Optimal Power Flow of Renewable-Integrated Power Systems Using a Gaussian Bare-Bones Levy-Flight Firefly Algorithm

This article proposes a Gaussian bare-bones Levy-flight firefly algorithm (GBLFA) and its modified version named MGBLFA for optimizing the various kinds of the different optimal power flow (OPF) problems in the presence of conventional thermal power generators and intermittent renewable energy resources such as solar photovoltaic (PV) and wind power (WE). Several objective functions, including fuel costs, emission, power loss, and voltage deviation, are considered in the OPF problem subject to economic, technical, and safety constraints. Also, the uncertainties of solar irradiance and wind speed are modeled using Weibull, lognormal probability distribution functions, and their influences are considered in the OPF problem. Proper cost functions associated with the power generation of PV and WE units are modeled. A comprehensive analysis of ten cases with various objectives on the IEEE 30-bus test system demonstrates the potential effects of renewable energies on the optimal scheduling of thermal power plants in a cost-emission-effective manner. Numerical results show the superiority of the proposed method over other state-of-the-art algorithms in finding optimal solutions for the OPF problems.