A hybrid cultural algorithm based on clonal selection principle for optimal generation scheduling of cascaded hydropower stations

To solve optimal generation scheduling problem of cascaded hydropower stations, a hybrid cultural algorithm based on clonal selection principle (HCA-CSA) is presented. HCA-CSA uses cultural algorithm (CA) as its framework and clonal selection algorithm (CSA) in population space. Considering the characteristics of CSA, three knowledge structures are redefined in belief space to improve the search purposefulness and directivity of CSA, so as to improve the searching convergence rate and precision. In addition, a recombination and a chaos search operation are adopted in belief space to accelerate convergence rate and precision of the proposed algorithm. HCA-CSA is first tested by several benchmark problems and then it is applied to a case study of optimal generation scheduling of the Three Gorges Cascaded Hydropower Stations. The results obtained show its efficiency on solving complex optimization problems, and it can be an alternative for optimal generation scheduling of cascaded hydropower stations.

[1]  J. Sharma,et al.  Short term hydro scheduling using two-phase neural network , 2002 .

[2]  Yanbin Yuan,et al.  An enhanced differential evolution algorithm for daily optimal hydro generation scheduling , 2008, Comput. Math. Appl..

[3]  Robert G. Reynolds,et al.  Knowledge-based solution to dynamic optimization problems using cultural algorithms , 2001 .

[4]  C. Coello,et al.  Cultured differential evolution for constrained optimization , 2006 .

[5]  Janis A. Bubenko,et al.  Application of Decomposition Techniques to Short-Term Operation Planning of Hydrothermal Power System , 1986 .

[6]  Robert G. Reynolds,et al.  Cultural swarms: modeling the impact of culture on social interaction and problem solving , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[7]  Kang Li.  A New Algorithm for Solving Function Optimization Problems with Inequality Constraints , 1999 .

[8]  M. Janga Reddy,et al.  Ant Colony Optimization for Multi-Purpose Reservoir Operation , 2006 .

[9]  Slobodan P. Simonovic,et al.  Optimal Operation of Reservoir Systems using Simulated Annealing , 2002 .

[10]  William W.-G. Yeh,et al.  Optimization of Real‐Time Hydrothermal System Operation , 1992 .

[11]  V. T. Chow,et al.  Discrete Differential Dynamic Programing Approach to Water Resources Systems Optimization , 1971 .

[12]  Xiaohui Yuan,et al.  Improved Self-Adaptive Chaotic Genetic Algorithm for Hydrogeneration Scheduling , 2008 .

[13]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[14]  Xia Qing,et al.  Optimal daily scheduling of cascaded plants using a new algorithm of nonlinear minimum cost network flow , 1988 .

[15]  J. Bubenko,et al.  Application of Decomposition Techniques to Short-Term Operation Planning of Hydrothermal Power System , 1986, IEEE Transactions on Power Systems.

[16]  Xiaohui Yuan,et al.  Short-term hydro-thermal scheduling using particle swarm optimization method , 2007 .

[17]  R. Reynolds.  AN INTRODUCTION TO CULTURAL ALGORITHMS , 2008 .