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Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control

Author

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  • Yusuf A. Sha’aban

    (School of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK)

  • Augustine Ikpehai

    (School of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK)

  • Bamidele Adebisi

    (School of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK)

  • Khaled M. Rabie

    (School of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK)

Abstract

The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university’s PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%.

Suggested Citation

  • Yusuf A. Sha’aban & Augustine Ikpehai & Bamidele Adebisi & Khaled M. Rabie, 2017. "Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control," Energies, MDPI, vol. 10(10), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1507-:d:113580
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    References listed on IDEAS

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    1. Olamide Jogunola & Bamidele Adebisi & Kelvin Anoh & Augustine Ikpehai & Mohammad Hammoudeh & Georgina Harris & Haris Gacanin, 2018. "Distributed Adaptive Primal Algorithm for P2P-ETS over Unreliable Communication Links," Energies, MDPI, vol. 11(9), pages 1-16, September.
    2. Baocheng Wang & Yafei Hu & Yu Xiao & Yi Li, 2018. "An EV Charging Scheduling Mechanism Based on Price Negotiation," Future Internet, MDPI, vol. 10(5), pages 1-11, May.

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