skip to main content
10.1145/3205455.3205492acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

A detailed comparison of meta-heuristic methods for optimising wave energy converter placements

Published:02 July 2018Publication History

ABSTRACT

In order to address environmental concerns and meet growing energy demand the development of green energy technology has expanded tremendously. One of the most promising types of renewable energy is ocean wave energy. While there has been strong research in the development of this technology to date there remain a number of technical hurdles to overcome. This research explores a type of wave energy converter (WEC) called a buoy. This work models a power station as an array of fully submerged three-tether buoys. The target problem of this work is to place buoys in a size-constrained environment to maximise power output. This article improves prior work by using a more detailed model and exploring the search space using a wide variety of search heuristics. We show that a hybrid method of stochastic local search combined with Nelder-Mead Simplex direct search performs better than previous search techniques.

References

  1. Hans-Georg Beyer and Hans-Paul Schwefel. 2002. Evolution strategies-A comprehensive introduction. Natural computing 1, 1 (2002), 3--52. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. BFM Child and Vengatesan Venugopal. 2010. Optimal configurations of wave energy device arrays. Ocean Engineering 37, 16 (2010), 1402--1417.Google ScholarGoogle ScholarCross RefCross Ref
  3. Duc-Cuong Dang and Per Kristian Lehre. 2016. Runtime analysis of non-elitist populations: From classical optimisation to partial information. Algorithmica 75, 3 (2016), 428--461. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. AD De Andrés, R Guanche, L Meneses, C Vidal, and IJ Losada. 2014. Factors that influence array layout on wave energy farms. Ocean Engineering 82 (2014), 32--41.Google ScholarGoogle ScholarCross RefCross Ref
  5. Benjamin Drew, Andrew R Plummer, and M Necip Sahinkaya. 2009. A review of wave energy converter technology. (2009).Google ScholarGoogle Scholar
  6. Aguston Eiben, Zbigniew Michalewicz, Marc Schoenauer, and Jim Smith. 2007. Parameter control in evolutionary algorithms. Parameter setting in evolutionary algorithms (2007), 19--46. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Carnegie Clean Energy. 2017. CETO 6 Design Update @ONLINE. (2017). https://s3-ap-southeast-2.amazonaws.com/website-sydney-1/media/2017/11/13134305/1738756.pdfGoogle ScholarGoogle Scholar
  8. Nikolaus Hansen. 2006. The CMA evolution strategy: a comparing review. Towards a new evolutionary computation (2006), 75--102.Google ScholarGoogle Scholar
  9. Jeffrey C Lagarias, James A Reeds, Margaret H Wright, and Paul E Wright. 1998. Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM Journal on optimization 9, 1 (1998), 112--147. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. JT Scruggs, SM Lattanzio, AA Taflanidis, and IL Cassidy. 2013. Optimal causal control of a wave energy converter in a random sea. Applied Ocean Research 42 (2013), 1--15.Google ScholarGoogle ScholarCross RefCross Ref
  11. N Yu Sergiienko. 2016. Frequency domain model of the three-tether WECs array. (2016).Google ScholarGoogle Scholar
  12. Rainer Storn and Kenneth Price. 1997. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization 11, 4 (1997), 341--359. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Tom W Thorpe et al. 1999. A brief review of wave energy. Harwell Laboratory, Energy Technology Support Unit.Google ScholarGoogle Scholar
  14. Markus Wagner, Jareth Day, and Frank Neumann. 2013. A fast and effective local search algorithm for optimizing the placement of wind turbines. Renewable Energy 51 (2013), 64--70.Google ScholarGoogle ScholarCross RefCross Ref
  15. GX Wu. 1995. Radiation and diffraction by a submerged sphere advancing in water waves of finite depth. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, Vol. 448. The Royal Society, 29--54.Google ScholarGoogle ScholarCross RefCross Ref
  16. Junhua Wu, Slava Shekh, Nataliia Y Sergiienko, Benjamin S Cazzolato, Boyin Ding, Frank Neumann, and Markus Wagner. 2016. Fast and effective optimisation of arrays of submerged wave energy converters. In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference. ACM, 1045--1052. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. A detailed comparison of meta-heuristic methods for optimising wave energy converter placements

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference
        July 2018
        1578 pages
        ISBN:9781450356183
        DOI:10.1145/3205455

        Copyright © 2018 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 2 July 2018

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate1,669of4,410submissions,38%

        Upcoming Conference

        GECCO '24
        Genetic and Evolutionary Computation Conference
        July 14 - 18, 2024
        Melbourne , VIC , Australia

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader