Collaborative strategies for building resilience against wildfires and severe weather conditions
In a groundbreaking move, electric utilities across the western United States are partnering with their state Public Utility Commissions (PUCs) to adopt a data-driven approach that aims to build a resilient and safe system at a reasonable cost.
This collaborative effort is focused on proactive, collaborative, and intelligent risk reduction, with the ultimate goal of creating a safer and more reliable grid for the future. The strategy is designed to inform decisions and planning under constantly changing conditions, ensuring the system remains robust and adaptable.
At the heart of this approach is the use of advanced AI-based risk modeling. These models help utilities identify and de-energize only circuits at risk of igniting a fire, thereby reducing customer impact and improving restoration times. The models are tailored for fire weather and extreme weather conditions and can forecast potential risks across multiple time horizons.
One of the key advantages of these data-driven models is their ability to use real-time changing weather and fuel conditions, something that static data cannot do. This allows utilities to establish a new climatology for their service territory, understanding what constitutes a "99th percentile" wind event or an unusually heavy snowstorm.
The insights from these models provide a granular, asset-level understanding of an electric utility's risk. This level of precision enables utilities to shift from geography-based asset improvements to risk-based investment strategies. As a result, utilities can continuously identify high-risk areas, ensuring efficient use of their budget for maximum risk reduction per dollar invested.
The benefits of this collaborative, data-driven model are crucial for enhancing community safety and optimizing resource allocation in electric utilities. For instance, during Public Safety Power Shutoff (PSPS) events, a data-driven model enables utilities to move from a broad-brush approach to a targeted and surgical one. This level of precision minimizes customer impact, improves restoration times, and maximizes community safety.
Moreover, electric utilities can now employ dynamic, high-resolution validated wildfire and weather models for risk prediction. Several western U.S. utilities, including Pacific Gas and Electric (PG&E), Southern California Edison (SCE), and San Diego Gas & Electric (SDG&E), use dynamic risk information to implement more precise and targeted public safety power shutoffs, minimizing customer impacts and optimizing restoration times.
The partnership between electric utilities and their state PUCs can evolve to exceed regulatory requirements, build a more resilient system, and protect the communities they serve. This partnership embraces a shared commitment to risk reduction, leveraging advanced data technology and weather intelligence.
In addition, electric utilities submit Wildfire Mitigation Plans to regulators, which often include asset hardening strategies. The data-driven approach allows utilities to track and demonstrate enhanced community safety to PUCs using metrics such as "risk-spend-efficiency" or similar cost benefit ratios.
This data-driven approach also extends beyond wildfire risk to inform strategies for extreme weather events. By adopting this approach, electric utilities are demonstrating a commitment to building a safer, more resilient, and cost-effective grid for the future.
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