A novel chaotic flower pollination algorithm for function optimization and constrained optimal power flow considering renewable energy sources - Daqaq, Fatima; Ouassaid, Mohammed; Kamel, Salah; Ellaia, Rachid; El-Naggar, Mohamed F. (Frontiers in Energy Research, 2022)
This paper presents an improved chaotic flower pollination algorithm (CFPA) with a view to handle the optimal power flow (OPF) problem integrating a hybrid wind and solar power and generate the optimal settings of generator power, bus voltages, shunt reactive power, and tap setting transformers. In spite of the benefits of FPA, it encounters two problems like other evolutionary algorithms: entrapment in local optima and slow convergence speed. Thus, to deal with these drawbacks and enhance the FPA searching accuracy, a hybrid optimization approach CFPA which combines the stochastic algorithm FPA that simulates the flowering plants process, with the chaos methodology is applied in this manuscript. Therefore, owing to the various non-linear constraints in OPF issue, a constraint handling technique named superiority of feasible solutions (SF) is embedded into CFPA. To confirm the performance of the chaotic FPA, a set of different well-known benchmark functions were employed for ten diverse chaotic maps, then the best map is tested on IEEE 30-bus and IEEE 57-bus test systems. The obtained results are analyzed statistically using Non-parametric Wilcoxon rank-sum test in view of evaluating their significance compared to the outcomes of the state-of-the-art metaheuristic algorithms such ant bee colony (ABC), grasshopper optimization algorithm (GOA), and dragonfly algorithm (DA). Additionally, experimental results illustrate that combining FPA with chaotic sequences is able to accelerate the convergence, provide better accuracy to find optimal solutions, and prove that CFPA (especially with the Sinusoidal map) is challenging in solving complex real-world problem.