Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir

Fernando Mainardi Fan (), Dirk Schwanenberg, Rodolfo Alvarado, Alberto Assis dos Reis, Walter Collischonn and Steffi Naumman
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Fernando Mainardi Fan: Universidade Federal do Rio Grande do Sul
Dirk Schwanenberg: Deltares
Rodolfo Alvarado: University of Duisburg-Essen (UDE)
Alberto Assis dos Reis: Companhia Energética de Minas Gerais S.A. (CEMIG)
Walter Collischonn: Universidade Federal do Rio Grande do Sul
Steffi Naumman: Advanced System Technology (AST) Branch of Fraunhofer IOSB

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2016, vol. 30, issue 10, No 18, 3609-3625

Abstract: Abstract Hydropower is the most important source of electricity in Brazil. It is subject to the natural variability of water yield. One building block of the proper management of hydropower assets is the short-term forecast of reservoir inflows as input for an online, event-based optimization of its release strategy. While deterministic forecasts and optimization schemes are the established techniques for short-term reservoir management, the use of probabilistic ensemble forecasts and multi-stage stochastic optimization techniques is receiving growing attention. The present work introduces a novel, mass conservative scenario tree reduction in combination with a detailed hindcasting and closed-loop control experiments for a multi-purpose hydropower reservoir in a tropical region in Brazil. The case study is the hydropower project Três Marias, which is operated with two main objectives: (i) hydroelectricity generation and (ii) flood control downstream. In the experiments, precipitation forecasts based on observed data, deterministic and probabilistic forecasts are used to generate streamflow forecasts in a hydrological model over a period of 2 years. Results for a perfect forecast show the potential benefit of the online optimization and indicate a desired forecast lead time of 30 days. In comparison, the use of actual forecasts of up to 15 days shows the practical benefit of operational forecasts, where stochastic optimization (15 days lead time) outperforms the deterministic version (10 days lead time) significantly. The range of the energy production rate between the different approaches is relatively small, between 78% and 80%, suggesting that the use of stochastic optimization combined with ensemble forecasts leads to a significantly higher level of flood protection without compromising the energy production.

Keywords: Hydrological forecasting; Short-term optimization; Ensemble forecasting; Real-time control; Flood mitigation; Três Marias Dam (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s11269-016-1377-8

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