Enhanced Virtual Inertia Control for Microgrids with High-Penetration Renewables Based on Whale Optimization

• Published in: Energies (Basel) Vol. 15; no. 23; p. 9254
• Main Authors: Faragalla, Asmaa, Abdel-Rahim, Omar, Orabi, Mohamed, Abdelhameed, Esam H.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2022
• Subjects: Algorithms; Alternative energy sources; Control methods; Controllers; Damping; Design; Distributed generation; Distributed generation (Electric power); Dynamic stability; Energy resources; Energy sources; Energy storage; Foraging behavior; Frequency control; frequency measurements; Frequency stability; Generators; H-infinity control; Mathematical optimization; microgrid; Noise generation; Noise measurement; Optimization; Optimization algorithms; Optimization techniques; Phase locked loops; phase-locked loop (PLL); Renewable energy sources; renewable energy sources (RESs); Robust control; Systems stability; Uncertainty; virtual inertia control; Whales & whaling; Egypt
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15239254

High penetration of renewable energy sources into isolated microgrids (µGs) is considered a critical challenge, as µGs’ operation at low inertia results in frequency stability problems. To solve this challenge, virtual inertia control based on an energy storage system is applied to enhance the inertia and damping properties of the µG. On the other hand, utilization of a phase-locked loop (PLL) is indispensable for measuring system frequency; however, its dynamics, such as measurement delay and noise generation, cause extra deterioration of frequency stability. In this paper, to improve µG frequency stability and minimize the impact of PLL dynamics, a new optimal frequency control technique is proposed. A whale optimization algorithm is used to enhance the virtual inertia control loop by optimizing the parameters of the virtual inertia controller with consideration of PLL dynamics and the uncertainties of system inertia. The proposed controller has been validated through comparisons with an optimized virtual inertia PI controller which is tuned utilizing MATLAB internal model control methodology and with H∞-based virtual inertia control. The results show the effectiveness of the proposed controller against different operating conditions and system disturbances and uncertainties.