BibTeX

@article{2409.03588v2,
Author        = {Matthias Pirlet and Adrien Bolland and Gilles Louppe and Damien Ernst},
Title         = {Cost Estimation in Unit Commitment Problems Using Simulation-Based
  Inference},
Eprint        = {2409.03588v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {The Unit Commitment (UC) problem is a key optimization task in power systems
to forecast the generation schedules of power units over a finite time period
by minimizing costs while meeting demand and technical constraints. However,
many parameters required by the UC problem are unknown, such as the costs. In
this work, we estimate these unknown costs using simulation-based inference on
an illustrative UC problem, which provides an approximated posterior
distribution of the parameters given observed generation schedules and demands.
Our results highlight that the learned posterior distribution effectively
captures the underlying distribution of the data, providing a range of possible
values for the unknown parameters given a past observation. This posterior
allows for the estimation of past costs using observed past generation
schedules, enabling operators to better forecast future costs and make more
robust generation scheduling forecasts. We present avenues for future research
to address overconfidence in posterior estimation, enhance the scalability of
the methodology and apply it to more complex UC problems modeling the network
constraints and renewable energy sources.},
Year          = {2024},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2409.03588v2},
File          = {2409.03588v2.pdf}
}