Collaborative Research: Enhancing Power System Resilience via Data-Driven Optimization

协作研究：通过数据驱动优化增强电力系统的弹性

• 批准号: 2037540
• 负责人: Chaoyue Zhao
• 金额: $ 9.34万
• 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: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2037540&HistoricalAwards=false
• 关键词: Collaborative; Research; Enhancing; Power; System

As the backbone of the U.S. energy infrastructure, the electric grid transmits power to the nation, with a revenue of around 400 billion dollars annually. The electric grid is vulnerable to a variety of weather-related natural disasters. The evaluation and mitigation of disruption-related risks and impacts are often computationally prohibitive due to the complexity of the power system, uncertainty of weather conditions, and the combinatorial nature of component failures. This project will advance the use of analytical models and scalable solution methods to assist system operators to better evaluate and mitigate disruptions. The PIs, as well as their graduate students, will collaborate with a U.S. Department of Energy national laboratory, which will facilitate connections with power systems operators.This award will study a new class of data-driven optimization methodologies to support strategic and operational planning in power systems management. As part of this research, the PIs will study probabilistic modeling of electricity grid disruptions based on meteorological and historical transmission availability data. These data will be incorporated in distributionally robust optimization (DRO) models to (a) conduct risk assessment analysis, (b) harden pre-disaster electricity grid, (c) take corrective actions during disasters, and (d) conduct post-disaster self-healing and system restoration. The DRO approach will allow the consideration of an exponential number of disruptions, as well as their probabilities of occurring, to be inferred from the analysis of the data. In addition, this project will investigate new DRO solution approaches based on mixed-integer programming.

电网作为美国能源基础设施的支柱，向全国输送电力，每年收入约4000亿美元。 电网很容易受到各种与天气有关的自然灾害的影响。 由于电力系统的复杂性、天气条件的不确定性以及组件故障的组合性质，对与中断相关的风险和影响的评估和缓解通常在计算上难以实现。 该项目将推进分析模型和可扩展解决方案方法的使用，以帮助系统运营商更好地评估和减轻干扰。 PI 及其研究生将与美国能源部国家实验室合作，该实验室将促进与电力系统运营商的联系。该奖项将研究一类新的数据驱动优化方法，以支持战略和运营规划电力系统管理。 作为这项研究的一部分，PI 将根据气象和历史传输可用性数据研究电网中断的概率模型。 这些数据将纳入分布式鲁棒优化（DRO）模型中，以（a）进行风险评估分析，（b）加固灾前电网，（c）在灾害期间采取纠正措施，以及（d）进行灾后自救- 治疗和系统恢复。 DRO 方法将允许考虑指数数量的中断及其发生的概率，并从数据分析中推断出来。此外，该项目还将研究基于混合整数编程的新 DRO 解决方案。

项目成果

Distributionally Robust Distribution Network Configuration Under Random Contingency

随机意外情况下的分布式鲁棒配电网络配置

• DOI: 10.1109/tpwrs.2020.2973596
• 发表时间: 2020-09
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Babaei, Sadra;Jiang, Ruiwei;Zhao, Chaoyue
• 通讯作者: Zhao, Chaoyue

A Data-Driven Model of Virtual Power Plants in Day-Ahead Unit Commitment

日前机组承诺中虚拟电厂的数据驱动模型

• DOI: 10.1109/tpwrs.2018.2890714
• 发表时间: 2019-11
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Babaei, Sadra;Zhao, Chaoyue;Fan, Lei
• 通讯作者: Fan, Lei

Distributionally Robust Optimal Power Flow with Uncertain Renewable Energy Output

可再生能源输出不确定的分布鲁棒最优潮流

• 发表时间: 2023-06-24
• 作者: Jia Yang;Jun Song;Chaoyue Zhao
• 通讯作者: Chaoyue Zhao