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.
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.
| Original language | English |
|---|---|
| Pages (from-to) | 754-772 |
| Number of pages | 19 |
| Journal | IEEE Transactions on Smart Grids |
| Volume | 16 |
| Issue number | 1 |
| Early online date | 21 Aug 2024 |
| DOIs | |
| Publication status | Published - 2025 |
| MoE publication type | A1 Journal article-refereed |
Keywords
- Electrical engineering
- small-world network model
- coupled energy-transportation networks
- Charging infrastructure planning
- electric vehicle charging stations
- Charging stations
- Electric vehicle charging
- User experience
- Planning
- Optimization
- complex systems theory
- Resilience
