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This paper deals with the train scheduling problem for metro networks taking into account time-dependent passenger origin–destination demands and train speed profiles. The aim is to adjust train schedules online according to time-dependent passenger demands so that passenger satisfaction and operational costs are jointly optimized. An extended passenger absorption model that explicitly includes time-dependent passenger origin–destination demands is developed, where the term "absorption" refers to passengers boarding trains. Then, the passenger absorption model is extended to a bi-level framework, where passenger demands and rolling stock availability are considered at the higher level, and detailed timetables and train speed profiles are included at the lower level. A bi-level model predictive control (MPC) approach is developed for the integrated problem. The optimization problems of both levels of the bi-level MPC approach can be converted into mixed-integer linear programming (MILP) problems, which enables us to solve them with existing MILP solvers. We then show that the recursive feasibility of both the higher-level and the lower-level optimization problems can be guaranteed. In this way, we can achieve real-time train scheduling for the metro system. Numerical experiments, based on real-life data from the Beijing metro network, illustrate the effectiveness of the extended passenger absorption model and the proposed bi-level MPC approach. • An extended passenger absorption model is developed to design train departure frequencies considering passenger demands. • A train scheduling model is presented to include detailed timetables, train speed profiles, and train orders. • A bi-level model predictive control approach is proposed for the train scheduling problem. [ABSTRACT FROM AUTHOR]

• The control interval constraint method can increase the control interval of MPC. • The water level constraint method can avoid unnecessary control of minor deviations. • The simultaneous use of the two methods can greatly reduce the gate control times. Model predictive control (MPC) is an effective real-time control method in dealing with water level deviations in canal pools. However, the control interval must be relatively small if some canal pools have short time delay characteristics, resulting in frequent gate control and limited use in practical engineering. In this paper, control interval constraint and water level soft constraint methods were proposed to handle frequent gate control problems in MPC. The MPC method with these constraint methods was tested on a simulation model of the Beijing–Shijiazhuang part of the Middle Route of the South-to-North Water Diversion Project (MRP) with two kinds of disturbances common to this project. The results indicate that the control interval constraint can increase the gate control interval and avoid back-and-forth gate opening adjustments without affecting the water level regulation. The water level constraint helps avoid unnecessary control to minor water level deviations. With both constraints used together, the control times can be reduced by 60% and 37%, respectively, in the two test disturbances. This control method meets the demand of large control intervals in multi-cascade pools. [ABSTRACT FROM AUTHOR]

• A novel tri-stage online dispatch framework for a microgrid is proposed. • The proposed framework coordinates the EV charging behavior at different timescales. • Multiple uncertainties introduced by EV, PV, and load are taken into account. • The real-world data of eight EVs in Beijing is collected to model EV charging behavior. This paper proposes a novel tri-stage online dispatch framework that coordinates the charging behaviors of electrical vehicles (EVs) within an AC/DC hybrid microgrid in the presence of uncertainties. A day-ahead scheduling model is proposed as the first stage to optimize the total operational cost for the next day (24 h). In the second stage, a receding horizon manner is adopted to adjust the day-ahead scheduling results, which can compensate for unpredictable disturbances. Both the first and second stages are operated with a time scale of one hour, which is, however, insufficient in capturing the operations of EVs. Hence, the third stage is introduced with the stochastic model predictive control (SMPC) method in a time scale of 10 min to further consider uncertainties of load, PV, EVs. The real-world behavioral data of eight private EVs in Beijing are used to evaluate the performance of our dispatch model. The simulation results show that compared with some traditional online dispatch methods, the total operational cost of the proposed dispatch framework is significantly reduced. [ABSTRACT FROM AUTHOR]

• A new comprehensive control method based on Model Predictive Control is proposed. • A new dynamic equation of Continuously Variable Transmission is proposed. • Take engine torque and clamping force as control objectives. • The method can improve transmission efficiency by 8.92% and fuel economy by 4.9%. This paper proposes an optimal comprehensive control framework to maximize the powertrain transmission efficiency and fuel economy meeting vehicle owners' travel needs. The proposed control framework consists of vehicle layers and verification. In the vehicle layer, creatively, each set of automotive Continuously Variable Transmission (CVT) supply equipment is equipped with original comprehensive controller that estimates engine fuel consumption and CVT efficiency based on a newly dynamic equation of CVT, engine torque, clamping force, and upcoming trip information. The controller obtains the optimal input of engine torque and clamping force by repeatedly solving the optimization problem of each sampling point in real-time. In the verification layer, with the powertrain system model received from the comprehensive controller, the robustness was verified by bench and hardware-in-the-loop simulation platform. The proposed comprehensive optimization approach can enhance accuracy and solve the multi-objective control problems compared with traditional methods. It is also scalable to the expanding CVT fleet and robust to uncertainties future system condition and in further to meet the vehicle owner's travel comfort and economy. The proposed method is studied using a prototypical driving platform developed at State Key Laboratory of Automotive Safety and Energy of Tsinghua University and detailed trip information extracted from 2019 BEIJING Transport Annual Report Survey. The main contribution of this study is to explore a novel way to optimize the comprehensive transmission efficiency of engine and CVT, compared with the Proportional Integral Derivative (PID) control, the comprehensive controller can improve the transmission efficiency by 8.92%, and reduce the fuel consumption by 4.9%, it also can save about 90 million liters of fuel and 70 million US dollar for CVT vehicles in Beijing. [ABSTRACT FROM AUTHOR]