智能电网中带有太阳能光伏发电和需求响应功能的智能家居负荷调度系统

IF 2.6 4区 工程技术 Q3 ENERGY & FUELS Frontiers in Energy Research Pub Date : 2024-08-07 DOI:10.3389/fenrg.2024.1322047
Lyu-Guang Hua, S. Haseeb Ali Shah, Baheej Alghamdi, Ghulam Hafeez, Safeer Ullah, Sadia Murawwat, Sajjad Ali, Muhammad Iftikhar Khan
{"title":"智能电网中带有太阳能光伏发电和需求响应功能的智能家居负荷调度系统","authors":"Lyu-Guang Hua, S. Haseeb Ali Shah, Baheej Alghamdi, Ghulam Hafeez, Safeer Ullah, Sadia Murawwat, Sajjad Ali, Muhammad Iftikhar Khan","doi":"10.3389/fenrg.2024.1322047","DOIUrl":null,"url":null,"abstract":"This study introduces a smart home load scheduling system that aims to address concerns related to energy conservation and environmental preservation. A comprehensive demand response (DR) model is proposed, which includes an energy consumption scheduler (ECS) designed to optimize the operation of smart appliances. The ECS utilizes various optimization algorithms, including particle swarm optimization (PSO), genetic optimization algorithm (GOA), wind-driven optimization (WDO), and the hybrid genetic wind-driven optimization (HGWDO) algorithm. These algorithms work together to schedule smart home appliance operations effectively under real-time price-based demand response (RTPDR). The efficient integration of renewable energy into smart grids (SGs) is challenging due to its time-varying and intermittent nature. To address this, batteries were used in this study to mitigate the fluctuations in renewable generation. The simulation results validate the effectiveness of our proposed approach in optimally addressing the smart home load scheduling problem with photovoltaic generation and DR. The system achieves the minimization of utility bills, pollutant emissions, and the peak-to-average demand ratio (PADR) compared to existing models. Through this study, we provide a practical and effective solution to enhance the efficiency of smart home energy management, contributing to sustainable practices and reducing environmental impact.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":"2012 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Smart home load scheduling system with solar photovoltaic generation and demand response in the smart grid\",\"authors\":\"Lyu-Guang Hua, S. Haseeb Ali Shah, Baheej Alghamdi, Ghulam Hafeez, Safeer Ullah, Sadia Murawwat, Sajjad Ali, Muhammad Iftikhar Khan\",\"doi\":\"10.3389/fenrg.2024.1322047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study introduces a smart home load scheduling system that aims to address concerns related to energy conservation and environmental preservation. A comprehensive demand response (DR) model is proposed, which includes an energy consumption scheduler (ECS) designed to optimize the operation of smart appliances. The ECS utilizes various optimization algorithms, including particle swarm optimization (PSO), genetic optimization algorithm (GOA), wind-driven optimization (WDO), and the hybrid genetic wind-driven optimization (HGWDO) algorithm. These algorithms work together to schedule smart home appliance operations effectively under real-time price-based demand response (RTPDR). The efficient integration of renewable energy into smart grids (SGs) is challenging due to its time-varying and intermittent nature. To address this, batteries were used in this study to mitigate the fluctuations in renewable generation. The simulation results validate the effectiveness of our proposed approach in optimally addressing the smart home load scheduling problem with photovoltaic generation and DR. The system achieves the minimization of utility bills, pollutant emissions, and the peak-to-average demand ratio (PADR) compared to existing models. Through this study, we provide a practical and effective solution to enhance the efficiency of smart home energy management, contributing to sustainable practices and reducing environmental impact.\",\"PeriodicalId\":12428,\"journal\":{\"name\":\"Frontiers in Energy Research\",\"volume\":\"2012 1\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Energy Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3389/fenrg.2024.1322047\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Energy Research","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3389/fenrg.2024.1322047","RegionNum":4,"RegionCategory":"工程技术","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

本研究介绍了一种智能家居负载调度系统,旨在解决与节能和环境保护有关的问题。研究提出了一个全面的需求响应(DR)模型,其中包括一个旨在优化智能家电运行的能耗调度器(ECS)。ECS 采用了多种优化算法,包括粒子群优化算法 (PSO)、遗传优化算法 (GOA)、风驱动优化算法 (WDO) 和混合遗传风驱动优化算法 (HGWDO)。这些算法相互配合,在基于价格的实时需求响应(RTPDR)下有效地安排智能家电的运行。由于可再生能源具有时变性和间歇性,因此将其有效集成到智能电网(SG)中具有挑战性。为解决这一问题,本研究使用电池来缓解可再生能源发电的波动。仿真结果验证了我们提出的方法在优化解决智能家居负载调度问题方面的有效性。与现有模型相比,该系统实现了公用事业账单、污染物排放和峰均需求比(PADR)的最小化。通过这项研究,我们为提高智能家居能源管理的效率提供了一个实用有效的解决方案,有助于可持续发展实践和减少对环境的影响。
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
Smart home load scheduling system with solar photovoltaic generation and demand response in the smart grid
This study introduces a smart home load scheduling system that aims to address concerns related to energy conservation and environmental preservation. A comprehensive demand response (DR) model is proposed, which includes an energy consumption scheduler (ECS) designed to optimize the operation of smart appliances. The ECS utilizes various optimization algorithms, including particle swarm optimization (PSO), genetic optimization algorithm (GOA), wind-driven optimization (WDO), and the hybrid genetic wind-driven optimization (HGWDO) algorithm. These algorithms work together to schedule smart home appliance operations effectively under real-time price-based demand response (RTPDR). The efficient integration of renewable energy into smart grids (SGs) is challenging due to its time-varying and intermittent nature. To address this, batteries were used in this study to mitigate the fluctuations in renewable generation. The simulation results validate the effectiveness of our proposed approach in optimally addressing the smart home load scheduling problem with photovoltaic generation and DR. The system achieves the minimization of utility bills, pollutant emissions, and the peak-to-average demand ratio (PADR) compared to existing models. Through this study, we provide a practical and effective solution to enhance the efficiency of smart home energy management, contributing to sustainable practices and reducing environmental impact.
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
Frontiers in Energy Research
Frontiers in Energy Research Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
3.90
自引率
11.80%
发文量
1727
审稿时长
12 weeks
期刊介绍: Frontiers in Energy Research makes use of the unique Frontiers platform for open-access publishing and research networking for scientists, which provides an equal opportunity to seek, share and create knowledge. The mission of Frontiers is to place publishing back in the hands of working scientists and to promote an interactive, fair, and efficient review process. Articles are peer-reviewed according to the Frontiers review guidelines, which evaluate manuscripts on objective editorial criteria
期刊最新文献
Grid-integrated solutions for sustainable EV charging: a comparative study of renewable energy and battery storage systems Research on the impact of digitalization on energy companies’ green transition: new insights from China Multi-objective-based economic and emission dispatch with integration of wind energy sources using different optimization algorithms Demand-side management scenario analysis for the energy-efficient future of Pakistan: Bridging the gap between market interests and national priorities Modeling and scheduling of utility-scale energy storage toward high-share renewable coordination
×
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