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  <doc>
    <id>323</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>preprint</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2020-08-24</completedDate>
    <publishedDate>2020-08-24</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Solving AC Optimal Power Flow with Discrete Decisions to Global Optimality</title>
    <abstract language="eng">We present a solution framework for general alternating current optimal power flow (AC OPF) problems that include discrete decisions.&#13;
The latter occur, for instance, in the context of the curtailment of renewables or the&#13;
switching of power generation units and transmission lines.&#13;
Our approach delivers globally optimal solutions and is provably convergent.&#13;
We model AC OPF problems with discrete decisions as mixed-integer nonlinear programs.&#13;
The solution method starts from a known framework that uses piecewise linear relaxations.&#13;
These relaxations are modeled as as mixed-integer linear programs and adaptively refined until some termination criterion is fulfilled.&#13;
In this work, we extend and complement this approach by problem-specific as well as very general algorithmic enhancements.&#13;
In particular, these are mixed-integer second-order cone programs as well as primal and dual cutting planes.&#13;
For example objective cuts and no-good-cuts help to compute good feasible solutions as where outer approximation constraints tighten the relaxations.&#13;
We present extensive numerical results for various AC OPF problems where discrete decisions play a major role.&#13;
Even for hard instances with a large proportion of discrete decisions, the method is able&#13;
to generate high quality solutions efficiently.&#13;
Furthermore, we compare our approach with state-of-the-art MINLP.&#13;
Our method outperforms all other algorithms.</abstract>
    <enrichment key="SubmissionStatus">under review</enrichment>
    <enrichment key="review.accepted_by">2</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Kevin-Martin Aigner</author>
    <author>Robert Burlacu</author>
    <author>Frauke Liers</author>
    <author>Alexander Martin</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed-Integer Nonlinear Programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Second-Order Cone Programming</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>AC Optimal Power Flow</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Discrete Decisions</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Piecewise Linear Relaxation</value>
    </subject>
    <collection role="institutes" number="">Friedrich-Alexander-Universität Erlangen-Nürnberg</collection>
    <collection role="subprojects" number="">A05</collection>
    <collection role="subprojects" number="">B06</collection>
    <collection role="subprojects" number="">B07</collection>
    <collection role="subprojects" number="">B10</collection>
    <file>https://opus4.kobv.de/opus4-trr154/files/323/discrete-ac-opf.pdf</file>
  </doc>
</export-example>
