processing on the travel characteristic parameters and the charging characteristic parameters to obtain probability density functions of each parameter.
3. The method for optimizing the layout of an electric vehicle charging station based on dynamic charging requirements according to claim 1, wherein the steps of dividing travel tracks of an electric vehicle and constructing a user travel chain comprise:
carrying out travel division according to the travel track data of the electric automobile to obtain the start and stop points of each section of travel of a user;
according to the starting and stopping points of the travel of the user, acquiring a travel track omega of the electric automobile in one day:
Ω＝{(x 1 ,y 1 ,t 1 ),(x 2 ,y 2 ,t 2 ),(x 3 ,y 3 ,t 3 ),...,(x n ,y n ,t n )}
wherein, (x) i ,y i ,t i ) The i-th point, x, representing the trajectory Ω i And y i Respectively representing the longitude and latitude of point i, t i Time representing point i;
and building a user travel chain according to the travel tracks of all the electric vehicles.
4. The method for optimizing the layout of an electric vehicle charging station based on dynamic charging demands according to claim 2, wherein the user traveling activity simulation is performed based on the characteristic parameters and the traveling chain to obtain a space-time distribution result of the charging demands, comprising:
simulating the travel of the electric automobile according to the characteristic parameters and the travel chain, and acquiring the space-time distribution of the charging demand;
and adjusting the position of the charging station according to the space-time distribution of the charging demand.
5. The method for optimizing the layout of an electric vehicle charging station based on dynamic charging requirements according to claim 4, wherein the step of obtaining the space-time distribution of the charging requirements by simulating the travel of the electric vehicle according to the characteristic parameters and the travel chain comprises the steps of:
randomly generating a vehicle model, an initial trip time and a battery residual capacity of each electric automobile according to probability density functions of trip characteristics and charging characteristic parameters;
according to a travel chain of a user, an initial position and a destination are allocated to each electric automobile, a get-on and get-off point of the next journey is randomly generated after the electric automobile reaches the destination, meanwhile, the residual electric quantity of the automobile is calculated, and whether a charging requirement is triggered or not is judged;
and repeating the process to simulate the strokes of all the electric vehicles and obtain the space-time distribution of the charging requirements.
6. The method of claim 5, wherein adjusting the charging station position based on the charging demand space-time distribution comprises:
according to the space-time distribution of the charging demands, eliminating charging stations with charging load demands lower than a threshold value;
setting the position of the electric vehicle triggering the charging demand but having a distance from the nearest charging station greater than a set threshold as a potential charging demand point;
the potential charging demand points are clustered into k categories using a k-means clustering method, generating k new charging station locations.
7. The electric vehicle charging station layout optimization method based on dynamic charging demand of claim 1, wherein the objective function of the charging station layout optimization model includes annual construction and operation costs F C1 Cost of time F C2 And penalty value F C3 ：
minF＝F C1 +F C2 +F C3
Annual construction and operation costs F C1 The method comprises the following steps of constructing cost Con and annual operation cost Cop, wherein the objective function formula is as follows:
wherein i is the charging station serial number, con i And Cop i Respectively representing the annual construction cost and the annual operation cost of the charging station i, r0 is the discount rate, nyear is the depreciation age, cg is the fixed investment cost, nchar is the number of charging piles in the station,the equivalent investment coefficient of the equipment cost is epsilon, the price of a single charging pile, and gamma, the conversion coefficient of the manual operation and maintenance cost of the equipment;
time cost F C2 The target function of (2) is:
F C2 ＝365·costF·λ
the costF is the distance between each charging demand point and the nearest charging station, and λ is the cost spent by the electric vehicle for each kilometer;
penalty value F C3 The target function of (2) is:
wherein C is a C is the sum of distance values exceeding a set value between all charging demand points and the nearest charging station b For the number of stations with the distance between two charging stations exceeding the set value, C c Is the number of charging stations that cannot meet the charging load demand.
8. The electric vehicle charging station layout optimization method based on dynamic charging requirements of claim 7, wherein constraints of the charging station layout optimization model are:
the charging demand point is within the service range of the nearest charging station:
d ij ≤S1
wherein d ij For the distance between the charging service station i and the charging demand point j, S1 is the service range set by the charging station;
charging service station spacing:
wherein d i1-i2 S2 is a distance threshold value between two charging stations;
carbon emission of electric automobile:
Carbon ijk ＝ω·d ijk ·W a
wherein d ijk Representing the distance of the vehicle k from the start point i to the end point j, W a Representing the power consumption rate of the electric vehicle. Omega represents CO of a vehicle when using electric energy 2 Emission rate, ζ is CO of a vehicle using gasoline 2 Emission rate.
9. The method of claim 1, wherein solving the charging station layout optimization model using a whale optimization algorithm based on the space-time distribution result of the charging demand comprises:
initializing related parameters of an algorithm, and randomly generating a first generation population;
determining the minimum load demand and the maximum load demand according to the space-time distribution result of the charging demand, and making a layout scheme;
and solving the addressing scheme by using a whale optimization algorithm, setting the maximum iteration times, and calculating the objective function value until the optimal objective function value is obtained or the maximum iteration times are reached, so as to obtain the final layout scheme.
10. An electric vehicle charging station layout optimization system based on dynamic charging demand, comprising:
the characteristic extraction module is used for acquiring travel characteristic parameters and charging characteristic parameters according to the running track of the electric automobile;
the travel chain building module is used for carrying out travel division on the travel track of the electric automobile and building a user travel chain;
the trip simulation module is used for carrying out user trip activity simulation based on the characteristic parameters and the trip chain to obtain a space-time distribution result of the charging demand;
the model building module is used for building a charging station layout optimization model taking the minimum comprehensive cost as a target and considering the carbon emission;
and the optimization solving module is used for solving the charging station layout optimization model by using a whale optimization algorithm based on the space-time distribution result of the charging demand, so as to obtain a final layout scheme.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117273181A (en) * | 2023-11-17 | 2023-12-22 | 天津平高易电科技有限公司 | Electric automobile charging scheduling method and system |
| CN117994116A (en) * | 2024-04-07 | 2024-05-07 | 国网江西省电力有限公司经济技术研究院 | Residential electric vehicle charging station constant volume method and system considering carbon reduction and synergy |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117273181A (en) * | 2023-11-17 | 2023-12-22 | 天津平高易电科技有限公司 | Electric automobile charging scheduling method and system |
| CN117273181B (en) * | 2023-11-17 | 2024-04-26 | 天津平高易电科技有限公司 | Electric automobile charging scheduling method and system |
| CN117994116A (en) * | 2024-04-07 | 2024-05-07 | 国网江西省电力有限公司经济技术研究院 | Residential electric vehicle charging station constant volume method and system considering carbon reduction and synergy |
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Application publication date: 20231110 |