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 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.

[1]  Fakhri Karray,et al.  Minimizing variance of reservoir systems operations benefits using soft computing tools , 2003, Fuzzy Sets Syst..

[2]  Demetris Koutsoyiannis,et al.  A parametric rule for planning and management of multiple‐reservoir systems , 1997 .

[3]  Ari Jolma,et al.  Fuzzy Model for Real-Time Reservoir Operation , 2002 .

[4]  Alessandro Ancarani,et al.  A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules , 2002 .

[5]  Jery R. Stedinger,et al.  HEURISTIC OPERATING POLICIES FOR RESERVOIR SYSTEM SIMULATION , 1991 .

[6]  G. K. Young.  Finding Reservoir Operating Rules , 1967 .

[7]  P. P. Mujumdar,et al.  Reservoir Operation Modelling with Fuzzy Logic , 2000 .

[8]  Jay R. Lund,et al.  Operating Rule Optimization for Missouri River Reservoir System , 1996 .

[9]  Michio Sugeno,et al.  Industrial Applications of Fuzzy Control , 1985 .

[10]  Daniel P. Loucks,et al.  Operating rules for multi-reservoir systems , 1994 .

[11]  V. Chandramouli,et al.  Deriving a General Operating Policy for Reservoirs Using Neural Network , 1996 .

[12]  Demetris Koutsoyiannis,et al.  Evaluation of the parameterization‐simulation‐optimization approach for the control of reservoir systems , 2003 .

[13]  Samuel O. Russell,et al.  Reservoir Operating Rules with Fuzzy Programming , 1996 .

[14]  S. Yakowitz.  Dynamic programming applications in water resources , 1982 .

[15]  Peter K. Kitanidis,et al.  Limitations of Deterministic Optimization Applied to Reservoir Operations , 1999 .

[16]  M. Karamouz,et al.  Annual and monthly reservoir operating rules generated by deterministic optimization , 1982 .

[17]  Lucien Duckstein,et al.  Fuzzy Rule-Based Modeling of Reservoir Operation , 1996 .

[18]  William W.-G. Yeh,et al.  Reservoir Management and Operations Models: A State‐of‐the‐Art Review , 1985 .

[19]  J. Lund,et al.  Derived Operating Rules for Reservoirs in Series or in Parallel , 1999 .

[20]  N. Bhaskar,et al.  Derivation of monthly reservoir release policies , 1980 .

[21]  V. Chandramouli,et al.  Multireservoir Modeling with Dynamic Programming and Neural Networks , 2001 .

[22]  R. P. Oliveira,et al.  Operating rules for multireservoir systems , 1997 .