CN111915150A - Electric public transportation system planning method - Google Patents

Electric public transportation system planning method Download PDF

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CN111915150A
CN111915150A CN202010663544.1A CN202010663544A CN111915150A CN 111915150 A CN111915150 A CN 111915150A CN 202010663544 A CN202010663544 A CN 202010663544A CN 111915150 A CN111915150 A CN 111915150A
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李兴华
王天佐
成诚
王洧
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Abstract

The invention relates to an electric public transportation system planning method, which comprises the following steps: acquiring public transport network information and expense information; establishing an electric bus network operation scheduling and charging facility layout optimization model aiming at minimizing the total cost of the electric bus system by using bus network information and cost information; and solving an electric bus network operation scheduling and charging facility layout optimization model by using a self-adaptive genetic algorithm of the continuous variation cross probability and the continuous variation probability to obtain the electric bus system relating to the electric bus network operation scheduling and the electric bus charging facility layout. Compared with the prior art, the scheme for arranging the electric bus operation scheduling plan and the charging facilities is optimized, the method is applicable to time-of-use electricity price, the utilization rate of the electric bus is improved, the electric bus charging scheduling is more flexible, off-peak charging can be realized, the electricity consumption cost is reduced, and the utilization rates of the electric bus and the charging facilities are improved.

Description

Electric public transportation system planning method
Technical Field
The invention relates to the field of public transportation systems, in particular to a planning method of an electric public transportation system.
Background
The bus system is one of ideal application scenes of pure electric vehicles due to the fact that lines and schedules are fixed. The electric bus has the characteristics of low noise, zero emission, high comfort and the like, is generally considered to replace the traditional diesel bus, and is the future development direction of a bus system.
However, due to the limitation of battery technology, the electric bus has limited cruising ability and long charging time, and the operation schedule of the electric bus needs to consider not only the scheduling schedule of the vehicles but also the charging schedule of the vehicles. In addition, the problem of the layout of the charging facilities is also a key difficult problem of bus electromotion, and the layout of the charging facilities and the operation and scheduling of the electric bus are mutually influenced, so that the optimization of the two aspects is a key technical difficulty of bus electromotion.
At present, researches respectively and independently aiming at electric bus operation scheduling and charging facility layout are carried out. In the aspect of electric bus operation Scheduling, chinese patents CN 107341563, CN 104615850, and CN 109636176 research an electric bus charging sequence plan of a single charging station, and patent CN 109934391 reduces the number of used vehicles by a heuristic algorithm based on a Vehicle Scheduling Problem (VSP), and patent CN 109615268 further considers a time-of-use electricity price scenario, and establishes a Vehicle operation Scheduling model with the minimum charging cost of the Vehicle on the same day as a target. On the other hand, patents CN 110705745 and CN 107392360 provide experience reference for modeling and optimizing the layout of the electric bus charging facility from the aspects of location capacity of the charging station and the number of charging facilities.
However, the electric bus operation scheduling and charging facility layout synchronous optimization technology is still insufficient. In addition, at present, the research on the scheduling of the electric buses is mostly based on a single-parking-lot single-line scene, and the consideration on a multi-parking-lot multi-line scene is lacked. In addition, the current research of the multi-default charging mode, namely the full-charging mode, lacks the modeling optimization of the more flexible partial charging mode.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide an electric public transportation system planning method.
The purpose of the invention can be realized by the following technical scheme:
an electric public transportation system planning method comprises the following steps:
step S1: acquiring bus network information and cost information, wherein the bus network information comprises bus network station information, inter-station idle running time length information and line operation schedule information, and the bus network station information comprises the number of vehicles at the head station and the tail station of each line in the network, configuration construction and power grid conditions;
step S2: establishing an electric bus network operation scheduling and charging facility layout optimization model aiming at minimizing the total cost of the electric bus system by using bus network information and cost information;
step S3: and solving an electric bus network operation scheduling and charging facility layout optimization model by using a self-adaptive genetic algorithm of the continuous variation cross probability and the continuous variation probability to obtain the electric bus system relating to the electric bus network operation scheduling and the electric bus charging facility layout.
The fee information includes an electricity rate charging standard including a single electricity rate charging standard and a time-of-use electricity rate charging standard.
The objective function of the electric bus network operation scheduling and charging facility layout optimization model is as follows:
Figure BDA0002579498630000021
the system comprises an electric bus system, an electric bus charging facility, an electric charge and an electric bus operation cost, wherein Z is the total cost of the electric bus system and consists of four parts, namely electric bus purchase cost, electric bus charging facility purchase cost, electric charge cost and electric bus operation cost; c. CbPurchase cost for electric bus individual, ccIndividual purchase cost for charging facilities, ceTo the price of electricity, ctFor the hourly operating costs of electric buses, eiThe amount of power consumed for shift i, tiFor the operating duration of shift i, EijIs a shiftAmount of change in battery level, T, at the beginning of j compared to the end of shift iijThe time length required for the vehicle to reach the starting station of shift j from the end of shift i; x is the number ofij1 is taken when the electric bus operates the shift j after the shift i is operated, or 0 is taken; y ispqAnd taking 1 when the charging event q is executed after the charging event p is finished for the charging pile, and otherwise, taking 0.
The constraint conditions of the electric bus network operation scheduling and charging facility layout optimization model comprise:
vehicle shift scheduling constraint:
Figure BDA0002579498630000022
Figure BDA0002579498630000023
Figure BDA0002579498630000024
Figure BDA0002579498630000031
and electric quantity consumption constraint:
Figure BDA0002579498630000032
Figure BDA0002579498630000033
Figure BDA0002579498630000034
charging and scheduling constraints:
Figure BDA0002579498630000035
Figure BDA0002579498630000036
Figure BDA0002579498630000037
Figure BDA0002579498630000038
Figure BDA0002579498630000039
Figure BDA00025794986300000310
Figure BDA00025794986300000311
Figure BDA00025794986300000312
Figure BDA00025794986300000313
Figure BDA00025794986300000314
Figure BDA00025794986300000315
Eij、Tijand lpThe calculation formula of (2) is as follows:
Figure BDA00025794986300000316
Figure BDA00025794986300000317
Figure BDA00025794986300000318
wherein i and j are numbers of the shift or the first and last stations, S is a set of all bus shifts, D is a set of all the first and last stations, U is a union of S and D, P and Q are numbers of charging events or the first and last stations, P is a set of all charging events, and Q is a union of P and D; a isiIs the start time of shift i,/iThe residual electric quantity of the vehicle after the shift i is finished; SOCmax,SOCminThe method comprises the following steps of setting upper and lower limits of battery electric quantity in advance; z is a radical ofip Taking 1 when charging event p is carried out after the operation shift i of the electric bus is finished, and taking 0, z otherwisepjTaking 1 when the operation shift j is carried out after the charging event p is carried out on the electric bus, and otherwise, taking 0; a ispTo the start time of the charging event p, tpDuration of charging for charging event p, lpThe remaining capacity of the electric bus before the charging event p begins; e.g. of the typeipThe amount of electricity consumed by the electric bus to travel to the charging station of the charging event p after the operation shift i is finished, epjThe amount of power consumed by the electric bus to travel to the j-start station of the shift after the end of the charging event p, tipThe time t consumed by the electric bus to travel to the charging station of the charging event p after the operation shift i is finishedpjThe time consumed by the electric bus to travel to the starting station of shift j after the charging event p is finished; t is tijTime consumed for electric bus to travel from shift i terminal to shift j starting station, eijThe electric bus consumes the electric quantity consumed by driving from the terminal station of shift i to the starting station of shift j; m isA sufficiently large number; f (l)p,tp) As a function of the charging withpAnd tpThe independent variable is the residual electric quantity of the electric bus after the charging event p is finished.
The continuous change cross probability of the self-adaptive genetic algorithm is as follows:
Figure BDA0002579498630000041
the continuous variation probability of the adaptive genetic algorithm is as follows:
Figure BDA0002579498630000042
wherein, Pc1、Pc2、Pm1、Pm2All constants are more than 0 and less than 1, G is the current genetic algebra, G is the maximum genetic algebra, f' is the fitness of the individual before the cross operation, f is the fitness of the individual before the mutation operation,
Figure BDA0002579498630000043
average fitness of all individuals of a population, fmaxIs the maximum fitness among all individuals in the population, kcTo adapt the rate of change of the cross probability with genetic algebra, kmThe change rate of the adaptive mutation probability along with the change of the genetic algebra.
The self-adaptive genetic algorithm is based on a feasible solution transformation method, namely, the crossing and mutation operations of the genetic algorithm are in a feasible solution range.
The self-adaptive genetic algorithm adopts an integer coding form, the number of chromosome gene bits is 2 times of the number of times of a shift of an electric public transport system a day, the sequence of odd gene bits from left to right represents the sequence of the shift from morning to evening, the number b (b is more than or equal to 1) of the odd gene bits of the chromosome represents the number of vehicles operating the shift, the number c (c is less than or equal to 0) of the even gene bits represents the number of charging stations, when c is 0, the number represents that the charging stations are not charged after the shift is finished, and when c is less than 0, the number represents that the charging stations with the number of c are charged after the shift is finished.
The adaptive genetic algorithm fitness function is calibrated by adopting the following formula:
Figure BDA0002579498630000051
wherein, TCreciThe reciprocal of the total cost, TC, of the electric public transportation system calculated for the chromosome individual decodingreci_minTC for all individuals in the populationreciMinimum value of, TCreci_maxTC for all individuals in the populationreciR is a small positive constant.
The electric bus network operation scheduling comprises electric bus fleet size, vehicle scheduling and vehicle charging scheduling, wherein the vehicle scheduling is multi-yard and multi-line scheduling, the vehicle charging scheduling comprises day charging scheduling and night charging, and the charging mode comprises partial charging.
The electric bus charging facilities are arranged, and the positions of the electric bus charging piles and the quantity of each position are arranged.
Compared with the prior art, the invention has the following advantages:
(1) solving the optimization model of the electric bus network operation scheduling and charging facility layout can simultaneously optimize the scheduling plan, the charging scheduling plan and the charging facility layout scheme of the electric buses, so that the planning of the electric bus system is closer to the actual situation.
(2) The method is not only suitable for single electricity price, but also suitable for electricity price charging scenes such as time-of-use electricity price and the like, so that the planning of the electric public transportation system is closer to the actual situation.
(3) And modeling is carried out from the line network level, and multi-yard and multi-line electric bus scheduling can be carried out.
(4) Considering the partial charging mode that the charging is not always full, compared with the charging mode that the charging is full, the electric bus charging scheduling is more flexible, the off-peak charging is realized, the electricity consumption cost is reduced, and the utilization rate of the electric bus and the charging facility is improved.
(5) By applying the improved adaptive genetic algorithm based on feasible solution transformation, the genetic algorithm can be prevented from being premature, and the optimization efficiency of the genetic algorithm is improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of an adaptive genetic algorithm of the present invention;
FIG. 3 is a schematic diagram of the adaptive genetic algorithm chromosome coding of the present invention;
FIG. 4 is a public transportation network diagram according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating a standard time variation of electricity rate charging according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating a variation of remaining battery power of electric buses in accordance with an embodiment of the present invention;
FIG. 7 is a diagram illustrating a variation of remaining battery power of an electric bus according to an embodiment of the present invention;
fig. 8 is a diagram illustrating a change of the charging power consumption of the electric bus system in each hour according to the embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
The embodiment provides an electric public transportation system planning method, as shown in fig. 1, including the following steps:
the first step, collecting operation data of each line in the public traffic line network and the line network. The bus network station information, the inter-station idle running time length information and the line operation schedule information are collected by consulting or looking up related websites for a bus company. The bus network station information mainly comprises the number of vehicles at the first station and the last station of each line in the network, configuration construction and power grid conditions, and stations with charging facilities arranged conditionally are obtained by evaluation and screening. The information of the idle running time between the stations can be obtained through field test or online map API by utilizing the period number of the crawler, and is used for calculating the scheduling time of vehicle scheduling or charging idle running. The line operation schedule information comprises departure stations and time, ending stations and time of all lines.
And secondly, collecting information of the construction and operation and maintenance costs of the electric bus system, wherein the information comprises the purchase and operation and maintenance costs of the electric bus to be purchased, the purchase and operation and maintenance costs of the charging facility, the electricity price charging standard, the driver wages and other related labor cost information.
And thirdly, calibrating the charging and discharging characteristics of the battery of the electric bus. The battery charge-discharge characteristics of the electric public transport vehicle can be determined by field operation tests or consultation of vehicle supplier labels.
And fourthly, constructing an electric bus operation scheduling and charging facility layout optimization model. The model is constructed by taking constraints of vehicle scheduling, electric quantity consumption, charging scheduling and the like of the electric bus into consideration and taking the minimum total cost of construction and operation and maintenance of the electric bus system as an optimization target.
The objective function of the model is as follows:
Figure BDA0002579498630000061
the system comprises an electric bus system, an electric bus charging facility, an electric charge and an electric bus operation cost, wherein Z is the total cost of the electric bus system and consists of four parts, namely electric bus purchase cost, electric bus charging facility purchase cost, electric charge cost and electric bus operation cost; c. CbPurchase cost for electric bus individual, ccIndividual purchase cost for charging facilities, ceTo the price of electricity, ctFor the hourly operating costs of electric buses, eiThe amount of power consumed for shift i, tiFor the operating duration of shift i, EijIs the amount of change in battery charge, T, at the beginning of shift j compared to the end of shift iijThe time length required for the vehicle to reach the starting station of shift j from the end of shift i; x is the number ofij1 is taken when the electric bus operates the shift j after the shift i is operated, or 0 is taken; y ispqAnd taking 1 when the charging event q is executed after the charging event p is finished for the charging pile, and otherwise, taking 0.
The model includes the following constraints:
vehicle shift scheduling constraint:
Figure BDA0002579498630000071
Figure BDA0002579498630000072
Figure BDA0002579498630000073
Figure BDA0002579498630000074
wherein, the constraint (2) indicates that each shift has only one electric bus to operate. Constraint (3) states that the electric bus will run the next shift after the end of the current operating shift. The constraint (4) indicates that the operation time of the front and the back shifts operated by the same electric bus is not overlapped. The constraint (5) defines the decision variable xijThe value range of (a).
And electric quantity consumption constraint:
Figure BDA0002579498630000075
Figure BDA0002579498630000076
Figure BDA0002579498630000077
wherein, constraint (6) limits the residual capacity of the battery of the electric public transport vehicle to be always at the upper limit SOCmaxAnd lower limit SOCminIn the meantime. Constraint (7) initializing the residual battery capacity of the electric bus to be SOC before the electric bus starts to operate the first shift in one daymax. Constraint (8) representing classAnd (5) the relation between the residual capacity of the battery of the electric bus at the end of the time j and the residual capacity at the end of the shift i.
Charging and scheduling constraints:
Figure BDA0002579498630000078
Figure BDA0002579498630000079
Figure BDA00025794986300000710
Figure BDA0002579498630000081
Figure BDA0002579498630000082
Figure BDA0002579498630000083
Figure BDA0002579498630000084
Figure BDA0002579498630000085
Figure BDA0002579498630000086
Figure BDA0002579498630000087
Figure BDA0002579498630000088
and the constraint (9) indicates that the next charging event is carried out on the same charging pile after the current charging event is finished. The constraint (10) indicates that charging is carried out on the charging station before the charging event is started and only one electric bus is operated after the operation of the shift is finished. The constraint (11) states that each charging post charges at most one electric bus at any time. The constraint (12) indicates that the next shift or the return to the yard can be operated after the charging of the electric bus is finished. The constraint (13) indicates that the two charging events before and after the same charging post do not overlap in time. The constraint (14) indicates that the charging time of an electric bus does not overlap with the time of a subsequent shift. Constraints (15) to (19) define the value ranges of the decision variables.
Calculating intermediate variables:
Figure BDA0002579498630000089
Figure BDA00025794986300000810
Figure BDA00025794986300000811
wherein the constraints (20) to (22) define an intermediate variable Eij、TijAnd lpThe calculation formula of (2).
The variables and parameter descriptions in the model are shown in table 1, and the present embodiment mainly builds the model for the demand of the early peak commuter passengers.
TABLE 1 model parameter description
Figure BDA0002579498630000091
Fifthly, improving the adaptive genetic algorithm solution model, wherein a flow chart of the improved adaptive genetic algorithm is shown in fig. 2, and the improved adaptive genetic algorithm has the following four characteristics:
(1) the improved self-adaptive genetic algorithm is based on a feasible solution transformation method, namely, the crossover and mutation operations of the genetic algorithm are in a feasible solution range, and an infeasible solution cannot be generated.
(2) The improved adaptive genetic algorithm adopts an integer coding form, and a chromosome coding schematic diagram is shown in figure 3. The chromosome gene locus number is 2 times of the number of times of a shift of an electric public transport system per day, the odd gene loci sequentially represent the sequence of the shift from morning to evening from left to right, the odd gene locus number b (b is more than or equal to 1) of the chromosome represents the number of vehicles operating the shift, the even gene locus number c (c is less than or equal to 0) represents the number of charging stations, when c is 0, the chromosome does not represent that the shift is ended and the charging stations with the number of c are charged, and when c is less than 0, the chromosome represents that the charging stations with the number of c are charged after the shift is ended.
(3) The cross probability P of the improved adaptive genetic algorithmcAnd the mutation probability PmVariable, is related to population individual fitness and genetic algebra, and the higher the population individual fitness, PcAnd PmThe smaller the number of genetic generations G (G ∈ [0, G ]]) The larger, PcThe smaller, PmThe larger the algorithm is, the earlier the algorithm is, and the optimization performance is improved. The calculation formulas of the adaptive cross probability and the adaptive variation probability are shown in formulas (23) to (24).
Figure BDA0002579498630000101
Wherein, Pc1、Pc2、Pm1、Pm2All constants are more than 0 and less than 1, G is the current genetic algebra, G is the maximum genetic algebra, f' is the fitness of the individual before the cross operation, f is the fitness of the individual before the mutation operation,
Figure BDA0002579498630000103
average fitness of all individuals of a population, fmaxFor population ownershipMaximum fitness in an individual, kcTo adapt the rate of change of the cross probability with genetic algebra, kmThe change rate of the adaptive mutation probability along with the change of the genetic algebra. The improved adaptive genetic algorithm can avoid the algorithm from being premature and improve the optimization efficiency of the algorithm.
(4) In order to improve the selection efficiency of the genetic algorithm, the improved adaptive genetic algorithm fitness function is calibrated by the following formula (25):
Figure BDA0002579498630000102
wherein, TCreciThe reciprocal of the total cost, TC, of the electric public transportation system calculated for the chromosome individual decodingreci_minTC for all individuals in the populationreciMinimum value of, TCreci_maxTC for all individuals in the populationreciR is a small positive constant. The fitness function calibration method can improve the selection efficiency of the genetic algorithm.
The following is a specific example:
in this example, the total cost required for replacing all diesel buses of 8 bus lines with plug-in conventional pure electric buses is optimized in a research scenario of 8 bus lines (a circuit diagram is shown in fig. 4) in Anchen town of Jiading district of hai city. In the example, 867 bus shifts are total in one day, a bus schedule is obtained through a designated bus official network, station conditions of all the first and last stations in the line are judged by combining map real scenes, the shift profile of each line is shown in table 2, and 12 starting and ending stations are related in total, wherein 5 stations are bus yards and charging facilities are arranged with conditions (charging stations are arranged only in the bus yards in the example), the rest starting and ending stations are small, buses can only stop temporarily, and the profiles of all the starting and ending stations in the network are shown in table 3. The travel time between stations is crawled using a high-end API because testing cannot be run on site.
TABLE 2 summary of line shifts
Figure BDA0002579498630000111
TABLE 3 overview of all the departure and destination stations in the public transport network
Site name Site numbering Longitude (G) Latitude Related circuit Whether or not to be used as a charging station
Bus station -1 121.16 31.29 1,2 √
Peace and quiet road pavilion old street station -2 121.15 31.30 4,6 √
North railway station of Anting pavilion -3 121.16 31.31 7 √
Shanghai racing car park -4 121.23 31.33 1,8 √
Yellow ferry bus station -5 121.21 31.27 7,8 √
Public transport changji east station -6 121.20 31.29 3
Yangguancun -7 121.14 31.30 3
Xiangfang highway victory south road -8 121.26 31.31 2
Lin gang village -9 121.25 31.28 5
Coxi village -10 121.25 31.27 4
Deng Jiajiao village -11 121.20 31.26 5,6
Anzhilugdong jiajiao cun -12 121.19 31.26
In the second step, the time-of-use electricity price charging standard of Shanghai city used in the present example is shown in fig. 5, and the construction of other electric public transportation systems and the value of the 3-year operation and maintenance cost model parameter are shown in table 4.
Table 4 electric public transport system construction and operation and maintenance cost model parameter value-taking table
Model parameters Means of Value taking
cb Cost of purchasing electric bus 1,500,000 yuan
cc Individual purchase cost of charging facility 450,000 yuan
ct Hourly operation cost of electric buses 25 yuan/hour
In the third step, the battery charging and discharging characteristics of the vehicle are calibrated by adopting a BYD K9 electric bus type. Since the test cannot be run in the field, this example assumes that the battery charge and discharge functions are all linear functions. The battery charge and discharge functions used in this example are defined as shown in table 5 and equations (26) to (29).
TABLE 5 Battery Charge-discharge function chart
Function(s) Description of functions
F(lp,tp) With lpAnd tpAs a function of charge of the argument
G(ti) With tiIs an independent variable, eiDischarge function as a dependent variable
G(tip) With tipIs an independent variable, eipDischarge function as a dependent variable
G(tpj) With tpjIs an independent variable, epjDischarge function as a dependent variable
Figure BDA0002579498630000121
Figure BDA0002579498630000122
Figure BDA0002579498630000123
Figure BDA0002579498630000124
The values of the model parameters related to the characteristics of the electric bus battery and the upper and lower limit thresholds of the residual electric quantity of the battery are shown in table 6.
Table 6 electric bus battery characteristic and model parameter value taking table related to battery residual capacity upper and lower limit threshold
Figure BDA0002579498630000125
And fifthly, based on the electric bus operation scheduling and charging facility layout optimization model constructed in the fourth step, values of relevant parameters of an improved genetic algorithm are used and are shown in table 7.
TABLE 7 table for values of parameters related to improved genetic algorithm
Algorithm parameters Means of Value taking
P Genetic algorithm population size 100
G Maximum evolution algebra 2000
r Smaller positive number in fitness scaling function 0.01
Pc1 Adaptive cross probability initial maximum 0.95
Pc2 Adaptive crossover probability initial minimum 0.85
kc Adaptive rate of change of cross probability with genetic algebra 1
Pm1 Adaptive initial maximum of mutation probability 0.1
Pm2 Adaptive initial minimum of mutation probability 0.05
km Rate of change of adaptive mutation probability with genetic algebra 0.2
After the fifth step is completed, the optimal solution electric bus system construction and 3-year operation and maintenance total cost obtained by solving is 154421053.88 yuan, including the electric bus acquisition cost 109500000 yuan (accounting for 70.9%), the electric bus charging facility acquisition cost 10350000 yuan (accounting for 6.7%), the electricity charge cost 14086341.38 (accounting for 9.1%) and the electric bus operation cost 20484712.5 yuan (accounting for 13.3%). The electric bus system comprises 73 electric buses and 21 charging piles, wherein one electric bus operates 11.88 shifts on average every day, serves 5.18 lines and runs 221.18 kilometers.
Fig. 6 shows a change of the remaining battery capacity of 73 electric buses within one 24-hour operating period, and specifically, fig. 7 shows a change of the remaining battery capacity of one electric bus within one 24-hour operating period. Therefore, the charging scheduling plan of the electric bus is very flexible due to the partial charging mode, a large amount of charging time is finished in the non-operation time period at night, occupation of time in the operation time period at daytime is reduced, and the electric bus resources are utilized to the maximum degree in the operation time period.
In addition, the charging power consumption of each hour of the electric public transportation system in a 24-hour operation period is shown in fig. 8. It can be seen that the charging power consumption of the electric bus system is opposite to the fluctuation trend of the time-of-use electricity price, which shows that the optimal electric bus charging scheduling plan maximally utilizes the time period with lower electricity price to charge, and the electricity consumption cost is maximally saved.
In the embodiment, under the development background that the electric public transportation system replaces the traditional public transportation system, the scheduling plan, the charging scheduling plan and the charging facility layout scheme of the electric public transportation are synchronously optimized by applying the improved adaptive genetic algorithm under the condition that the constraint of the original public transportation schedule is not changed, so that the total cost of construction, operation and maintenance of the electric public transportation system is minimum. The electric bus operation scheduling and charging facility arrangement scheme obtained by applying the model and the algorithm has better operation feasibility and economy, and can provide reference for planning, operation and management of the electric bus system.

Claims (10)

1. An electric public transportation system planning method is characterized by comprising the following steps:
step S1: acquiring bus network information and cost information, wherein the bus network information comprises bus network station information, inter-station idle running time length information and line operation schedule information, and the bus network station information comprises the number of vehicles at the head station and the tail station of each line in the network, configuration construction and power grid conditions;
step S2: establishing an electric bus network operation scheduling and charging facility layout optimization model aiming at minimizing the total cost of the electric bus system by using bus network information and cost information;
step S3: and solving an electric bus network operation scheduling and charging facility layout optimization model by using a self-adaptive genetic algorithm of the continuous variation cross probability and the continuous variation probability to obtain the electric bus system relating to the electric bus network operation scheduling and the electric bus charging facility layout.
2. The method as claimed in claim 1, wherein the charge information includes a power rate charge standard, and the power rate charge standard includes a single power rate charge standard and a time-of-use power rate charge standard.
3. The method for planning an electric public transportation system according to claim 1, wherein the objective function of the model for optimizing the operation scheduling and the layout of the charging facilities of the electric public transportation network is as follows:
Figure FDA0002579498620000011
the system comprises an electric bus system, an electric bus charging facility, an electric charge and an electric bus operation cost, wherein Z is the total cost of the electric bus system and consists of four parts, namely electric bus purchase cost, electric bus charging facility purchase cost, electric charge cost and electric bus operation cost; c. CbPurchase cost for electric bus individual, ccIndividual purchase cost for charging facilities, ceTo the price of electricity, ctFor the hourly operating costs of electric buses, eiThe amount of power consumed for shift i, tiFor the operating duration of shift i, EijIs the amount of change in battery charge, T, at the beginning of shift j compared to the end of shift iijThe time length required for the vehicle to reach the starting station of shift j from the end of shift i; x is the number ofij1 is taken when the electric bus operates the shift j after the shift i is operated, or 0 is taken; y ispqFor charging pile after charging event p is finishedThe line charge event q takes a 1, otherwise 0.
4. The method for planning the electric public transportation system according to claim 1, wherein the constraint conditions of the model for optimizing the operation scheduling and the layout of the charging facilities of the electric public transportation network comprise:
vehicle shift scheduling constraint:
Figure FDA0002579498620000012
Figure FDA0002579498620000021
Figure FDA0002579498620000022
Figure FDA0002579498620000023
and electric quantity consumption constraint:
Figure FDA0002579498620000024
Figure FDA0002579498620000025
Figure FDA0002579498620000026
charging and scheduling constraints:
Figure FDA0002579498620000027
Figure FDA0002579498620000028
Figure FDA0002579498620000029
Figure FDA00025794986200000210
Figure FDA00025794986200000211
Figure FDA00025794986200000212
Figure FDA00025794986200000213
Figure FDA00025794986200000214
Figure FDA00025794986200000215
Figure FDA00025794986200000216
Figure FDA00025794986200000220
Eij、Tijand lpThe calculation formula of (2) is as follows:
Figure FDA00025794986200000217
Figure FDA00025794986200000218
Figure FDA00025794986200000219
wherein i and j are numbers of the shift or the first and last stations, S is a set of all bus shifts, D is a set of all the first and last stations, U is a union of S and D, P and Q are numbers of charging events or the first and last stations, P is a set of all charging events, and Q is a union of P and D; a isiIs the start time of shift i,/iThe residual electric quantity of the vehicle after the shift i is finished; SOCmax,SOCminThe method comprises the following steps of setting upper and lower limits of battery electric quantity in advance; z is a radical ofipTaking 1 when charging event p is carried out after the operation shift i of the electric bus is finished, and taking 0, z otherwisepjTaking 1 when the operation shift j is carried out after the charging event p is carried out on the electric bus, and otherwise, taking 0; a ispTo the start time of the charging event p, tpDuration of charging for charging event p, lpThe remaining capacity of the electric bus before the charging event p begins; e.g. of the typeipThe amount of electricity consumed by the electric bus to travel to the charging station of the charging event p after the operation shift i is finished, epjThe amount of power consumed by the electric bus to travel to the j-start station of the shift after the end of the charging event p, tipThe time t consumed by the electric bus to travel to the charging station of the charging event p after the operation shift i is finishedpjThe time consumed by the electric bus to travel to the starting station of shift j after the charging event p is finished; t is tijTime consumed for electric bus to travel from shift i terminal to shift j starting station, eijThe electric bus consumes the electric quantity consumed by driving from the terminal station of shift i to the starting station of shift j; m is a sufficiently large number; f (l)p,tp) As a function of the charging withpAnd tpThe independent variable is the residual electric quantity of the electric bus after the charging event p is finished.
5. The method for planning an electric bus system according to claim 1, wherein the continuously varying cross probability of the adaptive genetic algorithm is:
Figure FDA0002579498620000031
the continuous variation probability of the adaptive genetic algorithm is as follows:
Figure FDA0002579498620000032
wherein, Pc1、Pc2、Pm1、Pm2All constants are more than 0 and less than 1, G is the current genetic algebra, G is the maximum genetic algebra, f' is the fitness of the individual before the cross operation, f is the fitness of the individual before the mutation operation,
Figure FDA0002579498620000033
average fitness of all individuals of a population, fmaxIs the maximum fitness among all individuals in the population, kcTo adapt the rate of change of the cross probability with genetic algebra, kmThe change rate of the adaptive mutation probability along with the change of the genetic algebra.
6. The method for planning an electric bus system according to claim 1, wherein the adaptive genetic algorithm is based on a feasible solution transformation method, that is, the crossover and mutation operations of the genetic algorithm are both in a feasible solution range.
7. The method as claimed in claim 1, wherein the adaptive genetic algorithm is an integer code, the number of the chromosome loci is 2 times of the number of shifts of the electric public transportation system per day, the odd loci sequentially represent the shift from the left to the right, the odd loci number b (b ≧ 1) of the chromosome represents the number of the vehicle operating the shift, the even loci number c (c ≦ 0) represents the number of the charging station, when c ≦ 0, it represents that the shift is not charged after the shift is finished, and when c <0, it represents that the charging station with the number of c is charged after the shift is finished.
8. The method for planning an electric bus system according to claim 1, wherein the adaptive genetic algorithm fitness function is calibrated using the following formula:
Figure FDA0002579498620000041
wherein, TCreciThe reciprocal of the total cost, TC, of the electric public transportation system calculated for the chromosome individual decodingreci_minTC for all individuals in the populationreciMinimum value of, TCreci_maxTC for all individuals in the populationreciR is a small positive constant.
9. The method as claimed in claim 1, wherein the electric public transportation network operation schedule includes electric public transportation fleet size, vehicle scheduling schedule and vehicle charging schedule, the vehicle scheduling schedule is multi-farm and multi-line scheduling schedule, the vehicle charging schedule includes day-time charging schedule and night-time charging, and the charging mode includes partial charging.
10. The electric public transportation system planning method according to claim 1, wherein the electric public transportation charging facility layout comprises position layout of electric public transportation charging piles and quantity layout of each position.
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