Electric Vehicle Charging Planning: A Complex Systems Perspective
Year: 2024
Type: article
Abstract: In this paper, we introduce an innovative framework for the strategic planning of electric vehicle (EV) charging infrastructure within interconnected energy-transportation networks. By harnessing the small-world network model and the advanced optimization capabilities of the Non-dominated Sorting Genetic Algorithm III (NSGA-III), we address the complex challenges of station placement and network design. Our application of the small-world theory ensures that charging stations are optimally interconnected, fostering network resilience and ensuring consistent service availability. We approach the infrastructure planning as a multi-objective optimization task with NSGA-III, focusing on cost minimization and the enhancement of network resilience and connectivity. Through simulations and empirical case studies, we demonstrate the efficacy of our model, which markedly improves the reliability and operational efficiency of EV charging networks. The findings of this study significantly advance the integrated planning and operation of energy and transportation networks, offering insightful contributions to the domain of sustainable urban mobility.
Source: IEEE Transactions on Smart Grid
Institutions: North China Electric Power University, Cornell University, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, Aalto University, Iowa State University, Chinese University of Hong Kong, Shenzhen, Tsinghua University, King Saud University, Southeast University, Sichuan University, Shanghai Jiao Tong University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G1922262774, G388593014
OpenAlex ID: W4401718397