Efficient Utilization of Flexible Transmission for Renewable Energy Integration

能源系统正在从化石燃料过渡到可再生能源，如太阳能和风能。 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面临一些技术困难。 这些挑战与可再生能源的可用性有关，风和阳光，取决于自然而不是燃料燃烧的可控过程。 该项目开发创新的软件工具，使灵活的传输，这是一个具有成本效益的解决方案，以应对这些挑战的部署。 由于这些发展，系统运营商将能够受益于一个新的资源，这是他们以前没有的。 特别是，该项目旨在采用灵活的传输，以减轻可再生能源的不确定性和不确定性，并促进更高水平的可再生能源生产。 在高效的数学建模和高性能计算的帮助下，本项目开发的模型速度快，适合实时操作。 由于可再生能源是经济和无排放的，该项目将对国民健康，繁荣和福利产生重大的积极影响。 此外，该项目还将整合电力工程教育中的计算方法和算法开发，以填补电力工程课程中急需的空白。该项目的目标是为当今存在的最大和最复杂的网络物理系统之一--北美电网频繁使用灵活的传输。 由于基础数学问题的计算负担，灵活的传输和发电调度的协同优化是不可能的。 具体而言，传输的灵活性，在可控阻抗的形式，引入非凸性的电力系统的操作是具有挑战性的有限的可用计算时间内处理。 该项目通过一种新颖的快速优化技术，利用潮流的数学结构，大大减少了这种计算负担。 因此，灵活传输的利用成为可能，这有助于降低运行成本并提高系统可靠性。 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不确定性和不确定性。该模型以随机优化为数学框架，对可再生能源的不确定性进行建模，以柔性输电和可控发电为决策变量进行优化。 算法分解和高性能计算，以减少随机优化所需的解决方案的时间，以确保快速和高效的计算。 这是该项目的重要组成部分，因为实时操作的计算时间不到五分钟。该项目的教育部分通过为电力工程专业的学生开发计算方法、算法设计和高性能计算的教育模块，丰富了电力工程课程。该奖项反映了NSF的法定使命，并通过使用基金会的智力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.