An Application of the Cuckoo Optimization Algorithm in the Optimal Operation of Hydropower Reservoir (Case Study: The Karun4 Reservoir)

Subject Areas : Article frome a thesis

Seyed Mohammad Hosseini-Moghari ¹ , Mohammad Moghadas ² , Shahab Raghinejad ³

1 - Ph.D Student of Water Resources Engineering, Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran.
2 - دانش آموخته کارشناسی ارشد مهندسی منابع آب، گروه آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج.
3 - دانشیار گروه آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج.

Received: 2015-04-09 Accepted : 2017-06-19 Published : 2017-05-22

Keywords: Genetic Algorithm, Cuckoo Optimization Algorithm, Optimal Operation, Evolutionary Algorithm,

Abstract :

Many algorithms and methods have been applied, so far, in order to operate reservoirs, optimally. Recently, the Cuckoo Optimization Algorithm (COA) as a new Evolutionary Algorithm (EA) has been utilized for solving a number of bench mark functions and real problems, and high performance and efficiency has been reported for solving difficult optimization problems. The main objective of the current study was an assessment of the COA efficiency in water resource systems in order to extract optimal operation policy for a reservoir with hydropower application. An application of this algorithm in a thirty-year operation of the Karun4 Reservoir was evaluated. The results were compared with those results of Genetic Algorithm (GA) outcomes. The results showed that the COA has a greater ability to reach optimal solution in comparison with the GA. The mean values of the objective functions were 6.34 and 9.28 for the COA and GA algorithms, respectively. Furthermore, the obtained results by the COA showed less difference to the global optimum.

References:

 مراجع

1)       برهانی­ دریان، ع. و ا.م.، مرادی. 1389. الگوریتم مورچگان پیوسته در بهینه­سازی بهره­برداری از سیستم­های چند مخزنی، مطالعه موردی: مخازن حوضه کرخه. آب و فاضلاب 76: 81-91.

2)       برهانی­ دریان، ع. و س.م. مرتضوی نائینی. 1387. مقایسه کاربرد روش­های کاوشی در بهره­برداری بهینه از منابع آب. آب و فاضلاب 68: 57-66.

3)        Ahmed, J.A., and A.K. Sarma. 2005. Genetic algorithm for optimal operating policy of a multipurpose reservoir.  Water Resour. Manage. 19: 145-161.

4)        Ben Alaya, A., A. Souissi, J. Tarhouni, and K. Ncib. 2003. Optimization of Nebhana reservoir water allocation by stochastic dynamic programming. Water Resour. Manage. 17: 259-272.

5)        Blanchini, F.,  and W. Ukovich. 1993. Linear programming approach to the control of discrete-time periodic systems with uncertain inputs. Optimization theory and applications. 78: 523-539.

6)        Cheng, C.T., W.C. Wang, D.M. Xu, and K.W., Chau. 2008. Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos. Water Resour. Manage. 22: 895-909.

7)        Chau, K.W., and F. Albermani. 2003. Knowledge-based system on optimum design of liquid retaining structures with genetic algorithms. J. Struct. Eng. 129: 1312-1321.

8)        Esat, V., and M.J. Hall. 1994. Water resources system optimization using genetic algorithms. Proceedings of first international conference on hydroinformatics, Balkema, Rotterdam, The Netherlands: 225-231.

9)        Foufoula-Georgiou, E., and P.K. Kitanidis. 1988. Gradient dynamic programming for stochastic optimal control of multidimensional water resources systems. Water Resour. Res. 24: 1345-1359.

10)    Goldberg, D.E., and C.H. Kuo. 1987. Genetic algorithms in pipeline optimization. J. Comput. in Civil Eng. 1: 128-141.

11)    Jian-Xia, C., H. Qiang, and W. Yi-Min. 2005. Genetic algorithms for optimal reservoir dispatching. Water Resour. Manage. 19: 321-331.

12)    Kumar, D.N., K.S. Raju, and B. Ashok. 2006. Optimal reservoir operation for irrigation of multiple crops using genetic algorithms. J. Irrig. Drain. Eng. 132: 123-129.

13)    Kumar, D.N., and M.J. Reddy. 2006. Ant colony optimization for multi-purpose reservoir operation. Water Resour. Manage. 20: 879-898.

14)    Labadie, J.W. 2004. Optimal operation of multireservoir systems: State-of-the-art review.  J. Water Resour. Plan. Manage.130: 93-111.

15)    LINDO. 2011. LINGO user’s manual.  LINDO System INC, http://www.lindo.com/. (Feb.  24, 2013).

16)    Mellal, M.A., S. Adjerid, E.J. Williams, and D. Benazzouz. 2012. Optimal replacement policy for

 

obsolete components using cuckoo optimization algorithm based-approach: Dependability context. J. Sci. Indust. Res. 71: 715-721.

17)    Mokhtari-Fard, M., R., Noroozian, and S. Molaei. 2012. Determining the optimal placement and capacity of DG in intelligent distribution networks under uncertainty demands by COA. The 2^nd Iranian Conference on Smart Grids, Tehran, Iran. 1-8.

18)    Momtahen, S., and A.B. Dariane. 2007. Direct search approaches using genetic algorithms for optimization of water reservoir operating policies. J. Water Resour. Plan. Manage.133: 202-209.

19)    Mousavi, S.J., K. Ponnambalam, and F. Karray. 2005. Reservoir operation using a dynamic programming fuzzy rule–based approach. Water Resour. Manage. 19: 655-672.

20)    Oliveira, R., and D.P. Loucks. 1997. Operating rules for multireservoir systems. Water Resour. Res. 33: 839-852.

21)    Ponnambalam, K., A., Vannelli, and T.E. Unny. 1989. An application of Karmarkar's interior-point linear programming algorithm for multi-reservoir operations optimization. Stochastic Hydrol. Hydrauli. 3: 17-29.

22)    Rajabioun, R. 2011. Cuckoo optimization algorithm. Appl. Soft Comput. 11: 5508-5518.

23)    Rani, D., and M.M. Moreira. 2010. Simulation–optimization modeling: A survey and potential application in reservoir systems operation. Water Resour. Manage. 24: 1107-1138.

24)    Reddy, M.J., and D.N. Kumar. 2006. Optimal reservoir operation using multi-objective evolutionary algorithm. Water Resour. Manage. 20: 861-878.

25)    Sharif, M., and R. Wardlaw. 2000. Multireservoir systems optimization using genetic algorithms: Case study. J. Comput. Civil Eng. 14: 255-263.

26)    Stedinger, J.R., B.F.  Sule, and D.P. Loucks. 1984. Stochastic dynamic programming models for reservoir operation optimization. Water Resour. Res. 20:1499-1505.

27)    Umamahesh, N.V., and P. Sreenivasulu. 1997. Technical communication: two-phase stochastic dynamic programming model for optimal operation of irrigation reservoir. Water Resour. Manage. 11: 395-406.

28)    Wurbs, R.A. 1993. Reservoir-system simulation and optimization models. J. Water Resour. Plan. Manage.119: 455-472.

29)    Yakowitz, S. 1982. Dynamic programming applications in water resources. Water Resour. Res. 18: 673-696.

30)    Yeh, W.W-G. 1985. Reservoir management and operations models: a state-of-the-art review. Water Resour. Res. 21: 1797-1818.