Abstract
Even with all of the benefits—like higher dependability—multi-microgrid design and implementation of a suitable control, management, and protection system still present substantial obstacles. Strong and dependable supervisory control is necessary for the seamless integration of distributed/renewable energy resources (DERs/RERs), energy storage systems (ESSs), and electric vehicles, as well as for the stable operation of the power grid with the highest possible power quality. The Internet of Everything (IoE) is critical in energy management and smart grid monitoring. In this sense, the Internet of Everything has transformed energy efficiency research and practice for industrial and smart city applications. The optimized bi-directional power-flow between the utility grid and prosumers with RERs will be made possible in smart grid operations through data exchange between grid components and data- driven solutions. Achieving the overall optimization of the energy systems is promising when it comes to the transportation sector and building energy demand. By combining artificial intelligence (AI), machine learning (ML), and data analytics, this dynamic collection of grid component databases—collected by smart meters, automatic meter readings (AMRs), intelligent electronic devices (IEDs), and phasor measurement units (PMUs)—can be leveraged to create data-driven solutions. With the aid of sophisticated data analysis tools, grid operators can extract valuable information from this data. Because microgrids use measurement devices, communication networks, and control layers, they can be categorized as cyber-physical systems (CPSs). As a result, microgrids are susceptible to various cyberattacks, including denial-of-service attacks, replay attacks, hijacking, and false data injection. As a result, fresh approaches to cyberattack detection and mitigation are needed. This talk addresses the technical aspects of power electronics-dominated grids’ (PEDGs') cyber resilience and issues with power quality, stability, and potential data- driven machine learning-based remedies. In This workshop, we try to introduce the concepts of AI for cybersecurity and cyber-resiliency. We also developed the idea of a cybersecurity body of knowledge (CyBoK) for power systems and power electronics-dominated grids (PEDGs). In the same fashion as software engineering body of knowledge (SWEBOK), CyBOK is meant to be a guide to the body of knowledge; the knowledge that it codies already exists in literature such as textbooks, academic research articles, technical reports, white papers, and standards.
By:
Dr. Hamid Reza Baghaee (hrbaghaee@modares.ac.ir )
Assistant Professor, Faculty of Electrical and Computer Engineering. (ECE), Tarbiat Modares University,
Jalal AleAhmad Nasr, Tehran, Iran, P.O.Box: 14115-111
Associate Research Professor, Department of Electrical Engineering., Amirkabir University of
Technology, Room 918, 9th floor, Abu-Reihan Building, Amirkabir University of Technology, Tehran, Iran,
P.O. Box: 15875-4413
