scholarly journals Impact of Climate Change on Wave Energy Resource in the Mediterranean Coast of Morocco

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2993
Author(s):  
Joan Pau Sierra ◽  
Ricard Castrillo ◽  
Marc Mestres ◽  
César Mösso ◽  
Piero Lionello ◽  
...  
Keyword(s):  
Climate Change ◽  
Wave Energy ◽  
Regional Climate ◽  
Energy Resource ◽  
Mediterranean Coast ◽  
Energy Output ◽  
Wave Power ◽  
Intergovernmental Panel ◽  
Wave Energy Converters ◽  
The Mediterranean

The increasing demand for energy and the impacts generated by CO2 emissions make it necessary to harness all possible renewable sources of energy, like wave power. Nevertheless, climate change may generate significant variations in the amount of wave energy available in a certain area. The aim of this paper is to study potential changes in the wave energy resource in the Mediterranean coast of Morocco due to climate change. To do this, wave datasets obtained by four institutes during the Coordinated Regional Climate Downscaling Experiment in the Mediterranean Region (Med-CORDEX) project are used. The future conditions correspond to the RCP4.5 and RCP8.5 scenarios from the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. The results show that projected future wave power is very similar to that of the present considering the whole area, although at some specific points there are slight changes that are more evident for the RCP8.5 scenario. Another remarkable result of this study is the significant increase of the temporal variability of wave power in future scenarios, in particular for RCP8.5. This will be detrimental for the deployment of wave energy converters in this area since their energy output will be more unevenly distributed over time, thus decreasing their efficiency.

scholarly journals The CIRCE Simulations: Regional Climate Change Projections with Realistic Representation of the Mediterranean Sea

2013 ◽  
Vol 94 (1) ◽  
pp. 65-81 ◽  
Author(s):  
S. Gualdi ◽  
S. Somot ◽  
L. Li ◽  
V. Artale ◽  
M. Adani ◽  
...  
Keyword(s):  
Climate Change ◽  
Mediterranean Sea ◽  
Regional Climate ◽  
Coupled Model ◽  
Regional Climate Change ◽  
Intergovernmental Panel ◽  
Steric Sea Level ◽  
Radiative Forcings ◽  
Scenario Period ◽  
The Mediterranean

In this article, the authors describe an innovative multimodel system developed within the Climate Change and Impact Research: The Mediterranean Environment (CIRCE) European Union (EU) Sixth Framework Programme (FP6) project and used to produce simulations of the Mediterranean Sea regional climate. The models include high-resolution Mediterranean Sea components, which allow assessment of the role of the basin and in particular of the air–sea feedbacks in the climate of the region. The models have been integrated from 1951 to 2050, using observed radiative forcings during the first half of the simulation period and the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario during the second half. The projections show a substantial warming (about 1.5°–2°C) and a significant decrease of precipitation (about 5%) in the region for the scenario period. However, locally the changes might be even larger. In the same period, the projected surface net heat loss decreases, leading to a weaker cooling of the Mediterranean Sea by the atmosphere, whereas the water budget appears to increase, leading the basin to lose more water through its surface than in the past. Overall, these results are consistent with the findings of previous scenario simulations, such as the Prediction of Regional Scenarios and Uncertainties for Defining European Climate Change Risks and Effects (PRUDENCE), Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES), and phase 3 of the Coupled Model Intercomparison Project (CMIP3). The agreement suggests that these findings are robust to substantial changes in the configuration of the models used to make the simulations. Finally, the models produce a 2021–50 mean steric sea level rise that ranges between +7 and +12 cm, with respect to the period of reference.

Hebridean Marine Energy Resources: Wave-Power Characterisation Using a Buoy Network

2012 ◽  
Author(s):  
Arne Vögler ◽  
Vengatesan Venugopal
Keyword(s):  
Wave Energy ◽  
Site Selection ◽  
Selection Process ◽  
Energy Resource ◽  
Energy Resources ◽  
Wave Power ◽  
Energy Futures ◽  
Wave Energy Converters ◽  
Marine Energy ◽  
Outer Hebrides

The Outer Hebrides of Scotland were identified as an area with a high wave power resource of 42.4kW/m. The Outer Hebrides of Scotland are currently targeted by a range of developers for demonstration and commercial developments of wave energy converters and current planning efforts are based on initial deployments by 2014. Technology providers with well advanced plans to develop the Hebridean wave resource include Aquamarine Power (Oyster) [1], Pelamis (P2) [2] and Voith Wavegen (OWC) [3]; all of these companies are partners in the Hebridean Marine Energy Futures project [4] to help move the industry into the commercialisation stage. As part of the Hebridean Marine Energy Futures project, a three year programme aimed at developing a high resolution wave energy resource map to support the site selection process of marine energy developers, a network of three wave measuring buoys was deployed 15km offshore in a depth of 60m and at distances of 11km between buoys. Measured wind and wave data from this buoy network for autumn 2011 are analysed and presented in this paper along with estimated wave power for the same duration.

scholarly journals Modeling Sea Ice Effects for Wave Energy Resource Assessments

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3482
Author(s):  
Ruth Branch ◽  
Gabriel García-Medina ◽  
Zhaoqing Yang ◽  
Taiping Wang ◽  
Fadia Ticona Rollano ◽  
...  
Keyword(s):  
Sea Ice ◽  
Wave Height ◽  
Wave Energy ◽  
Numerical Models ◽  
Energy Resource ◽  
Wave Climate ◽  
Wave Power ◽  
Wave Energy Converters ◽  
Wave Models ◽  
Mean Square Errors

Wave-generated power has potential as a valuable coastal resource, but the wave climate needs to be mapped for feasibility before wave energy converters are installed. Numerical models are used for wave resource assessments to quantify the amount of available power and its seasonality. Alaska is the U.S. state with the longest coastline and has extensive wave resources, but it is affected by seasonal sea ice that dampens the wave energy and the full extent of this dampening is unknown. To accurately characterize the wave resource in regions that experience seasonal sea ice, coastal wave models must account for these effects. The aim of this study is to determine how the dampening effects of sea ice change wave energy resource assessments in the nearshore. Here, we show that by combining high-resolution sea ice imagery with a sea ice/wave dampening parameterization in an unstructured grid, the Simulating Waves Nearshore (SWAN) model improves wave height predictions and demonstrates the extent to which wave power decreases when sea ice is present. The sea ice parametrization decreases the bias and root mean square errors of wave height comparisons with two wave buoys and predicts a decrease in the wave power of up to 100 kW/m in areas around Prince William Sound, Alaska. The magnitude of the improvement of the model/buoy comparison depends on the coefficients used to parameterize the wave–ice interaction.

scholarly journals Wave Energy Resource Harnessing Assessment in a Subtropical Coastal Region of the Pacific

2021 ◽  
Vol 9 (11) ◽  
pp. 1264
Author(s):  
Emiliano Gorr-Pozzi ◽  
Héctor García-Nava ◽  
Marco Larrañaga ◽  
Melissa G. Jaramillo-Torres ◽  
Manuel G. Verduzco-Zapata
Keyword(s):  
Wave Energy ◽  
Energy Resource ◽  
Coastal Region ◽  
Wave Power ◽  
Mexican Pacific ◽  
Wave Hindcast ◽  
Wave Energy Converters ◽  
Complete Test ◽  
Minimum Number ◽  
Decentralized Energy

Most wave energy converters (WECs) are designed to operate in high-latitude energetic seas, limiting their performance in regions usually dominated by milder conditions. The present study assesses the performance of complete test-stage WECs in farms that satisfy a decentralized energy scheme (DES) on the coast of Baja California, which is considered one of the most energetic regions along the Mexican Pacific. A high-resolution 11-year nearshore wave hindcast was performed and validated with Acoustic Doppler Current Profilers (ADCPs) data to characterize the wave energy resource in the study area. Two hotspots were identified from the wave power climatology. In these sites, the extractive capacities of seven well-known WEC technologies were determined based on their power matrices. Finally, the power extracted by small WEC farms, with the minimum number of devices required to satisfy a DES, was estimated. The studied region has moderate wave power availability with marked seasonality and low inter-annual variability. Out of all the evaluated devices, WaveDragon extracts the highest wave power; however, Pelamis has the best performance, with maximum monthly mean capacity factors up to 40%. Coupling WEC farms with storage modules or hybrid renewable systems are recommended to satisfy a continuous DES during the less energetic summer months.

scholarly journals HF Radars for Wave Energy Resource Assessment Offshore NW Spain

2021 ◽  
Vol 13 (11) ◽  
pp. 2070
Author(s):  
Ana Basañez ◽  
Vicente Pérez-Muñuzuri
Keyword(s):  
Wave Energy ◽  
Numerical Models ◽  
Wave Spectrum ◽  
Energy Resource ◽  
Electrical Energy ◽  
Resource Assessment ◽  
Electricity Production ◽  
Statistical Validation ◽  
Wave Energy Converters ◽  
Energy Converters

Wave energy resource assessment is crucial for the development of the marine renewable industry. High-frequency radars (HF radars) have been demonstrated to be a useful wave measuring tool. Therefore, in this work, we evaluated the accuracy of two CODAR Seasonde HF radars for describing the wave energy resource of two offshore areas in the west Galician coast, Spain (Vilán and Silleiro capes). The resulting wave characterization was used to estimate the electricity production of two wave energy converters. Results were validated against wave data from two buoys and two numerical models (SIMAR, (Marine Simulation) and WaveWatch III). The statistical validation revealed that the radar of Silleiro cape significantly overestimates the wave power, mainly due to a large overestimation of the wave energy period. The effect of the radars’ data loss during low wave energy periods on the mean wave energy is partially compensated with the overestimation of wave height and energy period. The theoretical electrical energy production of the wave energy converters was also affected by these differences. Energy period estimation was found to be highly conditioned to the unimodal interpretation of the wave spectrum, and it is expected that new releases of the radar software will be able to characterize different sea states independently.

scholarly journals Hardware-in-the-Loop Validation of Model Predictive Control of a Discrete Fluid Power Power Take-Off System for Wave Energy Converters

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3668
Author(s):  
Anders H. Hansen ◽  
Magnus F. Asmussen ◽  
Michael M. Bech
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Fluid Power ◽  
Wave Power ◽  
Reactive Control ◽  
Power Extraction ◽  
Wave Energy Converters ◽  
Extraction Algorithm ◽  
Energy Converters

Model predictive control based wave power extraction algorithms have been developed and found promising for wave energy converters. Although mostly proven by simulation studies, model predictive control based algorithms have shown to outperform classical wave power extraction algorithms such as linear damping and reactive control. Prediction models and objective functions have, however, often been simplified a lot by for example, excluding power take-off system losses. Furthermore, discrete fluid power forces systems has never been validated experimentally in published research. In this paper a model predictive control based wave power extraction algorithm is designed for a discrete fluid power power take-off system. The loss models included in the objective function are based on physical models of the losses associated with discrete force shifts and throttling. The developed wave power extraction algorithm directly includes the quantized force output and the losses models of the discrete fluid power system. The experimental validation of the wave power extraction algorithm developed in the paper shown an increase of 14.6% in yearly harvested energy when compared to a reactive control algorithm.

Wave-Powered Desalination

2015 ◽  
Author(s):  
Brian Stiber ◽  
Asfaw Beyene
Keyword(s):  
Climate Change ◽  
Population Growth ◽  
Wave Energy ◽  
Specific Region ◽  
Wave Power ◽  
Wave Energy Conversion ◽  
Coastal Regions ◽  
Sustainable Resources ◽  
Modeling Software ◽  
The Cost

Climate change, drought, population growth and increased energy and water costs are all forces driving exploration into alternative, sustainable resources. The abundance of untapped wave energy often presents an opportunity for research into exploiting this resource to meet the energy and water needs of populated coastal regions. This paper investigates the potential and impact of harnessing wave energy for the purpose of seawater desalination. First the SWAN wave modeling software was used to evaluate the size and character of the wave resource. These data are used to estimate the cost of water for wave-powered desalination taking a specific region as a case example. The results indicate that, although the cost of water from this technology is not economically competitive at this time, the large available resource confirms the viability of significantly supplementing current freshwater supplies. The results also confirm that research into the feasibility of wave power as a source of energy and water in the area is warranted, particularly as water and energy become more scarce and expensive coinciding with the maturity of commercial wave energy conversion.

scholarly journals An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets

2020 ◽  
Vol 12 (4) ◽  
pp. 2959-2970
Author(s):  
Maialen Iturbide ◽  
José M. Gutiérrez ◽  
Lincoln M. Alves ◽  
Joaquín Bedia ◽  
Ruth Cerezo-Mota ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Climate Adaptation ◽  
Regional Climate ◽  
Atmospheric Model ◽  
Future Climate Change ◽  
Intergovernmental Panel ◽  
Temperature And Precipitation ◽  
Scatter Plots ◽  
Projected Changes

Abstract. Several sets of reference regions have been used in the literature for the regional synthesis of observed and modelled climate and climate change information. A popular example is the series of reference regions used in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Adaptation (SREX). The SREX regions were slightly modified for the Fifth Assessment Report of the IPCC and used for reporting subcontinental observed and projected changes over a reduced number (33) of climatologically consistent regions encompassing a representative number of grid boxes. These regions are intended to allow analysis of atmospheric data over broad land or ocean regions and have been used as the basis for several popular spatially aggregated datasets, such as the Seasonal Mean Temperature and Precipitation in IPCC Regions for CMIP5 dataset. We present an updated version of the reference regions for the analysis of new observed and simulated datasets (including CMIP6) which offer an opportunity for refinement due to the higher atmospheric model resolution. As a result, the number of land and ocean regions is increased to 46 and 15, respectively, better representing consistent regional climate features. The paper describes the rationale for the definition of the new regions and analyses their homogeneity. The regions are defined as polygons and are provided as coordinates and a shapefile together with companion R and Python notebooks to illustrate their use in practical problems (e.g. calculating regional averages). We also describe the generation of a new dataset with monthly temperature and precipitation, spatially aggregated in the new regions, currently for CMIP5 and CMIP6, to be extended to other datasets in the future (including observations). The use of these reference regions, dataset and code is illustrated through a worked example using scatter plots to offer guidance on the likely range of future climate change at the scale of the reference regions. The regions, datasets and code (R and Python notebooks) are freely available at the ATLAS GitHub repository: https://github.com/SantanderMetGroup/ATLAS (last access: 24 August 2020), https://doi.org/10.5281/zenodo.3998463 (Iturbide et al., 2020).

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