Heng Wan

Extreme weather events, such as Heat Waves (HW) and Cold Snaps (CS), pose significant risks to the power grid. The United States (U.S.) Federal Energy Regulatory Commission Order No. 896 mandates regional coordination standards that account for extreme thermal events. However, the lack of a universal definition for extreme thermal events may lead to inconsistent compliance efforts among neighboring entities, undermining the reliability of the transmission system. This study directly addresses this challenge by systematically evaluating how varying technical choices in defining HW and CS fundamentally impact the characterization and ranking of extreme events for power grid reliability studies. We used 12 event definitions and multiple temperature spatial aggregation approaches to construct historical (1980–2024) regional extreme thermal event libraries across North American Electric Reliability Corporation (NERC) subregions in the conterminous U.S. We examined the sensitivity of event characteristics (e.g., duration, frequency, intensity, and spatial coverage) to different definitions. While some definitions produced similar libraries and top event rankings, definitions based on moving‐window‐averaged temperatures yielded markedly different characteristics. Spatial aggregation methods had minimal impact on heat wave or cold snap intensity, frequency and duration but significantly influenced spatial coverage. The top events identified across different aggregation methods were consistent, but their ranking order varied. These findings offer critical insights for characterizing and selecting extreme thermal events and for supporting local and cross‐regional coordination as required by reliability standards.

Power outage datasets report on the spatial and temporal characteristics of customers without electricity and provide insights into trends in reliability and compliance. As the availability and quality of such datasets vary, an understanding of fit-for-purpose is needed. We compare two U.S. outage datasets, EAGLE-I and IEEE, from 2018-2022 to evaluate their strengths and weaknesses for different scientific questions and identify complementarities. The datasets differ in collection methodologies and in temporal and spatial scales. EAGLE-I captures a snapshot of outages sourced from the web, whereas IEEE compiles data from utilities directly. We use outage statistics, correlations of daily Customers Interrupted (CI), and case studies of extreme events to illustrate their differences. Despite processing the datasets to a common spatial and temporal resolution, direct comparisons are challenging due to differences in their methodologies and data collection approaches. EAGLE-I covers a larger share of customers, contributing to systematically higher CI and the associated estimated Customer Minutes Interrupted (CMI). Pooled across NERC (NERC LTRA) regions, we find weak linear mapping in CI between the two datasets, indicating that, in general, their trends in reliability metrics would not be consistent. Consistency is higher for some NERC regions, specifically Florida, Northwest, Southwest Power Pool, and Texas. EAGLE-I is advantageous for fine-scale, event-focused analysis. IEEE is more suitable for long-term regional benchmarking and analyses that support mapping between power outages and evolving asset characteristics. We conclude that efforts to promote coordination and harmonization between outage datasets are needed to expand analytical capabilities about reliability events.

Coastal cities face increasing flood hazards due to climate change. Physical infrastructures, such as levees, are commonly used to reduce flood hazards. To effectively manage flood risks, it is important to understand the degree to which physical infrastructures change both hazard and exposure. For example, many studies suggest that levee construction causes an overall increase in risk because levees promote exposure growth to a greater degree than they reduce flood hazards. Although this so‐called “levee effect” is widely studied, there are knowledge gaps surrounding how uncertainties related to levee construction and flood risk translate into the occurrence and strength of the levee effect in coastal communities. Here, we use agent‐based modeling to simulate the influence of flood risk information pathways on the dynamics around the levee effect, first under idealized conditions and then within a real‐world coastal case study. We finally conduct a global sensitivity analysis to identify which model factors contribute to the strength of the levee effect. We find that, under idealized conditions, the strength of the levee effect is highly sensitive to economic (e.g., population growth) and engineering (e.g., levee failure) factors. However, under more complex coastal conditions, factors related to household behavior (e.g., risk aversion) are more influential on the strength of the levee effect. Overall, our findings emphasize the importance of capturing the interactions and uncertainties among multiple behavioral, economic, and engineering factors when measuring flood risk in coastal communities.

Electric vehicles are promoted as ‘clean’ technologies and offer promising reductions in transportation emissions. Nevertheless, their environmental benefits critically depend on the local electricity grid mix and the type of emission being considered. Here, we conduct a comparative life cycle assessment of the four dominant light-duty vehicle categories at both the global scale and in three representative countries: Norway, the US, and China. By analyzing different environmental indicators, particularly global warming potential and respiratory effects, and quantifying related parametric uncertainties, we reveal that the advantages of electric vehicles vary across these regions and across environmental impact types. While electric vehicles offer considerable decarbonization potential as the grid mix becomes cleaner, they might not mitigate other environmental impacts, such as increased respiratory effects on rural, low-income communities. Our results support stakeholders in identifying environmentally friendly vehicle and policy options while considering multiple factors, and emphasize the importance of tailored approaches over one-size-fits-all solutions in sustainable transportation.

Electric vehicles (EVs) are promoted as ‘clean’ technologies and offer promising reductions in transportation emissions. Nevertheless, their environmental benefits critically depend on the local electricity grid mix and the type of emission being considered. Here, we conduct a comparative life cycle assessment (LCA) of the four dominant light-duty vehicles (LDVs) categories at both the global scale and across three prominent countries leading the global EV market – Norway, the US, and China – each with a distinct electricity grid mix. By analyzing different environmental indicators, specifically emphasizing global warming potential and respiratory effects, and quantifying related parametric uncertainties (e.g. fuel economy, electric driving share and vehicle lifetime mileage), we reveal that the EV advantages vary across these regions and across different types of environmental impacts. Our results support stakeholders in finding the most environmentally friendly vehicle and policy interventions while considering multiple factors, emphasizing tailored approaches over one-size-fits-all solutions in sustainable transportation.

High-resolution income projections under different Shared Socioeconomic Pathways (SSPs) are essential for the climate change research communities to devise climate change adaptation and mitigation strategies. To generate income projections for Washington state, we obtain state-level GDP per capita projections and convert them into projected annual household income. The resulting state-level income projections are subsequently downscaled to the census block-level based on the Longitudinal Origin-Destination Employment Statistics (LODES) dataset. For accuracy assessment, we downscale historical income data from state- level to block- and block group-level and compare the downscaled results against the actual income data from LODES. County-level accuracy assessment is also conducted based on American Community Survey. The results demonstrate a good agreement (Average R² of 0.67, 0.8, and 0.99 for block-, block group-, and county-level, respectively) between the downscaled income data and the reference data, thereby validating the methodology employed. Our approach is applicable to other states for income projections, which can be utilized by a broader audience, including those involved in demographic analysis, economic research, and urban planning.

This paper introduces a classification of 1500V DC switch trip fault based on K-means algorithm. According to the trip duration at the time of the trip fault and the change of electrical volume, scatter diagram is drawn by Matlab, and the Euclidean distance from each point to each cluster center is calculated to complete the classification by comparing the distance sum between the new cluster center and the last round of cluster center. The proposed method aims to classify the DC switch trip faults of rail transit in detail and meet the requirements of rapid maintenance better. Through experimental comparison, it is concluded that when K=7, the SC coefficient of K-means performance index is 0.8326, which is the closest to 1 and the classification is complete compared with other K values, and the effect is good.