Profit based unit commitment: A parallel ABC approach using a workstation cluster

This paper proposes a parallel artificial bee colony (PABC) approach for committing generating units thereby maximizing the profit of generation companies. Profit based unit commitment (PBUC) must be obtained in a short time even though there is an increase in generating units. Nowadays, computing resources are available in plenty, and effective utilization of these resources will be advantageous for reducing the time complexity for a large scale power system. Here, the message passing interface based technique is used in the PABC algorithm in distributed and shared memory models. The time complexity and the solution quality with respect to the number of processors in a cluster are thoroughly analyzed. PABC for PBUC is tested for a power system ranging from 10 to 1000 generating units. Also the PABC is validated for economic dispatch and the unit commitment problem in a traditional power system on 40 and 10 unit systems, respectively.

[1]  Louis-A. Dessaint,et al.  Parallel computing environments and methods , 2000, Proceedings International Conference on Parallel Computing in Electrical Engineering. PARELEC 2000.

[2]  S. M. Shahidehpour,et al.  Unit commitment using a hybrid model between Lagrangian relaxation and genetic algorithm in competitive electricity markets , 2004 .

[3]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

[4]  Mietek A. Brdys,et al.  Grid Implementation of a Parallel Multiobjective Genetic Algorithm for Optimized Allocation of Chlorination Stations in Drinking Water Distribution Systems: Chojnice Case Study , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[5]  Rolf Hempel,et al.  The emergence of the MPI message passing standard for parallel computing , 1999 .

[6]  June Ho Park,et al.  Economic load dispatch using evolutionary algorithms , 1996, Proceedings of International Conference on Intelligent System Application to Power Systems.

[7]  P. Attaviriyanupap,et al.  A Hybrid LR-EP for Solving New Profit-Based UC Problem under Competitive Environment , 2002, IEEE Power Engineering Review.

[8]  Tomonobu Senjyu,et al.  Fast solution technique for large-scale unit commitment problem using genetic algorithm , 2003 .

[9]  Mitsuhisa Sato,et al.  An Experience with Super-Linear Speedup Achieved by Parallel Computing on a Workstation Cluster: Parallel Calculation of Density of States of Large Scale Cyclic Polyacenes , 1995, Parallel Comput..

[10]  S. M. Shahidehpour,et al.  Combination of Lagrangian-relaxation and linear-programming approaches for fuel-constrained unit-commitment problems , 1989 .

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

[12]  Gerald B. Sheblé,et al.  A profit-based unit commitment GA for the competitive environment , 2000 .

[13]  L. F. B. Baptistella,et al.  A Decomposition Approach to Problem of Unit Commitment Schedule for Hydrothermal Systems , 1980 .

[14]  S. Baskar,et al.  Hybrid real coded genetic algorithm solution to economic dispatch problem , 2003, Computers & electrical engineering.

[15]  Chi-Chang Chen,et al.  A Generic Parallel Computing Model for the Distributed Environment , 2006, 2006 Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT'06).

[16]  Zwe-Lee Gaing,et al.  Particle swarm optimization to solving the economic dispatch considering the generator constraints , 2003 .

[17]  P. K. Chattopadhyay,et al.  Evolutionary programming techniques for economic load dispatch , 2003, IEEE Trans. Evol. Comput..

[18]  Lingyun Wei,et al.  Design and implementation of a windows-based parallel computing environment for large scale optimization , 2004 .

[19]  S. Virmani,et al.  Implementation of a Lagrangian Relaxation Based Unit Commitment Problem , 1989, IEEE Power Engineering Review.

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

[21]  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.

[22]  Dingju Zhu,et al.  Application of Parallel Computing in Digital City , 2008, 2008 10th IEEE International Conference on High Performance Computing and Communications.

[23]  H. Monsef,et al.  A new approach for profit-based unit commitment using Lagrangian relaxation combined with ant colony search algorithm , 2008, 2008 43rd International Universities Power Engineering Conference.

[24]  G. Sheblé,et al.  Power generation operation and control — 2nd edition , 1996 .

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

[26]  Y. W. Wong,et al.  Genetic and genetic/simulated-annealing approaches to economic dispatch , 1994 .

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

[28]  Richi Nayak,et al.  A hybrid neural network and simulated annealing approach to the unit commitment problem , 2000 .

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

[30]  P. J. Van Den Bosch,et al.  A Solution of the Unit Commitment Problem Via Decomposition and Dynamic Programming , 1985, IEEE Transactions on Power Apparatus and Systems.

[31]  Narayana Prasad Padhy,et al.  Unit commitment problem under deregulated environment-a review , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[32]  T.A.A. Victoire,et al.  Unit commitment by a tabu-search-based hybrid-optimisation technique , 2005 .

[33]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[34]  M. Sydulu,et al.  New approach with muller method for profit based unit commitment , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[35]  Chun Che Fung,et al.  A low-cost parallel computing platform for power engineering applications , 2000 .

[36]  Deyu Qi,et al.  Implementations of Grid-Based Distributed Parallel Computing , 2006, First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06).

[37]  G. Sheblé,et al.  Genetic algorithm solution of economic dispatch with valve point loading , 1993 .

[38]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[39]  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.

[40]  Rahul Garg,et al.  ECONOMIC GENERATION AND SCHEDULING OF POWER BY GENETIC ALGORITHM , 2008 .