Abstract
This paper presents a discrete chaotic gravitational search algorithm (DCGSA) to solve the unit commitment (UC) problem. Gravitational search algorithm (GSA) has been applied to a wide scope of global optimization problems. However, GSA still suffers from the inherent disadvantages of trapping in local minima and the slow convergence rates. The UC problem is a discrete optimization problem and the original GSA and chaos which belong in the realm of continuous space cannot be applied directly. Thus in this paper a data discretization method is implemented after the population initialization to make the improved algorithm available for coping with discrete variables. Two chaotic systems, including logistic map and piece wise linear chaotic map, are used to generate chaotic sequences and to perform local search. The simulation was carried out on small-scale UC problem with six-unit system and ten-unit system. Simulation results show lower fuel cost than other methods such as quadratic model, selective pruning method and iterative linear algorithm, confirming the potential and effectiveness of the proposed DCGSA for the UC problem.
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References
Yuan, X., Su, A., Nie, H., Yuan, Y., Wang, L.: Unit commitment problem using enhanced particle swarm optimization algorithm. Soft. Comput. 15(1), 139–148 (2011)
Ting, T., Rao, M., Loo, C., Ngu, S.: Solving unit commitment problem using hybrid particle swarm optimization. J. Heuristics 9(6), 507–520 (2003)
Dudek, G.: Unit commitment by genetic algorithm with specialized search operators. Electr. Power Syst. Res. 72(3), 299–308 (2004)
Pappala, V., Erlich. I.: A new approach for solving the unit commitment problem by adaptive particle swarm optimization. In: IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, pp. 1–6 (2008)
Sun, L., Zhang, Y., Jiang, C.: A matrix real-coded genetic algorithm to the unit commitment problem. Electr. Power Syst. Res. 76(9), 716–728 (2006)
Roy, P.: Solution of unit commitment problem using gravitational search algorithm. Int. J. Electr. Power Energy Syst. 53, 85–94 (2013)
Shen, D., Jiang, T., Chen, W., Qian, S., Gao, S.: Improved chaotic gravitational search algorithms for global optimization. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1220–1226 (2015)
Dang, C., Li, M.: A floating-point genetic algorithm for solving the unit commitment problem. Eur. J. Oper. Res. 181(3), 1370–1395 (2007)
Padhy, N.: Unit commitment-a bibliographical survey. IEEE Trans. Power Syst. 19(2), 1196–1205 (2004)
May, R.: Simple mathematical models with very complicated dynamics. Nature 261(5560), 459–467 (1976)
Acknowledgment
This research was partially supported by the National Natural Science Foundation of China (Grant Nos. 61203325, 11572084, 11472061, and 61472284), the Shanghai Rising-Star Program (No. 14QA1400100) and JSPS KAKENHI Grant No. 15K00332 (Japan).
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Li, S., Jiang, T., Chen, H., Shen, D., Todo, Y., Gao, S. (2016). Discrete Chaotic Gravitational Search Algorithm for Unit Commitment Problem. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_67
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DOI: https://doi.org/10.1007/978-3-319-42294-7_67
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