Profit-Oriented False Data Injection on Electricity Market: Reviews, Analyses, and Insights

Zhang, Qiwei; Li, Fangxing; Shi, Qingxin; Tomsovic, Kevin; Sun, Jianhua; Ren, Lingyu

doi:10.1109/tii.2020.3036104

Cited by 26 publications

(6 citation statements)

References 49 publications

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“…The LMP pricing scheme has been widely adopted in U.S. electricity markets to provide economic signals to market participants. LMPs are defined as the marginal increase in dispatch costs versus the marginal increase in load consumption at a particular bus, as given in (2) [16].…”

Section: Preliminaries On Ed and Lmpsmentioning

confidence: 99%

“…Thus, this bilevel problem means that the upper-level problem tries to find a load sample, which makes the lower-level dispatch problem produce marginal pattern as indicated in the upper-level constraints. Multiple load samples can be obtained by solving problem (16) iteratively. The obtained load samples are integrated to enhance the original training dataset.…”

Section: B Dataset Enhancementmentioning

confidence: 99%

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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%

Section: B Dataset Enhancementmentioning

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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“…State estimation applies to topology errors (Ashok & Govindarasu, 2012), SCADA and PMU data (Yang et al, 2013), and DAE‐based dynamic models (Zhao et al, 2019). State estimation serves as a preprocessor for other computing subsystems, including real‐time wide‐area control (Raoufat et al, 2017) and electricity markets (Zhang, Li, et al, 2020).…”

Section: Cyber‐physical Framework Cybersecurity and Testbedsmentioning

confidence: 99%

Powerelectronics‐interfaced cyber‐physicalpower systems: A review on modeling, simulation, and cybersecurity

Cui

Zhang

Tomsovic

et al. 2022

WIREs Energy & Environment

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We present the review of two interlinked challenges in modern electric power systems: the transformation to a cyber‐physical system, and the integration of power electronics‐interfaced renewables. Electric power systems are being modernized with the integration of power electronics‐interfaced devices (PEID) and communication‐enabled cyber‐applications. This paper reviews the concepts, studies, and testbeds for cyber‐physical power systems (CPPS), as well as the modeling of power electronics‐based devices for physical power system stability simulations. The CPPS concept is introduced in the National Institute of Standard Technology framework for cyber‐physical systems, with an emphasis on CPPS subsystems. For the physical subsystem, PEID components are generalized into the primary source and the grid interface, while controllers are generalized as a reference generator and a reference tracker. Next, the cybersecurity research objectives are summarized, followed by a categorization of CPPS studies. Further, testbed techniques for integrating communication networks with power system simulation are reviewed. Also, challenges and future directions in the area of CPPS are discussed. This article is categorized under: Energy Infrastructure > Systems and Infrastructure

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“…Meanwhile, the MG based on the distributed optimization architecture presents a strong cyber-physical fusion characteristic, and its probability and degree of being affected by potential cyber-attacks are greatly increased. False data injection (FDI) attacks are the most typical and common attack forms [17][18][19], which will maliciously destroy the distributed control performance by injecting false information to corrupt the real information. The communication network is more vulnerable to FDI attacks, which brings higher risks to the safe and stable operation of MG.…”

Section: Introductionmentioning

confidence: 99%

Attack‐resilient event‐triggered distributed control strategy of microgrid based on multi‐objective optimization

Liu,

Wang,

Liu

et al. 2023

IET Generation Trans & Dist

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As an important form of renewable energy utilization, microgrid (MG) is considered to be the main bearing form of distributed generation in the future. One of the most concerning issues in the operation of MG is how to realize its economic dispatch (ED). Nowadays, distributed algorithms have been increasingly used to solve the economic dispatch problem of MG. However, the MG based on the distributed optimization architecture must bear higher cyber-attack risks. To address this issue, this paper investigates the distributed robust ED problem of MG. Firstly, a multi-objective dispatch model of MG using a linear weighted sum (LWS) algorithm is developed, which considers the environmental and economic costs. On this basis, an event-triggered fully distributed algorithm is proposed, which can effectively reduce communication times. Furthermore, an attack resilient strategy against false data injection (FDI) attacks is implemented in the proposed fully distributed algorithm, which has strong robustness against various colluding attacks and non-colluding attacks, and can eliminate incorrect measurement of incremental cost and power generation data. Finally, the effectiveness of the proposed distributed control strategy is demonstrated through case studies in this paper.

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Profit-Oriented False Data Injection on Electricity Market: Reviews, Analyses, and Insights

Cited by 26 publications

References 49 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

Powerelectronics‐interfaced cyber‐physicalpower systems: A review on modeling, simulation, and cybersecurity

Attack‐resilient event‐triggered distributed control strategy of microgrid based on multi‐objective optimization

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