arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1909.02753eess.SYcs.SY
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闭环:电网动态状态估计与反馈优化

Closing the Loop: Dynamic State Estimation and Feedback Optimization of Power Grids

Miguel Picallo, Saverio Bolognani, Florian Dörfler

摘要:

本文研究了在线反馈优化问题,以实时求解电网交流最优潮流。这包括基于在电网上执行的实时测量在时变条件下朝向最佳设定点连续地驱动可控功率注入和负载。然而,而不是假设无噪声的完整状态测量,如最近的反馈优化方法,我们连接一个动态状态估计使用可用的测量,并研究其动态相互作用的优化方案。我们证明了这种互连的稳定性和收敛的期望状态估计和控制输入的真实状态值和最优设定点分别。此外,我们限制了由此产生的随机误差。最后,我们使用高分辨率的消费数据的测试用例上显示的方法的有效性。

英文摘要:

This paper considers the problem of online feedback optimization to solve the AC Optimal Power Flow in real-time in power grids. This consists in continuously driving the controllable power injections and loads towards the optimal set-points in time-varying conditions based on real-time measurements performed on the grid. However, instead of assuming noise-free full state measurement like recent feedback optimization approaches, we connect a dynamic State Estimation using available measurements, and study its dynamic interaction with the optimization scheme. We certify stability of this interconnection and the convergence in expectation of the state estimate and the control inputs towards the true state values and optimal set-points respectively. Additionally, we bound the resulting stochastic error. Finally, we show the effectiveness of the approach on a test case using high resolution consumption data.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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