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Article Dans Une Revue Algorithms Année : 2022

Batch Acquisition for Parallel Bayesian Optimization—Application to Hydro-Energy Storage Systems Scheduling

Jan Gmys
Jean-François Toubeau
Nouredine Melab
  • Fonction : Auteur
Daniel Tuyttens
  • Fonction : Auteur
François Vallée

Résumé

Bayesian Optimization (BO) with Gaussian process regression is a popular framework for the optimization of time-consuming cost functions. However, the joint exploitation of BO and parallel processing capabilities remains challenging, despite intense research efforts over the last decade. In particular, the choice of a suitable batch-acquisition process, responsible for selecting promising candidate solutions for batch-parallel evaluation, is crucial. Even though some general recommendations can be found in the literature, many of its hyperparameters remain problem-specific. Moreover, the limitations of existing approaches in terms of scalability, especially for moderately expensive objective functions, are barely discussed. This work investigates five parallel BO algorithms based on different batch-acquisition processes, applied to the optimal scheduling of Underground Pumped Hydro-Energy Storage stations and classical benchmark functions. Efficient management of such energy-storage units requires parallel BO algorithms able to find solutions in a very restricted time to comply with the responsive energy markets. Our experimental results show that for the considered methods, a batch of four candidates is a good trade-off between execution speed and relevance of the candidates. Analysis of each method’s strengths and weaknesses indicates possible future research directions.
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Dates et versions

hal-03930078 , version 1 (09-01-2023)

Identifiants

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Maxime Gobert, Jan Gmys, Jean-François Toubeau, Nouredine Melab, Daniel Tuyttens, et al.. Batch Acquisition for Parallel Bayesian Optimization—Application to Hydro-Energy Storage Systems Scheduling. Algorithms, 2022, 15 (12), pp.446. ⟨10.3390/a15120446⟩. ⟨hal-03930078⟩
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