This paper presents a passivity-based distributed model predictive control algorithm that can provide minimum performance bound on power flow systems. The dynamics of large-scale power flow systems can be described by the transportation, conversion, and storage of energy among and across subsystems. By strategically choosing a passive output for each subsystem and augmenting each local model predictive controller with a special passivity constraint, the passivity property of each closed-loop subsystem can be guaranteed; in addition, the corresponding passivity index can be characterized by the designed parameters of the local controller. The passivity-preserving coupling between subsystems can be utilized to maintain passivity and the stability properties of the overall system. Moreover, the passivity index of the overall system can be characterized by the transition of passivity indices of subsystems based on interconnection topology. Based on the passivity index, the minimum performance bound of the overall power flow system can be characterized. Even if the structure of the power flow system changes, the entire system can remain stable and achieve a certain performance level. Simulation results of a fluid tank system show the effectiveness of the proposed algorithm.

中文翻译：

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基于无源性的可重构潮流系统具有保证性能的分布式模型预测控制

本文提出了一种基于被动的分布式模型预测控制算法，该算法可以对潮流系统提供最小的性能限制。大规模潮流系统的动力学可以通过子系统之间和跨子系统的能量传输、转换和存储来描述。通过为每个子系统策略性地选择一个被动输出，并用特殊的被动约束来增强每个局部模型预测控制器，可以保证每个闭环子系统的被动属性；此外，相应的无源指标可以通过本地控制器的设计参数来表征。子系统之间的无源保持耦合可用于保持整个系统的无源性和稳定性。而且，整个系统的无源指数可以通过基于互连拓扑的子系统无源指数的转变来表征。基于无源指数，可以表征整个潮流系统的最小性能界限。即使潮流系统的结构发生变化，整个系统也能保持稳定并达到一定的性能水平。流体罐系统的仿真结果表明了该算法的有效性。