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An Integrated Framework for Distributed Model Predictive Control of Large-Scale Power Networks

机译:大型电网分布式模型预测控制的集成框架

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摘要

This work proposes a novel integrated framework in order to model, simulate, and optimize potentially large-scale and complex networks, fusing an extended network modeling methodology and distributed model predictive control structures based on Lagrange multipliers. In concrete, a revised network modeling formulation is utilized, allowing extended networking capabilities with a novel, generic, and compact way of organizing network topology information. This optimization framework offers a number of very convenient features: distribution of the overall network control effort among the local agents; no overall network knowledge required; the introduction/elimination of a node only affects to its neighbors. Thus, this integrated framework offers a powerful and easy-to-use mathematical tool in order to analyze and manage a wide variety of today's large-scale network. Specifically, this methodology is applied here to a power network to show the benefit of the work developed.
机译:这项工作提出了一个新颖的集成框架,以对潜在的大规模复杂网络进行建模,仿真和优化,将扩展的网络建模方法和基于拉格朗日乘数的分布式模型预测控制结构融合在一起。具体而言,使用了经过修订的网络建模公式,从而允许通过新颖,通用且紧凑的组织网络拓扑信息的方式来扩展网络功能。该优化框架提供了许多非常方便的功能:在本地代理之间分配整个网络控制工作;不需要全面的网络知识;节点的引入/消除只会影响其邻居。因此,此集成框架提供了功能强大且易于使用的数学工具,以分析和管理当今各种大型网络。具体而言,此处将这种方法论应用于电力网络,以展示所开展工作的益处。

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