CAREER: Data-driven dynamic adaptive optimization for next generation power system operation

职业：数据驱动的下一代电力系统运行的动态自适应优化

• 批准号: 1751747
• 负责人: Xu Sun
• 金额: $ 50万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-15 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1751747&HistoricalAwards=false
• 关键词: CAREER; Data; driven; dynamic; adaptive

The objective of this Faculty Early Career Development Program (CAREER) project is to create a set of novel optimization models and algorithms for the operation of future electric power systems. The approach is to (1) develop efficient and robust algorithms for optimizing power flow and power network topology, which will be significantly faster, more accurate, and more scalable than the state-of-the-art approaches; (2) develop new techniques for harnessing large amount of data for modeling uncertainties in power system; (3) develop decision making algorithms for the real-time operation of power systems with substantial renewable, demand response, and distributed generation resources. The intellectual merits of the project lie in (1) the development of new insights and understanding of some key mathematical structures of a broad class of hard optimization problems involving networks, which are intrinsic to optimal power flow, network topology control, and dynamic decision making, and (2) leveraging these mathematical understanding to design rigorous and efficient algorithms for the mentioned problems. If successful, this research will not only provide transformative technologies for the operations of power grid, but will also strengthen intellectual ties between power engineering and industrial & operations engineering. The project will directly benefit the society at large by creating the next generation of operational tools to manage the future power grids, to help reduce power system operational cost, and to increase power system reliability and flexibility. The methodological contributions of the project will provide new tools for applications beyond electric power systems, such as for the operation of water and natural-gas networks and coordination of interconnected energy systems. The PI will actively pursue opportunities to bring power industry, academia, government, and national labs together to form synergistic discussions and collaborations on developing analytical methods for electric energy systems. The PI will also develop new education curriculum and outreach activities to contribute to the development of a new generation of multidisciplinary workforce for the nation's infrastructure industry.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.

学院早期职业发展计划(CALEAR)项目的目标是为未来电力系统的运行创造一套新的优化模型和算法。方法是(1)开发高效和健壮的优化潮流和电网拓扑的算法，比最先进的方法更快、更准确、更具可扩展性；(2)开发新的技术来利用大量数据来建模电力系统中的不确定性；(3)开发具有大量可再生、需求响应和分布式发电资源的电力系统实时运行的决策算法。该项目的智力优势在于(1)对涉及网络的一大类困难优化问题的一些关键数学结构有了新的见解和理解，这些问题是最优潮流、网络拓扑控制和动态决策的内在原因，以及(2)利用这些数学知识为上述问题设计严格而有效的算法。如果成功，这项研究不仅将为电网运营提供变革性技术，还将加强电力工程与工业与电力运营工程之间的智力联系。该项目将通过创造下一代运行工具来管理未来的电网，帮助降低电力系统运行成本，并提高电力系统的可靠性和灵活性，从而直接造福于整个社会。该项目的方法学贡献将为电力系统以外的应用提供新的工具，例如水和天然气网络的运行以及相互关联的能源系统的协调。国际电力协会将积极寻求机会，将电力行业、学术界、政府和国家实验室聚集在一起，就开发电力系统分析方法进行协同讨论和合作。PI还将开发新的教育课程和推广活动，为国家基础设施行业发展新一代多学科劳动力做出贡献。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Multistage Stochastic Unit Commitment Using Stochastic Dual Dynamic Integer Programming

• DOI: 10.1109/tpwrs.2018.2880996
• 发表时间: 2019-05
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Jikai Zou;Shabbir Ahmed;X. Sun