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• W4211085461 abstract "Distributed Generating (DG) units, Energy Storage Systems (ESS), Distributed Reactive Sources (DRS), and resilient loads make up the microgrid (MG), which can operate in both connected and isolated modes. Because the amount of power generated by Renewable Energy Sources (RES) such as Wind Energy Systems (WES) and Photovoltaic Energy Systems (PVES) is unpredictable, it becomes difficult for MGs planners to make judgments. In this article, the uncertainties caused by RES are resolved through the successful application of a hybrid optimization approach and the integration of hybrid DGs. The Teaching Learning Algorithm (TLA) is used in this study to determine the best site for DGs and reconfiguration, and heuristic fuzzy has been merged with TLA to handle multi-objectives such as total generation and emission cost minimization, and bus voltage deviation. In addition, the impact of replacing RES with hybrid DGs on RES performance is investigated. The ideal structures are determined by solving four different scenarios with the suggested approach, allowing DSO to make decisions with greater flexibility. The proposed technique is validated using a benchmark IEEE 33 bus system that has been converted into a microgrid. WES, PVES, and hybrid DGs are validated using a 24-hour daily load pattern with 24-hour load dispatching characteristic behaviors." @default.
• W4211085461 created "2022-02-13" @default.
• W4211085461 creator A5031441540 @default.
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• W4211085461 date "2022-06-01" @default.
• W4211085461 modified "2023-10-09" @default.
• W4211085461 title "Dynamic optimal distributed resource planning for renewable energy based interconnected microgrids using teaching learning algorithm" @default.
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• W4211085461 doi "https://doi.org/10.3233/jifs-212397" @default.
• W4211085461 hasPublicationYear "2022" @default.
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