Data-Driven Analysis and Optimization for Urban Energy Systems Equitable Resilience

Abstract

Electric vehicles (EVs) can be leveraged as power resources to support the grid operation in challenging scenarios, e.g., natural disasters or health crises such as the COVID-19 pandemic. This paper aims to enhance equity of power resilience in urban energy systems by means of strategic allocation of EV charging infrastructure. We first use data-driven approaches to infer the relationships between communities' power resilience equity and available EV charging infrastructure as well as other prominent social-demographic factors. This inference leads to the development of a machine learning model for power resilience inequity prediction. We further develop an optimization frame-work that jointly considers equitable resiliency and resource utilization to guide the optimized EV charging infrastructure allocation across the city. Case studies demonstrate the capability of the devised approach in enhancing power resilience equity in marginalized communities.