On learning to generate wind farm layouts

Authors:
Dennis Wilson;Emmanuel Awa;Sylvain Cussat-Blanc;Kalyan Veeramachaneni;Una-May O'Reilly
Affiliations:
CSAIL - MIT, Cambridge, MA, USA;Brandeis University, Waltham, MA, USA;University of Toulouse, Toulouse, France;CSAIL - MIT, Cambridge, MA, USA;CSAIL - MIT, Cambridge, MA, USA
Venue:
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Year:
2013

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Abstract

Optimizing a wind farm layout is a very complex problem that involves many local and global constraints such as inter-turbine wind interference or terrain peculiarities. Existing methods are either inefficient or, when efficient, take days or weeks to execute. Solutions are contextually sensitive to the specific values of the problem variables; when one value is modified, the algorithm has to be re-run from scratch. This paper proposes the use of a developmental model to generate farm layouts. Controlled by a gene regulatory network, virtual cells have to populate a simulated environment that represents the wind farm. When the cells' behavior is learned, this approach has the advantage that it is re-usable in different contexts; the same initial cell is responsive to a variety of environments and the layout generation takes few minutes instead of days.