An Evolutionary Programming Based Tabu Search Method for Unit Commitment Problem with Cooling-Banking Constraints

An Evolutionary Programming Based Tabu Search Method for Unit Commitment Problem with Cooling-Banking Constraints This paper presents a new approach to solve the short-term unit commitment problem using An Evolutionary Programming Based tabu search method with cooling and banking constraints. Numerical results are shown comparing the cost solutions and computation time obtained by using the evolutionary programming method and other conventional methods like dynamic programming, lagrangian relaxation.

[1]  Yong-Gang Wu,et al.  A diploid genetic approach to short-term scheduling of hydro-thermal system , 2000 .

[2]  Arthur I. Cohen,et al.  A Branch-and-Bound Algorithm for Unit Commitment , 1983, IEEE Transactions on Power Apparatus and Systems.

[3]  J. M. Ngundam,et al.  Optimal scheduling of large-scale hydrothermal power systems using the Lagrangian relaxation technique , 2000 .

[4]  Hong-Tzer Yang,et al.  Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions , 1996 .

[5]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[6]  Francisco D. Galiana,et al.  Unit commitment by simulated annealing , 1990 .

[7]  Shokri Z. Selim,et al.  A simulated annealing algorithm for the clustering problem , 1991, Pattern Recognit..

[8]  Michael Mascagni Book review:Simulated annealing and Boltzntann machines: A stochastic approach to combinatorial optimization and neural computing , 1989 .

[9]  Walter L. Snyder,et al.  Dynamic Programming Approach to Unit Commitment , 1987, IEEE Transactions on Power Systems.

[10]  S. M. Shahidehpour,et al.  Hydro-thermal, scheduling by tabu search and decomposition method , 1996 .

[11]  S. M. Shahidehpour,et al.  Short-term unit commitment expert system , 1990 .

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

[13]  Y. Y. Hong,et al.  Genetic Algorithms Based Economic Dispatch for Cogeneration Units Considering Multiplant Multibuyer Wheeling , 2002, IEEE Power Engineering Review.

[14]  J. Birge,et al.  Using integer programming to refine Lagrangian-based unit commitment solutions , 2000 .

[15]  Chiang-Tsung Huang,et al.  Dynamic security constrained multi-area unit commitment , 1991 .

[16]  R. Nieva,et al.  Lagrangian Reduction of Search-Range for Large-Scale Unit Commitment , 1987, IEEE Power Engineering Review.

[17]  A. G. Bakirtzis,et al.  Lambda of Lagrangian relaxation solution to unit commitment problem , 2000 .

[18]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[19]  Shokri Z. Selim,et al.  Integrating genetic algorithms, tabu search, and simulated annealing for the unit commitment problem , 1999 .

[20]  A. H. Mantawy,et al.  A simulated annealing algorithm for unit commitment , 1998 .

[21]  Narayana Prasad Padhy,et al.  Unit commitment using hybrid models: a comparative study for dynamic programming, expert system, fuzzy system and genetic algorithms , 2001 .

[22]  Eiichi Tanaka,et al.  An Evolutionary Programming Solution to the Unit Commitment Problem , 1997 .

[23]  Kit Po Wong,et al.  Combined genetic algorithm/simulated annealing/fuzzy set approach to short-term generation scheduling with take-or-pay fuel contract , 1996 .

[24]  M. R. Mohan,et al.  Optimal short-term hydrothermal scheduling using decomposition approach and linear programming method , 1992 .

[25]  Yuan-Yih Hsu,et al.  Fuzzy dynamic programming: an application to unit commitment , 1991 .

[26]  A. H. Mantawy,et al.  Unit commitment by tabu search , 1998 .

[27]  Hiroshi Sasaki,et al.  A solution method of unit commitment by artificial neural networks , 1992 .

[28]  Chuan-Ping Cheng,et al.  Unit commitment by Lagrangian relaxation and genetic algorithms , 2000 .

[29]  David B. Fogel,et al.  Evolutionary algorithms in theory and practice , 1997, Complex.