The Improved Binary-Real Coded Shuffled Frog Leaping Algorithm for Solving Short-Term Hydropower Generation Scheduling Problem in Large Hydropower Station

Abstract

The short-term hydro generation scheduling (STHGS) decomposed into unit commitment (UC) and economic load dispatch (ELD) subproblems is complicated problem with integer optimization, which has characteristics of high dimension, nonlinear and complex hydraulic and electrical constraints. In this study, the improved binary-real coded shuffled frog leaping algorithm (IBR-SFLA) is proposed to effectively solve UC and ELD subproblems, respectively. For IB-SFLA, the new grouping strategy is applied to overcome the grouping shortage of SFLA, and modified search strategies for each type of frog subpopulation based on normal cloud model (NCM) and chaotic theory are introduced to enhance search performance. The initialization strategy with chaos theory and adaptive frog activation mechanism are presented to strengthen performance of IR-SFLA on ELD subproblem. Furthermore, to solve ELD subproblem, the optimal economic operation table is formed using IR-SFLA and invoked from database. Moreover, reserve capacity supplement and repair, and minimum on and off time repairing strategies are applied to handle complex constraints in STHGS. Finally, the coupled external and internal model corresponding to UC and ELD subproblems is established and applied to solve STHGS problem in Three Gorges hydropower station. Simulation results obtained from IBR-SFLA are better than other compared algorithms with less water consumption. In conclusion, to solve STHGS optimization problem, the proposed IBR-SFLA presents outstanding performance on solution precision and convergence speed compared to traditional SFLA effectively and outperforms the rivals to get higher precision solution with improving the utilization rate of waterpower resources.

A modified shuffled frog leaping algorithm with inertia weight

“…This algorithm was motivated by the predatory habit of frog groups in a small pond and contains elements of local search and global information shuffling [1,2]. Due to its advantages of fast computation and excellent convergence performance, SFLA has been widely applied in optimization domains, such as parameter estimation [3], the unit commitment problem [4], wireless sensor networks (WSNs) design [5], integrated circuits design [6], scheduling problem [7], and machine learning [8]. However, with the increasing of the complexity and the dimension of the solving problem, the convergence speed and solution accuracy of SFLA decreases significantly, even the SFLA easily traps into local optima.…”

A modified shuffled frog leaping algorithm with inertia weight

“…This algorithm was motivated by the predatory habit of frog groups in a small pond and contains elements of local search and global information shuffling 10,11 . Due to its advantages of fast computation and excellent convergence performance, SFLA has been widely applied in optimization domains, such as parameter estimation 12 , the unit commitment problem 13 , wireless sensor networks (WSNs) design 14 , integrated circuits design 15 , scheduling problem 16 , and machine learning 17 . However, with the increasing complexity and the dimension of the solving problem, the convergence speed and solution accuracy of SFLA decreases significantly, even the SFLA easily traps into local optima.…”