Cyber-Vulnerability Analysis for Real-Time Power Market Operation

Zhang, Qiwei; Li, Fangxing

doi:10.1109/tsg.2021.3066398

Cited by 11 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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A Dataset for Electricity Market Studies on Western and Northeastern Power Grids in the United States

Zhang,

2023

Sci Data

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Efficient electricity market operations and cost-effective electricity generations are fundamental to a low-carbon energy future. The Western Electricity Coordinating Council (WECC) and Northeast Power Coordinating Council (NPCC) systems were built to provide efficient electrical grid simulation solutions for their respective U.S. regions. Data reuse for electricity economic studies remains a challenge due to the lack of credible and realistic economic data. This paper delivers a comprehensive dataset containing generator aggregations, generator costs, transmission limits, load distributions, and electricity prices for the WECC and NPCC systems based on real-world grid operation data at year 2020, including power plant geographic locations, generation profiles, regional power flow interchanges, and load distributions in both regions. The electricity price from the developed dataset is simulated based on the other items in the dataset, and we show that the variation of the simulated electricity price reasonably aligns with the real-world electricity price in both the WECC and NPCC regions. Overall, the developed dataset is of interest for various electricity market and economic studies, such as the economic dispatch and locational marginal price (LMP) analysis.

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Cyber-Vulnerability Analysis for Real-Time Power Market Operation

Cited by 11 publications

References 26 publications

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

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

Model-Measurement Data Integrity Attacks

A Dataset for Electricity Market Studies on Western and Northeastern Power Grids in the United States

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