Quantifying Grid Resiliency Using GFM with HELICS Co-Simulation for Enhancing Outage Management During the Extreme Weather Events

Background:  

Extreme weather events continue to threaten the security and reliability of power grids, making it crucial to predict outages and mitigate their electrical impact. While traditional outage prediction models primarily focus on physical disruptions, they do not quantify the actual electrical disturbances within the distribution system. By integrating outage prediction models with power system simulations and enhancing them with grid-forming inverters and battery energy storage systems, this project aims to improve outage response strategies and overall grid resilience.

Industry Need:  

Energy providers require more advanced tools to translate outage predictions into actionable strategies that strengthen grid resilience. Understanding the electrical impact of outages will improve emergency planning and restoration efforts. Additionally, utilizing grid-forming inverters and co-simulation techniques will enhance system reliability, minimize downtime, and validate emerging energy technologies to ensure stability during severe weather events.

Objectives:

This project seeks to integrate outage prediction models with transmission and distribution system simulations using HELICS co-simulation. The research will focus on quantifying the electrical impact of predicted outages, improving contingency planning, and enhancing grid resilience in regions vulnerable to extreme weather. By incorporating grid-forming inverter models into the distribution system, the study will evaluate their ability to mitigate disturbances and stabilize power supply.

Methodology:  

The research will begin with an analysis of historical weather and outage data in Connecticut to identify high-risk areas and develop a power distribution model. The transmission network will be implemented and validated using the TSAT platform, followed by the development and verification of distribution power network models in OpenDSS. Grid-forming inverter models will then be created in RTDS and integrated into the distribution network. The study will use HELICS to link TSAT, OpenDSS, and RTDS, creating a testbed for analyzing power system behavior. Outage prediction models will be incorporated into this co-simulation environment, where predicted outage locations will be translated into electrical nodes for further analysis. The project will assess grid impacts and validate mitigation strategies for improving outage management.

Deliverables:

The project will provide a detailed report on the integration of outage prediction models with power system simulations, demonstrating their effectiveness in predicting and managing outages. Research findings on grid-forming inverter integration and outage mitigation strategies will be published in peer-reviewed journals. The study will contribute to developing a scalable platform for assessing grid resilience, improving outage management, and enhancing emergency preparedness for extreme weather events.