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Potential for metro rail energy savings and emissions reduction via eco-driving

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  • Yuan, Weichang
  • Frey, H. Christopher

Abstract

Metro rail energy efficiency needs to be improved to compensate for growing capacity demand. Eco-driving aims to reduce energy consumption without affecting safety and passenger comfort. Estimates of energy savings from train eco-driving are typically based on theoretical speed trajectory optimization models. However, achievable energy savings from eco-driving should be assessed based on realistic trajectories. A Markov chain speed trajectory simulator calibrated to measured trajectories was used to simulate realistic inter-run variability in 1 Hz trajectories. The simulator was calibrated and applied to the Washington Metropolitan Area Transit Authority Metrorail system. Estimated energy consumption for each trajectory includes auxiliary loads and tractive effort to overcome resistive forces. Inter-run variability in estimated energy consumption implies opportunities for energy savings via eco-driving. Energy savings was quantified by comparing the lowest and average segment energy consumption. A segment is the one-way rail track between adjacent stations of each line. Simulated trajectories are similar to measured trajectories based on mean absolute error and coefficient of determination (R2) for the same operation mode sequence. Based on 100 simulations per segment, energy savings ranging from 5% to 50% among segments and from 14% to 18% at the system level can be achieved without modifying travel time. Energy savings lead to reduced electricity consumption and, therefore, reduced power generation emissions. The method demonstrated here to quantify opportunities for metro train energy conservation and emissions mitigation is broadly applicable to electric metro and commuter trains and rail segments. Implications for energy-efficient passenger rail planning and operation are discussed.

Suggested Citation

  • Yuan, Weichang & Frey, H. Christopher, 2020. "Potential for metro rail energy savings and emissions reduction via eco-driving," Applied Energy, Elsevier, vol. 268(C).
  • Handle: RePEc:eee:appene:v:268:y:2020:i:c:s0306261920304566
    DOI: 10.1016/j.apenergy.2020.114944
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    References listed on IDEAS

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    Cited by:

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    2. Kyoungho Ahn & Ahmed Aredah & Hesham A. Rakha & Tongchuan Wei & H. Christopher Frey, 2023. "Simple Diesel Train Fuel Consumption Model for Real-Time Train Applications," Energies, MDPI, vol. 16(8), pages 1-15, April.
    3. Mariano Gallo & Marilisa Botte & Antonio Ruggiero & Luca D’Acierno, 2020. "A Simulation Approach for Optimising Energy-Efficient Driving Speed Profiles in Metro Lines," Energies, MDPI, vol. 13(22), pages 1-17, November.
    4. Almaksour, Khaled & Krim, Youssef & Kouassi, N’guessan & Navarro, Nicolas & François, Bruno & Letrouvé, Tony & Saudemont, Christophe & Taunay, Lionel & Robyns, Benoit, 2021. "Comparison of dynamic models for a DC railway electrical network including an AC/DC bi-directional power station," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 244-266.
    5. Gonzalo Sánchez-Contreras & Adrián Fernández-Rodríguez & Antonio Fernández-Cardador & Asunción P. Cucala, 2023. "A Two-Level Fuzzy Multi-Objective Design of ATO Driving Commands for Energy-Efficient Operation of Metropolitan Railway Lines," Sustainability, MDPI, vol. 15(12), pages 1-24, June.
    6. Feng, Zongbao & Chen, Weiya & Liu, Yang & Chen, Hongyu & Skibniewski, Mirosław J., 2023. "Long-term equilibrium relationship analysis and energy-saving measures of metro energy consumption and its influencing factors based on cointegration theory and an ARDL model," Energy, Elsevier, vol. 263(PD).

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