A biased random key genetic algorithm approach for unit commitment problem

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6630 LNCS, Issue , 2011, Pages 327-339

  Roque, Luís A C ^a   Fontes, Dalila B M M ^b   Fontes, Fernando A C C ^c  

^a INSTITUTO SUPERIOR DE ENGENHARIA DO PORTO   (Portugal)

^b UNIVERSITY OF PORTO   (Portugal)

^c UNIVERSITY OF PORTO   (Portugal)

Author keywords

Electrical Power Generation; Genetic Algorithm; Optimization; Unit Commitment

Indexed keywords

CROSSOVER STRATEGIES; ELECTRICAL POWER GENERATION; ENERGY DEMANDS; ENERGY PRODUCTIONS; GENETIC ALGORITHM APPROACH; LARGE-SCALE POWER SYSTEMS; MINIMUM COST; PARENT SELECTION; RANDOM KEYS; REAL NUMBER; SPINNING RESERVES; THERMAL GENERATION UNIT; UNIT COMMITMENT; UNIT COMMITMENT PROBLEM; LARGE SCALE UNIT COMMITMENT PROBLEM; SPINNING RESERVE CONSTRAINTS; UNIT-COMMITMENT;

GENETIC ALGORITHMS; OPTIMIZATION;

CONSTRAINT SATISFACTION PROBLEMS; GENETIC ALGORITHMS;

EID: 79955789910     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-20662-7_28     Document Type: Conference Paper

References (28)

1. Abookazemi, K., Mustafa, M.W., Ahmad, H.: Structured Genetic Algorithm Technique for Unit Commitment Problem. International Journal of Recent Trends in Engineering 1(3), 135-139 (2009)
2. Arroyo, J.M., Conejo, A.J.: A parallel repair genetic algorithm to solve the unit commitment problem. IEEE Transactions on Power Systems 17, 1216-1224 (2002)
3. Bean, J.C.: Genetic Algorithms and Random Keys for Sequencing and Optimization. ORSA Journal on Computing 6(2) (1994)
4. Chen, Y.M., Wang, W.S.: Fast solution technique for unit commitment by particle swarm optimisation and genetic algorithm. International Journal of Energy Technology and Policy 5(4), 440-456 (2007)
5. Cheng, C.P., Liu, C.W., Liu, G.C.: Unit commitment by Lagrangian relaxation and genetic algorithms. IEEE Transactions on Power Systems 15, 707-714 (2000)
6. Cohen, A.I., Yoshimura, M.: A Branch-and-Bound Algorithm for Unit Commitment. IEEE Transactions on Power Apparatus and Systems 102, 444-451 (1983)
7. Dudek, G.: Unit commitment by genetic algorithm with specialized search operators. Electric Power Systems Research 72(3), 299-308 (2004)
8. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, New York (1989)
9. Gonçalves, J.F., Resende, M.G.C.: Biased random-key genetic algorithms for combinatorial optimization. Journal of Heuristics (2010), Published online (August 27, 2010). DOI: 10.1007/s10732-010-9143-1
10. Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
11. Jenkins, L., Purushothama, G.K.: Simulated annealing with local search-a hybrid algorithm for unit commitment. IEEE Transactions on Power Systems 18(1), 1218-1225 (2003)
12. Juste, K.A., Kita, H., Tanaka, E., Hasegawa, J.: An evolutionary programming solution to the unit commitment problem. IEEE Transactions on Power Systems 14(4), 1452-1459 (1999)
13. Kazarlis, S.A., Bakirtzis, A.G., Petridis, V.: A Genetic Algorithm Solution to the Unit Commitment Problem. IEEE Transactions on Power Systems 11, 83-92 (1996)
14. Lee, F.N.: Short-term unit commitment-a new method. IEEE Transactions on Power Systems 3(2), 421-428 (1998)
15. Merlin, A., Sandrin, P.: A new method for unit commitment at Electricit de France. IEEE Transactions on Power Apparatus Systems 2(3), 1218-1225 (1983)
16. Padhy, N.P.: Unit commitment using hybrid models: a comparative study for dynamic programming, expert system, fuzzy system and genetic algorithms. International Journal of Electrical Power & Energy Systems 23(8), 827-836 (2001)
17. Raglend, I.J., Padhy, N.P.: Comparison of Practical Unit Commitment Solutions. Electric Power Components and Systems 36(8), 844-863 (2008)
18. Rajan, C.C.A., Mohan, M.R.: An evolutionary programming-based tabu search method for solving the unit commitment problem. IEEE Transactions on Power Systems 19(1), 577-585 (2004)
19. Rajan, C.C.A.,Mohan, M.R.,Manivannan, K.: Refined simulated annealing method for solving unit commitment problem. In: Proceedings of the 2002 International Joint Conference on Neural Networks, IJCNN 2002, vol. 1, pp. 333-338. IEEE, Los Alamitos (2002)
20. Salam, S.: Unit commitment solution methods. Proceedings of World Academy of Science, Engineering and Technology 26, 600-605 (2007)
21. Senjyu, T., Yamashiro, H., Uezato, K., Funabashi, T.: A unit commitment problem by using genetic algorithm based on unit characteristic classification. IEEE Power Engineering Society Winter Meeting 1 (2002)
22. Simopoulos, D.N., Kavatza, S.D., Vournas, C.D.: Unit commitment by an enhanced simulated annealing algorithm. IEEE Transactions on Power Systems 21(1), 68-76 (2006)
23. Sriyanyong, P., Song, Y.H.: Unit commitment using particle swarm optimization combined with Lagrange relaxation. In: Power Engineering Society General Meeting, pp. 2752-2759. IEEE, Los Alamitos (2005)
24. Swarup, K.S., Yamashiro, S.: Unit Commitment Solution Methodology Using Genetic Algorithm. IEEE Transactions on Power Systems 17, 87-91 (2002)
25. Valenzuela, J., Smith, A.E.: A seeded memetic algorithm for large unit commitment problems. Journal of Heuristics 8(2), 173-195 (2002)
26. Viana, A., Sousa, J.P., Matos, M.A.: Fast solutions for UC problems by a new metaheuristic approach. IEEE Electric Power Systems Research 78, 1385-1389 (2008)
27. Xing, W., Wu, F.F.: Genetic algorithm based unit commitment with energy contracts. International Journal of Electrical Power & Energy Systems 24(5), 329-336 (2002)
28. Zhao, B., Guo, C.X., Bai, B.R., Cao, Y.J.: An improved particle swarm optimization algorithm for unit commitment. International Journal of Electrical Power & Energy Systems 28(7), 482-490 (2006)