Direct Search Approaches Using Genetic Algorithms for Optimization of Water Reservoir Operating Policies

Abstract

The direct search approach to determine optimal reservoir operating policies is proposed with a real coded genetic algorithm (GA) as the optimization method. The parameters of the policies are optimized using the objective values obtained from system simulations. Different reservoir release rules or forms, such as linear, piecewise linear, fuzzy rule base, and neural network, are applied to a single reservoir system and compared with conventional models such as stochastic dynamic programming and dynamic programming and regression. The results of historical and artificial time series simulations show that the GA models are generally superior in identifying better expected system performance. Parsimony of policy parameters is inferred as a principle for selecting the structure of the policy, and Fourier series can be helpful for reducing the number of parameters by defining the time variations of coefficients. The proposed method has shown to be flexible and robust in optimizing various types of policies, even in models that include nonlinear, nonseparable objective functions and constraints.