The effect of distributed electricity generation using natural gas on the electric and natural gas grids

Touretzky, Cara R.; McGuffin, Dana L.; Ziesmer, Jena C.; Bâldea, Michael

doi:10.1016/j.apenergy.2016.05.098

Cited by 41 publications

(8 citation statements)

References 52 publications

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“…Advanced and cost-effective drilling and production techniques like hydraulic fracturing and horizontal drilling have stimulated increased natural gas production from shale formations [1,2]. The coupling between natural gas systems and electricity systems has been increasing with rising deployment of gas-fired power plants and gas-fired combined heat and power systems [3][4][5][6][7][8]. Increasing residential and commercial heating demand also contributes to growing natural gas consumption and production [9].…”

Section: Introductionmentioning

confidence: 99%

Machine vision for natural gas methane emissions detection using an infrared camera

et al. 2020

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In a climate-constrained world, it is crucial to reduce natural gas methane emissions, which can potentially offset the climate benefits of replacing coal with gas. Optical gas imaging (OGI) is a widely-used method to detect methane leaks, but is labor-intensive and cannot provide leak detection results without operators' judgment. In this paper, we develop a computer vision approach to OGI-based leak detection using convolutional neural networks (CNN) trained on methane leak images to enable automatic detection. First, we collect ∼1 M frames of labeled video of methane leaks from different leaking equipment for training, validating and testing CNN model, covering a wide range of leak sizes (5.3-2051.6 gCH 4 /h) and imaging distances (4.6-15.6 m). Second, we examine different background subtraction methods to extract the methane plume in the foreground. Third, we then test three CNN model variants, collectively called GasNet, to detect plumes in videos taken at other pieces of leaking equipment. We assess the ability of GasNet to perform leak detection by comparing it to a baseline method that uses optical-flow based change detection algorithm. We explore the sensitivity of results to the CNN structure, with a moderate-complexity variant performing best across distances. We find that the detection accuracy (fraction of leak and non-leak images correctly identified by the algorithm) can reach as high as 99%, the overall detection accuracy can exceed 95% for a case across all leak sizes and imaging distances. Binary 1 arXiv:1904.08500v1 [cs.CV] 1 Apr 2019 detection accuracy exceeds 97% for large leaks (∼710 gCH 4 /h) imaged closely (∼5-7 m).At closer imaging distances (∼5-10 m), CNN-based models have greater than 94% accuracy across all leak sizes. At farthest distances (∼13-16 m), performance degrades rapidly, but it can achieve above 95% accuracy to detect large leaks (>950 gCH 4 /h). The GasNet-based computer vision approach could be deployed in OGI surveys to allow automatic vigilance of methane leak detection with high detection accuracy in the real world.

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Section: Introductionmentioning

confidence: 99%

Machine vision for natural gas methane emissions detection using an infrared camera

et al. 2020

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“…Several studies have focused on the electricity-gas system. Reference [30] focused on the interaction between the electricity grid and natural gas network on the residential demand side. The simulation results verified the advancement of a large number of small-scale generators for localized load balancing of the integrated system.…”

Section: Introductionmentioning

confidence: 99%

Integrated demand response for a load serving entity in multi-energy market considering network constraints

et al. 2019

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“…Other benefits of using distributed systems include: improved power quality, enhancement in system reliability and security, provision of power supply for rural or isolated areas, and increased overall electric power energy efficiency [3,4]. The application of distributed power generation systems also makes it possible to utilise the waste heat from them for either industrial processes or space heating and increase their overall energy efficiency [5][6][7]. In order to meet the dynamic demand of users, it is common for distributed power generation systems to utilise energy storage units as a back-up measure [8,9].…”

Section: Introductionmentioning

confidence: 99%

An Experimental and Simulation Study on Optimisation of the Operation of a Distributed Power Generation System with Energy Storage—Meeting Dynamic Household Electricity Demand

Xia

et al. 2019

Energies

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There are few experimental reports combining hybrid energy storage and diesel engine generators as the power source of distributed power generation systems. In this article, a distributed power generation with energy storage system (DG-ES) which contains a diesel engine generator and an energy storage unit is set up and tested in the laboratory to satisfy the dynamic changing load. The hybrid energy storage system is composed of a lead-acid battery and a supercapacitor. The DG-ES supplied power to meet the domestic load demand successfully under different seasons. A simulation model of this system was also set up in MATLAB software used to guide to the experimental operation of this system. The simulation results has 2.69% and 2.35% error. The results of the experimental tests show the DG-ES can provide enough power to meet the dynamic loads of the selected house, especially at peak time in both winter and summer time. Computational simulation results show that the performance of DG-ES is improved by 3.61% in summer and 1.86% in winter when the system’s operations are optimised.

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The effect of distributed electricity generation using natural gas on the electric and natural gas grids

Cited by 41 publications

References 52 publications

Machine vision for natural gas methane emissions detection using an infrared camera

Machine vision for natural gas methane emissions detection using an infrared camera

Integrated demand response for a load serving entity in multi-energy market considering network constraints

An Experimental and Simulation Study on Optimisation of the Operation of a Distributed Power Generation System with Energy Storage—Meeting Dynamic Household Electricity Demand

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