Learning-Based, Safety and Stability-Certified Microgrid Control

Lizhi Wang; Songyuan Zhang; Yifan Zhou; Chuchu Fan; Peng Zhang; Yacov A. Shamash

doi:10.1109/PESGM52003.2023.10253396

Learning-Based, Safety and Stability-Certified Microgrid Control

Lizhi Wang
, Songyuan Zhang
, Yifan Zhou
, Chuchu Fan
, Peng Zhang
, Yacov A. Shamash

Stony Brook University
Massachusetts Institute of Technology

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

2 Scopus citations

Abstract

A neural-Lyapunov-barrier-enabled, physics informed-learning-based control method is devised to provide certificated safe and stable hierarchical control of microgrids. The main contributions include: 1) a neural hierarchical control framework for microgrids with provable safety and stability guarantees; 2) a control Lyapunov barrier function (CLBF) considering the fast dynamics of distributed energy resources, loads, and networks in microgrids; 3) a physics-informed learning approach for CLBF-based neural hierarchical control synthesis, which learns safety and stability certificates and control policy simultaneously without a verification module. Case studies demonstrate the effectiveness of the approach in provably certifying the stability and safety of microgrids equipped with hierarchical inverter control.

Original language	English
Title of host publication	2023 IEEE Power and Energy Society General Meeting, PESGM 2023
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665464413
DOIs	https://doi.org/10.1109/PESGM52003.2023.10253396
State	Published - 2023
Event	2023 IEEE Power and Energy Society General Meeting, PESGM 2023 - Orlando, United States Duration: Jul 16 2023 → Jul 20 2023

Publication series

Name	IEEE Power and Energy Society General Meeting
Volume	2023-July
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2023 IEEE Power and Energy Society General Meeting, PESGM 2023
Country/Territory	United States
City	Orlando
Period	07/16/23 → 07/20/23

Keywords

Microgrid control
certified control
control Lyapunov barrier function
learning-based control
microgrid stability

Access to Document

10.1109/PESGM52003.2023.10253396

Cite this

@inproceedings{97eee69fe17341bc8c4d16224fe728d1,

title = "Learning-Based, Safety and Stability-Certified Microgrid Control",

abstract = "A neural-Lyapunov-barrier-enabled, physics informed-learning-based control method is devised to provide certificated safe and stable hierarchical control of microgrids. The main contributions include: 1) a neural hierarchical control framework for microgrids with provable safety and stability guarantees; 2) a control Lyapunov barrier function (CLBF) considering the fast dynamics of distributed energy resources, loads, and networks in microgrids; 3) a physics-informed learning approach for CLBF-based neural hierarchical control synthesis, which learns safety and stability certificates and control policy simultaneously without a verification module. Case studies demonstrate the effectiveness of the approach in provably certifying the stability and safety of microgrids equipped with hierarchical inverter control.",

keywords = "Microgrid control, certified control, control Lyapunov barrier function, learning-based control, microgrid stability",

author = "Lizhi Wang and Songyuan Zhang and Yifan Zhou and Chuchu Fan and Peng Zhang and Shamash, \{Yacov A.\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Power and Energy Society General Meeting, PESGM 2023 ; Conference date: 16-07-2023 Through 20-07-2023",

year = "2023",

doi = "10.1109/PESGM52003.2023.10253396",

language = "English",

series = "IEEE Power and Energy Society General Meeting",

publisher = "IEEE Computer Society",

booktitle = "2023 IEEE Power and Energy Society General Meeting, PESGM 2023",

}

Wang, L, Zhang, S, Zhou, Y, Fan, C, Zhang, P & Shamash, YA 2023, Learning-Based, Safety and Stability-Certified Microgrid Control. in 2023 IEEE Power and Energy Society General Meeting, PESGM 2023. IEEE Power and Energy Society General Meeting, vol. 2023-July, IEEE Computer Society, 2023 IEEE Power and Energy Society General Meeting, PESGM 2023, Orlando, United States, 07/16/23. https://doi.org/10.1109/PESGM52003.2023.10253396

Learning-Based, Safety and Stability-Certified Microgrid Control. / Wang, Lizhi; Zhang, Songyuan; Zhou, Yifan et al.
2023 IEEE Power and Energy Society General Meeting, PESGM 2023. IEEE Computer Society, 2023. (IEEE Power and Energy Society General Meeting; Vol. 2023-July).

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

TY - GEN

T1 - Learning-Based, Safety and Stability-Certified Microgrid Control

AU - Wang, Lizhi

AU - Zhang, Songyuan

AU - Zhou, Yifan

AU - Fan, Chuchu

AU - Zhang, Peng

AU - Shamash, Yacov A.

PY - 2023

Y1 - 2023

N2 - A neural-Lyapunov-barrier-enabled, physics informed-learning-based control method is devised to provide certificated safe and stable hierarchical control of microgrids. The main contributions include: 1) a neural hierarchical control framework for microgrids with provable safety and stability guarantees; 2) a control Lyapunov barrier function (CLBF) considering the fast dynamics of distributed energy resources, loads, and networks in microgrids; 3) a physics-informed learning approach for CLBF-based neural hierarchical control synthesis, which learns safety and stability certificates and control policy simultaneously without a verification module. Case studies demonstrate the effectiveness of the approach in provably certifying the stability and safety of microgrids equipped with hierarchical inverter control.

AB - A neural-Lyapunov-barrier-enabled, physics informed-learning-based control method is devised to provide certificated safe and stable hierarchical control of microgrids. The main contributions include: 1) a neural hierarchical control framework for microgrids with provable safety and stability guarantees; 2) a control Lyapunov barrier function (CLBF) considering the fast dynamics of distributed energy resources, loads, and networks in microgrids; 3) a physics-informed learning approach for CLBF-based neural hierarchical control synthesis, which learns safety and stability certificates and control policy simultaneously without a verification module. Case studies demonstrate the effectiveness of the approach in provably certifying the stability and safety of microgrids equipped with hierarchical inverter control.

KW - Microgrid control

KW - certified control

KW - control Lyapunov barrier function

KW - learning-based control

KW - microgrid stability

UR - https://www.scopus.com/pages/publications/85174742950

U2 - 10.1109/PESGM52003.2023.10253396

DO - 10.1109/PESGM52003.2023.10253396

M3 - Conference contribution

AN - SCOPUS:85174742950

T3 - IEEE Power and Energy Society General Meeting

BT - 2023 IEEE Power and Energy Society General Meeting, PESGM 2023

PB - IEEE Computer Society

T2 - 2023 IEEE Power and Energy Society General Meeting, PESGM 2023

Y2 - 16 July 2023 through 20 July 2023

ER -

Learning-Based, Safety and Stability-Certified Microgrid Control

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Keywords

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