Computational Management Science

, Volume 15, Issue 1, pp 55–86 | Cite as

A successive linear programming algorithm with non-linear time series for the reservoir management problem

Original Paper
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Abstract

This paper proposes a multi-stage stochastic programming formulation based on affine decision rules for the reservoir management problem. Our approach seeks to find a release schedule that balances flood control and power generation objectives while considering realistic operating conditions as well as variable water head. To deal with the non-convexity introduced by the variable water head, we implement a simple, yet effective, successive linear programming algorithm. We also introduce a novel non-linear inflow representation that captures serial correlation of arbitrary order. We test our method on a small real river system and discuss policy implications. Our results namely show that our method can decrease flood risk and increase production compared to the historical decisions, albeit at the cost of reduced final storages.

Keywords

Mathematical programming Stochastic processes Forecasting Risk analysis 

Notes

Acknowledgements

The authors would like to thank Grégory Émiel, Louis Delorme, Pierre-Marc Rondeau, Sara Séguin, Jasson Pina and Pierre-Luc Carpentier. This research was supported by NSERC/Hydro-Québec through the Industrial Research Chair on the Stochastic Optimization of Electricity Generation and Grant 386416-2010.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Polytechnique MontréalMontrealCanada
  2. 2.HEC MontréalMontrealCanada

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