Cost Estimation in Unit Commitment Problems Using Simulation-Based Inference

TL;DR

This paper introduces a simulation-based inference method to estimate unknown costs in the unit commitment problem, improving cost forecasting and scheduling robustness in power systems.

Contribution

It presents a novel approach using simulation-based inference to estimate unknown parameters in the UC problem, capturing their distribution from observed data.

Findings

Posterior distribution effectively captures data variability.

Method enables better cost forecasting from past data.

Framework adaptable to complex UC scenarios.

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.