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

Sponsor: National Science Foundation

PI: Zhaomiao Guo

This Faculty Early Career Development (CAREER) project aims to enhance the sustainability and resilience of transportation and power systems (TPSs) leveraging electric vehicles (EVs) and clean energy. A growing adoption of EVs strengthens the couplings between TPSs and provides unprecedented opportunities to optimize the sustainability and resilience of both systems. However, these opportunities cannot be materialized using traditional centralized approaches to address each infrastructure system separately due to the close couplings and decentralized decision makers involved. This project will address this fundamental challenge by offering a novel mechanism design viewpoint in TPSs planning and operation. The methodologies developed have the potential to benefit other critical infrastructure systems, where decentralized decision makers interact with each other over networks. Successful execution of this program will further promote EV adoption and grid integration of intermittent clean energy, which will mitigate energy and environmental impacts from both the transportation and power sectors. The integrated research and education activities will significantly improve the knowledge of both professionals and public audiences, including K-12 and college students from underrepresented groups, utility companies, and transportation planning agencies, on interplay of transportation electrification and clean energy.

The goal of this CAREER project is to advance the understanding on the mechanism design of decentralized TPSs. The CAREER project will create a unified methodological framework for a sustainable and resilient development of decentralized TPSs and enable large-scale adoption of EVs and intermittent clean energy. More specifically, from a mathematical modeling perspective, the research efforts will advance the knowledge on (1) network modeling strategies to elucidate the spatio-temporal 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. From a computational perspective, the research efforts 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.