Unit Commitment Using Particle Swarm-Based-Simulated Annealing Optimization Approach

In this paper, a new approach based on hybrid particle swarm-based-simulated annealing optimization (PSO-B-SA) for solving thermal unit commitment (UC) problems is proposed. The PSO-B-SA presented in this paper solves the two sub-problems simultaneously and independently; unit-scheduled problem that determines on/off status of units and the economic dispatch problem for production amount of generating units. Problem formulation of UC is defined as minimization of total objective function while satisfying all the associated constraints such as minimum up and down time, production limits and the required demand and spinning reserve. Simulation results show that the proposed approach can outperform the other solutions.

[1]  X. Yao.  Evolving Artificial Neural Networks , 1999 .

[2]  Anastasios G. Bakirtzis,et al.  A genetic algorithm solution to the unit commitment problem , 1996 .

[3]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

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

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

[6]  P. Sriyanyong,et al.  Unit commitment using particle swarm optimization combined with Lagrange relaxation , 2005, IEEE Power Engineering Society General Meeting, 2005.

[7]  C.D. Vournas,et al.  Unit Commitment by an Enhanced Simulated Annealing Algorithm , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[8]  K. Uezato,et al.  A fast technique for unit commitment problem by extended priority list , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[9]  Tomonobu Senjyu,et al.  A unit commitment problem by using genetic algorithm based on unit characteristic classification , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[10]  Gerald B. Sheblé,et al.  Solution of the unit commitment problem by the method of unit periods , 1990 .

[11]  Francisco D. Galiana,et al.  Towards a more rigorous and practical unit commitment by Lagrangian relaxation , 1988 .

[12]  S. M. Shahidehpour,et al.  An intelligent dynamic programming for unit commitment application , 1991 .

[13]  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.

[14]  L. L. Garver,et al.  Power Generation Scheduling by Integer Programming-Development of Theory , 1962, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems.

[15]  P. G. Lowery,et al.  Generating Unit Commitment by Dynamic Programming , 1966 .

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

[17]  A. Merlin,et al.  A New Method for Unit Commitment at Electricite De France , 1983, IEEE Transactions on Power Apparatus and Systems.

[18]  F. Albuyeh,et al.  Evaluation of Dynamic Programming Based Methods and Multiple area Representation for Thermal Unit Commitments , 1981, IEEE Transactions on Power Apparatus and Systems.

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

[20]  N. Sadati,et al.  Hybrid Particle Swarm-Based-Simulated Annealing Optimization Techniques , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[21]  T.O. Ting,et al.  A novel approach for unit commitment problem via an effective hybrid particle swarm optimization , 2006, IEEE Transactions on Power Systems.

[22]  Richard C. Wilson,et al.  An Application of Mixed-Integer Programming Duality to Scheduling Thermal Generating Systems , 1968 .

[23]  C.K. Pang,et al.  Optimal short-term thermal unit commitment , 1976, IEEE Transactions on Power Apparatus and Systems.

[24]  W. Ongsakul,et al.  Ant colony search algorithm for unit commitment , 2003, IEEE International Conference on Industrial Technology, 2003.

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

[26]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[27]  Costas Vournas,et al.  Unit commitment by an enhanced simulated annealing algorithm , 2006 .

[28]  A. Bakirtzis,et al.  A solution to the unit-commitment problem using integer-coded genetic algorithm , 2004, IEEE Transactions on Power Systems.