Hybrid cross entropy—cuckoo search algorithm for solving optimal power flow with renewable generators and controllable loads

Abstract

The demand of energy is increasing due to the growing population of the world and improvements of technology. One of the best significant solution techniques to fulfill this energy demand is utilization of renewable energy sources (RESs). Modern power systems, which integrate RESs, such as wind, small hydro or solar energy sources need to carry out the uncertainty by the accessibility of demanded or injected power. Therefore, it is necessary to consider uncertainty costs in optimal power flow (OPF) problems. This paper proposed a novel hybrid meta‐heuristic algorithm entitled cross entropy—cuckoo search algorithm (CE‐CSA). The application of levy flights in the cuckoo search algorithm (CSA) improves the local exploitation capability while the CE method is used in the initial stage for global exploration due to its fast convergence. The effectiveness of the proposed hybrid algorithm has been demonstrated in solving the OPF problem, considering RESs and controllable loads for different stochastic scenarios in a benchmark system to minimize the total operation cost. To verify its effectiveness, its performance is compared with the most advanced and recently proposed hybrid meta‐heuristic techniques. Simulation results show that the proposed algorithm can solve the OPF problems with RESs and controllable loads efficiently and can give better solutions compared to different techniques. The conventional statistical method called analysis of variance (ANOVA) test, Tukey honestly significant difference test, and Wilcoxon sign rank test are performed for comparative analysis of different techniques. The results of this test show the validation of CE‐CSA compared to different optimization techniques.

A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources

“…Various adjustments have been made to metaheuristic optimization approaches in the literature to address the issue of early convergence and provide an improved solution for the OPF problem, such as modified JAYA 31 , enhanced bacteria foraging algorithm (MBFA) 32 , SHADE-SF 33 , modified grasshopper optimization 34 , improved rao-2 algorithm 35 , boosted quasi-reflection jellyfish optimization algorithm 36 , hybrid cross entropy-cuckoo search algorithm 37 , hybrid TLTFWO 38 through the integration between the teaching and learning algorithm and turbulent flow of water algorithm, hybrid Mayfly algorithm and Aquila optimizer 39 . Accordingly, this study aims to develop a recent optimization technique named white shark optimization (WSO) to tackle the OPF, considering several real-world scenarios and the uncertainties associated with the generation and demand.…”

Multi‐objective optimal power flow using grasshopper optimization algorithm

“…Here, to implement and evaluate the performance of AVOA, it was tested on thirty six well standard benchmark functions. Recently, a novel hybrid meta-heuristic technique named cross entropy-cuckoo search algorithm (CE-CSA) 45 obtained by BSA, DE, PSO, GA, ABC and BBO algorithms. The rest of the manuscript is ordered as follows: Section 2 provides the mathematical expression of the OPF problem.…”

“…Here, to implement and evaluate the performance of AVOA, it was tested on thirty six well standard benchmark functions. Recently, a novel hybrid meta‐heuristic technique named cross entropy‐cuckoo search algorithm (CE‐CSA) 45 has successfully been implemented in solving the OPF problem, considering RESs and controllable loads for unlike stochastic scenarios in a established benchmark system. Moreover, in Reference 46, Abdel‐Basset et al proposed a new meta‐heuristic algorithm named memory‐based improved gorilla troops optimizer (MIGTO).…”