TY - JOUR
T1 - Online routing for smart electricity network under hybrid uncertainty
AU - Xu, Jianyu
AU - Sun, Qiuzhuang
AU - Mo, Huadong
AU - Dong, Daoyi
N1 - Funding Information:
The work is partially supported by an AI for Decision Shared Grant ( RG213518 ), the Australian Research Council’s Discovery Projects funding scheme under Project DP190101566 , and the Natural Science Foundation of China under Grant 72071071 . The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Francesco Vasca under the direction of Editor Thomas Parisini.
Funding Information:
His research interests include quantum control, machine learning and smart grids. Dr. Dong was awarded an ACA Temasek Young Educator Award by The Asian Control Association and is the recipient of an International Collaboration Award, a Discovery International Award and an Australian Post-Doctoral Fellowship from the Australian Research Council. He is also the recipient of a Humboldt Research Fellowship from the Alexander von Humboldt Foundation of Germany. He is the founding Chair of IEEE ACT/NSW Chapter, IEEE Control Systems Society, a Member-at-Large, Board of Governors, and was the Associate Vice President for Conferences & Meetings, IEEE Systems, Man and Cybernetics Society. He served as an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (2015–2021). He is currently an Associate Editor of IEEE Transactions on Cybernetics, and a Technical Editor of IEEE/ASME Transactions on Mechatronics.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/11
Y1 - 2022/11
N2 - In this study, an online routing problem for the optimal power flow (OPF) of smart grids subject to forecasting errors and cyber-attacks on data integrity is investigated. Energy data packages and flow-dispatch commands are transmitted over a communication network and corrupted by adversaries. In particular, we consider false data injection attacks that maliciously tamper with the data presented to grid operators. We develop an OPF model considering piecewise invariable load and power generation, which can be evaluated using a linear program. We extend the problem to an online setting, where data are sequentially observed, and adaptive strategies are required to optimize a metric, called the regret function, over time. We then incorporate the state-of-the-art adaptive change-point detection approach to develop an online routing algorithm that retains a sublinear regret in both the time horizon and number of change points. The applicability and effectiveness of the proposed algorithm were verified by numerical experiments on real-world smart grids.
AB - In this study, an online routing problem for the optimal power flow (OPF) of smart grids subject to forecasting errors and cyber-attacks on data integrity is investigated. Energy data packages and flow-dispatch commands are transmitted over a communication network and corrupted by adversaries. In particular, we consider false data injection attacks that maliciously tamper with the data presented to grid operators. We develop an OPF model considering piecewise invariable load and power generation, which can be evaluated using a linear program. We extend the problem to an online setting, where data are sequentially observed, and adaptive strategies are required to optimize a metric, called the regret function, over time. We then incorporate the state-of-the-art adaptive change-point detection approach to develop an online routing algorithm that retains a sublinear regret in both the time horizon and number of change points. The applicability and effectiveness of the proposed algorithm were verified by numerical experiments on real-world smart grids.
KW - Change-point detection
KW - Hybrid uncertainty
KW - Online routing
KW - Smart grid
UR - http://www.scopus.com/inward/record.url?scp=85136549244&partnerID=8YFLogxK
U2 - 10.1016/j.automatica.2022.110538
DO - 10.1016/j.automatica.2022.110538
M3 - Article
AN - SCOPUS:85136549244
SN - 0005-1098
VL - 145
JO - Automatica
JF - Automatica
M1 - 110538
ER -
