FENGBO-WIND - Farming the ENvironment into the Grid: Big data in Offshore Wind
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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.
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Potential Impact:
Offshore wind farms tap into an abundant source of renewable energy and can be scaled up in size (both farm size and number). Their location at sea does not place additional pressures on the use of land, although their environmental impacts at local and regional scales needs to be carefully assessed. They will undoubtedly play a very important role in the decarbonization of the energy mix required to avoid global warming, yet they are still relative expensive compared to other sources of energy.
The research in this project targets directly the reduction in the cost of offshore wind energy by developing the design tools and knowledge required to build and operate more efficient wind farms. This will have a direct economic impact by making wind energy more competitive, while increasing energy independence will benefit local economies. It will further have a societal impact by facilitating the expansion of a clean source of energy in an environmentally sympathetic manner, and in doing so it will expand our knowledge regarding the interactions between the wind farm and its local environment.
The FENGBO-WIND project will specifically target these issues in the context of the further development of offshore wind energy in China. It will produce the most detailed predictions to date of the future interactions between large farms and the local marine environment in coastal areas, with the high sediment concentrations that are characteristic of the East China Sea, while also constructing high-resolution simulations of the impact of typhoons on wind farms. The high-resolution simulations will be further used to build new performance forecasting strategies. The combination of this will provide new knowledge and tools to the local wind operators, while assessing the potential long-term impact on the local environment. It will also contribute to the formation of a more skilled workforce in both research skills and application to wind energy.
To maximize its impact, the project has been designed in close collaboration with energy operators, wind manufactures, policy institutes and marine research centres in China, as well with expert wind consultancies in the UK. This will ensure the relevance of the studies from the point of view of the Chinese wind energy industry, as well as of the conservation of the marine environment. While the solutions may focus on particular case studies, the methods will as much as possible address the more general problem, and many solutions will be applicable elsewhere. To facilitate this, all the software tools developed in the project are and will remain open source and will be developed using the highest best-practice standards in software development. This will stimulate the wider adoption of the new solution methods, both for further development by the research community and for application to specific site studies by wind farm design offices.
Imperial College London | LEAD_ORG |
Technical University of Munich | COLLAB_ORG |
National Supercomputing Center in Wuxi | PP_ORG |
Illinois Institute of Technology | PP_ORG |
State Grid Corporation of China | PP_ORG |
Queen's University of Belfast | PP_ORG |
Energy Research Institute | PP_ORG |
Envision Energy | PP_ORG |
State Oceanic Administration | PP_ORG |
DNV GL (UK) | PP_ORG |
Zhejiang University | PP_ORG |
Centre for Env Fisheries Aqua Sci CEFAS | PP_ORG |
University of Colorado at Boulder | PP_ORG |
FTI Consulting | PP_ORG |
University of Cambridge | PP_ORG |
John Graham | PI_PER |
Matthew Piggott | COI_PER |
Rafael Palacios Nieto | COI_PER |
Xiaowei Zhao | COI_PER |
James Percival | RESEARCH_PER |
Subjects by relevance
- Wind energy
- Renewable energy sources
- Farms
- Wind
- Wind farms
- Wind power stations
- Simulation
- Simulators
- Energy policy
Extracted key phrases
- Wind farm design office
- Offshore wind farm
- Efficient wind farm
- Offshore wind energy
- Chinese wind energy industry
- Art wind turbine design
- Local wind operator
- Wind project
- Expert wind consultancy
- Offshore farm
- Wind resource variability
- Wind manufacture
- Farm state
- Large farm
- Farm output