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Reduced-Order Dispatch Model for Simulating Marginal Emissions Factors for the United States Power Sector

机译:用于模拟美国电力部门边际排放因子的降序调度模型

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摘要

This study develops a reduced-order power plant dispatch model and uses it to simulate marginal emissions factors (MEFs) for the 2014-2017 United States (U.S.) electric grid at the North American Electric Reliability Corporation (NERC) regional level. MEFs help quantify the health, environmental, and climate change impacts caused by changes in marginal net electricity consumption, which could result, for example, from new technologies or policies. This study develops the model, validates it against historical data, and compares its simulated MEFs against historically derived regression-based MEFs. Our method accurately reproduces CO2, SO2, and NOx emissions for multiple U.S. NERC regions and years and enables us to analyze future scenarios that are absent from the historical data. Though historically derived regression-based MEFs are generally more accurate, our simulated MEFs provide a more nuanced picture of how clusters of low- or high-emitting power plants of similar production cost create large swings in MEFs throughout the day. Policymakers could use these dynamic MEFs to target demand-reduction strategies at high-emissions portions of the power plant merit order.
机译:这项研究开发了降阶发电厂调度模型,并使用它来模拟北美电力可靠性公司(NERC)区域级别的2014-2017年美国(美国)电网边际排放因子(MEF)。 MEF有助于量化由例如新技术或新政策引起的边际净电力消耗变化对健康,环境和气候变化的影响。这项研究开发了该模型,并根据历史数据对其进行了验证,并将其模拟的MEF与历史得出的基于回归的MEF进行了比较。我们的方法可以准确地再现多个美国NERC地区和年份的CO2,SO2和NOx排放量,并使我们能够分析历史数据中缺少的未来情景。尽管从历史上得出的基于回归的MEF通常更准确,但我们的模拟MEF可以更细致地了解生产成本相似的低排放或高排放发电厂集群如何在一天中造成MEF大幅波动。政策制定者可以使用这些动态的MEF,以电厂绩效指标中高排放部分的减少需求战略为目标。

著录项

  • 来源
    《Environmental Science & Technology》 |2019年第17期|10506-10513|共8页
  • 作者单位

    Carnegie Mellon Univ Dept Engn & Publ Policy Pittsburgh PA 15213 USA;

    Stanford Univ Dept Energy Resources Engn Stanford CA 94305 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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