Techno-Economic and Environmental Analysis of Grid-Connected Electric Vehicle Charging Station Using AI-Based Algorithm

Mohd Bilal, Ibrahim Alsaidan, Muhannad Alaraj, Fahad M. Almasoudi and Mohammad Rizwan
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Mohd Bilal: Department of Electrical Engineering, Delhi Technological University, Delhi 110042, India
Ibrahim Alsaidan: Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah 52571, Saudi Arabia
Muhannad Alaraj: Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah 52571, Saudi Arabia
Fahad M. Almasoudi: Department of Electrical Engineering, Faculty of Engineering, University of Tabuk, Tabuk 47913, Saudi Arabia
Mohammad Rizwan: Department of Electrical Engineering, Delhi Technological University, Delhi 110042, India

Mathematics, 2022, vol. 10, issue 6, 1-40

Abstract: The rapid growth of electric vehicles in India necessitates more power to energize such vehicles. Furthermore, the transport industry emits greenhouse gases, particularly SO 2 , CO 2 . The national grid has to supply an enormous amount of power on a daily basis due to the surplus power required to charge these electric vehicles. This paper presents the various hybrid energy system configurations to meet the power requirements of the electric vehicle charging station (EVCS) situated in the northwest region of Delhi, India. The three configurations are: (a) solar photovoltaic/diesel generator/battery-based EVCS, (b) solar photovoltaic/battery-based EVCS, and (c) grid-and-solar photovoltaic-based EVCS. The meta-heuristic techniques are implemented to analyze the technological, financial, and environmental feasibility of the three possible configurations. The optimization algorithm intends to reduce the total net present cost and levelized cost of energy while keeping the value of lack of power supply probability within limits. To confirm the solution quality obtained using modified salp swarm algorithm (MSSA), the popularly used HOMER software, salp swarm algorithm (SSA), and the gray wolf optimization are applied to the same problem, and their outcomes are equated to those attained by the MSSA. MSSA exhibits superior accuracy and robustness based on simulation outcomes. The MSSA performs much better in terms of computation time followed by the SSA and gray wolf optimization. MSSA results in reduced levelized cost of energy values in all three configurations, i.e., USD 0.482/kWh, USD 0.684/kWh, and USD 0.119/kWh in configurations 1, 2, and 3, respectively. Our findings will be useful for researchers in determining the best method for the sizing of energy system components.

Keywords: artificial intelligence; salp swarm algorithm; hybrid optimization of multiple energy resources; renewable energy; electric vehicle charging station (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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