Efficient Utilization of Flexible Transmission for Renewable Energy Integration

能源系统正在从化石燃料向太阳能和风能等可再生能源过渡。尽管可再生能源具有明显的环境优势，但大规模整合可再生能源仍面临一些技术难题。这些挑战与可再生能源（即风能和太阳能）的可用性取决于自然而不是燃烧燃料的可控过程这一事实有关。该项目开发了创新的软件工具，使灵活传输的部署成为可能，这是应对这些挑战的一种经济有效的解决方案。由于这些发展，系统操作人员将能够从以前无法获得的新资源中受益。特别是，本项目旨在采用柔性输电，以减轻可再生能源的不确定性和间歇性，促进更高水平的可再生能源生产。借助高效的数学建模和高性能计算，本项目开发的模型速度快，适合于实时运行。由于可再生能源经济且无排放，该项目将对国民的健康、繁荣和福利产生重大的积极影响。此外，本计画将整合电力工程教育的计算方法与演算法发展，以填补电力工程课程急需的空白。该项目的目标是使当今最大、最复杂的网络物理系统之一——北美电网——能够频繁地利用灵活传输。由于潜在数学问题的计算负担，柔性输电和发电调度的协同优化尚不可能实现。具体而言，传输灵活性以可控阻抗的形式引入了电力系统运行的非凸性，这些非凸性在有限的可用计算时间内难以处理。该项目通过一种新颖而快速的优化技术，利用潮流的数学结构，大大减少了这种计算负担。因此，利用柔性传输可以降低运行成本，提高系统可靠性。该项目还旨在通过利用传输灵活性来减轻与可再生能源发电相关的间歇性和不确定性。以随机优化为数学框架对可再生能源不确定性进行建模，并以柔性输电和可控发电为决策变量进行优化。采用算法分解和高性能计算来减少随机优化所需的求解时间，以保证快速高效的计算。这是该项目的一个重要组成部分，因为实时操作的计算时间不到5分钟。本计画的教育部分，透过为电力工程学生开发计算方法、演算法设计和高性能计算的教育模块，丰富电力工程课程。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Energy systems are transitioning from fossil fuels to renewable energy resources, such as solar and wind. Despite their obvious environmental advantages, large-scale integration of renewable energy faces a number of technical difficulties. These challenges are linked to the fact that the availability of renewable energy, i.e., wind and sunlight, depends on nature rather than the controllable process of burning a fuel. This project develops innovative software tools that enable the deployment of flexible transmission, which is a cost-effective solution to address these challenges. As a result of these developments, system operators will be able to benefit from a new resource that was not available to them before. Particularly, this project aims to employ flexible transmission in order to alleviate the uncertainty and intermittency of renewable energy resources, and facilitate higher levels of renewable energy production. With the help of efficient mathematical modeling and high-performance computing, the models developed in this project are fast and appropriate for real-time operation. As renewable energy is economical and emission-free, this project will have a significant positive impact on the national health, prosperity, and welfare. Additionally, this project will integrate computational methods and algorithm development in power engineering education to fill a much-needed gap in power engineering curriculum.The objective of this project is to enable frequent utilization of flexible transmission for one of the largest and most complex cyber-physical system that exists today: the North American power grid. Co-optimization of flexible transmission and generation dispatch is not possible yet due to the computational burden of the underlying mathematical problem. Specifically, transmission flexibility, in the form of controllable impedance, introduces non-convexities to power system operation that are challenging to handle within the limited available computational time. This project substantially reduces such computational burden through a novel and fast optimization technique, which exploits the mathematical structure of power flows. Consequently, utilization of flexible transmission can become possible which can help reduce the operation cost and improve the system reliability. This project also aims to mitigate the intermittencies and uncertainties associated with renewable generation by utilizing transmission flexibility. It employs stochastic optimization as the mathematical framework to model renewable energy uncertainties, and optimizes flexible transmission and controllable generation as the decision variables. Algorithm decomposition and high-performance computing are employed to reduce the solution time required for stochastic optimization in order to ensure fast and efficient computation. This is an essential component of the project as the computational time available for real-time operation is less than five minutes. The education component of this project enriches the power engineering curriculum, by developing educational modules, on computational methods, algorithm design, and high-performance computing for power engineering students.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.