Wind power is the renewable energy source with the widest and most successful deployment over the past 20 years, reaching 435 GW of global installed capacity in 2016. Of this, 148 GW is in Europe and Brazil leads the way in Latin American with 9GW, still far behind its estimated potential power of 145 GW.
However, the competitiveness of wind energy requires accurate wind resource assessments, wind farm design and short-term simulations in order to forecast power production for better production-demand response and management of the energy supply mix. In this scenario, the use of statistical or Computational Fluid Dynamics (CFD) microscale models to simulate the lower atmosphere is necessary to address two fundamental wind energy problems: siting (mainly focused on wind resource assessment and wind farm design) and forecast (mainly focused on short-term power prediction and delivering to the electricity network).
Both problems are in the scope of the HPC4E project, which considers key aspects of microscale modelling simulation, either as stand-alone CFD models or in connection with mesoscale models, by developing dynamical and statistical downscaling strategies. European (Iberdrola Renovables S.A, CIEMAT and BSC) and Brazilian (CER-UFPE and LNCC) partners are working in collaboration to improve wind and production modelling in complex terrains and to implement models on current and upcoming HPC environments.
The ultimate goal is to improve wind energy efficiency and exploitation (wind farm design, response to demand peaks, output prediction, etc.) and to contribute to the reduction of technical and financial uncertainties, thereby increasing security for investors. In addition to significantly reducing the cost of this renewable power, it will also help achieve global CO2 emission reduction targets.
HPC4E Researcher - Barcelona Supercomputing Center
This article also appeared in the LinkedIn Group. Join us!
Other LinkedIn articles: