Multi-strategy enhanced orchard algorithm for optimal integration of renewable energy sources and EV charging stations in microgrids

The integration of Electric Vehicle Charging Stations (EVCS) with renewable energy (RE)-based microgrids (MGs) introduces significant operational challenges due to the stochastic charging behavior of EVs and the intermittent nature of Renewable Energy Sources (RES) such as solar photovoltaic (PV) and wind turbines (WTs). These uncertainties may have a negative impact on voltage stability, power balance, and system reliability. To solve these problems, this research paper presents a Multi-Strategy Enhanced Orchard Optimization Algorithm (MSEOA) which combines several search strategies to enhance exploration capabilities, faster convergence, and more accurate solutions than the conventional Orchard Algorithm (OA). The suggested approach simultaneously determines the optimal sizing and placement of RES units and EVCS while coordinating EV charging (EVC) behavior. A multi-objective optimization (MOO) framework is formulated to minimize voltage deviation, real power losses (PLs), and tie-line power deviation in the MG. The optimization variables include the capacities and locations of solar PV, WTs, and EVCS units, while EVC management is regulated using coefficients associated with solar irradiance, wind velocity, and demand ratio. The suggested framework is implemented in MATLAB and tested on a general MG test system using four operating conditions. The performance of the suggested MSEOA based on the simulation results shows that the MSEOA approach drastically diminishes real PLs, voltage variance, as well as tie-line power variation by 17.8%, 14.6%, and 12.3%, respectively, compared to the conventional OA. In addition, the algorithm converges more quickly with a computational time of 18.92 s, which supports the effectiveness of the algorithm in the optimal planning and operation management of EV-based renewable MGs.