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边际相对较低。空间需求响应（DR），即控制整个地理区域的电力需求的方法，可用于提高电力系统稳定边际，并且可能比维持电力系统稳定的其他方法更便宜和/或对环境的影响更小。此外，增加DR的潜在用途会增加其价值，使负载聚合器和消费者更有吸引力地投资于能够实现DR的传感和通信技术，从而使所有DR应用受益。通过该项目获得的结果将为能源政策提供信息，例如，设计新的电力市场和/或市场外产品，以补偿负荷，以提高电力系统的稳定性。这项工作将有助于培养一名博士生，PI将为电力市场和优化的新研究生课程开发课程模块。此外，方法和结果将通过“空间DR情况说明书”传达给从业者。研究人员将调查现有的稳定裕度措施，并制定一份简短的措施清单，在准确性和计算复杂性之间表现出良好的权衡。然后，研究人员将制定空间DR问题的变体，考虑不同程度的现实性和复杂性。他们将开发算法来解决空间DR问题的变体，专注于以牺牲解决精度为代价提高可解性的近似的发展，例如，简化稳定边界的表示。此外，研究团队将对开发的算法进行分析和经验评估，以确定预期和最坏情况下的性能（在稳定裕度改进方面）。实证测试还将允许定量探索各种主题，包括：(i)柔性负载的参数如何影响DR提高电力系统稳定裕度的能力；（ii）可再生能源如何影响稳定边际，以及如何利用空间DR来减轻这些影响，从而提高可再生能源的渗透率；(3)空间DR值随可再生能源发电水平增加的变化规律。

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.