A New Approach To Economic Load Dispatch Of Power System Using Imperialist Competitive Algorithm

Electrical power industry restructuring has introduced a highly vibrant and competitive market which altered revolutionized many aspects of the power industry. In this changed scenario, the scarcity of energy resources, the ever increasing power generation cost, environmental concerns, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems due to local optimum solution convergence. In the past decade, Met heuristic optimization techniques especially Imperialist Competitive Algorithm (ICA) has gained an incredible recognition as the solution algorithm for such type of ED problems. The application of ICA in ED problem which is considered as the most complex optimization problem has been summarized in present paper.

[1]  A.G. Exposito,et al.  Short-term hydro-thermal coordination based on interior point nonlinear programming and genetic algorithms , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[2]  Leandro dos Santos Coelho,et al.  Economic dispatch optimization using hybrid chaotic particle swarm optimizer , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[3]  Cara MacNish Evolutionary programming techniques for testing students' code , 2000, ACSE '00.

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

[5]  Secundino Soares,et al.  A network flow model for short-term hydro-dominated hydrothermal scheduling problems , 1994 .

[6]  Malabika Basu,et al.  A simulated annealing-based goal-attainment method for economic emission load dispatch of fixed head hydrothermal power systems , 2005 .

[7]  R.-H. Liang,et al.  Scheduling of hydroelectric generations using artificial neural networks , 1994 .

[8]  S. J. Huang,et al.  Enhancement of Hydroelectric Generation Scheduling Using Ant Colony System-Based Optimization Approaches , 2001, IEEE Power Engineering Review.

[9]  P. K. Chattopadhyay,et al.  Evolutionary programming techniques for economic load dispatch , 2003, IEEE Trans. Evol. Comput..

[10]  Chunming Yang,et al.  A new particle swarm optimization technique , 2005, 18th International Conference on Systems Engineering (ICSEng'05).

[11]  A.H. Mantawy,et al.  A new tabu search algorithm for the long-term hydro scheduling problem , 2002, LESCOPE'02. 2002 Large Engineering Systems Conference on Power Engineering. Conference Proceedings.

[12]  Peter B. Luh,et al.  Hydroelectric generation scheduling with an effective differential dynamic programming algorithm , 1990 .

[13]  J. Waight,et al.  Experiences with Mixed Integer Linear Programming-Based Approaches in Short-Term Hydro Scheduling , 2001, IEEE Power Engineering Review.

[14]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[15]  Yanbin Yuan,et al.  A hybrid chaotic genetic algorithm for short-term hydro system scheduling , 2002, Math. Comput. Simul..

[16]  F. Y. Xu,et al.  Short-term hydro -thermal scheduling with Artificial Bee Colony , 2012, 2012 International Conference on Machine Learning and Cybernetics.

[17]  Caro Lucas,et al.  Colonial competitive algorithm: A novel approach for PID controller design in MIMO distillation column process , 2008, Int. J. Intell. Comput. Cybern..

[18]  C. Sagastizábal,et al.  Solving the unit commitment problem of hydropower plants via Lagrangian Relaxation and Sequential Quadratic Programming , 2005 .

[19]  K. C. Almeida,et al.  Short term hydrothermal scheduling with bilateral transactions via bundle method , 2007 .