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Optimization of Multireservoir Systems by Genetic Algorithm

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Abstract

Application of optimization techniques for determining the optimal operating policy of reservoirs is a major issue in water resources planning and management. As an optimization Genetic Algorithm, ruled by evolution techniques, have become popular in diversified fields of science. The main aim of this study is to explore the efficiency and effectiveness of genetic algorithm in optimization of multi-reservoirs. A computer code has been constructed for this purpose and verified by means of a reference problem with a known global optimum. Three reservoirs in the Colorado River Storage Project were optimized for maximization of energy production. Besides, a real-time approach utilizing a blend of online and a posteriori data was proposed. The results obtained were compared to the real operational data and genetic algorithm was found to be effective and can be utilized as an alternative technique to other traditional optimization techniques.

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References

  • Ahmed I, Lansey K (2001) Optimal operation of multi-reservoir systems under uncertainty. In: Phelps D (ed) Proc, world water and environmental resources congress. Environmental and Water Resources Institute of ASCE, Reston

    Google Scholar 

  • Ahmed JA, Sarma AK (2005) Genetic algorithm for optimal operating policy of a multipurpose reservoir. Water Resour Manage 19(2):145–161

    Article  Google Scholar 

  • Ansari N, Hou E (1999) Computational intelligence for optimization. Kluwer Academic, Boston

    Google Scholar 

  • Barros M, Tsai F, Yang S-L, Lopes J, Yeh W (2003) Optimization of large-scale hydropower system operations. J Water Resour Plan Manage 129(3):178–188

    Article  Google Scholar 

  • Bellman R (1957) Dynamic programming. Princeton University Press, Princeton

    Google Scholar 

  • Cai X, McKinney D, Lasdon L (2001) Solving nonlinear water management models using a combined genetic algorithm and linear programming approach. Adv Water Resour 24(6):667–676

    Article  Google Scholar 

  • Chandramouli V, Raman H (2001) Multireservoir modeling with dynamic programming and neural networks. J Water Resour Plan Manage 127(2):89–98

    Article  Google Scholar 

  • Cheng CT, Chau KW (2001) Fuzzy iteration methodology for reservoir flood control operation. J Am Water Resour Assoc 37(5):1381–1388

    Article  Google Scholar 

  • Cheng CT, Chau KW (2002) Three-person multi-objective conflict decision in reservoir flood control. Eur J Oper Res 142(3):625–631

    Article  Google Scholar 

  • Cheng CT, Chau KW (2004) Flood control management system for reservoirs in China. Environ Model Softw 19(12):1141–1150

    Article  Google Scholar 

  • Cheng CT, Wang WC, Xu DM, Chau KW (2008) Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos. Water Resour Manage 22(7):895–909

    Article  Google Scholar 

  • Cieniawski SE, Eheart JW, Ranjithan S (1995) Using genetic algorithms to solve a multiobjective groundwater monitoring problem. Water Resour Res 31(2):399–409

    Article  Google Scholar 

  • Davidson JW, Goulter IC (1995) Evolution program for the design of rectilinear branched distribution systems. J Comput Civ Eng, ASCE 9(2):112–121

    Article  Google Scholar 

  • De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. Doctoral dissertation, University of Michigan, Ann Arbor, MI

  • Esat V, Hall MJ (1994) Water resources system optimization using genetic algorithms. In: Proc. 1st int. conf. on hydroinformatics. Balkema Rotterdam, The Netherlands, pp 225–231

    Google Scholar 

  • Eshelman LJ, Schaffer JD (1993) Real-coded genetic algorithms and interval-schemata. In: Whitley LD (ed) Foundations of genetic algorithms 2. Morgan Kaufmann Publishers, San Mateo, pp 187–202

    Google Scholar 

  • Fahmy HS, King JP, Wentzel MW, Seton JA (1994) Economic optimization of river management using genetic algorithms. Paper No. 943034, ASAE 1994 Int. Summer Meeting, Am. Soc. of Agricultural Engrs., St. Joseph, Mich

  • Francini M (1996) Use of a genetic algorithm combined with a local search method for the automatic calibration of conceptual rainfall-runoff models. J Hydrol Sci 41(1):21–40

    Article  Google Scholar 

  • Gal S (1979) Optimal management of a multi-reservoir water supply system. Water Resour Res 15(4):737–749

    Article  Google Scholar 

  • Goldberg D (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley, Reading

    Google Scholar 

  • Goldberg DE (1991) Real coded genetic algorithms, virtual alphabets, and blocking. Complex Syst 5:139–167

    Google Scholar 

  • Hall WA, Harboe W, Yeh WW-G, Askew AJ (1969) Optimum firm power output from a two reservoir system by incremental dynamic programming. Water Resour Ctr Contrib, University of Calif 130:273–282

    Google Scholar 

  • Heidari M, Chow V, Kotovic P, Meredith D (1971) Discrete differential dynamic programming approach to water resources system optimization. Water Resour Res 7(2):273–282

    Article  Google Scholar 

  • Hınçal O (2008) Optimization of multireservoir systems by genetic algorithm. PhD Dissertation, Middle East Technical University, Ankara

  • Holland JH (1975) Adaptation in natural and artificial systems. MIT, Cambridge

    Google Scholar 

  • Howson HR, Sancho NGF (1975) A new algorithm for the solution of multistate dynamic programming problems. Math Program 8(1):104–116

    Article  Google Scholar 

  • Kumar DN, Baliarsingh F, Raju KS (2010) Optimal reservoir operation for flood control using folded dynamic programming. Water Resour Manage 24(6):1045–1064

    Article  Google Scholar 

  • Labadie JW (2004) Optimal operation of multireservoir systems: state-of-the-art-review. J Water Resour Plan Manage 130:93–111

    Article  Google Scholar 

  • Larson RE (1968) State increment dynamic programming. Elsevier Science, New York

    Google Scholar 

  • Liu P, Guo S, Xiong L, Li W, Zhang H (2006) Deriving reservoir refill operating rules by using the proposed DPNS Model. J Water Resour Manage 20(3):337–357

    Article  Google Scholar 

  • Liu X, Guo S, Liu P, Chen L, Li X (2010) Deriving optimal refill rules for multi-purpose reservoir operation. Water Resour Manage. doi:10.1007/s11269-010-9707-8

  • Loucks D, Dorfman P (1975) An evaluation of some linear decision rules in chance-constrained models for reservoir planning and operation. Water Resour Res 11(6):777–782

    Article  Google Scholar 

  • Michalewicz Z (1992) Genetic algorithms + data structures = evolution programs. Springer, New York

    Google Scholar 

  • Murray DM, Yakowitz S (1979) Constrained differential dynamic programming and its application to multireservoir control. Water Resour Res 15(5):1017–1027

    Article  Google Scholar 

  • Oliveira R, Loucks D (1997) Operating rules for multireservoir systems. Water Resour Res 33(4):839–852

    Article  Google Scholar 

  • Raman H, Chandramouli V (1996) Deriving a general operating policy for reservoirs using neural network. J Water Resour Plan Manage 122(5):342–347

    Article  Google Scholar 

  • Sharif M, Wardlaw R (2000) Multireservoir systems optimization using genetic algorithms: case study. J Comput Civ Eng 14(4):255–263

    Article  Google Scholar 

  • Wardlaw R, Sharif M (1999) Evaluation of genetic algorithms for optimal reservoir system operations. J Water Resour Plan Manage 125:25–33

    Article  Google Scholar 

  • Wang QJ (1991) The genetic algorithm and its application to calibrating conceptual rainfall–runoff models. Water Resour Res 27(9):2467–2471

    Article  Google Scholar 

  • Wright AH (1991) Genetic algorithms for real parameter optimization. In: Rawlins E (ed) Foundations of genetic algorithms. Morgan Kaufmann, Massachusetts, pp 205–218

    Google Scholar 

  • Young GK (1967) Finding reservoir operating rules. J Hydraul Div, ASCE 93(6):297–321

    Google Scholar 

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Authors and Affiliations

  1. Headquarters, Soyak Construction and Trading Co., Büyükdere Cad. No:38, Mecidiyeköy, Istanbul, Turkey

    Onur Hınçal

  2. Department of Civil Engineering, Middle East Technical University, Ankara, Turkey

    A. Burcu Altan-Sakarya

  3. İstanbul Aydın University, İstanbul, Turkey

    A. Metin Ger

Authors
  1. Onur Hınçal
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  2. A. Burcu Altan-Sakarya
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  3. A. Metin Ger
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Correspondence to A. Burcu Altan-Sakarya.

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Hınçal, O., Altan-Sakarya, A.B. & Metin Ger, A. Optimization of Multireservoir Systems by Genetic Algorithm. Water Resour Manage 25, 1465–1487 (2011). https://doi.org/10.1007/s11269-010-9755-0

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  • DOI: https://doi.org/10.1007/s11269-010-9755-0

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