Mathematical Programming Models and Algorithms for Oshore Wind Park Design

Research output: Book/ReportPh.D. thesis – Annual report year: 2018Research

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Mathematical Programming Models and Algorithms for Oshore Wind Park Design. / Fischetti, Martina.

DTU Management Engineering, 2017. 310 p.

Research output: Book/ReportPh.D. thesis – Annual report year: 2018Research

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@phdthesis{9c92d369d44d4476a4f085dbac7f362f,
title = "Mathematical Programming Models and Algorithms for Oshore Wind Park Design",
abstract = "Designing an offshore wind park is a complex process, involving several differentexpertises, and multiple tasks. In this thesis we developed MathematicalProgramming models and algorithms to help the wind park designers. In particular,we focused on two optimization problems arising at the design phase ofoffshore wind parks, namely the optimal allocation of turbines in a given siteand the connection of turbines through cables. We briefly touched upon theoptimization of offshore jacket foundations as well.This thesis was motivated and supervised by Vattenfall, a leading companyin wind park development and operation. Thanks to our close collaboration,the optimization problems have been described and modelled as they arise inpractical applications and they have been tested on real data. Our work provedto have a huge impact in practice, being able to increase park production andreduce costs. Having a sound optimization tool to help the designers allowsalso for different what-if analyses and scenario evaluations. This is of key valuefor Vattenfall, especially when looking at new technologies on the market.The mathematical optimization models and algorithms developed have beenconsidered of great interest also by the Operational Research (OR) community,and resulted in six journal papers. This thesis wants to follow the two-foldnature of our project, offering interesting material both to wind energy expertsand practitioners, and to OR experts. Therefore we alternate OR journalpapers, with practical examples and impact evaluations.Finally, we proposed an application of integrating Machine Learning and OR,where we investigate if a machine, trained on a large number of optimizedsolutions, can estimate the value of the optimized solution for new instances.This research question is of interest for all kinds of optimization problems, andis here studied on our specific wind farm application.",
author = "Martina Fischetti",
note = "Ph.D. afhandling",
year = "2017",
language = "English",
publisher = "DTU Management Engineering",

}

RIS

TY - BOOK

T1 - Mathematical Programming Models and Algorithms for Oshore Wind Park Design

AU - Fischetti, Martina

N1 - Ph.D. afhandling

PY - 2017

Y1 - 2017

N2 - Designing an offshore wind park is a complex process, involving several differentexpertises, and multiple tasks. In this thesis we developed MathematicalProgramming models and algorithms to help the wind park designers. In particular,we focused on two optimization problems arising at the design phase ofoffshore wind parks, namely the optimal allocation of turbines in a given siteand the connection of turbines through cables. We briefly touched upon theoptimization of offshore jacket foundations as well.This thesis was motivated and supervised by Vattenfall, a leading companyin wind park development and operation. Thanks to our close collaboration,the optimization problems have been described and modelled as they arise inpractical applications and they have been tested on real data. Our work provedto have a huge impact in practice, being able to increase park production andreduce costs. Having a sound optimization tool to help the designers allowsalso for different what-if analyses and scenario evaluations. This is of key valuefor Vattenfall, especially when looking at new technologies on the market.The mathematical optimization models and algorithms developed have beenconsidered of great interest also by the Operational Research (OR) community,and resulted in six journal papers. This thesis wants to follow the two-foldnature of our project, offering interesting material both to wind energy expertsand practitioners, and to OR experts. Therefore we alternate OR journalpapers, with practical examples and impact evaluations.Finally, we proposed an application of integrating Machine Learning and OR,where we investigate if a machine, trained on a large number of optimizedsolutions, can estimate the value of the optimized solution for new instances.This research question is of interest for all kinds of optimization problems, andis here studied on our specific wind farm application.

AB - Designing an offshore wind park is a complex process, involving several differentexpertises, and multiple tasks. In this thesis we developed MathematicalProgramming models and algorithms to help the wind park designers. In particular,we focused on two optimization problems arising at the design phase ofoffshore wind parks, namely the optimal allocation of turbines in a given siteand the connection of turbines through cables. We briefly touched upon theoptimization of offshore jacket foundations as well.This thesis was motivated and supervised by Vattenfall, a leading companyin wind park development and operation. Thanks to our close collaboration,the optimization problems have been described and modelled as they arise inpractical applications and they have been tested on real data. Our work provedto have a huge impact in practice, being able to increase park production andreduce costs. Having a sound optimization tool to help the designers allowsalso for different what-if analyses and scenario evaluations. This is of key valuefor Vattenfall, especially when looking at new technologies on the market.The mathematical optimization models and algorithms developed have beenconsidered of great interest also by the Operational Research (OR) community,and resulted in six journal papers. This thesis wants to follow the two-foldnature of our project, offering interesting material both to wind energy expertsand practitioners, and to OR experts. Therefore we alternate OR journalpapers, with practical examples and impact evaluations.Finally, we proposed an application of integrating Machine Learning and OR,where we investigate if a machine, trained on a large number of optimizedsolutions, can estimate the value of the optimized solution for new instances.This research question is of interest for all kinds of optimization problems, andis here studied on our specific wind farm application.

M3 - Ph.D. thesis

BT - Mathematical Programming Models and Algorithms for Oshore Wind Park Design

PB - DTU Management Engineering

ER -