Collaborative Research: Improving Power Grids Weather Resilience through Model-free Dimension Reduction and Stochastic Search for Optimal Hardening

合作研究：通过无模型降维和随机搜索优化强化来提高电网的耐候能力

• 批准号: 1923247
• 负责人: Jianhui Zhou
• 金额: $ 15.71万
• 依托单位: University of Virginia Main Campus
• 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: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923247&HistoricalAwards=false
• 关键词: Collaborative; Research; Improving; Power; Grids

Severe weather is the leading cause of power outages in the United States, leading to tremendous economic and social costs. Given days-ahead weather forecasts, hardening power lines can significantly mitigate cascading outrage risks, shorten the time to restore electricity, and therefore improve power grids weather resilience. Due to resource constraints, such as time and/or budget, it is important to identify the optimal hardening plan with the constraints. Given the large number of power lines on the grids, searching for the optimal subset of power lines of hardening is challenging. In this research, a novel searching approach for optimal hardening plan with practical constraints is studied. The method takes the advantages of high-dimensional data analysis from statistics and discrete optimization via stochastic simulation from operations research and makes fundamental theoretical and algorithmic advances to optimize power grids hardening plans and reduce cascading power outage risks in severe weather. Because of the broad economic and societal importance of power grids, the research has broader impact on the welfare and security of the country.This project develops a model-free dimension reduction method to improve the computational efficiency of discrete optimization via simulation for improving power grids weather resilience through optimal power line hardening. The dimension reduction method ranks the subsets of the transmission lines according to power loss caused by their disconnection from power grids. The new method does not need to assume a specific statistical joint model between power loss and all considered line combinations and only uses the marginal information of the line combinations, and thus are generally applicable for hardening planning. A new stochastic search algorithm that exploits the dimension reduction capability is then proposed to reduce the size of the effective search space given resource constraints when preparing for severe weather conditions. To improve computational efficiency, the stochastic search algorithm uses an informative Gaussian mixture prior to incorporate dimension reduction results while achieving asymptotical convergence and constructs a hierarchical sampling distribution using dimension reduction results. The model-free dimension reduction and stochastic search algorithm are generally applicable to a variety of other disciplines, e.g., the protection of other critical civil infrastructures such as road networks.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.

恶劣天气是美国停电的主要原因，导致巨大的经济和社会成本。考虑到几天前的天气预报，加固电力线可以显著降低连锁愤怒风险，缩短恢复电力的时间，从而提高电网的天气恢复能力。由于时间和/或预算等资源限制，确定具有约束的最佳强化计划非常重要。考虑到电网中有大量的电力线，寻找最优的电力线硬化子集是一个挑战。研究了一种具有实际约束条件的最优硬化方案搜索方法。该方法利用统计学的高维数据分析和运筹学的随机模拟离散优化的优势，在优化电网加固方案和降低恶劣天气下的级联停电风险方面取得了基础性的理论和算法进步。由于电网具有广泛的经济和社会重要性，因此该研究对国家的福利和安全具有更广泛的影响。本项目开发了一种无模型降维方法，通过模拟提高离散优化的计算效率，通过优化电力线硬化来提高电网的天气恢复能力。降维法根据输电线路脱离电网后的功率损失对输电线路子集进行排序。该方法不需要假设功率损耗与所有考虑的线材组合之间的特定统计联合模型，只使用线材组合的边缘信息，因此一般适用于硬化规划。在此基础上，提出了一种利用降维能力的随机搜索算法，在给定资源约束的情况下，在准备恶劣天气条件时减小有效搜索空间的大小。为了提高计算效率，随机搜索算法在实现渐近收敛的同时，先使用一个信息丰富的高斯混合来合并降维结果，并利用降维结果构建分层抽样分布。无模型降维和随机搜索算法一般适用于各种其他学科，例如其他关键民用基础设施的保护，如道路网络。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。