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!
Tag: Resilience
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.
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.