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Formal Asymptotic Analysis of Online Scheduling Algorithms for Plug-In Electric Vehicles’ Charging

Author

Listed:
  • Asad Ahmed

    (School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), H-12 Islamabad, Pakistan
    These authors contributed equally to this work.)

  • Osman Hasan

    (School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), H-12 Islamabad, Pakistan
    These authors contributed equally to this work.)

  • Falah Awwad

    (College of Engineering, United Arab Emirates University, Al-Ain 15551, UAE)

  • Nabil Bastaki

    (College of Engineering, United Arab Emirates University, Al-Ain 15551, UAE)

  • Syed Rafay Hasan

    (Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA)

Abstract

A large-scale integration of plug-in electric vehicles (PEVs) into the power grid system has necessitated the design of online scheduling algorithms to accommodate the after-effects of this new type of load, i.e., PEVs, on the overall efficiency of the power system. In online settings, the low computational complexity of the corresponding scheduling algorithms is of paramount importance for the reliable, secure, and efficient operation of the grid system. Generally, the computational complexity of an algorithm is computed using asymptotic analysis. Traditionally, the analysis is performed using the paper-pencil proof method, which is error-prone and thus not suitable for analyzing the mission-critical online scheduling algorithms for PEV charging. To overcome these issues, this paper presents a formal asymptotic analysis approach for online scheduling algorithms for PEV charging using higher-order-logic theorem proving, which is a sound computer-based verification approach. For illustration purposes, we present the complexity analysis of two state-of-the-art online algorithms: the Online cooRdinated CHARging Decision (ORCHARD) algorithm and online Expected Load Flattening (ELF) algorithm.

Suggested Citation

  • Asad Ahmed & Osman Hasan & Falah Awwad & Nabil Bastaki & Syed Rafay Hasan, 2018. "Formal Asymptotic Analysis of Online Scheduling Algorithms for Plug-In Electric Vehicles’ Charging," Energies, MDPI, vol. 12(1), pages 1-20, December.
    • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:19-:d:192461
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    References listed on IDEAS

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    1. Guille, Christophe & Gross, George, 2009. "A conceptual framework for the vehicle-to-grid (V2G) implementation," Energy Policy, Elsevier, vol. 37(11), pages 4379-4390, November.
    2. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Demand response in smart electricity grids equipped with renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 64-72.
    3. Sara Deilami, 2018. "Online Coordination of Plug-In Electric Vehicles Considering Grid Congestion and Smart Grid Power Quality," Energies, MDPI, vol. 11(9), pages 1-17, August.
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