Optimizing the Energy Efficiency of Electric Transportation Systems Operation Using a Genetic Algorithm

Influence of Infrastructure and Operating Conditions on Energy Performance of DC Transit Systems

“…The train model employs a mixed representation considering a single point to calculate the vehicle dynamics and a segment to determine the speed limit, accounting for train length and its track occupation, and ensuring that the entire train complies with speed limits. Train dynamics can be determined for the whole route by applying either a "time increment method" or a "distance increment method" to solve the dynamic model equation [14]; this is possible by assuming "RF" terms and dynamic train quantities that are constant within a simulation step (details are contained in [14]).…”

“…On the other hand, a better exploitation of regenerative braking energy can be achieved by means of energy storage systems [2,3] and reversible traction power substations (TPSs) [8], controlling train operations and driving profiles [9], practically exploiting the scheduled service time reserve [10], and even including cases of timetable disruption [11]. The latter, in particular, represents a high-level solution that does not require major changes to the infrastructure and can be implemented in the automatic train operation (ATO) subsystem for metros and the supervision center or the operation and control center (OCC) for railways [12][13][14][15][16]. Trains receive the parameters for driving to the stations ahead from information points (e.g., balises or radio tags) or from a continuous radio coverage system (e.g., CBTC or other train-to-wayside radio links).…”

“…The problem of train operation control in an electrified transport system involves nonlinear large-scale and complex equations with stochastic and operational constraints [49]. Thus, the optimal speed trajectory may be found with non-deterministic optimization methods, such as genetic algorithms or other random search techniques [13,14,50,51]. Since algorithms are generally computationally intensive, the calculation of consumed traction energy and receptivity is often achieved by applying simplified power supply network models or a limited number of degrees of freedom in the train trajectory and traffic quantities.…”

A comparison of performance metrics for balancing the power consumption of trains in a railway network by slight timetable adaptation

“…There is a lot of literature available that treats the energy-efficient operation of railway traffic. Most papers focus on reducing the energy consumption of the trains, which is possible by energy-efficient driving strategies (e.g., Doan et al 2014;Wang et al 2014;Li et al 2012;Miyatake and Ko 2010;Boschetti and Mariscotti 2014), operational measures (e.g., Ragunathan et al 2014;Hasegawa et al 2014;Kimura and Miyatake 2014) or timetabling (see Su et al 2013;Pen ˜a-Alcaraz et al 2011;Li and Lo 2014;Fournier et al 2012;Gong et al 2014), among others.…”