Optimization of Train Energy-Efficient Operation Using Simulated Annealing Algorithm - Intelligent Computing for Sustainable Energy and Environment - page 357

results still contradict with the requirements, therefore braking mode is started.
X3 is in the last section and a train starts coasting for a while from X3 and
changes to brake to stop in the station accurately.

Tabl e 2. Track Alignmemt and Speed Limit

Distance(m) Grade(%) Radius(m) V max (Km/h)
0 -160 0 3000 50

160-205 0 3000 65

205-663 -2.74 400 65

663-818 -2 0 70

818-985 -2.7 1000 70

985-1266 -0.91 0 75

1266-1738 -1.01 600 65

1738-2032 -0.33 0 30

2032-3034 0.13 1500 70

3034-3060 0.32 0 70

3060-3200 0.2 0 55

80

70

60

X1

X2

X3

50

40

30

Maximum allowed speed
Time−efficient speed
Energy−efficient speed

20

10

0

0 500 1000 1500 2000 2500 3000 3500

Distance (m)

Fig. 3. Train traction operation curve under different modes

According to the simulation results in Table. 3, after optimization, though
there is a slightly increase of running time by 4.56%, the energy consumption
between two stations decreased significantly by 27.58%. In the previous study,
Bocharnikov et al developed a model to optimize the timing of coasting as-
sociated with varying acceleration and deceleration rates with GA(2007). The
method was applied to a 8.53-km track segment, accordingly 31.27% of energy
consumption was saved albeit the travel time increased by 12.5%. It was found
that here proposed optimization is very effective.