YorkSpace has migrated to a new version of its software. Access our Help Resources to learn how to use the refreshed site. Contact diginit@yorku.ca if you have any questions about the migration.
 

Decentralized Topology Reconfiguration in Multiphase Distribution Networks

Loading...
Thumbnail Image

Date

2019-02

Authors

Srikantha, P.
Liu, J.

Journal Title

Journal ISSN

Volume Title

Publisher

IEEE Transactions on Signal and Information Processing over Networks

Abstract

The cyber-physical nature of the modern power grid allows active power entities to exchange information signals with one another to make intelligent local actuation decisions. Exacting effective coordination amongst these cyber-enabled entities by way of strategic signal exchanges is essential for accommodating highly fluctuating power components (e.g., renewables, electric vehicles, etc.) that are becoming prevalent in today's electric grid. As such, in this paper, we present a novel decentralized topology reconfiguration algorithm for the distribution network (DN) that allows the system to adapt in real time to unexpected perturbations and/or congestions to restore balance in loads across the feeder and improve the DN voltage profile. For this, individual agents residing in DN buses iteratively exchange signals with neighbouring nodes to infer the current state (e.g., power balance and voltage) of the system and utilize this information to make local line switching decisions. Strong convergence properties and optimality conditions of the proposed algorithm are established via theoretical studies evoking potential games and discrete concavity. Comparative simulation studies conducted on realistic DNs showcase the practical properties of the proposed algorithm.

Description

Keywords

smart grids, power system reliability, optimization, adaptive signal processing

Citation

J. Liu and P. Srikantha, "Decentralized Topology Reconfiguration in Multiphase Distribution Networks," in IEEE Transactions on Signal and Information Processing over Networks, vol. 5, no. 3, pp. 598-610, Sept. 2019.