Work Description

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

h
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
  • B. Li and J. L. Mathieu, "Analytical reformulation of chance-constrained optimal power flow with uncertain load control," 2015 IEEE Eindhoven PowerTech, Eindhoven, 2015, pp. 1-6. https://doi.org/10.1109/PTC.2015.7232803
  • B. Li, J. L. Mathieu and R. Jiang, "Distributionally Robust Chance Constrained Optimal Power Flow Assuming Log-Concave Distributions," 2018 Power Systems Computation Conference (PSCC), Dublin, 2018, pp. 1-7. https://doi.org/10.23919/PSCC.2018.8442927
  • B. Li, M. Vrakopoulou and J. L. Mathieu, "Chance Constrained Reserve Scheduling Using Uncertain Controllable Loads Part II: Analytical Reformulation," in IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 1618-1625, March 2019. https://doi.org/10.1109/TSG.2017.2773603
  • B. Li, R. Jiang and J. L. Mathieu, "Distributionally Robust Chance-Constrained Optimal Power Flow Assuming Unimodal Distributions With Misspecified Modes," in IEEE Transactions on Control of Network Systems, vol. 6, no. 3, pp. 1223-1234, Sept. 2019. https://doi.org/10.1109/TCNS.2019.2930872
  • B. Li, R. Jiang and J. L. Mathieu, "Distributionally robust risk-constrained optimal power flow using moment and unimodality information," 2016 IEEE 55th Conference on Decision and Control (CDC), Las Vegas, NV, 2016, pp. 2425-2430. https://doi.org/10.1109/CDC.2016.7798625
  • B. Li, S. D. Maroukis, Y. Lin and J. L. Mathieu, "Impact of uncertainty from load-based reserves and renewables on dispatch costs and emissions," 2016 North American Power Symposium (NAPS), Denver, CO, 2016, pp. 1-6. https://doi.org/10.1109/NAPS.2016.7747830
  • G. Martínez, J. Liu, B. Li, J. L. Mathieu and C. L. Anderson, "Enabling renewable resource integration: The balance between robustness and flexibility," 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, 2015, pp. 195-202. https://doi.org/10.1109/ALLERTON.2015.7447004
  • J. Liu, M. G. Martinez, B. Li, J. Mathieu and C. L. Anderson, "A Comparison of Robust and Probabilistic Reliability for Systems with Renewables and Responsive Demand," 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, 2016, pp. 2373-2380. https://doi.org/10.1109/HICSS.2016.297
  • Li, B., Jiang, R. & Mathieu, J.L. "Ambiguous risk constraints with moment and unimodality information." Math. Program. 173, 151–192 (2019). https://doi.org/10.1007/s10107-017-1212-x
  • M. Vrakopoulou, B. Li and J. L. Mathieu, "Chance Constrained Reserve Scheduling Using Uncertain Controllable Loads Part I: Formulation and Scenario-Based Analysis," in IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 1608-1617, March 2019. https://doi.org/10.1109/TSG.2017.2773627
  • Y. Zhang, S. Shen and J. L. Mathieu, "Data-driven optimization approaches for optimal power flow with uncertain reserves from load control," 2015 American Control Conference (ACC), Chicago, IL, 2015, pp. 3013-3018. https://doi.org/10.1109/ACC.2015.7171795
  • Y. Zhang, S. Shen and J. L. Mathieu, "Distributionally Robust Chance-Constrained Optimal Power Flow With Uncertain Renewables and Uncertain Reserves Provided by Loads," in IEEE Transactions on Power Systems, vol. 32, no. 2, pp. 1378-1388, March
  • Y. Zhang, S. Shen, B. Li and J. L. Mathieu, "Two-stage distributionally robust optimal power flow with flexible loads," 2017 IEEE Manchester PowerTech, Manchester, 2017, pp. 1-6. https://doi.org/10.1109/PTC.2017.7981202
Related items in Deep Blue Documents
Resource type
Last modified
  • 04/22/2020
Published
  • 06/11/2019
DOI
  • https://doi.org/10.7302/413q-2c95
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 Data. https://doi.org/10.7302/413q-2c95

Relationships

This work is not a member of any user collections.

Files (Count: 2; Size: 217 MB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
Documentation.docx 2019-06-10 2019-07-02 14.1 KB Open Access
Code.zip 2019-06-10 2019-06-10 217 MB Open Access

Download All Files (To download individual files, select them in the “Files” panel above)

Best for data sets < 3 GB. Downloads all files plus metadata into a zip file.



Best for data sets > 3 GB. Globus is the platform Deep Blue Data uses to make large data sets available.   More about Globus

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.