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

• 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

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.

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.该项目将通过为操作员提供准确模拟压力重大的电网所需的计算工具来带来变革性的变化。这将通过将新机器学习技术与先进的非线性优化算法和新型电源系统建模方法相结合来实现。该项目的智力优点包括开发新的解决方案算法，以在极端事件中在电力系统中遇到的优化问题。该项目的更广泛影响包括减轻气候变化的影响以及开发针对电力系统弹性的视频游戏样式模拟的教育努力。本着公民科学的精神，玩家对这些模拟的解决方案将形成一个众包数据集，该数据集将用于训练项目研究工作中的机器学习模型，结束研究和教育之间的循环。该项目的目的是开发基本理论和算法，以解决极端事件中强调的强调条件。准确地建模这些压力很大的条件会产生随机的混合构成非线性优化问题，这些问题与现有理论和算法相关。现有方法使用对非典型条件的不适用的假设来应对这些挑战，从而导致了极端事件，从而产生了较大的错误和弹性计划，这些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，这些算法可以准确地对功率流非线性，不确定性和离散决策进行建模，而无需牺牲计算速度和可靠性。为了实现这一目标，该项目将改善并结合替代功率流模型，混合智能编程求解器，机器学习技术和非线性优化，以创建量身定制的理论和算法，以供弹性应用。该奖项反映了NSF的法定任务，并通过使用基金会的知识优点和广泛的criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia rection the Action the奖项。

项目成果

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
• 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
• 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)
• 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