• Energy Research

We analyzed the peak spring tidal current speeds, annual mean tidal power densities ( T P D ) and annual energy production ( A E P ) obtained from experiment 06.1, referred as the “HYCOM model” throughout, of the three dimensional (3D), global model HYCOM in an area covering the Baja California Pacific and the Gulf of California. The HYCOM model is forced with astronomical tides and surface winds alone, and therefore is particularly suitable to assess the tidal current and wind-driven current contribution to in-stream energy resources. We find two areas within the Gulf of California, one in the Great Island Region and one in the Upper Gulf of California, where peak spring tidal flows reach speeds of 1.1 m per second. Second to fifth-generation tidal stream devices would be suitable for deployment in these two areas, which are very similar in terms of tidal in-stream energy resources. However, they are also very different in terms of sediment type and range in water depth, posing different challenges for in-stream technologies. The highest mean T P D value when excluding TPDs equal or less than 50 W m−2 (corresponding to the minimum velocity threshold for energy production) is of 172.8 W m−2, and is found near the town of San Felipe, at (lat lon) = (31.006–114.64); here energy would be produced during 39.00% of the time. Finally, wind-driven currents contribute very little to the mean T P D and the total A E P . Therefore, the device, the grid, and any energy storage plans need to take into account the periodic tidal current fluctuations, for optimal exploitation of the resources.

AbstractOffshore and near‐shore wind energy is one of the major renewable energy resources, which already make a substantial contribution to the energy supply in Europe. Nevertheless, development is still in the start‐up phase, with new projects being planned and implemented continuously. This development needs to be informed, to enable deployment at the most suitable/ideal locations. However, in order to predict the potential energy yield, forecasts have to be made. In this paper the authors present a scoping study. This study uses high‐resolution climate data to predict wind speeds at various heights, and various locations along the coast of Mexico. Several techniques for wind extrapolation are explored, alongside a new method based on data analysis. The results show clearly a strong sensitivity to the method chosen, and results have to be interpreted accordingly. Also, the impact of water vapor is shown, using a method that predicts the moist air density at the hub height. The paper indicates that valuable information can be gained from the analyses undertaken, prior to embarking on detailed, targeted studies. The paper concludes with recommendations for further work.

In this paper a generic methodology is presented that allows the impacts of climate change on wave energy generation from a wave energy converter (WEC) to be quantified. The methodology is illustrated by application to the Wave Hub site off the coast of Cornwall, UK. Control and future wave climates were derived using wind fields output from a set of climate change experiments. Control wave conditions were generated from wind data between 1961 and 2000. Future wave conditions were generated using two IPCC wind scenarios from 2061 to 2100, corresponding to intermediate and low greenhouse gas emissions (IPCC scenarios A1B and B1 respectively). The quantitative comparison between future scenarios and the control condition shows that the available wave power will increase by 2–3% in the A1B scenario. In contrast, the available wave power in the B1 scenario will decrease by 1–3%, suggesting, somewhat paradoxically, that efforts to reduce greenhouse gas emissions may reduce the wave energy resource. Meanwhile, the WEC energy will yield decrease by 2–3% in both A1B and B1 scenarios, which is mainly due to the relatively low efficiency of energy extraction from steeper waves by the specific WEC considered. Although those changes are relatively small compared to the natural variability, they may have significance when considered over the lifetime of a wave energy farm. Analysis of downtime under low and high thresholds suggests that the distribution of wave heights at the Wave Hub will have a wider spread due to the impacts of climate change, resulting in longer periods of generation loss. Conversely, the estimation of future changes in joint wave height-period distribution provides indications on how the response and power matrices of WECs could be modified in order to maintain or improve energy extraction in the future.

We present a review of wind energy development in Mexico, factors hampering this development, and proposals for solutions to address this hampering. This review is relevant in the context of climate change mitigation strategies and the achievement of the United Nations’ sustainable development goals. Wind energy can be harvested at competitive costs to solve society’s energy poverty and climate change problems. Firstly, we present the current wind energy installed capacity and wind power generation status globally and in Mexico and discuss why Mexico is lagging behind, particularly since 2020. Despite this lag, several state governors are still considering wind energy developments. The current economic context is then considered, with community wind energy as a solution forward for wind energy development, using a successful case study from the UK that has addressed energy poverty and provided an additional income source for an island community. Any community energy project using wind as its main energy resource relies on accurate wind energy assessment in its feasibility analysis. Thus, an evaluation of different wind energy atlases for Mexico was performed, which showed that models considering microscale processes could lead to a relative difference of more than 50% when compared to those that do not consider them. This led to the conclusion that microscale effects must be considered in wind energy characterization models. Furthermore, it is acknowledged that wind faces other challenges, such as the effect of future climate change scenarios, grid planning, and vulnerability and risk associated with tropical storms, which can be substantial in Mexico. Solutions are proposed in the form of possible wind power generation scenarios, planning and implementation of centralized and distributed transmission lines, and possible wind siting and technological choices to reduce the vulnerability and risk to tropical storms. Finally, we close with some future perspectives for researchers and decision-makers. The main conclusions are that sustainable growth can only be compatible with a transition to renewable sources of energy, energy community projects can address energy poverty and achieve sustainable development goals, wind energy feasibility studies need to include microscale effects, return of investment can be improved by siting the wind farms in regions of low vulnerability and risk to extreme events, and high-voltage transmission lines are crucial for sustainable development, even with the important role that distributed systems play. Finally, turbine growth and materials recycling, among other factors, must be considered when assessing the environmental impacts of wind farm decommissioning.

Local bathymetric quasi-periodic patterns of oscillation are identified from monthly profile surveys taken at two shore-perpendicular transects at the USACE field research facility in Duck, North Carolina, USA, spanning 24.5 years and covering the swash and surf zones. The chosen transects are the two furthest (north and south) from the pier located at the study site. Research at Duck has traditionally focused on one or more of these transects as the effects of the pier are least at these locations. The patterns are identified using singular spectrum analysis (SSA). Possible correlations with potential forcing mechanisms are discussed by 1) doing an SSA with same parameter settings to independently identify the quasi-periodic cycles embedded within three potentially linked sequences: monthly wave heights (MWH), monthly mean water levels (MWL) and the large scale atmospheric index known as the North Atlantic Oscillation (NAO) and 2) comparing the patterns within MWH, MWL and NAO to the local bathymetric patterns. The results agree well with previous patterns identified using wavelets and confirm the highly nonstationary behaviour of beach levels at Duck; the discussion of potential correlations with hydrodynamic and atmospheric phenomena is a new contribution. The study is then extended to all measured bathymetric profiles, covering an area of 1100m (alongshore) by 440m (cross-shore), to 1) analyse linear correlations between the bathymetry and the potential forcings using multivariate empirical orthogonal functions (MEOF) and linear correlation analysis and 2) identify which collective quasi-periodic bathymetric patterns are correlated with those within MWH, MWL or NAO, based on a (nonlinear) multichannel singular spectrum analysis (MSSA). (...continued in submitted paper) 50 pages, 3 tables, 8 figures

The Wind Power Density (WPD) is widely used for wind resource characterization. However, there is a significant level of uncertainty associated with its estimation. Here, we analyze the effect of sampling frequencies, averaging periods, and the length of time series on the WPD estimation. We perform this analysis using four approaches. First, we analytically evaluate the impact of assuming that the WPD can simply be computed from the cube of the mean wind speed. Second, the wind speed time series from two meteorological stations are used to assess the effect of sampling and averaging on the WPD. Third, we use numerical weather prediction model outputs and observational data to demonstrate that the error in the WPD estimate is also dependent on the length of the time series. Finally, artificial time series are generated to control the characteristics of the wind speed distribution, and we analyze the sensitivity of the WPD to variations of these characteristics. The WPD estimation error is expressed mathematically using a numerical-data-driven model. This numerical-data-driven model can then be used to predict the WPD estimation errors at other sites. We demonstrate that substantial errors can be introduced by choosing too short time series. Furthermore, averaging leads to an underestimation of the WPD. The error introduced by sampling is strongly site-dependent.