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Development and Application of Storage-Zone Decision Method for Long-Term Reservoir Operation Using the Dynamically Dimensioned Search Algorithm

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  • Shinuk Kang

    (K-water Research Institute, K-water)

  • Sangho Lee

    (Pukyong National University)

  • Taeuk Kang

    (Kyungsung University)

Abstract

A zone-based operation uses a simple method to make operational decisions based on rules established for storage zones. However, the division of a reservoir into appropriate storage zones involves complicated procedures. Using test functions to compare the three heuristic methods of a genetic algorithm, the shuffled complex evolution method developed by the University of Arizona, and the dynamically dimensioned search (DDS) method, we found that DDS might be useful for determining good global solutions to a problem with many decision variables. Hence, we applied the DDS method to determine three series of water levels for zoned reservoir storage division. The developed reservoir-operating method was applied to Andong Dam in the Republic of Korea. We derived zone boundaries using a generated dam inflow series and performed zone-based operations for validation using historical data under the assumption of known inflow data. The results showed improvements in the water supply reliability and vulnerability compared with historical data. The zone-based operating method derived from the present research could prove valuable because of its simplicity and practicality.

Suggested Citation

  • Shinuk Kang & Sangho Lee & Taeuk Kang, 2017. "Development and Application of Storage-Zone Decision Method for Long-Term Reservoir Operation Using the Dynamically Dimensioned Search Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 219-232, January.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:1:d:10.1007_s11269-016-1520-6
    DOI: 10.1007/s11269-016-1520-6
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    References listed on IDEAS

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    1. Ramesh Teegavarapu & Slobodan Simonovic, 2002. "Optimal Operation of Reservoir Systems using Simulated Annealing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(5), pages 401-428, October.
    2. A. Cancelliere & G. Giuliano & A. Ancarani & G. Rossi, 2002. "A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(1), pages 71-88, February.
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    Cited by:

    1. Mohammad Ehteram & Hojat Karami & Saeed Farzin, 2018. "Reducing Irrigation Deficiencies Based Optimizing Model for Multi-Reservoir Systems Utilizing Spider Monkey Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2315-2334, May.
    2. Fu Yan & Jianzhong Xu & Kumchol Yun, 2019. "Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information," Complexity, Hindawi, vol. 2019, pages 1-36, December.
    3. Seyed Mohammad Ashrafi & Alireza Borhani Dariane, 2017. "Coupled Operating Rules for Optimal Operation of Multi-Reservoir Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4505-4520, November.
    4. Youngkyu Jin & Sangho Lee, 2019. "Comparative Effectiveness of Reservoir Operation Applying Hedging Rules Based on Available Water and Beginning Storage to Cope with Droughts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(5), pages 1897-1911, March.

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