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Modeling an aggressive energy-efficiency scenario in long-range load forecasting for electric power transmission planning

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  • Sanstad, Alan H.
  • McMenamin, Stuart
  • Sukenik, Andrew
  • Barbose, Galen L.
  • Goldman, Charles A.

Abstract

Improving the representation of end-use energy efficiency, and of the effects of policies and programs to promote it, is an emergent priority for electricity load forecasting models and methods. This paper describes a “hybrid” load forecasting approach combining econometric and technological elements that is designed to meet this need, in a novel application to long-run electric power transmission planning in the western United States. A twenty-year load forecast incorporating significant increases in energy-efficiency programs and policies across multiple locations was developed in order to assess the potential of efficiency to reduce load growth and requirements for expanded transmission capacity. Load forecasting and transmission planning background is summarized, the theoretical and empirical aspects of the hybrid methodology described, and the assumptions, structure, data development, and results of the aggressive efficiency scenario are presented. The analysis shows that substantial electricity savings are possible in this scenario in most residential and commercial end-uses, and in the industrial sector, with magnitudes depending upon the specific end-use as well as upon the geographic location of the utility or other entity providing the electricity.

Suggested Citation

  • Sanstad, Alan H. & McMenamin, Stuart & Sukenik, Andrew & Barbose, Galen L. & Goldman, Charles A., 2014. "Modeling an aggressive energy-efficiency scenario in long-range load forecasting for electric power transmission planning," Applied Energy, Elsevier, vol. 128(C), pages 265-276.
    • Handle: RePEc:eee:appene:v:128:y:2014:i:c:p:265-276
    DOI: 10.1016/j.apenergy.2014.04.096
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    Cited by:

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    2. de la Rue du Can, Stephane & Pudleiner, David & Pielli, Katrina, 2018. "Energy efficiency as a means to expand energy access: A Uganda roadmap," Energy Policy, Elsevier, vol. 120(C), pages 354-364.
    3. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    4. Gençer, Emre & Torkamani, Sarah & Miller, Ian & Wu, Tony Wenzhao & O'Sullivan, Francis, 2020. "Sustainable energy system analysis modeling environment: Analyzing life cycle emissions of the energy transition," Applied Energy, Elsevier, vol. 277(C).
    5. Zhao, Huiru & Guo, Sen, 2016. "An optimized grey model for annual power load forecasting," Energy, Elsevier, vol. 107(C), pages 272-286.
    6. Tao Hong & Katarzyna Maciejowska & Jakub Nowotarski & Rafal Weron, 2014. "Probabilistic load forecasting via Quantile Regression Averaging of independent expert forecasts," HSC Research Reports HSC/14/10, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    7. Cadini, Francesco & Agliardi, Gian Luca & Zio, Enrico, 2017. "A modeling and simulation framework for the reliability/availability assessment of a power transmission grid subject to cascading failures under extreme weather conditions," Applied Energy, Elsevier, vol. 185(P1), pages 267-279.
    8. Partha Gangopadhyay & Narasingha Das, 2022. "Can Energy Efficiency Promote Human Development in a Developing Economy?," Sustainability, MDPI, vol. 14(21), pages 1-20, November.
    9. Jose R. Vargas-Jaramillo & Jhon A. Montanez-Barrera & Michael R. von Spakovsky & Lamine Mili & Sergio Cano-Andrade, 2019. "Effects of Producer and Transmission Reliability on the Sustainability Assessment of Power System Networks," Energies, MDPI, vol. 12(3), pages 1-21, February.
    10. Bonati, A. & De Luca, G. & Fabozzi, S. & Massarotti, N. & Vanoli, L., 2019. "The integration of exergy criterion in energy planning analysis for 100% renewable system," Energy, Elsevier, vol. 174(C), pages 749-767.

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