Comparison Between Robust and Stochastic Optimisation for Long-term Reservoir Management Under Uncertainty - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Water Resources Management Année : 2018

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

Résumé

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.
Fichier principal
Vignette du fichier
Paper_springer.pdf (2.43 Mo) Télécharger le fichier
ESM_1.pdf (99.05 Ko) Télécharger le fichier
ESM_2.pdf (61.86 Ko) Télécharger le fichier
ESM_3.pdf (329.59 Ko) Télécharger le fichier
ESM_4.pdf (2.38 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02265436 , version 1 (09-08-2019)

Identifiants

Citer

Thibaut Cuvelier, Pierre Archambeau, Benjamin J. Dewals, Quentin Louveaux. Comparison Between Robust and Stochastic Optimisation for Long-term Reservoir Management Under Uncertainty. Water Resources Management, 2018, 32 (5), ⟨10.1007/s11269-017-1893-1⟩. ⟨hal-02265436⟩
58 Consultations
192 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More