Comprehensive Optimization of Energy Consumption and Network Voltage Stability of Freight Multitrain Operation Based on amMOPSO Algorithm
Xinkun Tao, Qingyuan Wang, Mo Chen, Pengfei Sun, Xiaoyun Feng
Abstract
The Multi-train comprehensive optimization (MTCO) combined with the AC traction power supply system (TPSS) can achieve energy savings of traction substations. However, traditional energy-saving optimization methods can cause continuous voltage fluctuations in the power grid, which may endenger train security. To address this issue, this paper proposes a comprehensive optimization method for energy-saving and voltage-stability in multiple trains. Firstly, this paper establishes the Train-Track-Power grid (TTP) comprehensive optimization model for freight trains, which is composed of Train dynamics model, traction drive model, and TPSS. Considering the dynamic complex power and network power loss, a multi-objective function for train energy saving and voltage offset is constructed. Due to the strong coupling and complexity of the nonlinear TTP model, it is difficult to optimize algorithm solution and convergence accuracy is low, as well as the deficiencies in the distribution of non-dominated solution set during iterations, an adaptive multi-strategy multi-objective particle swarm optimization algorithm (amMOPSO) is proposed. Then, the effectiveness of the TTP model is validated through the measured data from the Shuohuang Railway in China. Finally, several classical and state-of-the-art algorithms are employed for experimental comparisons. Experimental results show that amMOPSO outperforms the other algorithms. And the feasibility of the freight multi-train MTCO method is verified through three comparative experiments. The energy consumption of the trains is reduced by 53.97%, the total voltage offset is reduced by 66.96%, and the utilization efficiency of RBE (Regenerative Braking Energy) is increased by 75.3% as demonstrated through the comprehensive optimization experiments involving three trains.