On evolutionary approaches to wind turbine placement with geo-constraints

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dc.contributor.authorLückehe, D.
dc.contributor.authorWagner, M.
dc.contributor.authorKramer, O.
dc.contributor.conferenceAnnual Conference on Genetic and Evolutionary Computation (GECCO) (11 Jul 2015 - 15 Jul 2015 : Madrid, Spain)
dc.contributor.editorSilva, S.
dc.date.issued2015
dc.description.abstractWind turbine placement, i.e., the geographical planning of wind turbine locations, is an important first step to an efficient integration of wind energy. The turbine placement problem becomes a difficult optimization problem due to varying wind distributions at different locations and due to the mutual interference in the wind field known as wake effect. Artificial and environmental geological constraints make the optimization problem even more difficult to solve. In our paper, we focus on the evolutionary turbine placement based on an enhanced wake effect model fed with real-world wind distributions. We model geo-constraints with realworld data from OpenStreetMap. Besides the realistic modeling of wakes and geo-constraints, the focus of the paper is on the comparison of various evolutionary optimization approaches. We propose four variants of evolution strategies with turbine-oriented mutation operators and compare to state-of-the-art optimizers like the CMA-ES in a detailed experimental analysis on three benchmark scenarios.
dc.description.statementofresponsibilityDaniel Lückehe, Markus Wagner, Oliver Kramer
dc.identifier.citationProceedings of the 2015 on Genetic and Evolutionary Computation Conference, 2015 / Silva, S. (ed./s), pp.1223-1230
dc.identifier.doi10.1145/2739480.2754690
dc.identifier.isbn9781450334723
dc.identifier.orcidWagner, M. [0000-0002-3124-0061]
dc.identifier.urihttp://hdl.handle.net/2440/108011
dc.language.isoen
dc.publisherACM press
dc.relation.ispartofseriesGECCO ’15
dc.rights©2015 ACM
dc.source.urihttp://doi.acm.org/10.1145/2739480.2754690
dc.subjectWind Power; Wind Farm Layout; Evolutionary Optimization; Self-Adaptation; CMA-ES
dc.titleOn evolutionary approaches to wind turbine placement with geo-constraints
dc.typeConference paper
pubs.publication-statusPublished

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