Wave Resource Assessment Research Articles

Identification of optimal sites for the deployment of wave energy converters: the importance of a technology-centred approach

Driven by climate issues and geopolitical uncertainties, Europe faces the need to transform its energy supply dramatically and quickly. Various renewable technologies are proposed as a medium- to long-term solution for an environmentally and economically sustainable energy mix: among the available solutions, wave energy converters (WECs) are attracting growing interest due to the large untapped wave energy potential in European seas. In this context, the choice of optimal locations for the use of wave energy is fundamental to limit the technological gap with other fully developed conversion technologies, and to ensure competitive energy costs. In this paper, we compare different possible strategies to identify suitable sites for the installation of WECs, namely the one based on pure analysis of the wave energy resource, and that considering the productivity of the device in different sea states, i.e., its power matrix. Using the performance matrices of notional WECs, particularly an Oscillating Surge Wave Energy Converter (OSWEC) and a Heaving Point Absorber (HPA), we estimate optimal locations on the Italian coasts and highlight the advantages and disadvantages of the two approaches. The analysis shows the importance of a technology-based approach for the spatial planning of future wave power plants and highlights significant differences compared to the approach that can be used for the preliminary identification of sites for wind farms and, especially, for photovoltaic plants. We use the obtained results to introduce the MORE-EST platform, a novel web-based tool for straightforward estimation of wave resources and WECs productivity in European seas. The proposed platform is able to integrate information on wave resource assessment, bathymetry, marine space use and technological features, and represents a tool aimed at researchers, WECs developers, and policy makers.

A scalable wave resource assessment methodology: Application to U.S. waters

Waves deliver large quantities of energy to populated coastlines around the world, and wave energy technology research and development has accelerated over the last two decades. Throughout this time national and regional resource assessments have utilized disparate methodologies, which can cause confusion and skepticism. In this work, we describe a theoretical wave resource assessment methodology that addresses many of the major areas of inconsistency and debate. Applying this revised methodology to U.S. waters, we find the theoretical U.S. wave energy resource to be 3300 TWh/yr, with region totals of 2000 TWh/yr in Alaska, 510 TWh/yr along the U.S. west coast, 380 TWh/yr in Hawaii, 290 TWh/yr along the east coast, 69 TWh/yr in the Gulf of Mexico, and 17 TWh/yr in Puerto Rico and the U.S. Virgin Islands. We also find significant uncertainty in these estimates associated with the underlying model dataset, which emphasizes the importance of thorough model validation and calibration as well as quantifying uncertainty.

Geospatial Analysis of Technical U.S. Wave Net Power Potential

Developing new technologies to utilize natural renewable energy resources and fluxes is critical to mitigate climate change and energy crisis. As a renewable energy resource, wave energy is more predictable and has a higher power density compared to solar and wind. However, the technology for wave energy harvesting is still relatively nascent. One of the major challenges as the sector developers is the lack of spatial–temporal representation of wave resources, or wave power generation, from a generic WEC.This paper proposes a novel Wave Net Power Assessment (WNPA) method by utilizing the theoretical Wave Energy Converter (WEC) power limits (including the radiation power limits, Budal’s limit, and gross wave power), and applies it to a decade of wave conditions across the USA East Coast, West Coast and Hawaiian Islands. The WNPA method assesses the wave resource from device point of view (unlike traditional gross wave resource assessment) and is generally applicable to WECs regardless of dimension, size, and degrees of freedom (DoFs). The geospatial application of the WNPA method allowed for the analysis of wave power production potential along U.S. coastlines and provides WEC developers meaningful data to make decisions about the best spatial region to deploy certain WECs. Numerical simulations are conducted for WECs with 5 different sizes, 3 different operating modes across the West Coast, East Coast, and Hawaii over 10 years. In addition, productivity, efficiency and gross power quality comparisons are made in terms of the size and degree-of-freedom for WECs deployed at certain ocean sites (e.g., PacWave).A few of the most important findings: (1) The net wave power is dramatically smaller than the gross wave power, which indicates the new WNPA method has a more conservative assessment of the wave power potential; (2) A heaving 10 m WEC is found to have the highest (compared to other sizes and degrees of freedom) absorption efficiency on the West Coast and Hawaii; (3) For smaller 2 m surging/pitching WECs, ocean sites on the East Coast (e.g., Diamond Shoals, NC) maximize power production. (4) There are distinct trade-offs between maximizing power or minimizing power quality and dependencies exists between the location of interest, the WEC size, and the priority degree of freedom. The proposed methodology can be utilized by WEC technology and project developers to conduct apriori preliminary analysis of WEC deployment locations and device characteristics.

Linking the long-term variability in global wave energy to swell climate and redefining suitable coasts for energy exploitation

The sustainability of wave energy linked to the intra- and inter-annual variability in wave climate is crucial in wave resource assessment. In this study, we quantify the dependency of stability of wave energy flux (power) on long-term variability of wind and wave climate to detect a relationship between them. We used six decades of re-analysis wind and simulated wave climate in the entire globe and using two 30-yearly periods, we showed that not only the previously suggested minimum period of 10 years for wave energy assessment appears to be insufficient for detecting the influence of climate variability, but also the selection period for wave energy assessment can lead to an over/underestimation of about 25% for wave power. In addition, we quantified the dependency of rates of change of wave power, wind speed and wave parameters and showed that the change in wave power is mainly a function of change in swell wave climate globally. Finally, we redefined the suitability of global hotspots for wave energy extraction using intra-annual fluctuation, long-term change, and the available wave power for the period of six decades. The results highlight the importance of climate variability in resource assessment, sustainability, and prioritizing the hotspots for future development.

Combined wind and wave resource assessment and energy extraction along the Indian coast

The present study analyses the combined offshore wind and wave resource potential along the Indian coast. The state-of-the-art ERA5 reanalysis datasets that provide wind fields at 100 m height have been used for the first time to assess wind resources along the Indian Coast. Further, wave parameters were generated using the WAVEWATCH III (WWIII) model. Long-term spatial air density was considered to find accurate wind power potential. The results reveal that the southern coast of India exhibits higher wind power density and moderate wave energy flux (up to 0.65 kW/m2 and 10 kW/m), respectively. This region has the lowest variability (coefficient of variation <1, skewness <1.5, and kurtosis <4) and less correlation (<0.4). A suitable hotspot is identified based on the collocation feasibility index and depth constraint after excluding conflicted regions. A comprehensive investigation of local wind and wave characteristics was performed at the hotspot. Further, the yearly and monthly cross-correlations between wind and wave at different time lags were analysed. Actual estimation of power generation is carried out with various combinations of wind turbine (SG8.0-167 DD) and wave energy converter (Oceantec). The outcome of the present study will help to initiate the development of joint offshore wind and wave energy farms in India.

Modeling Sea Ice Effects for Wave Energy Resource Assessments

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.

Joint Modelling of Wave Energy Flux and Wave Direction

In the context of wave resource assessment, the description of wave climate is usually confined to significant wave height and energy period. However, the accurate joint description of both linear and directional wave energy characteristics is essential for the proper and detailed optimization of wave energy converters. In this work, the joint probabilistic description of wave energy flux and wave direction is performed and evaluated. Parametric univariate models are implemented for the description of wave energy flux and wave direction. For wave energy flux, conventional, and mixture distributions are examined while for wave direction proven and efficient finite mixtures of von Mises distributions are used. The bivariate modelling is based on the implementation of the Johnson–Wehrly model. The examined models are applied on long-term measured wave data at three offshore locations in Greece and hindcast numerical wave model data at three locations in the western Mediterranean, the North Sea, and the North Atlantic Ocean. A global criterion that combines five individual goodness-of-fit criteria into a single expression is used to evaluate the performance of bivariate models. From the optimum bivariate model, the expected wave energy flux as function of wave direction and the distribution of wave energy flux for the mean and most probable wave directions are also obtained.

High-resolution hindcasts for U.S. wave energy resource characterization

The marine and hydrokinetic (MHK) industry is at an early stage of development and has the potential to play a significant role in diversifying the U.S. energy portfolio and reducing the U.S. carbon footprint. Wave energy is the largest among all the U.S. MHK energy resources, which include wave energy, ocean current, tidal-instream, ocean thermal energy conversion, and river-instream. Wave resource characterization is an essential step for regional wave energy assessments, Wave Energy Converter (WEC) project development, site selection and WEC design. The present paper provides an overview of a joint modelling effort by the Pacific Northwest National Laboratory and Sandia National Laboratories on high-resolution wave hindcasts to support the U.S. Department of Energy’s Water Power Technologies Office’s program of wave resource characterization, assessment and classifications in all US coastal regions. Topics covered include the modelling approach, model input requirements, model validation strategies, high performance computing resource requirements, model outputs and data management strategies. Examples of model setup and validation for different regions are provided along with application to development of classification systems, and analysis of regional wave climates. Lessons learned and technical challenges of the long-term, high-resolution regional wave hindcast are discussed.

Wave resource assessment and wave energy exploitation along the Indian coast

The present study investigates the feasibility of wave energy exploitation along the Indian coast using 19-year (2000–2018) hindcasted wave data generated by WAVEWATCH-III with a fine spatial resolution (0.1° × 0.1°) and temporal resolution (6 h). The wave model is validated against 12-month measured wave data collected at multiple buoy sites and compared with 19-year ERA5 data across the study area. The results show good agreement between model data and the other two datasets. The spatio-temporal variability analysis of wave energy flux reveals that southern coast of India has the mean wave energy flux ranging from 6 to 10 kW/m with a minimum monthly (<2.0), seasonal (<1.0) and annual (<0.2) variability which could be a promising area for future wave energy exploitation. A detailed analysis of wave power resource has been discussed for nine hotspot locations which were selected based on the optimum hotspot index, geographical restrictions on bathymetry and distance to the shore. Further, each hotspot has been analysed through combined scatter and energy diagrams. Finally, electrical power output is estimated at each hotspot using five wave energy converters and observed that the annual energy production at most of the locations is above 800 MWh with more than 70% availability.

A high-resolution, long-term wave resource assessment of Japan with wave–current effects

The largest source of uncertainty in estimating annual energy production from a wave energy converter is natural variability. Standards recommend the collection of long-term on-site records, but with a concurrent requirement of dense data coverage, it becomes intractable to assess resources even at the feasibility development stage. Here, we provide uncertainty estimates of wave energy resources along the coast of Japan at 1 km resolution based on 21-year long numerical simulation, assuring a mean absolute percentage error of 4%. The uncertainties of the annual, seasonal and monthly climatologies are provided, and the epistemic uncertainty due to the effects of ocean current and finite depth on wave power estimation are quantified. Additionally, a significant wave height threshold is applied to exclude severe storm periods when devices are not in operation. The estimated total available wave power in Japan is 115.1 GW, whereas with a 3 m significant wave height threshold, it reduces to about 70 GW. Wave modeling under strong influence of ocean current is yet another source of epistemic uncertainty and the local and remote effects of strong current field are discussed. Results from the wave resource assessment are accessible to the public via a user-friendly web-based Geographical Information System, which was developed to help foster the use of marine renewable energy.

Wave Energy Assessment in the South Aquitaine Nearshore Zone from a 44-Year Hindcast

Wave resource assessment is the first step toward the installation of a wave energy converter (WEC). To support initiatives for wave energy development in the southwest of France, a coastal wave database is built from a 44-year hindcast simulation with the spectral wave model SWAN (Simulating WAve Nearshore) run on a high-resolution unstructured grid. The simulation includes shallow-water processes such as refraction, shoaling, and breaking. The model is validated against a five-year coastal wave buoy recording. The study shows that most of the resource is provided by sea states with wave heights ranging from 2 to 5 m, with wave periods from 10 and 15 s, and coming from a very narrow angular sector. The long hindcast duration and the refined unstructured grid used for the simulation allow assessment of the spatiotemporal distribution of wave energy across the coastal area. On the one hand, large longshore variations of the resource caused by steep bathymetric gradients such as the Capbreton submarine canyon are underlined. On the other hand, the study highlights that no specific long-term trend can be extracted regarding the coastal wave energy resource evolution. The provided downscaled local wave resource information may be used to optimize the location and design of a future WEC that could be deployed in the region.

Selection index for Wave Energy Deployments (SIWED): A near-deterministic index for wave energy converters

This study introduces a novel index that accounts for the interactions of wave climate and wave energy converters, offering an unbiased approach that considers climate variability, survivability and energy production. Application of the index is done with use of a long-term wave hindcast, validated database for the North Sea. A detailed overall and monthly wave resource assessment reveals that mean expected wave resource is ≈15 kW/m, with higher nearshore values in December-January ≈20-25 kW/m. Lower magnitudes are met in July with values closer to ≈ 4-6 kW/m, as a general observation higher resource magnitude is expected at upper parts of the North Sea, with diminishing levels towards the English channel, the difference in available resource is almost half. The region favours "smaller" capacity devices for energy production, with capacity factors having encouraging results. The highest mean value for a capacity factor in the region is 25-32%, depending on device. However, the new index indicated that the highest capacity factor value should not be the determinant parameter. In fact, other locations have less energy production annnually, but exhibit significantly less variation in production patterns, and lower extreme values of return waves.

The Changes of Theoretical Wave Power from Offshore to Coast in the South-western Black Sea

This study concentrates on the changes of theoretical wave power from offshore to coastal regions of south-western Black Sea. The investigation also offers a long-term (31-year) wave power quantification analysis between 1979 and 2009 using the SWAN version 41.01AB numerical wave model. The wave resource assessment is performed in terms of its seasonal and monthly variability of wave power, annual wave power directional distributions and the comparison of the maximum and median values of some important wave power parameters in the west of Istanbul a major city in Turkey that straddles Europe and Asia across the Bosphorus Strait. For this analysis, 10 point locations distributed on two perpendicular lines (KA and KE) to the coastline with five different depths (5, 25, 50, 75, and 100 m) in the areas of interest were selected. The data needed was extracted from the dataset produced by [1, 2] using a calibrated nested layered wave hindcast model SWAN forced with CFSR winds. The results show that the wave energy resource in the study area is high, and the potential locations can be considered for extracting wave electrical power.

A techno-economic approach to wave energy resource assessment and development site identification

A careful and detailed wave resource assessment is essential for identification of a successful wave energy project. However, current approaches inherently limit application to specific locations and wave energy converter architectures, rather than providing for broad application across the industry. Generally, a site is identified based on either the gross resources, or an assessment of minimal conflict with current ocean users. This chosen location defines the wave characteristics and a specific Wave Energy Converter (WEC) technology is identified. The predetermined wave characteristics and associated device performance then determine the final power production. This is a ‘bottom-up’ type approach. The current work introduces a novel ‘top-down’ approach. The new approach incorporates a novel device-agnostic performance constraints and economic considerations to identify priority development sites over broad coastal regions. The technological constraints are represented by frequency and directional screening functions, derived from the power matrices of four conceptually distinct WEC devices. This screening process estimates the extractable portion of the wave resource. Subsequently, a Net Revenue Index (NRI) is developed that characterizes the economic potential based on proximity to existing electricity markets and site-specific installation costs. Hot-spots are identified based on the screened extractable resource and economic analyses to represent both long and short term development opportunities. This ‘top-down’ approach is demonstrated through a case study of the West Coast of Vancouver Island. For both the screened wave resource and NRI data, hot-spots formed from locations exhibiting values in the 90th percentiles are identified. If fully developed, the resource hot spots potential is ~67,000 GWh of annual energy delivery, while hot-spots based on NRI represent economic hot-spots present 21,000 GWh of annual energy delivery. Intersecting the two collections of hot-spots results in 336 km2 with an average of wave energy flux of 35 kW/m.

Wave energy exploitation in the Ionian Sea Hellenic coasts: spatial planning of potential wave power stations

ABSTRACTWave energy represents the ‘new entry’ to Renewable Energy Sources discussion, in the context of clean and sustainable energy solutions in the electricity production sector. This research describes a geo-spatial Multiple-Criteria Decision Analysis, based on the Geographic Information Systems technology, for the identification of the best location to deploy a potential Wave Energy Farm in the Ionian Sea, an area offshore the Greek mainland West coast. For this purpose, several factors are taken into consideration; restrictions such as protected areas, military exercise areas etc. and weighted factors such as distance to power grid, wave height etc. The wave resource assessment is completed through real data measurements and numerical wave model approximations. The results pinpoint the most suitable areas for installing the proposed wave power plants, i.e. near the SW coast of Corfu, in the West side of the Straits between Kefallonia and Zakynthos and near the broader area of Pylos, in the SW coast of Peloponnesus. The suggested methodology can be equally applied in other spatial planning cases too, being considered as a checklist, addressed to policy-makers and private investors.

Wave Resource Characterization Using an Unstructured Grid Modeling Approach

This paper presents a modeling study conducted on the central Oregon coast for wave resource characterization, using the unstructured grid Simulating WAve Nearshore (SWAN) model coupled with a nested grid WAVEWATCH III® (WWIII) model. The flexibility of models with various spatial resolutions and the effects of open boundary conditions simulated by a nested grid WWIII model with different physics packages were evaluated. The model results demonstrate the advantage of the unstructured grid-modeling approach for flexible model resolution and good model skills in simulating the six wave resource parameters recommended by the International Electrotechnical Commission in comparison to the observed data in Year 2009 at National Data Buoy Center Buoy 46050. Notably, spectral analysis indicates that the ST4 physics package improves upon the ST2 physics package’s ability to predict wave power density for large waves, which is important for wave resource assessment, load calculation of devices, and risk management. In addition, bivariate distributions show that the simulated sea state of maximum occurrence with the ST4 physics package matched the observed data better than with the ST2 physics package. This study demonstrated that the unstructured grid wave modeling approach, driven by regional nested grid WWIII outputs along with the ST4 physics package, can efficiently provide accurate wave hindcasts to support wave resource characterization. Our study also suggests that wind effects need to be considered if the dimension of the model domain is greater than approximately 100 km, or O (102 km).

Galway Bay – The 1/4 scale wave energy test site? A detailed wave energy resource assessment and investigation of scaling factors

Ireland offers a complete testing pathway for wave energy devices from small-scale testing in laboratory wave tanks to the Galway Bay Test Site (GBTS), located in a sheltered bay, and the full-scale Atlantic Marine Energy Test Site (AMETS) exposed to the Atlantic Ocean on its west coast. This research investigates the scaling relationships between the GBTS and the AMETS, and also with the Westwave commercial demonstration site. The paper presents (i) detailed wave resource assessments at GBTS and Westwave (ii) a scaling analysis methodology suitable for determining the scale relationships between two sites, and (iii) the appropriate scaling factors for wave energy test sites in Ireland which can be used by developers for prototype testing and commercial deployment. The assessment and scaling analyses were performed using 12-year model outputs from two high resolution wave models from January 2004–December 2015. The models were well-validated using available measured data. The resource assessments determined (1) mean and maximum conditions and (2) operational, high and extreme event conditions for significant wave height, energy period and power. Both annual and seasonal analyses are presented. The 12-year annual mean power was 3 kW/m at GBTS and 50 kW/m at Westwave. Three scaling approaches have been analysed to identify the appropriate scale ratios to be used to upscale GBTS to AMETS for wave height, energy period and power. The distribution fittings method was found to be the most accurate method for resource scalability between GBTS and both AMETS and Westwave sites. The scaling result shows GBTS, although often termed a ¼-scale test site, is not actually ¼-scale proportionate to AMETS or Wavewave. Scaling factors are presented in tabular format, which are based on the Froude scaling method, for seasonal windows that could be used to determine the scale of ocean energy device models for testing in the benign site of GBTS in Ireland to ensure it adequately upscales to the open sea exposed sites of AMETS and Westwave in Ireland.

An Improved Method for Energy and Resource Assessment of Waves in Finite Water Depths

For cost savings and ease of operation, nearshore regions have been considered as ideal regions for deploying wave energy converters (WECs) and wave farms. As the water depths of these regions may be frequently limited to 50 m or less, they can be considered as being transitional/intermediate to shallow when compared to the wave lengths of interest for wave energy conversion. Since the impact of water depths on propagation of waves is significant, it cannot be ignored in wave energy assessment. According to the basic wave theory, in order to work out accurate wave energy amounts in finite water depth, detailed wave spectral distributions should be given. However, for some practical reasons, there are still some cases where only scatter diagrams and/or the statistical wave parameters are available, whilst the detailed wave spectra are discarded. As a result, the assessments of wave energy and resources are frequently determined by ignoring the effect of water depths or using very simplified approximations. This research paper aims to develop more accurate approximation methods by utilising a number of available parameters such that a better estimate on the wave resource assessment can be achieved even if the detailed wave spectra are not available. As one important goal, the research can provide some important indications on how the measured wave data are effectively presented so that they can be very useful for assessing the wave energy resource, especially in the cases including the effects of finite water depths.

Assessment of Wave Energy in the South China Sea Based on GIS Technology

China is now the world’s largest user of coal and also has the highest greenhouse gas emissions associated with the mining and use of coal. Under today’s enormous pressures of the growing shortage of conventional energy sources and the need for emission reductions, the search for clean energy is the most effective strategy to address the energy crisis and global warming. This study utilized satellite remote sensing technology, geographic information system (GIS) technology, and simulated wave data for the South China Sea. The characteristic features of the wave energy were obtained by analysis through the wave resource assessment formula and the results were stored in a GIS database. Software for the evaluation of wave energy in the South China Sea was written. The results should provide accurate, efficient references for wave energy researchers and decision-makers. Based on a 24-year WW3 model simulation wave data and GIS technology, this study presented the characteristic of the wave energy in the SCS; results demonstrated that the SCS has the feasibility and viability for wave energy farming.

Calibrating wave resource assessments through application of the triple collocation technique

Numerical wave models are often used to hindcast wave conditions and predict the theoretical energy production from wave energy conversion (WEC) devices. It is widely acknowledged that numerical model suffer from bias's and uncertainties which ultimately affect the final predictions of WEC power. In this case study, a Simulating WAves Nearshore (SWAN) hindcast, based on the ECMWF wave and FNMOC wind boundary conditions, is used to predict sea states off the Canadian west coast and the triple collocation technique is applied to quantify the model result bias's, and systematic and random errors. To analyze the error and calibrate the results from the hindcast, two in-situ collocated wave measurement devices are deployed; A TriAxys wave measurement buoy and a Nortek AWAC. The triple collocation technique is used to compare the significant wave height and energy period parameters over a three-month period, from October to December 2014. The triple collocation technique assumes linear relationship between the measured value and true value, and outputs the bias, calibration slope and the measurement random error. This study implements two previously utilized calibration regimes, a single value and monthly calibration regime, as well as presenting two novel methods to improve the impact of the calibration; a bivariate calibration and a spectral calibration. The two standard calibration techniques resulted in negligible improvements in data correlation. The spectral method suffered from high computational cost and only 3.4% improvement in significant wave height correlation. The bivariate calibration regime, following the International Electrotechnical Commission (IEC) wave resource histogram parameters, resulted in 5% and 26% improvements in the significant wave height and energy period correlations respectively. Calibration of the SWAN hindcast reduced the gross wave energy transport values by 900 MWh, yet the final WEC production estimates only varied by 1.5 MWh but greatly improved their time-series correlation. It is shown that the triple collocation technique, under the bivariate distribution regime, provides a more realistic presentation of future WEC power production and eliminates known short-comings of numerical model outputs.