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In this paper, we provide extensive modeling of fast-charging electric vehicle stations (FCEVS) allied to a grid integrated renewable energy sources (RES like hydro and solar) system considering an electric vehicle (EV) demand characteristics, state of charge (SOC), battery ratings, arrival, and departure time. This aids in maximizing the revenue and lowering the grid energy utilization. Investigations are carried with a novel meta-heuristic technique known as the emperor penguin optimization (EPO) algorithm, which is utilized to optimize the FCEVS parameters and maximize net present value (NPV). The investigations wih EPO has shown significant improvent of 1% with particle swarm optimization (PSO) and 1.25% with crow search algorithm (CSA). Also, studies are carried using a probabilistic demand distribution based on EV behaviors considering a sequential Monte-Carlo approach with hourly intervals. The acquired economic deliberations with EPO algorithm are compared with PSO, CSA, and EPO extracts maximum revenue. Simulations with FCEVS fed by grid, RES, and hybrid grid-RES reveal that FCEVS fed by hybrid grid-RES extracts the highest NPV. Further, 78.09% increase in NPV is observed with RES feeds the Grid over Grid feeds FCEVS alone, 25.23% hike of NPV is observed with both RES and battery storage system (BSS) over RES system, 10.56% increase with considering EVs arrival time and 2.3% increase with considering flow of EVs. Further, simulations with detailed modeling of FCEVS enhance NPV values over the hybrid grid-RES system.

Ideal parameter distribution in renewable integrated rapid charging electric vehicle station