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Restoration of smart grids: Current status, challenges, and opportunities

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  • Fan, Dongming
  • Ren, Yi
  • Feng, Qiang
  • Liu, Yiliu
  • Wang, Zili
  • Lin, Jing

Abstract

Smart grids, which constitute intelligent infrastructure in smart cities, provide reliable, energy-efficient, and high-quality power to improve the standard of living while maintaining a sustainable environment. For self-healing, restoration is considered an effective and intelligent way to ensure that parts of the system remain active and even reconfigure automatically with external failures or attacks. Restoration has recently received considerable attention from researchers and engineers. Regarding the technical route of restoration, this survey reveals some common properties and identifies the research gaps in modeling frameworks, reconfiguration technologies, and optimization approaches. It presents a summary of the challenges and opportunities in restoration of smart grids. It is expected to facilitate the researchers in the advancement of effective and intelligent restoration of smart grids.

Suggested Citation

  • Fan, Dongming & Ren, Yi & Feng, Qiang & Liu, Yiliu & Wang, Zili & Lin, Jing, 2021. "Restoration of smart grids: Current status, challenges, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:rensus:v:143:y:2021:i:c:s1364032121002021
    DOI: 10.1016/j.rser.2021.110909
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    4. Hu, Yusha & Man, Yi, 2022. "Two-stage energy scheduling optimization model for complex industrial process and its industrial verification," Renewable Energy, Elsevier, vol. 193(C), pages 879-894.
    5. Xie, Haipeng & Tang, Lingfeng & Zhu, Hao & Cheng, Xiaofeng & Bie, Zhaohong, 2023. "Robustness assessment and enhancement of deep reinforcement learning-enabled load restoration for distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    6. Nasir Ayub & Usman Ali & Kainat Mustafa & Syed Muhammad Mohsin & Sheraz Aslam, 2022. "Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid," Forecasting, MDPI, vol. 4(4), pages 1-13, November.
    7. Gouda, El Mehdi & Benaouicha, Mustapha & Neu, Thibault & Fan, Yilin & Luo, Lingai, 2022. "Flow and heat transfer characteristics of air compression in a liquid piston for compressed air energy storage," Energy, Elsevier, vol. 254(PB).
    8. Arman Goudarzi & Farzad Ghayoor & Muhammad Waseem & Shah Fahad & Issa Traore, 2022. "A Survey on IoT-Enabled Smart Grids: Emerging, Applications, Challenges, and Outlook," Energies, MDPI, vol. 15(19), pages 1-32, September.

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