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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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基于改进的鱼优化算法,多微电网系统的合作最佳调度.

Dongmei Yan1,2,3, Hongkun Wang4,5,6, Yujie Gao1,2,3

  • 1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832003, China.

Scientific reports
|October 22, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的算法,以优化多微电网调度,提高可再生能源整合的准确性和速度. 该方法增加了所有参与者的收入,并减少了所有参与者的排放.

关键词:
鱼优化算法 鱼优化算法多个微电网系统的系统.最佳的安排时间表.分享能源储存共享能源储存.斯塔克尔伯格的游戏

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科学领域:

  • 电气工程 电气工程
  • 优化算法 优化算法
  • 可再生能源系统可再生能源系统

背景情况:

  • 在多微电网 (MMG) 系统中,新能源的高透率使最佳调度变得复杂.
  • 在MMG系统中,受试者之间的相互作用会影响解决方案的准确性和速度.

研究的目的:

  • 为共享能源存储的MMG系统开发一个优化的调度模型.
  • 为应对复杂的学科间互动和高可再生能源透率所带来的挑战.

主要方法:

  • 建立了一个双层最佳调度斯塔克尔伯格游戏模型,考虑共享的能量存储和主体间的互动.
  • 通过整合混乱地图,量子行为,高斯分布和非线性控制策略,提出了混乱高斯量子鱼优化算法 (CGQCOA).
  • 应用了改进的算法来解决优化调度模型.

主要成果:

  • 与原始算法相比,CGQCOA展示了优越的初始解决方案和增强的搜索功能,相对错误的显著减少 (高达98.74%).
  • 拟议的方法增加了Microgrid 1 (0.73%),Microgrid 2 (1.17%),Microgrid 3 (1.04%) 和共享存储 (1.91%) 的收入.
  • 在所有微电网中,污染物排放罚款成本显著下降 (5.9%至12.68%).

结论:

  • 开发的Stackelberg游戏模型和CGQCOA有效地解决了MMG系统的最佳调度问题.
  • 该方法提高了微电网和共享存储的运营收入,同时大幅减少了污染物排放.
  • 这些发现证实了在高可再生能源透率下,为高效和可持续的MMG运行提出的方法.