Solar forecasting methods for renewable energy integration
The higher penetration of renewable resources in the energy portfolios of several communities accentuates the need for accurate forecasting of variable resources (solar, wind, tidal) at several different temporal scales in order to achieve power grid balance. Solar generation technologies have exper...
Gespeichert in:
Veröffentlicht in: | Progress in energy and combustion science 2013-12, Vol.39 (6), p.535-576 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 576 |
---|---|
container_issue | 6 |
container_start_page | 535 |
container_title | Progress in energy and combustion science |
container_volume | 39 |
creator | Inman, Rich H. Pedro, Hugo T.C. Coimbra, Carlos F.M. |
description | The higher penetration of renewable resources in the energy portfolios of several communities accentuates the need for accurate forecasting of variable resources (solar, wind, tidal) at several different temporal scales in order to achieve power grid balance. Solar generation technologies have experienced strong energy market growth in the past few years, with corresponding increase in local grid penetration rates. As is the case with wind, the solar resource at the ground level is highly variable mostly due to cloud cover variability, atmospheric aerosol levels, and indirectly and to a lesser extent, participating gases in the atmosphere. The inherent variability of solar generation at higher grid penetration levels poses problems associated with the cost of reserves, dispatchable and ancillary generation, and grid reliability in general. As a result, high accuracy forecast systems are required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment. Here we review the theory behind these forecasting methodologies, and a number of successful applications of solar forecasting methods for both the solar resource and the power output of solar plants at the utility scale level. |
doi_str_mv | 10.1016/j.pecs.2013.06.002 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1524401809</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0360128513000294</els_id><sourcerecordid>1524401809</sourcerecordid><originalsourceid>FETCH-LOGICAL-c429t-f558bfbf0d8b61548eda8ea3c37713ba05416ba35a6b6a9d048db3eade5957953</originalsourceid><addsrcrecordid>eNp9kE1LxDAQhoMouK7-AU-9CF5aJ0mTtuJFxC9Y8KCCtzBNp2uWbrMmXWX_vV128ehphuF5Z5iHsXMOGQeurxbZimzMBHCZgc4AxAGb8LKQqeD645BNQGpIuSjVMTuJcQEAOtfVhF2_-g5D0vpAFuPg-nmypOHTN3E7SwL19IN1R8nYhPkmcf1A84CD8_0pO2qxi3S2r1P2_nD_dveUzl4en-9uZ6nNRTWkrVJl3dYtNGWtucpLarAklFYWBZc1gsq5rlEq1LXGqoG8bGpJ2JCqVFEpOWWXu72r4L_WFAezdNFS12FPfh0NVyLPgZdQjajYoTb4GAO1ZhXcEsPGcDBbUWZhtqLMVpQBbUZRY-hivx-jxa4N2FsX_5KiqICLCkbuZsfR-Oy3o2CiddRbatwobzCNd_-d-QWuZ37l</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1524401809</pqid></control><display><type>article</type><title>Solar forecasting methods for renewable energy integration</title><source>Access via ScienceDirect (Elsevier)</source><creator>Inman, Rich H. ; Pedro, Hugo T.C. ; Coimbra, Carlos F.M.</creator><creatorcontrib>Inman, Rich H. ; Pedro, Hugo T.C. ; Coimbra, Carlos F.M.</creatorcontrib><description>The higher penetration of renewable resources in the energy portfolios of several communities accentuates the need for accurate forecasting of variable resources (solar, wind, tidal) at several different temporal scales in order to achieve power grid balance. Solar generation technologies have experienced strong energy market growth in the past few years, with corresponding increase in local grid penetration rates. As is the case with wind, the solar resource at the ground level is highly variable mostly due to cloud cover variability, atmospheric aerosol levels, and indirectly and to a lesser extent, participating gases in the atmosphere. The inherent variability of solar generation at higher grid penetration levels poses problems associated with the cost of reserves, dispatchable and ancillary generation, and grid reliability in general. As a result, high accuracy forecast systems are required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment. Here we review the theory behind these forecasting methodologies, and a number of successful applications of solar forecasting methods for both the solar resource and the power output of solar plants at the utility scale level.</description><identifier>ISSN: 0360-1285</identifier><identifier>EISSN: 1873-216X</identifier><identifier>DOI: 10.1016/j.pecs.2013.06.002</identifier><identifier>CODEN: PECSDO</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Applied sciences ; Economic data ; Energy ; Energy economics ; Evolutionary forecasting methods ; Exact sciences and technology ; General, economic and professional studies ; Natural energy ; Solar energy ; Solar energy integration ; Solar forecasting ; Solar meteorology ; Solar variability ; Weather-dependent renewable energy</subject><ispartof>Progress in energy and combustion science, 2013-12, Vol.39 (6), p.535-576</ispartof><rights>2013 Elsevier Ltd</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c429t-f558bfbf0d8b61548eda8ea3c37713ba05416ba35a6b6a9d048db3eade5957953</citedby><cites>FETCH-LOGICAL-c429t-f558bfbf0d8b61548eda8ea3c37713ba05416ba35a6b6a9d048db3eade5957953</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.pecs.2013.06.002$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27901290$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Inman, Rich H.</creatorcontrib><creatorcontrib>Pedro, Hugo T.C.</creatorcontrib><creatorcontrib>Coimbra, Carlos F.M.</creatorcontrib><title>Solar forecasting methods for renewable energy integration</title><title>Progress in energy and combustion science</title><description>The higher penetration of renewable resources in the energy portfolios of several communities accentuates the need for accurate forecasting of variable resources (solar, wind, tidal) at several different temporal scales in order to achieve power grid balance. Solar generation technologies have experienced strong energy market growth in the past few years, with corresponding increase in local grid penetration rates. As is the case with wind, the solar resource at the ground level is highly variable mostly due to cloud cover variability, atmospheric aerosol levels, and indirectly and to a lesser extent, participating gases in the atmosphere. The inherent variability of solar generation at higher grid penetration levels poses problems associated with the cost of reserves, dispatchable and ancillary generation, and grid reliability in general. As a result, high accuracy forecast systems are required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment. Here we review the theory behind these forecasting methodologies, and a number of successful applications of solar forecasting methods for both the solar resource and the power output of solar plants at the utility scale level.</description><subject>Applied sciences</subject><subject>Economic data</subject><subject>Energy</subject><subject>Energy economics</subject><subject>Evolutionary forecasting methods</subject><subject>Exact sciences and technology</subject><subject>General, economic and professional studies</subject><subject>Natural energy</subject><subject>Solar energy</subject><subject>Solar energy integration</subject><subject>Solar forecasting</subject><subject>Solar meteorology</subject><subject>Solar variability</subject><subject>Weather-dependent renewable energy</subject><issn>0360-1285</issn><issn>1873-216X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAQhoMouK7-AU-9CF5aJ0mTtuJFxC9Y8KCCtzBNp2uWbrMmXWX_vV128ehphuF5Z5iHsXMOGQeurxbZimzMBHCZgc4AxAGb8LKQqeD645BNQGpIuSjVMTuJcQEAOtfVhF2_-g5D0vpAFuPg-nmypOHTN3E7SwL19IN1R8nYhPkmcf1A84CD8_0pO2qxi3S2r1P2_nD_dveUzl4en-9uZ6nNRTWkrVJl3dYtNGWtucpLarAklFYWBZc1gsq5rlEq1LXGqoG8bGpJ2JCqVFEpOWWXu72r4L_WFAezdNFS12FPfh0NVyLPgZdQjajYoTb4GAO1ZhXcEsPGcDBbUWZhtqLMVpQBbUZRY-hivx-jxa4N2FsX_5KiqICLCkbuZsfR-Oy3o2CiddRbatwobzCNd_-d-QWuZ37l</recordid><startdate>20131201</startdate><enddate>20131201</enddate><creator>Inman, Rich H.</creator><creator>Pedro, Hugo T.C.</creator><creator>Coimbra, Carlos F.M.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7U6</scope><scope>C1K</scope><scope>SOI</scope></search><sort><creationdate>20131201</creationdate><title>Solar forecasting methods for renewable energy integration</title><author>Inman, Rich H. ; Pedro, Hugo T.C. ; Coimbra, Carlos F.M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c429t-f558bfbf0d8b61548eda8ea3c37713ba05416ba35a6b6a9d048db3eade5957953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Applied sciences</topic><topic>Economic data</topic><topic>Energy</topic><topic>Energy economics</topic><topic>Evolutionary forecasting methods</topic><topic>Exact sciences and technology</topic><topic>General, economic and professional studies</topic><topic>Natural energy</topic><topic>Solar energy</topic><topic>Solar energy integration</topic><topic>Solar forecasting</topic><topic>Solar meteorology</topic><topic>Solar variability</topic><topic>Weather-dependent renewable energy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Inman, Rich H.</creatorcontrib><creatorcontrib>Pedro, Hugo T.C.</creatorcontrib><creatorcontrib>Coimbra, Carlos F.M.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><jtitle>Progress in energy and combustion science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Inman, Rich H.</au><au>Pedro, Hugo T.C.</au><au>Coimbra, Carlos F.M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Solar forecasting methods for renewable energy integration</atitle><jtitle>Progress in energy and combustion science</jtitle><date>2013-12-01</date><risdate>2013</risdate><volume>39</volume><issue>6</issue><spage>535</spage><epage>576</epage><pages>535-576</pages><issn>0360-1285</issn><eissn>1873-216X</eissn><coden>PECSDO</coden><abstract>The higher penetration of renewable resources in the energy portfolios of several communities accentuates the need for accurate forecasting of variable resources (solar, wind, tidal) at several different temporal scales in order to achieve power grid balance. Solar generation technologies have experienced strong energy market growth in the past few years, with corresponding increase in local grid penetration rates. As is the case with wind, the solar resource at the ground level is highly variable mostly due to cloud cover variability, atmospheric aerosol levels, and indirectly and to a lesser extent, participating gases in the atmosphere. The inherent variability of solar generation at higher grid penetration levels poses problems associated with the cost of reserves, dispatchable and ancillary generation, and grid reliability in general. As a result, high accuracy forecast systems are required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment. Here we review the theory behind these forecasting methodologies, and a number of successful applications of solar forecasting methods for both the solar resource and the power output of solar plants at the utility scale level.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.pecs.2013.06.002</doi><tpages>42</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0360-1285 |
ispartof | Progress in energy and combustion science, 2013-12, Vol.39 (6), p.535-576 |
issn | 0360-1285 1873-216X |
language | eng |
recordid | cdi_proquest_miscellaneous_1524401809 |
source | Access via ScienceDirect (Elsevier) |
subjects | Applied sciences Economic data Energy Energy economics Evolutionary forecasting methods Exact sciences and technology General, economic and professional studies Natural energy Solar energy Solar energy integration Solar forecasting Solar meteorology Solar variability Weather-dependent renewable energy |
title | Solar forecasting methods for renewable energy integration |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T11%3A37%3A11IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Solar%20forecasting%20methods%20for%20renewable%20energy%20integration&rft.jtitle=Progress%20in%20energy%20and%20combustion%20science&rft.au=Inman,%20Rich%20H.&rft.date=2013-12-01&rft.volume=39&rft.issue=6&rft.spage=535&rft.epage=576&rft.pages=535-576&rft.issn=0360-1285&rft.eissn=1873-216X&rft.coden=PECSDO&rft_id=info:doi/10.1016/j.pecs.2013.06.002&rft_dat=%3Cproquest_cross%3E1524401809%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1524401809&rft_id=info:pmid/&rft_els_id=S0360128513000294&rfr_iscdi=true |