Decentralized Model Predictive Control of Plug-in Electric Vehicles Charging based on the Alternating Direction Method of Multipliers

被引：0
|
作者：
Germana, Roberto [1 ]
Liberati, Francesco [1 ]
Di Giorgio, Alessandro [1 ]
机构：
[1] Sapienza Univ Rome, Dept Comp Control & Management Engn Antonio Ruber, Via Ariosto 25, I-00185 Rome, Italy
来源：
2020 28TH MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION (MED) | 2020年
基金：
欧盟地平线“2020”;
关键词：
D O I：
10.1109/med48518.2020.9183137
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a decentralized Model Predictive Control (MPC) for Plug-in Electric Vehicles (PEVs) charging, in presence of both network and drivers' requirements. The open loop optimal control problem at the basis of MPC is modeled as a consesus with regularization optimization problem and solved by means of the decentralized Alternating Direction Method of Multipliers (ADMM). Simulations performed on a realistic test case show the potential of the proposed control approach and allow to provide a preliminary evaluation of the compatibility between the required computational effort and the application in real time charging control system.
引用
收藏
页码：739 / 745
页数：7
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