State-of-the-art review on energy management and control of networked microgrids

被引:36
作者
Singh, Arvind R. [1 ,2 ]
Raju, D. Koteswara [3 ]
Raghav, L. Phani [4 ]
Kumar, R. Seshu [5 ]
机构
[1] Koneru Lakshmaiah Educ Fdn, Dept Elect & Elect Engn, Vijayawada, India
[2] Univ Pretoria, Dept Elect Elect & Comp Engn, Pretoria, South Africa
[3] Natl Inst Technol, Dept Elect Engn, Silchar, India
[4] Anil Neerukonda Inst Technol & Sci, Dept Elect Engn, Visakhapatnam, Andhra Pradesh, India
[5] KPR Inst Engn & Technol, Coimbatore, Tamil Nadu, India
关键词
Smart distribution network; Multi microgrids; Energy management; Microgrid operation; Control; ACTIVE DISTRIBUTION NETWORKS; MULTI-MICROGRIDS; FREQUENCY CONTROL; OPTIMIZATION; STRATEGY; MODEL;
D O I
10.1016/j.seta.2023.103248
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
As the United Nations plans to "ensure access to affordable, reliable, sustainable and modern energy for all," great attention is paid to deploying sustainable networked microgrids to fulfill the future energy demand. Several neighboring low-voltage microgrids in a fixed or dynamic electric boundary will form a Multi-Microgrid. The unprecedented deployment of microgrids, brilliant communication, and infrastructure enable the concept of multi-microgrids to be an operational exemplar in future distribution networks, and it paves the way to deliver critical loads against all odds. This paper provides a state-of-the-art review of the evolution of networked microgrids with deep insight into the most critical research areas, opportunities, and challenges in energy management and control. Their operation and control in coordination with medium voltage distribution networks in time-varying loads and intermittent characteristics of renewable-based distributed generation sources are challenging for both distribution network and microgrid operators. Further, the complexities involved in the multiple control layers in the multi-microgrid network need appropriate strategies for optimal sharing and trading among neighboring microgrids. Numerous solutions based on advanced distribution control, reinforcement learning, adaptive deep neural networks, and game theory were reported in the literature. A systematic review of various energy management strategies, optimization scheduling frameworks, and multi-microgrid voltage and frequency control strategies are presented. Then, a detailed survey on hierarchical, decentralized, and distributed control architectures is reported. Finally, a comprehensive overview of research objectives and more than 30 solution methodologies with respect to both energy management and control is illustrated.
引用
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页数:14
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