Multi-objective optimization and comparison framework for the design of Distributed Energy Systems
•We present two models for the optimal design of distributed energy systems.•We perform a comparison of the two methods in a case study with six buildings.•The total annual cost and the carbon emissions are the two objective functions.•Each method provides different results for each solution.•We pro...
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Veröffentlicht in: | Energy conversion and management 2019-01, Vol.180, p.473-495 |
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description | •We present two models for the optimal design of distributed energy systems.•We perform a comparison of the two methods in a case study with six buildings.•The total annual cost and the carbon emissions are the two objective functions.•Each method provides different results for each solution.•We propose a combined use of the two methodologies.
Energy supply in an economic and environmentally friendly configuration is a major challenge for urban energy systems. This paper presents two multi-objective models for the design of Distributed Energy Systems (DES) for satisfying local needs in heating, cooling and electricity. DES can be used to promote energy efficiency and reduce carbon emissions which is crucial to tackle climate change. A multi-objective MILP framework is presented, comparing two main methodologies of designing DES using Total Annual Cost (TAC) and carbon emissions as objective functions. The first one, namely “Method A”, which performs a simultaneous sizing and the second one, namely “Method B”, in which technologies and their respective capacity are predefined. Furthermore, three scenarios regarding the available technologies are developed to assess DES for both methods and a case study is carried out. Results show that in both methods Scenario 3 (a DES coupled with district heating network, microgrid and storage technologies) has the best performance. In terms of comparison between the two methods, “Method A” provides better solutions in terms of cost and emissions as it has more degrees of freedom. Structure, design and operational results are presented and discussed analytically, as well as the possibility of comparing the two methods for a more detailed design. |
doi_str_mv | 10.1016/j.enconman.2018.10.083 |
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Energy supply in an economic and environmentally friendly configuration is a major challenge for urban energy systems. This paper presents two multi-objective models for the design of Distributed Energy Systems (DES) for satisfying local needs in heating, cooling and electricity. DES can be used to promote energy efficiency and reduce carbon emissions which is crucial to tackle climate change. A multi-objective MILP framework is presented, comparing two main methodologies of designing DES using Total Annual Cost (TAC) and carbon emissions as objective functions. The first one, namely “Method A”, which performs a simultaneous sizing and the second one, namely “Method B”, in which technologies and their respective capacity are predefined. Furthermore, three scenarios regarding the available technologies are developed to assess DES for both methods and a case study is carried out. Results show that in both methods Scenario 3 (a DES coupled with district heating network, microgrid and storage technologies) has the best performance. In terms of comparison between the two methods, “Method A” provides better solutions in terms of cost and emissions as it has more degrees of freedom. Structure, design and operational results are presented and discussed analytically, as well as the possibility of comparing the two methods for a more detailed design.</description><identifier>ISSN: 0196-8904</identifier><identifier>EISSN: 1879-2227</identifier><identifier>DOI: 10.1016/j.enconman.2018.10.083</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Carbon ; Case studies ; Climate change ; Design ; Design engineering ; Distributed energy systems ; Distributed generation ; District heating ; District heating network ; Emissions ; Energy efficiency ; Methods ; MILP ; Multi-objective optimization ; Multiple objective analysis ; Optimization ; Renewables ; Storage</subject><ispartof>Energy conversion and management, 2019-01, Vol.180, p.473-495</ispartof><rights>2018 Elsevier Ltd</rights><rights>Copyright Elsevier Science Ltd. Jan 15, 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c379t-9a205ba62d0c333a304dd7ebbeb2e6afeda0b8f845a2c979dbd60db76e0555283</citedby><cites>FETCH-LOGICAL-c379t-9a205ba62d0c333a304dd7ebbeb2e6afeda0b8f845a2c979dbd60db76e0555283</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.enconman.2018.10.083$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Karmellos, M.</creatorcontrib><creatorcontrib>Mavrotas, G.</creatorcontrib><title>Multi-objective optimization and comparison framework for the design of Distributed Energy Systems</title><title>Energy conversion and management</title><description>•We present two models for the optimal design of distributed energy systems.•We perform a comparison of the two methods in a case study with six buildings.•The total annual cost and the carbon emissions are the two objective functions.•Each method provides different results for each solution.•We propose a combined use of the two methodologies.
Energy supply in an economic and environmentally friendly configuration is a major challenge for urban energy systems. This paper presents two multi-objective models for the design of Distributed Energy Systems (DES) for satisfying local needs in heating, cooling and electricity. DES can be used to promote energy efficiency and reduce carbon emissions which is crucial to tackle climate change. A multi-objective MILP framework is presented, comparing two main methodologies of designing DES using Total Annual Cost (TAC) and carbon emissions as objective functions. The first one, namely “Method A”, which performs a simultaneous sizing and the second one, namely “Method B”, in which technologies and their respective capacity are predefined. Furthermore, three scenarios regarding the available technologies are developed to assess DES for both methods and a case study is carried out. Results show that in both methods Scenario 3 (a DES coupled with district heating network, microgrid and storage technologies) has the best performance. In terms of comparison between the two methods, “Method A” provides better solutions in terms of cost and emissions as it has more degrees of freedom. Structure, design and operational results are presented and discussed analytically, as well as the possibility of comparing the two methods for a more detailed design.</description><subject>Carbon</subject><subject>Case studies</subject><subject>Climate change</subject><subject>Design</subject><subject>Design engineering</subject><subject>Distributed energy systems</subject><subject>Distributed generation</subject><subject>District heating</subject><subject>District heating network</subject><subject>Emissions</subject><subject>Energy efficiency</subject><subject>Methods</subject><subject>MILP</subject><subject>Multi-objective optimization</subject><subject>Multiple objective analysis</subject><subject>Optimization</subject><subject>Renewables</subject><subject>Storage</subject><issn>0196-8904</issn><issn>1879-2227</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNqFkM1OwzAQhC0EEqXwCsgS55SN0zjODVTKjwTiAJwtO94UhyYutltUnh5XhTOn1Y5mZrUfIec5THLI-WU3waFxQ6-GCYNcJHECojggo1xUdcYYqw7JCPKaZ6KG6TE5CaEDgKIEPiL6ab2MNnO6wybaDVK3ira33ypaN1A1GNq4fqW8DWltverxy_kP2jpP4ztSg8EuBupaemND9FavIxo6H9AvtvRlGyL24ZQctWoZ8Ox3jsnb7fx1dp89Pt89zK4fs6ao6pjVikGpFWcGmqIoVAFTYyrUGjVDrlo0CrRoxbRUrKmr2mjDweiKI5RlyUQxJhf73pV3n2sMUXZu7Yd0UrJcsKngomLJxfeuxrsQPLZy5W2v_FbmIHc8ZSf_eModz52eeKbg1T6I6YeNRS9DY5MTjfWJnTTO_lfxA6CHhJ4</recordid><startdate>20190115</startdate><enddate>20190115</enddate><creator>Karmellos, M.</creator><creator>Mavrotas, G.</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TB</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H8D</scope><scope>KR7</scope><scope>L7M</scope><scope>SOI</scope></search><sort><creationdate>20190115</creationdate><title>Multi-objective optimization and comparison framework for the design of Distributed Energy Systems</title><author>Karmellos, M. ; Mavrotas, G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c379t-9a205ba62d0c333a304dd7ebbeb2e6afeda0b8f845a2c979dbd60db76e0555283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Carbon</topic><topic>Case studies</topic><topic>Climate change</topic><topic>Design</topic><topic>Design engineering</topic><topic>Distributed energy systems</topic><topic>Distributed generation</topic><topic>District heating</topic><topic>District heating network</topic><topic>Emissions</topic><topic>Energy efficiency</topic><topic>Methods</topic><topic>MILP</topic><topic>Multi-objective optimization</topic><topic>Multiple objective analysis</topic><topic>Optimization</topic><topic>Renewables</topic><topic>Storage</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Karmellos, M.</creatorcontrib><creatorcontrib>Mavrotas, G.</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>Energy conversion and management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Karmellos, M.</au><au>Mavrotas, G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-objective optimization and comparison framework for the design of Distributed Energy Systems</atitle><jtitle>Energy conversion and management</jtitle><date>2019-01-15</date><risdate>2019</risdate><volume>180</volume><spage>473</spage><epage>495</epage><pages>473-495</pages><issn>0196-8904</issn><eissn>1879-2227</eissn><abstract>•We present two models for the optimal design of distributed energy systems.•We perform a comparison of the two methods in a case study with six buildings.•The total annual cost and the carbon emissions are the two objective functions.•Each method provides different results for each solution.•We propose a combined use of the two methodologies.
Energy supply in an economic and environmentally friendly configuration is a major challenge for urban energy systems. This paper presents two multi-objective models for the design of Distributed Energy Systems (DES) for satisfying local needs in heating, cooling and electricity. DES can be used to promote energy efficiency and reduce carbon emissions which is crucial to tackle climate change. A multi-objective MILP framework is presented, comparing two main methodologies of designing DES using Total Annual Cost (TAC) and carbon emissions as objective functions. The first one, namely “Method A”, which performs a simultaneous sizing and the second one, namely “Method B”, in which technologies and their respective capacity are predefined. Furthermore, three scenarios regarding the available technologies are developed to assess DES for both methods and a case study is carried out. Results show that in both methods Scenario 3 (a DES coupled with district heating network, microgrid and storage technologies) has the best performance. In terms of comparison between the two methods, “Method A” provides better solutions in terms of cost and emissions as it has more degrees of freedom. Structure, design and operational results are presented and discussed analytically, as well as the possibility of comparing the two methods for a more detailed design.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.enconman.2018.10.083</doi><tpages>23</tpages></addata></record> |
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subjects | Carbon Case studies Climate change Design Design engineering Distributed energy systems Distributed generation District heating District heating network Emissions Energy efficiency Methods MILP Multi-objective optimization Multiple objective analysis Optimization Renewables Storage |
title | Multi-objective optimization and comparison framework for the design of Distributed Energy Systems |
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