Intelligent scheduling method for energy saving operation of multi-train based on genetic algorithm and regenerative kinetic energy

• Published in: Journal of engineering (Stevenage, England) Vol. 2018; no. 16; pp. 1550 - 1554
• Main Authors: Zou, Bingqian, Gong, Lixiong, Yu, Ning, Chen, Jin
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.11.2018
• Subjects: departure time; energy consumption; energy consumption optimisation model; energy saving operation; evening peak; genetic algorithm; genetic algorithms; intelligent algorithm; intelligent control; intelligent control models; intelligent scheduling method; lowest total energy consumption; Matlab; morning peak; multidimensional state vector subspace; multitrain intelligent scheduling problem; rail traffic control; railways; regenerative kinetic energy; scheduling; The 2nd 2018 Asian Conference on Artificial Intelligence Technology (ACAIT 2018); time interval scheduling plan; total vehicle number; train departure interval; train operation diagram; multitrain intelligent scheduling problem; intelligent control; rail traffic control; energy saving operation; genetic algorithms; railways; energy consumption optimisation model; intelligent control models; intelligent scheduling method; genetic algorithm; evening peak; train operation diagram; departure time; regenerative kinetic energy; morning peak; multidimensional state vector subspace; scheduling; train departure interval; time interval scheduling plan; intelligent algorithm; total vehicle number; energy consumption; lowest total energy consumption; Matlab
• ISSN: 2051-3305; 2051-3305
• DOI: 10.1049/joe.2018.8273

An intelligent scheduling method for energy saving operation of multi-train is proposed based on genetic algorithm and regenerative kinetic energy. Considering the morning and evening peak, departure time, and total vehicle number, the energy consumption optimisation model of multidimensional state vector subspace of train is established for the train departure interval with the lowest total energy consumption. Two intelligent control models are used to formulate the time interval scheduling plan depending on whether to consider the morning and evening peak. The train operation diagram is solved by Matlab, and the validity and feasibility of the proposed algorithm are verified. This study provides an intelligent and efficient algorithm and solution for multi-train intelligent scheduling problem.