Chance-Constrained Outage Scheduling using a Machine Learning Proxy

Gal Dalal; Elad Gilboa; Shie Mannor; Louis Wehenkel

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arXiv · 1801.00500

Chance-Constrained Outage Scheduling using a Machine Learning Proxy

Gal Dalal·Elad Gilboa·Shie Mannor·Louis Wehenkel

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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.

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Gal Dalal, Elad Gilboa, Shie Mannor, Louis Wehenkel. 2018-01-01. Chance-Constrained Outage Scheduling using a Machine Learning Proxy. https://arxiv.org/abs/1801.00500

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