Modeling and design of solar + storage-powered community resilience hubs across California.

Distributed clean, reliable energy resources like solar plus battery storage (solar + storage) can reduce harmful emissions while supporting resilience. Solar + storage-powered resilience hubs provide energy for critical services during disasters while increasing human adaptive capacity year round. We studied where utility rates, local climate, and historical injustice make solar + storage resilience hubs more valuable and more challenging. We modeled the economic and climate impacts of outfitting candidate hub sites across California with solar + storage for everyday operations and identified designs and costs required to withstand a range of outages considering weather impacts on energy needs and availability. We integrated sociodemographic data to prioritize the siting of resilience hubs, to focus potential policy and funding priorities on regions where solar + storage for resilience hubs is hard or expensive, and where populations are most in need. We identified almost 20,000 candidate buildings with more than 8 GW of total rooftop solar potential capable of reducing CO2 emissions by 5 million tons per year while providing energy for community resilience. Hub capacity for one of the most challenging missions-providing emergency shelter during a power outage and smoke event-could have a statewide average lifetime cost of less than $2000 per seat. We identified regional challenges including insufficient rooftop solar capacity in cities, low sunlight in northern coastal California, and high costs driven by utility rate structures in Sacramento and the Imperial Valley. Results show that rates and net metering rules that incentivize solar + storage during everyday operations decrease resilience costs.

Density-functional fluctuation theory of crowds.

A primary goal of collective population behavior studies is to determine the rules governing crowd distributions in order to predict future behaviors in new environments. Current top-down modeling approaches describe, instead of predict, specific emergent behaviors, whereas bottom-up approaches must postulate, instead of directly determine, rules for individual behaviors. Here, we employ classical density functional theory (DFT) to quantify, directly from observations of local crowd density, the rules that predict mass behaviors under new circumstances. To demonstrate our theory-based, data-driven approach, we use a model crowd consisting of walking fruit flies and extract two functions that separately describe spatial and social preferences. The resulting theory accurately predicts experimental fly distributions in new environments and provides quantification of the crowd "mood". Should this approach generalize beyond milling crowds, it may find powerful applications in fields ranging from spatial ecology and active matter to demography and economics.