The aggressive integration of renewable resources such as wind and solar exposes the power system to uncertainty in power supply. One cannot control when the sun shines or when the wind blows. This NSF CAREER project aims to make electricity markets meaningfully accommodate such uncertainties in their daily operations. Current electricity market processes take either a reactive or a conservative view towards uncertainty. This project will bring transformative change by adopting a risk-sensitive design paradigm--one that strikes a healthy balance between the two extremes. The intellectual merits of the project include novel approaches to combine recent advances from multiple domains including optimization theory, machine learning and networked markets to enable large-scale renewable integration in the power grid. The broader impacts of the project include wide applicability of research outcomes to other engineering domains, open-source approach to simulation design, an interdisciplinary curriculum development plan, and community engagement efforts with K-12 students and the power industry.
Key research outcomes of this project will include risk-sensitive electricity market clearing formulations, efficient algorithmic frameworks to compute solutions to these formulations, and pricing design for market participants derived from these formulations. Some research tasks target implementable solutions for power systems. Others will develop new theoretical frameworks that apply broadly to other engineering context - for example, mechanisms to meaningfully combine historical measurements and simulated data in decision-making. Research outputs from computer simulations will be made available via GitHub and Jupyter Notebooks to make them verifiable and usable by other researchers. Inter-disciplinary course curricula combining power engineering with modern data science and economics will be developed. Teaching innovations will enhance classroom engagement and communication skills. Special attention will be paid to undergraduate involvement in research.
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.
