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.

电网的转换给系统操作员和实用程序带来了许多挑战。他们必须适应管理一组高度不确定和分布的资源，例如电动汽车和太阳能PV，同时运营数十年前设计的网格基础设施。这些挑战在分配系统中尤为严重，在分配系统中，网络传统上不密切监视，并且运营商缺乏基本信息来获得系统的准确实时操作状态。同时，随着系统年龄的增长，分销系统中的中断数量已经开始增加，并且负载变得更加动态。该提案的目的是通过开发新颖的算法和新见解来克服这些挑战，从而提高分配系统的效率和弹性。将围绕这些研究作用开发教育活动，以确保学生参与并向更广泛的社区进行宣传。该项目着重于三个推力：i）使用智能电表和其他传感器提供的大量数据的系统拓扑估算，在该数据中，网络可能包含循环，并且数据可能是高度异质的； ii）使用对功率流的新几何理解来表征操作点的可行性，从而导致可证明有效和最佳算法； iii）通过使用前两个推力的结果通过线路切换后立即恢复服务。这些调查带来了电力系统分析，优化和统计学习的工具，以实现分配系统运营的基本进步。特别是，这些推力使我们能够利用技术和理论的最新进展来开发及时，严格的算法，从而解决了电网的一些紧迫的工程问题。我们提出的项目的成功应用将使分配系统运营商能够回答各种“现在”和“如果”问题，这些问题是从那些具有大量分布资源的高度挥发性网格中得出的。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。