Abstract
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 language | English (US) |
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Title of host publication | ACC 2015 - 2015 American Control Conference |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 3013-3018 |
Number of pages | 6 |
ISBN (Electronic) | 9781479986842 |
DOIs | |
State | Published - Jul 28 2015 |
Externally published | Yes |
Event | 2015 American Control Conference, ACC 2015 - Chicago, United States Duration: Jul 1 2015 → Jul 3 2015 |
Publication series
Name | Proceedings of the American Control Conference |
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Volume | 2015-July |
ISSN (Print) | 0743-1619 |
Conference
Conference | 2015 American Control Conference, ACC 2015 |
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Country/Territory | United States |
City | Chicago |
Period | 7/1/15 → 7/3/15 |
Bibliographical note
Publisher Copyright:© 2015 American Automatic Control Council.