Reservoir Operation Using a Dynamic Programming Fuzzy Rule-Based Approach

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 a...

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Bibliographic Details
Published in:Water resources management 2005-10, Vol.19 (5), p.655-672
Main Authors: Mousavi, S.J, Ponnambalam, K, Karray, F
Format: Article
Language:English
Subjects:
algorithms
Dynamic programming
dynamic programming fuzzy rule-based model
Earth sciences
Earth, ocean, space
Exact sciences and technology
fuzzy logic
hydrologic models
Hydrology. Hydrogeology
irrigated farming
irrigation
irrigation water
mathematical models
Objective function
Operations research
Reservoir operation
Reservoir releases
reservoirs
Simulation
Studies
water management
Water resources
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Summary: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.
ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-005-3275-3