Multilaboratory project explores improvements to system planning and operation

Reliability is a concept that has been widely understood in the electric power industry for a long time, but the concept of resilience is still evolving.

Reliability means there are adequate resources to supply customers with electricity on a consistent basis. But suppose a system has been shut down, either by extreme weather, ransomware from cybercriminals, or even a squirrel?

A power system should adapt to any hazard and recover to regular operation as quickly as possible—a quality not captured by reliability, but rather resiliency.

This is why WETO is funding an effort to craft definitions and metrics that demonstrate and measure the resilience benefits that distributed energy resources, including wind energy, provide. WETO’s Microgrids, Infrastructure Resilience and Advanced Controls Launchpad (MIRACL) project, which involves four national laboratories, aims to improve the planning and operation of distributed wind systems. At Idaho National Laboratory (INL), a team of researchers is developing an electric-energy-delivery-system-focused resilience framework—a set of flexible guidelines that organizations can use to manage risk and inform capital investments.

Distributed wind energy systems exist to provide power locally—for example, to a community, family farm, factory, or microgrid—and can range in size from a 5-kilowatt wind turbine at a home to a multimegawatt wind turbine at a manufacturing facility. They can provide many resilience benefits, thanks to their ability to quickly change power output and support grid stability. It can even help bring power back online if the local grid experiences an outage.

The term resilience comes from the Latin resilio, meaning to spring back or rebound. This might sound simple enough, but as the INL team studied research done by national laboratories, government agencies, and utilities, they concluded that the power industry lacks a standardized definition of resilience.

Small wind turbine stands beside a building at sunset.

This distributed wind turbine, which supplies backup power to the Center for Advanced Energy Studies at INL, is an example of a system that could use the lab’s resilience metrics and framework to evaluate the benefits of distributed wind. Photo by J. Gentle, INL

“Every electric energy delivery system has its own characteristics and vulnerabilities,” said INL researcher Megan Culler. “A remote microgrid in Alaska might be resilient against extreme cold, and a distribution system in Texas might be resilient against hurricanes. Both of these systems are resilient against some hazards, but not necessarily against every threat out there.”

In the comprehensive literature review, “Distributed Wind Resilience Metrics for Electric Energy Delivery Systems,” the INL team zeroed in on a critical characteristic of resilience for electric energy delivery systems: Each system has its own unique needs and perspectives as a result of different geographies, resources, and stakeholders—a characteristic the team labels as the distinctiveness property. Recognizing there is no “one-size-fits-all” process for resilience, the distinctiveness property encompasses factors such as likely threats, geography, stakeholders, risk tolerance, and mitigations.

The distinctiveness property is captured in each step of INL’s new definition of resilience for electric energy delivery systems: “The resilience of an electric energy delivery system is described as a characteristic of the people, assets, and processes that make up the electric energy delivery system and its ability to identify, prepare for, and adapt to disruptive events (in the form of changing conditions) and recover rapidly from any disturbance to an acceptable state of operation.”

Resilience metrics and, more specifically, distributed wind resilience metrics, must come from a process that addresses this distinctiveness quality and is separate from well-established reliability processes.

Applying qualitative and quantitative metrics to distributed wind resilience, the INL team developed a set of flexible guidelines—or a resilience framework—comprising three stages:

  1. Planning. Uses future organizational needs and current system status to prepare for potential risks. Resiliency planning differs from traditional reliability planning in that it considers high-impact, potentially long-duration consequences.
  2. Operational. Seeks to respond to active risks as prudently and efficiently as possible. This is the most “hands-on” portion of resilience because many decisions must be made quickly to maintain system viability. When a hazard occurs, the people, processes, and systems detect the event and react to the extent of their capabilities.
  3. Future. Seeks to improve on current system resilience and feeds back into the planning stage to promote continuous improvement. This can involve direct work in the operational world to make repairs, upgrade components after they’ve failed, or make immediate changes to standard processes.
Diagram showing three stages of resilience—plan and evaluate, operate, and future—and the process steps and flow involved in each one.

The INL resilience framework includes three high-level stages of resilience, five core functions of resilience, and multiple process steps within each stage to guide users through planning and evaluating the resilience benefits from distributed wind. Figure from INL

    To guide a user through these three stages, the framework lays out five core functions that represent high-level capabilities a system must have for lifecycle resilience: identify, prepare, detect, adapt, recover. These core functions align closely with the core functions laid out in the National Institute of Standards and Technology cybersecurity framework for critical infrastructure.

    The INL resilience framework is then broken down further, offering detailed steps and examples and walking users through resilience-planning scenarios.

    INL will continue to contribute to the MIRACL initiative by providing an end-to-end, cyclical process for evaluating resilience and building it into the processes and assets that make up an energy delivery system. Ultimately, this process can be used to demonstrate the value of distributed wind beyond simple generation capacity and clean energy goals.

    Fall 2021 R&D Newsletter

    Explore previous editions of the Wind R&D Newsletter or browse articles by topic: