The growing integration of distributed energy resources (DERs) in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors. With large-scale DER penetration in distribution grids, traditional outage detection methods, which rely on customers report and smart meters' “last gasp” signals, will have poor performance, because renewable generators and storage and the mesh structure in urban distribution grids can continue supplying power after line outages. To address these challenges, we propose a data-driven outage monitoring approach based on the stochastic time series analysis with a theoretical guarantee. Specifically, we prove via power flow analysis that dependency of time-series voltage measurements exhibits significant statistical changes after line outages. This makes the theory on optimal change-point detection suitable to identify line outages. However, existing change point detection methods require post-outage voltage distribution, which are unknown in distribution systems. Therefore, we design a maximum likelihood estimator to directly learn distribution pa-rameters from voltage data. We prove the estimated parameters-based detection also achieves optimal performance, making it extremely useful for fast distribution grid outage identifications. Furthermore, since smart meters have been widely installed in distribution grids and advanced infrastructure (e.g., PMU) has not widely been available, our approach only requires voltage magnitude for quick outage identification. Simulation results show highly accurate outage identification in eight distribution grids with 17 configurations with and without DERs using smart meter data.
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Analysis of animal-related electric outages using species distribution models and community science data
Abstract Animal-related outages (AROs) are a prevalent form of outages in electrical distribution systems. Animal-infrastructure interactions vary across species and regions, underlining the need to study the animal-outage relationship in more species and diverse systems. Animal activity has been an indicator of reliability in the electrical grid system by describing temporal patterns in AROs. However, these ARO models have been limited by a lack of available species activity data, instead approximating activity based on seasonal patterns and weather dependency in ARO records and characteristics of broad taxonomic groups, e.g. squirrels. We highlight available resources to fill the ecological data gap limiting joint analyses between ecology and energy sectors. Species distribution modeling (SDM), a common technique to model the distribution of a species across geographic space and time, paired with community science data, provided us with species-specific estimates of activity to analyze alongside spatio-temporal patterns of ARO severity. We use SDM estimates of activity for multiple outage-prone bird species to examine whether diverse animal activity patterns were important predictors of ARO severity by capturing existing variation within animal-outage relationships. Low dimensional representation and single patterns of bird activity were important predictors of ARO severity in Massachusetts. However, both patterns of summer migrants and overwintering species showed some degree of importance, indicating that multiple biological patterns could be considered in future models of grid reliability. Making the best available resources from quantitative ecology known to outside disciplines can allow for more interdisciplinary data analyses between ecological and non-ecological systems. This can result in further opportunities to examine and validate the relationships between animal activity and grid reliability in diverse systems.
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- Award ID(s):
- 1940176
- NSF-PAR ID:
- 10392469
- Date Published:
- Journal Name:
- Environmental Research: Ecology
- Volume:
- 1
- Issue:
- 1
- ISSN:
- 2752-664X
- Page Range / eLocation ID:
- 011004
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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INTRODUCTION The Anthropocene is marked by an accelerated loss of biodiversity, widespread population declines, and a global conservation crisis. Given limited resources for conservation intervention, an approach is needed to identify threatened species from among the thousands lacking adequate information for status assessments. Such prioritization for intervention could come from genome sequence data, as genomes contain information about demography, diversity, fitness, and adaptive potential. However, the relevance of genomic data for identifying at-risk species is uncertain, in part because genetic variation may reflect past events and life histories better than contemporary conservation status. RATIONALE The Zoonomia multispecies alignment presents an opportunity to systematically compare neutral and functional genomic diversity and their relationships to contemporary extinction risk across a large sample of diverse mammalian taxa. We surveyed 240 species spanning from the “Least Concern” to “Critically Endangered” categories, as published in the International Union for Conservation of Nature’s Red List of Threatened Species. Using a single genome for each species, we estimated historical effective population sizes ( N e ) and distributions of genome-wide heterozygosity. To estimate genetic load, we identified substitutions relative to reconstructed ancestral sequences, assuming that mutations at evolutionarily conserved sites and in protein-coding sequences, especially in genes essential for viability in mice, are predominantly deleterious. We examined relationships between the conservation status of species and metrics of heterozygosity, demography, and genetic load and used these data to train and test models to distinguish threatened from nonthreatened species. RESULTS Species with smaller historical N e are more likely to be categorized as at risk of extinction, suggesting that demography, even from periods more than 10,000 years in the past, may be informative of contemporary resilience. Species with smaller historical N e also carry proportionally higher burdens of weakly and moderately deleterious alleles, consistent with theoretical expectations of the long-term accumulation and fixation of genetic load under strong genetic drift. We found weak support for a causative link between fixed drift load and extinction risk; however, other types of genetic load not captured in our data, such as rare, highly deleterious alleles, may also play a role. Although ecological (e.g., physiological, life-history, and behavioral) variables were the best predictors of extinction risk, genomic variables nonrandomly distinguished threatened from nonthreatened species in regression and machine learning models. These results suggest that information encoded within even a single genome can provide a risk assessment in the absence of adequate ecological or population census data. CONCLUSION Our analysis highlights the potential for genomic data to rapidly and inexpensively gauge extinction risk by leveraging relationships between contemporary conservation status and genetic variation shaped by the long-term demographic history of species. As more resequencing data and additional reference genomes become available, estimates of genetic load, estimates of recent demographic history, and accuracy of predictive models will improve. We therefore echo calls for including genomic information in assessments of the conservation status of species. Genomic information can help predict extinction risk in diverse mammalian species. Across 240 mammals, species with smaller historical N e had lower genetic diversity, higher genetic load, and were more likely to be threatened with extinction. Genomic data were used to train models that predict whether a species is threatened, which can be valuable for assessing extinction risk in species lacking ecological or census data. [Animal silhouettes are from PhyloPic]more » « less
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Abstract Aim Species distribution models (SDMs) are widely used to make predictions on how species distributions may change as a response to climatic change. To assess the reliability of those predictions, they need to be critically validated with respect to what they are used for. While ecologists are typically interested in how and where distributions will change, we argue that SDMs have seldom been evaluated in terms of their capacity to predict such change. Instead, typical retrospective validation methods estimate model's ability to predict to only one static time in future. Here, we apply two validation methods, one that predicts and evaluates a static pattern, while the other measures change and compare their estimates of predictive performance.
Location Fennoscandia.
Methods We applied a joint SDM to model the distributions of 120 bird species in four model validation settings. We trained models with a dataset from 1975 to 1999 and predicted species' future occurrence and abundance in two ways: for one static time period (2013–2016, ‘static validation’) and for a change between two time periods (difference between 1996–1999 and 2013–2016, ‘change validation’). We then measured predictive performance using correlation between predicted and observed values. We also related predictive performance to species traits.
Results Even though static validation method evaluated predictive performance as good, change method indicated very poor performance. Predictive performance was not strongly related to any trait.
Main Conclusions Static validation method might overestimate predictive performance by not revealing the model's inability to predict change events. If species' distributions remain mostly stable, then even an unfit model can predict the near future well due to temporal autocorrelation. We urge caution when working with forecasts of changes in spatial patterns of species occupancy or abundance, even for SDMs that are based on time series datasets unless they are critically validated for forecasting such change.
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Silva, Daniel de (Ed.)Biodiversity loss is a global ecological crisis that is both a driver of and response to environmental change. Understanding the connections between species declines and other components of human-natural systems extends across the physical, life, and social sciences. From an analysis perspective, this requires integration of data from different scientific domains, which often have heterogeneous scales and resolutions. Community science projects such as eBird may help to fill spatiotemporal gaps and enhance the resolution of standardized biological surveys. Comparisons between eBird and the more comprehensive North American Breeding Bird Survey (BBS) have found these datasets can produce consistent multi-year abundance trends for bird populations at national and regional scales. Here we investigate the reliability of these datasets for estimating patterns at finer resolutions, inter-annual changes in abundance within town boundaries. Using a case study of 14 focal species within Massachusetts, we calculated four indices of annual relative abundance using eBird and BBS datasets, including two different modeling approaches within each dataset. We compared the correspondence between these indices in terms of multi-year trends, annual estimates, and inter-annual changes in estimates at the state and town-level. We found correspondence between eBird and BBS multi-year trends, but this was not consistent across all species and diminished at finer, inter-annual temporal resolutions. We further show that standardizing modeling approaches can increase index reliability even between datasets at coarser temporal resolutions. Our results indicate that multiple datasets and modeling methods should be considered when estimating species population dynamics at finer temporal resolutions, but standardizing modeling approaches may improve estimate correspondence between abundance datasets. In addition, reliability of these indices at finer spatial scales may depend on habitat composition, which can impact survey accuracy.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/2752-664X/ac7eb5
