Cost Estimation in Unit Commitment Problems Using Simulation-Based Inference

Matthias Pirlet, Adrien Bolland, Gilles Louppe, Damien Ernst

Keywords: Machine Learning, Simulation-based Inference, Unit Commitment, Energy Markets, Probabilistic inverse problem, Neural surrogate

TL;DR: We propose a novel approach for estimating the parameters of the unit commitment problem using simulation-based inference, allowing to deduce the production costs of generation units based on the observed power generation schedule

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.

Submission Number: 23