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Analyzing at-scale distribution grid response to extreme temperatures

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  • Hanif, Sarmad
  • Mukherjee, Monish
  • Poudel, Shiva
  • Yu, Min Gyung
  • Jinsiwale, Rohit A.
  • Hardy, Trevor D.
  • Reeve, Hayden M.

Abstract

Threats against power grids continue to increase, as extreme weather conditions and natural disasters (extreme events) become more frequent. Hence, there is a need for the simulation and modeling of power grids to reflect realistic conditions during extreme events conditions, especially distribution systems. This paper presents a modeling and simulation platform for electric distribution grids which can estimate overall power demand during extreme weather conditions. The presented platform’s efficacy is shown by demonstrating estimation of electrical demand for (1) Electricity Reliability Council of Texas (ERCOT) during winter storm Uri in 2021, and (2) alternative hypothetical scenarios of integrating Distributed Energy Resources (DERs), weatherization, and load electrification. In comparing to the actual demand served by ERCOT during the winter storm Uri of 2021, the proposed platform estimates approximately 34 GW of peak capacity deficit.11These numbers are consistent with state-of-the-art prediction results published in the literature (Gruber et al., 2022). For the case of the future electrification of heating loads, peak capacity of 78 GW (124% increase) is estimated, which would be reduced to 47 GW (38% increase) with the adoption of efficient heating appliances and improved thermal insulation. Integrating distributed solar PV and storage into the grid causes improvement in the local energy utilization and hence reduces the potential unmet energy by 31% and 40%, respectively.

Suggested Citation

  • Hanif, Sarmad & Mukherjee, Monish & Poudel, Shiva & Yu, Min Gyung & Jinsiwale, Rohit A. & Hardy, Trevor D. & Reeve, Hayden M., 2023. "Analyzing at-scale distribution grid response to extreme temperatures," Applied Energy, Elsevier, vol. 337(C).
    • Handle: RePEc:eee:appene:v:337:y:2023:i:c:s0306261923002507
    DOI: 10.1016/j.apenergy.2023.120886
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    References listed on IDEAS

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    1. David P. Chassin & Jason C. Fuller & Ned Djilali, 2014. "GridLAB-D: An Agent-Based Simulation Framework for Smart Grids," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-12, June.
    2. Alexis Kwasinski, 2016. "Quantitative Model and Metrics of Electrical Grids’ Resilience Evaluated at a Power Distribution Level," Energies, MDPI, vol. 9(2), pages 1-27, February.
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    4. Mukherjee, Sayanti & Nateghi, Roshanak & Hastak, Makarand, 2018. "A multi-hazard approach to assess severe weather-induced major power outage risks in the U.S," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 283-305.
    5. Zwickl-Bernhard, Sebastian & Auer, Hans, 2022. "Demystifying natural gas distribution grid decommissioning: An open-source approach to local deep decarbonization of urban neighborhoods," Energy, Elsevier, vol. 238(PB).
    6. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Jeffrey A. Bennett & Claire N. Trevisan & Joseph F. DeCarolis & Cecilio Ortiz-García & Marla Pérez-Lugo & Bevin T. Etienne & Andres F. Clarens, 2021. "Extending energy system modelling to include extreme weather risks and application to hurricane events in Puerto Rico," Nature Energy, Nature, vol. 6(3), pages 240-249, March.
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    Cited by:

    1. Nan Ma & Ziwen Xu & Yijun Wang & Guowei Liu & Lisheng Xin & Dafu Liu & Ziyu Liu & Jiaju Shi & Chen Chen, 2024. "Strategies for Improving the Resiliency of Distribution Networks in Electric Power Systems during Typhoon and Water-Logging Disasters," Energies, MDPI, vol. 17(5), pages 1-16, March.

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