基于聚类方法的大规模电网监督MPC控制
Supervised MPC control of large-scale electricity networks via clustering methods
摘要：
本文介绍了一种控制方法，用于大规模电网，目标是有效地协调分布式发电机，以平衡意外的负荷变化与标称预测。为了减轻由于问题的大小而造成的困难，所提出的方法分为两个步骤。首先，网络被划分成集群，由几个可调度和不可调度的发电机，存储系统和负载。设计集群算法的目的是获得具有以下特征的集群：（i）它们必须紧凑，使发电机和负载之间的距离尽可能小;（ii）它们必须能够在内部最大限度地平衡负载变化。一旦完成网络分簇，设计了一个两层的控制系统。在较低层，本地模型预测控制器与每个集群相关联，用于管理可用的发电和存储元件以补偿本地负载变化。如果本地源不足以平衡集群的负载变化，则向监督层发送功率请求，监督层最佳地分配可从网络的其他集群获得的附加资源。为了提高该方法的可扩展性，监督程序的实现依赖于一个完全分布式的优化算法。IEEE 118总线系统是用来测试所提出的设计过程中的一个非平凡的情况。
英文摘要：
This paper describes a control approach for large-scale electricity networks, with the goal of efficiently coordinating distributed generators to balance unexpected load variations with respect to nominal forecasts. To mitigate the difficulties due to the size of the problem, the proposed methodology is divided in two steps. First, the network is partitioned into clusters, composed of several dispatchable and non dispatchable generators, storage systems, and loads. A clustering algorithm is designed with the aim of obtaining clusters with the following characteristics: (i) they must be compact, keeping the distance between generators and loads as small as possible; (ii) they must be able to internally balance load variations to the maximum possible extent. Once the network clustering has been completed, a two layer control system is designed. At the lower layer, a local Model Predictive Controller is associated to each cluster for managing the available generation and storage elements to compensate local load variations. If the local sources are not sufficient to balance the cluster's load variations, a power request is sent to the supervisory layer, which optimally distributes additional resources available from the other clusters of the network. To enhance the scalability of the approach, the supervisor is implemented relying on a fully distributed optimization algorithm. The IEEE 118-bus system is used to test the proposed design procedure in a non trivial scenario.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
