  In this paper, a multi-stage model for expansion co-planning of transmission
lines, Battery Energy Storages (BESs), and Wind Farms (WFs) is presented
considering resilience against extreme weather events. In addition to High
Voltage Alternating Current (HVAC) lines, Multi-Terminal Voltage Source
Converter (MTVSC) based High Voltage Direct Current (HVDC) lines are planned to
reduce the impact of high-risk events. To evaluate the system resilience
against hurricanes, probable hurricane speed (HS) scenarios are generated using
Monte Carlo Simulation (MCS). The Fragility Curve (FC) 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 (RPS) policy is modeled to
integrate high penetration of WFs. To deal with the wind power and load demand
uncertainties, a Chronological Time-Period Clustering (CTPC) algorithm is
introduced for extracting representative hours in each planning stage. A deep
learning approach based on Bi-directional Long Short-Term Memory (B-LSTM)
networks is presented to forecast the yearly peak loads. The Mixed-Integer
Linear Programming (MILP) formulation of the proposed model is solved using a
Benders Decomposition (BD) algorithm. A modified IEEE RTS test system is used
to evaluate the proposed model effectiveness.
