An open-source extendable model and corrective measure assessment of the 2021 texas power outage
An open-source extendable model and corrective measure assessment of the 2021 texas power outage
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DOI：
10.1016/j.adapen.2021.100056
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发表时间：
2021-11-19
影响因子：
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通讯作者：
Xie, Le
中科院分区：
文献类型：
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作者：
Wu, Dongqi;Zheng, Xiangtian;Xie, Le
2021年2月，史无前例的冬季风暴袭击了德克萨斯州，由于大范围的发电能力中断和创纪录的电力需求，造成至少69人死亡和450万客户中断。虽然仍有许多关于德克萨斯州发生如此大范围停电的原因、方式和原因的调查，但更广泛的宏观能源界必须根据连贯的电网模型和数据集制定政策。在本文中，我们合作发布了一个开源可扩展模型，该模型是合成的，但仍然提供了实际能源网的真实表示，并附有开源跨域数据集。这个简化的合成模型是根据我们所知的最佳数据资源进行校准的。基于这种开源合成网格模型，研究人员可以定量评估各种政策对减轻此类极端事件影响的影响。作为一个例子，在本文中，我们批判性地评估了几个纠正措施，可以减轻停电在这种极端天气条件下。揭示了切负荷的地区差异。该分析还量化了几种纠正措施的敏感性，以减轻停电的严重程度，在能源不服务（ENS）。这一办法和方法可推广到经历重大能源组合转型的其他区域。
Unprecedented winter storms that hit across Texas in February 2021 have caused at least 69 deaths and 4.5 million customer interruptions due to the wide-ranging generation capacity outage and record-breaking electricity demand. While much remains to be investigated on what, how, and why such wide-spread power outages occurred across Texas, it is imperative for the broader macro energy community to develop insights for policy making based on a coherent electric grid model and data set. In this paper, we collaboratively release an open-source extendable model that is synthetic but nevertheless provides a realistic representation of the actual energy grid, accompanied by open-source cross-domain data sets. This simplified synthetic model is calibrated to the best of our knowledge based on published data resources. Building upon this open-source synthetic grid model, researchers could quantitatively assess the impact of various policies on mitigating the impact of such extreme events. As an example, in this paper we critically assess several corrective measures that could have mitigated the blackout under such extreme weather conditions. We uncover the regional disparity of load shedding. The analysis also quantifies the sensitivity of several corrective measures with respect to mitigating the severity of the power outage, as measured in Energy-not-Served (ENS). This approach and methodology are generalizable for other regions experiencing significant energy portfolio transitions.