An optimal distributed RES sizing strategy in hybrid low voltage networks focused on EVs' integration
The new era of the energy sector has already begun, and therefore, new challenges need to be tackled. A major challenge that residential distribution grids are going to encounter with the integration of photovoltaic (PV) panels and electric vehicles (EVs) is the unsynchronized new demand and the tim...
Gespeichert in:
Veröffentlicht in: | IEEE access 2023-01, Vol.11, p.1-1 |
---|---|
Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 1 |
---|---|
container_issue | |
container_start_page | 1 |
container_title | IEEE access |
container_volume | 11 |
creator | Boglou, V. Karavas, C-S Karlis, A. Arvanitis, K. Palaiologou, I. |
description | The new era of the energy sector has already begun, and therefore, new challenges need to be tackled. A major challenge that residential distribution grids are going to encounter with the integration of photovoltaic (PV) panels and electric vehicles (EVs) is the unsynchronized new demand and the time-limited production of distributed generation, in combination with space limitations. Therefore, the necessity of energy storage systems (ESSs) is more than evident. ESSs have excessive manufacturing costs, implying that the purchase cost for residential users can be prohibitive. In the present work, a distributed optimal small-scale PV energy system sizing strategy is proposed, by considering the individual energy needs of each residence and their EVs. The strategy is formulated based on the demand of the households and EVs charging. By enabling the fuzzy cognitive maps theory, a graph is designed, aiming to establish the correlation among the individual energy parameters and the characteristics of the renewable energy sources (RES). The optimization results reveal that the adopted hybrid approach can reduce the energy cost significantly, up to almost 40%, while enabling distribution system operators (DSOs) to incorporate additional loads, without the need for network expansion. Finally, based on the extracted results, a short discussion about the concept of EVs' charging by residential RES is presented. |
doi_str_mv | 10.1109/ACCESS.2023.3245152 |
format | Article |
fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_proquest_journals_2778700067</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10044676</ieee_id><doaj_id>oai_doaj_org_article_693514cd5abf49888ed8b6435e68e2a5</doaj_id><sourcerecordid>2778700067</sourcerecordid><originalsourceid>FETCH-LOGICAL-c409t-99fa30392865c0ed6a26b3af9426d5370bb859067b3cd15093770c1a17e5533b3</originalsourceid><addsrcrecordid>eNpNkUtPGzEUhUeoSCDKL4CFpS5YJfgxfi2jKKVISJUa2q1lj-8Eh-k42A4o_PoaBlV4Y-vofOfaPk1zQfCcEKyvF8vlar2eU0zZnNGWE06PmlNKhJ4xzsSXT-eT5jznLa5LVYnL0wYWI4q7Ev7aAfmQSwpuX8CjX6s1yuE1jBtURVtgc0BhRA8Hl4JHQ3xBz3EodgNohPIS02NGfez2uaJxRKs_-araK1XREMevzXFvhwznH_tZ8_v76n75Y3b38-Z2ubibdS3WZaZ1bxlmmirBOwxeWCocs71uqfCcSeyc4hoL6VjnCceaSYk7YokEzhlz7Ky5nXJ9tFuzS_VZ6WCiDeZdiGljbCqhG8AIzThpO8-t61utlAKvnGgZB6GAWl6zvk1ZuxSf9pCL2cZ9Guv1DZVSyfqJQlYXm1xdijkn6P9PJdi81WOmesxbPeajnkpdTlQAgE8EblshBfsHONeK2g</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2778700067</pqid></control><display><type>article</type><title>An optimal distributed RES sizing strategy in hybrid low voltage networks focused on EVs' integration</title><source>IEEE Open Access Journals</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Boglou, V. ; Karavas, C-S ; Karlis, A. ; Arvanitis, K. ; Palaiologou, I.</creator><creatorcontrib>Boglou, V. ; Karavas, C-S ; Karlis, A. ; Arvanitis, K. ; Palaiologou, I.</creatorcontrib><description>The new era of the energy sector has already begun, and therefore, new challenges need to be tackled. A major challenge that residential distribution grids are going to encounter with the integration of photovoltaic (PV) panels and electric vehicles (EVs) is the unsynchronized new demand and the time-limited production of distributed generation, in combination with space limitations. Therefore, the necessity of energy storage systems (ESSs) is more than evident. ESSs have excessive manufacturing costs, implying that the purchase cost for residential users can be prohibitive. In the present work, a distributed optimal small-scale PV energy system sizing strategy is proposed, by considering the individual energy needs of each residence and their EVs. The strategy is formulated based on the demand of the households and EVs charging. By enabling the fuzzy cognitive maps theory, a graph is designed, aiming to establish the correlation among the individual energy parameters and the characteristics of the renewable energy sources (RES). The optimization results reveal that the adopted hybrid approach can reduce the energy cost significantly, up to almost 40%, while enabling distribution system operators (DSOs) to incorporate additional loads, without the need for network expansion. Finally, based on the extracted results, a short discussion about the concept of EVs' charging by residential RES is presented.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2023.3245152</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Charging ; Cognitive maps ; Costs ; Distributed Energy Networks ; Distributed generation ; Electric vehicles ; Energy costs ; Energy industry ; Energy storage ; EVs ; Fuzzy Cognitive Maps ; Households ; Hybrid Energy Systems ; Low voltage ; Optimization ; Photovoltaic cells ; Power quality ; Power system stability ; Production ; Production costs ; Renewable energy sources ; RES sizing ; Sizing ; Stability analysis ; Storage systems</subject><ispartof>IEEE access, 2023-01, Vol.11, p.1-1</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c409t-99fa30392865c0ed6a26b3af9426d5370bb859067b3cd15093770c1a17e5533b3</citedby><cites>FETCH-LOGICAL-c409t-99fa30392865c0ed6a26b3af9426d5370bb859067b3cd15093770c1a17e5533b3</cites><orcidid>0000-0002-8084-7919 ; 0000-0003-0666-0109 ; 0000-0002-5630-6143 ; 0000-0002-8191-1556</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10044676$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,27633,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Boglou, V.</creatorcontrib><creatorcontrib>Karavas, C-S</creatorcontrib><creatorcontrib>Karlis, A.</creatorcontrib><creatorcontrib>Arvanitis, K.</creatorcontrib><creatorcontrib>Palaiologou, I.</creatorcontrib><title>An optimal distributed RES sizing strategy in hybrid low voltage networks focused on EVs' integration</title><title>IEEE access</title><addtitle>Access</addtitle><description>The new era of the energy sector has already begun, and therefore, new challenges need to be tackled. A major challenge that residential distribution grids are going to encounter with the integration of photovoltaic (PV) panels and electric vehicles (EVs) is the unsynchronized new demand and the time-limited production of distributed generation, in combination with space limitations. Therefore, the necessity of energy storage systems (ESSs) is more than evident. ESSs have excessive manufacturing costs, implying that the purchase cost for residential users can be prohibitive. In the present work, a distributed optimal small-scale PV energy system sizing strategy is proposed, by considering the individual energy needs of each residence and their EVs. The strategy is formulated based on the demand of the households and EVs charging. By enabling the fuzzy cognitive maps theory, a graph is designed, aiming to establish the correlation among the individual energy parameters and the characteristics of the renewable energy sources (RES). The optimization results reveal that the adopted hybrid approach can reduce the energy cost significantly, up to almost 40%, while enabling distribution system operators (DSOs) to incorporate additional loads, without the need for network expansion. Finally, based on the extracted results, a short discussion about the concept of EVs' charging by residential RES is presented.</description><subject>Charging</subject><subject>Cognitive maps</subject><subject>Costs</subject><subject>Distributed Energy Networks</subject><subject>Distributed generation</subject><subject>Electric vehicles</subject><subject>Energy costs</subject><subject>Energy industry</subject><subject>Energy storage</subject><subject>EVs</subject><subject>Fuzzy Cognitive Maps</subject><subject>Households</subject><subject>Hybrid Energy Systems</subject><subject>Low voltage</subject><subject>Optimization</subject><subject>Photovoltaic cells</subject><subject>Power quality</subject><subject>Power system stability</subject><subject>Production</subject><subject>Production costs</subject><subject>Renewable energy sources</subject><subject>RES sizing</subject><subject>Sizing</subject><subject>Stability analysis</subject><subject>Storage systems</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkUtPGzEUhUeoSCDKL4CFpS5YJfgxfi2jKKVISJUa2q1lj-8Eh-k42A4o_PoaBlV4Y-vofOfaPk1zQfCcEKyvF8vlar2eU0zZnNGWE06PmlNKhJ4xzsSXT-eT5jznLa5LVYnL0wYWI4q7Ev7aAfmQSwpuX8CjX6s1yuE1jBtURVtgc0BhRA8Hl4JHQ3xBz3EodgNohPIS02NGfez2uaJxRKs_-araK1XREMevzXFvhwznH_tZ8_v76n75Y3b38-Z2ubibdS3WZaZ1bxlmmirBOwxeWCocs71uqfCcSeyc4hoL6VjnCceaSYk7YokEzhlz7Ky5nXJ9tFuzS_VZ6WCiDeZdiGljbCqhG8AIzThpO8-t61utlAKvnGgZB6GAWl6zvk1ZuxSf9pCL2cZ9Guv1DZVSyfqJQlYXm1xdijkn6P9PJdi81WOmesxbPeajnkpdTlQAgE8EblshBfsHONeK2g</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Boglou, V.</creator><creator>Karavas, C-S</creator><creator>Karlis, A.</creator><creator>Arvanitis, K.</creator><creator>Palaiologou, I.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-8084-7919</orcidid><orcidid>https://orcid.org/0000-0003-0666-0109</orcidid><orcidid>https://orcid.org/0000-0002-5630-6143</orcidid><orcidid>https://orcid.org/0000-0002-8191-1556</orcidid></search><sort><creationdate>20230101</creationdate><title>An optimal distributed RES sizing strategy in hybrid low voltage networks focused on EVs' integration</title><author>Boglou, V. ; Karavas, C-S ; Karlis, A. ; Arvanitis, K. ; Palaiologou, I.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c409t-99fa30392865c0ed6a26b3af9426d5370bb859067b3cd15093770c1a17e5533b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Charging</topic><topic>Cognitive maps</topic><topic>Costs</topic><topic>Distributed Energy Networks</topic><topic>Distributed generation</topic><topic>Electric vehicles</topic><topic>Energy costs</topic><topic>Energy industry</topic><topic>Energy storage</topic><topic>EVs</topic><topic>Fuzzy Cognitive Maps</topic><topic>Households</topic><topic>Hybrid Energy Systems</topic><topic>Low voltage</topic><topic>Optimization</topic><topic>Photovoltaic cells</topic><topic>Power quality</topic><topic>Power system stability</topic><topic>Production</topic><topic>Production costs</topic><topic>Renewable energy sources</topic><topic>RES sizing</topic><topic>Sizing</topic><topic>Stability analysis</topic><topic>Storage systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Boglou, V.</creatorcontrib><creatorcontrib>Karavas, C-S</creatorcontrib><creatorcontrib>Karlis, A.</creatorcontrib><creatorcontrib>Arvanitis, K.</creatorcontrib><creatorcontrib>Palaiologou, I.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Boglou, V.</au><au>Karavas, C-S</au><au>Karlis, A.</au><au>Arvanitis, K.</au><au>Palaiologou, I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An optimal distributed RES sizing strategy in hybrid low voltage networks focused on EVs' integration</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2023-01-01</date><risdate>2023</risdate><volume>11</volume><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>The new era of the energy sector has already begun, and therefore, new challenges need to be tackled. A major challenge that residential distribution grids are going to encounter with the integration of photovoltaic (PV) panels and electric vehicles (EVs) is the unsynchronized new demand and the time-limited production of distributed generation, in combination with space limitations. Therefore, the necessity of energy storage systems (ESSs) is more than evident. ESSs have excessive manufacturing costs, implying that the purchase cost for residential users can be prohibitive. In the present work, a distributed optimal small-scale PV energy system sizing strategy is proposed, by considering the individual energy needs of each residence and their EVs. The strategy is formulated based on the demand of the households and EVs charging. By enabling the fuzzy cognitive maps theory, a graph is designed, aiming to establish the correlation among the individual energy parameters and the characteristics of the renewable energy sources (RES). The optimization results reveal that the adopted hybrid approach can reduce the energy cost significantly, up to almost 40%, while enabling distribution system operators (DSOs) to incorporate additional loads, without the need for network expansion. Finally, based on the extracted results, a short discussion about the concept of EVs' charging by residential RES is presented.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2023.3245152</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-8084-7919</orcidid><orcidid>https://orcid.org/0000-0003-0666-0109</orcidid><orcidid>https://orcid.org/0000-0002-5630-6143</orcidid><orcidid>https://orcid.org/0000-0002-8191-1556</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2169-3536 |
ispartof | IEEE access, 2023-01, Vol.11, p.1-1 |
issn | 2169-3536 2169-3536 |
language | eng |
recordid | cdi_proquest_journals_2778700067 |
source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
subjects | Charging Cognitive maps Costs Distributed Energy Networks Distributed generation Electric vehicles Energy costs Energy industry Energy storage EVs Fuzzy Cognitive Maps Households Hybrid Energy Systems Low voltage Optimization Photovoltaic cells Power quality Power system stability Production Production costs Renewable energy sources RES sizing Sizing Stability analysis Storage systems |
title | An optimal distributed RES sizing strategy in hybrid low voltage networks focused on EVs' integration |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T09%3A01%3A11IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20optimal%20distributed%20RES%20sizing%20strategy%20in%20hybrid%20low%20voltage%20networks%20focused%20on%20EVs'%20integration&rft.jtitle=IEEE%20access&rft.au=Boglou,%20V.&rft.date=2023-01-01&rft.volume=11&rft.spage=1&rft.epage=1&rft.pages=1-1&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2023.3245152&rft_dat=%3Cproquest_doaj_%3E2778700067%3C/proquest_doaj_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2778700067&rft_id=info:pmid/&rft_ieee_id=10044676&rft_doaj_id=oai_doaj_org_article_693514cd5abf49888ed8b6435e68e2a5&rfr_iscdi=true |