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CAREER: Optimal Control of Energy Systems via Structured Neural Networks: A Convex Approach

职业：通过结构化神经网络优化能源系统控制：凸方法

基本信息

批准号：
1942326
负责人：
Baosen Zhang
金额：
$ 50万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942326&HistoricalAwards=false
关键词：
CAREER Optimal Control Energy Systems

项目摘要

The electric energy system is currently undergoing a period of unprecedented transformations. On the one hand, large-scale deployment of technologies such as rooftop solar and smart building management systems have the potential to make the power system more efficient, sustainable and reliable. On the other hand, achieving this promise has proven to be far from trivial, as many capabilities remain unused. Two primary systems of interest are the heating, ventilation, and air conditioning (HVAC) systems of commercial and industrial buildings, and distributed energy resources in the power distribution system. A fundamental challenge in controlling these systems is that their behaviors are often governed by complex dynamics with unknown parameters. For instance, the relationship between temperature setpoints in different zones and the HVAC power consumption is governed by a set of nonlinear high dimensional partial differential equations, whose parameters depend on detailed building characteristics that are difficult to measure in practice. Similarly, the distribution system is governed by nonlinear AC power flow equations, but since they are typically not monitored, their topology and line parameters are either not known or severely outdated.This CAREER proposal addresses this challenge by leveraging the significant amounts of measurement data that are now becoming available. Fundamentally different from many existing AI applications, the physical laws governing the behaviors of these systems---laws of thermodynamics for heat transfers in buildings and power flow equations---are well studied, but the system parameters are not known and cannot be easily measured. The goal of this project is to provide algorithms with provable guarantees that combine physical laws with data to safely and efficiently operate these energy systems. Specifically, we present a model-based framework that uses structured neural networks to achieve both model tractability and representability, by designing them to be convex from input to output. This project will tightly integrate research and education by working with the campus sustainability office and the local utility, thus training a generation of professionals qualified both in power systems and machine learning.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.

目前，电力系统正经历着前所未有的变革时期。一方面，屋顶太阳能和智能建筑管理系统等技术的大规模部署有可能使电力系统更加高效、可持续和可靠。另一方面，事实证明，实现这一承诺绝非易事，因为许多功能仍未使用。感兴趣的两个主要系统是商业和工业建筑的供暖、通风和空调(HVAC)系统，以及配电系统中的分布式能源。控制这些系统的一个根本挑战是，它们的行为往往由具有未知参数的复杂动力学所支配。例如，不同区域的温度设定值与暖通空调能耗之间的关系由一组非线性高维偏微分方程组决定，这些偏微分方程组的参数取决于实际中难以测量的详细建筑特性。同样，配电系统由非线性交流潮流方程控制，但由于它们通常不受监控，它们的拓扑和线路参数要么未知，要么严重过时。这份职业建议通过利用现在可用的大量测量数据来解决这一挑战。与许多现有的人工智能应用程序根本不同，控制这些系统行为的物理定律-建筑物内热传递的热力学定律和功率流方程-得到了很好的研究，但系统参数是未知的，也不容易测量。该项目的目标是为算法提供可证明的保证，将物理定律与数据相结合，以安全有效地运行这些能源系统。具体地说，我们提出了一个基于模型的框架，它使用结构化神经网络来实现模型的可处理性和代表性，通过将它们设计为从输入到输出的凸性。该项目将通过与校园可持续发展办公室和当地公用事业机构合作，将研究和教育紧密结合起来，从而培养一代在电力系统和机器学习方面都合格的专业人员。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。