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arXiv 2011.14937eess.SYcs.SY
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基于交叉熵的配电网可靠性有效评估

Efficient Assessment of Electricity Distribution Network Adequacy with the Cross-Entropy Method

Julian N. Betge, Barbera Droste, Jacco Heres, Simon H. Tindemans

摘要:

识别配电网络中的未来拥塞点是配电系统运营商面临的重要挑战。解决这一挑战的一个行之有效的方法是使用未来需求的概率模型来评估配电网的充足性。然而,计算成本可能成为一个严峻的挑战,评估大型概率电力需求预测模型与长期预测的视野。在本文中,蒙特卡罗方法的开发,以提高计算效率,获得资产超载概率从自底向上的随机需求模型。交叉熵优化的重要性抽样与传统的蒙特卡罗抽样进行对比。所提出的方法的基准测试结果表明,在这项工作中引入的重要性抽样为基础的方法是适合于估计罕见的超载概率的资产与少数客户。

英文摘要:

Identifying future congestion points in electricity distribution networks is an important challenge distribution system operators face. A proven approach for addressing this challenge is to assess distribution grid adequacy using probabilistic models of future demand. However, computational cost can become a severe challenge when evaluating large probabilistic electricity demand forecasting models with long forecasting horizons. In this paper, Monte Carlo methods are developed to increase the computational efficiency of obtaining asset overload probabilities from a bottom-up stochastic demand model. Cross-entropy optimised importance sampling is contrasted with conventional Monte Carlo sampling. Benchmark results of the proposed methods suggest that the importance sampling-based methods introduced in this work are suitable for estimating rare overload probabilities for assets with a small number of customers.

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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