Wildfire Electric Grid Resilience

Sandia Energy > Programs > Electric Grid > Wildfire Electric Grid Resilience

Protecting Our Electric Grid from Wildfires and Eliminating Grid Initiated Wildfires

Sandia’s wildfire grid resilience program aims to mitigate the ignition and decrease consequences of major wildfires through new tools and improved information pre-wildfire, early response during wildfires to increase safety and minimize damage, and accelerated recovery following wildfires to maximize energy availability.

Better wildfire modeling, monitoring, and planning tools allows significantly reduction in consequences once a wildfire ignites, and better grid devices can reduce the probabilities of grid ignited wildfires.

Responding early and effectively during wildfires is key to reducing wildfire consequences. A better understanding of wildfire ignition and lightning ignition probabilities can improve wildfire response time. Better grid component modeling and monitoring can reduce grid ignited wildfires and better uncertainty modeling and visualization can help decision makers understand when and if Public Safety Power Shutoffs (PSPS) are needed and when and if evacuations are needed.

graphic showing phases of wildfire mitigation

Why is wildfire grid resiliency important to national security?

Our vision is to have the right information available at the right time to maximize the resilience of the U.S. electric power grid and maximize the security of people to wildfires, this includes:

Knowing and predicting risks pre wildfire and developing tools aimed at planning for future wildfires
Responding early and effectively during wildfires
Accelerating recovery following wildfires

Sandia aims to help accelerate electric grid recovery efforts following wildfires by working closely with utilities and employing an agile optimized restoration process to ensure critical infrastructure is returned to service quickly and effectively.

Wildfire Resilience Program Goals

Wildfire Research Categories

Our research is divided into three main categories of action to help appropriately address wildfire needs: pre-wildfire, during wildfire, and post-wildfire.

Pre-Wildfire

Problem

Modeling wildfires depends on accurate vegetation characterization
Previously used in fire models to assess hazard to assets of interest has historically been static and years old.

Approach

Leveraging previous work from the Resilient Energy Systems-funded Lab Directed Research and Development (LDRD)
Generate ML-derived vegetation characterization from fusing satellite imagery and weather station data and pushing into wildfire simulations

Impact

Improve utilities ability to assess, plan, and adapt to wildfires
Pilot Sandia’s wildfire analytics product with Public Utility of New Mexico (PNM) to update their Hazard Fire Areas with Sandia’s developed wildfire risk geospatial dataset to provide PNM with daily situational awareness on fire risk

Problem

Determine wildfire risk to critical infrastructure in near-real-time and understand resulting impacts to the grid that could lead to cascading failure.

Approach

Apply machine learning to weather station data weather station data to determine near-real-time fuel moisture, leverage wildfire spread software and Sandia grid modeling to identify component damage.

Impact

Provide decision makers with an interactive map which shows our near-real-time fuels condition layer. This allows the user to run wildfire simulations and analyze component damage
The University of New Mexico is a project partner focusing on vegetation parameterization

Opportunity

Explore commercialization vectors for the technology through customer discovery interviews

Accomplishments

Conducted over 75 customer discovery interviews covering multiple industry verticals

Problem

Mitigation for wildfire threat is very expensive, utilities need a data-driven approach to compare and contrast investments.

Approach

Model impacts to wildfire risk based on mitigation type simulated, assessing how each one meets objectives, including grid hardening, fuel reduction with surrounding landowners, and vegetation mitgation.

Impact

Our modeling efforts will provide a data-driven decision support tool that assesses mitigation costs and benefits against specific objectives
Partnering with the Public Service Company of New Mexico (PNM) in Year 1, Western Area Power Administration in Year 2

During Wildfire

Problem

Lightning features key to fire ignition and grid/asset disruption have yet to be identified due to limitations of existing lightning datasets. Therefore, suppression efforts begin only after fire is large enough to be detected by remote-sensing or an asset is already malfunctioning or offline.

Approach

We will develop a novel lightning monitoring system that provides total lightning current (and energy), which we hypothesize is a key quantity for understanding and predicting ignition by lightning.

Impact

Lightning results in the most detrimental wildfire impact, accounting for over 50% of acreage burned worldwide and over 90% in Arctic regions.

By providing a presently nonexistent lightning total-current dataset from a well-instrumented asset location, this project will enable predictive early warning tools through identification of lightning’s key features in wildfire ignition, and act as pathfinder for future monitoring systems.

Problem

Humans are notoriously bad at understanding state uncertainty and probability. Prior research suggests that different representations of uncertainty can lead to different patterns of decisions. Such as decisions of whether or not and when to evacuate homes during wildfires, or the decision of when to do Public Safety Power Shutoffs (PSPS).

Approach

Grid operations (and modeling) involve complex, heterogeneous, uncertain information. How should that data be presented to support optimal decision making?

Impact

More optimal grid operation and planning decisions, because of a better understanding on how visualization of uncertainty affects those decisions.

Problem

An unknown time lag occurs between lightning ignition and fire detection such that, “there are no datasets that unambiguously relate igniting lightning to the corresponding wildfires” [Moris et al., 2020] and hence no data to train near-real-time prediction models that could enable preemptive response to suspect locations.

Approach

We will develop an experimentally-driven computational model to understand the predictability of lightning-ignited fire from first principles. We will leverage Sandia’s ongoing development of codes EMPIRE (plasma discharge physics/chemistry) and SIERRA (heat transport / fire reaction dynamics).

Impact

The recent rise in uncontrollable wildfire indicates an immediate need for early/preemptive suppression.
A first principles understanding of wildfire ignition by lightning will enable shortened lead time for lightning caused wildfire response and will provide guidance for lightning monitoring requirements and wildfire prediction tools.

Problem

The potential for fire ignition is proportional to the duration of the arc, and current protection schemes generally take 100 milliseconds to a second to operate

Solution

Develop fast, local, bi-directional, data-driven fault detection and location schemes for distribution systems, including DER high-penetrations, that operate in less than 4 milliseconds

Impact

Achieve sensitive protection schemes instead of Public Safety Power Shutoffs (PSPS)
Prototype box developed with custom DSP hardware for sampling, running developed algorithms, and making fault decisions – tested in microgrid
Involved with the Institute of Electrical and Electronics Engineers Power System Relaying Commitee D45 Reduction of Forest Fire Hazard
Participation in DOE’s Energy I-Corps program to better understand industry needs and refine prototype capabilities. Mentored by the Public Service Company of New Mexico (PNM).

Problem

The increased amount of electric vehicle charging load and the incapability of serving it during climate disasters will impact both the electric utility and the transportation sector, and result in jeopardizing the current evacuation plan.

Approach

Identify the impacts of increased electric vehicle penetration on both the transportation and electric grid during climate disaster evacuation plans.
Evaluate electric vehicle charging demand based on climate disaster scenarios (e.g. short/long distance evacuation requirements).
Find the optimal locations to establish new EV infrastructures and utilize the existing ones to ensure maximum electric vehicle utilization (e.g. maximum uptime, facilitation of commuting to shelters, grid assets, and backup power resources), and minimal evacuation time.

Impact

Impacts analyses of EV penetration on the power grid during evacuation
Solutions to satisfy the unserved EV load during evacuations
Multi-objective optimization models to maximize evacuation efficiency and utilization of EV supporting infrastructure.

Problem

The likelihood of high-voltage insulator failure from corona is expected to increase with surface contamination, natural aging, and carbon deposition from previous breakdowns.

Approach

Investigate laboratory techniques for artificial contamination and electrical aging
Procure grid insulators (purchased or donated)
Application of laboratory-based degradation techniques
High-voltage breakdown testing of clean and contaminated/aged insulators
Define risk metrics for contamination and aging at which point risk of failure increases
Develop failure thresholds and tracking criteria (working with wildfire propagation projects at Sandia) that can be used for grid health predictions in response to wildfires.

Impact

This information will create a basis for developing high-voltage insulator contamination and aging prediction tools that utilities can use for mitigation planning for parts of the grid impacted by wildfires.

Problem

Modeling wildfire smoke is a critical component of estimating decreases in solar energy production, and thus maintaining a resilient electric grid. However, wildfire dynamics are governed by complex climate – vegetation – human coupled processes. Further, smoke transport is a computationally challenging task, requiring high performance computing and typically seen as prohibitive at the large spatial and temporal scales required for grid planning efforts.

Approach

Develop a coupled landscape, climate, wildfire smoke modeling platform using existing tools, and made effective at scale with neural network surrogate models and machine learning.

Impact

Gridded measure of the reduction in potential solar energy at annual timesteps for the next 30 years.
Spatial smoke impact attribution, allowing each solar potential reduction pixel to describe the pixels where the smoke evolved from, permitting targeted forest management plans and explainable model output for managers and decision makers.

Post-Wildfire

Problem

Electrical outage time due to wildfires needs to be minimized.

Approach

Today, linemen are frequently stationed in areas affected by wildfires. They await safety approval to access damaged areas and then they proceed to assess and repair the affect grid infrastructure.

Impact

In partnership with electric grid utilities, identify and evaluate approaches to obtaining key missing information for wildfire grid recovery
Develop and demonstrate a wildfire grid recovery methodology that includes optimal restoration decisions

fire burning under transmission line — A California wildfire burns under a high voltage electrical transmission line.

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Contact

Brian J. Pierre, PhD

Manager, Electric Power Systems Research Department

(505) 284-7955

bjpierr@sandia.gov

Acknowledgement

Sandia’s Wildfire Resilience program is supported by the U.S. Department of Energy’s Office of Cybersecurity, Energy Security, and Emergency Response under the guidance of Dr. Joseph Dygert. Sandia National Laboratories is a multimission laboratory operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration.