An international research team has developed a novel AI-enhanced intrusion detection system (MSIDS) to detect and mitigate cyber threats in smart renewable energy grids. The system outperforms traditional intrusion detection models by achieving high accuracy, precision, and recall rates, while maintaining a low false positive rate. It utilizes supervised and unsupervised learning for threat detection, addressing known and unknown attacks, and features a multi-layer architecture with automated feature extraction and decision fusion. MSIDS's ability to provide low-latency detection allows for swift response to potential threats, ensuring uninterrupted energy distribution and enhancing decentralized security within the grid when deployed on edge computing nodes and smart meters.