Renewable energy plays an importance role in the road to net-zero greenhouse gas emissions, limiting the global temperature increase by up to 1.5°C by 2050. According to the latest report by the International Renewable Energy Agency, more renewable energy has been added to the grid than fossil fuels and nuclear energy combined since 2013 . According to GWEC , wind energy is a key solution in the global long-term energy mix. With continuous investments in the ongoing and future development plans, the total global offshore wind capacity is expected to increase tenfold to 270 GW by 2030 and approaching 2000 GW by 2050 , . Offshore wind operation and maintenance (O&M), with a large and growing fleet, market is expected to reach £9 billion per year by 2030 . Efficient O&M management ensure reliable and economic operation of wind energy assets and that is critical for the offshore energy industry in the long-term.
Recent advances in autonomous systems such as in  bring a new challenges and opportunities in maintenance modelling and management of offshore energy. This project will investigate the applicability and impacts of advanced technologies and analyse the data of energy systems including the use of the robotic autonomous systems for inspections & maintenance of offshore wind farms and the analysis of SCADA data and autonomous inspection data  available. This enables efficient utilisation of advanced maintenance strategies, such as condition-based maintenance  and opportunistic maintenance of offshore energy systems. In addition, abundant offshore wind energy can be utilised in a hybrid energy system to produce green hydrogen from wind , which contributes to the decarbonisation of our future transport, buildings, and industry. O&M modelling and simulation of such systems will also be investigated in this project using advanced methodologies related to data analysis, reliability engineering, maintenance modelling and simulation, and. The outcomes of this project can help improve the O&M management, reduce the maintenance cost, which is vital for the future development of offshore energy systems.
Interest applicants should have research experience in renewable energy, reliability and maintenance engineering or be willing to develop research knowledge and skills in these topics. All students with background in engineering, applied statistics, or computer science are encouraged to apply.