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Title:Formulations and methods for wind farm layout optimization
Author(s):Quan, Ning
Director of Research:Kim, Harrison
Doctoral Committee Chair(s):Kim, Harrison
Doctoral Committee Member(s):Thurston, Deborah L.; Ouyang, Yanfeng; Ho, Koki
Department / Program:Industrial&Enterprise Sys Eng
Discipline:Industrial Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):wind farm layout
layout optimization
robust wind farm
robust layout
wind farm design
wind farm optimization
wind farm micro-siting
turbine placement
Abstract:The use of wind energy in electricity generation around the world has increased steadily over the past few years as the world seeks to reduce the use of fossil fuels in response to concerns over climate change and air pollution. Utility scale wind power is generated at large wind farms with as many as a hundred wind turbines. The layout of turbines in the wind farm has important implications regarding maintenance costs, electrical infrastructure costs, and most importantly, wind farm power generation. The wind farm layout optimization problem seeks to find the optimal layout of turbines that minimizes power loss from placing turbines in the wake cones of upstream turbines. The problem has received plenty of attention from researchers, but there remains significant room for improvement in terms of dealing with non-convexity, use of heuristics, and robust layouts which are resistant to errors in wind predictions. The first part of this work proposes a novel mixed integer linear programming formulation that allows for unrestricted placement of turbines within the wind farm, while at the same time eliminating solution dependence on the initial layout common to other continuous formulations. The second part introduces a dual-decomposition method for getting a close bound on the optimal solutions to discrete formulations, thereby facilitating the use of heuristics by giving an objective estimate of solution quality. The final part presents a robust layout optimization formulation with minimal data requirements, as well as a modified greedy algorithm with feasibility guarantees for finding robust solutions.
Issue Date:2018-06-25
Rights Information:Copyright 2018 Ning Quan
Date Available in IDEALS:2018-09-27
Date Deposited:2018-08

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