A Cooperative Train Control Model for Energy Saving

Su, Shuai; Tang, Tao; Roberts, Clive

doi:10.1109/tits.2014.2334061

Cited by 112 publications

(66 citation statements)

References 27 publications

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“…Similar studies can be found in [12,13]. Su [14] proposed a cooperative train control model to efficiently use the regenerative energy by adjusting the departure time of the accelerating train. Gong [15] proposed an energy-efficient operation methodology for metro lines, including timetable optimization and the driving strategy optimization.…”

Section: %mentioning

confidence: 55%

“…In our previous work [14], a cooperative train control model has been studied, in which the regenerative energy is used better by adjusting the departure time. The simulation results show that the net energy consumption can be reduced by 11.34% for peak hours with combining the energy-efficient driving strategy and utilization of the regenerative energy.…”

Section: Regenerative Energymentioning

confidence: 99%

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Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model

Tang

Wang

2016

Energies

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Abstract:Increasing attention is being paid to the energy efficiency in metro systems to reduce the operational cost and to advocate the sustainability of railway systems. Classical research has studied the energy-efficient operational strategy and the energy-efficient system design separately to reduce the traction energy consumption. This paper aims to combine the operational strategies and the system design by analyzing how the infrastructure and vehicle parameters of metro systems influence the operational traction energy consumption. Firstly, a solution approach to the optimal train control model is introduced, which is used to design the Optimal Train Control Simulator(OTCS). Then, based on the OTCS, the performance of some important energy-efficient system design strategies is investigated to reduce the trains' traction energy consumption, including reduction of the train mass, improvement of the kinematic resistance, the design of the energy-saving gradient, increasing the maximum traction and braking forces, introducing regenerative braking and timetable optimization. As for these energy-efficient strategies, the performances are finally evaluated using the OTCS with the practical operational data of the Beijing Yizhuang metro line. The proposed approach gives an example to quantitatively analyze the energy reduction of different strategies in the system design procedure, which may help the decision makers to have an overview of the energy-efficient performances and then to make decisions by balancing the costs and the benefits.

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Section: %mentioning

confidence: 55%

Section: Regenerative Energymentioning

confidence: 99%

Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model

Tang

Wang

2016

Energies

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“…Su et al [78] proposed a cooperative train control model to reduce the energy consumption, and designed a numerical algorithm to obtain the optimal driving strategy with a given trip time, in which the variable traction forces, speed limits, and gradients are considered. Then Su et al [79] developed the model and designed a bisection method to solve the optimal timetable and speed profile. The results showed that the developed model can save 2.4% of energy for one trip in comparison with the energy-efficient driving method [25].…”

Section: Integrated Optimization Methodsmentioning

confidence: 99%

A Survey on Energy-Efficient Train Operation for Urban Rail Transit

Yang

Ning

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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314

133

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Due to rising energy prices and environmental concerns, the energy efficiency of urban rail transit has attracted much attention from both researchers and practitioners in recent years. Timetable optimization and energy-efficient driving, as two mainly used train operation methods in relation to the tractive energy saving, make major contributions in reducing the energy consumption that has been studied for a long time. Generally speaking, timetable optimization synchronizes the accelerating and braking actions of trains to maximize the utilization of regenerative energy, and energy-efficient driving optimizes the speed profile at each section to minimize the tractive energy consumption. In this paper, we present a fully comprehensive survey on energy-efficient train operation for urban rail transit. First, a general energy consumption distribution of urban rail trains is described. Second, the current literature on timetable optimization and energy-efficient driving is reviewed. Finally, according to the review work, it is concluded that the integrated optimization method jointly optimizing the timetable and speed profile has become a new tendency and ought to be paid more attention in future research.

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“…Intelligent Buildings cover all types of types, homes, shops, offices, industries, sports facilities, etc. In addition to multi-agent systems, other proposals have been made to optimize energy consumption such as linear programming [38,39], predictive models [40], decision models [41,42] embedded systems [43] and gradient models [44].…”

Section: Mas and Energy Optimization: A New Approachmentioning

confidence: 99%

Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

et al. 2018

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This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings.

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A Cooperative Train Control Model for Energy Saving

Cited by 112 publications

References 27 publications

Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model

Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model

A Survey on Energy-Efficient Train Operation for Urban Rail Transit

Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

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