When Hurricane Fiona hit Puerto Rico in 2022, it revealed significant weaknesses in the island’s energy system. Despite being a Category 1 hurricane, it caused a complete power outage, leaving residents without electricity for days or even weeks, which had serious health, safety, and economic impacts.

However, the situation also allowed researchers to gather valuable insights about energy resilience during extreme weather events. LUMA Energy, the private company managing Puerto Rico’s electricity grid since 2021, collected detailed outage data every 10 minutes as the hurricane approached.

A group of engineers from Princeton University is now analyzing this data to help LUMA Energy and other operators understand how to better manage their systems amid the increasing frequency of climate-related disasters, including hurricanes and heat waves.

This research has led to the creation of models that assess the risk of major blackouts like the one experienced during the hurricane and provide predictions on how future climate extremes might affect energy systems. The insights gained from these models may assist in improving the grid’s resilience to the worsening impacts anticipated due to climate change. Additionally, these models are designed to help energy providers meet their clean energy goals without compromising reliability.

“A key reason for shifting to clean energy is to mitigate severe climate impacts, including the rise in extreme weather events,” explained Ning Lin, a professor of civil and environmental engineering and the lead researcher on the project.

“However, renewable sources like solar and wind are more vulnerable to weather conditions than traditional fossil fuels, potentially making them less stable during adverse events. Our work is aimed at helping energy systems manage these risks while pursuing their clean energy objectives.”

CRESCENT: Understanding the Threats to Energy Systems from Climate Change

What happened during Hurricane Fiona?

The data showed that just before Fiona made landfall, Puerto Rico’s power grid went from operating at over 50% to a complete blackout in less than 10 minutes. This rapid decline indicates a situation called a cascading power failure, where an issue in one part of the grid triggers a failure throughout the entire system.

In a recent paper published in Nature Communications, the Princeton team presented a new model that assesses the risk of such cascading outages linked to hurricanes and other climate-related events. Named CRESCENT (Climate-induced Renewable Energy System Cascading Event), this model combines climate hazard data with grid vulnerability information to predict the odds of a large-scale blackout.

Using CRESCENT, researchers simulated 1,000 possible scenarios similar to Hurricane Fiona’s characteristics. These simulations helped identify recurring patterns in how the storm affected the grid, which can aid operators in pinpointing critical infrastructure and formulating strategies to prevent blackouts.

Interestingly, they discovered that if the Costa Sur power plant, Puerto Rico’s largest, failed early during the hurricane, the grid would actually be more resilient to a total blackout than if the failure occurred later in the storm.

“Initial failures of key infrastructure may sound counterintuitive, but they can actually help maintain grid stability, particularly when smaller components take on the load,” explained Luo Xu, a research scholar involved in the study.

Early failures can allow the grid to redistribute energy more effectively, which is critical in preventing a total collapse, as more extensive damage accumulates later in the storm.

The team suggests that insights from CRESCENT could be beneficial for both immediate and long-term planning of Puerto Rico’s energy grid. In the short term, operators can enhance the grid’s resilience against hurricanes by identifying this critical infrastructure.

In the long run, the model could inform Puerto Rico’s goal of fully decarbonizing its energy system by 2050, taking into account the vulnerabilities of renewable energy systems.

For example, the model indicates that strategically pairing energy storage with renewable sources can significantly lower the risk of a widespread blackout. Energy storage becomes increasingly vital once the proportion of rooftop solar systems exceeds 45%, which is a tipping point identified in the research.

“Unlike traditional energy generation, renewable sources lack the inertia to help stabilize the grid. As we incorporate more renewables, robust energy storage options or stabilizing mechanisms are crucial,” added co-author H. Vincent Poor, an electrical engineering professor.

REDUCER: Preparing for Power Needs in Advance of Extreme Weather

Power grid operators must predict energy demands hour by hour, every day of the year to ensure a steady supply.

Typically, this forecasting is manageable. A day ahead, operators analyze weather forecasts, historical demand trends, and expected consumer usage to decide which generators to activate to match supply and demand.

However, during hurricanes or other extreme weather, these forecasts can become wildly off-mark, often leading to blackouts or reliance on costly emergency generators.

In another recent study published in the Proceedings of the National Academy of Sciences, the Princeton research team introduced a new model called REDUCER (Risk-aware Electricity Dispatch Under Climate Extremes with Renewable integration) to enhance next-day energy supply planning for climate threats like hurricanes.

REDUCER significantly outperformed existing models, capturing potential energy demand losses more accurately during harsh climatic events. For instance, applying it to Puerto Rico’s grid prior to Hurricane Fiona reduced operational costs by 20% compared to standard day-ahead strategies and minimized reliance on additional energy sources.

“This approach helps prevent compounding disasters and allows both operators and consumers to concentrate on recovery,” noted Poor. “In the long run, it provides a more reliable energy supply and keeps electricity costs lower.”

The REDUCER model outdid others by considering risks to both distribution and transmission networks. If the electric grid is akin to a roadway system, the transmission lines are like highways and the distribution lines are local streets. Most operational models only focus on transmission risks, as incorporating the complexities of distribution networks is often too demanding computationally.

“Previously, operators couldn’t factor in that complexity in their existing next-day models,” Xu explained, emphasizing that REDUCER can compute these risks effectively and quickly.

The new model performed particularly well for energy systems with substantial rooftop solar installations, an important part of Puerto Rico’s energy transition. When raised solar levels were included in the simulations, REDUCER proved more cost-effective, while traditional models suffered higher costs.

“As extreme weather events escalate and renewable adoption rises, tools like REDUCER will be essential for making informed decisions during such crises,” Lin stated.

Preparing for Climate Change Challenges Ahead

Lin and Xu noted that CRESCENT and REDUCER tackle challenges that grid operators will face as climate change continues to escalate extreme weather incidents.

The researchers used Puerto Rico’s power grid during Hurricane Fiona as a case study, but they noted that these models could be adapted to various power grid designs and extreme weather scenarios.

“Climate change affects everyone, not just in Puerto Rico,” Lin pointed out, citing a 2024 article showing a 78% increase in weather-related outages in the U.S. from 2011 to 2021 compared to the previous decade.

“We hope our research will support energy systems everywhere in adapting to the risks posed by climate extremes,” Lin concluded.