A Measurement-Based Analysis of the Hydrokinetic Energy in the Gulf Stream

Machado, Maria Carolina P. M.; VanZwieten, James H.; Callou, Allan P. O.; Pinos, Isabella

doi:10.17736/jowe.2016.asr06

“…Each of these resource types are distributed globally with riverine energy primarily located in the worlds larger river systems, tidal energy along the coast and primarily at higher latitudes, and ocean current energy primarily along the western boundaries of the world's oceans. Ocean currents with average energy densities greater than 0.5 kW/m 2 are located at numerous locations along the western boundaries of the world's ocean basins [1], with average energy densities measured in excess of 3.0 kW/m 2 [2]. The technically extractable average ocean current energy from US waters has been estimated at 19 GW [3], with several other countries such as Japan, South Africa, Taiwan, the Philippines, and Brazil also having large scale ocean currents accessible off their coasts [1].…”

Section: Introductionmentioning

confidence: 99%

Influences of yaw angle and turbulence intensity on the performance of a 20 kW in-stream hydrokinetic turbine

Tian

¹

,

VanZwieten

²

,

Pyakurel

³

et al. 2016

Energy

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Three-dimensional transient Computational Fluid Dynamics (CFD) simulations are performed to study the hydrodynamic performance of an ocean current turbine with a 3.0 m diameter 3-bladed rotor. Simulations are based on the Reynolds Averaged Navier-Stokes (RANS) equations and the shear stress transport k-ω turbulent model is utilized. The influence of yaw angle and upstream turbulence intensity (TI) on the turbine performance is studied. The CFD method is first validated using existing experimental data and good agreement is obtained. The performance of the turbine, including power, thrust and wake characteristics are then studied at different tip speed ratios (TSR). The turbine obtains a maximum coefficient of power (Cp) of 0.4642 at TSR=6 and the coefficient of thrust (Ct) increases over the entire evaluated TSR range to a value of 0.8788 at a TSR=10. Simulations are also performed at four different yaw angles, 0°, 5°, 10° and 15° which show that both Cp and Ct decrease as yaw angle increases. Finally simulations of three different TIs, 3%, 6% and 9%, are performed and analyzed. Results show that TI minimally affects Cp and Ct for the considered TI range, but greatly influences the downstream wake structure.

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“…The ocean velocity field varies both with time and 3-D space. However, moored OCTs can primarily vary their vertical location and current shear is most prominent in the [26]. The ocean shear profile of these data with 4-hour intervals for one sample day is shown in Fig.…”

Section: A Statistical Ocean Current Shear Profile Characterizationmentioning

confidence: 99%

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Tang¹

2020

Preprint

0

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This paper presents a novel spatiotemporal optimization approach for maximizing the output power of an ocean current turbine (OCT) under uncertain ocean velocities. In order to determine output power, ocean velocities and the power consumed and generated by an OCT system are modeled. The stochastic behavior of ocean velocities is a function of time and location, which is modeled as a Gaussian process. The power of the OCT system is composed of three parts, including generated power, power for maintaining the system at an operating depth, and power consumed for changing the water depth to reach the maximum power. Two different algorithms, including model predictive control (MPC) as a model-based method and reinforcement learning (RL) as a learning-based method, are proposed to design the optimization structure, and comparative studies are presented. On one hand, the MPC based controller is faster in finding the optimal water depth, while the RL is also computationally feasible considering the required time for changing operating depth. On the other hand, the cumulative energy production of the RL algorithm is higher than the MPC method, which verifies that the learning-based RL algorithm can provide a better solution to address the uncertainties in renewable energy systems. Results verify the efficiency of both presented methods in maximizing the total power of an OCT system, where the total harnessed energy after 200 hours shows an over 18% increase compared to the baseline.

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“…Therefore, the ocean velocity's dependence on both time and water depth is of primary importance, in other words the ocean velocity should be determined at specific times t and operating depths z. In this paper, we use the data recorded in Southeast Florida by a bottom mounted acoustic Doppler current profile (ADCP) between February 2009 and March 2010 [23]. The ocean shear profile of these data with 4-hour intervals for one sample day is shown in Fig.…”

Section: A Statistical Ocean Current Shear Profile Characterizationmentioning

confidence: 99%

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Tang¹

2020

Preprint

1

0

View full text Add to dashboard Cite

This paper presents a novel spatiotemporal optimization approach for maximizing the output power of an ocean current turbine (OCT) under uncertain ocean velocities. In order to determine output power, ocean velocities and the power consumed and generated by an OCT system are modeled. The stochastic behavior of ocean velocities is a function of time and location, which is modeled as a Gaussian process. The power of the OCT system is composed of three parts, including generated power, power for maintaining the system at an operating depth, and power consumed for changing the water depth to reach the maximum power. Two different algorithms, including model predictive control (MPC) as a model-based method and reinforcement learning (RL) as a learning-based method, are proposed to design the optimization structure, and comparative studies are presented. On one hand, the MPC based controller is faster in finding the optimal water depth, while the RL is also computationally feasible considering the required time for changing operating depth. On the other hand, the cumulative energy production of the RL algorithm is higher than the MPC method, which verifies that the learning-based RL algorithm can provide a better solution to address the uncertainties in renewable energy systems. Results verify the efficiency of both presented methods in maximizing the total power of an OCT system, where the total harnessed energy after 200 hours shows an over 18% increase compared to the baseline.

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A Measurement-Based Analysis of the Hydrokinetic Energy in the Gulf Stream

Cited by 24 publications

References 1 publication

Influences of yaw angle and turbulence intensity on the performance of a 20 kW in-stream hydrokinetic turbine

Influences of yaw angle and turbulence intensity on the performance of a 20 kW in-stream hydrokinetic turbine

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

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