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An Effective Approach to Long-Term Optimal Operation of Large-Scale Reservoir Systems: Case Study of the Three Gorges System

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  • Fang-Fang Li
  • Jia-Hua Wei
  • Xu-Dong Fu
  • Xin-Yu Wan

Abstract

A new approach for optimization of long-term operation of large-scale reservoirs is presented, incorporating Incremental Dynamic Programming (IDP) and Genetic algorithm (GA) . The immense storage capacity of the large scale reservoirs enlarges feasible region of the operational decision variables, which leads to invalidation of traditional random heuristic optimization algorithms. Besides, long term raised problem dimension, which has a negative impact on reservoir operational optimization because of its non-linearity and non-convexity. The hybrid IDP-GA approach proposed exploits the validity of IDP for high dimensional problem with large feasible domain by narrowing the search space with iterations, and also takes the advantage of the efficiency of GA in solving highly non-linear, non-convex problems. IDP is firstly used to narrow down the search space with discrete d variables. Within the sub search space provided by IDP, GA searches the optimal operation scheme with continuous variables to improve the optimization precision. This hybrid IDP-GA approach was applied to daily optimization of the Three Gorges Project-Gezhouba cascaded hydropower system for annual evaluation from the year of 2004 to 2008. Contrast test shows hybrid IDP-GA approach outperforms both the univocal IDP and the classical GA. Another sub search space determined by actual operational data is also compared, and the hybrid IDP-GA approach saves about 10 times of computing resources to obtain similar increments. It is shown that the hybrid IDP GA approach would be a promising approach to dealing with long-term optimization problems of large-scale reservoirs. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • Fang-Fang Li & Jia-Hua Wei & Xu-Dong Fu & Xin-Yu Wan, 2012. "An Effective Approach to Long-Term Optimal Operation of Large-Scale Reservoir Systems: Case Study of the Three Gorges System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 4073-4090, November.
    • Handle: RePEc:spr:waterr:v:26:y:2012:i:14:p:4073-4090
    DOI: 10.1007/s11269-012-0131-0
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    1. V. Jothiprakash & Ganesan Shanthi, 2006. "Single Reservoir Operating Policies Using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(6), pages 917-929, December.
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    3. Yuan, Xiaohui & Yuan, Yanbin & Zhang, Yongchuan, 2002. "A hybrid chaotic genetic algorithm for short-term hydro system scheduling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 59(4), pages 319-327.
    4. Juran Ahmed & Arup Sarma, 2005. "Genetic Algorithm for Optimal Operating Policy of a Multipurpose Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(2), pages 145-161, April.
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    2. Bilal & Deepti Rani & Millie Pant & S. K. Jain, 2020. "Dynamic programming integrated particle swarm optimization algorithm for reservoir operation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 515-529, April.
    3. Majid Montaseri & Mahdi Hesami Afshar & Omid Bozorg-Haddad, 2015. "Development of Simulation-Optimization Model (MUSIC-GA) for Urban Stormwater Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4649-4665, October.
    4. Fang-Fang Li & Jun Qiu, 2015. "Multi-Objective Reservoir Optimization Balancing Energy Generation and Firm Power," Energies, MDPI, vol. 8(7), pages 1-15, July.
    5. Bilal & Deepti Rani & Millie Pant & S. K. Jain, 0. "Dynamic programming integrated particle swarm optimization algorithm for reservoir operation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-15.
    6. Mohammed Falah Allawi & Othman Jaafar & Mohammad Ehteram & Firdaus Mohamad Hamzah & Ahmed El-Shafie, 2018. "Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3373-3389, August.
    7. Sabah Fayaed & Ahmed El-Shafie & Othman Jaafar, 2013. "Integrated Artificial Neural Network (ANN) and Stochastic Dynamic Programming (SDP) Model for Optimal Release Policy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3679-3696, August.
    8. Asmadi Ahmad & Ahmed El-Shafie & Siti Razali & Zawawi Mohamad, 2014. "Reservoir Optimization in Water Resources: a Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3391-3405, September.
    9. Li, Fang-Fang & Qiu, Jun, 2016. "Multi-objective optimization for integrated hydro–photovoltaic power system," Applied Energy, Elsevier, vol. 167(C), pages 377-384.

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