Formulations and methods for wind farm layout optimization

Quan, Ning

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https://hdl.handle.net/2142/101499 Copy

Description

Title

Formulations and methods for wind farm layout optimization

Author(s)

Quan, Ning

Issue Date

2018-06-25

Director of Research (if dissertation) or Advisor (if thesis)

Kim, Harrison

Doctoral Committee Chair(s)

Kim, Harrison

Committee Member(s)

• Thurston, Deborah L.
• Ouyang, Yanfeng
• Ho, Koki

Department of Study

Industrial&Enterprise Sys Eng

Discipline

Industrial Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(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.

Graduation Semester

2018-08

Type of Resource

text

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http://hdl.handle.net/2142/101499

Copyright and License Information

Copyright 2018 Ning Quan

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