Publications

This paper introduces a potential approach for mitigating the effects of communication delays between multiple, closed-loop hardware-in-the-loop experiments which are virtually connected, yet physically separated. The approach consists of an analytical procedure for the compensation of communication delays, along with the supporting computational and communication infrastructure. The control design leverages tools for the design of observers for the compensation of measurement errors in systems with time-varying delays. The proposed methodology is validated through computer simulation and hardware experimentation connecting hardware-in-the-loop experiments conducted between laboratories separated by a distance of over 100 km.

Recent studies indicate emission factors used to generate bottom-up methane inventories may have considerable regional variability. The US’s Environmental Protection Agency’s emission factors for plugged and unplugged abandoned oil and gas wells are largely based on measurement of historic wells and estimated at 0.4 g and 31 g CH4 well-1 h-1, respectively. To investigate if these are representative of wells more recently abandoned, methane emissions were measured from 128 plugged and 206 unplugged abandoned wells in Colorado, finding the first super-emitting abandoned well (76 kg CH4 well-1 h-1) and average emissions of 0 and 586 g CH4 well-1 h-1, respectively. Combining these with other states’ measurements, we update the US emission factors to 1 and 198 g CH4 well-1 h-1, respectively. Correspondingly, annual methane emissions from the 3.4 million abandoned wells in the US are estimated at between 2.6 Tg, following current methodology, and 1.1 Tg, where emissions are disaggregated for well-type. In conclusion, this study identifies a new abandoned well-type, recently-producing orphaned, that contributes 74% to the total abandoned wells methane emissions. Including this new well-type in the bottom-up inventory suggests abandoned well emissions equates to between 22 and 49% of total emissions from US active oil and gas production operations.

Smart homes hold the potential for increasing energy efficiency, decreasing costs of energy use, decreasing the carbon footprint by including renewable resources, and trans-forming the role of the occupant. At the crux of the smart home is an efficient electric energy management system that is enabled by emerging technologies in the electricity grid and consumer electronics. This article presents a discussion of the state-of-the-art in electricity management in smart homes, the various enabling technologies that will accelerate this concept, and topics around consumer behavior with respect to energy usage.

Abstract. Recent interest in quantifying trace gas emissions from point sources, such as measuring methane (CH4) emissions from oil and gas wells, has resulted in several methods being used to estimate emissions from sources with emission rates below 200g CH4 hour−1. The choice of measurement approach depends on how close observers can get to the source, the instruments available and the meteorological/micrometeorological conditions. As such, static chambers, dynamic chambers, HiFlow measurements, Gaussian plume modelling 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, for comparison, we present estimates for the percentage difference between the measured emission and the known emission, A, and the average percentage difference for three repeat measurements, Ar , for emissions of 200 g CH4 h−1. Our results show that, even though the dynamic chamber repeatedly underestimates the emission, it is the most accurate for a single measurement and the accuracy improves with subsequent measurements (A = −11 %, Ar = −10 %). The single HiFlow emission estimate was also an underestimate, however, poor instrument precision resulted in reduced accuracy of emission estimate to becomes less accurate after repeat measurements (A = −16 %, Ar = −18 %). Of the far field methods, the bLs method underestimated emissions both for single and repeat measurements (A = −11 %, Ar = −7 %) while the GP method significantly overestimated the emissions (A = 33 %, Ar = 29 %) despite using the same meteorological and concentration data as input. Additionally, our results show that the accuracy and precision of the emission estimate increases as the flow rate of the source is increased for all methods. To our knowledge this is the first time that methods for measuring CH4 emissions from point sources less than 200 g CH4 h−1 have been quantitively assessed against a known reference source and each other.

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 and voltage 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.

Continuous emission monitoring (CM) solutions promise to detect large fugitive methane emissions in natural gas infrastructure sooner than traditional leak surveys, and quantification by CM solutions has been proposed as the foundation of measurement-based inventories. This study performed single-blind testing at a controlled release facility (release from 0.4 to 6400 g CH4/h) replicating conditions that were challenging but less complex than typical field conditions. Eleven solutions were tested, including point sensor networks and scanning/imaging solutions. Results indicated 90% probability of detection (POD) of 3-30 kg CH4/h; 6 of 11 solutions achieved POD >50%. False positive rates ranged from 0 to 79%. Six solutions estimated emission rates. For release rate of 0.1-1 kg/h, the solutions' mean relative errors ranged from -44% to +586% with single estimates between -97% and +2077%, with 4 solutions' upper uncertainty exceeding +900%. Above 1 kg/h, mean relative error was -40% to +93%, with two solutions within 20%, and single-estimate relative errors from -82% to +448%. The large variability in performance between CM solutions, coupled with highly uncertain detection, detection limit, and quantification results, indicate that the performance of individual CM solutions should be well understood before relying on results for internal emissions mitigation programs or regulatory reporting.

Advances in power electronics and their use in Miscellaneous Electric Loads (MELs) in buildings have resulted in increased interest in using low-voltage direct current (DC) power distribution as a replacement for the standard alternating current (AC) power distribution in buildings. Both systems require an endpoint converter to convert the distribution system voltage to the MELs voltage requirements. This study focused on the efficiency of these endpoint converters by testing pairs of AC/DC and DC/DC power converters powering the same load profile. In contrast to prior studies, which estimated losses based on data sheet efficiency and rated loads, in this study, we used part load data derived from real-world time-series load measurements of MELs and experimentally characterized efficiency curves for all converters. The measurements performed for this study showed no systematic efficiency advantage for commercially available DC/DC endpoint converters relative to comparable, commercially available AC/DC endpoint converters. For the eight appliances analyzed with the pair of converters tested, in 50%, the weighted energy efficiency of the DC/DC converter was higher, while, for the other 50%, the AC/DC converter was. Additionally, the measurements indicated that the common assumption of using either data sheet efficiency values or efficiency at full load may result in substantial mis-estimates of the system efficiency.

The recent interest in measuring methane (CH4) emissions from abandoned oil and gas wells has resulted in five methods being typically used. In line with the US Federal Orphaned Wells Program’s (FOWP) guidelines and the American Carbon Registry’s (ACR) protocols, quantification methods must be able to measure minimum emissions of 1 g of CH4 h−1 to within ±20%. To investigate if the methods meet the required standard, dynamic chambers, a Hi-Flow (HF) sampler, and a Gaussian plume (GP)-based approach were all used to quantify a controlled emission (Qav; g h−1) of 1 g of CH4 h−1. After triplicate experiments, the average accuracy (Ar; %) and the upper (Uu; %) and lower (Ul; %) uncertainty bounds of all methods were calculated. Two dynamic chambers were used, one following the ACR guidelines, and a second “mobile” chamber made from lightweight materials that could be constructed around a source of emission on a well head. The average emission calculated from the measurements made using the dynamic chamber (Qav = 1.01 g CH4 h−1, Ar = +0.9%), the mobile chamber (Qav = 0.99 g CH4 h−1, Ar = −1.4%), the GP approach (Qav = 0.97 g CH4 h−1, Ar = −2.6%), and the HF sampler (Qav = 1.02 g CH4 h−1, Ar = +2.2%) were all within ±3% of 1 g of CH4 h−1 and met the requirements of the FOWP and ACR protocols. The results also suggest that the individual measurements made using the dynamic chamber can quantify emissions of 1 g of CH4 h−1 to within ±6% irrespective of the design (material, number of parts, geometrical shape, and hose length), and changes to the construction or material specifications as defined via ACR make no discernible difference to the quantification uncertainty. Our tests show that a collapsible chamber can be easily constructed around the emission source on an abandoned well and be used to quantify emissions from abandoned wells in remote areas. To our knowledge, this is the first time that methods for measuring the CH4 emissions of 1 g of CH4 h−1 have been quantitively assessed against a known reference source and against each other.

Accurate methane emission estimates are essential for climate policy, yet current field methods often struggle with spatial constraints and source complexity. Ground-based mobile approaches frequently miss key plume features, introducing bias and uncertainty in emission rate estimates. This study addresses these limitations by using small unmanned aerial systems equipped with precision gas sensors to measure methane alongside co-released tracers. We tested whether arc-shaped flight paths and alternative ratio estimation methods could improve the accuracy of tracer-based emission quantification under real-world constraints. Controlled releases using ethane and nitrous oxide tracers showed that (1) arc flights provided stronger plume capture and higher correlation between methane and tracer concentrations than traditional flight paths; (2) the cumulative sum method yielded the lowest relative error (as low as 3.3%) under ideal mixing conditions; and (3) the arc flight pattern yielded the lowest relative error and uncertainty across all experimental configurations, demonstrating its robustness for quantifying methane emissions from downwind plume measurements. These findings demonstrate a practical and scalable approach to reducing uncertainty in methane quantification. The method is well-suited for challenging environments and lays the groundwork for future applications at the facility scale.

Quantitative optical gas imaging (QOGI) system can provide rapid quantification of leaks detected by optical gas imaging (OGI) cameras across the oil and gas supply chain. A comprehensive evaluation of the QOGI system’s quantification capability is needed for successful adoption of the technology. This study conducted single-blind experiments to examine the quantification performance of the FLIR QL320 QOGI system under near-field conditions at a pseudo-realistic, outdoor, controlled testing facility that mimics upstream and midstream natural gas operations. The study collected 357 individual measurements across 26 controlled releases with rates between 2 slpm to 88 slpm of compressed natural gas (CNG). The majority (75%) of measurements were within a quantification factor of 3 (quantification error of -67% to 200%) with individual errors between -90% and 831% (i.e. within a factor of 10). Quantification error decreased with increasing controlled release rates. Performance improved when viewing gas plumes against a clear sky as background and at calm wind speed conditions relative to other scenarios. Quantification error varied substantially when the same controlled releases were quantified from different camera positions.