Topology-Aware Lindblad Quantum Physics-Informed Spatio-Temporal Graph Neural Automatic Generation Control for Renewable-Rich Multi-Area Power Systems

This research tackles frequency regulation and automatic generation control (AGC) in multi‑area power systems that have a lot of renewable energy. Conventional AGC methods have trouble handling the random output of renewables and the ever‑changing structure of today’s grids. To overcome these issues, the project proposes a topology‑aware framework that combines two advanced tools: – **Lindblad quantum physics‑informed spatio‑temporal graph neural networks (STGNNs)** – **Multi‑agent reinforcement learning (MARL)** The power grid is represented as a dynamic graph, so the method can learn both the spatial relationships and the time‑varying behavior of interconnected control areas. Adding the Lindblad master equation introduces a quantum‑inspired, physics‑based constraint. This keeps the learning agents within

the limits set by conservation laws and system‑stability boundaries. With these constraints, the system can generate safe, real‑time optimal control actions even when generation and demand are heavily mismatched. The framework is tested with extensive simulations on standard multi‑area benchmark power systems. The evaluation looks at: – Frequency deviation – Tie‑line power fluctuations – Computational efficiency Results show a model that is robust, scalable, and mathematically sound, giving modern grid operators a reliable tool for managing highly uncertain, renewable‑rich environments.

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