The optimization of Wave Energy Converters (WECs) is a current topic related to the development of the ocean wave energy sector. Competition between developers is based mainly on the minimiza-tion of the Levelized Cost Of Energy (LCOE). A particular numerical methodology is adopted. Nemoh is used for hydrodynamic computation and the standard linear potential flow formulation is employed. However second order dynamic boundary conditions are neglected. Finally MATLAB and the open-source WEC-Sim are used to find the energy production of the device. Results are presented for a method of optimization as part of a project to optimize for a metric reflecting the LCOE of a multi-float configuration of a WaveSub device. Different design parameters are considered based on the WaveSub system: the float spacing and number, the float-reactor separation, the PTO tuning and the rated power. A genetic algorithm and a particle swarm optimization are used to observe the evolution of the minimum LCOE.

Design of an optimization scheme for the wavesub array / Faraggiana, E.; Masters, I.; Chapman, J.. - (2019), pp. 633-638.

Design of an optimization scheme for the wavesub array

Faraggiana E.;
2019

Abstract

The optimization of Wave Energy Converters (WECs) is a current topic related to the development of the ocean wave energy sector. Competition between developers is based mainly on the minimiza-tion of the Levelized Cost Of Energy (LCOE). A particular numerical methodology is adopted. Nemoh is used for hydrodynamic computation and the standard linear potential flow formulation is employed. However second order dynamic boundary conditions are neglected. Finally MATLAB and the open-source WEC-Sim are used to find the energy production of the device. Results are presented for a method of optimization as part of a project to optimize for a metric reflecting the LCOE of a multi-float configuration of a WaveSub device. Different design parameters are considered based on the WaveSub system: the float spacing and number, the float-reactor separation, the PTO tuning and the rated power. A genetic algorithm and a particle swarm optimization are used to observe the evolution of the minimum LCOE.
2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2935487