Improved gazelle optimization algorithm (IGOA)-based optimal design of solar/battery energy storage/EV charging station

Abstract

Small-scale photovoltaic (PV), battery energy storage systems (BESS), and electric vehicle charging stations have all been proposed and implemented as part of an integrated system in numerous cities worldwide to develop sustainable urban efficiency and dramatically increase the rate of utilization of solar energy resources. To scale PV and BESS and define BESS’s charging/discharging pattern, this manuscript demonstrates a grid-connected photovoltaic/battery energy storage/EV charging station optimization model (PBES). To minimize the cost of electricity, this study provides an optimization model for a grid-connected PBES. To solve this model, GOA-BESA is used. The model's optimal size and energy management technique are determined. Therefore, this manuscript proposes an intelligent search technique that combines the gazelle optimization algorithm (GOA) and is improved by utilizing the bald eagle search algorithm (BESA) which is named the improved gazelle optimization algorithm (IGOA). The IGOA is employed to simulate EV charging patterns and to calculate the EV charging demand at each time interval. By then the performance of the proposed methodology will be evaluated using MATLAB, and then, the proposed technique will be compared with existing techniques.