Grid Congestion

Grid congestion, encompassing various forms of network overload from traffic flow to power grids and data transmission, is a critical challenge demanding efficient management strategies. Current research focuses on developing sophisticated predictive models, often employing machine learning techniques like graph neural networks and deep reinforcement learning, to anticipate and mitigate congestion in diverse systems. These advancements aim to optimize resource allocation, improve system efficiency, and enhance overall performance across sectors ranging from transportation and energy distribution to large-scale computing and communication networks. The impact of this research is significant, offering solutions to reduce costs, improve safety, and enhance the sustainability of critical infrastructure.