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Assessing risks for New England's wholesale electricity market from wind power losses during extreme winter storms

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  • Akdemir, Kerem Ziya
  • Kern, Jordan D.
  • Lamontagne, Jonathan

Abstract

In the United States, New England faces difficulties from severe winter weather, during which its power grid simultaneously experiences high natural gas prices and electricity demand, leading to spikes in wholesale electricity prices. In recent years, a significant amount of offshore wind power capacity has been planned for the region, and previous studies have suggested the presence of offshore wind could lower emissions and market prices during cold snaps. However, there has been limited consideration of potential wind power losses during extreme winter weather due to excessive wind speeds, which could lead to sudden losses of wind power. This aim of this study is to quantify risks associated with sudden wind power losses during extreme winter weather, especially the potential for these events to cause spikes in the wholesale electricity price. Results suggest that these so-called wind turbine “cut-out” events likely represent a minor risk compared to the loss of wind power due to low wind speeds and sudden drops in wind speeds during summer, when demand for electricity is higher. Overall, the benefits of having offshore wind power during extreme winter weather appear to outweigh the risks associated with relatively rare cut-out events caused by excessive wind speeds.

Suggested Citation

  • Akdemir, Kerem Ziya & Kern, Jordan D. & Lamontagne, Jonathan, 2022. "Assessing risks for New England's wholesale electricity market from wind power losses during extreme winter storms," Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222007897
    DOI: 10.1016/j.energy.2022.123886
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    References listed on IDEAS

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    1. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren & Söder, Lennart, 2018. "Analysis of wind power intermittency based on historical wind power data," Energy, Elsevier, vol. 150(C), pages 482-492.
    2. Andrew Speake & Paul Donohoo-Vallett & Eric Wilson & Emily Chen & Craig Christensen, 2020. "Residential Natural Gas Demand Response Potential during Extreme Cold Events in Electricity-Gas Coupled Energy Systems," Energies, MDPI, vol. 13(19), pages 1-19, October.
    3. R. J. Barthelmie & S. C. Pryor, 2014. "Potential contribution of wind energy to climate change mitigation," Nature Climate Change, Nature, vol. 4(8), pages 684-688, August.
    4. Turconi, R. & O’Dwyer, C. & Flynn, D. & Astrup, T., 2014. "Emissions from cycling of thermal power plants in electricity systems with high penetration of wind power: Life cycle assessment for Ireland," Applied Energy, Elsevier, vol. 131(C), pages 1-8.
    5. Gualtieri, Giovanni & Secci, Sauro, 2012. "Methods to extrapolate wind resource to the turbine hub height based on power law: A 1-h wind speed vs. Weibull distribution extrapolation comparison," Renewable Energy, Elsevier, vol. 43(C), pages 183-200.
    6. Archer, C.L. & Simão, H.P. & Kempton, W. & Powell, W.B. & Dvorak, M.J., 2017. "The challenge of integrating offshore wind power in the U.S. electric grid. Part I: Wind forecast error," Renewable Energy, Elsevier, vol. 103(C), pages 346-360.
    7. Gualtieri, Giovanni & Secci, Sauro, 2011. "Comparing methods to calculate atmospheric stability-dependent wind speed profiles: A case study on coastal location," Renewable Energy, Elsevier, vol. 36(8), pages 2189-2204.
    8. Lin, Jin & Sun, Yuan-zhang & Cheng, Lin & Gao, Wen-zhong, 2012. "Assessment of the power reduction of wind farms under extreme wind condition by a high resolution simulation model," Applied Energy, Elsevier, vol. 96(C), pages 21-32.
    9. Wang, Jianzhou & Qin, Shanshan & Jin, Shiqiang & Wu, Jie, 2015. "Estimation methods review and analysis of offshore extreme wind speeds and wind energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 26-42.
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    Cited by:

    1. Wu, Han & Liang, Yan & Gao, Xiao-Zhi & Du, Pei, 2024. "Auditory-circuit-motivated deep network with application to short-term electricity price forecasting," Energy, Elsevier, vol. 288(C).
    2. Lin, Jianing & Bao, Minglei & Liang, Ziyang & Sang, Maosheng & Ding, Yi, 2022. "Spatio-temporal evaluation of electricity price risk considering multiple uncertainties under extreme cold weather," Applied Energy, Elsevier, vol. 328(C).
    3. Akdemir, Kerem Ziya & Robertson, Bryson & Oikonomou, Konstantinos & Kern, Jordan & Voisin, Nathalie & Hanif, Sarmad & Bhattacharya, Saptarshi, 2023. "Opportunities for wave energy in bulk power system operations," Applied Energy, Elsevier, vol. 352(C).

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