- Title
- Smart grid evolution : predictive control of distributed energy resources—A review
- Creator
- Babayomi, Oluleke; Zhang, Zhenbin; Dragicevic, Tomislav; Hu, Jiefeng; Rodriguez, Jose
- Date
- 2023
- Type
- Text; Journal article; Review
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/190663
- Identifier
- vital:17652
- Identifier
-
https://doi.org/10.1016/j.ijepes.2022.108812
- Identifier
- ISSN:0142-0615 (ISSN)
- Abstract
- As the smart grid evolves, it requires increasing distributed intelligence, optimization and control. Model predictive control (MPC) facilitates these functionalities for smart grid applications, namely: microgrids, smart buildings, ancillary services, industrial drives, electric vehicle charging, and distributed generation. Among these, this article focuses on providing a comprehensive review of the applications of MPC to the power electronic interfaces of distributed energy resources (DERs) for grid integration. In particular, the predictive control of power converters for wind energy conversion systems, solar photovoltaics, fuel cells and energy storage systems are covered in detail. The predictive control methods for grid-connected converters, artificial intelligence-based predictive control, open issues and future trends are also reviewed. The study highlights the potential of MPC to facilitate the high-performance, optimal power extraction and control of diverse sustainable grid-connected DERs. Furthermore, the study brings detailed structure to the artificial intelligence techniques that are beneficial to enhance performance, ease deployment and reduce computational burden of predictive control for power converters. © 2022 Elsevier Ltd
- Publisher
- Elsevier Ltd
- Relation
- International Journal of Electrical Power and Energy Systems Vol. 147, no. (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022 Elsevier Ltd
- Subject
- 4008 Electrical engineering; 4009 Electronics, sensors and digital hardware; 4601 Applied computing; Artificial intelligence; Distributed energy resources; Distributed generation; Grid-connected converter; Microgrid; Model predictive control; Power electronic converter; Smart grid
- Reviewed
- Funder
- This work was supported in part by the National R & D Program of China , (Grant No. 2022YFB4201700 ), in part by the General Program of the National Natural Science Foundation of China (Grant Nos. 51977124 , 52277191 , and 52277192 ), in part by the National Distinguished Expert (Youth Talent) Program of China (Grant No. 31390089963058 ), and in part by the Shenzhen Science and Technology Innovation Program (Grant Nos. JCYJ20210324132616040 and JCYJ20220530141010024 ).
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