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Published November 2017 | public
Journal Article Open

Distributed optimization decomposition for joint economic dispatch and frequency regulation


Economic dispatch and frequency regulation are typically viewed as fundamentally different problems in power systems and, hence, are typically studied separately. In this paper, we frame and study a joint problem that co-optimizes both slow timescale economic dispatch resources and fast timescale frequency regulation resources. We show how the joint problem can be decomposed without loss of optimality into slow and fast timescale sub-problems that have appealing interpretations as the economic dispatch and frequency regulation problems respectively. We solve the fast timescale sub-problem using a distributed frequency control algorithm that preserves network stability during transients. We solve the slow timescale subproblem using an efficient market mechanism that coordinates with the fast timescale sub-problem. We investigate the performance of our approach on the IEEE 24-bus reliability test system.

Additional Information

© 2017 IEEE. Manuscript received May 6, 2016; revised September 28, 2016 and January 25, 2017; accepted February 25, 2017. Date of publication March 14, 2017; date of current version October 18, 2017. This work was supported in part by ARPA-E Grant DE-AR0000226, in part by Los Alamos National Lab through an DoE Grant DE-AC52-06NA25396, in part by DTRA through Grant HDTRA 1-15-1-0003, in part by Skoltech, in part by NSF Grant 1545096 as a part of the NSF/DHS/DOT/NASA/NIH Cyber-Physical Systems Program, in part by NSF Grant NETS-1518941, in part by NSF Grant EPAS-1307794, in part by NSF CPS Grant CNS 1544771, in part by Johns Hopkins E2SHI Seed Grant, and in part by Johns Hopkins WSE startup funds. A preliminary and abridged version of this paper was presented at the 54th IEEE Conference on Decision and Control, Osaka, Japan, December 2015 [1]. Paper no. TPWRS-00700-2016. The authors would like to thank Ben Hobbs from Johns Hopkins and Steven H. Low from Caltech for insightful discussions.

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