An official website of the United States government
We focus on blackouts in electric distribution systems that have a large cost to customers. To quantify resilience to these events, we show how to calculate risk metrics from the historical outage data routinely collected by utilities' outage management systems. Risk is defined using a customer cost exceedance curve. The exceedance curve has a heavy tail that implies large fluctuations in large blackout costs, and this makes estimating the mean large cost in the usual way impractical. To avoid this problem, we use new resilience metrics describing the large event risk; these metrics are the probability of a large cost event, the annual log cost resilience index, and the average of the logarithm of the cost of large‐cost events or the slope magnitude of the tail on a log–log exceedance curve. Resilience can be improved by planned investments to upgrade system components or speed up restoration. The benefits that these investments would have had if they had been made in the past can be quantified by “rerunning history” with the effects of the investment included, and then recalculating the large event risk to find the improvement in resilience. An example using utility data shows a 2% reduction in the probability of a large cost event due to 10% wind hardening and 6%–7% reduction due to 10% faster restoration in two different areas of a distribution utility. This new data‐driven approach to quantify resilience and resilience investments is realistic and much easier to apply than complicated approaches based on modeling all the phases of resilience. Moreover, an appeal to improvements to past lived experience may well be persuasive to customers and regulators in making the case for resilience investments.
more »
« less
Towards using utility data to quantify how investments would have increased the wind resilience of distribution systems
We quantify resilience with metrics extracted from the historical outage data that is routinely recorded by many distribution utilities. The outage data is coordinated with wind data to relate average outage rates in an area to wind speed measured at a nearby weather station. A past investment in wind hardening would have reduced the outage rates, and the effect of this on metrics can be calculated by sampling a reduced number of historical outages and recomputing the metrics. This quantifies the impact that the hardening would have had on customers. This is a tangible way to relate an investment in wind resilience to the benefits it would have had on the lived experience of customers that could help make the case for the investment to the public and regulators. We also quantify the impact of earlier or faster restoration on customer metrics and compare this to the impact of investment in hardening. Overall, this is a new and straightforward approach to quantify resilience and justify resilience investments to stakeholders that is directly driven by utility data. The approach driven by data avoids complicated models or modeling assumptions.
more »
« less
- PAR ID:
- 10494985
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Power Systems
- ISSN:
- 0885-8950
- Page Range / eLocation ID:
- 1 to 12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Transmission utilities routinely collect detailed outage data, including resilience events in which outages bunch due to weather. The resilience events and associated metrics can readily be extracted from this historical outage data. Improvements such as asset hardening or investments in restoration lead to reduced outages or faster restoration. In this paper, we show how to rerun the historical events including the effects of the reduced outages or faster restorations to measure the resulting improvement in resilience metrics, thus quantifying the benefits of these investments. This is demonstrated with case studies for specific events (a derecho and a hurricane), and all large events or large thunderstorms in the Midwest USA. Instead of predicting future extreme events with models, which is very challenging, rerunning historical events readily quantifies the benefits of resilience investments if these investments had been made in the past. Rerunning historical events is particularly vivid in making the case for resilience investments as it quantifies the benefits for events actually experienced, rather than for uncertain future events.more » « less
-
It is useful to quantify electrical distribution system resilience based on historical performance. This paper systematically extracts resilience curves from historical utility outage data, extracts resilience metrics such as duration, average recovery rates, and maximum number of simultaneously outaged components, and examines the statistics of these resilience metrics for small, medium, and large events. The resilience metrics and their typical variabilities are expected to be helpful in predicting and bounding the likely outcomes of future resilience events. For example, we can calculate the restoration time that will be achieved with 95% confidence.more » « less
-
We automatically extract resilience events and novel outage and restore processes from standard transmission utility detailed outage data. This new processing is applied to the outage data collected in NERC’s Transmission Availability Data System to introduce and analyze statistics that quantify resilience of the transmission system against extreme weather events. These metrics (such as outage rate and duration, number of elements outaged, rated capacity outaged, restore duration, maximum simultaneous outages, and element-days lost) are calculated for all large weather-related events on the North American transmission system from 2015 to 2020 and then by extreme weather type that caused them such as hurricanes, tornadoes, and winter storms. Finally, we study how performance of the system changed with respect to the resilience metrics by season and year.more » « less
-
null (Ed.)Abstract Nine in ten major outages in the US have been caused by hurricanes. Long-term outage risk is a function of climate change-triggered shifts in hurricane frequency and intensity; yet projections of both remain highly uncertain. However, outage risk models do not account for the epistemic uncertainties in physics-based hurricane projections under climate change, largely due to the extreme computational complexity. Instead they use simple probabilistic assumptions to model such uncertainties. Here, we propose a transparent and efficient framework to, for the first time, bridge the physics-based hurricane projections and intricate outage risk models. We find that uncertainty in projections of the frequency of weaker storms explains over 95% of the uncertainty in outage projections; thus, reducing this uncertainty will greatly improve outage risk management. We also show that the expected annual fraction of affected customers exhibits large variances, warranting the adoption of robust resilience investment strategies and climate-informed regulatory frameworks.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1109/TPWRS.2023.3342729
