使用输入凸神经网络的暂时稳定性受限制的单元承诺
概括
本研究引入了一种新型的暂时稳定性受约束单元承诺 (TSC-UC) 模型,利用输入凸神经网络 (ICNNs). 该模型有效地评估了电力系统的短暂稳定性,提高了运行可靠性.
科学领域:
- 电气工程 电气工程
- 电力系统工程 电力系统工程
- 计算智能是一种计算智能.
背景情况:
- 传统的单位承诺 (UC) 模型往往忽视过渡性稳定性,可能导致运营风险.
- 评估短暂稳定性通常需要计算密集的时间域模拟或复杂的微分代数方程 (DAE) 离散.
- 将短暂稳定性约束集成到UC中,对于提高电力系统可靠性和安全性至关重要.
研究的目的:
- 开发一个计算效率高的暂时稳定性受约束单位承诺 (TSC-UC) 模型.
- 利用输入凸神经网络 (ICNN) 进行准确和快速的短暂稳定性评估.
- 在不依赖传统模拟方法的情况下,将基于ICNN的稳定性评估集成到UC框架中.
主要方法:
- 培训ICNN学习从预默认运行条件到过渡稳定性指数 (TSI) 的映射.
- 由于其凸的性质,将训练的ICNN编码为线性编程 (LP) 模型.
- 将TSC-UC模型分解为主问题和子问题 (网络可行性和暂时稳定性检查) 用于通过Benders分解进行代解决方案.
主要成果:
- 提出的基于ICNN的方法准确地评估了没有时间域模拟的瞬态稳定性.
- 经过培训的ICNN成功地被集成到UC模型中,作为一个精确的LP配方.
- 用Benders分解解决的分解TSC-UC模型有效地纳入了暂时稳定性约束.
结论:
- 开发的TSC-UC模型为电力系统运行规划提供了有效和高效的方法.
- 在优化框架内,ICNN的使用为实时暂时稳定性评估提供了一个有希望的替代方案.
- 提出的方法证明了在各种操作条件下确保电力系统稳定性的有效性.
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