In this paper, we propose a distributed model predictive control (MPC) scheme for economic dispatch of energy systems with a large number of active components. The scheme uses a distributed optimization algorithm that works over random communication networks and asynchronous updates, implying the resiliency of the proposed scheme with respect to communication problems, such as link failures, data packet drops, and delays. The distributed optimization algorithm is based on the augmented Lagrangian approach, where the dual of the considered convex economic dispatch problem is solved. Furthermore, in order to improve the convergence speed of the algorithm, we adapt Nesterov's accelerated gradient method and apply the warm start method to initialize the variables. We show through numerical simulations of a well-known case study the performance of the proposed scheme.

中文翻译：

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大规模能源系统经济调度的随机网络分布式增强拉格朗日方法


在本文中，我们提出了一种分布式模型预测控制（MPC）方案，用于具有大量活动组件的能源系统的经济调度。该方案使用在随机通信网络和异步更新上工作的分布式优化算法，这意味着所提出的方案对于链路故障、数据包丢失和延迟等通信问题具有弹性。分布式优化算法基于增强拉格朗日方法，解决了所考虑的凸经济调度问题的对偶问题。此外，为了提高算法的收敛速度，我们采用Nesterov的加速梯度法并采用热启动方法来初始化变量。我们通过一个著名案例研究的数值模拟展示了所提出方案的性能。