A Novel Trigonometric Mutation-Based Backtracking Search Algorithm for Solving Optimal Power Flow Problem Considering Renewable Energy Sources

Abstract

Renewable energy sources (RESs)−based optimal power flow (OPF) problem that imposes a higher degree of uncertainty and nonlinearities into the solution process is a great concern for the power system researchers. In the present work, to improve the convergence rate of the orthodox backtracking search algorithm (BSA), a highly intelligent trigonometric mutation−based BSA (TMBSA) is proposed. The proposed technique is used to minimize highly non-linear, non-convex, and uncertainty-induced power generation costs for thermal power units along with highly intermittent wind energy (WE) and tidal energy (TDE). Further, the power generation cost objective is considered with the valve-point loading effect and prohibited operating zones (POZs) to make the research work more pragmatic. To confirm the superiority of the proposed technique, simulation work is performed on a RESs-based modified IEEE 30−bus test system, and the results are compared with the BSA. The result analysis clearly shows that the proposed TMBSA outperforms the BSA in terms of delivering more high−quality, accurate results with a faster convergence speed while solving the uncertainty−based OPF problem.