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A Data‐Driven Approach to Assessing Supply Inadequacy Risks Due to Climate‐Induced Shifts in Electricity Demand

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  • Sayanti Mukherjee
  • Roshanak Nateghi

Abstract

The U.S. electric power system is increasingly vulnerable to the adverse impacts of extreme climate events. Supply inadequacy risk can result from climate‐induced shifts in electricity demand and/or damaged physical assets due to hydro‐meteorological hazards and climate change. In this article, we focus on the risks associated with the unanticipated climate‐induced demand shifts and propose a data‐driven approach to identify risk factors that render the electricity sector vulnerable in the face of future climate variability and change. More specifically, we have leveraged advanced supervised learning theory to identify the key predictors of climate‐sensitive demand in the residential, commercial, and industrial sectors. Our analysis indicates that variations in mean dew point temperature is the common major risk factor across all the three sectors. We have also conducted a statistical sensitivity analysis to assess the variability in the projected demand as a function of the key climate risk factor. We then propose the use of scenario‐based heat maps as a tool to communicate the inadequacy risks to stakeholders and decisionmakers. While we use the state of Ohio as a case study, our proposed approach is equally applicable to all other states.

Suggested Citation

  • Sayanti Mukherjee & Roshanak Nateghi, 2019. "A Data‐Driven Approach to Assessing Supply Inadequacy Risks Due to Climate‐Induced Shifts in Electricity Demand," Risk Analysis, John Wiley & Sons, vol. 39(3), pages 673-694, March.
    • Handle: RePEc:wly:riskan:v:39:y:2019:i:3:p:673-694
    DOI: 10.1111/risa.13192
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    References listed on IDEAS

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    1. Roshanak Nateghi & Seth D. Guikema & Yue (Grace) Wu & C. Bayan Bruss, 2016. "Critical Assessment of the Foundations of Power Transmission and Distribution Reliability Metrics and Standards," Risk Analysis, John Wiley & Sons, vol. 36(1), pages 4-15, January.
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    6. Roshanak Nateghi & Sayanti Mukherjee, 2017. "A multi-paradigm framework to assess the impacts of climate change on end-use energy demand," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-23, November.
    7. Eric Hirst, 1992. "Price and Cost Impacts of Utility DSM Programs," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 75-90.
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    Cited by:

    1. Ganguly, Prasangsha & Mukherjee, Sayanti, 2021. "A multifaceted risk assessment approach using statistical learning to evaluate socio-environmental factors associated with regional felony and misdemeanor rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    2. Pezalla, Simon & Obringer, Renee, 2023. "Evaluating the household-level climate-electricity nexus across three cities through statistical learning techniques," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    3. 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).
    4. Renee Obringer & Rohini Kumar & Roshanak Nateghi, 2020. "Managing the water–electricity demand nexus in a warming climate," Climatic Change, Springer, vol. 159(2), pages 233-252, March.
    5. Alipour, Panteha & Mukherjee, Sayanti & Nateghi, Roshanak, 2019. "Assessing climate sensitivity of peak electricity load for resilient power systems planning and operation: A study applied to the Texas region," Energy, Elsevier, vol. 185(C), pages 1143-1153.
    6. Obringer, Renee & Mukherjee, Sayanti & Nateghi, Roshanak, 2020. "Evaluating the climate sensitivity of coupled electricity-natural gas demand using a multivariate framework," Applied Energy, Elsevier, vol. 262(C).

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