Incremental based PSO for optimal power flow considering wind power producers

The optimal power flow (OPF) is a mathematical optimization computation used in electrical power systems engineering to determine the most efficient and cost-effective way to operate a power grid while meeting various constraints and objectives. Furthermore, nowadays, with the increasing assimilation of renewable energy resources like wind and solar power into the grid, the OPF becomes even more critical. These sources are variable and intermittent, and OPF can help manage their integration by adjusting conventional generation and grid infrastructure to maintain system stability and reliability. This manuscript presents how to acquire OPF in power systems with the incorporation of wind power producers (WPP). The objective function of this work is minimizing generation costs and network losses. A modified technique called incremental particle swarm optimization (IPSO) is proposed to resolve the non-convex and non-linear OPF hindrance effectually. IPSO is a variant of the classic PSO in which a new particle is included to the population at each iteration, and the new particle adjusts its position based on information from other particles with "social learning" rules. The WPP outputs are set varies to assess the effectiveness of IPSO OPF for various levels of WPP outputs. Simulations are performed for conventional OPF, PSO OPF, and IPSO OPF and evaluate the efficacy of the proposed method.