{"ID":2836513,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.21590","arxiv_id":"2511.21590","title":"An AI-Enabled Hybrid Cyber-Physical Framework for Adaptive Control in Smart Grids","abstract":"Evolving smart grids require flexible and adaptive control methods. A harmonized hybrid cyber-physical framework, which considers both physical and cyber layers and ensures adaptability, is one of the critical challenges to enable sustainable and scalable smart grids. This paper proposes a three-layer (physical, cyber, control) architecture, with an energy management system as the core of the system. Adaptive Dynamic Programming(ADP) and Artificial Intelligence-based optimization techniques are used for sustainability and scalability. The deployment is considered under two contingencies: Cloud Independent and cloud-assisted. They allow us to test the proposed model under a low-latency localized decision scenario and also under a centralized control scenario. The architecture is simulated on a standard IEEE 33-Bus system, yielding positive results. The proposed framework can ensure grid stability, optimize dispatch, and respond to ever-changing grid dynamics.","short_abstract":"Evolving smart grids require flexible and adaptive control methods. A harmonized hybrid cyber-physical framework, which considers both physical and cyber layers and ensures adaptability, is one of the critical challenges to enable sustainable and scalable smart grids. This paper proposes a three-layer (physical, cyber,...","url_abs":"https://arxiv.org/abs/2511.21590","url_pdf":"https://arxiv.org/pdf/2511.21590v2","authors":"[\"Muhammad Siddique\",\"Sohaib Zafar\"]","published":"2025-11-26T17:08:06Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}