Voltage Drops Improvement at Ferry Ports by Integrating Distributed Resources and Predictive Analytics(VDROP-PORT)

As the maritime industry shifts towards electrification, maintaining stable and efficient energy management at ferry charging stations becomes crucial. This project focuses on enhancing voltage drop at Charging Station Ports (CSPs) for electric ferries by optimizing the hosting capacity of photovoltaic (PV) systems and utilizing battery storage in public buildings. It employs real-time predictive control through machine learning (ML) algorithms to dynamically manage voltage during electric ferry charging events.The methodology involves a comprehensive system analysis to understand existing grid infrastructure and identify critical nodes vulnerable to voltage drops. An optimization model will be developed to maximize PV hosting capacity and minimize voltage drops. ML algorithms will predict and control voltage levels in real-time, improving grid reliability and energy efficiency. The project will validate its solutions through hardware-in-the-loop (HIL) simulations, ensuring their effectiveness in real-world scenarios.The proposed framework aligns with the European Commission's regulations on rooftop solar installations, leveraging these requirements to enhance PV integration and energy optimization. By addressing voltage drop issues, the project contributes to national sustainability goals and promotes the adoption of renewable energy solutions in the maritime sector. The outcomes include improved voltage stability at CSPs, enhanced PV system utilization, and a scalable approach that supports the global electrification of maritime transport and reduces carbon emissions.