Optimal power flow by means of improved adaptive differential evolution

Optimal power flow (OPF) problem is a large-scale, non-convex, multi-modal, and non-linear constrained optimization problem, which has been widely used in power system operation. Because of these features, solving the OPF problem is a very popular and challenging task in power system optimization. In recent years, many advanced optimization methods are employed to deal with the OPF problem. However, most of these methods are unconstrained. In this paper, an enhanced adaptive differential evolution (JADE) with self-adaptive penalty constraint handling technique, referred to as EJADE-SP, is proposed to obtain the optimal solution of the OPF problem. The EJADE-SP is an enhanced version of JADE, where four improvements are proposed to enhance the performance of JADE when solving the OPF problem: i) crossover rate (CR) sorting mechanism is introduced to allow individuals to inherit more good genes; ii) re-randomizing parameters (CR and scale factor F) to maintain the search efficiency and diversity; iii) dynamic population reduction strategy is used to accelerate convergence; and iv) self-adaptive penalty constraint handling technique is integrated to deal with the constraints. To verify the effectiveness of the proposed method, it is applied to the OPF problem on a modified IEEE 30-bus test system, which combines stochastic wind energy and solar energy with conventional thermal power generators. The simulation results demonstrate that the proposed approach can be an effective alternative for the OPF problem. (C) 2020 Elsevier Ltd. All rights reserved.