Title :

Parallel Bayesian Optimization for Optimal Scheduling of Underground Pumped Hydro-Energy Storage Systems

Author(s) :

Gobert, Maxime [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Gmys, Jan [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Inria Lille - Nord Europe
Toubeau, Jean-François [Auteur]
University of Mons [Belgium] [UMONS]
Melab, Nouredine [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Inria Lille - Nord Europe
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Tuyttens, Daniel [Auteur]
University of Mons [Belgium] [UMONS]
Vallée, François [Auteur]
University of Mons [Belgium] [UMONS]

Conference title :

IPDPSw PDCO - Parallel / Distributed Combinatorics and Optimization

City :

Lyon (remote)

Country :

France

Start date of the conference :

2022-05-27

Book title :

IEEE Xplore

Publication date :

2022-08-01

English keyword(s) :

Bayesian Optimization
Gaussian Process
Batch-based Parallelism
Optimization
Electrical Engineering

HAL domain(s) :

Informatique [cs]/Recherche opérationnelle [cs.RO]
Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
Sciences de l'ingénieur [physics]

English abstract : [en]

Underground Pumped Hydro-Energy Storage stations are sustainable options to enhance storage capacity and thus the flexibility of energy systems. Efficient management of such units requires high-performance optimization ...
Show more >Underground Pumped Hydro-Energy Storage stations are sustainable options to enhance storage capacity and thus the flexibility of energy systems. Efficient management of such units requires high-performance optimization algorithms able to find solutions in a very restricted timing to comply with the responsive energy markets. In this context, parallel computing offers a valuable solution to ensure appropriate decisions that maximize the profit of the station operator, while guaranteeing the safety of the energy network. This study investigates the use of three existing algorithms in Parallel Bayesian Optimization, namely q-EGO, BSP-EGO and TuRBO. The three algorithms have different inherent behaviors in terms of parallel potential and, even though TuRBO scales better, q-EGO remains the best choice regarding the final outcomes for all investigated batch sizes and manages to get up to 5 times more profits than other approaches.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL