Optimal unit commitment using equivalent linear minimum up and down time constraints

An optimal unit commitment for electric spot markets is presented in this paper. Unit commitment is a mixed integer and non-linear and complex combinatorial optimization problem which is difficult to be solved for large-scale power systems. This paper presents equivalent linear expression of the problem. Artificial generators are used to model loss of load and to avoid divergence. Mixed-integer linear programming is used to minimize the total energy dispatch cost in 24 hours of a day. A system as the same structure as Iranian power market is used to demonstrate the efficiency of the presented method. Simulation results are compared with the results of another approach. The results show the efficiency of the proposed method.

[1]  S..  SHORT-TERM GENERATION SCHEDULING WITH TRANSMISSION AND ENVIRONMENTAL CONSTRAINTS USING AN AUGMENTED LAGRANGIAN RELAXATION , 2009 .

[2]  Narayana Prasad Padhy,et al.  An ant colony system approach for unit commitment problem , 2006 .

[3]  Hugh Rudnick,et al.  Short-term hydrothermal generation scheduling model using a genetic algorithm , 2003 .

[4]  S. M. Shahidehpour,et al.  Optimal generation scheduling with ramping costs , 1993, Conference Proceedings Power Industry Computer Application Conference.

[5]  Weerakorn Ongsakul,et al.  Enhanced augmented Lagrangian Hopfield network for unit commitment , 2006 .

[6]  T. Senjyu,et al.  Fuzzy unit commitment scheduling using absolutely stochastic simulated annealing , 2006, IEEE Transactions on Power Systems.

[7]  A. Conejo,et al.  Optimal response of a thermal unit to an electricity spot market , 2000 .

[8]  Tomonobu Senjyu,et al.  A fast technique for unit commitment problem by extended priority list , 2003 .

[9]  Chuanwen Jiang,et al.  A matrix real-coded genetic algorithm to the unit commitment problem , 2006 .

[10]  Tomonobu Senjyu,et al.  Emerging solution of large-scale unit commitment problem by Stochastic Priority List , 2006 .

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

[12]  Xiaohong Guan,et al.  Unit Commitment with Identical Units: Successive Subproblem Solving Method Based on Lagrangian Relaxation , 2002, IEEE Power Engineering Review.

[13]  C.-P. Cheng,et al.  Unit commitment by annealing-genetic algorithm , 2002 .

[14]  Josef Kittler,et al.  Fast branch & bound algorithms for optimal feature selection , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[16]  C.E. Zoumas,et al.  A genetic algorithm solution approach to the hydrothermal coordination problem , 2004, IEEE Transactions on Power Systems.

[17]  Antonio J. Conejo,et al.  A Parallel Repair Genetic Algorithm to Solve the Unit Commitment Problem , 2002, IEEE Power Engineering Review.

[18]  Hanif D. Sherali,et al.  Evolution and state-of-the-art in integer programming , 2000 .

[19]  Hiroyuki Mori,et al.  Application of Priority-List-Embedded Tabu Search to Unit Commitment in Power Systems , 2001 .

[20]  M. R. Mohan,et al.  An evolutionary programming-based tabu search method for solving the unit commitment problem , 2004, IEEE Transactions on Power Systems.

[21]  W. Ongsakul,et al.  Unit commitment by enhanced adaptive Lagrangian relaxation , 2004, IEEE Transactions on Power Systems.

[22]  K. W. Edwin,et al.  Integer Programming Approach to the Problem of Optimal Unit Commitment with Probabilistic Reserve Determination , 1978, IEEE Transactions on Power Apparatus and Systems.

[23]  Narayana Prasad Padhy,et al.  Solution to Unit Commitment Problem with Spinning Reserve and Ramp Rate Constraints Using Ant Colony System , 2005 .