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

Yusuf A. Sha’aban, Augustine Ikpehai, Bamidele Adebisi and Khaled M. Rabie
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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

Energies, 2017, vol. 10, issue 10, 1-20

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%.

Keywords: plug-in electric vehicle; economic model predictive control (EMPC); vehicle-to-grid (V2G); grid-to-vehicle (G2V); optimisation; smart grid; vehicle-to-grid (V2G) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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