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

doi:10.1631/FITEE.1601826

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

Regular Papers | Updated：2022-05-19

- Energy management for multi-microgrid system based on model predictive control
- 基于模型预测控制的多微电网系统能量管理
- Frontiers of Information Technology & Electronic Engineering Vol. 19, Issue 11, Pages: 1340-1351(2018)
- Affiliations：
  
  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
- Author bio：
  
  [ "Ke-yong HU, E-mail: hukeyong@yeah.net" ]
- Funds：
- DOI：10.1631/FITEE.1601826
  CLC： TP273
- Received：17 December 2016，
  
  Revised：2018-11-27，
  
  Published：2018-11
- Accepted：
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Ke-yong HU, Wen-juan LI, Li-dong WANG, et al. Energy management for multi-microgrid system based on model predictive control[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(11): 1340-1351.
DOI：

Ke-yong HU, Wen-juan LI, Li-dong WANG, et al. Energy management for multi-microgrid system based on model predictive control[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(11): 1340-1351. DOI： 10.1631/FITEE.1601826.

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。

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.

关键词

Keywords

references

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