Optimal Energy Procurement for Geo-distributed Data Centers in Multi-timescale Electricity Markets
Optimal Energy Procurement for Geo-distributed Data Centers in Multi-timescale Electricity Markets
复制标题
多时间尺度电力市场中地理分布式数据中心的最优能源采购
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
P. Mieghem
中科院分区：
文献类型：
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作者：
Hale Çetinay;Saleh Soltan;F. Kuipers;Gil Zussman;P. Mieghem
本文研究了非线性交流（AC）和线性化直流（DC）潮流模型下电网的连锁故障。我们通过数值模拟，对比了四种测试网络中，在这两种潮流模型下，单条线路故障后连锁故障的演变情况。连锁故障模拟表明，直流模型所基于的假设（例如，忽略功率损耗、无功功率流和电压幅值变化）可能导致不准确且过于乐观的连锁故障预测。特别是在大型网络中，直流模型往往会高估供电率（连锁故障结束时的供电量与初始需求量之比）。因此，使用直流模型进行连锁故障预测可能会错误地反映连锁故障的严重程度。
In this paper, we study cascading failures in power grids under the nonlinear AC and linearized DC power flow models. We numerically compare the evolution of cascades after single line failures under the two flow models in four test networks. The cascade simulations demonstrate that the assumptions underlying the DC model (e.g., ignoring power losses, reactive power flows, and voltage magnitude variations) can lead to inaccurate and overly optimistic cascade predictions. Particularly, in large networks the DC model tends to overestimate the yield (the ratio of the demand supplied at the end of the cascade to the initial demand). Hence, using the DC model for cascade prediction may result in a misrepresentation of the gravity of a cascade.