In recent years, in the field of Multi-Agent Systems (MAS), significant progress has been
made in the research of algorithms that combine Reinforcement Learning (RL) with Attention …

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

This paper selects the whole microgrid system as the master and renewable energy, energy
storage, and load as the game's slave. It builds a master-slave game optimization model for …

Long-distance power support through High-voltage Direct Current (HVDC) has provided
feasible solutions for power dispatch and control problems in multi-area power systems …

Extreme weather, chain failures, and other events have increased the probability of wide-
area blackouts, which highlights the importance of rapidly and efficiently restoring the …

Compressed air energy storage (CAES) is considered to be one of the most promising large-
scale energy storage technologies to address the challenges of source-grid-load-storage …

With the increasing weight of wind and photovoltaic power (WPP) in grids, the uncertainty of
WPP has an increasing impact on power systems. The access to WPP raises the difficulty of …

The rise of renewable energy and distributed generation requires new approaches to
overcome the limitations of traditional methods. In this context, Graph Neural Networks are …

Real-time perception of the power system operating state with high resolution is essential for
enabling online dynamic security assessment. However, challenges associated with limited …

Due to the volatility and intermittency of renewable energy, the integration of a large amount
of renewable energy into the grid can have a significant impact on its stability and security. In …