Pursuing patterns in the statistics of utility data to analyze grid resilience

追踪公用事业数据统计模式以分析电网弹性

• 批准号: 2153163
• 负责人: Ian Dobson
• 金额: $ 34万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153163&HistoricalAwards=false
• 关键词: Pursuing; patterns; statistics; utility; data

This NSF project analyzes statistical patterns that have occurred in blackouts or outages of our nation's bulk electrical power transmission grid. The patterns will be extracted from standard utility data that record details of these outages. The project aims to explore and understand these statistical patterns to improve the grid resilience. For example, quantifying the typical patterns in component outages and recovery when severe weather hits the grid can inform making the grid more resilient. Quantifying the most likely patterns of how blackouts start and spread can detect the vulnerabilities to a cascading sequence of outages of grid components. The project complements the knowledge of individual blackouts and their mechanisms in the power engineering field with new approaches based on the statistical patterns occurring across all blackout-related outages in detailed utility data. Processing this real data is foundational to quantifying and preventing blackouts and grounding the field in observed data. New metrics calculated from observed data can transform detecting and help in fixing the most likely grid vulnerabilities. The intellectual merits of the project include deploying an interdisciplinary range of statistical and engineering approaches to analyze, confirm and explain patterns observed in detailed utility outage data. The broader impacts of the project include ways to improve the resilience of the nation’s transmission grid by quantifying and mitigating blackouts. The project will improve classroom education by developing interactive teaching methods.The project will statistically test heavy-tailed probability distributions, design robust resilience metrics, model outage timing with Poisson processes, extract outage and restore processes from data, adapt ideas from motifs from network theory to contingency lists, and identify mechanisms of outages from data. The project will also pursue an entirely new explanation of how the Zipf distribution of event size can arise from the power system engineering that responds to cascades of outages by mitigating those cascades. The expected outcomes include practical resilience event definitions, useful resilience metrics, and expanded risk-based contingency lists for transmission systems that can be calculated from standard utility data and that can be used to help utilities and regulators quantify and mitigate blackout risks.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.

这个NSF项目分析了我国大容量电力传输网停电或停电的统计模式。这些模式将从记录这些中断细节的标准公用事业数据中提取。该项目旨在探索和理解这些统计模式，以提高电网的弹性。例如，当恶劣天气袭击电网时，量化组件中断和恢复的典型模式可以使电网更具弹性。量化停电如何开始和蔓延的最有可能的模式可以检测到电网组件的级联停电序列的漏洞。该项目补充了个人停电及其机制的知识，在电力工程领域的新方法的基础上发生在所有停电相关的停电详细的公用事业数据的统计模式。处理这些真实的数据是量化和防止停电以及根据观测数据接地的基础。根据观察到的数据计算的新指标可以改变检测，并帮助修复最可能的网格漏洞。该项目的智力优势包括部署一系列跨学科的统计和工程方法，以分析，确认和解释在详细的公用事业停电数据中观察到的模式。该项目更广泛的影响包括通过量化和减轻停电来提高国家输电网的弹性。该项目将通过开发交互式教学方法来改善课堂教学，该项目将统计测试重尾概率分布，设计鲁棒的弹性度量，用泊松过程建模中断时间，从数据中提取中断和恢复过程，将网络理论的主题思想应用于应急列表，并从数据中识别中断机制。该项目还将寻求一种全新的解释，即事件大小的Zipf分布如何从电力系统工程中产生，该工程通过减轻这些级联来响应停电级联。预期成果包括实用的复原力事件定义、有用的复原力指标、和扩大的风险-基于输电系统的应急清单，可根据标准公用事业数据计算，并可用于帮助公用事业和监管机构量化和减轻停电风险。该奖项反映了NSF的法定使命，并通过利用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

项目成果

How Long is a Resilience Event in a Transmission System?: Metrics and Models Driven by Utility Data

传输系统中的弹性事件有多长？：公用事业数据驱动的指标和模型

• DOI: 10.1109/tpwrs.2023.3292328
• 发表时间: 2024
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Dobson, Ian;Ekisheva, Svetlana
• 通讯作者: Ekisheva, Svetlana

Assessing Transmission Resilience during Extreme Weather with Outage and Restore Processes

通过中断和恢复过程评估极端天气期间的传输弹性

• DOI: 10.1109/pmaps53380.2022.9810645
• 发表时间: 2022
• 期刊: Probability Methods Applied to Power Systems
• 作者: Ekisheva, Svetlana;Dobson, Ian;Rieder, Rachel;Norris, Jack
• 通讯作者: Norris, Jack

Towards using utility data to quantify how investments would have increased the wind resilience of distribution systems

• DOI: 10.1109/tpwrs.2023.3342729
• 发表时间: 2023-06
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Arslan Ahmad;I. Dobson

Grid Restoration After Extreme Weather Events

极端天气事件后的电网恢复

• DOI: 10.1109/isgteurope56780.2023.10407350
• 发表时间: 2023
• 期刊: Innovative Smart Grid Technologies Conference Europe
• 作者: Ekisheva, Svetlana;Pratt, Donna K.;Kachadurian, Maria;Martin, William G.;Norris, Jack;Dobson, Ian
• 通讯作者: Dobson, Ian

Models, Metrics, and Their Formulas for Typical Electric Power System Resilience Events

典型电力系统弹性事件的模型、指标及其公式

• DOI: 10.1109/tpwrs.2023.3300125
• 发表时间: 2023
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Dobson, Ian