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Using Predictive Weather Technology to Reduce Risks

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Julian Jackson's picture
Staff Writer, Energy Central BrightGreen PR

Julian Jackson is a writer whose interests encompass business and technology, cryptocurrencies, energy and the environment, as well as photography and film. His portfolio is here:...

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As more unstable weather encroaches globally, utilities are faced with challenges: how to prepare for these events, and improve resilience. Since 2000, the US has experienced a 67% increase in significant power outages caused by extreme weather-related events.

  • Weather is the most unpredictable element of complex utility operations and frequently leads to significant cost from damage to infrastructure

  • Extreme weather is especially challenging for medium size utilities that have a narrower margin of error than larger companies owing to smaller emergency crews and fewer financial resources

  • Mandated grid hardening programs are necessary, but require heavy, long-term investment in building infrastructure, and the cost is typically passed on to customers

  • Being unable to predict significant weather events early can lead to unnecessary expenses

One approach is to harden all vulnerable areas against the threats. This is an important strategy, but it suffers from increased costs, and it is impossible to fully protect against every contingency.

Another approach is to use weather-predicting technologies to guide risk management and complement the hardening programs. Storm Risk Analysis (SRA) can predict outage and weather risks with more lead time for emergency management personnel to react in a considered manner.

This will allow utilities to declare an Incident to FEMA, allocate resources, accurately anticipate significant weather events, reduce revenue losses to outages, and reassure customer that normal service will be restored as quickly as possible.

 

Assessing The Risks

There are many tools and services that can ensure that the best information is available to the decision makers in a system. The National Oceanic and Atmospheric Administration (NOAA) being perhaps the best-known of these. The process can be divided into three phases.

  • Site Hazard Assessment

  • Risk Assessment

  • Risk Management Strategies

Firstly the site must be assessed for hazards: is it at risk of flooding? Is it in a wild-fire prone area? How easy it it to access with repair vehicles in extreme weather (for example, snowy conditions). Hazards can be ranked on a scale, for example a five point spread from “very low” to “very high.”

The Risk Assessment module is composed of the following steps: Hazard Analysis, Exposure Analysis, Susceptibility Analysis, Coping and Adaptive Capacity Analysis, Risk Identification, and Risk Analysis. Each of those steps conforms with defined steps of a Risk Assessment methodology.

By using computer predictive models, this method can produce reports on where the most vulnerable areas are, enabling assignment of priorities for defending against dangerous challenges. Firstly, it can enable emergency management centers to track and predict storm damage, assign repair crews to appropriate locations, and liase with other services, such as Fire and Rescue to prepare a swift response.

The benefits from this approach are that companies can shorten outage duration and pinpoint expected damage for swift repair, thus improving reliability. Utilities can achieve cost savings from efficiently mobilizing restoration crews, as well as increasing the safety of crews restoring power under difficult conditions. They can also reduce or eliminate regulatory performance penalties. Good communication with customers will improve confidence as users receive high-quality, accurate updates based on data-driven insights.

 

Storm Risk Analytics Benefits

Storm Risk Analytics users can restore normal business operations as quickly as possible while keeping their workforce and customers safe. Ultimately, Storm Risk Analytics provides utility operation centers with greater guidance and preparedness so they can allocate necessary resources and restoration crews ahead of significant weather events. As data and computing models improve, perhaps using AI, predictive analysis will increase in value as a tool for utilities to enable greater resiliency in the face of increasingly severe weather.

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