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Cost-effective optimization of on-grid electric vehicle charging systems with integrated renewable energy and energy storage: An economic and reliability analysis
被引:5
|作者:
Bilal, Mohd
[1
]
Oladigbolu, Jamiu O.
[2
,3
]
Mujeeb, Asad
[4
]
Al-Turki, Yusuf A.
[2
,3
]
机构:
[1] Univ Johannesburg, Dept Elect & Elect Engn Technol, ZA-2006 Johannesburg, South Africa
[2] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia
[3] K A CARE Energy Res & Innovat Ctr, Jeddah 21589, Saudi Arabia
[4] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
关键词:
Saudi Arabia;
Electric Vehicle Charging Station (EVCS);
Improved Salp Swarm Algorithm (ISSA);
Sensitivity analysis;
Total net present cost (TNPC);
Levelized Cost of Electricity (LCOE);
RURAL ELECTRIFICATION;
SAUDI-ARABIA;
STATION;
WIND;
DESIGN;
FEASIBILITY;
AREA;
D O I:
10.1016/j.est.2024.113170
中图分类号:
TE [石油、天然气工业];
TK [能源与动力工程];
学科分类号:
0807 ;
0820 ;
摘要:
As urban areas expand and the demand for sustainable transportation solutions grows, optimizing infrastructure to support electric vehicles (EVs) becomes increasingly crucial. This study investigates the enhancement of electric vehicle charging systems (EVCS) in Saudi Arabia by leveraging its renewable energy potential. Specifically, the research explores the optimization of EVCS using hybrid renewable energy sources and battery storage systems across Riyadh, Jeddah, Mecca, and Medina. The methodology employs the Improved Salp Swarm Algorithm (ISSA) and compares its performance with the Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), and Flower Pollination Algorithm (FPA). This approach integrates battery energy storage, solar photovoltaic (SPV) panels, wind turbines, diesel generators, and grid connections, and evaluates these systems against technical, economic, and emission (TEE) metrics. Detailed sensitivity analysis assesses the impact of variables such as inflation rate, real discount rate, solar irradiance, and Lack of Power Supply Probability (LPSP) on system performance. Numerical results indicate that ISSA significantly outperforms other algorithms in TEE metrics across all scenarios, achieving the lowest Levelized Cost of Electricity (LCOE) of $0.0711/kWh in Medina. The SPV/WT/BESS configuration across all selected cities achieved zero CO2 emissions, while the optimal SPV/WT/ Grid configuration substantially reduced total net present cost (TNPC). For instance, in Riyadh, the TNPC was reduced to $95,692, with operating costs of $2845.42 and a renewable fraction (RF) of 67.9 %. In Jeddah, the TNPC was $96,576.59 with an LCOE of $0.1995/kWh. Mecca's optimal configuration achieved a TNPC of $93,272.57 and an RF of 56 %, while Medina's SPV/WT/Grid configuration reached the lowest LCOE and a TNPC of $79,573.71. Seasonal and diurnal analyses demonstrate the systems' adaptability to varying energy demands, with grid integration particularly beneficial during low renewable output periods. Increasing the LPSP index from 0 % to 5 % resulted in a cost reduction, with LCOE dropping from $1.02 to $0.4231 in Riyadh. This research provides a comprehensive framework for urban planners and policymakers to strategically deploy EV charging infrastructures that are both economically viable and environmentally sustainable.
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