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Collaborative Research: Learning and Optimizing Power Systems: A Geometric Approach

协作研究：学习和优化电力系统：几何方法

基本信息

批准号：
1807142
负责人：
Baosen Zhang
金额：
$ 22.5万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1807142&HistoricalAwards=false
关键词：
Collaborative Research Learning Optimizing Power

项目摘要

The transformations of the electrical grid present a plethora of challenges to system operators and utilities. They must adapt to manage a set of highly uncertain and distributed resources such as electric vehicles and solar PVs, while at the same time operating a grid infrastructure that was designed decades ago. These challenges are particularly acute in the distribution system, where the networks are traditionally not monitored closely, and operators lack the essential information to obtain an accurate real-time operational state of the system. At the same time, the number of outages in distribution systems has started to increase as the system ages, and the loads become more dynamic. The goal of this proposal is to overcome these challenges by developing novel algorithms and new insights that increase the efficiency and resilience of the distribution systems. Educational activities would be developed around these research thrusts to ensure diverse student participation and outreach to the broader community. The project focuses on three thrusts: i) system topology estimation using the wealth of data made available by smart meters and other sensors, where the network may contain loops and the data may be highly heterogeneous; ii) characterization of the feasibility of operating points using a new geometric understanding of power flow that leads to provably efficient and optimal algorithms; and iii) restoration of service right after outages through line switching by using the results from the first two thrusts. These investigations bring in tools from power system analysis, optimization, and statistical learning to enable fundamental advances in the distribution system operations. In particular, these thrusts allow us to leverage recent advances in both technology and theory to develop timely and rigorous algorithms that solve some pressing engineering problems for the power grids. Successful application of our proposed project will allow distribution system operators to answer various "what now" and "what if" questions deriving from those highly volatile grids with large amounts of distributed resources.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.

电网的转型给系统运营商和公用事业带来了大量挑战。他们必须适应管理一系列高度不确定和分布式的资源，如电动汽车和太阳能光伏，同时运营几十年前设计的电网基础设施。这些挑战在配电系统中尤其严重，在配电系统中，网络传统上不被密切监控，并且运营商缺乏必要的信息来获得系统的准确实时操作状态。与此同时，随着系统老化，配电系统中的停电次数开始增加，并且负荷变得更加动态。该提案的目标是通过开发新的算法和新的见解来克服这些挑战，以提高配电系统的效率和弹性。将围绕这些研究重点开展教育活动，以确保不同的学生参与和扩大到更广泛的社区。该项目侧重于三个方面：i）利用智能电表和其他传感器提供的大量数据进行系统拓扑估计，其中网络可能包含环路，数据可能高度异构; ii）利用对潮流的新几何理解表征操作点的可行性，从而产生可证明有效和最佳的算法;以及iii）通过使用来自前两个推力的结果，通过线路切换在停电之后立即恢复服务。这些调查带来了来自电力系统分析，优化和统计学习的工具，以实现配电系统运营的根本进步。特别是，这些推力使我们能够利用技术和理论的最新进展，开发及时和严格的算法，解决电网的一些紧迫的工程问题。我们建议的项目的成功应用将使配电系统运营商能够回答各种“现在怎么办”和“如果怎么办”的问题，这些问题来自于那些具有大量分布式资源的高度不稳定的电网。该奖项反映了NSF的法定使命，并已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。