scholarly journals Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

2020 ◽  
Author(s):  
Yufei Tang
Keyword(s):  
Output Power ◽  
Water Depth ◽  
Energy Production ◽  
Ocean Current ◽  
Total Power ◽  
Optimization Approach ◽  
Cumulative Energy ◽  
Renewable Energy Systems ◽  
Power Maximization ◽  
Current Turbine

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.

scholarly journals Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

2020 ◽  
Author(s):  
Yufei Tang
Keyword(s):  
Output Power ◽  
Water Depth ◽  
Energy Production ◽  
Ocean Current ◽  
Total Power ◽  
Optimization Approach ◽  
Cumulative Energy ◽  
Renewable Energy Systems ◽  
Power Maximization ◽  
Current Turbine

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.

scholarly journals Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

2020 ◽  
Author(s):  
Yufei Tang
Keyword(s):  
Output Power ◽  
Water Depth ◽  
Energy Production ◽  
Ocean Current ◽  
Total Power ◽  
Optimization Approach ◽  
Cumulative Energy ◽  
Renewable Energy Systems ◽  
Power Maximization ◽  
Current Turbine

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.

scholarly journals Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

2020 ◽  
Author(s):  
Yufei Tang
Keyword(s):  
Output Power ◽  
Water Depth ◽  
Energy Production ◽  
Ocean Current ◽  
Total Power ◽  
Optimization Approach ◽  
Cumulative Energy ◽  
Renewable Energy Systems ◽  
Power Maximization ◽  
Current Turbine

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.

Iterative In-Situ 3D Layout Optimization of a Reconfigurable Ocean Current Turbine Array Using Bayesian Optimization

2017 ◽  
Author(s):  
Ali Baheri ◽  
Praveen Ramaprabhu ◽  
Christopher Vermillion
Keyword(s):  
Optimization Technique ◽  
Complex Function ◽  
Interaction Model ◽  
Layout Optimization ◽  
Bayesian Optimization ◽  
Ocean Current ◽  
Optimization Approach ◽  
Evaluation Point ◽  
3D Layout ◽  
Current Turbine

In this paper, we present an online approach for optimizing the 3D layout of an ocean current turbine (OCT) array. Unlike towered turbines, most OCT concepts for Gulf Stream energy harvesting involve tethered systems. The replacement of towers with tethers provides the opportunity for OCTs to adjust their locations within some domain by paying out/in tether to adjust depth and manipulating control surfaces (elevators and rudders) to adjust longitudinal and lateral positions. The ability to adjust the OCT positions online provides the capacity to reconfigure the array layout in response to changing flow conditions; however, successful online array layout reconfiguration requires optimization schemes that are not only effective but also enable fast convergence to the optimal configuration. To address the above needs, we present a reconfigurable layout optimization algorithm with two novel features. First, we describe the location of each turbine through a small set of basis parameters; the number of basis parameters does not grow with increasing array size, thereby leading to an optimization that is not only computationally tractable but is also highly scalable. Secondly, we use Bayesian Optimization to optimize these basis parameters. Bayesian Optimization is a very powerful iterative optimization technique that, at every iteration, fuses a best-guess model of a complex function (array power as a function of basis parameters, in our case) with a characterization of the model uncertainty in order to determine the next evaluation point. Using a low-order analytical wake interaction model, we demonstrate the effectiveness of the proposed optimization approach for various array sizes.

scholarly journals Output Power Maximization of DFIG Wind Turbine using Linear MPC Technique

2021 ◽  
Vol 1878 (1) ◽  
pp. 012045
Author(s):  
S M Suboh ◽  
M S Hassan ◽  
N H Baharudin ◽  
K Ananda-Rao ◽  
N B Ahamad ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Output Power ◽  
Power Maximization

Advances in ocean current turbine blade design

2021 ◽  
Author(s):  
Hassan Mahfuz ◽  
Nicholas Asseff ◽  
Mohammad Wasim Akram ◽  
Fang Zhou ◽  
Takuya Suzuki ◽  
...  
Keyword(s):  
Turbine Blade ◽  
Ocean Current ◽  
Blade Design ◽  
Turbine Blade Design ◽  
Current Turbine

scholarly journals Energy Recovery Potential in Industrial and Municipal Wastewater Networks Using Micro-Hydropower in Spain

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 691
Author(s):  
Aida Mérida García ◽  
Juan Antonio Rodríguez Díaz ◽  
Jorge García Morillo ◽  
Aonghus McNabola
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Energy Recovery ◽  
Energy Production ◽  
Municipal Wastewater ◽  
Distribution Networks ◽  
Total Power ◽  
Energy Potential ◽  
Total Energy Consumption ◽  
Recovery Potential

The use of micro-hydropower (MHP) for energy recovery in water distribution networks is becoming increasingly widespread. The incorporation of this technology, which offers low-cost solutions, allows for the reduction of greenhouse gas emissions linked to energy consumption. In this work, the MHP energy recovery potential in Spain from all available wastewater discharges, both municipal and private industrial, was assessed, based on discharge licenses. From a total of 16,778 licenses, less than 1% of the sites presented an MHP potential higher than 2 kW, with a total power potential between 3.31 and 3.54 MW. This total was distributed between industry, fish farms and municipal wastewater treatment plants following the proportion 51–54%, 14–13% and 35–33%, respectively. The total energy production estimated reached 29 GWh∙year−1, from which 80% corresponded to sites with power potential over 15 kW. Energy-related industries, not included in previous investigations, amounted to 45% of the total energy potential for Spain, a finding which could greatly influence MHP potential estimates across the world. The estimated energy production represented a potential CO2 emission savings of around 11 thousand tonnes, with a corresponding reduction between M€ 2.11 and M€ 4.24 in the total energy consumption in the country.

scholarly journals Is Investing in Companies Manufacturing Solar Components a Lucrative Business? A Decision Tree Based Analysis

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 499
Author(s):  
Sebastian Klaudiusz Tomczak ◽  
Anna Skowrońska-Szmer ◽  
Jan Jakub Szczygielski
Keyword(s):  
Renewable Energy ◽  
Energy Production ◽  
Manufacturing Sector ◽  
Energy Systems ◽  
Product Portfolio ◽  
New Approach ◽  
Renewable Energy Systems ◽  
Pv Systems ◽  
Depth Analysis ◽  
A Company

In an era of increasing energy production from renewable sources, the demand for components for renewable energy systems has dramatically increased. Consequently, managers and investors are interested in knowing whether a company associated with the semiconductor and related device manufacturing sector, especially the photovoltaic (PV) systems manufacturers, is a money-making business. We apply a new approach that extends prior research by applying decision trees (DTs) to identify ratios (i.e., indicators), which discriminate between companies within the sector that do (designated as “green”) and do not (“red”) produce elements of PV systems. Our results indicate that on the basis of selected ratios, green companies can be distinguished from the red companies without an in-depth analysis of the product portfolio. We also find that green companies, especially operating in China are characterized by lower financial performance, thus providing a negative (and unexpected) answer to the question posed in the title.

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