The exhaustion of fossil fuels, environmental concerns, and difficulties in deploying smart grids have expedited the development of renewable energy distributed generators (REDGs) and electric vehicles (EVs). In recent decades, there has been a notable rise in the production and marketing of EVs. Previous research has proposed reactive power control solutions, including the use of power electronic converters associated with distributed generators (DGs) to alleviate voltage fluctuations. This research presents a strategy for the best integration of electric vehicles through bidirectional charging and renewable energy distributed generators inside power systems, with the objective of efficiently managing voltage, active power, and reactive power flows at interconnection points. Furthermore, it entails determining appropriate locations and dimensions for electric car charging stations through a comparative examination of computing time and iterations between the Hybrid Genetic Algorithm Improved Particle Swarm Optimization (HGAIPSO) and several other optimization methods, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Improved Particle Swarm Optimization (IPSO). This analysis was performed on the IEEE-118 bus system, incorporating Vehicle-to-Grid (V2G), Grid-to-Vehicle (G2V), and REDG allocations. The simulation results indicated that the suggested HGAIPSO approach is more rapid and effective regarding calculation time for complex networks, attaining optimal solutions with greater efficiency.
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