Reverse and Forward Engineering of Local Voltage Control in Distribution Networks
Abstract
The increasing penetration of renewable and distributed energy resources in distribution networks calls for real-time and distributed voltage control. In this article, we investigate local Volt/VAR control with a general class of control functions, and show that the power system dynamics with nonincremental local voltage control can be seen as a distributed algorithm for solving a well-defined optimization problem (reverse engineering). The reverse engineering further reveals a fundamental limitation of the nonincremental voltage control: the convergence condition is restrictive and prevents better voltage regulation at equilibrium. This motivates us to design two incremental local voltage control schemes based on the subgradient and pseudo-gradient algorithms, respectively, for solving the same optimization problem (forward engineering). The new control schemes decouple the dynamical property from the equilibrium property, and have much less restrictive convergence conditions. This article presents another step toward developing a new foundation—network dynamics as optimization algorithms—for distributed real-time control and optimization of future power networks.
Additional Information
© 2020 IEEE. Manuscript received October 7, 2019; accepted May 6, 2020. Date of publication May 12, 2020; date of current version February 26, 2021. This work was supported in part by the National Renewable Energy Laboratory under subcontracts APUP UGA-0-41026-120 and APUP UGA-0-41026-107, in part by the National Science Foundation through Grant CCF 1637598, Grant ECCS 1619352, and Grant CPS ECCS 1739355, and in part by the DOE under Contract DE-EE-0007998. Presented in part at the IEEE Conference on Decision and Control, Florence, Italy, 2013 [53], IEEE International Conference on Smart Grid Communications, Miami, FL, 2015 [54], and Annual Allerton Conference on Communication, Control, and Computing, Allerton, IL, 2015 [55]. Recommended by Associate Editor U. V. Shanbhag.Attached Files
Submitted - 1801.02015.pdf
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Additional details
- Eprint ID
- 103208
- Resolver ID
- CaltechAUTHORS:20200514-142330504
- National Renewable Energy Laboratory
- APUP UGA-0-41026-120
- National Renewable Energy Laboratory
- APUP UGA-0-41026-107
- NSF
- CCF-1637598
- NSF
- ECCS-1619352
- NSF
- ECCS-1739355
- Department of Energy (DOE)
- DE-EE-0007998
- Created
-
2020-05-14Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field