Assessment of Spatial and Temporal Modeling on Greenhouse Gas Emissions From Electricity Generation
This paper highlights the importance of precise assessments of greenhouse gas (GHG) emissions associated with power generation for effective policy making in environmental sustainability. The current assessment approaches based on historical data or estimated generation using energy models may not a...
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my.uniten.dspace-346532024-10-14T11:21:27Z Assessment of Spatial and Temporal Modeling on Greenhouse Gas Emissions From Electricity Generation Sarhan A. Ramachandaramurthy V.K. Sin T.C. Walker S.L. Salman B. Padmanaban S. 57203979904 6602912020 57212007867 37105142200 57195282152 18134802000 electric vehicle Energy energy storage GHG emissions renewable generation Digital storage Electric energy storage Electric loads Fossil fuels Fuel storage Gas emissions Greenhouse effect Greenhouse gases Renewable energy resources Sustainable development Electricity-generation Energy Generation mix Greenhouse gas emissions Load modeling Power- generations Renewable energy source Renewable generation Spatial and temporal modeling Techno-economics Electric vehicles This paper highlights the importance of precise assessments of greenhouse gas (GHG) emissions associated with power generation for effective policy making in environmental sustainability. The current assessment approaches based on historical data or estimated generation using energy models may not accurately reflect the reality of future power systems due to the impact of spatial-temporal and techno-economic characteristics of generation mix and load demands. To address this, the paper presents a comprehensive methodology for accurately quantifying the geographical and temporal variations in GHG emissions associated with generating units' operation, startup, and shutdown at an hourly resolution. The methodology is based on a detailed electricity model that considers various sources of generation, techno-economic, and spatial-temporal characteristics of system components. The study demonstrates the effectiveness of the methodology in quantifying GHG emissions in the IEEE RTS-GLMC system, with a focus on CO2, N2O, and CH4. The analysis reveals significant variations in GHG emissions among different generation buses and hours of the year, attributed to the high proportion of renewable energy in the generation mix. The paper emphasizes the inadequacy of examining marginal environmental impacts based on GHG emission intensity alone and suggests a more thorough analysis based on total GHG emissions generation. Finally, the paper emphasizes the crucial role of time-varying and marginal assessment techniques in identifying effective strategies for reducing GHG emissions in the electricity sector, including optimizing the operation and capacity of generation units, energy storage systems, and electric vehicles, including their locations. � 2013 IEEE. Final 2024-10-14T03:21:27Z 2024-10-14T03:21:27Z 2023 Article 10.1109/ACCESS.2023.3258923 2-s2.0-85151556917 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151556917&doi=10.1109%2fACCESS.2023.3258923&partnerID=40&md5=4ca2b8acd3dd0a709c46035561d7f5c0 https://irepository.uniten.edu.my/handle/123456789/34653 11 97478 97492 All Open Access Gold Open Access Green Open Access Institute of Electrical and Electronics Engineers Inc. Scopus |
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electric vehicle Energy energy storage GHG emissions renewable generation Digital storage Electric energy storage Electric loads Fossil fuels Fuel storage Gas emissions Greenhouse effect Greenhouse gases Renewable energy resources Sustainable development Electricity-generation Energy Generation mix Greenhouse gas emissions Load modeling Power- generations Renewable energy source Renewable generation Spatial and temporal modeling Techno-economics Electric vehicles |
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electric vehicle Energy energy storage GHG emissions renewable generation Digital storage Electric energy storage Electric loads Fossil fuels Fuel storage Gas emissions Greenhouse effect Greenhouse gases Renewable energy resources Sustainable development Electricity-generation Energy Generation mix Greenhouse gas emissions Load modeling Power- generations Renewable energy source Renewable generation Spatial and temporal modeling Techno-economics Electric vehicles Sarhan A. Ramachandaramurthy V.K. Sin T.C. Walker S.L. Salman B. Padmanaban S. Assessment of Spatial and Temporal Modeling on Greenhouse Gas Emissions From Electricity Generation |
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This paper highlights the importance of precise assessments of greenhouse gas (GHG) emissions associated with power generation for effective policy making in environmental sustainability. The current assessment approaches based on historical data or estimated generation using energy models may not accurately reflect the reality of future power systems due to the impact of spatial-temporal and techno-economic characteristics of generation mix and load demands. To address this, the paper presents a comprehensive methodology for accurately quantifying the geographical and temporal variations in GHG emissions associated with generating units' operation, startup, and shutdown at an hourly resolution. The methodology is based on a detailed electricity model that considers various sources of generation, techno-economic, and spatial-temporal characteristics of system components. The study demonstrates the effectiveness of the methodology in quantifying GHG emissions in the IEEE RTS-GLMC system, with a focus on CO2, N2O, and CH4. The analysis reveals significant variations in GHG emissions among different generation buses and hours of the year, attributed to the high proportion of renewable energy in the generation mix. The paper emphasizes the inadequacy of examining marginal environmental impacts based on GHG emission intensity alone and suggests a more thorough analysis based on total GHG emissions generation. Finally, the paper emphasizes the crucial role of time-varying and marginal assessment techniques in identifying effective strategies for reducing GHG emissions in the electricity sector, including optimizing the operation and capacity of generation units, energy storage systems, and electric vehicles, including their locations. � 2013 IEEE. |
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57203979904 |
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57203979904 Sarhan A. Ramachandaramurthy V.K. Sin T.C. Walker S.L. Salman B. Padmanaban S. |
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Article |
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Sarhan A. Ramachandaramurthy V.K. Sin T.C. Walker S.L. Salman B. Padmanaban S. |
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Sarhan A. |
title |
Assessment of Spatial and Temporal Modeling on Greenhouse Gas Emissions From Electricity Generation |
title_short |
Assessment of Spatial and Temporal Modeling on Greenhouse Gas Emissions From Electricity Generation |
title_full |
Assessment of Spatial and Temporal Modeling on Greenhouse Gas Emissions From Electricity Generation |
title_fullStr |
Assessment of Spatial and Temporal Modeling on Greenhouse Gas Emissions From Electricity Generation |
title_full_unstemmed |
Assessment of Spatial and Temporal Modeling on Greenhouse Gas Emissions From Electricity Generation |
title_sort |
assessment of spatial and temporal modeling on greenhouse gas emissions from electricity generation |
publisher |
Institute of Electrical and Electronics Engineers Inc. |
publishDate |
2024 |
_version_ |
1814060111319007232 |
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13.214268 |