Study on demand and supply operation using forecasting in power systems with extremely large integrations of photovoltaic generation

T. Masuta, Joao Gari da Silva Fonseca, Hideaki Ootake, A. Murata
{"title":"Study on demand and supply operation using forecasting in power systems with extremely large integrations of photovoltaic generation","authors":"T. Masuta, Joao Gari da Silva Fonseca, Hideaki Ootake, A. Murata","doi":"10.1109/ICSET.2016.7811758","DOIUrl":null,"url":null,"abstract":"The installed capacity of photovoltaic (PV) generation in Japan is currently more than 20 GW. This capacity has been rapidly increasing since the start of the feed-in tariff policy in 2012. The installed capacity after 2030 may be considerably larger than the current supposition if this trend continues. In this study, we conducted numerical simulations of the demand and supply operation with consideration of the unit commitment of conventional power generators using PV generation forecasting in economic-load dispatching control. This operation occurs in power systems with extremely large integrations of PV generation. The impact of PV generation forecasting errors on the demand and supply imbalance is quantitatively evaluated with consideration of the installed PV capacity as a parameter. Simulation results show that the impact of the forecasting errors becomes small for a larger value of the installed PV capacity. The results additionally reveal that efficient use of PV generation is more important in power systems with extremely large integrations of PV generation.","PeriodicalId":164446,"journal":{"name":"2016 IEEE International Conference on Sustainable Energy Technologies (ICSET)","volume":"213 0 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Sustainable Energy Technologies (ICSET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSET.2016.7811758","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

Abstract

The installed capacity of photovoltaic (PV) generation in Japan is currently more than 20 GW. This capacity has been rapidly increasing since the start of the feed-in tariff policy in 2012. The installed capacity after 2030 may be considerably larger than the current supposition if this trend continues. In this study, we conducted numerical simulations of the demand and supply operation with consideration of the unit commitment of conventional power generators using PV generation forecasting in economic-load dispatching control. This operation occurs in power systems with extremely large integrations of PV generation. The impact of PV generation forecasting errors on the demand and supply imbalance is quantitatively evaluated with consideration of the installed PV capacity as a parameter. Simulation results show that the impact of the forecasting errors becomes small for a larger value of the installed PV capacity. The results additionally reveal that efficient use of PV generation is more important in power systems with extremely large integrations of PV generation.
基于预测的超大光伏发电集成度电力系统供需运行研究
目前,日本的光伏发电装机容量超过20吉瓦。自2012年开始实施上网电价政策以来,这一产能一直在迅速增加。如果这一趋势持续下去,2030年后的装机容量可能会大大超过目前的假设。本文将光伏发电预测应用于经济负荷调度控制中,对考虑常规发电机组负荷的供需运行进行了数值模拟。这种操作发生在光伏发电集成度极高的电力系统中。以光伏装机容量为参数,定量评价了光伏发电预测误差对供需失衡的影响。仿真结果表明,光伏装机容量越大,预测误差的影响就越小。结果还表明,在光伏发电集成度极高的电力系统中,光伏发电的有效利用更为重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信