Energy, Mobility
ATELIER
ATELIER is a smart city project that demonstrates Positive Energy Districts (PEDs) within 8 European cities with sustainability and carbon...
CL WINDCON (Closed Loop Wind Farm Control) will address advanced modelling as well as the generation of open-loop and closed-loop control algorithms at farm level, considering the farm as a complete entity and performing real-time energy production optimisation. The project is funded by Horizon 2020 and will last until October 2019, involving 14 partners from 6 countries (Germany, Italy, Denmark, Netherlands, UK and Spain). CENER coordinates this consortium in which ZABALA Innovation Consulting also participates.
CHALLENGE
The main challenge for CL-Windcon is to carry out advanced control of large wind farms and wind turbines in a global way, while improving efficiency and reducing the cost of energy. Another challenge is to ensure that the new solutions can be applied to both existing and new wind farms.
SOLUTION
To achieve its goal, CL-Windcon will address advanced modelling as well as the generation of open-loop and closed-loop control algorithms at the farm level, considering the farm as a complete entity and performing real-time energy production optimisation. The aim is to provide the wind energy sector with new innovative solutions based on advanced wind farm control. This will require the development of dynamic modules that simulate its operation at a reasonable computational cost. The models generated in the project and the control algorithms will be validated. For this purpose, simulation tests will be carried out in wind tunnels and through real campaigns in wind farms. It is intended that the tools of the models generated in the project will be open source when the work is complete.
RESULTS
The expected results are ambitious, since the aim is to achieve a reduction of the global normalised cost of wind energy (LCOE) of 10%, in addition to increasing production at farm level by 4-5%, reducing operating costs by 4%, extending the useful life of the machine by 1%, and reducing the cost of component materials (due to lower loads) by 3 to 4%. In addition, an overall analysis of the economic and environmental impact of these technical improvements will be performed as a result of the project.
