Surrogate-Based Multiperiod, Multiobjective Reservoir Operation Optimization for Quality and Quantity Management

Abstract

Deriving optimal reservoir operation rules in a selective withdrawal framework (SWF) considering quality and quantity objectives is a challenging problem due to its computational burdens. To overcome the computational bottleneck, two surrogate models have been developed and coupled with the evolutionary algorithm in an adaptive-recursive framework to form the surrogate-based multiobjective optimization technique (SBMOOT). SBMOOT is used to derive the optimal reservoir operating strategies and the set of nondominated optimal solutions to enhance reservoir outflow water quality and maximize water supply and hydropower energy generation. The most desirable scenarios of the Pareto front have been identified to derive monthly operating rules in the SWF. The operating rules focusing on water supply, hydropower energy, and water quality objectives are estimated using polynomial regression technique. The performances of the operating rules, with and without regarding the uncertainty of inflows, have been compared with the historical operating strategy in Karkheh Reservoir, Khuzestan, Iran. The results show that water quality measure may be enhanced while maintaining desirable water supply and/or hydropower energy compared with the historical operating strategy of Karkheh Reservoir.