Real-Time learning to run Power Distribution Networks (#1237)

Authors

Urquizo Calderón, Javier

Pasmay Bohórquez, Patricia

Muñoz Zurita, Luis

Abstract

This study presents the development of a platform for real-time simulation and optimization of power distribution networks. The proposed system integrates OPAL-RT hardware and the ePHASORSIM module as the simulation tool, enabling dynamic modelling of network behaviour, while a phasor-based framework facilitates real-time phase analysis under variable loading conditions. A high-speed fibre optic communication infrastructure operating under the DNP3 protocol ensures reliable synchronization and data transmission from two substations to the simulation environment. In addition, artificial intelligence techniques are incorporated through large language models and reinforcement learning algorithms to generate automated operational recommendations aimed at optimizing power flow and improving decision-making processes. Software-based analysis is employed for voltage profile assessment, contingency scenario simulation, and operational stability evaluation in low-inertia distribution networks. By simulating multiple operating conditions, the platform enhances early fault detection and improves overall system reliability, reducing the impact of external disturbances on network performance. The proposed methodology is validated through technical studies conducted using the CYMDIST power distribution simulation software and automated diagnostics performed through specialized Python scripts for signal processing and data evaluation. The obtained results accurately reproduce network dynamics and optimal operational strategies, demonstrating the feasibility and robustness of the proposed approach. Furthermore, an interactive dashboard and a geographic information system interface are integrated to centralize real-time data visualization, providing operators with geospatial insight required for comprehensive network supervision, preventive maintenance planning, and rapid emergency response. The platform establishes a framework for future integration of advanced control strategies, intelligent monitoring systems, and large-scale distribution network applications.