SCC-PG: Advanced Learning for Energy Risk Tracking (ALERT)

SCC-PG：能源风险跟踪高级学习 (ALERT)

• 批准号: 1951813
• 负责人: Mladen Kezunovic
• 金额: $ 15万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951813&HistoricalAwards=false
• 关键词: SCC; PG; Advanced; Learning; Energy

The energy supply is the backbone of Smart and Connected Communities (S&CC). It ties together various energy stakeholders (providers, consumers, and services) across different social, economic and cultural layers, and among groups corresponding to the residential, commercial, or industrial settings. Recent statistics indicate that despite all the measures taken by the utility industry to maintain the reliability and security of the energy supply, the number of major blackouts throughout the world is increasing. Loss of electricity affects residents, students, private and public sector employees, small and large businesses, and critical city services, such as, police, firefighters, and first responders. The fundamental question is whether the data sciences and engineering, combined with social sciences and technology can help reduce the losses and related societal impacts. This project postulates that the combination of the Big Data spatiotemporal analytics and physical models will allow us to achieve predictive outage risk capabilities to address the mitigation options not presently available. The Advance Learning for Energy Risk Tracking (ALERT) approach will focus on predicting the outages and asset failure risks using historical data from utilities (outage, smart meter data, etc.), along with additional data from weather-related government and private sources (radar, satellite, ASOS, NLDN and NDFD, Vaisala), as well as topology and vegetation data. Such risk predictions will be shared with participating utility companies, and with their customers to mobilize mitigation measures, which needs a strong social study aspect to better understand customer behavior. Those measures may include: a) equipment repair/replacement and feeder switching actions, b) relocation of the volatile population, and local power back up for schools, businesses and essential city services, c) scheduling firefighter and emergency services for evacuation of at-risk populations, and d) dispatching police forces to prevent looting of vacated houses and businesses. The goal of the communities to minimize the risk of electricity supply failure and undesirable environmental impacts will be achieved by engaging community stakeholders in creating and sharing outage prediction risk maps to allow for individual and collaborative mitigation actions. The main objective is to build research capability to develop a methodology for predicting the risk of electricity outages, which emphasizes the S&CC aspects. To strengthen the community engagement, and to consolidate thinking in the research team and among the various stakeholder groups in metropolitan areas in Texas, different types of meetings will be held.. These multidisciplinary meetings will focus on defining ALERT goals: the need for data collection and behavioral pilots, the requirements for the integration platform, the logistics of the user portal implementations for the dissemination of ALERT messages and handling of false positive and false negative scenarios, as well as collection of panel polling data in future steps. The project will identify the research gaps and means of addressing them, the data requirements, and will develop a comprehensive network of contacts for each partner organization, and each prospective pilot participant.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.

能源供应是智能和连接社区（S＆CC）的骨干。它将各种能源利益相关者（提供者，消费者和服务）联系在一起，以及与住宅，商业或工业环境相对应的群体之间。最近的统计数据表明，尽管公用事业行业采取了维持能源供应的可靠性和安全性的所有措施，但全世界的主要停电数量仍在增加。电力损失会影响居民，学生，私营和公共部门员工，小型和大型企业以及关键的城市服务，例如警察，消防员和急救人员。基本问题是数据科学和工程以及社会科学和技术是否可以帮助减少损失和相关的社会影响。该项目假设大数据时空分析和物理模型的结合将使我们能够获得预测性的停电风险能力，以解决目前尚不可用的缓解选项。能源风险跟踪（警报）方法的提前学习将着重于使用公用事业（中断，智能电表数据等）的历史数据以及与天气相关的政府和私人资源的其他数据（使用历史数据）预测中断和资产故障风险（雷达，卫星，ASOS，NLDN和NDFD，Vaisala）以及拓扑和植被数据。 这种风险预测将与参与的公用事业公司共享，并与客户动员缓解措施，这需要强大的社会研究方面以更好地了解客户行为。这些措施可能包括：a）设备维修/更换和进料器切换动作，b）搬迁人口的搬迁，以及当地的学校备份学校，企业和基本城市服务，c）安排消防员和紧急服务以撤离AT-风险人群，d）派遣警察部队，以防止抢劫腾空的房屋和企业。通过让社区利益相关者参与创建和共享中断预测图以允许个人和协作缓解措施，将实现社区最大程度地减少电力供应失效风险和不良环境影响的风险的目标。主要目的是建立研究能力，以开发一种预测电量中断风险的方法，该方法强调了S＆CC方面。加强社区参与，并在得克萨斯州大都会地区的各个利益相关者群体中巩固思维，将举行不同类型的会议。.这些多学科会议将重点放在定义警报目标上：收集数据的需求和行为飞行员，集成平台的要求，用户门户网站实现的物流，用于传播警报消息，处理误报正面和假阴性场景，以及在未来步骤中收集面板投票数据。该项目将确定研究差距和解决方案的方法，数据要求，并将为每个合作伙伴组织建立全面的联系网络，每个准试验参与者。该奖项反映了NSF的法定任务，并被认为值得通过使用基金会的智力优点和更广泛的影响评估标准进行评估。