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  <doc>
    <id>7228</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
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    <language>eng</language>
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    <belongsToBibliography>1</belongsToBibliography>
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    <title language="eng">Model Reduction by Time Aggregation for Optimal Design of Energy Supply Systems by an MILP Hierarchical Branch and Bound Method</title>
    <abstract language="eng">Mixed-integer linear programming (MILP) methods have been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems effi- ciently. An original problem has been solved by dividing it into a relaxed optimal design problem at the upper level and optimal operation problems which are independent of one another at the lower level. In addition, some strategies have been proposed to enhance the computation efficiency furthermore. In this paper, a method of reducing model by time aggregation is proposed as a novel strategy to search design candidates efficiently in the relaxed optimal design problem at the upper level. In addition, the previous strategies are modified in accordance with the novel strategy. This method is realized only by clustering periods and averaging energy demands for clustered periods, while it guarantees to derive the optimal solu- tion. The method can decrease the number of design variables and constraints at the upper level, and thus may decrease the computation time at the upper level. Through a case study on the optimal design of a gas turbine cogeneration system, it is clarified how the model reduction is effective to enhance the computation efficiency in comparison and combination with the modified previous strategies.</abstract>
    <parentTitle language="eng">Proceedings of the 31st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2018)</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Ryohei Yokoyama</author>
    <submitter>Yuji Shinano</submitter>
    <author>Yuji Shinano</author>
    <author>Yuki Wakayama</author>
    <author>Tetsuya Wakui</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>9044</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
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    <language>jpn</language>
    <pageFirst>401</pageFirst>
    <pageLast>404</pageLast>
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    <title language="jpn">エネルギー供給システムの設計および運用の階層的関係を考慮した最適化（時間集約によるモデル縮約とその効果）</title>
    <title language="eng">Optimization of Energy Supply Systems in Consideration of Hierarchical Relationship Between Design and Operation(Model Reduction by Time Aggregation and Its Effect)</title>
    <abstract language="eng">A hierarchical mixed-integer linear programming method to solve optimal design problems of energy supply systems efficiently has been proposed. The original problem is solved by dividing it into a relaxed optimal design problem at the upper level and optimal operation problems which are independent of each other at the lower level. In this paper, a method of reducing model by time aggregation is proposed to search design solution candidates efficiently in the relaxed optimal design problem at the upper level. This method is realized only by clustering periods and averaging energy demands for clustered periods, while it guarantees to derive the optimal solution. On one hand, the method may decrease the number of design variables and costraints at the upper level, and thus the computation time at the upper level. On the other hand, it may increase the numbers of generated design solution candidates and solved optimal operation problems, and thus the computation time at both the levels. Though a case study on the optimal design of a cogeneration system, it is clarified how the model reduction is effective to enhance the computation efficiency.</abstract>
    <parentTitle language="jpn">エネルギー・資源学会第34回エネルギーシステム・経済・環境コンファレンス講演論文集</parentTitle>
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    <author>Yuki Wakayama</author>
    <submitter>Yuji Shinano</submitter>
    <author>Ryohei Yokoyama</author>
    <author>Yuji Shinano</author>
    <author>Tetsuya Wakui</author>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
    <collection role="institutes" number="aim">Applied Algorithmic Intelligence Methods</collection>
    <collection role="projects" number="MODAL-EnergyLab">MODAL-EnergyLab</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>9043</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>jpn</language>
    <pageFirst>259</pageFirst>
    <pageLast>260</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="jpn">階層的最適化によるエネルギ ー供給システムの最適設計・運用 (期間のクラスタリングに よるモデル縮約とその効果)</title>
    <title language="eng">Optimal Design and Operation of Energy Supply Systems by Hierarchical Optimization (Model Reduction by Clustering Periods and Its Effect)</title>
    <abstract language="eng">Ahierarchical mixed-integer linear programmingmethod has been proposed to solve optimal design problems of energy supply systems efficiently. In this paper, a method of reducing model by clustering periods is proposed to search design solution candidates efficiently in the relaxed optimal design problem at the upper level. This method is realized only by clustering periods and averaging energy demands for clustered periods, while it guarantees to derive the optimal solution.&#13;
Through acase study on the optimaldesign of a cogeneration system, ti is clarified how the model reduction si effective ot enhance the computation efficiency.</abstract>
    <parentTitle language="jpn">日本機械学会関西支部第93期定時総会講演会講演論文集</parentTitle>
    <enrichment key="PeerReviewed">no</enrichment>
    <enrichment key="SubmissionStatus">in press</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Yuki Wakayama</author>
    <submitter>Yuji Shinano</submitter>
    <author>Ryohei Yokoyama</author>
    <author>Yuji Shinano</author>
    <author>Tetsuya Wakui</author>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
    <collection role="institutes" number="aim">Applied Algorithmic Intelligence Methods</collection>
    <collection role="projects" number="MODAL-EnergyLab">MODAL-EnergyLab</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>9018</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>782</pageFirst>
    <pageLast>792</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>181</volume>
    <type>article</type>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Model reduction by time aggregation for optimal design of energy supply systems by an MILP hierarchical branch and bound method</title>
    <abstract language="eng">Mixed-integer linear programming (MILP) methods have been applied widely to optimal design of en- ergy supply systems in consideration of multi-period operation. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. An original problem has been solved by dividing it into a relaxed optimal design problem at the upper level and optimal operation problems which are independent of one another at the lower level. In addition, some strategies have been pro- posed to enhance the computation efficiency furthermore. In this paper, a method of reducing model by time aggregation is proposed as a novel strategy to search design candidates efficiently in the relaxed optimal design problem at the upper level. In addition, the previous strategies are modified in accor- dance with the novel strategy. This method is realized only by clustering periods and averaging energy demands for clustered periods, while it guarantees to derive the optimal solution. Thus, it may decrease the computation time at the upper level. Through a case study on the optimal design of a gas turbine cogeneration system, it is clarified how the model reduction is effective to enhance the computation efficiency in comparison and combination with the modified previous strategies.</abstract>
    <parentTitle language="eng">Energy</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Ryohei Yokoyama</author>
    <submitter>Yuji Shinano</submitter>
    <author>Yuji Shinano</author>
    <author>Yuki Wakayama</author>
    <author>Tetsuya Wakui</author>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
    <collection role="institutes" number="aim">Applied Algorithmic Intelligence Methods</collection>
    <collection role="projects" number="MODAL-EnergyLab">MODAL-EnergyLab</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
  <doc>
    <id>8066</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>782</pageFirst>
    <pageLast>792</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>181</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Model reduction by time aggregation for optimal design of energy supply systems by an MILP hierarchical branch and bound method</title>
    <abstract language="eng">Mixed-integer linear programming (MILP) methods have been applied widely to optimal design of energy supply systems in consideration of multi-period operation. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. An original problem has been solved by dividing it into a relaxed optimal design problem at the upper level and optimal operation problems which are independent of one another at the lower level. In addition, some strategies have been proposed to enhance the computation efficiency furthermore. In this paper, a method of reducing model by time aggregation is proposed as a novel strategy to search design candidates efficiently in the relaxed optimal design problem at the upper level. In addition, the previous strategies are modified in accordance with the novel strategy. This method is realized only by clustering periods and averaging energy demands for clustered periods, while it guarantees to derive the optimal solution. Thus, it may decrease the computation time at the upper level. Through a case study on the optimal design of a gas turbine cogeneration system, it is clarified how the model reduction is effective to enhance the computation efficiency in comparison and combination with the modified previous strategies.</abstract>
    <parentTitle language="eng">Energy</parentTitle>
    <identifier type="doi">https://doi.org/10.1016/j.energy.2019.04.066</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Ryohei Yokoyama</author>
    <submitter>Yuji Shinano</submitter>
    <author>Yuji Shinano</author>
    <author>Yuki Wakayama</author>
    <author>Tetsuya Wakui</author>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="Mathematical Algorithmic Intelligence">Mathematical Algorithmic Intelligence</collection>
    <collection role="institutes" number="ais2t">AI in Society, Science, and Technology</collection>
  </doc>
  <doc>
    <id>6740</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>14</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>3</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Multiobjective Optimal Design of a Gas Turbine Cogeneration Plant by a Revised Hierarchical Optimization Method</title>
    <abstract language="eng">To attain the highest performance of energy supply systems, it is necessary to rationally determine design specifications in consideration of operational strategies corresponding to energy demands. Mixed-integer linear programming (MILP) approaches have been applied widely to such optimal design problems. A MILP method utilizing the hierarchical relationship between design and operation variables have been proposed to solve them efficiently. However, it cannot necessarily be effective to multi-objective optimal design problems because of the existence of a large number of competing design candidates. In this paper, the hierarchical MILP method is revised from the viewpoint of computation efficiency so that it can be applied practically to multi-objective optimal design problems. At the lower level, the order of the optimal operation problems to be solved is changed based on incumbents obtained previously to increase a lower bound for the optimal value of the combined objective function and reduce the number of the optimal operation problems to be solved. At the upper level, a lower bound for the optimal value of the combined objective function is incorporated into the solution method to reduce the number of the design candidates to be generated. This revised hierarchical MILP method is applied to a multiobjective optimal design of a gas turbine cogeneration plant, and its validity and effectiveness are clarified.</abstract>
    <parentTitle language="eng">ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition</parentTitle>
    <identifier type="doi">doi:10.1115/GT2017-64296</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Ryohei Yokoyama</author>
    <submitter>Yuji Shinano</submitter>
    <author>Yuji Shinano</author>
    <author>Yuki Wakayama</author>
    <author>Tetsuya Wakui</author>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
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