Resiliency & Reliability

Forecast and backcast the impact of capital improvements on resiliency metrics in different environmental scenarios.

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Improve system-level resiliency, not just individual asset risk

Prioritize mitigation work based on how asset condition and behavior, vegetation proximity, and environmental conditions interact across the network in every scenario.

  • Demonstrate why certain feeders require specific hardening activities over others
  • Validate that mitigation work improved overall resiliency vs. improving individual operation-level metrics or simply shifted risk elsewhere
  • Forecast how risk, investment, and response operations need to change across different scenarios

Most resiliency programs still evaluate constituent risks independently, despite failures arising from interacting behaviors.

Vegetation teams analyze vegetation risks. Engineering teams focus on structure utilization. Emergency response teams focus on response scenarios. But HILF events rarely show up because any single threshold was exceeded in isolation. They emerge from a perfect storm of intersecting conditions across the network, such as:

  • Vegetation falling at just the right angle on a pole that is overutilized
  • Trimming vegetation in a windy corridor unwittingly increasing wind exposure and materially increasing failure risk on nearby poles
  • A hardened pole that ends up shifting mechanical stress to adjacent spans

You can have a perfect lens into vegetation, structure loading, and accessibility risk, and still have a very limited understanding of actual network resiliency. Mitigation work can successfully reduce individual risk vectors while leaving the overall resiliency and reliability risk profile largely unchanged.

From localized engineering assessments toward network-wide outcome modeling

From localized risk management To network-wide outcome modeling
Solving vegetation management, structure loading and clearance risks in separate workflows Modeling interacting characteristics together to understand concentrated failure exposure
Time-based inspections and workflow-specific violation thresholds Risk-based inspections in the context of every weather and operating scenario
Evaluating local hardening decisions asset-by-asset Understanding how hardening actions impact overall failure risk across the network

Doubling Network Capacity Across 183,000 km

Essential Energy — New South Wales, Australia


"By modeling each span individually, we found that in many parts, the capacity is twice as high as we previously thought. "

John Cleland
John Cleland CEO, Essential Energy

2x existing network capacity unlocked
Doubling Network Capacity Across 183,000 km

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