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• W3209502631 abstract "Power control and optimization are both crucial for the proper operation of a microgrid. However, in existing research, they are usually studied separately. Active and reactive powers are either maintained to constant values at device level or optimized at system level without considering frequency and voltage control of distributed converters. In this article, a holistic power control and optimization strategy is proposed for microgrids. Specifically, a model predictive control incorporated with the droop method is developed at device level to achieve load sharing and flexible power dispatching among distributed energy resources, which is feasible for both islanded and grid-connected modes. In addition, an evolutionary particle swarm optimization algorithm is designed at system level to generate the optimal active and reactive power setpoints, which are then sent to the device level for controlling inverters. The proposed power optimization scheme is able to mitigate voltage deviations and minimize the operational cost of the microgrid. Comprehensive case studies and real-time simulator test are provided to demonstrate the feasibility and efficacy of the proposed power control and optimization strategy." @default.
• W3209502631 created "2021-11-08" @default.
• W3209502631 creator A5004758745 @default.
• W3209502631 creator A5006859983 @default.
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• W3209502631 date "2022-08-01" @default.
• W3209502631 modified "2023-10-18" @default.
• W3209502631 title "A Holistic Power Management Strategy of Microgrids Based on Model Predictive Control and Particle Swarm Optimization" @default.
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• W3209502631 doi "https://doi.org/10.1109/tii.2021.3123532" @default.
• W3209502631 hasPublicationYear "2022" @default.
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