Neural Based Tabu Search method for solving unit commitment problem

This paper presents a new approach to solve short-term unit commitment problem (UCP) using Neural Based Tabu Search (NBTS). The solution of the unit commitment problem is a complex optimization problem. The exact solution of the UCP can be obtained by a complete enumeration of all feasible combinations of generating units, which could be very huge number. The unit commitment has commonly been formulated as a nonlinear, large scale, mixed-integer combinational optimization problem. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for next H hours. Neyveli Thermal Power Station II in India, demonstrates the effectiveness of the proposed approach. Numerical results are shown to compare the superiority of the cost solutions obtained using the Tabu Search (TS) method in reaching proper unit commitment.

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

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

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

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

[5]  N. Jimenez-Redondo,et al.  Unit commitment by Lagrangian relaxation and genetic algorithms [discussion and closure] , 2001 .

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

[7]  Teuvo Kohonen,et al.  An introduction to neural computing , 1988, Neural Networks.

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

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

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

[11]  K. Manivannan,et al.  Neural-based tabu search method for solving unit commitment problem , 2003 .

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

[13]  Wei-Jen Lee,et al.  Improve the unit commitment scheduling by using the neural network based short term load forecasting , 2004, Conference, 2004 IEEE Industrial and Commercial Power Systems Technical.

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

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

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

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

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

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

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

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

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

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

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

[25]  Emile H. L. Aarts,et al.  Simulated annealing and Boltzmann machines - a stochastic approach to combinatorial optimization and neural computing , 1990, Wiley-Interscience series in discrete mathematics and optimization.

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

[27]  S. M. Shahidehpour,et al.  Extended neighborhood search algorithm for constrained unit commitment , 1997 .

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

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