Peak Shaving Mechanism Employing a Battery Storage System (BSS) and Solar Forecasting

L. Yong
{"title":"Peak Shaving Mechanism Employing a Battery Storage System (BSS) and Solar Forecasting","authors":"L. Yong","doi":"10.37936/ecti-eec.2023212.249826","DOIUrl":null,"url":null,"abstract":"Maximum demand (kW) has contributed significantly to expensive electricity bills. A modern-day solution for overcoming the penalty demand charges is to utilize the peak shaving method. To perform peak shaving, a battery storage system (BSS) is used. This methodinvolves the charging and discharging of the battery during high and low demand respectively, thus reducing the penalty incurred from the electricity utility company. To charge the battery, a photovoltaic (PV) system is coupled with the BSS. There is currently no BSS algorithm in existence under the microgrid to shave maximum demand with the aid of solar forecasting. In this paper, an algorithm for the BSS to achieve peak shave will be developed with the use of solar PV forecasting. The load profile of a building is used in this study as a reference for future consumption. The developed algorithm releases the energy stored in the BSS to shave the critical demand based on solar forecasting and the BSS state of charge (SOC). In short, this algorithm provides a green solution for reducing the demand charges from the electricity company.","PeriodicalId":38808,"journal":{"name":"Transactions on Electrical Engineering, Electronics, and Communications","volume":"344 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions on Electrical Engineering, Electronics, and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37936/ecti-eec.2023212.249826","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

Abstract

Maximum demand (kW) has contributed significantly to expensive electricity bills. A modern-day solution for overcoming the penalty demand charges is to utilize the peak shaving method. To perform peak shaving, a battery storage system (BSS) is used. This methodinvolves the charging and discharging of the battery during high and low demand respectively, thus reducing the penalty incurred from the electricity utility company. To charge the battery, a photovoltaic (PV) system is coupled with the BSS. There is currently no BSS algorithm in existence under the microgrid to shave maximum demand with the aid of solar forecasting. In this paper, an algorithm for the BSS to achieve peak shave will be developed with the use of solar PV forecasting. The load profile of a building is used in this study as a reference for future consumption. The developed algorithm releases the energy stored in the BSS to shave the critical demand based on solar forecasting and the BSS state of charge (SOC). In short, this algorithm provides a green solution for reducing the demand charges from the electricity company.
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
采用电池储能系统(BSS)的调峰机制与太阳能预测
最大需求(kW)是造成昂贵电费的重要原因。克服惩罚性需求收费的现代解决方案是利用调峰方法。为了实现调峰,使用了电池存储系统(BSS)。该方法涉及电池在高需求和低需求时分别充电和放电,从而减少了电力公司的罚款。为了给电池充电,光伏(PV)系统与BSS耦合在一起。目前还没有一种BSS算法可以在微电网下借助太阳预报来实现最大需求的削减。本文将利用太阳能光伏发电预测,开发一种BSS实现调峰的算法。在本研究中,建筑物的负荷分布作为未来消耗的参考。该算法基于太阳能预测和BSS的荷电状态(SOC),释放存储在BSS中的能量来削减临界需求。简而言之,该算法为减少电力公司的需求收费提供了一种绿色解决方案。
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 去求助
来源期刊
Transactions on Electrical Engineering, Electronics, and Communications
Transactions on Electrical Engineering, Electronics, and Communications Engineering-Electrical and Electronic Engineering
CiteScore
1.60
自引率
0.00%
发文量
45
期刊最新文献
Improving Air Quality Prediction with a Hybrid Bi-LSTM and GAN Model Sentiment Analysis on Large-Scale Covid-19 Tweets using Hybrid Convolutional LSTM Based on Naïve Bayes Sentiment Modeling Collaborative Movie Recommendation System using Enhanced Fuzzy C-Means Clustering with Dove Swarm Optimization Algorithm A Performance of AFIRO among Asynchronous Iteration Strategy Metaheuristic Algorithms Particle Swarm Optimization Trained Feedforward Neural Network for Under-Voltage Load Shedding
×
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