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Estimation of the year-on-year volatility and the unpredictability of the United States energy system

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  • Evan D. Sherwin

    (Carnegie Mellon University)

  • Max Henrion

    (Carnegie Mellon University
    Lumina Decision Systems, Inc.)

  • Inês M. L. Azevedo

    (Carnegie Mellon University)

Abstract

Long-term projections of energy consumption, supply and prices heavily influence decisions regarding long-lived energy infrastructure. Predicting the evolution of these quantities over multiple years to decades is a difficult task. Here, we estimate year-on-year volatility and unpredictability over multi-decade time frames for many quantities in the US energy system using historical projections. We determine the distribution over time of the most extreme projection errors (unpredictability) from 1985 to 2014, and the largest year-over-year changes (volatility) in the quantities themselves from 1949 to 2014. Our results show that both volatility and unpredictability have increased in the past decade, compared to the three and two decades before it. These findings may be useful for energy decision-makers to consider as they invest in and regulate long-lived energy infrastructure in a deeply uncertain world.

Suggested Citation

  • Evan D. Sherwin & Max Henrion & Inês M. L. Azevedo, 2018. "Estimation of the year-on-year volatility and the unpredictability of the United States energy system," Nature Energy, Nature, vol. 3(4), pages 341-346, April.
    • Handle: RePEc:nat:natene:v:3:y:2018:i:4:d:10.1038_s41560-018-0121-4
    DOI: 10.1038/s41560-018-0121-4
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    Cited by:

    1. Gang He & Jiang Lin & Froylan Sifuentes & Xu Liu & Nikit Abhyankar & Amol Phadke, 2020. "Rapid cost decrease of renewables and storage accelerates the decarbonization of China’s power system," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    2. Gao, Yang & Ma, Shaoxiu & Wang, Tao & Miao, Changhong & Yang, Fan, 2022. "Distributed onshore wind farm siting using intelligent optimization algorithm based on spatial and temporal variability of wind energy," Energy, Elsevier, vol. 258(C).
    3. Bistline, John E.T. & Blanford, Geoffrey J., 2020. "Value of technology in the U.S. electric power sector: Impacts of full portfolios and technological change on the costs of meeting decarbonization goals," Energy Economics, Elsevier, vol. 86(C).
    4. Cotterman, Turner, 2019. "Why Rapid and Deep Decarbonization isn’t Simple: Linking Bottom-up Socio-technical Decision-making Insights with Top-down Macroeconomic Analyses," Conference papers 333088, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    5. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
    6. Mark Agyei-Sakyi & Yunfei Shao & Oppong Amos & Armah Marymargaret, 2021. "Determinants of Electricity Consumption and Volatility-Driven Innovative Roadmaps to One Hundred Percent Renewables for Top Consuming Nations in Africa," Sustainability, MDPI, vol. 13(11), pages 1-22, June.
    7. Wen, Xin & Jaxa-Rozen, Marc & Trutnevyte, Evelina, 2022. "Accuracy indicators for evaluating retrospective performance of energy system models," Applied Energy, Elsevier, vol. 325(C).
    8. Larisa Vazhenina & Elena Magaril & Igor Mayburov, 2022. "Resource Conservation as the Main Factor in Increasing the Resource Efficiency of Russian Gas Companies," Resources, MDPI, vol. 11(12), pages 1-14, December.

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