Equilibrium Optimizer: Insights, Balance, Diversity for Renewable Energy Resources Based Optimal Power Flow with Multiple Scenarios

Abstract

ABSTRACT Today, along with renewable energy sources such as wind generation units and solar photovoltaic systems, the power grid consists of traditional generating units. An approach for solving single-objective optimal power flow problems with the combination of renewable energy resources (RER-OPF) solar and wind power with conventional coal-based power stations is recommended in the proposed paper. In the proposed work, functions of lognormal and Weibull probability distribution are used, respectively, to forecast solar and wind outcomes. The objective feature includes the underestimation service charge and the standby charge for overestimating unusual non-conventional power generation. The quantitative and comparative results show that Equilibrium optimizer (EO) outperforms compare to Harris Hawks Optimizer (HHO), Grey Wolf Optimizer (GWO), Ions Motion Optimizer (IMO) and Success-History based Adaptive Differential Evolution (SHADE), which are all well-known optimization algorithms for solving RER-OPF problem. The EO optimizer provides the optimum value of each objective function and has merits in solving IEEE-30 bus-based RER-OPF problem, according to several evaluation criteria such as best value statistical criterion. Graphical Abstract