Publications

Abstract. Other Test Method 33A (OTM 33A) is a near-source flux measurement method developed by the Environmental Protection Agency (EPA) primarily used to locate and estimate emission fluxes of methane from oil and gas (O&G) production facilities without requiring site access. A recent national estimate of methane emissions from O&G production included a large number of flux measurements of upstream O&G facilities made using OTM 33A and concluded the EPA National Emission Inventory underestimates this sector by a factor of ∼2.1 (Alvarez et al., 2018). The study presented here investigates the accuracy of OTM 33A through a series of test releases performed at the Methane Emissions Technology Evaluation Center (METEC), a facility designed to allow quantified amounts of natural gas to be released from decommissioned O&G equipment to simulate emissions from real facilities (Fig. 1). This study includes test releases from single and multiple points, from equipment locations at different heights, and spanned methane release rates ranging from 0.16 to 2.15 kg h−1. Approximately 95 % of individual measurements (N=45) fell within ±70 % of the known release rate. A simple linear regression of OTM 33A versus known release rates at the METEC site gives an average slope of 0.96 with 95 % CI (0.66,1.28), suggesting that an ensemble of OTM 33A measurements may have a small but statistically insignificant low bias.

Methane emissions from the oil and gas industry (O&G) and other sources in the Barnett Shale region were estimated by constructing a spatially resolved emission inventory. Eighteen source categories were estimated using multiple data sets, including new empirical measurements at regional O&G sites and a national study of gathering and processing facilities. Spatially referenced activity data were compiled from federal and state databases and combined with O&G facility emission factors calculated using Monte Carlo simulations that account for high emission sites representing the very upper portion, or fat-tail, in the observed emissions distributions. Total methane emissions in the 25-county Barnett Shale region in October 2013 were estimated to be 72,300 (63,400–82,400) kg CH4 h–1. O&G emissions were estimated to be 46,200 (40,000–54,100) kg CH4 h–1 with 19% of emissions from fat-tail sites representing less than 2% of sites. Our estimate of O&G emissions in the Barnett Shale region was higher than alternative inventories based on the United States Environmental Protection Agency (EPA) Greenhouse Gas Inventory, EPA Greenhouse Gas Reporting Program, and Emissions Database for Global Atmospheric Research by factors of 1.5, 2.7, and 4.3, respectively. Gathering compressor stations, which accounted for 40% of O&G emissions in our inventory, had the largest difference from emission estimates based on EPA data sources. Our inventory's higher O&G emission estimate was due primarily to its more comprehensive activity factors and inclusion of emissions from fat-tail sites.

The Covid-19 pandemic had wide-ranging effects on how people lived, worked and learned. Consequently, electricity use was altered from pre-pandemic patterns. The deviation from expected electricity usage patterns in commercial properties due to the Covid-19 pandemic was analyzed in a medium-sized American city. The focus on a single community (1) allows usage to be linked specifically to the timing of public health and executive orders; and (2) provides a fine-grained, detailed understanding of usage in different property classifications (e.g. restaurants, hotels, schools, outpatient medical facilities, offices, and religious organizations). Electricity consumption data from 2019, adjusted for average daily temperature, were used to calculate expected use in 2020. Electricity usage was found to be lower than expected for most commercial property classes, but the timing and magnitude of these effects varied. For example, within the hospitality industry, hotels evidenced a larger and more sustained decrease in usage (–17%) as compared with restaurants (–11%). In addition, usage patterns for outpatient medical facilities can be linked to specific executive orders. Together, a heterogenous rate of electricity use is found to vary according to specific types of commercial properties. Practice relevance In much of the research regarding electricity usage, utility customers are placed into the broad categories of residential, commercial and industrial. These groups are viewed as homogeneous. This study examined different property classes within the commercial group during the Covid-19 pandemic. Although electricity usage was lower than expected for most commercial property classes, the timing and magnitude of these effects varied. For example, within the hospitality industry, hotels evidenced a larger and more sustained decrease in usage than did restaurants. In addition, usage patterns for outpatient medical facilities were linked to specific executive orders. Together, these data sources allowed for a detailed examination of Covid-19’s effect on electricity use for specific types of commercial properties and it revealed that their response to the pandemic varied widely. Thus, it is an oversimplification to view commercial properties as a single, homogeneous group.

Energy systems integration (ESI) enables the effective analysis, design, and control of these interactions and interdependencies along technical, economic, regulatory, and social dimensions. By focusing on the optimization of energy from all systems, across all pathways, and at all scales, we can better understand and make use of the co-benefits that result to increase reliability andperformance, reduce cost, and minimize environmental impacts. This white paper discusses systems integration and the research in new control architectures that are optimized at smaller scales but can be aggregated to optimize energy systems at any scale and would allow replicable energy solutions across boundaries of existing and new energy pathways.

Methane is a powerful greenhouse gas with a 25 times higher 100-year warming potential than carbon dioxide and is a target for mitigation to achieve climate goals. To control and curb methane emissions, estimates are required from the sources and sectors which are typically generated using bottom-up methods. However, recent studies have shown that national and international bottom-up approaches can significantly underestimate emissions. In this study, we present three bottom-up approaches used to estimate methane emissions from all emission sectors in the Denver-Julesburg basin, CO, USA. Our data show emissions generated from all three methods are lower than historic measurements. A Tier 1/2 approach using IPCC emission factors estimated 2022 methane emissions of 358 Gg (0.8% of produced methane lost by the energy sector), while a Tier 3 EPA-based approach estimated emissions of 269 Gg (0.2%). Using emission factors informed by contemporary and region-specific measurement studies, emissions of 212 Gg (0.2%) were calculated. The largest difference in emissions estimates were a result of using the Mechanistic Air Emissions Simulator (MAES) for the production and transport of oil and gas in the DJ basin. The MAES accounts for changes to regulatory practice in the DJ basin, which include comprehensive requirements for compressors, pneumatics, equipment leaks, and fugitive emissions, which were implemented to reduce emissions starting in 2014. The measurement revealed that normalized gas loss is predicted to have been reduced by a factor of 20 when compared to 10-year-old normalization loss measurements and a factor of 10 less than a nearby oil and production area (Delaware basin, TX); however, we suggest that more measurements should be made to ensure that the long-tail emission distribution has been captured by the modeling. This study suggests that regulations implemented by the Colorado Department of Public Health and Environment could have reduced emissions by a factor of 20, but contemporary regional measurements should be made to ensure these bottom-up calculations are realistic.

Continuous monitoring (CM) solutions can facilitate faster detection and repair of emissions compared to traditional survey methods. This study tested 13 CM solutions over 12 weeks using single-blind controlled testing. Controlled release rates ranged from 0.08 to 6.75 kg CH4 h−1 and lasted 18 min to 8 h. Six solutions demonstrated 90% method detection limits (DL90s) ranging from 0.5 [0.3, 0.6] kg CH4 h−1 to 6.7 [5.9, 8.0] kg CH4 h−1. Of the 6 solutions, 5 had low False Positive (FP) rates (7.8%–18.9%), and 4 had low False Negative (FN) rates (8.0%–34.1%). Similar to Ilonze et al., the results show that the tested solutions balance method sensitivity with low FP and FN rates. All scanning/imaging solutions achieved high localization precision and accuracy (≥40%) at the equipment unit level. Single quantification estimates exhibited high relative quantification errors, ranging from 33 [0.9, 66]%, 95% confidence interval (CI) to 1326 [1003, 1648]%, 95% CI for small emissions (between 0.1 and 1 kg CH4 h−1) and from 3 [−20, 26]%, 95% CI to 3578 [−2832, 9988]%, 95% CI for large emissions (>1 kg CH4 h−1). The mean detection time for all solutions ranged from 5 h to 5 days. Relative to previous studies, errors in quantification estimates decreased, as did FN and FP rates, with improved DL90s for 2 of the 4 retested solutions. However, the mean detection times increased for 2 solutions, remained constant for one solution, and decreased for 1 of the 4 retested solutions. These findings highlight that continuous, rigorous testing enhances solution performance, with notable improvements observed across multiple testing programs using the same test protocol.

Leaks in natural gas distribution networks are often initially detected by odor and then localized during operators' walking surveys. The size of the enhancement detected and the leak's proximity to infrastructure are used to determine leak severity of the leak and how quickly it is repaired. Methane (CH 4 ) enhancements on the surface above the leak change with atmospheric conditions, but currently the leak severity assessment is made without considering the environmental conditions and could result in misdiagnosis of the leak. This study has developed the “ESCAPE” model, a tool that can be used to estimate CH 4 enhancements on the surface above a leak using meteorological and leak data as input. Surface CH 4 concentrations calculated using the ESCAPE model agrees well with measurements made during controlled experiments (m = 0.95 and R 2 = 0.95). This study highlights the effect of micrometeorology on gas transport from the surface to the atmosphere, where the surface and the atmospheric conditions have the largest effect on the flux. Here, recommendations are made to improve industry best practices, including recording the meteorological conditions at the time of the leak detection, avoiding walking surveys on days where there are strong winds or strong solar irradiance and that known leak locations should be revisited and measured in different conditions. Mitigating CH 4 emissions from the gas distribution network is a cost-effective and economically realistic target. Distribution operators can be directed by the output of ESCAPE model to correctly identify and repair the largest distribution leaks and could reduce annual CH 4 emissions by 0.69 Tg, this may go some way to reducing energy sector emissions in accordance with the Paris Agreement. • Surface methane above pipeline leakage changes with atmospheric conditions. • Model developed to estimate surface methane using weather and leak data as input. • Model agrees with measurements made during controlled experiments, m = 0.95 & R2 = 0.95 • Shows need of using wind speed & irradiance to assess severity of distribution leaks. • Model helps identify/prioritize largest leaks for repair, reduces risk & GHG emission.

As smart grids introduce profound changes in the operation of the electric power industry, the need for efficient and robust resource allocation (RA) algorithms arises, especially due to the increasingly stochastic nature of availability of highly dispersed resources. A framework for solving the smart grid RA (SGRA) problem using a heuristic approach such as a genetic algorithm is presented in this paper. Similar challenges exist in resource allocation in the realm of computing. A comparison is drawn between SGRA and computing RA. Its application to a multi-agent-based distribution management system, used as an environment model, is also proposed. A path forward concludes the paper.