An Improved Human Evolutionary Optimization Algorithm for Optimal Integration of Hybrid Fast Charging Stations

To enhance the beneficial effects and reduce the adverse impacts of electric vehicles (EVs) on electrical distribution systems (EDSs), and to promote their growing adoption rate, it is essential to optimally allocate charging infrastructure and renewable energy-based distributed generation (RDGs), taking into account all aspects of technoeconomic-environmental advantages.The determination of locations and sizes for multiple targets is a crucial effort due to the complexities associated with solving non-linear power flow equations and varied planning and operational limitations, both equal and unequal.Further, the issue pertains to discrete and continuous variables.This work presents an enhanced form of the human evolutionary optimization algorithm (IHEOA) that incorporates an adaptive Gaussian random walk and a group hopping strategy to decrease computing complexity and enhance exploration by concentrating on favourable areas.The multi-objective optimization problem for minimizing loss, enhancing voltage stability, and reducing greenhouse gas (GHG) emissions is designed to address the optimal integration of hybrid fast charging stations (HFCSs) with photovoltaic systems (PVs), wind turbines (WTs), distribution static compensator (D-STATCOM) and capacitor banks (CBs).The computational efficiency of IHEOA is evaluated by simulations of several case studies on the IEEE 33-bus, incorporating grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scenarios.The findings demonstrate that IHEOA surpasses HEOA and other metaheuristic approaches for global optima and convergence characteristics.It is discovered that power losses are optimized by 31% and 67%, average voltage deviation (AVD) is decreased by 24% and 32%, and GHG emissions are reduced by 21% and 96% under G2V and V2G modes, respectively.The results underscore the suitability of the suggested methodology for addressing realtime complex optimization problems and for converting existing EDSs into sustainable clean energy systems.