163 research outputs found

    Resilient Distributed Energy Management for Systems of Interconnected Microgrids

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    In this paper, distributed energy management of interconnected microgrids, which is stated as a dynamic economic dispatch problem, is studied. Since the distributed approach requires cooperation of all local controllers, when some of them do not comply with the distributed algorithm that is applied to the system, the performance of the system might be compromised. Specifically, it is considered that adversarial agents (microgrids with their controllers) might implement control inputs that are different than the ones obtained from the distributed algorithm. By performing such behavior, these agents might have better performance at the expense of deteriorating the performance of the regular agents. This paper proposes a methodology to deal with this type of adversarial agents such that we can still guarantee that the regular agents can still obtain feasible, though suboptimal, control inputs in the presence of adversarial behaviors. The methodology consists of two steps: (i) the robustification of the underlying optimization problem and (ii) the identification of adversarial agents, which uses hypothesis testing with Bayesian inference and requires to solve a local mixed-integer optimization problem. Furthermore, the proposed methodology also prevents the regular agents to be affected by the adversaries once the adversarial agents are identified. In addition, we also provide a sub-optimality certificate of the proposed methodology.Comment: 8 pages, Conference on Decision and Control (CDC) 201

    Improvise, Adapt, Overcome: Dynamic Resiliency Against Unknown Attack Vectors in Microgrid Cybersecurity Games

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    Cyber-physical microgrids are vulnerable to rootkit attacks that manipulate system dynamics to create instabilities in the network. Rootkits tend to hide their access level within microgrid system components to launch sudden attacks that prey on the slow response time of defenders to manipulate system trajectory. This problem can be formulated as a multi-stage, non-cooperative, zero-sum game with the attacker and the defender modeled as opposing players. To solve the game, this paper proposes a deep reinforcement learning-based strategy that dynamically identifies rootkit access levels and isolates incoming manipulations by incorporating changes in the defense plan. A major advantage of the proposed strategy is its ability to establish resiliency without altering the physical transmission/distribution network topology, thereby diminishing potential instability issues. The paper also presents several simulation results and case studies to demonstrate the operating mechanism and robustness of the proposed strategy

    Cyber Security in Control of Grid-Tied Power Electronic Converters - Challenges and Vulnerabilities

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    Output Impedance Diffusion into Lossy Power Lines

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    Output impedances are inherent elements of power sources in the electrical grids. In this paper, we give an answer to the following question: What is the effect of output impedances on the inductivity of the power network? To address this question, we propose a measure to evaluate the inductivity of a power grid, and we compute this measure for various types of output impedances. Following this computation, it turns out that network inductivity highly depends on the algebraic connectivity of the network. By exploiting the derived expressions of the proposed measure, one can tune the output impedances in order to enforce a desired level of inductivity on the power system. Furthermore, the results show that the more "connected" the network is, the more the output impedances diffuse into the network. Finally, using Kron reduction, we provide examples that demonstrate the utility and validity of the method

    SIEMS: A Secure Intelligent Energy Management System for Industrial IoT Applications

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    © IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TII.2022.3165890In this work, we deploy a one-day-ahead prediction algorithm using a deep neural network for a fast-response BESS in an intelligent energy management system (I-EMS) that is called SIEMS. The main role of the SIEMS is to maintain the state of charge at high rates based on the one-day-ahead information about solar power, which depends on meteorological conditions. The remaining power is supplied by the main grid for sustained power streaming between BESS and end-users. Considering the usage of information and communication technology components in the microgrids, the main objective of this paper is focused on the hybrid microgrid performance under cyber-physical security adversarial attacks. Fast gradient sign, basic iterative, and DeepFool methods, which are investigated for the first time in power systems e.g. smart grid and microgrids, in order to produce perturbation for training data.Peer reviewe

    THE ELECTROMAGNETIC THREAT TO THE UNITED STATES: RECOMMENDATIONS FOR CONSEQUENCE MANAGEMENT

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    This thesis analyzes the threat of both electromagnetic pulse (EMP) and geomagnetic disturbances (GMD) to the U.S. Department of Homeland Security. EMP/GMD events are classified as low-probability/high-impact events that have potential catastrophic consequences to all levels of government as well as the civilian population of the United States. By reviewing current literature and conducting two thought experiments, this thesis determined that various critical infrastructure sectors and modern society are at risk of the effects of EMP/GMD events. Some of the most serious consequences of a large-scale EMP/GMD event include long-term power loss to large geographic regions, loss of modern medical services, and severe communication blackouts that could make recovery from these events extremely difficult. In an attempt to counteract and mitigate the risks of EMP/GMD events, resilience engineering concepts introduced several recommendations that could be utilized by policymakers to mitigate the effects of EMP or GMD events. Some of the recommendations include utilizing hardened micro-grid systems, black start options, and various changes to government agency organizations that would provide additional resilience and recovery to American critical infrastructure systems in a post-EMP/GMD environment.Captain, United States ArmyApproved for public release. Distribution is unlimited

    Enhancing Cyber-Resiliency of DER-based SmartGrid: A Survey

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    The rapid development of information and communications technology has enabled the use of digital-controlled and software-driven distributed energy resources (DERs) to improve the flexibility and efficiency of power supply, and support grid operations. However, this evolution also exposes geographically-dispersed DERs to cyber threats, including hardware and software vulnerabilities, communication issues, and personnel errors, etc. Therefore, enhancing the cyber-resiliency of DER-based smart grid - the ability to survive successful cyber intrusions - is becoming increasingly vital and has garnered significant attention from both industry and academia. In this survey, we aim to provide a systematical and comprehensive review regarding the cyber-resiliency enhancement (CRE) of DER-based smart grid. Firstly, an integrated threat modeling method is tailored for the hierarchical DER-based smart grid with special emphasis on vulnerability identification and impact analysis. Then, the defense-in-depth strategies encompassing prevention, detection, mitigation, and recovery are comprehensively surveyed, systematically classified, and rigorously compared. A CRE framework is subsequently proposed to incorporate the five key resiliency enablers. Finally, challenges and future directions are discussed in details. The overall aim of this survey is to demonstrate the development trend of CRE methods and motivate further efforts to improve the cyber-resiliency of DER-based smart grid.Comment: Submitted to IEEE Transactions on Smart Grid for Publication Consideratio
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