Impact of electric vehicles and wave energy systems on OPF of power networks using hybrid Osprey-PSO approach

Abstract

In this article, a novel optimization algorithm named hybrid Osprey Optimization and Particle Swarm Optimization (OOPSO) is proposed to solve the Probabilistic Optimal Power Flow (POPF) problem in power systems with high penetration of renewable energy resources (RERs), such as photovoltaic, wind, and wave energy and integration of electric vehicles (EVs). The algorithm's results are compared with established optimization algorithms like Osprey Optimization Algorithm (OOA), Particle Swarm Optimization (PSO), and Heap Optimization. Applied to the 30 and 118-bus IEEE test systems, the OOPSO algorithm efficiently deals with the uncertainties produced by RERs generation. This research study includes EV charging profiles for weekdays and weekends. It considers home and work-level charging scenarios. The RERs and EVs penetration ensure system reliability while minimizing generation costs and satisfying power flow constraints. Compared to existing algorithms, the OOPSO algorithm achieves superior performance, faster convergence, and higher solution accuracy. The simulation results show the effectiveness of OOPSO. It appears to be a robust tool for POPF, integrating RERs in power systems with EVs. This work highlights the potential of OOPSO as a feasible approach for power system optimization in the context of RERs and EV integration, paving the way for further research into unconventional grid optimization techniques.