Analysis of System and Interoperability Impact from Securing Communications for Distributed Energy Resources

Jacobs, Nicholas; Hossain-McKenzie, Shamina; Jose, Deepu; Saleem, Danish; Flora, Lai, Christine; Cordeiro, Patricia; Hasandka, Adarsh; Martin, Maurice; Howerter, Christopher M.

doi:10.1109/peci.2019.8698915

Cited by 22 publications

(20 citation statements)

References 5 publications

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“…Fig. 3(b) shows the recoveredP obtained by (7) which matches well with the ground-truth data in Fig. 3(a).…”

Section: Simulation Resultssupporting

confidence: 69%

“…This section presents the numerical results of recovering P obtained by solving problem (7) and using Algorithm 1 on a winter and summer data set respectively. For the winter data set, it turns out the solution to (7) is sufficiently good as there is no periodic HVAC activity that could potentially lead to biased D. However, for the summer data set with high level of HVAC activities, the post-processing step in Algorithm 1 turns out to be useful as it improves the separation of the correlated PV profiles from the sparse appliance events.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The computational complexity may grow fast with the matrix dimension, thereby calling for accelerated solutions such as alternating minimization [27] or adaptive updates using subspace learning approaches [28], which will be explored in the future to develop fast algorithms for solving (7). One main issue of the proposed recovery formulation (7) is that the L1-norm regularization may penalize the magnitude of nonzero entries of D. Hence, the solu-tionD tends to be smaller in magnitude than the actual values and is thus biased. We have observed through numerical studies that the presence of frequent HVAC activities during the summer days could make this issue worse.…”

Section: Spatio-temporal Load Recoverymentioning

confidence: 99%

“…In the numerical tests, we use a small positive threshold to reflect the numerical accuracy of the zero entries. Using the estimated support in M, one can recast the recovery problem (7) by neglecting the penalty term on Algorithm 1 Recovering matrices K and D 1: Input: Smart meter data Y and D-PMU data z. 2: Output: EstimatedK andD 3: Step 1: Solve the problem (7) to obtain the biased estimationK b andD b .…”

Section: Spatio-temporal Load Recoverymentioning

confidence: 99%

“…For example, to validate the security of electric vehicle (EV) charging command, one can estimate the start/end time of EV charging using the change points of the highrate household load profile [6]. Similarly, the dynamic load profile can also be used to verify residential photovoltaic (PV) systems' inverter control settings [7]. In addition to that, the real-time information on residential PV outputs is necessary for achieving effective protection design in distribution systems, as the solar generation level can significantly affect the fault current magnitude therein [8].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing the Spatio-Temporal Observability of Residential Loads

Lin¹,

Zhu²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Enhancing the spatio-temporal observability of residential loads is crucial for achieving secure and efficient operations in distribution systems with increasing penetration of distributed energy resources (DERs). This paper presents a joint inference framework for residential loads by leveraging the real-time measurements from distribution-level sensors. Specifically, smart meter data is available for almost every load with unfortunately low temporal resolution, while distribution synchrophasor data is at very fast rates yet available at limited locations. By combining these two types of data with respective strengths, the problem is cast as a matrix recovery one with much less number of observations than unknowns. To improve the recovery performance, we introduce two regularization terms to promote a lowrank plus sparse structure of the load matrix via a difference transformation. Accordingly, the recovery problem can be formulated as a convex optimization one which is efficiently solvable. Numerical tests using real residential load data demonstrate the effectiveness of our proposed approaches in identifying appliance activities and recovering the PV output profiles. arXiv:1910.00984v2 [eess.SY]

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“…Fig. 3(b) shows the recoveredP obtained by (7) which matches well with the ground-truth data in Fig. 3(a).…”

Section: Simulation Resultssupporting

confidence: 69%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Spatio-temporal Load Recoverymentioning

confidence: 99%

Section: Spatio-temporal Load Recoverymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing the Spatio-Temporal Observability of Residential Loads

Lin¹,

Zhu²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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False Data Injection Attacks in Power Systems

Ameli¹,

Hamed²

2023

Wiley Encyclopedia of Electrical and Electronics Engineering

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Power networks are among the most critical infrastructures of any society, since other infrastructures are dependent on power and energy systems. Recently, there has been a trend toward harnessing Information Technology (IT) in power networks to enhance their performance and improve their consumer centricity. This movement, however, has exposed power systems to cyberattacks. Among various types of cyber intrusions, False Data Injection Attack (FDIA) is the most important one, since it stealthily intercepts the legitimate traffic and/or injects malicious data into the system, thus resulting in severe physical and economic damages. There has been a substantial increase in the number of reported FDIAs in recent years, which indicates the necessity of extensive research on vulnerabilities of power systems to FDIAs, probable attack models, impacts of such intrusions, and the possible preventive and detective measures. On this basis, this article presents a comprehensive review of FDIAs in power networks, with a particular focus on attack targets and adversarial methods. Additionally, it elaborates on various types of FDIAs, their implementation steps and strategies, and the vulnerabilities of various components and schemes to this type of attack. Finally, the it briefly discusses FDIA detection techniques in power grids.

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A Framework for Information Security Risk Management from an Interoperability Perspective

Lubbe

Serfontein

2023

Communications in Computer and Information Science

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Analysis of System and Interoperability Impact from Securing Communications for Distributed Energy Resources

Cited by 22 publications

References 5 publications

Enhancing the Spatio-Temporal Observability of Residential Loads

Enhancing the Spatio-Temporal Observability of Residential Loads

False Data Injection Attacks in Power Systems

A Framework for Information Security Risk Management from an Interoperability Perspective

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