Supervised model predictive control of large‐scale electricity networks via clustering methods

Alessio La Bella; Pascal Klaus; Giancarlo Ferrari‐TrecateRiccardo Scattolini

首页> 外文期刊>Optimal Control Applications and Methods >Supervised model predictive control of large‐scale electricity networks via clustering methods

【24h】

Supervised model predictive control of large‐scale electricity networks via clustering methods

机译：Supervised model predictive control of large‐scale electricity networks via clustering methods

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

Abstract This article 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 nondispatchable 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 nontrivial scenario.

著录项

来源
《Optimal Control Applications and Methods》 |2022年第1期|44-64|共21页
作者
Alessio La Bella; Pascal Klaus; Giancarlo Ferrari‐TrecateRiccardo Scattolini;
展开▼
作者单位

Informazione e Bioingegneria, Politecnico di Milano;

École Polytechnique Fédérale de Lausanne;

展开▼
收录信息
原文格式 PDF
正文语种英语
中图分类应用数学;
关键词
graph clustering; large‐scale networks; power balancing; predictive control;

Supervised model predictive control of large‐scale electricity networks via clustering methods

摘要

著录项

相关主题

期刊订阅