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Control Strategies for Energy Efficiency at PNU’s Metro System

Author

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  • Wafaa Saleh

    (College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia
    Transport Engineering School of Engineering and The Built Environment, Edinburgh Napier University, Edinburgh EH10 5D, UK
    Visiting Professor.)

  • Shekaina Justin

    (College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia)

  • Ghada Alsawah

    (College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia
    Mechanical Engineering Department, Higher Technological Institute, 10th of Ramadan 44629, Egypt)

  • Tasneem Al Ghamdi

    (College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia)

  • Maha M. A. Lashin

    (College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia
    Mechanical Department, Faculty of Engineering-Shoubra, Banha University, Banha 13518, Egypt)

Abstract

It is broadly acknowledged that there is an urgent need to reduce carbon-based mobility systems and increase renewable energy alternatives. The automotive industry is one of the greatest consumers of energy in the world. It is fronted with many challenges that aim at reducing carbon emissions. Renewable energy costs are getting cheaper and more cost effective. However, well devised design and control strategies are also needed in order to optimize any systems that are adopted in this field. Previous research shows that the energy consumption for non-traction purposes may be of the same scale as the energy used to move rolling stock, and in some cases even larger. The Kingdom of Saudi Arabia is very interested in the implementation of policies that aim at reducing energy consumption and encouraging renewable energy programs. Under its Vision 2030 development program, the Kingdom of Saudi Arabia is looking to produce 30% of its energy from renewables and other sources, with solar energy playing an important role. The work presented in this paper is aimed at an investigation of design and control strategies to reduce energy consumption and to propose a cleaner source of energy to power Princess Nourah Bint Abdulrahman University’s Automated People Mover (PNU-APM). Two areas of applications have been investigated for adopting these types of technology. Firstly, a p-v solar energy option that could be adopted for implementation in potential applications since the metro system is already in full operation using electricity. Secondly, design and control strategies including exploiting solar energy for a metro operation are discussed and investigated. A number of strategies to reduce heating, ventilation, and air conditioning (HVAC) load, which happens to be the biggest energy consumer, have been discussed. Results show great potential in energy savings with adopting p-v solar sources as well as implementation of few suggested control strategies. Some deliberations of some of the drawbacks of solar energy are also offered.

Suggested Citation

  • Wafaa Saleh & Shekaina Justin & Ghada Alsawah & Tasneem Al Ghamdi & Maha M. A. Lashin, 2021. "Control Strategies for Energy Efficiency at PNU’s Metro System," Energies, MDPI, vol. 14(20), pages 1-13, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6660-:d:656428
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    References listed on IDEAS

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    5. Jia Feng & Xiamiao Li & Haidong Liu & Xing Gao & Baohua Mao, 2017. "Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands," Energies, MDPI, vol. 10(4), pages 1-20, March.
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    7. Wafaa Saleh & Shekaina Justin & Ghada Alsawah & Areej Malibari & Maha M A Lashin, 2021. "An Investigation into Conversion of a Fleet of Plug-in-Electric Golf Carts into Solar Powered Vehicles Using Fuzzy Logic Control," Energies, MDPI, vol. 14(17), pages 1-13, September.
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    Cited by:

    1. Shekaina Justin & Wafaa Saleh & Maha M. A. Lashin & Hind Mohammed Albalawi, 2023. "Modeling of Artificial Intelligence-Based Automated Climate Control with Energy Consumption Using Optimal Ensemble Learning on a Pixel Non-Uniformity Metro System," Sustainability, MDPI, vol. 15(18), pages 1-18, September.

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