Exploratory analysis of high-resolution power interruption data reveals spatial and temporal heterogeneity in electric grid reliability

Large-scale data analytics for resilient recovery services from power failures

“…12,30 The data we use in this work are commonly available to most distribution grid operators in the US and parts of the world. 9,13,18 Thus, this study demonstrates that energy service providers have the ability to adopt data science, 31,32 that is, to turn their own data into new knowledge, and to benchmark and improve recovery as well as infrastructure enhancement. This work also shows significant challenges for scaling up data analytics even from one US state to its neighbor, as service regions are often governed by different policies, as shown here by the different declaration of major events.…”

“…9,[14][15][16][17] Using individual failure events, however, does not allow a systematic study on whether recovery services are resilient, i.e., maintain a desirable performance despite the increasing severity of failure events. Second, most work on historic power failures and recovery has used data aggregated over townships or service regions and hours since the granular measurements are privately owned and thus difficult to obtain (see Ji et al, 9,13 Larsen et al, 11 and Dunn et al 18 and references therein). Advanced data collection and analysis are emerging from smart meter infrastructure and micro PMUs (Phasor measurement units).…”

Designing resilient decentralized energy systems: The importance of modeling extreme events and long-duration power outages

“…Electric power outages vary in magnitude and duration, in space, and over time (Larsen et al, 2020) (Dunn et al, 2019). Resilience planning, which seeks to mitigate the effects of high-impact low-probability extremes, should capture this variation as thoroughly as possible.…”

“…Although some studies have been given proprietary access to high-resolution data (Kankanala et al, 2014) (Nateghi et al, 2014) (Ji et al, 2016), such detailed data are held by utilities and generally not available. A second approach, with foresight, is to scrub interruption data from public-facing utility webpages in real time until a sufficiently large dataset is collected (Dunn et al, 2019). A third approach-the most accessible and general-is to use averaged interruption data filed annually by electric utilities while making assumptions about the shape of the underlying distribution of interruptions.…”

“…First, fresh analysis of raw power outage data is needed to generate reliability functions (i.e., probability distributions) for the likelihood and duration of different types of disruptions. Ideally, one would construct distributions by aggregating data from a wide cross-section of individual outage events and customer interruptions, as in Hossein Afsharinejad et al ( 2021) and Dunn et al (2019). Although utilities record these granular data, they are not typically available for public use.…”