The use of small wind turbines (SWTs) is an alternative energy strategy with increasing potential for satisfying in situ electrical demands and should be studied to promote social penetration. The Valley of Mexico Metropolitan Area (VMMA) has air pollution issues that need to be addressed. This has resulted in programs for monitoring atmospheric variables, such as wind speed. By selecting and using 3 years’ worth of available data, we developed a methodology to study the technical and economic feasibility of using SWTs in the VMMA. To this end, 28 SWT models were assessed at 18 locations to estimate annual energy production. In light of certain data characteristics, an adjustment to the power production was proposed for the specific case of using SWTs. Cash flow analysis and annualized net present value (ANPV) were used to determine economic feasibility for each location; furthermore, electric home feeds in the VMMA were considered to model local economic conditions. Similar wind conditions were observed within the VMMA; however, only two wind turbine and location models provided positive ANPV values. The extra annual benefit for each project was calculated by associating the cost per mitigation of CO₂ emissions, which may provide an economic strategy for promoting the penetration of this technology.

Wind speed turbulence intensity is a crucial parameter in designing the structure of wind turbines. The IEC61400 considers the Normal Turbulence Model (NTM) as a reference for fatigue load calculations for small and large wind turbines. La Ventosa is a relevant region for the development of the wind power sector in Mexico. However, in the literature, there are no studies on this important parameter in this zone. Therefore, we present an analysis of the turbulence intensity to improve the understanding of local winds and contribute to the development of reliable technical solutions. In this work, we experimentally estimate the turbulence intensity of the region and the wind shear exponent in terms of atmospheric stability to analyze the relation of these design parameters with the recommended standard for large and small wind turbines. The results showed that the atmosphere is strongly convective and stable in most of the eleven months studied. The turbulence intensity analysis showed that for a range of wind speeds between 2 and 24 m/s, some values of the variable measured were greater than those recommended by the standard, which corresponds to 388 hours of turbulence intensity being underestimated. This may lead to fatigue loads and cause structural damage to the technologies installed in the zone if they were not designed to operate in these wind speed conditions.

This study presents a comprehensive assessment of the offshore wind energy potential in Mexico across 40 years (1979–2018) of numerical simulations using the Weather Research and Forecasting (WRF) model and data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The WRF identifies three regions with moderate to good wind potential: off the north coast of Tamaulipas (Zone I), the northwest coast of Yucatan (Zone II), and the Gulf of Tehuantepec (Zone III). The analysis involves comparing 47 CMIP6 climate models with the WRF results and selecting the best performing models to obtain future projections for the short term (2040–2069) and the long term (2070–2099). Two ensemble-based strategies were implemented. The first one, which uses an intersection approach from which four CMIP6 models were considered, reveals positive percentage differences in Zone II for both future projections, especially for the long-term one. In Zones I and III, positive values are also observed near the coast, mainly for the long-term projection, but they are considerably lower compared to those in Zone II. The second ensemble strategy uses weight assignment through the Mean Absolute Percentage Error, so that a greater weight is given to the model that performed better in each particular zone, potentially providing more accurate results. The findings suggest the likelihood of increased offshore wind energy in these three zones of Mexico, for both short- and long-term future projections, with positive percentage differences of up to 10% in certain areas.

A versatile and widely used tool in atmospheric research and operational forecasting is the Weather Research and Forecasting (WRF) model. The WRF model utilizes advanced algorithms and physics parameterizations to simulate complicated atmospheric phenomena. However, this model relies on a configuration that encompasses spin‐up time, different planetary boundary layer (PBL), and land surface model (LSM) schemes to describe the physics near the surface. This work aims to optimize the configuration of the WRF model by evaluating the impact of the spin‐up period, PBL, and LSM schemes to obtain the best representation of the wind field to calculate the power production of a wind farm located in the complex terrain of La Rumorosa, Baja California, Mexico. This site was chosen because of the availability of data, the challenging conditions of the terrain complexity, and severe changes in the wind direction. Results indicate that 1‐day spin‐up is the best option to simulate and create a climatological wind speed data set for 1 month or even 1 year for the site under investigation. Moreover, the best statistical metrics are obtained when the Mellor–Yamada–Nakanishi–Niino Level 2.5 (MYNN2) PBL scheme is coupled with Noah‐multiphysics LSM. The best setup for the WRF model was used to simulate the performance of a wind turbine in the farm with excellent results.