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.

该 NSF CAREER 项目旨在将经济驱动的电力系统输电规划的计算效率提高三个数量级，从而将冗长的规划流程转变为敏捷流程，并为美国电网做好快速变化的能源和政策的准备该项目将为输电系统规划的设计和评估的基本方法和计算工具带来革命性的变化，这将通过探索建模、仿真、计算和设计方面的研究创新并将它们整合在一起形成一个解决方案来实现。该项目的整体解决方案的智力优点。包括 (1) 揭示一条技术路径，以实现经济驱动的输电规划性能加速三个数量级，(2) 推进电力输电系统的理解、建模和控制方面的知识，(3) 产生网络理论方面的新知识、数学方法和计算方法，(4) 在大规模网络系统扩展的设计和完善方面产生科学成果，该项目的更广泛影响包括 (1) 大大缩短经济驱动的输电规划的时间，提供更有效的输电扩展。战略，并释放巨大的经济效益，(2)为基础设施规划提供自动化设计方法，(3) 促进基础设施的跨部门和跨行业整合，(4) 提高人们对输电系统在整合清洁能源和应对气候变化方面的关键作用的认识。经济驱动的输电规划此类数学问题广泛存在于各种工程和社会问题中，尽管文献不断提供逐步改进的解决方案质量和处理非凸性的能力，但这些解决方法并不是如此。易处理或由于固有的计算挑战，该项目计划通过网络缩减、分解和图形处理单元（GPU）计算、基于人工智能（AI）的传输选项的创新和集成来解决这一问题。该项目将通过推广、游戏和课程开发、培训和软件来促进电力工程教育并培养跨学科思维。该奖项是 NSF 的法定使命，并通过使用基金会的知识进行评估，被认为值得支持。优点和更广泛的影响审查标准。