Analysis of high frequency photovoltaic solar energy fluctuations
•Household PV power is underestimated by up to 22% when using 15 min averages.•Fluctuations of household PV systems exceed those of both irradiance and PV parks.•Clear-sky conditions do not represent the worst-case for PV grid-integration.•Bimodality of irradiance requires temporal resolution in ord...
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Veröffentlicht in: | Solar energy 2020-08, Vol.206, p.381-389 |
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creator | Kreuwel, Frank P.M. Knap, Wouter H. Visser, Lennard R. van Sark, Wilfried G.J.H.M. Vilà-Guerau de Arellano, Jordi van Heerwaarden, Chiel C. |
description | •Household PV power is underestimated by up to 22% when using 15 min averages.•Fluctuations of household PV systems exceed those of both irradiance and PV parks.•Clear-sky conditions do not represent the worst-case for PV grid-integration.•Bimodality of irradiance requires temporal resolution in order of seconds.
Characterizing short-term variability of generated solar power is important for the integration of photovoltaic (PV) systems into the electrical grid. Using different kinds of high frequency, in-situ observations of both irradiance and generated PV power, we quantify insights on temporal averaging effects on the highest observed peaks and ramp rates, which closely relate to grid stability. We use measurements obtained at three specific spatial scales; a single point pyranometer, two household PV systems and a PV system typical for small medium businesses. We show that the 15-minute time resolution typically used for grid calculations significantly underestimates key dynamics at high temporal resolutions, such as ramp rates and maximum power output, at the local grid level. We find that absolute power peaks in the order of seconds are up to 18% higher compared to a 15-minute resolution for irradiance and up to 22% higher for a household PV system. For the largest PV system, the increase is limited to 11%. Furthermore, we find that the highest peaks solely occur under mixed-cloud conditions. Additionally, we show that the time interval-dependency of the largest power ramps is similar for all systems under research, ranging from ~20% at a 5-second interval to stabilizing at 70–80% between 5 and 10 min, which we can explain based on meteorological arguments. |
doi_str_mv | 10.1016/j.solener.2020.05.093 |
format | Article |
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Characterizing short-term variability of generated solar power is important for the integration of photovoltaic (PV) systems into the electrical grid. Using different kinds of high frequency, in-situ observations of both irradiance and generated PV power, we quantify insights on temporal averaging effects on the highest observed peaks and ramp rates, which closely relate to grid stability. We use measurements obtained at three specific spatial scales; a single point pyranometer, two household PV systems and a PV system typical for small medium businesses. We show that the 15-minute time resolution typically used for grid calculations significantly underestimates key dynamics at high temporal resolutions, such as ramp rates and maximum power output, at the local grid level. We find that absolute power peaks in the order of seconds are up to 18% higher compared to a 15-minute resolution for irradiance and up to 22% higher for a household PV system. For the largest PV system, the increase is limited to 11%. Furthermore, we find that the highest peaks solely occur under mixed-cloud conditions. Additionally, we show that the time interval-dependency of the largest power ramps is similar for all systems under research, ranging from ~20% at a 5-second interval to stabilizing at 70–80% between 5 and 10 min, which we can explain based on meteorological arguments.</description><identifier>ISSN: 0038-092X</identifier><identifier>EISSN: 1471-1257</identifier><identifier>DOI: 10.1016/j.solener.2020.05.093</identifier><language>eng</language><publisher>New York: Elsevier Ltd</publisher><subject>BSRN ; Cloud enhancement ; High frequencies ; Irradiance ; Maximum power ; Photovoltaic cells ; Photovoltaics ; PV grid integration ; Ramps ; Solar energy ; Solar power ; Solar power fluctuations ; Temporal averaging ; Time dependence</subject><ispartof>Solar energy, 2020-08, Vol.206, p.381-389</ispartof><rights>2020</rights><rights>Copyright Pergamon Press Inc. Aug 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c384t-fb243fd8c208c5075b41f02e22e4780a4b4b44c6e27cc01b89488f2510b14f903</citedby><cites>FETCH-LOGICAL-c384t-fb243fd8c208c5075b41f02e22e4780a4b4b44c6e27cc01b89488f2510b14f903</cites><orcidid>0000-0003-3288-0893</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0038092X20305922$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Kreuwel, Frank P.M.</creatorcontrib><creatorcontrib>Knap, Wouter H.</creatorcontrib><creatorcontrib>Visser, Lennard R.</creatorcontrib><creatorcontrib>van Sark, Wilfried G.J.H.M.</creatorcontrib><creatorcontrib>Vilà-Guerau de Arellano, Jordi</creatorcontrib><creatorcontrib>van Heerwaarden, Chiel C.</creatorcontrib><title>Analysis of high frequency photovoltaic solar energy fluctuations</title><title>Solar energy</title><description>•Household PV power is underestimated by up to 22% when using 15 min averages.•Fluctuations of household PV systems exceed those of both irradiance and PV parks.•Clear-sky conditions do not represent the worst-case for PV grid-integration.•Bimodality of irradiance requires temporal resolution in order of seconds.
Characterizing short-term variability of generated solar power is important for the integration of photovoltaic (PV) systems into the electrical grid. Using different kinds of high frequency, in-situ observations of both irradiance and generated PV power, we quantify insights on temporal averaging effects on the highest observed peaks and ramp rates, which closely relate to grid stability. We use measurements obtained at three specific spatial scales; a single point pyranometer, two household PV systems and a PV system typical for small medium businesses. We show that the 15-minute time resolution typically used for grid calculations significantly underestimates key dynamics at high temporal resolutions, such as ramp rates and maximum power output, at the local grid level. We find that absolute power peaks in the order of seconds are up to 18% higher compared to a 15-minute resolution for irradiance and up to 22% higher for a household PV system. For the largest PV system, the increase is limited to 11%. Furthermore, we find that the highest peaks solely occur under mixed-cloud conditions. Additionally, we show that the time interval-dependency of the largest power ramps is similar for all systems under research, ranging from ~20% at a 5-second interval to stabilizing at 70–80% between 5 and 10 min, which we can explain based on meteorological arguments.</description><subject>BSRN</subject><subject>Cloud enhancement</subject><subject>High frequencies</subject><subject>Irradiance</subject><subject>Maximum power</subject><subject>Photovoltaic cells</subject><subject>Photovoltaics</subject><subject>PV grid integration</subject><subject>Ramps</subject><subject>Solar energy</subject><subject>Solar power</subject><subject>Solar power fluctuations</subject><subject>Temporal averaging</subject><subject>Time dependence</subject><issn>0038-092X</issn><issn>1471-1257</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNqFkE1LAzEQhoMoWKs_QQh43nXy1c2epBS_oOBFwVvIpkmbZd3UZLew_96U9i5zmMO8874zD0L3BEoCZPHYlil0trexpEChBFFCzS7QjPCKFISK6hLNAJgsoKbf1-gmpRaAVERWM7Rc9rqbkk84OLzz2x120f6OtjcT3u_CEA6hG7Q3OEfoiI8p2wm7bjTDqAcf-nSLrpzukr079zn6enn-XL0V64_X99VyXRgm-VC4hnLmNtJQkEZAJRpOHFBLqeWVBM2bXNwsLK2MAdLImkvpqCDQEO5qYHP0cPLdx5APTINqwxjz9UlRzrOasZpklTipTAwpRevUPvofHSdFQB1pqVadaakjLQVCZVp57-m0Z_MLB5-nyfhMwW58tGZQm-D_cfgDX4t15A</recordid><startdate>202008</startdate><enddate>202008</enddate><creator>Kreuwel, Frank P.M.</creator><creator>Knap, Wouter H.</creator><creator>Visser, Lennard R.</creator><creator>van Sark, Wilfried G.J.H.M.</creator><creator>Vilà-Guerau de Arellano, Jordi</creator><creator>van Heerwaarden, Chiel C.</creator><general>Elsevier Ltd</general><general>Pergamon Press Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7ST</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0003-3288-0893</orcidid></search><sort><creationdate>202008</creationdate><title>Analysis of high frequency photovoltaic solar energy fluctuations</title><author>Kreuwel, Frank P.M. ; Knap, Wouter H. ; Visser, Lennard R. ; van Sark, Wilfried G.J.H.M. ; Vilà-Guerau de Arellano, Jordi ; van Heerwaarden, Chiel C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c384t-fb243fd8c208c5075b41f02e22e4780a4b4b44c6e27cc01b89488f2510b14f903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>BSRN</topic><topic>Cloud enhancement</topic><topic>High frequencies</topic><topic>Irradiance</topic><topic>Maximum power</topic><topic>Photovoltaic cells</topic><topic>Photovoltaics</topic><topic>PV grid integration</topic><topic>Ramps</topic><topic>Solar energy</topic><topic>Solar power</topic><topic>Solar power fluctuations</topic><topic>Temporal averaging</topic><topic>Time dependence</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kreuwel, Frank P.M.</creatorcontrib><creatorcontrib>Knap, Wouter H.</creatorcontrib><creatorcontrib>Visser, Lennard R.</creatorcontrib><creatorcontrib>van Sark, Wilfried G.J.H.M.</creatorcontrib><creatorcontrib>Vilà-Guerau de Arellano, Jordi</creatorcontrib><creatorcontrib>van Heerwaarden, Chiel C.</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Environment Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>Solar energy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kreuwel, Frank P.M.</au><au>Knap, Wouter H.</au><au>Visser, Lennard R.</au><au>van Sark, Wilfried G.J.H.M.</au><au>Vilà-Guerau de Arellano, Jordi</au><au>van Heerwaarden, Chiel C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis of high frequency photovoltaic solar energy fluctuations</atitle><jtitle>Solar energy</jtitle><date>2020-08</date><risdate>2020</risdate><volume>206</volume><spage>381</spage><epage>389</epage><pages>381-389</pages><issn>0038-092X</issn><eissn>1471-1257</eissn><abstract>•Household PV power is underestimated by up to 22% when using 15 min averages.•Fluctuations of household PV systems exceed those of both irradiance and PV parks.•Clear-sky conditions do not represent the worst-case for PV grid-integration.•Bimodality of irradiance requires temporal resolution in order of seconds.
Characterizing short-term variability of generated solar power is important for the integration of photovoltaic (PV) systems into the electrical grid. Using different kinds of high frequency, in-situ observations of both irradiance and generated PV power, we quantify insights on temporal averaging effects on the highest observed peaks and ramp rates, which closely relate to grid stability. We use measurements obtained at three specific spatial scales; a single point pyranometer, two household PV systems and a PV system typical for small medium businesses. We show that the 15-minute time resolution typically used for grid calculations significantly underestimates key dynamics at high temporal resolutions, such as ramp rates and maximum power output, at the local grid level. We find that absolute power peaks in the order of seconds are up to 18% higher compared to a 15-minute resolution for irradiance and up to 22% higher for a household PV system. For the largest PV system, the increase is limited to 11%. Furthermore, we find that the highest peaks solely occur under mixed-cloud conditions. Additionally, we show that the time interval-dependency of the largest power ramps is similar for all systems under research, ranging from ~20% at a 5-second interval to stabilizing at 70–80% between 5 and 10 min, which we can explain based on meteorological arguments.</abstract><cop>New York</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.solener.2020.05.093</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-3288-0893</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | BSRN Cloud enhancement High frequencies Irradiance Maximum power Photovoltaic cells Photovoltaics PV grid integration Ramps Solar energy Solar power Solar power fluctuations Temporal averaging Time dependence |
title | Analysis of high frequency photovoltaic solar energy fluctuations |
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