CN115619060B - Train energy-saving operation optimization method based on discrete interval working condition selection - Google Patents
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Abstract
The invention discloses a train energy-saving operation optimization method based on discrete interval working condition selection. Based on the running interval of the discrete train, the speed limit line reconstruction is carried out, the ant colony system algorithm is improved by adopting a differential edge mechanism, the working condition selection is directly carried out, the objective function and the pheromone heuristic factor which are both energy-saving and punctual are designed, the time compensation mechanism is integrated into the pheromone updating of the algorithm, and the working condition sequence required by the running of the train is obtained through optimization.
Description
Technical Field
The invention relates to the field of railway train operation optimization, and also belongs to the field of computers and the field of operation research, in particular to a train energy-saving operation optimization method based on discrete interval working condition selection.
Background
The energy consumption of the railway is increased year by year, wherein the energy consumed by the train operation accounts for the main part, and the optimization of the energy-saving operation of the train has important significance for reducing the energy consumption of the railway. The research on the energy-saving operation of the train not only needs to ensure the energy conservation and punctuality of the train operation, but also considers the applicability of the optimization method. The existing method mostly adopts a fixed working condition sequence, and cannot completely adapt to the line conditions with various changes such as gradient, gradient length, speed limit and the like, such as documents [1]HE D,ZHANG L,GUO S,et al.Energy-efficient Train Trajectory Optimization Based on Improved Differential Evolution Algorithm and Multi-part Model [ J ]. Journal of Cleaner Production,2021,304 (2): 127163.. Some methods optimize the speed profile based on discrete intervals, but there are speed tracking errors when the train is operated according to the optimized speed profile, as in documents [2]LUS,HILLMANSEN S,HO T K,et al.Single-Train Trajectory Optimization [ J ]. Intelligent Transportation Systems, IEEE Transactions on Intelligent Transportation Systems,2013,14 (2): 743-750.. The prior patent [3] discloses an energy-saving driving strategy optimizing method based on a discrete ant colony algorithm under a timing condition [ P ]. Miao, cao Yu, and the like (CN 109398426B, 2020-06-16), which is used for researching that the ant colony algorithm is adopted to select working conditions through discrete operation time, however, the method is used for dispersing the operation time, so that line conditions of the same subinterval in the optimization can be different, the quality of a solution is not high, so that excessive default restoration is adopted, and further negative influence is caused on an optimization result; in addition, the adopted basic ant colony algorithm has poor optimizing effect and low running speed. Therefore, a method suitable for various lines, avoiding speed tracking errors and improving the quality and the optimizing effect of solutions is sought, and the method has important value and research significance for optimizing the energy-saving operation of the train.
Disclosure of Invention
The invention constructs a train energy-saving operation optimization method based on discrete interval working condition selection. The method aims to overcome the defects that the existing method is not suitable for complex line conditions, has speed tracking errors, poor solution quality and optimization effect and the like. The running interval of the discrete train is used for adapting to complex and changeable railway line conditions such as gradient, slope length, speed limit and the like, the problem of low quality of solutions and default restoration is avoided by adopting speed limit line reconstruction, the working condition selection is directly carried out by using an improved ant colony system algorithm, the working condition sequence required by train running is obtained by optimizing, and the speed tracking error is avoided. Secondly, in order to solve the complex optimization problem with constraint after discrete, the optimization result and the operation speed are improved, and the ant colony system algorithm is improved by adopting a differential edge mechanism. In addition, in order to coordinate the constraint relation between energy conservation and punctual time so as to improve the quality of the solution, an objective function and a pheromone heuristic factor which are both designed, and a time compensation mechanism is integrated into the pheromone updating of the algorithm.
According to the train energy-saving operation optimization method based on the discrete interval working condition selection, firstly, a train operation interval is discrete into equidistant subintervals, then speed-limiting line reconstruction is carried out, an objective function considering energy conservation and punctual time is established, and then an improved ant colony system algorithm is adopted to select working conditions of maximum traction, partial traction cruising, idle running, partial braking cruising and maximum braking on the discrete subintervals according to constraint conditions, so that a working condition sequence required by train energy consumption minimum operation is optimized.
The train energy-saving operation optimizing method specifically comprises the following steps:
step1, dividing a train operation interval into N equidistant discrete subintervals SE i (i=1, 2,., N), subinterval length 10m;
step2, reconstructing a speed limit line based on a discrete subinterval, and when the limiting speed of the next subinterval is smaller than the limiting speed of the current subinterval, reversely calculating from the limiting speed of the next subinterval to the intersection with the current subinterval speed limit line according to the maximum braking working condition, wherein the formed speed curve is used as the reconstructed speed limit line;
and 3, amplifying the violating behavior by using a punishment function mechanism and multiplying the punishment factor by the violating degree to construct an objective function J considering energy conservation and punctuality, wherein the calculation mode is as follows:
J＝E+σ·ΔT
e is the total energy consumption of the train running in the whole interval; Δt is the train arrival time difference, Δt= |t-T set I (I); t is train running time; t (T) set Is a predetermined run time; sigma is a time violation penalty factor calculated as follows:
wherein δ is the allowable time error;
and 4, improving the ant colony system algorithm, and comparing optimal paths of two adjacent iterations, wherein different paths are used as difference edges, and additionally adding pheromones to the difference edges in a global pheromone updating rule of the basic ant colony system algorithm, wherein the calculation mode of the improved global pheromone updating rule is as follows:
′(r,s)＝(1-α)·τ(r,s)+α·Δτ 1 (r,s)+Δτ 2 (r,s)
wherein τ (r, s) is the pheromone of the current state r to the next state s; alpha is global pheromone evaporation coefficient, 0<α<1；Δτ 1 (r, s) global pheromone updating of a basic ant colony system algorithm; Δτ 2 (r, s) extra pheromone updating for the difference edge; l (L) ib The length of the optimal path for the current iteration; the item_best_tour is the current iteration optimal path; s is S Dif The difference edge after the comparison of the optimal paths of the two iterations;
and 5, selecting working conditions of maximum traction, partial traction cruising, idle running, partial braking cruising and maximum braking on a discrete subinterval by adopting an improved ant colony system algorithm based on the objective function in the step3, and optimizing to obtain a working condition sequence required by minimum train energy consumption operation, wherein the solving process is as follows:
step1: in subinterval SE i (i=1, 2,., N) selecting a train operating condition;
step2: calculating the energy consumption, speed and time of train operation, judging the default, and returning to Step1 if the default is found;
step3: based on the local pheromone updating rule, carrying out pheromone updating;
step4: if i < N, i=i+1 returns to Step1; otherwise, entering Step5;
step5: calculating an objective function J;
step6: based on the improved global pheromone updating rule, carrying out pheromone updating;
step7: and repeating Step1 to Step6 until the iteration is finished, and outputting an optimal working condition sequence table of train operation.
The improved ant colony system algorithm selection working condition specifically comprises the following steps:
(1) Operating mode selection
Wherein r is i Is subinterval SE i (i=1, 2,., N) operating conditions employed; allowed i Is subinterval SE i A set of selectable operating conditions; τ iu Is subinterval SE i The pheromone concentration of the medium working condition u; η (eta) iu Is subinterval SE i Heuristic factor of the medium working condition u; beta is a parameter for determining the relative weight of the pheromone and the heuristic factor, beta>0; q is in the range of 0,1]Random numbers uniformly distributed in the matrix; q 0 Is a fixed parameter, q is more than or equal to 0 0 Is less than or equal to 1; u is the probability of roulette through the roulette wheel according to the formulaThe working conditions of selection are as follows:
heuristic factor eta iu By an energy-saving heuristic factor eta 1 And a velocity heuristic factor eta 2 And (3) jointly determining:
η iu ＝η 1 ×η 2
η 1 ＝1/(E ir +λ 1 )
wherein E is ir Is SE i The energy consumption when the working condition r is selected in the subinterval; lambda (lambda) 1 Is an energy consumption balance factor; v ir Is SE i The outlet speed of the working condition r is selected in the subinterval; lambda (lambda) 2 Is a speed balance factor; omega is the radius of action of the speed heuristic factor;for the reference speed of the slope segment q, the calculation mode is as follows:
wherein L is q For the length of the slope segment q, t ref,q The reference running time on the slope section q under the condition of meeting the time constraint is calculated as follows:
T set ＝t ref,1 +…+t ref,q +…+t ref,n
wherein t is ref,1 The time from the maximum traction of the train to the running out of the slope section 1 is adopted as the reference running time of the slope section 1; t is t ref,n For the reference running time of the slope section n, adopting a train to enter the slope section n at a limited speed and adopting idle-maximum braking until reaching the time right to the station; t is t ref,2 A reference run time for segment 2; t is t ref,n-1 A reference run time for slope segment n-1; l (L) 2 Is the length of the slope section 2; l (L) n Is the length of the slope section n; v max,2 Limiting the speed of the slope section 2; v max,q Limiting speed for the slope section q; v max,n-1 Limiting the speed for the slope section n-1;
the reference running time of each slope segment is dynamically adjusted in real time according to the following two kinds of combined solution results:
wherein T is i For the actual operating time of the slope section i, t ref,q+1 A reference run time for segment q+1; l (L) q+1 Is the length of the slope segment q+1; v max,q+1 Limiting speed for slope segment q+1;
(2) Local pheromone update rules
τ iu ＝(1-ρ)·τ iu +ρ·τ 0
Wherein ρ is localPheromone evaporation coefficient, 0<ρ<1；τ 0 Is the initial pheromone concentration;
(3) Improved global pheromone update rules
Based on the improved difference-side global pheromone updating rule method, a time compensation mechanism is integrated, when the arrival time difference is overlarge, if the arrival time difference is larger than the preset running time, additional pheromone is applied to the maximum traction working condition, and if the arrival time difference is smaller than the preset running time, additional pheromone is applied to the idle working condition, and the specific global pheromone updating mode is obtained as follows:
wherein τ ir Is subinterval SE i Inner working condition r i Corresponding pheromones; Δτ ir Updating the basic global pheromone;updating the extra pheromone of the difference edge; j (J) iBest And (5) iterating an objective function corresponding to the optimal solution.
The constraint conditions of train operation are as follows:
(1) Speed constraint
The train is in a stationary state at the start point and the end point, and the speed requirement is within a limited speed range during the running of the section:
v(x O )＝0,v(x D )＝0
0≤v(x)≤V(x)
wherein v (x O ) Is the starting point position x o Is a running speed of the vehicle; v (x) D ) For the end position x D Is a running speed of the vehicle;v (x) is the running speed of position x; v (x) is the limiting speed of position x;
(2) Parking position restraint
The train starts from the starting point and reaches a specified stopping point:
x o ＝0,x D ＝L
wherein L is the length of the operation interval;
(3) Time constraint
Calculated operating time and predetermined operating time T of train set The same:
T(x o )＝0,T(x D )＝T set
wherein T (x) O ) Is the starting point position x o Is set to be a run time of (2); t (x) D ) For the end position x D Is set to be a run time of (2);
(4) Operating mode conversion constraint
(a) The maximum traction working condition can be converted into maximum traction, partial traction cruising and idle running, and can not be converted into partial braking cruising and maximum braking;
(b) The partial traction cruising working condition can be converted into maximum traction, partial traction cruising and idle running, and can not be converted into partial braking cruising and maximum braking;
(c) The idle running working condition can be converted into any working condition;
(d) The partial braking cruising working condition can be converted into idle running, partial braking cruising and maximum braking, and can not be converted into maximum traction and partial traction cruising;
(e) The maximum braking condition can be converted into idle running, partial braking cruising and maximum braking, and can not be converted into maximum traction and partial traction cruising.
The beneficial effects of the invention are as follows:
compared with the prior art, the train running interval is discretized into a plurality of equidistant subintervals, and the working conditions are selected to occur in a single environment due to the same line conditions in the subintervals, so that the train running interval can adapt to complex and changeable railway line conditions. The working condition sequence is obtained through direct optimization of the selected working conditions, instead of the speed curve, so that speed tracking errors are avoided. In addition, an improved ant colony system algorithm is provided, heuristic information from the difference between two adjacent iterative optimal solutions is considered, pheromone updating rules are improved, and knowledge quality is improved. In order to coordinate the constraint relation between energy conservation and punctual time, an objective function and a pheromone heuristic factor which are both considered are designed, and a time compensation mechanism is integrated into the pheromone updating of the algorithm.
Drawings
FIG. 1 is a flow chart for optimizing energy-saving operation of a train.
Fig. 2 is a schematic diagram of discrete subintervals.
Fig. 3 is a schematic diagram of a speed limit line correction.
Fig. 4 is a schematic diagram of the working condition selection of the ant colony algorithm.
Detailed Description
The present invention will be further described with reference to the following examples and drawings for the purpose of making the objects, technical solutions and features of the present invention more apparent, and the exemplary embodiments and descriptions of the present invention are provided for explaining the present invention without limiting the present invention thereto.
Examples:
the example selects the interval from the old palace station to the station Zhuang Qiao in Beijing also zhuang subway line. The total length of the interval line is 1982m, and the interval line comprises 13 slope sections; slope section 1, slope length 130m and slope 0%; slope section 2, slope length 336m and slope 0%; slope section 3, slope length 77m and slope-4 per mill; slope section 4, slope length 157m and slope of-6 per mill; slope section 5, slope length 61m and slope 0%; slope section 6, slope length 90m and slope 7 per mill; slope section 7, slope length 178m and slope 12 per mill; slope section 8, slope length 86m and slope 0%; slope section 9, slope length 290m and slope-2 per mill; slope section 10, slope length 363m, slope-3 per mill; slope section 11, slope length 34m and slope-2 per mill; slope section 12, slope length 38m and slope-1%; slope section 13, slope length 142m and slope 0%. The speed limit is typically three-step, the maximum speed limit in the distance from the old uterine station 466 m-1840 m is 80km/h, and the speed limit of other slope sections is 54km/h. The train running time in the interval is 135s, and the allowable error of the arrival time is 2s. The train adopts 6-section marshalling and three-movement three-towing, wherein the mass of a single-section traction power vehicle is 35t, the weight of a single-section trailer vehicle is 31t, and the total net weight of the train is 198t. While taking account of the load weight, the total traction mass thereof is assumed to be 250t. The traction and braking force characteristics of the train can be expressed as a function of speed:
the running resistance can be calculated from the following empirical formula:
W(v,x)＝3.858×10 -4 ×v 2 -0.064×v+2.965kN
step1, dividing a train operation interval into N equidistant discrete subintervals SE i (i=1, 2,., N), subinterval length 10m;
step2, reconstructing a speed limit line based on a discrete subinterval, and when the limiting speed of the next subinterval is smaller than the limiting speed of the current subinterval, reversely calculating from the limiting speed of the next subinterval to the intersection with the current subinterval speed limit line according to the maximum braking working condition, wherein the formed speed curve is used as the reconstructed speed limit line;
and 3, amplifying the violating behavior by using a punishment function mechanism and multiplying the punishment factor by the violating degree to construct an objective function J considering energy conservation and punctuality, wherein the calculation mode is as follows:
J＝E+σ·ΔT
e is the total energy consumption of the train running in the whole interval; Δt is the train arrival time difference, Δt= |t-T set I (I); t is train running time; t (T) set Is a predetermined run time; sigma is a time violation penalty factor calculated as follows:
wherein δ is the allowable time error;
and 4, improving the ant colony system algorithm, and comparing optimal paths of two adjacent iterations, wherein different paths are used as difference edges, and additionally adding pheromones to the difference edges in a global pheromone updating rule of the basic ant colony system algorithm, wherein the calculation mode of the improved global pheromone updating rule is as follows:
τ(5,s)＝(1-α)·τ(r,s)+α·Δτ 1 (r,s)+Δτ 2 (5,s)
wherein τ (r, s) is the pheromone of the current state r to the next state s; alpha is global pheromone evaporation coefficient, 0<α<1；Δτ 1 (r, s) global pheromone updating of a basic ant colony system algorithm; Δτ 2 (r, s) extra pheromone updating for the difference edge; l (L) ib The length of the optimal path for the current iteration; the item_best_tour is the current iteration optimal path; s is S Dif The difference edge after the comparison of the optimal paths of the two iterations;
and 5, selecting working conditions of maximum traction, partial traction cruising, idle running, partial braking cruising and maximum braking on a discrete subinterval by adopting an improved ant colony system algorithm based on the objective function in the step3, and optimizing to obtain a working condition sequence required by minimum train energy consumption operation, wherein the solving process is as follows:
step1: in subinterval SE i (i=1, 2,., N) selecting a train operating condition;
step2: calculating the energy consumption, speed and time of train operation, judging the default, and returning to Step1 if the default is found;
step3: based on the local pheromone updating rule, carrying out pheromone updating;
step4: if i < N, i=i+1 returns to Step1; otherwise, entering Step5;
step5: calculating an objective function J;
step6: based on the improved global pheromone updating rule, carrying out pheromone updating;
step7: and repeating Step1 to Step6 until the iteration is finished, and outputting an optimal working condition sequence table of train operation.
The results of the optimization are shown in table 1 after calculation. The actual running time of the train is 136.91s, the total energy consumption is 12.61 kW.h, and the running time is within the allowable error range. The working condition sequence table shows that the train adopts idle working conditions in about 81% of subintervals in the whole running process; except for the outbound stage, the train only adopts a traction working condition before the uphill section to improve the running speed, so that excessive speed drop is avoided; in addition, the braking mode is used in advance during the slope 12. Compared with the method proposed by the prior document, the method saves the energy consumption by about 29.1 percent, the running speed of the train can be adjusted according to the change of the line condition, and the method provides a flexible strategy for the running of the train by effectively adapting to the influence of the change of the line condition through a discrete subinterval. In addition, under the condition of using the optimization method, compared with the basic ant colony system algorithm, the energy consumption of the improved ant colony system algorithm is reduced by about 9.9%, and the effectiveness of a differential edge mechanism is proved.
Table 1 comparison of optimized results
Claims (1)
1. The train energy-saving operation optimization method based on the discrete interval working condition selection is characterized by comprising the following steps of:
step1, dividing a train operation interval into N equidistant discrete subintervals SE i (i=1, 2,., N), subinterval length 10m;
step2, reconstructing a speed limit line based on a discrete subinterval, and when the limiting speed of the next subinterval is smaller than the limiting speed of the current subinterval, reversely calculating from the limiting speed of the next subinterval to the intersection with the current subinterval speed limit line according to the maximum braking working condition, wherein the formed speed curve is used as the reconstructed speed limit line;
and 3, amplifying the violating behavior by using a punishment function mechanism and multiplying the punishment factor by the violating degree to construct an objective function J considering energy conservation and punctuality, wherein the calculation mode is as follows:
J＝E+σ·ΔT
e is the total energy consumption of the train running in the whole interval; Δt is the train arrival time difference, Δt= |t-T set I (I); t is train running time; t (T) set Is a predetermined run time; sigma is a time violation penalty factor calculated as follows:
wherein δ is the allowable time error;
and 4, improving the ant colony system algorithm, and comparing optimal paths of two adjacent iterations, wherein different paths are used as difference edges, and additionally adding pheromones to the difference edges in a global pheromone updating rule of the basic ant colony system algorithm, wherein the calculation mode of the improved global pheromone updating rule is as follows:
τ(r，s)＝(1-α)·τ(r，s)+α·Δτ 1 (r，s)+Δτ 2 (r，s)
wherein τ (r, s) is the pheromone of the current state r to the next state s; alpha is the evaporation coefficient of the global pheromone, and alpha is more than 0 and less than 1; Δτ 1 (r, s) global pheromone updating of a basic ant colony system algorithm; Δτ 2 (r, s) extra pheromone updating for the difference edge; l (L) ib The length of the optimal path for the current iteration; the item_best_tour is the current iteration optimal path; s is S Dif The difference edge after the comparison of the optimal paths of the two iterations;
and 5, selecting working conditions of maximum traction, partial traction cruising, idle running, partial braking cruising and maximum braking on a discrete subinterval by adopting an improved ant colony system algorithm based on the objective function in the step3, and optimizing to obtain a working condition sequence required by minimum train energy consumption operation, wherein the solving process is as follows:
step1: in subinterval SE i (i=1, 2,., N) selecting a train operating condition;
step2: calculating the energy consumption, speed and time of train operation, judging the default, and returning to Step1 if the default is found;
step3: based on the local pheromone updating rule, carrying out pheromone updating;
step4: if i < N, i=i+1 returns to Step1; otherwise, entering Step5;
step5: calculating an objective function J;
step6: based on the improved global pheromone updating rule, carrying out pheromone updating;
step7: and repeating Step1 to Step6 until the iteration is finished, and outputting an optimal working condition sequence table of train operation.
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| 北京地铁亦庄线列车节能驾驶研究;唐涛;荀径;曹芳;王义惠;宿帅;北京交通大学学报(第004期);19-24 * |
| 城际列车多区间节能运行优化方法研究;柴杨;刘成;孙鹏飞;王青元;;计算机仿真(第03期);111-116 * |
| 基于蚁群算法的列车节能驾驶策略优化算法研究;褚心童;张亚东;郭进;高豪;李耀;刘芯宏;铁道标准设计;第65卷(第002期);159-164 * |
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