Stochastic reservoir operation with data-driven modeling and inflow forecasting

Abstract

This work applied implicit stochastic optimization (ISO) refined by long-term mean inflow forecasting and instance-based learning for the operation of the Sobradinho reservoir, Brazil. For efficiency assessment, the reservoir was also operated by perfect-forecast deterministic optimization, the standard operating policy, stochastic dynamic programming and two parameterization-simulation-optimization models, which were compared in terms of vulnerability, reliability and resilience found in each of the 100 synthetic inflow scenarios they were applied to. Evidence of long-term persistence was found in Sobradinho's records and this was replicated in the scenarios. The ISO model was employed with forecast horizons of 0, 1, 3, 6, 9, 12, 18 and 24 months. The operations demonstrated that the model with forecast horizons of 3 months or more was less vulnerable than all other models, revealing that it may be used efficiently for reservoir operation.