Chance-Constrained Outage Scheduling using a Machine Learning Proxy
Computational Engineering, Finance, and Science
2018-01-03 v1
Abstract
Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to minimize operation-cost expectation while satisfying reliability-related constraints. We propose a distributed scenario-based chance-constrained optimization formulation for this problem. To tackle tractability issues arising in large networks, we use machine learning to build a proxy for predicting outcomes of power system operation processes in this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains cheaper and more reliable plans than other candidates.
Keywords
Cite
@article{arxiv.1801.00500,
title = {Chance-Constrained Outage Scheduling using a Machine Learning Proxy},
author = {Gal Dalal and Elad Gilboa and Shie Mannor and Louis Wehenkel},
journal= {arXiv preprint arXiv:1801.00500},
year = {2018}
}