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A Derivative-Free Hybrid Optimization Model for Short-Term Operation of a Multi-Objective Reservoir System Under Uncertainty

Author

Listed:
  • Duan Chen

    (Changjiang River Scientific Research Institute
    Hunan Provincial Key Laboratory of Key Technology on Hydropower Development)

  • Arturo S. Leon

    (Florida International University)

  • Qiuwen Chen

    (Nanjing Hydraulic Research Institute)

  • Ruonan Li

    (Chinese Academy of Sciences)

Abstract

Short-term operation of a multi-objective reservoir system under inflow uncertainty has been receiving increasing attention, however, major challenges for the optimization of this system still remain due to the multiple and often conflicting objectives, highly nonlinear constraints and uncertain parameters in which derivative information may not be directly available. Population-based optimization methods do not rely on derivatives while generally have a slow convergence. This study presents a hybrid optimization model for short-term operation of multi-objective reservoirs under uncertainty that is derivative free and has a relatively fast convergence. The model incorporates a local improvement method called Mesh Adaptive Direct Search (MADS) into a population-based method NSGA-II and has no requirement for differentiability, convexity and continuity of the optimization problem. The operation of a multi-objective and multi-reservoir system on the Columbia River under inflow uncertainty is used as a case study. Overall, the hybrid model outperforms optimization models based on either the NSGA-II only or the MADS only. The model is intended for conditions where derivative information of the optimization problem is unavailable, which could have a wide array of applications in water resources systems.

Suggested Citation

  • Duan Chen & Arturo S. Leon & Qiuwen Chen & Ruonan Li, 2018. "A Derivative-Free Hybrid Optimization Model for Short-Term Operation of a Multi-Objective Reservoir System Under Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(11), pages 3707-3721, September.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:11:d:10.1007_s11269-018-2014-5
    DOI: 10.1007/s11269-018-2014-5
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    References listed on IDEAS

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    1. D. Kumar & M. Reddy, 2006. "Ant Colony Optimization for Multi-Purpose Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(6), pages 879-898, December.
    2. Duan Chen & Ruonan Li & Qiuwen Chen & Desuo Cai, 2015. "Deriving Optimal Daily Reservoir Operation Scheme with Consideration of Downstream Ecological Hydrograph Through A Time-Nested Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3371-3386, July.
    3. Duan Chen & Qiuwen Chen & Arturo S. Leon & Ruonan Li, 2016. "A Genetic Algorithm Parallel Strategy for Optimizing the Operation of Reservoir with Multiple Eco-environmental Objectives," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(7), pages 2127-2142, May.
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