Assessment of Hybrid Wind-Wave Energy Resource for the NW Coast of Iberian Peninsula in a Climate Change Context

Abstract

Offshore renewable energy has a high potential for ensuring the successful implementation of the European decarbonization agenda planned for the near future. Hybrid wind-wave farms can reduce installation and maintenance costs, and increase the renewable energy availability of a location by compensating for the wind’s intermittent nature with good wave conditions. In addition, wave farms can provide protection to wind farms, and the combined wind/wave farm can provide coastal protection. This work aims to assess the future hybrid wind-wave energy resource for the northwest coast of Iberian Peninsula for the near future (2026–2045), under the RCP 8.5 greenhouse gas emission scenario. This assessment was accomplished by applying a Delphi classification method to define four categories, aiming to evaluate the richness (wind and wave energy availability, downtime), the variability (temporal variation), the environmental risk (extreme events), and cost parameters (water depth and distance to coast) of the wind and wave resources. The combined index (CI), which classifies the hybrid wind-wave resource, shows that most of the NW Iberian Peninsula presents good conditions (CI > 0.6) for exploiting energy from wind and wave resources simultaneously. Additionally, there are some particularly optimal areas (CI > 0.7), such as the region near Cape Roca, and the Galician coast.

Evaluation of the Worldwide Wave Energy Distribution Based on ERA5 Data and Altimeter Measurements

“…Finally, it should be highlighted that although the technology for the wave energy extraction is not yet fully effective, the very high dynamics as regards the offshore wind is expected to induce momentum to the wave energy industry as well [69,70]. From this perspective, a first step would be to collocate wave farms in the vicinity of the already operating wind farms; in this way, both the capital and operational costs would be substantially decreased [71,72]. Furthermore, another expected advantage of the future wave energy farms is that by absorbing the incoming wave energy in the nearshore, they can also provide coastal protection [73,74], bringing a beneficial role in the balance of the nearshore processes.…”

Flooding Conditions at Aveiro Port (Portugal) within the Framework of Projected Climate Change

“…The RMSE and Bias indicators show that the BCC-CSM1.1 GCM represents the best climate model to reproduce the IP west coast's extreme wave climate. These results show that model performance can be seasonal specific as [50,51] found that MIROC5 GCM was the best model to reproduce the area's annual mean wave conditions.…”

“…The skill of these CMIP5 GCMs was evaluated in previous studies [50,51] for the Northwest IP coast. This performance metric of GCMs has been applied [52][53][54][55][56][57] to explore the frequency and severity of climate extremes and has been useful to evaluate the climate models.…”

“…The coarse resolution of the BCC-CSM1.1 GCM is not suitable to be used at a regional scale, which makes necessary a dynamical downscaling to propagate the wave to a more satisfactory resolution at the port entrance. The third-generation spectral wave model SWAN [65] (an acronym for Simulating Waves Nearshore) was used through the Delft3D-WAVE module in an implementation similar to [50,51], following the recommendations of the International Electrotechnical Commission (IEC) for a stage 1 (reconnaissance) model [66]. The downscaling was accomplished by making a two-step approach, the first step is to downscale the selected GCM, and the latter is to simulate the scenarios by coupling WAVE with FLOW modules.…”

Economic Feasibility of Floating Offshore Wind Farms Considering Near Future Wind Resources: Case Study of Iberian Coast and Bay of Biscay

“…The ability of this multimodel ensemble to reproduce wind speed at 10 m asl in the study area was previously demonstrated by comparing numerical wind speed series with in situ data from buoys using different statistical metrics (percentage of error, overlap percentage, root-mean-square error, and percentage of difference in median wind speed series) [ 18 , 24 , 34 ]. In particular, according to F. Santos et al (2018), the percentage of error between the mean wind projected for each RCM and the mean wind at each buoy is around 10% for the whole Iberian Peninsula and around 8% for the buoys located in the area under study.…”