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Deep Learning Based Hurricane Resilient Coplanning of Transmission Lines, Battery Energy Storages, and Wind Farms by Mojtaba Moradi-Sepahvand; Turaj Amraee; Saleh Sadeghi Gougheri is a Engineering article available to read on EtoBox.

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

In this paper, a multi-stage model for expansion coplanning 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 Mix

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Author

Mojtaba Moradi-Sepahvand; Turaj Amraee; Saleh Sadeghi Gougheri

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Language

EN

Field

Engineering (Physical Sciences)