2019
DOI: 10.1109/tsg.2019.2904873
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Network Constrained Unit Commitment Under Cyber Attacks Driven Overloads

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Cited by 36 publications
(19 citation statements)
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“…Objective FDI RAS [182] Coordination between load redistribution (LR) attack and current-carrying elements (i.e., lines and generators) to show the potential damaging impacts of the coordinated attacks on IEEE 14-bus test system [183,184] Injecting arbitrary errors into certain state variables by FDIs against state estimation, which bypass the bad data detection [185] Formulizing the economic impacts of FDIs on IEEE 14-bus test system in real-time market operation [186] Targeting a single day of the (1) Australian electricity market trading mechanism and (2) IEEE 118-bus test system using an extended FDI cyberattack [187] Changing the price of electricity in a desirable direction as well as defending the system to react to FDIs on PJM 5-bus test system [188] Making cascade outages as well as imposing large damages into the 118-bus test system [189] Injecting false data into the process of security-constrained economic dispatch (as a trilevel model) on IEEE 14-bus test system [190] Targeting multiple tie-lines at the same time in economic dispatch problem (as a bilevel FDI and a mixed-integer linear programming problem) on IEEE 118-bus test system [191] • Manipulating the state variables based on only the data associated with the susceptance of tie-lines • Presenting suitable countermeasures to keep all buses hidden to the attackers on IEEE 9-, 14, 30, and 118-bus test systems [192] Reacting (in the form of real-time smart distribution network reconfiguration) to the detected FDIs over retailers in the electricity market (distributed generation retailers as well as electric vehicle and demand response retailers) on the 136-bus distribution system [193] Recovering the manipulated data on IEEE 30-and 118-bus test systems using a defense mechanism based on a statistical physical model and a generative adversarial network-based cyber model simultaneously [194] Responding to the FDIs expeditiously by re-dispatching the generation units on IEEE 24-and 118-bus test systems using OPF aiming at optimizing the system security [195] Proposing a cyber-secured unit commitment problem that is reliable against LR attacks on IEEE 118-bus tests system…”
Section: Frameworkmentioning
confidence: 99%
“…Objective FDI RAS [182] Coordination between load redistribution (LR) attack and current-carrying elements (i.e., lines and generators) to show the potential damaging impacts of the coordinated attacks on IEEE 14-bus test system [183,184] Injecting arbitrary errors into certain state variables by FDIs against state estimation, which bypass the bad data detection [185] Formulizing the economic impacts of FDIs on IEEE 14-bus test system in real-time market operation [186] Targeting a single day of the (1) Australian electricity market trading mechanism and (2) IEEE 118-bus test system using an extended FDI cyberattack [187] Changing the price of electricity in a desirable direction as well as defending the system to react to FDIs on PJM 5-bus test system [188] Making cascade outages as well as imposing large damages into the 118-bus test system [189] Injecting false data into the process of security-constrained economic dispatch (as a trilevel model) on IEEE 14-bus test system [190] Targeting multiple tie-lines at the same time in economic dispatch problem (as a bilevel FDI and a mixed-integer linear programming problem) on IEEE 118-bus test system [191] • Manipulating the state variables based on only the data associated with the susceptance of tie-lines • Presenting suitable countermeasures to keep all buses hidden to the attackers on IEEE 9-, 14, 30, and 118-bus test systems [192] Reacting (in the form of real-time smart distribution network reconfiguration) to the detected FDIs over retailers in the electricity market (distributed generation retailers as well as electric vehicle and demand response retailers) on the 136-bus distribution system [193] Recovering the manipulated data on IEEE 30-and 118-bus test systems using a defense mechanism based on a statistical physical model and a generative adversarial network-based cyber model simultaneously [194] Responding to the FDIs expeditiously by re-dispatching the generation units on IEEE 24-and 118-bus test systems using OPF aiming at optimizing the system security [195] Proposing a cyber-secured unit commitment problem that is reliable against LR attacks on IEEE 118-bus tests system…”
Section: Frameworkmentioning
confidence: 99%
“…It is then imperative for system operators to take corrective actions to mitigate the attacks' physical consequences and avoid any severe damage (e.g., cascading outages). In this regard, the studies in [36], [37] addressed some post-attack corrective actions to provide secured operating points.…”
Section: B Countermeasures Against Cyber-attacksmentioning
confidence: 99%
“…Compared with manipulating the control signal directly, FDI attacks are easier to achieve and more difficult to be detected due to their stealthiness 10‐12 . In this regard, the load redistribution (LR) attack is regarded as an important type of FDI attacks and it has attracted wide attention 13‐18 . Simulations in Reference 13 indicate that attackers can successfully implement LR attacks with incomplete network information.…”
Section: Introductionmentioning
confidence: 99%
“…However, most of the existing literatures usually chose operational costs as the objective function in the attack model, that is, the worst scenario in these papers refer to the most expensive operating scenario, thus the corresponding defense strategy could not resist the attacks that have a great impact on the operational security of grids. To address this issue, authors in References 16,17 develop a cyber‐secured corrective dispatch method and unit commitment (UC) programming model, respectively, but they only consider the overload of a single line. While in Reference 18, a bi‐level LR attack model is proposed and it is able to overload multiple lines simultaneously.…”
Section: Introductionmentioning
confidence: 99%