Power systems are the backbone of modern society, but high-impact and low-probability
natural disasters pose unprecedented challenges to power systems in recent years. Power …

Nowadays, learning-based modeling methods are utilized to build a precise forecast model
for renewable power sources. Computational Intelligence (CI) techniques have been …

We reviewed the present studies on the vulnerability and resilience of the energy ecosystem
(most parts of the energy ecosystem), considering extreme climate events. This study …

Permanently increasing penetration of converter-interfaced generation and renewable
energy sources (RESs) makes modern electrical power systems more vulnerable to low …

In this paper, an integrated multi-period model for long term expansion planning of electric
energy transmission grid, power generation technologies, and energy storage devices is …

Integration of smart grid technologies in distribution systems, particularly behind-the-meter
initiatives, has a direct impact on transmission network planning. This paper develops a …

In the past few decades, there have been multiple algorithms proposed for the purpose of
solving optimization problems including Machine Learning (ML) applications. Among these …

When facing severe weather events, a distribution system may suffer from the loss or failure
of one or more of its components, the so-called NK contingencies. Nevertheless, taking …

The publication trends and bibliometric analysis of the research landscape on the
applications of machine and deep learning in energy storage (MDLES) research were …

Large-scale utilization of natural gas for electrical power generation, and application of gas
facilities run by electricity has deepened the interconnection of electricity and natural gas …