CAREER: Harnessing Smart Grid Data to Enable Resilient and Efficient Electricity

职业：利用智能电网数据实现弹性和高效的电力

• 批准号: 1254549
• 负责人: Paul Hines
• 金额: $ 40万
• 依托单位: University of Vermont & State Agricultural College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1254549&HistoricalAwards=false
• 关键词: CAREER; Harnessing; Smart; Grid; Data

The objective of this research is to harness Smart Grid data (Big Data) to enable more resilient and efficient electricity. Three research sub-projects contribute to this goal. Project 1 combines a new ?Random Chemistry? computational algorithm with complex networks methods to find patterns of vulnerability in power systems, and uses the results to reduce cascading failure blackout risk. Project 2 transforms smart grid data into actionable information about the health of a power grid by looking at statistical properties (structured noise) in data from grid sensors. Projects 1 and 2 seeks to make power grids more resilient to fluctuations from renewable generation or weather events. Project 3 uses crowdsourcing to identify trends affecting residential energy consumption through a web-based energy efficiency social network. Intellectual MeritThis project integrates research ideas from diverse scientific disciplines, including complex systems, graph theory, data science, computational intelligence and crowdsourcing. Projects 1 and 2 use abstract complex systems approaches, while retaining critical information about the physics of power systems. By using data from real power systems the project will contribute to the emerging field of data science. The third project combines computational intelligence with crowdsourcing in a way that could open new ways to improve energy efficiency.Broader ImpactsThis project tests new educational approaches, including a unique LEGO-based grid simulator, and integrates smart grid data into new courses. New curriculum and a hands on ?smart grid road show? will be leveraged to attract students from diverse educational and demographic backgrounds to study electric energy.

这项研究的目的是利用智能电网数据（大数据），以实现更有弹性和更高效的电力。三个研究次级项目有助于实现这一目标。项目1结合了新的？随机化学？计算算法与复杂网络的方法，发现电力系统的脆弱性模式，并使用的结果，以减少连锁故障停电的风险。项目2通过查看来自电网传感器的数据中的统计属性（结构化噪声），将智能电网数据转换为有关电网健康状况的可操作信息。项目1和2旨在使电网更能适应可再生能源发电或天气事件的波动。项目3利用众包，通过一个基于网络的能源效率社会网络，确定影响住宅能源消费的趋势。智力优势该项目整合了来自不同科学学科的研究思想，包括复杂系统、图论、数据科学、计算智能和众包。项目1和2使用抽象的复杂系统方法，同时保留有关电力系统物理的关键信息。通过使用来自真实的电力系统的数据，该项目将有助于新兴的数据科学领域。第三个项目将计算智能与众包相结合，为提高能源效率开辟了新的途径。更广泛的影响该项目测试了新的教育方法，包括一个独特的基于乐高的电网模拟器，并将智能电网数据整合到新的课程中。新课程和动手？智能电网路演？将吸引来自不同教育和人口背景的学生学习电能。

项目成果

Real-world, Full-scale Validation of Power Balancing Services from Packetized Virtual Batteries

• DOI: 10.1109/isgt.2019.8791628
• 发表时间: 2019-02
• 期刊: 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
• 作者: Kate Desrochers;Vanessa Hines;Forrest Wallace;J. Slinkman;Andrew Giroux;Adil Khurram;Mahraz Amini;M. Almassalkhi;P. Hines