Chance-Constrained Outage Scheduling using a Machine Learning Proxy

Dalal, Gal; Gilboa, Elad; Mannor, Shie; Wehenkel, Louis

doi:10.1109/TPWRS.2018.2889237

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Chance-Constrained Outage Scheduling using a Machine Learning Proxy

Dalal, Gal; Gilboa, Elad; Mannor, Shie et al.

2019 • In IEEE Transactions on Power Systems, On-line early access

Peer Reviewed verified by ORBi

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https://hdl.handle.net/2268/233188

DOI
10.1109/TPWRS.2018.2889237

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Keywords :

Machine Learning; Artificial Intelligence; Electric Power Systems; Outage scheduling; Risk management; Reliability; Stochastic Optimization

Abstract :

[en] 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.

Disciplines :

Electrical & electronics engineering
Computer science

Author, co-author :

Dalal, Gal

Gilboa, Elad

Mannor, Shie

Wehenkel, Louis ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation

Language :

English

Title :

Chance-Constrained Outage Scheduling using a Machine Learning Proxy

Publication date :

2019

Journal title :

IEEE Transactions on Power Systems

ISSN :

0885-8950

Publisher :

Institute of Electrical and Electronics Engineers, United States - New Jersey

Volume :

On-line early access

Peer reviewed :

Peer Reviewed verified by ORBi

European Projects :

FP7 - 608540 - GARPUR - Generally Accepted Reliability Principle with Uncertainty modelling and through probabilistic Risk assessment

Funders :

CE - Commission Européenne

Available on ORBi :

since 25 February 2019

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