Fast Distributed Model Predictive Control for DC Microgrids

被引：2
|
作者：
Marepalli, Lalit Kishore [1 ]
Gajula, Kaushik [1 ]
Herrera, Luis [1 ]
机构：
[1] Univ Buffalo, Dept Elect Engn, Buffalo, NY 14260 USA
来源：
2020 IEEE 21ST WORKSHOP ON CONTROL AND MODELING FOR POWER ELECTRONICS (COMPEL) | 2020年
关键词：
model predictive control; alternating direction method of multipliers; distributed optimization; dc microgrids; MPC;
D O I：
10.1109/compel49091.2020.9265746
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Model Predictive Control (MPC) is used to regulate the voltage and desired current flows in a dc microgrid. The optimization problem is solved using a distributed approach, the alternating direction method of multipliers (ADMM). A four node dc electric system with simplified dynamics and a six node network with 6 state source models are used as case studies. Computation times are compared using CVX, quadprog and mpcqpsolver. Real-time control hardware in the loop (CHIL) implementation using Opal RT and Simulink is demonstrated. The system can be extended to include higher dynamic models such as Modular Multilevel Converter (MMC) stations.
引用
收藏
页码：419 / 424
页数：6
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