<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>234</id>
    <completedYear>2018</completedYear>
    <publishedYear/>
    <thesisYearAccepted>2019</thesisYearAccepted>
    <language>eng</language>
    <pageFirst>43</pageFirst>
    <pageLast>69</pageLast>
    <pageNumber>27</pageNumber>
    <edition/>
    <issue>33</issue>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2018-02-07</completedDate>
    <publishedDate>2018-02-07</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Global Optimization of Multilevel Electricity Market Models Including Network Design and Graph Partitioning</title>
    <abstract language="eng">We consider the combination of a network design and graph partitioning model in a multilevel framework for determining the optimal network expansion and the optimal zonal configuration of zonal pricing electricity markets, which is an extension of the model discussed in [25] that does not include a network design problem. The two classical discrete optimization problems of network design and graph partitioning together with nonlinearities due to economic modeling yield extremely challenging mixed-integer nonlinear multilevel models for which we develop two problem-tailored solution techniques. The first approach relies on an equivalent bilevel formulation and a standard KKT transformation thereof including novel primal-dual bound tightening techniques, whereas the second is a tailored generalized Benders decomposition. For the latter, we strengthen the Benders cuts of [25] by using the structure of the newly introduced network design subproblem. We prove for both methods that they yield global optimal solutions. Afterward, we compare the approaches in a numerical study and show that the tailored Benders approach clearly outperforms the standard KKT transformation. Finally, we present a case study that illustrates the economic effects that are captured in our model.</abstract>
    <parentTitle language="deu">Discrete Optimization</parentTitle>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Thomas Kleinert</author>
    <author>Martin Schmidt</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Network design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Graph partitioning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multilevel optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed-integer optimization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Electricity market design</value>
    </subject>
    <collection role="institutes" number="">Friedrich-Alexander-Universität Erlangen-Nürnberg</collection>
    <collection role="subprojects" number="">B08</collection>
    <file>https://opus4.kobv.de/opus4-trr154/files/234/price-zones-plus-net-design_oo_preprint_2018-10-29.pdf</file>
  </doc>
</export-example>
