Minimizing variance of reservoir systems operations benefits using soft computing tools
Soft computing based tools including fuzzy inference systems (FIS), artificial neural networks (ANN), and genetic algorithms (GA) are used here to tackle the minimization of variance of benefits from reservoir operation. Variance reduction is a very hard optimization problem and solvable only using implicit methods like simulation, especially if the problem is nonlinear. First, a recently developed stochastic optimization method develops the optimal release policy (which is simply the recommended release in each season) of the system whose objective function maximizes the expected benefits. The policy is then simulated for a long inflow series to provide the trajectories of optimal releases and storages of the reservoir. These trajectories are then used as input-output data to train an adaptive neuro fuzzy inference system (ANFIS) to obtain updated fuzzy operating rules. The ANFIS based fuzzy rules are simulated and compared with policies developed using a multiple regression analysis, a commonly used method in water resources optimization. As the ANFIS performed better, further, a parameterized T-norm operator is applied and its parameters (numbering only two) are optimized through GA but with the objective of variance reduction in the benefits achieved. Results compare the better performance of the ANFIS based policies with other methods such as stochastic dynamic programming and the original stochastic method to demonstrate the usefulness of GA optimized parameters of a T-norm fuzzy operator for variance reduction. (C) 2002 Elsevier B.V. All rights reserved.
soft computing fuzzy inference system genetic algorithm optimization