SCC-IRG Track1: ADVANCED LEARNING FOR ENERGY RISK TRACKING (ALERT)

SCC-IRG Track1：能源风险跟踪高级学习（警报）

• 批准号: 2125985
• 负责人: Mladen Kezunovic
• 金额: $ 149.88万
• 依托单位: Texas A&M Engineering Experiment Station
• 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: 2022-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125985&HistoricalAwards=false
• 关键词: SCC; IRG; Track1; ADVANCED; LEARNING

This NSF S&CC project aims to predict the State of Risk (SoR) of electricity outage occurrence and develop risk management and mitigation strategies to minimize the impact of outages. Currently, electric utilities are only able to reactively respond to outages. Consumer are left in a passive role of struggling to cope with the consequence without a preemptive option to manage the outage impacts. The project brings a transformative change that will allow utilities to predict outages, and then provide consumers with both individual and community mitigation measures. This will be achieved by increasing the S&CC awareness of how to deal with the outage impacts equitably and effectively. We will deploy advanced data analytics to train machine learning outage prediction algorithms using weather and historical outage data. The intellectual merits of the project include new risk prediction approaches, study of behavioral aspects of the outage prediction, and experiments that measure the effectiveness of predictive alert messages. Broader impacts include education and outreach efforts across PreK-20 students, their teachers and parents, through public services of museums and libraries by all-inclusive age-appropriate STEM programming. We will communicate with the broader community of citizens through the invited talks and videos at appropriate city offices in San Antonio, and at the headquarters of one of the major retail providers of electricity in Philadelphia. The emphasis on inclusive workforce development is broadly applicable and highly impactful to advance the S&CC human resource needs and resilience plans. To achieve the spatiotemporal prediction of the SoR, ALERT will perform integrative research by merging methodologies from several disciplines: a) Advanced Data Analytics (ADA); b) Social, Behavioral, and Economic Sciences (SBE); and c) Smart Grid Fundamentals (SGF). The project activity will integrate ADA and SGF data and physical power system models, respectively, and then design SBE interventions based on the survey and experimental data to define SoR models and make mitigation decisions to reduce outage risk. The innovation is in the physics-constrained and structured learning-based prediction of the SoR using big data from historical utility records, weather records, grid topology and vegetation data.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.

该NSF S CC项目旨在预测停电发生的风险状态（SoR），并制定风险管理和缓解策略，以最大限度地减少停电的影响。目前，电力公司只能被动地应对停电。消费者处于被动的角色，努力科普后果，而没有先发制人的选择来管理停电的影响。该项目带来了变革性的变化，使公用事业公司能够预测停电，然后为消费者提供个人和社区的缓解措施。这将通过提高S CC对如何公平有效地处理停运影响的认识来实现。我们将部署先进的数据分析，使用天气和历史停电数据来训练机器学习停电预测算法。该项目的智力价值包括新的风险预测方法，停电预测的行为方面的研究，以及测量预测警报消息的有效性的实验。更广泛的影响包括通过博物馆和图书馆的公共服务，通过所有包容性的适龄STEM方案，在PreK-20学生，他们的教师和家长中开展教育和外联工作。我们将通过在圣安东尼奥适当的城市办公室和费城一家主要零售电力供应商的总部邀请的讲座和视频与更广泛的公民社区进行沟通。对包容性劳动力发展的强调广泛适用，对推进S CC人力资源需求和复原力计划具有高度影响力。为了实现SoR的时空预测，ALERT将通过合并多个学科的方法进行综合研究：a）高级数据分析（ADA）; B）社会，行为和经济科学（SBE）;和c）智能电网基础（SGF）。该项目活动将分别整合ADA和SGF数据以及物理电力系统模型，然后根据调查和实验数据设计SBE干预措施，以定义SoR模型并做出缓解决策，以降低停电风险。该奖项的创新之处在于利用历史公用事业记录、天气记录、电网拓扑和植被数据等大数据，在物理约束和结构化学习的基础上对SoR进行预测。该奖项反映了NSF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。