Optimal Power Flow Using Improved Cross-Entropy Method

Abstract

An improved cross-entropy (CE) method assisted with a chaotic operator (CGSCE) is presented for solving the optimal power flow (OPF) problem. The introduction of the chaotic operator helps to enhance the exploration capability of the popular cross-entropy approach while the global best solution is preserved. To handle the constraints in the optimal power flow, an efficient constraint handling technique with no parameter adjustment is also introduced. The approach is tested on both the IEEE-30 bus system and the IEEE-57 bus system with different objective functions to verify its effectiveness in comparison with a few other methods reported in the literature. Simulation results confirm that the proposed method is capable of improving both the exploration ability and the convergence speed of the conventional cross-entropy method. It outperforms the original cross-entropy, its variant GSCE and other methods in most of the OPF study cases.

Giant Trevally Optimization Approach for Probabilistic Optimal Power Flow of Power Systems Including Renewable Energy Systems Uncertainty

“…As optimization problems continue to evolve, new techniques encompassing artificial intelligence, as well as the metaheuristic search-based optimization approaches were designed to tackle the D-OPF problem. Recent efforts focused on search-based optimization approaches, which include the genetic algorithm (GA) optimization method [10], particle swarm optimizer (PSO) method [11,12], differential evolution optimization method [13,14], enhanced genetic algorithms optimization method [15], gravitational searching algorithm (GSA) method [16,17], multi-phase searching optimization algorithm [18,19], improving colliding bodies method [20], improved PSO method [21], biogeography-based optimizing approach [22], fuzzy-based hybrid PSO method [23], blackhole optimization approach [24], imperialist competitive optimization algorithm [25], harmony search optimization algorithm [26], PSO hybrid with GSA method [27], grey wolf optimization technique [28], and bee colony optimization approach [29]. Additionally, many multi-objective functions have been introduced for the D-OPF in [30,31].…”