A Hybrid Wild Horses Optimization (WHO) and Dwarf Mongoose Optimization (DMO) method for optimum energy management in SG system

P. Ganesan, S. Arockia Edwin Xavier
{"title":"A Hybrid Wild Horses Optimization (WHO) and Dwarf Mongoose Optimization (DMO) method for optimum energy management in SG system","authors":"P. Ganesan, S. Arockia Edwin Xavier","doi":"10.1002/oca.3131","DOIUrl":null,"url":null,"abstract":"A hybrid technique is proposed for the energy management (EM) of smart grid (SG) systems. The proposed method integrates the Wild Horse Optimization (WHO) and dwarf mongoose optimization (DMO) methods; hence, it is named the WHO–DMO approach. The Micro‐grid (MG)‐tied system is a combination of battery, micro turbine (MT), photovoltaic (PV), and Wind Turbine (WT). The key aim of the proposed approach is to manage the resources and power of the SG model and reduce the cost of electricity. The objective of the system is to improve load demand. The WHO method is enhanced by the DMO method, which minimizes the objective of the system. Access to power demand, state of charge (SOC), and renewable energy sources (RESs) for storing elements are considered constraints of the system. The unit of renewable power systems relies on batteries as energy sources to stabilize and sustain stable and consistent output power throughout the operation. The proposed technique is done in MATLAB platform and its implementation is calculated using the existing methods. From the simulation, the proposed method has less cost and higher power than the existing methods.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optimal Control Applications and Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/oca.3131","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

A hybrid technique is proposed for the energy management (EM) of smart grid (SG) systems. The proposed method integrates the Wild Horse Optimization (WHO) and dwarf mongoose optimization (DMO) methods; hence, it is named the WHO–DMO approach. The Micro‐grid (MG)‐tied system is a combination of battery, micro turbine (MT), photovoltaic (PV), and Wind Turbine (WT). The key aim of the proposed approach is to manage the resources and power of the SG model and reduce the cost of electricity. The objective of the system is to improve load demand. The WHO method is enhanced by the DMO method, which minimizes the objective of the system. Access to power demand, state of charge (SOC), and renewable energy sources (RESs) for storing elements are considered constraints of the system. The unit of renewable power systems relies on batteries as energy sources to stabilize and sustain stable and consistent output power throughout the operation. The proposed technique is done in MATLAB platform and its implementation is calculated using the existing methods. From the simulation, the proposed method has less cost and higher power than the existing methods.
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
混合野马优化法(WHO)和矮獴优化法(DMO)用于优化 SG 系统的能源管理
针对智能电网(SG)系统的能源管理(EM)提出了一种混合技术。该方法综合了野马优化法(WHO)和矮獴优化法(DMO),因此被命名为WHO-DMO方法。微电网(MG)绑定系统是电池、微型涡轮机(MT)、光伏(PV)和风力涡轮机(WT)的组合。建议方法的主要目的是管理 SG 模型的资源和电力,并降低电力成本。该系统的目标是改善负荷需求。世界卫生组织方法通过 DMO 方法得到了加强,从而使系统目标最小化。获取电力需求、充电状态 (SOC) 和用于存储元素的可再生能源 (RES) 被视为系统的约束条件。可再生能源发电系统依靠电池作为能源,在整个运行过程中稳定并持续输出功率。所提出的技术是在 MATLAB 平台上完成的,其实现是通过现有方法计算得出的。模拟结果表明,与现有方法相比,建议的方法成本更低,功率更高。
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
自引率
0.00%
发文量
0
期刊最新文献
An optimal demand side management for microgrid cost minimization considering renewables Output feedback control of anti‐linear systems using adaptive dynamic programming Reachable set estimation of delayed Markovian jump neural networks based on an augmented zero equality approach Adaptive neural network dynamic surface optimal saturation control for single‐phase grid‐connected photovoltaic systems Intelligent integration of ANN and H‐infinity control for optimal enhanced performance of a wind generation unit linked to a power system
×
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