Data-driven stochastic optimization for cyber-physical system risk management : smart power grids with renewable energy

Authors
Yi, Yuan
ORCID
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Other Contributors
Xie, Wei
Sharkey, Thomas C.
Mitchell, John E.
Zhou, Zhi
Issue Date
2018-12
Keywords
Industrial and management engineering
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Unit commitment decision is the fundamental operational decision for smart grids. Yet, the inherent unpredictable nature on both supply and demand sides, and our limited prior information of underlying input models lead to both stochastic and input uncertainty. To provide reliable and cost-efficient operational unit commitment decisions, we propose a new data-driven stochastic unit commitment model to hedge against the input and stochastic uncertainties simultaneously. Built on that, we develop a novel parallel optimization-based framework that further controls the finite sampling error caused by sample average approximation.
Description
December 2018
School of Engineering
Department
Dept. of Industrial and Systems Engineering
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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