Cost Estimation in Unit Commitment Problems Using Simulation-Based Inference
This work addresses cost estimation for power system operators, but it is incremental as it builds on existing simulation-based inference methods applied to an illustrative UC problem.
The paper tackles the problem of estimating unknown costs in Unit Commitment (UC) problems for power systems by using simulation-based inference to approximate posterior distributions from observed generation schedules and demands, enabling better cost forecasting and robust scheduling.
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.