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 …

The synergy between deep learning and meta-heuristic algorithms presents a promising
avenue for tackling the complexities of energy-related modeling and forecasting tasks. While …

Wind power exhibits low controllability and is situated in dispersed geographical locations,
presenting complex coupling and aggregation characteristics in both temporal and spatial …

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 …

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

Deep learning shows immense potential for strengthening the cyber-resilience of renewable
energy supply chains. However, research gaps in comprehensive benchmarks, real-world …