Deterministic proxies for stochastic unit commitment during hurricanes-猫眼课题宝

DOI：

10.1049/gtd2.12107

发表时间：

2020-12

期刊：

IET Generation, Transmission & Distribution

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

F. Mohammadi;Fatemehalsadat Jafarishiadeh;Jiayue Xue;M. Sahraei-Ardakani;Ge Ou

恶劣的天气会破坏电网，威胁到电力供应的可靠性。在飓风的情况下，数十个元素可能会失败，这将导致停电。在这种情况下，预防性机组投入方法可以对概率故障预测进行建模并最大限度地减少停电。预防性随机机组组合是考虑故障预测以减少停电的有效方法。虽然随机机组组合产生高质量的解决方案，它是计算负担。因此，本文评估代理确定性方法与随机机组组合的解决方案的时间和质量相比，具有更轻的计算。调整后的旋转储备要求，工程判断为基础的规则，和强大的预防性操作的评估方法。数值结果得到的合成网格上的足迹得克萨斯州与2000辆巴士。结果表明，虽然一些代理方法，如标准旋转储备和调整旋转储备的6%至30%的旋转能力，可能不像随机方法那样有效，但其他方法，如鲁棒优化，提供了大部分的随机贝内，计算时间大大减少（85%）。蒙特卡罗模拟用于

Severe weather threatens the reliability of the power supply by damaging the network. In the case of hurricanes, tens of elements may fail, which would lead to power outages. Under such circumstances, preventive unit commitment methods can model the probabilistic failure forecasts and minimise the power outages. Preventive stochastic unit commitment is an effective method to consider failure forecasts to reduce the power outage. Although stochastic unit commitment produces high-quality solutions, it is computationally burdensome. Thus, this paper evaluates proxy deterministic methods with lighter computational compared with stochastic unit commitment on both the solution time and quality. Adjusted spinning reserve requirements, engineering judgment-based rules, and robust preventive operation are among the evaluated methods. Numerical results are obtained for the synthetic grid on the footprint of Texas with 2000 buses. The results suggest that while some proxy methods, such as standard spinning-reserve and adjusted spinning-reserve with 6% to 30% of the spinning capacity, may not be as effective as the stochastic method, others, such as robust optimisation, deliver the majority of the stochastic beneﬁts with substantially less (85%) computational time. Monte Carlo simulations are used to

Deterministic proxies for stochastic unit commitment during hurricanes

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