S. Perkin, Arnbjörg Arnardóttir, K. Sigurjonsson, Þorvaldur Jacobsen
{"title":"Performance of probabilistic disturbance forecasts in extreme weather on the Icelandic power system","authors":"S. Perkin, Arnbjörg Arnardóttir, K. Sigurjonsson, Þorvaldur Jacobsen","doi":"10.1109/PMAPS47429.2020.9183655","DOIUrl":null,"url":null,"abstract":"An extreme weather event affected the Icelandic power system on the 10th and 11th of December 2019, causing dozens of disturbances and multiple instances of unserved energy. Landsnet, the Icelandic Transmission System Operator, has been developing disturbance probability forecast models as one means of improving situational awareness. This paper provides an ex-post analysis of these models during the extreme weather event. The disturbance forecasts provided useful information at a regional scale, and showed sensitivity to exogenous data. Opportunities to improve disturbance probability models are identified and regulatory drivers are highlighted.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PMAPS47429.2020.9183655","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
An extreme weather event affected the Icelandic power system on the 10th and 11th of December 2019, causing dozens of disturbances and multiple instances of unserved energy. Landsnet, the Icelandic Transmission System Operator, has been developing disturbance probability forecast models as one means of improving situational awareness. This paper provides an ex-post analysis of these models during the extreme weather event. The disturbance forecasts provided useful information at a regional scale, and showed sensitivity to exogenous data. Opportunities to improve disturbance probability models are identified and regulatory drivers are highlighted.