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Project title

Enhancing Offshore Wind-Power-to-X Integration with Machine Learning: A Sustainable Energy Frontier

Primary Funding Agency

Taighde �ireann-Research Ireland

Co-Funding Organisation(s)

Lead Organisation

Trinity College Dublin (TCD)

Lead Applicant

Not listed

Project Abstract

This proposed research aims to leverage machine learning (ML) techniques for optimizing the integration of Power-to-X (PtX) systems, including electrolyzers and hydrogen storage, within offshore wind energy grids. The challenges posed by the intermittent nature of renewable sources, such as offshore wind, combined with the complexities of PtX systems, necessitate advanced solutions for optimal operation and control. As the world seeks cleaner and more sustainable energy solutions, the integration of PtX systems into offshore wind energy grids presents a promising path towards efficient energy production and storage. However, the intermittent nature of wind energy and the complexities introduced by PtX technologies pose significant challenges. Our primary aim is to optimize the operation of Multi-Carrier Energy Systems (MCESs) in offshore environments, ensuring the seamless integration of PtX systems, including the development of ML-driven optimization models to dynamically manage PtX systems' operation, aligning them with variable wind energy inputs for optimal energy production and storage, while also utilizing ML algorithms to predict energy loads in offshore environments, enabling real-time adjustments in PtX system operation to meet energy demands efficiently. Additionally, we focus on implementing ML-based control strategies to enhance the adaptability and responsiveness of PtX processes, improving hydrogen production under varying wind conditions. Addressing environmental uncertainties through ML models is also a crucial aspect, ensuring the resilience and reliability of offshore wind-PtX systems. Our research seeks to answer critical questions about ML's role in optimizing PtX integration, energy load prediction, process control, and environmental uncertainty mitigation. The implications of our findings extend beyond academia, contributing to the advancement of offshore renewable energy technologies and the global transition towards cleaner and more sustainable energy practices.

Grant Approved

�93,000.00

Research Hub

Climate Change

Research Theme

3. Climate Solutions, Transition Management and Opportunities

Start Date

03/01/2025

Initial Projected Completion Date

03/01/2028