A Distributed-MPC Framework for Voltage Control Under Discrete Time-Wise Variable Generation/Load
A Distributed-MPC Framework for Voltage Control Under Discrete Time-Wise Variable Generation/Load
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DOI：
10.1109/tpwrs.2023.3266763
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发表时间：
2022-02
影响因子：
6.6
通讯作者：
Ramij Raja Hossain;Ratnesh Kumar
中科院分区：
文献类型：
--
作者：
Ramij Raja Hossain;Ratnesh Kumar
本文提出了一种分布式预测设计的实时电压控制下变化的负载和发电配置文件在离散的时间间隔。现有的控制设计包括集中式方法，提供最佳解决方案，但可扩展性较低，易受单点故障/攻击，以及分散或本地化的方法，具有增加的可扩展性和攻击弹性，但缺乏最优性。所提出的分布式解决方案提供了这两种方法的有吸引力的功能，其中相邻节点共享其本地信息，以获得最佳的解决方案，同时保持可扩展性和单点故障/攻击的弹性。我们首先介绍了集中式版本的电压控制问题，假设电网的可观测性，然后将其转移到分布式版本的基础上总线明智和区域明智的网络分解。分布式版本是通过交替方向乘法器（ADMM），总线式分解，需要一套完整的本地测量，而只有一部分的本地测量（保证每个区域的区域电网可观测性）是需要的区域式分解，沿着与邻居到邻居的通信。此外，利用测量数据的可用性，该框架包括一个分布式方法来估计底层网络图的导纳矩阵$\mathbf {Y}$。该框架针对IEEE-30、IEEE-57总线传输系统和IEEE-123总线配电系统进行了验证，并且可以容忍一定程度的发电/负荷预测不确定性、建模误差和通信故障;此外，其内置冗余支持攻击检测。
This article presents a distributed predictive design for real-time voltage control under changing load and generation profiles at discrete intervals. The existing control designs include centralized approach, providing an optimal solution but less scalable and susceptible to single-point failures/attacks, as well as decentralized or localized approach, having increased scalability and attack resilience but lacking optimality. The proposed distributed solution offers the attractive features of both approaches, where the neighboring nodes share their local information to attain an optimal solution while retaining scalability and resilience to single-point failures/attacks. We first introduce the centralized version of the voltage control problem assuming grid observability and then transfer it to the distributed versions based on both bus-wise and area-wise decompositions of the network. The distributed version is solved via alternating direction method of multipliers (ADMM) that, for bus-wise decomposition, needs a full set of local measurements, whereas only a partial set of local measurements (that guarantee area-wise grid observability for each area) is needed for area-wise decomposition, along with neighbor-to-neighbor communications. Additionally, leveraging the availability of measurement data, the framework includes a distributed method to estimate the admittance matrix $\mathbf {Y}$ of the underlying network graph. The proposed framework is validated against IEEE-30, IEEE-57 bus transmission systems, and IEEE-123 bus distribution systems and can tolerate certain levels of generation/load prediction uncertainties, modeling errors, and communication failures; plus, its in-built redundancy supports attack detection.