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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.21590 |
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| _version_ | 1866918220998377472 |
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| author | Siddique, Muhammad Zafar, Sohaib |
| author_facet | Siddique, Muhammad Zafar, Sohaib |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21590 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An AI-Enabled Hybrid Cyber-Physical Framework for Adaptive Control in Smart Grids Siddique, Muhammad Zafar, Sohaib Machine Learning 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. |
| title | An AI-Enabled Hybrid Cyber-Physical Framework for Adaptive Control in Smart Grids |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.21590 |