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How Does the Coupling of Real-World Policies with Optimization Models Expand the Practicality of Solutions in Reservoir Operation Problems?

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A Correction to this article was published on 23 September 2021

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Abstract

This study first compares how different formulations of a reservoir operation problem with conflicting objectives affect the quality of the generated solution set. Six models were developed for comparative analysis: three using dynamic programming and three using the evolutionary multi-objective direct policy search (EMODPS) algorithm. Subsequently, to improve the quality of the generated solution set, an EMODPS model was selected and coupled with a zone-based hedging policy that has been currently applied in real-world reservoir operations. The proposed methodology was applied to a multipurpose reservoir in South Korea. Among the different models, the EMODPS-Gaussian model with three parameters outperformed dynamic programming models. Moreover, coupling the status quo zone-based hedging rule with the optimization model improved the average duration of failure from the supply side by 37.4%. On the other hand, when measured from the demand side, the results indicated that the magnitude of failure also improved by 4.15% at the cost of frequency of water deficit. The overall results of this study suggest that the integrative use of optimization models with hedging rules is potentially applicable in future drought mitigation measures.

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Availability of Data and Material

The datasets generated and analyzed during the current study are available on Mendeley Data (https://data.mendeley.com/datasets/9ntcdscwpd/4).

Code Availability

All codes that support the findings of this study are available on Github (https://github.com/gijoo-kim/M3O_Boryeong_application).

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References

  • Barros MT, Tsai FT, Yang SL, Lopes JE, Yeh WW (2003) Optimization of large-scale hydropower system operations. J Water Resour Plan Manag 129(3):178–188

    Article  Google Scholar 

  • Bayazit M, Ünal N (1990) Effects of hedging on reservoir performance. Water Resour Res 26(4):713–719

    Article  Google Scholar 

  • Belaineh G, Peralta RC, Hughes TC (1999) Simulation/optimization modeling for water resources management. J Water Resour Plan Manag 125(3):154–161

    Article  Google Scholar 

  • Bellman R (1957) A Markovian decision process. J Appl Math Mech pp 679–684

  • Bertsekas DP (2005) Dynamic programming and suboptimal control: A survey from ADP to MPC. Eur J Control 11(4–5):310–334

    Article  Google Scholar 

  • Birhanu K, Alamirew T, Dinka MO, Ayalew S, Aklog D (2014) Optimizing reservoir operation policy using chance constraint nonlinear programming for Koga irrigation dam. Ethiopia Water Resour Manag 28(14):4957–4970

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Fayaed SS, El-Shafie A, Jaafar O (2013) Integrated artificial neural network (ANN) and stochastic dynamic programming (SDP) model for optimal release policy. Water Resour Manag 27(10):3679–3696

    Article  Google Scholar 

  • Feng ZK, Niu WJ, Jiang ZQ, Qin H, Song ZG et al (2020) Monthly operation optimization of cascade hydropower reservoirs with dynamic programming and Latin hypercube sampling for dimensionality reduction. Water Resour Manag 34(6):2029–2041

    Article  Google Scholar 

  • Giuliani M, Castelletti A, Pianosi F, Mason E, Reed PM (2016) Curses, tradeoffs, and scalable management: Advancing evolutionary multiobjective direct policy search to improve water reservoir operations. J Water Resour Plan Manag 142(2):04015050

    Article  Google Scholar 

  • Giuliani M, Li Y, Cominola A, Denaro S, Mason E, Castelletti A (2016b) A Matlab toolbox for designing multi-objective optimal operations ofwater reservoir systems. Environ Model Softw 85:293–298

    Article  Google Scholar 

  • Hadka D, Reed P (2012) Diagnostic assessment of search controls and failure modes in many-objective evolutionary optimization. Evol Comput 20(3):423–452

    Article  Google Scholar 

  • Hashimoto T, Stedinger JR, Loucks DP (1982) Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation. Water Resour Res 18(1):14–20

    Article  Google Scholar 

  • Herman JD, Quinn JD, Steinschneider S, Giuliani M, Fletcher S (2020) Climate adaptation as a control problem: Review and perspectives on dynamic water resources planning under uncertainty. Water Resour Res 56(2):e24389

    Article  Google Scholar 

  • Horn J, Nafpliotis N, Goldberg DE (1994) A niched pareto genetic algorithm for multiobjective optimization. In: Proceedings of the first IEEE conference on evolutionary computation, IEEE, pp 82–87

  • Javadi A, Hussain M, Sherif M, Farmani R (2015) Multi-objective optimization of different management scenarios to control seawater intrusion in coastal aquifers. Water Resour Manag 29(6):1843–1857

    Article  Google Scholar 

  • Kasprzyk JR, Reed PM, Kirsch BR, Characklis GW (2009) Managing population and drought risks using many-objective water portfolio planning under uncertainty. Water Resour Res 45(12)

  • Kim GJ, Seo SB, Kim YO (2019a) Elicitation of drought alternatives based on water policy council and the role of shared vision model. J Korea Water Resour Assoc 52(6):429–440

    Google Scholar 

  • Kim GJ, Yoon HN, Seo SB, Kim YO (2019b) Application of shared vision planning for drought mitigation climate change adaptation council in Korea. In: Proc., 38th IAHR World Congress, International Association for Hydro-Environment Engineering and Research. https://doi.org/10.3850/38WC092019-1599

  • Kim GJ, Kim YO, Reed PM (2021) Improving the robustness of reservoir operations with stochastic dynamic programming. J Water Resour Plan Manag 147(7):04021030

  • Klemeš V (1987) One hundred years of applied storage reservoir theory. Water Resour Manag 1(3):159–175

    Article  Google Scholar 

  • Koutsoyiannis D, Economou A (2003) Evaluation of the parameterization-simulation-optimization approach for the control of reservoir systems. Water Resour Res 39(6)

  • Labadie JW (2004) Optimal operation of multireservoir systems: State-of-the-art review. J Water Resour Plan Manag 130(2):93–111

    Article  Google Scholar 

  • Liu Y, Qin H, Mo L, Wang Y, Chen D, Pang S, Yin X (2019a) Hierarchical flood operation rules optimization using multi-objective cultured evolutionary algorithm based on decomposition. Water Resour Manag 33(1):337–354

    Article  Google Scholar 

  • Liu D, Guo S, Liu P, Xiong L, Zou H, Tian J, Zhang J (2019b) Optimisation of water-energy nexus based on its diagram in cascade reservoir system. J Hydrol 569:347–358

    Article  Google Scholar 

  • Nicklow J, Reed P, Savic D, Dessalegne T, Harrell L, Chan-Hilton A, Karamouz M, Minsker B, Ostfeld A, Singh A et al. (2010) State of the art for genetic algorithms and beyond in water resources planning and management. J Water Resour Plan Manag 136(4):412–432

    Article  Google Scholar 

  • Park J, Sandberg IW (1991) Universal approximation using radial-basis-function networks. Neural Comput 3(2):246–257

    Article  Google Scholar 

  • Powell WB (2007) Approximate dynamic programming: Solving the curses of dimensionality, vol 703. John Wiley & Sons

    Book  Google Scholar 

  • Quinn JD, Reed PM, Giuliani M, Castelletti A (2019) What is controlling our control rules? Opening the black box of multireservoir operating policies using time-varying sensitivity analysis. Water Resour Res 55(7):5962–5984

    Article  Google Scholar 

  • Reed PM, Hadka D, Herman JD, Kasprzyk JR, Kollat JB (2013) Evolutionary multiobjective optimization in water resources: The past, present, and future. AdvWater Resour 51:438–456

    Google Scholar 

  • Rosenstein MT, Barto AG (2001) Robot weightlifting by direct policy search. International Joint Conference on Artificial Intelligence, Citeseer 17:839–846

    Google Scholar 

  • Saadat M, Asghari K (2019) Feasibility improved stochastic dynamic programming for optimization of reservoir operation. Water Resour Manag 33(10):3485–3498

    Article  Google Scholar 

  • Salazar JZ, Reed PM, Quinn JD, Giuliani M, Castelletti A (2017) Balancing exploration, uncertainty and computational demands in many objective reservoir optimization. Adv Water Resour 109:196–210

    Article  Google Scholar 

  • Seo SB, Kim YO, Kang SU (2019) Time-varying discrete hedging rules for drought contingency plan considering long-range dependency in streamflow. Water Resour Manag 33(8):2791–2807

    Article  Google Scholar 

  • Shih JS, ReVelle C (1995) Water supply operations during drought: A discrete hedging rule. Eur J Oper Res 82(1):163–175

    Article  Google Scholar 

  • Stedinger JR, Sule BF, Loucks DP (1984) Stochastic dynamic programming models for reservoir operation optimization. Water Resour Res 20(11):1499–1505

    Article  Google Scholar 

  • Tejada-Guibert JA, Johnson SA, Stedinger JR (1995) The value of hydrologic information in stochastic dynamic programming models of a multireservoir system. Water Resour Res 31(10):2571–2579

    Article  Google Scholar 

  • Tsitsiklis JN, Van Roy B (1996) Feature-based methods for large scale dynamic programming. Mach Learn 22(1–3):59–94

    Google Scholar 

  • Yakowitz S (1982) Dynamic programming applications in water resources. Water Resour Res 18(4):673–696

    Article  Google Scholar 

  • Yang CC, Chang LC, Chen CS, Yeh MS (2009) Multi-objective planning for conjunctive use of surface and subsurface water using genetic algorithm and dynamics programming. Water Resour Manag 23(3):417–437

    Article  Google Scholar 

  • Yeh WWG (1985) Reservoir management and operations models: A state-of-the-art review. Water Resour Res 21(12):1797–1818

    Article  Google Scholar 

  • You JY, Cai X (2008a) Hedging rule for reservoir operations: 1. A theoretical analysis Water Resour Res 44(1):W01415

    Google Scholar 

  • You JY, Cai X (2008b) Hedging rule for reservoir operations: 2. A numerical model Water Resour Res 44(1):W0141

    Google Scholar 

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Funding

This study received funding from K-water under "Study in the assessment of drought response capability and improvement plan of coordinated dams-weirs operation for river systems" and the BK21 PLUS research program of the National Research Foundation of Korea. The authors also wish to thank the Institute of Engineering Research, and the Institute of Construction Environmental Engineering at Seoul National University and providing research facilities for this work.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Seoul National University, Seoul, 08826, South Korea

    Gi Joo Kim & Young-Oh Kim

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  1. Gi Joo Kim
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Contributions

Gi Joo Kim and Young-Oh Kim contributed to the study conception and design. Material preparation, data collection and analysis were performed by Gi Joo Kim. The first draft of the manuscript was written by Gi Joo Kim, and Young-Oh Kim commented on previous versions of the manuscript. Gi Joo Kim and Young-Oh Kim read and approved the final manuscript.

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Correspondence to Young-Oh Kim.

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The original online version of this article was revised: The original version of this article unfortunately contained a mistake in Equations 1, 5 and 15.

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Kim, G.J., Kim, YO. How Does the Coupling of Real-World Policies with Optimization Models Expand the Practicality of Solutions in Reservoir Operation Problems?. Water Resour Manage 35, 3121–3137 (2021). https://doi.org/10.1007/s11269-021-02862-y

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