A novel two-objective optimization computational framework for a two-body heaving wave energy converter

Lamprini Pavlidou and Demos C. Angelides

Renewable Energy, 2022, vol. 191, issue C, 510-534

Abstract: The present research introduces a novel two-objective optimization computational framework, named H∞ optimization criterion-constrained Two-Objective Optimization Genetic Algorithm (H∞ − cTOOGA). Within this optimization framework, the objectives considered are: (i) the sprung mass isolation, and (ii) the wave energy extraction of a heaving buoy coupled with a sprung (overlying) mass. In such a two-body heaving wave energy converter (WEC), the satisfaction of both objectives is pursued by proper selection of the buoy geometry (i.e., radius r and draft Dr) and the characteristics of the power take-off (PTO). The interrelation among potential buoy geometries and PTO characteristics is performed on the basis of a critical condition. The critical condition refers to the formation of equal double peaks in the sprung mass acceleration response curve and is imposed as the sprung mass acceleration limit that is not allowed to be exceeded throughout the entire frequency range of interest. The two-objective optimization is realized with r, Dr and the tuning ratio δOPT being the decision variables. The value of δOPT is constrained by the value of the critical tuning ratio δcrit, that is, δOPT <δcrit. This ensures the minimization of the maximum sprung mass acceleration response. Then, the Pareto-optimal solutions result to optimal wave energy extraction.

Keywords: Fixed-points theory; H∞optimization criterion; Genetic algorithms; Two-body heaving wave energy converter; Sprung mass isolation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148122004670
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:191:y:2022:i:c:p:510-534

DOI: 10.1016/j.renene.2022.04.006

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().