Modeling and Optimizing the Charge of Electric Vehicles with Genetic ‎Algorithm in the Presence of Renewable Energy Sources

Document Type : Research paper


1 Department of Occupational Therapy, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, ‎‎50200, Thailand‎

2 Faculty of Public Health, Universitas Indonesia; Faculty of Law, Universitas Krisnadwipayana

3 Department of Medical Electronics, M S Ramaiah Institute of Technology, Bengaluru.‎

4 Al-Nisour University College, Iraq

5 AL-Balqa Applied University , AL-Huson College University , P. O. Box 50, AL-Huson 21510, Jordan, AL-Huson

6 Sohar University, Faculty of Education & Arts, Sultanate of Oman

7 Department of Pharmacology, saveetha dental College and hospital, saveetha institute of medical and technical ‎sciences, chennai, india

8 Department of Dentistry, Kut University College, Kut, Wasit, 52001, Iraq

9 Department of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Iraq

10 Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-‎Qar, Iraq

11 Department of Legal Disciplines, Kazan Federal Uninersity, Kazan, Russia

12 ‎Institut Agama Islam Negeri Palopo, Indonesia


In recent years, as a result of remarkable increase in energy industry, discrimination between lower and higher loads as well as economic crisis which pestered a majority of countries; hence the usage of power plants became a significant issue. In addition, growing consumption of power and inexistence of valid source in satisfying the requirements has brought different problems such as diminish of fossil fuel resources, adversarial environmental influences, universal growth of Greenhouse Gases (GHGs). The associated issues have created technologies compatible with situations including Electric Vehicles (EVs). Regarding the efficiency of two-side exchange of energy within these vehicles, if there was a connection among the number of them and net under management and intelligent monitor of organization stability, so they can treat like a virtual tiny energy plant with start- up speed and free of cost. This paper presented the modeling and optimizing of the charge of electric vehicles with genetic algorithm in the presence of renewable energy sources. According to the results of this study, the cost of the HEV charge connected to the net is 75.88% less than the EV compared to the payment costs of the car (dis)charge in optimal patterns.


  1. Alayi et al., “Technical and environmental analysis of photovoltaic and solar water heater cogeneration system: a case study of Saveh City”, Int. J. Low-Carbon Tech., vol. 16, pp.447-53, 2021.
  2. Alayi et al., “Optimization, sensitivity analysis, and techno-economic evaluation of a multi-source system for an urban community: a case study”, Renew. Energy. Res. App., vol. 3, no. 1, pp. 21-30, 2022.
  3. Frank, “Plug-in hybrid vehicles for a sustainable future”, American Sci., vol. 95, p. 158, 2007.
  4. Alayi, H. Harasii, H. Pourderogar, “Modeling and optimization of photovoltaic cells with GA algorithm”, J. Robot Control, vol. 2, no. 1, pp. 35-41, 2021.
  5. Alayi et al., “Optimal load frequency control of island microgrids via a PID controller in the presence of wind turbine and PV”, Sustainability, vol. 13, no. 19, pp. 10728, 2021.
  6. Afraz et al., “Generation scheduling of active distribution network with renewable ‎energy resources considering demand response management”, J. Oper. Autom. Power. Eng., vol. 9, no. 2, pp. 132-43, 2021.
  7. Samaras, K. Meisterling, “Life cycle assessment of greenhouse gas emissions from Plug-in hybrid vehicles: Implications for Policy”, Environ. Sci. Tech., vol. 42, pp. 3170-6, 2008.
  8. Alayi et al., “Modeling and reviewing the reliability and multi-objective optimization of wind-turbine system and photovoltaic panel with intelligent algorithms”, Clean Energy, vol. 5, no. 4, pp. 713-30, 2021.
  9. Alayi, H. Rouhi, “Techno-economic analysis of electrical energy generation from urban waste in Hamadan, Iran”, Int. J. Design Nature Ecodynamics, vol. 15, no. 3, pp. 337-341, 2020.‏
  10. Kempton, J. Tomic, “Vehicle-to-grid power fundamentals: Calculating capacity and net revenue”, J. Power Sources, vol. 144, pp. 268-79, 2005.
  11. Lund, W. Kempton, “Integration of renewable energy into the transport and electricity sectors through V2G”, Energy Policy, vol. 36, pp. 3578-87, 2008.
  12. Huston et al., “Intelligent scheduling of hybrid and electric vehicle storage capacity in a parking lot for profit maximization in grid power transactions”, Energy 2030 Conf., 2008.
  13. Camus et al., “Impact of plug-in Hybrid Electric Vehicles in the Portuguese electric utility system”, Power Eng. Energy Electr. Drives, 2009.
  14. Clement et al., “Coordinated charging of multiple plug-in hybrid electric vehicles in residential distribution grids”, Power Syst. Conf. Expos., 2009.
  15. Guille, G. Gross, “A conceptual framework for the vehicle-to-grid (V2G) implementation”, Energy Policy, vol. 37, pp. 4379-90, 2009.
  16. Saber, G. Venayagamoorthy, “Intelligent unit commitment with vehicle-to-grid - A cost emission optimization”, J. Power Sources, vol. 195, pp. 898-911, 2010.
  17. Sioshansi, R. Fagiani, V. Marano, “Cost and emissions impacts of plug-in hybrid vehicles on the Ohio Power”, Energy Policy, vol. 38, PP. 6703-12, 2010.
  18. Sioshansi, P. Denholm, “The value of Plug-in hybrid electric vehicles as grid resources”, Energy J., vol. 31, PP.1-23, 2010.
  19. Acha et al., “Effects of optimised plug-in hybrid vehicle charging strategies on electric distribution network losses”, Transm. Distrib. Conf. Expos., 2010.
  20. Han, K. Sezaki, “Development of an Optimal vehicle-to-grid aggregator for frequency regulation”, Smart Grid, IEEE Trans., vol. 1, PP. 65-72, 2010.
  21. White, K. Zhang, “Using vehicle-to-grid technology for frequency regulation and peak-load reduction”, J. Power Sources, vol.196, PP. 3972-80, 2011.
  22. Kiviluma, P. Meibom, “Methodology for modeling plug-in electric vehicles in the power system and cost estimates for a system with either smart or dumb electric vehicles”, Energy , vol.36, PP.1758-1767, 2011.
  23. Longo, F. Foiadelli, W. Yaïci, “Simulation and optimisation study of the integration of distributed generation and electric vehicles in smart residential district”, Int. J. Energy Environ. Eng., vol. 10, no. 3, pp. 271-85, 2019.
  24. Petrusic, A. Janjic, “Renewable energy tracking and optimization in a hybrid electric vehicle charging station”, Appl. Sci., vol. 11, no. 1, pp. 245, 2021.