EAGER: Conditional Risk Measures for Reducing Cascading Failures

EAGER：减少级联故障的条件风险措施

• 批准号: 1451047
• 负责人: Ruiwei Jiang
• 金额: $ 30万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2015-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451047&HistoricalAwards=false
• 关键词: EAGER; Conditional; Risk; Measures; Reducing

In an interdependent system (e.g., power grids and financial markets), an undesirable event could spread and cause even more severe economic and/or social consequences, also known as cascading failures. Typical examples include the blackout in Northeastern America triggered by a tripped transmission line in 2003, and the recent global recession stemming from the U.S. subprime mortgage market. To protect an interdependent system, a system operator need to better understand the threats of cascading failures when undesirable conditions are realized, and accordingly make better operational and recourse plans. This award supports exploratory research to study conditional risk measures on quantifying the threats of cascading failures, and show how they can help improve operational decision makings in many practical applications. The successful implementation of this project will provide effective decision support tools for system operators to identify cascading failures and reduce potential impacts. At meanwhile, this project will provide new teaching materials for undergraduate- and graduate-level courses on related research topics. Underrepresented Ph.D. students will be motivated to participate in the research and educational activities. This project aims to explore a class of conditional risk measures which generalize many classical risk measures including the Conditional Value-at-Risk. Because of the quotient expressions and the distributional ambiguity, it is technically challenging to incorporate these conditional risk measures in stochastic optimization models. This project will explore reformulation and approximation approaches in a data-driven context. More specifically, distributional robust versions of the conditional risk measures and their reformulations under various distributional ambiguity settings will be explored, sample approximations of the conditional risk measures based on available historical data will be investigated, and effective solution algorithms to handle the conditional risk measures will be formulated. The successful completion of this project will lead to new knowledge in the stochastic optimization literature, including functional optimization analyses, the value of historical data, and cutting planes for stochastic integer programs.

在一个相互依赖的系统中（例如，电网和金融市场），不良事件可能蔓延并导致甚至更严重的经济和/或社会后果，也称为级联故障。典型的例子包括2003年由输电线路跳闸引发的美国东北部停电，以及最近由美国次级抵押贷款市场引发的全球经济衰退。为了保护一个相互依赖的系统，系统运营商需要更好地了解级联故障的威胁时，实现不良条件，并相应地制定更好的操作和补救计划。该奖项支持探索性研究，研究量化级联故障威胁的条件风险措施，并展示它们如何在许多实际应用中帮助改善运营决策。该项目的成功实施将为系统运营商提供有效的决策支持工具，以识别级联故障并减少潜在影响。同时，该项目将为相关研究课题的本科和研究生课程提供新的教材。博士学位不足鼓励学生参与研究及教育活动。本项目旨在探讨一类条件风险度量，它推广了包括条件风险价值在内的许多经典风险度量。由于商表达式和分布模糊性，将这些条件风险度量纳入随机优化模型在技术上具有挑战性。这个项目将探索在数据驱动的背景下的重新制定和近似方法。更具体地说，分布鲁棒版本的条件风险措施和他们的重新制定下各种分布模糊设置将进行探讨，样本近似的条件风险措施的基础上，现有的历史数据将进行调查，有效的解决方案算法来处理的条件风险措施将制定。该项目的成功完成将导致随机优化文献中的新知识，包括函数优化分析，历史数据的价值，以及随机整数规划的切割平面。