scholarly journals Advances in quantifying power plant CO2 emissions with OCO-2

2021 ◽  
Vol 264 ◽  
pp. 112579
Author(s):  
Ray Nassar ◽  
Jon-Paul Mastrogiacomo ◽  
William Bateman-Hemphill ◽  
Callum McCracken ◽  
Cameron G. MacDonald ◽  
...  
Keyword(s):  
Power Plant ◽  
Co2 Emissions

scholarly journals Estimating CO2 Emissions from Large Scale Coal-Fired Power Plants Using OCO-2 Observations and Emission Inventories

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 811
Author(s):  
Yaqin Hu ◽  
Yusheng Shi
Keyword(s):  
Power Plant ◽  
Co2 Emissions ◽  
Atmospheric Carbon Dioxide ◽  
Power Plants ◽  
Large Scale ◽  
Global Climate ◽  
Gaussian Model ◽  
Point Sources ◽  
Emission Inventories ◽  
Bottom Up

The concentration of atmospheric carbon dioxide (CO2) has increased rapidly worldwide, aggravating the global greenhouse effect, and coal-fired power plants are one of the biggest contributors of greenhouse gas emissions in China. However, efficient methods that can quantify CO2 emissions from individual coal-fired power plants with high accuracy are needed. In this study, we estimated the CO2 emissions of large-scale coal-fired power plants using Orbiting Carbon Observatory-2 (OCO-2) satellite data based on remote sensing inversions and bottom-up methods. First, we mapped the distribution of coal-fired power plants, displaying the total installed capacity, and identified two appropriate targets, the Waigaoqiao and Qinbei power plants in Shanghai and Henan, respectively. Then, an improved Gaussian plume model method was applied for CO2 emission estimations, with input parameters including the geographic coordinates of point sources, wind vectors from the atmospheric reanalysis of the global climate, and OCO-2 observations. The application of the Gaussian model was improved by using wind data with higher temporal and spatial resolutions, employing the physically based unit conversion method, and interpolating OCO-2 observations into different resolutions. Consequently, CO2 emissions were estimated to be 23.06 ± 2.82 (95% CI) Mt/yr using the Gaussian model and 16.28 Mt/yr using the bottom-up method for the Waigaoqiao Power Plant, and 14.58 ± 3.37 (95% CI) and 14.08 Mt/yr for the Qinbei Power Plant, respectively. These estimates were compared with three standard databases for validation: the Carbon Monitoring for Action database, the China coal-fired Power Plant Emissions Database, and the Carbon Brief database. The comparison found that previous emission inventories spanning different time frames might have overestimated the CO2 emissions of one of two Chinese power plants on the two days that the measurements were made. Our study contributes to quantifying CO2 emissions from point sources and helps in advancing satellite-based monitoring techniques of emission sources in the future; this helps in reducing errors due to human intervention in bottom-up statistical methods.

scholarly journals Comparative Environmental, Economic, and Jobs Impacts in the USA of Renewable Energy Compared to Carbon Capture, Utilization, and Storage

2021 ◽  
pp. 1-7
Author(s):  
Roger H Bezdek ◽  
Keyword(s):  
Renewable Energy ◽  
Environmental Economic ◽  
Power Plant ◽  
Co2 Emissions ◽  
Carbon Capture ◽  
Power Plants ◽  
Economic Benefits ◽  
The Usa ◽  
Coal Power ◽  
And Storage

This paper assesses the relative economic and jobs benefits of retrofitting an 847 MW USA coal power plant with carbon capture, utilization, and storage (CCUS) technology compared to replacing the plant with renewable (RE) energy and battery storage. The research had two major objectives: 1) Estimate the relative environmental, economic, and jobs impacts of CCUS retrofit of the coal plant compared to its replacement by the RE scenario; 2) develop metrics that can be used to compare the jobs impacts of coal fueled power plants to those of renewable energy. The hypotheses tested are: 1) The RE option will reduce CO2 emissions more than the CCUS option. We reject this hypothesis: We found that the CCUS option will reduce CO2 emissions more than the RE option. 2) The RE option will generate greater economic benefits than the CCUS option. We reject this hypothesis: We found that the CCUS option will create greater economic and jobs benefits than the RE option. 3) The RE option will create more jobs per MW than the CCUS option. We reject this hypothesis: We found that the CCUS option will create more jobs per MW more than the RE option. We discuss the implications of these findings.

Reducing CO2 Emissions of a Coal-Fired Power Plant via Accelerated Weathering of Limestone: Carbon Capture Efficiency and Environmental Safety

2020 ◽  
Vol 54 (7) ◽  
pp. 4528-4535
Author(s):  
Julia S. Kirchner ◽  
Andrew Berry ◽  
Frank Ohnemüller ◽  
Bernhard Schnetger ◽  
Egon Erich ◽  
...  
Keyword(s):  
Power Plant ◽  
Co2 Emissions ◽  
Carbon Capture ◽  
Environmental Safety ◽  
Capture Efficiency ◽  
Accelerated Weathering

scholarly journals Towards space based verification of CO<sub>2</sub> emissions from strong localized sources: fossil fuel power plant emissions as seen by a CarbonSat constellation

2011 ◽  
Vol 4 (4) ◽  
pp. 5147-5182
Author(s):  
V. A. Velazco ◽  
M. Buchwitz ◽  
H. Bovensmann ◽  
M. Reuter ◽  
O. Schneising ◽  
...  
Keyword(s):  
Power Plant ◽  
Systematic Error ◽  
Co2 Emissions ◽  
Power Plants ◽  
Fossil Fuel ◽  
Systematic Errors ◽  
Random Errors ◽  
Power Plant Emissions ◽  
Global Coverage ◽  
Plant Emissions

Abstract. Carbon dioxide (CO2) is the most important man-made greenhouse gas (GHG) that cause global warming. With electricity generation through fossil-fuel power plants now as the economic sector with the largest source of CO2, power plant emissions monitoring has become more important than ever in the fight against global warming. In a previous study done by Bovensmann et al. (2010), random and systematic errors of power plant CO2 emissions have been quantified using a single overpass from a proposed CarbonSat instrument. In this study, we quantify errors of power plant annual emission estimates from a hypothetical CarbonSat and constellations of several CarbonSats while taking into account that power plant CO2 emissions are time-dependent. Our focus is on estimating systematic errors arising from the sparse temporal sampling as well as random errors that are primarily dependent on wind speeds. We used hourly emissions data from the US Environmental Protection Agency (EPA) combined with assimilated and re-analyzed meteorological fields from the National Centers of Environmental Prediction (NCEP). CarbonSat orbits were simulated as a sun-synchronous low-earth orbiting satellite (LEO) with an 828-km orbit height, local time ascending node (LTAN) of 13:30 (01:30 p.m.) and achieves global coverage after 5 days. We show, that despite the variability of the power plant emissions and the limited satellite overpasses, one CarbonSat can verify reported US annual CO2 emissions from large power plants (≥5 Mt CO2 yr−1) with a systematic error of less than ~4.9 % for 50 % of all the power plants. For 90 % of all the power plants, the systematic error was less than ~12.4 %. We additionally investigated two different satellite configurations using a combination of 5 CarbonSats. One achieves global coverage everyday but only samples the targets at fixed local times. The other configuration samples the targets five times at two-hour intervals approximately every 6th day but only achieves global coverage after 5 days. From the statistical analyses, we found, as expected, that the random errors improve by approximately a factor of two if 5 satellites are used. On the other hand, more satellites do not result in a large reduction of the systematic error. The systematic error is somewhat smaller for the CarbonSat constellation configuration achieving global coverage everyday. Finally, we recommend the CarbonSat constellation configuration that achieves daily global coverage.

Analysis of Gas-Steam Combined Cycles With Natural Gas Reforming and CO2 Capture

2005 ◽  
Vol 127 (3) ◽  
pp. 545-552 ◽  
Author(s):  
Alessandro Corradetti ◽  
Umberto Desideri
Keyword(s):  
Power Plant ◽  
Natural Gas ◽  
Co2 Emissions ◽  
Co2 Capture ◽  
Carbon Dioxide Emissions ◽  
Research Work ◽  
Combined Cycle ◽  
Co2 Absorption ◽  
Syngas Production ◽  
Natural Gas Reforming

In the last several years greenhouse gas emissions, and, in particular, carbon dioxide emissions, have become a major concern in the power generation industry and a large amount of research work has been dedicated to this subject. Among the possible technologies to reduce CO2 emissions from power plants, the pretreatment of fossil fuels to separate carbon from hydrogen before the combustion process is one of the least energy-consuming ways to facilitate CO2 capture and removal from the power plant. In this paper several power plant schemes with reduced CO2 emissions were simulated. All the configurations were based on the following characteristics: (i) syngas production via natural gas reforming; (ii) two reactors for CO-shift; (iii) “precombustion” decarbonization of the fuel by CO2 absorption with amine solutions; (iv) combustion of hydrogen-rich fuel in a commercially available gas turbine; and (v) combined cycle with three pressure levels, to achieve a net power output in the range of 400 MW. The base reactor employed for syngas generation is the ATR (auto thermal reformer). The attention was focused on the optimization of the main parameters of this reactor and its interaction with the power section. In particular the simulation evaluated the benefits deriving from the postcombustion of exhaust gas and from the introduction of a gas-gas heat exchanger. All the components of the plants were simulated using ASPEN PLUS software, and fixing a reduction of CO2 emissions of at least 90%. The best configuration showed a thermal efficiency of approximately 48% and CO2 specific emissions of 0.04 kg/kWh.

scholarly journals A remote sensing technique for global monitoring of power plant CO<sub>2</sub> emissions from space and related applications

2010 ◽  
Vol 3 (4) ◽  
pp. 781-811 ◽  
Author(s):  
H. Bovensmann ◽  
M. Buchwitz ◽  
J. P. Burrows ◽  
M. Reuter ◽  
T. Krings ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Wind Speed ◽  
Power Plant ◽  
Co2 Emissions ◽  
Kyoto Protocol ◽  
Inverse Modeling ◽  
Point Sources ◽  
Atmospheric Co2 ◽  
Independent Verification ◽  
Anthropogenic Co2

Abstract. Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas (GHG) causing global warming. The atmospheric CO2 concentration increased by more than 30% since pre-industrial times – primarily due to burning of fossil fuels – and still continues to increase. Reporting of CO2 emissions is required by the Kyoto protocol. Independent verification of reported emissions, which are typially not directly measured, by methods such as inverse modeling of measured atmospheric CO2 concentrations is currently not possible globally due to lack of appropriate observations. Existing satellite instruments such as SCIAMACHY/ENVISAT and TANSO/GOSAT focus on advancing our understanding of natural CO2 sources and sinks. The obvious next step for future generation satellites is to also constrain anthropogenic CO2 emissions. Here we present a promising satellite remote sensing concept based on spectroscopic measurements of reflected solar radiation and show, using power plants as an example, that strong localized CO2 point sources can be detected and their emissions quantified. This requires mapping the atmospheric CO2 column distribution at a spatial resolution of 2×2 km2 with a precision of 0.5% (2 ppm) or better. We indicate that this can be achieved with existing technology. For a single satellite in sun-synchronous orbit with a swath width of 500 km, each power plant (PP) is overflown every 6 days or more frequent. Based on the MODIS cloud mask data product we conservatively estimate that typically 20 sufficiently cloud free overpasses per PP can be achieved every year. We found that for typical wind speeds in the range of 2–6 m/s the statistical uncertainty of the retrieved PP CO2 emission due to instrument noise is in the range 1.6–4.8 MtCO2/yr for single overpasses. This corresponds to 12–36% of the emission of a mid-size PP (13 MtCO2/yr). We have also determined the sensitivity to parameters which may result in systematic errors such as atmospheric transport and aerosol related parameters. We found that the emission error depends linearly on wind speed, i.e., a 10% wind speed error results in a 10% emission error, and that neglecting enhanced aerosol concentrations in the PP plume may result in errors in the range 0.2–2.5 MtCO2/yr, depending on PP aerosol emission. The discussed concept has the potential to contribute to an independent verification of reported anthropogenic CO2 emissions and therefore could be an important component of a future global anthropogenic GHG emission monitoring system. This is of relevance in the context of Kyoto protocol follow-on agreements but also allows detection and monitoring of a variety of other strong natural and anthropogenic CO2 and CH4 emitters. The investigated instrument is not limited to these applications as it has been specified to also deliver the data needed for global regional-scale CO2 and CH4 surface flux inverse modeling.

Air-Based Bottoming-Cycles for Water-Free Hybrid Solar Gas-Turbine Power Plants

2013 ◽  
Vol 135 (10) ◽  
Author(s):  
Raphaël Sandoz ◽  
James Spelling ◽  
Björn Laumert ◽  
Torsten Fransson
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Co2 Emissions ◽  
Power Plants ◽  
Levelized Cost Of Electricity ◽  
Minimum Capital ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Novel Concept ◽  
Gas Turbine Power Plant

A thermoeconomic model of a novel hybrid solar gas-turbine power plant with an air-based bottoming cycle has been developed, allowing its thermodynamic, economic, and environmental performance to be analyzed. Multi-objective optimization has been performed to identify the trade-offs between two conflicting objectives: minimum capital cost and minimum specific CO2 emissions. In-depth thermoeconomic analysis reveals that the additional bottoming cycle significantly reduces both the levelized cost of electricity and the environmental impact of the power plant (in terms of CO2 emissions and water consumption) when compared to a simple gas-turbine power plant without bottoming cycle. Overall, the novel concept appears to be a promising solution for sustainable power generation, especially in water-scarce areas.

scholarly journals Towards space-borne monitoring of localized CO<sub>2</sub> emissions: an instrument concept and first performance assessment

2020 ◽  
Author(s):  
Johan Strandgren ◽  
David Krutz ◽  
Jonas Wilzewski ◽  
Carsten Paproth ◽  
Ilse Sebastian ◽  
...  
Keyword(s):  
Power Plant ◽  
Performance Assessment ◽  
Co2 Emissions ◽  
Co2 Emission ◽  
Power Plants ◽  
Point Sources ◽  
Source Strength ◽  
Spectral Window ◽  
Instrument Noise ◽  
Noise Error

Abstract. The UNFCCC (United Nations Framework Convention on Climate Change) requires the nations of the world to report their carbon dioxide (CO2) emissions. Independent verification of these reported emissions is a corner stone for advancing towards emission accounting and reduction measures agreed upon in the Paris agreement. In this paper, we present the concept and first performance assessment of a compact space-borne imaging spectrometer that could support the task of "monitoring, verification, reporting" (MVR) of CO2 emissions worldwide. With a single spectral window in the short-wave infrared spectral region and a spatial resolution of 50 x 50 m2, the goal is to reliably estimate the CO2 emissions from localized sources down to a source strength of approx. 1 MtCO2 yr-1, hence complementing other planned CO2 monitoring missions, like the planned European Carbon Constellation (CO2M). Resolving CO2 plumes also from medium-sized power plants (1–10 MtCO2 yr-1) is of key importance for independent quantification of CO2 emissions from the coal-fired power plant sector. Through radiative transfer simulations, including a realistic instrument noise model and a global trial ensemble covering various geophysical scenarios, it is shown that an instrument noise error of 1.1 ppm (1σ) can be achieved for the retrieval of the column-averaged dry-air mole fraction of CO2 (XCO2). Despite limited amount of information from a single spectral window and a relatively coarse spectral resolution, scattering by atmospheric aerosol and cirrus can be partly accounted for in the XCO2 retrieval, with deviations of at most 4.0 ppm from the true abundance for 68 % of the scenes in the global trial ensemble. We further simulate the ability of the proposed instrument concept to observe CO2 plumes from single power plants in an urban area using high-resolution CO2 emission and surface albedo data for the city of Indianapolis. Given the preliminary instrument design and the corresponding instrument noise error, emission plumes from point sources with an emission rate down to the order of 0.3 MtCO2 yr-1 can be resolved, i.e. well below the target source strength of 1 MtCO2 yr-1. Hence, the proposed instrument concept could be able to resolve and quantify the CO2 plumes from localized point sources responsible for approx. 90 % of the power plant CO2 emission budget, assuming global coverage through a fleet of sensors and favorable conditions with respect to illumination and particle scattering.

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