Web-Based Platform for Evaluation of Resilient and Transactive Smart-Grids

• URL: http://arxiv.org/abs/2206.05550v1
• Date: Sat, 11 Jun 2022 15:34:33 GMT
• Title: Web-Based Platform for Evaluation of Resilient and Transactive Smart-Grids
• Authors: Himanshu Neema, Harsh Vardhan, Carlos Barreto, and Xenofon Koutsoukos
• Abstract summary: Transactive Energy (TE) is an emerging approach for managing increasing DERs in the smart-grids through economic and control techniques. We present a comprehensive web-based platform for evaluating resilience of smart-grids against a variety of cyber- and physical-attacks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Today's smart-grids have seen a clear rise in new ways of energy generation, transmission, and storage. This has not only introduced a huge degree of variability, but also a continual shift away from traditionally centralized generation and storage to distributed energy resources (DERs). In addition, the distributed sensors, energy generators and storage devices, and networking have led to a huge increase in attack vectors that make the grid vulnerable to a variety of attacks. The interconnection between computational and physical components through a largely open, IP-based communication network enables an attacker to cause physical damage through remote cyber-attacks or attack on software-controlled grid operations via physical- or cyber-attacks. Transactive Energy (TE) is an emerging approach for managing increasing DERs in the smart-grids through economic and control techniques. Transactive Smart-Grids use the TE approach to improve grid reliability and efficiency. However, skepticism remains in their full-scale viability for ensuring grid reliability. In addition, different TE approaches, in specific situations, can lead to very different outcomes in grid operations. In this paper, we present a comprehensive web-based platform for evaluating resilience of smart-grids against a variety of cyber- and physical-attacks and evaluating impact of various TE approaches on grid performance. We also provide several case-studies demonstrating evaluation of TE approaches as well as grid resilience against cyber and physical attacks.

Related papers

• Threat analysis and adversarial model for Smart Grids [1.7482569079741024]
  The cyber domain of this smart power grid opens a new plethora of threats. Different stakeholders including regulation bodies, industry and academy are making efforts to provide security mechanisms to mitigate and reduce cyber-risks. Recent work shows a lack of agreement among grid practitioners and academic experts on the feasibility and consequences of academic-proposed threats. This is in part due to inadequate simulation models which do not evaluate threats based on attackers full capabilities and goals.
  arXiv  Detail & Related papers  (2024-06-17T16:33:46Z)
• GAN-GRID: A Novel Generative Attack on Smart Grid Stability Prediction [53.2306792009435]
  We propose GAN-GRID a novel adversarial attack targeting the stability prediction system of a smart grid tailored to real-world constraints. Our findings reveal that an adversary armed solely with the stability model's output, devoid of data or model knowledge, can craft data classified as stable with an Attack Success Rate (ASR) of 0.99.
  arXiv  Detail & Related papers  (2024-05-20T14:43:46Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Digital Twin-Empowered Smart Attack Detection System for 6G Edge of Things Networks [2.3464026676834813]
  We introduce a digital twin-empowered smart attack detection system for 6G EoT networks. It monitors and simulates physical assets in real time, enhancing security. Our system excels in proactive threat detection, ensuring 6G EoT network security.
  arXiv  Detail & Related papers  (2023-10-05T14:06:04Z)
• Non-Intrusive Electric Load Monitoring Approach Based on Current Feature Visualization for Smart Energy Management [51.89904044860731]
  We employ computer vision techniques of AI to design a non-invasive load monitoring method for smart electric energy management. We propose to recognize all electric loads from color feature images using a U-shape deep neural network with multi-scale feature extraction and attention mechanism.
  arXiv  Detail & Related papers  (2023-08-08T04:52:19Z)
• Enhancing Cyber-Resiliency of DER-based SmartGrid: A Survey [15.633226785669203]
  This survey aims to provide a systematical and comprehensive review regarding the cyber-resiliency enhancement (CRE) of DER-based smart grid. An integrated threat modeling method is tailored for the hierarchical DER-based smart grid with special emphasis on vulnerability identification and impact analysis. A CRE framework is subsequently proposed to incorporate the five key resiliency enablers.
  arXiv  Detail & Related papers  (2023-05-09T10:59:56Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey [1.9084046244608193]
  The integration of several cutting-edge technologies has introduced several security and privacy vulnerabilities. Recent research trends have shown that False Data Injection (FDI) attacks are becoming one of the most malicious cyber threats within the entire smart grid paradigm.
  arXiv  Detail & Related papers  (2021-11-28T22:25:15Z)
• A Taxonomy of Cyber Defence Strategies Against False Data Attacks in Smart Grid [3.88835600711547]
  Modern electric power grid, known as the Smart Grid, has fast transformed the isolated and centrally controlled power system to a fast and massively connected cyber-physical system. The synergy of a vast number of cyber-physical entities has allowed the Smart Grid to be much more effective and sustainable in meeting the growing global energy challenges. However, it has also brought with it a large number of vulnerabilities resulting in breaches of data integrity, confidentiality and availability.
  arXiv  Detail & Related papers  (2021-03-30T05:36:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.