Renewable Energy Analysis: Geospatial Analysis and Maps (U.S.)
This presentation provides an overview of renewable energy levelized cost of energy using geospatial calculations and maps.
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This presentation provides an overview of renewable energy levelized cost of energy using geospatial calculations and maps.
Many advances have been made in building technology to help save energy, but influencing the behavior of the occupants is still necessary to achieve low-energy use targets. One of the most practical ways to influence and change occupant behaviors is through incentives. Developing incentives for energy-saving and quantifying the impact of occupant behaviors are both active areas of research. Here, we propose a data analytics framework for detecting changes in occupant behaviors, which will help build an analytics feedback loop from behavior impact to incentive design. The framework has two major parts. The first forecasts energy consumption for each occupant, while the second determines a probability distribution for changes in energy consumption. The parts are interchangeable with other existing machine learning and statistical methods. A specific instantiation of the framework, using kernel ridge-regression for forecasting and k-means to find an empirical behavior distribution, is described in detail. An HVAC use-case with 5 different incentivized behaviors is used as an example to show that the framework can detect behavior changes induced by incentives. Furthermore, we show that some simpler behavior-change detection methods do not work, further justifying the use of advanced analytics.
Building retrofit is one of the key strategies for cities to reduce energy use and GHG emissions. The historical information about changes to buildings is crucial to infer the buildings’ current energy system efficiency levels and to identify candidate buildings for retrofit. In general, a building permit is required before the start of any construction activity of a building, such as changing building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Therefore, the permits are a potentially good resource for mining information on the city’s retrofit history. In this study, we use the permit dataset from the city of San Francisco as a case study. Location and time information from the dataset is also used to depict the retrofit timeline of each building and the whole building stock. The type of work of the permit is inferred from the descriptive text by a machine learning model. At last, the limitations of the current permit dataset and potential improvements on the permit data management are discussed to better utilize the information in the future.
In view of the great interest in liquid argon neutrino detectors, the 40 Ar(γ,γ') 40 Ar reaction was revisited to guide a calculation of the neutral current neutrino cross section at supernova energies. Using the nuclear resonance fluorescence technique with a monoenergetic, 99% linearly polarized photon beam, in this work we report a three-fold increase in magnetic dipole strength at around 10 MeV in 40 Ar. Based on shell-model calculations, and using the experimentally identified transitions, the neutral current neutrino cross sections for low-energy reactions on 40 Ar are calculated.
Abstract We in-situ observe the ultrafast dynamics of trapped carriers in organic methyl ammonium lead halide perovskite thin films by ultrafast photocurrent spectroscopy with a sub-25 picosecond time resolution. Upon ultrafast laser excitation, trapped carriers follow a phonon assisted tunneling mechanism and a hopping transport mechanism along ultra-shallow to shallow trap states ranging from 1.72–11.51 millielectronvolts and is demonstrated by time-dependent and independent activation energies. Using temperature as an energetic ruler, we map trap states with ultra-high energy resolution down to < 0.01 millielectronvolt. In addition to carrier mobility of ~4 cm 2 V −1 s −1 and lifetime of ~1 nanosecond, we validate the above transport mechanisms by highlighting trap state dynamics, including trapping rates, de-trapping rates and trap properties, such as trap density, trap levels, and capture-cross sections. In this work we establish a foundation for trap dynamics in high defect-tolerant perovskites with ultra-fast temporal and ultra-high energetic resolution.
Previous measurements of neutron energy using fast plastic scintillators while operating the Fusion Z Pinch Experiment (FuZE) constrained the energy of any yield-producing deuteron beams to less than 4.65 keV. FuZE has since been operated at increasingly higher input power, resulting in increased plasma current and larger fusion neutron yields. A detailed experimental study of the neutron energy isotropy in these regimes applies more stringent limits to possible contributions from beam-target fusion. The FuZE device operated at -25 kV charge voltage has resulted in average plasma currents of 370 kA and D–D fusion neutron yields of $4\times10^7 \pm 4\times10^6$ neutrons per discharge. Measurements of the neutron energy isotropy under these operating conditions demonstrates the energy of deuteron beams is less than $7.4 \pm 5.6^\mathrm{(stat)} \pm 3.7^\mathrm{(syst)}$ keV. Characterization of the detector response has reduced the number of free parameters in the fit of the neutron energy distribution, improving the confidence in the forward-fit method. Gamma backgrounds have been measured and the impact of these contributions on the isotropy results have been studied. Additionally, a time dependent measurement of the isotropy has been resolved for the first time, indicating increases to possible deuteron beam energies at late times. This suggests the possible growth of m = 0 instabilities at the end of the main radiation event but confirms that the majority of the neutron production exhibits isotropy consistent with thermonuclear origin.
Electric motors in commercial and industrial applications account for almost a third of total electric grid load in the United States. With these applications representing such a significant level of energy use, increasing efficiency in these applications can help substantially increase resilience and lower total energy costs. This report examines stationary high-efficiency electric motor technologies and their supply chains.
The 2022 IPCC Sixth Assessment Report tells a worrying tale of global warming that temperatures will exceed beyond a 1.5 °C increase in the near future. Higher global temperatures result in more frequent and extreme weather events, irreversible ecosystem changes, and will contribute to detrimental impacts on human health. Anthropogenic greenhouse gas emissions— including those from burning fossil fuels for energy use—are the primary drivers of climate change. Organizations like the U.S Department of Energy’s (DOE) Office of Fossil Energy and Carbon Management (FECM) are working to decrease the environmental impacts of fossil fuels as society works towards net-zero emissions. To complete this goal, society must reduce its dependence on fossil fuels and move towards cleaner energy sources such as biofuels.
As part of a longer-term energy efficiency and solar development program on the Coeur d'Alene reservation, the Coeur d'Alene Tribe completed energy efficiency measures and deployed a solar photovoltaic system at its Senior Housing Complex. The project generated nearly 68,000 kWh of solar energy in its first year, and reduced energy used at the senior housing complex by 95,192 kWh, saving $7,144.56 in energy costs, and saving 48.1 metric tons of carbon dioxide.
A‐STEP is a first‐of‐its‐kind, integrated, open‐source software tool aimed at guiding freight rail decarbonization decision‐making. It has tools for studying energy use details for individual trains, networks of trains, battery and hydrogen charging stations, national energy sourcing and pricing, and overall decarbonization costs and environmental impacts. It gives analysts an ability to study the challenges of making such change happen. Completely amenable to analyst specified inputs and parameter values, it can be customized to provide outputs for a wide variety of assumptions about future energy conditions and technological advances. Written in Python, C++, and VB.Net, A‐STEP can be implemented on both Windows and Linux‐based platforms.
The expected rapid growth in electric vehicle deployment will inevitably be followed by a corresponding rise in the supply of end-of-life vehicles and their lithium-ion batteries (LIBs). The batteries may be reused, but will eventually be spent and provide a potential domestic resource that can help supply materials for future battery production. However, commercial recycling processes depend on profits from recovery of cobalt, use of which is being reduced in new cathode chemistries. The U.S. Department of Energy, therefore, established the ReCell Center in early 2019 to develop robust LIB recycling technology that would be economical even for batteries that contain no cobalt. The central feature of the technology is recovery of the cathode material with its unique crystalline cathode morphology intact in order to retain its value and functionality. Other materials are recovered as well in order to maximize revenues and minimize waste-handling costs. Analysis and modeling serve to evaluate and compare process options so that we can identify those that will be most economical while still minimizing energy use and environmental impacts. This paper provides background and describes highlights of the center’s first 2 years of operation.
Building occupancy significantly impacts energy use, timing for demand impacts, and is a significant source of uncertainty in building energy models. There are relatively few sources that define building occupancy schedules and number of occupants per building or space type. More importantly, these sources define traditional schedules that are likely not to reflect the true occupancy of a given building. We construct traffic-based occupancy schedules which are more responsive to changes in mobility patterns, and which can realistically estimate occupant arrivals, departures, and counts in individual buildings.
Energy consumption in residential buildings is primarily driven by space conditioning applications. Space heating and cooling, on average, consume approximately 50% of the energy in the residential buildings in the U.S. The primary energy use due to infiltration is more than 2.8 Quads, which is 29% of primary energy consumption attributable to fenestration and building envelope components in residential buildings in US in 2010. There are advanced air barrier technologies and construction practices to reduce air leakage in buildings, which are currently available in the market. However, the lack of adequate information on their impact on energy consumption and the durability of buildings has caused the slow adoption of these technologies and methods. In the past, the authors developed an online calculator that estimates the potential energy and cost savings in major U.S., Canadian and Chinese cities from improvement in airtightness in commercial buildings. In 2018–2019, the calculator was expanded to add moisture transfer calculations, given that air leakage through the building envelope can have a significant impact on moisture transfer. The calculator is again being expanded by adding residential and additional commercial building data. In this paper, we present the impact of airtightness in residential buildings on energy consumption and moisture transfer. The study includes the analysis of airtightness in 52 major cities in the U.S. and five cities in Canada on a residential building that includes a crawlspace and has a gas furnace.
Data set including 17 fields of information for 141,506,665 U.S. buildings. Advantages of this dataset include: Resolution - actionable data and models for individual U.S. buildings; Flexibility - ability to modify data, models, and analysis for any simulation-informed use case; Scalability - dynamic archetypes can be used as representative models for any geography of interest; Accuracy - error rates for total energy use at city-scale of 1-5%
The heats of formation of carbonate, bicarbonate and bicarbonate/hydroxide metal complexes and hydrated versions these complexes of Mg 2+ , Ca 2+ , Fe 2+ , and Cd 2+ are predicted from atomization energies using correlated molecular orbital theory at the CCSD(T) level extrapolated to the complete basis set limit following the Feller-Peterson-Dixon (FPD) approach. Using the calculated gas phase values and the available experimental solid state values, the cohesive energies of selected minerals were calculated. The gas phase decomposition energies into MO, CO 2 and H 2 O follow the order Mg ~ Ca > Cd ~ Fe, and correlate with the hardness of the metal +2 ions. Gas phase hydration energies show that the order is Mg > Fe > Ca ~ Cd. There are a number of bulk hydrated Mg and Ca complexes that occur as minerals but there are few if any for Fe and Cd, suggesting that a number of factors are important in determining the stability of the bulk mineral hydrates. The FPD heats of formation were used to benchmark a range of density functional theory exchange-correlation functionals, including those commonly used in solid state mineral calculations. None of the functionals provided chemical accuracy agreement (± 1 kcal/mol) with the FPD results The best functionals are ωB97X and ωB97X-D with mean average unsigned errors of 10 kcal/mol.
This study examined the use of zoned ventilation systems using a coupled CONTAM/EnergyPlus model for new California dwellings. Several smart control strategies were developed with a target of halving ventilation-related energy use, largely through reducing dwelling ventilation rates based on zone occupancy. The controls were evaluated based on the annual energy consumption relative to continuously operating non-zoned, code-compliant mechanical ventilation systems. The systems were also evaluated from an indoor air quality perspective using the equivalency approach, where the annual personal concentration of a contaminant for a control strategy is compared to the personal concentration that would have occurred using a continuously operating, non-zoned system. Individual occupant personal concentrations were calculated for the following contaminants of concern: moisture, CO2, particles, and a generic contaminant. Zonal controls that saved energy by reducing outside airflow achieved typical reductions in ventilation-related energy of 10% to 30%, compared to the 7% savings from the unzoned control. However, this was at the expense of increased personal concentrations for some contaminants in most cases. In addition, care is required in the design and evaluation of zonal controls, because control strategies may reduce exposure to some contaminants, while increasing exposure to others.
High-performance districts and communities offer opportunities for reducing energy use, emissions, and costs, and can be instrumental in helping cities achieve their climate goals. The design of such communities requires identification of opportunities early on and their re-evaluation throughout the planning process. There is a need for energy modeling tools that connect 3D Computer-Aided Design (CAD) platforms to simulation engines, enabling detailed energy analysis of districts within the workflows and tools used by practitioners. This paper introduces the Dragonfly and URBANoptTM combined toolset that supports the creation of urban models from a range of geometry formats typically used by designers and planners, and provides an integrated pathway to simulate district-scale energy systems. The toolset is piloted by a global architecture and master planning firm to evaluate several key urban-scale technical questions for the design of a district in Chicago. The findings indicate that, while energy savings can be achieved through traditional architectural studies and enhancements to individual building efficiency, the modeling toolset helps identify additional savings and insights that can be achieved when considering district-scale energy systems. Finally, this study demonstrates how the Dragonfly/URBANopt toolset can integrate with master planning workflows, thereby enabling an iterative performance-based design process.
Shading and daylighting systems affect cooling, heating, and lighting energy use by modulating solar radiation through the building façade. Characterizing shading systems holistically and accurately helps designers and engineers evaluate shading systems to achieve energy and non-energy performance goals. These complex fenestration systems can be modeled using Bidirectional Scattering Distribution Functions (BSDF), which map incident radiation to hemispherical distributions of outgoing radiation. Data-driven, tabulated BSDFs are derived from interpolated goniophotometer measured data, then sampled during the raytracing calculation. A peak extraction (PE) algorithm was developed to circumvent limits in BSDF angular resolution, where the specular peak is extracted during simulation by evaluating the BSDF in the through direction and surrounding region. The objective of this study was to validate this measurement and modeling workflow using field monitored data from a full scale testbed with eleven installed fabrics of different weaves, openness factors, and colors and assess the accuracy of the workflow under different adaptation and contrast conditions. Test conditions were limited to clear sky conditions with the sun in the field of view. Results showed that, for tensor tree datasets, vertical illuminance, solar luminance (2.5° apex), and daylight glare probability (DGP) were predicted to within a mean bias error (MBE) error of -456 lx (-12.3%), -3.46e5 (-38.4%), and -0.042 (-7.8%) when full PE occurred. With a binary classification of glare/ no glare, DGP was predicted accurately with a true positive rate of 0.98 and true negative rate of 1.0 using tensor tree data and less accurately with Klems BSDF data, particularly for cases of no glare. The workflow may be of insufficient accuracy to distinguish borderline performance between fabrics using the four-point glare scale, particularly under low adaptation, high contrast daylit conditions. Errors were due to reductions in peak shape and intensity across the BSDF interpolation and data reduction workflow. Future work is needed to better preserve measurement fidelity during interpolation and sampling, which in turn will improve PE performance.