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

Ladda ner artikelhttps://doi.org/10.3384/ecp201709

Ingår i: 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

Visa mer +

Publicerad: 2020-01-24

ISBN: 978-91-7929-897-5

ISSN: 1650-3686 (tryckt), 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


Stephen Boyd and Lieven Vandenberghe. Introduction to Applied Linear Algebra. Vectors, Matrices, and Least Squares. Cambridge University Press, 2018. ISBN 978-1316518960.

Maarten Breckpot, Oscar Mauricio Agudelo, and Bart L.R. De Moor. Flood Control with Model Predictive Control for River Systems with Water Reservoirs. Irrigation and Drainage Engineering, 139:532–531, 2013a.doi:10.1061/(ASCE)IR.1943-4774.0000577.

Maarten Breckpot, Oscar Mauricio Agudelo, Pieter Meert, Patrick Willems, and Bart De Moor. Flood control of the Demer by using Model Predictive Control. Control Engineering Practice, 21(12):1776–1787, December 2013b. doi:10.1016/j.conengprac.2013.08.008.

Michael B. Butts, Anne Katrine V. Falk, Yunqing Xuan, and Ian D. Cluckie. Integrating meteorological and uncertainty information in ?ood forecasting: the FLOODRELIEF project. IAHS, 313:385–397, July 2007.

Dines Krishnamoorthy, Bjarne Foss, and Sigurd Skogestad. A distributed algorithm for scenario-based model predictive control using primal decomposition. IFAC PapersOnLine, 51(18):351–356, 2018.

Bernt Lie. Final report: KONTRAKT NR INAN-140122 Optimal Control of Dalsfos Flood Gates- control algorithm, 2014.

R. Timothy Marler and Jasbir S. Arora. The weighted um method for multi-objective optimization: new insights. Structural and Multidisciplinary Optimization, 41(6):853–862, Jun 2010. ISSN 1615-1488. doi:10.1007/s00158-009-0460-7. URL https://doi.org/10.1007/s00158-009-0460-7.

Hasan Arshad Nasir, Tony Zhao, Algo Carè, Quan J. Wang, and Erik Weyer. Ef?cient river management using stochastic MPC and ensemble forecast of uncertain in-?ows. IFAC PapersOnLine, 51(5):37–42, 2018. doi:10.1016/j.ifacol.2018.06.196.

NVE. The Norwegian Water Resources and Energy Directorate, 2018. URL https://temakart.nve.no/link/?link=vannkraft&layer=0,8&field=kdbNr&value=7332&buffer=3000.

Sebastian Peitz and Michael Dellnitz. A survey of recent trends in multiobjective optimal control — surrogate models, feedback control and objective reduction. Mathematical and Computational Applications, 23(30):1–33, 2018. ISSN 2297-8747. doi:10.3390/mca23020030. URL http://www.mdpi.com/2297-8747/23/2/30.

Luciano Raso, Dirk Schwanenberg, Nick van de Giesen, and Peter Jules van Overloop. Short-term optimal operation of water systems using ensemble forecasts. Advances in Water Resources, 71:200–208, September 2014. doi:10.1016/j.advwatres.2014.06.009.

Dirk Schwanenberg, Fernando Mainardi Fan, Stef? Naumann, Julio Issao Kuwajima, Rodolfo Alvarado Montero, and Alberto Assis dos Reis. Short-Term Reservoir Optimization for Flood Mitigation under Meteorological and Hydrological Forecast Uncertainty. Water Resour Manage, 29(5):1635–1651, March 2015. ISSN 0920-4741. doi:10.1007/s11269-014-0899-1. URL https://link.springer.com/article/10.1007/s11269-014-0899-1.

Citeringar i Crossref