Work Description

Title: Data-driven approaches to managing uncertain load control in sustainable power systems (project outputs) Open Access Deposited

http://creativecommons.org/licenses/by/4.0/
Attribute Value
Methodology
  • 1. stochastic optimization techniques 2. optimal power flow analysis with renewable generation and demand response
Description
  • The project outputs summarize all the publications, talks, and codes we accomplished under this NSF funding. In the project, we develop methodologies to manage uncertainty in future electric power systems and quantify how uncertainty affects power system sustainability.

  • Talks, papers, and poster in Deep Blue:  http://hdl.handle.net/2027.42/149653
Creator
Depositor
  • libowen@umich.edu
Contact information
Discipline
Funding agency
  • National Science Foundation (NSF)
ORSP grant number
  • CCF-1442495
Keyword
Citations to related material
Resource type
Last modified
  • 08/21/2019
Published
  • 06/11/2019
DOI
License
To Cite this Work:
Bowen Li, Yiling Zhang, Siqian Shen, Johanna Mathieu (2019). Data-driven approaches to managing uncertain load control in sustainable power systems (project outputs) [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/413q-2c95

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