Comparison of Fuzzy and Nonfuzzy Optimal Reservoir Operating Policies

This paper compares reservoir operating policies obtained from fuzzy and nonfuzzy explicit stochastic dynamic programming. The reservoir operation problem for the Mansour Eddahbi dam in Morocco can be formulated as either a classical stochastic dynamic programming (SDP) problem, where the objective function stresses energy maximization with particular volumes being released for irrigation, or a fuzzy stochastic dynamic programming (FSDP) problem, in which both hydropower generation and irrigation are considered as fuzzy constraints and aggregated by the weighting method. System performance is estimated from simulation based on continuous reoptimization models using either the cost-to-go function generated by the SDP algorithm, or the membership function generated by the FSDP algorithm. Despite major differences in the mathematical representation of operating objectives and/or constraints, we show that both formulations yield similar measures of system performance.

[1]  H.-J. Zimmermann Multi-Person Decision Making in Fuzzy Environments , 1987 .

[2]  Uzay Kaymak,et al.  A sensitivity analysis approach to introducing weight factors into decision functions in fuzzy multicriteria decision making , 1998, Fuzzy Sets Syst..

[3]  Timothy K. Gates,et al.  Planning Reservoir Operations with Imprecise Objectives , 1997 .

[4]  Ying Li,et al.  Numerical Solution of Continuous-State Dynamic Programs Using Linear and Spline Interpolation , 1993, Oper. Res..

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

[6]  John W. Nicklow,et al.  Discrete-Time Optimal Control for Water Resources Engineering and Management , 2000 .

[7]  Janusz Kacprzyk,et al.  Fuzzy dynamic programming , 1999 .

[8]  Haralambos V. Vasiliadis,et al.  Bayesian stochastic optimization of reservoir operation using uncertain forecasts , 1992 .

[9]  Lynn E. Johnson,et al.  A COMPARISON OF TWO METHODS FOR MULTIOBJECTIVE OPTIMIZATION FOR RESERVOIR OPERATION , 1996 .

[10]  Marnik Vanclooster,et al.  Deriving efficient reservoir operating rules using flexible stochastic dynamic programming , 2001 .

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

[12]  Richard N. Palmer,et al.  Value of Seasonal Flow Forecasts in Bayesian Stochastic Programming , 1997 .

[13]  Lucien Duckstein,et al.  Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological and Engineering Systems , 1995 .

[14]  Slobodan P. Simonovic,et al.  Modeling uncertainty in reservoir loss functions using fuzzy sets , 1999 .

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

[16]  A. Bárdossy,et al.  Fuzzy regression in hydrology , 1990 .

[17]  H. Zimmermann Fuzzy sets, decision making, and expert systems , 1987 .

[18]  E. R. Gurocak FUZZY MULTIPLE CRITERIA DECISION MAKING FOR NATURAL RESOURCE PROJECTS , 1996 .

[19]  B. F. Sule,et al.  Stochastic dynamic programming models for reservoir operation optimization , 1984 .

[20]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[21]  William S. Butcher,et al.  STOCHASTIC DYNAMIC PROGRAMMING FOR OPTIMUM RESERVOIR OPERATION1 , 1971 .

[22]  Brigitte Werners,et al.  Aggregation Models in Mathematical Programming , 1988 .

[23]  R. Yager Fuzzy decision making including unequal objectives , 1978 .

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

[25]  Sharon A. Johnson,et al.  The Value of Hydrologic Information in Stochastic Dynamic Programming Models of a Multireservoir System , 1995 .

[26]  Sharon A. Johnson,et al.  Comparison of two approaches for implementing multireservoir operating policies derived using stochastic dynamic programming , 1993 .

[27]  J. Stedinger,et al.  Sampling stochastic dynamic programming applied to reservoir operation , 1990 .

[28]  J. Stedinger,et al.  Water resource systems planning and analysis , 1981 .