Electrified railways are becoming a popular transport medium and these consume a large amount of electrical energy. Environmental concerns demand reduction in energy use and peak power demand of railway systems. Furthermore, high transmission losses in DC railway systems make local storage of energy an increasingly attractive option. An optimisation framework based on genetic algorithms is developed to optimise a DC electric rail network in terms of a comprehensive set of decision variables including storage size, charge/discharge power limits, timetable and train driving style/trajectory to maximise benefits of energy storage in reducing railway peak power and energy consumption. Experimental results for the considered real-world networks show a reduction of energy consumption in the range 15%–30% depending on the train driving style, and reduced power peaks.

电气化铁路正在成为一种流行的运输媒介，并且会消耗大量电能。环境问题要求减少能源使用和铁路系统的峰值电力需求。此外，直流铁路系统中的高传输损耗使本地能量存储成为越来越有吸引力的选择。开发了基于遗传算法的优化框架，根据包括存储容量、充放电功率限制、时间表和列车驾驶方式/轨迹在内的一组综合决策变量优化直流电力铁路网络，以最大限度地提高储能在降低能耗方面的优势。铁路峰值功率和能耗。所考虑的真实世界网络的实验结果表明，根据列车驾驶方式，能耗降低了 15%–30%，