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Reservoir Operation Using a Dynamic Programming Fuzzy Rule–Based Approach

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  • S. Mousavi
  • K. Ponnambalam
  • F. Karray

Abstract

A dynamic programming fuzzy rule–based (DPFRB) model for optimal operation of reservoirs system is presented in this paper. In the first step, a deterministic dynamic programming (DP) model is used to develop the optimal set of inflows, storage volumes, and reservoir releases. These optimal values are then used as inputs to a fuzzy rule–based (FRB) model to establish the general operating policies in the second step. Subsequently, the operating policies are evaluated in a simulation model. During the simulation step, the parameters of the FRB model are optimized after which the algorithm gets back to the second step in a feedback loop to establish the new set of operating rules using the optimized parameters. This iterative approach improves the value of the performance function of the simulation model and continues until the satisfaction of predetermined stopping criteria. This method results in deriving the operating policies, which are robust against the uncertainty of inflows. These policies are derived by using long-term synthetic inflows and an objective function that minimizes its variance. The DPFRB performance is tested and compared to a model, which uses the commonly used multiple regression–based operating rules. Results show that the DPFRB performs well in terms of satisfying the system target performances and computational requirements. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • S. Mousavi & K. Ponnambalam & F. Karray, 2005. "Reservoir Operation Using a Dynamic Programming Fuzzy Rule–Based Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(5), pages 655-672, October.
    • Handle: RePEc:spr:waterr:v:19:y:2005:i:5:p:655-672
    DOI: 10.1007/s11269-005-3275-3
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    References listed on IDEAS

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    1. 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.
    2. D. Panigrahi & P. Mujumdar, 2000. "Reservoir Operation Modelling with Fuzzy Logic," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 14(2), pages 89-109, April.
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    5. Deepti Rani & Maria Moreira, 2010. "Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(6), pages 1107-1138, April.
    6. Vartika Paliwal & Aniruddha D. Ghare & Ashwini B. Mirajkar & Neeraj Dhanraj Bokde & Andrés Elías Feijóo Lorenzo, 2019. "Computer Modeling for the Operation Optimization of Mula Reservoir, Upper Godavari Basin, India, Using the Jaya Algorithm," Sustainability, MDPI, vol. 12(1), pages 1-21, December.
    7. Seyed-Mohammad Hosseini-Moghari & Reza Morovati & Mohammad Moghadas & Shahab Araghinejad, 2015. "Optimum Operation of Reservoir Using Two Evolutionary Algorithms: Imperialist Competitive Algorithm (ICA) and Cuckoo Optimization Algorithm (COA)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3749-3769, August.
    8. Li Chuangang & Ji Changming & Wang Boquan & Liu Minghao & Li Rongbo, 2017. "The Hydropower Station Output Function and its Application in Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 159-172, January.
    9. Alcigeimes Celeste & Max Billib, 2012. "Improving Implicit Stochastic Reservoir Optimization Models with Long-Term Mean Inflow Forecast," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(9), pages 2443-2451, July.
    10. Rama Mehta & Sharad Jain, 2009. "Optimal Operation of a Multi-Purpose Reservoir Using Neuro-Fuzzy Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(3), pages 509-529, February.
    11. Arvin Samadi-koucheksaraee & Iman Ahmadianfar & Omid Bozorg-Haddad & Seyed Amin Asghari-pari, 2019. "Gradient Evolution Optimization Algorithm to Optimize Reservoir Operation Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 603-625, January.
    12. Tan, Qiao-feng & Lei, Xiao-hui & Wen, Xin & Fang, Guo-hua & Wang, Xu & Wang, Chao & Ji, Yi & Huang, Xian-feng, 2019. "Two-stage stochastic optimal operation model for hydropower station based on the approximate utility function of the carryover stage," Energy, Elsevier, vol. 183(C), pages 670-682.
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