Deep Learning Based Hurricane Resilient Coplanning of Transmission Lines, Battery Energy Storages, and Wind Farms

Mojtaba Moradi-Sepahvand, Turaj Amraee*, Saleh Sadeghi Gougheri

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)


In this article, a multistage model for expansion coplanning of transmission lines, battery energy storages, and wind farms (WFs) is presented considering resilience against extreme weather events. In addition to high-voltage alternating current lines, multiterminal voltage source converter based high-voltage direct current lines are planned to reduce the impact of high-risk events. To evaluate the system resilience against hurricanes, probable hurricane speed scenarios are generated using Monte Carlo simulation. The fragility curve concept is utilized for calculating the failure probability of lines due to extreme hurricanes. Based on each hurricane damage, the probable scenarios are incorporated in the proposed model. Renewable portfolio standard policy is modeled to integrate high penetration of WFs. To deal with the wind power and load demand uncertainties, a chronological time-period clustering algorithm is introduced for extracting representative hours in each planning stage. A deep learning approach based on bidirectional long short-term memory networks is presented to forecast the yearly peak loads. The mixed-integer linear programming formulation of the proposed model is solved using a Benders decomposition algorithm. A modified IEEE RTS test system is used to evaluate the proposed model effectiveness.

Original languageEnglish
Pages (from-to)2120-2131
Number of pages12
JournalIEEE Transactions on Industrial Informatics
Issue number3
Publication statusPublished - 1 Mar 2022
Externally publishedYes


  • Bidirectional long short-term memory (B-LSTM)
  • chronological time-period clustering (CTPC)
  • deep learning
  • energy storage
  • extreme weather events
  • transmission expansion planning (TEP)
  • wind farm (WF)


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