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: 1662589
• 负责人: Chaoyue Zhao
• 金额: $ 18.63万
• 依托单位: Oklahoma State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1662589&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.

作为美国能源基础设施的支柱，电网向全国输送电力，每年的收入约为4000亿美元。电网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，评估和缓解与中断相关的风险和影响通常在计算上是困难的。该项目将推进分析模型和可扩展解决方案方法的使用，以协助系统操作员更好地评估和减少中断。PI和他们的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的优化方法，以支持电力系统管理中的战略和运营规划。作为这项研究的一部分，PIS将根据气象和历史传输可用性数据研究电网中断的概率建模。这些数据将被纳入分布式稳健优化(DRO)模型，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)进行灾后自我修复和系统恢复。DRO方法将允许从数据分析中推断出指数级数量的中断及其发生的概率。此外，该项目还将研究基于混合整数规划的新的DRO解决方案。

项目成果

Resilient Transmission Hardening Planning in a High Renewable Penetration Era

• DOI: 10.1109/tpwrs.2018.2872893
• 发表时间: 2019-03
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: A. Bagheri;Chaoyue Zhao;F. Qiu;Jianhui Wang

Backup capacity coordination with renewable energy certificates in a regional electricity market

区域电力市场中备用容量与可再生能源证书的协调

• DOI: 10.1080/24725854.2018.1440672
• 发表时间: 2018
• 期刊: IISE Transactions
• 影响因子: 2.6
• 作者: Zhou, Yingjue;Liu, Tieming;Zhao, Chaoyue
• 通讯作者: Zhao, Chaoyue

Data-driven risk-averse stochastic optimization with Wasserstein metric

• DOI: 10.1016/j.orl.2018.01.011
• 发表时间: 2018-03
• 期刊: Oper. Res. Lett.
• 作者: Chaoyue Zhao;Yongpei Guan

Allocating distributed generators for resilient distribution system under uncertain probability distribution of natural disasters

自然灾害概率分布不确定下的弹性配电系统分布式发电机配置

• DOI: 10.1109/pesgm.2017.8274133
• 发表时间: 2017
• 期刊: 2017 IEEE Power & Energy Society General Meeting
• 作者: Babaei, Sadra;Zhao, Chaoyue;Ding, Tao
• 通讯作者: Ding, Tao

Distributionally Robust Reliability Assessment for Transmission System Hardening Plan Under $N-k$ Security Criterion

• DOI: 10.1109/tr.2019.2893138
• 发表时间: 2019-04
• 期刊: IEEE Transactions on Reliability
• 影响因子: 5.9
• 作者: A. Bagheri;Chaoyue Zhao