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Understanding the Resilience of the Power Grid

Photo by Jerry Wang via Unsplash

We’ve had a flurry of publications in advancing the understanding of the resilience of the power grid to extreme weather events in recent weeks! Hopefully these insights can be used to help improve the grid, so that we can all have reliable power even under climate change. The first paper describes a hybrid Machine Learning approach that integrates data from physical simulations to quantify the different levels of impact we can expect for different storms and infrastructural configurations [link]. This type of model has been used to evaluate proposed physical upgrades to the existing power grid and perform a cost-benefit analysis using real data from Connecticut [link]. This is a new type of planning for these types of projects, and can help make sure that the funding provided by the US Infrastructure Investment and Jobs Act is spent wisely. My co-authors and I firmly believe in the impact and novelty of this technology, and have even applied for a patent [link], as mentioned in a previous post. This work is just the beginning of the development of more sophisticated AI-powered digital twin models of the power grid that help us prepare for the future!

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Patents Research Products

Framework For Evaluating Effectiveness of Adaptive Change

Just finished work on my second patent, one for a machine-learning based framework capable of quantifying the effectiveness of adaptive change in infrastructural systems. This is a tricky problem because as hazards get more severe, investments in infrastructural systems can appear to be less effective than they actually are.

Preliminary Results Applying this Approach to Infrastructure in Connecticut. Figure by William Hughes.

The trick is to build a weather-related impact model that is also trained on various technical aspects of the infrastructure. If the training data is good enough, the model will learn how these aspects influence the reliability of the system, and then you can use that model to test out various configurations of infrastructure.

We’re hoping that this new approach will be useful for power utilities, regulators, and others to optimize how they plan and adapt their systems to climate change.

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Patents Research Products

Damage and Restoration Prediction Patent

Originally conceived back in the Fall of 2018, I’m very happy to report that our patent for an empirical impact prediction system that predicts both the magnitude of the impacts, as well at the time to restoration of service has been accepted by the US Patent Office. For details, please see this link.

Basic Workflow of Patented System

It has broad claims, and expires in late 2039! It also includes several options that would make it particularly useful for real-time damage and restoration estimation. If you think you might be interested in licensing this technology, get in touch with UConn Technology Commercialization Services!