This perspective article introduces the Harvard Clean Energy Project (CEP), a theory-driven search for the next generation of organic solar cell materials. We give a broad overview of its setup and infrastructure, present first results, and outline upcoming developments.CEP has established an automated, high-throughput, in silico framework to study potential candidate structures for organic photovoltaics. The current project phase is concerned with the characterization of millions of molecular motifs using first-principles quantum chemistry. The scale of this study requires a correspondingly large computational resource which is provided by distributed volunteer computing on IBM's World Community Grid. The results are compiled and analyzed in a reference database and will be made available for public use. In addition to finding specific candidates with certain properties, it is the goal of CEP to illuminate and understand the structure-property relations in the domain of organic electronics. Such insights can open the door to a rational and systematic design of future high-performance materials. The computational work in CEP is tightly embedded in a collaboration with experimentalists, who provide valuable input and feedback to the project.
Forecasting solar energy generation is a challenging task because of the variety of solar power systems and weather regimes encountered. Inaccurate forecasts can result in substantial economic losses and power system reliability issues. One of the key challenges is the unavailability of a consistent and robust set of metrics to measure the accuracy of a solar forecast. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, and applications) that were developed as part of the U.S. Department of Energy SunShot Initiative's efforts to improve the accuracy of solar forecasting. In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design-of-experiments methodology in conjunction with response surface, sensitivity analysis, and nonparametric statistical testing methods. The three types of forecasting improvements are (i) uniform improvements when there is not a ramp, (ii) ramp forecasting magnitude improvements, and (iii) ramp forecasting threshold changes. Day-ahead and 1-hourahead forecasts for both simulated and actual solar power plants are analyzed. The results show that the proposed metrics can efficiently evaluate the quality of solar forecasts and assess the economic and reliability impacts of improved solar forecasting. Sensitivity analysis results show that (i) all proposed metrics are suitable to show the changes in the accuracy of solar forecasts with uniform forecasting improvements, and (ii) the metrics of skewness, kurtosis, and Rényi entropy are specifically suitable to show the changes in the accuracy of solar forecasts with ramp forecasting improvements and a ramp forecasting threshold.
Using planewave pseudopotential density functional theory and classical molecular dynamics simulations, we investigate the transport of noble gases through a family of two-dimensional hydrocarbon polymer membranes that generalize the “porous graphene” (PG) material synthesized by Bieri et al. by insertion of (E)-stilbene (ES) groups. We find that density functional theory overestimates the barrier height and empirical dispersion corrections underestimate the barrier height, compared to reference MP2/cc-pVTZ calculations on PG. The barrier height for noble gas transport is greatly reduced from PG to PG-ES1, but additional increases in the size of the pore in PG-ES2 and PG-ES3 lead to an attractive potential well instead of a repulsive barrier. Using the computed potential energy surfaces, we compute pressure- and temperature-driven tunneling probabilities of He isotopes, and refit an improved classical force-field. Using classical molecular dynamics simulations, we find that PG-ES1 has an He permeance of 6 × 106 GPU, which is 90 times greater than that of PG, and demonstrate high selectivity for He versus CH4, Ar, and CO2. These results indicate that PG-ES1 is a promising membrane material for separating He from natural gas, and separating He isotopes by tunneling differences.
Persistent social disparities in the adoption of distributed energy resources (DERs) have prompted calls for enabling more equitable uptake. However, there are indications that limits inherent to grid infrastructure may hinder DER adoption. In this study we analysed grid limits to new DER integration across California's two largest utility territories. We found that grid limits reduce access to solar photovoltaics to less than half of households served by these two utilities, and may hinder California's electric vehicle adoption and residential load electrification goals. We connected these results to demographic characteristics and found that grid limits also exacerbate existing inequities: households in increasingly Black-identifying and disadvantaged census block groups have disproportionately less access to new solar photovoltaic capacity based on circuit hosting capacity. Our results illuminate the need for equity goals to be an input in the design of policies for prioritizing grid upgrades.
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