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Water Resources Management

, Volume 32, Issue 5, pp 1901–1911 | Cite as

Multi-Objective Simulation-Optimization Model for Long-term Reservoir Operation using Piecewise Linear Hedging Rule

  • K. Srinivasan
  • Kranthi Kumar
Article

Abstract

An efficiently parameterized and appropriately structured piecewise linear hedging rule is formulated and included within a multi-objective simulation-optimization (S-O) framework that seeks to obtain Pareto-optimal solutions for the long-term hedged operation of a single water supply reservoir. Two conflicting objectives, namely, “minimize the total shortage ratio” and “minimize the maximum shortage” are considered in the S-O framework, while explicit specification of constraints is avoided in the optimization module. Evolutionary search based non-dominated sorting genetic algorithm is used as the driver, which is linked to the simulation engine that invokes the piecewise linear hedging rule within the S-O framework. Preconditioning of the multi-objective stochastic search of the time-varying piecewise linear hedging model is effected by feeding initial feasible solutions sampled from the Pareto-optimal front of a simple constant hedging parameter model, which has resulted in significant improvement of the Pareto-optimality and the computational efficiency.

Keywords

Reservoir hedging Piecewise linear hedging rule Multi-objective GA Simulation-optimization Preconditioning 

Notes

Acknowledgements

The authors acknowledge the “high performance computing environment” facility provided by the Indian Institute of Technology Madras, India for carrying out this research work. The authors also wish to thank the anonymous reviewer and the associate editor for the comments and suggestions that were useful in enhancing the quality of presentation of the manuscript.

References

  1. Afshar A, Shafii M, Haddad OB (2011) Optimizing multi-reservoir operation rules: an improved HBMO approach. J Hydroinf 13(1):121.  https://doi.org/10.2166/hydro.2010.061 CrossRefGoogle Scholar
  2. Ahmed JA, Sarma AK (2005) Genetic algorithm for optimal operating policy of a multipurpose reservoir. Water Resour Manag 19(2):145–161.  https://doi.org/10.1007/s11269-005-2704-7 CrossRefGoogle Scholar
  3. Chang LC (2008) Guiding rational reservoir flood operation using penalty-type genetic algorithm. J Hydrol 354(1-4):65–74.  https://doi.org/10.1016/j.jhydrol.2008.02.021 CrossRefGoogle Scholar
  4. Dariane A, Momtahen S (2009) Optimization of multireservoir systems operation using modified direct search genetic algorithm. J Water Resour Plan 135(3):141–148.  https://doi.org/10.1061/(ASCE)0733-9496(2009)135:3(141) CrossRefGoogle Scholar
  5. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197.  https://doi.org/10.1109/4235.996017 CrossRefGoogle Scholar
  6. Hadka D, Reed P (2012) Borg: an auto-adaptive many-objective evolutionary computing framework. Evol Comput 21(2):231–259.  https://doi.org/10.1162/EVCO_a_00075 CrossRefGoogle Scholar
  7. Hamlat A, Errih M, Guidoum A (2013) Simulation of water resources management scenarios in western Algeria watersheds using WEAP model. Arab J Geosci 6(7):2225–2236.  https://doi.org/10.1007/s12517-012-0539-0 CrossRefGoogle Scholar
  8. Jain SK, Bhunya PK (2008) Reliability, resilience and vulnerability of a multipurpose storage reservoir/Confiance, résilience et vulnérabilité d’un barrage multi-objectifs. Hydrol Sci J 53(2):434–447.  https://doi.org/10.1623/hysj.53.2.434 CrossRefGoogle Scholar
  9. Jebbo BEA, Awchi TA (2016) Simulation model for Mosul dam reservoir using HEC-ResSim 3.0 package. ZANCO J Pure Appl Sci 28(2):92–98Google Scholar
  10. Kang L, Zhang S, Ding Y, He X (2016) Extraction and preference ordering of multireservoir water supply rules in dry years. Water 8(1).  https://doi.org/10.3390/w8010028
  11. Klipsch JD, Hurst MB (2013) HEC-ResSim reservoir system simulation User’s manual. Davis: Hydrologic Engineering Center, Institute for Water ResourcesGoogle Scholar
  12. Kramer O (2010) A review of constraint-handling techniques for evolution strategies. Appl Comput Intell Soft Comput 2010:1–11.  https://doi.org/10.1155/2010/185063 CrossRefGoogle Scholar
  13. Labadie JW (2004) Optimal operation of multireservoir systems: state-of-the-art review. J Water Resour Plan Manag 130(2):93–111.  https://doi.org/10.1061/(ASCE)0733-9496(2004)130:2(93) CrossRefGoogle Scholar
  14. Maier HR, Kapelan Z, Kasprzyk J et al (2014) Evolutionary algorithms and other metaheuristics in water resources: current status, research challenges and future directions. Environ Model Softw 62:271–299.  https://doi.org/10.1016/j.envsoft.2014.09.013 CrossRefGoogle Scholar
  15. NIH (1987) Storage yield analysis. Technical report no. UM-16, National Institute of Hydrology, Roorkee, IndiaGoogle Scholar
  16. Oliveira R, Loucks DP (1997) Operating rules for multireservoir systems. Water Resour Res 33(4):839–852.  https://doi.org/10.1029/96WR03745 CrossRefGoogle Scholar
  17. Rani D, Moreira MM (2010) Simulation-optimization modeling: a survey and potential application in reservoir systems operation. Water Resour Manag 24(6):1107–1138.  https://doi.org/10.1007/s11269-009-9488-0 CrossRefGoogle Scholar
  18. Shiau J-T (2009) Optimization of reservoir hedging rules using multiobjective genetic algorithm. J Water Resour Plan Manag 135(5):355–363.  https://doi.org/10.1061/(ASCE)0733-9496(2009)135:5(355) CrossRefGoogle Scholar
  19. Shiau JT (2011) Analytical optimal hedging with explicit incorporation of reservoir release and carryover storage targets. Water Resour Res 47(1).  https://doi.org/10.1029/2010WR009166
  20. Shih JS, ReVelle C (1995) Water supply operations during drought: a discrete hedging rule. Eur J Oper Res 82(1):163–175.  https://doi.org/10.1016/0377-2217(93)E0237-R CrossRefGoogle Scholar
  21. Srinivasan K, Philipose MC (1996) Evaluation and selection of hedging policies using stochastic reservoir simulation. Water Resour Manag 10(3):163–188.  https://doi.org/10.1007/BF00424201 CrossRefGoogle Scholar
  22. Vrugt JA, Robinson BA (2007) Improved evolutionary optimization from genetically adaptive multimethod search. Proc Natl Acad Sci 104(3):708–711.  https://doi.org/10.1073/pnas.0610471104 CrossRefGoogle Scholar
  23. Yates D, Sieber J, Purkey D, Huber-Lee A (2005) WEAP21 - a demand-, priority-, and preference-driven water planning model. Part 1: model characteristics. Water Int 30(4):487–500.  https://doi.org/10.1080/02508060508691893 CrossRefGoogle Scholar
  24. Zhao J, Cai X, Wang Z (2011) Optimality conditions for a two-stage reservoir operation problem. Water Resour Res 47(8).  https://doi.org/10.1029/2010WR009971

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringIndian Institute of Technology MadrasChennaiIndia

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