A decision-centric approach for techno-economic optimization and environmental assessment of standalone and grid-integrated renewable-powered electric vehicle charging stations under multiple planning horizons

The automotive sector is undergoing a transitional phase towards electrification with the goal of achieving climate sustainability. Renewable-powered electric vehicle (EV) charging infrastructure can play a vital role in this paradigm shift. To support and provide a roadmap to this transition, the current study aims to investigate a sustainable planning approach for optimal renewable-powered electric vehicle charging stations (EVCS) within institutional premises. A feasibility analysis for multiple renewable-based EV charging station configurations has been carried out based on multi-objective techno-economic and environmental indicators including Levelized cost of energy (LCOE), Net present cost (NPC), and carbon emissions. The proposed planning approach has been applied under Fast charging for private vehicles and on-board charging for Ez-bikes and shuttles (FCOBC) and Medium- charging for private vehicles and on-board charging for Ez-bikes and shuttles (MCOBC). Results reveal that a PV system backed with a utility grid enabling both energy purchase and sale during peak hours and offpeak hours respectively remained the most feasible configuration for EV charging infrastructure experiencing an LCOE of $0.1289/kWh to $0.1262/kWh and $0.1168/kWh to $0.1161/kWh for both FCOBC and MCOBC, respectively. Along with the environmental analysis, a forest absorbing carbon emissions analysis has been performed to provide a policy optimization for the future environmental outlook. Results validations have been undertaken for levelized average tariffs of single point supply (above 66 kV) of different government-owned distribution companies, which indicated the commercial efficacy of the proposed EV charging infrastructure, where the LCOE is lower than the real-time operational tariffs within the region. Finally, a sensitivity analysis is carried out under different uncertainties to check the optimization behavior of proposed systems.