TY - GEN
T1 - Techno-Economic Optimization of Grid Connected Electric Vehicle Charging Station
T2 - 9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024
AU - Bilal, Mohd
AU - Bokoro, Pitshou N.
AU - Sharma, Gulshan
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This research evaluates the integration of renewable energy sources with grid-connected systems to power electric vehicle charging stations (EVCSs) in Riyadh, Kingdom of Saudi Arabia (KSA), underscoring its importance for sustainable development. An eco-friendly EVCS model is proposed and apply a novel metaheuristic optimization technique, the Enhanced Bald Eagle Search Algorithm (EBESA), to determine the optimal sizing of system components. Our objective is to minimize the total net present cost (TNPC) and the levelized cost of energy (LCOE) while ensuring reliability as quantified by the loss of power supply probability (LPSP). The performance of the EBESA is compared with alternative algorithms such as the Bald Eagle Search Algorithm (BESA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO), assessing their efficacy in system component sizing. Our findings indicate that the optimal EVCS configuration is a solar photovoltaic/wind turbine (PV/WT) grid-tied system, achieving a LCOE of $0.0796/kWh, a TNPC of $102,042. our study aims to guide policy-making and investment in the expansion of EV charging infrastructure, with broader implications for developing countries.
AB - This research evaluates the integration of renewable energy sources with grid-connected systems to power electric vehicle charging stations (EVCSs) in Riyadh, Kingdom of Saudi Arabia (KSA), underscoring its importance for sustainable development. An eco-friendly EVCS model is proposed and apply a novel metaheuristic optimization technique, the Enhanced Bald Eagle Search Algorithm (EBESA), to determine the optimal sizing of system components. Our objective is to minimize the total net present cost (TNPC) and the levelized cost of energy (LCOE) while ensuring reliability as quantified by the loss of power supply probability (LPSP). The performance of the EBESA is compared with alternative algorithms such as the Bald Eagle Search Algorithm (BESA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO), assessing their efficacy in system component sizing. Our findings indicate that the optimal EVCS configuration is a solar photovoltaic/wind turbine (PV/WT) grid-tied system, achieving a LCOE of $0.0796/kWh, a TNPC of $102,042. our study aims to guide policy-making and investment in the expansion of EV charging infrastructure, with broader implications for developing countries.
KW - Charging station
KW - Electric vehicle
KW - Optimization approach
KW - Solar photovoltaic
KW - Wind turbine
UR - http://www.scopus.com/inward/record.url?scp=85217878946&partnerID=8YFLogxK
U2 - 10.1109/ICAST61769.2024.10856459
DO - 10.1109/ICAST61769.2024.10856459
M3 - Conference contribution
AN - SCOPUS:85217878946
T3 - IEEE International Conference on Adaptive Science and Technology, ICAST
BT - Proceedings of the 2024 IEEE 9th International Conference on Adaptive Science and Technology, ICAST 2024
PB - IEEE Computer Society
Y2 - 24 October 2024 through 26 October 2024
ER -
