TransEnergy - a tool for energy storage optimization, peak power and energy consumption reduction in DC electric railway systems
TransEnergy - a tool for energy storage optimization, peak power and energy consumption reduction in DC electric railway systems
复制标题
TransEnergy - 直流电动铁路系统储能优化、峰值功率和能耗降低的工具
DOI：
10.1016/j.est.2020.101425
复制
发表时间：
2020
影响因子：
9.4
通讯作者：
Fletcher D
中科院分区：
文献类型：
--
作者：
Fletcher D
电气化铁路是电力的大用户，而此时电网供应转换为可再生能源生产使电网供应更不可预测，环境问题要求减少能源使用。这些发展使得希望控制和减少铁路系统的总能量使用和峰值功率需求。虽然交流系统具有良好的再生能力，但直流系统的高传输损耗使得本地储能成为一个更具吸引力的选择。建立了一个模型，该模型将通用的、可配置的数据库驱动的通用铁路网络模型与代表直流电气化铁路的供电网络集成。这项工作的目的是作为一个高层次的设计工具，探索系统范围内的行为之前，详细的最终设计建模的具体技术。为了验证我们的方法，列车运动和电力需求的预测进行了比较，从默西铁路网络在英国的数据。模拟一整天的交通的维拉尔线默西铁路（237服务的两条路线）的假设能量存储可在每个变电站显示存储效率的依赖时间表和交通密度在特定位置。该模型与遗传算法相结合，以优化系统参数（存储大小，充电/放电功率限制，时间表，列车驾驶风格/轨迹），也可以识别的情况下，指定的存储技术将有很少的影响峰值功率和能耗。
Electrified railways are large users of electrical power at a time when grid supply conversion to renewable energy production is making supply to the grid less predictable and environmental concerns demand reduction in energy use. These developments make it desirable to control and reduce both total energy usage and peak power demand of railway systems. While AC systems have a well-developed ability to regenerate power to the grid, high transmission losses in DC systems make local storage of energy a more attractive option.A model has been created integrating a versatile and configurable database-driven generic rail network model with a power supply network representative of DC electric railways. The work is intended as a high-level design tool to explore system wide behaviors prior to detailed final design modelling of specific technologies. To validate our method, predictions of train motion and power demand have been compared with data from the Merseyrail network in the UK. Simulating a full day of traffic for the Wirral Line of Merseyrail (237 services on two routes) with the assumption of energy storage being available at each electrical sub-station revealed the dependence of storage effectiveness on the timetable and traffic density at specific locations. The model is combined with a genetic algorithm to optimise system parameters (storage size, charge/discharge power limits, timetable, train driving style/trajectory) and also enables identification of cases in which poorly specified storage technology would have little impact on peak power and energy consumption.