Two-Layer Genetic Algorithm for the Charge Scheduling of Electric Vehicles Nikolaos T. Milas 1 , Dimitris A. Mourtzis 2 , Panagiotis I. Giotakos 3 , Emmanuel C. Tatakis 1 1 UNIVERSITY OF PATRAS, Laboratory of Electromechanical Energy Conversion, Electrical and Computer Engineering Department, Rion-Patras, Greece 26504, Tel.: +30.2610.996414, E-Mail: nmilas@ece.upatras.gr, e.c.tatakis@ece.upatras.gr, URL: http://lemec.ece.upatras.gr 2 UNIVERSITY OF PATRAS, Laboratory for Manufacturing Systems and Automation (LMS), Mechanical Engineering and Aeronautics Department, Rion-Patras, Greece 26504, Tel.: +30.2610.910160, E-Mail: mourtzis@lms.mech.upatras.gr URL: http://lms.mech.upatras.gr 3 FOUNDATION FOR RESEARCH AND TECHNOLOGY HELLAS(FORTH), Institute of Chemical Engineering Sciences (ICEHT), Laboratory of Energy Processes, Stadiou Street Platani, Patra, Greece 26504, Tel.:+302610965238, E-mail: pgiotakos@forth.iceht.gr URL: www.iceht.forth.gr Acknowledgements This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the project “Strengthening Human Resources Research Potential via Doctorate Research” (MIS-5000432), implemented by the State Scholarships Foundation (Ι). Keywords «Electric vehicle», «Charge Scheduling», «Genetic Algorithm», «Optimisation», «OPC-UA» Abstract The advent of Electric Vehicles (EV) may introduce disturbances to the operation of the Power Grid, due to the great demands of electric power that is required during the simultaneous charging of large EV fleets. Towards this end, novel approaches for the management of the EV charging have been proposed in recent literature. Nevertheless, the implementation of a framework that allows flexibility in the definition of the decision-making objectives, along with user-defined criteria is still a challenge. Towards addressing this challenge, a framework for the smart charging of EVs is presented in this paper. The smart charging is facilitated by a two-layer Genetic Algorithm that operates in Charging Stations with various types of chargers that are connected to multiple charging points in a resource-sharing manner. The benefits of the proposed approach are the fast optimisation time, the inclusion of user- defined criteria, and the extraction of feasible solutions considering the availability of the chargers in the station. The communications between the EV and the Charging Station are facilitated by the Open Platform Communications–Unified Architecture (OPC-UA) standard. The proposed algorithm succeeds into finding competitive solutions even during charging scenarios with conflicting criteria. Introduction The degradation of the environment dictates the adoption of non-conventional means of transportation. Towards this end, the Electric Vehicles (EVs) are eligible candidates to take the place of conventional vehicles, especially for urban transportations. The subject of the charge scheduling of EVs has been Two-Layer Genetic Algorithm for the Charge Scheduling of Electric Vehicles MILAS Nikolaos EPE'20 ECCE Europe ISBN: 978-9-0758-1536-8 – IEEE: CFP20850-ART P.1 Assigned jointly to the European Power Electronics and Drives Association & the Institute of Electrical and Electronics Engineers (IEEE) Authorized licensed use limited to: University of Patras. Downloaded on June 11,2021 at 11:01:46 UTC from IEEE Xplore. Restrictions apply.