Data-driven optimization approaches for optimal power flow with uncertain reserves from load control

Yiling Zhang, Siqian Shen, Johanna L. Mathieu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations


Aggregations of electric loads, like heating and cooling systems, can be controlled to help the power grid balance supply and demand, but the amount of balancing reserves available from these resources is uncertain. In this paper, we investigate data-driven optimization methods that are suited to dispatching power systems with uncertain balancing reserves provided by load control. Specifically, we consider a chance-constrained optimal power flow problem in which we aim to satisfy constraints that include random variables either jointly with a specified probability or individually with different risk tolerance levels. We focus on the realistic case in which we do not have full knowledge of the uncertainty distributions and compare distribution-free approaches with several stochastic optimization methods. We conduct experimental studies on the IEEE 9-bus test system assuming uncertainty in load, load-control reserve capacities, and renewable energy generation. The results show the computational efficacy of the distributionally robust approach and its flexibility in trading off between cost and robustness of solutions driven by data.

Original languageEnglish (US)
Title of host publicationACC 2015 - 2015 American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479986842
StatePublished - Jul 28 2015
Externally publishedYes
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: Jul 1 2015Jul 3 2015

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2015 American Control Conference, ACC 2015
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2015 American Automatic Control Council.


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