A new evolutionary algorithm for non-linear economic dispatch

Reduce fossil fuel resources; increasing established new power generation unit costs; and ever growing demand for electric energy necessitate optimal economic dispatch (ED) in today's electric power systems. Modern heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution for ED problems. One of the recently proposed evolutionary algorithms is the Shuffled Frog Leaping Algorithm (SFLA). In the original SFLA, every frog updates its position according to the best solution, because of the influence of the local best solution, every frog will constringe about the local best solution quickly. In this paper a new method is proposed to modify the worst frog's position. This proposed approach is called Modified Shuffle Frog Leaping Algorithm (MSFLA). Also, in order to improve the algorithm's stability and the ability to search the global optimum, a Chaotic Local Search (CLS) is used to get rid of the local optima. The proposed algorithm, called Chaotic Modified Shuffled Frog Leaping Algorithm (CMSFLA), is used to solve the ED problem considering the valve-point loading effects, multi-fuel and prohibited operating zones. The proposed algorithm is tested on different sample systems and its results are compared with other methods.

[1]  Chern-Lin Chen,et al.  Branch-and-bound scheduling for thermal generating units , 1993 .

[2]  Wei-Chiang Hong,et al.  Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model , 2009 .

[3]  Jong-Bae Park,et al.  An Improved Particle Swarm Optimization for Nonconvex Economic Dispatch Problems , 2010, IEEE Transactions on Power Systems.

[4]  Tunchan Cura,et al.  Particle swarm optimization approach to portfolio optimization , 2009 .

[5]  A. Rahimi-Vahed,et al.  A novel hybrid multi-objective shuffled frog-leaping algorithm for a bi-criteria permutation flow shop scheduling problem , 2009 .

[6]  Qi Wu,et al.  The hybrid forecasting model based on chaotic mapping, genetic algorithm and support vector machine , 2010, Expert Syst. Appl..

[7]  Ali Maroosi,et al.  Application of shuffled frog-leaping algorithm on clustering , 2009 .

[8]  Nima Amjady,et al.  Solution of nonconvex and nonsmooth economic dispatch by a new Adaptive Real Coded Genetic Algorithm , 2010, Expert Syst. Appl..

[9]  G. Cheng,et al.  On the efficiency of chaos optimization algorithms for global optimization , 2007 .

[10]  Kevin E Lansey,et al.  Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm , 2003 .

[11]  P. K. Chattopadhyay,et al.  Solving complex economic load dispatch problems using biogeography-based optimization , 2010, Expert Syst. Appl..

[12]  Taher Niknam,et al.  A new fuzzy adaptive particle swarm optimization for non-smooth economic dispatch , 2010 .

[13]  Donald E. Grierson,et al.  Comparison among five evolutionary-based optimization algorithms , 2005, Adv. Eng. Informatics.

[14]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[15]  W. Lin,et al.  Nonconvex Economic Dispatch by Integrated Artificial Intelligence , 2001, IEEE Power Engineering Review.

[16]  G. L. Viviani,et al.  Hierarchical Economic Dispatch for Piecewise Quadratic Cost Functions , 1984, IEEE Transactions on Power Apparatus and Systems.

[17]  Kevin E Lansey,et al.  Application of the Shuffled Frog Leaping Algorithm for the Optimization of a General Large-Scale Water Supply System , 2009 .

[18]  Ouaténi Diallo,et al.  Melnikov analysis of chaos in a general epidemiological model , 2007 .

[19]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[20]  Whei-Min Lin,et al.  An Improved Tabu Search for Economic Dispatch with Multiple Minima , 2002, IEEE Power Engineering Review.

[21]  Chao-Lung Chiang,et al.  Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels , 2005, IEEE Transactions on Power Systems.

[22]  S. Khamsawang,et al.  DSPSO–TSA for economic dispatch problem with nonsmooth and noncontinuous cost functions , 2010 .

[23]  G. Sheblé,et al.  Genetic algorithm solution of economic dispatch with valve point loading , 1993 .

[24]  AmjadyNima,et al.  Solution of nonconvex and nonsmooth economic dispatch by a new Adaptive Real Coded Genetic Algorithm , 2010 .

[25]  Kwang Y. Lee,et al.  Fuel-cost minimisation for both real-and reactive-power dispatches , 1984 .

[26]  Ching-Tzong Su,et al.  New approach with a Hopfield modeling framework to economic dispatch , 2000 .

[27]  Muzaffar Eusuff,et al.  Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization , 2006 .

[28]  G. Sheblé,et al.  Power generation operation and control — 2nd edition , 1996 .

[29]  Malcolm Irving,et al.  Economic dispatch of generators with prohibited operating zones: a genetic algorithm approach , 1996 .

[30]  Alireza Rahimi-Vahed,et al.  A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem , 2007, Comput. Ind. Eng..