集成的数据驱动拓重建和风险意识重构,用于在不完全可观测条件下的弹性发电系统
Sipei Sun1, Ning Li1, Liang Zhang1
1State Grid Henan Electric Power Company Luohe Power Supply Company, Luohe, 462000, Henan, People's Republic of China.
Scientific reports
|August 20, 2025
概括
本研究引入了一个数据驱动的框架,以识别电网拓,评估风险,并优化重新配置,即使有不完整的数据. 该方法提高了电网弹性和在不确定的条件下管理.
科学领域:
- 电气工程 电气工程
- 数据科学数据科学数据科学
- 网络优化 网络优化
背景情况:
- 配电网络面临的挑战是来自SCADA,GIS和日志等各种来源的碎片化和不完整的数据.
- 准确的网络拓对于有效的风险评估和运营管理至关重要.
研究的目的:
- 开发一个统一的数据驱动框架,用于拓识别,风险量化和电力分发网络的重新配置优化.
- 为了应对现实世界电网运营中不完整和不整的数据所带来的挑战.
主要方法:
- 利用图形卷积网络 (GCN) 来从异质,不完整的数据中重建网络拓.
- 制定了一个基于场景的风险评估模型,包括当地脆弱性和空间风险传播.
- 开发了一种双层重配置优化模型,以尽量减少风险和切换成本,同时最大限度地恢复负载.
主要成果:
- 在合成分发系统上,峰节点风险降低了52.7%.
- 在90%的测试场景中恢复了超过94%的总需求.
- 在100个故障情况下,每个场景的可处理计算时间在9分钟以下.
结论:
- 拟议的框架为弹性分布网络管理提供了一个可扩展和适应的解决方案.
- 证明有能力有效处理不确定性和分散的数字基础设施.
- 通过详细的可视化,强调了结果的可解释性和操作相关性.
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