CN107704975A - Subway train energy-saving run optimization method and system based on biogeography algorithm - Google Patents
Subway train energy-saving run optimization method and system based on biogeography algorithm Download PDFInfo
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Abstract
The present invention is directed to rail transit train operation energy consumption problem, it is proposed that a kind of subway train energy-saving run optimization method and system based on biogeography algorithm.Biogeography algorithm proposed by the present invention optimizes in two stages, the optimal energy-saving driving curve of biogeography algorithm thorough search train is utilized in the first stage, the rate curve of optimized operation is obtained on the premise of meeting running conditions of vehicle, run time and langding accuracy and constraining, reaches the effect of energy-conservation.Second stage optimizes for timetable run time, biogeography algorithm optimizes to dwell time increment of each train in each website, table at the time of optimizing original train operation, make same service area there are more trains the same time simultaneously out of the station, improve the utilization rate of regeneration energy.Multiple target of the present invention to train, high-dimensional problem has preferable improvement, convergence rate and precision for Optimal Parameters also have obvious effect, and under the given normal service condition of train, the formulation for the energy-saving run and timetable of train in actual track has good result with optimization.
Description
1 technical field
The present invention relates to a kind of optimization method of rail transit train energy saving in running, specifically, it is related to a kind of base
In the subway train energy-saving run optimization method of biogeography algorithm.
2 background technologies
It is train power supply that 33kV three-phase alternating voltages are formed 1500V DC voltages by traction substation through 24 pulse wave rectifiers,
Train obtains traction voltage to carry out a running under power of standing using pantograph or conductor rail, and running is according to operating mode between whole station
The number of transfer point and position adjust, and different Adjusted Options obtains different speed operation curves.
During multi-train movement, during train braking, regenerative braking energy feedback is produced to power network or the 3rd rail, is
Energy is provided in the tractor-trailer train of same service area's synchronization.Run time and the dwell time of train are adjusted, is obtained same
The quantity of service area's synchronization train out of the station is different.
In single vehicles running, due to the difference of train operating mode so that energy consumption during train operation is not
Together；During multi-train movement, due to the run time of train and the difference of dwell time so that same in same service area
Regeneration capacity usage ratio is different caused by the braking at moment.The energy consumption of train in the process of running how is saved, reaches energy consumption
Minimum, saving financial cost becomes the problem of control field technical staff needs to consider at present.
3 content of the invention
Technical problem：Train is many in the affected factor of running, and many uncertain factors occurred,
Energy consumption can be caused to have unnecessary loss.But the process often optimized is all independent, the situation of optimization is also relatively simple, right
In high-dimensional optimization aim also without effective solution.For the train run between station, it is difficult to ensure parameter
Accuracy, so as to be difficult to ensure that the effect of energy saving optimizing.
Technical scheme：In order to overcome above mentioned problem, a kind of new intelligent algorithm biogeography algorithm and track traffic are arranged
Car operation two benches energy saving optimizing combines, i.e. optimal speed performance curve in the first stage, by the speed operating mode after optimization
Application of curve is on second stage multi-train movement, the timetable that stops of adjusting and optimizing train.It so both compensate for traditional intelligence
The deficiency of algorithm, also meet train and more effectively save in the process of running.Also reached while train normal operation is ensured
The purpose of energy-conservation is arrived.
A kind of track traffic energy-saving run prioritization scheme based on biogeography algorithm proposed by the present invention, it is characterised in that
Based on biogeography algorithm, the process of two independent operatings of train is combined.Ensure it is punctual on schedule under conditions of,
The train speed performance curve of first stage is optimized, using the number of change working point and position as variable, after obtaining optimization
Speed-optimization curve, be finally reached the effect of energy saving optimizing；The rate curve that train is run between station applies to more trains
In operation, by changing adjustment train in the dwell time of website, make there are more trains to enter in unified service area synchronization
It is outbound, finally regenerating braking energy is fully used, reach energy-saving effect.
Stage 1：Train selection operating mode operation target be under train normal operation, one station between run when,
Change the operating mode rate curve of train, every kind of operating scheme can obtain corresponding power consumption values, realize the minimum of train power consumption.
The object function of Optimized model can be described as：
In formula, TiFor the Train Schedule of section i timetables requirement, unit s；E(Ti) it is train actual motion energy
Consumption, unit kwh；SiThe actual range of train operation between station, unit m；TsFor after optimization during train actual motion
Between, unit s；EsFor energy consumption in train journey after optimization, unit kwh；SSFor the train operation distance after optimization, unit is
m。
In formula, a+b+c=1.A is the weight of Train Schedule, and b is the weight of energy consumption in train journey, and c is train operation
The weight of distance.D is the late penalty factor of train operation, e be train operation range difference away from penalty factor, a, b and c's takes
Value is set according to the demand of practical problem, and their value depends on the requirement on schedule to run time, and operation energy consumption is most
Small requirement and the required precision of range ability.According to pertinent literature.
The constraints of rate curve Optimized model
Ts≤Ti (2)
D1≥lcoast (3)
Di-1≤Di (4)
v0=0, vn=0,0≤vi≤vmax (5)
ai≤amax (7)
(2) actual run time can not be more than the given standard time between formula represents train station；(3) formula represents that train enters first
The constraint that row coasting operation has to comply with, D1Represent the position of first time coasting, lcoastRepresent train coasting since starting to
Necessary beeline；(4) formula represents the position constraint of train coasting transfer point；(5) formula represents train in the process of running
Speed is no more than maximal rate, v0Represent the initial velocity of train operation, vnRepresent the end speed of train operation, viRepresent row
The initial velocity that car is currently run, vmaxRepresent the maximal rate of train operation；(6) a in formulaFRepresent to add caused by the hauling capacity of a locomotive
Speed, agRepresent acceleration caused by grade resistance, afRepresent datum drag caused by acceleration, Δ s represent train operation away from
From；vi-1Represent the end speed that train is currently run.(7) a iniRepresent train acceleration, amaxRepresent peak acceleration.Reference columns
Car operation relevant parameter setting, launch train peak acceleration is 0.56m/s2, braking peak acceleration is -1m/s2.When wherein
Between unit be s, the unit of distance is m, and the unit of speed is km/h, and the unit of acceleration is m/s2。
With { s0,s1,s2,…sN-1Represent a circuit on each change working point position, optimization dimension is N, BBO
The specific optimization process of algorithm is as follows：
Step 1 sets BBO algorithm parameters；Since section 1；
Step 2 generates the index vector of multiple N-dimensionals, as change working point according to the line length run between train station
Number, wherein N is even number；
Step 3 generates initial habitat population, any H for choosing habitatiIndex vector XiInitialized.
Step 4 calculates the suitability degree of each habitat, for different suitability degree Hi, habitat is arranged from good to secondary
Row；
Step 5 determines whether required optimal result by comparing, if optimal result, then output valve, and flow
Terminate.Otherwise step Step 6 is continued；
Step 6 calculates the mobility and mutation rate of each habitat, is migrated and mutation operation, recalculates and inhabites
The suitability degree H on groundi, return to step Step 4；
If each Dimensionality optimizations of Step 7 are completed, terminate；If also dimension does not optimize completion, return to step Step
2 carry out next range optimization.
By the change working point number of train operation station and the optimization of position, changing operation side between the station of train
Formula, it is rational to increase coasting operating condition, it is finally reached the purpose of energy-conservation.
Stage 2：In order to obtain more preferable energy-saving effect, while energy-saving train operation strategy is studied, also by timetable
Optimization problem account for, the two is combined and modeled with bilayer model, designs a two stage prioritization scheme,
Also global timetable is optimized while using algorithm optimization train driving strategy, so as to obtain more preferable Energy Saving Strategy.
It is required that the energy that consumption is minimum, as follows to the object function of overall control of consuming energy：
In formula, E (Ti) represent train running interval total energy consumption, unit kwh；TiFor i-th train operation when
Between.
Invention considers safety index and energy consumption index, to obtain optimal energy conservation object.Therefore basic constraints is set
Put as follows:(1) only increased for the larger website of the part volume of the flow of passengers, dwell time, i.e. tI, j>=0, i represent i-th train, j
Represent j-th of website, unit s；
(2) same time, the energy consumption of incoming train are all absorbed by train departure, i.e. ESystem=ELead, unit kw
h；(3) the total time change in train operation cycle is limited in rational time range；
(4) in train travelling process, the loss of energy in a variety of ways is not considered；
(5) the operation minimum spacing of train can not be less than Δ S, unit m.
Subway line m platform composition, then can be formed m × n time allocation matrix, had altogether by n car parallel connection
Mxn element.Each correction of one train of element representation in the dwell time of a website, during with BBO algorithms, by this
Individual matrix regards one group of solution of corresponding energy consumption in train journey as.Each group of solution correspond to a prioritization scheme, i.e. train dwelling time
Correction table.
The step of BBO algorithm optimizations, is as follows：
Step 1 sets algorithm parameter, parameter initialization；
Step 2 generates initial habitat dimension, i.e. dwell time correction initial matrix X0, first entered according to constraints
Row validity is screened；
Step 3 calculates the suitability degree H of each habitati, for different suitability degree Hi, habitat is arranged from good to secondary
Row；
Step 4 determines whether required optimal result by comparing, if optimal result, then output valve, and flow
Terminate.Otherwise step Step 5 is continued；
Step 5 calculates the mobility and mutation rate of each habitat, is migrated and mutation operation, recalculates and inhabites
The suitability degree H on groundi, return to step Step 3.
Updated by iteration, the train time table after being optimized, when adjusting train actual run time and stopping
Between, regenerative braking energy consumption is fully used, be finally reached the purpose of energy-conservation.
Finally obtain energy optimization index：
η is expressed as energy optimization rate, i.e. fractional energy savings；
W0、W1, Δ W be expressed as optimization before, optimization after energy consumption in train journey and its difference between the two, unit kWh.
If η is just, representing optimized scheme power consumption values is less than former scheme.η is bigger, the global optimization effect of optimization operating mode scheme
Fruit is better.
Beneficial effect：For biogeography optimized algorithm of the present invention not only to high-dimensional, the control of multiple target has good result, and
And also there are quick optimization characteristics to parameter.In the case where keeping normal operation, speed performance curve is not only optimized, and make
The dwell time of train website can also be adjusted according to physical condition.The optimization in two stages causes the overall energy consumption drop of train
It is low, it is finally reached the effect of energy-saving and emission-reduction.
Brief description of the drawings
Fig. 1 is the control structure figure of the track train running optimizatin based on biogeography algorithm.
Embodiment：
Rail transit train running optimizatin method combination accompanying drawing proposed by the present invention based on biogeography algorithm and specific
Details are as follows for embodiment：
The present invention is that parameter is optimized under normal operation in guarantee subway train using biogeography algorithm optimization
Method.Stage 1：The target of train selection operating mode operation is under train normal operation, when being run between a station, is changed
The operating mode rate curve of changing train, every kind of operating scheme can obtain corresponding power consumption values, realize the minimum of train power consumption.It is excellent
Changing the object function of model can be described as：
In formula, TiFor the Train Schedule of section i timetables requirement, unit s；E(Ti) it is train actual motion energy
Consumption, unit kwh；SiThe actual range of train operation between station, unit m；TsFor when train actual motion is total after optimization
Between, unit s；EsFor energy consumption in train journey after optimization, unit kwh；SSFor the train operation distance after optimization, unit is
m。
In formula, a+b+c=1.A is the weight of Train Schedule, and b is the weight of energy consumption in train journey, and c is train operation
The weight of distance.D is the late penalty factor of train operation, e be train operation range difference away from penalty factor, a, b and c's takes
Value is set according to the demand of practical problem, and their value depends on the requirement on schedule to run time, and operation energy consumption is most
Small requirement and the required precision of range ability.According to pertinent literature, a=0.3, b=0.4, c=0.3.
The constraints of rate curve Optimized model
Ts≤Ti (11)
D1≥lcoast (12)
Di-1≤Di (13)
v0=0, vn=0,0≤vi≤vmax (14)
ai≤amax (16)
(2) actual run time can not be more than the given standard time between formula represents train station；(3) formula represents that train enters first
The constraint that row coasting operation has to comply with, D1Represent the position of first time coasting, lcoastRepresent train coasting since starting to
Necessary beeline；(4) formula represents the position constraint of train coasting transfer point；(5) formula represents train in the process of running
Speed is no more than maximal rate, v0Represent the initial velocity of train operation, vnRepresent the end speed of train operation, viRepresent row
The initial velocity that car is currently run, vmaxRepresent the maximal rate of train operation；(6) a in formulaFRepresent to add caused by the hauling capacity of a locomotive
Speed, agRepresent acceleration caused by grade resistance, afRepresent datum drag caused by acceleration, Δ s represent train operation away from
From；vi-1Represent the end speed that train is currently run.(7) a iniRepresent train acceleration, amaxRepresent peak acceleration.Reference columns
Car operation relevant parameter setting, launch train peak acceleration is 0.56m/s2, braking peak acceleration is -1m/s2.When wherein
Between unit be s, the unit of distance is m, and the unit of speed is km/h, and the unit of acceleration is m/s2。
With { s0,s1,s2,…sN-1Represent a circuit on each change working point position, optimization dimension is N, BBO
The specific optimization process of algorithm is as follows：
Step 1 sets BBO algorithm parameters；Since first section；
Step 2 generates the index vector of multiple N-dimensionals, as change working point according to the line length run between train station
Number, wherein N is even number；
Step 3 generates initial habitat population, any H for choosing habitatiIndex vector XiInitialized.
Step 4 calculates the suitability degree of each habitat, for different suitability degree Hi, habitat is arranged from good to secondary
Row, typically take the turnover rate i=1 of habitat；
Step 5 determines whether required optimal result by comparing, if optimal result, then output valve, and flow
Terminate.Otherwise step Step 6 is continued；
Step 6 calculates the mobility and mutation rate of each habitat, is migrated and mutation operation, recalculates and inhabites
The suitability degree H on groundi, return to step Step 4；
Step 7 takes N=2 respectively, and 4,6,8, if each Dimensionality optimization is completed, terminate；If also dimension has not optimized
Cheng Ze, return to step Step 2 carry out next range optimization.
By the change working point number of train operation station and the optimization of position, changing operation side between the station of train
Formula, it is rational to increase coasting operating condition, it is finally reached the purpose of energy-conservation.
Stage 2：In order to obtain more preferable energy-saving effect, while energy-saving train operation strategy is studied, also by timetable
Optimization problem account for, the two is combined and modeled with bilayer model, designs a two stage prioritization scheme,
Also global timetable is optimized while using algorithm optimization train driving strategy, so as to obtain more preferable Energy Saving Strategy.
It is required that the energy that consumption is minimum, as follows to the object function of overall control of consuming energy：
In formula, E (Ti) represent train running interval total energy consumption, unit kwh；TiFor i-th train operation when
Between.
Invention considers safety index and energy consumption index, to obtain optimal energy conservation object.Therefore basic constraints is set
Put as follows:
(1) only increased for the larger website of the part volume of the flow of passengers, dwell time, i.e. tI, j>=0, i represent i-th train,
J represents j-th of website, unit s；
(2) same time, the energy consumption of incoming train are all absorbed by train departure, i.e. ESystem=ELead, unit kw
h；
(3) the total time change in train operation cycle is limited between -12s~12s, i.e. -12s≤tI, j≤12s；
(4) in train travelling process, the loss of energy in a variety of ways, i.e. Δ E=0 are not considered；
(5) the operation minimum spacing of train can not be less than 800m, i.e. Δ S >=800, unit m.
Subway line m platform composition, then can be formed m × n time allocation matrix, had altogether by n car parallel connection
Mxn element.Each correction of one train of element representation in the dwell time of a website, during with BBO algorithms, by this
Individual matrix regards one group of solution of corresponding energy consumption in train journey as.Each group of solution correspond to a prioritization scheme, i.e. train dwelling time
Correction table.
The step of BBO algorithm optimizations, is as follows：
Step 1 sets algorithm parameter, parameter initialization；
Step 2 generates initial habitat dimension, i.e. dwell time correction initial matrix X0, first entered according to constraints
Row validity is screened；
Step 3 calculates the suitability degree H of each habitati, for different suitability degree Hi, habitat is arranged from good to secondary
Row, typically take the turnover rate i=1 of habitat；
Step 4 determines whether required optimal result by comparing, if optimal result, then output valve, and flow
Terminate.Otherwise step Step 5 is continued；
Step 5 calculates the mobility and mutation rate of each habitat, is migrated and mutation operation, recalculates and inhabites
The suitability degree H on groundi, return to step Step 3.
Updated by iteration, the train time table after being optimized, when adjusting train actual run time and stopping
Between, regenerative braking energy consumption is fully used, be finally reached the purpose of energy-conservation.
Finally obtain energy optimization index：
η is expressed as energy optimization rate, i.e. fractional energy savings；
W0、W1, Δ W be expressed as optimization before, optimization after energy consumption in train journey and its difference between the two, unit kWh.
If η is just, representing optimized scheme power consumption values is less than former scheme.η is bigger, the global optimization effect of optimization operating mode scheme
Fruit is better.
Above-mentioned specific implementation is the preferable realization of the present invention, and certainly, the present invention can also have other various embodiments,
In the case of without departing substantially from spirit of the invention and essence, those skilled in the art work as can make various phases according to the present invention
The change and deformation answered, but these corresponding changes and deformation should all belong to the scope of the claims of the present invention.
Claims (2)
1. a kind of subway train energy-saving run optimized algorithm, it is characterised in that this method utilizes biogeography algorithm by train operation
Two independent stages combine, the high-dimensional optimization problem of multiple target is completed using biogeography algorithm.In the first rank
Section, train motor self-characteristic, line slope and bend speed limit are taken into account, complete the authenticity of train actual motion；
Second stage, the flow of the people actually got on or off the bus is taken into account, while ensureing that the stream of people gets on or off the bus safely, complete the excellent of train
Change operation.Two independent stages are combined into optimization with biogeography algorithm, compensate for traditional intelligence algorithm for higher-dimension
The weak point of multi-objective problem is spent, while train is reached more preferable energy-saving effect.
2. according to a kind of subway train energy-saving run optimized algorithm of the requirement of right 1, it is characterised in that train motor itself is led
Draw the utilization ratio that characteristic decides train energy in the process of running, line slope and bend speed limit decide the actual mistake of train
The authenticity and validity of journey, make train travelling process closer to reality.During train dwelling, according to the people that gets on or off the bus of reality
Flow completes stopping for train, while ensureing safe, improves the utilization rate to the time, makes train Effec-tive Function.Specifically such as
Under：
It is interior in the first stage, using the number of change working point and position as variable, and with train train motor self-characteristic,
The conditions such as circuit, the gradient and bend speed limit optimize as reference frame and constraints, and the object function of Optimized model is：
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In formula, TiFor the Train Schedule of section i timetables requirement, unit s；TsFor train actual run time after optimization,
Unit is s；E(Ti) it is train actual motion energy consumption, unit kwh；EsFor energy consumption in train journey after optimization, unit kw
h；SiThe actual range of train operation between station, unit m；SSFor the train operation distance after optimization, unit m, a are row
The weight of car run time, b be energy consumption in train journey weight, c be train operation distance weight, a+b+c=1.D is train
Run late penalty factor, e be train operation range difference away from penalty factor, a, b and c value is according to the need of practical problem
Ask and set, their value depends on the requirement on schedule to run time, and the minimum of operation energy consumption requires and range ability
Required precision.
The constraints of rate curve Optimized model
Ts≤Ti (2)
D1≥lcoast (3)
Di-1≤Di (4)
v0=0, vn=0,0≤vi≤vmax (5)
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ai≤amax (7)
(2) actual run time can not be more than the given standard time between formula represents train station；(3) it is lazy to represent that train is carried out first for formula
The constraint that row operation has to comply with, D1Represent the position of first time coasting, lcoastRepresent that coasting must since starting to for train
The beeline of palpus；(4) formula represents the position constraint of train coasting transfer point；(5) formula represents train speed in the process of running
No more than maximal rate, v0Represent the initial velocity of train operation, vnRepresent the end speed of train operation, viRepresent that train is worked as
The initial velocity of preceding operation, vmaxRepresent the maximal rate of train operation；(6) a in formulaFAcceleration caused by the hauling capacity of a locomotive is represented,
agRepresent acceleration caused by grade resistance, afAcceleration caused by datum drag is represented, Δ s represents the distance of train operation；
vi-1Represent the end speed that train is currently run.(7) a iniRepresent train acceleration, amaxRepresent peak acceleration.Reference train
The relevant parameter setting of operation, launch train peak acceleration is 0.56m/s2, braking peak acceleration is -1m/s2.When wherein
Between unit be s, the unit of distance is m, and the unit of speed is km/h, and the unit of acceleration is m/s2。
With { s0,s1,s2,…sN-1The position of each change working point on a circuit in section is represented, optimization dimension is N,
The specific optimization process of BBO algorithms is as follows：
Step 1 sets BBO algorithm parameters；Since section 1；
Step 2 generates the index vector of multiple N-dimensionals, as change working point is individual according to the line length run between train station
Number, wherein N is even number；
Step 3 generates initial habitat population, any H for choosing habitatiIndex vector XiInitialized.
Step 4 calculates the suitability degree of each habitat, for different suitability degree Hi, habitat is arranged from good to secondary；
Step 5 determines whether required optimal result, if optimal result, then output valve, flow terminate by comparing.
Otherwise step Step 6 is continued；
Step 6 calculates the mobility and mutation rate of each habitat, is migrated and mutation operation, recalculates habitat
Suitability degree Hi, return to step Step 4；
If each Dimensionality optimizations of Step 7 are completed, terminate；If also dimension does not optimize completion, return to step Step 2 enters
The next range optimization of row.
By the way that the change working point number of train operation station and continuing to optimize for position, the data after each renewal are carried out
Comparative analysis, the power consumption values according to needed for system, realize continuing to optimize for system.
Matrix timetable is formed as variable in the dwell time increment of each website using each train in second stage, and with
The flow of the people actually got on or off the bus is reference frame and constraints, and the object function of Optimized model is：
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<mi>E</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>T</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>8</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula, E (Ti) represent train running interval total energy consumption, unit kwh；TiFor the time of i-th train operation.
Invention considers safety index and energy consumption index, to obtain optimal energy conservation object.Therefore the setting of basic constraints is such as
Under：
(1) only increased for the larger website of the part volume of the flow of passengers, dwell time, i.e. tI, j>=0, i represent i-th train, j tables
Show j-th of website, unit s；
(2) same time, the energy consumption of incoming train are all absorbed by train departure, i.e. ESystem=ELead, unit kwh；
(3) the total time change in train operation cycle was limited in certain reasonable time；
(4) in train travelling process, the loss of energy in a variety of ways is not considered；
(5) the operation minimum spacing of train can not be less than Δ S, unit m.
Subway line is made up of n car parallel connection, m platform, then can form m × n time allocation matrix, and i represents optimization
Which for habitat, mxn element altogether.Amendment of each one train of element representation in the dwell time of a website
Amount, during with BBO algorithms as, this matrix is regarded to one group of solution of corresponding energy consumption in train journey.Each group of solution correspond to one it is excellent
Change scheme, i.e. train dwelling time complexity curve scale.
The step of BBO algorithm optimizations, is as follows：
Step 1 sets algorithm parameter, parameter initialization；
Step 2 generates initial habitat dimension, i.e. dwell time correction initial matrix X0, first carried out according to constraints effective
Property screening；
Step 3 calculates the suitability degree H of each habitati, for different suitability degree Hi, habitat is arranged from good to secondary；
Step 4 determines whether required optimal result, if optimal result, then output valve, flow terminate by comparing.
Otherwise step Step 5 is continued；
Step 5 calculates the mobility and mutation rate of each habitat, is migrated and mutation operation, recalculates habitat
Suitability degree Hi, return to step Step 3.
Table data read-out at the time of constantly updating, by the judgement comparative analysis to the data after each iteration, according to required
The power consumption values for the system wanted, realize continuing to optimize for system.
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