My recently published paper, “A Data-Driven Decision Support Tool for Anticipating Tropical Storm Impacts to the United States Power Grid,” has the potential to be my most impactful yet. We developed a system that can accurately predict the impacts that tropical storms have on the power grid in the Continental United States. While not the first system of its kind, we demonstrate accurate predictions for a wide range of types and magnitude of storms. It has the potential to be a powerful decision-making support tool that helps managers in government and at utilities prepare more effectively for the worst types of natural hazards. For more details, please see our article: 10.1109/ACCESS.2024.3442768
Category: Article
Academic Articles in progress or in print.
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!
Major storms are always much much worse than more typical events. In terms of power outages, they can be many orders of magnitude more impactful than typical events: sometimes cause about the same number of power outages in a day that a power utility would otherwise see over the course of a year.
But extremes are hard to predict with machine learning for two major reasons.
- They’re rare events, and therefore you have few examples to learn from.
- Their impacts don’t fit the trends of more moderate events well and are much much larger.
But because these storms are so disruptive, they are very important to be able to predict accurately. In the journal Weather and Climate Extremes, we’ve just published a paper Improved Quantitative Prediction of Power Outages caused by Extreme Weather Events, which describes a data intensive approach of empirically predicting the impacts of storms like hurricanes on the power grid.
Not only do the described methods have superior accuracy, because we consider a range meteorological factors not typically considered in impact analysis we’re able to describe a diversity of factors that can contribute to the most extreme impacts of weather.
Tree trimming is a go-to solution for many power utilities to reduce the number of power outages caused by storms. The effects of this are intuitive: because so many outages are caused by trees in storms, cutting back the trees will reduce outages.
Quantify or predicting this reduction is much more difficult, and yet, so critical for effective cost-benefit analysis. In Dynamic Modeling of the Effect of Vegetation Management on Weather-Related Power Outages published in Electric Power Systems Research we do just that by building a dynamic machine learning power outage prediction model, using the amount of aggressive tree trimming as one of the predictor variables. With this dynamic model we were able to run a counter-factual analysis to determine that historical tree trimming has reduced outages in Connecticut somewhere between 25 and 42%, and that additional tree trimming could have further reductions in outages in the future.
We just got a new article published in a special issue of Sustainability. It uses statistical methods to quantify the effects of Enhanced Tree Trimming (a rigorous tree trimming standard being used by Eversource), differentiating between storms of different intensities. We show that this type of tree trimming is able to reduce power outages during all types of storms, but the effects diminish somewhat for strong storms: which can cause much more severe damage like downed trees.
We’re hoping that with these results, utilities and regulators will be better able to prioritize measures to make the power grid more reliable, and help prepare for an increasingly uncertain climate. Check it out here. Check out the full text of the article here.