微电网的随机能源管理包括两点估计方法,移动存储和模糊的多目标增强灰狼优化器
Serajuddin Habibi1, Reza Effatnejad2, Mahdi Hedayati1
1Department of Electrical Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran.
Scientific reports
|January 18, 2024
概括
这项研究优化了使用新型灰狼优化器的可再生微电网能源管理,降低了成本和排放,同时增加了可再生能力. 动态存储和需求响应有效地减轻了不确定性.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 优化技术 优化技术
背景情况:
- 微电网对于整合可再生能源至关重要.
- 在不确定性下管理能源资源和需求是复杂的.
- 优化微电网运行需要平衡成本,排放和容量.
研究的目的:
- 开发可再生微电网的随机能源管理和调度策略.
- 为了最大限度地利用可再生能源的容量,同时最大限度地降低运营和排放成本.
- 评估动态存储和需求响应对微电网性能的影响.
主要方法:
- 使用两点估计方法 (2m+1 PEM) 来处理不确定性.
- 使用多目标增强灰狼优化器 (MOEGWO) 进行决策变量优化.
- 集成的物流混乱映射和模糊的决策,以提高优化.
主要成果:
- 与静态系统相比,动态移动储能系统 (MESS) 降低了高达34.78%的成本,并增加了7.62%的容量.
- 需求响应应用减轻了不确定性对成本和容量的负面影响.
- 在降低成本和提高容量方面,MOEGWO的表现优于多目标粒子群优化.
结论:
- 拟议的随机能源管理方法有效地将成本和排放量降到最低.
- 动态存储和需求响应对于强大的可再生微电网运行至关重要.
- MOEGWO是一个优越的算法,用于复杂的微电网优化问题.
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