Optimization and Simulation of Multiple Reservoir Systems

TLDR

The study extends an implicitly stochastic optimization scheme to multiple reservoir systems and demonstrates its application to a two‑river system under 28 different conditions. The method uses a three‑step cyclic process—optimizing reservoir operations for a given streamflow set, regressing to derive operating rules, and evaluating those rules in a simulation with a separate data set—repeating until stopping criteria are met, and requires two contemporaneous streamflow series and synthetic series. The approach successfully generated operating rules for the two‑river system across all 28 scenarios, illustrating its effectiveness for multiple reservoir systems.

Abstract

An implicitly stochastic optimization scheme previously described in the literature is extended to consider multiple reservoir systems. The scheme comprises a three‐step cyclic procedure that attempts to improve the initial operating rules for the system. The system requires two sets of contemporaneous streamflow series to be used in the simulation model and synthetically generated series are required for this purpose. The three‐step cycle begins with an optimization of reservoir operations for a given set of streamflows. The optimal operations from the solution are then analyzed in a regression procedure to obtain a set of operating rules. These rules are evaluated in a simulation model using a different set of data. Based on the simulation results, bounds are placed on operations and cycle returns to the optimization model. The cycle continues until one of the stopping rules is satisfied. The use of the scheme to generate operating rules for multiple reservoir systems is illustrated for a two‐river system under a set of 28 different conditions.