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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2018-236
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2018-236
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 03 Jul 2018

Research article | 03 Jul 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Analysis of the effects of biases in ESP forecasts on electricity production in hydropower reservoir management

Richard Arsenault1,2 and Pascal Côté2 Richard Arsenault and Pascal Côté
  • 1Department of construction engineering, École de technologie supérieure, Montreal, H3C 1K3, Canada
  • 2Quebec Power Operations, Rio Tinto, Jonquière, G7S 4R5, Canada

Abstract. This paper presents an analysis of the effects of biased Extended Streamflow Prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon testbed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data was fed to the hydrological model and the synthetic streamflow thus generated was considered as a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method’s response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test-bed in which the entire system is precisely and accurately modeled. The ensemble climate data forecasts, the hydrological modeling and ESP forecast generation, optimization model and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focused on one hydropower system both with and without minimum base load constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore increases the odds of forced spillage by attempting to maximize short-term profit by keeping a higher net head. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5% allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider optimal trajectories.

Richard Arsenault and Pascal Côté
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Richard Arsenault and Pascal Côté
Richard Arsenault and Pascal Côté
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Latest update: 15 Dec 2018
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Short summary
Hydrological forecasting allows hydropower system operators to make the most efficient use of the available water as possible. Accordingly, hydrolgists have been aiming at improving the quality of these forecasts. This work looks at the impacts of improving systematic errors in a forecasting scheme on the hydropower generation using a few decision-aiding tools that are used operationally by hydropower utilities. We find that the impacts differ according to the hydropower system characteristics.
Hydrological forecasting allows hydropower system operators to make the most efficient use of...
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