- Title
- Economic dispatch of model predictive controlled distributed power generation
- Creator
- Shan, Yinghao; Hu, Jiefeng; Liu, Huashan
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/189078
- Identifier
- vital:17389
- Identifier
-
https://doi.org/10.1109/PRECEDE51386.2021.9680878
- Identifier
- ISBN:9781665425575 (ISBN)
- Abstract
- Conventionally, for model predictive power control (MPPC) of voltage source inverter (VSI) in a grid-connected microgrid, active power reference is often associated with dc power source while reactive power reference is often given as a constant zero. There is rare literature completely investigating the changing rationale of dynamic change of power references when the dc power source is invariable. In this paper, an economic dispatch method optimizing power flows based on evolutionary algorithms is proposed to dynamically determine the power references. From the microgrid's hierarchical view, the MPPC at the device level is combined with the economic dispatch at the system level, via determining the common power references. Then, the system-level optimized power flow with the minimized cost is directly sent to the device-level power converters, enhancing the two levels' connection. The validity and effectiveness of the proposed strategy have been demonstrated by simulation results. © 2021 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 6th IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2021, Jinan, 20-22 November 2021, 6th IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2021 p. 836-839
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021 IEEE
- Subject
- Economic dispatch; Microgrid; Model predictive control; Power management
- Reviewed
- Funder
- Natural Science Foundation of Shanghai,
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