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• W4285170820 abstract "Networked microgrids (MGs) with inverter-based and droopcontrolled distributed energy resources (DERs) require operational optimization with guaranteed stability performance to ensure the stable energy supply with minimum cost, yet it remains an open challenge. Additionally, the discrete nature of MGs leads to convergence issues to existing optimization methods thereby leading to difficulties obtaining feasible solutions for large-scale networks. This article develops a paradigm for discrete droop control to improve microgrids controllability in managing voltage and frequency fluctuations. With the emergence of Internet of Things, the computational tasks are distributed among local resources. The utilized Distributed and Asynchronous Surrogate Lagrangian Relaxation (DA-SLR) method distributes the optimization tasks among the MGs and efficiently coordinates the distributed subsystems. A small-signal model of the operational optimization is then developed to verify the systems stability. Numerous case studies have proven the DA-SLRs efficacy in comparison to various variations of the alternating direction of multipliers method (ADMM)." @default.
• W4285170820 created "2022-07-14" @default.
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• W4285170820 date "2022-01-01" @default.
• W4285170820 modified "2023-10-14" @default.
• W4285170820 title "Computationally Distributed and Asynchronous Operational Optimization of Droop-Controlled Networked Microgrids" @default.
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• W4285170820 doi "https://doi.org/10.1109/oajpe.2022.3188733" @default.
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