CN110443406B - Layered planning method for electric vehicle charging facility and distributed power supply - Google Patents

Layered planning method for electric vehicle charging facility and distributed power supply Download PDF

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CN110443406B
CN110443406B CN201910578420.0A CN201910578420A CN110443406B CN 110443406 B CN110443406 B CN 110443406B CN 201910578420 A CN201910578420 A CN 201910578420A CN 110443406 B CN110443406 B CN 110443406B
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张旭
冯华
杨强
孙思扬
颜文俊
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State Grid Zhejiang Integrated Energy Service Co ltd
Zhejiang University ZJU
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Abstract

The invention discloses a layered planning method for an electric vehicle charging facility and a distributed power supply. According to the method, the optimal positions and capacities of various charging facilities and distributed power supplies of the electric automobile are gradually solved through a layered planning method, and the complexity of an optimization problem is greatly reduced.

Description

Layered planning method for electric vehicle charging facility and distributed power supply
Technical Field
The invention relates to a planning problem of an electric vehicle charging facility and a distributed power supply in an urban power distribution network, in particular to a hierarchical planning method of the electric vehicle charging facility and the distributed power supply.
Background
With the rapid development of economy, the problems of resource shortage and environmental pollution become more and more serious. Renewable new energy sources such as wind power, photovoltaic power generation and the like, and electric vehicles are becoming the main development direction of the energy industry because of the characteristics of energy conservation and environmental protection. With the increasing technical level of renewable power sources and electric vehicles and the support of national policies, renewable power sources and electric vehicles on a certain scale are put into demonstration operation at present, and the modes of industrialization and commercialization are gradually improved. With the increase of popularity, renewable power sources and electric vehicle charging will have a great influence on the power grid. Therefore, how to plan the charging facilities of the distributed renewable power sources and the electric vehicles in the city is a problem that must be solved at present.
Disclosure of Invention
Aiming at the defects of the conventional electric vehicle charging facility and distributed power supply planning method, the invention aims to provide a layered planning method for the electric vehicle charging facility and the distributed power supply.
The purpose of the invention is realized by the following technical means: a hierarchical planning method for an electric vehicle charging facility and a distributed power supply comprises the following steps:
the method comprises the following steps of (1) obtaining the fast charging probability of the electric automobile and the daily variation curve of the slow charging probability in a residential area and a commercial area by utilizing the existing trip survey data of the electric automobile;
step (2), obtaining several typical sunrise force curves of the distributed power supply by utilizing a K-means clustering algorithm according to the output data of the existing distributed power supply;
step (3), obtaining a typical daily variation curve of the basic load of the power distribution network according to historical data;
step (4), the installation address of the electric automobile slow charging station is a traffic or power distribution node where each residential area and each commercial office building in the city are located;
step (5), selecting the maximum value of the charging probability in the residential area and the commercial office building according to the daily variation curve of the low-speed charging probability of the electric vehicle in the residential area and the commercial office building obtained in the step (1), and multiplying the maximum value by the electric vehicle remaining capacity of the residential area or the commercial office building to obtain the number of charging piles of the low-speed charging station of the electric vehicle;
step (6), counting the number of electric vehicles of each node in the urban traffic network, and calculating the position of an electric vehicle quick charging station which enables the sum of the charging travel distances of all the electric vehicles to be minimum;
step (7), calculating the total number of electric vehicles required to be served by each charging station according to the position of the electric vehicle quick charging station obtained in the step (6), and further calculating the number of charging piles and waiting positions for enabling the electric vehicle quick charging station to obtain the daily maximum profit according to the daily change curve of the electric vehicle quick charging load obtained in the step (1);
and (8) further solving an optimal problem based on the positions and capacities of the electric vehicle slow charging station and the electric vehicle fast charging station obtained in the steps (4) to (7) and the typical output curve of the distributed power supply obtained in the step (3), so as to obtain the optimal position and capacity of the distributed power supply.
Further, in the step (2), several typical sunrise force curves of the distributed power supply are calculated in MATLAB by using a K-means clustering algorithm.
Further, in the step (6), the position of the electric vehicle quick charging station that minimizes the sum of the charging travel distances of all the electric vehicles is calculated, specifically as follows:
the optimization target is as follows:
Figure GDA0003509635600000021
the constraint conditions are:
Figure GDA0003509635600000022
wherein D is the sum of the charging running distances of all the electric automobiles, and nLIs the total number of traffic nodes and,
Figure GDA0003509635600000023
is the number of electric vehicles on the traffic node l,
Figure GDA0003509635600000024
the distance from the traffic node l to the nearest electric vehicle rapid charging station is shown; disminThe distance that the electric automobile can run with the minimum residual capacity is obtained.
Further, in the step (7), the number of the charging piles and the waiting positions for enabling the electric vehicle rapid charging station to obtain the maximum daily profit is calculated, and the calculation method specifically includes the following steps:
the optimization target is as follows:
Figure GDA0003509635600000025
the constraint conditions are: s is more than or equal to 1 and less than or equal to smax、1≤w≤wmax
Wherein E (t) ═ cpB-(cqQ+crR+ccMc+cwMw) The annual profit of the electric vehicle quick charging station is obtained;
Figure GDA0003509635600000026
the annual fixed cost of the electric vehicle rapid charging station is achieved; r ═ λ PNThe number of rejected charged electric vehicles in the electric vehicle quick charging station is determined;
Figure GDA0003509635600000027
the number of the electric vehicles which are queued in the electric vehicle quick charging station; b ═ s ρ (1-P)N) The number of the electric vehicle charging piles which are working in the electric vehicle quick charging station is the number of the electric vehicle charging piles;
Figure GDA0003509635600000028
the probability that n vehicles exist in the electric vehicle quick charging station is shown;
Figure GDA0003509635600000029
the probability that the electric vehicle quick charging station has no vehicle to charge is determined;
Figure GDA00035096356000000210
the service intensity of the electric vehicle rapid charging station is shown; m is a group ofcThe number of idle charging piles in the electric vehicle quick charging station is s-B, Mww-Q; the number of idle waiting positions in the electric vehicle quick charging station is shown; s and w are the number of charging piles and waiting positions in the electric vehicle quick charging station respectively; lambda and mu are respectively the automobile arrival rate and the charging pile service efficiency of the electric automobile quick charging station; n is s + w, which is the total capacity of the electric vehicle rapid charging station; r is the equipment depreciation rate; m is the equipment service life; i all right anglec,iwUnit prices of the charging pile and the waiting position are respectively; i isfThe fixed cost of the electric vehicle quick charging station is achieved; c. Cp cq cr cc cwCharging profit, waiting cost, refusing cost, charging pile idle cost and waiting position idle cost respectively; smax，wmaxThe maximum number of charging piles and waiting positions, respectively.
Further, the step (8) comprises the following specific steps:
solving an optimal problem
Figure GDA0003509635600000031
The optimal position and capacity of the distributed power supply can be obtained;
wherein the content of the first and second substances,
Figure GDA0003509635600000032
is the sum of active power loss of the distribution network in one day;
Figure GDA0003509635600000033
sum of reactive power losses of the distribution network for one day;
Figure GDA0003509635600000034
the sum of voltage fluctuation amplitudes of the power distribution network in one day; sigma1σ2σ3Is a weight factor, and σ1+σ2+σ3＝1；PLt,QLt,VDtRespectively the active loss, the reactive loss and the voltage fluctuation of an original power distribution network which is not connected with the charging of the electric automobile and the distributed power supply at the time t; n is a radical ofh,NL,NBThe number of typical sunrise curves of the distributed power supply, the number of branches in the power distribution network and the number of nodes are included; rij,XijIs the resistance and reactance of branch i-j; p is a radical ofij,h,t,qij,h,tActive power and reactive power of the branch i-j at the time t and under the h distributed power supply typical sunrise curve; vi,h,tIs the voltage of the node i at the time t and under the h-th distributed power supply typical sunrise curve;
the constraint conditions are as follows:
Figure GDA0003509635600000035
Figure GDA0003509635600000036
Figure GDA0003509635600000037
Figure GDA0003509635600000038
0.95pu≤Vi,h,t≤1.05pu
Figure GDA0003509635600000041
Figure GDA0003509635600000042
wherein S isilIs the correlation factor of node i with branch l,
Figure GDA0003509635600000043
active power and reactive power of a substation at the time t and under the h-th distributed power supply typical sunrise curve,
Figure GDA0003509635600000044
is the active power and reactive power of the distributed power supply at the moment of the I node t, Iij,h,tIs the current for branch i-j at time t and the typical sunrise curve of the h distributed power supply,
Figure GDA0003509635600000045
charging active power and reactive power of the electric automobile at the moment of the i node t and under the h distributed power supply typical sunrise curve,
Figure GDA0003509635600000046
charging active power and reactive power of the base load of the power distribution network at the moment of the i node t and under the h distributed power supply typical sunrise curve,
Figure GDA0003509635600000047
is the upper limit of the permeability of the distributed power source,
Figure GDA0003509635600000048
is the maximum distributed power source power of node i,
Figure GDA0003509635600000049
is the power factor at distributed generator inode time t and at the h distributed generator typical sunrise curve.
The invention has the following beneficial effects: according to the invention, through a layered planning method, the optimal positions and capacities of various charging facilities and distributed power supplies of the electric automobile are gradually solved, the optimal positions and capacities of the low-speed charging stations, the high-speed charging stations and the distributed power supplies of the electric automobile in a city can be solved, meanwhile, the complexity of the optimization problem can be greatly simplified, and the solving time can be shortened.
Drawings
FIG. 1: and (4) a power distribution network topology schematic diagram.
FIG. 2: and (4) a traffic network topology schematic diagram.
FIG. 3: daily variation graphs of the electric vehicle fast charging probability and the slow charging probability in the residential and commercial districts.
FIG. 4: typical daily output profiles for distributed power supplies.
FIG. 5: typical daily load profile.
FIG. 6: a position diagram of an electric vehicle rapid charging station.
Detailed Description
In the following, a detailed description of the algorithm is given by taking a distribution network of some IEEE 53 node and a traffic network of some 25 nodes as examples, and a series of experiments prove the effectiveness of the proposed method.
The distribution network of IEEE 53 nodes is shown in figure 1, and the traffic network of 25 nodes is shown in figure 2; wherein, the traffic nodes 3, 5, 10, 11 and 13 are connected with residential districts, and the traffic nodes 1, 4, 16, 19 and 22 are connected with commercial office buildings; the connection between the distribution network and the traffic network is shown in table 1; the quantity of cars is 1000, and the quantity of electric cars in each residential district is the same as that in each business office.
TABLE 1
Figure GDA0003509635600000051
Step (1), obtaining the daily variation curves of the fast charging probability and the slow charging probability in the residential area and the commercial area of the electric vehicle by using the existing travel survey data of the electric vehicle, as shown in fig. 3;
step (2), calculating several typical daily output curves of the distributed power supply by using a K-means clustering algorithm function in MATLAB according to the output data of the existing distributed power supply, wherein the data adopts fan output data of a certain wind power plant in Zhejiang province, and after normalization calculation, 4 typical daily output curves can be summarized by using the K-means function in MATLAB, as shown in FIG. 4;
step (3), obtaining a typical daily variation curve of the normalized power distribution network basic load according to historical data, as shown in fig. 5;
step (4), the placing address of the electric vehicle slow charging station is a traffic or power distribution node where each residential area and each commercial office building in the city are located, namely, residential area slow charging piles are placed at traffic nodes 3, 5, 10, 11 and 13, and commercial area slow charging piles are placed at traffic nodes 1, 4, 16, 19 and 22;
step (5), according to the daily variation curves of the slow charging probabilities of the electric vehicles in the residential area and the commercial office building, which are obtained in the step (1), selecting the maximum values of the charging probabilities in the residential area and the commercial office building, namely 0.410 and 0.245, and multiplying the maximum values by the electric vehicle holding capacity of the residential area or the commercial office building, namely 1000/5 being 200, so that the charging pile numbers of the electric vehicle slow charging stations in the residential area and the commercial area are 82 and 49 respectively;
step (6), counting the number of electric vehicles of each node in the urban traffic network, and calculating the position of the electric vehicle rapid charging station which enables the sum of the charging travel distances of all the electric vehicles to be minimum, wherein the optimization target is as follows:
Figure GDA0003509635600000052
wherein D is the sum of the charging running distances of all the electric automobiles, and nLIs the total number of traffic nodes and,
Figure GDA0003509635600000061
is the number of electric vehicles on the traffic node l,
Figure GDA0003509635600000062
the distance from the traffic node l to the nearest electric vehicle rapid charging station can be calculated by a Floyd algorithm; the constraints of the optimization problem are:
Figure GDA0003509635600000063
wherein disminThe distance that the electric automobile can run with the minimum residual electric quantity is obtained; solving the above optimization problem can result in: the positions of the electric vehicle rapid charging stations are at traffic nodes 2, 17 and 22, as shown in fig. 6;
step (7), calculating the total number of electric vehicles required to be served by each charging station according to the position of the electric vehicle quick charging station obtained in the step (6), and further calculating the number of charging piles and waiting positions for enabling the electric vehicle quick charging station to obtain the daily maximum profit according to the daily change curve of the electric vehicle quick charging load obtained in the step (1); the optimization target is as follows:
Figure GDA0003509635600000064
wherein E (t) ═ cpB-(cqQ+crR+ccMc+cwMw) The annual profit of the electric vehicle quick charging station is obtained,
Figure GDA0003509635600000065
is the annual fixed cost of the electric vehicle rapid charging station, and R is lambda PNThe rejected number of the charged electric vehicles in the electric vehicle quick charging station,
Figure GDA0003509635600000066
the number of the electric vehicles which are queued in the electric vehicle quick charging station is B ═ s rho (1-P)N) The number of the working electric vehicle charging piles in the electric vehicle quick charging station,
Figure GDA0003509635600000067
is the probability of n vehicles in the electric vehicle rapid charging station,
Figure GDA0003509635600000068
is the probability that the electric vehicle quick charging station has no vehicle to charge,
Figure GDA0003509635600000069
is the service intensity of the electric vehicle rapid charging station, McThe number of idle charging piles in the electric vehicle quick charging station is s-B, Mww-Q, the number of idle waiting positions in the electric vehicle quick charging station, s, w is the number of charging piles and waiting positions in the electric vehicle quick charging station, λ, μ is the vehicle arrival rate and charging pile service efficiency of the electric vehicle quick charging station, N ═ s + w, the total capacity of the electric vehicle quick charging station, r is the equipment depreciation rate, m is the equipment service life, i is the equipment service life, andc,iwunit price, I, of charging pile and waiting position, respectivelyfIs the fixed cost of the electric vehicle rapid charging station, cp cq cr cc cwCharging profit, waiting cost, refusing cost, charging pile idle cost and waiting position idle cost respectively; the constraints of this problem are: s is more than or equal to 1 and less than or equal to smax、1≤w≤wmaxWherein s ismax，wmaxThe maximum number of charging piles and the maximum number of waiting positions are respectively; let cp＝5($),cq＝1($),cr＝2($),cc＝0.5($),cw＝0.05($),smax＝40and wmax＝40,r＝0.08,m＝10,ic＝2000($),iw＝8140($),If163000($), and the quick charging station of the traffic node 2 is calculated to set 14 charging piles and 7 waiting positions; the rapid charging station of the traffic node 17 is provided with 20 charging piles and 8 waiting positions; the rapid charging station of the traffic node 22 is provided with 15 charging piles and 7 waiting positions;
and (8) further solving an optimal problem based on the positions and the capacities of the electric vehicle slow charging station and the electric vehicle fast charging station obtained in the steps (4) to (7) and the typical output curve of the distributed power supply obtained in the step (3)
Figure GDA0003509635600000071
The optimal position and capacity of the distributed power supply can be obtained. Wherein the content of the first and second substances,
Figure GDA0003509635600000072
is the sum of the active power losses of the distribution network in one day,
Figure GDA0003509635600000073
the sum of the reactive power losses of the distribution network for one day,
Figure GDA0003509635600000074
is the sum of voltage fluctuation amplitude, sigma, of a distribution network in one day1σ2σ3Is a weight factor, and σ1+σ2+σ3＝1，PLt,QLt,VDtThe active loss, the reactive loss and the voltage fluctuation at the time t of the original power distribution network without the charging of the electric automobile and the distributed power supply are respectively Nh,NL,NBIs the number of typical sunrise curves of the distributed power supply, the number of branches and the number of nodes in the power distribution network, Rij,XijIs the resistance and reactance of the branch i-j, pij,h,t,qij,h,tIs the active power and the reactive power of the branch i-j at the moment t and the h distributed power supply typical sunrise curve, Vi,h,tIs section (III)The voltage of the point i under the t moment and the h distributed power supply typical sunrise curve; the constraints of the optimization problem are as follows:
Figure GDA0003509635600000075
Figure GDA0003509635600000076
0.95pu≤Vi,h,t≤1.05pu、
Figure GDA0003509635600000077
wherein S isilIs the correlation factor of node i with branch l,
Figure GDA0003509635600000078
active power and reactive power of a substation at the time t and under the h-th distributed power supply typical sunrise curve,
Figure GDA0003509635600000079
is the active power and reactive power of the distributed power supply at the moment of the I node t, Iij,h,tIs the current of branch i-j at time t and the typical sunrise curve of the h distributed power supply,
Figure GDA00035096356000000710
charging active power and reactive power of the electric automobile at the moment of the i node t and under the typical sunrise curve of the h distributed power supply,
Figure GDA00035096356000000711
charging active power and reactive power of the base load of the power distribution network at the moment of the i node t and under the h distributed power supply typical sunrise curve,
Figure GDA00035096356000000712
is the upper limit of the permeability of the distributed power source,
Figure GDA00035096356000000713
is the maximum distributed power source power of node i,
Figure GDA00035096356000000714
the power factors of the distributed power supply i node at the time t and the h distributed power supply under a typical daily output curve are obtained; through calculation, the location capacity of the optimal distributed power supply is shown in table 2:
TABLE 2
Figure GDA0003509635600000081
The above description is only for the preferred embodiment of the present invention and is not intended to limit the scope of the present invention, and all equivalent flow transformations made by using the contents of the specification and drawings, or applied directly or indirectly to other related technical fields, are included in the scope of the present invention.

Claims (3)

1. A layered planning method for an electric vehicle charging facility and a distributed power supply is characterized by comprising the following steps:
the method comprises the following steps of (1) obtaining the fast charging probability of the electric automobile and the daily variation curve of the slow charging probability in a residential area and a commercial area by utilizing the existing trip survey data of the electric automobile;
step (2), obtaining several typical sunrise force curves of the distributed power supply by utilizing a K-means clustering algorithm according to the output data of the existing distributed power supply;
step (3), obtaining a typical daily variation curve of the basic load of the power distribution network according to historical data;
step (4), the installation address of the electric vehicle slow charging station is a traffic or power distribution node where each residential area and each commercial office building in the city are located;
step (5), selecting the maximum value of the charging probability in the residential area and the commercial office building according to the daily variation curve of the low-speed charging probability of the electric vehicle in the residential area and the commercial office building obtained in the step (1), and multiplying the maximum value by the electric vehicle remaining amount of the residential area or the commercial office building to obtain the charging pile number of the low-speed charging station of the electric vehicle;
step (6), counting the number of electric vehicles of each node in the urban traffic network, and calculating the position of an electric vehicle quick charging station which enables the sum of the charging travel distances of all the electric vehicles to be minimum;
step (7), calculating the total number of electric vehicles required to be served by each charging station according to the position of the electric vehicle quick charging station obtained in the step (6), and further calculating the number of charging piles and waiting positions for enabling the electric vehicle quick charging station to obtain the maximum daily profit according to the electric vehicle quick charging probability obtained in the step (1);
step (8), further solving an optimal problem based on the positions and capacities of the electric vehicle slow charging station and the electric vehicle fast charging station obtained in the steps (4) to (7) and the typical sunrise curve of the distributed power supply obtained in the step (2), and obtaining the optimal position and capacity of the distributed power supply;
in the step (7), the number of the charging piles and the waiting positions for enabling the electric vehicle quick charging station to obtain the maximum daily profit is calculated, and the calculation method specifically comprises the following steps:
the optimization target is as follows:
Figure FDA0003539008830000011
the constraint conditions are: s is more than or equal to 1 and less than or equal to smax、1≤w≤wmax
Wherein E (t) ═ cpB-(cqQ+crR+ccMc+cwMw) And E (t) is the annual profit of the electric vehicle quick charging station;
Figure FDA0003539008830000012
i is the annual fixed cost of the electric vehicle rapid charging station; r ═ λ PNR is the rejected number of the charged electric vehicles in the electric vehicle quick charging station;
Figure FDA0003539008830000013
q is the number of electric vehicles queuing in the electric vehicle quick charging station; b ═ s ρ (1-P)N) B is the number of the electric automobile charging piles working in the electric automobile quick charging station;
Figure FDA0003539008830000021
Pnthe probability that n vehicles exist in the electric vehicle quick charging station is shown;
Figure FDA0003539008830000022
P0the probability that the electric vehicle quick charging station has no vehicle to charge is determined;
Figure FDA0003539008830000023
ρ is the service intensity of the electric vehicle rapid charging station; mc＝s-B，McThe number of idle charging piles in the electric vehicle rapid charging station is shown; mw＝w-Q，MwThe number of idle waiting positions in the electric vehicle quick charging station is shown; s and w are the number of charging piles and waiting positions in the electric vehicle rapid charging station respectively; lambda and mu are respectively the automobile arrival rate and the charging pile service efficiency of the electric automobile quick charging station; n is s + w, and N is the total capacity of the electric vehicle quick charging station; r is the equipment depreciation rate; m is the equipment service life; i.e. ic,iwUnit prices of the charging pile and the waiting position are respectively; i isfThe fixed cost of the electric vehicle quick charging station is achieved; c. Cp cq cr cc cwCharging profit, waiting cost, refusing cost, charging pile idle cost and waiting position idle cost respectively; smax，wmaxThe maximum number of charging piles and the maximum number of waiting positions are respectively;
the step (8) comprises the following specific steps:
solving an optimal problem
Figure FDA0003539008830000024
The optimal position and capacity of the distributed power supply can be obtained;
wherein the content of the first and second substances,
Figure FDA0003539008830000025
PLItis the sum of active power loss of the distribution network in one day;
Figure FDA0003539008830000026
QLItis the sum of the reactive power loss of the distribution network in one day;
Figure FDA0003539008830000027
VDItis the sum of voltage fluctuation amplitudes of the power distribution network in one day; sigma1 σ2 σ3Is a weight factor, and σ1+σ2+σ3＝1；PLt,QLt,VDtRespectively realizing active loss, reactive loss and voltage fluctuation of an original power distribution network which is not connected with electric automobile charging and a distributed power supply at the time t; n is a radical ofh,NL,NBThe number of typical sunrise curves of the distributed power supply, the number of branches in the power distribution network and the number of nodes are included; rij,XijIs the resistance and reactance of branch i-j; p is a radical ofij,h,t,qij,h,tActive power and reactive power of the branch i-j at the time t and under the h distributed power supply typical sunrise curve; vi,h,tIs the voltage of the node i at the time t and under the h-th distributed power supply typical sunrise curve;
the constraint conditions are as follows:
Figure FDA0003539008830000031
Figure FDA0003539008830000032
Figure FDA0003539008830000033
Figure FDA0003539008830000034
0.95pu≤Vi,h,t≤1.05pu
|Iij,h,t|≤1.0pu、
Figure FDA0003539008830000035
Figure FDA0003539008830000036
wherein S isilIs the correlation factor of node i with branch l,
Figure FDA0003539008830000037
is the active power and reactive power of the substation at time t and the h-th distributed power supply typical sunrise curve,
Figure FDA0003539008830000038
is the active power and reactive power of the distributed power supply at the moment of the I node t, Iij,h,tIs the current for branch i-j at time t and the typical sunrise curve of the h distributed power supply,
Figure FDA0003539008830000039
charging active power and reactive power of the electric automobile at the moment of the i node t and under the h distributed power supply typical sunrise curve,
Figure FDA00035390088300000310
charging active power and reactive power of the base load of the power distribution network at the moment of the i node t and under the h distributed power supply typical sunrise curve,
Figure FDA00035390088300000311
is the upper limit of the permeability of the distributed power source,
Figure FDA00035390088300000312
is the maximum distributed power source power of node i,
Figure FDA00035390088300000313
is the power factor at distributed generator inode time t and at the h distributed generator typical sunrise curve.
2. The hierarchical planning method for electric vehicle charging facilities and distributed power supplies according to claim 1, characterized in that several typical sunrise curves of the distributed power supplies are calculated in MATLAB in the step (2) by using a K-means clustering algorithm.
3. The hierarchical planning method for electric vehicle charging facilities and distributed power supplies according to claim 1, wherein in the step (6), the position of the electric vehicle fast charging station which minimizes the sum of the charging travel distances of all electric vehicles is calculated as follows:
the optimization target is as follows:
Figure FDA0003539008830000041
the constraint conditions are:
Figure FDA0003539008830000042
wherein D is the sum of the charging running distances of all the electric automobiles, and nLIs the total number of traffic nodes and,
Figure FDA0003539008830000043
is the number of electric vehicles on the traffic node l,
Figure FDA0003539008830000044
the distance from the traffic node l to the nearest electric vehicle rapid charging station is shown; disminThe distance that the electric automobile can run with the minimum residual capacity is obtained.
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