Techno-economic feasibility analysis of an electric vehicle charging station for an International Airport in Chattogram, Bangladesh

The transportation system is one of the crucial requirements of day-to-day human life. The car is one of the most attractive modes of transportation system for human beings. The demand for electric vehicles (EVs) has been growing dramatically in the last few years to reduce environmental impacts. This article provides a proposed electric vehicle charging station (EVCS) development with detailed planning and comprehensive analysis for Shah Amanat (CGP) International Airport, Chattogram, Bangladesh. A load profile prediction method is developed by utilizing Fuzzy logic and RNN-LSTM, taking account of climate data, battery state of charge (SOC), vehicle occupancy, and flight schedules. After predicting the load profile, an average load of 10.54 MWh/day is generated as output. This load profile is used in optimization software to optimize the operational and economic performance of the components and determine the most cost-effective design of the EVCS. Four scenarios with different resource combinations were explored, and Scenario-3 (S3): grid-tied Photovoltaic (PV) and wind turbine (WT) configuration was found to be the optimal scenario, with a cost of energy (COE) of $0.041/kWh based on the renewable fraction (84.3 %) and profitability index (5.55). Given these results and a suitable charging tariff ($0.140/kWh), the estimated profit was calculated to be $0.22 M/year. In comparison to the grid-only scenario (Base Scenario), Net present cost (NPC), COE, and emission can be lowered by 84 %, 63 %, and 75 %, respectively. As EVs have yet to enter the country's market, the feasibility research will assist decision-makers in determining the best adoption of integrating EVCS using renewable energy.

Automated summary

This article proposes the development of an electric vehicle charging station (EVCS) at Shah Amanat (CGP) International Airport in Chattogram, Bangladesh. Using fuzzy logic and RNN-LSTM, a load profile prediction method is developed to consider climate data, battery state of charge (SOC), vehicle occupancy, and flight schedules. The optimal scenario is found to be the grid-tied Photovoltaic (PV) and wind turbine (WT) configuration, with a cost of energy (COE) of $0.041/kWh and an estimated profit of $0.22 M/year.