CAREER: A Decentralized Optimization Framework for Next-Gen Transportation and Power Systems with Large-scale Transportation Electrification

This Faculty Early Career Development (CAREER) project aims to enhance the sustainability and resilience of transportation and power systems (TPSs) in response to rapid deployment of electric vehicles (EVs) and clean energy. Traditional, system-specific approaches are often inadequate or unable to address close couplings and decentralized decision-making scheme.

This research meets this fundamental challenge by offering a novel mechanism design for system-level planning and operation. The methodologies developed have the potential to extend to other infrastructure systems, where heterogeneous stakeholders interact with each other over a large-scale network. Research findings will help inform future strategies for EV adoption and grid integration of intermittent clean energy sources.

The integrated research and education activities are intended to facilitate knowledge transfer to students, practitioners, and the public, including K-12 and college students, utility companies, and transportation planning agencies.

The scientific goal of this CAREER project is to advance the understanding of the mechanism design of decentralized TPSs. More specifically, the research efforts will advance the knowledge on (1) network modeling strategies to elucidate the spatiotemporal interactions among heterogeneous and decentralized stakeholders with incomplete information, (2) optimal information sensing and sharing strategies for decentralized TPSs, and (3) equity-aware market mechanism design to optimize TPSs leveraging EVs and clean energy.

Meanwhile, it will integrate convexification, decomposition, and variational analysis theories to cope with the computational challenges brought by multi-agent interaction, multi-stage decision-making, and multi-dimensional scenarios for TPSs planning and operation.

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.