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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Probabilistic reliability assessment and case studies for predicted energy savings in residential buildings

This study aims to (1) investigate the key influential parameters (KIPs) in estimating the uncertainty of energy savings using a residential building energy simulation model and (2) perform uncertainty quantification for energy savings for several different scenarios. The proposed methodology was successfully applied to the calculation of uncertainties associated with residential energy retrofits using two test houses designed for pre- and post-retrofit cases. Uncertainties were determined using basic parameters that might be supplied to an energy model and then reevaluated based on an audit of the KIPs identified, resulting in substantially reduced uncertainty. Of four different scenarios, the most uncertain scenario estimated the annual energy savings from the retrofit would be between 18% and 51% at a 95% confidence level, and the least uncertain scenario estimated the annual savings would be between 26% and 40% at a 95% confidence level. The actual measured annual savings from the two test houses was 28%, which shows an agreement with the uncertainty analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

A probabilistic perspective on thermodynamic parameter uncertainties: Understanding aqueous speciation of mercury

Speciation plays an important role in determining the fate and transport of metals in terrestrial surface and subsurface systems. Equilibrium speciation modeling in aqueous systems relies on thermodynamic constants (log K values) of complexes, which are subject to uncertainties. In this study, using Monte Carlo (MC) simulations with Latin hypercube sampling (LHS) we systematically analyze the propagation of thermodynamic constant uncertainty through speciation modeling of an inorganic mercury-sulfide-chloride-water system. We find that seemingly small variances of the input log K normal distributions can lead to output species concentrations spanning multiple orders of magnitude, with highly skewed probability distributions. When equilibrium with mineral metacinnabar (β-HgS(s)) is neglected, the relative uncertainty of each output species is strongly positively correlated with the skewness of its concentration probability distribution, i.e., the lowest uncertainty occurs when the species concentration probability distribution is the most negatively skewed as its concentration approaches the total element concentration limit. The highest uncertainty in the identity of dominant species is located around species equivalence points. For cases where the mineral equilibrium is included, we derive analytical probability density functions for the log concentrations of all major species in the system. The mineral log K uncertainty is found to be an important contributor to all output concentration uncertainties. The analysis of combined effects of both pH and total sulfide concentration on output concentration uncertainties shows that high concentration uncertainties occur under highly sulfidic alkaline conditions and low uncertainties at low pH and sulfide concentrations. Finally, an analysis as presented here can distinguish between conditions that require a full uncertainty analysis and those for which the classical deterministic speciation modeling suffices.

58 GEOSCIENCES↗
DOE OSTI2017

A probabilistic application of oil and gas data for exploration stage geothermal reservoir assessment in the Appalachian Basin

Geothermal energy is a renewable, widespread, baseload energy source that has the potential to supply a large portion of the world’s energy needs. High development risk, specifically high drilling costs combined with uncertainty in subsurface flow properties, have hampered growth of geothermal energy development. One option to reduce subsurface risk is to target locations where reservoir data are already available, including sedimentary basins where there has been extensive hydrocarbon production. Sedimentary basins are widespread and often have suitable geothermal gradients. This chapter presents a low-cost methodology that can be used at the pre-drilling exploration phase of a low-temperature geothermal project to predict the location of high-productivity and low-risk reservoirs, applied to a case study of the Appalachian Basin in the eastern United States. Because only averaged reservoir data were available for analysis, the technique uses a Monte Carlo simulation of the Reservoir Flow Capacity (RFC) and Reservoir Productivity Index (RPI) for over 1800 individual reservoirs, using liquid water or supercritical carbon dioxide (sCO 2 ) as the reservoir fluid. RFC is used as an initial screening tool to identify high-priority geologic formations and reservoirs. Here our results indicate that 99% of the reservoirs in the basin are of insufficient quality for geothermal heat production with water as the assumed fluid and without using stimulation technologies associated with Enhanced Geothermal Systems (EGS). RPI is also used as an alternative metric to identify high-productivity reservoirs. When comparing the RPI results to natural gas production volumes from several selected reservoirs, the predicted RPI proves to be a good estimate of flow potential for porous reservoirs, but overestimates flow by a factor of three for fractured or vuggy reservoirs. A greater uncertainty ought to be utilized when applying the RPI equation to non-porous media, therefore we recommend utilizing the RPI metric when higher resolution data are available for analysis. This methodology can be utilized in other basins that have experienced hydrocarbon exploration and production, to highlight low-risk reservoirs or geologic formations for further geothermal development.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Probabilistic cost estimation methods for treatment of water extracted during CO 2 storage and EOR

Extraction and treatment of in situ water can minimize risk for large-scale CO 2 injection in saline aquifers during carbon capture, utilization, and storage (CCUS), and for enhanced oil recovery (EOR). Additionally, treatment and reuse of oil and gas produced waters for hydraulic fracturing will conserve scarce fresh-water resources. Each treatment step, including transportation and waste disposal, generates economic and engineering challenges and risks; these steps should be factored into a comprehensive assessment. We expand the water treatment model (WTM) coupled within the sequestration system model CO 2 -PENS and use chemistry data from seawater and proposed injection sites in Wyoming, to demonstrate the relative importance of different water types on costs, including little-studied effects of organic pretreatment and transportation. We compare the WTM with an engineering water treatment model, utilizing energy costs and transportation costs. Specific energy costs for treatment of Madison Formation brackish and saline base cases and for seawater compared closely between the two models, with moderate differences for scenarios incorporating energy recovery. Transportation costs corresponded for all but low flow scenarios (<5000 m 3 /d). Some processes that have high costs (e.g., truck transportation) do not contribute the most variance to overall costs. Other factors, including feed-water temperature and water storage costs, are more significant contributors to variance. These results imply that the WTM can provide good estimates of treatment and related process costs (AACEI equivalent level 5, concept screening, or level 4, study or feasibility), and the complex relationships between processes when extracted waters are evaluated for use during CCUS and EOR site development.

20 FOSSIL-FUELED POWER PLANTS↗