An important objective of the operation of multi-purpose reservoirs is the mitigation of flood risks in downstream river reaches. Under the assumptions of reservoirs with finite storage volumes, a key factor for its effective use during flood events is the proper timing of detention measures under consideration of forecast uncertainty. Operational flow forecasting systems support this task by providing deterministic or probabilistic inflow forecasts and decision support components to assess optimum release strategies. We focus on the decision support component and propose a deterministic optimization and its extension to an adaptive multi-stage stochastic optimization. These techniques are used to compute release trajectories of the reservoirs over a finite forecast horizon of up to 15 days by integrating a nonlinear gradient-based optimization algorithm and a simulation model of the water system. The framework has been implemented for a reservoir system operated by the Brazilian Companhia Energética de Minas Gerais S.A. (CEMIG). We exemplary present results obtained for the operation of the Tres Marias reservoir in the Brazilian state of Minas Gerais with a catchment area of near 55,000 km2. The focus of our discussion is the impact of forecast uncertainty and its consideration in the optimization procedure. We compare the performance of the deterministic and multi-stage stochastic optimization techniques and show the superiority of the stochastic approach.
Schwanenberg, Dirk; Mainardo Fan, Fernando; Naumann, Steffi; Kuwajima, Julio; Alvarado Montero, Rodolfo; and Assis Dos Reis, Alberto, "Short-Term Reservoir Optimization By Stochastic Optimization To Mitigate Downstream Flood Risks" (2014). CUNY Academic Works.