Disturbance-Resilient Two-Area LFC via RBBMO-Optimized Hybrid Fuzzy–Fractional with Auxiliary PI(1+DD) Controller Considering RES/ESS Integration and EVs Support

Saleh A. Alnefaie (), Abdulaziz Alkuhayli and Abdullah M. Al-Shaalan
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Saleh A. Alnefaie: Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Abdulaziz Alkuhayli: Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Abdullah M. Al-Shaalan: Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia

Mathematics, 2025, vol. 13, issue 23, 1-52

Abstract: This study examines dual-area load–frequency control (LFC) in the context of significant renewable energy integration, energy storage systems (ESSs), and collective electric vehicle (EV) involvement. We propose a RBBMO-FO-FuzzyPID+PI(1+DD) hybrid controller in which fractional-order fuzzy regulation shapes the ACE, while an auxiliary PI(1+DD) path adds damping without steady-state penalty. Across ideal linear plants, 3% governor-rate constraints (GRC), and stressed conditions that include contract violations in Area-2, renewable power variations, and partial EV State of Charge (SoC 50–70%), EV participation yields systematic gains for all controller families, and the magnitude depends on the control architecture. Baseline methods improve by 15–25% with EVs, whereas advanced designs—especially the proposed controller—benefit by 25–45%, revealing a clear synergy between controller intelligence and EV flexibility. With EV support, the proposed controller limits frequency overshoot to 0.055 Hz (a 20–55% reduction versus peers), caps the nadir at −0.065 Hz (15–41% better undershoot), and attains 3.5–4.5 s settling (25–61% faster than competitors), while holding tie-line deviations within ±0.02 Hz. Optimization studies confirm the algorithmic advantage: RBBMO achieves 30% lower cost than BBOA and converges 25% faster; EV integration further reduces cost by 15% and speeds convergence by 12%. A strong correlation between optimization cost and closed-loop performance (r 2 ≈ 0.95) validates the tuning strategy. Collectively, the results establish the proposed hybrid controller with EV participation as a new benchmark for robust, system-wide frequency regulation in renewable-rich multi-area grids.

Keywords: red-billed blue magpie optimizer (RBBMO); LFC; renewable penetration; electric-vehicle and power system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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