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CLC number: TP273

On-line Access: 2018-12-14

Received: 2016-12-17

Revision Accepted: 2017-04-17

Crosschecked: 2018-11-27

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Ke-yong Hu


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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.11 P.1340-1351


Energy management for multi-microgrid system based on model predictive control

Author(s):  Ke-yong Hu, Wen-juan Li, Li-dong Wang, Shi-hua Cao, Fang-ming Zhu, Zhou-xiang Shou

Affiliation(s):  Qianjiang College, Hangzhou Normal University, Hangzhou 310018, China; more

Corresponding email(s):   hukeyong@yeah.net

Key Words:  Microgrids, Energy management]> Renewable energy, MControllable energy

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",
publisher="Zhejiang University Press & Springer",

%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

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

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.


摘要:为降低多微电网能量管理优化算法计算复杂度,提出一种基于模型预测控制能量优化管理方法。首先,采用分解协调实现供需平衡,协调多微电网系统剩余能量,使供电成本最小化。然后,根据多微电网潮流特性,建立能量管理模型并提出能量优化问题。接着,采用对偶分解法将优化问题分为两部分,引入基于全局优化的分布式预测控制算法,经过算法迭代与协调实现最优解。仿真结果表明,该方法能实时向用户提供所需能源,提高可再生能源利用率。此外,与粒子群算法(particle swarm optimization,PSO)进行比较。比较结果表明,该算法具有更好性能、更快收敛速度和更高效率。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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