No Arabic abstract
Forming (hybrid) AC/DC microgrids (MGs) has become a promising manner for the interconnection of various kinds of distributed generators that are inherently AC or DC electric sources. This paper addresses the distributed asynchronous power control problem of hybrid microgrids, considering imperfect communication due to non-identical sampling rates and communication delays. To this end, we first formulate the optimal power control problem of MGs and devise a synchronous algorithm. Then, we analyze the impact of asynchrony on optimal power control and propose an asynchronous iteration algorithm based on the synchronous version. By introducing a random clock at each iteration, different types of asynchrony are fitted into a unified framework, where the asynchronous algorithm is converted into a fixed-point problem based on the operator splitting method, leading to a convergence proof. We further provide an upper bound estimation of the time delay in the communication. Moreover, the real-time implementation of the proposed algorithm in both AC and DC MGs is introduced. By taking the power system as a solver, the controller is simplified by reducing one order and the power loss can be considered. Finally, a benchmark MG is utilized to verify the effectiveness and advantages of the proposed algorithm.
Motivated by the fact that the location(s) and structural properties of the pinning node(s) affect the algebraic connectivity of a network with respect to the reference value and thereby, its dynamic performance, this paper studies the application of intelligent single and multiple pinning of distributed cooperative secondary control of distributed generators (DGs) in islanded microgrid operation. It is shown that the intelligent selection of a pinning set based on the degree of connectivity and distance of leader DG(s) from the rest of the network improves the transient performance for microgrid voltage and frequency regulation. The efficacy of the distributed control strategy based on the proposed algorithms is illustrated via numerical results simulating typical scenarios for a variety of microgrid configurations.
Recently we studied communication delay in distributed control of untimed discrete-event systems based on supervisor localization. We proposed a property called delay-robustness: the overall system behavior controlled by distributed controllers with communication delay is logically equivalent to its delay-free counterpart. In this paper we extend our previous work to timed discrete-event systems, in which communication delays are counted by a special clock event {it tick}. First, we propose a timed channel model and define timed delay-robustness; for the latter, a polynomial verification procedure is presented. Next, if the delay-robust property does not hold, we introduce bounded delay-robustness, and present an algorithm to compute the maximal delay bound (measured by number of ticks) for transmitting a channeled event. Finally, we demonstrate delay-robustness on the example of an under-load tap-changing transformer.
This paper considers the distributed sampled-data control problem of a group of mobile robots connected via distance-induced proximity networks. A dwell time is assumed in order to avoid chattering in the neighbor relations that may be caused by abrupt changes of positions when updating information from neighbors. Distributed sampled-data control laws are designed based on nearest neighbour rules, which in conjunction with continuous-time dynamics results in hybrid closed-loop systems. For uniformly and independently initial states, a sufficient condition is provided to guarantee synchronization for the system without leaders. In order to steer all robots to move with the desired orientation and speed, we then introduce a number of leaders into the system, and quantitatively establish the proportion of leaders needed to track either constant or time-varying signals. All these conditions depend only on the neighborhood radius, the maximum initial moving speed and the dwell time, without assuming a prior properties of the neighbor graphs as are used in most of the existing literature.
Optimal power flow (OPF) is an important technique for power systems to achieve optimal operation while satisfying multiple constraints. The traditional OPF are mostly centralized methods which are executed in the centralized control center. This paper introduces a totally Distributed DC Optimal Power Flow (DDCOPF) method for future power systems which have more and more distributed generators. The proposed method is based on the Distributed Economic Dispatch (DED) method and the Distributed State Estimation (DSE) method. In this proposed scheme, the DED method is used to achieve the optimal power dispatch with the lowest cost, and the DSE method provides power flow information of the power system to the proposed DDCOPF algorithm. In the proposed method, the Auto-Regressive (AR) model is used to predict the load variation so that the proposed algorithm can prevent overflow. In addition, a method called constraint algorithm is developed to correct the results of DED with the proposed correction algorithm and penalty term so that the constraints for the power system will not be violated. Different from existing research, the proposed method is completely distributed without need for any centralized facility.
In this work, a dynamic system is controlled by multiple sensor-actuator agents, each of them commanding and observing parts of the systems input and output. The different agents sporadically exchange data with each other via a common bus network according to local event-triggering protocols. From these data, each agent estimates the complete dynamic state of the system and uses its estimate for feedback control. We propose a synthesis procedure for designing the agents state estimators and the event triggering thresholds. The resulting distributed and event-based control system is guaranteed to be stable and to satisfy a predefined estimation performance criterion. The approach is applied to the control of a vehicle platoon, where the methods trade-off between performance and communication, and the scalability in the number of agents is demonstrated.