New Strategy Based on Combined Use of Genetic Algorithm and Gradient to Solve the UC Problem: Theoretical Investigation and Comparative Study

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    This paper presents a comparative study between a new strategy based on hybrid  Gradient-Genetic Algorithm method and metaheuristic methods for solving  Unit Commitment problem. Strategies have been applied on the IEEE electrical network 14 bus test system for a variable load profile during a discretized margin of time (24-hour time requirement). The right choice of the initial population and the best knowledge of the technical constraints specific to each generator (power balance constraints, Spinning reserve constraints, minimum up time, minimum down time ) suggests the possibility of obtaining improvements in the time execution. The adopted strategy has presented high performance both for minimizing the production cost and for the rapidity of convergence to optimal solutions and is promising compared to Genetic algorithm.



  • Keywords

    Unit commitment ; Optimization ; Scheduling ; Genetic Algorithm ; Gradient method

  • References

      [1] S. Kharbash, W. Wang, All-Terminal Network Reliability Optimization in Fading Environment via Cross Entropy Method, IEEE Trans on Power Systems 2010, 978-1-4244-6404-3/10.

      [2] S, Jr WJ, Powell Jr JD, Rayburn JC. Dynamic programming approach to unit commitment, IEEE Trans on Power Systems 1987; 2:339-350

      [3] Sahbi Marrouchi, Moez ben hessine and Souad Chebbi, “New strategy based on Combined Use of Particle Swarm Optimization and Gradient methods to solve the Unit Commitment Problem”, 15th IEEE International Conference on Environment and Electrical Engineering (EEEIC), Rome, Italy, 10-13 Juin 2015.

      [4] Sahbi Marrouchi, Souad Chebbi, “Combined Use of Particle Swarm Optimization and Genetic Algorithm Methods to Solve the Unit Commitment Problem”, 16th International conference on Sciences and Techniques of Automatic control & computer engineering (STA), Monastir, Tunisia, December 21-23, 2014.

      [5] X. Guan, P B Luh, H Yan, J A Amalfi- An optimization-based method for unit commitment. Electric power and energy systems, Vol.14, No.1, Feb 1992, pp.9-17

      [6] Ouyang Z, Shahidehpour S.M. -An intelligent dynamic programming for unit commitment application, IEEE Trans on Power Systems 1991; 6(3):1203-1209.

      [7] Sahbi MARROUCHI, Souad CHEBBI, “Unit Commitment Optimization Using Gradient-Genetic Algorithm and Fuzzy Logic Approaches”, Studies in Computational Intelligence, Vol. 319, pp. 687-710 Springer, 2014.

      [8] Zhuang F, Galiana FD. -Towards a more rigorous and practical unit commitment by Lagrangian relaxation, IEEE Trans on Power Systems 1988; 3 (2):763-772.

      [9] T. Logenthiran and Dipti Srinivasan, ‘‘Particle Swarm Optimization for Unit Commitment Problem’’,IEEE Transactions on Power Systems, 2010.

      [10] C. P. Cheng, C. W. Liu, C. C. Liu. ‘‘Unit commitment by annealing-genetic algorithm”, Electrical Power and Energy Systems 24 (2002) 149-158.

      [11] P. G. Lowery, ‘‘Generating unit commitment by dynamic programming”, IEEE Trans. on Power Apparatus and Systems’’, vol. 85, no. 5, pp. 422- 426, 1966.

      [12] C.K. Pang and NY G. B. Sheble ‘‘evaluation of dynamic programming based methods and multiple area representation for thermal unit commitments’’, IEEE Transactions on Power Apparatus and Systems, Vol. 1PAS-100, No. 3, March 1981.

      [13] A. Merlin and P. Sandrin, “A new method for unit commitment at Electricity De France, IEEE Transactions on Power Apparatus and Systems, Vol. PAS-102, No. 5, May 1983.

      [14] W. Ongsakul, and N. Petcharaks,“Unit Commitment by Enhanced Adaptive Lagrangian Relaxation’’, IEEE Transactions on Power Systems, VOL. 19, NO. 1, Febrary 2004.

      [15] Dimitris N. Simopoulos, Stavroula D. Kavatza, and Costas D. Vournas, “Unit Commitment by an Enhanced Simulated Annealing Algorithm’’,IEEE Manuscript received April 22, 2005; revised July 29, 2005. Paper no. TPWRS-00234-2005.

      [16] Tim T. Maifeld and Gerald B. Sheble, ‘‘Genetic-based unitcommitment algorithm”, IEEE Transactions on Power Systems, Vol. 1 1, No. 3, August 1996.

      [17] A. Rudolf and R. Bayrleithner, ‘‘A Genetic Algorithm for Solving the Unit Commitment Problem of a Hydro-Thermal Power System’’,IEEE Transactions on Power Systems, Vol. 14, No. 4, November 1999.

      [18] Hiroyuki Mori,Osamu Matsuzaki and Tama-Ku,‘‘Embedding the Priority List into Tabu Search for Unit Commitment’’,IEEE Transactions on Power Systems, 2001.

      [19] Sahbi Marrouchi, Nesrine Amor, Moez Ben Hessine and Souad Chebbi, “Theoretical Investigation of Combined Use of PSO, Tabu Search and Lagrangian Relaxation methods to solve the Unit Commitment Problem”, Advances in Science, Technology and Engineering Systems Journal 3 (1), 357-365,2018.

      [20] C. P. Cheng, C. W. Liu, C. C. Liu. -Unit commitment by annealing-genetic algorithm, Electrical Power and Energy Systems 24 (2002) 149-158, 2002.

      [21] H. AboElFotoh and L. Al-Sumait, - A Neural Approach to Topological Optimization of Communication Networks, with Reliability Constraint, IEEE Transactions on Reliability, vol. 50, no. 4, 397-408 2001.




Article ID: 16322
DOI: 10.14419/ijet.v7i3.13.16322

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.