Abstract
To obtain optimal operating rules for storage reservoirs, large numbers of simulation and optimization models have been developed over the past several decades, which vary significantly in their mechanisms and applications. As every model has its own limitations, the selection of appropriate model for derivation of reservoir operating rule curves is difficult and most often there is a scope for further improvement as the model selection depends on data available. Hence, evaluation and modifications related to the reservoir operation remain classical. In the present study a Genetic Algorithm model has been developed and applied to Pechiparai reservoir in Tamil Nadu, India to derive the optimal operational strategies. The objective function is set to minimize the annual sum of squared deviation form desired irrigation release and desired storage volume. The decision variables are release for irrigation and other demands (industrial and municipal demands), from the reservoir. Since the rule curves are derived through random search it is found that the releases are same as that of demand requirements. Hence based on the present case study it is concluded that GA model could perform better if applied in real world operation of the reservoir.
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References
Aly, A. H. and Peralta, R. C., 1999, ‘Comparison of a genetic algorithm and mathematical programming to the design of ground water clean up system’ Water Resources Research 35(8), 2415–2426.
Ahmed, J. A. and Sarma, A. K., 2005, ‘Genetic algorithm for optimal operating policy of a multi-purpose reservoir’, Water Resources Management 19(2), 145–161.
Chan Hilton, A. B. and Culver, T. B., 2005, ‘Ground water remediation design under uncertainty using genetic algorithm’, J. Water Resour. Plng. and Mgmt. ASCE 131(1), 25–34.
Dandy, G. C. and Engelhardt, 2001, ‘The optimal scheduling of water main replacement using genetic algorithm’ J. Water Resour. Plng. and Mgmt. ASCE 127(4), 214–223.
Dandy, G. C. Simpson, A. R. and Murphy. L. J., 1996, ‘An improved genetic algorithm for pipe network optimization’, Water Resources Research 32(2), 449–458.
Deb Kalyanmoy, 1995, ‘Optimization for Engineering Design’. Prentice Hall of India.
Doorenbos, S. and Pruitt, W. E., 1977, ‘Guidelines for predicting crop water requirements, Irrigation and Drainage Paper 24’, Food and Agriculture Organization of the United Nations, Rome, Italy.
East, V. and Hall, M. J., 1994. ‘Water resources system optimization using genetic algorithms’. Proc., 1st Int. Conf. On Hydroinformatics, Baikema, Rotteram, The Netherlands, 225–231.
Espinoza, F. P. Minska, B. S. and Goldberg, D. E., 2005, ‘Adaptive hybrid genetic algorithm for ground water remediation design’, J. Water Resour. Plng. and Mgmt. ASCE 131(1), 14–24.
Fahmy, H. S. King, J. P., Wentzel, M. W. and Seton, J. A., 1994., ‘Economic optimization of river management using genetic algorithms.’ Paper no.943034, ASCE 1994 Int.Summer Meeting, Am. Soc. of Agricultural Engrs., St. Joseph, Mich.
Goldberg, D. E., 1989., ‘Genetic algorithms in search, optimization and machine learning’, Addison-Wesley, Reading, Mass.
Holland, J. H., 1975, ‘Adaptation in natural and artificial systems’, University of Michiyan Press annarbov, Cambridge Mass.
Kim, T. and Heo, J. H., 2004, ‘Multi-reseroir system optimization using mulit-objective genetic algorithms’ Proceedings of the World Water and Environmental Resources Congress, 27th Jun. To 1st Jul. WERI, ASCE, Salt Lake City UT, 1–10.
Muleta, M. K. and Nicklow, J. W., 2005, ‘Decision support for watershed management using evolutionary algorithms’ J. Water Resour. Plng. and Mgmt. ASCE 131(1), 35–44.
Oliveira, R. and Loucks, D. P., 1997, ‘Operating rules for multireserovir system’, Water Resources Research 33(4), 839–852.
Reed, P., Minsker, B. S. and Goldberg, D. E., 2003, ‘Simplifying multiobjective optimization: An automated design methodology for the nondominated sorted genetic algorithm-II’, Water Resources Research 39(7), 1196 doi:10.1029/2002 WR001483.
Ritzel, B. J., Eheart, J. W. and Ranjithan, S., 1994, ‘Using genetic algorithms to solve a multiple objective ground water pollution containment’, Water Resources Research 30(5), 1589–1604.
Savic, D. A. and Walters, G. A., 1997, ‘Genetic algorithms for least-cost design of water distribution networks.’ J. Water Resour. Plng. and Mgmt. ASCE 123(2), 67–77.
Sharif, M. and Wardlaw, R., 2000, ‘Multireservoir System Optimization Using genetic algorithms. Case Study.’ J. Comp.in Civ. Engrg. ASCE 14(4),255–263.
Wang, Q. J., 1991, ‘The genetic algorithm and its application to calibrating conceptual rainfall-runoff models’, Water Resources Research 27(9), 2467–2471.
Wardlaw, R. and Sharif, M., 1999, ‘Evaluation of genetic algorithms for optimal reservoir system operation.’ J. Water Resour. Plng. and Mgmt. ASCE 125(1), 25–33.
Wu, Z. Y. and Simpson, A. R., 2001, ‘Competent genetic evolutionary optimization of water distribution systems’, J. Comput. in Civil Engg. 15(2), 89–101.
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Jothiprakash, V., Shanthi, G. Single Reservoir Operating Policies Using Genetic Algorithm. Water Resour Manage 20, 917–929 (2006). https://doi.org/10.1007/s11269-005-9014-y
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DOI: https://doi.org/10.1007/s11269-005-9014-y