Development, deployment and experimental test on the novel tethered system GEM for tidal current energy exploitation

Coiro, Domenico P.; Troise, G.; Scherillo, Fabio; Marco, Agostino De; Calise, Giuseppe; Bizzarrini, Nadia

doi:10.1016/j.renene.2017.01.040

“…This OCT has been extended to include two variable buoyancy tanks and a 607 m long mooring cable that attaches to the sea floor at a depth of 325 m [32]. These model parameters roughly follow the prototype systems from IHI Corp. [33], [34] and the University of Naples [35], but with the variable ballast tanks sized to operate at a depth of 50 m when half filled with ballast water and operating at the Gulf Stream's mean flow speed off Southeast Florida of 1.6 m/s. This allows for the dynamic spatial control and response.…”

Section: B Mathematical Model Of Output Power Of Octmentioning

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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“…This OCT model has been extended to include two variable buoyancy tanks and a 607 m long mooring cable that attaches to the sea floor at a depth of 325 m [27]. These model parameters roughly follow the prototype systems from IHI Corp. [28], [29] and the University of Naples [30], but with a single rotor and variable ballast tanks sized to operate at a depth of 50 m when half filled with ballast water and operating at the Gulf Stream's mean flow speed off Southeast Florida of 1.6 m/s. This allows for the dynamic spatial control and response.…”

Section: B Mathematical Model Of Oct Output Powermentioning

confidence: 99%

“…where is usually set as a small value, such as 0.05. The Q-learning algorithm using greedy approach is presented in Algorithm 3, which is used for maximizing the net power of OCT system according to the state space (29), the action space (30), and the reward function (31). As it is presented in this algorithm, the action (depth change) is…”

Section: Rl-based Optimization Designmentioning

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 number of tidal stream resource assessments are included in the special issue, discussing the tidal resource of the Bosphorus [15], Puget Sound [16], the Massachusetts coast [17], Rathlin Sound in Northern Ireland [18], and the famous Pentland Firth [19]. These studies consider issues that include characterising the shape of the velocity profile at highly energetic sites [20], turbulence [21,22], representing energy extraction in three-dimensional (3D) models [19,23], the impact of waves on the tidal resource [24], and novel floating tidal devices [25]. It is notable that the majority of tidal resource assessments in the special issue that included a modelling component used 3D models as standard.…”

Section: Take Down Policymentioning

confidence: 99%

Special issue: Wave and tidal resource characterization

Neill¹,

Yang

²

,

Hashemi

³

2017

Renewable Energy

0

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Development, deployment and experimental test on the novel tethered system GEM for tidal current energy exploitation

Cited by 32 publications

References 1 publication

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Special issue: Wave and tidal resource characterization

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