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.
