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: 1662774
• 负责人: Ruiwei Jiang
• 金额: $ 21.55万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1662774&HistoricalAwards=false
• 关键词: Collaborative; Research; Enhancing; Power; System

As the backbone of the U.S. energy infrastructure, the electric grid transmits about 4 trillion kilowatthours of electricity to power the nation. The grid is vulnerable to a variety of weather-related disruptions. 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，电网向全国输送约4万亿千瓦时的电力。电网很容易受到各种与天气有关的干扰。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，评估和减轻与中断相关的风险和影响在计算上往往是难以实现的。该项目将推进分析模型和可扩展解决方案方法的使用，以帮助系统运营商更好地评估和减轻中断。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。该合同将研究一类新的数据驱动优化方法，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，pi将研究基于气象和历史传输可用性数据的电网中断概率建模。这些数据将被纳入分布式鲁棒优化（DRO）模型，以(a)进行风险评估分析，(b)加强灾前电网，(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方法将允许考虑指数数量的中断，以及它们发生的概率，从数据分析中推断出来。此外，本项目将研究基于混合整数规划的新的DRO解决方法。

项目成果

Ambiguous Chance-Constrained Binary Programs under Mean-Covariance Information

• DOI: 10.1137/17m1158707
• 发表时间: 2016-09
• 期刊: SIAM J. Optim.
• 作者: Yiling Zhang;Ruiwei Jiang;Siqian Shen

Distributionally Robust Contingency-Constrained Unit Commitment

• DOI: 10.1109/tpwrs.2017.2699121
• 发表时间: 2018
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Chaoyue Zhao;Ruiwei Jiang