SOLAR BASED ELECTRIC VEHICLE CHARRING STATION USING GENETIC ALGORITHM

Solar

For the first time, a comprehensive model of rapid charging EV stations that are connected to a hybrid grid-RES system, such as solar, mini-hydro, and wind, has been provided. This model takes into consideration the features of EV demand as well as arrival time, departure time, state of charge, and battery capacity. This helps to maximize profits while also reducing the amount of electricity drawn from the grid. The hybrid genetic with pattern search (hGPS) method is used to run simulations for the very first time, marking a significant milestone in the development of metaheuristic techniques. They are used to tune the system characteristics of the charging station, which is referred to as the NPV. This helps to maximize the net present value. The studies are carried out with the assistance of a sequential Monte Carlo simulation and a probabilistic distribution of the EV demand determined by the behaviors of EVs and the hourly intervals. The economic considerations acquired using the hybrid genetic with pattern search (hGPS) algorithm reveal that hGPS optimizes profit when compared to the Genetic Algorithm (GA) and the Pattern Search (PS) algorithms. These comparisons were made using the hGPS method. It is also evident that the suggested method reduces the effect of the grid on the system network. This is accomplished by placing a limit on the quantity of power that may be traded between the system network and the grid

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