Analysis of Microgrid Energy Management Under the Background of Energy Internet

Guodong ZHANG; Kai LIU

doi:10.12096/j.2096-4528.pgt.18239

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Analysis of Microgrid Energy Management Under the Background of Energy Internet

Energy Internet | 更新时间：2025-06-06

- Analysis of Microgrid Energy Management Under the Background of Energy Internet
- Power Generation Technology Vol. 40, Issue 1, Pages: 17-21(2019)
- 作者机构：
  
  1.山东科技大学电气信息系, 山东省济南市 253500
  2.洛阳供电公司, 河南省洛阳市 471000
- 作者简介：
- 基金信息：
  
  Ministry of Education's Cooperative Education Program Project(201702064021);Research Project of Jinan Campus of Shandong University of Science and Technology(JNJG2017203)
- DOI：10.12096/j.2096-4528.pgt.18239
  CLC：
- Received：19 November 2018，
  
  Published：26 February 2019
- 稿件说明：
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Guodong ZHANG, Kai LIU. Analysis of Microgrid Energy Management Under the Background of Energy Internet[J]. Power Generation Technology, 2019, 40(1): 17-21.
DOI：

Guodong ZHANG, Kai LIU. Analysis of Microgrid Energy Management Under the Background of Energy Internet[J]. Power Generation Technology, 2019, 40(1): 17-21. DOI： 10.12096/j.2096-4528.pgt.18239.

摘要

能源互联网背景下，可再生能源并网规模日益扩大。由于可再生能源发电功率具有很强的随机性，电力系统运行中出现了所谓“双侧随机问题”，影响安全稳定运行。如何对各种可再生能源进行经济有效的能量管理，是实现能源互联网的关键技术之一。为实现微电网运行的整体优化，对包含各种分布式电源的微电网，建立以综合发电成本最低、环境效益最好作为优化目标的多目标优化模型。利用遗传算法，进行能量管理的优化研究。以联网运行的含有多种分布式电源的微电网为算例进行仿真计算，仿真结果表明了优化模型及算法的有效性。

Abstract

Under the background of energy internet

the scale of renewable energy interconnection is increasing day by day. Due to the strong randomness of renewable energy generation power

the so-called "bilateral stochastic problems" appears in the operation of the power system

which affects the safety and stability. How to manage all kinds of renewable energy economically and effectively is one of the key technologies to realize the energy internet. In order to realize the overall optimization of microgrid operation

a multi-objective optimization model was established for microgrid including various distributed generators (DG) with the lowest comprehensive power generation cost and the best environmental benefits as the optimization objectives. The genetic algorithm is used to optimize the energy management. The simulation results of a microgrid with multiple DGs running on the grid show the effectiveness of the optimization model and algorithm.

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references

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