Publications

Abstract. Recent interest in measuring methane (CH4) emissions from abandoned oil and gas infrastructure has resulted in several methods being continually used to quantify point source emissions less than 200 g CH4 h−1. The choice of measurement approach depends on how close observers can come to the source, the instruments available, and the meteorological/micrometeorological conditions. As such, static chambers, dynamic chambers, Bacharach Hi Flow Sampler (BHFS) measurements, Gaussian plume (GP) modeling, and backward Lagrangian stochastic (bLs) models have all been used, but there is no clear understanding of the accuracy or precision of each method. To address this, we copy the experimental design for each of the measurement methods to make single field measurements of a known source, to simulate single measurement field protocol, and then make repeat measurements to generate an understanding of the accuracy and precision of each method. Here, we present estimates for the average percentage difference between the measured emission and the known emission for three repeat measurements, Ar, for emissions of 40 to 200 g CH4 h−1. The static chamber data were not presented because of safety concerns during the experiments. Both the dynamic chamber (Ar = −10 %, −8 %, and −10 % at emission rates of 40, 100, and 200 g CH4 h−1, respectively) and BHFS (Ar = −18 %, −16 %, and −18 %) repeatedly underestimate the emissions, but the dynamic chamber had better accuracy. The standard deviation of emissions from these direct measurement methods remained relatively constant for emissions between 40 and 200 g CH4 h−1. For the far-field methods, the bLs method generally underestimated emissions (Ar = +6 %, −6 %, and −7 %) while the GP method significantly overestimated the emissions (Ar = +86 %, +57 %, and +29 %) despite using the same meteorological and concentration data as input. Variability in wind speed, wind direction, and atmospheric stability over the 20 min averaging period are likely to propagate through to large variability in the emission estimate, making these methods less precise than the direct measurement methods. To our knowledge, this is the first time that methods for measuring CH4 emissions from point sources between 40 and 200 g CH4 h−1 have been quantitatively assessed against a known reference source and against each other.

In Rwanda, most small-scale hydropower systems are connected to the national grid to supply additional generation capacity. The Rwandan rivers are characterized by low flow-rates and a majority of plants are below 5 MW generation capacity. The purpose of this study is to provide a scientific overview of positive and negative factors affecting the sustainability of small-scale hydropower plants in Rwanda. Based on interviews, field observation, and secondary data for 17 plants, we found that the factors contributing to small-scale hydropower plant sustainability are; favorable regulations and policies supporting sale of electricity to the national grid, sufficient annual rainfall, and suitable topography for run-of-river hydropower plants construction. However, a decrease in river discharge during the dry season affects electricity production while the rainy season is characterized by high levels of sediment and soil erosion. This shortens turbine lifetime, causes unplanned outages, and increases maintenance costs. Further, there is a need to increase local expertise to reduce maintenance cost. Our analysis identifies environmental factors related to the amount and quality of water as the main current problem and potential future threat to the sustainability of small-scale hydropower. The findings are relevant for energy developers, scholars, and policy-makers in Rwanda and East Africa.

This study evaluated multiple commercially available continuous monitoring (CM) point sensor network (PSN) solutions under single-blind controlled release testing conducted at operational upstream and midstream oil and natural gas (O&G) sites. During releases, PSNs reported site-level emission rate estimates of 0 kg/h between 38-86% of the time. When non-zero site-level emission rate estimates were provided, no linear correlation between release rate and reported emission rate estimate was observed. The average, aggregated across all PSN solutions during releases, shows 5% of mixing ratio readings at downwind sensors were greater than the site's baseline plus two standard deviations. Four of six total PSN solutions tested during this field campaign provided site-level emission rate estimates with the site average relative error ranging from -100% to 24% for solution D, -100% to -43% for solution E, -25% for solution F (solution F was only at one site), and -99% to 430% for solution G, with an overall average of -29% across all sites and solutions. Of all the individual site-level emission rate estimates, only 11% were within +/- 2.5 kg/h of the study team's best estimate of site-level emissions at the time of the releases.

In this paper, we propose a robust control strategy for reducing system frequency deviation, caused by load fluctuation and renewable sources, in a smart microgrid system with attached storage. Frequency deviations are associated with renewable energy sources because of their inherent variability. In this work, we consider a microgrid where fossil fuel generators and renewable energy sources are combined with a reasonably sized, fast acting battery-based storage system. We develop robust control strategies for frequency deviation reduction, despite the presence of significant (model) uncertainties. The advantages of our approach are illustrated by comparing system frequency deviation between the proposed system (designed via μ synthesis) and the reference system which uses governors and conventional PID control to cope with load and renewable energy source transients. All the simulations are conducted in the Matlab™ and Simulink™ environment.

Methane (CH4) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH4 emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions, however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH4 emissions at the facility-level. Near-field quantification of methane (CH4) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian Plume (GP) and backward Lagrangian Stochastic (bLS), by comparing calculated CH4 emissions to controlled single-point emissions of between 0.4 and 5.2 kg CH4 h-1. Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. Comparison shows the bLS approach showed better predictive performance with twice as many emission estimates were within a factor of two (FAC2) of the known emission rates compared to those calculated using the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows the lateral and vertical alignment of source and sensor plays a critical role in emission estimations as measurements made closer to the plume centerline and at a distance between 40 to 80 m downwind yielded the best FAC2 agreement. High wind meander degraded ability of both approaches to generate representative emissions particularly with the GP approach as it violates the modelling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While it is likely that the results presented here are suitable for informing leak detection technology in relatively flat unvegetated environments, it is currently unknown if these findings will be applicable in more vertiginous or heavily vegetated oil and gas producing regions of the Marcellus or Uinta Basins.

Atmospheric methane emissions from active natural gas production sites in normal operation were quantified using an inverse Gaussian method (EPA's OTM 33a) in four major U.S. basins/plays: Upper Green River (UGR, Wyoming), Denver-Julesburg (DJ, Colorado), Uintah (Utah), and Fayetteville (FV, Arkansas). In DJ, Uintah, and FV, 72–83% of total measured emissions were from 20% of the well pads, while in UGR the highest 20% of emitting well pads only contributed 54% of total emissions. The total mass of methane emitted as a percent of gross methane produced, termed throughput-normalized methane average (TNMA) and determined by bootstrapping measurements from each basin, varied widely between basins and was (95% CI): 0.09% (0.05–0.15%) in FV, 0.18% (0.12–0.29%) in UGR, 2.1% (1.1–3.9%) in DJ, and 2.8% (1.0–8.6%) in Uintah. Overall, wet-gas basins (UGR, DJ, Uintah) had higher TNMA emissions than the dry-gas FV at all ranges of production per well pad. Among wet basins, TNMA emissions had a strong negative correlation with average gas production per well pad, suggesting that consolidation of operations onto single pads may reduce normalized emissions (average number of wells per pad is 5.3 in UGR versus 1.3 in Uintah and 2.8 in DJ).

Methane (CH4) is emitted during extraction, processing, and transport processes in the natural gas industry. As a powerful greenhouse gas, CH4 releases are harmful to the environment. Operators aim to minimize methane loss, and continuous monitoring using low-cost fence-line sensors are now being developed to observe methane enhancements downwind of operations. However, it is not clear how useful these systems are and whether they can be used to quantify emissions or simply identify the presence of a leak. To investigate this, we deployed 4 calibrated low-cost sensors 30 m from emissions of known rates over a 48-h period. The aims were to determine: (1) how much of the time a fence-line system would detect a leakage event from a single, point source of the size typically seen at oil and gas production well pads and (2) how accurately a fence-line system can estimate CH4 emissions using a relatively simple downwind dispersion method. Our results show that during a 48-h measurement period, the fence-line sensor network could detect CH4 releases of 84 g h−1 40% of the time and 100% of the time for emissions greater than 167 g h−1 using an enhancement threshold of 2 ppm. A Gaussian plume inversion based on binned centerline, maximum measured concentrations and the WindTrax Lagrangian particle model were each tested. With these models, average estimated emissions were within ±50% of a known emission rate in 24 h and ±25% in 48 h; however, estimated individual 20-min average emissions vary by more than a factor of 10. A simple Gaussian plume inversion using all of the measured concentrations produced unreasonable average emission estimates because of the inability of the equation to parameterize lateral dispersion at distances less than 100 m when the sensor was on the edge of the plume. This study provides evidence to support the use of low-cost sensors as autonomous fence-line monitoring systems to detect and potentially quantify emissions. If the sensors are properly calibrated and sensor deployment location is optimized for prevailing wind directions at each site, fence-line systems could be used routinely to quantify emissions from oil and gas infrastructure.

Numerous recent calls have been made for policy design research to embed itself throughout the policy process and explore avenues for matching tools and targets. These calls have argued that policy design research, while emphasizing the content and the choice of design, has been under-leveraged, particularly in exploring rationales for effectiveness. In this paper, we conduct a comparative case study to explore variation in participation rates for two similarly categorized solar policies across two mid-sized cities. In this regard, three contextual factors are examined, including the population characteristics, the existing configuration of policies, and the physical environment, which all contribute to shaping policy effectiveness. We argue that policy design is situated within an explicit context and that without capturing the context, the effectiveness of policies may not translate if diffused.