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Electric power and broadband have become essential services for modern economies, but utilities face substantial challenges in providing disruption-free access. Recent legislation, including the US Infrastructure Investment and Jobs Act of 2021, has allocated enormous resources toward improving infrastructure systems. Historically, undergrounding has enhanced system reliability but has been cost effective only in densely populated areas. We investigate the conditions under which undergrounding becomes cost effective, particularly when co-deployed with fiber optic lines. We introduce a novel data-driven cost-benefit model and conduct a detailed localized case study in Shrewsbury, Massachusetts. The results indicate that when undergrounding is viable, aggressively co-undergrounding yields the highest net benefit. This finding is robust across various assumptions. Importantly, our model highlights the importance of assumptions regarding undergrounding’s effectiveness in reducing outages. Our model is readily deployable to other study areas, providing effective decision-making capabilities even with limited data. 

The rapid expansion of intermittent grid-tied solar capacity is making the job of balancing electricity's real-time supply and demand increasingly challenging. To address the problem, recent work proposes mechanisms for actively controlling solar power output to the grid by enabling software to cap it as a fraction of its time-varying maximum output. Utilities can use these mechanisms to dynamically share the grid's solar capacity by controlling the solar output at each site. However, while enforcing an equal fraction of each solar site's time-varying maximum output results in "fair" short-term contributions of solar power, it does not result in "fair" long-term contributions of solar energy. This discrepancy arises from fundamental differences in enforcing "fair" access to the grid to contribute solar energy, compared to analogous fair-sharing in networks and processors. In this paper, we present a centralized and distributed algorithm to enable control of distributed solar capacity that enforces fair grid energy access. We implement our algorithm and evaluate it on synthetic data and real data across 18 solar sites. We show that traditional rate allocation, which enforces equal rates, results in solar sites contributing up to 18.9% less energy than an algorithm that enforces fair grid energy access over a single month. 

Abstract The Surface Water and Ocean Topography (SWOT) satellite has the potential to transform global hydrologic science by offering simultaneous and synoptic estimates of river discharge and other hydraulic variables. Discharge is estimated from SWOT observations of water surface elevation, width, and slope. A first assessment using just the highest quality SWOT measurements, over the first 15 months (March 2023–July 2024) of the mission evaluated at 65 gauged reaches shows results consistent with pre‐launch expectations. SWOT estimates track discharge dynamics without relying on any gauge information: median correlation is 0.73, with a correlation interquartile range of 0.51–0.89. SWOT estimates capture discharge magnitude correctly in some cases but are biased (median bias is 50%) in others. There are already a total of 11,274 ungauged global locations with highest quality SWOT measurements where SWOT discharge is expected to accurately track discharge variations: this value will increase as SWOT data record length grows, algorithms are refined and SWOT measurements are reprocessed. This first look indicates that SWOT discharge is performing as expected for SWOT data that achieve performance requirements, providing observed information on discharge variations in ungauged basins globally.