Overview of ocean power technology

Wilberforce, Tabbi; Hassan, Zaki El; Durrant, Andrew; Thompson, J. F.; Soudan, Bassel; Olabi, Abdul Ghani

doi:10.1016/j.energy.2019.03.068

“…The main challenges in developing MHK based energy are high investment and maintenance costs (e.g., hostile operating environment) and difficulty of integration their produced electricity to the electrical grid (e.g., remote areas and deep sea) [5], [6]. There are many approaches to address the high cost challenges, such as lower the cost of turbine design or optimized operational strategy [7], and this study focuses on maximizing the output power of an ocean current turbine (OCT).…”

Section: Introductionmentioning

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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“…Wave energy has the capacity to travel for long distances with low energy loss. For instance, the wind storm originated from the "Western Atlantic Ocean" have the capacity to travel toward the "Western Cost of Europe" with a very minimal loss of the energy [6,7]. It is revealed that the wave energy systems can produce up to 90% of the global energy demand.…”

Section: Introductionmentioning

confidence: 99%

“…Illustration of onshore, offshore and offshore categories for Wave Energy Systems (WES)[6] Kinds of Wave Energy Systems (WES)[6] …”

mentioning

confidence: 99%

Wave energy in the UK: Current scope, challenges and prognostications

Memon

¹

,

Lawal

²

,

Tariq

³

et al. 2020

STVE

2

0

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This paper reviews the current status, challenges and prognostications of wave energy systems (WES), which have momentous scope in the UK that could deliver the UK's net zero energy target by 2050. In Britain, there are 43 primary seaside towns around the coast in which 37 are in England that encompass a collective population of 2.9 million and signify around 5.7 % of the population of England as a whole where the zero energy coastline house projects can be initiated with WES. The progress in the development of standalone WES for a vision of zero energy coastline houses is still in its initial stages. This paper exhibited a brief review of the onshore, nearshore and offshore WES technologies, particular focus was made to the scope in the UK. The feasibility and efficiency of WES study imply that the power take off (PTO) efficiency is crucially important as the overall output depends on the optimum energy harness from WES that will improve its competitive prowess with other renewable technologies and further reduce the cost of WES manufacturing. This study implicates one of prognostications that developing marine energy resources in the UK can save 60 metric tons of carbon dioxide by 2025. This study also concluded that WES can pose environmental challenges such as alterations of water column to biota and sea-bed habitats, dredging, noise and vibrations. The prognostication of zero energy coastline houses arises due to the location of the UK, which is one of the major determinants for the future success of wave energy projects, as the UK is located at the long fetch of the Atlantic Ocean and has the wind direction from the west. The available resource to harness wave energy in the UK is around 120 GW.

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“…The main challenges in developing MHK based energy are high investment and maintenance costs (e.g., hostile operating environment) and difficulty of integration their produced electricity to the electrical grid (e.g., remote areas and deep sea). There are many approaches to address the high cost challenges, such as lowering the cost of turbine design or optimizing the operational strategy [5], and this study focuses on maximizing the output power of an ocean current turbine (OCT).…”

Section: Introductionmentioning

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.

show abstract

Overview of ocean power technology

Cited by 140 publications

References 47 publications

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Wave energy in the UK: Current scope, challenges and prognostications

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

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