城市轨道交通车-储协同利用再生能的APSO-MPC双层优化方法OA

With the rapid development of urban rail transit,operational energy consumption has in-creased rapidly.Improving the utilization of regenerative braking energy has therefore become an im-portant means of reducing energy consumption.Regenerative energy utilization is constrained by the spatiotemporal matching of train operations,and the simultaneous braking of multiple trains can cause traction-network voltage fluctuations.Although wayside energy storage systems can effectively ad-dress these problems,most existing charging and discharging strategies are based only on current train-storage states and do not consider how future coordination events may affect current decisions.To im-prove the foresight of such strategies,this study proposes a two-level optimization method based on Adaptive Particle Swarm Optimization and Model Predictive Control(APSO-MPC)for the coopera-tive utilization of regenerative braking energy by trains and wayside storage systems.First,a regenera-tive energy calculation model that accounts for dynamic conversion losses and line-transmission losses is developed.In the inner layer,an adaptive prediction horizon triggered by coordination events is de-signed;model predictive control is used to predict regenerative energy utilization during future coordi-nation events and optimize the charging and discharging strategy of the energy storage systems in a receding-horizon manner.In the outer layer,adaptive particle swarm optimization searches for the opti-mal speed profile of each train,using the regenerative energy utilization returned by the inner layer as the fitness function.Experiments based on data from an urban railway line in China show that,compared with APSO combined with a default charging and discharging strategy,the proposed method increases indirect regenerative energy utilization by 16.91 kWh and reduces the number of charging and discharging operations by 28.Model predictive control performs best when the prediction horizon contains three dis-charge events.The minimum and average computation times per step are 0.001 2 s and 0.027 2 s,re-spectively,satisfying the requirements for online optimization.The method also demonstrates good re-covery capability in response to unexpected timetable adjustments.