Understanding spatiotemporal correlations in power systems is crucial for maintaining grid
stability, reliability, and efficiency. By discerning connections between spatial and temporal …

Convolutional neural networks a type of deep learning technology, are used for forecasting
future power usage. The mean absolute error, mean square error, root mean square error …

Identifying statistical trends for a wide range of practical power system applications,
including sustainable energy forecasting, demand response, energy decomposition, and …

To improve the resilience of distribution networks (DNs) in the event of extreme natural
disasters such as typhoons and rainstorms, it is imperative to efficiently implement …

Soft open points (SOPs) can perform load shifting and voltage improvement, and are critical
to improving distribution service restoration (DSR) during power outages. Although multiple …

With the increase of distributed generations, the problems related to lack of inertia and
damping support for cascaded-type power networks are becoming more and more …

As natural disasters become more frequent and severe, ensuring a resilient communications
infrastructure is of paramount importance for effective disaster response and recovery. This …

This paper proposes a frequency security-constrained optimal restoration scheduling
framework for active distribution networks (FRSDN). The approach leverages distributed …

Harvesting wind energy is constrained by its generation availability and variability. Energy
storage systems (ESSs) partly address this limitation by absorbing the generation volatility …

Extreme events such as earthquakes, floods, or wars could cause severe grid faults and
large-scale outages in the distribution network. The active islanding technology can be used …