Modified augmented hopfield neural network for optimal thermal unit commitment

This paper develops a novel solution methodology of the Thermal Unit Commitment Problem (TUCP) using modified Augmented Hopfield Network (AHN) with enhanced performance. The modifications are mandatory to eliminate the error that conventional AHN structure is reported to suffer from. This error originates from the mapping process, the corner stone in using AHN as an optimization tool. A new solution algorithm is developed by combining the AHN with the proposed modifications. In order to verify the effectiveness of the new algorithm, it is applied and tested to some examples reported in literature and the solution is then compared with that obtained by counterpart Artificial Intelligence (AI) techniques. Unlike other AI techniques, the solution obtained using the modified AHN is more optimal and satisfying all the operating constraints.

[1]  M. Aganagic,et al.  A practical resource scheduling with OPF constraints , 1995 .

[2]  H. A. Smolleck,et al.  A fuzzy logic approach to unit commitment , 1997 .

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

[4]  A. J. Svoboda,et al.  Short-term resource scheduling with ramp constraints [power generation scheduling] , 1997 .

[5]  K. S. Swarp,et al.  Unit Connuitment Solution Methodology Using Genetic Algorithm , 2002, IEEE Power Engineering Review.

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

[7]  Yonghua Song,et al.  Modern optimisation techniques in power systems , 1999 .

[8]  A. Ebenezer Jeyakumar,et al.  A tabu search based hybrid optimization approach for a fuzzy modelled unit commitment problem , 2006 .

[9]  M. Shahidehpour,et al.  Price-based unit commitment: a case of Lagrangian relaxation versus mixed integer programming , 2005, IEEE Transactions on Power Systems.

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

[11]  A. Y. Abdelaziz,et al.  A hybrid HNN-QP approach for dynamic economic dispatch problem , 2008 .

[12]  Bruce F. Wollenberg,et al.  A unit commitment expert system (power system control) , 1988 .

[13]  Abdel-aal Hassan Ismail. Mantawy.  Unit commitment by artificial intelligence techniques. , 1997 .

[14]  Mark O'Malley,et al.  Augmented Hopfield network for unit commitment and economic dispatch , 1997 .

[15]  V. Palanisamy,et al.  A dynamic programming based fast computation Hopfield neural network for unit commitment and economic dispatch , 2007 .

[16]  F. J. Heredia,et al.  Optimum short-term hydrothermal scheduling with spinning reserve through network flows , 1995 .

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

[18]  Mark O'Malley,et al.  Initialisation of the augmented Hopfield network for improved generator scheduling , 2002 .

[19]  Mark O'Malley,et al.  Augmented Hopfield network for constrained generator scheduling , 1999 .

[20]  A. Y. Abdelaziz,et al.  Economic Dispatch Using an Enhanced Hopfield Neural Network , 2008 .

[21]  C. Christober Asir Rajan,et al.  An evolutionary programming based simulated annealing method for solving the unit commitment problem , 2007 .

[22]  Chuangxin Guo,et al.  An improved particle swarm optimization algorithm for unit commitment , 2006 .

[23]  A. J. Svoboda,et al.  A new unit commitment method , 1997 .

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

[25]  Weerakorn Ongsakul,et al.  Ramp rate constrained unit commitment by improved priority list and augmented Lagrange Hopfield network , 2008 .