Publications

To achieve UN Sustainable Development Goal 7 (SDG7) by 2030, better rural electrification is required. Most current projects provide electricity inefficiently to rural users rather using stand-alone designs. As most remaining unserved users are located in difficult-to-reach areas, IEA has pointed out that the marginal cost of rural electrification is increasing. Earlier work indicates that networking rural villages would be more cost-effective than prior models. This work uses modified fuzzy inference to improve power distribution costs by improving the optimality of distribution network design.

A large fraction of the developing world utilizes biomass fuels for cooking. Air pollution from these stoves represents a significant health hazard. Electric cooking has been identified as a potential method to deliver clean, cost-effective, cooking in the developing world, with particular emphasis on electric pressure cookers (EPCs) that are more energy-efficient than cooking in unpressurized vessels. Most EPCs in use today are designed for fast, on-grid, cooking using large heating elements, and it is unclear how these large, cyclic, power loads would impact minigrids. This study uses an electro-thermal model to investigate how a range of EPC power ratings would impact their use in minigrids. Results indicate that the lower efficiency of lower-power units increases minigrid energy requirements in a non-trivial way relative to higher-power units. Finally, this study also indicates that periods of higher power consumption for higher-power units occurs infrequently, and thus minigrids may be capable of handling EPCs for cooking staples such as rice and beans.

Recent emphasis on decreasing methane emissions from oil and gas production and transport has stimulated the development of multiple regulatory and voluntary reporting programs.  These programs include monitoring and verification requirements, commonly known as MMRV (methane monitoring, reporting and verification).  An underlying assumption in these programs is the use of advanced methods to estimate emissions, including continuously installed sensors at facilities and aerial, satellite and driving survey methods. These methods provide emission estimates at the scale of major sources or entire facilities. Regulatory programs in the USA and EU are increasingly tying these estimates to substantial financial risks while encouraging anonymous 3rd party measurements, raising the stakes for using these methods.  This abstract reviews recent studies of these methods, and reviews four areas of concern.  First, multiple commonly used methods display accuracy problems which are likely to be present in most methods. Recent studies study assessed methods at production and midstream facilities onshore in the USA.  Two methods deployed simultaneously at 14 midstream facilities disagreed by 2:1 averaged across all facilities and by more than 2:1 at 6 of the facilities.  Other studies in U.S.A production and European midstream have identified similar accuracy issues.  Second, ‘measurement informed inventory’ methods, which use full-facility estimates to update emissions reporting, remain poorly developed and unevenly implemented.  While one study found that survey methods identified large emitters and operators corrected reporting, another study found that most aerial detections did not result in effective corrections to inventory estimates.  Third, methods used to extrapolate facility-scale estimates to basin scale have unaddressed uncertainties.  Recent work indicates that 9-49% of plumes detected by aircraft methods are due to maintenance emissions, which are poorly characterized by anonymous aerial sampling.  Additionally, extrapolation methods poorly estimate short emission events, resulting in a significant potential to over-estimate of emissions.  Conversely, random non-detects of exhaust emissions likely under-estimates emissions from engines and combustors by a factor of 2 or more.  These errors both shift emission between sectors and may result in significant bias. Additional control inputs, better GIS data, and improved methods are required to better estimate regional emissions.   Finally, recent studies of continuous emissions monitors in both controlled and field tests indicate poor quantification accuracy in controlled testing, and poorer accuracy in field conditions.  While advanced methods show promise for improving emissions detection and mitigation, consumers of these data need to be aware of the performance of these methods and account for bias, uncertainty, and variability of emissions estimates when constructing programs that utilize these estimates.

Twelve next generation emission measurement (NGEM) technologies completed single-blind testing at the Methane Emissions Technology Evaluation Center in 2018. This is the first series of tests to evaluate a wide variety of NGEM solutions including handheld, mobile, and continuous monitoring methods using comparable, repeatable protocols. Results assess performance of detection, localization and quantification, albeit with limited statistical significance due to a low number of tests. Overall, a higher detection rate is observed for handheld and mobile solutions than for continuous monitoring solutions. Compared to when a single emission source is present, a decline in detection rate is observed across all methods when multiple, steady emission sources are present. Localization by handheld and mobile solutions is more accurate than continuous monitoring solutions. These results support the common perception that detections by continuous monitoring systems will need to be confirmed and pinpointed by a follow-up inspection. Finally, this and other controlled release experiments, have been performed across a limited range of environmental conditions. To develop robust probability of detection curves needed for demonstrating emission reduction potential of leak detection and repair programs, new protocols are needed to evaluate methods across a wide range of metrological conditions and emission scenarios in a cost-effective manner.

Adapting turbocharger performance maps to a form suitable for dynamic simulations is challenging for the following reasons: (1) the amount of available data is typically limited, (2) data are typically not provided for the entire operating range of the compressor and turbine and (3) the performance data are non-linear. To overcome these challenges, curve fits are typically generated using the performance data individually for each device. The process, however, can take un-economical amounts of effort to implement for a range of compressors and turbines. This article introduces a method to implement non-dimensional performance maps thereby allowing a range of turbochargers to be modeled from the same performance data, reducing the effort required to implement models of different sizes. The non-dimensional maps seek to model the performance of compressor and turbine families in which the geometry of the rotor and housing are similar and allow the turbocharger to be scaled for simulation in much the same way used to design customized sizes of turbochargers. A method to match the non-dimensional compressor map to engine performance targets by selecting the compressor diameter is presented, as well as a method to match the turbine to the selected compressor.

This paper discusses how to achieve high PV penetrations for a hybrid diesel-PV micro-grid via the means of a cloud forecasting system. The micro-grid control system utilizes cloud forecasts to forecast PV transients and manage online diesel generation. Simulation results for the test micro-grid showed that an array size of 2 MW achieved an energy penetration of 30.8% with ≈11.5 annual faults. Additionally, the maximum return on investment was achieved at this array size. With this forecasting system, one could expect to save a minimum of 3.7% more diesel in comparison to a conventional PV-diesel control system.

Wide-area aerial methods provide comprehensive screening of methane emissions from oil and gas (O & G) facilities in production basins. Emission detections ("plumes") from these studies are also frequently scaled to the basin level, but little is known regarding the uncertainties during scaling. This study analyzed an aircraft field study in the Denver-Julesburg basin to quantify how often plumes identified maintenance events, using a geospatial inventory of 12,629 O & G facilities. Study partners (7 midstream and production operators) provided the timing and location of 5910 maintenance events during the 6 week study period. Results indicated three substantial uncertainties with potential bias that were unaddressed in prior studies. First, plumes often detect maintenance events, which are large, short-duration, and poorly estimated by aircraft methods: 9.2 to 46% (38 to 52%) of plumes on production were likely known maintenance events. Second, plumes on midstream facilities were both infrequent and unpredictable, calling into question whether these estimates were representative of midstream emissions. Finally, 4 plumes attributed to O & G (19% of emissions detected by aircraft) were not aligned with any O & G location, indicating that the emissions had drifted downwind of some source. It is unclear how accurately aircraft methods estimate this type of plume; in this study, it had material impact on emission estimates. While aircraft surveys remain a powerful tool for identifying methane emissions on O & G facilities, this study indicates that additional data inputs, e.g., detailed GIS data, a more nuanced analysis of emission persistence and frequency, and improved sampling strategies are required to accurately scale plume estimates to basin emissions.

Traditional bottom-up (BU) methods estimate methane emissions from oil and gas facilities by multiplying activity data with emission factors. Top-down (TD) methods measure methane emissions from all sources across an entire site and often use BU estimates to attribute emissions specifically to natural gas, often through ethane-to-methane ratio analysis. However, traditional BU methods do not adequately account for variations in throughput and failure conditions, which can significantly impact gas composition and emission rates. The Mechanistic Air Emissions Simulator (MAES) is a computer model developed to estimate methane and other hydrocarbon emissions from oil and gas facilities. MAES employs two distinct modeling approaches: mechanistic models and traditional methods. MAES uses mechanistic models to estimate emissions based on fluid flow through equipment and equipment states, offering a detailed, process-oriented emissions representation. In contrast, the traditional methods utilized by MAES estimate emissions by applying activity data multiplied by emission factor distributions, offering a statistical approach grounded in empirical data. This study applies MAES to assess emission impacts on three vintages of production wellpads in the Denver-Julesburg (DJ) basin: a wellpad with two stages of separation ("old" facility), one with three stages of separation ("current" facility), and a tankless wellpad ("future" facility). The study found that increased throughput led to higher methane emissions in older wellpads but not in tankless future facilities. Additionally, MAES showed that failure conditions, like stuck dump valves, increased emission rates and affected the ethane-to-methane ratio, which could vary by 2.15 times depending on facility configuration. These findings underscore the importance of incorporating variability in facility operations into emissions estimates to improve accuracy and guide effective mitigation strategies.