CLC number: TP273
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2018-11-27
Cited: 0
Clicked: 7726
Ke-yong Hu, Wen-juan Li, Li-dong Wang, Shi-hua Cao, Fang-ming Zhu, Zhou-xiang Shou. Energy management for multi-microgrid system based on model predictive control[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(11): 1340-1351.
@article{title="Energy management for multi-microgrid system based on model predictive control",
author="Ke-yong Hu, Wen-juan Li, Li-dong Wang, Shi-hua Cao, Fang-ming Zhu, Zhou-xiang Shou",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="11",
pages="1340-1351",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601826"
}
%0 Journal Article
%T Energy management for multi-microgrid system based on model predictive control
%A Ke-yong Hu
%A Wen-juan Li
%A Li-dong Wang
%A Shi-hua Cao
%A Fang-ming Zhu
%A Zhou-xiang Shou
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 11
%P 1340-1351
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601826
TY - JOUR
T1 - Energy management for multi-microgrid system based on model predictive control
A1 - Ke-yong Hu
A1 - Wen-juan Li
A1 - Li-dong Wang
A1 - Shi-hua Cao
A1 - Fang-ming Zhu
A1 - Zhou-xiang Shou
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 11
SP - 1340
EP - 1351
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601826
Abstract: To reduce the computation complexity of the optimization algorithm used in energy management of a multi-microgrid system, an energy optimization management method based on model predictive control is presented. The idea of decomposition and coordination is adopted to achieve the balance between power supply and user demand, and the power supply cost is minimized by coordinating surplus energy in the multi-microgrid system. The energy management model and energy optimization problem are established according to the power flow characteristics of microgrids. A dual decomposition approach is imposed to decompose the optimization problem into two parts, and a distributed predictive control algorithm based on global optimization is introduced to achieve the optimal solution by iteration and coordination. The proposed method has been verified by simulation, and simulation results show that the proposed method provides the demanded energy to consumers in real time, and improves renewable energy efficiency. In addition, the proposed algorithm has been compared with the particle swarm optimization (PSO) algorithm. The results show that compared with PSO, the proposed method has better performance, faster convergence, and significantly higher efficiency.
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