Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/114298
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Type: Journal article
Title: Distributed Secondary Control and Management of Islanded Microgrids via Dynamic Weights
Author: Li, Q.
Peng, C.
Wang, M.
Chen, M.
Guerrero, J.M.
Abbott, D.
Citation: IEEE Transactions on Smart Grid, 2019; 10(2):2196-2207
Publisher: IEEE
Issue Date: 2019
ISSN: 1949-3053
1949-3061
Statement of
Responsibility: 
Qiang Li, Congbo Peng, Minglin Wang, Minyou Chen, Josep M. Guerrero, Derek Abbott
Abstract: The averaging algorithm for consensus is widely used as a distributed secondary method for the control and management of microgrids. However, during each iteration it may break the system balance obtained by the primary control. In this paper, a distributed and networked method for the control and management of islanded microgrids is proposed, in which there is an agent based communication network as the top layer over a microgrid as the bottom layer. Further, a systematic method is presented to derive a set of distributed control laws for agents from any given communication network, where only nearest neighbor information is needed. The control laws consist of two terms, dynamic and fixed weights, in which the term with dynamic weights reassigns outputs of distributed generators in order to reach di erent targets. Moreover, this method o ers a convenient way to achieve di erent targets of control and management by substituting a parameter in the control laws with dynamic weights. More importantly, the control laws with dynamic weights never break the system balance during iterations. We formally show that if agents apply the control laws to regulate distributed generators, their outputs will iteratively satisfy the given targets. Finally, simulations are carried out to evaluate the performance of the control laws. The results show that equal outputs, proportional outputs and the optimal incremental cost are obtained. Moreover, the voltage and frequency are still stable, when fluctuations of load demand and environmental conditions are considered.
Keywords: Distributed control; energy management; microgrids; multi-agent system (MAS); networked control systems; secondary control
Rights: © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
RMID: 0030081778
DOI: 10.1109/TSG.2018.2791398
Appears in Collections:Electrical and Electronic Engineering publications

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