Reference : Comparison Between Robust and Stochastic Optimisation for Long-term Reservoir Managem...
Scientific journals : Article
Engineering, computing & technology : Civil engineering
Engineering, computing & technology : Computer science
Physical, chemical, mathematical & earth Sciences : Mathematics
Comparison Between Robust and Stochastic Optimisation for Long-term Reservoir Management Under Uncertainty
[fr] Comparaison entre l'optimisation stochastique et robuste dans le cadre de la gestion de réservoirs à long terme sous incertitude
Cuvelier, Thibaut mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation : Optimisation discrète >]
Archambeau, Pierre mailto [Université de Liège - ULiège > Département ArGEnCo > HECE (Hydraulics in Environnemental and Civil Engineering) >]
Dewals, Benjamin mailto [Université de Liège - ULiège > Département ArGEnCo > Hydraulics in Environmental and Civil Engineering >]
Louveaux, Quentin mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation : Optimisation discrète >]
Water Resources Management
Springer Science & Business Media B.V.
Yes (verified by ORBi)
[en] Long-term reservoir management ; Rule curve ; Stochastic optimisation ; Robust optimisation
[en] 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.
Researchers ; Professionals
This is a post-peer-review, pre-copyedit version of an article published in Water Resources Management. The final authenticated version is available online at:
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