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Chance-Constrained Outage Scheduling Using a Machine Learning Proxy
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DOI：10.1109/TPWRS.2018.2889237.png)
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Abstract
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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 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.
Keywords:
Outage Scheduling
Stochastic Optimization
Scenario Optimization
Chance Constraints
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