Comparison Between Robust and Stochastic Optimisation for Long-term Reservoir Management Under Uncertainty

Thibaut Cuvelier (), Pierre Archambeau, Benjamin Dewals and Quentin Louveaux
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Thibaut Cuvelier: Université de Liège
Pierre Archambeau: Université de Liège
Benjamin Dewals: Université de Liège
Quentin Louveaux: Université de Liège

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 5, No 5, 1599-1614

Abstract: Abstract Long-term reservoir management often uses bounds on the reservoir level, between which the operator can work. However, these bounds are not always kept up-to-date with the latest knowledge about the reservoir drainage area, and thus become obsolete. The main difficulty with bounds computation is to correctly take into account the high uncertainty about the inflows to the reservoir. In this article, we propose a methodology to derive minimum bounds while providing formal guarantees about the quality of the obtained solutions. The uncertainty is embedded using either stochastic or robust programming in a model-predictive-control framework. We compare the two paradigms to the existing solution for a case study and find that the obtained solutions vary substantially. By combining the stochastic and the robust approaches, we also assign a confidence level to the solutions obtained by stochastic programming. The proposed methodology is found to be both efficient and easy to implement. It relies on sound mathematical principles, ensuring that a global optimum is reached in all cases.

Keywords: Long-term reservoir management; Rule curve; Stochastic optimisation; Robust optimisation (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11269-017-1893-1

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