Data-Driven Analysis and Optimization for Urban Energy Systems Equitable Resilience
57th Annual Conference on Information Sciences and Systems, CISS 2023
; 2023.
Article
in English
| Scopus | ID: covidwho-2314264
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. © 2023 IEEE.
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
57th Annual Conference on Information Sciences and Systems, CISS 2023
Year:
2023
Document Type:
Article
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