Abstract
In this paper, the Unit Commitment (UC) problem is presented and solved, following an innovative approach based on a metaheuristic procedure. The problem consists on deciding which electric generators must be committed, over a given planning horizon, and on defining the production levels that are required for each generator, so that load and spinning reserve requirements are verified, at minimum production costs. Due to its complexity, exact methods proved to be inefficient when real size problems were considered. Therefore, heuristic methods have for long been developed and, in recent years, metaheuristics have also been applied with some success to the problem. Methods like Simulated Annealing, Tabu Search and Evolutionary Programming can be found in several papers, presenting results that are sufficiently interesting to justify further research in the area. In this paper, a resolution framework based on GRASP – Greedy Randomized Adaptive Search Procedure – is presented. To obtain a general optimisation tool, capable of solving different problem variants and of including several objectives, the operations involved in the optimisation process do not consider any particular characteristics of the classical UC problem. Even so, when applied to instances with very particular structures, the computational results show the potential of this approach.
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References
K. Aoki et al., Unit commitment in a large-scale power system including fuel constrained thermal and pumped-storage hydro, IEEE Trans. Power Systems 4 (1987) 1077–1084.
J.F. Bard, Short-term scheduling of thermal-electric generators using Lagrangian relaxation, Oper. Res. 36(5) (1988) 756–766.
A.I. Cohen and M. Yoshimura, A branch-and-bound algorithm for unit commitment, IEEE Trans. Power Apparatus Systems 102(2) (1983) 444–451.
D. Dasgupta and D. McGrevor, Thermal unit commitment using genetic algorithms, IEE Proc. Generation Transmission Distribution 141 (1994) 459–465.
M. Ehrgott and X. Gandibleux, A survey and annotated bibliography of multiobjective combinatorial optimization, OR Spektrum 22 (2000) 425–460.
T. Feo and M. Resende, Greedy randomized adaptive search procedures, J. Global Optim. 6 (1995) 109–133.
P. Festa and M. Resende, GRASP: An annotated bibliography, in: Essays and Surveys on Metaheuristics, eds. P. Hansen and C. Ribeiro (Kluwer Academic, Dordrecht, 2001).
B.F. Hobbs et al., The Next Generation of Electric Power Unit Commitment Models (Kluwer Academic, Dordrecht, 2001).
S.-J. Huang and C.-L. Huang, Application of genetic-based neural networks to thermal unit commitment, IEEE Trans. Power Systems 12 (1997) 654–660.
S. Kazarlis, A. Bakirtzis and V. Petridis, A genetic algorithm solution to the unit commitment problem, IEEE Trans. Power Systems 11 (1996) 83–92.
F. Lee, Short term unit commitment – a new method, IEEE Trans. Power Systems 3 (1988) 421–428.
A. Mantawy, Y.L. Abdel-Magid and S.Z. Selim, A simulated annealing algorithm for unit commitment, IEEE Trans. Power Systems 13 (1998) 197–204.
A. Mantawy, Y.L. Abdel-Magid and S.Z. Selim, Unit commitment by tabu search, IEE Proc. Generation Transmission Distribution 145 (1998) 56–65.
A. Merlin and P. Sandrin, A new method for unit commitment at Electricite de France, IEEE Trans. PAS 102 (1983) 1218–1225.
Z. Ouyang and S. Shahidehpour, A multi-stage intelligent system for unit commitment, IEEE Trans. Power Systems 7 (1992) 639–646.
C.R. Reeves, Modern Heuristiques Techniques for Combinatorial Problems (McGraw-Hill, New York, 1995).
H. Sasaki, M. Watanabe and R. Yokoyama, A solution method of unit commitment by artificial neural networks, IEEE Trans. Power Systems 7 (1992) 974–981.
S. Sen and D.P. Kothari, Optimal thermal generating unit commitment: a review, Electrical Power Energy Systems 20 (1998) 443–451.
G. Sheblé, Computational Auction Mechanisms for Restructured Power Industry Operation (Kluwer Academic, Dordrecht, 1999).
A. Viana and J.P. Sousa, Using metaheuristics in multiobjective resource constrained project scheduling, European J. Oper. Res. 120 (2000) 359–374.
A. Wood and B.F. Wollenberg, Power Generation Operation and Control (Wiley, New York, 1996).
S. Yin Wa Wong, An enhanced simulated annealing approach to unit commitment, Electrical Power Energy Systems 20 (1998) 359–368.
F. Zhuang and F.D. Galiana, Unit commitment by simulated annealing, IEEE Trans. Power Systems 5 (1990) 311–318.
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Viana, A., de Sousa, J.P. & Matos, M. Using GRASP to Solve the Unit Commitment Problem. Annals of Operations Research 120, 117–132 (2003). https://doi.org/10.1023/A:1023326413273
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DOI: https://doi.org/10.1023/A:1023326413273