uni-leipzig-open-access/json/we.2816

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T15:23:06Z","timestamp":1704727386821},"reference-count":25,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004895","name":"European Social Fund","doi-asserted-by":"publisher","award":["10033950","100602743"]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wind Energy"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Though wind power predictions have been consistently improved in the last decade, persistent reasons for remaining uncertainties are sudden large changes in wind speed, so\u2010called ramps. Here, we analyse the occurrence of ramp events in a wind farm in Eastern Germany and the performance of a wind power prediction tool in forecasting these events for forecasting horizons of 15 and 30\u2009min. Results on the seasonality of ramp events and their diurnal cycle are presented for multiple ramp definition thresholds. Ramps were found to be most frequent in March and April and least frequent in November and December. For the analysis, the wind power prediction tool is fed by different wind velocity forecast products, for example, numerical weather prediction (NWP) model and measurement data. It is shown that including observational wind speed data for very short\u2010term wind power forecasts improves the performance of the power prediction tool compared to the NWP reference, both in terms of ramp detection and in decreasing the mean absolute error between predicted and generated wind power. This improvement is enhanced during ramp events, highlighting the importance of wind observations for very short\u2010term wind power prediction.<\/jats:p>","DOI":"10.1002\/we.2816","type":"journal-article","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T10:05:54Z","timestamp":1681293954000},"page":"573-588","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Analysing wind power ramp events and improving very short\u2010term wind power predictions by including wind speed observations"],"prefix":"10.1002","volume":"26","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-4938-328X","authenticated-orcid":false,"given":"Moritz","family":"Lochmann","sequence":"first","affiliation":[{"name":"Leipzig Institute for Meteorology Leipzig University  Leipzig Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6699-7040","authenticated-orcid":false,"given":"Heike","family":"Kalesse\u2010Los","sequence":"additional","affiliation":[{"name":"Leipzig Institute for Meteorology Leipzig University  Leipzig Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1896-1574","authenticated-orcid":false,"given":"Michael","family":"Sch\u00e4fer","sequence":"additional","affiliation":[{"name":"Leipzig Institute for Meteorology Leipzig University  Leipzig Germany"}]},{"given":"Ingrid","family":"Heinrich","sequence":"additional","affiliation":[{"name":"LEM\u2010Software  Leipzig Germany"}]},{"given":"Ronny","family":"Leinweber","sequence":"additional","affiliation":[{"name":"Deutscher Wetterdienst  Lindenberg Germany"}]}],"member":"311","published-online":{"date-parts":[[2023,4,12]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"Umweltbundesamt.Erneuerbare Energien in Zahlen.https:\/\/www.umweltbundesamt.de\/themen\/klimaenergie\/erneuerbare-energien\/erneuerbare-energien-in-zahlen;2022."},{"key":"e_1_2_10_3_1","unstructured":"Agora\u2010Energiewende.Stromnetze f\u00fcr 65 Prozent Erneuerbare bis 2030. Zw\u00f6lf Ma\u00dfnahmen f\u00fcr den synchronen Ausbau von Netzen und Erneuerbaren Energien \u00a0Agora\u2010Energiewende;2018.\u00a0Tech. Rep."},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00703\u2010020\u201000736\u20103"},{"key":"e_1_2_10_5_1","doi-asserted-by":"crossref","unstructured":"PotterCW GrimitE NijssenB.Potential benefits of a dedicated probabilistic rapid ramp event forecast tool. 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