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Wave energy is one of the most concentrated ocean renewable energy resources. Although wave energy has been studied extensively for more than four decades, there is no large commercial installation for wave energy production or a consensus framework on how to exploit this resource. Wave energy is a complex resource that directly depends on two meteorological parameters, which produced significant fluctuations of wave energy in both temporal and spatial criteria. This paper presents a new concept called Energy Event, to analyze meteorological data generated by WaveWatch III over 36 years in the U.S. to characterize and assess wave energy behavior using the peak-over-threshold methodology. This methodology used extreme statistics, segmented the wave energy with different thresholds, and assessed wave energy production on a temporal and spatial framework. Three areas were studied in this paper, including the Gulf of Mexico, the East and West U.S. Coasts. The results indicated that wave energy behaved as a two-state energy system with each state having independent characteristics. The main difference among the three studied areas was the constant baseline of wave power density, with the West Coast having the highest constant baseline and the Gulf of Mexico having the lowest baseline. [ABSTRACT FROM AUTHOR]

The impact of surface wave assimilation on hurricane track and intensity forecasts has been investigated using a fully coupled air-ocean-wave tropical cyclone data assimilation and forecast modeling system. A new 3DVAR wave assimilation method in the Navy Coupled Ocean Data Assimilation system (NCODA) maps the 1D wave energy spectra from buoys to 2D directional wave energy spectra using the maximum likelihood method (MLM) and corrects the wave model forecast component directional wave energy spectra. The Coupled Ocean/Atmosphere Mesoscale Prediction System for Tropical Cyclone Prediction (COAMPS-TC) is used to conduct three Hurricane Harvey (2017) airocean-wave coupled data assimilation and forecasting experiments with and without the wave data assimilation. Hurricane Harvey traversed through the Western Gulf of Mexico from 24 August to 1 September, 2017 and made landfall in the Texas and Louisiana coast. Validation of track, maximum wind speed, significant wave height, and mean absolute wave periods show wave assimilation of the 1D wave energy spectra from 13 National Data Buoy Center (NDBC) buoys reduced the forecast errors of these parameters compared to experiments without the wave assimilation. In spite of this positive outcome, the wave assimilation is unable to reduce Harvey's 0-120 h forecast mean wave direction errors and correlation compared to the NDBC buoy time series. [ABSTRACT FROM AUTHOR]

This paper summarizes a comprehensive feasibility assessment of six offshore renewable energy sources in the Gulf of Mexico (GOM) to potentially provide utilityscale electricity from the Outer Continental Shelf (federal waters) and state waters to land-based grids. The primary objective is to inform future energy planning. The authors evaluated offshore wind, wave energy, tidal energy, ocean-based solar photovoltaics (PV), ocean current, and ocean thermal energy conversion. Of these energy resources, offshore wind was the most viable option and was the focus of the second part of the study. The technical challenges of developing offshore wind in the GOM are discussed including hurricane design for turbines and substructures, as well as turbine solutions to overcome lower wind regimes. In addition, advantages to offshore wind development in the GOM are described including proximity to oil and gas supply chains. Economic analysis using established cost models at the National Renewable Energy Laboratory identified hypothetical project locations where net value of offshore wind was the highest, and the levelized cost of energy (LCOE) was calculated for three sites: Port Isabel, Port Arthur, and Pensacola. Offshore wind LCOE in the GOM was found to be higher than those in sites along the north and mid-Atlantic coasts, but decreasing cost trajectories indicate the possibility of economic viability for locations in Texas and west Louisiana after 2030. The extrapolated 2030 LCOE values range from $73/MWh (Site 1, Port Isabel) and $79/MWh (Site 3, Port Arthur) to $91/MWh (Site 5, Pensacola). [ABSTRACT FROM AUTHOR]

Different concepts and methods have been proposed and developed by many researchers to harvest ocean wave energy. In this paper, a new self-adjustable wave energy converter concept is presented, which changes its inertia through ballasting and de-ballasting using sea water. The trigger of ballasting and de-ballasting is controlled by the critical wave period. Therefore, the self-adjustable wave energy converter is able to interact at resonance with the ocean waves at two different resonant bandwidths. Ten years real wave data with hourly resolution from a selected location in Gulf of Mexico was used in this paper to decide the critical wave period and other parameters of the wave energy converter. The annual energy performance of the self-adjustable wave energy converter was also estimated and compared with non-adjustable wave energy converter with similar dimensions. Structural analysis including both static and fatigue analysis was performed on the self-adjustable wave energy converter to determine its survivability with the real ocean wave data. The results show that the self-adjustable wave energy converter is able to capture more energy than non-adjustable wave energy converter, and is able to survive during the hash ocean wave conditions. [ABSTRACT FROM AUTHOR]

The ocean plays host to a vast array of sources of energy. These energies simply need to be developed efficiently and effectively, as well as harmonized and collected. This paper will present and discuss two major sources of sustainable ocean energy—wind and wave—which can be combined effectively into one multi-purpose structure. These dynamic structures are emerging as useful in many international settings. However, the United States' (U.S.) areas are slow to adopt. This research presents a hierarchical analysis for decision makers to utilize in strategic offshore location planning with regard to these platforms. Surprisingly, the Gulf of Mexico (GOM) proves to be in a favorable position for entities considering use of such multi-purpose platforms. • Combinatory analysis of energy collection modes. • Provides a conceptual framework using a hierarchical analysis tool. • Optimal location decisions for U.S. coastal regions. [ABSTRACT FROM AUTHOR]

Observational data are successfully assessed to investigate wave power (wave energy flux per unit of wave-crest) trends within four coastal regions around the US, a parameter that is deemed vital to those responsible for coastal protection and community resilience. This study tests for shifting observational inter-annual wave power trends using a newly developed, unique, United States Army Corps of Engineers Quality Controlled Consistent Measurement Archive, and offers a viable methodology to remove documented observational time series data discontinuations. This study is one of the first to show spatially and temporally comparative observational and model wave power results, providing new information on the accuracy of model wave power estimates, while showcasing in situ wave power trends at 29 sites around the U.S. coastline. Overall, the majority of the eastern Pacific Ocean and Hawaii wave power trends are downward, with mixed slope wave power trends apparent within the Atlantic Ocean and the Gulf of Mexico. Observational and model results are similar with respect to timing, but not magnitude, of wave power peaks in long-term inter-annual trends, with the moored buoy data presenting smaller wave power ranges for two (eastern Pacific Ocean and Hawaii) of the four regions. Additionally, the detection of a noticeable variability in the wave power trend direction within each region suggests that site-specific wave power trends should not be generalised to represent a large region. This work demonstrates that observational data are essential in local and regional wave climate studies to accurately estimate wave power for coastal planners and engineers. [ABSTRACT FROM AUTHOR]

Probabilistic approaches are frequently used to describe irregular activity data to assist the design and development of devices. Unfortunately, useful estimations are not always feasible due to the large noise in the data modeled, as it occurs when estimating the sea waves potential for electricity generation. In this work we propose a simple methodology based on the use of joint probability models that allow discriminating extreme values, collected from measurements as pairs of independent points, while allowing the preservation of the essential statistics of the measurements. The outcome of the proposed methodology is an equivalent data series where large-amplitude fluctuations are suppressed and, therefore, can be used for design purposes. For the evaluation of the proposed method, we used year-long databases of hourly-collected measurements of the wave's height and period, performed at maritime buoys located in the Gulf of Mexico. These measurements are used to obtain a fluctuations-reduced representation of the energy potential of the waves that can be useful, for instance, for the design of electric generators. [ABSTRACT FROM AUTHOR]

Abstract: The statistical behavior of wave energy at a site strongly depends on the wave characteristics. Wave energy converters fail to produce energy when there are no sufficiently available wave heights. Hence, evaluation of return periods and risk values of the minimum wave height becomes important for wave energy studies. A time index representing the minimum wave height is proposed here for ocean wave applications. Persistence plays a significant role in the calculation of return period and risk. Although ignoring the serial independence makes calculations easy, it leads to overestimations of the real status. In this paper, return periods and risk values are compared with each other by taking into consideration independent and dependent situations. Application of the study is achieved for the stations located in the Pacific Ocean and Gulf of Mexico. [Copyright &y& Elsevier]

Ocean renewable energy is a promising inexhaustible source of renewable energy, with an estimated harnessing potential of approximately 337 GW worldwide, which could re-shape the power generation mix. As with other sources of renewables, however, wave energy has an intermittent and irregular nature, which is a major concern for power system stability. Consequently, in order to integrate wave energy into power grids, it must be forecasted. This paper proposes using optimised deep learning neural networks to forecast the wave energy flux, and other wave parameters. In particular, we use moth-flame optimisation as the central decision-making unit to configure the deep neural network structure and the proper input data selection. Besides, the moth-flame optimisation algorithm was modified to improve its search space mechanisms. The forecasting skills are assessed using 13 datasets from locations across the Pacific and Atlantic coasts, and the Gulf of Mexico. The proposed optimised deep neural network performs well at all the sites, especially over short-term horizons, where it outperforms statistical and physics-based approaches. • We propose a new methodology for short-term wave forecasting. • A Deep Neural Network structure is used to perform the forecasts. • Moth-Flame Optimizer is used to "shape and fine-tune" sensitive hyperparameters. • A broad spectrum of wave data from NDBC and ISDM operators were used for testing. • Results revealed promising forecasting accuracy, especially in short-range horizons. [ABSTRACT FROM AUTHOR]