Collaborative Research: An Intelligent Restoration System for a Self-healing Smart Grid

合作研究：自愈智能电网的智能恢复系统

• 批准号: 1408486
• 负责人: Wei Sun
• 金额: $ 21万
• 依托单位: South Dakota State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2015-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1408486&HistoricalAwards=false
• 关键词: Collaborative; Research; Intelligent; Restoration; System

The overall goal of this project is to achieve a self-healing smart grid by creating an intelligent power restoration system. As such, the power system restoration remains a great concern in the evolving smart grid. The lack of online adaptive restoration tools leads to an incredibly long time for system recovery after a major blackout, due to the stochastic nature and uncertainties associated renewables such as wind and solar power resources. The proposed intelligent power restoration system is to create a self-healing power grid by overcoming the challenges that prevent electric power grid from system recovery. The advances are in the development of an intelligent power restoration system that will provide system operators with real-time adaptive restoration actions by including the use of renewable energy in the restoration process and assisting system planners to make long-term investment decisions on blackstart resources including renewable resources. This project will have a significant impact on national grid, sustainable energy and energy security. The potential economic impact is expected to save the loss of several millions of dollars that result from blackouts. The short and long term societal impact addresses national needs, especially for highly interconnected power system, in terms of sustainability, security, and reliability. Situational intelligence in power system restoration minimizes impacts of blackouts and leads to rapid recovery from natural disasters such as earthquakes, hurricanes and malicious attacks. The proposed research will promote teaching, training and learning about smart grid to graduate and undergraduate students; provide hands-on experiences to power engineers in automated power system restoration; and offer learning platform to K-12 students to cultivate the interest in power and energy systems with overarching goal of recruiting underrepresented minority, women and Native Americans students.This collaborative project proposes to apply emerging computational intelligence techniques including scalable adaptive dynamic programming based on cellular computational networks to design the power restoration system. The goals are; (1) to develop an online adaptive restoration tool using advanced scalable computational intelligence techniques; (2) investigate a novel scheme to use renewable resources in system restoration; (3) explore a blackstart unit investment strategy to improve the self-healing capability; and (4) implement and demonstrate the feasibility of new system on real-time utility power systems.

该项目的总体目标是通过创建智能电力恢复系统来实现自我修复的智能电网。 因此，电力系统恢复仍然是发展中的智能电网中的一个重要问题。 由于风能和太阳能等可再生能源的随机性和不确定性，在线自适应恢复工具的缺乏导致大停电后系统恢复的时间非常长。 提出的智能电力恢复系统是通过克服阻碍电网系统恢复的挑战来创建自愈电网。这些进展是在开发智能电力恢复系统，该系统将通过在恢复过程中使用可再生能源，并协助系统规划者对包括可再生资源在内的黑启动资源做出长期投资决策，为系统运营商提供实时自适应恢复行动。 该项目将对国家电网、可持续能源和能源安全产生重大影响。 潜在的经济影响预计将节省停电造成的数百万美元的损失。 短期和长期的社会影响解决了国家的需求，特别是高度互联的电力系统，在可持续性，安全性和可靠性方面。 电力系统恢复中的情景智能最大限度地减少了停电的影响，并导致从地震，飓风和恶意攻击等自然灾害中快速恢复。这项研究将促进研究生和本科生对智能电网的教学、培训和学习，为电力工程师提供自动电力系统恢复的实践经验;并为K-12学生提供学习平台，培养他们对电力和能源系统的兴趣，其总体目标是招募代表性不足的少数民族，这个合作项目提出应用新兴的计算智能技术，包括基于蜂窝计算网络的可扩展自适应动态规划来设计电力恢复系统。目标是：（1）利用先进的可扩展计算智能技术开发在线自适应恢复工具;（2）研究在系统恢复中使用可再生资源的新方案;（3）探索提高自愈能力的黑启动单元投资策略;以及（4）在实时公用电力系统上实现和演示新系统的可行性。