基于离散微区间工况选择的列车节能运行优化方法

Optimization Method for Train Energy-Saving Operation Based on Working Condition Selection in Discrete Micro-Interval

列车节能运行优化可降低列车的运行能耗,从而降低轨道交通运营成本,但对于坡度、坡长、限速等条件多变的线路,既有节能优化方法难以给出合理的工况组合方案,因此提出基于离散微区间工况选择的列车节能运行优化方法。首先,将列车运行区间离散为等距的微区间,建立节能运行优化模型;其次,通过考虑相邻2个迭代最优解之间差异的启发效应,对蚁群系统算法(ACS算法)进行改进,提出改进的蚁群系统算法(ACSd算法);然后,采用ACSd算法在微区间中直接选择运行工况;最后,将节能和准时的要求同时纳入目标函数和算法的启发因子,并提出调节信息素浓度的时间补偿机制处理时间误差。以北京亦庄地铁线某个多坡段区间为例,将所提方法与既有能量分配法的优化结果进行对比,并对分别采用ACSd算法和ACS算法选择运行工况所获得的优化结果进行对比。结果表明:基于微区间工况选择的优化方法较能量分配方法降低能耗29.1%;采用ACSd算法进行列车节能运行优化较ACS算法降低能耗9.9%。

Optimization of train energy-saving operation can reduce energy consumption during train operation,hus reducing the operation cost of rail transit.However,for the railway lines with varying gradients,slope lengths,speed limits and other conditions,it is difficult to give a reasonable combination scheme of working conditions regarding the existing optimization methods for energy-saving.Therefore,an optimization method for train energy-saving operation based on working condition selection in discrete micro-interval is proposed.Firstly,the train operation interval is discretized into equidistant micro-intervals,and the optimization model for energy-saving operation is established.Secondly,an improved Ant Colony System algorithm(ACSd algorithm)is proposed by considering the heuristic effect of the difference between the optimal solutions of two adjacent iterations,which improves Ant Colony System algorithm(ACS algorithm).Then,the working condition is directly selected in the micro-intervals through ACSd algorithm.Finally,the requirements for energy-saving and punctuality are simultaneously incorporated into the objective function and the heuristic factors of the algorithm,and a time compensation mechanism is proposed for adjusting the pheromone concentration in order to deal with time errors.Taking a multi-slope interval of Beijing Subway YIZHUANG Line as an instance,the optimization results of the proposed method are compared with those of the existing energy allocation method,and the optimization results obtained by ACSd algorithm and ACS algorithm to select working conditions are compared individually.The results show that the optimization method based on working condition selection in micro-interval can reduce the energy consumption by 29.1%compared with the energy allocation method;and ACSd algorithm that is used to optimize train energy-saving operation can reduce the energy consumption by 9.9%compared with ACS algorithm.