FENGBO-WIND - Farming the ENvironment into the Grid: Big data in Offshore Wind

Completed

Renewable Energy Sources(Wind Energy)

Basic and strategic applied research

PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics)
ENGINEERING AND TECHNOLOGY (Civil Engineering)
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering)
ENVIRONMENTAL SCIENCES (Earth Systems and Environmental Sciences)

Not Cross-cutting

Professor JM Graham
Aeronautics
Imperial College London

Standard

EPSRC

03 July 2017

02 January 2021

42 months

£812,415

Energy

London

Energy : Energy

Professor JM Graham, Aeronautics, Imperial College London

Dr R Palacios Nieto, Aeronautics, Imperial College London
Dr MD Piggott, Earth Science and Engineering, Imperial College London
Dr X Zhao, School of Engineering, University of Warwick

Project Contact, National Supercomputing Center, Wuxi (NSCC-Wuxi), China
Project Contact, DNV GL (UK)
Project Contact, State Grid Corporation of China
Project Contact, Queen's University Belfast
Project Contact, The Centre for Environment, Fisheries & Aquaculture Science (CEFAS)
Project Contact, University of the Highlands and Islands
Project Contact, Energy Research Institute (ERI), China
Project Contact, Envision Energy, China
Project Contact, FTI Consulting, USA
Project Contact, State Oceanic Administration, China
Project Contact, University of Colorado at Boulder
Project Contact, Illinois Institute of Technology
Project Contact, Argyll College
Project Contact, Zhejiang University, China
Project Contact, University of Cambridge

The proposed project will develop an integrated computational simulation approach capable of handling the complex interactions between the local atmosphere, the coastal ocean and sedimentary environment, farm aerodynamics, turbine response and grid integration in offshore wind farms. This will target a substantial reduction in the cost of energy in offshore wind by exploiting: high-fidelity optimization of array design and operation, tailored to a specific site and able to deal with realistic marine atmospheric boundary layer conditions, in particular the very slow dissipation of rotor wakes; combined with big-data analysis of very-large-scale simulations of the whole system under extreme conditions, to minimize integrity risks without overly conservative safety factors. Both situations will be investigated within the context of the development of offshore farms off the Chinese coast, which brings particular challenges regarding coastal characteristics (e.g. high sediment concentrations) and extreme events (in particular typhoons).To achieve this we propose a multiscale approach to wind farm design and network integration that considers, first, a more accurate characterisation of extreme events (and active mitigation strategies) in the analysis through highly-resolved computer simulation; second, new optimization techniques for the design and operation of wind farms that allow for sustained power extraction using relevant knowledge of both the marine atmosphere and individual turbine (aeroservoelastic) dynamics; and third, robust grid design and operation strategies that accommodate wind resource variability and maximise the sustainability of energy generation. FENGBO-WIND will carry out the most ambitious computer simulations to date on farm dynamics and farm/environment interaction, to build physics-based predictive capabilities on farm output and investigate long-term interactions between farms and their local environment. An interdisciplinary consortium of experts, including Earth/environmental scientists, civil and electrical engineers, and fluid dynamicists, have been assembled to tackle this challenging computational problem. The team will have access to (1) the world's largest supercomputer (Sunway TaihuLight) to carry out full system simulations of energy output and farm state for specific environmental scenarios, (2) operational data from existing wind farms off the Chinese coast as well as conditions at a target site through a partnership with a local grid company, and (3) performance data for a state-of-the-art wind turbine design from the leading Chinese manufacturer. The results will be benchmarked against state-of-the-art industrial design tools and protocols for grid integration for offshore wind farms

08/01/18