Roshni Anna Jacob

Ali Mahboub Rad, Roshni Anna Jacob, Bikash Poudel et al.

Small modular reactors (SMRs) are increasingly considered for flexible power generation; however, many dynamic studies still neglect the thermodynamic coupling between the primary and secondary loops that is essential for accurate assessment of load-following capability. In this study, we develop a hybrid dynamic framework that couples an equation-based model of the NuScale integral pressurized water reactor, including the reactor, primary loop, and moving-boundary helical-coil once-through steam generator, with a physics-based secondary steam cycle comprising the valve, turbine, condenser, and feedwater pump. This approach enforces mass and energy conservation across the coupled system while preserving physically consistent flow interactions across the domain boundary. The integrated model reproduces nominal design-point conditions and is used to analyze a 5% step load rejection under five control strategies, including a decentralized three-loop control architecture for the valve, feedwater pump, and control rods. The results show that partial control strategies are insufficient for efficient and safe operation, whereas simultaneous action of all three actuators stabilizes steam pressure, limits adverse thermal excursions in the primary loop and maintains acceptable steam generator operating margins during load-following maneuvers. Compared with a conventional linear steam-cycle representation, the coupled framework captures dynamic back-pressure and variable turbine enthalpy drop that are otherwise neglected, leading to different predictions of transient behavior and required steam flow. These findings show that thermodynamically coupled, physics-based steam-cycle models are needed for more accurate assessment of the operational flexibility, efficiency and safety margins of SMRs under realistic load-following conditions.

Roshni Anna Jacob, Prithvi Poddar, Jaidev Goel et al.

Extreme weather events and cyberattacks can cause component failures and disrupt the operation of power distribution networks (DNs), during which reconfiguration and load shedding are often adopted for resilience enhancement. This study introduces a topology-aware graph reinforcement learning (RL) framework for outage management that embeds higher-order topological features of the DN into a graph-based RL model, enabling reconfiguration and load shedding to maximize energy supply while maintaining operational stability. Results on the modified IEEE 123-bus feeder across 300 diverse outage scenarios demonstrate that incorporating the topological data analysis (TDA) tool, persistence homology (PH), yields 9-18% higher cumulative rewards, up to 6% increase in power delivery, and 6-8% fewer voltage violations compared to a baseline graph-RL model. These findings highlight the potential of integrating RL with TDA to enable self-healing in DNs, facilitating fast, adaptive, and automated restoration.