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

本CAREER提案以电网为重点，旨在将基础理论和算法转化为分布式能源（DERs）的突破性实时优化和控制方法。在这方面，首要目标是克服目前与DERs大规模整合有关的技术和操作障碍，其中：(a)按常规做法部署可再生能源降低了电能质量和可靠性，(b)现有的网络优化方法可能无法在时间尺度上提供与可再生能源电力系统动态相匹配的解决方案，以及(c)针对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局部控制之间的时间尺度分离范式的转变-在今天的配电网中占主导地位，其中纠偏和局部规则作为实时电压调节和辅助服务供应的基础-到der积极参与电网运行并利用实时网络级协调以寻求提高效率和可靠性的操作。DER协调设计使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暑期科学、技术、工程和数学（STEM）研究所和大学前发展计划的讲座，吸引初高中学生。为了在研究和教育之间架起桥梁，学院将以网络在线优化和电力系统优化为主题开设课程，并将促进本科生的研究。科罗拉多大学博尔德分校将建立一个优化和学习工作组，以协同自主系统跨学科研究主题，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。提出的研究利用时变优化模型在动态环境中运行的网络，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)原则性算法步骤采用来自网络的测量来绕过对网络模型的需求；ii)算法在执行在线决策算法时，通过从用户反馈中学习用户效用函数，将人纳入循环；iii)算法与电网在闭环中实现，以识别动态并有效地充当反馈控制器；iv)算法促进低复杂性、分布式和可扩展的架构。将提供收敛速度、时变最优解跟踪、最大约束违反和算法计算复杂性之间的基本权衡。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.