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Continued advances in technology have led to falling costs and a dramatic increase in the aggregate amount of solar capacity installed across the world. A drawback of increased solar penetration is the potential for supply-demand mismatches in the grid due to the intermittent nature of solar generation. While energy storage can be used to mask such problems, we argue that there is also a need to explicitly control the rate of solar generation of each solar array in order to achieve high penetration while also handling supply-demand mismatches. To address this issue, we present the notion of smart solar arrays that can actively modulate their solar output based on the notion of proportional fairness. We present a decentralized algorithm based on Lagrangian optimization that enables each smart solar array to make local decisions on its fair share of solar power it can inject into the grid and then present a sense-broadcast-respond protocol to implement our decentralized algorithm into smart solar arrays. We also study the benefits of using energy storage when we rate control solar. To do so, we present a decentralized algorithm to charge and discharge batteries for each smart solar. Our evaluation on a city-scale dataset shows that our approach enables 2.6× more solar penetration while causing smart arrays to reduce their output by as little as 12.4%. By employing an adaptive gradient approach, our decentralized algorithm has 3 to 30× faster convergence. Finally, we demonstrate energy storage can help netmeter more solar energy while ensuring fairness and grid constraints are met. 

Although photothermal imaging was originally designed to detect individual molecules that do not emit or small nanoparticles that do not scatter, the technique is now being applied to image and spectroscopically characterize larger and more sophisticated nanoparticle structures that scatter light strongly. Extending photothermal measurements into this regime, however, requires revisiting fundamental assumptions made in the interpretation of the signal. Herein, we present a theoretical analysis of the wavelength-resolved photothermal image and its extension to the large particle scattering regime, where we find the photothermal signal to inherit a nonlinear dependence upon pump intensity, together with a contraction of the full-width-at-half-maximum of its point spread function. We further analyze theoretically the extent to which photothermal spectra can be interpreted as an absorption spectrum measure, with deviations between the two becoming more prominent with increasing pump intensities. Companion experiments on individual 10, 20, and 100 nm radius gold nanoparticles evidence the predicted nonlinear pump power dependence and image contraction, verifying the theory and demonstrating new aspects of photothermal imaging relevant to a broader class of targets. 

Solvated electrons are powerful reducing agents capable of driving some of the most energetically expensive reduction reactions. Their generation under mild and sustainable conditions remains challenging though. Using near-ultraviolet irradiation under low-intensity one-photon conditions coupled with electrochemical and optical detection, we show that the yield of solvated electrons in water is increased more than 10 times for nanoparticle-decorated electrodes compared to smooth silver electrodes. Based on the simulations of electric fields and hot carrier distributions, we determine that hot electrons generated by plasmons are injected into water to form solvated electrons. Both yield enhancement and hot carrier production spectrally follow the plasmonic near-field. The ability to enhance solvated electron yields in a controlled manner by tailoring nanoparticle plasmons opens up a promising strategy for exploiting solvated electrons in chemical reactions. 

Residential solar installations are becoming increasingly popular among homeowners. However, renters and homeowners living in shared buildings cannot go solar as they do not own the shared spaces. Community-owned solar arrays and energy storage have emerged as a solution, which enables ownership even when they do not own the property or roof. However, such community-owned systems do not allow individuals to control their share for optimizing a home’s electricity bill. To overcome this limitation, inspired by the concept of virtualization in operating systems, we propose virtual community-owned solar and storage—a logical abstraction to allow individuals to independently control their share of the system. We argue that such individual control can benefit all owners and reduce their reliance on grid power. We present mechanisms and algorithms to provide a virtual solar and battery abstraction to users and understand their cost benefits. In doing so, our comparison with a traditional community-owned system shows that our AutoShare approach can achieve the same global savings of 43% while providing independent control of the virtual system. Further, we show that independent energy sharing through virtualization provides an additional 8% increase in savings to individual owners.

 

While ride-sharing has emerged as a popular form of transportation in urban areas due to its on-demand convenience, it has become a major contributor to carbon emissions, with recent studies suggesting it is 47% more carbon-intensive than personal car trips. In this paper, we examine the feasibility, costs, and carbon benefits of using electric bike-sharing—a low carbon form of ride-sharing—as a potential substitute for shorter ride-sharing trips, with the overall goal of greening the ride-sharing ecosystem. Using public datasets from New York City, our analysis shows that nearly half of the taxi and rideshare trips in New York are shorts trips of less than 3.5km, and that biking is actually faster than using a car for ultra-short trips of 2km or less. We analyze the cost and carbon benefits of different levels of ride substitution under various scenarios. We find that the additional bikes required to satisfy increased demand from ride substitution increases sub-linearly and results in 6.6% carbon emission reduction for 10% taxi ride substitution. Moreover, this reduction can be achieved through a hybrid mix that requires only a quarter of the bikes to be electric bikes, which reduces system costs. We also find that expanding bike-share systems to new areas that lack bike-share coverage requires additional investments due to the need for new bike stations and bike capacity to satisfy demand but also provides substantial carbon emission reductions. Finally, frequent station repositioning can reduce the number of bikes needed in the system by up to a third for a minimal increase in carbon emissions of 2% from the trucks required to perform repositioning, providing an interesting tradeoff between capital costs and carbon emissions. 

While ride-sharing has emerged as a popular form of transportation in urban areas due to its on-demand convenience, it has become a major contributor to carbon emissions, with recent studies suggesting it is 47% more carbon-intensive than personal car trips. In this paper, we examine the feasibility, costs, and carbon benefits of using electric bike-sharing---a low carbon form of ride-sharing---as a potential substitute for shorter ride-sharing trips, with the overall goal of greening the ride-sharing ecosystem. Using public datasets from New York City, our analysis shows that nearly half of the taxi and rideshare trips in New York are shorts trips of less than 3.5km, and that biking is actually faster than using a car for ultra-short trips of 2km or less. We analyze the cost and carbon benefits of different levels of ride substitution under various scenarios. We find that the additional bikes required to satisfy increased demand from ride substitution increases sub-linearly and results in 6.6% carbon emission reduction for 10% taxi ride substitution. Moreover, this reduction can be achieved through a hybrid mix that requires only a quarter of the bikes to be electric bikes, which reduces system costs. We also find that expanding bike-share systems to new areas that lack bike-share coverage requires additional investments due to the need for new bike stations and bike capacity to satisfy demand but also provides substantial carbon emission reductions. Finally, frequent station repositioning can reduce the number of bikes needed in the system by up to a third for a minimal increase in carbon emissions of 2% from the trucks required to perform repositioning, providing an interesting tradeoff between capital costs and carbon emissions. 

Buildings consume over 40% of the total energy in modern societies, and improving their energy efficiency can significantly reduce our energy footprint. In this article, we present WattScale, a data-driven approach to identify the least energy-efficient buildings from a large population of buildings in a city or a region. Unlike previous methods such as least-squares that use point estimates, WattScale uses Bayesian inference to capture the stochasticity in the daily energy usage by estimating the distribution of parameters that affect a building. Further, it compares them with similar homes in a given population. WattScale also incorporates a fault detection algorithm to identify the underlying causes of energy inefficiency. We validate our approach using ground truth data from different geographical locations, which showcases its applicability in various settings. WattScale has two execution modes—(i) individual and (ii) region-based, which we highlight using two case studies. For the individual execution mode, we present results from a city containing >10,000 buildings and show that more than half of the buildings are inefficient in one way or another indicating a significant potential from energy improvement measures. Additionally, we provide probable cause of inefficiency and find that 41%, 23.73%, and 0.51% homes have poor building envelope, heating, and cooling system faults, respectively. For the region-based execution mode, we show that WattScale can be extended to millions of homes in the U.S. due to the recent availability of representative energy datasets.