Solving wind farm layout optimization with mixed integer programs and constraint programs

The wind farm layout optimization problem is concerned with the optimal location of turbines within a fixed geographical area to maximize profit under stochastic wind conditions. Previously, it has been modeled as a maximum diversity (or p-dispersion-sum) problem, but such a formulation cannot captu...

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Autores principales:	PeterY. Zhang, DavidA. Romero, J.Christopher Beck, CristinaH. Amon
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2014
Materias:	90C90 Application of mathematical programming 90C11 Mixed integer programming Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/d0b31b2dad84468291a5ee1a9a742568
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spelling	oai:doaj.org-article:d0b31b2dad84468291a5ee1a9a7425682021-12-02T05:00:40ZSolving wind farm layout optimization with mixed integer programs and constraint programs2192-440610.1007/s13675-014-0024-5https://doaj.org/article/d0b31b2dad84468291a5ee1a9a7425682014-08-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2192440621000320https://doaj.org/toc/2192-4406The wind farm layout optimization problem is concerned with the optimal location of turbines within a fixed geographical area to maximize profit under stochastic wind conditions. Previously, it has been modeled as a maximum diversity (or p-dispersion-sum) problem, but such a formulation cannot capture the nonlinearity of aerodynamic interactions among multiple wind turbines. We present the first constraint programming (CP) and mixed integer linear programming (MIP) models that incorporate such nonlinearity. Our empirical results indicate that the relative performance between these two models reverses when the wind scenario changes from a simple to a more complex one. We then extend these models to include landowner participation and noise constraints. With the additional constraints, the MIP-based decomposition outperforms CP in almost all cases. We also propose an improvement to the previous maximum diversity model and demonstrate that the improved model solves more problem instances.PeterY. ZhangDavidA. RomeroJ.Christopher BeckCristinaH. AmonElsevierarticle90C90Application of mathematical programming90C11 Mixed integer programmingApplied mathematics. Quantitative methodsT57-57.97Electronic computers. Computer scienceQA75.5-76.95ENEURO Journal on Computational Optimization, Vol 2, Iss 3, Pp 195-219 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	90C90 Application of mathematical programming 90C11 Mixed integer programming Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
spellingShingle	90C90 Application of mathematical programming 90C11 Mixed integer programming Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95 PeterY. Zhang DavidA. Romero J.Christopher Beck CristinaH. Amon Solving wind farm layout optimization with mixed integer programs and constraint programs
description	The wind farm layout optimization problem is concerned with the optimal location of turbines within a fixed geographical area to maximize profit under stochastic wind conditions. Previously, it has been modeled as a maximum diversity (or p-dispersion-sum) problem, but such a formulation cannot capture the nonlinearity of aerodynamic interactions among multiple wind turbines. We present the first constraint programming (CP) and mixed integer linear programming (MIP) models that incorporate such nonlinearity. Our empirical results indicate that the relative performance between these two models reverses when the wind scenario changes from a simple to a more complex one. We then extend these models to include landowner participation and noise constraints. With the additional constraints, the MIP-based decomposition outperforms CP in almost all cases. We also propose an improvement to the previous maximum diversity model and demonstrate that the improved model solves more problem instances.
format	article
author	PeterY. Zhang DavidA. Romero J.Christopher Beck CristinaH. Amon
author_facet	PeterY. Zhang DavidA. Romero J.Christopher Beck CristinaH. Amon
author_sort	PeterY. Zhang
title	Solving wind farm layout optimization with mixed integer programs and constraint programs
title_short	Solving wind farm layout optimization with mixed integer programs and constraint programs
title_full	Solving wind farm layout optimization with mixed integer programs and constraint programs
title_fullStr	Solving wind farm layout optimization with mixed integer programs and constraint programs
title_full_unstemmed	Solving wind farm layout optimization with mixed integer programs and constraint programs
title_sort	solving wind farm layout optimization with mixed integer programs and constraint programs
publisher	Elsevier
publishDate	2014
url	https://doaj.org/article/d0b31b2dad84468291a5ee1a9a742568
work_keys_str_mv	AT peteryzhang solvingwindfarmlayoutoptimizationwithmixedintegerprogramsandconstraintprograms AT davidaromero solvingwindfarmlayoutoptimizationwithmixedintegerprogramsandconstraintprograms AT jchristopherbeck solvingwindfarmlayoutoptimizationwithmixedintegerprogramsandconstraintprograms AT cristinahamon solvingwindfarmlayoutoptimizationwithmixedintegerprogramsandconstraintprograms
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Solving wind farm layout optimization with mixed integer programs and constraint programs

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