SAIDI Constrained Economic Planning and Utilization of Central Storage in Rural Distribution Networks
This paper forms a framework for allocating a central electric energy storage (EES) in discrete communities, forming as segmentations along the rural feeders, where the installation of cross connects are not economic or even feasible. In this framework, EESs that centrally can be installed in each c...
Gespeichert in:
| Veröffentlicht in: | IEEE systems journal 2019-03, Vol.13 (1), p.842-853 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 853 |
|---|---|
| container_issue | 1 |
| container_start_page | 842 |
| container_title | IEEE systems journal |
| container_volume | 13 |
| creator | Narimani, Afsaneh Nourbakhsh, Ghavameddin Arefi, Ali Ledwich, Gerard F. Walker, Geoffrey R. |
| description | This paper forms a framework for allocating a central electric energy storage (EES) in discrete communities, forming as segmentations along the rural feeders, where the installation of cross connects are not economic or even feasible. In this framework, EESs that centrally can be installed in each community are owned and operated by an aggregator/retailer, trading bidirectional energy transactions with both the grid and the customers. The objective is to find optimum investment in storage capacity in rural feeders for minimum annual energy purchase cost using energy arbitrage opportunities, while maintaining an allowable level of system average interruption duration index (SAIDI). As a part of EES sizing and siting optimization in this approach, annual hourly network data are utilized for charge/discharge scheduling and constraint, and for system reliability assessment-using the k-means clustering technique. This method is applied to a rural network in Queensland, Australia, and the results are examined to show the effectiveness of this method. |
| doi_str_mv | 10.1109/JSYST.2018.2852630 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_8411550</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8411550</ieee_id><sourcerecordid>2185733263</sourcerecordid><originalsourceid>FETCH-LOGICAL-c344t-6d194d711c232f1a9b52e586180407a65f9638bdc6933c2504af69db17315fec3</originalsourceid><addsrcrecordid>eNo9kMtOwzAQRSMEEqXwA7CxxDrF40diL6uUR1EFiLQLVpaTOJVLaxcnEYKvJ32I1YxG585oThRdAx4BYHn3nH_k8xHBIEZEcJJQfBINQNI0loSy031PYgGCnUcXTbPCmAueykFk8vF0MkWZd00btHWmQveld35jS_S21s5Zt0TaVWjR2rX91a31DvkaZcb1_BrlrQ96aZB16L3bDSa2X2SLbg--mPbbh8_mMjqr9boxV8c6jBYP9_PsKZ69Pk6z8SwuKWNtnFQgWZUClISSGrQsODFcJCAww6lOeC0TKoqqTCSlJeGY6TqRVQEpBV6bkg6j28PebfBfnWlatfJdcP1JRaB_mNJeTU-RA1UG3zTB1Gob7EaHHwVY7XSqvU6106mOOvvQzSFkjTH_AcEAOMf0D_3ZcRE</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2185733263</pqid></control><display><type>article</type><title>SAIDI Constrained Economic Planning and Utilization of Central Storage in Rural Distribution Networks</title><source>IEEE Electronic Library (IEL)</source><creator>Narimani, Afsaneh ; Nourbakhsh, Ghavameddin ; Arefi, Ali ; Ledwich, Gerard F. ; Walker, Geoffrey R.</creator><creatorcontrib>Narimani, Afsaneh ; Nourbakhsh, Ghavameddin ; Arefi, Ali ; Ledwich, Gerard F. ; Walker, Geoffrey R.</creatorcontrib><description>This paper forms a framework for allocating a central electric energy storage (EES) in discrete communities, forming as segmentations along the rural feeders, where the installation of cross connects are not economic or even feasible. In this framework, EESs that centrally can be installed in each community are owned and operated by an aggregator/retailer, trading bidirectional energy transactions with both the grid and the customers. The objective is to find optimum investment in storage capacity in rural feeders for minimum annual energy purchase cost using energy arbitrage opportunities, while maintaining an allowable level of system average interruption duration index (SAIDI). As a part of EES sizing and siting optimization in this approach, annual hourly network data are utilized for charge/discharge scheduling and constraint, and for system reliability assessment-using the k-means clustering technique. This method is applied to a rural network in Queensland, Australia, and the results are examined to show the effectiveness of this method.</description><identifier>ISSN: 1932-8184</identifier><identifier>EISSN: 1937-9234</identifier><identifier>DOI: 10.1109/JSYST.2018.2852630</identifier><identifier>CODEN: ISJEB2</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject><![CDATA[<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> k-means</tex-math> </inline-formula> </named-content> clustering ; Batteries ; Cluster analysis ; Clustering ; Cost benefits analysis ; Distribution management ; Economics ; Electric energy storage ; electric energy storage (EES) ; Energy storage ; Feeders ; genetic algorithm (GA) ; Investment ; Planning ; Power system reliability ; Reliability ; Reliability analysis ; Rural communities ; Storage capacity ; System reliability ; Vector quantization]]></subject><ispartof>IEEE systems journal, 2019-03, Vol.13 (1), p.842-853</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c344t-6d194d711c232f1a9b52e586180407a65f9638bdc6933c2504af69db17315fec3</citedby><cites>FETCH-LOGICAL-c344t-6d194d711c232f1a9b52e586180407a65f9638bdc6933c2504af69db17315fec3</cites><orcidid>0000-0002-8989-2812 ; 0000-0001-7474-0309 ; 0000-0001-8137-9507 ; 0000-0001-9679-7306</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8411550$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8411550$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Narimani, Afsaneh</creatorcontrib><creatorcontrib>Nourbakhsh, Ghavameddin</creatorcontrib><creatorcontrib>Arefi, Ali</creatorcontrib><creatorcontrib>Ledwich, Gerard F.</creatorcontrib><creatorcontrib>Walker, Geoffrey R.</creatorcontrib><title>SAIDI Constrained Economic Planning and Utilization of Central Storage in Rural Distribution Networks</title><title>IEEE systems journal</title><addtitle>JSYST</addtitle><description>This paper forms a framework for allocating a central electric energy storage (EES) in discrete communities, forming as segmentations along the rural feeders, where the installation of cross connects are not economic or even feasible. In this framework, EESs that centrally can be installed in each community are owned and operated by an aggregator/retailer, trading bidirectional energy transactions with both the grid and the customers. The objective is to find optimum investment in storage capacity in rural feeders for minimum annual energy purchase cost using energy arbitrage opportunities, while maintaining an allowable level of system average interruption duration index (SAIDI). As a part of EES sizing and siting optimization in this approach, annual hourly network data are utilized for charge/discharge scheduling and constraint, and for system reliability assessment-using the k-means clustering technique. This method is applied to a rural network in Queensland, Australia, and the results are examined to show the effectiveness of this method.</description><subject><![CDATA[<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> k-means</tex-math> </inline-formula> </named-content> clustering]]></subject><subject>Batteries</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Cost benefits analysis</subject><subject>Distribution management</subject><subject>Economics</subject><subject>Electric energy storage</subject><subject>electric energy storage (EES)</subject><subject>Energy storage</subject><subject>Feeders</subject><subject>genetic algorithm (GA)</subject><subject>Investment</subject><subject>Planning</subject><subject>Power system reliability</subject><subject>Reliability</subject><subject>Reliability analysis</subject><subject>Rural communities</subject><subject>Storage capacity</subject><subject>System reliability</subject><subject>Vector quantization</subject><issn>1932-8184</issn><issn>1937-9234</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMtOwzAQRSMEEqXwA7CxxDrF40diL6uUR1EFiLQLVpaTOJVLaxcnEYKvJ32I1YxG585oThRdAx4BYHn3nH_k8xHBIEZEcJJQfBINQNI0loSy031PYgGCnUcXTbPCmAueykFk8vF0MkWZd00btHWmQveld35jS_S21s5Zt0TaVWjR2rX91a31DvkaZcb1_BrlrQ96aZB16L3bDSa2X2SLbg--mPbbh8_mMjqr9boxV8c6jBYP9_PsKZ69Pk6z8SwuKWNtnFQgWZUClISSGrQsODFcJCAww6lOeC0TKoqqTCSlJeGY6TqRVQEpBV6bkg6j28PebfBfnWlatfJdcP1JRaB_mNJeTU-RA1UG3zTB1Gob7EaHHwVY7XSqvU6106mOOvvQzSFkjTH_AcEAOMf0D_3ZcRE</recordid><startdate>201903</startdate><enddate>201903</enddate><creator>Narimani, Afsaneh</creator><creator>Nourbakhsh, Ghavameddin</creator><creator>Arefi, Ali</creator><creator>Ledwich, Gerard F.</creator><creator>Walker, Geoffrey R.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-8989-2812</orcidid><orcidid>https://orcid.org/0000-0001-7474-0309</orcidid><orcidid>https://orcid.org/0000-0001-8137-9507</orcidid><orcidid>https://orcid.org/0000-0001-9679-7306</orcidid></search><sort><creationdate>201903</creationdate><title>SAIDI Constrained Economic Planning and Utilization of Central Storage in Rural Distribution Networks</title><author>Narimani, Afsaneh ; Nourbakhsh, Ghavameddin ; Arefi, Ali ; Ledwich, Gerard F. ; Walker, Geoffrey R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c344t-6d194d711c232f1a9b52e586180407a65f9638bdc6933c2504af69db17315fec3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic><![CDATA[<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> k-means</tex-math> </inline-formula> </named-content> clustering]]></topic><topic>Batteries</topic><topic>Cluster analysis</topic><topic>Clustering</topic><topic>Cost benefits analysis</topic><topic>Distribution management</topic><topic>Economics</topic><topic>Electric energy storage</topic><topic>electric energy storage (EES)</topic><topic>Energy storage</topic><topic>Feeders</topic><topic>genetic algorithm (GA)</topic><topic>Investment</topic><topic>Planning</topic><topic>Power system reliability</topic><topic>Reliability</topic><topic>Reliability analysis</topic><topic>Rural communities</topic><topic>Storage capacity</topic><topic>System reliability</topic><topic>Vector quantization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Narimani, Afsaneh</creatorcontrib><creatorcontrib>Nourbakhsh, Ghavameddin</creatorcontrib><creatorcontrib>Arefi, Ali</creatorcontrib><creatorcontrib>Ledwich, Gerard F.</creatorcontrib><creatorcontrib>Walker, Geoffrey R.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><jtitle>IEEE systems journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Narimani, Afsaneh</au><au>Nourbakhsh, Ghavameddin</au><au>Arefi, Ali</au><au>Ledwich, Gerard F.</au><au>Walker, Geoffrey R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>SAIDI Constrained Economic Planning and Utilization of Central Storage in Rural Distribution Networks</atitle><jtitle>IEEE systems journal</jtitle><stitle>JSYST</stitle><date>2019-03</date><risdate>2019</risdate><volume>13</volume><issue>1</issue><spage>842</spage><epage>853</epage><pages>842-853</pages><issn>1932-8184</issn><eissn>1937-9234</eissn><coden>ISJEB2</coden><abstract>This paper forms a framework for allocating a central electric energy storage (EES) in discrete communities, forming as segmentations along the rural feeders, where the installation of cross connects are not economic or even feasible. In this framework, EESs that centrally can be installed in each community are owned and operated by an aggregator/retailer, trading bidirectional energy transactions with both the grid and the customers. The objective is to find optimum investment in storage capacity in rural feeders for minimum annual energy purchase cost using energy arbitrage opportunities, while maintaining an allowable level of system average interruption duration index (SAIDI). As a part of EES sizing and siting optimization in this approach, annual hourly network data are utilized for charge/discharge scheduling and constraint, and for system reliability assessment-using the k-means clustering technique. This method is applied to a rural network in Queensland, Australia, and the results are examined to show the effectiveness of this method.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JSYST.2018.2852630</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-8989-2812</orcidid><orcidid>https://orcid.org/0000-0001-7474-0309</orcidid><orcidid>https://orcid.org/0000-0001-8137-9507</orcidid><orcidid>https://orcid.org/0000-0001-9679-7306</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1932-8184 |
| ispartof | IEEE systems journal, 2019-03, Vol.13 (1), p.842-853 |
| issn | 1932-8184 1937-9234 |
| language | eng |
| recordid | cdi_ieee_primary_8411550 |
| source | IEEE Electronic Library (IEL) |
| subjects | <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> k-means</tex-math> </inline-formula> </named-content> clustering Batteries Cluster analysis Clustering Cost benefits analysis Distribution management Economics Electric energy storage electric energy storage (EES) Energy storage Feeders genetic algorithm (GA) Investment Planning Power system reliability Reliability Reliability analysis Rural communities Storage capacity System reliability Vector quantization |
| title | SAIDI Constrained Economic Planning and Utilization of Central Storage in Rural Distribution Networks |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-03T23%3A31%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=SAIDI%20Constrained%20Economic%20Planning%20and%20Utilization%20of%20Central%20Storage%20in%20Rural%20Distribution%20Networks&rft.jtitle=IEEE%20systems%20journal&rft.au=Narimani,%20Afsaneh&rft.date=2019-03&rft.volume=13&rft.issue=1&rft.spage=842&rft.epage=853&rft.pages=842-853&rft.issn=1932-8184&rft.eissn=1937-9234&rft.coden=ISJEB2&rft_id=info:doi/10.1109/JSYST.2018.2852630&rft_dat=%3Cproquest_RIE%3E2185733263%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2185733263&rft_id=info:pmid/&rft_ieee_id=8411550&rfr_iscdi=true |