A fractional-order multiple-model type-2 fuzzy control for interconnected power systems incorporating renewable energies and demand response
Yan S.-R. Dai Y. Shakibjoo A.D. Zhu L. Taghizadeh S. Ghaderpour E. Mohammadzadeh A.
December 2024Elsevier Ltd
Energy Reports
2024#12187 - 196 pp.
Frequency regulation in Multi-region Interconnected Power Systems (MIPS), incorporating wind turbine systems, energy storage units, and demand response, is a challenging control problem. The problem involves maintaining grid stability, integrating variable renewable energy sources, enhancing grid resilience, optimizing energy storage and demand response capabilities, and ensuring regulatory compliance. Effective frequency regulation is a key problem for reliable and sustainable power system operation. In this paper, complex and uncertain dynamics in various components of a MIPS are modeled using multiple first-order dynamic fractional-order Type-2 Fuzzy Logic Systems (T2-FLSs). The models are evaluated, and the best possible dynamic model is chosen. With the best possible model, an optimal controller is designed. The stability and optimality analysis are presented through a Linear Matrix Inequality (LMI) approach. The adaptive laws of T2-FLSs are derived such that some LMI-based conditions are satisfied. The designed controller does not depend on the dynamics of MIPS. The uncertainties of time-varying load, wind energy, and solar power are modeled using T2-FLSs. The suggested LMI technique presents adaptation laws for T2-FLSs to ensure stability in the presage of natural disturbances and errors. The designed scheme is validated by applying to a practical 39-bus IEEE test system that includes the wind farms, demand response, and energy storage systems. The simulation results under various conditions verify the usefulness of the suggested controller. The comparisons with conventional controllers demonstrate that the suggested approach is more effective under uncertainties and natural perturbations.
Adaptive control , Frequency regulation , LMI , Type-2 fuzzy
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School of Management, Guangdong University of Science and Technology, Dongguan, 523083, China
College of Architecture and Civil Engineering, Shenyang University of Technology, Shenyang, China
Department of Electrical Engineering, Ahrar Institute of Technology and Higher Education, Rasht, Iran
Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
Department of Earth Sciences, Sapienza University of Rome, Piazzale Aldo-Moro, 5, Rome, 00185, Italy
Department of Computational and Data Science, Astana IT University, Astana, Kazakhstan
School of Management
College of Architecture and Civil Engineering
Department of Electrical Engineering
Department of Electrical Engineering
Department of Earth Sciences
Department of Computational and Data Science
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