Prototyping a Reconfigurable Electrical Power Network Modelin Simulink for Load Balancing via Transmission Line Switching

The increasing complexity of modern power systems, driven by the integration of renewable energy sources and decentralized generation, has amplified the need for adaptive and reconfigurable control strategies. This study presents the development and validation of a modular simulation model of an electrical power network in MATLAB/Simulink 2024a, aimed at real-time load balancing through dynamic transmission line switching. The model integrates Simscape Electrical (Specialized Power Systems) blocks to replicate key components, including a three-phase programmable voltage source, transmission lines, and dynamically switchable loads. Mathematical formulations based on Kirchhoff’s laws are used to represent the nodal power balance and load distribution. Switching logic is implemented to simulate sequential load engagement and disengagement under various operational conditions. The model structure supports real-time observation of transient behavior through voltage, current, and power measurements at critical network nodes. Simulation scenarios demonstrate system response to load switching events, including voltage sags, current surges, and power flow redistribution. The results confirm the model’s ability to maintain stability and accurately reflect dynamic system characteristics. This framework provides engineers with a flexible tool for analyzing load balancing strategies, testing protection algorithms, and exploring control mechanisms in reconfigurable power networks. The novelty of the research lies in the practical implementation of topological reconfiguration within a Simulink environment, bridging the gap between theoretical optimization approaches and hands-on simulation. This makes the model suitable for academic purposes, engineering education, and prototyping of smart grid solutions. Future developments may include integration of renewable energy sources, fault modeling, and machine learning algorithms for automated switching decisions and anomaly detection.