International Journal of Electrical and Computer Engineering (IJECE) Vol. 10, No. 5, October 2020, pp. 5093~5107 ISSN: 2088-8708, DOI: 10.11591/ijece.v10i5.pp5093-5107  5093 Journal homepage: http://ijece.iaescore.com/index.php/IJECE Optimal scheduling of smart microgrids considering electric vehicle battery swapping stations J. Garcia-Guarin 1 , W. Infante 2 , J. Ma 3 , D. Alvarez 4 , S. Rivera 5 1,4,5 Department of Electrical Engineering, Faculty of Engineering, Universidad Nacional de Colombia, Colombia 2,3 Department of Electrical Engineering, Faculty of Engineering, University of Sydney, Australia Article Info ABSTRACT Article history: Received Mar 22, 2020 Revised Apr 24, 2020 Accepted May 3, 2020 Smart microgrids belong to a set of networks that operate independently. These networks have technologies such as electric vehicle battery swapping stations that aim to economic welfare with own resources. Improper handling of electric vehicles services represents overload, congestion or surplus energy. This study addresses both management and support of electric vehiculos battery swapping stations. The formulation of a decision matrix examines economically viable alternatives that contributes to scheduling battery swapping stations. The decision matrix is implemented to manage the swapping, charging and discharging of electric vehicles. In addition, the smart microgrid model evaluates operation issues. The smart microgrid model used extends with considerations of demand response, generators with renewable energies, the wholesale market, local market and electricity spot price for electric vehicles. Additionally, uncertainty issues related to the planning for the infrastructure of the electric vehicle battery swapping station, variability of electricity prices, weather conditions and load forecasting are used. Mentioned stakeholders must maximize their economic benefits optimizing their uncertain day-ahead resources. The proposed hybrid optimization algorithm supports aggregator decision making. This algorithm achieves a 72% reduction in total cost. This percentage of feasible reduction is obtained by calculating the random solution with respect to the suboptimal solution. Keywords: Electric vehicle Microgrids Optimization Scheduling Swapping stations Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Sergio Rivera, Department of Electrical Engineering, Universidad Nacional de Colombia, Carrera 30 Número 45-03, Bogotá, Colombia. Email: srriverar@unal.edu.co NOMENCLATURE Indexes: f Time zero for EV batteries L Loads i Distributed generation (DG) units M Markets j PV units S Scenarios k External suppliers t Periods E Energy storage systems Variables: DG P Active power generation (kW) ESS P  Discharge power of ESS (kW) ext P External supplied power (kW) EV P  Discharge power of EV (kW)