SOURCE-LINKED INTELLIGENCE

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

CORDIS · observation · Publication date unknown

on 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 a

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

263393.28

unit

EUR