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: 2316675
• 负责人: Xu Sun
• 金额: $ 50万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316675&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.

这个教师早期职业发展计划（职业）项目的目的是为未来电力系统的运行创建一系列新颖的优化模型和算法。该方法是（1）开发出高效且可靠的算法来优化功率流和电力网络拓扑，这将比最先进的方法更快，更准确且更可扩展； （2）开发新技术，用于利用大量数据来建模电源系统中的不确定性； （3）为具有实质性可再生，需求响应和分布式生成资源的电力系统实时操作制定决策算法。该项目的智力优点在于（1）新见解的发展以及对涉及网络的一系列重要的硬性优化问题的一些关键数学结构的理解，这些问题涉及网络，这些问题本质上是最佳的功率流，网络拓扑控制和动态决策，以及（2）利用这些数学理解来设计严格有效的算法，以解决所提到的问题。如果成功，这项研究不仅将为电网运营提供变革性技术，而且还将加强动力工程与工业与运营工程之间的智力关系。该项目将通过创建下一代运营工具来管理未来的电网，帮助降低电源系统运营成本，并提高电力系统的可靠性和灵活性，从而直接受益于整个社会。该项目的方法论贡献将为电力系统以外的应用提供新工具，例如水和天然气网络的运行以及互连能源系统的协调。 PI将积极寻求将电力行业，学术界，政府和国家实验室融合在一起的机会，以在开发电能系统的分析方法上进行协同讨论和合作。 PI还将开发新的教育课程和外展活动，以为全国基础设施行业的新一代多学科劳动力发展做出贡献。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。