Towards Optimal and Executable Distribution Grid Restoration Planning With a Fine-Grained Power-Communication Interdependency Model

Liu, Xin; Zhang, Bo; Chen, Bo; Aved, Alex; Jin, Dong

doi:10.1109/tsg.2022.3149973

Title: Towards Optimal and Executable Distribution Grid Restoration Planning With a Fine-Grained Power-Communication Interdependency Model

Journal Article · Tue Feb 08 00:00:00 EST 2022 · IEEE Transactions on Smart Grid

DOI:https://doi.org/10.1109/tsg.2022.3149973· OSTI ID:1905063

^[1]; Zhang, Bo ^[1];

^[2];

^[3]; Jin, Dong ^[4]

Illinois Institute of Technology, Chicago, IL (United States)
Argonne National Lab. (ANL), Lemont, IL (United States)
Air Force Research Lab. (AFRL), Rome, NY (United States)
Univ. of Arkansas, Fayetteville, AR (United States)

Distribution service restoration (DSR) under natural disasters is always a critical and challenging problem for utility companies. An effective solution must not ignore the power-communication interdependency as various systems are getting increasingly connected in the Smart Grid era. In this paper, we propose a two-layer distribution system model with both power and communication components. Based on this model, we formulate the restoration process as a routing problem that schedules the path and action sequence of utility crews that involves repairing damaged components, closing power switches, and enabling communication paths between the control center and remote field devices. Further, we develop a simulation-based method to quantitatively evaluate the restoration process with public reference models of large-scale power systems. The experimental results show that our method improves the total restored energy up to 57.6% and reduces the recovery time up to 63% by considering the power-communication interdependency.

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Research Organization:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Organization:: US Air Force Office of Scientific Research (AFOSR); Maryland Procurement Office; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: AC02-06CH11357; YIP FA9550-17-1-0240; H98230-18-D-0007; 34230

OSTI ID:: 1905063

Journal Information:: IEEE Transactions on Smart Grid, Vol. 13, Issue 3; ISSN 1949-3053

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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