EAGER: Renewables: Demand response algorithms to improve electric power system stability margins

可再生能源渗透率高的电力系统运行的稳定裕度相对较低。空间需求响应(DR)，即控制整个地理区域的电力需求的方法，可以用来提高电力系统的稳定裕度，并且可能比其他维持电力系统稳定的方法更便宜和/或对环境的影响更小。此外，增加灾难恢复的潜在用途会增加灾难恢复的价值，使其对负载聚合器和消费者更具吸引力，投资于支持灾难恢复的传感和通信技术，进而使所有灾难恢复应用受益。通过该项目获得的结果将为能源政策提供参考，例如，设计新的电力市场和/或市场外产品来补偿负荷，以提高电力系统的稳定性。这项工作将有助于培训一名博士生，国际电力协会将为新的电力市场和优化研究生课程开发一个课程模块。此外，这些方法和结果将通过“空间DR事实说明书”传达给从业者。调查人员将调查现有的稳定裕度衡量标准，并制定一份在准确性和计算复杂性之间进行良好权衡的衡量标准简短清单。然后，研究人员将考虑不同程度的现实主义和复杂性，制定空间DR问题的变体。他们将开发算法来解决空间DR问题的变体，专注于开发以牺牲解精度为代价提高可解性的近似方法，例如，稳定性裕度的简化表示法。此外，研究小组将对开发的算法进行分析和经验评估，以确定预期和最差情况下的性能(在稳定性边际改进方面)。经验测试还将允许定量探讨各种主题，包括：(1)灵活负荷的参数如何影响可再生能源提高电力系统稳定裕度的能力；(2)可再生能源如何影响稳定裕度，以及如何利用空间可再生能源来缓解这些影响，从而提高可再生能源的渗透率；以及(3)空间可再生能源价值如何随着可再生发电水平的增加而变化。

Power systems with high penetrations of renewables operate with relatively low stability margins. Spatial demand response (DR), i.e., ways of controlling the demand for electricity throughout a geographic area, can be used to improve power system stability margins and may be less expensive and/or less environmentally impactful than alternative approaches to maintain power system stability. Additionally, increasing the potential uses of DR increases its value, making it more attractive for load aggregators and consumers to invest in sensing and communication technologies that enable it, in turn benefiting all DR applications. Results obtained through the the project will inform energy policy, for example, the design of new electricity markets and/or out-of market products to compensate loads for improving power system stability. The work will contribute to the training of one PhD student and the PI will develop a course module for a new graduate course on electricity markets and optimization. Additionally, the methods and results will be communicated to practitioners through "Spatial DR Fact Sheets."The investigators will survey existing stability margin measures and develop a short list of measures that exhibit a good trade-off between accuracy and computational complexity. The researchers will then formulate variants of the spatial DR problem, considering varying levels of realism and complexity. They will develop algorithms to solve variants of the spatial DR problem, focusing on the development of approximations that improve solvability at the expense of solution accuracy, for example, simplified representations of stability margins. Additionally, the research team will analytically and empirically evaluate the developed algorithms to determine expected and worst-case performance (in terms of stability margin improvement). Empirical testing will also allow for quantitatively exploring a variety of topics including: (i) how the parameters of flexible load impact the ability of DR to improve power system stability margins; (ii) how renewables impact stability margins and how spatial DR can be used to mitigate those impacts, enabling higher penetrations of renewables; and (iii) how the value of spatial DR changes as the level of renewable generation increases.