In situ near-shore wave resource assessment in the Fiji Islands

Ram, Krishnil R.; Narayan, Sumesh; Ahmed, M. Rafiuddin; Nakavulevu, P.; Lee, Young‐Ho

doi:10.1016/j.esd.2014.09.002

Cited by 11 publications

(8 citation statements)

References 9 publications

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“…The oscillating water column device works using an air turbine (Pico [4], LIMPET, Sakata, Mutriku [5], Mighty Whale [6], Ocean Energy, SPERBOY [7], Oceanlinx [8], and REWEC3 [9]); the oscillating bodies work with a hydraulic motor, a hydraulic turbine, and a linear electrical generator (AquaBuOY [10], IPS Buoy [11], FO3 [12], PowerBuoy [12], Wavebob [13], Pelamis [14,15], PS Frog [16], SEAREV [17], AWS (Archimedes Waveswing Submerged) [18], WaveRoller [19], and Oyster [20]); and the overtopping concept works with a low-head hydraulic turbine (TAPCHAN [21], SSG (Sea Slot-cone Generator) [22], and Wave Dragon [23]). They can also be classified by considering the water depth that they were designed to operate in; fixed (less than 50 m of depth or onshore) or floating (more than 50 m of depth) or the distance to shore [24]. Other WECs are: the Wavestar, which produces electricity due to the motion of the floats that are attached by arms to the platform [25]; the SeaBeavl, which is a "taut-moored dual-body" WEC designed to improve the maintenance process [26]; and Falcão et al analyze the hydrodynamics of IPS Buoy [27].…”

Section: Introductionmentioning

confidence: 99%

Economic Feasibility of Wave Energy Farms in Portugal

et al. 2018

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This paper develops a methodology to determine the economic feasibility of implementing offshore wave energy farms on the Portuguese continental coast. This methodology follows several phases: the geographic phase, the energy phase, the economic phase, and the restrictions phase. First, in the geographic phase, the height and the period of the waves, the bathymetry, the distance from the farm to the shore, from farm to shipyard, and from farm to port, are calculated. In the energy phase the energy produced by each wave energy converter is determined, and in the economic phase, the parameters calculated in the previous phases are used as input to find the economic parameters. Finally, in the restrictions phase, a limitation by the bathymetry will be added to the economic maps, whose value will be different depending on the floating offshore wave energy converter (WEC). In this study, three wave energy converters have been considered, Pelamis, AquaBuOY, and Wave Dragon, and several scenarios for electric tariffs have been taken into account. The results obtained indicate what the best WEC is for this study in terms of its levelized cost of energy (LCOE), internal rate of return (IRR), and net present value (NPV), and where the best area is to install wave energy farms.

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Section: Introductionmentioning

confidence: 99%

Economic Feasibility of Wave Energy Farms in Portugal

et al. 2018

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“…Ocean waves have a good potential to generate electricity (power) along the coastlines [9]. As cited by Ram et al [8], USA has a wave power potential of 240 GW, Republic of China has a wave power potential of 125 GW while Coastline of India has a wave energy potential of 6 GW/year respectively. Fiji has a sea to land area ratio of 70 with a coastline of 1129 km having wave power potential of 29 GW and 0.5% of these wave energy is enough to meet the daily demand of the Fiji Islands.…”

Section: Introductionmentioning

confidence: 99%

Time-Variations of Wave-Energy and Forecasting Power Availability using Different Techniques

Kumar¹,

Ahmed²,

Khan³

2020

Australasian Fluid Mechanics Conference (AFMC)

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Wave energy has captured the attention of island nations that have a significantly larger sea area compared to the land area. An accurate forecasting model is required to correctly predict the availability of wave energy with all the required variables clearly defined. In this work, wave energy forecasting models: artificial neural network, regression and time series techniques were developed using wave height and wave period data. The models were tested using the wave data measured near Muani, Kadavu in Fiji for 14 months. The performance of each model is assessed using mean squared error, root mean square error, mean absolute error and coefficient of determination. The empirical results reveal that the artificial neural network model is more efficient and accurate in forecasting wave energy in comparison to the regression and time series models.

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“…The Orkney archipelago, situated in the north of Scotland is estimated to have a mean wave power of 10-25 kW/m and has been earmarked to contribute 550 MW to the total 1.6 GW wave and tidal energy capacity from twelve leased sites by 2020 [5]. An assessment of the near-shore wave energy potential for the southern pacific islands of Fiji showed that mean wave power of 9.81 kW/m was detected at a depth of 15 m in the west of the main island while energy flux of around 28.78 kW/m was identified near Kadavu island at a depth of 18 m [6]. The Cape Verde Islands, located off the coast of West Africa in the central Atlantic Ocean, witnessed a wave power exceeding 7 kW/m in coastal areas neighboring the islands, with the summer season recording around half the wave potential of the winter season [7].…”

Section: The Long-term Vision Of the Government Of Mauritius Is Tomentioning

confidence: 99%

Assessment of the wave potential at selected hydrology and coastal environments around a tropical island, case study: Mauritius

Doorga

Chinta

Gooroochurn

et al. 2018

Int J Energy Environ Eng

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Waves are the dominant influence on coastal morphology and ecosystem structure of tropical islands. The geographical positioning of Mauritius near to the Tropic of Capricorn ensures that the eastern regions benefit from the persistent southeast trade winds which influence the incoming surface waves. In this study, we present the high dependence of the wave regimes of windward offshore site on the trade winds. The higher occurrence of incoming waves in the winter season directed in the southeast direction indicates that the trade winds are more prevalent in the winter season. Storms within the extratropical South Atlantic, Indian and Pacific oceans generally propagate towards the east such that extratropical South Atlantic swell energy spreads through the entire Indian Ocean. Since waves are very directional and tend to get shadowed by land masses, Mauritius situated in the line of sight from those sources end up in the shadow region due to the geographical location of Reunion island. In this study, we support the explanation on how the western region of the island gets influenced by episodic swell events. A detailed wave energy resource assessment is provided for different targeted coastal environments around the island. It is revealed that the mean wave power observed in the summer season at one of the sites can attain 28.8 kW/m and is found to be lower as compared to the winter season (31.7 kW/m).

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In situ near-shore wave resource assessment in the Fiji Islands

Cited by 11 publications

References 9 publications

Economic Feasibility of Wave Energy Farms in Portugal

Economic Feasibility of Wave Energy Farms in Portugal

Time-Variations of Wave-Energy and Forecasting Power Availability using Different Techniques

Assessment of the wave potential at selected hydrology and coastal environments around a tropical island, case study: Mauritius

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