A reliable optimization framework using ensembled successive history adaptive differential evolutionary algorithm for optimal power flow problems

Abstract

The Optimal Power Flow (OPF) is a primary tool in planning and installing power systems. It attempts to minimize the operating costs associated with generating and transmitting electrical power by modifying control parameters to satisfy environmental, economic, and operational constraints. Implementing an efficient and robust optimization algorithm for the above-said problem is critical to achieving such a typical objective. Therefore, this paper introduces and evaluates new variants of the Successive History-based Adaptive Differential Evolutionary (SHADE) algorithm called ESHADE, SHADE-SFS, and SHADE-SAP to solve the OPF problems with equality and inequality constraints. Generally, the static penalty approach is widely used for eliminating infeasible solutions discovered during the search phase when searching for feasible solutions. This approach requires the accurate selection of penalty coefficients, accomplished through the trial-and-error method. The proposed ESHADE algorithm is formulated using Self-Adaptive Penalty (SAP) and Superiority of Feasible Solution (SFS) mechanisms to obtain feasible solutions for OPF problems. Two IEEE bus systems are used to demonstrate the effectiveness of the proposed algorithm in handling OPF problems. The fuel cost and active power loss obtained by the proposed algorithm are better than other state-of-the-art algorithms. The results reveal that the proposed framework offers significant advantages over other algorithms.