arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2211.05855eess.SYcs.AIcs.SY
收藏

基于深度强化学习的TSO-DSO协同拥塞管理的鲁棒N-1 secure HV网格灵活性估计

Robust N-1 secure HV Grid Flexibility Estimation for TSO-DSO coordinated Congestion Management with Deep Reinforcement Learning

University of Kassel(卡塞尔大学);Fraunhofer Institute for Energy Economics and Energy System Technology (IEE)(弗劳恩霍夫能源经济与能源系统技术研究所)

Zhenqi Wang, Sebastian Wende-von Berg, Martin Braun

摘要:

目前,来自高压电网中分布式能源的电能质量灵活性在TSO电网中的电网拥塞管理中起着更加关键和重要的作用。这项工作提出了一种多阶段深度强化学习方法,以估计TSO-DSO接口处的PQ灵活性(PQ区域),并以某种方式识别每个操作点的DER PQ设定点,可以协调网状高压电网中的DER,为输电网提供灵活性。在估计过程中,我们考虑的稳态电网的限制和鲁棒性的电压曲线对不确定性和N-1的安全标准,热线负载,现实生活中的电网运营规划应用程序至关重要。使用深度强化学习(DRL)进行PQ灵活性估计是同类中的第一个。此外,我们的方法考虑N-1安全标准的网状电网和鲁棒性对不确定性直接在优化任务提供了一个新的视角,除了常见的松弛模式,在寻找一个解决方案的数学最优潮流(OPF)。最后,在PQ区域估计的计算效率的显着改善是所提出的方法的亮点。

英文摘要:

Nowadays, the PQ flexibility from the distributed energy resources (DERs) in the high voltage (HV) grids plays a more critical and significant role in grid congestion management in TSO grids. This work proposed a multi-stage deep reinforcement learning approach to estimate the PQ flexibility (PQ area) at the TSO-DSO interfaces and identifies the DER PQ setpoints for each operating point in a way, that DERs in the meshed HV grid can be coordinated to offer flexibility for the transmission grid. In the estimation process, we consider the steady-state grid limits and the robustness in the resulting voltage profile against uncertainties and the N-1 security criterion regarding thermal line loading, essential for real-life grid operational planning applications. Using deep reinforcement learning (DRL) for PQ flexibility estimation is the first of its kind. Furthermore, our approach of considering N-1 security criterion for meshed grids and robustness against uncertainty directly in the optimization tasks offers a new perspective besides the common relaxation schema in finding a solution with mathematical optimal power flow (OPF). Finally, significant improvements in the computational efficiency in estimation PQ area are the highlights of the proposed method.

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。

↑