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Spatio-temporal impacts of a utility’s efficiency portfolio on the distribution grid

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  • Granderson, Jessica
  • Fernandes, Samuel
  • Touzani, Samir
  • Lee, Chih-Cheng
  • Crowe, Eliot
  • Sheridan, Margaret

Abstract

Energy Efficiency has historically focused on delivering savings to offset growth in energy supply. Today’s growing emphasis on decarbonization of the energy supply is driving renewables adoption and increased interest in electrification. As a result, energy efficiency is being assessed not just in its ability to offset load growth, but also for its ability to alleviate location-specific constraints on transmission and distribution infrastructure. This work demonstrates that advanced measurement and verification modeling techniques can be used to estimate the spatio-temporal grid impact of a portfolio of energy efficiency programs. It extends measurement-based methods to an entire Demand Side Management portfolio and uses a single model to predict annual as well as seasonal building energy use with near-zero bias. In addition, new metrics are introduced to assess grid level impacts of energy efficiency. The results show that the efficiency program portfolio delivers savings of over 12% at the territory-wide proxy level, with substation and feeder level savings ranging from 0.4% to 26%, and −5%-42% respectively. These savings impacted 1.0%–1.4% of the energy used at these locations in the grid. This work provides a methodology with potential to connect efficiency with distribution planning, carrying implications for non-wires alternatives and targeted delivery of efficiency programs.

Suggested Citation

  • Granderson, Jessica & Fernandes, Samuel & Touzani, Samir & Lee, Chih-Cheng & Crowe, Eliot & Sheridan, Margaret, 2020. "Spatio-temporal impacts of a utility’s efficiency portfolio on the distribution grid," Energy, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:energy:v:212:y:2020:i:c:s0360544220317771
    DOI: 10.1016/j.energy.2020.118669
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    References listed on IDEAS

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    1. Granderson, Jessica & Touzani, Samir & Custodio, Claudine & Sohn, Michael D. & Jump, David & Fernandes, Samuel, 2016. "Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings," Applied Energy, Elsevier, vol. 173(C), pages 296-308.
    2. Arnaudo, Monica & Topel, Monika & Laumert, Björn, 2020. "Techno-economic analysis of demand side flexibility to enable the integration of distributed heat pumps within a Swedish neighborhood," Energy, Elsevier, vol. 195(C).
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    4. Granderson, Jessica & Price, Phillip N. & Jump, David & Addy, Nathan & Sohn, Michael D., 2015. "Automated measurement and verification: Performance of public domain whole-building electric baseline models," Applied Energy, Elsevier, vol. 144(C), pages 106-113.
    5. Kevin Novan & Aaron Smith, 2018. "The Incentive to Overinvest in Energy Efficiency: Evidence from Hourly Smart-Meter Data," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 5(3), pages 577-605.
    6. Mejia, Mario A. & Melo, Joel D. & Zambrano-Asanza, Sergio & Padilha-Feltrin, Antonio, 2020. "Spatial-temporal growth model to estimate the adoption of new end-use electric technologies encouraged by energy-efficiency programs," Energy, Elsevier, vol. 191(C).
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    Cited by:

    1. Mirfin, Anthony & Xiao, Xun & Jack, Michael W., 2024. "TOWST: A physics-informed statistical model for building energy consumption with solar gain," Applied Energy, Elsevier, vol. 369(C).
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    3. Ahir, Rajesh K. & Chakraborty, Basab, 2021. "A meta-analytic approach for determining the success factors for energy conservation," Energy, Elsevier, vol. 230(C).

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