Cyber-Vulnerability Analysis for Real-Time Power Market Operation

Zhang, Qiwei; Li, Fangxing

doi:10.1109/tsg.2021.3066398

Cited by 16 publications

(2 citation statements)

References 26 publications

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“…This phenomenon is also identified as "system pattern regions" in [18] and [19]. When the loading level steps out of the interval, the pattern changes instantaneously with a step change of LMP [20].…”

Section: Preliminaries On Ed and Lmpsmentioning

confidence: 99%

Building Marginal Pattern Library With Unbiased Training Dataset for Enhancing Model-Free Load-ED Mapping

Zhang

Feng

et al. 2022

IEEE Open J. Power Energy

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Input-output mapping for a given power system problem, such as loads versus economic dispatch (ED) results, has been demonstrated to be learnable through artificial intelligence (AI) techniques, including neural networks. However, the process of identifying and constructing a comprehensive dataset for the training of such input-output mapping remains a challenge to be solved. Conventionally, load samples are generated by a predefined distribution, and then ED is solved based on those load samples to form training datasets, but this paper demonstrates that such dataset generation is biased regarding load-ED mapping. The marginal unit and line congestion (i.e., marginal pattern) exhibit a unique characteristic called "step change" in which the marginal pattern changes when the load goes from one critical loading level (CLL) to another, and there is no change of marginal units within the interval of the two adjacent CLLs. Those loading intervals differ significantly in size. The randomly generated training dataset overfills intervals with large sizes and underfits intervals with small sizes, so it is biased. In this paper, three algorithms are proposed to construct a marginal pattern library to examine this bias according to different computational needs, and an enhancement algorithm is proposed to eliminate the bias for the load-ED dataset generation. Three illustrative test cases demonstrate the proposed algorithms, and comparative studies are constructed to show the superiority of the enhanced, unbiased dataset.

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Section: Preliminaries On Ed and Lmpsmentioning

confidence: 99%

Building Marginal Pattern Library With Unbiased Training Dataset for Enhancing Model-Free Load-ED Mapping

Zhang

Feng

et al. 2022

IEEE Open J. Power Energy

Self Cite

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“…A cyber attack against load forecasting [31] is investigated to misguide operators to make unsuitable decisions for electricity delivery. A cyber-vulnerability analysis model is developed in [32], where the attack patches include unit bids, generation capacities, and line ratings. Cyber attacks against critical network parameters are investigated in [33] to gain unlawful benefits from electricity markets.…”

Section: Introductionmentioning

confidence: 99%

Model-Measurement Data Integrity Attacks

Lin

Yan

Zhao

et al. 2023

IEEE Trans. Smart Grid

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The vulnerabilities of information and communication technology (ICT) infrastructures leave room for cyber attacks threatening the reliable operations of power systems. Based on the real-world evidence of the Ukraine power grid attack and the popular technical discussion that cyber attacks could be launched at the control-center level, this paper reveals a new attack strategy: model-measurement data integrity (MMI) attack. Instead of compromising measurements only, we investigate the possibility where network parameters are coordinately manipulated when constructing false data injection attack (FDIA) vectors. Furthermore, we model cyber adversaries' possible behavior of co-planning the manipulated measurement channels and parameter attack vectors prior to the launch of FDIAs. The revealed MMI attack strategy allows a drastic reduction of measurement channels to compromise in run-time for keeping the stealth property. Simulations in the IEEE 14-bus test system and the IEEE 118-bus test system demonstrate the feasibility of the revealed MMI attack strategy.

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