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

AitF：智能电网中的算法挑战：控制、优化

基本信息

批准号：
1637598
负责人：
Steven Low
金额：
$ 75万
依托单位：
California Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-10-01 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637598&HistoricalAwards=false
关键词：
AitF Algorithmic challenges smart grids

项目摘要

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.

该项目将解决电网转型所面临的算法挑战。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。未来的网格将更加分布式、动态和开放，在稳定、可靠、安全和高效的运行中出现了令人生畏的挑战。这个项目将推动控制，优化和学习的边界，以开发实际的解决方案，其中一些困难。它将推进一般网络物理系统科学及其在智能电网中的应用。它将通过将研究与教育课程紧密结合以及对女学生和少数民族学生的培训来支持教育和多样性。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。具体来说，该项目将重点关注智能电网等网络物理网络面临的三个核心算法挑战：控制、优化和学习。首先，该项目将开发一种基于优化的方法来设计信息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，本计画将发展一个新的指数规划的凸松弛层次，其基础是相对熵最佳化。这将立即产生一个全新的方法来解决最优潮流（OPF）的问题，这是众多的电力系统应用的基础，一般是非凸和NP-难的。第三，该项目将开发方法来学习一个接近最优的策略，尽管在运行时无法访问目标函数。这将允许电力系统在真实的时间内“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在真实的时间内获得。

项目成果

期刊论文数量（72）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Scalable Reinforcement Learning for Multiagent Networked Systems

DOI：
10.1287/opre.2021.2226
发表时间：
2019-12
期刊：
Oper. Res.
影响因子：
0
作者：
Guannan Qu;A. Wierman;N. Li
通讯作者：
Guannan Qu;A. Wierman;N. Li

Black-Box Acceleration of Monotone Convex Program Solvers

单调凸程序求解器的黑盒加速

DOI：
10.1287/opre.2022.2352
发表时间：
2022
期刊：
Operations Research
影响因子：
2.7
作者：
London, Palma;Vardi, Shai;Eghbali, Reza;Wierman, Adam
通讯作者：
Wierman, Adam

Control Regularization for Reduced Variance Reinforcement Learning

DOI：
发表时间：
2019-05
期刊：
影响因子：
0
作者：
Richard Cheng;Abhinav Verma;G. Orosz;Swarat Chaudhuri;Yisong Yue;J. Burdick
通讯作者：
Richard Cheng;Abhinav Verma;G. Orosz;Swarat Chaudhuri;Yisong Yue;J. Burdick

Distributed load-side control: Coping with variation of renewable generations

分布式负荷侧控制：应对可再生能源发电的变化