Abstract:With the ongoing large-scale implementation of electric vehicles (EVs), the exploration of a more flexible approach to maintain fair interaction between EVs and the power grid is urgently required. This paper presents an aggregator-based interactive charging management scheme adopting interruptible load (IL) pricing, in which the EV aggregator will respond to the load control command of the grid in an EV interactive mode. Charging managements are carried out according to battery state-of-charge and the EV departure time in EV charging stations. A power-altering charging (PAC) control method is proposed to dispatch the EVs charging fairly in a station and guarantee EV owners' preferences. The method does not require classical iterative procedures or heavy computations; furthermore, it is beneficial for EVs to depart earlier than expected for reasons beyond keeping homeostatic charging. The proposed scheme, which is tested to charge individual EVs well according to its preference, was implemented as part of an "EV Beijing" project. The proposed management scheme provides new insight into EV charging strategy and provides another choice to EV users.
As a new type of transportation serving the suburban passengers, the medium-speed maglev (MSM) is gradually becoming the focus of scholars. This paper addressess the speed profile optimization problem for MSM train operations by integrating the power supply system and train control system under various constraints. Unlike the model for optimizing the mechanical energy of the train, this approach aims at the lowest energy consumption of the substation for the MSM system. First, a speed profile optimization model for the MSM train is built by combining the mathematical control model of the long stator synchronous linear motor and dynamic equation of train, in which the stator current is the control variable. Next, a dedicated dynamic programming approach is proposed to solve the optimization model. The results of the numerical experiments show that the proposed model outperforms the model that only considers the train mechanical energy, and the energy consumption is reduced by 10.3% and 6.5% in the two case studies, respectively. Furthermore, the relationship between energy consumption and travel time is analyzed to reflect the optimal results of the proposed model limited to different fixed travel time.
Background:The Auxiliary Stopping Area (ASA) is the special section that possesses power supply rail and personnel evacuation facilities, whose quantities and locations in a line are of great significance to reduce construction cost and improve transportation efficiency for the middle-to-high speed maglev.Aim: This paper focuses on optimizing the length and location of the ASA for the middle-to-high speed maglev system to improve the robustness of maglev line.Methods: Two evaluation indexes which reflect the ASA restricts on the train operation process was proposed. A model for optimizing the setting of the ASA is constructed, and solved by the genetic algorithm.Results:The result of numerical examples shows that the proposed method can effectively improve the performances of the ASA.Conclusion: This paper proposed two indexes to reflect the impact of station settings on train operations, which provides a method to optimize the ASA from qualitative optimization to quantitative optimization.
Short-term forecasting of OD (origin to destination) passenger flow on high-speed rail (HSR) is one of the critical tasks in rail traffic management. This paper proposes a hybrid model to explore the impact of the train service frequency (TSF) of the HSR on the passenger flow. The model is composed of two parts. One is the Holt-Winters model, which takes advantage of time series characteristics of passenger flow. The other part considers the changes of TSF for the OD in different time during a day. The two models are integrated by the minimum absolute value method to generate the final hybrid model. The operational data of BeijingShanghai high-speed railway from 2012 to 2016 are used to verify the effectiveness of the model. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of the TSF.
This paper addresses the energy‐efficient train timetabling problem for MSM systems, where both propulsion and suspension energy consumption are considered. The timetable design problem is modelled as a bi‐level model for a complete two‐way MSM line. The upper level determines the train departure time at the first station, which makes train operations more convenient for passengers. The lower level uses an empirical description of the train energy consumption as a function of segment running times, and an energy‐efficient timetable optimization model is built. In doing so, all the services in both directions along a certain planning horizon are considered while attending to a known passengers’ demand. Moreover, the convenience of considering energy consumption as part of a broad objective function that includes other relevant costs is pointed out. Then, a unified sequential solution algorithm is developed for an efficient and accurate solution of the bi‐level model. Experiments show that the proposed framework can generate a holographic timetable of energy‐efficient MSM containing multi‐dimensional variables such as time, space, velocity, and electrical quantities.
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