A novel MPC-based cascaded control for multi-area smart grids: Tackling renewable energy and EV integration challenges.

Muhammad S Tolba, Muhammad Majid Gulzar, Ali Arishi, Mohamed Soliman, Ali Faisal Murtaza
{"title":"A novel MPC-based cascaded control for multi-area smart grids: Tackling renewable energy and EV integration challenges.","authors":"Muhammad S Tolba, Muhammad Majid Gulzar, Ali Arishi, Mohamed Soliman, Ali Faisal Murtaza","doi":"10.1016/j.isatra.2025.06.024","DOIUrl":null,"url":null,"abstract":"<p><p>This paper presents an advanced cascaded control scheme for load frequency regulation in multi-area power systems incorporating renewable energy sources (RES) and electric vehicles (EVs). The proposed design (Model predictive control cascaded with one plus proportional-integral control cascaded with tilt control in parallel with one plus fractional-order integral derivative controller (MPC-((1+PI)-(T+(1+I<sup>λ</sup>D<sup>μ</sup>)))) combines predictive, tilt, and fractional-order dynamics to improve adaptability and robustness under uncertainties. Controller parameters are tuned using the Lyrebird Optimization Algorithm (LOA), ensuring fast convergence and effective global search. Simulation results under varying operational conditions, including nonlinearity effects such as Generation Rate Constraints (GRC), Governor Dead Band (GDB), and Communication Time Delays (CTD), confirm the controller's superiority. It achieves a 96.4 % ITAE reduction, 98.6 % undershoot mitigation, and a settling time of just 5.8 s outperforming existing benchmark strategies (GOA: PDf+(0.75+PI), CBOA: PI-PD, JSA: PI, and ARA: 1+PID).</p>","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISA transactions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.isatra.2025.06.024","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

This paper presents an advanced cascaded control scheme for load frequency regulation in multi-area power systems incorporating renewable energy sources (RES) and electric vehicles (EVs). The proposed design (Model predictive control cascaded with one plus proportional-integral control cascaded with tilt control in parallel with one plus fractional-order integral derivative controller (MPC-((1+PI)-(T+(1+IλDμ)))) combines predictive, tilt, and fractional-order dynamics to improve adaptability and robustness under uncertainties. Controller parameters are tuned using the Lyrebird Optimization Algorithm (LOA), ensuring fast convergence and effective global search. Simulation results under varying operational conditions, including nonlinearity effects such as Generation Rate Constraints (GRC), Governor Dead Band (GDB), and Communication Time Delays (CTD), confirm the controller's superiority. It achieves a 96.4 % ITAE reduction, 98.6 % undershoot mitigation, and a settling time of just 5.8 s outperforming existing benchmark strategies (GOA: PDf+(0.75+PI), CBOA: PI-PD, JSA: PI, and ARA: 1+PID).

基于多区域智能电网的新型mpc级联控制:应对可再生能源和电动汽车集成挑战。
本文提出了一种适用于可再生能源和电动汽车多区域电力系统负荷频率调节的先进级联控制方案。所提出的模型预测控制与一加比例积分控制级联,倾斜控制与一加分数阶积分导数控制器(MPC-(1+PI)-(T+(1+ i - λ dμ)))))并联,结合了预测、倾斜和分数阶动力学,以提高不确定性下的适应性和鲁棒性。控制器参数使用Lyrebird优化算法(LOA)进行调整,确保快速收敛和有效的全局搜索。在不同运行条件下的仿真结果,包括非线性影响,如产生率约束(GRC),总督死区(GDB)和通信时间延迟(CTD),证实了控制器的优越性。它实现了96.4 %的ITAE降低,98.6 %的低偏差缓解,并且稳定时间仅为5.8 s,优于现有的基准策略(GOA: PDf+(0.75+PI), CBOA: PI- pd, JSA: PI和ARA: 1+PID)。
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