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Front. Inform. Technol. Electron. Eng.  2018, Vol. 19 Issue (11): 1340-1352    
    
Energy management for multi-microgrid system based on model predictive control
Ke-yong HU, Wen-juan LI, Li-dong WANG, Shi-hua CAO, Fang-ming ZHU, Zhou-xiang SHOU
Qianjiang College, Hangzhou Normal University, Hangzhou 310018, China   MOE Key Laboratory of Special Purpose Equipment and Advanced Manufacturing Technology, Zhejiang University of Technology, Hangzhou 310014, China
School of Information Science and Engineering, Hangzhou Normal University, Hangzhou 311121, China
Energy management for multi-microgrid system based on model predictive control
Ke-yong HU, Wen-juan LI, Li-dong WANG, Shi-hua CAO, Fang-ming ZHU, Zhou-xiang SHOU
Qianjiang College, Hangzhou Normal University, Hangzhou 310018, China 
MOE Key Laboratory of Special Purpose Equipment and Advanced Manufacturing Technology, Zhejiang University of Technology, Hangzhou 310014, China
School of Information Science and Engineering, Hangzhou Normal University, Hangzhou 311121, China
 全文: PDF 
摘要： 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 mini-
mized 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.
关键词： Microgrids;  Energy management;  Predictive control;  Renewable energy;  Controllable energy    
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 mini-
mized 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.
Key words: Microgrids    Energy management    Predictive control    Renewable energy    Controllable energy
收稿日期: 2016-12-17 出版日期: 2019-06-13
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Ke-yong HU
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Li-dong WANG
Shi-hua CAO
Fang-ming ZHU
Zhou-xiang SHOU

引用本文:

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. Front. Inform. Technol. Electron. Eng., 2018, 19(11): 1340-1352.

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http://www.zjujournals.com/xueshu/fitee/CN/    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2018/V19/I11/1340

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