Development of particle swarm optimization algorithm for optimal multiple reservoir operation (case study: Dez dam)

Document Type : Original Article

Authors

1 Associate Professor of Water Engineering ,University of Lorestan, Khorramabad, Iran

2 Assistant Professor of Water Engineering ,University of Lorestan, Khorramabad, Iran,

Abstract

Abstract
 
Due to the limitation of quantity and quality of water resources, the occurrence of long droughts and increasing of populations, construction of multi-objective reservoirs and optimal operation of these reservoirs is very important.In this research, the optimal operation of the Dez dam with multi-objective of supply agricultural and potable water, environment, flood control and electrical power generation  is investigated. For this purpose, the particle swarm optimization (PSO) algorithm is modified and the new mutated algorithm (DMPSO) is developed. Then the proposed method (DMPSO) is applied for optimal operation of the multiple Dez  reservoir. The results of this research are compared with PSO and standard operation Procedure (SOP). The results show that DMPSO algorithm has the best distribution of monthly release to have less shortage in comparison with PSO and SOP. The annual shortage ratio for  PSO, DMPSO and SOP  are 28%, 25%, 47%  respectively, which represent the high-capability of DMPSO in optimal operation of reservoirs

Keywords

References

 
Abbass, H.A. 2001. MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach. Conference Evolutionary Computation, in Seoul May. 1(4):207 – 214.
Barros, M., Tsai, F., Yang, S., Lopes, J., Yeh, W.2003. Optimization of Large-Acale Hydropower System Operation. J.water Resour. Plann. Manage. 129(3):178-188.
Bazaraa, M.S., Sherali, H.D., Shetty,  C.M. 2006.Nonlinear Programming: Theory and Algorithms 3rd Edition. John wiley and sons,Inc., Hoboken, New Jersey.
Bellman, R.1957. Dynamic programming. Princeton, N.J.: Princeton University Press.
Bozorg Haddad, O., Afshar, A., Marino, A. 2011. Multireservoir optimisation in discrete and continuous domains. Water Management. 164(4): 57–72.
Bozorg Haddad, O., Afshar, A., Marino M.A.2006. Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization. Water Resources Management .20(4): 661–680.
Dorigo, M.1996. Ant System: Optimization by a Colony of Cooperating Agents. IEEE T SYST, .26(1):29-41.
Geem, Z.W.,  Kim,  J.H.,  Loganathan, G.V.2001.  A New Heuristic Optimization Algorithm: Harmony Search. SIMULATION. 76(2):60-68.
Hakimi-Asiabar, M., Ghodsypour,  S.H, Kerachian, R. 2010.Deriving operating policies for multi-objective reservoir systems: Application of Self-Learning Genetic Algorithm, Applied Soft Computing .10(4):1151–1163.
Heidari, M., chow, V.T., Kokotovic, P.V., Meredith, D.D.1972. Discrete Differenti a Dynamic programing approach to Water ResourceS systems Optimization. Water Resources Research.7 (2):1-14
Holland, J.H. 1975. Adaption in natural and artificial systems. University of Michigan Press.
Jacobson D, Myne D.1970. Differential dynamic programming.American Elsevier Publishing Company, New York, New York.
Jalali1. M.R.,  Afshar,  A.,  Mariño,  M.A.2007. Optimal Operation of Multi-reservoir Systems: State-of-the-Art Review. International Journal of Civil Engineerng. 5(4):284-301.
Jalali1, M.R., Afshar, A., Marino, M.A.2006.  Improved Ant Colony Optimization Algorithm for Reservoir Operation. Scientia Iranica. 13(3): 295-302.
Kennedy, J.,  Eberhart, R.1995.  Particle Swarm Optimization. IEEE International Conference on Nov/Dec. 4(4): 1942 – 1948.
Kougias, I.,  Theodossiou, N. 2011. Optimization of multi-reservoir management using Harmony Search Algorithm (HAS). 3rd Intern. Conf. on Environmental Management, Engineering, Planning and Economics At Greece.
Kumar, D.N., Reddy, M.J.2007.  Multipurpose Reservoir Operation Using Particle Swarm Optimization. J. Water Resour. Plann. Manage.133(1):192-201.
Labadie, J.W . 2004. Optimal Operation of Multi-reservoir Systems: State-of-the-Art Review”. J. Water Resour. Plann. Manage. 130(2): 93-111.
Murray, D.M, Yakowitz, S.J.1979.  Constrained Differential Dynamic Programming and Its Application to Multi-reservoir Control. Water Resources Research.15(5): 1017-1027.
Nandalal, K.D.W., Bogardi, J.J.2007. Dynamic programming based operation of reservoirs: applicability and limits. Cambridge University Press, Cambridge.
Rani, D., Moreira, M.M. 2010. Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation. Water Resource Manage. 24(2):1107–1138.
Rossman, L.A. 2000. EPANET 2 user’s manual, Reports EPA/600/R-00/057. US Environ. Prot. Agency, Cincinnati, Ohio.
Shih, J.S., ReVelle, C. 1994. Water-supply operations during drought: Continuous hedging rule.Journal of water resources planning and management. 120(5): 613-629.
Srinivasan, K., Philipose, M.1998. Effect of hedging on over-year reservoir performance." Water resources management. 12(2): 95-120.
Wardlaw,  R.,  Sharif, M.1999.  Evaluation of genetic algorithms for optimal reservoir system reservoir system operation. Water Resour. Plann. Manage. 125(2):25-33.

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