CN111861145A - Method for configuring service area electric vehicle charging station considering highway network - Google Patents

Method for configuring service area electric vehicle charging station considering highway network Download PDF

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CN111861145A
CN111861145A CN202010606692.XA CN202010606692A CN111861145A CN 111861145 A CN111861145 A CN 111861145A CN 202010606692 A CN202010606692 A CN 202010606692A CN 111861145 A CN111861145 A CN 111861145A
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吴志
李奥
顾伟
周苏洋
孙琦润
刘鹏翔
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Southeast University
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Abstract

The invention discloses a method for configuring an electric vehicle charging station in a service area considering a highway network, and belongs to the field of power distribution of a power system. The method comprises the following steps: the method comprises the steps of firstly, collecting data of all parts in the system, modeling a high-speed road network, an electric automobile use rule and an electric automobile charging demand in the system, and constructing a charging station demand prediction model to obtain charging demand data. And secondly, establishing an optimization model by taking the maximum income of the annual charging station as a target and considering land cost, equipment cost, operation cost, charging loss cost and the like to obtain the optimal installation capacity of the charging station in the service area to be transformed. The charging demand simulation model constructed by the method considers the influence of an actual road network on the demand and improves the accuracy of the charging demand prediction. The method constructs an optimization model with the maximum annual charging station income as a target, and can effectively solve the problem of capacity allocation of the charging stations in the service area.

Description

Method for configuring service area electric vehicle charging station considering highway network
Technical Field
The invention relates to a method for configuring an electric vehicle charging station in a service area considering a highway network, and belongs to the field of power distribution of a power system.
Background
In recent years, the government of our country has vigorously carried out energy-saving and emission-reduction policies, and the power grid is developing towards the directions of high efficiency, flexibility, intelligence and sustainability. The electric automobile as an important component of the smart grid is rapidly developed, and China also becomes the largest electric automobile market all over the world. Meanwhile, electric vehicle charging facilities are also continuously perfected, various charging stations gradually enter the user, and research on optimal configuration of electric vehicle charging stations also becomes a key point of concern at home and abroad. With the continuous improvement of the battery technology of the electric automobile, the use of the electric automobile for long-distance travel becomes possible, and the construction of a large number of highway electric automobile charging stations is urgent. Therefore, the method for configuring the highway electric vehicle charging station is researched, the optimal configuration of the electric vehicle charging station is better realized, and the method has obvious engineering significance and economic value.
Disclosure of Invention
The invention provides a service area electric vehicle charging station configuration method considering a highway network, which is used for constructing an optimization model by taking the maximum annual charging station profit as a target and effectively solving the problem of capacity configuration of the service area charging stations.
The invention adopts the following technical scheme for solving the technical problems:
A method for configuring an electric vehicle charging station in a service area considering a highway network comprises the following steps:
step 1, collecting data of all parts in the system, modeling a high-speed road network, an electric vehicle use rule and an electric vehicle charging demand in the system, and constructing a charging station demand prediction model by using a Monte Carlo method to obtain charging demand data;
step 2, aiming at the maximum income of a charging station in an annual expressway service area, and specifically considering five aspects of land cost, equipment cost, operation and maintenance cost, charging loss cost and charging income of the charging station;
step 3, considering the actual conditions of the existing service area and the construction standard conditions of the charging station, and taking the number of charging piles and the maximum service capacity of the charging station as constraint conditions;
and 4, solving by using the mixed integer scale type to obtain the optimal configuration of the charging pile of the charging station.
In step 1, the highway network is modeled as follows:
traffic network G ═ (V, EW) consists of set of vertices V ═ V i1, 2,., n, and an edge set E ═ v ·ij|vi∈V，vjE V, i ≠ j } and road section weight W ═ Wij|vijE, E, wherein V is a set of all nodes of the traffic network G, E is a set of all directed arcs of the traffic network G, W is a set of all road weights of the traffic network G, and the weights represent the length of road sections or the congestion degree of the road sections in practice;
After the road network topology is obtained, the value is quantitatively assigned according to the weight in the actual road network pair model, and the leading edge matrix E is set as (a)ij)n×nThe assignment is performed by using equation (1):
Figure BDA0002559419410000021
wherein: a isijAs distance between nodes of the road network, vijIs a component edge, v, of a node i and a node j in a road networkiFor the ith node in the road network, vjIs the jth node in the road network; e is an edge set; finally obtaining a road network adjacent matrix W
Figure BDA0002559419410000022
In the formula: w is a12Is the distance between node 1 and node 2, w21Distance of node 2 from node 1, w23Distance of node 2 from node 3, w32Is the distance between node 3 and node 2; infinity represents a nodeviAnd vjThere is no link between the links, 0 means there is no distance between the same nodes;
the electric automobile model in the road network is as follows:
Figure BDA0002559419410000023
EV in the formula (3) is an electric vehicle parameter set comprising CharFor the set of charging characteristic parameters, TrFor the travel parameter set, the meaning of the individual specific parameters is for the charging characteristic parameter: csNumber t of charging station for receiving chargingarrTime to reach charging station, tdTo end the charging time, PcFor charging power, EcFor electric vehicle power consumption per kilometer, EevIs the total electric quantity, SOC of the electric automobile0Is the initial total electric quantity, SOC of the electric automobiletIs the residual electric quantity at the t moment of the electric automobile, SOCfFor electric automobile electric quantity early warning, SOC endFor the end of charge, WEV，nThe required electric quantity for the nth electric vehicle, tcCharging time for the electric vehicle;
for the driving characteristic parameters: n is a radical ofEVNumber of electric vehicles to be simulated, D0For the initial position of the electric vehicle, DendFor the end of travel position of the electric vehicle, DtElectric vehicle position at time t, DsFor the charging station position, tsFor the travel time of electric vehicle, VeFor the running speed of the electric vehicle, Ns，tNumber of electric vehicles to be charged at time t, Ws，iFor the i-th charging station, RpThe number n is the number of the electric automobile;
in the step 1, the modeling process of the use rule and the charging requirement of the electric automobile is as follows:
the travel time distribution of the electric automobile and the starting and stopping nodes of the user in the road network are obtained according to research, the travel time of the user accords with normal distribution, and simulation is carried out according to scenes such as morning and evening peaks
Initial electric quantity SOC of electric automobile0：
Figure BDA0002559419410000031
In the formula: mu and sigma are the mean and variance of the initial electric quantity distribution of the electric vehicle, x is time, EevThe electric quantity is the battery capacity of the electric automobile; the electric automobile has the residual capacity at the moment t
SOCt＝SOC0-Ec×Δl (5)
Wherein, Delta l is the driving distance of the electric automobile at the moment t, EcThe power consumption of the electric automobile;
trigger value SOC for charging requirement of electric vehicle user f:
Figure BDA0002559419410000032
In the formula: mu and sigma are respectively the average and variance of the electric quantity distribution of the user demand trigger value of the electric automobile, x is the specific time of day, EevThe electric quantity is the battery capacity of the electric automobile;
when the remaining capacity SOCtLower than trigger electric quantity SOCfThe charging requirement is generated:
SOCt<SOCf(7)
at this time, the nth electric vehicle requires the electric quantity W for chargingEV，nComprises the following steps:
WEV，n＝SOCend-SOCt(8) SOC in formula (8)endFor the amount of electricity at the time of charge stop, SOCtThe residual electric quantity of the electric automobile;
electric automobile charging station i's demand electric quantity Ws，i：
Figure BDA0002559419410000033
Wherein n isiDividing a day into 24 time periods for the number of charging vehicles at the ith charging station; when the electric automobile arrives at a charging station and generates a charging demand, the required charging time tc：
tc＝WEV，n/Pc(10)
Wherein P iscFor charging power, WEV，nThe required electric quantity is charged for the nth electric automobile;
end time t of chargingd：
td＝tarr+tc(11)
Wherein t isarrTime for electric vehicle to arrive at charging station, tcIs the charging time;
determining an actual running path of each electric automobile through a Floyd algorithm according to the initial position information and the end position information of each electric automobile; according to the travel time distribution and the electric vehicle battery residual capacity information of the electric vehicles, each electric vehicle is allowed to enter a road network, the charging demand distribution condition of a service area to be modified in the road network is reasonably obtained, and the arrival time, the stay time and the charge state information of each electric vehicle are obtained.
The establishment process of the charging station model of the expressway service area in the step 2 is as follows:
taking the maximum annual total income related to the highway electric vehicle charging station as an objective function, and specifically comprising annual land cost ClAnnual equipment purchasing and installing cost CstrAnnual electric vehicle charging station operation cost CopAnnual charging loss cost ClossCharging income Pro；
The specific form of the objective function is shown in equation (12):
max Profit＝Pro-Ci(12)
wherein Profit is the annual total revenue of the charging station, ProAnnual charging revenue for charging stations, CiAnnual investment cost for charging stations; the function aims at maximizing annual total income of the charging station, takes charging loss caused by incapability of meeting the charging requirement into consideration, and takes the investment cost of station building into consideration;
(1) land cost:
Cl＝Cd·Sc(13)
wherein C islAs a service areaCharging station land cost, CdCost per unit for charging station land, ScThe land area is a charging station;
(2) cost of equipment
Considering that the DC fast charging pile is charged, the quantity of the transformer and other equipment is related to the quantity of the charging pile, so the equipment cost CstrComprises the following steps:
Cstr＝f(s) (14)
wherein C isstr(s) is a function related to the number of charging piles, and s is the planned number of fast charging piles;
(3) operating costs
The operation and maintenance cost of the electric automobile charging pile every year is approximately considered to be in direct proportion to the investment and construction cost, and the operation cost of the charging station is shown as the formula (15):
Cop＝Cstr+NpCp(15)
Wherein C isopOperating cost for charging stations in the service area of the highway, proportion of operating maintenance cost to construction investment cost, NpNumber of staff being charging stations, CpThe staff of the charging station pay each year;
(4) annual investment cost conversion model
Annual investment cost CiExpressed as:
Ci＝R·(Cl+Cstr+Cop) (16)
wherein C islFor charging station land costs, CstrFor charging station equipment costs, CopFor the operating cost of the charging station, R is an annual conversion coefficient, and is specifically calculated as shown in formula (17):
Figure BDA0002559419410000051
wherein r is0For discount rate, y is the planned operating age;
(5) cost of charging loss
The charge demand lost per hour, expressed as follows:
Figure BDA0002559419410000052
Figure BDA0002559419410000053
Figure BDA0002559419410000054
Figure BDA0002559419410000055
equation (18) represents the hourly power loss in the workday scenario
Figure BDA0002559419410000056
S is the charging station charging pile number, u is the charging station utilization rate parameter, PcTo charge the charging post with power,
Figure BDA0002559419410000057
the working day hourly demand is obtained according to the demand simulation model; equation (19) represents the hourly power loss in the holiday scenario
Figure BDA0002559419410000058
S is the charging station charging pile number, PcTo charge the charging post with power,
Figure BDA0002559419410000059
the demand is the hourly charging demand of the holiday days obtained according to the demand simulation model; equations (20), (21) represent the power loss cost, p, for holidays and weekdays on a single daysUnit price, p, for selling electricity for charging stations bPurchasing electricity unit price for a charging station;
annual charging loss cost C after obtaining single-day charging demandlossExpressed as:
Closs＝(Closs，week*5+Closs，weekend*2)*52*(ps-pb) (22)
in the formula (22), Closs，weekFor charging losses in a workday scenario, Closs，weekendFor a charging loss in a holiday scenario, constant 5 represents 5 weekdays a week, constant 2 represents two holidays a week, and constant 52 represents 52 weeks a year;
(6) charging income:
when the expected charging income is calculated, the income which can be met by the charging demand of the charging station every hour is calculated, and then the charging loss caused by the insufficient number of the charging piles is subtracted;
charging profit ProThe specific calculation formula is as follows:
Figure BDA0002559419410000061
Pro＝Pw·52·(ps-pb)-Closs(23)
wherein
Figure BDA0002559419410000062
And
Figure BDA0002559419410000063
charging demands, p, for working days and holidays, respectivelysUnit price, p, for selling electricity for charging stationsbPurchase price of electricity for charging station, PwIs the total charge requirement. ClossTo reduce the annual charging loss cost, u is a charging station usage parameter.
In step 3, the constraint of the number of the charging piles is represented as:
smin<s<smax(24)
wherein: sminThe number of charging piles installed for the charging stations in the expressway service area is the minimum; s is the number of charging piles of the charging station; smaxThe number of charging piles which are installed for the charging stations in the expressway service area at most;
maximum service capacity constraint of charging station, i.e. maximum charging capacity of charging station per hour does not exceed maximum installation capacity
Wcmax≤s·Pc(25)
Wherein: pcFor charging power, WcmaxAnd the maximum charging capacity per hour is set for the charging station.
The invention has the following beneficial effects:
(1) the charging demand simulation model constructed by the invention considers the influence of an actual road network, a user travel rule and an electric vehicle driving path on the demand, and improves the accuracy of the charging demand prediction.
(2) The method constructs an optimization model with the maximum annual charging station income as a target, and can effectively solve the problem of capacity allocation of the charging stations in the service area. Meanwhile, in order to reduce the construction investment cost of the charging station, the existing expressway service area is planned.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a flow chart for generating a typical intraday highway electric vehicle charging demand profile.
FIG. 3 is a traffic network topology diagram.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
Considering a method for configuring an electric vehicle charging station in a service area of a highway network, as shown in fig. 1, the method comprises the following steps:
step 1, collecting data of all parts in the system, modeling a high-speed road network, an electric vehicle use rule and an electric vehicle charging demand in the system, and constructing a charging station demand prediction model by using a Monte Carlo method to obtain charging demand data;
Step 2, aiming at the maximum income of a charging station in an annual expressway service area, and specifically considering five aspects of land cost, equipment cost, operation and maintenance cost, charging loss cost and charging income of the charging station;
step 3, considering the actual conditions of the existing service area, the conditions of the charging station construction standard and the like, and taking the number of charging piles and the maximum service capacity of the charging station as constraint conditions;
and 4, solving by using the mixed integer scale type to obtain the optimal configuration of the charging pile of the charging station.
Considering a method for configuring an electric vehicle charging station in a service area of a highway network, in step 1, the highway network is modeled as follows:
the traffic road network G (V, E, W) is composed of a set of vertices V (V)i1, 2,., n, and an edge set E ═ v ·ij|vi∈V，vjE V, i ≠ j } and road section weight W ═ Wij|vijE, E, wherein V is a set of all nodes of the traffic network G, E is a set of all directed arcs of the traffic network G, W is a set of all road weights of the traffic network G, and the weights represent the length of road sections or the congestion degree of the road sections in practice. As shown in fig. 3, a traffic network topology. The two-way arrows in the figure represent two-way roads, the nodes represent charging stations at the entrances and exits of the highways or at intersections or service areas, v 4、v6Indicating that the charging station is located at a geographic location of the traffic network.
After the road network topology is obtained, the value can be quantitatively assigned according to the weight in the actual road network pair model, and the leading edge matrix E is (a)ij)n×nThe assignment is performed by using equation (1):
Figure BDA0002559419410000081
wherein: a isijAs distance between nodes of the road network, vijIs a component edge, v, of a node i and a node j in a road networkiFor the ith node in the road network, vjIs the jth node in the road network;
the final available road network adjacent matrix W is
Figure BDA0002559419410000082
In the formula: w is a12Is the distance between node 1 and node 2, w21Distance of node 2 from node 1, w23Distance of node 2 from node 3, w32Is the distance between node 3 and node 2; infinity represents a node viAnd vjWith no links between links, 0 tableThere is no distance between the same nodes, and these segments do not exist in the road network. The electric automobile model in the road network is as follows:
Figure BDA0002559419410000083
EV in the formula (3) is an electric vehicle parameter set comprising CharFor the set of charging characteristic parameters, TrFor the travel parameter set, the meaning of the individual specific parameters is for the charging characteristic parameter: csNumber t of charging station for receiving chargingarrTime to reach charging station, tdTo end the charging time, PcFor charging power, EcFor electric vehicle power consumption per kilometer, EevIs the total electric quantity, SOC of the electric automobile0Is the initial total electric quantity, SOC of the electric automobile tIs the residual electric quantity at the t moment of the electric automobile, SOCfFor electric automobile electric quantity early warning, SOCendFor the end of charge, WEV，nThe required electric quantity for the nth electric vehicle, tcAnd charging the electric automobile.
For the driving characteristic parameters: n is a radical ofEVNumber of electric vehicles to be simulated, D0For the initial position of the electric vehicle, DendFor the end of travel position of the electric vehicle, DtElectric vehicle position at time t, DsFor the charging station position, tsFor the travel time of electric vehicle, VeFor the running speed of the electric vehicle, Ns，tNumber of electric vehicles to be charged at time t, Ws，iFor the i-th charging station, RpAnd n is the electric automobile number.
The travel time distribution of the electric automobile and the starting and stopping nodes of the user in the road network can be obtained according to research, the travel time of the user accords with normal distribution, and simulation can be carried out according to scenes such as morning and evening peaks.
Initial electric quantity SOC of electric automobile0：
Figure BDA0002559419410000091
In the formula: mu and sigma are the mean and variance of the initial electric quantity distribution of the electric vehicle, x is time, EevThe electric quantity of the battery of the electric automobile. The electric automobile has the residual capacity at the moment t
SOCt＝SOC0-EcX Δ l (5) where Δ l is the distance traveled by the electric vehicle at time t, EcThe power consumption of the electric automobile is reduced.
Trigger value SOC for charging requirement of electric vehicle user f:
Figure BDA0002559419410000092
In the formula: mu and sigma are respectively the average and variance of the electric quantity distribution of the user demand trigger value of the electric automobile, x is the specific time of day, EevThe electric quantity of the battery of the electric automobile.
When the remaining capacity SOCtLower than trigger electric quantity SOCfThe charging requirement is generated:
SOCt<SOCf(7)
at this time, the nth electric vehicle requires the electric quantity W for chargingEV，nComprises the following steps:
WEV，n＝SOCend-SOCt(8) SOC in formula (8)endFor the amount of electricity at the time of charge stop, SOCtThe residual electric quantity of the electric automobile.
Electric automobile charging station i's demand electric quantity Ws，i：
Figure BDA0002559419410000093
Wherein n isiThe number of charging vehicles for the ith charging station may be divided into 24 time periods. When the electric automobile arrives at a charging station and generates a charging demand, the required charging time tc：
tc＝WEV，n/Pc(10)
Wherein P iscFor charging power, WEV，nAnd charging the required electric quantity for the nth electric automobile.
End time t of chargingd：
td＝tarr+tc(11)
Wherein t isarrTime for electric vehicle to arrive at charging station, tcIs the charging time.
And determining the actual running path of each electric automobile through a Floyd algorithm according to the initial position information and the end position information of each electric automobile. According to the travel time distribution and the battery residual capacity information of the electric automobiles, each electric automobile is driven into a road network, the charging demand distribution situation of a service area to be modified in the road network can be reasonably obtained, the information of the arrival time, the residence time, the charge state and the like of each electric automobile is obtained, and a specific flow chart is shown as a flow chart of the charging demand distribution situation of the electric automobiles on a highway in a typical day generated by an attached figure 2.
Considering the configuration method of the electric vehicle charging station in the service area of the highway network, in step 2, the method comprises the following steps of establishing a highway service area planning model:
comprehensively considering the concerns of all interest bodies, taking the maximum annual total income related to the highway electric vehicle charging station as an objective function, and specifically comprising the annual land cost ClAnnual equipment purchasing and installing cost CstrAnnual electric vehicle charging station operation cost CopAnnual charging loss cost ClossCharging income Pro。
The specific form of the objective function is shown in equation (12):
max Profit＝Pro-Ci(12)
wherein Profit is the annual total revenue of the charging station, ProAnnual charging revenue for charging stations, CiAnnual investment cost of the charging station is saved. The function aims at maximizing annual total income of the charging station, takes charging loss caused by incapability of meeting charging requirements into consideration, and takes investment cost for building the station into consideration.
(1) Land cost:
Cl＝Cd·Sc(13)
wherein C islCharging the service area with land costs, CdCost per unit for charging station land, ScIs a charging station land area.
(2) Cost of equipment
The equipment cost of the charging station mainly comprises a charging pile, a transformer and other equipment. Direct current quick charging pile is considered in the research, and the quantity of transformers and other equipment is related to the quantity of charging piles, so the equipment cost C strComprises the following steps:
Cstr＝f(s) (14)
wherein C isstrFor charging station equipment costs, f(s) is a function related to the number of charging piles, s is the planned number of fast charging piles.
(3) Operating costs
The operating costs of service area charging stations include mainly equipment operation maintenance and personnel wages. The operation maintenance cost that service area charging station filled electric pile receives the influence of multiple factors such as geographical region, frequency of use, maintenance level, is difficult to accurate quantization, considers approximately that electric automobile fills electric pile annual operation maintenance cost and its investment construction cost directly proportional, and charging station operation cost is as shown in (15):
Cop＝Cstr+NpCp(15)
wherein C isopOperating cost for charging stations in the service area of the highway, proportion of operating maintenance cost to construction investment cost, NpNumber of staff being charging stations, CpThe staff of the charging station pay each year.
(4) Annual investment cost conversion model
In the objective function of the model, the investment cost of the planning operation year needs to be converted into annual investment cost to complete the calculation of the annual income, and the conversion rate r is considered during conversion0。
Annual investment cost CiCan be expressed as:
Ci＝R·(Cl+Cstr+Cop) (16)
wherein C islFor charging station land costs, CstrFor charging stationPreparation cost, CopFor the operating cost of the charging station, R is an annual conversion coefficient, and is specifically calculated as shown in the formula 3-5:
Figure BDA0002559419410000111
Wherein r is0To discount, y is the planned operational age.
(5) Cost of charging loss:
the charge demand lost per hour, expressed as follows:
Figure BDA0002559419410000112
Figure BDA0002559419410000113
Figure BDA0002559419410000114
Figure BDA0002559419410000115
equation (18) represents the hourly power loss in the workday scenario
Figure BDA0002559419410000116
And (4) calculating, wherein s is the charging pile number of the charging station, and u is the utilization rate parameter of the charging station. PcTo charge the charging post with power,
Figure BDA0002559419410000117
the daily hourly demand is obtained according to the demand simulation model. Equation (19) represents the hourly power loss in the holiday scenario
Figure BDA0002559419410000118
S is the charging station charging pile number, PcTo charge the charging post with power,
Figure BDA0002559419410000119
the charging requirements of the solar energy power generation system are obtained according to the holiday day and the hour of the demand simulation model. Equations (20), (21) represent the power loss cost, p, for holidays and weekdays on a single daysUnit price, p, for selling electricity for charging stationsbAnd purchasing the electricity unit price for the charging station.
Annual charging loss cost C after obtaining single-day charging demandlossCan be expressed as:
Closs＝(Closs，week*5+Closs，weekend*2)*52*(ps-pb) (22)
in the formula (22), Closs，weekFor charging losses in a workday scenario, Closs，weekendFor a charging loss in a holiday scenario, constant 5 represents 5 weekdays a week, constant 2 represents two holidays a week, and constant 52 represents 52 weeks a year.
(6) Charging income:
the benefits of the charging station for the highway service area are mainly from the benefits of providing the charging service for the electric vehicle users. When the expected charging income is calculated, the income which can be met by the charging demand of each hour of the charging station is calculated firstly, and then the charging loss caused by the insufficient quantity of the charging piles is subtracted.
Charging profit ProThe specific calculation formula is as follows:
Figure BDA0002559419410000121
Pro＝Pw·52·(ps-pb)-Closs(23)
wherein
Figure BDA0002559419410000122
And
Figure BDA0002559419410000123
charging demands, p, for working days and holidays, respectivelysUnit price, p, for selling electricity for charging stationsbPurchase price of electricity for charging station, PwTo total charge demand。ClossTo reduce the annual charging loss cost, u is a charging station usage parameter.
In step 3, constraints of the highway electric vehicle charging station planning model are included.
When the existing expressway service area is transformed, factors such as the area of the existing service area need to be considered so as to determine the number s of charging piles which are installed mostmax. According to the design specification GB50966-2014 of electric vehicle charging stations, the minimum number of charging piles in the constructed charging stations is more than 3, so that the number of the charging piles which are installed at the minimum number in the charging stations in the service area of the expressway is smin. The charging pile quantity constraint may be expressed as:
smin<s<smax(24)
it is also considered that the charging station maximum service capacity constraint, i.e., the maximum charge per hour of the charging station, does not exceed the maximum installed capacity.
Wcmax≤s·Pc(25)
The above embodiments are merely illustrative of the technical ideas of the present invention, and the description thereof is specific and detailed, but the scope of the present invention should not be limited thereby, and any modifications made on the basis of the technical ideas proposed by the present invention are within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (5)

1. A method for configuring an electric vehicle charging station in a service area considering a highway network is characterized by comprising the following steps:
step 1, collecting data of all parts in the system, modeling a high-speed road network, an electric vehicle use rule and an electric vehicle charging demand in the system, and constructing a charging station demand prediction model by using a Monte Carlo method to obtain charging demand data;
step 2, aiming at the maximum income of a charging station in an annual expressway service area, and specifically considering five aspects of land cost, equipment cost, operation and maintenance cost, charging loss cost and charging income of the charging station;
step 3, considering the actual conditions of the existing service area and the construction standard conditions of the charging station, and taking the number of charging piles and the maximum service capacity of the charging station as constraint conditions;
and 4, solving by using the mixed integer scale type to obtain the optimal configuration of the charging pile of the charging station.
2. The method for configuring electric vehicle charging stations in consideration of service areas of highway network as claimed in claim 1, wherein in step 1, said highway network is modeled as follows:
the traffic road network G (V, E, W) is composed of a set of vertices V (V)i1, 2,., n, and an edge set E ═ v · ij|vi∈V，vjE V, i ≠ j } and road section weight W ═ Wij|vijThe method comprises the following steps that E is formed, wherein V is a set of all nodes of a traffic network G, E is a set of all directed arc sections of the traffic network G, W is a set of all road section weights of the traffic network G, and the weights represent the length of road sections or the congestion degree of the road sections in practice;
after the road network topology is obtained, the value is quantitatively assigned according to the weight in the actual road network pair model, and the leading edge matrix E is set as (a)ij)n×nThe assignment is performed by using equation (1):
Figure FDA0002559419400000011
wherein: a isijAs distance between nodes of the road network, vijIs a component edge, v, of a node i and a node j in a road networkiFor the ith node in the road network, vjIs the jth node in the road network; e is an edge set; finally obtaining a road network adjacent matrix W
Figure FDA0002559419400000012
In the formula: w is a12Is the distance between node 1 and node 2, w21Distance of node 2 from node 1, w23Distance of node 2 from node 3, w32Is the distance between node 3 and node 2; infinityRepresenting a node viAnd vjThere is no link between the links, 0 means there is no distance between the same nodes;
the electric automobile model in the road network is as follows:
Figure FDA0002559419400000021
EV in the formula (3) is an electric vehicle parameter set comprising CharFor the set of charging characteristic parameters, TrFor the travel parameter set, the meaning of the individual specific parameters is for the charging characteristic parameter: csNumber t of charging station for receiving charging arrTime to reach charging station, tdTo end the charging time, PcFor charging power, EcFor electric vehicle power consumption per kilometer, EevIs the total electric quantity, SOC of the electric automobile0Is the initial total electric quantity, SOC of the electric automobiletIs the residual electric quantity at the t moment of the electric automobile, SOCfFor electric automobile electric quantity early warning, SOCendFor the end of charge, WEV，nThe required electric quantity for the nth electric vehicle, tcCharging time for the electric vehicle;
for the driving characteristic parameters: n is a radical ofEVNumber of electric vehicles to be simulated, D0For the initial position of the electric vehicle, DendFor the end of travel position of the electric vehicle, DtElectric vehicle position at time t, DsFor the charging station position, tsFor the travel time of electric vehicle, VeFor the running speed of the electric vehicle, Ns，tNumber of electric vehicles to be charged at time t, Ws，iFor the i-th charging station, RpAnd n is the electric automobile number.
3. The method for configuring electric vehicle charging stations in service areas considering highway networks according to claim 1, wherein in step 1, the modeling process of the usage rules and the charging requirements of electric vehicles comprises the following steps:
the travel time distribution of the electric automobile and the starting and stopping nodes of the user in the road network are obtained according to research, the travel time of the user accords with normal distribution, and simulation is carried out according to scenes such as morning and evening peaks
Initial electric quantity SOC of electric automobile0：
Figure FDA0002559419400000022
In the formula: mu and sigma are the mean and variance of the initial electric quantity distribution of the electric vehicle, x is time, EevThe electric quantity is the battery capacity of the electric automobile; the electric automobile has the residual capacity at the moment t
SOCt＝SOC0-Ec×Δl (5)
Wherein, Delta l is the driving distance of the electric automobile at the moment t, EcThe power consumption of the electric automobile;
trigger value SOC for charging requirement of electric vehicle userf:
Figure FDA0002559419400000023
In the formula: mu and sigma are respectively the average and variance of the electric quantity distribution of the user demand trigger value of the electric automobile, x is the specific time of day, EevThe electric quantity is the battery capacity of the electric automobile;
when the remaining capacity SOCtLower than trigger electric quantity SOCfThe charging requirement is generated:
SOCt<SOCf(7)
at this time, the nth electric vehicle requires the electric quantity W for chargingEV，nComprises the following steps:
WEV，n＝SOCend-SOCt(8)
SOC in formula (8)endFor the amount of electricity at the time of charge stop, SOCtThe residual electric quantity of the electric automobile;
electric automobile charging station i's demand electric quantity Ws，i：
Figure FDA0002559419400000031
Wherein n isiDividing a day into 24 time periods for the number of charging vehicles at the ith charging station; when the electric automobile arrives at a charging station and generates a charging demand, the required charging time tc：
tc＝WEV，n/Pc(10)
Wherein P iscFor charging power, WEV，nThe required electric quantity is charged for the nth electric automobile;
end time t of chargingd：
td＝tarr+tc(11)
Wherein t isarrTime for electric vehicle to arrive at charging station, tcIs the charging time;
Determining an actual running path of each electric automobile through a Floyd algorithm according to the initial position information and the end position information of each electric automobile; according to the travel time distribution and the electric vehicle battery residual capacity information of the electric vehicles, each electric vehicle is allowed to enter a road network, the charging demand distribution condition of a service area to be modified in the road network is reasonably obtained, and the arrival time, the stay time and the charge state information of each electric vehicle are obtained.
4. The method as claimed in claim 1, wherein the step 2 of establishing the charging station model of the service area of the expressway is as follows:
taking the maximum annual total income related to the highway electric vehicle charging station as an objective function, and specifically comprising annual land cost ClAnnual equipment purchasing and installing cost CstrAnnual electric vehicle charging station operation cost CopAnnual charging loss cost ClossCharging income Pro；
The specific form of the objective function is shown in equation (12):
max Profit＝Pro-Ci(12)
wherein Profit is the annual total revenue of the charging station, ProAnnual charging revenue for charging stations, CiAnnual investment cost for charging stations; the function aims at maximizing annual total income of the charging station, takes charging loss caused by incapability of meeting the charging requirement into consideration, and takes the investment cost of station building into consideration;
(1) Land cost:
Cl＝Cd·Sc(13)
wherein C islCharging the service area with land costs, CdCost per unit for charging station land, ScThe land area is a charging station;
(2) cost of equipment
Considering that the DC fast charging pile is charged, the quantity of the transformer and other equipment is related to the quantity of the charging pile, so the equipment cost CstrComprises the following steps:
Cstr＝f(s) (14)
wherein C isstr(s) is a function related to the number of charging piles, and s is the planned number of fast charging piles;
(3) operating costs
The operation and maintenance cost of the electric automobile charging pile every year is approximately considered to be in direct proportion to the investment and construction cost, and the operation cost of the charging station is shown as the formula (15):
Cop＝Cstr+NpCp(15)
wherein C isopOperating cost for charging stations in the service area of the highway, proportion of operating maintenance cost to construction investment cost, NpNumber of staff being charging stations, CpThe staff of the charging station pay each year;
(4) annual investment cost conversion model
Annual investment cost CiExpressed as:
Ci＝R·(Cl+Cstr+Cop) (16)
wherein C islFor charging station land costs, CstrFor charging station equipment costs, CopFor the operating cost of the charging station, R is an annual conversion coefficient, and is specifically calculated as shown in formula (17):
Figure FDA0002559419400000041
wherein r is0For discount rate, y is the planned operating age;
(5) cost of charging loss
The charge demand lost per hour, expressed as follows:
Figure FDA0002559419400000042
Figure FDA0002559419400000051
Figure FDA0002559419400000052
Figure FDA0002559419400000053
Equation (18) represents the hourly power loss in the workday scenario
Figure FDA0002559419400000054
S is the charging station charging pile number, u is the charging station utilization rate parameter, PcTo charge the charging post with power,
Figure FDA0002559419400000055
the working day hourly demand is obtained according to the demand simulation model; equation (19) represents the hourly power loss in the holiday scenario
Figure FDA0002559419400000056
S is the charging station charging pile number, PcTo charge the charging post with power,
Figure FDA0002559419400000057
to be according to the requirementsThe charging requirement of each hour on the holiday days obtained by the simulation model; equations (20), (21) represent the power loss cost, p, for holidays and weekdays on a single daysUnit price, p, for selling electricity for charging stationsbPurchasing electricity unit price for a charging station;
annual charging loss cost C after obtaining single-day charging demandlossExpressed as:
Closs＝(Closs，week*5+Closs，weekend*2)*52*(ps-pb) (22)
in the formula (22), Closs，weekFor charging losses in a workday scenario, Closs，weekendFor a charging loss in a holiday scenario, constant 5 represents 5 weekdays a week, constant 2 represents two holidays a week, and constant 52 represents 52 weeks a year;
(6) charging income:
when the expected charging income is calculated, the income which can be met by the charging demand of the charging station every hour is calculated, and then the charging loss caused by the insufficient number of the charging piles is subtracted;
charging profit ProThe specific calculation formula is as follows:
Figure FDA0002559419400000058
Pro＝Pw·52·(ps-pb)-Closs(23)
Wherein
Figure FDA0002559419400000059
And
Figure FDA00025594194000000510
charging demands, p, for working days and holidays, respectivelysUnit price, p, for selling electricity for charging stationsbPurchase price of electricity for charging station, PwTo total charge requirement, ClossTo reduce the annual charging loss cost, u is a charging station usage parameter.
5. The method for configuring electric vehicle charging stations in service areas considering the highway network as claimed in claim 1, wherein in step 3, the charging pile number constraint is expressed as:
smin<s<smax(24)
wherein: sminThe number of charging piles installed for the charging stations in the expressway service area is the minimum; s is the number of charging piles of the charging station; smaxThe number of charging piles which are installed for the charging stations in the expressway service area at most;
maximum service capacity constraint of charging station, i.e. maximum charging capacity of charging station per hour does not exceed maximum installation capacity
Wcmax≤s·Pc(25)
Wherein: pcFor charging power, WcmaxAnd the maximum charging capacity per hour is set for the charging station.
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