Browsing by Author "Zimmerle, Daniel, advisor"

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Open Access
Development of a plume identification algorithm for optical gas imaging of natural gas emissions that requires no human intervention
(Colorado State University. Libraries, 2020) Martinez, Marcus M., author; Zimmerle, Daniel, advisor; Marchese, Anthony, advisor; von Fischer, Joe, committee member
Recent growth in natural gas production in the United States has increased focus on reducing greenhouse gas emissions from the natural gas supply chain. Methane, the primary constituent of natural gas, is also a potent greenhouse gas. Optical gas imaging (OGI) is frequently used for emission detection in upstream and midstream sectors of the natural gas supply chain. Current OGI methods typically use mid-range infrared video cameras tuned to absorption lines of light hydrocarbons to make natural gas emissions visible to human operators. Prior studies of camera output have used human interpretation to determine if an emission is visible in the video stream, making it difficult to standardize measures of visibility between tests or to automate large test suites. This work presents a signal processing method which separates the background scene from the gas plume when used in controlled test conditions where video is collected in both leaking and non-leaking conditions. The method utilizes a novel frequency-based method that detects the high-frequency motion of the gas plume in the video stream. After background removal, the size of the gas plume can be quantified by thresholding the detected plume and measuring its size relative to the camera's field of view. The resulting metric eliminates the need for human evaluation of video streams. To demonstrate application of the method, multiple cameras were used to develop a relationship between emission rate and plume visibility over a range of viewing distances. Tests were conducted at the Methane Emissions Technology Evaluation Center, on CSU's Foothills Campus, using six identical OGI cameras (FLIR G300a camera cores with 38 mm lenses) to image the emission from multiple directions at a range 1 to 6 m. Gas was released from a mock well head at 17 to 196 g/h, with wind speeds of 1.8 to 3.0 m/s. Comparison with expert evaluation was used to set and validate the threshold levels; a 90% probability of detection requires a plume covering at least 13.8% of the camera's field of view. Testing indicated a linear relationship between emission rate and plume coverage fractions at a distance of 1 to 2 m, regardless of the viewing angle. Beyond 2 m, plume coverage drops rapidly, approaching the noise floor. While test conditions were limited, sufficient data was collected to demonstrate method functionality and its applicability to evaluating OGI emission detection systems.
Open Access
Efficiency of AC vs. DC distribution systems in commercial buildings
(Colorado State University. Libraries, 2022) Santos, Arthur Felício Barbaro dos, author; Young, Peter, advisor; Zimmerle, Daniel, advisor; Cale, James, committee member; Clark, Maggie, committee member
Decarbonization and modernization of the grid, electrification of transportation, and energy storage are some of the trends pushing towards the significant growth of power electronics in the past few decades. The massive application of such devices has increased the interest in direct current (DC) power distribution as an alternative to the conventional alternating current (AC) distribution systems in residential and commercial buildings. This increase in non-linear loads, however, substantially increases current harmonics, which compromises the lifespan, efficiency, and/or operability of distribution components, such as transformers and protection equipment. Additionally, when comparing the efficiency of AC vs. DC distribution systems, the literature is often based on simulation studies rather than real measured data. In this regard, this study focuses on three major topics: a) Harmonic cancellation within building circuits; b) Endpoint use efficiency comparison for AC and DC in-building distribution systems; and c) A cautionary note on using smart plugs for research data acquisition. The analyses are based on recorded power consumption data from office-based appliances, made by smart plugs, combined with detailed characterization of sampled Miscellaneous Electric Loads (MELs') power converters. While harmonic cancellation studies often assume that AC converters operate across their rated power range, measured realistic power profiles reported in this work show that MELs operate below 40% of rated power the majority of the time when not in standby mode. This makes the harmonic cancellation significantly lower than that predicted when using full-range power assumptions, which could provide incorrect guidance to building design engineers. In contrast, increased diversity of MELs increases harmonic cancellation. Blending typical office loads with lighting, for instance, improves the harmonic cancellation to near the levels predicted by traditional methods. Regarding the endpoint efficiency of AC and DC distribution systems, no systematic efficiency advantage was found, when endpoint AC/DC converters were compared to a similar, commercially available, DC/DC converter powering the same load profile. That goes in the opposite direction of prior studies, which estimate converters' efficiency based on datasheet information or the efficiency at rated load.
Open Access
Informing methane emissions inventories using facility aerial measurements at midstream natural gas facilities
(Colorado State University. Libraries, 2023) Brown, Jenna A., author; Windom, Bret, advisor; Zimmerle, Daniel, advisor; Blanchard, Nathaniel, committee member
Increased interest in greenhouse gas (GHG) emissions, including recent legislative action and voluntary programs, has increased attention on quantifying, and ultimately reducing, methane emissions from the natural gas supply chain. While inventories used for public or corporate GHG policies have traditionally utilized bottom-up (BU) methods to estimate emissions, the validity of such inventories has been questioned. To align with climate initiatives, multiple reporting programs are transitioning away from BU methods to utilizing full-facility measurements using airborne, satellite or drone (top-down (TD)) techniques to inform, improve, or validate inventories. This study utilized full-facility estimates from two independent TD methods at 15 midstream natural gas facilities in the U.S.A., and were compared with a contemporaneous daily inventory assembled by the facility operator, employing comprehensive inventory methods. Methods produced multiple full-facility methane estimates at each facility, resulting in 801 individual paired estimates (same facility, same day), and robust mean estimates for each facility. Mean estimates for each facility, aggregated across all facilities, differed by 28% [10% to 43%] for the first deployment and nearly 2:1 (49% [32% to 68%]) the second deployment. Estimates from the two TD methods statistically agreed in 12% (97 of 801) of the paired measurements. These data suggest that one or both methods did not produce accurate facility-level estimates, at a majority of facilities and in aggregate across all facilities. Operator inventories, which included extensions to capture sources beyond regular inventory requirements and to integrate local measurements, estimated significantly lower emissions than the TD estimates for 96% (1535 of 1589) of the paired comparisons. Significant disagreement is observed at most facilities, both between the two TD methods and between the TD estimates and operator inventory. Overall results were coupled with two case studies where TD estimates at two pre-selected facilities were coupled with comprehensive onsite measurements to understand factors driving the divergence between TD and BU inventory emissions estimates. In 3 of 4 paired comparisons between the intensive onsite estimates and one of the TD methods, the intensive on-site work did not conclusively diagnose the difference in estimates. In these cases, the preponderance of evidence suggests that the TD methods mis-estimate emissions an unknown fraction of the time, for unknown reasons. The results presented here have two implications. Firstly, these findings have important implications for the construction of voluntary and regulatory reporting programs that rely on emission estimates for reporting, fees or penalties, or for studies using full-facility estimates to aggregate TD emissions to basin or regional estimates. Secondly, the TD full-facility measurement methods need to undergo further testing, characterization, and potential improvement specifically tailored for complex midstream facilities.
Open Access
Long duration measurements of pneumatic controller emissions on onshore natural gas gathering stations
(Colorado State University. Libraries, 2019) Luck, Benjamin Kendell, author; Quinn, Jason, advisor; Zimmerle, Daniel, advisor; Marchese, Anthony, committee member; von Fischer, Joseph, committee member
Over the last 15 years, advances in hydraulic fracturing have led to a boom of natural gas production the United States and abroad. The combustion of natural gas produces less carbon dioxide (CO2) than the combustion of other fossil fuels per unit of energy released, making it an attractive option for reducing emissions from power generation and transportation industries. Uncombusted methane (CH4) has a global warming potential (GWP) of 86 times that of CO2 on 20 year time scales and a GWP of global warming potential 32 times greater than CO2 on a 100 year time scale. The increase in supply chain throughput has led to concerns regarding the greenhouse gas contributions of CH4 from accidental or operational leaks from natural gas infrastructure. Automated, pneumatic actuated valves are used to control process variables on stations in all sectors of the natural gas industry. Pneumatic valve controllers (PCs) vent natural gas to the atmosphere during their normal operation and are a significant source of fugitive emissions from the natural gas supply chain. This paper outlines the work that was done to improve the characterization of emissions from PCs using long duration measurements. This work was performed as part of the Department of Energy funded Gathering Emission Factor (GEF) study. A thermal mass flow meter based emission measurement system was developed to perform direct measurements of pneumatic controller emissions over multiday periods. This measurement system was developed based on methods used in previous studies, with design modifications made to meet site safety regulations, power supply constraints and measurement duration targets. Emissions were measured from 72 PCs at 16 gathering compressor stations between June, 2017 and May, 2018. The average emission rate of 72 PCs was 10.86 scfh [+4.31/-3.60], which is 91.2% of the EPA's current emission factor for PCs on gathering compressor stations. The mean measurement duration of these 72 samples was 76.8 hours. Due to potential biases associated with flow meter errors, updates to EPA emission factors based on these data are not proposed. However, because all previous studies to quantify PC emissions used short sampling times (typically ≤15 minutes) the long duration measurements provided insight into previously unobserved PC emissions behavior. A panel of industry experts assessed the emissions recordings and found that 30 PCs (42% of measured devices) had emissions patterns or rates that were inconsistent with their design. 73% of emissions measured during this study were attributed to these 30 PCs that were malfunctioning from an emissions perspective. It was also found that PC emission rates are more variable over time than previously thought. Due to this high temporal variability, the short duration observations currently used by leak detection programs to identify malfunctioning equipment have a low probability of providing accurate characterizations of PC emissions. Many natural gas companies are investigating ways to improve the efficiency of their operations and reduce rates of natural gas leakage in their systems. The data presented in this paper improves the characterization of emissions behavior from a significant emission source in the production, processing and transmission sectors of the natural gas supply chain and has implications for organizations with an interest in reducing emissions from PCs.
Open Access
Methane emissions from gathering pipeline networks, distribution systems, agriculture, waste management and natural sources
(Colorado State University. Libraries, 2016) Pickering, Cody, author; Bradley, Thomas, advisor; Zimmerle, Daniel, advisor; Sheehan, John, committee member
Climate change has influenced United States policymakers and industry professionals alike to minimize greenhouse gas emissions; including methane, the second most abundant greenhouse gas. The recent focus on quantifying methane emissions is not only motivated by its abundance but also the high global warming potential of the gas, which is 86 times greater than that of carbon dioxide on a 20-year timescale. Techniques to quantify methane emissions can be broken into three categories: component level, facility level, and basin level. In this study component level measurements and published emission estimates were used in Monte Carlo models to estimate regional methane emissions from three different source categories: natural gas gathering pipeline networks, natural gas distributions systems, and non-oil and gas sources such as: agriculture, waste management, lakes, ponds, rivers, wetlands and geological seepage. These estimates are designed to support a regional estimate including all methane sources for comparison against top-down emission estimates from aircraft measurements in the same region. Gathering pipeline networks are a sector of the natural gas supply chain for which little methane emissions data are available. In this study leak detection was performed on 96 kilometers of underground plastic pipeline and above-ground components including 56 pigging facilities and 39 block valves. Only one leak was located on an underground pipeline, however, it accounted for 83% of total measured emissions. Methane emissions estimated using a Monte-Carlo model for the 4684 km of gathering pipeline in the study area were 400 [+214%/-87%] kg/h (95% CI). This estimate is statistically similar to estimates based on emission factors from EPA’s 2015 Greenhouse Gas Reporting Program and is approximately 1% [0.1% to 3.2%] of the 39 Mg/h estimated in a prior aircraft measurement of the study region. The wide uncertainty range is due to two factors: one, the small sample size relative to the total gathering system in the study area and two, the presence of only one underground pipeline leak to characterize a range of possible emissions. The study also investigates what fraction of gathering pipelines in a basin must be measured to understand the maximum probable impact gathering line emissions could have on a basin level emission estimate. Distribution systems are a sector of the natural gas supply chain that has been analyzed and measured in recent years due to the attention they received in a 1992 study showing that they contribute approximately 25% of total methane emissions from the natural gas supply chain. The only distribution company in the study region provided data and access to their system for measurement during this study. During the field campaign, 129 of 239 metering and regulating stations were visited and 34 of 87 documented leaks from PHMSA surveys were visited. When scaling measured emissions to the eight counties in the study region, pneumatic emissions dominate, accounting for 2.8 [+37%/-31%] kg/h (95% CI) or 53% [42%-64%] of total emissions from measured sources. When including customer meters, the total distribution system in the 8 county study region contributes approximately 0.05% [0.02% to 0.12%] of the 39 Mg/h found in a prior aircraft measurement of the study region. While this study shows that the distribution system measurements are not a major contributor of emissions in this basin, it does not imply emissions are negligible on a national scale, since the rural regions in the study area had relatively little distribution infrastructure, and other distribution systems that may be older or constructed with materials that have higher leak rates, such as cast iron or unprotected steel. A detailed emission estimate from non-oil and gas sources was performed including poultry, cattle, swine, rice cultivation, landfills, wastewater treatment, wetlands, rivers, ponds and lakes, and geological seepage. This analysis supported emission estimates of previous work suggesting that cattle are the largest source of biogenic methane in this region. This analysis also indicates the importance of understanding geological seepage due to the large contribution that it may have to methane emissions from non-oil and gas sources. This analysis concludes that methane emissions from gathering pipeline networks, distributions systems, agricultural practices, waste management systems and natural sources contribute a small, but non-negligible, fraction of total methane emissions for this particular region which includes large-scale natural gas production. While methane emissions from the analyzed sources are proportionally low in the study region they are not necessarily proportionally small on a state, national or global scale.
Open Access
Modeling energy systems using large data sets
(Colorado State University. Libraries, 2024) Duggan, Gerald P., author; Young, Peter, advisor; Zimmerle, Daniel, advisor; Bradley, Thomas, committee member; Carter, Ellison, committee member
Modeling and simulation are playing an increasingly import role in the sciences, and science is having a broader impact on policy definition at a local, national, and global scale. It is therefore important that simulations which impact policy produce high-quality results. The veracity of these models depend on many factors, including the quality of input data, the verification process for the simulations, and how result data are transformed into conclusions. Input data often comes from multiple sources and it is difficult to create a single, verified data set. This dissertation describes the challenges in creating a research-quality, verified and aggregated data set. It then offers solutions to these challenges, then illustrates the process using three case studies of published modeling and simulation results from different application domains.
Open Access
Networked rural electrification – optimal network design under complex topography
(Colorado State University. Libraries, 2022) Li, Jerry Chun-Fung, author; Young, Peter, advisor; Zimmerle, Daniel, advisor; Cale, Jim, committee member; Cross, Jeni Eileen, committee member
The 7th of United Nations' Sustainable Development Goals (SDG7) aims to "ensure access to affordable, reliable, sustainable and modern energy for all" by 2030. While substantial progresses have been made in the last few years, 759 million people in rural areas still have no or limited access to electricity. Due to the distances and geographical complexity of rural areas, providing electricity to this unserved population is very costly. As IEA recently pointed out, rural electrification is increasingly costly. With the current electrification approach, it is expected that 660 million people will remain without electricity access by 2030. In addition, accurate planning for small rural power system is difficult as both demand and energy resource forecasts are highly uncertain. Thus, achieving SDG7 is very challenging. In this research, a Networked Rural Electrification framework has been proposed. This approach can potentially accelerate SDG7 by reducing system cost, enhancing reliability, and offering installation flexibility for small communities in remote areas. In this framework, villages and generation facilities are connected via an optimal, low voltage network that can be built with inexpensive poles and cables. To make this approach economically feasible, cost for building the network is crucial. A specific difficulty associated with this approach is the anisotropicity of search space for optimal design of the power distribution network, which results from complex topographical variations in these rural areas. Traditional optimization methods are not suitable for designing this network because of computational complexity, accuracy requirement, and practical implementation considerations. To address the issues, new computation methods and tools have been developed. These include (i) Multiplier-accelerated A* (MAA*) and (ii) Adaptive Multiplier-accelerated A* (AMAA*) algorithms, which resolve the computational complexity problem by significantly reduce computation time while maintaining good optimality, and (iii) Levelized Interpolative Genetic Algorithm (LIGA) which, when used in conjunction with A*, MAA*, or AMAA*, provides viable alternative plans to tackle unexpected route change problem right before or even during project implementation, and (iv) a fuzzy rule-based system for further network topology optimization.
Open Access
Optimization of daytime fuel consumption for a hybrid diesel and photovoltaic industrial micro-grid
(Colorado State University. Libraries, 2017) Dufrane, Stacey, author; Marchese, Anthony, advisor; Zimmerle, Daniel, advisor; Suryanarayanan, Siddharth, committee member; Bradley, Thomas, committee member
The work to be presented will examine the optimization of daytime diesel fuel consumption for a hybrid diesel and photovoltaic (PV) industrial micro-grid with no energy storage. The micro-grid utilizes a control system developed to forecast PV transients and manage the diesel generators providing electrical supply to the micro-grid. The work focuses on optimization of daytime fuel consumption when PV generation is available. Simulations were utilized to minimize diesel consumption while maintaining secure operations by controlling both PV curtailment and diesel generation. The control system utilizes a cloud forecast system based upon sky imaging, developed by CSIRO (Australia), to predict the presence of cloud cover in concentric "rings" around the sun's position in the sky. The control system utilizes these cloud detections to establish supervisory settings for PV and diesel generation. Work included methods to optimize control response for the number of rings around the sun, studied the use of two different sizes of generators to allow for increased PV utilization, and modification of generator controller settings to reduce fault occurrence. The work indicates that increasing the number of rings used to create the PV forecast has the greatest impact on reducing the number of faults, while having a minimal impact on the total diesel consumption. Additionally, increasing the total number of generators in the system increases PV utilization and decreases fuel consumption.
Open Access
Performance of continuous emission monitoring systems at operating oil and gas facilities
(Colorado State University. Libraries, 2024) Day, Rachel Elizabeth, author; Riddick, Stuart, advisor; Zimmerle, Daniel, advisor; Blanchard, Nathaniel, committee member; Marzolf, Greg, committee member
Globally, demand to reduce methane (CH4) emissions has become paramount and the oil and natural gas (O&G) sector is highlighted as one of the main contributors, being the largest industrial emission source at ≈30%. In efforts to follow legislation of CH4 emission reductions, O&G operators, emission measurement solution companies, and researchers have been testing various techniques and technologies to accurately measure and quantify CH4 emissions. As recent changes to U.S. legislative policies in the Greenhouse Gas Reporting Program (GHGRP) and Inflation Reduction Act (IRA) are imposing a methane waste emission charge beginning in 2024, O&G operators are looking for more continuous and efficient methods to effectively measure emissions at their facilities. Prior to these policy updates, bottom-up measurement methods were the main technique used for reporting yearly emissions to the GHGRP, which involves emission factors and emission source activity data. Top-down measurement methods such as fly-overs with airplanes, drones, or satellites, can provide snap in time surveys of the overall site emissions. With prior research showing the variance between top-down and bottom-up emission estimates, O&G operators have become interested in continuous emissions monitoring systems (CEMs) for their sites to see emission activity continually overtime. A type of CEM, a continuous monitoring (CM) point sensor network (PSN), monitors methane emissions continuously with sensors mounted at the perimeter of O&G sites. CM PSN solutions have become appealing, as they could potentially offer a relatively cost effective and autonomous method of identifying sporadic and fugitive leaks. This study evaluated multiple commercially available CM PSN solutions under single-blind controlled release testing conducted at operational upstream and midstream 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.
Open Access
Statistical analysis of the challenges to high penetration of wind energy
(Colorado State University. Libraries, 2014) O'Connell, Matthew, author; Marchese, Anthony, advisor; Zimmerle, Daniel, advisor; Young, Peter, committee member
Grid penetration of renewable energy technologies, especially wind power, is higher than ever and continues to increase. The inherent stochastic variability of wind makes predicting wind, and thus power generation difficult. Generating companies usually don't openly share power output predictions or historical generation data which increases the level of complexity when determining new wind plant locations or estimating delivered grid level power. This work focuses on statistical data analysis and advanced data modeling related to wind power forecasting and generation. The first part of this thesis uses power output logs from several wind plants and a well-known forecasting method to determine energy storage requirements for individual wind plant contract firming. Forecasts of varying accuracy are used to characterize storage requirements based on contract period length, forecast lead time, and forecast accuracy. Results show that forecast error distributions are effected more by forecast accuracy and lead time than wind plant size and location. The biggest reductions in produced power deviations can be achieved by increasing forecast accuracy and decreasing forecast lead time. The second part of this work develops a statistical analysis which allows estimation of contract firming requirements for a specific wind plant location without the need for time series wind and forecast data. The developed method requires only a wind speed and forecasting error distribution. Using these distributions, deviations between forecast to produced power and energy can be estimated. Results from comparing to historical time series data show this method is accurate to within 10% of actual amounts. Since distributions are much more easily attained than historical time series data, this analysis is useful for developers when evaluating potential new locations. The third part of this work uses a pattern matching algorithm to recognize wind ramp events and separate the forecasting error due to timing from the forecasting error due to magnitude. Wind ramp detection is achieved by developing a pattern matching algorithm which is also shown to work in identifying start and stop transients in electrical device current draw. The analysis confirms wind ramp events can be detected by calculating a bimodal ranking value from a histogram of power data, and the effects of forecast timing and magnitude can be separated from overall forecasting errors. The results of this analysis show magnitude errors contribute more in large wind ramp events, while timing errors contribute more in small ramp events.