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
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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+Iλ[jls-end-space/]Dμ[jls-end-space/])))) 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).

Original languageEnglish
Pages (from-to)143-169
Number of pages27
JournalISA Transactions
Volume165
DOIs
StatePublished - Oct 2025

Bibliographical note

Publisher Copyright:
© 2025 International Society of Automation. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Keywords

  • Electric vehicles (EVs)
  • Load frequency control (LFC)
  • Lyrebird optimization algorithm (LOA)
  • Model predictive control (MPC)
  • Smart grids

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Instrumentation
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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