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arXiv 1104.0183eess.SYcs.SYmath.OCphysics.soc-ph
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精确且高效的算法用于发现输电电网中风力发电的极端随机事件

Exact and Efficient Algorithm to Discover Extreme Stochastic Events in Wind Generation over Transmission Power Grids

Los Alamos National Laboratory(洛斯阿拉莫斯国家实验室);New Mexico Consortium(新墨西哥联盟);University of Arizona(亚利桑那大学);The University of Texas at Austin(德克萨斯大学奥斯汀分校)

Michael Chertkov, Mikhail Stepanov, Feng Pan, Ross Baldick

AI总结:

提出一种精确且高效的算法,在比例控制假设和对数凹概率分布下,发现输电电网中与风力发电间歇性相关的极端随机事件。

摘要:

在本文中,我们延续了[M. Chertkov, F. Pan, M. Stepanov, Predicting Failures in Power Grids: The Case of Static Overloads, IEEE Smart Grid 2011]的思路,提出了一种新算法,用于发现静态电网中与风力发电机间歇发电相关的最可能极端随机事件。在标准发电的比例控制(或其他低参数控制)以及可再生能源发电的对数凹概率分布(假设从风力预报中已知)的情况下,该算法变得精确且高效(多项式时间)。我们以IEEE RTS-96输电模型为例,分别添加10%、20%和30%的可再生能源发电,展示了该算法发现极端事件的能力。我们观察到,如果可再生能源的渗透足够多样化和分布广泛,故障概率可能增加,但也可能随着渗透率的提高而降低。

英文摘要:

In this manuscript we continue the thread of [M. Chertkov, F. Pan, M. Stepanov, Predicting Failures in Power Grids: The Case of Static Overloads, IEEE Smart Grid 2011] and suggest a new algorithm discovering most probable extreme stochastic events in static power grids associated with intermittent generation of wind turbines. The algorithm becomes EXACT and EFFICIENT (polynomial) in the case of the proportional (or other low parametric) control of standard generation, and log-concave probability distribution of the renewable generation, assumed known from the wind forecast. We illustrate the algorithm's ability to discover problematic extreme events on the example of the IEEE RTS-96 model of transmission with additions of 10%, 20% and 30% of renewable generation. We observe that the probability of failure may grow but it may also decrease with increase in renewable penetration, if the latter is sufficiently diversified and distributed.

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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