CAREER: Synthesis of Feedback-based Online Algorithms for Power Grids

该CAREER提案专注于电网，旨在将基础理论和算法转化为分布式能源（DER）的突破性实时优化和控制方法。在这方面，总体目标是克服与大规模整合减少灾害风险机制相关的现有技术和业务障碍，其中：（a）以通常的商业实践部署DER已经降低了电力质量和可靠性，（B）现有的网络优化方法可能无法在与具有DER的电力系统的动态相匹配的时间尺度上提供解决方案，以及（c）用户偏好和舒适度的综合模型可能无法真实地捕捉用户的目标。该研究计划寻求从经济优化和本地控制之间的时间尺度分离的范式转变-在当今的配电网中占主导地位，其中校正和本地化规则作为实时电压调节和辅助服务提供的基础-到DER积极参与电网运营并利用实时网络级协调来寻求提高效率和可靠性的操作。DER协调是经过设计的，以便DER可以学习最大限度地提高用户的偏好，同时帮助系统级频率和电压控制。一项综合教育和推广计划将通过夏季科学、技术、工程和数学（STEM）研究学院和大学预科发展方案讲座吸引初中和高中学生。为了连接研究和教育，PI将开发以网络在线优化和电力系统优化为主题的课程，并将促进本科生的研究。科罗拉多大学博尔德分校将成立一个优化和学习工作组，与自治系统跨学科研究主题协同，将校园内的教师和学生聚集在一起，促进多学科研究和教育。拟议的研究利用动态环境中运行的网络的时变优化模型，并寻求开发具有紧密集成的反馈和学习组件的实时优化架构。所提出的基于反馈的在线算法具有以下关键属性：i）原则性的算法步骤采用来自网络的测量以绕过对网络模型的需要; ii）算法通过在在线决策算法的执行期间从用户的反馈学习用户的效用函数而将人包括在循环中; iii）算法在电力网络的闭环中实现，以确认动态并有效地充当反馈控制器;以及iv）算法促进低复杂性、分布式和可扩展的架构。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This CAREER proposal focuses on power grids, and aims to translate foundational theory and algorithms into breakthrough real-time optimization and control approaches for distributed energy resources (DERs). In this context, the overarching goal is to overcome current technological and operational barriers associated with the large-scale integration of DERs, where: (a) the deployment of DERs with business-as-usual practices has decreased power-quality and reliability, (b) existing network optimization approaches may fail to provide solutions at a time scale that matches the dynamics of power systems with DERs, and (c) synthetic models for users’ preferences and comfort may not capture the users’ goals truthfully. The research plan seeks a shift from a paradigm with a time-scale separation between economic optimization and local control – predominant in today's distribution grids, where corrective and localized rules serve as a basis for real-time voltage regulation and ancillary-service provisioning – to operations where DERs actively partake into grid operations and leverage real-time network-level coordination to seek increased efficiency and reliability. DER coordination is engineered so that DERs can learn to maximize users' preferences, while aiding system-level frequency and voltage control. An integrated education and outreach plan will engage middle- and high-school students through a summer Science, Technology, Engineering and Mathematics (STEM) Research Academy and lectures for the Pre-Collegiate Development Program. To bridge research and education, the PI will develop courses on the themes of online optimization for networks and optimization of power systems, and will promote undergraduate student research. A working group on optimization and learning will be created at the University of Colorado Boulder in synergy with the Autonomous Systems Interdisciplinary Research Theme, to bring together faculty and students across the campus and stimulate multi-disciplinary research and education.The proposed research leverages time-varying optimization models for networks operating in dynamic environments, and seeks to develop real-time optimization architectures with tightly-integrated feedback and learning components. The proposed feedback-based online algorithms have the following key attributes: i) Principled algorithmic steps employ measurements from the network to bypass the need for a network model; ii) Algorithms include humans in the loop by learning the users' utility functions from users' feedback during the execution of the online decision algorithm; iii) Algorithms are implemented in closed loop with the power network to acknowledge dynamics and effectively act as feedback controllers; and, iv) Algorithms promote low-complexity, distributed, and scalable architectures. Fundamental tradeoffs between convergence rate, tracking of time-varying optimal solutions, maximum constraint violation, and computational complexity of the algorithms will be offered.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.