Publications

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. Therefore, there is attention on 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. Estimates from the two TD methods statistically agreed in 2 of 28 paired measurements. 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 40 of 43 paired comparisons. Significant disagreement is observed at most facilities, both between the two TD methods and between the TD estimates and operator inventory. These findings have two implications. Firstly, improving inventory estimates will require additional on-site or ground-based diagnostic screening and measurement of all sources. Secondly, the TD full-facility measurement methods need to undergo further testing, characterization, and potential improvement specifically tailored for complex midstream facilities.

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.

Access to water for domestic and irrigation uses remains limited across Sub-Saharan Africa, particularly in rural areas. While the technical feasibility of implementing innovative technologies to improve water supply has been often evaluated, less is known about the drivers that motivate users to pay for obtaining the inherent benefits of improved water supply. In this article, the determinants of the marginal willingness to pay (mWTP) for improved domestic and irrigation water are investigated. For this purpose, a contingent valuation study is performed in rural Rwanda. This study uses survey data from 316 households situated in 8 villages, in which ~20% of the households receive electricity thanks to microgrids powered by solar energy. It is found that the mWTP for improved domestic water supply is influenced by the proportion of children in the household, business ownership, and satisfaction with water quantity and quality. The mWTP for irrigation is determined by respondent education, business ownership and crop area planted. This paper contributes to the literature by allowing a comparison of the determinants of the mWTP for improved domestic water supply to the determinants of the mWTP for irrigation. Another contribution is to examine the mWTP for improved water infrastructure in partially electrified villages. Identifying areas with high marginal benefits from water can guide infrastructure investment and electrification efforts while improving well-being and increasing revenues in rural areas.

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.

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.