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Carbon Dioxide (CO2) Emissions from Electricity: The Influence of The North Atlantic Oscillation

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  • Curtis, John
  • Lynch, Muireann Á.
  • Zubiate, Laura

Abstract

The North Atlantic Oscillation (NAO) is a large-scale circulation pattern driving climate variability in north-western Europe. In recent years there has been an increasing deployment of wind-powered generation technology, i.e. wind farms, on electricity networks across Europe. As this deployment increases it is important to understand how climate variability will affect both wind powered and non-renewable power generation. This study extends the literature by assessing the impact of NAO, via wind-power generation, on carbon dioxide emissions from the wider electricity system. A Monte Carlo approach is used to model NAO phases, generate hourly wind speed time series data, electricity demand and fuel input data. A unit commitment, least-cost economic dispatch model is used to simulate an entire electricity system, modelled on the all-island Irish electricity system. Our results confirm that the NAO has a significant impact on monthly mean wind speeds, wind power output, and carbon dioxide emissions from the entire electricity system. The impact of NAO on emissions obviously depends on the level of wind penetration within an electricity system but our results indicate that emissions intensity within the Irish electricity system could vary by as much as 10% depending on the NAO phase within the next few years. The emissions intensity of the electricity system will vary with the NAO phase.

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  • Curtis, John & Lynch, Muireann Á. & Zubiate, Laura, 2015. "Carbon Dioxide (CO2) Emissions from Electricity: The Influence of The North Atlantic Oscillation," Papers WP510, Economic and Social Research Institute (ESRI).
    • Handle: RePEc:esr:wpaper:wp510
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    2. Curtis, John & Lynch, Muireann Á. & Zubiate, Laura, 2016. "The impact of the North Atlantic Oscillation on electricity markets: A case study on Ireland," Energy Economics, Elsevier, vol. 58(C), pages 186-198.
    3. Hong, Taehoon & Jeong, Kwangbok & Koo, Choongwan, 2018. "An optimized gene expression programming model for forecasting the national CO2 emissions in 2030 using the metaheuristic algorithms," Applied Energy, Elsevier, vol. 228(C), pages 808-820.
    4. Cradden, Lucy C. & McDermott, Frank & Zubiate, Laura & Sweeney, Conor & O'Malley, Mark, 2017. "A 34-year simulation of wind generation potential for Ireland and the impact of large-scale atmospheric pressure patterns," Renewable Energy, Elsevier, vol. 106(C), pages 165-176.
    5. Plaga, Leonie Sara & Bertsch, Valentin, 2023. "Methods for assessing climate uncertainty in energy system models — A systematic literature review," Applied Energy, Elsevier, vol. 331(C).
    6. Slednev, Viktor & Bertsch, Valentin & Ruppert, Manuel & Fichtner, Wolf, 2017. "Highly resolved optimal renewable allocation planning in power systems under consideration of dynamic grid topology," MPRA Paper 79706, University Library of Munich, Germany.
    7. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.

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