Potential impacts of climate warming and increased summer heat stress on the electric grid: a case study for a large power transformer (LPT) in the Northeast United States

Article

Abstract

Large power transformers (LPTs) are critical yet vulnerable components of the power grid. More frequent and intense heat waves or high temperatures can degrade their operational lifetime and increase the risk of premature failure. Without adequate preparedness, a widespread situation could ultimately lead to prolonged grid disruption and incur excessive economic costs. Here, we investigate the potential impact of climate warming and corresponding shifts in summertime “hot days” on a selected LPT located in the Northeast United States. We apply an analogue method, which detects the occurrence of hot days based on the salient, associated large-scale atmospheric conditions, to assess the risk of future change in their occurrence. Compared with the more conventional approach that relies on climate model-simulated daily maximum temperature, the analogue method produces model medians of late twentieth century hot day frequency that are more consistent with observation and have stronger inter-model consensus. Under the climate warming scenarios, multi-model medians of both model daily maximum temperature and the analogue method indicate strong decadal increases in hot day frequency by the late twenty-first century, but the analogue method improves model consensus considerably. The decrease of transformer lifetime with temperature increase is further assessed. The improved inter-model consensus of the analogue method is viewed as a promising step toward providing actionable information for a more stable, reliable, and environmentally responsible national grid.

Keywords

Electric grid Heat wave Climate change General circulation model Synoptic atmospheric conditions 

Notes

Acknowledgements

We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI), and the WCRP’s Working Group on Coupled Modeling (WGCM) for their roles in making available the WCRP CMIP5 multi-model dataset. We thank the NOAA Climate Prediction Center for the global gridded precipitation observations and the NASA Global Modeling and Assimilation Office for the MERRA reanalysis data.

Supplementary material

10584_2017_2114_MOESM1_ESM.docx (1.1 mb)
ESM 1(DOCX 1169 kb)

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  • Xiang Gao
    • 1
  • C. Adam Schlosser
    • 1
  • Eric R. Morgan
    • 2
  1. 1.Joint Program on the Science and Policy of Global ChangeMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.MIT Lincoln LaboratoryLexingtonUSA

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