AitF: Algorithmic challenges in smart grids: control, optimization & learning

该项目将解决电网改造背后的算法挑战。在可持续发展的推动下，社会正处于能源系统历史性转型的风口浪尖。未来电网的稳定、可靠、安全和高效运行面临着艰巨的挑战，未来电网将更加分布式、动态和开放。这个项目将突破控制、优化的界限，并学会为其中一些困难开发实用的解决方案。它将推动一般网络物理系统的科学及其在智能电网中的应用方面的最先进水平。它将通过将研究与教育课程以及对女性和少数民族学生的培训紧密结合起来，支持教育和多样性。将在该项目中开发的理论和算法将直接有助于能源系统向更可持续的未来的历史性转变。具体地说，该项目将专注于网络物理网络(如智能电网)面临的三个核心算法挑战：控制、优化和学习。首先，这个项目将开发一种基于优化的方法来设计数字物理系统的反馈控制器，使闭环系统渐近稳定，并且闭环系统的每个平衡点都是给定优化问题的最优解。其次，该项目将开发一种新的基于相对熵优化的指数规划的凸松弛层次。这将立即产生一种解决最优潮流(OPF)问题的全新方法，该问题是许多电力系统应用的基础，通常是非凸的和NP-Hard的。第三，该项目将开发有效地学习接近最优的策略的方法，尽管在运行时无法访问目标函数。这将使电力系统能够实时“学习优化”，解决电力系统中最大的挑战之一--关于系统的数据太昂贵或不可能实时获得。

This project will tackle the algorithmic challenges underlying the transformation of the power grid. Society is at the cusp of a historic transformation of our energy systems, driven by sustainability. Daunting challenges arise in the stable, reliable, secure, and efficient operation of the future grid that will be much more distributed, dynamic, and open. This project will push the boundaries of control, optimization, and learning to develop practical solutions to some of these difficulties. It will advance state of the art in both the science of general cyber-physical systems and its application to smart grids. It will support education and diversity through a tight integration of the research with educational courses and the training of female and minority students. The theory and algorithms to be developed in this project will contribute directly towards the historic transformation of energy systems to a more sustainable future. Specifically, the project will focus on three core algorithmic challenges facing cyber-physical networks such as a smart grid: control, optimization, and learning. First, this project will develop an optimization-based approach to the design of feedback controllers for cyber-physical systems so that the closed-loop system is asymptotically stable, and every equilibrium point of the closed-loop system is an optimal solution of a given optimization problem. Second, this project will develop a new hierarchy of convex relaxations for exponential programs based on relative entropy optimization. This will immediately yield a fundamentally new approach for solving Optimal Power Flow (OPF) problems, which underlie numerous power system applications and are non-convex and NP-hard in general. Third, this project will develop methods to learn a policy that is near-optimal efficiently, despite not having access to the objective function at run time. This will allow power systems to "learn to optimize" in real time, addressing one of the biggest challenges in power systems -- that data about the system is too expensive or impossible to obtain in real time.