Multitime-Scale Optimal Dispatch of Railway FTPSS Based on Model Predictive Control

Chen, Minwu; Cheng, Zhe; Liu, Yuanli; Cheng, Yuehua; Tian, Zhongbei

doi:10.1109/tte.2020.2992693

Cited by 47 publications

(18 citation statements)

References 38 publications

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“…value). Regarding the efficiency of the ESS, recent research on railway ESS has established the efficiency for battery ESS to be 80%, and the efficiency of the super-capacitor ESS is 98% [20,41]. It is reasonable to consider in this study a unitary efficiency for the ESS if recent technological advancements on supercapacitors and in silicon carbide (SiC) transistors are taken into consideration, and if the focus of this study is on the capabilities of the FLC and the GA optimization algorithms.…”

Section: Materials and Frameworkmentioning

confidence: 99%

“…Two-level hierarchical EMS were proposed in [16], [17][18][19], [20] and [21]. In [16] is proposed a hierarchical two-level EMS to charge wayside ESS, having an FLC to manage in real-time the net balance of the power, and a GA as an ultimate optimization strategy having the predictions and the cost of electricity as a reference for the FLC (the generated reference will be an extra input variable of the FLC).…”

mentioning

confidence: 99%

“…In [17][18][19] is introduced a hierarchical structure for the RSG comprising two levels, and were able to achieve up to 45 % cost reduction and 40 % reduction on energy consumption, for a case study with an actual rail route and trains of an existing commercial brand. The paper in [20] proposes a two-level EMS considering a day-ahead dispatch and an intra-day feedback correction. In [21], the AC railway with RES and ESS is targeted to be managed by a two-level hierarchical management structure, having not only to optimize the energy consumption but also to provide power quality by reducing the negative sequence currents and to contributing to reactive power compensation.…”

mentioning

confidence: 99%

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Towards Smart Railways: A Charging Strategy for Railway Energy Storage Systems

Morais¹,

Afonso²,

Martins³

2021

EAI Endorsed Transactions on Energy Web

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The huge power requirements of future railways require the usage of energy-efficient strategies towards a more intelligent railway system. The usage of on-board energy storage systems enables better usage of the traction energy with a higher degree of freedom. In this article is proposed a top-level charging controller for the on-board and wayside railway energy storage systems. Its structure comprehends two processing levels: a real-time fuzzy logic controller for each energy storage system, and a genetic algorithm meta-heuristic, that remotely and automatically tune the fuzzy rules weight. As global results, the reduction of regenerated energy is 22.3% with the fuzzy logic controller. With the optimization strategy, this reduction can be further extended to 28.7%. The need for a smart railway framework is also discussed towards a realistic implementation of such charging strategy. Thus, with a high degree of flexibility, the efficiency of railway energy systems can be increased with the proposed framework.

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Section: Materials and Frameworkmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Towards Smart Railways: A Charging Strategy for Railway Energy Storage Systems

Morais¹,

Afonso²,

Martins³

2021

EAI Endorsed Transactions on Energy Web

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“…At present, there are some studies devoted to researching novel TPSS and its energy management strategy. M. Chen [18] has proposed a topology scheme of installing hybrid energy storage system (HESS) on DC bus of AC-DC-AC traction substation to collect and utilize regenerative braking energy of train. However, the effect of different train positions on power transmission efficiency was not considered in this study.…”

Section: Introductionmentioning

confidence: 99%

The Flexible Smart Traction Power Supply System and Its Hierarchical Energy Management Strategy

et al. 2021

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With the increasing requirements of the railway sector for electrified railways and the development of society, the traction power supply system needs to become more flexible, economic and reliable. The traditional traction power supply system has many problems due to its own topology, such as negative sequence current, load fluctuation, low utilization rate of regenerative braking energy, and inability to use the distributed generation along the railway. In order to solve these problems and make the traction power supply system (TPSS) more flexible, efficient and reliable, a novel TPSS called flexible smart traction power supply system (FSTPSS) is proposed. This traction power supply system consists of AC-DC-AC traction substation and the microgrid along the railway. It can effectively mitigate the problems of negative sequence current, improve regenerative braking energy utilization and distributed generation utilization. A hierarchical energy management strategy is also proposed here, which can effectively improve the operating economy of FSTPSS and reduce the load fluctuation of FSTPSS. And based on the actual load measurement power of a traction substation of China's Beijing-Shanghai high-speed railway, the proposed energy management strategy has been verified for the economic improvement and load fluctuation reduction of TPSS.

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“…There has been growing application of fuel cell (FC) across multiple industries in recent years in response to declining fossil fuel reserves, as well as worsening trends in environmental pollution and climate change. Among various types of fuel cells, the proton exchange membrane fuel cell (PEMFC) has the advantages of low operating temperature, high power density, fast response, great stability and environmental friendliness, making it suitable for several kinds of power generation applications (including mobile generator, static power, and distributed generation systems) [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Controller Based on Distributed Deep Reinforcement Learning for PEMFC Air Supply System

2021

IEEE Access

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Multitime-Scale Optimal Dispatch of Railway FTPSS Based on Model Predictive Control

Cited by 47 publications

References 38 publications

Towards Smart Railways: A Charging Strategy for Railway Energy Storage Systems

Towards Smart Railways: A Charging Strategy for Railway Energy Storage Systems

The Flexible Smart Traction Power Supply System and Its Hierarchical Energy Management Strategy

Intelligent Controller Based on Distributed Deep Reinforcement Learning for PEMFC Air Supply System

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