This paper evaluates the application of Physics-Informed Neural Networks (PINNs), which directly integrate physics into a learning framework, to smart grid modeling to address data scarcity and physical consistency issues. Through three experiments (interpolation, cross-validation, and episodic path prediction), we compare the performance of PINNs with XGBoost, Random Forest, and linear regression. PINNs trained using physics-based loss functions (power balancing, operational constraints, and grid stability enhancement) demonstrate superior generalization performance in terms of error reduction compared to data-driven models. Specifically, PINNs maintain a low MAE under dynamic grid operation and reliably capture state transitions in both random and expert-driven control scenarios, whereas existing models exhibit unstable performance. While PINNs exhibited some performance degradation under extreme operating conditions, they consistently maintained physical plausibility, demonstrating their essential role in safety-critical applications. This study contributes to establishing PINNs as an alternative smart grid modeling tool that combines data-driven flexibility with first-principles rigor, advances real-time grid control and scalable digital twins, and highlights the need for physics-aware architectures in mission-critical energy systems.
