PROJECT TITLE :

Managing Hydroelectric Reservoirs Over an Extended Horizon Using Benders Decomposition With a Memory Loss Assumption

ABSTRACT:

Traditional stochastic programming strategies are widely used for solving hydroelectric reservoirs management problems under uncertainty. With these methods, random parameters are described employing a situation tree possessing an unstructured topology. So, ancient ways will probably handle high-order time-correlation effects, however their computational necessities grow exponentially with the branching level used to represent parameters (e.g., load, inflows, costs). Consequently, random parameters must be discretized terribly coarsely and, so, numerical solutions of mid-term optimization models can be quite sensitive to little perturbations to the tree parameters. During this paper, we propose a brand new approach for managing high-capability reservoirs over an extended horizon (one–three years). We partition the look horizon in two stages and assume that a memory loss happens at the top of the primary stage. We propose a new Benders decomposition algorithm designed specifically to use this simplification. The low memory demand of our method permits to consider a much higher branching level than would be potential with previous methods. The proposed approach is tested on a 104-week production designing drawback with stochastic inflows.