Chance-Constrained Outage Scheduling using a Machine Learning Proxy

Abstract

peer reviewedOutage 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 data-driven distributed 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. All our code (matlab) is publicly available at https://github.com/galdl/outage scheduling