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Techno-economic hardening strategies to enhance distribution system resilience against earthquake

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  • Venkateswaran V, Balaji
  • Saini, Devender Kumar
  • Sharma, Madhu

Abstract

The electrical distribution grid is unremittingly vulnerable to natural disasters. Many researchers propose strategies mainly based on grid-side solutions to improve critical load's survivability during the targeted emergency period. However, the main agenda of resilience enhancement is to improve the overall system resilience. Therefore, this paper proposes a proactive framework that combines the grid-side and demand-side solutions to enhance the overall system resilience. Here, the grid-side approach presents optimal hardening of the distribution grid by using resilient energy storage units (ESUs), underground cables (UCs), and the demand-side by using home battery inverters (HBIs) & its communication infrastructure. For resilient hardening against earthquakes, it is essential to identify it's all possible occurrences. Therefore, a Monte-Carlo-based probabilistic earthquake hazard model is developed through which the vulnerability is characterized using the peak ground acceleration (PGA) model and fragility curves. For optimized hardening investments, the vulnerable zones of the system are identified via clustering algorithms. With the formulated mixed-integer nonlinear problem, the optimal ESUs and UCs are identified for each cluster. The proposed methodology is tested on a real-world 156-bus distribution system of Dehradun district, India, under seismic zone IV.

Suggested Citation

  • Venkateswaran V, Balaji & Saini, Devender Kumar & Sharma, Madhu, 2021. "Techno-economic hardening strategies to enhance distribution system resilience against earthquake," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021002209
    DOI: 10.1016/j.ress.2021.107682
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    4. Artis, Reza & Assili, Mohsen & Shivaie, Mojtaba, 2022. "A seismic-resilient multi-level framework for distribution network reinforcement planning considering renewable-based multi-microgrids," Applied Energy, Elsevier, vol. 325(C).
    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. Venkatasubramanian, Balaji V. & Panteli, Mathaios, 2023. "Power system resilience during 2001–2022: A bibliometric and correlation analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    7. Hasanzad, Fardin & Rastegar, Hasan, 2022. "Application of optimal hardening for improving resilience of integrated power and natural gas system in case of earthquake," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    8. Lu, Qin & Zhang, Wei, 2022. "Integrating dynamic Bayesian network and physics-based modeling for risk analysis of a time-dependent power distribution system during hurricanes," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    9. Hughes, William & Zhang, Wei & Cerrai, Diego & Bagtzoglou, Amvrossios & Wanik, David & Anagnostou, Emmanouil, 2022. "A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    10. Oboudi, Mohammad Hossein & Mohammadi, Mohammad, 2024. "Two-Stage Seismic Resilience Enhancement of Electrical Distribution Systems," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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