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Sustainability
Volume 14
Issue 22
10.3390/su142214928
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Article

Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation

by
Ahmed M. Hussien
1,
Jonghoon Kim
2,
Abdulaziz Alkuhayli
3,
Mohammed Alharbi
3,
Hany M. Hasanien
4,*,
Marcos Tostado-Véliz
5,
Rania A. Turky
1 and
Francisco Jurado
5
1
Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt
2
Energy Storage and Conversion Laboratory, Department of Electrical Engineering, Chungnam National University, Daejeon 34134, Korea
3
Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
4
Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
5
Department of Electrical Engineering, Superior Polytechnic School of Linares, University of Jaén, 23700 Linares, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(22), 14928; https://doi.org/10.3390/su142214928
Submission received: 15 October 2022 / Revised: 7 November 2022 / Accepted: 8 November 2022 / Published: 11 November 2022
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Abstract

The present research produces a new technique for the optimum operation of an isolated microgrid (MGD) based on an enhanced block-sparse adaptive Bayesian algorithm (EBSABA). To update the proportional-integral (PI) controller gains online, the suggested approach considers the impact of the actuating error signal as well as its magnitude. To reach a compromise result between the various purposes, the Response Surface Methodology (RSMT) is combined with the sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms. To demonstrate the success of the novel approach, a benchmark MGD is evaluated in three different Incidents: (1) removing the MGD from the utility (islanding mode); (2) load variations under islanding mode; and (3) a three-phase fault under islanding mode. Extensive simulations are run to test the new technique using the PSCAD/EMTDC program. The validity of the proposed optimizer is demonstrated by comparing its results with those obtained using the least mean and square root of exponential method (LMSRE) based adaptive control, SFO, and PSO methodologies. The study demonstrates the superiority of the proposed EBSABA over the LMSRE, SFO, and PSO approaches in the system’s transient reactions.
Keywords: adaptive control; enhanced block-sparse adaptive Bayesian algorithm; microgrid; response surface methodology adaptive control; enhanced block-sparse adaptive Bayesian algorithm; microgrid; response surface methodology

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MDPI and ACS Style

Hussien, A.M.; Kim, J.; Alkuhayli, A.; Alharbi, M.; Hasanien, H.M.; Tostado-Véliz, M.; Turky, R.A.; Jurado, F. Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation. Sustainability 2022, 14, 14928. https://doi.org/10.3390/su142214928

AMA Style

Hussien AM, Kim J, Alkuhayli A, Alharbi M, Hasanien HM, Tostado-Véliz M, Turky RA, Jurado F. Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation. Sustainability. 2022; 14(22):14928. https://doi.org/10.3390/su142214928

Chicago/Turabian Style

Hussien, Ahmed M., Jonghoon Kim, Abdulaziz Alkuhayli, Mohammed Alharbi, Hany M. Hasanien, Marcos Tostado-Véliz, Rania A. Turky, and Francisco Jurado. 2022. "Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation" Sustainability 14, no. 22: 14928. https://doi.org/10.3390/su142214928

APA Style

Hussien, A. M., Kim, J., Alkuhayli, A., Alharbi, M., Hasanien, H. M., Tostado-Véliz, M., Turky, R. A., & Jurado, F. (2022). Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation. Sustainability, 14(22), 14928. https://doi.org/10.3390/su142214928

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