Consensus control in complex network systems with microgrid applications

<p>Consensus in complex networks has attracted much attention in various communities due to their extensive applications such as power grids regulation, formation task of unmanned air vehicles and cooperative surveillance. Distributed consensus control generally focuses on achieving an agreement for the states in networks by designing a control protocol for each agent based on its local information collected within the neighboring area. The communication networks and the data transmission strategies are two essential aspects that are considered in consensus control. In addition, consensus controllers are often applied to the regulation of microgrids, which are modern small-scale power distribution systems. This thesis is dedicated to studying consensus control in complex networks and microgrid applications. The work in this thesis can be broadly divided into three parts, which are summarized below.</p> <p>Firstly, the network with multilayered cluster structure is studied, and a correlated event-triggered consensus controller is proposed. The traditional complex network only considers a single-layer network, which has limitations to reflect the characteristics of the real-world systems. Also, the agents are often formed in groups in systems like microgrids and unmanned air vehicles. The study of the multilayered cluster networks has great potential to describe the details of the network. In addition, the conventional communication approaches for controllers are continuous or periodic, which are inefficient considering the limitation of communication bandwidth in practice. An event-triggered communication mechanism is proposed to avoid such issues. Furthermore, an event predictive algorithm is proposed to avoid continuous detection, which improves the efficiency of data monitoring by sensors.</p> <p>Secondly, the impact of periodically switching topologies on the consensus value and state dynamics of multiagent systems. A methodology is developed to quantitatively analyze the convergence under different patterns of switching topologies, which reveals the impact of switching intervals and switching patterns on agent states. The numerical relationship between the switching interval and the consensus value under switching patterns is provided, which describes how the consensus value dynamically changes according to different switching intervals. It is shown that for large switching interval, the switching patterns have a significant influence on state dynamics. Furthermore, the analysis methodology is extended to high-order multiagent systems, where the switching interval and the switching patterns are shown to have similar impact. The numerical examples are provided for simulation to validate the effectiveness of the analysis.</p> <p>Lastly, the consensus controller for multilayered cluster networks is implemented to regulate thermostatically controlled loads (TCLs) and energy storage systems (ESSs) in microgrids. For the regulation of TCLs, the model of the TCLs is proposed, which assumes the controllers are constructed at each TCLs. Also, there are multiple TCLs installed in each building, which are interconnected to form a microgrid. The buildings are mutually connected to form a microgrid community, and communities are allowed to connect with each other for support. The controllers could achieve power sharing and fair comfort levels among TCLs in different buildings. For the regulation of ESSs, the model and the hierarchical control scheme for microgrid clusters are demonstrated. The primary control implements the traditional droop control, which only requires the local information, while the secondary control applies to each microgrid to achieve power sharing and voltage regulation among each ESSs in the microgrid. The tertiary control provides the overall regulation to coordinate the mutually connected microgrids. The hierarchical control network is formed by multiple layers with different dynamics and time scales, which serve various control purposes. The simulation provides the control performance under different situations such as load changing and plug and play to validate the effectiveness of the controller.</p>