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Bounding US electricity demand in 2050

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  • Schweizer, Vanessa J.
  • Morgan, M. Granger

Abstract

Limiting climate change requires a radical shift in energy supply and use. Because of time lags in capital investments, the political process, and the climate system, potential developments decades from now must be considered for energy policy decisions today. Traditionally, scenario analysis and forecasting are used to conceptualize the future; however, past energy demand forecasts have performed poorly displaying overconfidence, or a tendency to overly discount the tails of a distribution of possibilities under uncertainty. This study demonstrates a simple analytical approach to bound US electricity demand in 2050. Long-term electricity demand is parsed into two terms — an expected, or “business-as-usual,” term and a “new demand” term estimated explicitly to account for possible technological changes in response to climate change. Under a variety of aggressive adaptation and mitigation conditions, low or high growth in GDP, and modest or substantial improvements in energy intensity, US electricity demand could be as little as 3100TWh or as much as 17,000TWh in 2050. Electrification of the US transportation sector could introduce the largest share of new electricity demand. Projections for expected electricity demand are most sensitive to assumptions about the rate of reduction of US electricity intensity per unit GDP.

Suggested Citation

  • Schweizer, Vanessa J. & Morgan, M. Granger, 2016. "Bounding US electricity demand in 2050," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 215-223.
    • Handle: RePEc:eee:tefoso:v:105:y:2016:i:c:p:215-223
    DOI: 10.1016/j.techfore.2015.09.001
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    Citations

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    Cited by:

    1. Li, Francis G.N. & Trutnevyte, Evelina, 2017. "Investment appraisal of cost-optimal and near-optimal pathways for the UK electricity sector transition to 2050," Applied Energy, Elsevier, vol. 189(C), pages 89-109.
    2. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    3. Fózer, Dániel & Volanti, Mirco & Passarini, Fabrizio & Varbanov, Petar Sabev & Klemeš, Jiří Jaromír & Mizsey, Péter, 2020. "Bioenergy with carbon emissions capture and utilisation towards GHG neutrality: Power-to-Gas storage via hydrothermal gasification," Applied Energy, Elsevier, vol. 280(C).
    4. Rizzati, Massimiliano & De Cian, Enrica & Guastella, Gianni & Mistry, Malcolm N. & Pareglio, Stefano, 2022. "Residential electricity demand projections for Italy: A spatial downscaling approach," Energy Policy, Elsevier, vol. 160(C).
    5. Yi Liang & Dongxiao Niu & Ye Cao & Wei-Chiang Hong, 2016. "Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission," Energies, MDPI, vol. 9(11), pages 1-22, November.
    6. Wesseh, Presley K. & Lin, Boqiang, 2021. "Bulk storage technologies in imperfect electricity markets under time-of-use pricing: Implications for the environment and social welfare," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    7. Berntsen, Philip B. & Trutnevyte, Evelina, 2017. "Ensuring diversity of national energy scenarios: Bottom-up energy system model with Modeling to Generate Alternatives," Energy, Elsevier, vol. 126(C), pages 886-898.

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