在极端天气的多维不确定性下的弹性电网基于层次深度Q网络的优化
Shimeng Dong1, Chao Wang1, Yan Zhang2
1The State Grid Electric Power Dispatching Control Center, Beijing, China.
Scientific reports
|July 10, 2025
概括
本研究介绍了一个深度Q网络 (DQN) 框架,以提高电网对抗极端天气和可再生能源整合挑战的弹性. 该模型显著降低了负载减排和成本,同时提高了系统可靠性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 极端天气事件和可再生能源的整合暴露了现代电力系统的漏洞.
- 增强的弹性,运营灵活性和网络安全对于电力系统稳定性至关重要.
- 目前的系统正在努力动态地适应天气引起的干扰和需求变化.
研究的目的:
- 开发一个创新的优化框架,以整合可再生能源和储能系统.
- 通过使用先进的决策模型,提高电力系统的弹性和运营灵活性.
- 为了应对来自天气事件和需求波动的不确定性,实现实时系统适应.
主要方法:
- 充分利用深度Q网络 (DQN) 为一个连贯的决策模型.
- 整合可再生能源,储能系统和弹性指标.
- 利用对现实的118节点分布网络的广泛案例研究.
主要成果:
- 实现了高达35%的负载减排.
- 在弹性得分上表现出30%的提升.
- 与传统方法相比,报告了1500万欧元的额外成本节约.
结论:
- 拟议的DQN框架为实时电力系统适应提供了一个强大的解决方案.
- 该模型有效地平衡了经济效率和系统可靠性.
- 这项研究为下一代电力系统的可持续和弹性能源管理设定了新的基准.
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