MIT WVU NSF

Date: October 19, 2024

Location:
In-Person: Room 3-370, 33 Massachusetts Ave, MIT (Map)
Online: Zoom Link (TBA)

Overview

Join us for an in-depth workshop on "Enabling Cyber-Resilient Distribution Systems with Edge Inverter-Based Resources (IBR)". This event will bring together industry experts, researchers, and practitioners to explore advanced strategies for enhancing the resilience and security of distribution systems incorporating IBR at the edge.

Through a series of focused sessions inspired by an NSF project, participants will gain valuable insights into monitoring, visibility, and data-driven approaches to detecting cyber anomalies and threats, and distributed optimization and control-based mitigation for enabling resilient electric distribution systems.

Workshop Goals

Speakers and Workshop Agenda

Welcome and Background: 9:00 AM - 9:10 AM

Session 1: 9:10 AM - 10:40 AM: Monitoring and Visibility of IBR in Electric Distribution Systems: This session will focus on the system modeling, importance of monitoring and visibility in distribution systems with integrated IBR. Participants will explore the latest advancements in real-time monitoring to ensure the resiliency and security of the grid.

  1. Jessica Bian, Vice President, Grid Services, Grid-X Partners and past IEEE PES President, “The New Grid: Reinvention and Resilience”
  2. Marija Ilic, Senior Research Scientist at Laboratory for Information and Decision Systems (LIDS) and Adjunct Professor in Electrical Engineering, Massachusetts Institute of Technology, “Three different ways of integrating IBRs: What do we know and what is tradeoff”
  3. Rajeev K Singh, Professor and Head, Department of Electrical Engineering, Indian Institute of Technology, Varanasi, “Electric Vehicle Charging in Distribution System: Standard, Protocols and Monitoring”
  4. Panayiotis (Panos) Moutis, Assistant Professor of Electrical Engineering, City College of New York, “Digital twins for real time monitoring of inverter dominated grids”

Session 2: 10:50 AM -12:20 PM: Data-Driven/ML for Cyber Anomalies Detection and Classification: In this session, data-driven approaches and machine learning techniques will be discussed for detecting cyber anomalies and threats in distribution systems. The session will also cover classification of data anomalies and events.

  1. Le Xie, Gordon McKay Professor of Electrical Engineering, John A. Paulson School of Engineering and Applied Sciences (SEAS) at Harvard University, “Exploring the Capabilities of Large Language Models in Edge-Level Power Electronic Circuitry”
  2. Ali Abur, University Distinguished Professor of Electrical Engineering, Northeastern University, “Distribution system state and topology monitoring using a mixed set of measurements”
  3. Deepjyoti Deka, Research Scientist, MIT Energy Initiative, “Trackable topology and parameter estimation in distribution grids with limited edge-data”
  4. Anurag Srivastava, Lane Professor and Department Chair of Computer Science and Electrical Engineering, West Virginia University, “Anomaly-Aware Distributed Control for DER-Rich Distribution System”

Session 3: 1:30 PM - 3:00 PM: Distributed Optimization and Control: In this session, data-driven approaches and machine learning assisted distributed optimization techniques will be discussed for control at the edge and for enabling the resilience of IBR-integrated networks.

  1. Priya Donti, Assistant Professor and the Silverman (1968) Family Career Development Professor in Electrical Engineering and Computer Science, Massachusetts Institute of Technology, co-founder and Chair of Climate Change AI, “Optimization-in-the-loop ML for power grid operations”
  2. Na Li, Winokur Family Professor of Electrical Engineering and Applied Mathematics in the School of Engineering and Applied Sciences (SEAS) at Harvard University, and Xin Chen, Assistant Professor, Electrical and Computer Engineering at Texas A&M University, “Distributed Model-free Optimal Coordination of IBRs for Grid-level Voltage Control”
  3. Vineet J. Nair, Massachusetts Institute of technology, “Data-driven distributed optimization, markets, and control for an IBR-rich grid edge”
  4. Abhishek Dubey, Associate Professor of Computer Science and Senior Research Scientist at the Institute for Software Integrated Systems, Vanderbilt University; and Chief Science officer and Co-Founder at Mobius AI, Inc., “Sequential Decision Making for Optimal and Resilient Bi-Directional Integration of Electric Vehicles into Grid”

Session 4: 3:10 PM - 4:40 PM: Data-Driven Mitigation for Cyber-resilient Distribution Systems: In this session, techniques for mitigating the impact of cyber threats will be discussed in distribution systems and to enhance the resilience of IBR-integrated networks.

  1. Arvind Tiwari, Program Manager, GE Research, “Cyber-resilient Control for the DER-rich Electric Distribution System”
  2. Elli Ntakou, Manager, System Resiliency and Reliability, Eversource Energy, “Eversource’s resilience & climate adaptation planning”
  3. Babak Enyati, Senior Director of Grid Modernization, LUMA Energy, “Grid Enhancing Technologies Under High Penetration of Distributed Energy Resources”
  4. Anuradha Annaswamy, Director of the Active-Adaptive Control Laboratory at Massachusetts Institute of Technology, “Resilience at the Grid-Edge Using Trustable DERs”

Closing Remarks: 4:40 PM - 4:50 PM

Who Should Attend

This workshop is ideal for professionals, researchers, and academics involved in the fields of power systems, cybersecurity, optimization, data science, machine learning, digital automation, and related disciplines. Whether you are looking to deepen your knowledge or collaborate with others in the industry, this workshop offers valuable insights and practical strategies for enabling cyber-resilient distribution systems.

Registration

Don't miss the opportunity to engage with leading experts and advance your understanding of cyber resilience in modern distribution systems.


Contact Us

For more information or inquiries about the workshop, please contact:

Anuradha M Annaswamy

Anurag Srivastava