Wind Energy Group
The Wind energy Group is a recent creation group with research lines in consolidation; it comprises five researchers, undergrad and grad students in renewable energy engineering. The group has experience in international collaboration with the University of Reading (one Newton Fund project), The University of Strathclyde in the United Kingdom, and DTU Delft in the Netherlands, which are still active. The main research lines with experimental equipment and facilities are classified as follows:
Resource assessment modeling
Analysis of meteorological modeling into wind power applications. In this area, the main goal is to assess the reliability of reanalysis data and dynamical modeling to represent wind power conditions, power production, and capacity factors. The main challenges are understanding variability and complementarity with solar and storage systems in different Mexican regions to model high renewable energy penetration scenarios according to electric demand. In addition, forecasting is also a challenge, and it may be based on meteorological modeling to estimate 24-hour forecasting accordingly for the Mexican electric market. Finally, to increase the spatial resolution of dynamical modeling to study the interaction between wind turbine wakes in wind farm arrays or small wind turbine interactions with the environment.
Computational fluid dynamics:
The group develops numerical codes to solve turbulent flow mass and momentum conservation equations. We consider the Reynolds Averaged Navier-Stokes (RANS) formulation and we explore various alternatives for specifying the boundary conditions and the closure problem. Recently, we have considered developing models for fluid-structure interactions to understand the mechanical energy transfer from the wind to a system that modifies its position due to the aerodynamic interaction. In our context, the obvious application of these physical conditions is the wind interaction with turbine blades. Also, we are interested in the possibility of using artificial intelligence tools to solve the closure problem in turbulence, and other machine learning tools to control the dynamics of wind turbines.
Experimental aerodynamics:
The group has designed and recently built a small wind tunnel with modular, individually-controlled small fans that drive the flow through the working section. It is expected that the time-average velocity profile and turbulence level can be controlled to at least partially mimic the dynamics in the atmospheric boundary layer. The goal is to test the performance of scaled wind turbines under conditions similar to those in the earth's atmosphere. It is expected that the observations in the wind tunnel will provide experimental information to verify the numerical models.
Data science:
Artificial Intelligence methods are applied to analyze problems related to wind dynamics, power generation and distributed generation, based on datasets: Clustering methods to classify wind states in sites; network theory and machine learning to multiscale evaluation of regional and national wind states; neural networks to forecast power generation and electricity demand to integrate smart microgrids.
Power electronics
Modeling, analysis, design, simulation, and implementation of power electronic devices, are applied in energy conditioning for the wind turbine integration at the electrical networks, generating an energy conversion system. Low-scale experimental prototypes validate the energy conversion systems' functionality and robustness. The test systems' technical characteristics are reflected, whose analysis is carried out in real-time using the OPAL-RT Technologies® simulator, test-bench for wind turbines, power electronics devices, and an electrical network emulator.
Wind turbines Integration at the electrical networks:
Analysis of the problems inherent to integrating many wind turbines in the electrical grids and the management of their contribution (regulating voltage and frequency, wind farms operation, etc.) to guarantee the sustainability and security of supply. In addition, the technical challenges associated with integrating wind power into power systems are addressed. These challenges include the effects of wind power on the electrical system, the operating cost, power quality, and power imbalances (grid stability). Finally, solutions are presented to improve the management of wind generation and increase its penetration in the whole of electricity production.