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.

Keywords:

• Reservoir operation optimization
• implicit stochastic optimization
• instance-based learning
• k-nearest neighbor algorithm
• long-term memory

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors acknowledge the Foundation for Research and Technological Innovation Support of the State of Sergipe, Brazil (FAPITEC/SE) for the financial support.

Notes on contributors

Raul Fontes Santana

Raul Fontes Santana holds a bachelor's degree in Civil Engineering from the Federal University of Sergipe, Brazil and a Master's degree from the Graduate Program in Engineering and Environmental Sciences of the same university. This paper is the result of part of his Master's research supervised by the second author.

Alcigeimes B. Celeste

Alcigeimes B. Celeste is an Associate Professor at the Department of Civil Engineering of the Federal University of Sergipe, Brazil. He holds both M.Eng. (2002) and Dr.Eng. (2004) degrees from Ehime University, Japan. His research interests include reservoir design & operation optimization, hydroinformatics, deterministic and stochastic programming, data-driven modeling, heuristic optimization, stochastic hydrology, and hydrological modeling.