- Title
- A holistic power management strategy of microgrids based on model predictive control and particle swarm optimization
- Creator
- Shan, Yinghao; Hu, Jiefeng; Liu, Huashan
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/186854
- Identifier
- vital:16978
- Identifier
-
https://doi.org/10.1109/TII.2021.3123532
- Identifier
- ISBN:1551-3203 (ISSN)
- 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. © 2005-2012 IEEE.
- Publisher
- IEEE Computer Society
- Relation
- IEEE Transactions on Industrial Informatics Vol. 18, no. 8 (2022), p. 5115-5126
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021 IEEE
- Subject
- 40 Engineering; 46 Information and computing sciences; Microgrids; Model predictive control (MPC); Particle swarm optimization (PSO); Power control and optimization
- Reviewed
- Funder
- This work was sponsored in part by the Shanghai Sailing Program under Grant 21YF1400100, in part by the Shanghai Rising-Star Program under Grant 19QA1400400, in part by the Natural Science Foundation of Shanghai under Grant 21ZR1401100, in part by the Fundamental Research Funds for the Central Universities under Grant 2232021D-38 and Grant 2232020G-09, and in part by the Initial Research Funds for Young Teachers of Donghua University. Paper no. TII-21-2491.
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