Citation: Agrawal, H.; Talwariya, A.; Gill, A.; Singh, A.; Alyami, H.; Alosaimi, W.; Ortega-Mansilla, A. A Fuzzy-Genetic-Based Integration of Renewable Energy Sources and E-Vehicles. Energies 2022, 15, 3300. https://doi.org/10.3390/en15093300 Academic Editors: Dimitrios Katsaprakakis and Surender Reddy Salkuti Received: 14 March 2022 Accepted: 26 April 2022 Published: 30 April 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). energies Article A Fuzzy-Genetic-Based Integration of Renewable Energy Sources and E-Vehicles Himanshi Agrawal 1 , Akash Talwariya 1 , Amandeep Gill 2 , Aman Singh 3,4, * , Hashem Alyami 5 , Wael Alosaimi 6 and Arturo Ortega-Mansilla 3,7 1 Department of Electrical Engineering, JECRC University, Rajasthan 303905, India; himanshi199612agrawal@gmail.com (H.A.); akash.talwariya@gmail.com (A.T.) 2 Department of Electrical Engineering, Chandigarh University, Punjab 140413, India; aamangill.87@gmail.com 3 Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain; arturo.ortega@uneatlantico.es 4 Faculty of Engineering, Universidade Internacional do Cuanza, Estrada Nacional 250, Bairro Kaluapanda, Cuito EN 250, Bié, Angola 5 Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia; hyami@tu.edu.sa 6 Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia; w.osaimi@tu.edu.sa 7 Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico * Correspondence: aman.singh@uneatlantico.es Abstract: E-Vehicles are used for transportation and, with a vehicle-to-grid optimization approach, they may be used for supplying a backup source of energy for renewable energy sources. Renewable energy sources are integrated to maintain the demand of consumers, mitigate the active and reactive power losses, and maintain the voltage profile. Renewable energy sources are not supplied all day and, to meet the peak demand, extra electricity may be supplied through e-Vehicles. E-Vehicles with random integration may cause system unbalancing problems and need a solution. The objective of this paper is to integrate e-Vehicles with the grid as a backup source of energy through the grid-to- vehicle optimization approach by reducing active and reactive power losses and maintaining voltage profile. In this paper, three case studies are discussed: (i) integration of renewable energy sources alone; (ii) integration of e-Vehicles alone; (iii) integration of renewable energy sources and e-Vehicles in hybrid mode. The simulation results show the effectiveness of the integration and the active and reactive power losses are minimum when we used the third case. Keywords: renewable energy sources; E-Vehicle charging station; fuzzy logic approach; genetic algorithm 1. Introduction Energy demand at the consumer end is rapidly increasing and needs the attention of distribution network operators to maintain the demand with fewer losses by maintaining the voltage profile. Energy is generated by centralized installed power plants and supplied to consumers through large transmission and distribution networks. Approximately 35% of the electricity is lost as transmission and distribution losses throughout the process. It is also estimated that the electricity demand will rise to 900 GW by 2030 and meeting that demand with conventional energy sources may result in a 59% rise in environmental pollution [1]. A suitable solution is provided by renewable energy sources (RESs) installed near load centers to meet the demand of consumers; the suitable integration may reduce environmental pollution as well as transmission and distribution losses [2]. RESs are green energy generation sources installed at the consumer end. RESs consist of solar photovoltaic, wind turbines, microhydro, etc. [3]. RES integration with different kinds of connected sources, random energy consumption profiles, and high losses increase the complexity of the system. The integration of RES is essential with integrated approaches and the Energies 2022, 15, 3300. https://doi.org/10.3390/en15093300 https://www.mdpi.com/journal/energies