两种MPC算法在实际微电网系统中降低需求费用的比较

Yun Xue, M. Todd, S. Ula, M. Barth, A. Martinez-Morales
{"title":"两种MPC算法在实际微电网系统中降低需求费用的比较","authors":"Yun Xue, M. Todd, S. Ula, M. Barth, A. Martinez-Morales","doi":"10.1109/PVSC.2016.7749947","DOIUrl":null,"url":null,"abstract":"This paper describes an evaluation between two model predictive control (MPC) algorithms for microgrid energy management combined with solar production and battery energy storage for demand charge reduction in a real-world microgrid system. The first control algorithm is a constant threshold MPC (CT-MPC) that works well on a system with relatively stable solar generation and a well-known building load profile. CT-MPC can maintain the on-peak demand under a certain value during the entire on-peak rate period. The second control algorithm is an adjusting demand threshold MPC (ADT-MPC). ADT-MPC can better deal with unpredictable solar generation and/or changing building loads. The on-peak threshold under this algorithm is adjusted to the optimal value during the on-peak rate period. As expected, The CT-MPC algorithm performs well when coupled with accurate forecast models while the ADT-MPC algorithm excels when forecasting is more unpredictable.","PeriodicalId":6524,"journal":{"name":"2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC)","volume":"8 1","pages":"1875-1880"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"A comparison between two MPC algorithms for demand charge reduction in a real-world microgrid system\",\"authors\":\"Yun Xue, M. Todd, S. Ula, M. Barth, A. Martinez-Morales\",\"doi\":\"10.1109/PVSC.2016.7749947\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes an evaluation between two model predictive control (MPC) algorithms for microgrid energy management combined with solar production and battery energy storage for demand charge reduction in a real-world microgrid system. The first control algorithm is a constant threshold MPC (CT-MPC) that works well on a system with relatively stable solar generation and a well-known building load profile. CT-MPC can maintain the on-peak demand under a certain value during the entire on-peak rate period. The second control algorithm is an adjusting demand threshold MPC (ADT-MPC). ADT-MPC can better deal with unpredictable solar generation and/or changing building loads. The on-peak threshold under this algorithm is adjusted to the optimal value during the on-peak rate period. As expected, The CT-MPC algorithm performs well when coupled with accurate forecast models while the ADT-MPC algorithm excels when forecasting is more unpredictable.\",\"PeriodicalId\":6524,\"journal\":{\"name\":\"2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC)\",\"volume\":\"8 1\",\"pages\":\"1875-1880\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PVSC.2016.7749947\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PVSC.2016.7749947","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

摘要

本文介绍了两种模型预测控制(MPC)算法在实际微电网系统中结合太阳能发电和电池储能的微电网能源管理中的评估。第一种控制算法是恒阈值MPC (CT-MPC),该算法适用于相对稳定的太阳能发电系统和众所周知的建筑负荷分布。CT-MPC可以在整个峰期费率期内将峰期需求维持在一定值以下。第二种控制算法是需求阈值调节MPC算法(ADT-MPC)。ADT-MPC可以更好地处理不可预测的太阳能发电和/或不断变化的建筑负荷。该算法的峰值阈值在峰值速率周期内调整为最优值。正如预期的那样,CT-MPC算法在与准确的预测模型相结合时表现良好,而ADT-MPC算法在预测更不可预测时表现出色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A comparison between two MPC algorithms for demand charge reduction in a real-world microgrid system
This paper describes an evaluation between two model predictive control (MPC) algorithms for microgrid energy management combined with solar production and battery energy storage for demand charge reduction in a real-world microgrid system. The first control algorithm is a constant threshold MPC (CT-MPC) that works well on a system with relatively stable solar generation and a well-known building load profile. CT-MPC can maintain the on-peak demand under a certain value during the entire on-peak rate period. The second control algorithm is an adjusting demand threshold MPC (ADT-MPC). ADT-MPC can better deal with unpredictable solar generation and/or changing building loads. The on-peak threshold under this algorithm is adjusted to the optimal value during the on-peak rate period. As expected, The CT-MPC algorithm performs well when coupled with accurate forecast models while the ADT-MPC algorithm excels when forecasting is more unpredictable.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Boosting the efficiency of III-V/Si tandem solar cells Bandgap and carrier transport engineering of quantum confined mixed phase nanocrystalline/amorphous silicon Improving the radiation hardness of space solar cells via nanophotonic light trapping A comparison between two MPC algorithms for demand charge reduction in a real-world microgrid system Enhancing grain growth and boosting Voc in CZTSe absorber layers — Is Ge doping the answer?
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1