Resilient Power System Black-start Restoration Model Considering Uncertain Renewable Energy

Yi Lin; Meng Li; Yuchen Tang; Wei Lin; Zhiming Chen; Zekai Wang; Tao Ding

doi:10.1109/ICARCV57592.2022.10004336

Resilient Power System Black-start Restoration Model Considering Uncertain Renewable Energy

Yi Lin
, Meng Li
, Yuchen Tang
, Wei Lin
, Zhiming Chen
, Zekai Wang
, Tao Ding

State Grid Fujian Electric Power Co. Ltd
State Key Laboratory of Electrical Insulation and Power Equipment

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper presents a novel multi-stage stochastic sequential black-start restoration model for the power transmission system with high penetration of renewable energy sources (RESs) after large-scale blackouts. Considering the uncertainties of RESs and the sequential restoration of the network, a two-stage generator model is designed to collaboratively dispatch the black-start units, RESs, and other thermal units during the restoration process. Finally, a modified New England 10-unit-39-bus test power system is employed to verify the effectiveness of the proposed model. Case studies indicate that the proposed model can coordinate the restoration of thermal units, RESs, and power loads. And RESs can effectively support the load restoration during the black-start process.

Original language	English
Title of host publication	2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	540-544
Number of pages	5
ISBN (Electronic)	9781665476874
DOIs	https://doi.org/10.1109/ICARCV57592.2022.10004336
State	Published - 2022
Externally published	Yes
Event	17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022 - Singapore, Singapore Duration: 11 Dec 2022 → 13 Dec 2022

Publication series

Name	2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022

Conference

Conference	17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022
Country/Territory	Singapore
City	Singapore
Period	11/12/22 → 13/12/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Renewable energy sources
load restoration
power system black-start
stochastic optimization

Access to Document

10.1109/ICARCV57592.2022.10004336

Cite this

Lin, Y., Li, M., Tang, Y., Lin, W., Chen, Z., Wang, Z., & Ding, T. (2022). Resilient Power System Black-start Restoration Model Considering Uncertain Renewable Energy. In 2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022 (pp. 540-544). (2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICARCV57592.2022.10004336

Lin, Yi ; Li, Meng ; Tang, Yuchen et al. / Resilient Power System Black-start Restoration Model Considering Uncertain Renewable Energy. 2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 540-544 (2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022).

@inproceedings{9a79b3ba402344f1a8b14e468160c82e,

title = "Resilient Power System Black-start Restoration Model Considering Uncertain Renewable Energy",

abstract = "This paper presents a novel multi-stage stochastic sequential black-start restoration model for the power transmission system with high penetration of renewable energy sources (RESs) after large-scale blackouts. Considering the uncertainties of RESs and the sequential restoration of the network, a two-stage generator model is designed to collaboratively dispatch the black-start units, RESs, and other thermal units during the restoration process. Finally, a modified New England 10-unit-39-bus test power system is employed to verify the effectiveness of the proposed model. Case studies indicate that the proposed model can coordinate the restoration of thermal units, RESs, and power loads. And RESs can effectively support the load restoration during the black-start process.",

keywords = "Renewable energy sources, load restoration, power system black-start, stochastic optimization",

author = "Yi Lin and Meng Li and Yuchen Tang and Wei Lin and Zhiming Chen and Zekai Wang and Tao Ding",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022 ; Conference date: 11-12-2022 Through 13-12-2022",

year = "2022",

doi = "10.1109/ICARCV57592.2022.10004336",

language = "英语",

series = "2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "540--544",

booktitle = "2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022",

}

Lin, Y, Li, M, Tang, Y, Lin, W, Chen, Z, Wang, Z & Ding, T 2022, Resilient Power System Black-start Restoration Model Considering Uncertain Renewable Energy. in 2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022. 2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022, Institute of Electrical and Electronics Engineers Inc., pp. 540-544, 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022, Singapore, Singapore, 11/12/22. https://doi.org/10.1109/ICARCV57592.2022.10004336

Resilient Power System Black-start Restoration Model Considering Uncertain Renewable Energy. / Lin, Yi; Li, Meng; Tang, Yuchen et al.
2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 540-544 (2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Resilient Power System Black-start Restoration Model Considering Uncertain Renewable Energy

AU - Lin, Yi

AU - Li, Meng

AU - Tang, Yuchen

AU - Lin, Wei

AU - Chen, Zhiming

AU - Wang, Zekai

AU - Ding, Tao

PY - 2022

Y1 - 2022

N2 - This paper presents a novel multi-stage stochastic sequential black-start restoration model for the power transmission system with high penetration of renewable energy sources (RESs) after large-scale blackouts. Considering the uncertainties of RESs and the sequential restoration of the network, a two-stage generator model is designed to collaboratively dispatch the black-start units, RESs, and other thermal units during the restoration process. Finally, a modified New England 10-unit-39-bus test power system is employed to verify the effectiveness of the proposed model. Case studies indicate that the proposed model can coordinate the restoration of thermal units, RESs, and power loads. And RESs can effectively support the load restoration during the black-start process.

AB - This paper presents a novel multi-stage stochastic sequential black-start restoration model for the power transmission system with high penetration of renewable energy sources (RESs) after large-scale blackouts. Considering the uncertainties of RESs and the sequential restoration of the network, a two-stage generator model is designed to collaboratively dispatch the black-start units, RESs, and other thermal units during the restoration process. Finally, a modified New England 10-unit-39-bus test power system is employed to verify the effectiveness of the proposed model. Case studies indicate that the proposed model can coordinate the restoration of thermal units, RESs, and power loads. And RESs can effectively support the load restoration during the black-start process.

KW - Renewable energy sources

KW - load restoration

KW - power system black-start

KW - stochastic optimization

UR - https://www.scopus.com/pages/publications/85146700660

U2 - 10.1109/ICARCV57592.2022.10004336

DO - 10.1109/ICARCV57592.2022.10004336

M3 - 会议稿件

AN - SCOPUS:85146700660

T3 - 2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022

SP - 540

EP - 544

BT - 2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022

Y2 - 11 December 2022 through 13 December 2022

ER -

Lin Y, Li M, Tang Y, Lin W, Chen Z, Wang Z et al. Resilient Power System Black-start Restoration Model Considering Uncertain Renewable Energy. In 2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 540-544. (2022 17th International Conference on Control, Automation, Robotics and Vision, ICARCV 2022). doi: 10.1109/ICARCV57592.2022.10004336

Resilient Power System Black-start Restoration Model Considering Uncertain Renewable Energy

Abstract

Publication series

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UN SDGs

Keywords

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