Optimization of Multipurpose Reservoir Operation with Application Particle Swarm Optimization Algorithm

Document Type : Research Paper

Authors

1 Ph.D. Student of Water Resources, Dept. of Irrigation and Reclamation, College of Tech. and Agriculture Eng., Pardis of Agriculture and Natural Resources, Tehran University

2 Assoc. Prof. of Irrigation and Reclamation, College of Tech. and Agriculture Eng., Pardis of Agriculture and Natural Resources, Tehran University

Abstract

Optimal operation of multipurpose reservoirs is one of the complex and sometimes nonlinear problems in the field of multi-objective optimization. Evolutionary algorithms are optimization tools that search decision space using simulation of natural biological evolution and present a set of points as the optimum solutions of problem. In this research, application of multi-objective particle swarm optimization (MOPSO) in optimal operation of Bazoft reservoir with different objectives, including generating hydropower energy, supplying downstream demands (drinking, industry and agriculture), recreation and flood control have been considered. In this regard, solution sets of the MOPSO algorithm in bi-combination of objectives and compromise programming (CP) using different weighting and power coefficients have been first compared that the MOPSO algorithm in all combinations of objectives is more capable than the CP to find solution with appropriate distribution and these solutions have dominated the CP solutions. Then, ending points of solution set from the MOPSO algorithm and nonlinear programming (NLP) results have been compared. Results showed that the MOPSO algorithm with 0.3 percent difference from the NLP results has more capability to present optimum solutions in the ending points of solution set.

Keywords

References

1-Elbeltagi, E., Hegazy, T., and Grierson, G. (2005). “Comparison among five evolutionary-based optimization algorithms.” J. of Advanced Engineering Informatics, 19(1), 43-53.
2-Blum, C., and Roli, A. (2003). “Metaheuristic in combinatorial optimization: Overview and conceptual comparision.” J. of ACM Computing Surveys, 35(3), 268-308.
3-Rajabpur, R., and Afshar, M.H. (2008). “Optimized operation of serial pump stations using the PSO algorithm.” J. of Water and Wastewater, 66, 56-66. (In Persian)
4-Chow, V.T., Maidment, D.R., and Mays, L.W. (1988). Applied hydrology, McGraw-Hill, New York.
5-Meraji, S.H., Afshar, M.H., and Afshar, A. (2006). “Reservoir operation by particle swarm optimization algorithm.” 7th International Conference of Civil Engineering, Tehran, Iran.
6-Balter, A.M., and Fontane, D.G. (2006). “A multiobjective particle swarm optimization model for reservoir operations and planning.” Proceeding of International Conference on Computing and Decision Making in Civil and Building Engineering, Montreal, 1544-1552.
7-Coello, C.A., and Lechunga, M.S. (2002). “MOPSO: A proposal for multiple objective particle swarm optimization.” Proceeding of IEEE Congress on Evolutionary Computation, IEEE Service Center. Piscataway, NJ, 2, 1677-1681.
8-Kumar, D., and Reddy, J. (2007). “Multiple reservoir operation using particle swarm optimization.” J. of Water Resources Planning and Management, 133(3), 192-202.
9-Kennedy, J., and Eberhart, R. (1995). “Particle swarm optimization.” Proceeding of the International Conference on Neural Networks, Perth, Austalia, 1942-1948.
10-Parsopoulos, K.E., and Vrahatis, M.N. (2002). “Particle swarm optimization method in multiobjective problems.” Proceedings of the 2002 ACM Symposium on Applied Computing, Madraid, Spain, 603-607.
11-Zeleny, M. (1973). “Compromise programming.” Cocharane, M., Zeleny, M. (Eds.), Multiple criteria decision making, University of Southern Carolina Press, Columbia.
12-Asgharpour, M.J. (2007). Multicriteria decision making, Tehran University Press, Tehran. (In Persian)
Journal of Water and Wastewater; Ab va Fazilab ( in persian )
Volume 23, Issue 4 - Serial Number 4
Serial number:84
December 2012
Pages 97-105

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