Conference article

Flood Management of Lake Toke: MPC Operation under Uncertainty

Itsaso Menchacatorre
University of South-Eastern Norway, Porsgrunn, Norway

Roshan Sharma
University of South-Eastern Norway, Porsgrunn, Norway

Beathe Furenes
Skagerak Kraft AS, Porsgrunn, Norway

Bernt Lie
University of South-Eastern Norway, Porsgrunn, Norway

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Published in: Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden

Linköping Electronic Conference Proceedings 170:2, s. 9-16

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Published: 2020-01-24

ISBN: 978-91-7929-897-5

ISSN: 1650-3686 (print), 1650-3740 (online)


A deterministic reference tracking model predictive control (MPC) is in use at Skagerak Kraft for flood management of Lake Toke in Norway. An operational inflow estimate is used to predict the optimal gate opening at Dalsfos power station, with required constraints set by the Norwegian Water Resource and Energy Directorate (NVE). The operational inflow estimate is based on the meteorological forecast, and is uncertain; this may lead to broken concession requirements and unnecessary release of water through the floodgates. Currently not utilized, the meteorological uncertainty is quantified by an ensemble of possible weather forecasts. In this paper, quantified inflow uncertainty is studied and how this affects the operation of the current, deterministic MPC solution. Next, we develop an alternative, stochastic MPC solution based on multi objective optimization which directly takes the inflow uncertainty into consideration. A comparison of the results from both approaches concludes that the stochastic MPC solution seems to give better control by reducing the amount of water released through the flood gates. Furthermore, with less frequent update of the control signal, the benefit of the stochastic MPC is expected to increase.


model predictive control, hydrology, uncertainty, multi objective optimization


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