一个信息差距决策理论和改进的基于梯度的优化器,用于在配电网络中强大优化可再生能源系统
Fude Duan1, Ali Basem2, Sadek Habib Ali3
1School of Intelligent Transportation, Nanjing Vocational College of Information Technology, Jiangsu, 210000, Nanjing, China.
Scientific reports
|January 2, 2025
概括
这项研究优化了可再生能源在配电网络中的整合,使用了一种新的多目标优化器. 分散的可再生能源比混合系统提供更高的性能,在需求不确定性下提高电网可靠性.
科学领域:
- 电气工程 电气工程
- 优化理论 优化理论
- 可再生能源系统可再生能源系统
背景情况:
- 辐射分布网络需要有效地整合可再生能源.
- 可再生发电和网络需求的不确定性带来了重大挑战.
- 现有的优化方法可能会与复杂的多目标问题作斗争.
研究的目的:
- 开发一个强大的模糊多目标框架,以优化分散和混合可再生能源资源.
- 提出一种新的多目标改进梯度基础优化器 (MOIGBO),以提高融合.
- 分析资源分配 (分散与混合) 和不确定性对网络性能的影响.
主要方法:
- 实施了一个强大的模糊的多目标优化框架.
- 开发了一种新的MOIGBO,将罗森布洛克的技术结合起来.
- 信息差距决策理论 (IGDT) 用于确定最大不确定性半径 (MUR) 和系统稳定性.
主要成果:
- MOIGBO有效地平衡了目标,在帕雷托方面实现了最佳的解决方案.
- 与混合配置相比,分散的可再生能源资源分配显示出更高的性能.
- 在30%的不确定性风险下实现了最大的系统稳定性,并为资源生产和负载需求制定了特定的MUR.
结论:
- 拟议的MOIGBO是优化可再生能源在配电网络中的整合的强大工具.
- 分散的可再生能源系统在改善不确定性下网络运行方面比混合系统更有效.
- 该框架为管理可再生能源预测和需求模式的不确定性提供了有价值的见解.
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