Multi Objective Multireservoir Optimization in Fuzzy Environment for River Sub Basin Development and Management
- 1
- 2
Abstract
In this paper, a multi objective, multireservoir operation model is proposed using Genetic algorithm (GA) under fuzzy environment. A monthly Multi Objective Genetic Algorithm Fuzzy Optimization (MOGAFU-OPT) model for the present study is developed in ‘C’ Language. The GA parameters i.e. population size, number of generations, crossover probability, and mutation probability are decided based on optimized val-ues of fitness function. The GA operators adopted are stochastic remainder selection, one point crossover and binary mutation. Initially the model is run for maximization of irrigation releases. Then the model is run for maximization of hydropower production. These objectives are fuzzified by assuming a linear membership function. These fuzzified objectives are simultaneously maximized by defining level of satisfaction (?) and then maximizing it. This approach is applied to a multireservoir system in Godavari river sub basin in Ma-harashtra State, India. Problem is formulated with 4 reservoirs and a barrage. The optimal operation policy for maximization of irrigation releases, maximization of hydropower production and maximization of level of satisfaction is presented for existing demand in command area. This optimal operation policy so deter-mined is compared with the actual average operation policy for Jayakwadi Stage-I reservoir.
- R. A. Wurbs, “Modelling and analysis of reservoir system operation,” NJ: Prentice Hall PTR, Prentice-Hall Inc., 1996.
- W. W-G. Yeh, “Reservoir management and operations models: A state-of-the-art review,” Water Resour. Res., Vol. 21, No. 12, pp. 1797–1818, 1985.
- J. W. Labadie, “Optimal operation of multireservoir sys-tems: State-of-the-art review,” J. Water Resour. Plan. and Manage., Vol. 130, No. 2, pp. 93–111, 2004.
- J. H. Holland, “Adaptation in natural and artificial sys-tems,” University of Michiyan Press annarbov, Cam-bridge Mass, 1975.
- D. E. Goldberg, “Genetic algorithms in search, optimiza-tion and machine learning,” Addison-Wesley Publishing Co., Inc., Reading MA, 1989.
- R. Oliveira and D. P. Loucks, “Operating rules for multi-reservoir systems,” Water Resour. Res., Vol. 33, No. 4, pp. 839–852, 1997.
- R. Wardlaw and M. Sharif, “Evaluation of genetic algo-rithm for optimal reservoir system operation,” J. Water Resour. Plan. and Manage., Vol. 125, No. 1, pp. 25–33, 1999.
- M. Sharif and R. Wardlaw, “Multireservoir systems op-timization using genetic algorithms: Case study,” J. Compu. in Civil Engrg., Vol. 14, No. 4, pp. 255–263, 2000.
- L. C. Chang and C. C. Yang, “Optimizing the rule curves for multi-reservoir operations using a genetic algorithm and HEC-5,” J. Hydrosci. and Hydra. Engrg., Vol. 20, No. 1, pp. 59–75, 2002.
- K. Srinivasa Raju and D. Nagesh Kumar, “Irrigation planning using genetic algorithms,” Water Resour. Man-age., Vol. 18, pp. 163–176, 2004.
- J. A. Ahmed and A. K. Sarma, “Genetic algorithm for optimal operating policy of a multipurpose reservoir,” Water Resour. Manag., Vol. 19, pp. 145–161, 2005.
- L. F. R. Reis, G. A. Walters, D. E. Savic, and F. H. Chaudhry, “Multi-reservoir operation planning using hy-brid genetic algorithm and linear programming (GA-LP): An alternative stochastic approach,” Water Resour. Manag., Vol. 19, pp. 831–848, 2005.
- V. Jothiprakash and Ganesan Shanthi, “Single reservoir operating policies using genetic algorithm,” Water Re-sour. Manag., Vol. 20, pp. 917–929, 2006.
- J. X. Chang, G. Huang, and Y. M. Wang, “Genetic algo-rithms for optimal reservoir dispatching,” Water Resour. Manag., Vol. 19, pp. 321–331, 2005.
- L. F. R. Reis, F. T. Bessler, G. A. Walters, and D. Savic, “Water supply reservoir operation by combined genetic algorithm-linear programming (GA-LP) approach,” Wa-ter Resour. Manag., Vol. 20, pp. 227–255, 2006.