结合储能系统能源套利和电能质量应用的模型预测控制框架

IF 3.3 Q3 ENERGY & FUELS IEEE Open Access Journal of Power and Energy Pub Date : 2024-08-29 DOI:10.1109/OAJPE.2024.3451501
Ujjwol Tamrakar;Niranjan Bhujel;Tu A. Nguyen;Raymond H. Byrne;Babu Chalamala
{"title":"结合储能系统能源套利和电能质量应用的模型预测控制框架","authors":"Ujjwol Tamrakar;Niranjan Bhujel;Tu A. Nguyen;Raymond H. Byrne;Babu Chalamala","doi":"10.1109/OAJPE.2024.3451501","DOIUrl":null,"url":null,"abstract":"Energy storage systems (ESSs) are a flexible resource that will be vital to meet the aggressive clean energy targets of the future. However, the economic gains from ESSs can be limited due to large capital investments and monetization challenges. It is thus desirable to utilize ESSs for multiple techno-economic benefits to justify deployment costs. In this work, a framework to simultaneously dispatch ESSs for energy arbitrage and power quality applications is presented. More specifically, a model predictive control (MPC)-based framework that can dispatch energy storage to accomplish multiple techno-economic objectives is proposed. This is achieved without impacting market revenues while satisfying all power system and ESS constraints. Simulation results indicate that power quality applications such as voltage regulation and power factor correction can be stacked with arbitrage without significantly impacting arbitrage revenues and in some cases even improving the revenues. A controller-hardware-in-the-loop (CHIL) study of the proposed framework is also performed to demonstrate the practical feasibility of the framework.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10659047","citationCount":"0","resultStr":"{\"title\":\"A Model Predictive Control Framework for Combining Energy Arbitrage and Power Quality Applications From Energy Storage Systems\",\"authors\":\"Ujjwol Tamrakar;Niranjan Bhujel;Tu A. Nguyen;Raymond H. Byrne;Babu Chalamala\",\"doi\":\"10.1109/OAJPE.2024.3451501\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Energy storage systems (ESSs) are a flexible resource that will be vital to meet the aggressive clean energy targets of the future. However, the economic gains from ESSs can be limited due to large capital investments and monetization challenges. It is thus desirable to utilize ESSs for multiple techno-economic benefits to justify deployment costs. In this work, a framework to simultaneously dispatch ESSs for energy arbitrage and power quality applications is presented. More specifically, a model predictive control (MPC)-based framework that can dispatch energy storage to accomplish multiple techno-economic objectives is proposed. This is achieved without impacting market revenues while satisfying all power system and ESS constraints. Simulation results indicate that power quality applications such as voltage regulation and power factor correction can be stacked with arbitrage without significantly impacting arbitrage revenues and in some cases even improving the revenues. A controller-hardware-in-the-loop (CHIL) study of the proposed framework is also performed to demonstrate the practical feasibility of the framework.\",\"PeriodicalId\":56187,\"journal\":{\"name\":\"IEEE Open Access Journal of Power and Energy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10659047\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Access Journal of Power and Energy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10659047/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Access Journal of Power and Energy","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10659047/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

摘要

储能系统(ESS)是一种灵活的资源,对于实现未来积极的清洁能源目标至关重要。然而,由于资本投资巨大和货币化方面的挑战,ESS 的经济收益可能有限。因此,最好能利用 ESS 带来多重技术经济效益,以证明部署成本的合理性。在这项工作中,提出了一种同时为能源套利和电能质量应用调度 ESS 的框架。更具体地说,本文提出了一个基于模型预测控制(MPC)的框架,该框架可以调度储能以实现多个技术经济目标。在满足所有电力系统和 ESS 约束条件的同时,还不会影响市场收入。仿真结果表明,电压调节和功率因数校正等电能质量应用可以与套利叠加,而不会对套利收入产生重大影响,在某些情况下甚至可以提高收入。此外,还对拟议框架进行了控制器-硬件-在环 (CHIL) 研究,以证明该框架的实际可行性。
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 Model Predictive Control Framework for Combining Energy Arbitrage and Power Quality Applications From Energy Storage Systems
Energy storage systems (ESSs) are a flexible resource that will be vital to meet the aggressive clean energy targets of the future. However, the economic gains from ESSs can be limited due to large capital investments and monetization challenges. It is thus desirable to utilize ESSs for multiple techno-economic benefits to justify deployment costs. In this work, a framework to simultaneously dispatch ESSs for energy arbitrage and power quality applications is presented. More specifically, a model predictive control (MPC)-based framework that can dispatch energy storage to accomplish multiple techno-economic objectives is proposed. This is achieved without impacting market revenues while satisfying all power system and ESS constraints. Simulation results indicate that power quality applications such as voltage regulation and power factor correction can be stacked with arbitrage without significantly impacting arbitrage revenues and in some cases even improving the revenues. A controller-hardware-in-the-loop (CHIL) study of the proposed framework is also performed to demonstrate the practical feasibility of the framework.
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
CiteScore
7.80
自引率
5.30%
发文量
45
审稿时长
10 weeks
期刊最新文献
A Novel Dual-Rotor Homopolar AC Machine Learning Power Systems Waveform Incipient Patterns Through Few-Shot Meta-Learning Data Driven Real-Time Dynamic Voltage Control Using Decentralized Execution Multi-Agent Deep Reinforcement Learning Global Research Priorities for Holistic Integration of Water and Power Systems Floating Neutral Detection Using Actual Generation of Form 2S Meters
×
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