IEEE Smart Grid Interview
A Framework to Quantify Resilience Built from the Customer Perspective
Sean Morash, Senior Consultant, EnerNex
smorash@enernex.com
865-770-4879
Aaron Snyder, Ph.D., Director of Grid Technology Consulting
aaron@enernex.com
865-770-4849
In this interview, Sean Morash and Aaron Snyder answer questions from their webinar, A Framework to Quantify Resilience Built from the Customer Perspective, originally presented on September 12, 2019. For more details regarding these questions, please view the webinar on-demand on the IEEE SG Resource Center.
Does this approach to new metrics potentially impact how utilities would report CAIDI?
It depends. The IEEE standard defines CAIDI, the customer average interruption duration index representing the average time required to restore service, as the sum of customer minutes of interruption over the total number of customers interrupted. If the interruption is only measured considering the supply/source from the utility standpoint, this may not change (so a meter would report loss of voltage on the utility side of the meter, and the time it took to restore). However, so-called “behind the meter” (BTM) supply on the customer side of the meter may or may not impact whether the meter reports. If the meter only considers the utility supply, there is no impact and clearly if the metric involves both the utility and BTM supply, this metric could be impacted. An additional consideration is that the new resilience metric does not require the 5-minute duration to register as an outage.
How does resilience relate to the topology of the network?
Network topology is one aspect of being resilient for one particular source, the grid, or for resources that are supplying the grid. Where there is more than one supply path or source for a load or group of loads, the resilience would be higher.
Would these metrics also help enable customers define what resources they would invest in to meet their need?
That is certainly a potential application of these metrics as different resource mixes would necessarily have different resilience scores and costs, providing a method to rank different investment choices. It would also help a customer define their needs more clearly against the length of use to remain resilient to loss of supply as the primary source outage increases in duration.
Is it possible that these metrics would be used by third party providers to help the customer balance their needs with potential behind the meter resources?
That is certainly a potential application of these metrics as different resource mixes would necessarily have different resilience scores and costs, providing a method to rank different investment choices.
In definitions of a resilient system, is it always defined as a system that provides power 100% of the time that it is desired? If so, then when designing such a resilient electric system, who should determine the bounds in terms of system cost and what should they be?
There might never be a perfect system (collection of supplies, beyond just the grid) that provides power 100% of the time to all parties that desire it. Regardless, such a system is likely not economically justifiable. One hundred percent of the time desired is therefore the upper bound of the solution. It would be up to the appropriate party to determine the bounds of system cost to achieve a particular resilience target, just as they do for a particular reliability target today. An individual might do it for themselves, a neighborhood could pool together (community energy storage), a provider might reinforce the grid and add alternative supplies (distributed energy resources) for portions or all of their territory, or it could be done on a very macro level with regulators or legislators taking action.
How could your introduced metric expand to interdependent power and gas infrastructures?
At the moment, those interdependent infrastructures are implicit in the calculation when considering a collection of resources. This likely could be made a more explicit dependency after more study.
How does this tie into microgrids that can operate in parallel with the grid?
The resiliency metrics we propose work explicitly with any collection of resources operating in any fashion. A microgrid can be seen to improve both reliability and resilience and this would be born out through application of the metrics/calculations.