CAREER: A Multi-faceted Framework to Enable Computationally Efficient Evaluation and Automatic Design for Large-scale Economics-driven Transmission Planning

职业生涯：一个多方面的框架，可实现大规模经济驱动的输电规划的计算高效评估和自动设计

• 批准号: 2339956
• 负责人: Rui Bo
• 金额: $ 50万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-09-01 至 2029-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339956&HistoricalAwards=false
• 关键词: CAREER; Multi; faceted; Framework; Enable

This NSF CAREER project aims to improve the computational efficiency of economics driven transmission planning for electric power systems by up to three orders of magnitude, in order to transform lengthy planning processes toward an agile process and prepare the US power grids for rapidly changing energy and policy landscape. The project will bring transformative change in the fundamental methods and computational tools for the design and evaluation of electric power transmission system planning. This will be achieved by exploring research innovations in modeling, simulation, computing and design and integrating them together to form a holistic solution. The intellectual merits of the project include (1) revealing a technological path to achieving up to three orders of magnitude performance speedup for economics-driven transmission planning, (2) advancing knowledge in understanding, modeling and control of electrical transmission systems, (3) producing new knowledge in network theory, mathematical methods and computational methods, (4) producing scientific findings in design and refinement for large-scale networked system expansion. The broader impacts of the project include (1) dramatically shortening the timeline of economics driven transmission planning, providing more effective transmission expansion strategies, and unleashing immense economic benefits, (2) providing an automated design approach for infrastructure planning, (3) facilitating cross-sector and cross-industry integration of infrastructures, (4) raising awareness of electric transmission system’s critical role in integrating clean energy and combating climate change.The economics-driven transmission planning problem can be characterized as a large-scale mathematical optimization with chronology. Such mathematical problems are widely present in various engineering and social problems. Although the literature continues to offer gradually improved solution quality and ability to handle non-convexity, these solution methods are not tractable or scalable to large-scale realistic systems due to the inherent computational challenges. This NSF CAREER project plans to address it through innovations in and integration of network reduction, decomposition and graphics processing unit (GPU) computing, artificial intelligence (AI)-based transmission option design, and parametric analysis-based refinement. The project will promote power engineering education and foster interdisciplinary thinking through outreach, game and course development, training and software.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NSF职业项目旨在提高经济学驱动电力系统的传输计划的计算效率，最多三个数量级，以将冗长的计划过程转变为敏捷过程，并为美国电力网络做好准备，以迅速改变能源和政策格局。该项目将带来用于设计和评估电力传输系统计划的基本方法和计算工具的变革性变化。这将通过探索在建模，模拟，计算和设计中的研究创新并将它们集成在一起以形成整体解决方案来实现。该项目的智力优点包括（1）揭示一条技术途径，以达到经济学驱动的传输计划的最多三个数量级绩效的速度，（2）在理解，建模和控制电气传输系统方面的知识，（3）在网络理论，数学方法和计算方法中产生新知识，（（4）在设计和范围内生产大型系统，以实现大型系统。 The broader impacts of the project include (1) dramatically shortening the timeline of economics driven transmission planning, providing more effective transmission expansion strategies, and unleashing immense economic benefits, (2) providing an automated design approach for infrastructure planning, (3) developing cross-sector and cross-industry integration of infrastructures, (4) raising awareness of electric transmission system’s critical role in integrating clean energy and combating climate change.The经济学驱动的传输计划问题可以被描述为按时间顺序排列的大规模数学优化。这种数学问题在各种工程和社会问题中都广泛提出。尽管文献继续提供了逐渐提高的解决方案质量和处理非跨性别的能力，但由于继承计算挑战，这些解决方案方法无法实现或可扩展到大型现实系统。这个NSF职业项目计划通过降低网络，分解和图形处理单元（GPU）计算，人工智能（AI）基于基于人工智能的传输选项设计以及基于参数分析的改进来解决它。该项目将通过宣传，游戏和课程开发，培训和软件来促进动力工程教育，并促进跨学科思维。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，通过评估诚实地表示支持。