Cost Estimation in Unit Commitment Problems Using Simulation-Based Inference
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| Title: | Cost Estimation in Unit Commitment Problems Using Simulation-Based Inference |
|---|---|
| Authors: | Pirlet, Matthias, Bolland, Adrien, Louppe, Gilles, Ernst, Damien |
| Source: | NeurIPS workshop: Data-driven and Differentiable Simulations, Surrogates, and Solvers, Vancouver, Canada [CA], du 9 décembre 2024 au 15 décembre 2024 |
| Publication Year: | 2024 |
| Subject Terms: | Computer Science - Learning, simulation-based inference, Machine learning, Energy markets, Unit Commitment, Deep Learning, Engineering, computing & technology, Computer science, Electrical & electronics engineering, Ingénierie, informatique & technologie, Sciences informatiques, Ingénierie électrique & électronique |
| Description: | 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 researchto 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. |
| Document Type: | conference paper http://purl.org/coar/resource_type/c_5794 conferenceObject peer reviewed |
| Language: | English |
| Access URL: | https://orbi.uliege.be/handle/2268/321782 |
| Rights: | open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess |
| Accession Number: | edsorb.321782 |
| Database: | ORBi |
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