CN111311017A - Urban rail transit train operation schedule and speed operation curve optimization method - Google Patents

Urban rail transit train operation schedule and speed operation curve optimization method Download PDF

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CN111311017A
CN111311017A CN202010141777.5A CN202010141777A CN111311017A CN 111311017 A CN111311017 A CN 111311017A CN 202010141777 A CN202010141777 A CN 202010141777A CN 111311017 A CN111311017 A CN 111311017A
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贺德强
郭松林
陈彦君
邓建新
阿必德
广宽
简汉青
张朗
滕小亮
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Abstract

The invention discloses an urban rail transit train operation schedule and speed operation curve optimization method, which comprises the steps of obtaining urban rail trains, urban rail lines, operation schedules and basic data of passenger flows; under the constraint condition of an operation schedule, calculating to obtain an optimized speed operation curve and a driving strategy; calculating a traction power curve and a braking power curve between stations according to the speed operation curve and the driving strategy; establishing a passenger platform waiting time and transfer waiting time calculation model based on the train running schedule and the passenger flow data; aiming at the operation characteristics among multiple trains and multiple stations, a schedule optimization model is established, and the utilization rate of regenerative braking energy is improved; the two models are combined to establish a comprehensive optimization model, and the optimized departure interval and the optimized stop time are obtained.

Description

Urban rail transit train operation schedule and speed operation curve optimization method
Technical Field
The invention belongs to the technical field of urban rail transit list energy-saving optimization, and particularly relates to an urban rail transit train operation schedule and speed operation curve optimization method.
Background
In recent years, with the rapid expansion of urban scale, urban rail transit trains have been widely popularized worldwide due to their characteristics of high frequency, large capacity, comfort, convenience, rapidness and the like. However, due to rising energy prices and concerns about environmental issues, urban rail transit operations face ever-increasing pressure. Meanwhile, with the continuous expansion of the scale of urban rail transit, the total energy consumption is increased rapidly, and great pressure is caused to an urban power supply system. By the end of 2019, 185 urban rail transit operation lines are opened in 39 cities in China, and the total mileage of the operation lines reaches 6600 kilometers. In 2018, the total electric energy consumption of urban rail transit in China is as high as 400 hundred million kilowatt-hours, and the traction energy consumption accounts for 45.3 percent of the total electric energy consumption and is as high as 180 hundred million kilowatt-hours on average in China.
However, the urban rail transit train is one of the important transportation means for people to go out, improves the operation service quality, gives passengers a more comfortable trip feeling, and is the direction of continuous efforts of operation departments. In 2018, the total passenger capacity in China is 210.7 hundred million people, which is increased by 25.9 hundred million people and 14 percent compared with 2017. In addition, in 2018, the minimum departure interval of national urban rail transit peak hours is 265 seconds on average, and 10 lines entering 120 seconds and less are provided, wherein 115 seconds of the No. 9 line of the Shanghai subway is the shortest, 118 seconds of the No. 3 line of the Guangzhou subway are less than 118 seconds, the minimum departure interval of the No. 1 line, 2 lines, 4 lines, 5 lines and 10 lines of the Beijing subway, 6 lines and 11 lines of the Shanghai subway and the No. 1 line of the Chengdu subway is 120 seconds in total at the peak hour minimum departure interval. The increase of departure density improves the service quality of urban rail trains, reduces the waiting time of passengers, and directly leads to the increase of the total energy consumption of the system.
In the daily operation process of the urban rail transit train, the traction energy consumption generally accounts for 40% -60% of the total energy consumption, wherein about 33% of energy can be converted into regenerative braking energy which is stored in a vehicle-mounted energy storage device or an energy storage device along the line, and can also be directly utilized by the traction train in the same power supply interval or fed back to a power supply network. Therefore, the traction energy consumption is reduced by fully utilizing the idle working condition; by adjusting the arrival time and departure time of a plurality of trains and platforms, the synchronous time of the traction train and the braking train in the same power supply interval is maximized, so that the utilization rate of regenerative braking energy is improved, and therefore, the method has important significance for optimizing the speed operation curve and the utilization rate of the regenerative braking energy of the urban rail transit train.
Disclosure of Invention
The invention aims to: aiming at the problems of huge energy consumption and low utilization rate of regenerative braking energy of the existing urban rail transit train, the invention optimizes the speed operation curve, departure interval and stop time of the train on the basis of safety and accuracy, thereby reducing the total energy consumption of the system and simultaneously reducing the waiting time of passengers. In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides an urban rail transit train operation schedule and speed operation curve optimization method, which comprises the following steps:
s01: acquiring basic data of an urban rail train, basic data of an urban rail line, basic data of an urban rail train operation schedule and basic data of passenger flow;
s02: aiming at the running process of a single train between single stations, calculating an optimized inter-station energy-saving speed running curve and driving strategy by taking a traction force coefficient, a braking force coefficient, a cruising speed, an idling working condition conversion point and a braking working condition conversion point as independent variables and aiming at minimizing running traction energy consumption between stations; calculating the change curves of traction power and braking power between stations along with time according to the calculated energy-saving speed running curve between stations and the driving strategy;
s03: establishing a calculation model of passenger platform waiting time and transfer waiting time based on the arrival time and departure time of different trains at the platform in the operation schedule and the change rule of passenger flow along with time as targets;
s04: aiming at the running process among multiple vehicles and multiple stations and aiming at improving the utilization rate of regenerative braking energy, establishing a regenerative braking energy matrix matching model based on the inter-station traction power and braking power data obtained in S02;
s05: establishing a genetic algorithm by taking two targets in S03 and S04 as target functions of a comprehensive optimization model, simulating by using an MATLAB running program, and adjusting basic data of an urban rail train operation schedule to obtain the optimized basic data of the urban rail train operation schedule;
s06: and outputting an optimization result, and automatically storing the optimization result after the simulation is finished in a corresponding folder according to the file name and the storage position in the MATLAB running program code, wherein the output result comprises an energy-saving running time schedule and an energy-saving speed running curve of the urban rail train.
Preferably, in step S01, the basic data of the urban rail train includes a train weight, a traction braking characteristic, a train length, a maximum speed limit, a davis coefficient, and a passenger carrying capacity; basic data of the urban rail line comprise inter-station kilometer posts, ramps, bends, speed limit and power supply interval setting; the basic data of the urban rail train operation schedule comprises inter-station running time, departure intervals, station stopping time and service time; the passenger flow basic data comprises a passenger starting station, a passenger terminal station, an arrival time and an exit time.
Preferably, in the step S02, the operation process of the train between the single stations in the single train further includes the following sub-steps:
s0201: performing mass point processing on the urban rail train and performing stress analysis including traction force, braking force, basic resistance and additional resistance so as to establish a mechanical model;
s0202: constraints are imposed on the force model, including traction force range, braking force range, speed variation range, acceleration range, boundary conditions, travel distance variation range, and travel time variation range.
S0203: establishing an energy-saving operation optimization model between single train stations, designing a genetic algorithm and solving the model by using MATLAB simulation software;
the optimization model for the energy-saving operation between the single train stations meets the following requirements:
Figure BDA0002399331740000031
wherein E isTIs the traction energy consumption, C is the departure times, N-1 is the station spacing number,
Figure BDA0002399331740000032
is the running time of the train at the nth inter-station distance, FT(t) tractive effort at time t, v (t) train speed at time t, vmaxIs the maximum speed at which the train is operating,
Figure BDA0002399331740000033
is the maximum tractive effort, t, specified by the train tractive characteristic curvetotalIs the actual inter-station running time, and x is the variation range of the inter-station running time,
Figure BDA0002399331740000034
is the actual running distance between the nth stations, phi is the variation range of the running distance between the stations, epsilon is the discrete precision of time, atIs the acceleration of the train at time t, amaxIs the maximum acceleration allowed, α, β are the traction coefficient and the braking coefficient, Ft(v) Is the tractive effort at train speed v, FB(v) Is the braking force at a train speed v, FR(v) Is the basic train resistance at speed v, FC(s) is the curve addition of the train at the displacement sResistance, FG(s) is the ramp added drag of the train at displacement s, MtotalIs the total mass of the train.
Preferably, in the simulation process, MATLAB simulation software is used, the traction force coefficient, the braking force coefficient, the maximum train running speed, the idling condition conversion point and the braking condition conversion point are used as independent variables, time is discretized, and the traction force and the braking force at each moment are recorded to obtain a traction force positive value and braking force negative value database.
Further preferably, the calculation model in step S03 specifically includes the following sub-steps:
s0301: obtaining arrival time and departure time of each train at a certain station according to a train schedule;
s0302: obtaining the arrival time of passengers according to the passenger OD data, judging whether the passengers need to be transferred according to the passenger OD data, if the passengers need to be transferred, obtaining the arrival time and departure time of trains on different lines at the transfer station according to the train schedule, and calculating the waiting time for passenger transfer
Figure BDA0002399331740000041
And calculating the total transfer waiting time t of the passengertra(ii) a Wherein the waiting time for passenger transfer
Figure BDA0002399331740000042
Satisfies the following conditions:
Figure BDA0002399331740000043
Figure BDA0002399331740000044
wherein, twalkIs the traveling time of the passenger at the transfer station, mu is a coefficient for judging the sequence of the arrival of the trains on different lines at the transfer station, and the total transfer waiting time t of the passengertraSatisfies the following conditions:
Figure BDA0002399331740000045
if the transfer is not needed, the average waiting time of the passengers at the platform is calculated according to the OD data of the passengers
Figure BDA0002399331740000046
Calculating the arrival passenger flow volume of the kth time interval
Figure BDA0002399331740000047
And calculating the total waiting time t of the passenger at the platformplaWherein the average waiting time of the stations
Figure BDA0002399331740000048
Satisfies the following conditions:
Figure BDA0002399331740000049
Figure BDA00023993317400000410
Figure BDA00023993317400000411
wherein,
Figure BDA00023993317400000412
is a coefficient for judging the passenger arriving at the station, and is shown in figure 3 in the specification,
Figure BDA00023993317400000413
is that the omega +1 train is on the line lkThe arrival time of the nth station of (a),
Figure BDA00023993317400000414
is passenger piOn the line lkThe arrival time of the nth station of (a),
Figure BDA00023993317400000415
is the ω th train on lineRoad lkIs the screen door closing time, e is the maximum waiting time of passengers at the platform,
Figure BDA00023993317400000416
is a line lkThe departure interval of (a);
the arrival passenger volume
Figure BDA00023993317400000417
Satisfies the following conditions:
Figure BDA0002399331740000051
Figure BDA0002399331740000052
Figure BDA0002399331740000053
wherein n is the passenger's origin station, m is the passenger's destination station,
Figure BDA0002399331740000054
is a discrete time interval;
total station waiting time t of the passengerplaSatisfies the following conditions:
Figure BDA0002399331740000055
further preferably, the establishing of the regenerative braking energy matrix matching model in step S04 specifically includes the following substeps:
s0401: carrying out feature description on data of inter-station traction power and braking power, and establishing a power characteristic description matrix equation;
s0402: describing the power characteristic of the platform in the waiting period, discretizing the platform waiting time, and combining the power value corresponding to each moment, the traction braking characteristic and the power supply interval to form a matrix equation;
s0403: and establishing a multi-column multi-station regenerative braking energy matrix matching model according to a matrix equation.
The scheme is further preferable, and the multi-column multi-station regenerative braking energy matrix matching model meets the following requirements:
Figure BDA0002399331740000056
wherein,
Figure BDA0002399331740000057
is the regenerative braking energy being utilized, thIs the interval between the departure of the vehicle,
Figure BDA0002399331740000058
is the stop time of the nth station, ε is the discrete time accuracy, λ (n-1, n) is the coefficient to determine if the nth and nth stations are in the same power supply interval, pi,n,tIs the traction power, p, between stations n in the ith supply intervali,n,bIs the braking power between stations n in the ith power supply interval,
Figure BDA0002399331740000059
and
Figure BDA00023993317400000510
is the upper and lower limit of the waiting time of the nth station,
Figure BDA00023993317400000511
and
Figure BDA00023993317400000512
the upper and lower limits of the departure interval.
Further preferably, the step S05 specifically includes the following sub-steps:
s0501: introducing a weight coefficient w according to the calculation model of the passenger waiting time acquired in the step S03 and the multi-row inter-station regenerative braking energy matrix matching model acquired in the step S041And w2Establishing a multi-objective comprehensive optimization model which comprises the most waiting time of passengersTwo optimization targets of small and maximum regenerative braking energy utilization;
s0502: establishing a multi-target genetic algorithm, taking departure intervals and station stop time of a train as independent variables, subtracting passenger waiting time from regenerative braking energy utilization amount as a fitness function, firstly setting the variation range of the independent variables, then setting the characteristics of maximum population scale, chromosome length, maximum iteration times, crossover probability, variation probability and the like in the genetic algorithm, and solving a comprehensive optimization model by using MATLAB simulation software.
Preferably, the step S06 of outputting the optimization result specifically includes: the energy-saving system comprises an energy-saving speed operation curve, energy-saving inter-station operation time, inter-station energy consumption, a power curve, an optimized departure interval, station stop time and total system energy consumption.
In summary, due to the adoption of the technical scheme, the invention has the following beneficial effects:
the method comprehensively considers the optimization of the total energy consumption of the system and the waiting time of passengers, reduces the total energy consumption of the urban rail train system, reduces the waiting time of the passengers and improves the service quality, and the method can quickly obtain an energy-saving speed operation curve through a single-train energy-saving optimization model and has small error; and the utilization rate of regenerative braking energy is improved by optimizing train departure intervals and station stop time of a platform.
Drawings
FIG. 1 is a flow chart of an energy-saving optimization method for an urban rail transit train according to the invention;
FIG. 2 is a four-phase energy-saving speed operating curve of a typical train in an embodiment of the present invention;
FIG. 3 is a comparison graph of the waiting time classification of the passenger transfer station according to the present invention;
FIG. 4 is a passenger arrival wait time classification chart of the present invention;
fig. 5 is a graph of traffic intensity at an early peak time of day in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings by way of examples of preferred embodiments. It should be noted, however, that the numerous details set forth in the description are merely for the purpose of providing the reader with a thorough understanding of one or more aspects of the invention, even though such aspects of the invention may be practiced without these specific details.
As shown in fig. 1, according to an aspect of the present invention, the present invention provides an urban rail transit train operation schedule and speed operation curve optimization method, a specific flow of the method is described in detail below by an embodiment, taking a nanning rail transit No. 1 line B-type train as an example, as shown in fig. 1, the optimization method includes the following steps:
s01: acquiring basic data (train weight, traction brake characteristics, train length, maximum speed limit, Davis coefficient and passenger carrying capacity) of a Nanning track traffic No. 1 line B type train, basic data (inter-station kilometer posts, ramps, bends, speed limit and power supply interval settings) of an urban rail line, basic data (inter-station running time, departure interval, stop time and service time) of an urban rail train running schedule and basic data (passenger starting station, terminal station, arrival time and departure time) of passenger flow;
s02: aiming at the running process of a single train between single stations, taking a traction force coefficient, a braking force coefficient, a cruising speed, an idle running condition conversion point and a braking condition conversion point as independent variables, aiming at the minimum running traction energy consumption between the stations, and under the constraint condition of a running schedule, aiming at the actual running situation of the single train between the stations, calculating an optimized inter-station speed running curve and a driving strategy, for example, a typical four-stage energy-saving speed running curve is shown in figure 2, the train is converted into constant-speed running after accelerating to a speed limit by using the maximum traction force, the traction force is equal to resistance at the moment, when the train runs to the optimal idle running condition conversion point, the traction force becomes zero, the train overcomes the resistance and runs in idle running, and finally braking is carried out at the braking point; calculating a change curve of the traction power and the braking power along with time according to the energy-saving speed operation curve and the driving strategy; simulating based on the obtained energy-saving speed running curve and the driving strategy, and discretizing time in the simulation process, wherein in the invention, the discretization precision is 0.1 second, and the traction force and the braking force at each moment are recorded to obtain a traction force and braking force database which comprises the magnitude and the positive and negative of the force;
s03: establishing a passenger platform waiting time and transfer waiting time calculation model based on the arrival time and departure time of different trains at the platform in the operation schedule and the change rule of passenger flow along with time as targets;
s04: aiming at the running process among multiple vehicles and multiple stations, aiming at improving the utilization rate of regenerative braking energy, establishing a multiple vehicle optimization model;
s05: establishing a genetic algorithm by taking two targets in S03 and S04 as an objective function of the comprehensive optimization model, adjusting basic data (departure interval and stop time) of an urban rail train operation schedule of the train to obtain the basic data (departure interval and stop time) of the optimized urban rail train operation schedule,
s06: and outputting an optimization result, automatically storing the optimization result after the simulation is finished in a corresponding folder according to the file name and the storage position in the MATLAB simulation operation program code, automatically storing the corresponding optimization result in a specified folder after the MATLAB simulation is finished each time, and outputting the optimization result, wherein the output result comprises an energy-saving speed operation curve, inter-station operation time, inter-station energy consumption, a power curve, an optimized departure interval, station stop time and total system energy consumption of the urban rail train.
Wherein:
traction braking characteristics: the curve of the change of the train traction force and the train braking force along with the speed is given by the self attribute of the train;
davis coefficient: fR(v)＝A+B·v(t)+C·v2(t) calculating the basic train running resistance by adopting a classical davis equation, wherein A, B and C are davis coefficients and are given by the attributes of the train;
basic data of the circuit: setting the distance between adjacent stations, the thousands of ramps, the radius, the length and the kilometers of the curve, the speed limit value and the kilometers of a speed limit interval and a power supply interval;
cruising speed: the speed of the train when the train keeps running at a constant speed; the idle condition switching point is as follows: the traction coefficient and the braking force coefficient are both zero; braking condition switching point: the traction coefficient is zero and the braking coefficient is [0,1 ].
The step S02 (for the operation process of the train at the single station) further includes the following sub-steps:
s0201: performing mass point processing on the urban rail train and performing stress analysis including traction force, braking force, basic resistance and additional resistance so as to establish a mechanical model;
s0202: constraints are imposed on the mechanical model including a tractive effort range, a braking effort range, a speed variation range, an acceleration range, boundary conditions, a travel distance variation range, and a travel time variation range.
S0203: establishing an energy-saving operation optimization model between single train stations, designing a genetic algorithm by taking a traction force coefficient, a braking force coefficient, a maximum train operation speed, an idling condition conversion point and a braking condition conversion point as independent variables, and solving the model by using MATLAB simulation software.
The optimization model for the energy-saving operation between the single train stations (the single train energy-saving optimization model) meets the following requirements:
Figure BDA0002399331740000081
wherein E isTIs the traction energy consumption, C is the departure times, N-1 is the station spacing number,
Figure BDA0002399331740000082
is the running time of the train at the nth inter-station distance, FT(t) tractive effort at time t, v (t) train speed at time t, vmaxIs the maximum speed at which the train is operating,
Figure BDA0002399331740000083
is the maximum tractive effort, t, specified by the train tractive characteristic curvetotalIs the actual inter-station running time, and x is the variation range of the inter-station running time,
Figure BDA0002399331740000084
is the actual running distance between the nth stations, phi is the variation range of the running distance between the stations, epsilonIs a discrete precision of time, atIs the acceleration of the train at time t, amaxIs the maximum acceleration allowed, α, β are the traction coefficient and the braking coefficient, Ft(v) Is the tractive effort at train speed v, FB(v) Is the braking force at a train speed v, FR(v) Is the basic train resistance at speed v, FC(s) is the additional resistance of the train on a curve at a displacement s, FG(s) is the ramp added drag of the train at displacement s, MtotalIs the total mass of the train.
In the present invention, the step S03 specifically includes the following sub-steps:
s0301: obtaining arrival time and departure time of each train at a certain station according to a train schedule; specifically, the arrival time and departure time of each train at stations such as Guangxi university are obtained according to a train schedule;
s0302: obtaining the arrival time of passengers according to the OD (Origin-to-Destination) data of the passengers, judging whether the passengers need to transfer according to the OD data of the passengers, if so, obtaining the arrival time and departure time of trains on different lines at a transfer station according to a train schedule, and calculating the waiting time for the passengers to transfer
Figure BDA0002399331740000091
And calculating the total transfer waiting time t of the passengertra(ii) a Wherein the waiting time for passenger transfer
Figure BDA0002399331740000092
Satisfies the following conditions:
Figure BDA0002399331740000093
Figure BDA0002399331740000094
wherein, twalkThe passengers travel at the transfer station in the same station in the transfer mode of the railway station for 10s, and travel at the opposite station in the sunny square in the up-down transfer mode for walkingThe line time is 60s, mu is a coefficient for judging the sequence of arriving at the transfer station by different lines, the concrete judgment measure is shown in figure 3,
Figure BDA0002399331740000095
and
Figure BDA0002399331740000096
is a line lkThe arrival time and departure time of the upper omega train at station n,
Figure BDA0002399331740000097
and
Figure BDA0002399331740000098
is a line lvThe arrival time and departure time of the upper omega train at station n,
Figure BDA0002399331740000099
and
Figure BDA00023993317400000910
is a line lvThe arrival time and departure time of the upper omega +1 train at the station n are the same, in case 1, when the passenger travels for a period of time to reach the transfer station, the train is just stopped at the station, and in case 2, when the passenger reaches the transfer station, the passenger does not stop the train, so the passenger needs to wait for the next train; the total transfer waiting time t of the passengertraSatisfies the following conditions:
Figure BDA00023993317400000911
wherein: OD data: including passenger origin, destination, time to enter and time to exit; if the transfer is not needed, the average waiting time of the passengers at the platform is calculated according to the OD data of the passengers
Figure BDA00023993317400000912
Calculating the arrival passenger flow volume of the kth time interval
Figure BDA00023993317400000913
And calculating the total waiting time t of the passenger at the platformplaWherein the average waiting time of the stations
Figure BDA00023993317400000914
Satisfies the following conditions:
Figure BDA00023993317400000915
wherein,
Figure BDA00023993317400000916
Figure BDA00023993317400000917
wherein,
Figure BDA0002399331740000101
is a coefficient for judging the passenger arriving at the station, as shown in figure 4,
Figure BDA0002399331740000102
and
Figure BDA0002399331740000103
is a line lkThe arrival time and departure time of the upper omega train at station n,
Figure BDA0002399331740000104
and
Figure BDA0002399331740000105
is a line lkThe arrival time and departure time of the upper omega +1 train at the station n,
Figure BDA0002399331740000106
and
Figure BDA0002399331740000107
is a line lkThe upper omega +2 train arrives at the station nThe time and the departure time of the train are calculated,
Figure BDA0002399331740000108
is that the train is on the line lkThe stop time of the upper station n,
Figure BDA0002399331740000109
is a line lkThe departure interval of (1) is that the passenger arrives at the platform with the train just stopped, the passenger does not need to wait, while in (2) the passenger arrives at the platform without the train stopped, and can only wait for the next train, delta is the screen door closing time,
Figure BDA00023993317400001010
is passenger piOn the line lkThe arrival time at the nth station of (a), e is the maximum waiting time of the passenger at the station;
wherein,
Figure BDA00023993317400001011
0 represents that the passenger just can catch up with the train when arriving at the station, and 1 represents that the passenger needs to wait for the next train; μ: +1 denotes line lνGet on train ratio line lkThe getting-on train arrives at the transfer station late, -1 represents the line lνGet on train ratio line lkThe upper train arrives at the transfer station early; the following table 1 shows the traffic distribution situation of the Nanning subway No. 1 line at the early peak period:
TABLE 1
Figure BDA00023993317400001012
The average passenger flow at each station every 10 minutes during the early peak hours is recorded in table 1 and used to calculate the total waiting time of the passengers.
As shown in FIG. 5, the figure describes the passenger flow data of each platform in two power supply intervals near the transfer station of the Nanning subway No. 1 line 6:00-9:00 in the morning, and the arriving passenger flow of the kth time interval is calculated according to the passenger OD data, and the arriving passenger flow
Figure BDA00023993317400001013
Satisfies the following conditions:
Figure BDA00023993317400001014
Figure BDA00023993317400001015
wherein n is the passenger's origin station, m is the passenger's destination station,
Figure BDA0002399331740000111
is a discrete time interval, which may be 10min, 20min or 30 min; total station waiting time t of the passengerplaSatisfies the following conditions:
Figure BDA0002399331740000112
in the present invention, the step S04 specifically includes the following sub-steps:
s0401: according to the inter-station traction power and braking power data obtained in the step S02, performing characteristic description on the data, and establishing a matrix equation for describing power characteristics;
s0402: describing the power characteristic of the platform in the waiting period, discretizing the platform waiting time, and combining the power value corresponding to each moment, the traction braking characteristic and the power supply interval to form a matrix equation;
s0403: establishing a multi-row vehicle multi-station regenerative braking energy matrix matching model; the multi-train multi-station regenerative braking energy matrix matching model comprises the following steps:
Figure BDA0002399331740000113
wherein,
Figure BDA0002399331740000114
is the regenerative braking energy being utilized, thIs the interval between the departure of the vehicle,
Figure BDA0002399331740000115
is the stop time of the nth station, ε is the discrete time accuracy, λ (n-1, n) is the coefficient to determine if the nth and nth stations are in the same power supply interval, pi，n，tIs the traction power, p, between stations n in the ith supply intervali，n，bIs the braking power between stations n in the ith power supply interval,
Figure BDA0002399331740000116
and
Figure BDA0002399331740000117
is the upper and lower limit of the waiting time of the nth station,
Figure BDA0002399331740000118
and
Figure BDA0002399331740000119
is the upper and lower limits of the departure interval;
in the present invention, the step S05 specifically includes the following sub-steps:
s0501: introducing a weight coefficient w according to the passenger waiting time calculation model acquired in the step S03 and the multi-row inter-station regenerative braking energy matrix matching model acquired in the step S041And w2And establishing a multi-objective comprehensive optimization model, wherein the model comprises two optimization objectives of minimum passenger waiting time and maximum regenerative braking energy utilization.
S0502: establishing a multi-target genetic algorithm, taking departure intervals and station stop time of a train as independent variables, subtracting passenger waiting time from regenerative braking energy utilization amount as a fitness function, firstly setting the variation range of the independent variables, then setting the characteristics of maximum population scale, chromosome length, maximum iteration times, crossover probability, variation probability and the like in the genetic algorithm, and solving a comprehensive optimization model by using MATLAB simulation software.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be construed as the protection scope of the present invention.

Claims (9)

1. The method for optimizing the operation schedule and the speed operation curve of the urban rail transit train is characterized by comprising the following steps of: the method comprises the following steps:
s01: acquiring basic data of an urban rail train, basic data of an urban rail line, basic data of an urban rail train operation schedule and basic data of passenger flow;
s02: aiming at the running process of a single train between single stations, calculating an optimized inter-station energy-saving speed running curve and driving strategy by taking a traction force coefficient, a braking force coefficient, a cruising speed, an idling working condition conversion point and a braking working condition conversion point as independent variables and aiming at minimizing running traction energy consumption between stations; calculating the change curves of traction power and braking power between stations along with time according to the calculated energy-saving speed running curve between stations and the driving strategy;
s03: establishing a calculation model of passenger platform waiting time and transfer waiting time based on the arrival time and departure time of different trains at the platform in the operation schedule and the change rule of passenger flow along with time as targets;
s04: aiming at the running process among multiple vehicles and multiple stations and aiming at improving the utilization rate of regenerative braking energy, establishing a regenerative braking energy matrix matching model based on the inter-station traction power and braking power data obtained in S02;
s05: establishing a genetic algorithm by taking two targets in S03 and S04 as target functions of a comprehensive optimization model, simulating by using an MATLAB running program, and adjusting basic data of an urban rail train operation schedule to obtain the optimized basic data of the urban rail train operation schedule;
s06: and outputting an optimization result, and automatically storing the optimization result after the simulation is finished in a corresponding folder according to the file name and the storage position in the MATLAB running program code, wherein the output result comprises an energy-saving running time schedule and an energy-saving speed running curve of the urban rail train.
2. The urban rail transit train operation schedule and speed operation curve optimization method according to claim 1, characterized in that: in step S01, the basic data of the urban rail train includes a train weight, a traction braking characteristic, a train length, a maximum speed limit, a davis coefficient, and a passenger carrying capacity; basic data of the urban rail line comprise inter-station kilometer posts, ramps, bends, speed limit and power supply interval setting; the basic data of the urban rail train operation schedule comprises inter-station running time, departure intervals, station stopping time and service time; the passenger flow basic data comprises a passenger starting station, a passenger terminal station, an arrival time and an exit time.
3. The urban rail transit train operation schedule and speed operation curve optimization method according to claim 1, characterized in that: the operation process of the single train between the single stations in the step S02 further includes the following sub-steps:
s0201: performing mass point processing on the urban rail train and performing stress analysis including traction force, braking force, basic resistance and additional resistance so as to establish a mechanical model;
s0202: constraints are imposed on the force model, including traction force range, braking force range, speed variation range, acceleration range, boundary conditions, travel distance variation range, and travel time variation range.
S0203: establishing an energy-saving operation optimization model between single train stations, designing a genetic algorithm and solving the model by using MATLAB simulation software;
the optimization model for the energy-saving operation between the single train stations meets the following requirements:
Figure FDA0002399331730000021
wherein E isTIs the traction energy consumption, C is the departure times, N-1 is the station spacing number,
Figure FDA0002399331730000022
is the running time of the train at the nth inter-station distance, FT(t) Is the tractive effort at time t, v (t) is the train speed at time t, vmaxIs the maximum speed at which the train is operating,
Figure FDA0002399331730000023
is the maximum tractive effort, t, specified by the train tractive characteristic curvetotalIs the actual inter-station running time, and x is the variation range of the inter-station running time,
Figure FDA0002399331730000024
is the actual running distance between the nth stations, phi is the variation range of the running distance between the stations, epsilon is the discrete precision of time, atIs the acceleration of the train at time t, amaxIs the maximum acceleration allowed, α, β are the traction coefficient and the braking coefficient, Ft(v) Is the tractive effort at train speed v, FB(v) Is the braking force at a train speed v, FR(v) Is the basic train resistance at speed v, FC(s) is the additional resistance of the train on a curve at a displacement s, FG(s) is the ramp added drag of the train at displacement s, MtotalIs the total mass of the train.
4. The urban rail transit train operation schedule and speed operation curve optimization method according to claim 3, characterized in that: in the simulation process, a traction force coefficient, a braking force coefficient, the maximum train running speed, an idling working condition conversion point and a braking working condition conversion point are used as independent variables, time is discretized, and the traction force and the braking force at each moment are recorded to obtain a traction force positive value and braking force negative value database.
5. The urban rail transit train operation schedule and speed operation curve optimization method according to claim 1, characterized in that: the calculation model of step S03 specifically includes the following sub-steps:
s0301: obtaining arrival time and departure time of each train at a certain station according to a train schedule;
s0302: passenger arrival acquisition based on passenger OD dataThe station time, and whether the passenger needs to be transferred or not is judged according to the passenger OD data, if the passenger needs to be transferred, the arrival time and departure time of the trains in different lines at the transfer station are obtained according to the train schedule, and the passenger transfer waiting time is calculated
Figure FDA0002399331730000031
And calculating the total transfer waiting time t of the passengertra(ii) a Wherein the waiting time for passenger transfer
Figure FDA0002399331730000032
Satisfies the following conditions:
Figure FDA0002399331730000033
Figure FDA0002399331730000034
wherein, twalkIs the traveling time of the passenger at the transfer station, mu is a coefficient for judging the sequence of the arrival of the trains on different lines at the transfer station, and the total transfer waiting time t of the passengertraSatisfies the following conditions:
Figure FDA0002399331730000035
if the transfer is not needed, the average waiting time of the passengers at the platform is calculated according to the OD data of the passengers
Figure FDA0002399331730000036
Calculating the arrival passenger flow volume of the kth time interval
Figure FDA0002399331730000037
And calculating the total waiting time t of the passenger at the platformplaWherein the average waiting time of the stations
Figure FDA0002399331730000038
Satisfies the following conditions:
Figure FDA0002399331730000039
Figure FDA00023993317300000310
Figure FDA00023993317300000311
wherein,
Figure FDA00023993317300000312
is a coefficient for judging the passenger arriving at the station, and is shown in figure 3 in the specification,
Figure FDA00023993317300000313
is that the omega +1 train is on the line lkThe arrival time of the nth station of (a),
Figure FDA00023993317300000314
is passenger piOn the line lkThe arrival time of the nth station of (a),
Figure FDA00023993317300000315
is the ω -th train on line lkIs the screen door closing time, e is the maximum waiting time of passengers at the platform,
Figure FDA00023993317300000316
is a line lkThe departure interval of (a);
the arrival passenger volume
Figure FDA00023993317300000317
Satisfies the following conditions:
Figure FDA00023993317300000318
Figure FDA0002399331730000041
Figure FDA0002399331730000042
wherein n is the passenger's origin station, m is the passenger's destination station,
Figure FDA0002399331730000043
is a discrete time interval;
total station waiting time t of the passengerplaSatisfies the following conditions:
Figure FDA0002399331730000044
6. the urban rail transit train operation schedule and speed operation curve optimization method according to claim 1, characterized in that: the establishing of the regenerative braking energy matrix matching model in the step S04 specifically includes the following substeps:
s0401: carrying out feature description on data of inter-station traction power and braking power, and establishing a power characteristic description matrix equation;
s0402: describing the power characteristic of the platform in the waiting period, discretizing the platform waiting time, and combining the power value corresponding to each moment, the traction braking characteristic and the power supply interval to form a matrix equation;
s0403: and establishing a multi-column multi-station regenerative braking energy matrix matching model according to a matrix equation.
7. The urban rail transit train operation schedule and speed operation curve optimization method according to claim 6, characterized in that: the multi-train multi-station regenerative braking energy matrix matching model meets the following requirements:
Figure FDA0002399331730000045
wherein,
Figure FDA0002399331730000046
is the regenerative braking energy being utilized, thIs the interval between the departure of the vehicle,
Figure FDA0002399331730000047
is the stop time of the nth station, ε is the discrete time accuracy, λ (n-1, n) is the coefficient to determine if the nth and nth stations are in the same power supply interval, pi,n,tIs the traction power, p, between stations n in the ith supply intervali,n,bIs the braking power between stations n in the ith power supply interval,
Figure FDA0002399331730000048
and
Figure FDA0002399331730000049
is the upper and lower limit of the waiting time of the nth station,
Figure FDA00023993317300000410
and
Figure FDA00023993317300000411
the upper and lower limits of the departure interval.
8. The urban rail transit train operation schedule and speed operation curve optimization method according to claim 1, characterized in that: the step S05 specifically includes the following sub-steps:
s0501: introducing a weight coefficient w according to the calculation model of the passenger waiting time acquired in the step S03 and the multi-row inter-station regenerative braking energy matrix matching model acquired in the step S041And w2Establishing a multi-target comprehensive optimization model which comprises two optimization targets of minimum passenger waiting time and maximum regenerative braking energy utilization；
S0502: establishing a multi-target genetic algorithm, taking departure intervals and station stop time of a train as independent variables, subtracting passenger waiting time from regenerative braking energy utilization amount as a fitness function, firstly setting the variation range of the independent variables, then setting the characteristics of maximum population scale, chromosome length, maximum iteration times, crossover probability, variation probability and the like in the genetic algorithm, and solving a comprehensive optimization model by using MATLAB simulation software.
9. The urban rail transit train operation schedule and speed operation curve optimization method according to claim 1, characterized in that: the step S06 of outputting the optimization result specifically includes: the energy-saving system comprises an energy-saving speed operation curve, energy-saving inter-station operation time, inter-station energy consumption, a power curve, an optimized departure interval, station stop time and total system energy consumption.
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