Tao Sun

Extreme weather events can severely disrupt critical infrastructure, triggering cascading effects on power, transportation, and essential services. However, standard weather and climate datasets often lack specialized variables necessary for hazard assessments. We present a unified dataset of infrastructure-critical weather and climate variables across the United States at 0.25° resolution, covering daily or sub-daily intervals from 1979 to 2100. The dataset includes temperature, dew point, wind gusts, precipitation partitioned by rain, snow, and freezing rain or ice pellets, lightning, and wildfire metrics. Historical conditions (1979-2023) are synthesized from observations and reanalysis products, while future projections are derived from 14 CMIP6 global climate models (historical, SSP245, and SSP585 experiments). Physically based and data-driven methods are used to estimate variables not directly provided by existing models. By integrating these variables into a single unified dataset, we enable consistent, high-resolution assessments of weather-related infrastructure risks across past and future periods, supporting wide-ranging applications in energy, transportation, water resources, emergency management, and beyond.

Distributed energy resources are promising solutions for household energy affordability and resilience as weather extremes and ageing infrastructure intensify grid-reliability risks. Here we present a comprehensive nationwide assessment of over 500,000 US households, evaluating economic and back-up viability of solar–battery systems. We find that 60% of households could reduce electricity costs with average savings of 15%, whereas 63% of households could achieve affordable back-up power during power outages covering an average of 51% of their essential energy needs. However, these benefits show limited alignment with areas of greatest need, particularly in regions facing high outage risks. We also identify notable disparities in access to solar and battery, with less-populated and disadvantaged communities showing consistently lower viability. These findings demonstrate the need for targeted policy interventions to ensure equitable access to solar–battery benefits, especially as states transition from net energy metering to other electricity tariff policies.

A majority of US households can reduce energy costs and access affordable backup power during outages through rooftop solar and battery storage. Policymakers need to evaluate and adopt measures to ensure high-outage-risk and energy-burdened communities have equitable access to these adaptation solutions as climate impacts intensify outages.

The timing of electricity consumption is increasingly important for grid operations. In response, households are being encouraged to alter their daily usage patterns through demand response and time-varying pricing, although it is unknown if they are aware of these patterns. Here we introduce an energy literacy concept, ’load shape awareness’, and apply it to a sample of California residents (n = 186) who provided their household’s hourly electricity data and completed an energy use questionnaire. Choosing from four prominent load shape designations, half of respondents (51%) correctly identified their dominant load shape before COVID-19 shelter-in-place (SIP) orders while only one-third (31%) did so during SIP orders. Those aware of their load shape were more likely to have chosen evening peak, the most frequent dominant shape in the electricity data. Our work provides proof of principle for the load shape awareness concept, which could prove useful in designing energy conservation interventions and helping consumers adapt to an evolving energy system.

Understanding how the COVID-19 pandemic has altered electricity consumption can provide insights into society’s responses to future shocks and other extreme events. We quantify changes in electricity consumption in 58 different countries/regions around the world from January-October 2020 and examine how those changes relate to government restrictions, health outcomes, GDP, mobility metrics, and electricity sector characteristics in different countries. We cluster the timeseries of electricity consumption changes to identify impact groupings that capture systematic differences in timing, depth of initial changes, and recovery rate, revealing substantial heterogeneity. Results show that stricter government restrictions and larger decreases in mobility (particularly retail and recreation) are most tightly linked to decreases in electricity consumption, although these relationships are strongest during the initial phase of the pandemic. We find indications that decreases in electricity consumption relate to pre-pandemic sensitivity to holidays, suggesting a new direction for future research.

The death spiral hypothesis in electric utility represents a positive feedback phenomenon in which a regulated utility is driven to financial instability by rising prices and declining demand. We establish conditions for the existence of death spiral and conditions of stable adoption of distributed energy resources. We show in particular that linear tariffs always induce death spiral when the fixed operating cost of the utility rises beyond a certain threshold. For two-part tariffs with connection and volumetric charges, the Ramsey pricing, which myopically optimizes social welfare subject to the revenue adequacy constraint, induces a stable equilibrium. The Ramsey pricing, however, inhibits renewable adoption with a high connection charge. In contrast, a two-part tariff with a small connection charge results in a stable adoption process with a higher level of renewable adoption and greater long-term total consumer surplus. Market data are used to illustrate various solar adoption scenarios.