Distributed Secondary Control and Management of Islanded Microgrids via Dynamic Weights

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: 1) dynamic and 2) fixed weights, in which the term with dynamic weights reassigns outputs of distributed generators in order to reach different targets. Moreover, this method offers a convenient way to achieve different 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.