An integrated optimization model of metro energy consumption based on regenerative energy and passenger transfer

He, Deqiang; Yang, Yanjie; Chen, Yanjun; Deng, Jianxin; Shan, Sheng; Liu, Jian-Ren; Li, Xianwang

doi:10.1016/j.apenergy.2020.114770

Cited by 40 publications

(12 citation statements)

References 41 publications

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“…With the development of GIS, detailed information of POIs can be extracted through a map application programming interface (API), such as Gaode Map, which is an effective supplement that helps to reflect the land use situation ( 51 ). As a result, 13 types of POIs of Chengdu in 2018 are selected as general land use characteristics (see Figure 4).…”

Section: Methodsmentioning

confidence: 99%

Analysis of the Integration Usage Patterns of Multiple Shared Mobility Modes and Metro System

Zhang

2021

Transportation Research Record

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Although metro systems are established in many Asian cities including Chengdu, they have yet to cover every corner of a city. Understanding the transfer behavior of passengers can provide insight into achieving efficient and sustainable urban transport systems. Combining shared mobility programs with metro to improve the weaknesses of traditional feeder modes is viewed as the most promising line of business in sustainable transportation for the near future. Therefore, this study aims to comprehend the factors affecting the usage regularity of shared mobility by deepening the knowledge on endogenous and exogenous effects, and integrating two modes, namely bike-sharing and ridesourcing. Two systems are cross-compared, first in respect of their travel characteristics. Then, a binary logistic model is employed to capture the influences of trip characteristics and travel environment characteristics on their usage frequency. Researchers found that trip distance is significantly associated with users’ mode options, indicating that bike-sharing and ridesourcing mainly serve short-distance and long-distance transfer users, respectively, although some users may be confused which feeder mode to choose for the journeys of 2 km to 4 km. There were also meteorological and temporal influences, with the competition and complementation of multiple shared mobility feeder modes being likely to change under extreme weather conditions, during peak hours, or on weekends. Besides, metro-shared mobility users value the accessibility of two kinds of transport service, which is affected by the metro station and its surrounding built environment. This study and the proposed policy implications are helpful for embracing a sustainable mobility design from general optimum.

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Section: Methodsmentioning

confidence: 99%

Analysis of the Integration Usage Patterns of Multiple Shared Mobility Modes and Metro System

Zhang

2021

Transportation Research Record

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“…Technologies for energy-efficient railway systems have been researched for several decades [6][7][8][9]. The energy-saving operation of railways relies on technological advancements in many fields, including the development of train propulsion systems [10], advances in train traction power supply systems [11][12][13], and advanced train dispatch and driving technology [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Train Speed Profile Planning Method for Induction Motor Traction System

Gao

Tang

2021

Energies

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Optimizing the operating speed curve of trains without adding new energy storage facilities is essential in the energy-saving operation of railways. In this paper, we propose an optimal train speed curve planning method for driving trains more energy efficiently. A refined traction energy evaluation model for induction motor propulsion systems is first presented. The proposed model considers the efficiency of the traction motor at different operating points and the efficiency of the inverter and gearbox. Then, the optimal energy-efficient speed profile problem is transformed into a multistep decision problem and solved using dynamic programming (DP). To verify the effectiveness of the proposed method, a case study was conducted on an actual subway line. The results obtained indicate that the speed curve produced by the proposed method results in a 20% energy consumption saving compared with the speed curve for actual operations. Furthermore, the results of comparison with a genetic algorithm indicate that the DP algorithm is better able to satisfy the constraints of the train traction system. Solving the optimal speed curve using the proposed method and programming the onboard controller of the train according to the optimal speed curve enables the train to be driven with greater energy efficiency.

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“…Due to the rapid development of urban railway system, the electric energy consumption is still an unsolved problem that concerned by many researchers. The energy consumption mainly comes from station facilities, such as lighting system, air-condition system and so on, and vehicle traction which consumes most of the electric power [16]. The all-day traction current distribution can reflect not only the characteristics of metro operation, but also the load variations in DC railway system.…”

Section: Introductionmentioning

confidence: 99%

“…Case studies, which based on the new model and combined by route condition, speed limitation and traction power energy in Nanning Metro Line 1 and 2, were also carried out. Results show that the approach can reduce the energy consumption by approximately 48.25% for Line 1 and 39.80% for Line 2 [16].…”

Section: Introductionmentioning

confidence: 99%

Temporal Fluctuated Features of Metro Traction Load Current in DC Railway

et al. 2020

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24-hour traction current based on Wuhan Metro Line 6 are reported. The fluctuation features, which corresponding to the interval of vehicle operation, indicate that there are four types of inrush current periodically appearing throughout the all-day waveforms. The first type consists of two positive sharp waves. The second type is saddle shaped, and consists of two positive peaks and an anti-peak. The third type consists of two double peaks followed by a sharp peak about 1 minute later. The fourth type consists of two hump shaped waveforms. The whole operation time can be divided into the peak and off-peak hours according to these repetitive segments. The interval of any two adjacent vehicles is 9 minutes and 11 minutes, respectively, in peak and off-peak hours. The distribution and the positive average value of feeder current per hour from 6:00-24:00 are counted and analyzed. Results show that the current distribution in off-peak hours is more concentrated in a narrower range than that in peak hours. The differences in averages between various 15-minute periods in an hour can be as high as tens of amperes, due to the occurrence time of repetitive segments. The rail potential has relatively consistent variation with feeder currents, but it matched best with the sum of up-line and down-line in a same section instead of one of the two lines.

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An integrated optimization model of metro energy consumption based on regenerative energy and passenger transfer

Cited by 40 publications

References 41 publications

Analysis of the Integration Usage Patterns of Multiple Shared Mobility Modes and Metro System

Analysis of the Integration Usage Patterns of Multiple Shared Mobility Modes and Metro System

An Optimal Train Speed Profile Planning Method for Induction Motor Traction System

Temporal Fluctuated Features of Metro Traction Load Current in DC Railway

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