TY - JOUR
T1 - Identifying Security Vulnerabilities in Electricity Market Operations Induced by Weakly Detectable Network Parameter Errors
AU - Lin, Yuzhang
AU - Abur, Ali
AU - Xu, Hanchen
N1 - Funding Information:
Manuscript received May 27, 2020; accepted June 28, 2020. Date of publication July 7, 2020; date of current version October 23, 2020. This work was supported in part by the ERC Program of the National Science Foundation under NSF Award EEC-1041877 and in part by the NSF Award 1947617. Paper no. TII-20-2641. (Corresponding author: Ali Abur.) Yuzhang Lin is with the Electrical and Computer Engineering Department, University of Mass-Lowell, Lowell 01854 USA (e-mail: yuzhang_lin@uml.edu).
Publisher Copyright:
© 2005-2012 IEEE.
PY - 2021/1
Y1 - 2021/1
N2 - In this article, a new security vulnerability in electricity market operations is identified. It involves certain parameters in the network model database whose errors, by nature, are difficult to detect and identify. These errors can either occur due to unintentional reasons or be maliciously introduced by cyber-adversaries. It is shown that by impacting the injection shift factors and transmission line congestion patterns, these errors may exert biases on locational marginal prices (LMPs), and thus impact the revenues received by the holders of financial transmission rights (FTRs). A method is then developed for identifying the network parameters whose errors are difficult to detect and may have severe impacts on the LMPs and FTR revenues. Simulation results in the IEEE 57-bus system are presented to illustrate and verify the analysis and the proposed method. The proposed framework can be used to conduct cyber-vulnerability assessment for power system model databases.
AB - In this article, a new security vulnerability in electricity market operations is identified. It involves certain parameters in the network model database whose errors, by nature, are difficult to detect and identify. These errors can either occur due to unintentional reasons or be maliciously introduced by cyber-adversaries. It is shown that by impacting the injection shift factors and transmission line congestion patterns, these errors may exert biases on locational marginal prices (LMPs), and thus impact the revenues received by the holders of financial transmission rights (FTRs). A method is then developed for identifying the network parameters whose errors are difficult to detect and may have severe impacts on the LMPs and FTR revenues. Simulation results in the IEEE 57-bus system are presented to illustrate and verify the analysis and the proposed method. The proposed framework can be used to conduct cyber-vulnerability assessment for power system model databases.
KW - Anomaly detection
KW - cybersecurity
KW - electricity market
KW - financial transmission right (FTR)
KW - locational marginal price (LMP)
KW - parameter estimation
KW - state estimation (SE)
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U2 - 10.1109/TII.2020.3007424
DO - 10.1109/TII.2020.3007424
M3 - Article
AN - SCOPUS:85096033273
SN - 1551-3203
VL - 17
SP - 627
EP - 636
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 1
M1 - 9134954
ER -