Challenges in Real World Optimisation Using Evolutionary Computing
نویسندگان
چکیده
Costs for traction energy in DC electric rail transit systems depend on the energy actually demanded in the substations as well as on average power peaks there. Both are strongly influenced by the applied train timetable, because synchronous powering of multiple trains causes high power peaks whereas coordinated powering and braking of trains leads to good usage of regenerative energy from braking and consequently less energy need at the substation. This paper examines the possibilities of train running time modification in order to reduce power peaks and energy consumption. The problem can be described as the search for an optimal distribution of a train’s running time reserve along its ride. Due to the very high complexity of the problem, the non-linearities in the model for the electric network and the very large search space, the application of Genetic Algorithms is proposed and examined in a case study for one line of the Berlin suburban railway network.
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