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CAREER: Overcoming Nonlinearities, Uncertainties, and Discreteness to Mitigate the Impacts of Extreme Events on Electric Power Systems

职业：克服非线性、不确定性和离散性，减轻极端事件对电力系统的影响

基本信息

批准号：
2145564
负责人：
Daniel Molzahn
金额：
$ 50万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-02-01 至 2027-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145564&HistoricalAwards=false
关键词：
CAREER Overcoming Nonlinearities Uncertainties Discreteness

项目摘要

This NSF CAREER project aims to develop algorithms for optimizing the planning and operation of electric power systems in the context of extreme events such as wildfires, hurricanes, evacuations, etc. The project focuses on three key computational challenges: nonlinearities associated with the physical models of electric grids, uncertainties from wind and solar generators and failures of system components, and discrete choices such as where to upgrade infrastructure. The project will bring transformative change by providing operators with the computational tools needed to accurately model heavily stressed power grids. This will be achieved by combining new machine learning techniques with advanced nonlinear optimization algorithms and novel power system modeling methods. The project’s intellectual merits include the development of new solution algorithms for the optimization problems encountered in power systems during extreme events. The broader impacts of the project include mitigating the impacts of climate change as well as educational efforts to develop video game style simulations focused on power system resiliency. In the spirit of citizen science, the players' solutions to these simulations will form a crowdsourced dataset that will be used to train the machine learning models in the project's research efforts, closing the loop between research and education.The goal of this project is to develop the fundamental theory and algorithms for addressing the heavily stressed conditions inherent to power systems during extreme events. Accurately modeling these heavily stressed conditions yields stochastic mixed-integer nonlinear optimization problems that are intractable with existing theory and algorithms. Existing approaches address these challenges using assumptions that are inapplicable for the atypical conditions inherent to extreme events, resulting in large errors and resiliency plans that fail to adequately reduce the impacts of extreme events. This project will develop new algorithms that can accurately model power flow nonlinearities, uncertainties, and discrete decisions without sacrificing computational speed and reliability. To accomplish this, the project will improve and combine alternative power flow models, mixed-integer programming solvers, machine learning techniques, and nonlinear optimization to create tailored theory and algorithms for resiliency applications.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.

这个NSF CAREER项目旨在开发算法，用于在野火，飓风，疏散等极端事件的背景下优化电力系统的规划和运行。该项目侧重于三个关键的计算挑战：与电网物理模型相关的非线性，风能和太阳能发电机的不确定性以及系统组件的故障，和离散的选择，如在哪里升级基础设施。该项目将通过为运营商提供精确建模压力巨大的电网所需的计算工具来带来变革。这将通过将新的机器学习技术与先进的非线性优化算法和新的电力系统建模方法相结合来实现。该项目的智力优势包括为极端事件期间电力系统中遇到的优化问题开发新的解决方案算法。该项目更广泛的影响包括减轻气候变化的影响，以及开发专注于电力系统弹性的视频游戏风格模拟的教育工作。本着公民科学的精神，参与者对这些模拟的解决方案将形成一个众包数据集，用于在项目的研究工作中训练机器学习模型，闭合研究和教育之间的循环。该项目的目标是开发基础理论和算法，以解决极端事件期间电力系统固有的严重压力条件。精确建模这些严重的压力条件下产生的随机混合整数非线性优化问题，是棘手的与现有的理论和算法。现有的方法使用不适用于极端事件固有的非典型条件的假设来应对这些挑战，导致大的错误和弹性计划无法充分减少极端事件的影响。该项目将开发新的算法，可以准确地模拟潮流的非线性，不确定性和离散决策，而不牺牲计算速度和可靠性。为了实现这一目标，该项目将改进并联合收割机替代潮流模型、混合整数规划求解器、机器学习技术和非线性优化，为弹性应用创建量身定制的理论和算法。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Restoring AC Power Flow Feasibility from Relaxed and Approximated Optimal Power Flow Models

从松弛和近似最优潮流模型恢复交流潮流的可行性

DOI：
10.23919/acc55779.2023.10156521
发表时间：
2023
期刊：
Proceedings of the American Control Conference
影响因子：
0
作者：
Taheri, Babak;Molzahn, Daniel K.
通讯作者：
Molzahn, Daniel K.

Optimizing Transmission Infrastructure Investments to Support Line De-energization for Mitigating Wildfire Ignition Risk

DOI：
10.48550/arxiv.2203.10176
发表时间：
2022-03
期刊：
ArXiv
影响因子：
0
作者：
A. Kody;Ryan Piansky;D. Molzahn
通讯作者：
A. Kody;Ryan Piansky;D. Molzahn

Improving distribution system resilience by undergrounding lines and deploying mobile generators

通过埋设线路和部署移动发电机来提高配电系统的弹性

DOI：
10.1016/j.epsr.2022.108804
发表时间：
2023
期刊：
Electric Power Systems Research
影响因子：
3.9
作者：
Taheri, Babak;Molzahn, Daniel K.;Grijalva, Santiago
通讯作者：
Grijalva, Santiago

Sharing the Load: Considering Fairness in De-energization Scheduling to Mitigate Wildfire Ignition Risk using Rolling Optimization

DOI：
10.1109/cdc51059.2022.9993295
发表时间：
2022-04
期刊：
2022 IEEE 61st Conference on Decision and Control (CDC)
影响因子：
0
作者：
A. Kody;Amanda M. West;D. Molzahn
通讯作者：
A. Kody;Amanda M. West;D. Molzahn

Power systems optimization under uncertainty: A review of methods and applications

DOI：
10.1016/j.epsr.2022.108725
发表时间：
2023-01
期刊：
Electric Power Systems Research
影响因子：
3.9
作者：
Line A. Roald;David Pozo;A. Papavasiliou;D. Molzahn;J. Kazempour;A. Conejo
通讯作者：
Line A. Roald;David Pozo;A. Papavasiliou;D. Molzahn;J. Kazempour;A. Conejo