<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>30081</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>100</pageFirst>
    <pageLast>115</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>3</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-01-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Model Predictive Control-Based Decision-Making Strategy for Residential Microgrids</title>
    <abstract language="eng">This work presents the development of a decision-making strategy for fulfilling the power and heat demands of small residential neighborhoods. The decision on the optimal operation of a microgrid is based on the model predictive control (MPC) rolling horizon. In the design of the residential microgrid, the new approach different technologies, such as photovoltaic (PV) arrays, micro-combined heat and power (micro-CHP) units, conventional boilers and heat and electricity storage tanks are considered. Moreover, electricity transfer between the microgrid components and the national grid are possible. The MPC problem is formulated as a mixed integer linear programming (MILP) model. The proposed novel approach is applied to two case studies: one without electricity storage, and one integrated microgrid with electricity storage. The results show the benefits of considering the integrated microgrid, as well as the advantage of including electricity storage.</abstract>
    <parentTitle language="eng">Eng</parentTitle>
    <identifier type="url">https://www.mdpi.com/2673-4117/3/1/9</identifier>
    <identifier type="doi">10.3390/eng3010009</identifier>
    <identifier type="issn">2673-4117</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Publikationsweg">Open Access</enrichment>
    <enrichment key="Fprofil">1 Energiewende und Dekarbonisierung / Energy Transition and Decarbonisation</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Evgenia</firstName>
      <lastName>Mechleri</lastName>
    </author>
    <submitter>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </submitter>
    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </author>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
    </author>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
</export-example>
