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

Fernando Mainardi Fan,Dirk Schwanenberg,Walter Collischonn,Alberto Assis Dos Reis,Steffi Naumman,Rodolfo Alvarado

doi:10.1007/s11269-016-1377-8

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 Tres 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.

Highlights

Hydropower is the most important source of electricity in Brazil
Deterministic and probabilistic forecasts force a short-term optimization model to operate the Três Marias Hydroelectric Power Plant (HPP) reservoir over a 2-years test period
Main focus is the assessment of the added value of probabilistic forecast, the novel mass-conservative scenario tree reduction technique and multi-stage-stochastic optimization in comparison to their deterministic counterparts

Summary

Introduction

Hydropower is the most important source of electricity in Brazil. The remaining electricity is provided mostly by thermal power plants using biomass, coal, natural gas, and nuclear power, but generally at higher costs. A nationwide transmission network allows for integrated management of the energy production. This management is done by a central organization called Operador Nacional do Sistema (ONS), whose objective is to optimize electricity production by increasing production at plants with lower operation costs and decreasing it at plants where these costs are higher. Operational costs of hydropower are lower than for thermal power plants; there is a strong economic reason to maximize the proportion of energy generated from hydropower (Hamlet et al 2002). Hydropower is dependent on weather and climate, which are naturally variable, leading to risks of power production shortage

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Conclusion