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Economic Scheduling of Residential Plug-In (Hybrid) Electric Vehicle (PHEV) Charging

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  • Maigha

    (Department of Electrical and Computer Engineering, Missouri University of Science & Technology, 301 W. 16th Street, Rolla, MO 65409, USA)

  • Mariesa L. Crow

    (Department of Electrical and Computer Engineering, Missouri University of Science & Technology, 301 W. 16th Street, Rolla, MO 65409, USA)

Abstract

In the past decade, plug-in (hybrid) electric vehicles (PHEVs) have been widely proposed as a viable alternative to internal combustion vehicles to reduce fossil fuel emissions and dependence on petroleum. Off-peak vehicle charging is frequently proposed to reduce the stress on the electric power grid by shaping the load curve. Time of use (TOU) rates have been recommended to incentivize PHEV owners to shift their charging patterns. Many utilities are not currently equipped to provide real-time use rates to their customers, but can provide two or three staggered rate levels. To date, an analysis of the optimal number of levels and rate-duration of TOU rates for a given consumer demographic versus utility generation mix has not been performed. In this paper, we propose to use the U.S. National Household Travel Survey (NHTS) database as a basis to analyze typical PHEV energy requirements. We use Monte Carlo methods to model the uncertainty inherent in battery state-of-charge and trip duration. We conclude the paper with an analysis of a different TOU rate schedule proposed by a mix of U.S. utilities. We introduce a centralized scheduling strategy for PHEV charging using a genetic algorithm to accommodate the size and complexity of the optimization.

Suggested Citation

  • Maigha & Mariesa L. Crow, 2014. "Economic Scheduling of Residential Plug-In (Hybrid) Electric Vehicle (PHEV) Charging," Energies, MDPI, vol. 7(4), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:4:p:1876-1898:d:34435
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    References listed on IDEAS

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    1. Egbue, Ona & Long, Suzanna, 2012. "Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions," Energy Policy, Elsevier, vol. 48(C), pages 717-729.
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    Cited by:

    1. Su Su & Yong Hu & Tiantian Yang & Shidan Wang & Ziqi Liu & Xiangxiang Wei & Mingchao Xia & Yutaka Ota & Koji Yamashita, 2018. "Research on an Electric Vehicle Owner-Friendly Charging Strategy Using Photovoltaic Generation at Office Sites in Major Chinese Cities," Energies, MDPI, vol. 11(2), pages 1-19, February.
    2. Shyang-Chyuan Fang & Bwo-Ren Ke & Chen-Yuan Chung, 2017. "Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System," Energies, MDPI, vol. 10(7), pages 1-20, June.
    3. Afaq Ahmad & Muhammad Khalid & Zahid Ullah & Naveed Ahmad & Mohammad Aljaidi & Faheem Ahmed Malik & Umar Manzoor, 2022. "Electric Vehicle Charging Modes, Technologies and Applications of Smart Charging," Energies, MDPI, vol. 15(24), pages 1-32, December.
    4. Benslama Sami & Nasri Sihem & Salsabil Gherairi & Cherif Adnane, 2019. "A Multi-Agent System for Smart Energy Management Devoted to Vehicle Applications: Realistic Dynamic Hybrid Electric System Using Hydrogen as a Fuel," Energies, MDPI, vol. 12(3), pages 1-20, February.
    5. Mohammad Rasoul Narimani & Maigha & Jhi-Young Joo & Mariesa Crow, 2017. "Multi-Objective Dynamic Economic Dispatch with Demand Side Management of Residential Loads and Electric Vehicles," Energies, MDPI, vol. 10(5), pages 1-18, May.
    6. Fattahi, Abbas & Nahavandi, Ali & Jokarzadeh, Mohammadreza, 2018. "A comprehensive reserve allocation method in a micro-grid considering renewable generation intermittency and demand side participation," Energy, Elsevier, vol. 155(C), pages 678-689.
    7. Lixing Chen & Zhong Chen & Xueliang Huang & Long Jin, 2016. "A Study on Price-Based Charging Strategy for Electric Vehicles on Expressways," Energies, MDPI, vol. 9(5), pages 1-18, May.

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