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Title: Coordinated Transmission and Distribution AC Optimal Power Flow

Abstract

The current separate transmission and distribution energy management system faces multiple challenges associated with integrating distributed generators (DGs) into future grids. These challenges, such as a voltage rise issue resulting in curtailment of DGs, are difficult to solve via the current separate energy management. Thus, coordination between transmission and distribution is suggested, and a coordinated transmission and distribution AC optimal power flow (TDOPF) is proposed in this paper. A mathematical TDOPF model is established and analyzed in a master-slave structure. A heterogeneous decomposition algorithm (HGD), which is inspired by heterogeneous transmission and distribution characteristics, is proposed to solve the TDOPF in a distributed manner. The HGD is compared to other typical multi-area OPF decomposition algorithms and the differences are discussed. Numerical tests verify the benefit of the TDOPF to both transmission and distribution systems. The TDOPF improves economic operations, mitigates voltage rises, and decreases boundary bus mismatches. Hence, more DGs can be accommodated by the grid. In addition, a series of numerical tests indicate that the HGD competitively solves the TDOPF.

Authors:
; ; ORCiD logo;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
National Key Basic Research Program of China; National Natural Science Foundation of China (NNSFC); USDOE Office of Electricity Delivery and Energy Reliability
OSTI Identifier:
1464769
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
IEEE Transactions on Smart Grid
Additional Journal Information:
Journal Volume: 9; Journal Issue: 2; Journal ID: ISSN 1949-3053
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
decomposition; distributed generator (DG); distribution; energy management; optimal power flow (OPF); transmission

Citation Formats

Li, Zhengshuo, Guo, Qinglai, Sun, Hongbin, and Wang, Jianhui. Coordinated Transmission and Distribution AC Optimal Power Flow. United States: N. p., 2018. Web. doi:10.1109/tsg.2016.2582221.
Li, Zhengshuo, Guo, Qinglai, Sun, Hongbin, & Wang, Jianhui. Coordinated Transmission and Distribution AC Optimal Power Flow. United States. doi:10.1109/tsg.2016.2582221.
Li, Zhengshuo, Guo, Qinglai, Sun, Hongbin, and Wang, Jianhui. Thu . "Coordinated Transmission and Distribution AC Optimal Power Flow". United States. doi:10.1109/tsg.2016.2582221.
@article{osti_1464769,
title = {Coordinated Transmission and Distribution AC Optimal Power Flow},
author = {Li, Zhengshuo and Guo, Qinglai and Sun, Hongbin and Wang, Jianhui},
abstractNote = {The current separate transmission and distribution energy management system faces multiple challenges associated with integrating distributed generators (DGs) into future grids. These challenges, such as a voltage rise issue resulting in curtailment of DGs, are difficult to solve via the current separate energy management. Thus, coordination between transmission and distribution is suggested, and a coordinated transmission and distribution AC optimal power flow (TDOPF) is proposed in this paper. A mathematical TDOPF model is established and analyzed in a master-slave structure. A heterogeneous decomposition algorithm (HGD), which is inspired by heterogeneous transmission and distribution characteristics, is proposed to solve the TDOPF in a distributed manner. The HGD is compared to other typical multi-area OPF decomposition algorithms and the differences are discussed. Numerical tests verify the benefit of the TDOPF to both transmission and distribution systems. The TDOPF improves economic operations, mitigates voltage rises, and decreases boundary bus mismatches. Hence, more DGs can be accommodated by the grid. In addition, a series of numerical tests indicate that the HGD competitively solves the TDOPF.},
doi = {10.1109/tsg.2016.2582221},
journal = {IEEE Transactions on Smart Grid},
issn = {1949-3053},
number = 2,
volume = 9,
place = {United States},
year = {2018},
month = {3}
}