Energy Efficient Based Optimized Renewable Energy Systems (ORES) Using QPSO Technique for Normalized Cost of Energy (NCE)

V. Pushpabala, C. ChristoberAsirRajan
{"title":"Energy Efficient Based Optimized Renewable Energy Systems (ORES) Using QPSO Technique for Normalized Cost of Energy (NCE)","authors":"V. Pushpabala, C. ChristoberAsirRajan","doi":"10.1109/ICSCAN53069.2021.9526488","DOIUrl":null,"url":null,"abstract":"Numbers and charts of the world renewable energy agency and the International Energy Agency have lately shown that renewable energy sources (RES) have exceeded natural gas and have taken second place as a power source. The intermittent nature of RES, however, causes a large triple-dimensional problem: system costs, environmental impacts and system reliability. Studies have shown that various RES integrated into Optimized Renewable Energy Systems (ORES) may effectively manage these problems by applying appropriate optimization methods. This article provides a technique for optimising electricity produced by an example of the need for loads as the load of typical buildings to meet an ORES. Quantum Particle Swarm Optimization Technique (QPSO) is used as an algorithm to search for optimisation because of its advantages compared with other methods to reduce the standardised costs of energy, between production and demand taking account of losses;defined the problem and introduce the objective function with fitness in mind. The structure of the algorithm was created with MATLAB software.","PeriodicalId":393569,"journal":{"name":"2021 International Conference on System, Computation, Automation and Networking (ICSCAN)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on System, Computation, Automation and Networking (ICSCAN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSCAN53069.2021.9526488","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Numbers and charts of the world renewable energy agency and the International Energy Agency have lately shown that renewable energy sources (RES) have exceeded natural gas and have taken second place as a power source. The intermittent nature of RES, however, causes a large triple-dimensional problem: system costs, environmental impacts and system reliability. Studies have shown that various RES integrated into Optimized Renewable Energy Systems (ORES) may effectively manage these problems by applying appropriate optimization methods. This article provides a technique for optimising electricity produced by an example of the need for loads as the load of typical buildings to meet an ORES. Quantum Particle Swarm Optimization Technique (QPSO) is used as an algorithm to search for optimisation because of its advantages compared with other methods to reduce the standardised costs of energy, between production and demand taking account of losses;defined the problem and introduce the objective function with fitness in mind. The structure of the algorithm was created with MATLAB software.
基于归一化能源成本的QPSO优化可再生能源系统(ORES)
世界可再生能源机构和国际能源机构的数据和图表最近显示,可再生能源(RES)已经超过天然气,成为第二大能源。然而,可再生能源的间歇性导致了一个巨大的三维问题:系统成本、环境影响和系统可靠性。研究表明,通过采用适当的优化方法,将各种可再生能源系统集成到优化可再生能源系统(ORES)中可以有效地管理这些问题。本文提供了一种优化电力产生的技术,通过一个例子,需要负载作为典型建筑物的负载,以满足一个ORES。采用量子粒子群优化技术(Quantum Particle Swarm Optimization Technique, QPSO)作为一种搜索优化算法,在考虑损失的情况下,降低生产和需求之间的标准化能源成本,与其他方法相比具有优势。利用MATLAB软件建立了算法的结构。
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:604180095
Book学术官方微信