This project focuses on developing an energy-efficient shipboard power system (SPS) by integrating variable-speed diesel generators, battery-supported solar PV systems, a combined cooling, heat, and power unit, a power-to-thermal conversion unit, and thermal storage to minimize power conversion losses. Additionally, an uncertainty-resilient voyage planning framework will be formulated, considering cruising plans, speed limits, and voyage durations while accounting for both high-probability uncertainties and low-probability extreme events. A data-driven grey box learning module, powered by a pre-trained physics-informed neural network, will enhance real-time decision-making for resilient voyage planning. Furthermore, the study will address power quality challenges during the transition from grid-connected docking to islanded voyage mode, investigating issues like voltage unbalance, harmonic distortion, and frequency variations. To mitigate these problems, storage-based power quality improvement techniques and advanced filtering schemes will be developed, ensuring stable and reliable SPS operations.

The integration of behind-the-meter (BTM) loads and distributed energy resources (DERs) presents challenges for electric grids due to limited visibility, data inaccuracy, and restricted sensor deployment, especially in low-voltage networks. This project aims to enhance grid resilience through data-driven estimation, robust optimization, outage detection, and fast restoration strategies. By improving situational awareness with minimal sensor data and optimizing pre- and post-outage operations, the project will ensure efficient grid management. A hardware-in-loop (HIL) validation using an advanced ADNMS testbed will demonstrate real-world applicability, offering scalable solutions for modernizing distribution networks, particularly in resource-constrained regions like India.